High-Resolution Air Pollution Mapping with Google Street View Cars: Exploiting Big DataClick to copy article linkArticle link copied!
- Joshua S. Apte
- Kyle P. Messier
- Shahzad Gani
- Michael Brauer
- Thomas W. Kirchstetter
- Melissa M. Lunden
- Julian D. Marshall
- Christopher J. Portier
- Roel C.H. Vermeulen
- Steven P. Hamburg
Abstract
Air pollution affects billions of people worldwide, yet ambient pollution measurements are limited for much of the world. Urban air pollution concentrations vary sharply over short distances (≪1 km) owing to unevenly distributed emission sources, dilution, and physicochemical transformations. Accordingly, even where present, conventional fixed-site pollution monitoring methods lack the spatial resolution needed to characterize heterogeneous human exposures and localized pollution hotspots. Here, we demonstrate a measurement approach to reveal urban air pollution patterns at 4–5 orders of magnitude greater spatial precision than possible with current central-site ambient monitoring. We equipped Google Street View vehicles with a fast-response pollution measurement platform and repeatedly sampled every street in a 30-km2 area of Oakland, CA, developing the largest urban air quality data set of its type. Resulting maps of annual daytime NO, NO2, and black carbon at 30 m-scale reveal stable, persistent pollution patterns with surprisingly sharp small-scale variability attributable to local sources, up to 5–8× within individual city blocks. Since local variation in air quality profoundly impacts public health and environmental equity, our results have important implications for how air pollution is measured and managed. If validated elsewhere, this readily scalable measurement approach could address major air quality data gaps worldwide.
1 Introduction
2 Materials and Methods
2.1 Measurement Platform
2.2 Study Area Description and Sampling Protocol
2.3 Data Reduction
2.4 Stability Analysis: Monte Carlo Subsampling
2.5 Data Mining: Spatial Patterns
3 Results and Discussion
3.1 Spatial Patterns and Hotspots
3.2 Distance-Decay Relationships
3.3 Localized Concentration Peaks
3.4 Scaling and Future Prospects
Supporting Information
The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.est.7b00891.
Text, figures and tables with detailed information on experimental methods, QA/QC procedures, sampling protocols, data reduction/analysis techniques, supplemental analyses, and a data dictionary (PDF)
High-resolution concentration data (XLS)
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.
Acknowledgment
We gratefully acknowledge the contributions of K. Tuxen-Bettman, A. Raman, R. Moore, L. Vincent, C. Owens, D. Herzl, O. Puryear, A. Teste, M. Gordon, B. Beveridge, L. Clark, M. Chu Baird, C. Ely, A. Roy, J. Choi, R. Alvarez, S. Fruin, D. Holstius, P. Martien, and the Google Street View and Aclima mobile platform teams. Funding was provided by a grant from Signe Ostby and Scott Cook to Environmental Defense Fund. JSA was supported by a Google Earth Engine Research Award, MML is employed by Aclima, Inc., and TWK has been an investigator on unrelated research sponsored by Aclima. Data will be made available in an interactive online archive.
References
This article references 47 other publications.
- 1Apte, J. S.; Marshall, J. D.; Brauer, M.; Cohen, A. J. Addressing global mortality from ambient PM2.5 Environ. Sci. Technol. 2015, 49, 8057– 8066 DOI: 10.1021/acs.est.5b01236Google Scholar1https://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.
- 2Lim, S. S.; Vos, T.; Flaxman, A. D.; Danaei, G.; Shibuya, K.; Adair-Rohani, H.; Amann, M.; Anderson, H. R.; Andrews, K. G.; Aryee, M. A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990–2010: A systematic analysis for the Global Burden of Disease Study 2010 Lancet 2012, 380, 2224– 2260 DOI: 10.1016/S0140-6736(12)61766-8Google Scholar2https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BC3s3isFClug%253D%253D&md5=f35c63bad4b58d5266a7ee7c4512569bA comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990-2010: a systematic analysis for the Global Burden of Disease Study 2010Lim Stephen S; Vos Theo; Flaxman Abraham D; Danaei Goodarz; Shibuya Kenji; Adair-Rohani Heather; Amann Markus; Anderson H Ross; Andrews Kathryn G; Aryee Martin; Atkinson Charles; Bacchus Loraine J; Bahalim Adil N; Balakrishnan Kalpana; Balmes John; Barker-Collo Suzanne; Baxter Amanda; Bell Michelle L; Blore Jed D; Blyth Fiona; Bonner Carissa; Borges Guilherme; Bourne Rupert; Boussinesq Michel; Brauer Michael; Brooks Peter; Bruce Nigel G; Brunekreef Bert; Bryan-Hancock Claire; Bucello Chiara; Buchbinder Rachelle; Bull Fiona; Burnett Richard T; Byers Tim E; Calabria Bianca; Carapetis Jonathan; Carnahan Emily; Chafe Zoe; Charlson Fiona; Chen Honglei; Chen Jian Shen; Cheng Andrew Tai-Ann; Child Jennifer Christine; Cohen Aaron; Colson K Ellicott; Cowie Benjamin C; Darby Sarah; Darling Susan; Davis Adrian; Degenhardt Louisa; Dentener Frank; Des Jarlais Don C; Devries Karen; Dherani Mukesh; Ding Eric L; Dorsey E Ray; Driscoll Tim; Edmond Karen; Ali Suad Eltahir; Engell Rebecca E; Erwin Patricia J; Fahimi Saman; Falder Gail; Farzadfar Farshad; Ferrari Alize; Finucane Mariel M; Flaxman Seth; Fowkes Francis Gerry R; Freedman Greg; Freeman Michael K; Gakidou Emmanuela; Ghosh Santu; Giovannucci Edward; Gmel Gerhard; Graham Kathryn; Grainger Rebecca; Grant Bridget; Gunnell David; Gutierrez Hialy R; Hall Wayne; Hoek Hans W; Hogan Anthony; Hosgood H Dean 3rd; Hoy Damian; Hu Howard; Hubbell Bryan J; Hutchings Sally J; Ibeanusi Sydney E; Jacklyn Gemma L; Jasrasaria Rashmi; Jonas Jost B; Kan Haidong; Kanis John A; Kassebaum Nicholas; Kawakami Norito; Khang Young-Ho; Khatibzadeh Shahab; Khoo Jon-Paul; Kok Cindy; Laden Francine; Lalloo Ratilal; Lan Qing; Lathlean Tim; Leasher Janet L; Leigh James; Li Yang; Lin John Kent; Lipshultz Steven E; London Stephanie; Lozano Rafael; Lu Yuan; Mak Joelle; Malekzadeh Reza; Mallinger Leslie; Marcenes Wagner; March Lyn; Marks Robin; Martin Randall; McGale Paul; McGrath John; Mehta Sumi; Mensah George A; Merriman Tony R; Micha Renata; Michaud Catherine; Mishra Vinod; Mohd Hanafiah Khayriyyah; Mokdad Ali A; Morawska Lidia; Mozaffarian Dariush; Murphy Tasha; Naghavi Mohsen; Neal Bruce; Nelson Paul K; Nolla Joan Miquel; Norman Rosana; Olives Casey; Omer Saad B; Orchard Jessica; Osborne Richard; Ostro Bart; Page Andrew; Pandey Kiran D; Parry Charles D H; Passmore Erin; Patra Jayadeep; Pearce Neil; Pelizzari Pamela M; Petzold Max; Phillips Michael R; Pope Dan; Pope C Arden 3rd; Powles John; Rao Mayuree; Razavi Homie; Rehfuess Eva A; Rehm Jurgen T; Ritz Beate; Rivara Frederick P; Roberts Thomas; Robinson Carolyn; Rodriguez-Portales Jose A; Romieu Isabelle; Room Robin; Rosenfeld Lisa C; Roy Ananya; Rushton Lesley; Salomon Joshua A; Sampson Uchechukwu; Sanchez-Riera Lidia; Sanman Ella; Sapkota Amir; Seedat Soraya; Shi Peilin; Shield Kevin; Shivakoti Rupak; Singh Gitanjali M; Sleet David A; Smith Emma; Smith Kirk R; Stapelberg Nicolas J C; Steenland Kyle; Stockl Heidi; Stovner Lars Jacob; Straif Kurt; Straney Lahn; Thurston George D; Tran Jimmy H; Van Dingenen Rita; van Donkelaar Aaron; Veerman J Lennert; Vijayakumar Lakshmi; Weintraub Robert; Weissman Myrna M; White Richard A; Whiteford Harvey; Wiersma Steven T; Wilkinson James D; Williams Hywel C; Williams Warwick; Wilson Nicholas; Woolf Anthony D; Yip Paul; Zielinski Jan M; Lopez Alan D; Murray Christopher J L; Ezzati Majid; AlMazroa Mohammad A; Memish Ziad ALancet (London, England) (2012), 380 (9859), 2224-60 ISSN:.BACKGROUND: Quantification of the disease burden caused by different risks informs prevention by providing an account of health loss different to that provided by a disease-by-disease analysis. No complete revision of global disease burden caused by risk factors has been done since a comparative risk assessment in 2000, and no previous analysis has assessed changes in burden attributable to risk factors over time. METHODS: We estimated deaths and disability-adjusted life years (DALYs; sum of years lived with disability [YLD] and years of life lost [YLL]) attributable to the independent effects of 67 risk factors and clusters of risk factors for 21 regions in 1990 and 2010. We estimated exposure distributions for each year, region, sex, and age group, and relative risks per unit of exposure by systematically reviewing and synthesising published and unpublished data. We used these estimates, together with estimates of cause-specific deaths and DALYs from the Global Burden of Disease Study 2010, to calculate the burden attributable to each risk factor exposure compared with the theoretical-minimum-risk exposure. We incorporated uncertainty in disease burden, relative risks, and exposures into our estimates of attributable burden. FINDINGS: In 2010, the three leading risk factors for global disease burden were high blood pressure (7·0% [95% uncertainty interval 6·2-7·7] of global DALYs), tobacco smoking including second-hand smoke (6·3% [5·5-7·0]), and alcohol use (5·5% [5·0-5·9]). In 1990, the leading risks were childhood underweight (7·9% [6·8-9·4]), household air pollution from solid fuels (HAP; 7·0% [5·6-8·3]), and tobacco smoking including second-hand smoke (6·1% [5·4-6·8]). Dietary risk factors and physical inactivity collectively accounted for 10·0% (95% UI 9·2-10·8) of global DALYs in 2010, with the most prominent dietary risks being diets low in fruits and those high in sodium. Several risks that primarily affect childhood communicable diseases, including unimproved water and sanitation and childhood micronutrient deficiencies, fell in rank between 1990 and 2010, with unimproved water and sanitation accounting for 0·9% (0·4-1·6) of global DALYs in 2010. However, in most of sub-Saharan Africa childhood underweight, HAP, and non-exclusive and discontinued breastfeeding were the leading risks in 2010, while HAP was the leading risk in south Asia. The leading risk factor in Eastern Europe, most of Latin America, and southern sub-Saharan Africa in 2010 was alcohol use; in most of Asia, North Africa and Middle East, and central Europe it was high blood pressure. Despite declines, tobacco smoking including second-hand smoke remained the leading risk in high-income north America and western Europe. High body-mass index has increased globally and it is the leading risk in Australasia and southern Latin America, and also ranks high in other high-income regions, North Africa and Middle East, and Oceania. INTERPRETATION: Worldwide, the contribution of different risk factors to disease burden has changed substantially, with a shift away from risks for communicable diseases in children towards those for non-communicable diseases in adults. These changes are related to the ageing population, decreased mortality among children younger than 5 years, changes in cause-of-death composition, and changes in risk factor exposures. New evidence has led to changes in the magnitude of key risks including unimproved water and sanitation, vitamin A and zinc deficiencies, and ambient particulate matter pollution. The extent to which the epidemiological shift has occurred and what the leading risks currently are varies greatly across regions. In much of sub-Saharan Africa, the leading risks are still those associated with poverty and those that affect children. FUNDING: Bill & Melinda Gates Foundation.
