Global Estimates and Long-Term Trends of Fine Particulate Matter Concentrations (1998–2018)Click to copy article linkArticle link copied!
- Melanie S. Hammer*Melanie S. Hammer*Email: [email protected]Department of Energy, Environmental & Chemical Engineering, Washington University in St. Louis, St. Louis, Missouri 63130, United StatesDepartment of Physics and Atmospheric Science, Dalhousie University, Halifax, N.S. B3H3J5, CanadaMore by Melanie S. Hammer
- Aaron van DonkelaarAaron van DonkelaarDepartment of Energy, Environmental & Chemical Engineering, Washington University in St. Louis, St. Louis, Missouri 63130, United StatesDepartment of Physics and Atmospheric Science, Dalhousie University, Halifax, N.S. B3H3J5, CanadaMore by Aaron van Donkelaar
- Chi LiChi LiDepartment of Physics and Atmospheric Science, Dalhousie University, Halifax, N.S. B3H3J5, CanadaDepartment of Chemistry, University of California, Berkeley, Berkeley, California 94720, United StatesMore by Chi Li
- Alexei LyapustinAlexei LyapustinEarth Sciences Division, NASA Goddard Space Flight Center, Greenbelt, Maryland 20771, United StatesGoddard Earth Sciences Technology and Research, Universities Space Research Association, Greenbelt, Maryland 20771, United StatesMore by Alexei Lyapustin
- Andrew M. SayerAndrew M. SayerEarth Sciences Division, NASA Goddard Space Flight Center, Greenbelt, Maryland 20771, United StatesGoddard Earth Sciences Technology and Research, Universities Space Research Association, Greenbelt, Maryland 20771, United StatesMore by Andrew M. Sayer
- N. Christina HsuN. Christina HsuEarth Sciences Division, NASA Goddard Space Flight Center, Greenbelt, Maryland 20771, United StatesMore by N. Christina Hsu
- Robert C. LevyRobert C. LevyEarth Sciences Division, NASA Goddard Space Flight Center, Greenbelt, Maryland 20771, United StatesMore by Robert C. Levy
- Michael J. GarayMichael J. GarayJet Propulsion Laboratory, California Institute of Technology, Pasadena, California 91125-0002, United StatesMore by Michael J. Garay
- Olga V. KalashnikovaOlga V. KalashnikovaJet Propulsion Laboratory, California Institute of Technology, Pasadena, California 91125-0002, United StatesMore by Olga V. Kalashnikova
- Ralph A. KahnRalph A. KahnEarth Sciences Division, NASA Goddard Space Flight Center, Greenbelt, Maryland 20771, United StatesMore by Ralph A. Kahn
- Michael BrauerMichael BrauerSchool of Population and Public Health, The University of British Columbia, 2206 East Mall, Vancouver, British Columbia V6T1Z3, CanadaInstitute for Health Metrics and Evaluation, University of Washington, Seattle 98121, United StatesMore by Michael Brauer
- Joshua S. ApteJoshua S. ApteDepartment of Civil, Architectural and Environmental Engineering, University of Texas at Austin, Austin, Texas 78712, United StatesMore by Joshua S. Apte
- Daven K. HenzeDaven K. HenzeDepartment of Mechanical Engineering, University of Colorado Boulder, Boulder, Colorado 80309, United StatesMore by Daven K. Henze
- Li ZhangLi ZhangCIRES, University of Colorado, Boulder, Colorado 80309, United StatesGlobal Systems Division, Earth System Research Laboratory, NOAA, Boulder, Colorado 80309, United StatesMore by Li Zhang
- Qiang ZhangQiang ZhangMinistry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, ChinaCollaborative Innovation Center for Regional Environmental Quality, Beijing 100084, ChinaMore by Qiang Zhang
- Bonne FordBonne FordDepartment of Atmospheric Science, Colorado State University, Fort Collins 80523-1019, United StatesMore by Bonne Ford
- Jeffrey R. PierceJeffrey R. PierceDepartment of Atmospheric Science, Colorado State University, Fort Collins 80523-1019, United StatesMore by Jeffrey R. Pierce
- Randall V. MartinRandall V. MartinDepartment of Energy, Environmental & Chemical Engineering, Washington University in St. Louis, St. Louis, Missouri 63130, United StatesDepartment of Physics and Atmospheric Science, Dalhousie University, Halifax, N.S. B3H3J5, CanadaHarvard-Smithsonian Center for Astrophysics, Cambridge, Massachusetts 02138, United StatesMore by Randall V. Martin
Abstract
Exposure to outdoor fine particulate matter (PM2.5) is a leading risk factor for mortality. We develop global estimates of annual PM2.5 concentrations and trends for 1998–2018 using advances in satellite observations, chemical transport modeling, and ground-based monitoring. Aerosol optical depths (AODs) from advanced satellite products including finer resolution, increased global coverage, and improved long-term stability are combined and related to surface PM2.5 concentrations using geophysical relationships between surface PM2.5 and AOD simulated by the GEOS-Chem chemical transport model with updated algorithms. The resultant annual mean geophysical PM2.5 estimates are highly consistent with globally distributed ground monitors (R2 = 0.81; slope = 0.90). Geographically weighted regression is applied to the geophysical PM2.5 estimates to predict and account for the residual bias with PM2.5 monitors, yielding even higher cross validated agreement (R2 = 0.90–0.92; slope = 0.90–0.97) with ground monitors and improved agreement compared to all earlier global estimates. The consistent long-term satellite AOD and simulation enable trend assessment over a 21 year period, identifying significant trends for eastern North America (−0.28 ± 0.03 μg/m3/yr), Europe (−0.15 ± 0.03 μg/m3/yr), India (1.13 ± 0.15 μg/m3/yr), and globally (0.04 ± 0.02 μg/m3/yr). The positive trend (2.44 ± 0.44 μg/m3/yr) for India over 2005–2013 and the negative trend (−3.37 ± 0.38 μg/m3/yr) for China over 2011–2018 are remarkable, with implications for the health of billions of people.
Introduction
Methods
Satellite AOD Sources
Simulated Relationship of Surface PM2.5 and Total Column AOD
Combined PM2.5 Estimated from Satellites and Simulation
Hybrid PM2.5 Estimates
Results and Discussion
Supporting Information
The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.est.0c01764.
Detailed description of satellite AOD sources, GEOS-Chem simulation, and algorithm for calculating PM2.5 estimates (PDF)
Terms & Conditions
Most electronic Supporting Information files are available without a subscription to ACS Web Editions. Such files may be downloaded by article for research use (if there is a public use license linked to the relevant article, that license may permit other uses). Permission may be obtained from ACS for other uses through requests via the RightsLink permission system: http://pubs.acs.org/page/copyright/permissions.html.
Acknowledgments
This work was supported by the Natural Sciences and Engineering Research Council (NSERC), the Energy Policy Institute at the University of Chicago, and the Health Effects Institute. M.H. was partially supported by the Killam Trusts. GEOS-Chem input files were obtained from the GEOS-Chem Data Portal enabled by Compute Canada.
References
This article references 87 other publications.
- 1
GBD 2016 risk factors:
Gakidou, E.; Afshin, A.; Abajobir, A. A.; Abate, K. H.; Abbafati, C.; Abbas, K. M.; Abd-Allah, F.; Abdulle, A. M.; Abera, S. F.; Aboyans, V. Global, Regional, and National Comparative Risk Assessment of 84 Behavioural, Environmental and Occupational, and Metabolic Risks or Clusters of Risks, 1990–2016: A Systematic Analysis for the Global Burden of Disease Study 2016. Lancet 2017, 390 (10100), 1345– 1422, DOI: 10.1016/S0140-6736(17)32366-8Google ScholarThere is no corresponding record for this reference. - 2Health Effects Institute. State of Global Air 2019; Health Effects Institute, 2019.Google ScholarThere is no corresponding record for this reference.
- 3The Economic Consequences of Outdoor Air Pollution; OECD: Paris, 2016; DOI: DOI: 10.1787/9789264257474-en .Google ScholarThere is no corresponding record for this reference.
- 4Greenstone, M.; Fan, C. Q. Introducing the Air Quality Life Index Twelve Facts about Particulate Air Pollution, Human Health, and Global Policy Index; University of Chicago, 2018.Google ScholarThere is no corresponding record for this reference.
- 5Martin, R. V.; Brauer, M.; van Donkelaar, A.; Shaddick, G.; Narain, U.; Dey, S. No One Knows Which City Has the Highest Concentration of Fine Particulate Matter. Atmos. Environ. X 2019, 3, 100040, DOI: 10.1016/j.aeaoa.2019.100040Google Scholar5https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3cXitFyhurzM&md5=d070c05b858cf958b8de28f242d83e34No one knows which city has the highest concentration of fine particulate matterMartin, Randall V.; Brauer, Michael; van Donkelaar, Aaron; Shaddick, Gavin; Narain, Urvashi; Dey, SagnikAtmospheric Environment: X (2019), 3 (), 100040CODEN: AEXTBX; ISSN:2590-1621. (Elsevier Ltd.)Exposure to ambient fine particulate matter (PM2.5) is the leading global environmental risk factor for mortality and disease burden, with assocd. annual global welfare costs of trillions of dollars. Examd. within is the ability of current data to answer a basic question about PM2.5, namely the location of the city with the highest PM2.5 concn. The ability to answer this basic question serves as an indicator of scientific progress to assess global human exposure to air pollution and as an important component of efforts to reduce its impacts. Despite the importance of PM2.5, we find that insufficient monitoring data exist to answer this basic question about the spatial pattern of PM2.5 at the global scale. Only 24 of 234 countries have more than 3 monitors per million inhabitants, while d. is an order of magnitude lower in the vast majority of the world's countries, with 141 having no regular PM2.5 monitoring at all. The global mean population distance to nearest PM2.5 monitor is 220 km, too large for exposure assessment. Efforts to fill in monitoring gaps with ests. from satellite remote sensing, chem. transport modeling, and statistical models have biases at individual monitor locations that can exceed 50μg m-3. Realization of such an integrated framework will facilitate accurate identification of the location of the city with the highest PM2.5 concn. and play a key role in tracking the progress of efforts to reduce the global impacts of air pollution.
- 6West, J. J.; Cohen, A.; Dentener, F.; Brunekreef, B.; Zhu, T.; Armstrong, B.; Bell, M. L.; Brauer, M.; Carmichael, G.; Costa, D. L.; Dockery, D. W.; Kleeman, M.; Krzyzanowski, M.; Künzli, N.; Liousse, C.; Lung, S.-C. C.; Martin, R. V.; Pöschl, U.; Pope, C. A.; Roberts, J. M.; Russell, A. G.; Wiedinmyer, C. What We Breathe Impacts Our Health: Improving Understanding of the Link between Air Pollution and Health. Environ. Sci. Technol. 2016, 50 (10), 4895– 4904, DOI: 10.1021/acs.est.5b03827Google Scholar6https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC28XkvVaisL0%253D&md5=de12920f352a18b4b7d816e22b99836e"What We Breathe Impacts Our Health: Improving Understanding of the Link between Air Pollution and Health"West, J. Jason; Cohen, Aaron; Dentener, Frank; Brunekreef, Bert; Zhu, Tong; Armstrong, Ben; Bell, Michelle L.; Brauer, Michael; Carmichael, Gregory; Costa, Dan L.; Dockery, Douglas W.; Kleeman, Michael; Krzyzanowski, Michal; Kunzli, Nino; Liousse, Catherine; Lung, Shih-Chun Candice; Martin, Randall V.; Poschl, Ulrich; Pope, C. Arden; Roberts, James M.; Russell, Armistead G.; Wiedinmyer, ChristineEnvironmental Science & Technology (2016), 50 (10), 4895-4904CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)Air pollution contributes to the premature deaths of millions of people each year around the world, and air quality problems are growing in many developing nations. While past policy efforts have succeeded in reducing particulate matter and trace gases in North America and Europe, adverse health effects are found at even these lower levels of air pollution. Future policy actions will benefit from improved understanding of the interactions and health effects of different chem. species and source categories. Achieving this new understanding requires air pollution scientists and engineers to work increasingly closely with health scientists. In particular, research is needed to better understand the chem. and phys. properties of complex air pollutant mixts., and to use new observations provided by satellites, advanced in situ measurement techniques, and distributed micro monitoring networks, coupled with models, to better characterize air pollution exposure for epidemiol. and toxicol. research, and to better quantify the effects of specific source sectors and mitigation strategies.
- 7Gupta, P.; Levy, R. C.; Mattoo, S.; Remer, L. A.; Munchak, L. A. A Surface Reflectance Scheme for Retrieving Aerosol Optical Depth over Urban Surfaces in MODIS Dark Target Retrieval Algorithm. Atmos. Meas. Tech. 2016, 9 (7), 3293– 3308, DOI: 10.5194/amt-9-3293-2016Google ScholarThere is no corresponding record for this reference.
- 8Sayer, A. M.; Hsu, N. C.; Lee, J.; Kim, W. V.; Dutcher, S. T. Validation, Stability, and Consistency of MODIS Collection 6.1 and VIIRS Version 1 Deep Blue Aerosol Data Over Land. J. Geophys. Res.: Atmos. 2019, 124 (8), 4658– 4688, DOI: 10.1029/2018JD029598Google ScholarThere is no corresponding record for this reference.
- 9Hsu, N. C.; Lee, J.; Sayer, A. M.; Kim, W.; Bettenhausen, C.; Tsay, S.-C. VIIRS Deep Blue Aerosol Products Over Land: Extending the EOS Long-Term Aerosol Data Records. J. Geophys. Res.: Atmos. 2019, 124 (7), 4026– 4053, DOI: 10.1029/2018JD029688Google ScholarThere is no corresponding record for this reference.
- 10Lyapustin, A.; Wang, Y.; Korkin, S.; Huang, D. MODIS Collection 6 MAIAC Algorithm. Atmos. Meas. Tech. 2018, 11 (10), 5741– 5765, DOI: 10.5194/amt-11-5741-2018Google ScholarThere is no corresponding record for this reference.
- 11Garay, M. J.; Kalashnikova, O. V.; Bull, M. A. Development and Assessment of a Higher-Spatial-Resolution (4.4km) MISR Aerosol Optical Depth Product Using AERONET-DRAGON Data. Atmos. Chem. Phys. 2017, 17 (8), 5095– 5106, DOI: 10.5194/acp-17-5095-2017Google Scholar11https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXhsV2gu7jK&md5=1d799611e00627bafcc01e03f86b00e2Development and assessment of a higher-spatial-resolution (4.4 km) MISR aerosol optical depth product usingGaray, Michael J.; Kalashnikova, Olga V.; Bull, Michael A.Atmospheric Chemistry and Physics (2017), 17 (8), 5095-5106CODEN: ACPTCE; ISSN:1680-7324. (Copernicus Publications)Since early 2000, the Multi-angle Imaging SpectroRadiometer (MISR) instrument on NASA's Terra satellite has been acquiring data that have been used to produce aerosol optical depth (AOD) and particle property retrievals at 17.6 km spatial resoln. Capitalizing on the capabilities provided by multi-angle viewing, the current operational (Version 22) MISR algorithm performs well, with about 75% of MISR AOD retrievals globally falling within 0.05 or 20% ×AOD of paired validation data from the groundbased Aerosol Robotic Network (AERONET). This paper describes the development and assessment of a prototype version of a higher-spatial-resoln. 4.4 km MISR aerosol optical depth product compared against multiple AERONET Distributed Regional Aerosol Gridded Observations Network (DRAGON) deployments around the globe. In comparisons with AERONET-DRAGON AODs, the 4.4 km resoln. retrievals show improved correlation (r D 0:9595), smaller RMSE (0.0768), reduced bias (-0.0208), and a larger fraction within the expected error envelope (80.92 %) relative to the Version 22 MISR retrievals.
- 12Franklin, M.; Kalashnikova, O. V.; Garay, M. J. Size-Resolved Particulate Matter Concentrations Derived from 4.4 Km-Resolution Size-Fractionated Multi-Angle Imaging SpectroRadiometer (MISR) Aerosol Optical Depth over Southern California. Remote Sens. Environ. 2017, 196, 312– 323, DOI: 10.1016/j.rse.2017.05.002Google ScholarThere is no corresponding record for this reference.
- 13Gelaro, R.; McCarty, W.; Suarez, M. J.; Todling, R.; Molod, A.; Takacs, L.; Randles, C. A.; Darmenov, A.; Bosilovich, M. G.; Reichle, R.; Wargan, K.; Coy, L.; Cullather, R.; Draper, C.; Akella, S.; Buchard, V.; Conaty, A.; da Silva, A. M.; Gu, W.; Kim, G.-K.; Koster, R.; Lucchesi, R.; Merkova, D.; Nielsen, J. E.; Partyka, G.; Pawson, S.; Putman, W.; Rienecker, M.; Schubert, S. D.; Sienkiewicz, M.; Zhao, B. The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). J. Clim. 2017, 30 (14), 5419– 5454, DOI: 10.1175/JCLI-D-16-0758.1Google Scholar13https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BB38%252FosVSqsQ%253D%253D&md5=959df25d738b268ddc11d400569a79d1The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2)Gelaro Ronald; McCarty Will; Todling Ricardo; Molod Andrea; Darmenov Anton; Bosilovich Michael G; Reichle Rolf; da Silva Arlindo; Kim Gi-Kong; Koster Randal; Pawson Steven; Putman William; Rienecker Michele; Schubert Siegfried D; Suarez Max J; Draper Clara; Buchard Virginie; Takacs Lawrence; Wargan Krzysztof; Coy Lawrence; Akella Santha; Conaty Austin; Gu Wei; Lucchesi Robert; Merkova Dagmar; Nielsen Jon Eric; Partyka Gary; Sienkiewicz Meta; Randles Cynthia; Cullather Richard; Zhao BinJournal of climate (2017), Volume 30 (Iss 13), 5419-5454 ISSN:0894-8755.The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) is the latest atmospheric reanalysis of the modern satellite era produced by NASA's Global Modeling and Assimilation Office (GMAO). MERRA-2 assimilates observation types not available to its predecessor, MERRA, and includes updates to the Goddard Earth Observing System (GEOS) model and analysis scheme so as to provide a viable ongoing climate analysis beyond MERRA's terminus. While addressing known limitations of MERRA, MERRA-2 is also intended to be a development milestone for a future integrated Earth system analysis (IESA) currently under development at GMAO. This paper provides an overview of the MERRA-2 system and various performance metrics. Among the advances in MERRA-2 relevant to IESA are the assimilation of aerosol observations, several improvements to the representation of the stratosphere including ozone, and improved representations of cryospheric processes. Other improvements in the quality of MERRA-2 compared with MERRA include the reduction of some spurious trends and jumps related to changes in the observing system, and reduced biases and imbalances in aspects of the water cycle. Remaining deficiencies are also identified. Production of MERRA-2 began in June 2014 in four processing streams, and converged to a single near-real time stream in mid 2015. MERRA-2 products are accessible online through the NASA Goddard Earth Sciences Data Information Services Center (GES DISC).
- 14Marais, E. A.; Jacob, D. J.; Jimenez, J. L.; Campuzano-Jost, P.; Day, D. A.; Hu, W.; Krechmer, J.; Zhu, L.; Kim, P. S.; Miller, C. C.; Fisher, J. A.; Travis, K.; Yu, K.; Hanisco, T. F.; Wolfe, G. M.; Arkinson, H. L.; Pye, H. O. T.; Froyd, K. D.; Liao, J.; McNeill, V. F. Aqueous-Phase Mechanism for Secondary Organic Aerosol Formation from Isoprene: Application to the Southeast United States and Co-Benefit of SO2 Emission Controls. Atmos. Chem. Phys. 2016, 16 (3), 1603– 1618, DOI: 10.5194/acp-16-1603-2016Google Scholar14https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC28XpsVSmsL0%253D&md5=d6606d8207351df09d7c698711e7c623Aqueous-phase mechanism for secondary organic aerosol formation from isoprene: application to the southeast United States and co-benefit of SO2 emission controlsMarais, E. A.; Jacob, D. J.; Jimenez, J. L.; Campuzano-Jost, P.; Day, D. A.; Hu, W.; Krechmer, J.; Zhu, L.; Kim, P. S.; Miller, C. C.; Fisher, J. A.; Travis, K.; Yu, K.; Hanisco, T. F.; Wolfe, G. M.; Arkinson, H. L.; Pye, H. O. T.; Froyd, K. D.; Liao, J.; McNeill, V. F.Atmospheric Chemistry and Physics (2016), 16 (3), 1603-1618CODEN: ACPTCE; ISSN:1680-7324. (Copernicus Publications)Isoprene emitted by vegetation is an important precursor of secondary org. aerosol (SOA), but the mechanism and yields are uncertain. Aerosol is prevailingly aq. under the humid conditions typical of isoprene-emitting regions. Here we develop an aq.-phase mechanism for isoprene SOA formation coupled to a detailed gas-phase isoprene oxidn. scheme. The mechanism is based on aerosol reactive uptake coeffs. (γ) for water-sol. isoprene oxidn. products, including sensitivity to aerosol acidity and nucleophile concns. We apply this mechanism to simulation of aircraft (SEAC4RS) and ground-based (SOAS) observations over the southeast US in summer 2013 using the GEOS-Chem chem. transport model. Emissions of nitrogen oxides (NOx ≡ NO + NO2) over the southeast US are such that the peroxy radicals produced from isoprene oxidn. (ISOPO2) react significantly with both NO (high-NOx pathway) and HO2 (low-NOx pathway), leading to different suites of isoprene SOA precursors. We find a mean SOA mass yield of 3.3% from isoprene oxidn., consistent with the obsd. relationship of total fine org. aerosol (OA) and formaldehyde (a product of isoprene oxidn.). Isoprene SOA prodn. is mainly contributed by two immediate gasphase precursors, isoprene epoxydiols (IEPOX, 58% of isoprene SOA) from the low-NOx pathway and glyoxal (28 %) from both low- and high-NOx pathways. This speciation is consistent with observations of IEPOX SOA from SOAS and SEAC4RS. Observations show a strong relationship between IEPOX SOA and sulfate aerosol that we explain as due to the effect of sulfate on aerosol acidity and vol. Isoprene SOA concns. increase as NOx emissions decrease (favoring the low-NOx pathway for isoprene oxidn.), but decrease more strongly as SO2 emissions decrease (due to the effect of sulfate on aerosol acidity and vol.). The US Environmental Protection Agency (EPA) projects 2013-2025 decreases in anthropogenic emissions of 34% for NOx (leading to a 7% increase in isoprene SOA) and 48% for SO2 (35% decrease in isoprene SOA). Reducing SO2 emissions decreases sulfate and isoprene SOA by a similar magnitude, representing a factor of 2 co-benefit for PM2.5 from SO2 emission controls.
- 15Pye, H. O. T.; Chan, A. W. H.; Barkley, M. P.; Seinfeld, J. H. Global Modeling of Organic Aerosol: The Importance of Reactive Nitrogen (NOX and NO3). Atmos. Chem. Phys. 2010, 10 (22), 11261– 11276, DOI: 10.5194/acp-10-11261-2010Google Scholar15https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3MXmvFCit7w%253D&md5=06bd441b244ac3ede8d916e918c150b6Global modeling of organic aerosol: the importance of reactive nitrogen (NOx and NO3)Pye, H. O. T.; Chan, A. W. H.; Barkley, M. P.; Seinfeld, J. H.Atmospheric Chemistry and Physics (2010), 10 (22), 11261-11276CODEN: ACPTCE; ISSN:1680-7316. (Copernicus Publications)Reactive nitrogen compds., specifically NOx and NO3, likely influence global org. aerosol levels. To assess these interactions, GEOS-Chem, a chem. transport model, is updated to include improved biogenic emissions (following MEGAN v2.1/2.04), a new org. aerosol tracer lumping scheme, aerosol from nitrate radical (NO3) oxidn. of isoprene, and NOx-dependent monoterpene and sesquiterpene aerosol yields. As a result of significant nighttime terpene emissions, fast reaction of monoterpenes with the nitrate radical, and relatively high aerosol yields from NO3 oxidn., biogenic hydrocarbon-NO3 reactions are expected to be a major contributor to surface level aerosol concns. in anthropogenically influenced areas such as the United States. By including aerosol from nitrate radical oxidn. in GEOS-Chem, terpene (monoterpene + sesquiterpene) aerosol approx. doubles and isoprene aerosol is enhanced by 30 to 40% in the Southeast United States. In terms of the global budget of org. aerosol, however, aerosol from nitrate radical oxidn. is somewhat minor (slightly more than 3 Tg/yr) due to the relatively high volatility of org.-NO3 oxidn. products in the yield parameterization. Globally, 69 to 88 Tg/yr of org. aerosol is predicted to be produced annually, of which 14-15 Tg/yr is from oxidn. of monoterpenes and sesquiterpenes and 8-9 Tg/yr from isoprene.
- 16Ginoux, P.; Prospero, J. M.; Gill, T. E.; Hsu, N. C.; Zhao, M. Global-Scale Attribution of Anthropogenic and Natural Dust Sources and Their Emission Rates Based on MODIS Deep Blue Aerosol Products. Rev. Geophys. 2012, DOI: 10.1029/2012RG000388Google ScholarThere is no corresponding record for this reference.
- 17Zhang, L.; Kok, J. F.; Henze, D. K.; Li, Q.; Zhao, C. Improving Simulations of Fine Dust Surface Concentrations over the Western United States by Optimizing the Particle Size Distribution. Geophys. Res. Lett. 2013, 40 (12), 3270– 3275, DOI: 10.1002/grl.50591Google Scholar17https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3sXhtFajtLzI&md5=b3efd76080e14cd5682622767de2f897Improving simulations of fine dust surface concentrations over the western United States by optimizing the particle size distributionZhang, Li; Kok, Jasper F.; Henze, Daven K.; Li, Qinbin; Zhao, ChunGeophysical Research Letters (2013), 40 (12), 3270-3275CODEN: GPRLAJ; ISSN:1944-8007. (Wiley-Blackwell)To improve ests. of remote contributions of dust to fine particulate matter (PM2.5) in the western United States, new dust particle size distributions (PSDs) based upon scale-invariant fragmentation theory (KokPSD) with constraints from in situ measurements (IMPPSD) are implemented in a chem. transport model (GEOS-Chem). Compared to initial simulations, this leads to redns. in the mass of emitted dust particles with radii <1.8 μm by 40%-60%. Consequently, the root-mean-square error in simulated fine dust concns. compared to springtime surface observations in the western United States is reduced by 67%-81%. The ratio of simulated fine to coarse PM mass is also improved, which is not achievable by redns. in total dust emissions. The IMPPSD best represents the PSD of dust transported from remote sources and reduces modeled PM2.5 concns. up to 5 μg/m3 over the western United States, which is important when considering sources contributing to nonattainment of air quality stds.
- 18Philip, S.; Martin, R. V.; Snider, G.; Weagle, C. L.; van Donkelaar, A.; Brauer, M.; Henze, D. K.; Klimont, Z.; Venkataraman, C.; Guttikunda, S. K.; Zhang, Q. Anthropogenic Fugitive, Combustion and Industrial Dust Is a Significant, Underrepresented Fine Particulate Matter Source in Global Atmospheric Models. Environ. Res. Lett. 2017, 12 (4), 044018, DOI: 10.1088/1748-9326/aa65a4Google Scholar18https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1cXmvVSht7w%253D&md5=b686869f2f7c48acdadf35d5e14506bbAnthropogenic fugitive, combustion and industrial dust is a significant, underrepresented fine particulate matter source in global atmospheric modelsPhilip, Sajeev; Martin, Randall V.; Snider, Graydon; Weagle, Crystal L.; van Donkelaar, Aaron; Brauer, Michael; Henze, Daven K.; Klimont, Zbigniew; Venkataraman, Chandra; Guttikunda, Sarath K.; Zhang, QiangEnvironmental Research Letters (2017), 12 (4), 044018/1-044018/7CODEN: ERLNAL; ISSN:1748-9326. (IOP Publishing Ltd.)Global measurements of the elemental compn. of fine particulate matter across several urban locations by the Surface Particulate Matter Network reveal an enhanced fraction of anthropogenic dust compared to natural dust sources, esp. over Asia. We develop a global simulation of anthropogenic fugitive, combustion, and industrial dust which, to our knowledge, is partially missing or strongly underrepresented in global models. We est. 2-16 μmg m-3 increase in fine particulate mass concn. across East and South Asia by including anthropogenic fugitive, combustion, and industrial dust emissions. A simulation including anthropogenic fugitive, combustion, and industrial dust emissions increases the correlation from 0.06 to 0.66 of simulated fine dust in comparison with Surface Particulate Matter Network measurements at 13 globally dispersed locations, and reduces the low bias by 10% in total fine particulate mass in comparison with global in situ observations. Global population-weighted PM2.5 increases by 2.9 μg m-3 (10%). Our assessment ascertains the urgent need of including this underrepresented fine anthropogenic dust source into global bottom-up emission inventories and global models.
- 19Giglio, L.; Randerson, J. T.; van der Werf, G. R. Analysis of Daily, Monthly, and Annual Burned Area Using the Fourth-Generation Global Fire Emissions Database (GFED4). J. Geophys. Res.: Biogeosci. 2013, 118 (1), 317– 328, DOI: 10.1002/jgrg.20042Google ScholarThere is no corresponding record for this reference.
- 20Li, M.; Zhang, Q.; Kurokawa, J.; Woo, J.-H.; He, K.; Lu, Z.; Ohara, T.; Song, Y.; Streets, D. G.; Carmichael, G. R.; Cheng, Y.; Hong, C.; Huo, H.; Jiang, X.; Kang, S.; Liu, F.; Su, H.; Zheng, B. MIX: A Mosaic Asian Anthropogenic Emission Inventory under the International Collaboration Framework of the MICS-Asia and HTAP. Atmos. Chem. Phys. 2017, 17 (2), 935– 963, DOI: 10.5194/acp-17-935-2017Google Scholar20https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXosFart7s%253D&md5=89cdde3610d7a091d6a61c44d57b3cc6MIX: a mosaic Asian anthropogenic emission inventory under the international collaboration framework of the MICS-Asia and HTAPLi, Meng; Zhang, Qiang; Kurokawa, Jun-ichi; Woo, Jung-Hun; He, Kebin; Lu, Zifeng; Ohara, Toshimasa; Song, Yu; Streets, David G.; Carmichael, Gregory R.; Cheng, Yafang; Hong, Chaopeng; Huo, Hong; Jiang, Xujia; Kang, Sicong; Liu, Fei; Su, Hang; Zheng, BoAtmospheric Chemistry and Physics (2017), 17 (2), 935-963CODEN: ACPTCE; ISSN:1680-7324. (Copernicus Publications)The MIX inventory is developed for the years 2008 and 2010 to support the Model Inter-Comparison Study for Asia (MICS-Asia) and the Task Force on Hemispheric Transport of Air Pollution (TF HTAP) by a mosaic of up-to-date regional emission inventories. Emissions are estd. for all major anthropogenic sources in 29 countries and regions in Asia. We conducted detailed comparisons of different regional emission inventories and incorporated the best available ones for each region into the mosaic inventory at a uniform spatial and temporal resoln. Emissions are aggregated to five anthropogenic sectors: power, industry, residential, transportation, and agriculture. We est. the total Asian emissions of 10 species in 2010 as follows: 51.3 Tg SO2, 52.1 Tg NOx, 336.6 Tg CO, 67.0 Tg NMVOC (non-methane volatile org. compds.), 28.8 Tg NH3, 31.7 Tg PM10, 22.7 Tg PM2.5, 3.5 Tg BC, 8.3 Tg OC, and 17.3 Pg CO2. Emissions from China and India dominate the emissions of Asia for most of the species. We also estd. Asian emissions in 2006 using the same methodol. of MIX. The relative change rates of Asian emissions for the period of 2006-2010 are estd. as follows: -8.1% for SO2, C19.2% for NOx, C3.9% for CO, C15.5% for NMVOC, C1.7% for NH3, -3.4% for PM10, -1.6% for PM2.5, C5.5% for BC, C1.8% for OC, and C19.9% for CO2. Model-ready speciated NMVOC emissions for SAPRC-99 and CB05 mechanisms were developed following a profile-assignment approach. Monthly gridded emissions at a spatial resoln. of 0.25° ×0.25° are developed and can be accessed.
- 21Travis, K. R.; Jacob, D. J.; Fisher, J. A.; Kim, P. S.; Marais, E. A.; Zhu, L.; Yu, K.; Miller, C. C.; Yantosca, R. M.; Sulprizio, M. P.; Thompson, A. M.; Wennberg, P. O.; Crounse, J. D.; St. Clair, J. M.; Cohen, R. C.; Laughner, J. L.; Dibb, J. E.; Hall, S. R.; Ullmann, K.; Wolfe, G. M.; Pollack, I. B.; Peischl, J.; Neuman, J. A.; Zhou, X. Why Do Models Overestimate Surface Ozone in the Southeast United States?. Atmos. Chem. Phys. 2016, 16 (21), 13561– 13577, DOI: 10.5194/acp-16-13561-2016Google Scholar21https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXhsFKmur8%253D&md5=4eeba560c682155ebb56302d01e8daafWhy do models overestimate surface ozone in the Southeast United States?Travis, Katherine R.; Jacob, Daniel J.; Fisher, Jenny A.; Kim, Patrick S.; Marais, Eloise A.; Zhu, Lei; Yu, Karen; Miller, Christopher C.; Yantosca, Robert M.; Sulprizio, Melissa P.; Thompson, Anne M.; Wennberg, Paul O.; Crounse, John D.; St. Clair, Jason M.; Cohen, Ronald C.; Laughner, Joshua L.; Dibb, Jack E.; Hall, Samuel R.; Ullmann, Kirk; Wolfe, Glenn M.; Pollack, Illana B.; Peischl, Jeff; Neuman, Jonathan A.; Zhou, XianliangAtmospheric Chemistry and Physics (2016), 16 (21), 13561-13577CODEN: ACPTCE; ISSN:1680-7324. (Copernicus Publications)Ozone pollution in the Southeast US involves complex chem. driven by emissions of anthropogenic nitrogen oxide radicals (NOx ≃ NO+NO2) and biogenic isoprene. Model ests. of surface ozone concns. tend to be biased high in the region and this is of concern for designing effective emission control strategies to meet air quality stds. We use detailed chem. observations from the SEAC4RS aircraft campaign in August and Sept. 2013, interpreted with the GEOS-Chem chem. transport model at 0.25° × 0.3125° horizontal resoln., to better understand the factors controlling surface ozone in the Southeast US. We find that the National Emission Inventory (NEI) for NOx from the US Environmental Protection Agency (EPA) is too high. This finding is based on SEAC4RS observations of and its oxidn. products, surface network observations of nitrate wet deposition fluxes, and OMI satellite observations of tropospheric NO2 columns. Our results indicate that NEI NOx emissions from mobile and industrial sources must be reduced by 30-60 %, dependent on the assumption of the contribution by soil NOx emissions. Upper-tropospheric NO2 from lightning makes a large contribution to satellite observations of tropospheric NO2 that must be accounted for when using these data to est. surface NOx emissions. We find that only half of isoprene oxidn. proceeds by the high-NOx pathway to produce ozone; this fraction is only moderately sensitive to changes in NOx emissions because isoprene and NOx emissions are spatially segregated. GEOS-Chem with reduced NOx emissions provides an unbiased simulation of ozone observations from the aircraft and reproduces the obsd. ozone prodn. efficiency in the boundary layer as derived from a regression of ozone and NOx oxidn. products. However, the model is still biased high by 6 ± 14 ppb relative to obsd. surface ozone in the Southeast US. Ozone sondes launched during midday hours show a 7 ppb ozone decrease from 1.5 km to the surface that GEOS-Chem does not capture. This bias may reflect a combination of excessive vertical mixing and net ozone prodn. in the model boundary layer.
- 22World Health Organization. WHO Global Ambient Air Quality Database (Update 2018); WHO: Geneva, 2018.Google ScholarThere is no corresponding record for this reference.
- 23Kumar, N.; Chu, A.; Foster, A. An Empirical Relationship between PM2.5 and Aerosol Optical Depth in Delhi Metropolitan. Atmos. Environ. 2007, 41 (21), 4492– 4503, DOI: 10.1016/j.atmosenv.2007.01.046Google Scholar23https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2sXlslWks70%253D&md5=b964ef372fec8230d04424f593c06b1dAn empirical relationship between PM2.5 and aerosol optical depth in Delhi MetropolitanKumar, Naresh; Chu, Allen; Foster, AndrewAtmospheric Environment (2007), 41 (21), 4492-4503CODEN: AENVEQ; ISSN:1352-2310. (Elsevier Ltd.)Atm. remote sensing offers a unique opportunity to compute indirect ests. of air quality, which are critically important for the management and surveillance of air quality in megacities of developing countries, particularly in India and China, which have experienced elevated concn. of air pollution but lack adequate spatial-temporal coverage of air pollution monitoring. This article examines the relationship between aerosol optical depth (AOD) estd. from satellite data at 5 km spatial resoln. and the mass of fine particles ≤2.5 μm in aerodynamic diam. (PM2.5) monitored on the ground in Delhi Metropolitan where a series of environmental laws have been instituted in recent years. PM2.5 monitored at 113 sites were collocated by time and space with the AOD computed using the data from Moderate Resoln. Imaging Spectroradiometer (MODIS onboard the Terra satellite). MODIS data were acquired from NASA's Goddard Space Flight Center Earth Sciences Distributed Active Archive Center (DAAC). Our anal. shows a significant pos. assocn. between AOD and PM2.5. After controlling for weather conditions, a 1% change in AOD explains 0.52±0.202% and 0.39±0.15% change in PM2.5 monitored within ±45 and 150 min intervals of AOD data. This relationship will be used to est. air quality surface for previous years, which will allow us to examine the time-space dynamics of air pollution in Delhi following recent air quality regulations, and to assess exposure to air pollution before and after the regulations and its impact on health.
- 24Liu, Y.; Sarnat, J. A.; Kilaru, V.; Jacob, D. J.; Koutrakis, P. Estimating Ground-Level PM2.5 in the Eastern United States Using Satellite Remote Sensing. Environ. Sci. Technol. 2005, 39 (9), 3269– 3278, DOI: 10.1021/es049352mGoogle Scholar24https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2MXitVyntrc%253D&md5=c04f7c467c6241b52e1bc370466962a2Estimating Ground-Level PM2.5 in the Eastern United States Using Satellite Remote SensingLiu, Yang; Sarnat, Jeremy A.; Kilaru, Vasu; Jacob, Daniel J.; Koutrakis, PetrosEnvironmental Science and Technology (2005), 39 (9), 3269-3278CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)An empirical model based on the regression between daily PM2.5 (particles with aerodynamic diams. of less than 2.5 μm) concns. and aerosol optical thickness (AOT) measurements from the multiangle imaging spectroradiometer (MISR) was developed and tested using data from the eastern United States during the period of 2001. Overall, the empirical model explained 48% of the variability in PM2.5 concns. The root-mean-square error of the model was 6.2 μg/m3 with a corresponding av. PM2.5 concn. of 13.8 μg/m3. When PM2.5 concns. greater than 40 μg/m3 were removed, model results were shown to be unbiased estimators of observations. Several factors, such as planetary boundary layer height, relative humidity, season, and other geog. attributes of monitoring sites, were found to influence the assocn. between PM2.5 and AOT. The findings of this study illustrate the strong potential of satellite remote sensing in regional ambient air quality monitoring as an extension to ground networks. With the continual advancement of remote sensing technol. and global data assimilation systems, AOT measurements derived from satellite remote sensors may provide a cost-effective approach as a supplemental source of information for detg. ground-level particle concns.
- 25de Hoogh, K.; Chen, J.; Gulliver, J.; Hoffmann, B.; Hertel, O.; Ketzel, M.; Bauwelinck, M.; van Donkelaar, A.; Hvidtfeldt, U. A.; Katsouyanni, K.; Klompmaker, J.; Martin, R. V.; Samoli, E.; Schwartz, P. E.; Stafoggia, M.; Bellander, T.; Strak, M.; Wolf, K.; Vienneau, D.; Brunekreef, B.; Hoek, G. Spatial PM2.5, NO2, O3 and BC Models for Western Europe – Evaluation of Spatiotemporal Stability. Environ. Int. 2018, 120, 81– 92, DOI: 10.1016/j.envint.2018.07.036Google Scholar25https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1cXhsVeksbzL&md5=931ca9f9793b88aef579dccb827e4090Spatial PM2.5, NO2, O3 and BC models for Western Europe - Evaluation of spatiotemporal stabilityde Hoogh, Kees; Chen, Jie; Gulliver, John; Hoffmann, Barbara; Hertel, Ole; Ketzel, Matthias; Bauwelinck, Mariska; van Donkelaar, Aaron; Hvidtfeldt, Ulla A.; Katsouyanni, Klea; Klompmaker, Jochem; Martin, Randal V.; Samoli, Evangelia; Schwartz, Per E.; Stafoggia, Massimo; Bellander, Tom; Strak, Maciej; Wolf, Kathrin; Vienneau, Danielle; Brunekreef, Bert; Hoek, GerardEnvironment International (2018), 120 (), 81-92CODEN: ENVIDV; ISSN:0160-4120. (Elsevier Ltd.)In order to investigate assocns. between air pollution and adverse health effects consistent fine spatial air pollution surfaces are needed across large areas to provide cohorts with comparable exposures. The aim of this paper is to develop and evaluate fine spatial scale land use regression models for four major health relevant air pollutants (PM2.5, NO2, BC, O3) across Europe. We developed West-European land use regression models (LUR) for 2010 estg. annual mean PM2.5, NO2, BC and O3 concns. (including cold and warm season ests. for O3). The models were based on AirBase routine monitoring data (PM2.5, NO2 and O3) and ESCAPE monitoring data (BC), and incorporated satellite observations, dispersion model ests., land use and traffic data. Kriging was performed on the residual spatial variation from the LUR models and added to the exposure ests. One model was developed using all sites (100%). Robustness of the models was evaluated by performing a five-fold hold-out validation and for PM2.5 and NO2 addnl. with independent comparison at ESCAPE measurements. To evaluate the stability of each model's spatial structure over time, sep. models were developed for different years (NO2 and O3: 2000 and 2005; PM2.5: 2013). The PM2.5, BC, NO2, O3 annual, O3 warm season and O3 cold season models explained resp. 72%, 54%, 59%, 65%, 69% and 83% of spatial variation in the measured concns. Kriging proved an efficient technique to explain a part of residual spatial variation for the pollutants with a strong regional component explaining resp. 10%, 24% and 16% of the R2 in the PM2.5, O3 warm and O3 cold models. Explained variance at fully independent sites vs the internal hold-out validation was slightly lower for PM2.5 (65% vs 66%) and lower for NO2 (49% vs 57%). Predictions from the 2010 model correlated highly with models developed in other years at the overall European scale. We developed robust PM2.5, NO2, O3 and BC hybrid LUR models. At the West-European scale models were robust in time, becoming less robust at smaller spatial scales. Models were applied to 100 × 100 m surfaces across Western Europe to allow for exposure assignment for 35 million participants from 18 European cohorts participating in the ELAPSE study.
- 26Ma, Z.; Hu, X.; Huang, L.; Bi, J.; Liu, Y. Estimating Ground-Level PM 2.5 in China Using Satellite Remote Sensing. Environ. Sci. Technol. 2014, 48 (13), 7436– 7444, DOI: 10.1021/es5009399Google Scholar26https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXpt1yqsrs%253D&md5=00fe05f7ca3d36ff090287cb15ad5cf2Estimating Ground-Level PM2.5 in China Using Satellite Remote SensingMa, Zongwei; Hu, Xuefei; Huang, Lei; Bi, Jun; Liu, YangEnvironmental Science & Technology (2014), 48 (13), 7436-7444CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)Estg. ground-level PM2.5 from satellite-derived aerosol optical depth (AOD) using a spatial statistical model is a promising new method to evaluate the spatial and temporal characteristics of PM2.5 exposure in a large geog. region. However, studies outside North America have been limited due to the lack of ground PM2.5 measurements to calibrate the model. Taking advantage of the newly established national monitoring network, we developed a national-scale geog. weighted regression (GWR) model to est. daily PM2.5 concns. in China with fused satellite AOD as the primary predictor. The results showed that the meteorol. and land use information can greatly improve model performance. The overall cross-validation (CV) R2 is 0.64 and root mean squared prediction error (RMSE) is 32.98 μg/m3. The mean prediction error (MPE) of the predicted annual PM2.5 is 8.28 μg/m3. Our predicted annual PM2.5 concns. indicated that over 96% of the Chinese population lives in areas that exceed the Chinese National Ambient Air Quality Std. (CNAAQS) Level 2 std. Our results also confirmed satellite-derived AOD in conjunction with meteorol. fields and land use information can be successfully applied to extend the ground PM2.5 monitoring network in China.
- 27Song, W.; Jia, H.; Huang, J.; Zhang, Y. A Satellite-Based Geographically Weighted Regression Model for Regional PM2.5 Estimation over the Pearl River Delta Region in China. Remote Sens. Environ. 2014, 154, 1– 7, DOI: 10.1016/j.rse.2014.08.008Google ScholarThere is no corresponding record for this reference.
- 28van Donkelaar, A.; Martin, R. V.; Brauer, M.; Boys, B. L. Use of Satellite Observations for Long-Term Exposure Assessment of Global Concentrations of Fine Particulate Matter. Environ. Health Perspect. 2015, 123 (2), 135– 143, DOI: 10.1289/ehp.1408646Google Scholar28https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BC2M3jtVaisA%253D%253D&md5=1178880e6f850f7acacdb93ea2c50f02Use of satellite observations for long-term exposure assessment of global concentrations of fine particulate mattervan Donkelaar Aaron; Martin Randall V; Brauer Michael; Boys Brian LEnvironmental health perspectives (2015), 123 (2), 135-43 ISSN:.BACKGROUND: More than a decade of satellite observations offers global information about the trend and magnitude of human exposure to fine particulate matter (PM2.5). OBJECTIVE: In this study, we developed improved global exposure estimates of ambient PM2.5 mass and trend using PM2.5 concentrations inferred from multiple satellite instruments. METHODS: We combined three satellite-derived PM2.5 sources to produce global PM2.5 estimates at about 10 km × 10 km from 1998 through 2012. For each source, we related total column retrievals of aerosol optical depth to near-ground PM2.5 using the GEOS-Chem chemical transport model to represent local aerosol optical properties and vertical profiles. We collected 210 global ground-based PM2.5 observations from the literature to evaluate our satellite-based estimates with values measured in areas other than North America and Europe. RESULTS: We estimated that global population-weighted ambient PM2.5 concentrations increased 0.55 μg/m3/year (95% CI: 0.43, 0.67) (2.1%/year; 95% CI: 1.6, 2.6) from 1998 through 2012. Increasing PM2.5 in some developing regions drove this global change, despite decreasing PM2.5 in some developed regions. The estimated proportion of the population of East Asia living above the World Health Organization (WHO) Interim Target-1 of 35 μg/m3 increased from 51% in 1998-2000 to 70% in 2010-2012. In contrast, the North American proportion above the WHO Air Quality Guideline of 10 μg/m3 fell from 62% in 1998-2000 to 19% in 2010-2012. We found significant agreement between satellite-derived estimates and ground-based measurements outside North America and Europe (r = 0.81; n = 210; slope = 0.68). The low bias in satellite-derived estimates suggests that true global concentrations could be even greater. CONCLUSIONS: Satellite observations provide insight into global long-term changes in ambient PM2.5 concentrations. Satellite-derived estimates and ground-based PM2.5 observations from this study are available for public use.
- 29van Donkelaar, A.; Martin, R. V.; Brauer, M.; Hsu, N. C.; Kahn, R. A.; Levy, R. C.; Lyapustin, A.; Sayer, A. M.; Winker, D. M. Global Estimates of Fine Particulate Matter Using a Combined Geophysical-Statistical Method with Information from Satellites, Models, and Monitors. Environ. Sci. Technol. 2016, 50 (7), 3762– 3772, DOI: 10.1021/acs.est.5b05833Google Scholar29https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC28XjvVyjurY%253D&md5=600d1e11d3e1b145924d1ccc11cf9758Global Estimates of Fine Particulate Matter using a Combined Geophysical-Statistical Method with Information from Satellites, Models, and Monitorsvan Donkelaar, Aaron; Martin, Randall V.; Brauer, Michael; Hsu, N. Christina; Kahn, Ralph A.; Levy, Robert C.; Lyapustin, Alexei; Sayer, Andrew M.; Winker, David M.Environmental Science & Technology (2016), 50 (7), 3762-3772CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)We estd. global fine particulate matter (PM2.5) concns. using information from satellite-, simulation- and monitor-based sources by applying a Geog. Weighted Regression (GWR) to global geophys. based satellite-derived PM2.5 ests. Aerosol optical depth from multiple satellite products (MISR, MODIS Dark Target, MODIS and SeaWiFS Deep Blue, and MODIS MAIAC) was combined with simulation (GEOS-Chem) based upon their relative uncertainties as detd. using ground-based sun photometer (AERONET) observations for 1998-2014. The GWR predictors included simulated aerosol compn. and land use information. The resultant PM2.5 ests. were highly consistent (R2 = 0.81) with out-of-sample cross-validated PM2.5 concns. from monitors. The global population-weighted annual av. PM2.5 concns. were 3-fold higher than the 10 μg/m3 WHO guideline, driven by exposures in Asian and African regions. Ests. in regions with high contributions from mineral dust were assocd. with higher uncertainty, resulting from both sparse ground-based monitoring, and challenging conditions for retrieval and simulation. This approach demonstrates that the addn. of even sparse ground-based measurements to more globally continuous PM2.5 data sources can yield valuable improvements to PM2.5 characterization on a global scale.
- 30Shaddick, G.; Thomas, M. L.; Green, A.; Brauer, M.; van Donkelaar, A.; Burnett, R.; Chang, H. H.; Cohen, A.; van Dingenen, R.; Dora, C.; Gumy, S.; Liu, Y.; Martin, R.; Waller, L. A.; West, J.; Zidek, J. V.; Prüss-Ustün, A. Data Integration Model for Air Quality: A Hierarchical Approach to the Global Estimation of Exposures to Ambient Air Pollution. J. R. Stat. Soc. Ser. C (Applied Stat. 2018, 67 (1), 231– 253, DOI: 10.1111/rssc.12227Google ScholarThere is no corresponding record for this reference.
- 31Di, Q.; Koutrakis, P.; Schwartz, J. A Hybrid Prediction Model for PM2.5 Mass and Components Using a Chemical Transport Model and Land Use Regression. Atmos. Environ. 2016, 131, 390– 399, DOI: 10.1016/j.atmosenv.2016.02.002Google Scholar31https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC28XjtFOqsr0%253D&md5=a424117770a1601fce71ca7122f8872fA hybrid prediction model for PM2.5 mass and components using a chemical transport model and land use regressionDi, Qian; Koutrakis, Petros; Schwartz, JoelAtmospheric Environment (2016), 131 (), 390-399CODEN: AENVEQ; ISSN:1352-2310. (Elsevier Ltd.)GEOS-Chem, a chem. transport model, provides time-space continuous ests. of atm. pollutants including PM2.5 and its major components, but model predictions are not highly correlated with ground monitoring data. In addn., its spatial resoln. is usually too coarse to characterize the spatial pattern in pollutant concns. in urban environments. Our objective was to calibrate daily GEOS-Chem simulations using ground monitoring data and incorporating meteorol. variables, land-use terms and spatial-temporal lagged terms. Major PM2.5 components of our interest include sulfate, nitrate, org. carbon, elemental carbon, ammonium, sea salt and dust. We used a backward propagation neural network to calibrate GEOS-Chem predictions with a spatial resoln. of 0.500° × 0.667° using monitoring data collected during the period from 2001 to 2010 for the Northeastern United States. Subsequently, we made predictions at 1 km × 1 km grid cells. We detd. the accuracy of the spatial-temporal predictions using ten-fold cross-validation and "leave-one-day-out" cross-validation techniques. We found a high total R2 for PM2.5 mass (all data R2 0.85, yearly values: 0.80-0.88) and PM2.5 components (R2 for individual components were around 0.70-0.80). Our model makes it possible to assess spatially- and temporally-resolved short- and long-term exposures to PM2.5 mass and components for epidemiol. studies.
- 32Friberg, M. D.; Kahn, R. A.; Holmes, H. A.; Chang, H. H.; Sarnat, S. E.; Tolbert, P. E.; Russell, A. G.; Mulholland, J. A. Daily Ambient Air Pollution Metrics for Five Cities: Evaluation of Data-Fusion-Based Estimates and Uncertainties. Atmos. Environ. 2017, 158, 36– 50, DOI: 10.1016/j.atmosenv.2017.03.022Google Scholar32https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXkslyisro%253D&md5=ef702846b2a25b8d422b758431b78964Daily ambient air pollution metrics for five cities: Evaluation of data-fusion-based estimates and uncertaintiesFriberg, Mariel D.; Kahn, Ralph A.; Holmes, Heather A.; Chang, Howard H.; Sarnat, Stefanie Ebelt; Tolbert, Paige E.; Russell, Armistead G.; Mulholland, James A.Atmospheric Environment (2017), 158 (), 36-50CODEN: AENVEQ; ISSN:1352-2310. (Elsevier Ltd.)Spatiotemporal characterization of ambient air pollutant concns. is increasingly relying on the combination of observations and air quality models to provide well-constrained, spatially and temporally complete pollutant concn. fields. Air quality models, in particular, are attractive, as they characterize the emissions, meteorol., and physiochem. process linkages explicitly while providing continuous spatial structure. However, such modeling is computationally intensive and has biases. The limitations of spatially sparse and temporally incomplete observations can be overcome by blending the data with ests. from a phys. and chem. coherent model, driven by emissions and meteorol. inputs. We recently developed a data fusion method that blends ambient ground observations and chem.-transport-modeled (CTM) data to est. daily, spatially resolved pollutant concns. and assocd. correlations. In this study, we assess the ability of the data fusion method to produce daily metrics (i.e., 1-h max, 8-h max, and 24-h av.) of ambient air pollution that capture spatiotemporal air pollution trends for 12 pollutants (CO, NO2, NOx, O3, SO2, PM10, PM2.5, and five PM2.5 components) across five metropolitan areas (Atlanta, Birmingham, Dallas, Pittsburgh, and St. Louis), from 2002 to 2008. Three sets of comparisons are performed: (1) the CTM concns. are evaluated for each pollutant and metropolitan domain, (2) the data fusion concns. are compared with the monitor data, (3) a comprehensive cross-validation anal. against obsd. data evaluates the quality of the data fusion model simulations across multiple metropolitan domains. The resulting daily spatial field ests. of air pollutant concns. and uncertainties are not only consistent with observations, emissions, and meteorol., but substantially improve CTM-derived results for nearly all pollutants and all cities, with the exception of NO2 for Birmingham. The greatest improvements occur for O3 and PM2.5. Squared spatiotemporal correlation coeffs. range between simulations and observations detd. using cross-validation across all cities for air pollutants of secondary and mixed origins are R2 = 0.88-0.93 (O3), 0.81-0.89 (SO4), 0.67-0.83 (PM2.5), 0.52-0.72 (NO3), 0.43-0.80 (NH4), 0.32-0.51 (OC), and 0.14-0.71 (PM10). Results for more spatially heterogeneous (larger spatial gradients) pollutants of primary origin (NOx, CO, SO2 and EC), tend to be better than those for relatively homogeneous pollutants of secondary origin. Generally, background concns. and spatial concn. gradients reflect interurban airshed complexity and the effects of regional transport, whereas daily spatial pattern variability shows intra-urban consistency in the fused data. With sufficiently high CTM spatial resoln., traffic-related pollutants exhibit gradual concn. gradients that peak toward the urban centers. Ambient pollutant concn. uncertainty ests. for the fused data are both more accurate and smaller than those for either the observations or the model simulations alone.
- 33Kunzli, N. Assessment of Deaths Attributable to Air Pollution: Should We Use Risk Estimates Based on Time Series or on Cohort Studies?. Am. J. Epidemiol. 2001, 153 (11), 1050– 1055, DOI: 10.1093/aje/153.11.1050Google Scholar33https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BD3MzislCrtg%253D%253D&md5=e5f8a849ec24c24dbef10b04885f1610Assessment of deaths attributable to air pollution: should we use risk estimates based on time series or on cohort studies?Kunzli N; Medina S; Kaiser R; Quenel P; Horak F Jr; Studnicka MAmerican journal of epidemiology (2001), 153 (11), 1050-5 ISSN:0002-9262.Epidemiologic studies are crucial to the estimation of numbers of deaths attributable to air pollution. In this paper, the authors present a framework for distinguishing estimates of attributable cases based on time-series studies from those based on cohort studies, the latter being 5-10 times larger. The authors distinguish four categories of death associated with air pollution: A) air pollution increases both the risk of underlying diseases leading to frailty and the short term risk of death among the frail; B) air pollution increases the risk of chronic diseases leading to frailty but is unrelated to timing of death; C) air pollution is unrelated to risk of chronic diseases but short term exposure increases mortality among persons who are frail; and D) neither underlying chronic disease nor the event of death is related to air pollution exposure. Time-series approaches capture deaths from categories A and C, whereas cohort studies assess cases from categories A, B, and C. In addition, years of life lost can only be derived from cohort studies, where time to death is the outcome, while in time-series studies, death is a once-only event (no dimension in time). The authors conclude that time-series analyses underestimate cases of death attributable to air pollution and that assessment of the impact of air pollution on mortality should be based on cohort studies.
- 34Brook, R. D.; Rajagopalan, S.; Pope, C. A.; Brook, J. R.; Bhatnagar, A.; Diez-Roux, A. V.; Holguin, F.; Hong, Y.; Luepker, R. V.; Mittleman, M. A.; Peters, A.; Siscovick, D.; Smith, S. C.; Whitsel, L.; Kaufman, J. D. American Heart Association Council on Epidemiology and Prevention, Council on the Kidney in Cardiovascular Disease, and Council on Nutrition, Physical Activity and Metabolism. Particulate Matter Air Pollution and Cardiovascular Disease. Circulation 2010, 121 (21), 2331– 2378, DOI: 10.1161/CIR.0b013e3181dbece1Google Scholar34https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3cXmslGmu78%253D&md5=bbe8d6ace13d0364865a8364e5b6000bParticulate Matter Air Pollution and Cardiovascular Disease: An Update to the Scientific Statement From the American Heart AssociationBrook, Robert D.; Rajagopalan, Sanjay; Pope, C. Arden, III; Brook, Jeffrey R.; Bhatnagar, Aruni; Diez-Roux, Ana V.; Holguin, Fernando; Hong, Yuling; Luepker, Russell V.; Mittleman, Murray A.; Peters, Annette; Siscovick, David; Smith, Sidney C., Jr.; Whitsel, Laurie; Kaufman, Joel D.Circulation (2010), 121 (21), 2331-2378CODEN: CIRCAZ; ISSN:0009-7322. (Lippincott Williams & Wilkins)A review. In 2004, the first American Heart Assocn. scientific statement on "Air Pollution and Cardiovascular Disease" concluded that exposure to particulate matter (PM) air pollution contributes to cardiovascular morbidity and mortality. In the interim, numerous studies have expanded our understanding of this assocn. and further elucidated the physiol. and mol. mechanisms involved. The main objective of this updated American Heart Assocn. scientific statement is to provide a comprehensive review of the new evidence linking PM exposure with cardiovascular disease, with a specific focus on highlighting the clin. implications for researchers and healthcare providers. The writing group also sought to provide expert consensus opinions on many aspects of the current state of science and updated suggestions for areas of future research. On the basis of the findings of this review, several new conclusions were reached, including the following: Exposure to PM < 2.5 μm in diam. (PM2.5) over a few hours to weeks can trigger cardiovascular disease-related mortality and nonfatal events; longer-term exposure (eg, a few years) increases the risk for cardiovascular mortality to an even greater extent than exposures over a few days and reduces life expectancy within more highly exposed segments of the population by several months to a few years; redns. in PM levels are assocd. with decreases in cardiovascular mortality within a time frame as short as a few years; and many credible pathol. mechanisms have been elucidated that lend biol. plausibility to these findings. It is the opinion of the writing group that the overall evidence is consistent with a causal relationship between PM2.5 exposure and cardiovascular morbidity and mortality. This body of evidence has grown and been strengthened substantially since the first American Heart Assocn. scientific statement was published. Finally, PM2.5 exposure is deemed a modifiable factor that contributes to cardiovascular morbidity and mortality.
- 35Pope, C. A. Mortality Effects of Longer Term Exposures to Fine Particulate Air Pollution: Review of Recent Epidemiological Evidence. Inhalation Toxicol. 2007, 19 (sup1), 33– 38, DOI: 10.1080/08958370701492961Google Scholar35https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2sXhtVKmtLjN&md5=7d2899758d840ff75a9da75e9485facaMortality effects of longer term exposures to fine particulate air pollution: review of recent epidemiological evidencePope, C. Arden, IIIInhalation Toxicology (2007), 19 (Suppl. 1), 33-38CODEN: INHTE5; ISSN:0895-8378. (Informa Healthcare)A review. This article evaluates the dynamic exposure-response relation between particulate matter air pollution (PM) and mortality risk by integrating epidemiol. evidence from studies that use different time scales of exposure. The evidence suggests that short-term exposure studies are observing more than just harvesting or mortality displacement. There is little evidence of short-term compensatory redn. in deaths, and estd. PM effects are generally larger for intermediate and longer term time scales of exposure. Although proximity in time matters, with most recent exposure having the largest health impact, there is evidence that the short-term exposure studies capture only a small amt. of the overall health effects of long-term repeated exposure to PM. The overall epidemiol. evidence suggests that adverse health effects are dependent on both exposure concns. and length of exposure, and that long-term exposures have larger, more persistent cumulative effects than short-term exposures.
- 36Yitshak-Sade, M.; Bobb, J. F.; Schwartz, J. D.; Kloog, I.; Zanobetti, A. The Association between Short and Long-Term Exposure to PM2.5 and Temperature and Hospital Admissions in New England and the Synergistic Effect of the Short-Term Exposures. Sci. Total Environ. 2018, 639, 868– 875, DOI: 10.1016/j.scitotenv.2018.05.181Google Scholar36https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1cXhtVWksbzF&md5=61df9f72355a96299d14892cbb07fabdThe association between short and long-term exposure to PM2.5 and temperature and hospital admissions in New England and the synergistic effect of the short-term exposuresYitshak-Sade, Maayan; Bobb, Jennifer F.; Schwartz, Joel D.; Kloog, Itai; Zanobetti, AntonellaScience of the Total Environment (2018), 639 (), 868-875CODEN: STENDL; ISSN:0048-9697. (Elsevier B.V.)Particulate matter < 2.5 μm in diam. (PM2.5) and heat are strong predictors of morbidity, yet few studies have examd. the effects of long-term exposures on non-fatal events, or assessed the short and long-term effect on health simultaneously. We jointly investigated the assocn. of short and long-term exposures to PM2.5 and temp. with hospital admissions, and explored the modification of the assocns. with the short-term exposures by one another and by temp. variability. Daily ZIP code counts of respiratory, cardiac and stroke admissions of adults ≥65 (N = 2,015,660) were constructed across New-England (2001-2011). Daily PM2.5 and temp. exposure ests. were obtained from satellite-based spatio-temporally resolved models. For each admission cause, a Poisson regression was fit on short and long-term exposures, with a random intercept for ZIP code. Modifications of the short-term effects were tested by adding interaction terms with temp., PM2.5 and temp. variability. Assocns. between short and long-term exposures were obsd. for all of the outcomes, with stronger effects of long-term exposures to PM2.5. For respiratory admissions, the short-term PM2.5 effect (percent increase per IQR) was larger on warmer days (1.12% vs. -0.53%) and in months of higher temp. variability (1.63% vs. -0.45%). The short-term temp. effect was higher in months of higher temp. variability as well. For cardiac admissions, the PM2.5 effect was larger on colder days (0.56% vs. -0.30%) and in months of higher temp. variability (0.99% vs. -0.56%). We obsd. synergistic effects of short-term exposures to PM2.5, temp. and temp. variability. Long-term exposures to PM2.5 were assocd. with larger effects compared to short-term exposures.
- 37Liang, F.; Xiao, Q.; Gu, D.; Xu, M.; Tian, L.; Guo, Q.; Wu, Z.; Pan, X.; Liu, Y. Satellite-Based Short- and Long-Term Exposure to PM2.5 and Adult Mortality in Urban Beijing, China. Environ. Pollut. 2018, 242, 492– 499, DOI: 10.1016/j.envpol.2018.06.097Google Scholar37https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1cXhtlSlu77M&md5=ef653841992788e03f5e3f5a2b20f5cbSatellite-based short- and long-term exposure to PM2.5 and adult mortality in urban Beijing, ChinaLiang, Fengchao; Xiao, Qingyang; Gu, Dongfeng; Xu, Meimei; Tian, Lin; Guo, Qun; Wu, Ziting; Pan, Xiaochuan; Liu, YangEnvironmental Pollution (Oxford, United Kingdom) (2018), 242 (Part_A), 492-499CODEN: ENPOEK; ISSN:0269-7491. (Elsevier Ltd.)Severe and persistent haze accompanied by high concns. of fine particulate matter (PM2.5) has become a great public health concern in urban China. However, research on the health effects of PM2.5 in China has been hindered by the lack of high-quality exposure ests. In this study, we assessed the excess mortality assocd. with both short- and long-term exposure to ambient PM2.5 simultaneously using satellite-derived exposure data at a high spatiotemporal resoln. Adult registries of non-accidental, respiratory and cardiovascular deaths in urban Beijing in 2013 were collected. Exposure levels were estd. from daily satellite-based PM2.5 concns. at 1 km spatial resoln. from 2004 to 2013. Mixed Poisson regression models were fitted to est. the cause-specific mortality in assocn. with PM2.5 exposures. With the mutual adjustment of short- and long-term exposure of PM2.5, the percent increases assocd. with every 10 μg/m3 increase in short-term PM2.5 exposure were 0.09% (95% CI: -0.14%, 0.33%; lag 01), 1.02% (95% CI: 0.08%, 1.97%; lag 04) and 0.09% (95% CI: -0.23%, 0.42%; lag 01) for non-accidental, respiratory and cardiovascular mortality, resp.; those attributable to every 10 μg/m3 increase in long-term PM2.5 exposure (9-yr moving av.) were 16.78% (95% CI: 10.58%, 23.33%), 44.14% (95% CI: 20.73%, 72.10%) and 3.72% (95% CI: -3.75%, 11.77%), resp.
- 38Sayer, A. M.; Munchak, L. A.; Hsu, N. C.; Levy, R. C.; Bettenhausen, C.; Jeong, M.-J. MODIS Collection 6 Aerosol Products: Comparison between Aqua’s e-Deep Blue, Dark Target, and “Merged” Data Sets, and Usage Recommendations. J. Geophys. Res. Atmos. 2014, 119 (24), 13965– 13989, DOI: 10.1002/2014JD022453Google Scholar38https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXhtVKmtrk%253D&md5=65b66f5ca479d2855e4737d3d514be7bMODIS Collection 6 aerosol products: Comparison between Aqua's e-Deep Blue, Dark Target, and "merged" data sets, and usage recommendationsSayer, A. M.; Munchak, L. A.; Hsu, N. C.; Levy, R. C.; Bettenhausen, C.; Jeong, M.-J.Journal of Geophysical Research: Atmospheres (2014), 119 (24), 13965-13989CODEN: JGRDE3; ISSN:2169-8996. (Wiley-Blackwell)The Moderate Resoln. Imaging Spectroradiometer (MODIS) Atmospheres data product suite includes three algorithms applied to retrieve midvisible aerosol optical depth (AOD): the Enhanced Deep Blue (DB) and Dark Target (DT) algorithms over land, and a DT over-water algorithm. All three have been refined in the recent "Collection 6" (C6) MODIS reprocessing. In particular, DB has been expanded to cover vegetated land surfaces as well as brighter desert/urban areas. Addnl., a new "merged" data set which draws from all three algorithms is included in the C6 products. This study is intended to act as a point of ref. for new and experienced MODIS data users with which to understand the global and regional characteristics of the C6 DB, DT, and merged data sets, based on MODIS Aqua data. This includes validation against Aerosol Robotic Network (AERONET) observations at 111 sites, focused toward regional and categorical (surface/aerosol type) anal. Neither algorithm consistently outperforms the other, although in many cases the retrieved AOD and the level of its agreement with AERONET are very similar. In many regions the DB, DT, and merged data sets are all suitable for quant. applications, bearing in mind that they cannot be considered independent, while in other cases one algorithm does consistently outperform the other. Usage recommendations and caveats are thus somewhat complicated and regionally dependent.
- 39Levy, R. C.; Mattoo, S.; Munchak, L. A.; Remer, L. A.; Sayer, A. M.; Patadia, F.; Hsu, N. C. The Collection 6 MODIS Aerosol Products over Land and Ocean. Atmos. Meas. Tech. 2013, 6 (11), 2989– 3034, DOI: 10.5194/amt-6-2989-2013Google ScholarThere is no corresponding record for this reference.
- 40Sayer, A. M.; Hsu, N. C.; Bettenhausen, C.; Jeong, M.-J.; Holben, B. N.; Zhang, J. Global and Regional Evaluation of Over-Land Spectral Aerosol Optical Depth Retrievals from SeaWiFS. Atmos. Meas. Tech. 2012, 5 (7), 1761– 1778, DOI: 10.5194/amt-5-1761-2012Google ScholarThere is no corresponding record for this reference.
- 41Hsu, N. C.; Jeong, M.-J.; Bettenhausen, C.; Sayer, A. M.; Hansell, R.; Seftor, C. S.; Huang, J.; Tsay, S.-C. Enhanced Deep Blue Aerosol Retrieval Algorithm: The Second Generation. J. Geophys. Res. Atmos. 2013, 118 (16), 9296– 9315, DOI: 10.1002/jgrd.50712Google ScholarThere is no corresponding record for this reference.
- 42Diner, D. J.; Beckert, J. C.; Reilly, T. H.; Bruegge, C. J.; Conel, J. E.; Kahn, R. A.; Martonchik, J. V.; Ackerman, T. P.; Davies, R.; Gerstl, S. A. W.; Gordon, H. R.; Muller, J.; Myneni, R. B.; Sellers, P. J.; Pinty, B.; Verstraete, M. M. Multi-Angle Imaging SpectroRadiometer (MISR) Instrument Description and Experiment Overview. IEEE Trans. Geosci. Remote Sens. 1998, 36 (4), 1072– 1087, DOI: 10.1109/36.700992Google ScholarThere is no corresponding record for this reference.
- 43Martonchik, J. V.; Kahn, R. A.; Diner, D. J. Retrieval of Aerosol Properties over Land Using MISR Observations. In Satellite Aerosol Remote Sensing over Land; Springer: Berlin, 2009; pp 267– 293. DOI: DOI: 10.1007/978-3-540-69397-0_9 .Google ScholarThere is no corresponding record for this reference.
- 44Garay, M. J.; Witek, M. L.; Kahn, R. A.; Seidel, F. C.; Limbacher, J. A.; Bull, M. A.; Diner, D. J.; Hansen, E. G.; Kalashnikova, O. V.; Lee, H.; Nastan, A. M.; Yu, Y. Introducing the 4.4km Spatial Resolution Multi-Angle Imaging SpectroRadiometer (MISR) Aerosol Product. Atmos. Meas. Tech. 2020, 13 (2), 593– 628, DOI: 10.5194/amt-13-593-2020Google ScholarThere is no corresponding record for this reference.
- 45van Donkelaar, A.; Martin, R. V.; Brauer, M.; Kahn, R.; Levy, R.; Verduzco, C.; Villeneuve, P. J. Global Estimates of Ambient Fine Particulate Matter Concentrations from Satellite-Based Aerosol Optical Depth: Development and Application. Environ. Health Perspect. 2010, 118 (6), 847– 855, DOI: 10.1289/ehp.0901623Google Scholar45https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3cXovVKhtro%253D&md5=eba9677d030d59d6c3ed27c887a336c7Global estimates of ambient fine particulate matter concentrations from satellite-based aerosol optical depth: development and applicationvan Donkelaar, Aaron; Martin, Randall V.; Brauer, Michael; Kahn, Ralph; Levy, Robert; Verduzco, Carolyn; Villeneuve, Paul J.Environmental Health Perspectives (2010), 118 (6), 847-855CODEN: EVHPAZ; ISSN:0091-6765. (U. S. Department of Health and Human Services, Public Health Services)Epidemiol. and health impact studies of fine particulate matter with diam. < 2.5 μm (PM2.5) are limited by the lack of monitoring data, esp. in developing countries. Satellite observations offer valuable global information about PM2.5 concns. In this study, we developed a technique for estg. surface PM2.5 concns. from satellite observations. We mapped global ground-level PM2.5 concns. using total column aerosol optical depth (AOD) from the MODIS (Moderate Resoln. Imaging Spectroradiometer) and MISR (Multiangle Imaging Spectroradiometer) satellite instruments and coincident aerosol vertical profiles from the GEOS-Chem global chem. transport model. We detd. that global ests. of long-term av. (1 Jan. 2001 to 31 Dec. 2006) PM2.5 concns. at approx. 10 km × 10 km resoln. indicate a global population-weighted geometric mean PM2.5 concn. of 20 μg/m3. The World Health Organization Air Quality PM2.5 Interim Target-1 (35 μg/m3 annual av.) is exceeded over central and eastern Asia for 38% and for 50% of the population, resp. Annual mean PM2.5 concns. exceed 80 μg/m3 over eastern China. Our evaluation of the satellite-derived est. with ground-based in situ measurements indicates significant spatial agreement with North American measurements (r = 0.77; slope = 1.07; n = 1057) and with noncoincident measurements elsewhere (r = 0.83; slope = 0.86; n = 244). The 1 SD of uncertainty in the satellite-derived PM2.5 is 25%, which is inferred from the AOD retrieval and from aerosol vertical profile errors and sampling. The global population-weighted mean uncertainty is 6.7 μg/m3. Satellite-derived total-column AOD, when combined with a chem. transport model, provides ests. of global long-term av. PM2.5 concns.
- 46Van Donkelaar, A.; Martin, R. V.; Park, R. J. Estimating Ground-Level PM 2.5 Using Aerosol Optical Depth Determined from Satellite Remote Sensing. J. Geophys. Res. 2006, 111, 21201, DOI: 10.1029/2005JD006996Google Scholar46https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2sXhvValtL8%253D&md5=95eff5143f7b35eaf63e8f207d7ba88bEstimating ground-level PM2.5 using aerosol optical depth determined from satellite remote sensingvan Donkelaar, Aaron; Martin, Randall V.; Park, Rokjin J.Journal of Geophysical Research, [Atmospheres] (2006), 111 (D21), D21201/1-D21201/10CODEN: JGRDE3 ISSN:. (American Geophysical Union)The authors assess the relation of ground-level fine particulate matter (PM2.5) concns. for 2000-2001 measured as part of the Canadian National Air Pollution Surveillance (NAPS) network and the U.S. Air Quality System (AQS), vs. remotesensed PM2.5 detd. from aerosol optical depths (AOD) measured by the Moderate Resoln. Imaging Spectroradiometer (MODIS) and the Multiangle Imaging Spectroradiometer (MISR) satellite instruments. A global chem. transport model (GEOS-CHEM) was used to simulate the factors affecting the relation between AOD and PM2.5. AERONET AOD was used to evaluate the method (r = 0.71, N = 48, slope = 0.69). The authors find significant spatial variation of the annual mean ground-based measurements with PM2.5 detd. from MODIS (r = 0.69, N = 199, slope = 0.82) and MISR (r = 0.58, N = 199, slope = 0.57). Excluding California significantly increases the resp. slopes and correlations. The relative vertical profile of aerosol extinction is the most important factor affecting the spatial relation between satellite and surface measurements of PM2.5; neglecting this parameter would reduce the spatial correlation to 0.36. In contrast, temporal variation in AOD is the most influential parameter affecting the temporal relation between satellite and surface measurements of PM2.5; neglecting daily variation in this parameter would decrease the correlation in eastern North America from 0.5-0.8 to <0.2. Other simulated aerosol properties, such as effective radius and extinction efficiency have a minor role temporally, but do influence the spatial correlation. Global mapping of PM2.5 from both MODIS and MISR reveals annual mean concns. of 40-50 μg/m3 over northern India and China.
- 47Molod, A.; Takacs, L.; Suarez, M.; Bacmeister, J. Development of the GEOS-5 Atmospheric General Circulation Model: Evolution from MERRA to MERRA2. Geosci. Model Dev. 2015, 8 (5), 1339– 1356, DOI: 10.5194/gmd-8-1339-2015Google ScholarThere is no corresponding record for this reference.
- 48Lu, Z.; Zhang, Q.; Streets, D. G. Sulfur Dioxide and Primary Carbonaceous Aerosol Emissions in China and India. Atmos. Chem. Phys. 2011, 11, 9839– 9864, DOI: 10.5194/acp-11-9839-2011Google Scholar48https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3MXhs1GqtbbN&md5=21b539ac27dec3c14d8db6024287dfd5Sulfur dioxide and primary carbonaceous aerosol emissions in China and India, 1996-2010Lu, Z.; Zhang, Q.; Streets, D. G.Atmospheric Chemistry and Physics (2011), 11 (18), 9839-9864CODEN: ACPTCE; ISSN:1680-7316. (Copernicus Publications)China and India are the 2 largest anthropogenic aerosol generating countries in the world. The authors develop a new inventory of SO2 (SO2) and primary carbonaceous aerosol (i.e., black and org. C, BC and OC) emissions from these 2 countries for the period 1996-2010, using a technol.-based methodol. Emissions from major anthropogenic sources and open biomass burning are included, and time-dependent trends in activity rates and emission factors are incorporated in the calcn. Year-specific monthly temporal distributions for major sectors and gridded emissions at a resoln. of 0.1° × 0.1° distributed by multiple year-by-year spatial proxies are also developed. In China, the interaction between economic development and environmental protection causes large temporal variations in the emission trends. From 1996 to 2000, emissions of all 3 species showed a decreasing trend (by 9 %-17 %) due to a slowdown in economic growth, a decline in coal use in nonpower sectors, and the implementation of air pollution control measures. With the economic boom after 2000, emissions from China changed dramatically. BC and OC emissions increased by 46% and 33% to 1.85 TG and 4.03 Tg in 2010. SO2 emissions 1st increased by 61% to 34.0 TG in 2006, and then decreased by 9.2% to 30.8 Tg in 2010 due to the wide application of flue-gas desulfurization (FGD) equipment in power plants. Driven by the remarkable energy consumption growth and relatively lax emission controls, emissions from India increased by 70 %, 41 %, and 35% to 8.81 TG, 1.02 Tg, and 2.74 Tg in 2010 for SO2, BC, and OC, resp. Monte Carlo simulations are used to quantify the emission uncertainties. The av. 95% confidence intervals (CIs) of SO2, BC, and OC emissions are -16 %-17 %, -43 %-93 %, and -43 %-80% for China, and -15 %-16 %, -41 %-87 %, and -44 %-92% for India, resp. S content, fuel use, and S retention of hard coal and the actual FGD removal efficiency are the main contributors to the uncertainties of SO2 emissions. Biofuel combustion related parameters (i.e., technol. divisions, fuel use, and emission factor determinants) are the largest source of OC uncertainties, whereas BC emissions are also sensitive to the parameters of coal combustion in the residential and industrial sectors and the coke-making process. Comparing the results with satellite observations, the trends of estd. emissions in both China and India are in good agreement with the trends of aerosol optical depth (AOD) and SO2 retrievals obtained from different satellites.
- 49Holben, B. N.; Eck, T. F.; Slutsker, I.; Tanré, D.; Buis, J. P.; Setzer, A.; Vermote, E.; Reagan, J. A.; Kaufman, Y. J.; Nakajima, T.; Lavenu, F.; Jankowiak, I.; Smirnov, A. AERONET—A Federated Instrument Network and Data Archive for Aerosol Characterization. Remote Sens. Environ. 1998, 66 (1), 1– 16, DOI: 10.1016/S0034-4257(98)00031-5Google ScholarThere is no corresponding record for this reference.
- 50Eck, T. F.; Holben, B. N.; Reid, J. S.; Dubovik, O.; Smirnov, A.; O’Neill, N. T.; Slutsker, I.; Kinne, S. Wavelength Dependence of the Optical Depth of Biomass Burning, Urban, and Desert Dust Aerosols. J. Geophys. Res. Atmos. 1999, 104 (D24), 31333– 31349, DOI: 10.1029/1999JD900923Google ScholarThere is no corresponding record for this reference.