- 3Pope, C. A.; Dockery, D. W. Health effects of fine particulate air pollution: Lines that connect J. Air Waste Manage. Assoc. 2006, 56, 709– 742 DOI: 10.1080/10473289.2006.10464485Google Scholar3https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD28Xmt1ygs7k%253D&md5=7aaf80b762054e234b73d653096e18f2Health effects of fine particulate air pollution: lines that connectPope, C. Arden, III; Dockery, Douglas W.Journal of the Air & Waste Management Association (2006), 56 (6), 709-742CODEN: JAWAFC; ISSN:1096-2247. (Air & Waste Management Association)A review. Efforts to understand and mitigate the health effects of participate matter (PM) air pollution have a rich and interesting history. This review focuses on six substantial lines of research that have been pursued since 1997 that have helped elucidate our understanding about the effects of PM on human health. There has been substantial progress in the evaluation of PM health effects at different time-scales of exposure and in the exploration of the shape of the concn.-response function. There has also been emerging evidence of PM-related cardiovascular health effects and growing knowledge regarding interconnected general pathophysiol. pathways that link PM exposure with cardiopulmonary morbidity and mortality. Despite important gaps in scientific knowledge and continued reasons for some skepticism, a comprehensive evaluation of the research findings provides persuasive evidence that exposure to fine particulate air pollution has adverse effects on cardiopulmonary health. Although much of this research has been motivated by environmental public health policy, these results have important scientific, medical, and public health implications that are broader than debates over legally mandated air quality stds.
- 4Brook, R. D.; Rajagopalan, S.; Pope, C. A., III; Brook, J. R.; Bhatnagar, A.; Diez-Roux, A. V.; Holguin, F.; Hong, Y.; Luepker, R. V.; Mittleman, M. A. Particulate matter air pollution and cardiovascular disease: An update to the scientific statement from the American Heart Association Circulation 2010, 121, 2331– 2378 DOI: 10.1161/CIR.0b013e3181dbece1Google Scholar4https://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.
- 5Carvalho, H. The air we breathe: differentials in global air quality monitoring Lancet Respir. Med. 2016, 4, 603– 605 DOI: 10.1016/S2213-2600(16)30180-1Google Scholar5https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BC2s3ht1yitA%253D%253D&md5=1aa08dd6124df8bd2e5543312cccb104The air we breathe: differentials in global air quality monitoringCarvalho HelotonioThe Lancet. Respiratory medicine (2016), 4 (8), 603-605 ISSN:.There is no expanded citation for this reference.
- 6Brauer, M.; Freedman, G.; Frostad, J.; van Donkelaar, A.; Martin, R. V.; Dentener, F.; Dingenen, R. v.; Estep, K.; Amini, H.; Apte, J. S. Ambient air pollution exposure estimation for the Global Burden of Disease 2013 Environ. Sci. Technol. 2016, 50, 79– 88 DOI: 10.1021/acs.est.5b03709Google Scholar6https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXhvVyit7bM&md5=0ff17c54d051acef99c5a703b91d4c2fAmbient Air Pollution Exposure Estimation for the Global Burden of Disease 2013Brauer, Michael; Freedman, Greg; Frostad, Joseph; van Donkelaar, Aaron; Martin, Randall V.; Dentener, Frank; Dingenen, Rita van; Estep, Kara; Amini, Heresh; Apte, Joshua S.; Balakrishnan, Kalpana; Barregard, Lars; Broday, David; Feigin, Valery; Ghosh, Santu; Hopke, Philip K.; Knibbs, Luke D.; Kokubo, Yoshihiro; Liu, Yang; Ma, Stefan; Morawska, Lidia; Sangrador, Jose Luis Texcalac; Shaddick, Gavin; Anderson, H. Ross; Vos, Theo; Forouzanfar, Mohammad H.; Burnett, Richard T.; Cohen, AaronEnvironmental Science & Technology (2016), 50 (1), 79-88CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)Ambient air pollution exposure is a major risk factor for global disease. Assessing the impact of air pollution on population health and evaluating trends relative to other major risk factors requires regularly updated, accurate, spatially resolved exposure ests. This work combined satellite-based ests., chem. transport model simulations, and ground measurements from 79 countries to produce global ests. of annual av. fine particle (PM2.5) and O3 concns. at 0.1° × 0.1° spatial resoln. for 5-yr intervals from 1990 to 2010 and year 2013. These ests. were used to assess population-weighted mean concns. for 1990-2013 for 188 countries. In 2013, 87% of the world population lived in areas exceeding the World Health Organization air quality guideline (10 μg/m3 PM2.5 annual av.). From 1990 to 2013, global population-weighted PM2.5 increased 20.4%, driven by trends in southern and southeastern Asia and China. Decreases in population-weighted mean PM2.5 concns. were evident in most high income countries. Population-weighted mean O3 concns. increased globally 8.9% from 1990 to 2013, with increases in most countries; modest decreases occurred in North America, parts of Europe, and several southeastern Asia countries.
- 7Zhang, K. M.; Wexler, A. S.; Zhu, Y. F.; Hinds, W. C.; Sioutas, C. Evolution of particle number distribution near roadways. Part II: the ‘Road-to-Ambient’ process Atmos. Environ. 2004, 38, 6655– 6665 DOI: 10.1016/j.atmosenv.2004.06.044Google Scholar7https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2cXptV2nsrg%253D&md5=aec367aa36e1d526fa688ac01341854fEvolution of particle number distribution near roadways. Part II. The 'Road-to-Ambient' processZhang, K. Max; Wexler, Anthony S.; Zhu, Yi Fang; Hinds, William C.; Sioutas, ConstantinosAtmospheric Environment (2004), 38 (38), 6655-6665CODEN: AENVEQ; ISSN:1352-2310. (Elsevier B.V.)The 'road-to-ambient' evolution of particle no. distributions near the 405 and 710 freeways in Los Angeles, California, in both summer and winter, were analyzed and then simulated by a multi-component sectional aerosol dynamic model. Condensation/evapn. and diln. were demonstrated to be the major mechanisms in altering aerosol size distribution, while coagulation and deposition play minor roles. Seasonal effects were significant with winters generally less dynamic than summers. A large no. of particles grew into the >10 nm range around 30-90 m downwind of the freeways. Beyond 90 m some shrink to <10 nm range and some continued growing to >100 nm as a result of competition between partial pressure and vapor pressure. Particle compns. probably change dramatically as components adapt to decreasing gas-phase concn. due to diln., so no. distribution evolution is also an evolution of compn. As a result, people who live within about 90 m of roadways are exposed to particle sizes and compns. that others are not.