- 51Giles, D. M.; Sinyuk, A.; Sorokin, M. G.; Schafer, J. S.; Smirnov, A.; Slutsker, I.; Eck, T. F.; Holben, B. N.; Lewis, J. R.; Campbell, J. R.; Welton, E. J.; Korkin, S. V.; Lyapustin, A. I. Advancements in the Aerosol Robotic Network (AERONET) Version 3 Database – Automated near-Real-Time Quality Control Algorithm with Improved Cloud Screening for Sun Photometer Aerosol Optical Depth (AOD) Measurements. Atmos. Meas. Tech. 2019, 12 (1), 169– 209, DOI: 10.5194/amt-12-169-2019Google Scholar51https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1MXhtlGhtbvN&md5=342ef7b49f925189ef4675428813c5bbAdvancements in the Aerosol Robotic Network (AERONET) Version 3 database - automated near-real-time quality control algorithm with improved cloud screening for Sun photometer aerosol optical depth (AOD) measurementsGiles, David M.; Sinyuk, Alexander; Sorokin, Mikhail G.; Schafer, Joel S.; Smirnov, Alexander; Slutsker, Ilya; Eck, Thomas F.; Holben, Brent N.; Lewis, Jasper R.; Campbell, James R.; Welton, Ellsworth J.; Korkin, Sergey V.; Lyapustin, Alexei I.Atmospheric Measurement Techniques (2019), 12 (1), 169-209CODEN: AMTTC2; ISSN:1867-8548. (Copernicus Publications)The Aerosol Robotic Network (AERONET) has provided highly accurate, ground-truth measurements of the aerosol optical depth (AOD) using Cimel Electronique Sun- sky radiometers for more than 25 years. In Version 2 (V2) of the AERONET database, the near-real-time AOD was semiautomatically quality controlled utilizing mainly cloudscreening methodol., while addnl. AOD data contaminated by clouds or affected by instrument anomalies were removed manually before attaining quality-assured status (Level 2.0). The large growth in the no. of AERONET sites over the past 25 years resulted in significant burden to the manual quality control of millions of measurements in a consistent manner. The AERONET Version 3 (V3) algorithm provides fully automatic cloud screening and instrument anomaly quality controls. All of these new algorithm updates apply to near-real-time data as well as post-fielddeployment processed data, and AERONET reprocessed the database in 2018. A full algorithm redevelopment provided the opportunity to improve data inputs and corrections such as unique filter-specific temp. characterizations for all visible and near-IR wavelengths, updated gaseous and water vapor absorption coeffs., and ancillary data sets. The Level 2.0 AOD quality-assured data set is now available within a month after post-field calibration, reducing the lag time from up to several months. Near-real-time estd. uncertainty is detd. using data qualified as V3 Level 2.0 AOD and considering the difference between the AOD computed with the pre-field calibration and AOD computed with pre-field and post-field calibration. This assessment provides a near-real-time uncertainty est. for which av. differences of AOD suggest a + 0.02 bias and one sigma uncertainty of 0.02, spectrally, but the bias and uncertainty can be significantly larger for specific instrument deployments. Long-term monthly avs. analyzed for the entire V3 and V2 databases produced av. differences (V3-V2) of +0.002 with a ±0.02 SD (std. deviation), yet monthly avs. calcd. using time-matched observations in both databases were analyzed to compute an av. difference of -0.002 with a ±0.004 SD. The high statistical agreement in multiyear monthly averaged AOD validates the advanced automatic data quality control algorithms and suggests that migrating research to the V3 database will corroborate most V2 research conclusions and likely lead to more accurate results in some cases.
- 52Li, Z.; Zhao, X.; Kahn, R.; Mishchenko, M.; Remer, L.; Lee, K.-H.; Wang, M.; Laszlo, I.; Nakajima, T.; Maring, H. Uncertainties in Satellite Remote Sensing of Aerosols and Impact on Monitoring Its Long-Term Trend: A Review and Perspective. Ann. Geophys. 2009, 27 (7), 2755– 2770, DOI: 10.5194/angeo-27-2755-2009Google ScholarThere is no corresponding record for this reference.
- 53van Donkelaar, A.; Martin, R. V.; Spurr, R. J. D.; Drury, E.; Remer, L. A.; Levy, R. C.; Wang, J. Optimal Estimation for Global Ground-Level Fine Particulate Matter Concentrations. J. Geophys. Res. Atmos. 2013, 118 (11), 5621– 5636, DOI: 10.1002/jgrd.50479Google ScholarThere is no corresponding record for this reference.
- 54Brunsdon, C.; Fotheringham, A. S.; Charlton, M. E. Geographically Weighted Regression: A Method for Exploring Spatial Nonstationarity. Geogr. Anal. 1996, 28 (4), 281– 298, DOI: 10.1111/j.1538-4632.1996.tb00936.xGoogle ScholarThere is no corresponding record for this reference.
- 55Fotheringham, A. S.; Charlton, M. E.; Brunsdon, C. Geographically Weighted Regression: A Natural Evolution of the Expansion Method for Spatial Data Analysis. Environ. Plan. A Econ. Sp. 1998, 30 (11), 1905– 1927, DOI: 10.1068/a301905Google ScholarThere is no corresponding record for this reference.
- 56Jin, X.; Fiore, A. M.; Curci, G.; Lyapustin, A.; Civerolo, K.; Ku, M.; van Donkelaar, A.; Martin, R. V. Assessing Uncertainties of a Geophysical Approach to Estimate Surface Fine Particulate Matter Distributions from Satellite-Observed Aerosol Optical Depth. Atmos. Chem. Phys. 2019, 19 (1), 295– 313, DOI: 10.5194/acp-19-295-2019Google Scholar56https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1MXitlSgu7Y%253D&md5=9ec67dc30f456bc71537e4498ded0996Assessing uncertainties of a geophysical approach to estimate surface fine particulate matter distributions from satellite-observed aerosol optical depthJin, Xiaomeng; Fiore, Arlene M.; Curci, Gabriele; Lyapustin, Alexei; Civerolo, Kevin; Ku, Michael; van Donkelaar, Aaron; Martin, Randall V.Atmospheric Chemistry and Physics (2019), 19 (1), 295-313CODEN: ACPTCE; ISSN:1680-7324. (Copernicus Publications)Health impact analyses are increasingly tapping the broad spatial coverage of satellite aerosol optical depth (AOD) products to est. human exposure to fine particulate matter (PM2.5). We use a forward geophys. approach to derive ground-level PM2.5 distributions from satellite AOD at 1 km2 resoln. for 2011 over the northeastern US by applying relationships between surface PM2.5 and column AOD (calcd. offline from speciated mass distributions) from a regional air quality model (CMAQ; 12×12 km2 horizontal resoln.). Seasonal av. satellite-derived PM2.5 reveals more spatial detail and best captures obsd. surface PM2.5 levels during summer. At the daily scale, however, satellite-derived PM2.5 is not only subject to measurement uncertainties from satellite instruments, but more importantly to uncertainties in the relationship between surface PM2.5 and column AOD. Using 11 ground-based AOD measurements within 10 km of surface PM2.5 monitors, we show that uncertainties in modeled PM2.5/AOD can explain more than 70% of the spatial and temporal variance in the total uncertainty in daily satellite-derived PM2.5 evaluated at PM2.5 monitors. This finding implies that a successful geophys. approach to deriving daily PM2.5 from satellite AOD requires model skill at capturing day-to-day variations in PM2.5/AOD relationships. Overall, we est. that uncertainties in the modeled PM2.5/AOD lead to an error of 11 μgm-3 in daily satellite-derived PM2.5, and uncertainties in satellite AOD lead to an error of 8 μgm-3. Using multi-platform ground, airborne, and radiosonde measurements, we show that uncertainties of modeled PM2.5/AOD are mainly driven by model uncertainties in aerosol column mass and speciation, while model representation of relative humidity and aerosol vertical profile shape contributes some systematic biases. The parameterization of aerosol optical properties, which dets. the mass extinction efficiency, also contributes to random uncertainty, with the size distribution being the largest source of uncertainty and hygroscopicity of inorg. salt the second largest. Future efforts to reduce uncertainty in geophys. approaches to derive surface PM2.5 from satellite AOD would thus benefit from improving model representation of aerosol vertical distribution and aerosol optical properties, to narrow uncertainty in satellite-derived PM2.5.
- 57Wang, Y.; Chen, Y. Significant Climate Impact of Highly Hygroscopic Atmospheric Aerosols in Delhi, India. Geophys. Res. Lett. 2019, 46 (10), 5535– 5545, DOI: 10.1029/2019GL082339Google Scholar57https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1MXhtFGqsL3P&md5=8ec0bcc186719f30c0c727a042ba4f06Significant Climate Impact of Highly Hygroscopic Atmospheric Aerosols in Delhi, IndiaWang, Yu; Chen, YingGeophysical Research Letters (2019), 46 (10), 5535-5545CODEN: GPRLAJ; ISSN:1944-8007. (Wiley-Blackwell)Hygroscopicity of aerosol (κchem) is a key factor affecting its direct and indirect climate effects, however, long-term observation in Delhi is absent. Here we demonstrate an approach to derive κchem from publicly available data sets and validate it (bias of 5%-30%) with long-term observations in Beijing. Using this approach, we report the first estn. of κchem in Delhi and discuss its climate implications. The bulk-averaged κchem of aerosols in Delhi is estd. to be 0.42 ω 0.07 during 2016-2018, implying a higher activation ability as cloud condensation nuclei in Delhi compared with Beijing and continental avs. worldwide. To activate a 0.1-μm particle, it averagely requires just a supersatn. of ∼0.18% ω 0.015% in Delhi but ∼0.3% (Beijing), 0.28%-0.31% (Asia, Africa, and South America) and ∼0.22% (Europe and North America). Our results imply that representing κchem of Delhi using Asian/Beijing av. may result in a significant underestimation of aerosol climate effects.
- 58Wang, Y.; Wang, Y.; Wang, L.; Petäjä, T.; Zha, Q.; Gong, C.; Li, S.; Pan, Y.; Hu, B.; Xin, J.; Kulmala, M. Increased Inorganic Aerosol Fraction Contributes to Air Pollution and Haze in China. Atmos. Chem. Phys. 2019, 19 (9), 5881– 5888, DOI: 10.5194/acp-19-5881-2019Google Scholar58https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1MXhtVahsbzF&md5=ff14544ef6838597365f560ee3c3bc50Increased inorganic aerosol fraction contributes to air pollution and haze in ChinaWang, Yonghong; Wang, Yuesi; Wang, Lili; Petaja, Tuukka; Zha, Qiaozhi; Gong, Chongshui; Li, Sixuan; Pan, Yuepeng; Hu, Bo; Xin, Jinyuan; Kulmala, MarkkuAtmospheric Chemistry and Physics (2019), 19 (9), 5881-5888CODEN: ACPTCE; ISSN:1680-7324. (Copernicus Publications)The detailed formation mechanism of an increased no. of haze events in China is still not very clear. Here, we found that reduced surface visibility from 1980 to 2010 and an increase in satellite-derived columnar concns. of inorg. precursors from 2002 to 2012 are connected with each other. Typically, higher inorg. mass fractions lead to increased aerosol water uptake and light-scattering ability in elevated relative humidity. Satellite observation of aerosol precursors of NO2 and SO2 showed increased concns. during the study period. Our in situ measurement of aerosol chem. compn. in Beijing also confirmed increased contribution of inorg. aerosol fraction as a function of the increased particle pollution level. Our investigations demonstrate that the increased inorg. fraction in the aerosol particles is a key component in the frequently occurring haze days during the study period, and particularly the redn. of nitrate, sulfate and their precursor gases would contribute towards better visibility in China.
- 59He, Q.; Zhou, G.; Geng, F.; Gao, W.; Yu, W. Spatial Distribution of Aerosol Hygroscopicity and Its Effect on PM2.5 Retrieval in East China. Atmos. Res. 2016, 170, 161– 167, DOI: 10.1016/j.atmosres.2015.11.011Google Scholar59https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXhvFKit7nI&md5=62f9efb8bde811d0cb777bd51f747609Spatial distribution of aerosol hygroscopicity and its effect on PM2.5 retrieval in East ChinaHe, Qianshan; Zhou, Guangqiang; Geng, Fuhai; Gao, Wei; Yu, WeiAtmospheric Research (2016), 170 (), 161-167CODEN: ATREEW; ISSN:0169-8095. (Elsevier B.V.)The hygroscopic properties of aerosol particles have strong impact on climate as well as visibility in polluted areas. Understanding of the scattering enhancement due to water uptake is of great importance in linking dry aerosol measurements with relevant ambient measurements, esp. for satellite retrievals. In this study, an observation-based algorithm combining meteorol. data with the particulate matter (PM) measurement was introduced to est. spatial distribution of indicators describing the integrated humidity effect in East China and the main factors impacting the hygroscopicity were explored. Investigation of 1 yr data indicates that the larger mass extinction efficiency αext values (> 9.0 m2/g) located in middle and northern Jiangsu Province, which might be caused by particulate org. material (POM) and sulfate aerosol from industries and human activities. The high level of POM in Jiangsu Province might also be responsible for the lower growth coeff. γ value in this region. For the inland junction provinces of Jiangsu and Anhui, a considerable higher hygroscopic growth region in East China might be attributed to more hygroscopic particles mainly comprised of inorg. salts (e.g., sulfates and nitrates) from several large-scale industrial districts distributed in this region. Validation shows good agreement of calcd. PM2.5 mass concns. with in situ measurements in most stations with correlative coeffs. of over 0.85, even if several defective stations induced by station location or seasonal variation of aerosol properties in this region. This algorithm can be used for more accurate surface level PM2.5 retrieval from satellite-based aerosol optical depth (AOD) with combination of the vertical correction for aerosol profile.
- 60Deng, X.; Tie, X.; Zhou, X.; Wu, D.; Zhong, L.; Tan, H.; Li, F.; Huang, X.; Bi, X.; Deng, T. Effects of Southeast Asia Biomass Burning on Aerosols and Ozone Concentrations over the Pearl River Delta (PRD) Region. Atmos. Environ. 2008, 42 (36), 8493– 8501, DOI: 10.1016/j.atmosenv.2008.08.013Google Scholar60https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1cXht1yrsr3J&md5=2198749d70946650574ec1893a2d7fc3Effects of Southeast Asia biomass burning on aerosols and ozone concentrations over the Pearl River Delta (PRD) regionDeng, Xuejiao; Tie, Xuexi; Zhou, Xiuji; Wu, Dui; Zhong, Liuju; Tan, Haobo; Li, Fei; Huang, Xiaoying; Bi, Xueyan; Deng, TaoAtmospheric Environment (2008), 42 (36), 8493-8501CODEN: AENVEQ; ISSN:1352-2310. (Elsevier Ltd.)The rapid increases in urbanization and human activities in the Pearl River Delta (PRD) region (China) have important impacts on regional air quality. In addn. to local anthropogenic emissions which are major driving forces for poor air quality in this region, biomass burning in Southeast Asia has also important contribution on aerosol and ozone concns. in the PRD region. This effect is analyzed by using satellite data, ground measurements, and models. MODIS aerosol optical depth (AOD) distribution in March 2006 shows a clear enhancement in AOD between Southeast Asia and the PRD region. With detail wind anal., 2 distinguished conditions are classified, i.e., Condition-1 (PRD is under influence of the biomass burning from Southeast Asia) and Condition-2 (PRD is not under influence of the biomass burning from Southeast Asia). The characterizations of aerosol, UV, and ozone in Guangzhou city (located in the PRD region) under these 2 conditions are analyzed. The analyses suggest that aerosols and CO concns. are higher in Condition-1 than in Condition-2; while the UV intensity and O3 concns. are lower in Condition-1 than in Condition-2. In Condition-1, the enhanced aerosol concns. from the Southeast Asia biomass burning produce redn. of UV intensity, and thus decreases the formation of ozone in Guangzhou.
- 61Zhang, M.; Wang, Y.; Ma, Y.; Wang, L.; Gong, W.; Liu, B. Spatial Distribution and Temporal Variation of Aerosol Optical Depth and Radiative Effect in South China and Its Adjacent Area. Atmos. Environ. 2018, 188, 120– 128, DOI: 10.1016/j.atmosenv.2018.06.028Google Scholar61https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1cXhtF2gtr7M&md5=525df9e74237f60b575eb37828a32a1fSpatial distribution and temporal variation of aerosol optical depth and radiative effect in South China and its adjacent areaZhang, Ming; Wang, Yi; Ma, Yingying; Wang, Lunche; Gong, Wei; Liu, BomingAtmospheric Environment (2018), 188 (), 120-128CODEN: AENVEQ; ISSN:1352-2310. (Elsevier Ltd.)The spatio-temporal characteristics of aerosol loading over South China from 2001 to 2016 were investigated using aerosol optical depth (AOD) from the Moderate Resoln. Imaging Spectroradiometer (MODIS) and NO2 from the Ozone Monitoring Instrument (OMI). AOD values were high in the central part and low in the southeast and northwest parts of South China. High AOD (larger than 0.7) were found in the Pearl River Delta, Nanning, and Hanoi (Vietnam). The seasonal av. AOD was high in spring (approx. 0.7) and low in winter (approx. 0.4). Generally, an increasing trend of AOD was found from 2001 to 2004 and a decreasing trend from 2004 to 2016 in the continent due to the change in pollutant discharging, which was verified by annual NO2 data. Furthermore, the aerosol radiative effect (ARE) was calcd. using the Mesoscale Atm. Global Irradiance Code (MAGIC) and MODIS AOD time series. The spatial distribution and temporal variation of ARE at surface showed similar patterns to AOD, with high values occurring in the Pearl River Delta (-39 W/m2), Hanoi (-36 W/m2), and Nanning (-30 W/m2). From 2001 to 2016, ARE at surface in South China decreased by approx. 4 W/m2 with the highest value (-24.75 W/m2) occurring in 2007.
- 62Yao, L.; Yang, L.; Yuan, Q.; Yan, C.; Dong, C.; Meng, C.; Sui, X.; Yang, F.; Lu, Y.; Wang, W. Sources Apportionment of PM2.5 in a Background Site in the North China Plain. Sci. Total Environ. 2016, 541, 590– 598, DOI: 10.1016/j.scitotenv.2015.09.123Google Scholar62https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXhs1ShtLnK&md5=151c202b19a52335555edfc448e8429eSources apportionment of PM2.5 in a background site in the North China PlainYao, Lan; Yang, Lingxiao; Yuan, Qi; Yan, Chao; Dong, Can; Meng, Chuanping; Sui, Xiao; Yang, Fei; Lu, Yaling; Wang, WenxingScience of the Total Environment (2016), 541 (), 590-598CODEN: STENDL; ISSN:0048-9697. (Elsevier B.V.)To better understand PM2.5 sources and potential source regions, a field study was conducted from Jan. 2011 to Nov. 2011 at a background site, Yellow River Delta National Nature Reserve (YRDNNR), in the North China Plain. Pos. matrix factorization (PMF) anal. and a potential source contribution function (PSCF) model assessed the data, which showed YRDNNR experiences serious air pollution. PM2.5 concns. at YRDNNR were 71.2, 92.7, 97.1 and 62.5 μg/m3 in spring, summer, autumn, and winter, resp.; 66.0% of daily samples exhibited higher concns. than the national air quality std. PM2.5 mass closure showed remarkable seasonal variations. SO42-, NO3-, and NH4+ were the dominant PM2.5 fractions in summer (58.0%); PM2.5 was characterized by a high org. aerosol load (40.2%) in winter. PMF anal. indicated secondary SO42- and NO3- (54.3%), biomass burning (15.8%), industry (10.7%), crustal matter (8.3%), vehicles (5.2%), and Cu smelting (4.9%) were important PM2.5 sources at YRDNNR on an annual av. The secondary SO42- and NO3- source was probably industrial coal combustion. PSCF anal. indicated a significant PM2.5 regional impact at YRDNNR year round. Local emissions may be non-negligible at YRDNNR in summer. Results provided a scientific basis to develop regional PM2.5 control strategies.
- 63Timmermans, R.; Kranenburg, R.; Manders, A.; Hendriks, C.; Segers, A.; Dammers, E.; Zhang, Q.; Wang, L.; Liu, Z.; Zeng, L.; Denier van der Gon, H.; Schaap, M. Source Apportionment of PM2.5 across China Using LOTOS-EUROS. Atmos. Environ. 2017, 164, 370– 386, DOI: 10.1016/j.atmosenv.2017.06.003Google Scholar63https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXhtVamt77P&md5=56787d58e398527ad648a4af376369eeSource apportionment of PM2.5 across China using LOTOS-EUROSTimmermans, R.; Kranenburg, R.; Manders, A.; Hendriks, C.; Segers, A.; Dammers, E.; Zhang, Q.; Wang, L.; Liu, Z.; Zeng, L.; Denier van der Gon, H.; Schaap, M.Atmospheric Environment (2017), 164 (), 370-386CODEN: AENVEQ; ISSN:1352-2310. (Elsevier Ltd.)China's population is exposed to high levels of particulate matter (PM) due to its strong economic growth and assocd. urbanization and industrialization. To support policy makers to develop cost effective mitigation strategies it is of crucial importance to understand the emission sources as well as formation routes responsible for high pollution levels. In this study we applied the LOTOS-EUROS model with its module to track the contributions of predefined source sectors to China for the year 2013 using the MEIC emission inventory. It is the first application of the model system to a region outside Europe. The source attribution was aimed to provide insight in the sector and area of origin of PM2.5 for the cities of Beijing and Shanghai. The source attribution shows that on av. about half of the PM2.5 pollution in both cities originates from the municipality itself. About a quarter of the PM2.5 comes from the neighboring provinces, whereas the remaining quarter is attributed to long range transport from anthropogenic and natural components. Residential combustion, transport, and industry are identified as the main sources with comparable contributions allocated to these sectors. The importance of the sectors varies throughout the year and differs slightly between the cities. During winter, urban contributions from residential combustion are dominant, whereas industrial and traffic contributions with a larger share of regional transport are more important during summer. The evaluation of the model results against satellite and in-situ observations shows the ability of the LOTOS-EUROS model to capture many features of the variability in particulate matter and its precursors in China. The model shows a systematic underestimation of particulate matter concns., esp. in winter. This illustrates that modeling particulate matter remains challenging as it comes to components like secondary org. aerosol and suspended dust as well as emissions and formation of PM during winter time haze situations. All in all, the LOTOS-EUROS system proves to be a powerful tool for policy support applications outside Europe as the intermediate complexity of the model allows the assessment of the area and sector of origin over decadal time periods.
- 64Zong, Z.; Wang, X.; Tian, C.; Chen, Y.; Fu, S.; Qu, L.; Ji, L.; Li, J.; Zhang, G. PMF and PSCF Based Source Apportionment of PM2.5 at a Regional Background Site in North China. Atmos. Res. 2018, 203, 207– 215, DOI: 10.1016/j.atmosres.2017.12.013Google Scholar64https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1cXosVGguw%253D%253D&md5=403751ecb43d65f0761291b5cd88c674PMF and PSCF based source apportionment of PM2.5 at a regional background site in North ChinaZong, Zheng; Wang, Xiaoping; Tian, Chongguo; Chen, Yingjun; Fu, Shanfei; Qu, Lin; Ji, Ling; Li, Jun; Zhang, GanAtmospheric Research (2018), 203 (), 207-215CODEN: ATREEW; ISSN:0169-8095. (Elsevier B.V.)To apportion regional PM2.5 (atm. particles with aerodynamic diam. < 2.5 μm) source types and their geog. pattern in North China, 120 daily PM2.5 samples on Beihuangcheng Island (BH, a regional background site in North China) were collected from August 20th, 2014 to Sept. 15th, 2015 showing one-year period. After the chem. analyses on carbonaceous species, water-sol. ions and inorg. elements, various approaches, such as Mann-Kendall test, chem. mass closure, ISORROPIA II model, Pos. Matrix Factorization (PMF) linked with Potential Source Contribution Function (PSCF), were used to explore the PM2.5 speciation, sources, and source regions. Consequently, distinct seasonal variations of PM2.5 and its main species were found and could be explained by varying emission source characteristics. Based on PMF model, seven source factors for PM2.5 were identified, which were coal combustion + biomass burning, vehicle emission, mineral dust, ship emission, sea salt, industry source, refined chrome industry with the contribution of 48.21%, 30.33%, 7.24%, 6.63%, 3.51%, 3.2%, and 0.88%, resp. In addn., PSCF anal. using the daily contribution of each factor from PMF result suggested that Shandong peninsula and Hebei province were identified as the high potential region for coal combustion + biomass burning; Beijing-Tianjin-Hebei (BTH) region was the main source region for industry source; Bohai Sea and East China Sea were found to be of high source potential for ship emission; Geog. region located northwest of BH Island was possessed of high probability for sea salt; Mineral dust presumably came from the region of Mongolia; Refined chrome industry mostly came from Liaoning, Jilin province; The vehicle emission was primarily of BTH region origin, centering on metropolises, such as Beijing and Tianjin. These results provided precious implications for PM2.5 control strategies in North China.
- 65Lee, H.-H.; Iraqui, O.; Gu, Y.; Yim, S. H.-L.; Chulakadabba, A.; Tonks, A. Y.-M.; Yang, Z.; Wang, C. Impacts of Air Pollutants from Fire and Non-Fire Emissions on the Regional Air Quality in Southeast Asia. Atmos. Chem. Phys. 2018, 18 (9), 6141– 6156, DOI: 10.5194/acp-18-6141-2018Google Scholar65https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1cXhtV2ktLbM&md5=38e819f42f7a6f076debc80b2c0c5bbaImpacts of air pollutants from fire and non-fire emissions on the regional air quality in Southeast AsiaLee, Hsiang-He; Iraqui, Oussama; Gu, Yefu; Yim, Steve Hung-Lam; Chulakadabba, Apisada; Tonks, Adam Yiu-Ming; Yang, Zhengyu; Wang, ChienAtmospheric Chemistry and Physics (2018), 18 (9), 6141-6156CODEN: ACPTCE; ISSN:1680-7324. (Copernicus Publications)Severe haze events in Southeast Asia caused by particulate pollution have become more intense and frequent in recent years. Widespread biomass burning occurrences and particulate pollutants from human activities other than biomass burning play important roles in degrading air quality in Southeast Asia. In this study, numerical simulations have been conducted using the Weather Research and Forecasting (WRF) model coupled with a chem. component (WRF-Chem) to quant. examine the contributions of aerosols emitted from fire (i.e., biomass burning) vs. non-fire (including fossil fuel combustion, and road dust, etc.) sources to the degrdn. of air quality and visibility over Southeast Asia. These simulations cover a time period from 2002 to 2008 and are driven by emissions from (a) fossil fuel burning only, (b) biomass burning only, and (c) both fossil fuel and biomass burning. The model results reveal that 39 % of obsd. low-visibility days (LVDs) can be explained by either fossil fuel burning or biomass burning emissions alone, a further 20 % by fossil fuel burning alone, a further 8 % by biomass burning alone, and a further 5 % by a combination of fossil fuel burning and biomass burning. Anal. of an 24 h PM2.5 air quality index (AQI) indicates that the case with coexisting fire and non-fire PM2.5 can substantially increase the chance of AQI being in the moderate or unhealthy pollution level from 23 to 34 %. The premature mortality in major Southeast Asian cities due to degrdn. of air quality by particulate pollutants is estd. to increase from ∼4110 per yr in 2002 to ∼6540 per yr in 2008. In addn., we demonstrate the importance of certain missing non-fire anthropogenic aerosol sources including anthropogenic fugitive and industrial dusts in causing urban air quality degrdn. An expt. of using machine learning algorithms to forecast the occurrence of haze events in Singapore is also explored in this study. All of these results suggest that besides minimizing biomass burning activities, an effective air pollution mitigation policy for Southeast Asia needs to consider controlling emissions from non-fire anthropogenic sources.
- 66Singh, N.; Murari, V.; Kumar, M.; Barman, S. C.; Banerjee, T. Fine Particulates over South Asia: Review and Meta-Analysis of PM2.5 Source Apportionment through Receptor Model. Environ. Pollut. 2017, 223, 121– 136, DOI: 10.1016/j.envpol.2016.12.071Google Scholar66https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXjvFOntA%253D%253D&md5=cf6392741686046851e0eed8991277b7Fine particulates over South Asia: Review and meta-analysis of PM2.5 source apportionment through receptor modelSingh, Nandita; Murari, Vishnu; Kumar, Manish; Barman, S. C.; Banerjee, TirthankarEnvironmental Pollution (Oxford, United Kingdom) (2017), 223 (), 121-136CODEN: ENPOEK; ISSN:0269-7491. (Elsevier Ltd.)Fine particulates (PM2.5) constitute dominant proportion of airborne particulates and have been often assocd. with human health disorders, changes in regional climate, hydrol. cycle and more recently to food security. Intrinsic properties of particulates are direct function of sources. This initiates the necessity of conducting a comprehensive review on PM2.5 sources over South Asia which in turn may be valuable to develop strategies for emission control. Particulate source apportionment (SA) through receptor models is one of the existing tool to quantify contribution of particulate sources. Review of 51 SA studies were performed of which 48 (94%) were appeared within a span of 2007-2016. Almost half of SA studies (55%) were found concd. over few typical urban stations (Delhi, Dhaka, Mumbai, Agra and Lahore). Due to lack of local particulate source profile and emission inventory, pos. matrix factorization and principal component anal. (62% of studies) were the primary choices, followed by chem. mass balance (CMB, 18%). Metallic species were most regularly used as source tracers while use of org. mol. markers and gas-to-particle conversion were min. Among all the SA sites, vehicular emissions (mean ± sd: 37 ± 20%) emerged as most dominating PM2.5 source followed by industrial emissions (23 ± 16%), secondary aerosols (22 ± 12%) and natural sources (20 ± 15%). Vehicular emissions (39 ± 24%) also identified as dominating source for highly polluted sites (PM2.5>100 μgm-3, n = 15) while site specific influence of either or in combination of industrial, secondary aerosols and natural sources were recognized. Source specific trends were considerably varied in terms of region and seasonality. Both natural and industrial sources were most influential over Pakistan and Afghanistan while over Indo-Gangetic plain, vehicular, natural and industrial emissions appeared dominant. Influence of vehicular emission was found single dominating source over southern part while over Bangladesh, both vehicular, biomass burning and industrial sources were significant.
- 67Gherboudj, I.; Naseema Beegum, S.; Ghedira, H. Identifying Natural Dust Source Regions over the Middle-East and North-Africa: Estimation of Dust Emission Potential. Earth-Sci. Rev. 2017, 165, 342– 355, DOI: 10.1016/j.earscirev.2016.12.010Google ScholarThere is no corresponding record for this reference.
- 68Weagle, C. L.; Snider, G.; Li, C.; van Donkelaar, A.; Philip, S.; Bissonnette, P.; Burke, J.; Jackson, J.; Latimer, R.; Stone, E.; Abboud, I.; Akoshile, C.; Anh, N. X.; Brook, J. R.; Cohen, A.; Dong, J.; Gibson, M. D.; Griffith, D.; He, K. B.; Holben, B. N.; Kahn, R.; Keller, C. A.; Kim, J. S.; Lagrosas, N.; Lestari, P.; Khian, Y. L.; Liu, Y.; Marais, E. A.; Martins, J. V.; Misra, A.; Muliane, U.; Pratiwi, R.; Quel, E. J.; Salam, A.; Segev, L.; Tripathi, S. N.; Wang, C.; Zhang, Q.; Brauer, M.; Rudich, Y.; Martin, R. V. Global Sources of Fine Particulate Matter: Interpretation of PM 2.5 Chemical Composition Observed by SPARTAN Using a Global Chemical Transport Model. Environ. Sci. Technol. 2018, DOI: 10.1021/acs.est.8b01658Google ScholarThere is no corresponding record for this reference.
- 69Nayebare, S. R.; Aburizaiza, O. S.; Khwaja, H. A.; Siddique, A.; Hussain, M. M.; Zeb, J.; Khatib, F.; Carpenter, D. O.; Blake, D. R. Chemical Characterization and Source Apportionment of PM 2.5 in Rabigh, Saudi Arabia. Aerosol Air Qual. Res. 2016, 16, 3114– 3129, DOI: 10.4209/aaqr.2015.11.0658Google Scholar69https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1cXkvVOktbw%253D&md5=30649bc1971c138299d01a8f5d3b9278Chemical characterization and source apportionment of PM♂.♂ in Rabigh, Saudi ArabiaNayebare, Shedrack R.; Aburizaiza, Omar S.; Khwaja, Haider A.; Siddique, Azhar; Hussaini, Mirza M.; Zeb, Jahan; Khatib, Fida; Carpenters, David O.; Blake, Donald R.Aerosol and Air Quality Research (2016), 16 (12), 3114-3129CODEN: AAQRAV; ISSN:1680-8584. (Taiwan Association for Aerosol Research)The present study describes the measurement, chem. characterization and delineation of sources of fine particulate matter (PM♂.♂) in Rabigh, Saudi Arabia. The 24-h PM♂.♂ was collected from May 6th June 17th, 2013. The sources of various air pollutants and their characterization was carried by computations of Enrichment Factor (EF), Pos. Matrix Factorization (PMF) and Backward-in-time Trajectories. The 24-h PM♂.♂ showed significant temporal variability with av. (37 ±16.2 μg m♂3) exceeding the WHO guideline (20 μg m♂3) by 2 fold. SO♂2♂, NO♂♂, NH♂♂ and Cl♂ ions dominated the ionic components. Two broad categories of aerosol Trace Elements (Ths) sources were defined as anthropogenic (Ni, V, Zn, Pb, S, Lu and Br) and soilknistal derived (Si, Rb, Ti, Fe, Mn, Mg, K, Sr, Cr, Ca, Cu, Na and Al) elements from computations of EF. Anthropogenic elements originated primarily from fossil-fuel combustion, automobile and industrial emissions. A factor anal. model (PMF) indicated the major sources of PM♂.♂ as Soil (Si, Al, Ti, Fe, Mg, K and Ca); Industrial Dust (Ca, Fe♂, Al, and Si); Fossil-Fuel combustion (V, Ni, Pb, Lu, Cu, Zn, NH♂♂, SO♂2♂ and BC); Vehicular Emissions (NO♂♂, C♂O♂2♂, V and BC) and Sea Sprays (Cl♂ and Na). Backward-in-time trajectories showed a significant contribution by long distance transport of fine aerosols to the overall daily PM♂.♂ levels. Results are consistent with previous studies and highlight the need for more comprehensive research into particulate air pollution in Rabigh and the neighboring areas. This is essential for the formulation of sustainable guidelines on air pollutant emissions in Saudi Arabia and the whole Middle East.
- 70Leibensperger, E. M.; Mickley, L. J.; Jacob, D. J.; Chen, W.-T.; Seinfeld, J. H.; Nenes, A.; Adams, P. J.; Streets, D. G.; Kumar, N.; Rind, D. Climatic Effects of 1950–2050 Changes in US Anthropogenic Aerosols – Part 2: Climate Response. Atmos. Chem. Phys. 2012, 12 (7), 3349– 3362, DOI: 10.5194/acp-12-3349-2012Google Scholar70https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC38XptFelsL4%253D&md5=b67842e3a51422a690b085c0ca385571Climatic effects of 1950-2050 changes in US anthropogenic aerosols - part 2: climate responseLeibensperger, E. M.; Mickley, L. J.; Jacob, D. J.; Chen, W.-T.; Seinfeld, J. H.; Nenes, A.; Adams, P. J.; Streets, D. G.; Kumar, N.; Rind, D.Atmospheric Chemistry and Physics (2012), 12 (7), 3349-3362CODEN: ACPTCE; ISSN:1680-7316. (Copernicus Publications)We investigate the climate response to changing US anthropogenic aerosol sources over the 1950-2050 period by using the NASA GISS general circulation model (GCM) and comparing to obsd. US temp. trends. Time-dependent aerosol distributions are generated from the GEOS-Chem chem. transport model applied to historical emission inventories and future projections. Radiative forcing from US anthropogenic aerosols peaked in 1970-1990 and has strongly declined since due to air quality regulations. We find that the regional radiative forcing from US anthropogenic aerosols elicits a strong regional climate response, cooling the central and eastern US by 0.5-1.0 °C on av. during 1970-1990, with the strongest effects on max. daytime temps. in summer and autumn. Aerosol cooling reflects comparable contributions from direct and indirect (cloud-mediated) radiative effects. Absorbing aerosol (mainly black carbon) has negligible warming effect. Aerosol cooling reduces surface evapn. and thus decreases pptn. along the US east coast, but also increases the southerly flow of moisture from the Gulf of Mexico resulting in increased cloud cover and pptn. in the central US. Observations over the eastern US show a lack of warming in 1960-1980 followed by very rapid warming since, which we reproduce in the GCM and attribute to trends in US anthropogenic aerosol sources. Present US aerosol concns. are sufficiently low that future air quality improvements are projected to cause little further warming in the US (0.1 °C over 2010-2050). We find that most of the warming from aerosol source controls in the US has already been realized over the 1980-2010 period.
- 71Klimont, Z.; Smith, S. J.; Cofala, J. The Last Decade of Global Anthropogenic Sulfur Dioxide: 2000–2011 Emissions. Environ. Res. Lett. 2013, 8 (1), 14003– 14006, DOI: 10.1088/1748-9326/8/1/014003Google Scholar71https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3sXhsFeisb3F&md5=0baecab9161b1fbb9a2695fc697675c6The last decade of global anthropogenic sulfur dioxide: 2000-2011 emissionsKlimont, Z.; Smith, S. J.; Cofala, J.Environmental Research Letters (2013), 8 (1), 014003CODEN: ERLNAL; ISSN:1748-9326. (IOP Publishing Ltd.)The evolution of global and regional anthropogenic SO2 emissions in the last decade has been estd. through a bottom-up calcn. After increasing until about 2006, we est. a declining trend continuing until 2011. However, there is strong spatial variability, with North America and Europe continuing to reduce emissions, with an increasing role of Asia and international shipping. China remains a key contributor, but the introduction of stricter emission limits followed by an ambitious program of installing flue gas desulfurization on power plants resulted in a significant decline in emissions from the energy sector and stabilization of total Chinese SO2 emissions. Comparable mitigation strategies are not yet present in several other Asian countries and industrial sectors in general, while emissions from international shipping are expected to start declining soon following an international agreement to reduce the sulfur content of fuel oil. The estd. trends in global SO2 emissions are within the range of representative concn. pathway (RCP) projections and the uncertainty previously estd. for the year 2005.
- 72Curier, L.; Kranenburg, R.; Timmermans, R.; Segers, A.; Eskes, H.; Schaap, M. Synergistic Use of LOTOS-EUROS and NO2 Tropospheric Columns to Evaluate the NOX Emission Trends Over Europe 2014, 239– 245, DOI: 10.1007/978-94-007-5577-2_41Google ScholarThere is no corresponding record for this reference.
- 73Simon, H.; Reff, A.; Wells, B.; Xing, J.; Frank, N. Ozone Trends Across the United States over a Period of Decreasing NOx and VOC Emissions. Environ. Sci. Technol. 2015, 49 (1), 186– 195, DOI: 10.1021/es504514zGoogle Scholar73https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXitVWks7zP&md5=19d13dc9b543f41cb03e0ca916a60ef6Ozone Trends Across the United States over a Period of Decreasing NOx and VOC EmissionsSimon, Heather; Reff, Adam; Wells, Benjamin; Xing, Jia; Frank, NeilEnvironmental Science & Technology (2015), 49 (1), 186-195CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)This work evaluated ambient O3 trends at urban, suburban, and rural monitoring sites across the US over a period of decreasing NOx and volatile org. compd. (VOC) emissions, 1998-2013. Decreasing O3 trends generally occurred in summer, in less urbanized areas, and at the upper end of the O3 distribution. Conversely, increasing O3 trends generally occurred in winter, in more urbanized areas, and at the lower end of the O3 distribution. The 95th percentile O3 concns. decreased at urban, suburban, and rural monitors by 1-2 ppb/yr in summer and 0.5-1 ppb/yr in winter. In summer, there were increasing and decreasing trends in 5th percentile O3 concns. of <0.5 ppb/yr at urban and suburban monitors; 5th percentile O3 concns. at rural monitors decreased by up to 1 ppb/yr. In winter, 5th percentile O3 concns. generally increased by 0.1-1 ppb/yr. Results demonstrated the large scale success of US control strategies to decrease peak O3 concns. Also, results indicated that as anthropogenic NOx emissions decreased, the O3 distribution has been compressed, leading to less spatiotemporal variability.
- 74Xing, J.; Mathur, R.; Pleim, J.; Hogrefe, C.; Gan, C.-M.; Wong, D. C.; Wei, C.; Gilliam, R.; Pouliot, G. Observations and Modeling of Air Quality Trends over 1990–2010 across the Northern Hemisphere: China, the United States and Europe. Atmos. Chem. Phys. 2015, 15 (5), 2723– 2747, DOI: 10.5194/acp-15-2723-2015Google Scholar74https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXksFehu7w%253D&md5=ad43252d28e9750d1acb3665d37d4e0cObservations and modeling of air quality trends over 1990-2010 across the Northern Hemisphere: China, the United States and EuropeXing, J.; Mathur, R.; Pleim, J.; Hogrefe, C.; Gan, C.-M.; Wong, D. C.; Wei, C.; Gilliam, R.; Pouliot, G.Atmospheric Chemistry and Physics (2015), 15 (5), 2723-2747CODEN: ACPTCE; ISSN:1680-7324. (Copernicus Publications)Trends in air quality across the Northern Hemisphere over a 21-yr period (1990-2010) were simulated using the Community Multiscale Air Quality (CMAQ) multiscale chem. transport model driven by meteorol. from Weather Research and Forecasting (WRF) simulations and internally consistent historical emission inventories obtained from EDGAR. Thorough comparison with several ground observation networks mostly over Europe and North America was conducted to evaluate the model performance as well as the ability of CMAQ to reproduce the obsd. trends in air quality over the past 2 decades in three regions: eastern China, the continental United States and Europe. The model successfully reproduced the obsd. decreasing trends in SO2, NO2, 8 h O3 maxima, SO2-4 and elemental carbon (EC) in the US and Europe. However, the model fails to reproduce the decreasing trends in NO-3 in the US, potentially pointing to uncertainties of NH3 emissions. The model failed to capture the 6-yr trends of SO2 and NO2 in CN-API (China - Air Pollution Index) from 2005 to 2010, but reproduced the obsd. pattern of O3 trends shown in three World Data Center for Greenhouse Gases (WDCGG) sites over eastern Asia. Due to the coarse spatial resoln. employed in these calcns., predicted SO2 and NO2 concns. are underestimated relative to all urban networks, i.e., US-AQS (US - Air Quality System; normalized mean bias (NMB) = -38% and -48%), EU-AIRBASE (European Air quality data Base; NMB = -18 and -54%) and CN-API (NMB = -36 and -68%). Conversely, at the rural network EU-EMEP (European Monitoring and Evaluation Program), SO2 is overestimated (NMB from 4 to 150%) while NO2 is simulated well (NMB within =15%) in all seasons. Correlations between simulated and obsd. O3 wintertime daily 8 h maxima (DM8) are poor compared to other seasons for all networks. Better correlation between simulated and obsd. SO2-4 was found compared to that for SO2. Underestimation of summer SO2-4 in the US may be assocd. with the uncertainty in pptn. and assocd. wet scavenging representation in the model. The model exhibits worse performance for NO-3 predictions, particularly in summer, due to high uncertainties in the gas/particle partitioning of NO-3 as well as seasonal variations of NH3 emissions. There are high correlations (R > 0.5) between obsd. and simulated EC, although the model underestimates the EC concn. by 65% due to the coarse grid resoln. as well as uncertainties in the PM speciation profile assocd. with EC emissions. The almost linear response seen in the trajectory of modeled O3 changes in eastern China over the past 2 decades suggests that control strategies that focus on combined control of NOx and volatile org. compd. (VOC) emissions with a ratio of 0.46 may provide the most effective means for O3 redns. for the region devoid of nonlinear response potentially assocd. with NOx or VOC limitation resulting from alternate strategies. The response of O3 is more sensitive to changes in NOx emissions in the eastern US because the relative abundance of biogenic VOC emissions tends to reduce the effectiveness of VOC controls. Increasing NH3 levels offset the relative effectiveness of NOx controls in reducing the relative fraction of aerosol NO-3 formed from declining NOx emissions in the eastern US, while the control effectiveness was assured by the simultaneous control of NH3 emission in Europe.
- 75Li, C.; Martin, R. V.; van Donkelaar, A.; Boys, B. L.; Hammer, M. S.; Xu, J.-W.; Marais, E. A.; Reff, A.; Strum, M.; Ridley, D. A.; Crippa, M.; Brauer, M.; Zhang, Q. Trends in Chemical Composition of Global and Regional Population-Weighted Fine Particulate Matter Estimated for 25 Years. Environ. Sci. Technol. 2017, 51, 11185, DOI: 10.1021/acs.est.7b02530Google Scholar75https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXhsVKmtbvL&md5=80a1f1359ff52f5aef28bca04d82b60dTrends in Chemical Composition of Global and Regional Population-Weighted Fine Particulate Matter Estimated for 25 YearsLi, Chi; Martin, Randall V.; van Donkelaar, Aaron; Boys, Brian L.; Hammer, Melanie S.; Xu, Jun-Wei; Marais, Eloise A.; Reff, Adam; Strum, Madeleine; Ridley, David A.; Crippa, Monica; Brauer, Michael; Zhang, QiangEnvironmental Science & Technology (2017), 51 (19), 11185-11195CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)The authors interpreted in-situ and satellite observations using a chem. transport model (GEOS-Chem, down-scaled to 0.1° × 0.1°) to understand global trends in population-weighted mean chem. compn. of fine particulate matter (PM2.5). Trends in obsd. and simulated population-weighted mean PM2.5 compn. for 1989-2013 were highly consistent for PM2.5 (-2.4 vs. -2.4%/yr), secondary inorg. aerosols (-4.3 vs. -4.1%/yr), org. aerosols (OA, -3.6 vs. -3.0%/yr) and black carbon (-4.3 vs. -3.9%/yr) over North America, as well as SO42- (-4.7 vs. -5.8%/yr) over Europe. Simulated trends for 1998-2013 also had overlapping 95% confidence intervals with satellite-derived trends in population-weighted mean PM2.5 for 20 of 21 global regions. For 1989-2013, most (79%) simulated increase in global population-weighted mean PM2.5 of 0.28 μg/m3-yr was explained by significantly (p <0.05) increasing OA (0.10 μg/m3-yr), NO3- (0.05 μg/m3-yr), SO42- (0.04 μg/m3-yr), and NH4+ (0.03 μg/m3-yr). These four components predominantly drive trends in population-weighted mean PM2.5 over populous regions of south Asia (0.94 μg/m3-yr), east Asia (0.66 μg/m3-yr), west Europe (-0.47 μg/m3-yr), and North America (-0.32 μg/m3-yr). Area-weighted and population-weighted mean PM2.5 compn. trends differed significantly.
- 76Weatherhead, E. C.; Reinsel, G. C.; Tiao, G. C.; Meng, X.-L.; Choi, D.; Cheang, W.-K.; Keller, T.; DeLuisi, J.; Wuebbles, D. J.; Kerr, J. B.; Miller, A. J.; Oltmans, S. J.; Frederick, J. E. Factors Affecting the Detection of Trends: Statistical Considerations and Applications to Environmental Data. J. Geophys. Res. Atmos. 1998, 103 (D14), 17149– 17161, DOI: 10.1029/98JD00995Google ScholarThere is no corresponding record for this reference.
- 77Weatherhead, E. C.; Stevermer, A. J.; Schwartz, B. E. Detecting Environmental Changes and Trends. Phys. Chem. Earth, Parts A/B/C 2002, 27 (6–8), 399– 403, DOI: 10.1016/S1474-7065(02)00019-0Google ScholarThere is no corresponding record for this reference.
- 78Boys, B. L.; Martin, R. V.; van Donkelaar, A.; MacDonell, R. J.; Hsu, N. C.; Cooper, M. J.; Yantosca, R. M.; Lu, Z.; Streets, D. G.; Zhang, Q.; Wang, S. W. Fifteen-Year Global Time Series of Satellite-Derived Fine Particulate Matter. Environ. Sci. Technol. 2014, 48 (19), 11109– 11118, DOI: 10.1021/es502113pGoogle Scholar78https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXhsVOntL7F&md5=7da0219c9c69350c750ccc34b7e4c36fFifteen-Year Global Time Series of Satellite-Derived Fine Particulate MatterBoys, B. L.; Martin, R. V.; van Donkelaar, A.; MacDonell, R. J.; Hsu, N. C.; Cooper, M. J.; Yantosca, R. M.; Lu, Z.; Streets, D. G.; Zhang, Q.; Wang, S. W.Environmental Science & Technology (2014), 48 (19), 11109-11118CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)Ambient fine particulate matter (PM2.5) is a leading environmental risk factor for premature mortality. This work used aerosol optical depth (AOD) measurements from 2 satellite instruments, multi-angle imaging spectroradiometer and sea viewing wide field of vision sensor, to produce a unified 15-yr global time series (1998-2012) of ground-level PM2.5 concns. at a 1° x 1° resoln. The GEOS-chem chem. transport model related each individual AOD retrieval to ground-level PM2.5 concn. Four broad areas displaying significant, spatially coherent, annual trends were examd. in detail: eastern USA (-0.39 ± 0.10 μg/m3-yr), Arabian Peninsula (0.81 ± 0.21 μg/m3-yr), southern Asia (0.93 ± 0.22 μg/m3-yr), and eastern Asia (0.79 ± 0.27 μg/m3-yr). Over the dense in-situ observation period, 1999-2012, the linear tendency for the eastern USA (-0.37 ± 0.13 μg/m3-yr) agreed well with in-situ measurements (-0.38 ± 0.06 μg/m3-yr). A GEOS-Chem simulation showed secondary inorg. aerosols largely explained the obsd. PM2.5 trend over the eastern USA and southern and eastern Asia; mineral dust largely explained the obsd. trend over the Arabian Peninsula.
- 79Klimont, Z.; Kupiainen, K.; Heyes, C.; Purohit, P.; Cofala, J.; Rafaj, P.; Borken-Kleefeld, J.; Schöpp, W. Global Anthropogenic Emissions of Particulate Matter Including Black Carbon. Atmos. Chem. Phys. 2017, 17 (14), 8681– 8723, DOI: 10.5194/acp-17-8681-2017Google Scholar79https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXhs1ahtbnK&md5=63758734475b7aad97d3d3614c7abc63Global anthropogenic emissions of particulate matter including black carbonKlimont, Zbigniew; Kupiainen, Kaarle; Heyes, Chris; Purohit, Pallav; Cofala, Janusz; Rafaj, Peter; Borken-Kleefeld, Jens; Schoepp, WolfgangAtmospheric Chemistry and Physics (2017), 17 (14), 8681-8723CODEN: ACPTCE; ISSN:1680-7324. (Copernicus Publications)This paper presents a comprehensive assessment of historical (1990-2010) global anthropogenic particulate matter (PM) emissions including the consistent and harmonized calcn. of mass-based size distribution (PM1, PM2.5, PM10), as well as primary carbonaceous aerosols including black carbon (BC) and org. carbon (OC). The ests. were developed with the integrated assessment model GAINS, where source- and region-specific technol. characteristics are explicitly included. This assessment includes a no. of previously unaccounted or often misallocated emission sources, i.e. kerosene lamps, gas flaring, diesel generators, refuse burning; some of them were reported in the past for selected regions or in the context of a particular pollutant or sector but not included as part of a total est. Spatially, emissions were calcd. for 172 source regions (as well as international shipping), presented for 25 global regions, and allocated to 0.5° × 0.5° longitude-latitude grids. No independent ests. of emissions from forest fires and savannah burning are provided and neither windblown dust nor unpaved roads emissions are included. We est. that global emissions of PM have not changed significantly between 1990 and 2010, showing a strong decoupling from the global increase in energy consumption and, consequently, CO2 emissions, but there are significantly different regional trends, with a particularly strong increase in East Asia and Africa and a strong decline in Europe, North America, and the Pacific region. This in turn resulted in important changes in the spatial pattern of PM burden, e.g.European, North American, and Pacific contributions to global emissions dropped from nearly 30% in 1990 to well below 15% in 2010, while Asia's contribution grew from just over 50% to nearly two-thirds of the global total in 2010. For all PM species considered, Asian sources represented over 60% of the global anthropogenic total, and residential combustion was the most important sector, contributing about 60% for BC and OC, 45% for PM2.5, and less than 40% for PM10, where large combustion sources and industrial processes are equally important. Global anthropogenic emissions of BC were estd. at about 6.6 and 7.2 Tg in 2000 and 2010, resp., and represent about 15% of PM2.5 but for some sources reach nearly 50%, i.e. for the transport sector. Our global BC nos. are higher than previously published owing primarily to the inclusion of new sources. This PM est. fills the gap in emission data and emission source characterization required in air quality and climate modeling studies and health impact assessments at a regional and global level, as it includes both carbonaceous and non-carbonaceous constituents of primary particulate matter emissions. The developed emission dataset has been used in several regional and global atm. transport and climate model simulations within the ECLIPSE (Evaluating the Climate and Air Quality Impacts of Short-Lived Pollutants) project and beyond, serves better parameterization of the global integrated assessment models with respect to representation of black carbon and org. carbon emissions, and built a basis for recently published global particulate no. ests.
- 80Wang, S.; Zhang, Q.; Martin, R. V.; Philip, S.; Liu, F.; Li, M.; Jiang, X.; He, K. Satellite Measurements Oversee China’s Sulfur Dioxide Emission Reductions from Coal-Fired Power Plants. Environ. Res. Lett. 2015, 10 (11), 114015, DOI: 10.1088/1748-9326/10/11/114015Google Scholar80https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXkvFektLc%253D&md5=dac16852613d6e5fc5b141bd38640001Satellite measurements oversee China's sulfur dioxide emission reductions from coal-fired power plantsWang, Siwen; Zhang, Qiang; Martin, Randall V.; Philip, Sajeev; Liu, Fei; Li, Meng; Jiang, Xujia; He, KebinEnvironmental Research Letters (2015), 10 (11), 114015/1-114015/9CODEN: ERLNAL; ISSN:1748-9326. (IOP Publishing Ltd.)To evaluate the real redns. in sulfur dioxide (SO2) emissions from coal-fired power plants in China, Ozone Monitoring Instrument (OMI) remote sensing SO2 columns were used to inversely model the SO2 emission burdens surrounding 26 isolated power plants before and after the effective operation of their flue gas desulfurization (FGD) facilities. An improved two-dimensional Gaussian fitting method was developed to est. SO2 burdens under complex background conditions, by using the accurate local background columns and the customized fitting domains for each target source. The OMI-derived SO2 burdens before effective FGD operation were correlated well with the bottom-up emission ests. (R = 0.92), showing the reliability of the OMI-derived SO2 burdens as a linear indicator of the assocd. source strength. OMI observations indicated that the av. lag time period between installation and effective operation of FGD facilities at these 26 power plants was around 2 years, and no FGD facilities have actually operated before the year 2008. The OMI estd. av. SO2 removal equivalence (56.0%) was substantially lower than the official report (74.6%) for these 26 power plants. Therefore, it has been concluded that the real redns. of SO2 emissions in China assocd. with the FGD facilities at coal-fired power plants were considerably diminished in the context of the current weak supervision measures.
- 81Fioletov, V. E.; McLinden, C. A.; Krotkov, N.; Li, C.; Joiner, J.; Theys, N.; Carn, S.; Moran, M. D. A Global Catalogue of Large SO2 Sources and Emissions Derived from the Ozone Monitoring Instrument. Atmos. Chem. Phys. 2016, 16 (18), 11497– 11519, DOI: 10.5194/acp-16-11497-2016Google Scholar81https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC28XhslKrsb%252FO&md5=2f8666380021f626f03a119b6c792196A global catalogue of large SO2 sources and emissions derived from the Ozone Monitoring InstrumentFioletov, Vitali E.; McLinden, Chris A.; Krotkov, Nickolay; Li, Can; Joiner, Joanna; Theys, Nicolas; Carn, Simon; Moran, Mike D.Atmospheric Chemistry and Physics (2016), 16 (18), 11497-11519CODEN: ACPTCE; ISSN:1680-7324. (Copernicus Publications)Sulfur dioxide (SO2) measurements from the Ozone Monitoring Instrument (OMI) satellite sensor processed with the new principal component anal. (PCA) algorithm were used to detect large point emission sources or clusters of sources. The total of 491 continuously emitting point sources releasing from about 30 kt yr-1 to more than 4000 kt yr-1 of SO2 per yr have been identified and grouped by country and by primary source origin: volcanoes (76 sources); power plants (297); smelters (53); and sources related to the oil and gas industry (65). The sources were identified using different methods, including through OMI measurements themselves applied to a new emission detection algorithm, and their evolution during the 2005-2014 period was traced by estg. annual emissions from each source. For volcanic sources, the study focused on continuous degassing, and emissions from explosive eruptions were excluded. Emissions from degassing volcanic sources were measured, many for the first time, and collectively they account for about 30 % of total SO2 emissions estd. from OMI measurements, but that fraction has increased in recent years given that cumulative global emissions from power plants and smelters are declining while emissions from oil and gas industry remained nearly const. Anthropogenic emissions from the USA declined by 80 % over the 2005-2014 period as did emissions from western and central Europe, whereas emissions from India nearly doubled, and emissions from other large SO2-emitting regions (South Africa, Russia, Mexico, and the Middle East) remained fairly const. In total, OMI-based ests. account for about a half of total reported anthropogenic SO2 emissions; the remaining half is likely related to sources emitting less than 30 kt yr-1 and not detected by OMI.
- 82Zhai, S.; Jacob, D. J.; Wang, X.; Shen, L.; Li, K.; Zhang, Y.; Gui, K.; Zhao, T.; Liao, H. Fine Particulate Matter (PM2.5) Trends in China, 2013–2018: Separating Contributions from Anthropogenic Emissions and Meteorology. Atmos. Chem. Phys. 2019, 19, 11031– 11041, DOI: 10.5194/acp-19-11031-2019Google Scholar82https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1MXhvFWqtr%252FI&md5=0f104c5a63f55ac96a2f74bf522924b1Fine particulate matter (PM2.5) trends in China, 2013-2018: separating contributions from anthropogenic emissions and meteorologyZhai, Shixian; Jacob, Daniel J.; Wang, Xuan; Shen, Lu; Li, Ke; Zhang, Yuzhong; Gui, Ke; Zhao, Tianliang; Liao, HongAtmospheric Chemistry and Physics (2019), 19 (16), 11031-11041CODEN: ACPTCE; ISSN:1680-7324. (Copernicus Publications)Fine particulate matter (PM2.5) is a severe air pollution problem in China. Observations of PM2.5 have been available since 2013 from a large network operated by the China National Environmental Monitoring Center (CNEMC). The data show a general 30%-50% decrease in annual mean PM2.5 across China over the 2013-2018 period, averaging at -5.2μg m-3 a 1. Trends in the five megacity cluster regions targeted by the government for air quality control are -9.3 ± 1.8μg m-3 a 1 (±95% confidence interval) for Beijing-Tianjin-Hebei, -6.1 ± 1.1μg m-3 a 1 for the Yangtze River Delta, -2.7±0.8μg m-3 a 1 for the Pearl River Delta, -6.7 ± 1.3μg m-3 a 1 for the Sichuan Basin, and -6.5 ± 2.5μg m-3 a 1 for the Fenwei Plain (Xi'an). Concurrent 2013-2018 observations of sulfur dioxide (SO2), carbon monoxide (CO) show that the declines in PM2.5 are qual. consistent with drastic controls of emissions from coal combustion. However, there is also a large meteorol. driven interannual variability in PM2.5 that complicates trend attribution. We used a stepwise multiple linear regression (MLR) model to quantify this meteorol. contribution to the PM2.5 trends across China. The MLR model correlates the 10 d PM2.5 anomalies to wind speed, pptn., relative humidity, temp., and 850 hPa meridional wind velocity (V850). The meteorol.-cor. PM2.5 trends after removal of the MLR meteorol. contribution can be viewed as being driven by trends in anthropogenic emissions.
- 83Apte, J. S.; Marshall, J. D.; Cohen, A. J.; Brauer, M. Addressing Global Mortality from Ambient PM 2.5. Environ. Sci. Technol. 2015, 49 (13), 8057– 8066, DOI: 10.1021/acs.est.5b01236Google Scholar83https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXhtVShtb3P&md5=9deade47f08dc87bbe7572d6199be8e4Addressing Global Mortality from Ambient PM2.5Apte, Joshua S.; Marshall, Julian D.; Cohen, Aaron J.; Brauer, MichaelEnvironmental Science & Technology (2015), 49 (13), 8057-8066CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)Ambient fine particulate matter (PM2.5) has a large, well-documented global burden of disease. This work used high-resoln. (10 km, global-coverage) concn. data and cause-specific integrated exposure-response functions developed for the Global Burden of Disease 2010 to assess how regional and global improvements in ambient air quality could reduce attributable mortality from PM2.5. Overall, an aggressive global program of PM2.5 mitigation in accord with World Health Organization interim guidelines could avoid 750,000 (23%) of the 3.2 million deaths/yr currently (2010) attributable to ambient PM2.5. Modest improvements in PM2.5 in relatively clean regions (North America, Europe) would result in surprisingly large avoided mortality, due to demog. factors and the non-linear concn.-response relationship which describes the risk of PM in relation to several important causes of death. Major air quality improvements would be required to substantially reduce mortality from PM2.5 in more polluted regions, e.g., China and India. Forecasted demog. and epidemiol. transitions in India and China imply that to maintain PM2.5-attributable mortality rates (deaths/100,000 people-yr) const., av. PM2.5 concns. would need to decline by ∼20-30% over the next 15 years to merely offset increases in PM2.5-attributable mortality from aging populations. An effective program to deliver clean air to the most polluted regions could avoid several hundred thousand premature deaths each year.
- 84van Donkelaar, A.; Martin, R. V.; Li, C.; Burnett, R. T. Regional Estimates of Chemical Composition of Fine Particulate Matter Using a Combined Geoscience-Statistical Method with Information from Satellites, Models, and Monitors. Environ. Sci. Technol. 2019, 53 (5), 2595– 2611, DOI: 10.1021/acs.est.8b06392Google Scholar84https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1MXitVyhsLs%253D&md5=ae0e49f5157b4f6bc45fa8460b307a1fRegional Estimates of Chemical Composition of Fine Particulate Matter Using a Combined Geoscience-Statistical Method with Information from Satellites, Models, and Monitorsvan Donkelaar, Aaron; Martin, Randall V.; Li, Chi; Burnett, Richard T.Environmental Science & Technology (2019), 53 (5), 2595-2611CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)An accurate fine-resoln. surface of the chem. compn. of fine particulate matter (PM2.5) would offer valuable information for epidemiol. studies and health impact assessments. We develop geoscience-derived ests. of PM2.5 compn. from a chem. transport model (GEOS-Chem) and satellite observations of aerosol optical depth, and statistically fuse these ests. with ground-based observations using a geog. weighted regression over North America to produce a spatially complete representation of sulfate, nitrate, ammonium, black carbon, org. matter, mineral dust, and sea-salt over 2000-2016. Significant long-term agreement is found with cross-validation sites over North America (R2 = 0.57-0.96), with the strongest agreement for sulfate (R2 = 0.96), nitrate (R2 = 0.90), and ammonium (R2 = 0.86). We find that North American decreases in population-weighted fine particulate matter (PM2.5) concns. since 2000 have been most heavily influenced by regional changes in sulfate and org. matter. Regionally, the relative importance of several chem. components are found to change with PM2.5 concn., such as higher PM2.5 concns. having a larger proportion of nitrate and a smaller proportion of sulfate. This data set offers information for research into the health effects of PM2.5 chem. components.
- 85Snider, G.; Weagle, C. L.; Martin, R. V.; van Donkelaar, A.; Conrad, K.; Cunningham, D.; Gordon, C.; Zwicker, M.; Akoshile, C.; Artaxo, P.; Anh, N. X.; Brook, J.; Dong, J.; Garland, R. M.; Greenwald, R.; Griffith, D.; He, K.; Holben, B. N.; Kahn, R.; Koren, I.; Lagrosas, N.; Lestari, P.; Ma, Z.; Vanderlei Martins, J.; Quel, E. J.; Rudich, Y.; Salam, A.; Tripathi, S. N.; Yu, C.; Zhang, Q.; Zhang, Y.; Brauer, M.; Cohen, A.; Gibson, M. D.; Liu, Y. SPARTAN: A Global Network to Evaluate and Enhance Satellite-Based Estimates of Ground-Level Particulate Matter for Global Health Applications. Atmos. Meas. Tech. 2015, 8 (1), 505– 521, DOI: 10.5194/amt-8-505-2015Google Scholar85https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXktl2ntL4%253D&md5=280b4be54cee22f1166487d98c56de3bSPARTAN: a global network to evaluate and enhance satellite-based estimates of ground-level particulate matter for global health applicationsSnider, G.; Weagle, C. L.; Martin, R. V.; van Donkelaar, A.; Conrad, K.; Cunningham, D.; Gordon, C.; Zwicker, M.; Akoshile, C.; Artaxo, P.; Anh, N. X.; Brook, J.; Dong, J.; Garland, R. M.; Greenwald, R.; Griffith, D.; He, K.; Holben, B. N.; Kahn, R.; Koren, I.; Lagrosas, N.; Lestari, P.; Ma, Z.; Vanderlei Martins, J.; Quel, E. J.; Rudich, Y.; Salam, A.; Tripathi, S. N.; Yu, C.; Zhang, Q.; Zhang, Y.; Brauer, M.; Cohen, A.; Gibson, M. D.; Liu, Y.Atmospheric Measurement Techniques (2015), 8 (1), 505-521CODEN: AMTTC2; ISSN:1867-8548. (Copernicus Publications)Ground-based observations have insufficient spatial coverage to assess long-term human exposure to fine particulate matter (PM2.5) at the global scale. Satellite remote sensing offers a promising approach to provide information on both short- and long-term exposure to PM2.5 at local-to-global scales, but there are limitations and outstanding questions about the accuracy and precision with which groundlevel aerosol mass concns. can be inferred from satellite remote sensing alone. A key source of uncertainty is the global distribution of the relationship between annual av. PM2.5 and discontinuous satellite observations of columnar aerosol optical depth (AOD). We have initiated a global network of ground-level monitoring stations designed to evaluate and enhance satellite remote sensing ests. for application in health-effects research and risk assessment. This Surface PARTiculate mAtter Network (SPARTAN) includes a global federation of ground-level monitors of hourly PM2.5 situated primarily in highly populated regions and collocated with existing ground-based sun photometers that measure AOD. The instruments, a three-wavelength nephelometer and impaction filter sampler for both PM2.5 and PM10, are highly autonomous. Hourly PM2.5 concns. are inferred from the combination of weighed filters and nephelometer data. Data from existing networks were used to develop and evaluate network sampling characteristics. SPARTAN filters are analyzed for mass, black carbon, water-sol. ions, and metals. These measurements provide, in a variety of regions around the world, the key data required to evaluate and enhance satellite-based PM2.5 ests. used for assessing the health effects of aerosols. Mean PM2.5 concns. across sites vary by more than 1 order of magnitude. Our initial measurements indicate that the ratio of AOD to ground-level PM2.5 is driven temporally and spatially by the vertical profile in aerosol scattering. Spatially this ratio is also strongly influenced by the mass scattering efficiency.
- 86Snider, G.; Weagle, C. L.; Murdymootoo, K. K.; Ring, A.; Ritchie, Y.; Stone, E.; Walsh, A.; Akoshile, C.; Anh, N. X.; Balasubramanian, R.; Brook, J.; Qonitan, F. D.; Dong, J.; Griffith, D.; He, K.; Holben, B. N.; Kahn, R.; Lagrosas, N.; Lestari, P.; Ma, Z.; Misra, A.; Norford, L. K.; Quel, E. J.; Salam, A.; Schichtel, B.; Segev, L.; Tripathi, S.; Wang, C.; Yu, C.; Zhang, Q.; Zhang, Y.; Brauer, M.; Cohen, A.; Gibson, M. D.; Liu, Y.; Martins, J. V.; Rudich, Y.; Martin, R. V. Variation in Global Chemical Composition of PM 2.5: Emerging Results from SPARTAN. Atmos. Chem. Phys. 2016, 16 (15), 9629– 9653, DOI: 10.5194/acp-16-9629-2016Google Scholar86https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC28XhslWrtbvO&md5=9468aef60c80bf3594a8a2223edd8294Variation in global chemical composition of PM2:5: emerging results from SPARTANSnider, Graydon; Weagle, Crystal L.; Murdymootoo, Kalaivani K.; Ring, Amanda; Ritchie, Yvonne; Stone, Emily; Walsh, Ainsley; Akoshile, Clement; Xuan, Anh Nguyen; Balasubramanian, Rajasekhar; Brook, Jeff; Qonitan, Fatimah D.; Dong, Jinlu; Griffith, Derek; He, Kebin; Holben, Brent N.; Kahn, Ralph; Lagrosas, Nofel; Lestari, Puji; Ma, Zongwei; Misra, Amit; Norford, Leslie K.; Quel, Eduardo J.; Salam, Abdus; Schichtel, Bret; Segev, Lior; Tripathi, Sachchida; Wang, Chien; Yu, Chao; Zhang, Qiang; Zhang, Yuxuan; Brauer, Michael; Cohen, Aaron; Gibson, Mark D.; Liu, Yang; Vanderlei, Martins J.; Rudich, Yinon; Martin, Randall V.Atmospheric Chemistry and Physics (2016), 16 (15), 9629-9653CODEN: ACPTCE; ISSN:1680-7324. (Copernicus Publications)The Surface Particulate mAtter Network (SPARTAN) is a long-term project that includes characterization of chem. and phys. attributes of aerosols from filter samples collected worldwide. This paper discusses the ongoing efforts of SPARTAN to define and quantify major ions and trace metals found in fine particulate matter (PM2.5). Our methods infer the spatial and temporal variability of PM2.5 in a cost-effective manner. Gravimetrically weighed filters represent multi-day avs. of PM2.5, with a collocated nephelometer sampling air continuously. SPARTAN instruments are paired with AErosol RObotic NETwork (AERONET) sun photometers to better understand the relationship between ground-level PM2.5 and columnar aerosol optical depth (AOD). We have examd. the chem. compn. of PM2.5 at 12 globally dispersed, densely populated urban locations and a site at Mammoth Cave (US) National Park used as a background comparison. So far, each SPARTAN location has been active between the years 2013 and 2016 over periods of 2-26 mo, with an av. period of 12 mo per site. These sites have collectively gathered over 10 years of quality aerosol data. The major PM2.5 constituents across all sites (relative contribution±SD) are ammoniated sulfate (20%±11 %), crustal material (13.4% ±9.9 %), equiv. black carbon (11.9%±8.4 %), ammonium nitrate (4.7%±3.0 %), sea salt (2.3%± 1.6 %), trace element oxides (1.0%±1.1 %), water (7.2% ±3.3 %) at 35% RH, and residual matter (40%±24 %). Anal. of filter samples reveals that several PM2.5 chem. components varied by more than an order of magnitude between sites. Ammoniated sulfate ranges from 1.1 μgm-3 (Buenos Aires, Argentina) to 17 μgm-3 (Kanpur, India in the dry season). Ammonium nitrate ranged from 0.2 μgm-3 (Mammoth Cave, in summer) to 6.8 μgm-3 (Kanpur, dry season). Equivalent black carbon ranged from 0.7 μgm-3 (Mammoth Cave) to over 8 μgm-3 (Dhaka, Bangladesh and Kanpur, India). Comparison of SPARTAN vs. coincident measurements from the Interagency Monitoring of Protected Visual Environments (IMPROVE) network at Mammoth Cave yielded a high degree of consistency for daily PM2.5 (r2 = 0.76, slope = 1.12), daily sulfate (r2 = 0.86, slope = 1.03), and mean fractions of all major PM2.5 components (within 6 %). Major ions generally agree well with previous studies at the same urban locations (e.g. sulfate fractions agree within 4% for 8 out of 11 collocation comparisons). Enhanced anthropogenic dust fractions in large urban areas (e.g. Singapore, Kanpur, Hanoi, and Dhaka) are apparent from high Zn . Al ratios. The expected water contribution to aerosols is calcd. via the hygroscopicity parameter κv for each filter. Mean aggregate values ranged from 0.15 (Ilorin) to 0.28 (Rehovot). The all-site parameter mean is 0.20±0.04. Chem. compn. and water retention in each filter measurement allows inference of hourly PM2.5 at 35% relative humidity by merging with nephelometer measurements. These hourly PM2.5 ests. compare favorably with a beta attenuation monitor (MetOne) at the nearby US embassy in Beijing, with a coeff. of variation r2 = 0.67 (n = 3167), compared to r2 = 0.62 when κv was not considered. SPARTAN continues to provide an open-access database of PM2.5 compositional filter information and hourly mass collected from a global federation of instruments.
- 87CIESIN (Center for International Earth Science Information Network). Gridded Population of the World Version 4; NASA Socioeconomic Data and Applications Center (SEDAC): Palisades, NY, 2017; pp 1– 21. DOI: DOI: 10.1128/AAC.03728-14 .Google ScholarThere is no corresponding record for this reference.