- 8Karner, A. A.; Eisinger, D. S.; Niemeier, D. A. Near-roadway air quality: Synthesizing the findings from real-world data Environ. Sci. Technol. 2010, 44, 5334– 5344 DOI: 10.1021/es100008xGoogle Scholar8https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3cXns1Wrtb4%253D&md5=ac6e9c38c90964168688cc4094594073Near-Roadway Air Quality: Synthesizing the Findings from Real-World DataKarner, Alex A.; Eisinger, Douglas S.; Niemeier, Deb A.Environmental Science & Technology (2010), 44 (14), 5334-5344CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)Despite increasing regulatory attention and literature linking roadside air pollution to health outcomes, studies on near roadway air quality have not yet been well synthesized. We employ data collected from 1978 as reported in 41 roadside monitoring studies, encompassing more than 700 air pollutant concn. measurements, published as of June 2008. Two types of normalization, background and edge-of-road, were applied to the obsd. concns. Local regression models were specified to the concn.-distance relationship and anal. of variance was used to det. the statistical significance of trends. Using an edge-of-road normalization, almost all pollutants decay to background by 115-570 m from the edge of road; using the more std. background normalization, almost all pollutants decay to background by 160-570 m from the edge of road. Differences between the normalization methods arose due to the likely bias inherent in background normalization, since some reported background values tend to under-predict (be lower than) actual background. Changes in pollutant concns. with increasing distance from the road fell into one of three groups: at least a 50% decrease in peak/edge-of-road concn. by 150 m, followed by consistent but gradual decay toward background (e.g., carbon monoxide, some ultrafine particulate matter no. concns.); consistent decay or change over the entire distance range (e.g., benzene, nitrogen dioxide); or no trend with distance (e.g., particulate matter mass concns.).
- 9Zhu, Y. F.; Hinds, W. C.; Kim, S.; Shen, S.; Sioutas, C. Study of ultrafine particles near a major highway with heavy-duty diesel traffic Atmos. Environ. 2002, 36, 4323– 4335 DOI: 10.1016/S1352-2310(02)00354-0Google Scholar9https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD38Xms1Gru7k%253D&md5=7815ecf468fb4a5a9c63d3f8dad5714fStudy of ultrafine particles near a major highway with heavy-duty diesel trafficZhu, Yifang; Hinds, William C.; Kim, Seongheon; Shen, Si; Sioutas, ConstantinosAtmospheric Environment (2002), 36 (27), 4323-4335CODEN: AENVEQ; ISSN:1352-2310. (Elsevier Science Ltd.)Motor vehicle emissions usually constitute the most significant source of ultrafine particles (diam. <0.1 μm) in an urban environment. Y. Zhu, et al. (2002, accepted for publication) conducted systematic measurements of ultra-fine particle concn. and size distribution near a highway dominated by gasoline vehicles. The reported study compared these measurements with those made on Interstate 710 in Los Angeles, California. The 710 freeway was selected because >25% of vehicles are heavy-duty diesel trucks. Particle no. concn. and size distribution from 6 to 220 nm were measured by a condensation particle counter and scanning mobility particle sizer, resp. Measurements were made 17, 20, 30, 90, 150, and 300 m downwind and 200 m upwind from the center of the freeway. At each sampling site, CO and black carbon (BC) concns. were also measured with a Dasibi CO monitor and an aethalometer, resp. The range of av. CO, BC, and total particulate no. concns. at 17 m was 1.9-2.6 ppm, 20.3-24.8 μg/m3, and 1.8×105-3.5×105/cm3, resp. Relative CO, BC, and particle no. concns. decreased exponentially and tracked each other well as distance from the freeway increased. Atm. dispersion and coagulation appeared to contribute to the rapid decrease in particle no. concn. and change in particle size distribution with increasing distance from the freeway. Av. traffic flow during sampling was 12,180 vehicles/h with >25% of vehicles heavy-duty diesel trucks. Ultra-fine particle no. concn. measured 300 m downwind from the freeway was indistinguishable from the upwind background concn. Data may be used to est. exposure to ultra-fine particles near major highways.
- 10Marshall, J. D.; Nethery, E.; Brauer, M. Within-urban variability in ambient air pollution: Comparison of estimation methods Atmos. Environ. 2008, 42, 1359– 1369 DOI: 10.1016/j.atmosenv.2007.08.012Google Scholar10https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1cXhtlGjurc%253D&md5=11eaeb0b662cb0a671027d6eabb023f7Within-urban variability in ambient air pollution: Comparison of estimation methodsMarshall, Julian D.; Nethery, Elizabeth; Brauer, MichaelAtmospheric Environment (2008), 42 (6), 1359-1369CODEN: AENVEQ; ISSN:1352-2310. (Elsevier Ltd.)An important component of air quality management and health risk assessment is improved by understanding of spatial and temporal variability in pollutant concns. We compare, for Vancouver, Canada, three approaches for estg. within-urban spatiotemporal variability in ambient concns.: spatial interpolation of monitoring data; an empirical/statistical model based on geog. analyses ("land-use regression"; LUR); and an Eulerian grid model (community multiscale air quality model, CMAQ). Four pollutants are considered-nitrogen oxide (NO), nitrogen dioxide (NO2), carbon monoxide, and ozone-represent varying levels of spatiotemporal heterogeneity. Among the methods, differences in central tendencies (mean, median) and variability (std. deviation) are modest. LUR and CMAQ perform well in predicting concns. at monitoring sites (av. abs. bias: <50% for NO; <20% for NO2). Monitors (LUR) offer the greatest (least) temporal resoln.; LUR (monitors) offers the greatest (least) spatial resoln. Of note, the length scale of spatial variability is shorter for LUR (units: km; 0.3 for NO, 1 for NO2) than for the other approaches (3-6 for NO, 4-6 for NO2), indicating that the approaches offer different information about spatial attributes of air pollution. Results presented here suggest that for investigations incorporating spatiotemporal variability in ambient concns., the findings may depend on which estn. method is employed.
- 11Morello-Frosch, R.; Pastor, M.; Sadd, J. Environmental justice and Southern California’s “riskscape”: the distribution of air toxics exposures and health risks among diverse communities Urban Affairs Review 2001, 36, 551– 578 DOI: 10.1177/10780870122184993Google ScholarThere is no corresponding record for this reference.
- 12Apte, J. S.; Kirchstetter, T. W.; Reich, A. H.; Deshpande, S. J.; Kaushik, G.; Chel, A.; Marshall, J. D.; Nazaroff, W. W. Concentrations of fine, ultrafine, and black carbon particles in auto-rickshaws in New Delhi, India Atmos. Environ. 2011, 45, 4470– 4480 DOI: 10.1016/j.atmosenv.2011.05.028Google Scholar12https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3MXotlCgu7w%253D&md5=87209d4751f23f18d941714ed1a7669cConcentrations of fine, ultrafine, and black carbon particles in auto-rickshaws in New Delhi, IndiaApte, Joshua S.; Kirchstetter, Thomas W.; Reich, Alexander H.; Deshpande, Shyam J.; Kaushik, Geetanjali; Chel, Arvind; Marshall, Julian D.; Nazaroff, William W.Atmospheric Environment (2011), 45 (26), 4470-4480CODEN: AENVEQ; ISSN:1352-2310. (Elsevier Ltd.)Concns. of air pollutants from vehicles are elevated along roadways, indicating that human exposure in transportation microenvironments may not be adequately characterized by centrally located monitors. Results are reported from ∼180 h of real-time measurements of fine particle and black carbon mass concn. (PM2.5, BC) and ultrafine particle no. concn. (PN) inside a common vehicle, the auto-rickshaw, in New Delhi, India. Measured exposure concns. are much higher in this study (geometric mean for ∼60 trip-averaged concns.: 190 μg m-3 PM2.5, 42 μg m-3 BC, 280 × 103 particles cm-3; GSD ∼1.3 for all three pollutants) than reported for transportation microenvironments in other megacities. In-vehicle concns. exceeded simultaneously measured ambient levels by 1.5× for PM2.5, 3.6× for BC, and 8.4× for PN. Short-duration peak concns. (averaging time: 10 s), attributable to exhaust plumes of nearby vehicles, were greater than 300 μg m-3 for PM2.5, 85 μg m-3 for BC, and 650 × 103 particles cm-3 for PN. The incremental increase of within-vehicle concn. above ambient levels-which is attributed to in- and near-roadway emission sources-accounted for 30%, 68% and 86% of time-averaged in-vehicle PM2.5, BC and PN concns., resp. Based on these results, it is established that one's exposure during a daily commute by auto-rickshaw in Delhi is as least as large as full-day exposures experienced by urban residents of many high-income countries. This study illuminates an environmental health concern that may be common in many populous, low-income cities.
- 13Boogaard, H.; Kos, G. P. A.; Weijers, E. P.; Janssen, N. A. H.; Fischer, P. H.; van der Zee, S. C.; de Hartog, J. J.; Hoek, G. Contrast in air pollution components between major streets and background locations: Particulate matter mass, black carbon, elemental composition, nitrogen oxide and ultrafine particle number Atmos. Environ. 2010, 45, 650– 658 DOI: 10.1016/j.atmosenv.2010.10.033Google ScholarThere is no corresponding record for this reference.