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(17)
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(9)
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https://doi.org/10.1038/s41598-024-64851-6
- Shuangming Zhao, Yuchen Fan, Pengxiang Zhao, Ali Mansourian, Hung Chak Ho. How do taxi drivers expose to fine particulate matter (PM2.5) in a Chinese megacity: a rapid assessment incorporating with satellite-derived information and urban mobility data. International Journal of Health Geographics 2024, 23
(1)
https://doi.org/10.1186/s12942-024-00368-5
- Yangchen Di, Xizhang Gao, Haijiang Liu, Baolin Li, Cong Sun, Yecheng Yuan, Yong Ni. Accuracy assessment on eight public PM2.5 concentration datasets across China. Atmospheric Environment 2024, 338 , 120799. https://doi.org/10.1016/j.atmosenv.2024.120799
- Vaishali Jain, Avideep Mukherjee, Soumya Banerjee, Sandeep Madhwal, Michael H. Bergin, Prakash Bhave, David Carlson, Ziyang Jiang, Tongshu Zheng, Piyush Rai, Sachchida Nand Tripathi. A hybrid approach for integrating micro-satellite images and sensors network-based ground measurements using deep learning for high-resolution prediction of fine particulate matter (PM2.5) over an indian city, lucknow. Atmospheric Environment 2024, 338 , 120798. https://doi.org/10.1016/j.atmosenv.2024.120798
- Shuai Wang, Mengyuan Zhang, Hui Zhao, Peng Wang, Sri Harsha Kota, Qingyan Fu, Hongliang Zhang. Extracting regional and temporal features to improve machine learning for hourly air pollutants in urban India. Atmospheric Environment 2024, 338 , 120834. https://doi.org/10.1016/j.atmosenv.2024.120834
- Xiwen Song, Di Wu, Yi Su, Yang Li, Qing Li. Review of health effects driven by aerosol acidity: Occurrence and implications for air pollution control. Science of The Total Environment 2024, 955 , 176839. https://doi.org/10.1016/j.scitotenv.2024.176839
- Malia SQ. Murphy, Kasim E. Abdulaziz, Éric Lavigne, Erica Erwin, Yanfang Guo, Alysha LJ. Dingwall-Harvey, David Stieb, Mark C. Walker, Shi Wu Wen, Hwashin Hyun Shin. Association between prenatal air pollutant exposure and autism spectrum disorders in young children: A matched case-control study in Canada. Environmental Research 2024, 261 , 119706. https://doi.org/10.1016/j.envres.2024.119706
- Carson Welker, Jeffrey Huang, Harish Ramakrishna. Air Quality and Cardiovascular Mortality: Analysis of Recent Data. Journal of Cardiothoracic and Vascular Anesthesia 2024, 38
(11)
, 2801-2804. https://doi.org/10.1053/j.jvca.2024.07.042
- Ming Chen, Zhuoyue Ren, Shibo Bi. Impact of green space patterns on PM2.5 levels: A local climate zone perspective. Journal of Cleaner Production 2024, 478 , 143975. https://doi.org/10.1016/j.jclepro.2024.143975
- Sylvester Dodzi Nyadanu, Gizachew A. Tessema, Ben Mullins, Maayan Yitshak-Sade, Gavin Pereira. Critical periods of maternal exposure to ambient fine particulate matter and the risks of stillbirth and spontaneous preterm birth in Western Australia. Building and Environment 2024, 52 , 112267. https://doi.org/10.1016/j.buildenv.2024.112267
- Feiran Wang, Shasha Cheng, Ming Chen, Shulei Cheng. Does sub-provincial fiscal decentralization reform improve energy transition? Evidence from a county-level quasi-natural experiment in China. Journal of Cleaner Production 2024, 6 , 144156. https://doi.org/10.1016/j.jclepro.2024.144156
- Shuai Yin, Chong Shi, Husi Letu, Akihiko Ito, Huazhe Shang, Dabin Ji, Lei Li, Sude Bilige, Tangzhe Nie, Kunpeng Yi, Meng Guo, Zhongyi Sun, Ao Li. Reconstruction of PM2.5 Concentrations in East Asia on the Basis of a Wide–Deep Ensemble Machine Learning Framework and Estimation of the Potential Exposure Level from 1981 to 2020. Engineering 2024, 248 https://doi.org/10.1016/j.eng.2024.09.025
- Lopamudra Chakraborti, John Voorheis. Is air pollution increasing in poorer localities of Mexico? Evidence from PM 2.5 satellite data. Environment and Development Economics 2024, 115 , 1-18. https://doi.org/10.1017/S1355770X24000251
- Yaqi Wang, Yang Yuan, Shaocai Mo, Fang Wang, Jing Wei, Yao Yao, Yi Zeng, Yunquan Zhang. Individual and joint exposures to PM2.5 constituents and mortality risk among the oldest-old in China. Science China Life Sciences 2024, 383 https://doi.org/10.1007/s11427-024-2718-9
- Xueqing Wang, Jia Zhu, Ke Li, Lei Chen, Yang Yang, Yongqi Zhao, Xu Yue, Yixuan Gu, Hong Liao. Meteorology-driven trends in PM2.5 concentrations and related health burden over India. Atmospheric Research 2024, 308 , 107548. https://doi.org/10.1016/j.atmosres.2024.107548
- Rajmal Jat, Bhola Ram Gurjar, Sachin D. Ghude, Prafull P. Yadav. Wintertime source apportionment of PM2.5 pollution in million plus population cities of India using WRF-Chem simulation. Modeling Earth Systems and Environment 2024, 10
(5)
, 6065-6082. https://doi.org/10.1007/s40808-024-02119-8
- Blandine Le Provost, Marie-Élise Parent, Paul J. Villeneuve, Claudia M. Waddingham, Jeffrey R. Brook, Eric Lavigne, Rose Dugandzic, Shelley A. Harris. Residential exposure to ambient fine particulate matter (PM2.5) and nitrogen dioxide (NO2) and incident breast cancer among young women in Ontario, Canada. Cancer Epidemiology 2024, 92 , 102606. https://doi.org/10.1016/j.canep.2024.102606
- Jiahong Xu, Yan Shi, Guanhao He, Yanfei Guo, Ye Ruan, Jianxiong Hu, Qijiong Zhu, Zhiqing Chen, Shuru Liang, Yuan Zheng, Zhongguo Huang, Siwen Yu, Ruotong Zhu, Xiaomei Dong, Fan Wu, Wenjun Ma, Tao Liu. Effects of Long‐Term Exposure to Ambient Formaldehyde on Hypertension and Angina Pectoris Symptoms: Evidence From the WHO SAGE Cohort Study. Journal of the American Heart Association 2024, 13
(19)
https://doi.org/10.1161/JAHA.124.035341
- Dongliang Dang, Xiaobing Li, Xin Lyu, Shiliang Liu, Huashun Dou, Mengyuan Li, Kai Wang, Wanyu Cao. Changing of the coordination of socioeconomic development and the environment as sustainable development progresses. Geography and Sustainability 2024, 68 https://doi.org/10.1016/j.geosus.2024.09.009
- Ruijing Ni, Hang Su, Richard T. Burnett, Yuming Guo, Yafang Cheng. Long-term exposure to PM2.5 has significant adverse effects on childhood and adult asthma: A global meta-analysis and health impact assessment. One Earth 2024, 396 https://doi.org/10.1016/j.oneear.2024.09.022
- Wenhua Yu, Tingting Ye, Zhuying Chen, Rongbin Xu, Jiangning Song, Shanshan Li, Yuming Guo. Global analysis reveals region-specific air pollution exposure inequalities. One Earth 2024, 382 https://doi.org/10.1016/j.oneear.2024.09.017
- Taveen S. Kapoor, Chimurkar Navinya, Adishree Apte, Nishit J. Shetty, Pradnya Lokhande, Sujit Singh, Sadashiva Murthy B. M., Meena Deswal, Jitender S. Laura, Akila Muthalagu, Asif Qureshi, Ankur Bhardwaj, Ramya Sunder Raman, Yang Lian, G. Pandithurai, Pooja Chaudhary, Baerbel Sinha, Shahadev Rabha, Binoy K. Saikia, Tanveer Ahmad Najar, Arshid Jehangir, Sauryadeep Mukherjee, Abhijit Chatterjee, Harish C. Phuleria, Rajan K. Chakrabarty, Chandra Venkataraman. Spatial Distribution in Surface Aerosol Light Absorption Across India. Geophysical Research Letters 2024, 51
(18)
https://doi.org/10.1029/2024GL110089
- T. Athira, V. Agilan. Analysing Long-Term Trends in Monthly PM2.5 Concentrations Over India Using a Satellite-Derived Dataset. Aerosol Science and Engineering 2024, 45 https://doi.org/10.1007/s41810-024-00260-6
- Lee T. Murray, Eric M. Leibensperger, Loretta J. Mickley, Amos P. K. Tai. Estimating future climate change impacts on human mortality and crop yields via air pollution. Proceedings of the National Academy of Sciences 2024, 121
(39)
https://doi.org/10.1073/pnas.2400117121
- Chun-yu Guo, Jin-sheng Zhou. Can policy advantages be transformed into environmental benefits? City-level evidence from a quasi-natural experiment in China. Clean Technologies and Environmental Policy 2024, 123 https://doi.org/10.1007/s10098-024-03020-9
- Sarath K. Guttikunda. Designating Airsheds in India for Urban and Regional Air Quality Management. Air 2024, 2
(3)
, 247-257. https://doi.org/10.3390/air2030015
- Georgia M.C. Dyer, Sasha Khomenko, Deepti Adlakha, Susan Anenberg, Martin Behnisch, Geoff Boeing, Manuel Esperon-Rodriguez, Antonio Gasparrini, Haneen Khreis, Michelle C. Kondo, Pierre Masselot, Robert I. McDonald, Federica Montana, Rich Mitchell, Natalie Mueller, M. Omar Nawaz, Enrico Pisoni, Rafael Prieto-Curiel, Nazanin Rezaei, Hannes Taubenböck, Cathryn Tonne, Daniel Velázquez-Cortés, Mark Nieuwenhuijsen. Exploring the nexus of urban form, transport, environment and health in large-scale urban studies: A state-of-the-art scoping review. Environmental Research 2024, 257 , 119324. https://doi.org/10.1016/j.envres.2024.119324
- Ramzi Ibrahim, Hoang Nhat Pham, Sarju Ganatra, Zulqarnain Javed, Khurram Nasir, Sadeer Al-Kindi. Social Phenotyping for Cardiovascular Risk Stratification in Electronic Health Registries. Current Atherosclerosis Reports 2024, 26
(9)
, 485-497. https://doi.org/10.1007/s11883-024-01222-6
- Daniel J. Kilpatrick, Peiyin Hung, Elizabeth Crouch, Stella Self, Jeremy Cothran, Dwayne E. Porter, Jan M. Eberth. Geographic Variations in Urban‐Rural Particulate Matter (PM
2.5
) Concentrations in the United States, 2010–2019. GeoHealth 2024, 8
(9)
https://doi.org/10.1029/2023GH000920
- Lifeng Zhu, Yang Yuan, Fatemeh Mayvaneh, Haitong Sun, Yunquan Zhang, Chengyang Hu. Maternal ozone exposure lowers infant’s birthweight: A nationwide cohort of over 4 million livebirths in Iran. Ecotoxicology and Environmental Safety 2024, 283 , 116840. https://doi.org/10.1016/j.ecoenv.2024.116840
- Zhizheng Cai, Runnig Chen, Mengxia Yang, Frank A. La Sorte, Yu Chen, Jiayu Wu. Addressing critical gaps in protected area coverage for bird habitats in China. Journal of Environmental Management 2024, 368 , 122263. https://doi.org/10.1016/j.jenvman.2024.122263
- Hareef baba shaeb Kannemadugu, Sandelger Dorligjav, Alok Taori, Rajashree Vinod Bothale, Prakash Chauhan. Long term trends in global air pollution potential and its application to ventilation corridors. Air Quality, Atmosphere & Health 2024, 17
(9)
, 2057-2071. https://doi.org/10.1007/s11869-024-01563-w
- Prince M. Amegbor, Clive E. Sabel, Laust H. Mortensen, Amar J. Mehta, Mark W. Rosenberg. Early-life air pollution and green space exposures as determinants of stunting among children under age five in Sub-Saharan Africa. Journal of Exposure Science & Environmental Epidemiology 2024, 34
(5)
, 787-801. https://doi.org/10.1038/s41370-023-00572-8
- Hengheng Zhao, Ke Gui, Wenrui Yao, Nanxuan Shang, Xutao Zhang, Yuanxin Liang, Yurun Liu, Lei Li, Yu Zheng, Zhili Wang, Hong Wang, Junying Sun, Huizheng Che, Xiaoye Zhang. Relative contributions of component-segregated aerosols to trends in aerosol optical depth over land (2007–2019): Insights from CAMS aerosol reanalysis. Atmospheric Environment 2024, 333 , 120676. https://doi.org/10.1016/j.atmosenv.2024.120676
- Ali M. Mustafa, Kevin J. Psoter, Kirsten Koehler, Nancy Lin, Meredith McCormack, Edward Chen, Robert A. Wise, Michelle Sharp. The Association Between Air Pollution and Lung Function in Sarcoidosis and Implications for Health Disparities. CHEST 2024, 160 https://doi.org/10.1016/j.chest.2024.08.049
- Risto Conte Keivabu, Ugofilippo Basellini, Emilio Zagheni. Racial disparities in deaths related to extreme temperatures in the United States. One Earth 2024, 7
(9)
, 1630-1637. https://doi.org/10.1016/j.oneear.2024.08.013
- Ya'nan Guo, Linsheng Yang, Li Wang, Hairong Li, Quansheng Ge. Assessment of ecological civilization construction from the perspective of environment and health in China. Eco-Environment & Health 2024, 3
(3)
, 281-289. https://doi.org/10.1016/j.eehl.2024.02.008
- Yixin Guo, Lin Zhang, Wilfried Winiwarter, Hans J.M. van Grinsven, Xiaolin Wang, Ke Li, Da Pan, Zehui Liu, Baojing Gu. Ambitious nitrogen abatement is required to mitigate future global PM2.5 air pollution toward the World Health Organization targets. One Earth 2024, 14 https://doi.org/10.1016/j.oneear.2024.08.007
- Risto Conte Keivabu, Marco Cozzani, Joshua Wilde. Temperature and fertility: Evidence from Spain. Population Studies 2024, 19 , 1-15. https://doi.org/10.1080/00324728.2024.2382152
- Amaury de Souza, Deniz Özonur, Elias Silva de Medeiros, Ivana Pobocikova, Jose Francisco de Oliveira-Júnior, Jose roberto Zenteno Jimenez, Kelvy Rosalvo Alencar Cardoso, Marcel Carvalho Abreu, Wagner Alessandro Pansera, Guilherme Henrique Cavazzana. Spatiotemporal of Particulate Matter (PM
2.5
) and Ozone (O
3
) in Eastern Northeast Brazil. Ozone: Science & Engineering 2024, , 1-14. https://doi.org/10.1080/01919512.2024.2388597
- Yixin Guo, Hao Zhao, Wilfried Winiwarter, Jinfeng Chang, Xiaolin Wang, Mi Zhou, Petr Havlik, David Leclere, Da Pan, David Kanter, Lin Zhang. Aspirational nitrogen interventions accelerate air pollution abatement and ecosystem protection. Science Advances 2024, 10
(33)
https://doi.org/10.1126/sciadv.ado0112
- Xiaoyang Chen, Wenhao Zhang, Jiacheng He, Lili Zhang, Hong Guo, Juan Li, Xingfa Gu. Mapping PM2.5 concentration from the top-of-atmosphere reflectance of Himawari-8 via an ensemble stacking model. Atmospheric Environment 2024, 330 , 120560. https://doi.org/10.1016/j.atmosenv.2024.120560
- Pei-Ting Yao, Xing Peng, Li-Ming Cao, Li-Wu Zeng, Ning Feng, Ling-Yan He, Xiao-Feng Huang. Evaluation of a new real-time source apportionment system of PM2.5 and its implication on rapid aging of vehicle exhaust. Science of The Total Environment 2024, 937 , 173449. https://doi.org/10.1016/j.scitotenv.2024.173449
- Dimitris Stratoulias, Narissara Nuthammachot, Racha Dejchanchaiwong, Perapong Tekasakul, Gregory R. Carmichael. Recent Developments in Satellite Remote Sensing for Air Pollution Surveillance in Support of Sustainable Development Goals. Remote Sensing 2024, 16
(16)
, 2932. https://doi.org/10.3390/rs16162932
- Jiajun Luo, Andrew Craver, Zhihao Jin, Liang Zheng, Karen Kim, Tamar Polonsky, Christopher O. Olopade, Jayant M. Pinto, Habibul Ahsan, Briseis Aschebrook-Kilfoy. Contextual Deprivation, Race and Ethnicity, and Income in Air Pollution and Cardiovascular Disease. JAMA Network Open 2024, 7
(8)
, e2429137. https://doi.org/10.1001/jamanetworkopen.2024.29137
- Yaqi Wang, Minjin Peng, Chengyang Hu, Yu Zhan, Yao Yao, Yi Zeng, Yunquan Zhang. Excess deaths and loss of life expectancy attributed to long-term NO2 exposure in the Chinese elderly. Ecotoxicology and Environmental Safety 2024, 281 , 116627. https://doi.org/10.1016/j.ecoenv.2024.116627
- Yi-Dan Zhang, Daniel Bogale Odo, Jia-Xin Li, Li-Xin Hu, Hui-Ling Qiu, Yu-Ting Xie, Gang-Long Zhou, Yuan-Zhong Zhou, Guang-Hui Dong, Luke D. Knibbs, Bo-Yi Yang. Greenspace and burden of infectious illnesses among children in 49 low- and middle-income countries. Cell Reports Sustainability 2024, 1
(8)
, 100150. https://doi.org/10.1016/j.crsus.2024.100150
- Li Han, Meng Han, Yiwen Wang, Hua Wang, Jiqiang Niu. Spatial and temporal characteristic of PM2.5 and influence factors in the Yellow River Basin. Frontiers in Public Health 2024, 12 https://doi.org/10.3389/fpubh.2024.1403414
- Chandra Venkataraman, Abhinav Anand, Sujit Maji, Neeldip Barman, Dewashish Tiwari, Kaushik Muduchuru, Arushi Sharma, Ganesh Gupta, Ankur Bhardwaj, Diksha Haswani, Delwin Pullokaran, Kajal Yadav, Ramya Sunder Raman, Mohd. Imran, Gazala Habib, Taveen Singh Kapoor, Gupta Anurag, Renuka Sharma, Harish C. Phuleria, Adnan Mateen Qadri, Gyanesh Kumar Singh, Tarun Gupta, Abisheg Dhandapani, R. Naresh Kumar, Sauryadeep Mukherjee, Abhijit Chatterjee, Shahadev Rabha, Binoy K. Saikia, Prasenjit Saikia, Dilip Ganguly, Pooja Chaudhary, Baerbel Sinha, Sayantee Roy, Akila Muthalagu, Asif Qureshi, Yang Lian, Govindan Pandithurai, Laxmi Prasad, Sadashiva Murthy, Sandeep Singh Duhan, Jitender S. Laura, Anil Kumar Chhangani, Tanveer Ahmad Najar, Arshid Jehangir, Amit P. Kesarkar, Vikas Singh. Drivers of PM
2.5
Episodes and Exceedance in India: A Synthesis From the COALESCE Network. Journal of Geophysical Research: Atmospheres 2024, 129
(14)
https://doi.org/10.1029/2024JD040834
- Wei Huang, Hongbing Xu, Jing Wu, Minghui Ren, Yang Ke, Jie Qiao. Toward cleaner air and better health: Current state, challenges, and priorities. Science 2024, 385
(6707)
, 386-390. https://doi.org/10.1126/science.adp7832
- Mischa Young, Georges A. Tanguay, Gavin MacGregor, Juste Rajaonson. Determinants of honeybee hive survival and its implications for urban biodiversity in Toronto and Montreal: A Canadian case study. Landscape and Urban Planning 2024, 247 , 105066. https://doi.org/10.1016/j.landurbplan.2024.105066
- Rainald Borck, Philipp Schrauth. Urban pollution: A global perspective. Journal of Environmental Economics and Management 2024, 126 , 103013. https://doi.org/10.1016/j.jeem.2024.103013
- Andrew Loh, Donghwi Kim, Joon Geon An, Narin Choi, Un Hyuk Yim. Characteristics of sub-micron aerosols in the Yellow Sea and its environmental implications. Marine Pollution Bulletin 2024, 204 , 116556. https://doi.org/10.1016/j.marpolbul.2024.116556
- Hao Yin, Erin E McDuffie, Randall V Martin, Michael Brauer. Global health costs of ambient PM2·5 from combustion sources: a modelling study supporting air pollution control strategies. The Lancet Planetary Health 2024, 8
(7)
, e476-e488. https://doi.org/10.1016/S2542-5196(24)00098-6
- Xiuling Zhao, Tong Wu, Weiqi Zhou, Lijian Han, Andreas M. Neophytou. Reducing air pollution does not necessarily reduce related adults' mortality burden: Variations in 177 countries with different economic levels. Science of The Total Environment 2024, 933 , 173037. https://doi.org/10.1016/j.scitotenv.2024.173037
- Ruiman Zhong, André Victor Ribeiro Amaral, Paula Moraga. Spatial data fusion adjusting for preferential sampling using integrated nested Laplace approximation and stochastic partial differential equation. Journal of the Royal Statistical Society Series A: Statistics in Society 2024, 42 https://doi.org/10.1093/jrsssa/qnae058
- Stephen Mainzer, Emily L. Pakhtigian, . Blue and red tides in the Chesapeake Bay watershed: Examining political and environmental framings of collective action during the 2016 and 2020 elections. PLOS ONE 2024, 19
(6)
, e0298962. https://doi.org/10.1371/journal.pone.0298962
- Asmita Mukherjee, Jagabandhu Panda. A study on the urban growth and dynamics over 16 major cities of India. Journal of Earth System Science 2024, 133
(2)
https://doi.org/10.1007/s12040-024-02280-9
- Zining Liu, Cheng Wan. Air pollution and the burden of long‐term care: Evidence from China. Health Economics 2024, 33
(6)
, 1241-1265. https://doi.org/10.1002/hec.4816
- Fabrizio Bernardi, Risto Conte Keivabu. Poor air quality at school and educational inequality by family socioeconomic status in Italy. Research in Social Stratification and Mobility 2024, 91 , 100932. https://doi.org/10.1016/j.rssm.2024.100932
- Kirat Singh, Tapas Peshin, Shayak Sengupta, Sumil K Thakrar, Christopher W Tessum, Jason D Hill, Inês M L Azevedo, Stephen P Luby. Air pollution mortality from India’s coal power plants: unit-level estimates for targeted policy. Environmental Research Letters 2024, 19
(6)
, 064016. https://doi.org/10.1088/1748-9326/ad472a
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- 1
GBD 2016 risk factors:
Gakidou, E.; Afshin, A.; Abajobir, A. A.; Abate, K. H.; Abbafati, C.; Abbas, K. M.; Abd-Allah, F.; Abdulle, A. M.; Abera, S. F.; Aboyans, V. Global, Regional, and National Comparative Risk Assessment of 84 Behavioural, Environmental and Occupational, and Metabolic Risks or Clusters of Risks, 1990–2016: A Systematic Analysis for the Global Burden of Disease Study 2016. Lancet 2017, 390 (10100), 1345– 1422, DOI: 10.1016/S0140-6736(17)32366-8There is no corresponding record for this reference. - 2Health Effects Institute. State of Global Air 2019; Health Effects Institute, 2019.There is no corresponding record for this reference.
- 3The Economic Consequences of Outdoor Air Pollution; OECD: Paris, 2016; DOI: DOI: 10.1787/9789264257474-en .There is no corresponding record for this reference.
- 4Greenstone, M.; Fan, C. Q. Introducing the Air Quality Life Index Twelve Facts about Particulate Air Pollution, Human Health, and Global Policy Index; University of Chicago, 2018.There is no corresponding record for this reference.
- 5Martin, R. V.; Brauer, M.; van Donkelaar, A.; Shaddick, G.; Narain, U.; Dey, S. No One Knows Which City Has the Highest Concentration of Fine Particulate Matter. Atmos. Environ. X 2019, 3, 100040, DOI: 10.1016/j.aeaoa.2019.1000405https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3cXitFyhurzM&md5=d070c05b858cf958b8de28f242d83e34No one knows which city has the highest concentration of fine particulate matterMartin, Randall V.; Brauer, Michael; van Donkelaar, Aaron; Shaddick, Gavin; Narain, Urvashi; Dey, SagnikAtmospheric Environment: X (2019), 3 (), 100040CODEN: AEXTBX; ISSN:2590-1621. (Elsevier Ltd.)Exposure to ambient fine particulate matter (PM2.5) is the leading global environmental risk factor for mortality and disease burden, with assocd. annual global welfare costs of trillions of dollars. Examd. within is the ability of current data to answer a basic question about PM2.5, namely the location of the city with the highest PM2.5 concn. The ability to answer this basic question serves as an indicator of scientific progress to assess global human exposure to air pollution and as an important component of efforts to reduce its impacts. Despite the importance of PM2.5, we find that insufficient monitoring data exist to answer this basic question about the spatial pattern of PM2.5 at the global scale. Only 24 of 234 countries have more than 3 monitors per million inhabitants, while d. is an order of magnitude lower in the vast majority of the world's countries, with 141 having no regular PM2.5 monitoring at all. The global mean population distance to nearest PM2.5 monitor is 220 km, too large for exposure assessment. Efforts to fill in monitoring gaps with ests. from satellite remote sensing, chem. transport modeling, and statistical models have biases at individual monitor locations that can exceed 50μg m-3. Realization of such an integrated framework will facilitate accurate identification of the location of the city with the highest PM2.5 concn. and play a key role in tracking the progress of efforts to reduce the global impacts of air pollution.
- 6West, J. J.; Cohen, A.; Dentener, F.; Brunekreef, B.; Zhu, T.; Armstrong, B.; Bell, M. L.; Brauer, M.; Carmichael, G.; Costa, D. L.; Dockery, D. W.; Kleeman, M.; Krzyzanowski, M.; Künzli, N.; Liousse, C.; Lung, S.-C. C.; Martin, R. V.; Pöschl, U.; Pope, C. A.; Roberts, J. M.; Russell, A. G.; Wiedinmyer, C. What We Breathe Impacts Our Health: Improving Understanding of the Link between Air Pollution and Health. Environ. Sci. Technol. 2016, 50 (10), 4895– 4904, DOI: 10.1021/acs.est.5b038276https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC28XkvVaisL0%253D&md5=de12920f352a18b4b7d816e22b99836e"What We Breathe Impacts Our Health: Improving Understanding of the Link between Air Pollution and Health"West, J. Jason; Cohen, Aaron; Dentener, Frank; Brunekreef, Bert; Zhu, Tong; Armstrong, Ben; Bell, Michelle L.; Brauer, Michael; Carmichael, Gregory; Costa, Dan L.; Dockery, Douglas W.; Kleeman, Michael; Krzyzanowski, Michal; Kunzli, Nino; Liousse, Catherine; Lung, Shih-Chun Candice; Martin, Randall V.; Poschl, Ulrich; Pope, C. Arden; Roberts, James M.; Russell, Armistead G.; Wiedinmyer, ChristineEnvironmental Science & Technology (2016), 50 (10), 4895-4904CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)Air pollution contributes to the premature deaths of millions of people each year around the world, and air quality problems are growing in many developing nations. While past policy efforts have succeeded in reducing particulate matter and trace gases in North America and Europe, adverse health effects are found at even these lower levels of air pollution. Future policy actions will benefit from improved understanding of the interactions and health effects of different chem. species and source categories. Achieving this new understanding requires air pollution scientists and engineers to work increasingly closely with health scientists. In particular, research is needed to better understand the chem. and phys. properties of complex air pollutant mixts., and to use new observations provided by satellites, advanced in situ measurement techniques, and distributed micro monitoring networks, coupled with models, to better characterize air pollution exposure for epidemiol. and toxicol. research, and to better quantify the effects of specific source sectors and mitigation strategies.
- 7Gupta, P.; Levy, R. C.; Mattoo, S.; Remer, L. A.; Munchak, L. A. A Surface Reflectance Scheme for Retrieving Aerosol Optical Depth over Urban Surfaces in MODIS Dark Target Retrieval Algorithm. Atmos. Meas. Tech. 2016, 9 (7), 3293– 3308, DOI: 10.5194/amt-9-3293-2016There is no corresponding record for this reference.
- 8Sayer, A. M.; Hsu, N. C.; Lee, J.; Kim, W. V.; Dutcher, S. T. Validation, Stability, and Consistency of MODIS Collection 6.1 and VIIRS Version 1 Deep Blue Aerosol Data Over Land. J. Geophys. Res.: Atmos. 2019, 124 (8), 4658– 4688, DOI: 10.1029/2018JD029598There is no corresponding record for this reference.
- 9Hsu, N. C.; Lee, J.; Sayer, A. M.; Kim, W.; Bettenhausen, C.; Tsay, S.-C. VIIRS Deep Blue Aerosol Products Over Land: Extending the EOS Long-Term Aerosol Data Records. J. Geophys. Res.: Atmos. 2019, 124 (7), 4026– 4053, DOI: 10.1029/2018JD029688There is no corresponding record for this reference.
- 10Lyapustin, A.; Wang, Y.; Korkin, S.; Huang, D. MODIS Collection 6 MAIAC Algorithm. Atmos. Meas. Tech. 2018, 11 (10), 5741– 5765, DOI: 10.5194/amt-11-5741-2018There is no corresponding record for this reference.
- 11Garay, M. J.; Kalashnikova, O. V.; Bull, M. A. Development and Assessment of a Higher-Spatial-Resolution (4.4km) MISR Aerosol Optical Depth Product Using AERONET-DRAGON Data. Atmos. Chem. Phys. 2017, 17 (8), 5095– 5106, DOI: 10.5194/acp-17-5095-201711https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXhsV2gu7jK&md5=1d799611e00627bafcc01e03f86b00e2Development and assessment of a higher-spatial-resolution (4.4 km) MISR aerosol optical depth product usingGaray, Michael J.; Kalashnikova, Olga V.; Bull, Michael A.Atmospheric Chemistry and Physics (2017), 17 (8), 5095-5106CODEN: ACPTCE; ISSN:1680-7324. (Copernicus Publications)Since early 2000, the Multi-angle Imaging SpectroRadiometer (MISR) instrument on NASA's Terra satellite has been acquiring data that have been used to produce aerosol optical depth (AOD) and particle property retrievals at 17.6 km spatial resoln. Capitalizing on the capabilities provided by multi-angle viewing, the current operational (Version 22) MISR algorithm performs well, with about 75% of MISR AOD retrievals globally falling within 0.05 or 20% ×AOD of paired validation data from the groundbased Aerosol Robotic Network (AERONET). This paper describes the development and assessment of a prototype version of a higher-spatial-resoln. 4.4 km MISR aerosol optical depth product compared against multiple AERONET Distributed Regional Aerosol Gridded Observations Network (DRAGON) deployments around the globe. In comparisons with AERONET-DRAGON AODs, the 4.4 km resoln. retrievals show improved correlation (r D 0:9595), smaller RMSE (0.0768), reduced bias (-0.0208), and a larger fraction within the expected error envelope (80.92 %) relative to the Version 22 MISR retrievals.
- 12Franklin, M.; Kalashnikova, O. V.; Garay, M. J. Size-Resolved Particulate Matter Concentrations Derived from 4.4 Km-Resolution Size-Fractionated Multi-Angle Imaging SpectroRadiometer (MISR) Aerosol Optical Depth over Southern California. Remote Sens. Environ. 2017, 196, 312– 323, DOI: 10.1016/j.rse.2017.05.002There is no corresponding record for this reference.
- 13Gelaro, R.; McCarty, W.; Suarez, M. J.; Todling, R.; Molod, A.; Takacs, L.; Randles, C. A.; Darmenov, A.; Bosilovich, M. G.; Reichle, R.; Wargan, K.; Coy, L.; Cullather, R.; Draper, C.; Akella, S.; Buchard, V.; Conaty, A.; da Silva, A. M.; Gu, W.; Kim, G.-K.; Koster, R.; Lucchesi, R.; Merkova, D.; Nielsen, J. E.; Partyka, G.; Pawson, S.; Putman, W.; Rienecker, M.; Schubert, S. D.; Sienkiewicz, M.; Zhao, B. The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). J. Clim. 2017, 30 (14), 5419– 5454, DOI: 10.1175/JCLI-D-16-0758.113https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BB38%252FosVSqsQ%253D%253D&md5=959df25d738b268ddc11d400569a79d1The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2)Gelaro Ronald; McCarty Will; Todling Ricardo; Molod Andrea; Darmenov Anton; Bosilovich Michael G; Reichle Rolf; da Silva Arlindo; Kim Gi-Kong; Koster Randal; Pawson Steven; Putman William; Rienecker Michele; Schubert Siegfried D; Suarez Max J; Draper Clara; Buchard Virginie; Takacs Lawrence; Wargan Krzysztof; Coy Lawrence; Akella Santha; Conaty Austin; Gu Wei; Lucchesi Robert; Merkova Dagmar; Nielsen Jon Eric; Partyka Gary; Sienkiewicz Meta; Randles Cynthia; Cullather Richard; Zhao BinJournal of climate (2017), Volume 30 (Iss 13), 5419-5454 ISSN:0894-8755.The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) is the latest atmospheric reanalysis of the modern satellite era produced by NASA's Global Modeling and Assimilation Office (GMAO). MERRA-2 assimilates observation types not available to its predecessor, MERRA, and includes updates to the Goddard Earth Observing System (GEOS) model and analysis scheme so as to provide a viable ongoing climate analysis beyond MERRA's terminus. While addressing known limitations of MERRA, MERRA-2 is also intended to be a development milestone for a future integrated Earth system analysis (IESA) currently under development at GMAO. This paper provides an overview of the MERRA-2 system and various performance metrics. Among the advances in MERRA-2 relevant to IESA are the assimilation of aerosol observations, several improvements to the representation of the stratosphere including ozone, and improved representations of cryospheric processes. Other improvements in the quality of MERRA-2 compared with MERRA include the reduction of some spurious trends and jumps related to changes in the observing system, and reduced biases and imbalances in aspects of the water cycle. Remaining deficiencies are also identified. Production of MERRA-2 began in June 2014 in four processing streams, and converged to a single near-real time stream in mid 2015. MERRA-2 products are accessible online through the NASA Goddard Earth Sciences Data Information Services Center (GES DISC).
- 14Marais, E. A.; Jacob, D. J.; Jimenez, J. L.; Campuzano-Jost, P.; Day, D. A.; Hu, W.; Krechmer, J.; Zhu, L.; Kim, P. S.; Miller, C. C.; Fisher, J. A.; Travis, K.; Yu, K.; Hanisco, T. F.; Wolfe, G. M.; Arkinson, H. L.; Pye, H. O. T.; Froyd, K. D.; Liao, J.; McNeill, V. F. Aqueous-Phase Mechanism for Secondary Organic Aerosol Formation from Isoprene: Application to the Southeast United States and Co-Benefit of SO2 Emission Controls. Atmos. Chem. Phys. 2016, 16 (3), 1603– 1618, DOI: 10.5194/acp-16-1603-201614https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC28XpsVSmsL0%253D&md5=d6606d8207351df09d7c698711e7c623Aqueous-phase mechanism for secondary organic aerosol formation from isoprene: application to the southeast United States and co-benefit of SO2 emission controlsMarais, E. A.; Jacob, D. J.; Jimenez, J. L.; Campuzano-Jost, P.; Day, D. A.; Hu, W.; Krechmer, J.; Zhu, L.; Kim, P. S.; Miller, C. C.; Fisher, J. A.; Travis, K.; Yu, K.; Hanisco, T. F.; Wolfe, G. M.; Arkinson, H. L.; Pye, H. O. T.; Froyd, K. D.; Liao, J.; McNeill, V. F.Atmospheric Chemistry and Physics (2016), 16 (3), 1603-1618CODEN: ACPTCE; ISSN:1680-7324. (Copernicus Publications)Isoprene emitted by vegetation is an important precursor of secondary org. aerosol (SOA), but the mechanism and yields are uncertain. Aerosol is prevailingly aq. under the humid conditions typical of isoprene-emitting regions. Here we develop an aq.-phase mechanism for isoprene SOA formation coupled to a detailed gas-phase isoprene oxidn. scheme. The mechanism is based on aerosol reactive uptake coeffs. (γ) for water-sol. isoprene oxidn. products, including sensitivity to aerosol acidity and nucleophile concns. We apply this mechanism to simulation of aircraft (SEAC4RS) and ground-based (SOAS) observations over the southeast US in summer 2013 using the GEOS-Chem chem. transport model. Emissions of nitrogen oxides (NOx ≡ NO + NO2) over the southeast US are such that the peroxy radicals produced from isoprene oxidn. (ISOPO2) react significantly with both NO (high-NOx pathway) and HO2 (low-NOx pathway), leading to different suites of isoprene SOA precursors. We find a mean SOA mass yield of 3.3% from isoprene oxidn., consistent with the obsd. relationship of total fine org. aerosol (OA) and formaldehyde (a product of isoprene oxidn.). Isoprene SOA prodn. is mainly contributed by two immediate gasphase precursors, isoprene epoxydiols (IEPOX, 58% of isoprene SOA) from the low-NOx pathway and glyoxal (28 %) from both low- and high-NOx pathways. This speciation is consistent with observations of IEPOX SOA from SOAS and SEAC4RS. Observations show a strong relationship between IEPOX SOA and sulfate aerosol that we explain as due to the effect of sulfate on aerosol acidity and vol. Isoprene SOA concns. increase as NOx emissions decrease (favoring the low-NOx pathway for isoprene oxidn.), but decrease more strongly as SO2 emissions decrease (due to the effect of sulfate on aerosol acidity and vol.). The US Environmental Protection Agency (EPA) projects 2013-2025 decreases in anthropogenic emissions of 34% for NOx (leading to a 7% increase in isoprene SOA) and 48% for SO2 (35% decrease in isoprene SOA). Reducing SO2 emissions decreases sulfate and isoprene SOA by a similar magnitude, representing a factor of 2 co-benefit for PM2.5 from SO2 emission controls.
- 15Pye, H. O. T.; Chan, A. W. H.; Barkley, M. P.; Seinfeld, J. H. Global Modeling of Organic Aerosol: The Importance of Reactive Nitrogen (NOX and NO3). Atmos. Chem. Phys. 2010, 10 (22), 11261– 11276, DOI: 10.5194/acp-10-11261-201015https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3MXmvFCit7w%253D&md5=06bd441b244ac3ede8d916e918c150b6Global modeling of organic aerosol: the importance of reactive nitrogen (NOx and NO3)Pye, H. O. T.; Chan, A. W. H.; Barkley, M. P.; Seinfeld, J. H.Atmospheric Chemistry and Physics (2010), 10 (22), 11261-11276CODEN: ACPTCE; ISSN:1680-7316. (Copernicus Publications)Reactive nitrogen compds., specifically NOx and NO3, likely influence global org. aerosol levels. To assess these interactions, GEOS-Chem, a chem. transport model, is updated to include improved biogenic emissions (following MEGAN v2.1/2.04), a new org. aerosol tracer lumping scheme, aerosol from nitrate radical (NO3) oxidn. of isoprene, and NOx-dependent monoterpene and sesquiterpene aerosol yields. As a result of significant nighttime terpene emissions, fast reaction of monoterpenes with the nitrate radical, and relatively high aerosol yields from NO3 oxidn., biogenic hydrocarbon-NO3 reactions are expected to be a major contributor to surface level aerosol concns. in anthropogenically influenced areas such as the United States. By including aerosol from nitrate radical oxidn. in GEOS-Chem, terpene (monoterpene + sesquiterpene) aerosol approx. doubles and isoprene aerosol is enhanced by 30 to 40% in the Southeast United States. In terms of the global budget of org. aerosol, however, aerosol from nitrate radical oxidn. is somewhat minor (slightly more than 3 Tg/yr) due to the relatively high volatility of org.-NO3 oxidn. products in the yield parameterization. Globally, 69 to 88 Tg/yr of org. aerosol is predicted to be produced annually, of which 14-15 Tg/yr is from oxidn. of monoterpenes and sesquiterpenes and 8-9 Tg/yr from isoprene.
- 16Ginoux, P.; Prospero, J. M.; Gill, T. E.; Hsu, N. C.; Zhao, M. Global-Scale Attribution of Anthropogenic and Natural Dust Sources and Their Emission Rates Based on MODIS Deep Blue Aerosol Products. Rev. Geophys. 2012, DOI: 10.1029/2012RG000388There is no corresponding record for this reference.
- 17Zhang, L.; Kok, J. F.; Henze, D. K.; Li, Q.; Zhao, C. Improving Simulations of Fine Dust Surface Concentrations over the Western United States by Optimizing the Particle Size Distribution. Geophys. Res. Lett. 2013, 40 (12), 3270– 3275, DOI: 10.1002/grl.5059117https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3sXhtFajtLzI&md5=b3efd76080e14cd5682622767de2f897Improving simulations of fine dust surface concentrations over the western United States by optimizing the particle size distributionZhang, Li; Kok, Jasper F.; Henze, Daven K.; Li, Qinbin; Zhao, ChunGeophysical Research Letters (2013), 40 (12), 3270-3275CODEN: GPRLAJ; ISSN:1944-8007. (Wiley-Blackwell)To improve ests. of remote contributions of dust to fine particulate matter (PM2.5) in the western United States, new dust particle size distributions (PSDs) based upon scale-invariant fragmentation theory (KokPSD) with constraints from in situ measurements (IMPPSD) are implemented in a chem. transport model (GEOS-Chem). Compared to initial simulations, this leads to redns. in the mass of emitted dust particles with radii <1.8 μm by 40%-60%. Consequently, the root-mean-square error in simulated fine dust concns. compared to springtime surface observations in the western United States is reduced by 67%-81%. The ratio of simulated fine to coarse PM mass is also improved, which is not achievable by redns. in total dust emissions. The IMPPSD best represents the PSD of dust transported from remote sources and reduces modeled PM2.5 concns. up to 5 μg/m3 over the western United States, which is important when considering sources contributing to nonattainment of air quality stds.
- 18Philip, S.; Martin, R. V.; Snider, G.; Weagle, C. L.; van Donkelaar, A.; Brauer, M.; Henze, D. K.; Klimont, Z.; Venkataraman, C.; Guttikunda, S. K.; Zhang, Q. Anthropogenic Fugitive, Combustion and Industrial Dust Is a Significant, Underrepresented Fine Particulate Matter Source in Global Atmospheric Models. Environ. Res. Lett. 2017, 12 (4), 044018, DOI: 10.1088/1748-9326/aa65a418https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1cXmvVSht7w%253D&md5=b686869f2f7c48acdadf35d5e14506bbAnthropogenic fugitive, combustion and industrial dust is a significant, underrepresented fine particulate matter source in global atmospheric modelsPhilip, Sajeev; Martin, Randall V.; Snider, Graydon; Weagle, Crystal L.; van Donkelaar, Aaron; Brauer, Michael; Henze, Daven K.; Klimont, Zbigniew; Venkataraman, Chandra; Guttikunda, Sarath K.; Zhang, QiangEnvironmental Research Letters (2017), 12 (4), 044018/1-044018/7CODEN: ERLNAL; ISSN:1748-9326. (IOP Publishing Ltd.)Global measurements of the elemental compn. of fine particulate matter across several urban locations by the Surface Particulate Matter Network reveal an enhanced fraction of anthropogenic dust compared to natural dust sources, esp. over Asia. We develop a global simulation of anthropogenic fugitive, combustion, and industrial dust which, to our knowledge, is partially missing or strongly underrepresented in global models. We est. 2-16 μmg m-3 increase in fine particulate mass concn. across East and South Asia by including anthropogenic fugitive, combustion, and industrial dust emissions. A simulation including anthropogenic fugitive, combustion, and industrial dust emissions increases the correlation from 0.06 to 0.66 of simulated fine dust in comparison with Surface Particulate Matter Network measurements at 13 globally dispersed locations, and reduces the low bias by 10% in total fine particulate mass in comparison with global in situ observations. Global population-weighted PM2.5 increases by 2.9 μg m-3 (10%). Our assessment ascertains the urgent need of including this underrepresented fine anthropogenic dust source into global bottom-up emission inventories and global models.
- 19Giglio, L.; Randerson, J. T.; van der Werf, G. R. Analysis of Daily, Monthly, and Annual Burned Area Using the Fourth-Generation Global Fire Emissions Database (GFED4). J. Geophys. Res.: Biogeosci. 2013, 118 (1), 317– 328, DOI: 10.1002/jgrg.20042There is no corresponding record for this reference.
- 20Li, M.; Zhang, Q.; Kurokawa, J.; Woo, J.-H.; He, K.; Lu, Z.; Ohara, T.; Song, Y.; Streets, D. G.; Carmichael, G. R.; Cheng, Y.; Hong, C.; Huo, H.; Jiang, X.; Kang, S.; Liu, F.; Su, H.; Zheng, B. MIX: A Mosaic Asian Anthropogenic Emission Inventory under the International Collaboration Framework of the MICS-Asia and HTAP. Atmos. Chem. Phys. 2017, 17 (2), 935– 963, DOI: 10.5194/acp-17-935-201720https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXosFart7s%253D&md5=89cdde3610d7a091d6a61c44d57b3cc6MIX: a mosaic Asian anthropogenic emission inventory under the international collaboration framework of the MICS-Asia and HTAPLi, Meng; Zhang, Qiang; Kurokawa, Jun-ichi; Woo, Jung-Hun; He, Kebin; Lu, Zifeng; Ohara, Toshimasa; Song, Yu; Streets, David G.; Carmichael, Gregory R.; Cheng, Yafang; Hong, Chaopeng; Huo, Hong; Jiang, Xujia; Kang, Sicong; Liu, Fei; Su, Hang; Zheng, BoAtmospheric Chemistry and Physics (2017), 17 (2), 935-963CODEN: ACPTCE; ISSN:1680-7324. (Copernicus Publications)The MIX inventory is developed for the years 2008 and 2010 to support the Model Inter-Comparison Study for Asia (MICS-Asia) and the Task Force on Hemispheric Transport of Air Pollution (TF HTAP) by a mosaic of up-to-date regional emission inventories. Emissions are estd. for all major anthropogenic sources in 29 countries and regions in Asia. We conducted detailed comparisons of different regional emission inventories and incorporated the best available ones for each region into the mosaic inventory at a uniform spatial and temporal resoln. Emissions are aggregated to five anthropogenic sectors: power, industry, residential, transportation, and agriculture. We est. the total Asian emissions of 10 species in 2010 as follows: 51.3 Tg SO2, 52.1 Tg NOx, 336.6 Tg CO, 67.0 Tg NMVOC (non-methane volatile org. compds.), 28.8 Tg NH3, 31.7 Tg PM10, 22.7 Tg PM2.5, 3.5 Tg BC, 8.3 Tg OC, and 17.3 Pg CO2. Emissions from China and India dominate the emissions of Asia for most of the species. We also estd. Asian emissions in 2006 using the same methodol. of MIX. The relative change rates of Asian emissions for the period of 2006-2010 are estd. as follows: -8.1% for SO2, C19.2% for NOx, C3.9% for CO, C15.5% for NMVOC, C1.7% for NH3, -3.4% for PM10, -1.6% for PM2.5, C5.5% for BC, C1.8% for OC, and C19.9% for CO2. Model-ready speciated NMVOC emissions for SAPRC-99 and CB05 mechanisms were developed following a profile-assignment approach. Monthly gridded emissions at a spatial resoln. of 0.25° ×0.25° are developed and can be accessed.
- 21Travis, K. R.; Jacob, D. J.; Fisher, J. A.; Kim, P. S.; Marais, E. A.; Zhu, L.; Yu, K.; Miller, C. C.; Yantosca, R. M.; Sulprizio, M. P.; Thompson, A. M.; Wennberg, P. O.; Crounse, J. D.; St. Clair, J. M.; Cohen, R. C.; Laughner, J. L.; Dibb, J. E.; Hall, S. R.; Ullmann, K.; Wolfe, G. M.; Pollack, I. B.; Peischl, J.; Neuman, J. A.; Zhou, X. Why Do Models Overestimate Surface Ozone in the Southeast United States?. Atmos. Chem. Phys. 2016, 16 (21), 13561– 13577, DOI: 10.5194/acp-16-13561-201621https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXhsFKmur8%253D&md5=4eeba560c682155ebb56302d01e8daafWhy do models overestimate surface ozone in the Southeast United States?Travis, Katherine R.; Jacob, Daniel J.; Fisher, Jenny A.; Kim, Patrick S.; Marais, Eloise A.; Zhu, Lei; Yu, Karen; Miller, Christopher C.; Yantosca, Robert M.; Sulprizio, Melissa P.; Thompson, Anne M.; Wennberg, Paul O.; Crounse, John D.; St. Clair, Jason M.; Cohen, Ronald C.; Laughner, Joshua L.; Dibb, Jack E.; Hall, Samuel R.; Ullmann, Kirk; Wolfe, Glenn M.; Pollack, Illana B.; Peischl, Jeff; Neuman, Jonathan A.; Zhou, XianliangAtmospheric Chemistry and Physics (2016), 16 (21), 13561-13577CODEN: ACPTCE; ISSN:1680-7324. (Copernicus Publications)Ozone pollution in the Southeast US involves complex chem. driven by emissions of anthropogenic nitrogen oxide radicals (NOx ≃ NO+NO2) and biogenic isoprene. Model ests. of surface ozone concns. tend to be biased high in the region and this is of concern for designing effective emission control strategies to meet air quality stds. We use detailed chem. observations from the SEAC4RS aircraft campaign in August and Sept. 2013, interpreted with the GEOS-Chem chem. transport model at 0.25° × 0.3125° horizontal resoln., to better understand the factors controlling surface ozone in the Southeast US. We find that the National Emission Inventory (NEI) for NOx from the US Environmental Protection Agency (EPA) is too high. This finding is based on SEAC4RS observations of and its oxidn. products, surface network observations of nitrate wet deposition fluxes, and OMI satellite observations of tropospheric NO2 columns. Our results indicate that NEI NOx emissions from mobile and industrial sources must be reduced by 30-60 %, dependent on the assumption of the contribution by soil NOx emissions. Upper-tropospheric NO2 from lightning makes a large contribution to satellite observations of tropospheric NO2 that must be accounted for when using these data to est. surface NOx emissions. We find that only half of isoprene oxidn. proceeds by the high-NOx pathway to produce ozone; this fraction is only moderately sensitive to changes in NOx emissions because isoprene and NOx emissions are spatially segregated. GEOS-Chem with reduced NOx emissions provides an unbiased simulation of ozone observations from the aircraft and reproduces the obsd. ozone prodn. efficiency in the boundary layer as derived from a regression of ozone and NOx oxidn. products. However, the model is still biased high by 6 ± 14 ppb relative to obsd. surface ozone in the Southeast US. Ozone sondes launched during midday hours show a 7 ppb ozone decrease from 1.5 km to the surface that GEOS-Chem does not capture. This bias may reflect a combination of excessive vertical mixing and net ozone prodn. in the model boundary layer.
- 22World Health Organization. WHO Global Ambient Air Quality Database (Update 2018); WHO: Geneva, 2018.There is no corresponding record for this reference.
- 23Kumar, N.; Chu, A.; Foster, A. An Empirical Relationship between PM2.5 and Aerosol Optical Depth in Delhi Metropolitan. Atmos. Environ. 2007, 41 (21), 4492– 4503, DOI: 10.1016/j.atmosenv.2007.01.04623https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2sXlslWks70%253D&md5=b964ef372fec8230d04424f593c06b1dAn empirical relationship between PM2.5 and aerosol optical depth in Delhi MetropolitanKumar, Naresh; Chu, Allen; Foster, AndrewAtmospheric Environment (2007), 41 (21), 4492-4503CODEN: AENVEQ; ISSN:1352-2310. (Elsevier Ltd.)Atm. remote sensing offers a unique opportunity to compute indirect ests. of air quality, which are critically important for the management and surveillance of air quality in megacities of developing countries, particularly in India and China, which have experienced elevated concn. of air pollution but lack adequate spatial-temporal coverage of air pollution monitoring. This article examines the relationship between aerosol optical depth (AOD) estd. from satellite data at 5 km spatial resoln. and the mass of fine particles ≤2.5 μm in aerodynamic diam. (PM2.5) monitored on the ground in Delhi Metropolitan where a series of environmental laws have been instituted in recent years. PM2.5 monitored at 113 sites were collocated by time and space with the AOD computed using the data from Moderate Resoln. Imaging Spectroradiometer (MODIS onboard the Terra satellite). MODIS data were acquired from NASA's Goddard Space Flight Center Earth Sciences Distributed Active Archive Center (DAAC). Our anal. shows a significant pos. assocn. between AOD and PM2.5. After controlling for weather conditions, a 1% change in AOD explains 0.52±0.202% and 0.39±0.15% change in PM2.5 monitored within ±45 and 150 min intervals of AOD data. This relationship will be used to est. air quality surface for previous years, which will allow us to examine the time-space dynamics of air pollution in Delhi following recent air quality regulations, and to assess exposure to air pollution before and after the regulations and its impact on health.
- 24Liu, Y.; Sarnat, J. A.; Kilaru, V.; Jacob, D. J.; Koutrakis, P. Estimating Ground-Level PM2.5 in the Eastern United States Using Satellite Remote Sensing. Environ. Sci. Technol. 2005, 39 (9), 3269– 3278, DOI: 10.1021/es049352m24https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2MXitVyntrc%253D&md5=c04f7c467c6241b52e1bc370466962a2Estimating Ground-Level PM2.5 in the Eastern United States Using Satellite Remote SensingLiu, Yang; Sarnat, Jeremy A.; Kilaru, Vasu; Jacob, Daniel J.; Koutrakis, PetrosEnvironmental Science and Technology (2005), 39 (9), 3269-3278CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)An empirical model based on the regression between daily PM2.5 (particles with aerodynamic diams. of less than 2.5 μm) concns. and aerosol optical thickness (AOT) measurements from the multiangle imaging spectroradiometer (MISR) was developed and tested using data from the eastern United States during the period of 2001. Overall, the empirical model explained 48% of the variability in PM2.5 concns. The root-mean-square error of the model was 6.2 μg/m3 with a corresponding av. PM2.5 concn. of 13.8 μg/m3. When PM2.5 concns. greater than 40 μg/m3 were removed, model results were shown to be unbiased estimators of observations. Several factors, such as planetary boundary layer height, relative humidity, season, and other geog. attributes of monitoring sites, were found to influence the assocn. between PM2.5 and AOT. The findings of this study illustrate the strong potential of satellite remote sensing in regional ambient air quality monitoring as an extension to ground networks. With the continual advancement of remote sensing technol. and global data assimilation systems, AOT measurements derived from satellite remote sensors may provide a cost-effective approach as a supplemental source of information for detg. ground-level particle concns.
- 25de Hoogh, K.; Chen, J.; Gulliver, J.; Hoffmann, B.; Hertel, O.; Ketzel, M.; Bauwelinck, M.; van Donkelaar, A.; Hvidtfeldt, U. A.; Katsouyanni, K.; Klompmaker, J.; Martin, R. V.; Samoli, E.; Schwartz, P. E.; Stafoggia, M.; Bellander, T.; Strak, M.; Wolf, K.; Vienneau, D.; Brunekreef, B.; Hoek, G. Spatial PM2.5, NO2, O3 and BC Models for Western Europe – Evaluation of Spatiotemporal Stability. Environ. Int. 2018, 120, 81– 92, DOI: 10.1016/j.envint.2018.07.03625https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1cXhsVeksbzL&md5=931ca9f9793b88aef579dccb827e4090Spatial PM2.5, NO2, O3 and BC models for Western Europe - Evaluation of spatiotemporal stabilityde Hoogh, Kees; Chen, Jie; Gulliver, John; Hoffmann, Barbara; Hertel, Ole; Ketzel, Matthias; Bauwelinck, Mariska; van Donkelaar, Aaron; Hvidtfeldt, Ulla A.; Katsouyanni, Klea; Klompmaker, Jochem; Martin, Randal V.; Samoli, Evangelia; Schwartz, Per E.; Stafoggia, Massimo; Bellander, Tom; Strak, Maciej; Wolf, Kathrin; Vienneau, Danielle; Brunekreef, Bert; Hoek, GerardEnvironment International (2018), 120 (), 81-92CODEN: ENVIDV; ISSN:0160-4120. (Elsevier Ltd.)In order to investigate assocns. between air pollution and adverse health effects consistent fine spatial air pollution surfaces are needed across large areas to provide cohorts with comparable exposures. The aim of this paper is to develop and evaluate fine spatial scale land use regression models for four major health relevant air pollutants (PM2.5, NO2, BC, O3) across Europe. We developed West-European land use regression models (LUR) for 2010 estg. annual mean PM2.5, NO2, BC and O3 concns. (including cold and warm season ests. for O3). The models were based on AirBase routine monitoring data (PM2.5, NO2 and O3) and ESCAPE monitoring data (BC), and incorporated satellite observations, dispersion model ests., land use and traffic data. Kriging was performed on the residual spatial variation from the LUR models and added to the exposure ests. One model was developed using all sites (100%). Robustness of the models was evaluated by performing a five-fold hold-out validation and for PM2.5 and NO2 addnl. with independent comparison at ESCAPE measurements. To evaluate the stability of each model's spatial structure over time, sep. models were developed for different years (NO2 and O3: 2000 and 2005; PM2.5: 2013). The PM2.5, BC, NO2, O3 annual, O3 warm season and O3 cold season models explained resp. 72%, 54%, 59%, 65%, 69% and 83% of spatial variation in the measured concns. Kriging proved an efficient technique to explain a part of residual spatial variation for the pollutants with a strong regional component explaining resp. 10%, 24% and 16% of the R2 in the PM2.5, O3 warm and O3 cold models. Explained variance at fully independent sites vs the internal hold-out validation was slightly lower for PM2.5 (65% vs 66%) and lower for NO2 (49% vs 57%). Predictions from the 2010 model correlated highly with models developed in other years at the overall European scale. We developed robust PM2.5, NO2, O3 and BC hybrid LUR models. At the West-European scale models were robust in time, becoming less robust at smaller spatial scales. Models were applied to 100 × 100 m surfaces across Western Europe to allow for exposure assignment for 35 million participants from 18 European cohorts participating in the ELAPSE study.
- 26Ma, Z.; Hu, X.; Huang, L.; Bi, J.; Liu, Y. Estimating Ground-Level PM 2.5 in China Using Satellite Remote Sensing. Environ. Sci. Technol. 2014, 48 (13), 7436– 7444, DOI: 10.1021/es500939926https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXpt1yqsrs%253D&md5=00fe05f7ca3d36ff090287cb15ad5cf2Estimating Ground-Level PM2.5 in China Using Satellite Remote SensingMa, Zongwei; Hu, Xuefei; Huang, Lei; Bi, Jun; Liu, YangEnvironmental Science & Technology (2014), 48 (13), 7436-7444CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)Estg. ground-level PM2.5 from satellite-derived aerosol optical depth (AOD) using a spatial statistical model is a promising new method to evaluate the spatial and temporal characteristics of PM2.5 exposure in a large geog. region. However, studies outside North America have been limited due to the lack of ground PM2.5 measurements to calibrate the model. Taking advantage of the newly established national monitoring network, we developed a national-scale geog. weighted regression (GWR) model to est. daily PM2.5 concns. in China with fused satellite AOD as the primary predictor. The results showed that the meteorol. and land use information can greatly improve model performance. The overall cross-validation (CV) R2 is 0.64 and root mean squared prediction error (RMSE) is 32.98 μg/m3. The mean prediction error (MPE) of the predicted annual PM2.5 is 8.28 μg/m3. Our predicted annual PM2.5 concns. indicated that over 96% of the Chinese population lives in areas that exceed the Chinese National Ambient Air Quality Std. (CNAAQS) Level 2 std. Our results also confirmed satellite-derived AOD in conjunction with meteorol. fields and land use information can be successfully applied to extend the ground PM2.5 monitoring network in China.
- 27Song, W.; Jia, H.; Huang, J.; Zhang, Y. A Satellite-Based Geographically Weighted Regression Model for Regional PM2.5 Estimation over the Pearl River Delta Region in China. Remote Sens. Environ. 2014, 154, 1– 7, DOI: 10.1016/j.rse.2014.08.008There is no corresponding record for this reference.
- 28van Donkelaar, A.; Martin, R. V.; Brauer, M.; Boys, B. L. Use of Satellite Observations for Long-Term Exposure Assessment of Global Concentrations of Fine Particulate Matter. Environ. Health Perspect. 2015, 123 (2), 135– 143, DOI: 10.1289/ehp.140864628https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BC2M3jtVaisA%253D%253D&md5=1178880e6f850f7acacdb93ea2c50f02Use of satellite observations for long-term exposure assessment of global concentrations of fine particulate mattervan Donkelaar Aaron; Martin Randall V; Brauer Michael; Boys Brian LEnvironmental health perspectives (2015), 123 (2), 135-43 ISSN:.BACKGROUND: More than a decade of satellite observations offers global information about the trend and magnitude of human exposure to fine particulate matter (PM2.5). OBJECTIVE: In this study, we developed improved global exposure estimates of ambient PM2.5 mass and trend using PM2.5 concentrations inferred from multiple satellite instruments. METHODS: We combined three satellite-derived PM2.5 sources to produce global PM2.5 estimates at about 10 km × 10 km from 1998 through 2012. For each source, we related total column retrievals of aerosol optical depth to near-ground PM2.5 using the GEOS-Chem chemical transport model to represent local aerosol optical properties and vertical profiles. We collected 210 global ground-based PM2.5 observations from the literature to evaluate our satellite-based estimates with values measured in areas other than North America and Europe. RESULTS: We estimated that global population-weighted ambient PM2.5 concentrations increased 0.55 μg/m3/year (95% CI: 0.43, 0.67) (2.1%/year; 95% CI: 1.6, 2.6) from 1998 through 2012. Increasing PM2.5 in some developing regions drove this global change, despite decreasing PM2.5 in some developed regions. The estimated proportion of the population of East Asia living above the World Health Organization (WHO) Interim Target-1 of 35 μg/m3 increased from 51% in 1998-2000 to 70% in 2010-2012. In contrast, the North American proportion above the WHO Air Quality Guideline of 10 μg/m3 fell from 62% in 1998-2000 to 19% in 2010-2012. We found significant agreement between satellite-derived estimates and ground-based measurements outside North America and Europe (r = 0.81; n = 210; slope = 0.68). The low bias in satellite-derived estimates suggests that true global concentrations could be even greater. CONCLUSIONS: Satellite observations provide insight into global long-term changes in ambient PM2.5 concentrations. Satellite-derived estimates and ground-based PM2.5 observations from this study are available for public use.
- 29van Donkelaar, A.; Martin, R. V.; Brauer, M.; Hsu, N. C.; Kahn, R. A.; Levy, R. C.; Lyapustin, A.; Sayer, A. M.; Winker, D. M. Global Estimates of Fine Particulate Matter Using a Combined Geophysical-Statistical Method with Information from Satellites, Models, and Monitors. Environ. Sci. Technol. 2016, 50 (7), 3762– 3772, DOI: 10.1021/acs.est.5b0583329https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC28XjvVyjurY%253D&md5=600d1e11d3e1b145924d1ccc11cf9758Global Estimates of Fine Particulate Matter using a Combined Geophysical-Statistical Method with Information from Satellites, Models, and Monitorsvan Donkelaar, Aaron; Martin, Randall V.; Brauer, Michael; Hsu, N. Christina; Kahn, Ralph A.; Levy, Robert C.; Lyapustin, Alexei; Sayer, Andrew M.; Winker, David M.Environmental Science & Technology (2016), 50 (7), 3762-3772CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)We estd. global fine particulate matter (PM2.5) concns. using information from satellite-, simulation- and monitor-based sources by applying a Geog. Weighted Regression (GWR) to global geophys. based satellite-derived PM2.5 ests. Aerosol optical depth from multiple satellite products (MISR, MODIS Dark Target, MODIS and SeaWiFS Deep Blue, and MODIS MAIAC) was combined with simulation (GEOS-Chem) based upon their relative uncertainties as detd. using ground-based sun photometer (AERONET) observations for 1998-2014. The GWR predictors included simulated aerosol compn. and land use information. The resultant PM2.5 ests. were highly consistent (R2 = 0.81) with out-of-sample cross-validated PM2.5 concns. from monitors. The global population-weighted annual av. PM2.5 concns. were 3-fold higher than the 10 μg/m3 WHO guideline, driven by exposures in Asian and African regions. Ests. in regions with high contributions from mineral dust were assocd. with higher uncertainty, resulting from both sparse ground-based monitoring, and challenging conditions for retrieval and simulation. This approach demonstrates that the addn. of even sparse ground-based measurements to more globally continuous PM2.5 data sources can yield valuable improvements to PM2.5 characterization on a global scale.
- 30Shaddick, G.; Thomas, M. L.; Green, A.; Brauer, M.; van Donkelaar, A.; Burnett, R.; Chang, H. H.; Cohen, A.; van Dingenen, R.; Dora, C.; Gumy, S.; Liu, Y.; Martin, R.; Waller, L. A.; West, J.; Zidek, J. V.; Prüss-Ustün, A. Data Integration Model for Air Quality: A Hierarchical Approach to the Global Estimation of Exposures to Ambient Air Pollution. J. R. Stat. Soc. Ser. C (Applied Stat. 2018, 67 (1), 231– 253, DOI: 10.1111/rssc.12227There is no corresponding record for this reference.
- 31Di, Q.; Koutrakis, P.; Schwartz, J. A Hybrid Prediction Model for PM2.5 Mass and Components Using a Chemical Transport Model and Land Use Regression. Atmos. Environ. 2016, 131, 390– 399, DOI: 10.1016/j.atmosenv.2016.02.00231https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC28XjtFOqsr0%253D&md5=a424117770a1601fce71ca7122f8872fA hybrid prediction model for PM2.5 mass and components using a chemical transport model and land use regressionDi, Qian; Koutrakis, Petros; Schwartz, JoelAtmospheric Environment (2016), 131 (), 390-399CODEN: AENVEQ; ISSN:1352-2310. (Elsevier Ltd.)GEOS-Chem, a chem. transport model, provides time-space continuous ests. of atm. pollutants including PM2.5 and its major components, but model predictions are not highly correlated with ground monitoring data. In addn., its spatial resoln. is usually too coarse to characterize the spatial pattern in pollutant concns. in urban environments. Our objective was to calibrate daily GEOS-Chem simulations using ground monitoring data and incorporating meteorol. variables, land-use terms and spatial-temporal lagged terms. Major PM2.5 components of our interest include sulfate, nitrate, org. carbon, elemental carbon, ammonium, sea salt and dust. We used a backward propagation neural network to calibrate GEOS-Chem predictions with a spatial resoln. of 0.500° × 0.667° using monitoring data collected during the period from 2001 to 2010 for the Northeastern United States. Subsequently, we made predictions at 1 km × 1 km grid cells. We detd. the accuracy of the spatial-temporal predictions using ten-fold cross-validation and "leave-one-day-out" cross-validation techniques. We found a high total R2 for PM2.5 mass (all data R2 0.85, yearly values: 0.80-0.88) and PM2.5 components (R2 for individual components were around 0.70-0.80). Our model makes it possible to assess spatially- and temporally-resolved short- and long-term exposures to PM2.5 mass and components for epidemiol. studies.
- 32Friberg, M. D.; Kahn, R. A.; Holmes, H. A.; Chang, H. H.; Sarnat, S. E.; Tolbert, P. E.; Russell, A. G.; Mulholland, J. A. Daily Ambient Air Pollution Metrics for Five Cities: Evaluation of Data-Fusion-Based Estimates and Uncertainties. Atmos. Environ. 2017, 158, 36– 50, DOI: 10.1016/j.atmosenv.2017.03.02232https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXkslyisro%253D&md5=ef702846b2a25b8d422b758431b78964Daily ambient air pollution metrics for five cities: Evaluation of data-fusion-based estimates and uncertaintiesFriberg, Mariel D.; Kahn, Ralph A.; Holmes, Heather A.; Chang, Howard H.; Sarnat, Stefanie Ebelt; Tolbert, Paige E.; Russell, Armistead G.; Mulholland, James A.Atmospheric Environment (2017), 158 (), 36-50CODEN: AENVEQ; ISSN:1352-2310. (Elsevier Ltd.)Spatiotemporal characterization of ambient air pollutant concns. is increasingly relying on the combination of observations and air quality models to provide well-constrained, spatially and temporally complete pollutant concn. fields. Air quality models, in particular, are attractive, as they characterize the emissions, meteorol., and physiochem. process linkages explicitly while providing continuous spatial structure. However, such modeling is computationally intensive and has biases. The limitations of spatially sparse and temporally incomplete observations can be overcome by blending the data with ests. from a phys. and chem. coherent model, driven by emissions and meteorol. inputs. We recently developed a data fusion method that blends ambient ground observations and chem.-transport-modeled (CTM) data to est. daily, spatially resolved pollutant concns. and assocd. correlations. In this study, we assess the ability of the data fusion method to produce daily metrics (i.e., 1-h max, 8-h max, and 24-h av.) of ambient air pollution that capture spatiotemporal air pollution trends for 12 pollutants (CO, NO2, NOx, O3, SO2, PM10, PM2.5, and five PM2.5 components) across five metropolitan areas (Atlanta, Birmingham, Dallas, Pittsburgh, and St. Louis), from 2002 to 2008. Three sets of comparisons are performed: (1) the CTM concns. are evaluated for each pollutant and metropolitan domain, (2) the data fusion concns. are compared with the monitor data, (3) a comprehensive cross-validation anal. against obsd. data evaluates the quality of the data fusion model simulations across multiple metropolitan domains. The resulting daily spatial field ests. of air pollutant concns. and uncertainties are not only consistent with observations, emissions, and meteorol., but substantially improve CTM-derived results for nearly all pollutants and all cities, with the exception of NO2 for Birmingham. The greatest improvements occur for O3 and PM2.5. Squared spatiotemporal correlation coeffs. range between simulations and observations detd. using cross-validation across all cities for air pollutants of secondary and mixed origins are R2 = 0.88-0.93 (O3), 0.81-0.89 (SO4), 0.67-0.83 (PM2.5), 0.52-0.72 (NO3), 0.43-0.80 (NH4), 0.32-0.51 (OC), and 0.14-0.71 (PM10). Results for more spatially heterogeneous (larger spatial gradients) pollutants of primary origin (NOx, CO, SO2 and EC), tend to be better than those for relatively homogeneous pollutants of secondary origin. Generally, background concns. and spatial concn. gradients reflect interurban airshed complexity and the effects of regional transport, whereas daily spatial pattern variability shows intra-urban consistency in the fused data. With sufficiently high CTM spatial resoln., traffic-related pollutants exhibit gradual concn. gradients that peak toward the urban centers. Ambient pollutant concn. uncertainty ests. for the fused data are both more accurate and smaller than those for either the observations or the model simulations alone.
- 33Kunzli, N. Assessment of Deaths Attributable to Air Pollution: Should We Use Risk Estimates Based on Time Series or on Cohort Studies?. Am. J. Epidemiol. 2001, 153 (11), 1050– 1055, DOI: 10.1093/aje/153.11.105033https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BD3MzislCrtg%253D%253D&md5=e5f8a849ec24c24dbef10b04885f1610Assessment of deaths attributable to air pollution: should we use risk estimates based on time series or on cohort studies?Kunzli N; Medina S; Kaiser R; Quenel P; Horak F Jr; Studnicka MAmerican journal of epidemiology (2001), 153 (11), 1050-5 ISSN:0002-9262.Epidemiologic studies are crucial to the estimation of numbers of deaths attributable to air pollution. In this paper, the authors present a framework for distinguishing estimates of attributable cases based on time-series studies from those based on cohort studies, the latter being 5-10 times larger. The authors distinguish four categories of death associated with air pollution: A) air pollution increases both the risk of underlying diseases leading to frailty and the short term risk of death among the frail; B) air pollution increases the risk of chronic diseases leading to frailty but is unrelated to timing of death; C) air pollution is unrelated to risk of chronic diseases but short term exposure increases mortality among persons who are frail; and D) neither underlying chronic disease nor the event of death is related to air pollution exposure. Time-series approaches capture deaths from categories A and C, whereas cohort studies assess cases from categories A, B, and C. In addition, years of life lost can only be derived from cohort studies, where time to death is the outcome, while in time-series studies, death is a once-only event (no dimension in time). The authors conclude that time-series analyses underestimate cases of death attributable to air pollution and that assessment of the impact of air pollution on mortality should be based on cohort studies.
- 34Brook, R. D.; Rajagopalan, S.; Pope, C. A.; Brook, J. R.; Bhatnagar, A.; Diez-Roux, A. V.; Holguin, F.; Hong, Y.; Luepker, R. V.; Mittleman, M. A.; Peters, A.; Siscovick, D.; Smith, S. C.; Whitsel, L.; Kaufman, J. D. American Heart Association Council on Epidemiology and Prevention, Council on the Kidney in Cardiovascular Disease, and Council on Nutrition, Physical Activity and Metabolism. Particulate Matter Air Pollution and Cardiovascular Disease. Circulation 2010, 121 (21), 2331– 2378, DOI: 10.1161/CIR.0b013e3181dbece134https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3cXmslGmu78%253D&md5=bbe8d6ace13d0364865a8364e5b6000bParticulate Matter Air Pollution and Cardiovascular Disease: An Update to the Scientific Statement From the American Heart AssociationBrook, Robert D.; Rajagopalan, Sanjay; Pope, C. Arden, III; Brook, Jeffrey R.; Bhatnagar, Aruni; Diez-Roux, Ana V.; Holguin, Fernando; Hong, Yuling; Luepker, Russell V.; Mittleman, Murray A.; Peters, Annette; Siscovick, David; Smith, Sidney C., Jr.; Whitsel, Laurie; Kaufman, Joel D.Circulation (2010), 121 (21), 2331-2378CODEN: CIRCAZ; ISSN:0009-7322. (Lippincott Williams & Wilkins)A review. In 2004, the first American Heart Assocn. scientific statement on "Air Pollution and Cardiovascular Disease" concluded that exposure to particulate matter (PM) air pollution contributes to cardiovascular morbidity and mortality. In the interim, numerous studies have expanded our understanding of this assocn. and further elucidated the physiol. and mol. mechanisms involved. The main objective of this updated American Heart Assocn. scientific statement is to provide a comprehensive review of the new evidence linking PM exposure with cardiovascular disease, with a specific focus on highlighting the clin. implications for researchers and healthcare providers. The writing group also sought to provide expert consensus opinions on many aspects of the current state of science and updated suggestions for areas of future research. On the basis of the findings of this review, several new conclusions were reached, including the following: Exposure to PM < 2.5 μm in diam. (PM2.5) over a few hours to weeks can trigger cardiovascular disease-related mortality and nonfatal events; longer-term exposure (eg, a few years) increases the risk for cardiovascular mortality to an even greater extent than exposures over a few days and reduces life expectancy within more highly exposed segments of the population by several months to a few years; redns. in PM levels are assocd. with decreases in cardiovascular mortality within a time frame as short as a few years; and many credible pathol. mechanisms have been elucidated that lend biol. plausibility to these findings. It is the opinion of the writing group that the overall evidence is consistent with a causal relationship between PM2.5 exposure and cardiovascular morbidity and mortality. This body of evidence has grown and been strengthened substantially since the first American Heart Assocn. scientific statement was published. Finally, PM2.5 exposure is deemed a modifiable factor that contributes to cardiovascular morbidity and mortality.
- 35Pope, C. A. Mortality Effects of Longer Term Exposures to Fine Particulate Air Pollution: Review of Recent Epidemiological Evidence. Inhalation Toxicol. 2007, 19 (sup1), 33– 38, DOI: 10.1080/0895837070149296135https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2sXhtVKmtLjN&md5=7d2899758d840ff75a9da75e9485facaMortality effects of longer term exposures to fine particulate air pollution: review of recent epidemiological evidencePope, C. Arden, IIIInhalation Toxicology (2007), 19 (Suppl. 1), 33-38CODEN: INHTE5; ISSN:0895-8378. (Informa Healthcare)A review. This article evaluates the dynamic exposure-response relation between particulate matter air pollution (PM) and mortality risk by integrating epidemiol. evidence from studies that use different time scales of exposure. The evidence suggests that short-term exposure studies are observing more than just harvesting or mortality displacement. There is little evidence of short-term compensatory redn. in deaths, and estd. PM effects are generally larger for intermediate and longer term time scales of exposure. Although proximity in time matters, with most recent exposure having the largest health impact, there is evidence that the short-term exposure studies capture only a small amt. of the overall health effects of long-term repeated exposure to PM. The overall epidemiol. evidence suggests that adverse health effects are dependent on both exposure concns. and length of exposure, and that long-term exposures have larger, more persistent cumulative effects than short-term exposures.
- 36Yitshak-Sade, M.; Bobb, J. F.; Schwartz, J. D.; Kloog, I.; Zanobetti, A. The Association between Short and Long-Term Exposure to PM2.5 and Temperature and Hospital Admissions in New England and the Synergistic Effect of the Short-Term Exposures. Sci. Total Environ. 2018, 639, 868– 875, DOI: 10.1016/j.scitotenv.2018.05.18136https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1cXhtVWksbzF&md5=61df9f72355a96299d14892cbb07fabdThe association between short and long-term exposure to PM2.5 and temperature and hospital admissions in New England and the synergistic effect of the short-term exposuresYitshak-Sade, Maayan; Bobb, Jennifer F.; Schwartz, Joel D.; Kloog, Itai; Zanobetti, AntonellaScience of the Total Environment (2018), 639 (), 868-875CODEN: STENDL; ISSN:0048-9697. (Elsevier B.V.)Particulate matter < 2.5 μm in diam. (PM2.5) and heat are strong predictors of morbidity, yet few studies have examd. the effects of long-term exposures on non-fatal events, or assessed the short and long-term effect on health simultaneously. We jointly investigated the assocn. of short and long-term exposures to PM2.5 and temp. with hospital admissions, and explored the modification of the assocns. with the short-term exposures by one another and by temp. variability. Daily ZIP code counts of respiratory, cardiac and stroke admissions of adults ≥65 (N = 2,015,660) were constructed across New-England (2001-2011). Daily PM2.5 and temp. exposure ests. were obtained from satellite-based spatio-temporally resolved models. For each admission cause, a Poisson regression was fit on short and long-term exposures, with a random intercept for ZIP code. Modifications of the short-term effects were tested by adding interaction terms with temp., PM2.5 and temp. variability. Assocns. between short and long-term exposures were obsd. for all of the outcomes, with stronger effects of long-term exposures to PM2.5. For respiratory admissions, the short-term PM2.5 effect (percent increase per IQR) was larger on warmer days (1.12% vs. -0.53%) and in months of higher temp. variability (1.63% vs. -0.45%). The short-term temp. effect was higher in months of higher temp. variability as well. For cardiac admissions, the PM2.5 effect was larger on colder days (0.56% vs. -0.30%) and in months of higher temp. variability (0.99% vs. -0.56%). We obsd. synergistic effects of short-term exposures to PM2.5, temp. and temp. variability. Long-term exposures to PM2.5 were assocd. with larger effects compared to short-term exposures.
- 37Liang, F.; Xiao, Q.; Gu, D.; Xu, M.; Tian, L.; Guo, Q.; Wu, Z.; Pan, X.; Liu, Y. Satellite-Based Short- and Long-Term Exposure to PM2.5 and Adult Mortality in Urban Beijing, China. Environ. Pollut. 2018, 242, 492– 499, DOI: 10.1016/j.envpol.2018.06.09737https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1cXhtlSlu77M&md5=ef653841992788e03f5e3f5a2b20f5cbSatellite-based short- and long-term exposure to PM2.5 and adult mortality in urban Beijing, ChinaLiang, Fengchao; Xiao, Qingyang; Gu, Dongfeng; Xu, Meimei; Tian, Lin; Guo, Qun; Wu, Ziting; Pan, Xiaochuan; Liu, YangEnvironmental Pollution (Oxford, United Kingdom) (2018), 242 (Part_A), 492-499CODEN: ENPOEK; ISSN:0269-7491. (Elsevier Ltd.)Severe and persistent haze accompanied by high concns. of fine particulate matter (PM2.5) has become a great public health concern in urban China. However, research on the health effects of PM2.5 in China has been hindered by the lack of high-quality exposure ests. In this study, we assessed the excess mortality assocd. with both short- and long-term exposure to ambient PM2.5 simultaneously using satellite-derived exposure data at a high spatiotemporal resoln. Adult registries of non-accidental, respiratory and cardiovascular deaths in urban Beijing in 2013 were collected. Exposure levels were estd. from daily satellite-based PM2.5 concns. at 1 km spatial resoln. from 2004 to 2013. Mixed Poisson regression models were fitted to est. the cause-specific mortality in assocn. with PM2.5 exposures. With the mutual adjustment of short- and long-term exposure of PM2.5, the percent increases assocd. with every 10 μg/m3 increase in short-term PM2.5 exposure were 0.09% (95% CI: -0.14%, 0.33%; lag 01), 1.02% (95% CI: 0.08%, 1.97%; lag 04) and 0.09% (95% CI: -0.23%, 0.42%; lag 01) for non-accidental, respiratory and cardiovascular mortality, resp.; those attributable to every 10 μg/m3 increase in long-term PM2.5 exposure (9-yr moving av.) were 16.78% (95% CI: 10.58%, 23.33%), 44.14% (95% CI: 20.73%, 72.10%) and 3.72% (95% CI: -3.75%, 11.77%), resp.
- 38Sayer, A. M.; Munchak, L. A.; Hsu, N. C.; Levy, R. C.; Bettenhausen, C.; Jeong, M.-J. MODIS Collection 6 Aerosol Products: Comparison between Aqua’s e-Deep Blue, Dark Target, and “Merged” Data Sets, and Usage Recommendations. J. Geophys. Res. Atmos. 2014, 119 (24), 13965– 13989, DOI: 10.1002/2014JD02245338https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXhtVKmtrk%253D&md5=65b66f5ca479d2855e4737d3d514be7bMODIS Collection 6 aerosol products: Comparison between Aqua's e-Deep Blue, Dark Target, and "merged" data sets, and usage recommendationsSayer, A. M.; Munchak, L. A.; Hsu, N. C.; Levy, R. C.; Bettenhausen, C.; Jeong, M.-J.Journal of Geophysical Research: Atmospheres (2014), 119 (24), 13965-13989CODEN: JGRDE3; ISSN:2169-8996. (Wiley-Blackwell)The Moderate Resoln. Imaging Spectroradiometer (MODIS) Atmospheres data product suite includes three algorithms applied to retrieve midvisible aerosol optical depth (AOD): the Enhanced Deep Blue (DB) and Dark Target (DT) algorithms over land, and a DT over-water algorithm. All three have been refined in the recent "Collection 6" (C6) MODIS reprocessing. In particular, DB has been expanded to cover vegetated land surfaces as well as brighter desert/urban areas. Addnl., a new "merged" data set which draws from all three algorithms is included in the C6 products. This study is intended to act as a point of ref. for new and experienced MODIS data users with which to understand the global and regional characteristics of the C6 DB, DT, and merged data sets, based on MODIS Aqua data. This includes validation against Aerosol Robotic Network (AERONET) observations at 111 sites, focused toward regional and categorical (surface/aerosol type) anal. Neither algorithm consistently outperforms the other, although in many cases the retrieved AOD and the level of its agreement with AERONET are very similar. In many regions the DB, DT, and merged data sets are all suitable for quant. applications, bearing in mind that they cannot be considered independent, while in other cases one algorithm does consistently outperform the other. Usage recommendations and caveats are thus somewhat complicated and regionally dependent.
- 39Levy, R. C.; Mattoo, S.; Munchak, L. A.; Remer, L. A.; Sayer, A. M.; Patadia, F.; Hsu, N. C. The Collection 6 MODIS Aerosol Products over Land and Ocean. Atmos. Meas. Tech. 2013, 6 (11), 2989– 3034, DOI: 10.5194/amt-6-2989-2013There is no corresponding record for this reference.
- 40Sayer, A. M.; Hsu, N. C.; Bettenhausen, C.; Jeong, M.-J.; Holben, B. N.; Zhang, J. Global and Regional Evaluation of Over-Land Spectral Aerosol Optical Depth Retrievals from SeaWiFS. Atmos. Meas. Tech. 2012, 5 (7), 1761– 1778, DOI: 10.5194/amt-5-1761-2012There is no corresponding record for this reference.
- 41Hsu, N. C.; Jeong, M.-J.; Bettenhausen, C.; Sayer, A. M.; Hansell, R.; Seftor, C. S.; Huang, J.; Tsay, S.-C. Enhanced Deep Blue Aerosol Retrieval Algorithm: The Second Generation. J. Geophys. Res. Atmos. 2013, 118 (16), 9296– 9315, DOI: 10.1002/jgrd.50712There is no corresponding record for this reference.
- 42Diner, D. J.; Beckert, J. C.; Reilly, T. H.; Bruegge, C. J.; Conel, J. E.; Kahn, R. A.; Martonchik, J. V.; Ackerman, T. P.; Davies, R.; Gerstl, S. A. W.; Gordon, H. R.; Muller, J.; Myneni, R. B.; Sellers, P. J.; Pinty, B.; Verstraete, M. M. Multi-Angle Imaging SpectroRadiometer (MISR) Instrument Description and Experiment Overview. IEEE Trans. Geosci. Remote Sens. 1998, 36 (4), 1072– 1087, DOI: 10.1109/36.700992There is no corresponding record for this reference.
- 43Martonchik, J. V.; Kahn, R. A.; Diner, D. J. Retrieval of Aerosol Properties over Land Using MISR Observations. In Satellite Aerosol Remote Sensing over Land; Springer: Berlin, 2009; pp 267– 293. DOI: DOI: 10.1007/978-3-540-69397-0_9 .There is no corresponding record for this reference.
- 44Garay, M. J.; Witek, M. L.; Kahn, R. A.; Seidel, F. C.; Limbacher, J. A.; Bull, M. A.; Diner, D. J.; Hansen, E. G.; Kalashnikova, O. V.; Lee, H.; Nastan, A. M.; Yu, Y. Introducing the 4.4km Spatial Resolution Multi-Angle Imaging SpectroRadiometer (MISR) Aerosol Product. Atmos. Meas. Tech. 2020, 13 (2), 593– 628, DOI: 10.5194/amt-13-593-2020There is no corresponding record for this reference.