- 14Jerrett, M.; Arain, A.; Kanaroglou, P.; Beckerman, B.; Potoglou, D.; Sahsuvaroglu, T.; Morrison, J.; Giovis, C. A review and evaluation of intraurban air pollution exposure models J. Exposure Anal. Environ. Epidemiol. 2005, 15, 185– 204 DOI: 10.1038/sj.jea.7500388Google Scholar14https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2MXitV2nsLo%253D&md5=0496867b19128403143544dad76fa16dA review and evaluation of intraurban air pollution exposure modelsJerrett, Michael; Arain, Altaf; Kanaroglou, Pavlos; Beckerman, Bernardo; Potoglou, Dimitri; Sahsuvaroglu, Talar; Morrison, Jason; Giovis, ChrisJournal of Exposure Analysis and Environmental Epidemiology (2005), 15 (2), 185-204CODEN: JEAEE9; ISSN:1053-4245. (Nature Publishing Group)A review. The development of models to assess air pollution exposures within cities for assignment to subjects in health studies was identified as a priority area for future research. This paper reviews models for assessing intra-urban exposure under 6 classes, including: (i) proximity-based assessments, (ii) statistical interpolation, (iii) land use regression models, (iv) line dispersion models, (v) integrated emission-meteorol. models, and (vi) hybrid models combining personal or household exposure monitoring with one of the preceding methods. The modeling procedures and results are enriched with applied examples from Hamilton, Canada. In addn., we qual. evaluate the models based on key criteria important to health effects assessment research. Hybrid models appear well suited to overcoming the problem of achieving population representative samples while understanding the role of exposure variation at the individual level. Remote sensing and activity-space anal. will complement refinements in pre-existing methods, and with expected advances, the field of exposure assessment may help to reduce scientific uncertainties that now impede policy intervention aimed at protecting public health.
- 15Hankey, S.; Marshall, J. D. Land use regression models of on-road particulate air pollution (particle number, black carbon, PM2.5, particle size) using mobile monitoring Environ. Sci. Technol. 2015, 49, 9194– 9202 DOI: 10.1021/acs.est.5b01209Google Scholar15https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXhtFSrtLnP&md5=4f4460e84f8c5ed99412e423fae75624Land Use Regression Models of On-Road Particulate Air Pollution (Particle Number, Black Carbon, PM2.5, Particle Size) Using Mobile MonitoringHankey, Steve; Marshall, Julian D.Environmental Science & Technology (2015), 49 (15), 9194-9202CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)Land use regression (LUR) models typically use fixed-site monitoring; this work used mobile monitoring as a cost-effective alternative for LUR model development. Bicycle-based, mobile measurements (∼85 h) during rush-hour in Minneapolis, Minnesota, was used to build LUR models for particulate concns. (particle no. [PN], black carbon [BC], fine particulate matter [PM2.5], particle size). A total of 1224 sep. LUR models were developed and examd. by varying pollutant, time-of-day, and time-series data spatiotemporal smoothing method. Base-case LUR models had modest goodness-of-fit (adjusted R2, ∼0.5 PN; ∼0.4 PM2.5; 0.35 BC; ∼0.25 particle size), low bias (<4%) and abs. bias (2-18%), and included predictor variables which captured proximity to and d. of emission sources. Measurement spatial d. resulted in a large model-building dataset (n = 1101 concn. ests.); ∼25% of buffer variables were selected at spatial scales <100 m, suggesting on-road particle concns. change on small spatial scales. LUR model R2 improved as sampling runs were completed, with diminishing benefits after ∼40 h data collection. Spatial auto-correlation of model residuals indicated the models performed poorly where emission source spatiotemporal resoln. (i.e., traffic congestion) was poor. Results suggested LUR modeling from mobile measurements is possible, but more work could usefully inform best practices.
- 16Reyes, J. M.; Serre, M. L. An LUR/BME framework to estimate PM2.5 explained by on road mobile and stationary sources Environ. Sci. Technol. 2014, 48, 1736– 1744 DOI: 10.1021/es4040528Google Scholar16https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXisVaqsg%253D%253D&md5=a13dfd85a5b418ac2f12867af9219191An LUR/BME Framework to Estimate PM2.5 Explained by on Road Mobile and Stationary SourcesReyes, Jeanette M.; Serre, Marc L.Environmental Science & Technology (2014), 48 (3), 1736-1744CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)Knowledge of particulate matter concns. <2.5 μm in diam. (PM2.5) across the United States is limited due to sparse monitoring across space and time. Epidemiol. studies need accurate exposure ests. to properly investigate potential morbidity and mortality. Previous works have used geostatistics and land use regression (LUR) sep. to quantify exposure. This work combines both methods by incorporating a large area variability LUR model that accounts for on road mobile emissions and stationary source emissions along with data that take into account incompleteness of PM2.5 monitors into the modern geostatistical Bayesian Maximum Entropy (BME) framework to est. PM2.5 across the United States from 1999 to 2009. A cross-validation was done to det. the improvement of the est. due to the LUR incorporation into BME. These results were applied to known diseases to det. predicted mortality coming from total PM2.5 as well as PM2.5 explained by major contributing sources. This method showed a mean squared error redn. of over 21.89% oversimple kriging. PM2.5 explained by on road mobile emissions and stationary emissions contributed to nearly 568 090 and 306 316 deaths, resp., across the United States from 1999 to 2007.
- 17Kerckhoffs, J.; Hoek, G.; Messier, K. P.; Brunekreef, B.; Meliefste, K.; Klompmaker, J. O.; Vermeulen, R. Comparison of ultrafine particle and black carbon concentration predictions from a mobile and short-term stationary land-use regression model Environ. Sci. Technol. 2016, 50, 12894– 12902 DOI: 10.1021/acs.est.6b03476Google Scholar17https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC28Xhslyju7bO&md5=8e0e286336f842bda652f98cf77574a3Comparison of Ultrafine Particle and Black Carbon Concentration Predictions from a Mobile and Short-Term Stationary Land-Use Regression ModelKerckhoffs, Jules; Hoek, Gerard; Messier, Kyle P.; Brunekreef, Bert; Meliefste, Kees; Klompmaker, Jochem O.; Vermeulen, RoelEnvironmental Science & Technology (2016), 50 (23), 12894-12902CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)Mobile and short-term monitoring campaigns are increasingly used to develop land use regression (LUR) models for ultra-fine particles (UFP) and black carbon (BC). It is not yet established whether LUR models based on mobile or short-term stationary measurements result in comparable models and concn. predictions. This work compared LUR models based on stationary (30 min) and mobile UFP and BC measurements in a single campaign. An elec. car collected repeated stationary and mobile measurements in Amsterdam and Rotterdam, Netherlands. A total of 2964 road segments and 161 stationary sites were sampled over two seasons. The main comparison was based on predicted concns. of mobile and stationary monitoring LUR models at 12,682 residential addresses in Amsterdam. Predictor variables in the mobile and stationary LUR model were comparable, resulting in highly correlated predictions at external residential addresses (R2 = 0.89 for UFP and 0.88 for BC). Mobile model predictions were, on av., 1.41 and 1.91 times higher than stationary model predictions for UFP and BC, resp. LUR models based on mobile and stationary monitoring predicted highly correlated UFP and BC concn. surfaces; predicted concns. based on mobile measurements were systematically higher.
- 18Snyder, E. G.; Watkins, T. H.; Solomon, P. A.; Thoma, E. D.; Williams, R. W.; Hagler, G. S. W.; Shelow, D.; Hindin, D. A.; Kilaru, V. J.; Preuss, P. W. The changing paradigm of air pollution monitoring Environ. Sci. Technol. 2013, 47, 11369– 11377 DOI: 10.1021/es4022602Google Scholar18https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3sXhtlektr3L&md5=04be15e19300cd103b92f730f8d805f3The Changing Paradigm of Air Pollution MonitoringSnyder, Emily G.; Watkins, Timothy H.; Solomon, Paul A.; Thoma, Eben D.; Williams, Ronald W.; Hagler, Gayle S. W.; Shelow, David; Hindin, David A.; Kilaru, Vasu J.; Preuss, Peter W.Environmental Science & Technology (2013), 47 (20), 11369-11377CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)The air pollution monitoring paradigm is rapidly changing due to recent advances in: development of portable, lower-cost air pollution sensors which report data in near-real time at high-time resoln.; increased computational and visualization capabilities; and wireless communication/infrastructure. It is possible these advances can support traditional air quality monitoring by supplementing ambient air monitoring and enhancing compliance monitoring. Sensors are beginning to provide individuals and communities necessary tools to understand their environmental exposure; these individual and community-based data strategies can be developed to reduce pollution exposure and to understand links to health indicators. Topics discussed include: current state of sensor science; supplementing routine ambient air monitoring networks; expanding the conversation with communities and citizens; enhancing source compliance monitoring; monitoring personal exposure; challenges; and opportunities for solns.: a changing role for government.
- 19West, J. J.; Cohen, A.; Dentener, F.; Brunekreef, B.; Zhu, T.; Armstrong, B.; Bell, M. L.; Brauer, M.; Carmichael, G.; Costa, D. L. What we breathe impacts our health: Improving understanding of the link between air pollution and health Environ. Sci. Technol. 2016, 50, 4895– 4904 DOI: 10.1021/acs.est.5b03827Google Scholar19https://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.
- 20Whitby, K. T.; Clark, W. E.; Marple, V. A.; Sverdrup, G. M.; Sem, G. J.; Willeke, K.; Liu, B. Y. H.; Pui, D. Y. H. Characterization of California aerosols--I. Size distributions of freeway aerosol Atmos. Environ. 1975, 9, 463– 482 DOI: 10.1016/0004-6981(75)90107-9Google ScholarThere is no corresponding record for this reference.
- 21Westerdahl, D.; Fruin, S.; Sax, T.; Fine, P. M.; Sioutas, C. Mobile platform measurements of ultrafine particles and associated pollutant concentrations on freeways and residential streets in Los Angeles Atmos. Environ. 2005, 39, 3597– 3610 DOI: 10.1016/j.atmosenv.2005.02.034Google Scholar21https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2MXlt1Wms7g%253D&md5=7c710956781e16dff612f20883c4af3eMobile platform measurements of ultrafine particles and associated pollutant concentrations on freeways and residential streets in Los AngelesWesterdahl, Dane; Fruin, Scott; Sax, Todd; Fine, Philip M.; Sioutas, ConstantinosAtmospheric Environment (2005), 39 (20), 3597-3610CODEN: AENVEQ; ISSN:1352-2310. (Elsevier B.V.)Recent health studies have reported that ultrafine particles (UFP) (<0.1 μm in diam.) may be responsible for some of the adverse health effects attributed to particulate matter. In urban areas UFP are produced by combustion sources such as vehicle exhaust, and by secondary formation in the atm. While UFP can be monitored, few studies have explored the impact of local primary sources in urban areas (including mobile sources on freeways) on the temporal and spatial distribution of UFP. This paper describes the integration of multiple monitoring technologies on a mobile platform designed to characterize UFP and assocd. pollutants, and the application of this platform in a study of UFP no. concns. and size distributions in Los Angeles. Monitoring technologies included 2 condensation particle counters (TSI Model 3007 and TSI 3022A) and scanning mobility particle sizers for UFP. Real-time measurements made of NOx (by chemiluminescence), black C (BC) (by light absorption), particulate matter-phase PAH (by UV ionization), and particle length (by diffusional charging) showed high correlations with UFP nos., (r2 = 0.78 for NO, 0.76 for BC, 0.69 for PAH, and 0.88 for particle length). Av. concns. of UFP and related pollutants varied by location, road type, and truck traffic vols., suggesting a relation between these concns. and truck traffic d.
- 22Hudda, N.; Gould, T.; Hartin, K.; Larson, T. V.; Fruin, S. A. Emissions from an international airport increase particle number concentrations 4-fold at 10 km downwind Environ. Sci. Technol. 2014, 48, 6628– 6635 DOI: 10.1021/es5001566Google Scholar22https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXoslOrs70%253D&md5=ba41b31e733c463edaf5b8e44bf1a912Emissions from an International Airport Increase Particle Number Concentrations 4-fold at 10 km DownwindHudda, Neelakshi; Gould, Tim; Hartin, Kris; Larson, Timothy V.; Fruin, Scott A.Environmental Science & Technology (2014), 48 (12), 6628-6635CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)This work measured the spatial pattern of particle no. (PN) concns. downwind from the Los Angeles International Airport (LAX) with an instrumented vehicle which enabled coverage of larger areas than allowed by traditional stationary measurements. LAX emissions adversely affected air quality much farther than reported in previous studies. At least a 2-fold increase in PN concns. over un-impacted baseline PN concns. was measured for most daytime hours in an ∼60 km2 area which extended 16 km (10 mi) downwind, and a 4- to 5-fold increase 8-10 km (5-6 mi) downwind. Locations of max. PN concns. were aligned to eastern, downwind jet trajectories during prevailing westerly winds; 8 km downwind concns. exceeded 75,000 particles/cm3, more than the av. freeway PN concn. in Los Angeles. During infrequent northerly winds, the impact area remained large, but shifted to south of the airport. A freeway length which would cause an impact equiv. to that measured in this work (i.e., PN concn. increases weighted by impacted area) was estd. to be 280-790 km. The total freeway length in Los Angeles is 1500 km. Results suggested airport emissions are a major source of PN in Los Angeles and are of the same general magnitude as the entire urban freeway network. They also indicated major airport air quality impact areas may be seriously underestimated.
- 23Larson, T.; Henderson, S. B.; Brauer, M. Mobile monitoring of particle light absorption coefficient in an urban area as a basis for land use regression Environ. Sci. Technol. 2009, 43, 4672– 4678 DOI: 10.1021/es803068eGoogle Scholar23https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1MXktFemu7Y%253D&md5=2272c625e697e1b1f41abb4c1b3ed6eeMobile Monitoring of Particle Light Absorption Coefficient in an Urban Area as a Basis for Land Use RegressionLarson, Timothy; Henderson, Sarah B.; Brauer, MichaelEnvironmental Science & Technology (2009), 43 (13), 4672-4678CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)Land use regression (LUR) is used to map air pollutant concn. spatial variability for risk assessment, epidemiol., and air quality management. Conventional LUR requires long-term measurements at multiple sites, so application to particulate matter has been limited. Mobile monitoring characterized spatial variability in carbon black concns. for LUR modeling. A particle soot absorption photometer in a moving vehicle measured the absorption coeff. (σap) in summer during peak afternoon traffic at 39 sites. LUR modeled the mean and 25th, 50th, 75th, and 90th percentile values of the distribution of 10-s measurements for each site. Model performance (detd. by R2) was higher for the 25th and 50th percentiles (0.72 and 0.68, resp.) than for the mean, 75th, and 90th percentiles (0.51, 0.55, and 0.54, resp.). Performance was similar to that reported for conventional LUR models of NO2 and NO in this region (116 sites) and better than that for mean σap from fixed-location samplers (25 sites). Models of the mean, 75th, and 90th percentiles favored predictors describing truck, rather than total, traffic. This approach is applicable to other urban areas to facilitate development of LUR models for particulate matter.
- 24Hasenfratz, D.; Saukh, O.; Walser, C.; Hueglin, C.; Fierz, M.; Arn, T.; Beutel, J.; Thiele, L. Deriving high-resolution urban air pollution maps using mobile sensor nodes Pervasive and Mobile Computing 2015, 16 (Part B) 268– 285 DOI: 10.1016/j.pmcj.2014.11.008Google ScholarThere is no corresponding record for this reference.
- 25Bukowiecki, N.; Dommen, J.; Prevot, A. S. H.; Richter, R.; Weingartner, E.; Baltensperger, U. A mobile pollutant measurement laboratory-measuring gas phase and aerosol ambient concentrations with high spatial and temporal resolution Atmos. Environ. 2002, 36, 5569– 5579 DOI: 10.1016/S1352-2310(02)00694-5Google ScholarThere is no corresponding record for this reference.
- 26Pirjola, L.; Parviainen, H.; Hussein, T.; Valli, A.; Hameri, K.; Aaalto, P.; Virtanen, A.; Keskinen, J.; Pakkanen, T. A.; Makela, T. ″Sniffer″ - a novel tool for chasing vehicles and measuring traffic pollutants Atmos. Environ. 2004, 38, 3625– 3635 DOI: 10.1016/j.atmosenv.2004.03.047Google ScholarThere is no corresponding record for this reference.
- 27Kolb, C.; Herndon, S. C.; McManus, J. B.; Shorter, J. H.; Zahniser, M. S.; Nelson, D. D.; Jayne, J. T.; Canagaratna, M. R.; Worsnop, D. R. Mobile laboratory with rapid response instruments for real-time measurement of urban and regional trace gas and particulate distributions and emissions source characteristics Environ. Sci. Technol. 2004, 38, 5694– 5703 DOI: 10.1021/es030718pGoogle ScholarThere is no corresponding record for this reference.
- 28Brantley, H. L.; Hagler, G. S. W.; Kimbrough, E. S.; Williams, R. W.; Mukerjee, S.; Neas, L. M. Mobile air monitoring data-processing strategies and effects on spatial air pollution trends Atmos. Meas. Tech. 2014, 7, 2169– 2183 DOI: 10.5194/amt-7-2169-2014Google ScholarThere is no corresponding record for this reference.
- 29Hagemann, R.; Corsmeier, U.; Kottmeier, C.; Rinke, R.; Wieser, A.; Vogel, B. Spatial variability of particle number concentrations and NOx in the Karlsruhe (Germany) area obtained with the mobile laboratory ‘AERO-TRAM’ Atmos. Environ. 2014, 94, 341– 352 DOI: 10.1016/j.atmosenv.2014.05.051Google Scholar29https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXhtFehtrfP&md5=c48d3e76d6180add350d3eb5523b9b32Spatial variability of particle number concentrations and NOx in the Karlsruhe (Germany) area obtained with the mobile laboratory 'AERO-TRAM'Hagemann, Rowell; Corsmeier, Ulrich; Kottmeier, Christoph; Rinke, Rayk; Wieser, Andreas; Vogel, BernhardAtmospheric Environment (2014), 94 (), 341-352CODEN: AENVEQ; ISSN:1352-2310. (Elsevier Ltd.)For the first time in Germany, we obtained high-resoln. spatial distributions of particle nos. and nitrogen oxides in an urban agglomeration using a tram system. In comparison to particle nos. the NOx concn. decreased much faster with a significantly steeper gradient when going from the inner city to the surrounding area. In case of NOx the decrease was 70% while for particle no. concn. it was only 50%. We found an area in the rural surrounding with a second increase of particle nos. without simultaneous enhanced NOx levels. The source of the high particle nos. could be ascribed to industry emissions about 5-10 km away. The mean spatial distribution of particle no. concn. depended on wind direction, wind velocity and boundary layer stability. The dependency was particularly strong in the rural area affected by industrial emissions, where individual wind directions led to concn. differences of up to 25%. The particulate concn. was 40% higher during low wind velocities (1-5 m s-1) than during high wind velocities (>5 m s-1). We obsd. similar findings for the impact of boundary layer stability on particle nos. concn. Particle pollution was 40% higher for stable stratification compared to neutral or unstable cases.