- 45van Donkelaar, A.; Martin, R. V.; Brauer, M.; Kahn, R.; Levy, R.; Verduzco, C.; Villeneuve, P. J. Global Estimates of Ambient Fine Particulate Matter Concentrations from Satellite-Based Aerosol Optical Depth: Development and Application. Environ. Health Perspect. 2010, 118 (6), 847– 855, DOI: 10.1289/ehp.090162345https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3cXovVKhtro%253D&md5=eba9677d030d59d6c3ed27c887a336c7Global estimates of ambient fine particulate matter concentrations from satellite-based aerosol optical depth: development and applicationvan Donkelaar, Aaron; Martin, Randall V.; Brauer, Michael; Kahn, Ralph; Levy, Robert; Verduzco, Carolyn; Villeneuve, Paul J.Environmental Health Perspectives (2010), 118 (6), 847-855CODEN: EVHPAZ; ISSN:0091-6765. (U. S. Department of Health and Human Services, Public Health Services)Epidemiol. and health impact studies of fine particulate matter with diam. < 2.5 μm (PM2.5) are limited by the lack of monitoring data, esp. in developing countries. Satellite observations offer valuable global information about PM2.5 concns. In this study, we developed a technique for estg. surface PM2.5 concns. from satellite observations. We mapped global ground-level PM2.5 concns. using total column aerosol optical depth (AOD) from the MODIS (Moderate Resoln. Imaging Spectroradiometer) and MISR (Multiangle Imaging Spectroradiometer) satellite instruments and coincident aerosol vertical profiles from the GEOS-Chem global chem. transport model. We detd. that global ests. of long-term av. (1 Jan. 2001 to 31 Dec. 2006) PM2.5 concns. at approx. 10 km × 10 km resoln. indicate a global population-weighted geometric mean PM2.5 concn. of 20 μg/m3. The World Health Organization Air Quality PM2.5 Interim Target-1 (35 μg/m3 annual av.) is exceeded over central and eastern Asia for 38% and for 50% of the population, resp. Annual mean PM2.5 concns. exceed 80 μg/m3 over eastern China. Our evaluation of the satellite-derived est. with ground-based in situ measurements indicates significant spatial agreement with North American measurements (r = 0.77; slope = 1.07; n = 1057) and with noncoincident measurements elsewhere (r = 0.83; slope = 0.86; n = 244). The 1 SD of uncertainty in the satellite-derived PM2.5 is 25%, which is inferred from the AOD retrieval and from aerosol vertical profile errors and sampling. The global population-weighted mean uncertainty is 6.7 μg/m3. Satellite-derived total-column AOD, when combined with a chem. transport model, provides ests. of global long-term av. PM2.5 concns.
- 46Van Donkelaar, A.; Martin, R. V.; Park, R. J. Estimating Ground-Level PM 2.5 Using Aerosol Optical Depth Determined from Satellite Remote Sensing. J. Geophys. Res. 2006, 111, 21201, DOI: 10.1029/2005JD00699646https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2sXhvValtL8%253D&md5=95eff5143f7b35eaf63e8f207d7ba88bEstimating ground-level PM2.5 using aerosol optical depth determined from satellite remote sensingvan Donkelaar, Aaron; Martin, Randall V.; Park, Rokjin J.Journal of Geophysical Research, [Atmospheres] (2006), 111 (D21), D21201/1-D21201/10CODEN: JGRDE3 ISSN:. (American Geophysical Union)The authors assess the relation of ground-level fine particulate matter (PM2.5) concns. for 2000-2001 measured as part of the Canadian National Air Pollution Surveillance (NAPS) network and the U.S. Air Quality System (AQS), vs. remotesensed PM2.5 detd. from aerosol optical depths (AOD) measured by the Moderate Resoln. Imaging Spectroradiometer (MODIS) and the Multiangle Imaging Spectroradiometer (MISR) satellite instruments. A global chem. transport model (GEOS-CHEM) was used to simulate the factors affecting the relation between AOD and PM2.5. AERONET AOD was used to evaluate the method (r = 0.71, N = 48, slope = 0.69). The authors find significant spatial variation of the annual mean ground-based measurements with PM2.5 detd. from MODIS (r = 0.69, N = 199, slope = 0.82) and MISR (r = 0.58, N = 199, slope = 0.57). Excluding California significantly increases the resp. slopes and correlations. The relative vertical profile of aerosol extinction is the most important factor affecting the spatial relation between satellite and surface measurements of PM2.5; neglecting this parameter would reduce the spatial correlation to 0.36. In contrast, temporal variation in AOD is the most influential parameter affecting the temporal relation between satellite and surface measurements of PM2.5; neglecting daily variation in this parameter would decrease the correlation in eastern North America from 0.5-0.8 to <0.2. Other simulated aerosol properties, such as effective radius and extinction efficiency have a minor role temporally, but do influence the spatial correlation. Global mapping of PM2.5 from both MODIS and MISR reveals annual mean concns. of 40-50 μg/m3 over northern India and China.
- 47Molod, A.; Takacs, L.; Suarez, M.; Bacmeister, J. Development of the GEOS-5 Atmospheric General Circulation Model: Evolution from MERRA to MERRA2. Geosci. Model Dev. 2015, 8 (5), 1339– 1356, DOI: 10.5194/gmd-8-1339-2015There is no corresponding record for this reference.
- 48Lu, Z.; Zhang, Q.; Streets, D. G. Sulfur Dioxide and Primary Carbonaceous Aerosol Emissions in China and India. Atmos. Chem. Phys. 2011, 11, 9839– 9864, DOI: 10.5194/acp-11-9839-201148https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3MXhs1GqtbbN&md5=21b539ac27dec3c14d8db6024287dfd5Sulfur dioxide and primary carbonaceous aerosol emissions in China and India, 1996-2010Lu, Z.; Zhang, Q.; Streets, D. G.Atmospheric Chemistry and Physics (2011), 11 (18), 9839-9864CODEN: ACPTCE; ISSN:1680-7316. (Copernicus Publications)China and India are the 2 largest anthropogenic aerosol generating countries in the world. The authors develop a new inventory of SO2 (SO2) and primary carbonaceous aerosol (i.e., black and org. C, BC and OC) emissions from these 2 countries for the period 1996-2010, using a technol.-based methodol. Emissions from major anthropogenic sources and open biomass burning are included, and time-dependent trends in activity rates and emission factors are incorporated in the calcn. Year-specific monthly temporal distributions for major sectors and gridded emissions at a resoln. of 0.1° × 0.1° distributed by multiple year-by-year spatial proxies are also developed. In China, the interaction between economic development and environmental protection causes large temporal variations in the emission trends. From 1996 to 2000, emissions of all 3 species showed a decreasing trend (by 9 %-17 %) due to a slowdown in economic growth, a decline in coal use in nonpower sectors, and the implementation of air pollution control measures. With the economic boom after 2000, emissions from China changed dramatically. BC and OC emissions increased by 46% and 33% to 1.85 TG and 4.03 Tg in 2010. SO2 emissions 1st increased by 61% to 34.0 TG in 2006, and then decreased by 9.2% to 30.8 Tg in 2010 due to the wide application of flue-gas desulfurization (FGD) equipment in power plants. Driven by the remarkable energy consumption growth and relatively lax emission controls, emissions from India increased by 70 %, 41 %, and 35% to 8.81 TG, 1.02 Tg, and 2.74 Tg in 2010 for SO2, BC, and OC, resp. Monte Carlo simulations are used to quantify the emission uncertainties. The av. 95% confidence intervals (CIs) of SO2, BC, and OC emissions are -16 %-17 %, -43 %-93 %, and -43 %-80% for China, and -15 %-16 %, -41 %-87 %, and -44 %-92% for India, resp. S content, fuel use, and S retention of hard coal and the actual FGD removal efficiency are the main contributors to the uncertainties of SO2 emissions. Biofuel combustion related parameters (i.e., technol. divisions, fuel use, and emission factor determinants) are the largest source of OC uncertainties, whereas BC emissions are also sensitive to the parameters of coal combustion in the residential and industrial sectors and the coke-making process. Comparing the results with satellite observations, the trends of estd. emissions in both China and India are in good agreement with the trends of aerosol optical depth (AOD) and SO2 retrievals obtained from different satellites.
- 49Holben, B. N.; Eck, T. F.; Slutsker, I.; Tanré, D.; Buis, J. P.; Setzer, A.; Vermote, E.; Reagan, J. A.; Kaufman, Y. J.; Nakajima, T.; Lavenu, F.; Jankowiak, I.; Smirnov, A. AERONET—A Federated Instrument Network and Data Archive for Aerosol Characterization. Remote Sens. Environ. 1998, 66 (1), 1– 16, DOI: 10.1016/S0034-4257(98)00031-5There is no corresponding record for this reference.
- 50Eck, T. F.; Holben, B. N.; Reid, J. S.; Dubovik, O.; Smirnov, A.; O’Neill, N. T.; Slutsker, I.; Kinne, S. Wavelength Dependence of the Optical Depth of Biomass Burning, Urban, and Desert Dust Aerosols. J. Geophys. Res. Atmos. 1999, 104 (D24), 31333– 31349, DOI: 10.1029/1999JD900923There is no corresponding record for this reference.
- 51Giles, D. M.; Sinyuk, A.; Sorokin, M. G.; Schafer, J. S.; Smirnov, A.; Slutsker, I.; Eck, T. F.; Holben, B. N.; Lewis, J. R.; Campbell, J. R.; Welton, E. J.; Korkin, S. V.; Lyapustin, A. I. Advancements in the Aerosol Robotic Network (AERONET) Version 3 Database – Automated near-Real-Time Quality Control Algorithm with Improved Cloud Screening for Sun Photometer Aerosol Optical Depth (AOD) Measurements. Atmos. Meas. Tech. 2019, 12 (1), 169– 209, DOI: 10.5194/amt-12-169-201951https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1MXhtlGhtbvN&md5=342ef7b49f925189ef4675428813c5bbAdvancements in the Aerosol Robotic Network (AERONET) Version 3 database - automated near-real-time quality control algorithm with improved cloud screening for Sun photometer aerosol optical depth (AOD) measurementsGiles, David M.; Sinyuk, Alexander; Sorokin, Mikhail G.; Schafer, Joel S.; Smirnov, Alexander; Slutsker, Ilya; Eck, Thomas F.; Holben, Brent N.; Lewis, Jasper R.; Campbell, James R.; Welton, Ellsworth J.; Korkin, Sergey V.; Lyapustin, Alexei I.Atmospheric Measurement Techniques (2019), 12 (1), 169-209CODEN: AMTTC2; ISSN:1867-8548. (Copernicus Publications)The Aerosol Robotic Network (AERONET) has provided highly accurate, ground-truth measurements of the aerosol optical depth (AOD) using Cimel Electronique Sun- sky radiometers for more than 25 years. In Version 2 (V2) of the AERONET database, the near-real-time AOD was semiautomatically quality controlled utilizing mainly cloudscreening methodol., while addnl. AOD data contaminated by clouds or affected by instrument anomalies were removed manually before attaining quality-assured status (Level 2.0). The large growth in the no. of AERONET sites over the past 25 years resulted in significant burden to the manual quality control of millions of measurements in a consistent manner. The AERONET Version 3 (V3) algorithm provides fully automatic cloud screening and instrument anomaly quality controls. All of these new algorithm updates apply to near-real-time data as well as post-fielddeployment processed data, and AERONET reprocessed the database in 2018. A full algorithm redevelopment provided the opportunity to improve data inputs and corrections such as unique filter-specific temp. characterizations for all visible and near-IR wavelengths, updated gaseous and water vapor absorption coeffs., and ancillary data sets. The Level 2.0 AOD quality-assured data set is now available within a month after post-field calibration, reducing the lag time from up to several months. Near-real-time estd. uncertainty is detd. using data qualified as V3 Level 2.0 AOD and considering the difference between the AOD computed with the pre-field calibration and AOD computed with pre-field and post-field calibration. This assessment provides a near-real-time uncertainty est. for which av. differences of AOD suggest a + 0.02 bias and one sigma uncertainty of 0.02, spectrally, but the bias and uncertainty can be significantly larger for specific instrument deployments. Long-term monthly avs. analyzed for the entire V3 and V2 databases produced av. differences (V3-V2) of +0.002 with a ±0.02 SD (std. deviation), yet monthly avs. calcd. using time-matched observations in both databases were analyzed to compute an av. difference of -0.002 with a ±0.004 SD. The high statistical agreement in multiyear monthly averaged AOD validates the advanced automatic data quality control algorithms and suggests that migrating research to the V3 database will corroborate most V2 research conclusions and likely lead to more accurate results in some cases.
- 52Li, Z.; Zhao, X.; Kahn, R.; Mishchenko, M.; Remer, L.; Lee, K.-H.; Wang, M.; Laszlo, I.; Nakajima, T.; Maring, H. Uncertainties in Satellite Remote Sensing of Aerosols and Impact on Monitoring Its Long-Term Trend: A Review and Perspective. Ann. Geophys. 2009, 27 (7), 2755– 2770, DOI: 10.5194/angeo-27-2755-2009There is no corresponding record for this reference.
- 53van Donkelaar, A.; Martin, R. V.; Spurr, R. J. D.; Drury, E.; Remer, L. A.; Levy, R. C.; Wang, J. Optimal Estimation for Global Ground-Level Fine Particulate Matter Concentrations. J. Geophys. Res. Atmos. 2013, 118 (11), 5621– 5636, DOI: 10.1002/jgrd.50479There is no corresponding record for this reference.
- 54Brunsdon, C.; Fotheringham, A. S.; Charlton, M. E. Geographically Weighted Regression: A Method for Exploring Spatial Nonstationarity. Geogr. Anal. 1996, 28 (4), 281– 298, DOI: 10.1111/j.1538-4632.1996.tb00936.xThere is no corresponding record for this reference.
- 55Fotheringham, A. S.; Charlton, M. E.; Brunsdon, C. Geographically Weighted Regression: A Natural Evolution of the Expansion Method for Spatial Data Analysis. Environ. Plan. A Econ. Sp. 1998, 30 (11), 1905– 1927, DOI: 10.1068/a301905There is no corresponding record for this reference.
- 56Jin, X.; Fiore, A. M.; Curci, G.; Lyapustin, A.; Civerolo, K.; Ku, M.; van Donkelaar, A.; Martin, R. V. Assessing Uncertainties of a Geophysical Approach to Estimate Surface Fine Particulate Matter Distributions from Satellite-Observed Aerosol Optical Depth. Atmos. Chem. Phys. 2019, 19 (1), 295– 313, DOI: 10.5194/acp-19-295-201956https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1MXitlSgu7Y%253D&md5=9ec67dc30f456bc71537e4498ded0996Assessing uncertainties of a geophysical approach to estimate surface fine particulate matter distributions from satellite-observed aerosol optical depthJin, Xiaomeng; Fiore, Arlene M.; Curci, Gabriele; Lyapustin, Alexei; Civerolo, Kevin; Ku, Michael; van Donkelaar, Aaron; Martin, Randall V.Atmospheric Chemistry and Physics (2019), 19 (1), 295-313CODEN: ACPTCE; ISSN:1680-7324. (Copernicus Publications)Health impact analyses are increasingly tapping the broad spatial coverage of satellite aerosol optical depth (AOD) products to est. human exposure to fine particulate matter (PM2.5). We use a forward geophys. approach to derive ground-level PM2.5 distributions from satellite AOD at 1 km2 resoln. for 2011 over the northeastern US by applying relationships between surface PM2.5 and column AOD (calcd. offline from speciated mass distributions) from a regional air quality model (CMAQ; 12×12 km2 horizontal resoln.). Seasonal av. satellite-derived PM2.5 reveals more spatial detail and best captures obsd. surface PM2.5 levels during summer. At the daily scale, however, satellite-derived PM2.5 is not only subject to measurement uncertainties from satellite instruments, but more importantly to uncertainties in the relationship between surface PM2.5 and column AOD. Using 11 ground-based AOD measurements within 10 km of surface PM2.5 monitors, we show that uncertainties in modeled PM2.5/AOD can explain more than 70% of the spatial and temporal variance in the total uncertainty in daily satellite-derived PM2.5 evaluated at PM2.5 monitors. This finding implies that a successful geophys. approach to deriving daily PM2.5 from satellite AOD requires model skill at capturing day-to-day variations in PM2.5/AOD relationships. Overall, we est. that uncertainties in the modeled PM2.5/AOD lead to an error of 11 μgm-3 in daily satellite-derived PM2.5, and uncertainties in satellite AOD lead to an error of 8 μgm-3. Using multi-platform ground, airborne, and radiosonde measurements, we show that uncertainties of modeled PM2.5/AOD are mainly driven by model uncertainties in aerosol column mass and speciation, while model representation of relative humidity and aerosol vertical profile shape contributes some systematic biases. The parameterization of aerosol optical properties, which dets. the mass extinction efficiency, also contributes to random uncertainty, with the size distribution being the largest source of uncertainty and hygroscopicity of inorg. salt the second largest. Future efforts to reduce uncertainty in geophys. approaches to derive surface PM2.5 from satellite AOD would thus benefit from improving model representation of aerosol vertical distribution and aerosol optical properties, to narrow uncertainty in satellite-derived PM2.5.
- 57Wang, Y.; Chen, Y. Significant Climate Impact of Highly Hygroscopic Atmospheric Aerosols in Delhi, India. Geophys. Res. Lett. 2019, 46 (10), 5535– 5545, DOI: 10.1029/2019GL08233957https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1MXhtFGqsL3P&md5=8ec0bcc186719f30c0c727a042ba4f06Significant Climate Impact of Highly Hygroscopic Atmospheric Aerosols in Delhi, IndiaWang, Yu; Chen, YingGeophysical Research Letters (2019), 46 (10), 5535-5545CODEN: GPRLAJ; ISSN:1944-8007. (Wiley-Blackwell)Hygroscopicity of aerosol (κchem) is a key factor affecting its direct and indirect climate effects, however, long-term observation in Delhi is absent. Here we demonstrate an approach to derive κchem from publicly available data sets and validate it (bias of 5%-30%) with long-term observations in Beijing. Using this approach, we report the first estn. of κchem in Delhi and discuss its climate implications. The bulk-averaged κchem of aerosols in Delhi is estd. to be 0.42 ω 0.07 during 2016-2018, implying a higher activation ability as cloud condensation nuclei in Delhi compared with Beijing and continental avs. worldwide. To activate a 0.1-μm particle, it averagely requires just a supersatn. of ∼0.18% ω 0.015% in Delhi but ∼0.3% (Beijing), 0.28%-0.31% (Asia, Africa, and South America) and ∼0.22% (Europe and North America). Our results imply that representing κchem of Delhi using Asian/Beijing av. may result in a significant underestimation of aerosol climate effects.
- 58Wang, Y.; Wang, Y.; Wang, L.; Petäjä, T.; Zha, Q.; Gong, C.; Li, S.; Pan, Y.; Hu, B.; Xin, J.; Kulmala, M. Increased Inorganic Aerosol Fraction Contributes to Air Pollution and Haze in China. Atmos. Chem. Phys. 2019, 19 (9), 5881– 5888, DOI: 10.5194/acp-19-5881-201958https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1MXhtVahsbzF&md5=ff14544ef6838597365f560ee3c3bc50Increased inorganic aerosol fraction contributes to air pollution and haze in ChinaWang, Yonghong; Wang, Yuesi; Wang, Lili; Petaja, Tuukka; Zha, Qiaozhi; Gong, Chongshui; Li, Sixuan; Pan, Yuepeng; Hu, Bo; Xin, Jinyuan; Kulmala, MarkkuAtmospheric Chemistry and Physics (2019), 19 (9), 5881-5888CODEN: ACPTCE; ISSN:1680-7324. (Copernicus Publications)The detailed formation mechanism of an increased no. of haze events in China is still not very clear. Here, we found that reduced surface visibility from 1980 to 2010 and an increase in satellite-derived columnar concns. of inorg. precursors from 2002 to 2012 are connected with each other. Typically, higher inorg. mass fractions lead to increased aerosol water uptake and light-scattering ability in elevated relative humidity. Satellite observation of aerosol precursors of NO2 and SO2 showed increased concns. during the study period. Our in situ measurement of aerosol chem. compn. in Beijing also confirmed increased contribution of inorg. aerosol fraction as a function of the increased particle pollution level. Our investigations demonstrate that the increased inorg. fraction in the aerosol particles is a key component in the frequently occurring haze days during the study period, and particularly the redn. of nitrate, sulfate and their precursor gases would contribute towards better visibility in China.
- 59He, Q.; Zhou, G.; Geng, F.; Gao, W.; Yu, W. Spatial Distribution of Aerosol Hygroscopicity and Its Effect on PM2.5 Retrieval in East China. Atmos. Res. 2016, 170, 161– 167, DOI: 10.1016/j.atmosres.2015.11.01159https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXhvFKit7nI&md5=62f9efb8bde811d0cb777bd51f747609Spatial distribution of aerosol hygroscopicity and its effect on PM2.5 retrieval in East ChinaHe, Qianshan; Zhou, Guangqiang; Geng, Fuhai; Gao, Wei; Yu, WeiAtmospheric Research (2016), 170 (), 161-167CODEN: ATREEW; ISSN:0169-8095. (Elsevier B.V.)The hygroscopic properties of aerosol particles have strong impact on climate as well as visibility in polluted areas. Understanding of the scattering enhancement due to water uptake is of great importance in linking dry aerosol measurements with relevant ambient measurements, esp. for satellite retrievals. In this study, an observation-based algorithm combining meteorol. data with the particulate matter (PM) measurement was introduced to est. spatial distribution of indicators describing the integrated humidity effect in East China and the main factors impacting the hygroscopicity were explored. Investigation of 1 yr data indicates that the larger mass extinction efficiency αext values (> 9.0 m2/g) located in middle and northern Jiangsu Province, which might be caused by particulate org. material (POM) and sulfate aerosol from industries and human activities. The high level of POM in Jiangsu Province might also be responsible for the lower growth coeff. γ value in this region. For the inland junction provinces of Jiangsu and Anhui, a considerable higher hygroscopic growth region in East China might be attributed to more hygroscopic particles mainly comprised of inorg. salts (e.g., sulfates and nitrates) from several large-scale industrial districts distributed in this region. Validation shows good agreement of calcd. PM2.5 mass concns. with in situ measurements in most stations with correlative coeffs. of over 0.85, even if several defective stations induced by station location or seasonal variation of aerosol properties in this region. This algorithm can be used for more accurate surface level PM2.5 retrieval from satellite-based aerosol optical depth (AOD) with combination of the vertical correction for aerosol profile.
- 60Deng, X.; Tie, X.; Zhou, X.; Wu, D.; Zhong, L.; Tan, H.; Li, F.; Huang, X.; Bi, X.; Deng, T. Effects of Southeast Asia Biomass Burning on Aerosols and Ozone Concentrations over the Pearl River Delta (PRD) Region. Atmos. Environ. 2008, 42 (36), 8493– 8501, DOI: 10.1016/j.atmosenv.2008.08.01360https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1cXht1yrsr3J&md5=2198749d70946650574ec1893a2d7fc3Effects of Southeast Asia biomass burning on aerosols and ozone concentrations over the Pearl River Delta (PRD) regionDeng, Xuejiao; Tie, Xuexi; Zhou, Xiuji; Wu, Dui; Zhong, Liuju; Tan, Haobo; Li, Fei; Huang, Xiaoying; Bi, Xueyan; Deng, TaoAtmospheric Environment (2008), 42 (36), 8493-8501CODEN: AENVEQ; ISSN:1352-2310. (Elsevier Ltd.)The rapid increases in urbanization and human activities in the Pearl River Delta (PRD) region (China) have important impacts on regional air quality. In addn. to local anthropogenic emissions which are major driving forces for poor air quality in this region, biomass burning in Southeast Asia has also important contribution on aerosol and ozone concns. in the PRD region. This effect is analyzed by using satellite data, ground measurements, and models. MODIS aerosol optical depth (AOD) distribution in March 2006 shows a clear enhancement in AOD between Southeast Asia and the PRD region. With detail wind anal., 2 distinguished conditions are classified, i.e., Condition-1 (PRD is under influence of the biomass burning from Southeast Asia) and Condition-2 (PRD is not under influence of the biomass burning from Southeast Asia). The characterizations of aerosol, UV, and ozone in Guangzhou city (located in the PRD region) under these 2 conditions are analyzed. The analyses suggest that aerosols and CO concns. are higher in Condition-1 than in Condition-2; while the UV intensity and O3 concns. are lower in Condition-1 than in Condition-2. In Condition-1, the enhanced aerosol concns. from the Southeast Asia biomass burning produce redn. of UV intensity, and thus decreases the formation of ozone in Guangzhou.
- 61Zhang, M.; Wang, Y.; Ma, Y.; Wang, L.; Gong, W.; Liu, B. Spatial Distribution and Temporal Variation of Aerosol Optical Depth and Radiative Effect in South China and Its Adjacent Area. Atmos. Environ. 2018, 188, 120– 128, DOI: 10.1016/j.atmosenv.2018.06.02861https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1cXhtF2gtr7M&md5=525df9e74237f60b575eb37828a32a1fSpatial distribution and temporal variation of aerosol optical depth and radiative effect in South China and its adjacent areaZhang, Ming; Wang, Yi; Ma, Yingying; Wang, Lunche; Gong, Wei; Liu, BomingAtmospheric Environment (2018), 188 (), 120-128CODEN: AENVEQ; ISSN:1352-2310. (Elsevier Ltd.)The spatio-temporal characteristics of aerosol loading over South China from 2001 to 2016 were investigated using aerosol optical depth (AOD) from the Moderate Resoln. Imaging Spectroradiometer (MODIS) and NO2 from the Ozone Monitoring Instrument (OMI). AOD values were high in the central part and low in the southeast and northwest parts of South China. High AOD (larger than 0.7) were found in the Pearl River Delta, Nanning, and Hanoi (Vietnam). The seasonal av. AOD was high in spring (approx. 0.7) and low in winter (approx. 0.4). Generally, an increasing trend of AOD was found from 2001 to 2004 and a decreasing trend from 2004 to 2016 in the continent due to the change in pollutant discharging, which was verified by annual NO2 data. Furthermore, the aerosol radiative effect (ARE) was calcd. using the Mesoscale Atm. Global Irradiance Code (MAGIC) and MODIS AOD time series. The spatial distribution and temporal variation of ARE at surface showed similar patterns to AOD, with high values occurring in the Pearl River Delta (-39 W/m2), Hanoi (-36 W/m2), and Nanning (-30 W/m2). From 2001 to 2016, ARE at surface in South China decreased by approx. 4 W/m2 with the highest value (-24.75 W/m2) occurring in 2007.
- 62Yao, L.; Yang, L.; Yuan, Q.; Yan, C.; Dong, C.; Meng, C.; Sui, X.; Yang, F.; Lu, Y.; Wang, W. Sources Apportionment of PM2.5 in a Background Site in the North China Plain. Sci. Total Environ. 2016, 541, 590– 598, DOI: 10.1016/j.scitotenv.2015.09.12362https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXhs1ShtLnK&md5=151c202b19a52335555edfc448e8429eSources apportionment of PM2.5 in a background site in the North China PlainYao, Lan; Yang, Lingxiao; Yuan, Qi; Yan, Chao; Dong, Can; Meng, Chuanping; Sui, Xiao; Yang, Fei; Lu, Yaling; Wang, WenxingScience of the Total Environment (2016), 541 (), 590-598CODEN: STENDL; ISSN:0048-9697. (Elsevier B.V.)To better understand PM2.5 sources and potential source regions, a field study was conducted from Jan. 2011 to Nov. 2011 at a background site, Yellow River Delta National Nature Reserve (YRDNNR), in the North China Plain. Pos. matrix factorization (PMF) anal. and a potential source contribution function (PSCF) model assessed the data, which showed YRDNNR experiences serious air pollution. PM2.5 concns. at YRDNNR were 71.2, 92.7, 97.1 and 62.5 μg/m3 in spring, summer, autumn, and winter, resp.; 66.0% of daily samples exhibited higher concns. than the national air quality std. PM2.5 mass closure showed remarkable seasonal variations. SO42-, NO3-, and NH4+ were the dominant PM2.5 fractions in summer (58.0%); PM2.5 was characterized by a high org. aerosol load (40.2%) in winter. PMF anal. indicated secondary SO42- and NO3- (54.3%), biomass burning (15.8%), industry (10.7%), crustal matter (8.3%), vehicles (5.2%), and Cu smelting (4.9%) were important PM2.5 sources at YRDNNR on an annual av. The secondary SO42- and NO3- source was probably industrial coal combustion. PSCF anal. indicated a significant PM2.5 regional impact at YRDNNR year round. Local emissions may be non-negligible at YRDNNR in summer. Results provided a scientific basis to develop regional PM2.5 control strategies.
- 63Timmermans, R.; Kranenburg, R.; Manders, A.; Hendriks, C.; Segers, A.; Dammers, E.; Zhang, Q.; Wang, L.; Liu, Z.; Zeng, L.; Denier van der Gon, H.; Schaap, M. Source Apportionment of PM2.5 across China Using LOTOS-EUROS. Atmos. Environ. 2017, 164, 370– 386, DOI: 10.1016/j.atmosenv.2017.06.00363https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXhtVamt77P&md5=56787d58e398527ad648a4af376369eeSource apportionment of PM2.5 across China using LOTOS-EUROSTimmermans, R.; Kranenburg, R.; Manders, A.; Hendriks, C.; Segers, A.; Dammers, E.; Zhang, Q.; Wang, L.; Liu, Z.; Zeng, L.; Denier van der Gon, H.; Schaap, M.Atmospheric Environment (2017), 164 (), 370-386CODEN: AENVEQ; ISSN:1352-2310. (Elsevier Ltd.)China's population is exposed to high levels of particulate matter (PM) due to its strong economic growth and assocd. urbanization and industrialization. To support policy makers to develop cost effective mitigation strategies it is of crucial importance to understand the emission sources as well as formation routes responsible for high pollution levels. In this study we applied the LOTOS-EUROS model with its module to track the contributions of predefined source sectors to China for the year 2013 using the MEIC emission inventory. It is the first application of the model system to a region outside Europe. The source attribution was aimed to provide insight in the sector and area of origin of PM2.5 for the cities of Beijing and Shanghai. The source attribution shows that on av. about half of the PM2.5 pollution in both cities originates from the municipality itself. About a quarter of the PM2.5 comes from the neighboring provinces, whereas the remaining quarter is attributed to long range transport from anthropogenic and natural components. Residential combustion, transport, and industry are identified as the main sources with comparable contributions allocated to these sectors. The importance of the sectors varies throughout the year and differs slightly between the cities. During winter, urban contributions from residential combustion are dominant, whereas industrial and traffic contributions with a larger share of regional transport are more important during summer. The evaluation of the model results against satellite and in-situ observations shows the ability of the LOTOS-EUROS model to capture many features of the variability in particulate matter and its precursors in China. The model shows a systematic underestimation of particulate matter concns., esp. in winter. This illustrates that modeling particulate matter remains challenging as it comes to components like secondary org. aerosol and suspended dust as well as emissions and formation of PM during winter time haze situations. All in all, the LOTOS-EUROS system proves to be a powerful tool for policy support applications outside Europe as the intermediate complexity of the model allows the assessment of the area and sector of origin over decadal time periods.
- 64Zong, Z.; Wang, X.; Tian, C.; Chen, Y.; Fu, S.; Qu, L.; Ji, L.; Li, J.; Zhang, G. PMF and PSCF Based Source Apportionment of PM2.5 at a Regional Background Site in North China. Atmos. Res. 2018, 203, 207– 215, DOI: 10.1016/j.atmosres.2017.12.01364https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1cXosVGguw%253D%253D&md5=403751ecb43d65f0761291b5cd88c674PMF and PSCF based source apportionment of PM2.5 at a regional background site in North ChinaZong, Zheng; Wang, Xiaoping; Tian, Chongguo; Chen, Yingjun; Fu, Shanfei; Qu, Lin; Ji, Ling; Li, Jun; Zhang, GanAtmospheric Research (2018), 203 (), 207-215CODEN: ATREEW; ISSN:0169-8095. (Elsevier B.V.)To apportion regional PM2.5 (atm. particles with aerodynamic diam. < 2.5 μm) source types and their geog. pattern in North China, 120 daily PM2.5 samples on Beihuangcheng Island (BH, a regional background site in North China) were collected from August 20th, 2014 to Sept. 15th, 2015 showing one-year period. After the chem. analyses on carbonaceous species, water-sol. ions and inorg. elements, various approaches, such as Mann-Kendall test, chem. mass closure, ISORROPIA II model, Pos. Matrix Factorization (PMF) linked with Potential Source Contribution Function (PSCF), were used to explore the PM2.5 speciation, sources, and source regions. Consequently, distinct seasonal variations of PM2.5 and its main species were found and could be explained by varying emission source characteristics. Based on PMF model, seven source factors for PM2.5 were identified, which were coal combustion + biomass burning, vehicle emission, mineral dust, ship emission, sea salt, industry source, refined chrome industry with the contribution of 48.21%, 30.33%, 7.24%, 6.63%, 3.51%, 3.2%, and 0.88%, resp. In addn., PSCF anal. using the daily contribution of each factor from PMF result suggested that Shandong peninsula and Hebei province were identified as the high potential region for coal combustion + biomass burning; Beijing-Tianjin-Hebei (BTH) region was the main source region for industry source; Bohai Sea and East China Sea were found to be of high source potential for ship emission; Geog. region located northwest of BH Island was possessed of high probability for sea salt; Mineral dust presumably came from the region of Mongolia; Refined chrome industry mostly came from Liaoning, Jilin province; The vehicle emission was primarily of BTH region origin, centering on metropolises, such as Beijing and Tianjin. These results provided precious implications for PM2.5 control strategies in North China.
- 65Lee, H.-H.; Iraqui, O.; Gu, Y.; Yim, S. H.-L.; Chulakadabba, A.; Tonks, A. Y.-M.; Yang, Z.; Wang, C. Impacts of Air Pollutants from Fire and Non-Fire Emissions on the Regional Air Quality in Southeast Asia. Atmos. Chem. Phys. 2018, 18 (9), 6141– 6156, DOI: 10.5194/acp-18-6141-201865https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1cXhtV2ktLbM&md5=38e819f42f7a6f076debc80b2c0c5bbaImpacts of air pollutants from fire and non-fire emissions on the regional air quality in Southeast AsiaLee, Hsiang-He; Iraqui, Oussama; Gu, Yefu; Yim, Steve Hung-Lam; Chulakadabba, Apisada; Tonks, Adam Yiu-Ming; Yang, Zhengyu; Wang, ChienAtmospheric Chemistry and Physics (2018), 18 (9), 6141-6156CODEN: ACPTCE; ISSN:1680-7324. (Copernicus Publications)Severe haze events in Southeast Asia caused by particulate pollution have become more intense and frequent in recent years. Widespread biomass burning occurrences and particulate pollutants from human activities other than biomass burning play important roles in degrading air quality in Southeast Asia. In this study, numerical simulations have been conducted using the Weather Research and Forecasting (WRF) model coupled with a chem. component (WRF-Chem) to quant. examine the contributions of aerosols emitted from fire (i.e., biomass burning) vs. non-fire (including fossil fuel combustion, and road dust, etc.) sources to the degrdn. of air quality and visibility over Southeast Asia. These simulations cover a time period from 2002 to 2008 and are driven by emissions from (a) fossil fuel burning only, (b) biomass burning only, and (c) both fossil fuel and biomass burning. The model results reveal that 39 % of obsd. low-visibility days (LVDs) can be explained by either fossil fuel burning or biomass burning emissions alone, a further 20 % by fossil fuel burning alone, a further 8 % by biomass burning alone, and a further 5 % by a combination of fossil fuel burning and biomass burning. Anal. of an 24 h PM2.5 air quality index (AQI) indicates that the case with coexisting fire and non-fire PM2.5 can substantially increase the chance of AQI being in the moderate or unhealthy pollution level from 23 to 34 %. The premature mortality in major Southeast Asian cities due to degrdn. of air quality by particulate pollutants is estd. to increase from ∼4110 per yr in 2002 to ∼6540 per yr in 2008. In addn., we demonstrate the importance of certain missing non-fire anthropogenic aerosol sources including anthropogenic fugitive and industrial dusts in causing urban air quality degrdn. An expt. of using machine learning algorithms to forecast the occurrence of haze events in Singapore is also explored in this study. All of these results suggest that besides minimizing biomass burning activities, an effective air pollution mitigation policy for Southeast Asia needs to consider controlling emissions from non-fire anthropogenic sources.
- 66Singh, N.; Murari, V.; Kumar, M.; Barman, S. C.; Banerjee, T. Fine Particulates over South Asia: Review and Meta-Analysis of PM2.5 Source Apportionment through Receptor Model. Environ. Pollut. 2017, 223, 121– 136, DOI: 10.1016/j.envpol.2016.12.07166https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXjvFOntA%253D%253D&md5=cf6392741686046851e0eed8991277b7Fine particulates over South Asia: Review and meta-analysis of PM2.5 source apportionment through receptor modelSingh, Nandita; Murari, Vishnu; Kumar, Manish; Barman, S. C.; Banerjee, TirthankarEnvironmental Pollution (Oxford, United Kingdom) (2017), 223 (), 121-136CODEN: ENPOEK; ISSN:0269-7491. (Elsevier Ltd.)Fine particulates (PM2.5) constitute dominant proportion of airborne particulates and have been often assocd. with human health disorders, changes in regional climate, hydrol. cycle and more recently to food security. Intrinsic properties of particulates are direct function of sources. This initiates the necessity of conducting a comprehensive review on PM2.5 sources over South Asia which in turn may be valuable to develop strategies for emission control. Particulate source apportionment (SA) through receptor models is one of the existing tool to quantify contribution of particulate sources. Review of 51 SA studies were performed of which 48 (94%) were appeared within a span of 2007-2016. Almost half of SA studies (55%) were found concd. over few typical urban stations (Delhi, Dhaka, Mumbai, Agra and Lahore). Due to lack of local particulate source profile and emission inventory, pos. matrix factorization and principal component anal. (62% of studies) were the primary choices, followed by chem. mass balance (CMB, 18%). Metallic species were most regularly used as source tracers while use of org. mol. markers and gas-to-particle conversion were min. Among all the SA sites, vehicular emissions (mean ± sd: 37 ± 20%) emerged as most dominating PM2.5 source followed by industrial emissions (23 ± 16%), secondary aerosols (22 ± 12%) and natural sources (20 ± 15%). Vehicular emissions (39 ± 24%) also identified as dominating source for highly polluted sites (PM2.5>100 μgm-3, n = 15) while site specific influence of either or in combination of industrial, secondary aerosols and natural sources were recognized. Source specific trends were considerably varied in terms of region and seasonality. Both natural and industrial sources were most influential over Pakistan and Afghanistan while over Indo-Gangetic plain, vehicular, natural and industrial emissions appeared dominant. Influence of vehicular emission was found single dominating source over southern part while over Bangladesh, both vehicular, biomass burning and industrial sources were significant.
- 67Gherboudj, I.; Naseema Beegum, S.; Ghedira, H. Identifying Natural Dust Source Regions over the Middle-East and North-Africa: Estimation of Dust Emission Potential. Earth-Sci. Rev. 2017, 165, 342– 355, DOI: 10.1016/j.earscirev.2016.12.010There is no corresponding record for this reference.
- 68Weagle, C. L.; Snider, G.; Li, C.; van Donkelaar, A.; Philip, S.; Bissonnette, P.; Burke, J.; Jackson, J.; Latimer, R.; Stone, E.; Abboud, I.; Akoshile, C.; Anh, N. X.; Brook, J. R.; Cohen, A.; Dong, J.; Gibson, M. D.; Griffith, D.; He, K. B.; Holben, B. N.; Kahn, R.; Keller, C. A.; Kim, J. S.; Lagrosas, N.; Lestari, P.; Khian, Y. L.; Liu, Y.; Marais, E. A.; Martins, J. V.; Misra, A.; Muliane, U.; Pratiwi, R.; Quel, E. J.; Salam, A.; Segev, L.; Tripathi, S. N.; Wang, C.; Zhang, Q.; Brauer, M.; Rudich, Y.; Martin, R. V. Global Sources of Fine Particulate Matter: Interpretation of PM 2.5 Chemical Composition Observed by SPARTAN Using a Global Chemical Transport Model. Environ. Sci. Technol. 2018, DOI: 10.1021/acs.est.8b01658There is no corresponding record for this reference.