- 30Van den Bossche, J.; Peters, J.; Verwaeren, J.; Botteldooren, D.; Theunis, J.; De Baets, B. Mobile monitoring for mapping spatial variation in urban air quality: Development and validation of a methodology based on an extensive dataset Atmos. Environ. 2015, 105, 148– 161 DOI: 10.1016/j.atmosenv.2015.01.017Google Scholar30https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXmtFWrtA%253D%253D&md5=57ec6fa6edf6c110e23bb2fe9c35cc0bMobile monitoring for mapping spatial variation in urban air quality: Development and validation of a methodology based on an extensive datasetVan den Bossche, Joris; Peters, Jan; Verwaeren, Jan; Botteldooren, Dick; Theunis, Jan; De Baets, BernardAtmospheric Environment (2015), 105 (), 148-161CODEN: AENVEQ; ISSN:1352-2310. (Elsevier Ltd.)Mobile monitoring is increasingly used as an addnl. tool to acquire air quality data at a high spatial resoln. However, given the high temporal variability of urban air quality, a limited no. of mobile measurements may only represent a snapshot and not be representative. In this study, the impact of this temporal variability on the representativeness is investigated and a methodol. to map urban air quality using mobile monitoring is developed and evaluated.A large set of black carbon (BC) measurements was collected in Antwerp, Belgium, using a bicycle equipped with a portable BC monitor (micro-aethalometer). The campaign consisted of 256 and 96 runs along two fixed routes (2 and 5 km long). Large gradients over short distances and differences up to a factor of 10 in mean BC concns. aggregated at a resoln. of 20 m are obsd. Mapping at such a high resoln. is possible, but a lot of repeated measurements are required. After computing a trimmed mean and applying background normalization, depending on the location 24-94 repeated measurement runs (median of 41) are required to map the BC concns. at a 50 m resoln. with an uncertainty of 25%. When relaxing the uncertainty to 50%, these nos. reduce to 5-11 (median of 8) runs. We conclude that mobile monitoring is a suitable approach for mapping the urban air quality at a high spatial resoln., and can provide insight into the spatial variability that would not be possible with stationary monitors. A careful set-up is needed with a sufficient no. of repetitions in relation to the desired reliability and spatial resoln. Specific data processing methods such as background normalization and event detection have to be applied.
- 31Peters, J.; Theunis, J.; Van Poppel, M.; Berghmans, P. Monitoring PM10 and ultrafine particles in urban environments using mobile measurements Aerosol Air Qual. Res. 2013, 13, 509– 522 DOI: 10.4209/aaqr.2012.06.0152Google ScholarThere is no corresponding record for this reference.
- 32Efron, B. Bootstrap methods: another look at the jackknife Ann. Stat. 1979, 7, 1– 26 DOI: 10.1214/aos/1176344552Google ScholarThere is no corresponding record for this reference.
- 33Dons, E.; Int Panis, L.; Van Poppel, M.; Theunis, J.; Wets, G. Personal exposure to black carbon in transport microenvironments Atmos. Environ. 2012, 55, 392– 398 DOI: 10.1016/j.atmosenv.2012.03.020Google Scholar33https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC38Xot1ejt7g%253D&md5=bb216b05539090ae7a11ed46d88287bfPersonal exposure to Black Carbon in transport microenvironmentsDons, Evi; Int Panis, Luc; Van Poppel, Martine; Theunis, Jan; Wets, GeertAtmospheric Environment (2012), 55 (), 392-398CODEN: AENVEQ; ISSN:1352-2310. (Elsevier Ltd.)We evaluated personal exposure of 62 individuals to the air pollutant Black Carbon, using 13 portable aethalometers while keeping detailed records of their time-activity pattern and whereabouts. Concns. encountered in transport are studied in depth and related to trip motives. The evaluation comprises more than 1500 trips with different transport modes. Measurements were spread over two seasons. Results show that 6% of the time is spent in transport, but it accounts for 21% of personal exposure to Black Carbon and approx. 30% of inhaled dose. Concns. in transport were 2-5 times higher compared to concns. encountered at home. Exposure was highest for car drivers, and car and bus passengers. Concns. of Black Carbon were only half as much when traveling by bike or on foot; when incorporating breathing rates, dose was found to be twice as high for active modes. Lowest in transport' concns. were measured in trains, but nevertheless these concns. are double the concns. measured at home. Two thirds of the trips are car trips, and those trips showed a large spread in concns. In-car concns. are higher during peak hours compared to off-peak, and are elevated on weekdays compared to Saturdays and even more so on Sundays. These findings result in significantly higher exposure during car commute trips (motive Work'), and lower concns. for trips with motive Social and leisure'. Because of the many factors influencing exposure in transport, travel time is not a good predictor of integrated personal exposure or inhaled dose.
- 34Zhou, Y.; Levy, J. I. Factors influencing the spatial extent of mobile source air pollution impacts: a meta-analysis BMC Public Health 2007, 7, 89 DOI: 10.1186/1471-2458-7-89Google Scholar34https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BD2szksV2itQ%253D%253D&md5=dd3d92c929cbb36909dd1c04a8d7b900Factors influencing the spatial extent of mobile source air pollution impacts: a meta-analysisZhou Ying; Levy Jonathan IBMC public health (2007), 7 (), 89 ISSN:.BACKGROUND: There has been growing interest among exposure assessors, epidemiologists, and policymakers in the concept of "hot spots", or more broadly, the "spatial extent" of impacts from traffic-related air pollutants. This review attempts to quantitatively synthesize findings about the spatial extent under various circumstances. METHODS: We include both the peer-reviewed literature and government reports, and focus on four significant air pollutants: carbon monoxide, benzene, nitrogen oxides, and particulate matter (including both ultrafine particle counts and fine particle mass). From the identified studies, we extracted information about significant factors that would be hypothesized to influence the spatial extent within the study, such as the study type (e.g., monitoring, air dispersion modeling, GIS-based epidemiological studies), focus on concentrations or health risks, pollutant under study, background concentration, emission rate, and meteorological factors, as well as the study's implicit or explicit definition of spatial extent. We supplement this meta-analysis with results from some illustrative atmospheric dispersion modeling. RESULTS: We found that pollutant characteristics and background concentrations best explained variability in previously published spatial extent estimates, with a modifying influence of local meteorology, once some extreme values based on health risk estimates were removed from the analysis. As hypothesized, inert pollutants with high background concentrations had the largest spatial extent (often demonstrating no significant gradient), and pollutants formed in near-source chemical reactions (e.g., nitrogen dioxide) had a larger spatial extent than pollutants depleted in near-source chemical reactions or removed through coagulation processes (e.g., nitrogen oxide and ultrafine particles). Our illustrative dispersion model illustrated the complex interplay of spatial extent definitions, emission rates, background concentrations, and meteorological conditions on spatial extent estimates even for non-reactive pollutants. Our findings indicate that, provided that a health risk threshold is not imposed, the spatial extent of impact for mobile sources reviewed in this study is on the order of 100-400 m for elemental carbon or particulate matter mass concentration (excluding background concentration), 200-500 m for nitrogen dioxide and 100-300 m for ultrafine particle counts. CONCLUSION: First, to allow for meaningful comparisons across studies, it is important to state the definition of spatial extent explicitly, including the comparison method, threshold values, and whether background concentration is included. Second, the observation that the spatial extent is generally within a few hundred meters for highway or city roads demonstrates the need for high resolution modeling near the source. Finally, our findings emphasize that policymakers should be able to develop reasonable estimates of the "zone of influence" of mobile sources, provided that they can clarify the pollutant of concern, the general site characteristics, and the underlying definition of spatial extent that they wish to utilize.
- 35Zhu, Y.; Hinds, W. C.; Kim, S.; Sioutas, C. Concentration and size distribution of ultrafine particles near a major highway J. Air Waste Manage. Assoc. 2002, 52, 1032– 1042 DOI: 10.1080/10473289.2002.10470842Google Scholar35https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BD38votlyhtA%253D%253D&md5=94f97350aaf59e808cd7770bbe874111Concentration and size distribution of ultrafine particles near a major highwayZhu Yifang; Hinds William C; Kim Seongheon; Sioutas ConstantinosJournal of the Air & Waste Management Association (1995) (2002), 52 (9), 1032-42 ISSN:1096-2247.Motor vehicle emissions usually constitute the most significant source of ultrafine particles (diameter <0.1 microm) in an urban environment, yet little is known about the concentration and size distribution of ultrafine particles in the vicinity of major highways. In the present study, particle number concentration and size distribution in the size range from 6 to 220 nm were measured by a condensation particle counter (CPC) and a scanning mobility particle sizer (SMPS), respectively. Measurements were taken 30, 60, 90, 150, and 300 m downwind, and 300 m upwind, from Interstate 405 at the Los Angeles National Cemetery. At each sampling location, concentrations of CO, black carbon (BC), and particle mass were also measured by a Dasibi CO monitor, an aethalometer, and a DataRam, respectively. The range of average concentration of CO, BC, total particle number, and mass concentration at 30 m was 1.7-2.2 ppm, 3.4-10.0 microg/m3, 1.3-2.0 x 10(5)/cm3, and 30.2-64.6 microg/m3, respectively. For the conditions of these measurements, relative concentrations of CO, BC, and particle number tracked each other well as distance from the freeway increased. Particle number concentration (6-220 nm) decreased exponentially with downwind distance from the freeway. Data showed that both atmospheric dispersion and coagulation contributed to the rapid decrease in particle number concentration and change in particle size distribution with increasing distance from the freeway. Average traffic flow during the sampling periods was 13,900 vehicles/hr. Ninety-three percent of vehicles were gasoline-powered cars or light trucks. The measured number concentration tracked traffic flow well. Thirty meters downwind from the freeway, three distinct ultrafine modes were observed with geometric mean diameters of 13, 27, and 65 nm. The smallest mode, with a peak concentration of 1.6 x 10(5)/cm3, disappeared at distances greater than 90 m from the freeway. Ultrafine particle number concentration measured 300 m downwind from the freeway was indistinguishable from upwind background concentration. These data may be used to estimate exposure to ultrafine particles in the vicinity of major highways.
- 36Both, A. F.; Balakrishnan, A.; Joseph, B.; Marshall, J. D. Spatiotemporal aspects of real-time PM2.5: Low- and middle-income neighborhoods in Bangalore, India Environ. Sci. Technol. 2011, 45, 5629– 5636 DOI: 10.1021/es104331wGoogle ScholarThere is no corresponding record for this reference.
- 37Watson, J. G.; Chow, J. C. Estimating middle-, neighborhood-, and urban-scale contributions to elemental carbon in Mexico City with a rapid response aethalometer J. Air Waste Manage. Assoc. 2001, 51, 1522– 1528 DOI: 10.1080/10473289.2001.10464379Google Scholar37https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD38Xks1KnsQ%253D%253D&md5=21da24f0a76a9af744f4fa06c5ab3630Estimating middle-, neighborhood-, and urban-scale contributions to elemental carbon in Mexico City with a rapid response aethalometerWatson, John G.; Chow, Judith C.Journal of the Air & Waste Management Association (2001), 51 (11), 1522-1528CODEN: JAWAFC; ISSN:1096-2247. (Air & Waste Management Association)A successive moving av. subtraction method was developed and applied to black carbon measured over 5-min intervals at a downtown site near many small emitters and at a suburban residential site within the urban plume, but distant from specific emitters. Short-duration pulses assumed to originate from nearby sources were subtracted from concns. at each site and were summed to est. middle-scale (0.1-1 km) contributions. The difference of the remaining baselines at urban and suburban monitors was interpreted as the contribution to the downtown monitor from source emissions mixed over a neighborhood scale (1-5 km). Baseline at the suburban site was interpreted as the contribution of the mixt. of black carbon sources for the entire city. When applied to 24-day periods from Feb. and Mar. 1997 in Mexico City, the anal. showed 65% of the 24-h black carbon was part of the urban mixt.; 23% originated in the neighborhood surrounding the monitor and 12% was contributed from nearby sources. Results indicate a fixed-site monitor can reasonably represent exposure in the surrounding neighborhood, even when many local sources, e.g., diesel vehicle exhaust, affects the monitor.
- 38Seinfeld, J. H.; Pandis, S. N. Atmospheric Chemistry and Physics: From Air Pollution to Climate Change, 2nd ed.; Wiley-Interscience: Hoboken, NJ, 2006.Google ScholarThere is no corresponding record for this reference.
- 39Dallmann, T. R.; Harley, R. A.; Kirchstetter, T. W. Effects of diesel particle filter retrofits and accelerated fleet turnover on drayage truck emissions at the Port of Oakland Environ. Sci. Technol. 2011, 45, 10773– 10779 DOI: 10.1021/es202609qGoogle Scholar39https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3MXhtlyltL7O&md5=fb48254e37adb98cb05613b0599be899Effects of Diesel Particle Filter Retrofits and Accelerated Fleet Turnover on Drayage Truck Emissions at the Port of OaklandDallmann, Timothy R.; Harley, Robert A.; Kirchstetter, Thomas W.Environmental Science & Technology (2011), 45 (24), 10773-10779CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)Heavy-duty diesel drayage trucks have a disproportionate impact on the air quality of communities surrounding major freight-handling facilities. In an attempt to mitigate this impact, the state of California has mandated new emission control requirements for drayage trucks accessing ports and rail yards in the state beginning in 2010. This control rule prompted an accelerated diesel particle filter (DPF) retrofit and truck replacement program at the Port of Oakland. The impact of this program was evaluated by measuring emission factor distributions for diesel trucks operating at the Port of Oakland prior to and following the implementation of the emission control rule. Emission factors for black carbon (BC) and oxides of nitrogen (NOx) were quantified in terms of grams of pollutant emitted per kg of fuel burned using a carbon balance method. Concns. of these species along with carbon dioxide were measured in the exhaust plumes of individual diesel trucks as they drove by en route to the Port. A comparison of emissions measured before and after the implementation of the truck retrofit/replacement rule shows a 54 ± 11% redn. in the fleet-av. BC emission factor, accompanied by a shift to a more highly skewed emission factor distribution. Although only particulate matter mass redns. were required in the first year of the program, a significant redn. in the fleet-av. NOx emission factor (41 ± 5%) was obsd., most likely due to the replacement of older trucks with new ones.
- 40Jenkin, M. E. Analysis of sources and partitioning of oxidant in the UK—Part 2: contributions of nitrogen dioxide emissions and background ozone at a kerbside location in London Atmos. Environ. 2004, 38, 5131– 5138 DOI: 10.1016/j.atmosenv.2004.05.055Google ScholarThere is no corresponding record for this reference.
- 41Riley, E. A.; Schaal, L.; Sasakura, M.; Crampton, R.; Gould, T. R.; Hartin, K.; Sheppard, L.; Larson, T.; Simpson, C. D.; Yost, M. G. Correlations between short-term mobile monitoring and long-term passive sampler measurements of traffic-related air pollution Atmos. Environ. 2016, 132, 229– 239 DOI: 10.1016/j.atmosenv.2016.03.001Google Scholar41https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC28XktVyjtrk%253D&md5=595bd2a2aa3520becfd5caf66b17c2beCorrelations between short-term mobile monitoring and long-term passive sampler measurements of traffic-related air pollutionRiley, Erin A.; Schaal, LaNae; Sasakura, Miyoko; Crampton, Robert; Gould, Timothy R.; Hartin, Kris; Sheppard, Lianne; Larson, Timothy; Simpson, Christopher D.; Yost, Michael G.Atmospheric Environment (2016), 132 (), 229-239CODEN: AENVEQ; ISSN:1352-2310. (Elsevier Ltd.)Mobile monitoring has provided a means for broad spatial measurements of air pollutants that are otherwise impractical to measure with multiple fixed site sampling strategies. However, the larger the mobile monitoring route the less temporally dense measurements become, which may limit the usefulness of short-term mobile monitoring for applications that require long-term avs. To investigate the stationarity of short-term mobile monitoring measurements, we calcd. long term medians derived from a mobile monitoring campaign that also employed 2-wk integrated passive sampler detectors (PSD) for NOx, Ozone, and nine volatile org. compds. at 43 intersections distributed across the entire city of Baltimore, MD. This is one of the largest mobile monitoring campaigns in terms of spatial extent undertaken at this time. The mobile platform made repeat measurements every third day at each intersection for 6-10 min at a resoln. of 10 s. In two-week periods in both summer and winter seasons, each site was visited 3-4 times, and a temporal adjustment was applied to each dataset. We present the correlations between eight species measured using mobile monitoring and the 2-wk PSD data and observe correlations between mobile NOx measurements and PSD NOx measurements in both summer and winter (Pearson's r = 0.84 and 0.48, resp.). The summer season exhibited the strongest correlations between multiple pollutants, whereas the winter had comparatively few statistically significant correlations. In the summer CO was correlated with PSD pentanes (r = 0.81), and PSD NOx was correlated with mobile measurements of black carbon (r = 0.83), two ultrafine particle count measures (r = 0.8), and intermodal (1-3 μm) particle counts (r = 0.73). Principal Component Anal. of the combined PSD and mobile monitoring data revealed multipollutant features consistent with light duty vehicle traffic, diesel exhaust and crankcase blow by. These features were more consistent with published source profiles of traffic-related air pollutants than features based on the PSD data alone. Short-term mobile monitoring shows promise for capturing long-term spatial patterns of traffic-related air pollution, and is complementary to PSD sampling strategies.