- 69Nayebare, S. R.; Aburizaiza, O. S.; Khwaja, H. A.; Siddique, A.; Hussain, M. M.; Zeb, J.; Khatib, F.; Carpenter, D. O.; Blake, D. R. Chemical Characterization and Source Apportionment of PM 2.5 in Rabigh, Saudi Arabia. Aerosol Air Qual. Res. 2016, 16, 3114– 3129, DOI: 10.4209/aaqr.2015.11.065869https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1cXkvVOktbw%253D&md5=30649bc1971c138299d01a8f5d3b9278Chemical characterization and source apportionment of PM♂.♂ in Rabigh, Saudi ArabiaNayebare, Shedrack R.; Aburizaiza, Omar S.; Khwaja, Haider A.; Siddique, Azhar; Hussaini, Mirza M.; Zeb, Jahan; Khatib, Fida; Carpenters, David O.; Blake, Donald R.Aerosol and Air Quality Research (2016), 16 (12), 3114-3129CODEN: AAQRAV; ISSN:1680-8584. (Taiwan Association for Aerosol Research)The present study describes the measurement, chem. characterization and delineation of sources of fine particulate matter (PM♂.♂) in Rabigh, Saudi Arabia. The 24-h PM♂.♂ was collected from May 6th June 17th, 2013. The sources of various air pollutants and their characterization was carried by computations of Enrichment Factor (EF), Pos. Matrix Factorization (PMF) and Backward-in-time Trajectories. The 24-h PM♂.♂ showed significant temporal variability with av. (37 ±16.2 μg m♂3) exceeding the WHO guideline (20 μg m♂3) by 2 fold. SO♂2♂, NO♂♂, NH♂♂ and Cl♂ ions dominated the ionic components. Two broad categories of aerosol Trace Elements (Ths) sources were defined as anthropogenic (Ni, V, Zn, Pb, S, Lu and Br) and soilknistal derived (Si, Rb, Ti, Fe, Mn, Mg, K, Sr, Cr, Ca, Cu, Na and Al) elements from computations of EF. Anthropogenic elements originated primarily from fossil-fuel combustion, automobile and industrial emissions. A factor anal. model (PMF) indicated the major sources of PM♂.♂ as Soil (Si, Al, Ti, Fe, Mg, K and Ca); Industrial Dust (Ca, Fe♂, Al, and Si); Fossil-Fuel combustion (V, Ni, Pb, Lu, Cu, Zn, NH♂♂, SO♂2♂ and BC); Vehicular Emissions (NO♂♂, C♂O♂2♂, V and BC) and Sea Sprays (Cl♂ and Na). Backward-in-time trajectories showed a significant contribution by long distance transport of fine aerosols to the overall daily PM♂.♂ levels. Results are consistent with previous studies and highlight the need for more comprehensive research into particulate air pollution in Rabigh and the neighboring areas. This is essential for the formulation of sustainable guidelines on air pollutant emissions in Saudi Arabia and the whole Middle East.
- 70Leibensperger, E. M.; Mickley, L. J.; Jacob, D. J.; Chen, W.-T.; Seinfeld, J. H.; Nenes, A.; Adams, P. J.; Streets, D. G.; Kumar, N.; Rind, D. Climatic Effects of 1950–2050 Changes in US Anthropogenic Aerosols – Part 2: Climate Response. Atmos. Chem. Phys. 2012, 12 (7), 3349– 3362, DOI: 10.5194/acp-12-3349-201270https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC38XptFelsL4%253D&md5=b67842e3a51422a690b085c0ca385571Climatic effects of 1950-2050 changes in US anthropogenic aerosols - part 2: climate responseLeibensperger, E. M.; Mickley, L. J.; Jacob, D. J.; Chen, W.-T.; Seinfeld, J. H.; Nenes, A.; Adams, P. J.; Streets, D. G.; Kumar, N.; Rind, D.Atmospheric Chemistry and Physics (2012), 12 (7), 3349-3362CODEN: ACPTCE; ISSN:1680-7316. (Copernicus Publications)We investigate the climate response to changing US anthropogenic aerosol sources over the 1950-2050 period by using the NASA GISS general circulation model (GCM) and comparing to obsd. US temp. trends. Time-dependent aerosol distributions are generated from the GEOS-Chem chem. transport model applied to historical emission inventories and future projections. Radiative forcing from US anthropogenic aerosols peaked in 1970-1990 and has strongly declined since due to air quality regulations. We find that the regional radiative forcing from US anthropogenic aerosols elicits a strong regional climate response, cooling the central and eastern US by 0.5-1.0 °C on av. during 1970-1990, with the strongest effects on max. daytime temps. in summer and autumn. Aerosol cooling reflects comparable contributions from direct and indirect (cloud-mediated) radiative effects. Absorbing aerosol (mainly black carbon) has negligible warming effect. Aerosol cooling reduces surface evapn. and thus decreases pptn. along the US east coast, but also increases the southerly flow of moisture from the Gulf of Mexico resulting in increased cloud cover and pptn. in the central US. Observations over the eastern US show a lack of warming in 1960-1980 followed by very rapid warming since, which we reproduce in the GCM and attribute to trends in US anthropogenic aerosol sources. Present US aerosol concns. are sufficiently low that future air quality improvements are projected to cause little further warming in the US (0.1 °C over 2010-2050). We find that most of the warming from aerosol source controls in the US has already been realized over the 1980-2010 period.
- 71Klimont, Z.; Smith, S. J.; Cofala, J. The Last Decade of Global Anthropogenic Sulfur Dioxide: 2000–2011 Emissions. Environ. Res. Lett. 2013, 8 (1), 14003– 14006, DOI: 10.1088/1748-9326/8/1/01400371https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3sXhsFeisb3F&md5=0baecab9161b1fbb9a2695fc697675c6The last decade of global anthropogenic sulfur dioxide: 2000-2011 emissionsKlimont, Z.; Smith, S. J.; Cofala, J.Environmental Research Letters (2013), 8 (1), 014003CODEN: ERLNAL; ISSN:1748-9326. (IOP Publishing Ltd.)The evolution of global and regional anthropogenic SO2 emissions in the last decade has been estd. through a bottom-up calcn. After increasing until about 2006, we est. a declining trend continuing until 2011. However, there is strong spatial variability, with North America and Europe continuing to reduce emissions, with an increasing role of Asia and international shipping. China remains a key contributor, but the introduction of stricter emission limits followed by an ambitious program of installing flue gas desulfurization on power plants resulted in a significant decline in emissions from the energy sector and stabilization of total Chinese SO2 emissions. Comparable mitigation strategies are not yet present in several other Asian countries and industrial sectors in general, while emissions from international shipping are expected to start declining soon following an international agreement to reduce the sulfur content of fuel oil. The estd. trends in global SO2 emissions are within the range of representative concn. pathway (RCP) projections and the uncertainty previously estd. for the year 2005.
- 72Curier, L.; Kranenburg, R.; Timmermans, R.; Segers, A.; Eskes, H.; Schaap, M. Synergistic Use of LOTOS-EUROS and NO2 Tropospheric Columns to Evaluate the NOX Emission Trends Over Europe 2014, 239– 245, DOI: 10.1007/978-94-007-5577-2_41There is no corresponding record for this reference.
- 73Simon, H.; Reff, A.; Wells, B.; Xing, J.; Frank, N. Ozone Trends Across the United States over a Period of Decreasing NOx and VOC Emissions. Environ. Sci. Technol. 2015, 49 (1), 186– 195, DOI: 10.1021/es504514z73https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXitVWks7zP&md5=19d13dc9b543f41cb03e0ca916a60ef6Ozone Trends Across the United States over a Period of Decreasing NOx and VOC EmissionsSimon, Heather; Reff, Adam; Wells, Benjamin; Xing, Jia; Frank, NeilEnvironmental Science & Technology (2015), 49 (1), 186-195CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)This work evaluated ambient O3 trends at urban, suburban, and rural monitoring sites across the US over a period of decreasing NOx and volatile org. compd. (VOC) emissions, 1998-2013. Decreasing O3 trends generally occurred in summer, in less urbanized areas, and at the upper end of the O3 distribution. Conversely, increasing O3 trends generally occurred in winter, in more urbanized areas, and at the lower end of the O3 distribution. The 95th percentile O3 concns. decreased at urban, suburban, and rural monitors by 1-2 ppb/yr in summer and 0.5-1 ppb/yr in winter. In summer, there were increasing and decreasing trends in 5th percentile O3 concns. of <0.5 ppb/yr at urban and suburban monitors; 5th percentile O3 concns. at rural monitors decreased by up to 1 ppb/yr. In winter, 5th percentile O3 concns. generally increased by 0.1-1 ppb/yr. Results demonstrated the large scale success of US control strategies to decrease peak O3 concns. Also, results indicated that as anthropogenic NOx emissions decreased, the O3 distribution has been compressed, leading to less spatiotemporal variability.
- 74Xing, J.; Mathur, R.; Pleim, J.; Hogrefe, C.; Gan, C.-M.; Wong, D. C.; Wei, C.; Gilliam, R.; Pouliot, G. Observations and Modeling of Air Quality Trends over 1990–2010 across the Northern Hemisphere: China, the United States and Europe. Atmos. Chem. Phys. 2015, 15 (5), 2723– 2747, DOI: 10.5194/acp-15-2723-201574https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXksFehu7w%253D&md5=ad43252d28e9750d1acb3665d37d4e0cObservations and modeling of air quality trends over 1990-2010 across the Northern Hemisphere: China, the United States and EuropeXing, J.; Mathur, R.; Pleim, J.; Hogrefe, C.; Gan, C.-M.; Wong, D. C.; Wei, C.; Gilliam, R.; Pouliot, G.Atmospheric Chemistry and Physics (2015), 15 (5), 2723-2747CODEN: ACPTCE; ISSN:1680-7324. (Copernicus Publications)Trends in air quality across the Northern Hemisphere over a 21-yr period (1990-2010) were simulated using the Community Multiscale Air Quality (CMAQ) multiscale chem. transport model driven by meteorol. from Weather Research and Forecasting (WRF) simulations and internally consistent historical emission inventories obtained from EDGAR. Thorough comparison with several ground observation networks mostly over Europe and North America was conducted to evaluate the model performance as well as the ability of CMAQ to reproduce the obsd. trends in air quality over the past 2 decades in three regions: eastern China, the continental United States and Europe. The model successfully reproduced the obsd. decreasing trends in SO2, NO2, 8 h O3 maxima, SO2-4 and elemental carbon (EC) in the US and Europe. However, the model fails to reproduce the decreasing trends in NO-3 in the US, potentially pointing to uncertainties of NH3 emissions. The model failed to capture the 6-yr trends of SO2 and NO2 in CN-API (China - Air Pollution Index) from 2005 to 2010, but reproduced the obsd. pattern of O3 trends shown in three World Data Center for Greenhouse Gases (WDCGG) sites over eastern Asia. Due to the coarse spatial resoln. employed in these calcns., predicted SO2 and NO2 concns. are underestimated relative to all urban networks, i.e., US-AQS (US - Air Quality System; normalized mean bias (NMB) = -38% and -48%), EU-AIRBASE (European Air quality data Base; NMB = -18 and -54%) and CN-API (NMB = -36 and -68%). Conversely, at the rural network EU-EMEP (European Monitoring and Evaluation Program), SO2 is overestimated (NMB from 4 to 150%) while NO2 is simulated well (NMB within =15%) in all seasons. Correlations between simulated and obsd. O3 wintertime daily 8 h maxima (DM8) are poor compared to other seasons for all networks. Better correlation between simulated and obsd. SO2-4 was found compared to that for SO2. Underestimation of summer SO2-4 in the US may be assocd. with the uncertainty in pptn. and assocd. wet scavenging representation in the model. The model exhibits worse performance for NO-3 predictions, particularly in summer, due to high uncertainties in the gas/particle partitioning of NO-3 as well as seasonal variations of NH3 emissions. There are high correlations (R > 0.5) between obsd. and simulated EC, although the model underestimates the EC concn. by 65% due to the coarse grid resoln. as well as uncertainties in the PM speciation profile assocd. with EC emissions. The almost linear response seen in the trajectory of modeled O3 changes in eastern China over the past 2 decades suggests that control strategies that focus on combined control of NOx and volatile org. compd. (VOC) emissions with a ratio of 0.46 may provide the most effective means for O3 redns. for the region devoid of nonlinear response potentially assocd. with NOx or VOC limitation resulting from alternate strategies. The response of O3 is more sensitive to changes in NOx emissions in the eastern US because the relative abundance of biogenic VOC emissions tends to reduce the effectiveness of VOC controls. Increasing NH3 levels offset the relative effectiveness of NOx controls in reducing the relative fraction of aerosol NO-3 formed from declining NOx emissions in the eastern US, while the control effectiveness was assured by the simultaneous control of NH3 emission in Europe.
- 75Li, C.; Martin, R. V.; van Donkelaar, A.; Boys, B. L.; Hammer, M. S.; Xu, J.-W.; Marais, E. A.; Reff, A.; Strum, M.; Ridley, D. A.; Crippa, M.; Brauer, M.; Zhang, Q. Trends in Chemical Composition of Global and Regional Population-Weighted Fine Particulate Matter Estimated for 25 Years. Environ. Sci. Technol. 2017, 51, 11185, DOI: 10.1021/acs.est.7b0253075https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXhsVKmtbvL&md5=80a1f1359ff52f5aef28bca04d82b60dTrends in Chemical Composition of Global and Regional Population-Weighted Fine Particulate Matter Estimated for 25 YearsLi, Chi; Martin, Randall V.; van Donkelaar, Aaron; Boys, Brian L.; Hammer, Melanie S.; Xu, Jun-Wei; Marais, Eloise A.; Reff, Adam; Strum, Madeleine; Ridley, David A.; Crippa, Monica; Brauer, Michael; Zhang, QiangEnvironmental Science & Technology (2017), 51 (19), 11185-11195CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)The authors interpreted in-situ and satellite observations using a chem. transport model (GEOS-Chem, down-scaled to 0.1° × 0.1°) to understand global trends in population-weighted mean chem. compn. of fine particulate matter (PM2.5). Trends in obsd. and simulated population-weighted mean PM2.5 compn. for 1989-2013 were highly consistent for PM2.5 (-2.4 vs. -2.4%/yr), secondary inorg. aerosols (-4.3 vs. -4.1%/yr), org. aerosols (OA, -3.6 vs. -3.0%/yr) and black carbon (-4.3 vs. -3.9%/yr) over North America, as well as SO42- (-4.7 vs. -5.8%/yr) over Europe. Simulated trends for 1998-2013 also had overlapping 95% confidence intervals with satellite-derived trends in population-weighted mean PM2.5 for 20 of 21 global regions. For 1989-2013, most (79%) simulated increase in global population-weighted mean PM2.5 of 0.28 μg/m3-yr was explained by significantly (p <0.05) increasing OA (0.10 μg/m3-yr), NO3- (0.05 μg/m3-yr), SO42- (0.04 μg/m3-yr), and NH4+ (0.03 μg/m3-yr). These four components predominantly drive trends in population-weighted mean PM2.5 over populous regions of south Asia (0.94 μg/m3-yr), east Asia (0.66 μg/m3-yr), west Europe (-0.47 μg/m3-yr), and North America (-0.32 μg/m3-yr). Area-weighted and population-weighted mean PM2.5 compn. trends differed significantly.
- 76Weatherhead, E. C.; Reinsel, G. C.; Tiao, G. C.; Meng, X.-L.; Choi, D.; Cheang, W.-K.; Keller, T.; DeLuisi, J.; Wuebbles, D. J.; Kerr, J. B.; Miller, A. J.; Oltmans, S. J.; Frederick, J. E. Factors Affecting the Detection of Trends: Statistical Considerations and Applications to Environmental Data. J. Geophys. Res. Atmos. 1998, 103 (D14), 17149– 17161, DOI: 10.1029/98JD00995There is no corresponding record for this reference.
- 77Weatherhead, E. C.; Stevermer, A. J.; Schwartz, B. E. Detecting Environmental Changes and Trends. Phys. Chem. Earth, Parts A/B/C 2002, 27 (6–8), 399– 403, DOI: 10.1016/S1474-7065(02)00019-0There is no corresponding record for this reference.
- 78Boys, B. L.; Martin, R. V.; van Donkelaar, A.; MacDonell, R. J.; Hsu, N. C.; Cooper, M. J.; Yantosca, R. M.; Lu, Z.; Streets, D. G.; Zhang, Q.; Wang, S. W. Fifteen-Year Global Time Series of Satellite-Derived Fine Particulate Matter. Environ. Sci. Technol. 2014, 48 (19), 11109– 11118, DOI: 10.1021/es502113p78https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXhsVOntL7F&md5=7da0219c9c69350c750ccc34b7e4c36fFifteen-Year Global Time Series of Satellite-Derived Fine Particulate MatterBoys, B. L.; Martin, R. V.; van Donkelaar, A.; MacDonell, R. J.; Hsu, N. C.; Cooper, M. J.; Yantosca, R. M.; Lu, Z.; Streets, D. G.; Zhang, Q.; Wang, S. W.Environmental Science & Technology (2014), 48 (19), 11109-11118CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)Ambient fine particulate matter (PM2.5) is a leading environmental risk factor for premature mortality. This work used aerosol optical depth (AOD) measurements from 2 satellite instruments, multi-angle imaging spectroradiometer and sea viewing wide field of vision sensor, to produce a unified 15-yr global time series (1998-2012) of ground-level PM2.5 concns. at a 1° x 1° resoln. The GEOS-chem chem. transport model related each individual AOD retrieval to ground-level PM2.5 concn. Four broad areas displaying significant, spatially coherent, annual trends were examd. in detail: eastern USA (-0.39 ± 0.10 μg/m3-yr), Arabian Peninsula (0.81 ± 0.21 μg/m3-yr), southern Asia (0.93 ± 0.22 μg/m3-yr), and eastern Asia (0.79 ± 0.27 μg/m3-yr). Over the dense in-situ observation period, 1999-2012, the linear tendency for the eastern USA (-0.37 ± 0.13 μg/m3-yr) agreed well with in-situ measurements (-0.38 ± 0.06 μg/m3-yr). A GEOS-Chem simulation showed secondary inorg. aerosols largely explained the obsd. PM2.5 trend over the eastern USA and southern and eastern Asia; mineral dust largely explained the obsd. trend over the Arabian Peninsula.
- 79Klimont, Z.; Kupiainen, K.; Heyes, C.; Purohit, P.; Cofala, J.; Rafaj, P.; Borken-Kleefeld, J.; Schöpp, W. Global Anthropogenic Emissions of Particulate Matter Including Black Carbon. Atmos. Chem. Phys. 2017, 17 (14), 8681– 8723, DOI: 10.5194/acp-17-8681-201779https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXhs1ahtbnK&md5=63758734475b7aad97d3d3614c7abc63Global anthropogenic emissions of particulate matter including black carbonKlimont, Zbigniew; Kupiainen, Kaarle; Heyes, Chris; Purohit, Pallav; Cofala, Janusz; Rafaj, Peter; Borken-Kleefeld, Jens; Schoepp, WolfgangAtmospheric Chemistry and Physics (2017), 17 (14), 8681-8723CODEN: ACPTCE; ISSN:1680-7324. (Copernicus Publications)This paper presents a comprehensive assessment of historical (1990-2010) global anthropogenic particulate matter (PM) emissions including the consistent and harmonized calcn. of mass-based size distribution (PM1, PM2.5, PM10), as well as primary carbonaceous aerosols including black carbon (BC) and org. carbon (OC). The ests. were developed with the integrated assessment model GAINS, where source- and region-specific technol. characteristics are explicitly included. This assessment includes a no. of previously unaccounted or often misallocated emission sources, i.e. kerosene lamps, gas flaring, diesel generators, refuse burning; some of them were reported in the past for selected regions or in the context of a particular pollutant or sector but not included as part of a total est. Spatially, emissions were calcd. for 172 source regions (as well as international shipping), presented for 25 global regions, and allocated to 0.5° × 0.5° longitude-latitude grids. No independent ests. of emissions from forest fires and savannah burning are provided and neither windblown dust nor unpaved roads emissions are included. We est. that global emissions of PM have not changed significantly between 1990 and 2010, showing a strong decoupling from the global increase in energy consumption and, consequently, CO2 emissions, but there are significantly different regional trends, with a particularly strong increase in East Asia and Africa and a strong decline in Europe, North America, and the Pacific region. This in turn resulted in important changes in the spatial pattern of PM burden, e.g.European, North American, and Pacific contributions to global emissions dropped from nearly 30% in 1990 to well below 15% in 2010, while Asia's contribution grew from just over 50% to nearly two-thirds of the global total in 2010. For all PM species considered, Asian sources represented over 60% of the global anthropogenic total, and residential combustion was the most important sector, contributing about 60% for BC and OC, 45% for PM2.5, and less than 40% for PM10, where large combustion sources and industrial processes are equally important. Global anthropogenic emissions of BC were estd. at about 6.6 and 7.2 Tg in 2000 and 2010, resp., and represent about 15% of PM2.5 but for some sources reach nearly 50%, i.e. for the transport sector. Our global BC nos. are higher than previously published owing primarily to the inclusion of new sources. This PM est. fills the gap in emission data and emission source characterization required in air quality and climate modeling studies and health impact assessments at a regional and global level, as it includes both carbonaceous and non-carbonaceous constituents of primary particulate matter emissions. The developed emission dataset has been used in several regional and global atm. transport and climate model simulations within the ECLIPSE (Evaluating the Climate and Air Quality Impacts of Short-Lived Pollutants) project and beyond, serves better parameterization of the global integrated assessment models with respect to representation of black carbon and org. carbon emissions, and built a basis for recently published global particulate no. ests.
- 80Wang, S.; Zhang, Q.; Martin, R. V.; Philip, S.; Liu, F.; Li, M.; Jiang, X.; He, K. Satellite Measurements Oversee China’s Sulfur Dioxide Emission Reductions from Coal-Fired Power Plants. Environ. Res. Lett. 2015, 10 (11), 114015, DOI: 10.1088/1748-9326/10/11/11401580https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXkvFektLc%253D&md5=dac16852613d6e5fc5b141bd38640001Satellite measurements oversee China's sulfur dioxide emission reductions from coal-fired power plantsWang, Siwen; Zhang, Qiang; Martin, Randall V.; Philip, Sajeev; Liu, Fei; Li, Meng; Jiang, Xujia; He, KebinEnvironmental Research Letters (2015), 10 (11), 114015/1-114015/9CODEN: ERLNAL; ISSN:1748-9326. (IOP Publishing Ltd.)To evaluate the real redns. in sulfur dioxide (SO2) emissions from coal-fired power plants in China, Ozone Monitoring Instrument (OMI) remote sensing SO2 columns were used to inversely model the SO2 emission burdens surrounding 26 isolated power plants before and after the effective operation of their flue gas desulfurization (FGD) facilities. An improved two-dimensional Gaussian fitting method was developed to est. SO2 burdens under complex background conditions, by using the accurate local background columns and the customized fitting domains for each target source. The OMI-derived SO2 burdens before effective FGD operation were correlated well with the bottom-up emission ests. (R = 0.92), showing the reliability of the OMI-derived SO2 burdens as a linear indicator of the assocd. source strength. OMI observations indicated that the av. lag time period between installation and effective operation of FGD facilities at these 26 power plants was around 2 years, and no FGD facilities have actually operated before the year 2008. The OMI estd. av. SO2 removal equivalence (56.0%) was substantially lower than the official report (74.6%) for these 26 power plants. Therefore, it has been concluded that the real redns. of SO2 emissions in China assocd. with the FGD facilities at coal-fired power plants were considerably diminished in the context of the current weak supervision measures.
- 81Fioletov, V. E.; McLinden, C. A.; Krotkov, N.; Li, C.; Joiner, J.; Theys, N.; Carn, S.; Moran, M. D. A Global Catalogue of Large SO2 Sources and Emissions Derived from the Ozone Monitoring Instrument. Atmos. Chem. Phys. 2016, 16 (18), 11497– 11519, DOI: 10.5194/acp-16-11497-201681https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC28XhslKrsb%252FO&md5=2f8666380021f626f03a119b6c792196A global catalogue of large SO2 sources and emissions derived from the Ozone Monitoring InstrumentFioletov, Vitali E.; McLinden, Chris A.; Krotkov, Nickolay; Li, Can; Joiner, Joanna; Theys, Nicolas; Carn, Simon; Moran, Mike D.Atmospheric Chemistry and Physics (2016), 16 (18), 11497-11519CODEN: ACPTCE; ISSN:1680-7324. (Copernicus Publications)Sulfur dioxide (SO2) measurements from the Ozone Monitoring Instrument (OMI) satellite sensor processed with the new principal component anal. (PCA) algorithm were used to detect large point emission sources or clusters of sources. The total of 491 continuously emitting point sources releasing from about 30 kt yr-1 to more than 4000 kt yr-1 of SO2 per yr have been identified and grouped by country and by primary source origin: volcanoes (76 sources); power plants (297); smelters (53); and sources related to the oil and gas industry (65). The sources were identified using different methods, including through OMI measurements themselves applied to a new emission detection algorithm, and their evolution during the 2005-2014 period was traced by estg. annual emissions from each source. For volcanic sources, the study focused on continuous degassing, and emissions from explosive eruptions were excluded. Emissions from degassing volcanic sources were measured, many for the first time, and collectively they account for about 30 % of total SO2 emissions estd. from OMI measurements, but that fraction has increased in recent years given that cumulative global emissions from power plants and smelters are declining while emissions from oil and gas industry remained nearly const. Anthropogenic emissions from the USA declined by 80 % over the 2005-2014 period as did emissions from western and central Europe, whereas emissions from India nearly doubled, and emissions from other large SO2-emitting regions (South Africa, Russia, Mexico, and the Middle East) remained fairly const. In total, OMI-based ests. account for about a half of total reported anthropogenic SO2 emissions; the remaining half is likely related to sources emitting less than 30 kt yr-1 and not detected by OMI.
- 82Zhai, S.; Jacob, D. J.; Wang, X.; Shen, L.; Li, K.; Zhang, Y.; Gui, K.; Zhao, T.; Liao, H. Fine Particulate Matter (PM2.5) Trends in China, 2013–2018: Separating Contributions from Anthropogenic Emissions and Meteorology. Atmos. Chem. Phys. 2019, 19, 11031– 11041, DOI: 10.5194/acp-19-11031-201982https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1MXhvFWqtr%252FI&md5=0f104c5a63f55ac96a2f74bf522924b1Fine particulate matter (PM2.5) trends in China, 2013-2018: separating contributions from anthropogenic emissions and meteorologyZhai, Shixian; Jacob, Daniel J.; Wang, Xuan; Shen, Lu; Li, Ke; Zhang, Yuzhong; Gui, Ke; Zhao, Tianliang; Liao, HongAtmospheric Chemistry and Physics (2019), 19 (16), 11031-11041CODEN: ACPTCE; ISSN:1680-7324. (Copernicus Publications)Fine particulate matter (PM2.5) is a severe air pollution problem in China. Observations of PM2.5 have been available since 2013 from a large network operated by the China National Environmental Monitoring Center (CNEMC). The data show a general 30%-50% decrease in annual mean PM2.5 across China over the 2013-2018 period, averaging at -5.2μg m-3 a 1. Trends in the five megacity cluster regions targeted by the government for air quality control are -9.3 ± 1.8μg m-3 a 1 (±95% confidence interval) for Beijing-Tianjin-Hebei, -6.1 ± 1.1μg m-3 a 1 for the Yangtze River Delta, -2.7±0.8μg m-3 a 1 for the Pearl River Delta, -6.7 ± 1.3μg m-3 a 1 for the Sichuan Basin, and -6.5 ± 2.5μg m-3 a 1 for the Fenwei Plain (Xi'an). Concurrent 2013-2018 observations of sulfur dioxide (SO2), carbon monoxide (CO) show that the declines in PM2.5 are qual. consistent with drastic controls of emissions from coal combustion. However, there is also a large meteorol. driven interannual variability in PM2.5 that complicates trend attribution. We used a stepwise multiple linear regression (MLR) model to quantify this meteorol. contribution to the PM2.5 trends across China. The MLR model correlates the 10 d PM2.5 anomalies to wind speed, pptn., relative humidity, temp., and 850 hPa meridional wind velocity (V850). The meteorol.-cor. PM2.5 trends after removal of the MLR meteorol. contribution can be viewed as being driven by trends in anthropogenic emissions.
- 83Apte, J. S.; Marshall, J. D.; Cohen, A. J.; Brauer, M. Addressing Global Mortality from Ambient PM 2.5. Environ. Sci. Technol. 2015, 49 (13), 8057– 8066, DOI: 10.1021/acs.est.5b0123683https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXhtVShtb3P&md5=9deade47f08dc87bbe7572d6199be8e4Addressing Global Mortality from Ambient PM2.5Apte, Joshua S.; Marshall, Julian D.; Cohen, Aaron J.; Brauer, MichaelEnvironmental Science & Technology (2015), 49 (13), 8057-8066CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)Ambient fine particulate matter (PM2.5) has a large, well-documented global burden of disease. This work used high-resoln. (10 km, global-coverage) concn. data and cause-specific integrated exposure-response functions developed for the Global Burden of Disease 2010 to assess how regional and global improvements in ambient air quality could reduce attributable mortality from PM2.5. Overall, an aggressive global program of PM2.5 mitigation in accord with World Health Organization interim guidelines could avoid 750,000 (23%) of the 3.2 million deaths/yr currently (2010) attributable to ambient PM2.5. Modest improvements in PM2.5 in relatively clean regions (North America, Europe) would result in surprisingly large avoided mortality, due to demog. factors and the non-linear concn.-response relationship which describes the risk of PM in relation to several important causes of death. Major air quality improvements would be required to substantially reduce mortality from PM2.5 in more polluted regions, e.g., China and India. Forecasted demog. and epidemiol. transitions in India and China imply that to maintain PM2.5-attributable mortality rates (deaths/100,000 people-yr) const., av. PM2.5 concns. would need to decline by ∼20-30% over the next 15 years to merely offset increases in PM2.5-attributable mortality from aging populations. An effective program to deliver clean air to the most polluted regions could avoid several hundred thousand premature deaths each year.
- 84van Donkelaar, A.; Martin, R. V.; Li, C.; Burnett, R. T. Regional Estimates of Chemical Composition of Fine Particulate Matter Using a Combined Geoscience-Statistical Method with Information from Satellites, Models, and Monitors. Environ. Sci. Technol. 2019, 53 (5), 2595– 2611, DOI: 10.1021/acs.est.8b0639284https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1MXitVyhsLs%253D&md5=ae0e49f5157b4f6bc45fa8460b307a1fRegional Estimates of Chemical Composition of Fine Particulate Matter Using a Combined Geoscience-Statistical Method with Information from Satellites, Models, and Monitorsvan Donkelaar, Aaron; Martin, Randall V.; Li, Chi; Burnett, Richard T.Environmental Science & Technology (2019), 53 (5), 2595-2611CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)An accurate fine-resoln. surface of the chem. compn. of fine particulate matter (PM2.5) would offer valuable information for epidemiol. studies and health impact assessments. We develop geoscience-derived ests. of PM2.5 compn. from a chem. transport model (GEOS-Chem) and satellite observations of aerosol optical depth, and statistically fuse these ests. with ground-based observations using a geog. weighted regression over North America to produce a spatially complete representation of sulfate, nitrate, ammonium, black carbon, org. matter, mineral dust, and sea-salt over 2000-2016. Significant long-term agreement is found with cross-validation sites over North America (R2 = 0.57-0.96), with the strongest agreement for sulfate (R2 = 0.96), nitrate (R2 = 0.90), and ammonium (R2 = 0.86). We find that North American decreases in population-weighted fine particulate matter (PM2.5) concns. since 2000 have been most heavily influenced by regional changes in sulfate and org. matter. Regionally, the relative importance of several chem. components are found to change with PM2.5 concn., such as higher PM2.5 concns. having a larger proportion of nitrate and a smaller proportion of sulfate. This data set offers information for research into the health effects of PM2.5 chem. components.
- 85Snider, G.; Weagle, C. L.; Martin, R. V.; van Donkelaar, A.; Conrad, K.; Cunningham, D.; Gordon, C.; Zwicker, M.; Akoshile, C.; Artaxo, P.; Anh, N. X.; Brook, J.; Dong, J.; Garland, R. M.; Greenwald, R.; Griffith, D.; He, K.; Holben, B. N.; Kahn, R.; Koren, I.; Lagrosas, N.; Lestari, P.; Ma, Z.; Vanderlei Martins, J.; Quel, E. J.; Rudich, Y.; Salam, A.; Tripathi, S. N.; Yu, C.; Zhang, Q.; Zhang, Y.; Brauer, M.; Cohen, A.; Gibson, M. D.; Liu, Y. SPARTAN: A Global Network to Evaluate and Enhance Satellite-Based Estimates of Ground-Level Particulate Matter for Global Health Applications. Atmos. Meas. Tech. 2015, 8 (1), 505– 521, DOI: 10.5194/amt-8-505-201585https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXktl2ntL4%253D&md5=280b4be54cee22f1166487d98c56de3bSPARTAN: a global network to evaluate and enhance satellite-based estimates of ground-level particulate matter for global health applicationsSnider, G.; Weagle, C. L.; Martin, R. V.; van Donkelaar, A.; Conrad, K.; Cunningham, D.; Gordon, C.; Zwicker, M.; Akoshile, C.; Artaxo, P.; Anh, N. X.; Brook, J.; Dong, J.; Garland, R. M.; Greenwald, R.; Griffith, D.; He, K.; Holben, B. N.; Kahn, R.; Koren, I.; Lagrosas, N.; Lestari, P.; Ma, Z.; Vanderlei Martins, J.; Quel, E. J.; Rudich, Y.; Salam, A.; Tripathi, S. N.; Yu, C.; Zhang, Q.; Zhang, Y.; Brauer, M.; Cohen, A.; Gibson, M. D.; Liu, Y.Atmospheric Measurement Techniques (2015), 8 (1), 505-521CODEN: AMTTC2; ISSN:1867-8548. (Copernicus Publications)Ground-based observations have insufficient spatial coverage to assess long-term human exposure to fine particulate matter (PM2.5) at the global scale. Satellite remote sensing offers a promising approach to provide information on both short- and long-term exposure to PM2.5 at local-to-global scales, but there are limitations and outstanding questions about the accuracy and precision with which groundlevel aerosol mass concns. can be inferred from satellite remote sensing alone. A key source of uncertainty is the global distribution of the relationship between annual av. PM2.5 and discontinuous satellite observations of columnar aerosol optical depth (AOD). We have initiated a global network of ground-level monitoring stations designed to evaluate and enhance satellite remote sensing ests. for application in health-effects research and risk assessment. This Surface PARTiculate mAtter Network (SPARTAN) includes a global federation of ground-level monitors of hourly PM2.5 situated primarily in highly populated regions and collocated with existing ground-based sun photometers that measure AOD. The instruments, a three-wavelength nephelometer and impaction filter sampler for both PM2.5 and PM10, are highly autonomous. Hourly PM2.5 concns. are inferred from the combination of weighed filters and nephelometer data. Data from existing networks were used to develop and evaluate network sampling characteristics. SPARTAN filters are analyzed for mass, black carbon, water-sol. ions, and metals. These measurements provide, in a variety of regions around the world, the key data required to evaluate and enhance satellite-based PM2.5 ests. used for assessing the health effects of aerosols. Mean PM2.5 concns. across sites vary by more than 1 order of magnitude. Our initial measurements indicate that the ratio of AOD to ground-level PM2.5 is driven temporally and spatially by the vertical profile in aerosol scattering. Spatially this ratio is also strongly influenced by the mass scattering efficiency.
- 86Snider, G.; Weagle, C. L.; Murdymootoo, K. K.; Ring, A.; Ritchie, Y.; Stone, E.; Walsh, A.; Akoshile, C.; Anh, N. X.; Balasubramanian, R.; Brook, J.; Qonitan, F. D.; Dong, J.; Griffith, D.; He, K.; Holben, B. N.; Kahn, R.; Lagrosas, N.; Lestari, P.; Ma, Z.; Misra, A.; Norford, L. K.; Quel, E. J.; Salam, A.; Schichtel, B.; Segev, L.; Tripathi, S.; Wang, C.; Yu, C.; Zhang, Q.; Zhang, Y.; Brauer, M.; Cohen, A.; Gibson, M. D.; Liu, Y.; Martins, J. V.; Rudich, Y.; Martin, R. V. Variation in Global Chemical Composition of PM 2.5: Emerging Results from SPARTAN. Atmos. Chem. Phys. 2016, 16 (15), 9629– 9653, DOI: 10.5194/acp-16-9629-201686https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC28XhslWrtbvO&md5=9468aef60c80bf3594a8a2223edd8294Variation in global chemical composition of PM2:5: emerging results from SPARTANSnider, Graydon; Weagle, Crystal L.; Murdymootoo, Kalaivani K.; Ring, Amanda; Ritchie, Yvonne; Stone, Emily; Walsh, Ainsley; Akoshile, Clement; Xuan, Anh Nguyen; Balasubramanian, Rajasekhar; Brook, Jeff; Qonitan, Fatimah D.; Dong, Jinlu; Griffith, Derek; He, Kebin; Holben, Brent N.; Kahn, Ralph; Lagrosas, Nofel; Lestari, Puji; Ma, Zongwei; Misra, Amit; Norford, Leslie K.; Quel, Eduardo J.; Salam, Abdus; Schichtel, Bret; Segev, Lior; Tripathi, Sachchida; Wang, Chien; Yu, Chao; Zhang, Qiang; Zhang, Yuxuan; Brauer, Michael; Cohen, Aaron; Gibson, Mark D.; Liu, Yang; Vanderlei, Martins J.; Rudich, Yinon; Martin, Randall V.Atmospheric Chemistry and Physics (2016), 16 (15), 9629-9653CODEN: ACPTCE; ISSN:1680-7324. (Copernicus Publications)The Surface Particulate mAtter Network (SPARTAN) is a long-term project that includes characterization of chem. and phys. attributes of aerosols from filter samples collected worldwide. This paper discusses the ongoing efforts of SPARTAN to define and quantify major ions and trace metals found in fine particulate matter (PM2.5). Our methods infer the spatial and temporal variability of PM2.5 in a cost-effective manner. Gravimetrically weighed filters represent multi-day avs. of PM2.5, with a collocated nephelometer sampling air continuously. SPARTAN instruments are paired with AErosol RObotic NETwork (AERONET) sun photometers to better understand the relationship between ground-level PM2.5 and columnar aerosol optical depth (AOD). We have examd. the chem. compn. of PM2.5 at 12 globally dispersed, densely populated urban locations and a site at Mammoth Cave (US) National Park used as a background comparison. So far, each SPARTAN location has been active between the years 2013 and 2016 over periods of 2-26 mo, with an av. period of 12 mo per site. These sites have collectively gathered over 10 years of quality aerosol data. The major PM2.5 constituents across all sites (relative contribution±SD) are ammoniated sulfate (20%±11 %), crustal material (13.4% ±9.9 %), equiv. black carbon (11.9%±8.4 %), ammonium nitrate (4.7%±3.0 %), sea salt (2.3%± 1.6 %), trace element oxides (1.0%±1.1 %), water (7.2% ±3.3 %) at 35% RH, and residual matter (40%±24 %). Anal. of filter samples reveals that several PM2.5 chem. components varied by more than an order of magnitude between sites. Ammoniated sulfate ranges from 1.1 μgm-3 (Buenos Aires, Argentina) to 17 μgm-3 (Kanpur, India in the dry season). Ammonium nitrate ranged from 0.2 μgm-3 (Mammoth Cave, in summer) to 6.8 μgm-3 (Kanpur, dry season). Equivalent black carbon ranged from 0.7 μgm-3 (Mammoth Cave) to over 8 μgm-3 (Dhaka, Bangladesh and Kanpur, India). Comparison of SPARTAN vs. coincident measurements from the Interagency Monitoring of Protected Visual Environments (IMPROVE) network at Mammoth Cave yielded a high degree of consistency for daily PM2.5 (r2 = 0.76, slope = 1.12), daily sulfate (r2 = 0.86, slope = 1.03), and mean fractions of all major PM2.5 components (within 6 %). Major ions generally agree well with previous studies at the same urban locations (e.g. sulfate fractions agree within 4% for 8 out of 11 collocation comparisons). Enhanced anthropogenic dust fractions in large urban areas (e.g. Singapore, Kanpur, Hanoi, and Dhaka) are apparent from high Zn . Al ratios. The expected water contribution to aerosols is calcd. via the hygroscopicity parameter κv for each filter. Mean aggregate values ranged from 0.15 (Ilorin) to 0.28 (Rehovot). The all-site parameter mean is 0.20±0.04. Chem. compn. and water retention in each filter measurement allows inference of hourly PM2.5 at 35% relative humidity by merging with nephelometer measurements. These hourly PM2.5 ests. compare favorably with a beta attenuation monitor (MetOne) at the nearby US embassy in Beijing, with a coeff. of variation r2 = 0.67 (n = 3167), compared to r2 = 0.62 when κv was not considered. SPARTAN continues to provide an open-access database of PM2.5 compositional filter information and hourly mass collected from a global federation of instruments.
- 87CIESIN (Center for International Earth Science Information Network). Gridded Population of the World Version 4; NASA Socioeconomic Data and Applications Center (SEDAC): Palisades, NY, 2017; pp 1– 21. DOI: DOI: 10.1128/AAC.03728-14 .There is no corresponding record for this reference.
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The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.est.0c01764.
Detailed description of satellite AOD sources, GEOS-Chem simulation, and algorithm for calculating PM2.5 estimates (PDF)
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