- 42Hu, S.; Fruin, S.; Kozawa, K.; Mara, S.; Paulson, S. E.; Winer, A. M. A wide area of air pollutant impact downwind of a freeway during pre-sunrise hours Atmos. Environ. 2009, 43, 2541– 2549 DOI: 10.1016/j.atmosenv.2009.02.033Google Scholar42https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1MXks1ygurc%253D&md5=af0ed9dd4bfdf88b7756281991965ab6A wide area of air pollutant impact downwind of a freeway during pre-sunrise hoursHu, Shishan; Fruin, Scott; Kozawa, Kathleen; Mara, Steve; Paulson, Suzanne E.; Winer, Arthur M.Atmospheric Environment (2009), 43 (16), 2541-2549CODEN: AENVEQ; ISSN:1352-2310. (Elsevier Ltd.)We have obsd. a wide area of air pollutant impact downwind of a freeway during pre-sunrise hours in both winter and summer seasons. In contrast, previous studies have shown much sharper air pollutant gradients downwind of freeways, with levels above background concns. extending only 300 m downwind of roadways during the day and up to 500 m at night. In this study, real-time air pollutant concns. were measured along a 3600 m transect normal to an elevated freeway 1-2 h before sunrise using an elec. vehicle mobile platform equipped with fast-response instruments. In winter pre-sunrise hours, the peak ultrafine particle (UFP) concn. (∼95 000 cm-3) occurred immediately downwind of the freeway. However, downwind UFP concns. as high as ∼40 000 cm-3 extended at least 1200 m from the freeway, and did not reach background levels (∼15 000 cm-3) until a distance of about 2600 m. UFP concns. were also elevated over background levels up to 600 m upwind of the freeway. Other pollutants, such as NO and particle-bound polycyclic arom. hydrocarbons, exhibited similar long-distance downwind concn. gradients. In contrast, air pollutant concns. measured on the same route after sunrise, in the morning and afternoon, exhibited the typical daytime downwind decrease to background levels within ∼300 m as found in earlier studies. Although pre-sunrise traffic vols. on the freeway were much lower than daytime congestion peaks, downwind UFP concns. were significantly higher during pre-sunrise hours than during the daytime. UFP and NO concns. were also strongly correlated with traffic counts on the freeway. We assoc. these elevated pre-sunrise concns. over a wide area with a nocturnal surface temp. inversion, low wind speeds, and high relative humidity. Observation of such wide air pollutant impact area downwind of a major roadway prior to sunrise has important exposure assessment implications since it demonstrates extensive roadway impacts on residential areas during pre-sunrise hours, when most people are at home.
- 43Brinkhoff, T. Major agglomerations of the world. http://citypopulation.de/world/Agglomerations.html 2016, (accessed February 14, 2017).Google ScholarThere is no corresponding record for this reference.
- 44Zeger, S. L.; Thomas, D.; Dominici, F.; Samet, J. M.; Schwartz, J.; Dockery, D.; Cohen, A. Exposure measurement error in time-series studies of air pollution: concepts and consequences Environ. Health Persp. 2000, 108, 419– 426 DOI: 10.1289/ehp.00108419Google ScholarThere is no corresponding record for this reference.
- 45Sheppard, L.; Burnett, R. T.; Szpiro, A. A.; Kim, S.-Y.; Jerrett, M.; Pope, C. A.; Brunekreef, B. Confounding and exposure measurement error in air pollution epidemiology Air Qual., Atmos. Health 2012, 5, 203– 216 DOI: 10.1007/s11869-011-0140-9Google Scholar45https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BC2srmslSrtQ%253D%253D&md5=d90a61b600c6cd21d96484a718aa7c5cConfounding and exposure measurement error in air pollution epidemiologySheppard Lianne; Burnett Richard T; Szpiro Adam A; Kim Sun-Young; Jerrett Michael; Pope C Arden 3rd; Brunekreef BertAir quality, atmosphere, & health (2012), 5 (2), 203-216 ISSN:1873-9318.Studies in air pollution epidemiology may suffer from some specific forms of confounding and exposure measurement error. This contribution discusses these, mostly in the framework of cohort studies. Evaluation of potential confounding is critical in studies of the health effects of air pollution. The association between long-term exposure to ambient air pollution and mortality has been investigated using cohort studies in which subjects are followed over time with respect to their vital status. In such studies, control for individual-level confounders such as smoking is important, as is control for area-level confounders such as neighborhood socio-economic status. In addition, there may be spatial dependencies in the survival data that need to be addressed. These issues are illustrated using the American Cancer Society Cancer Prevention II cohort. Exposure measurement error is a challenge in epidemiology because inference about health effects can be incorrect when the measured or predicted exposure used in the analysis is different from the underlying true exposure. Air pollution epidemiology rarely if ever uses personal measurements of exposure for reasons of cost and feasibility. Exposure measurement error in air pollution epidemiology comes in various dominant forms, which are different for time-series and cohort studies. The challenges are reviewed and a number of suggested solutions are discussed for both study domains.
- 46de Nazelle, A.; Seto, E.; Donaire-Gonzalez, D.; Mendez, M.; Matamala, J.; Nieuwenhuijsen, M. J.; Jerrett, M. Improving estimates of air pollution exposure through ubiquitous sensing technologies Environ. Pollut. 2013, 176, 92– 99 DOI: 10.1016/j.envpol.2012.12.032Google Scholar46https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3sXktFWntb8%253D&md5=7ab98c38bc3da1c2fecc5e5553574d92Improving estimates of air pollution exposure through ubiquitous sensing technologiesde Nazelle, Audrey; Seto, Edmund; Donaire-Gonzalez, David; Mendez, Michelle; Matamala, Jaume; Nieuwenhuijsen, Mark J.; Jerrett, MichaelEnvironmental Pollution (Oxford, United Kingdom) (2013), 176 (), 92-99CODEN: ENPOEK; ISSN:0269-7491. (Elsevier Ltd.)Traditional methods of exposure assessment in epidemiol. studies often fail to integrate important information on activity patterns, which may lead to bias, loss of statistical power, or both in health effects ests. Novel sensing technologies integrated with mobile phones offer potential to reduce exposure measurement error. We sought to demonstrate the usability and relevance of the CalFit smartphone technol. to track person-level time, geog. location, and phys. activity patterns for improved air pollution exposure assessment. We deployed CalFit-equipped smartphones in a free-living population of 36 subjects in Barcelona, Spain. Information obtained on phys. activity and geog. location was linked to space-time air pollution mapping. We found that information from CalFit could substantially alter exposure ests. For instance, on av. travel activities accounted for 6% of time and 24% of their daily inhaled NO2. Due to the large no. of mobile phone users, this technol. potentially provides an unobtrusive means of enhancing epidemiol. exposure data at low cost.
- 47Nyhan, M.; Grauwin, S.; Britter, R.; Misstear, B.; McNabola, A.; Laden, F.; Barrett, S. R. H.; Ratti, C. “Exposure Track”—the impact of mobile-device-based mobility patterns on quantifying population exposure to air pollution Environ. Sci. Technol. 2016, 50, 9671– 9681 DOI: 10.1021/acs.est.6b02385Google Scholar47https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC28XhtlaktbvF&md5=5d34bf4e28f0d8ce3a9ba21b8f01fdce"Exposure Track"-The Impact of Mobile-Device-Based Mobility Patterns on Quantifying Population Exposure to Air PollutionNyhan, Marguerite; Grauwin, Sebastian; Britter, Rex; Misstear, Bruce; McNabola, Aonghus; Laden, Francine; Barrett, Steven R. H.; Ratti, CarloEnvironmental Science & Technology (2016), 50 (17), 9671-9681CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)Air pollution is recognized as the single largest environmental and human health threat. Many environmental epidemiol. studies have quantified the health impacts of population exposure to pollution. In previous studies, exposure ests. at the population level have not considered spatial and temporal varying populations in the study regions. Thus, in the first study of it is kind, measured population activity patterns representing several million people were used to evaluate population-weighted exposure to air pollution on a city-wide scale. Mobile and wireless devices yielded information concerning where and when people are present, these collective activity patterns were detd. using counts of connections to cellular networks. Population-weighted exposure to PM2.5 in New York City (NYC), i.e., Active Population Exposure, was evaluated using population activity patterns and spatiotemporal PM2.5 concns. vs. Home Population Exposure, which assumed a static population distribution using Census data. Areas of relatively higher population-weighted exposure were concd. in different districts within NYC in both scenarios, but were more centralized for the Active Population Exposure scenario. Population-weighted exposure computed in each NYC district for the Active scenario were statistically significantly (p <0.05) different from the Home scenario for most districts. Temporal variability of the Active population-weighted exposure detd. in districts were significantly different (p <0.05) during the day and at night. Evaluating population exposure to air pollution using spatiotemporal population mobility patterns warrants consideration in future environmental epidemiol. studies linking air quality and human health.
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- Isabella M. Dressel, Mary Angelique G. Demetillo, Laura M. Judd, Scott J. Janz, Kimberly P. Fields, Kang Sun, Arlene M. Fiore, Brian C. McDonald, Sally E. Pusede. Daily Satellite Observations of Nitrogen Dioxide Air Pollution Inequality in New York City, New York and Newark, New Jersey: Evaluation and Application. Environmental Science & Technology 2022, 56
(22)
, 15298-15311. https://doi.org/10.1021/acs.est.2c02828
- Provat K. Saha, Albert A. Presto, Steve Hankey, Benjamin N. Murphy, Chris Allen, Wenwen Zhang, Julian D. Marshall, Allen L. Robinson. National Exposure Models for Source-Specific Primary Particulate Matter Concentrations Using Aerosol Mass Spectrometry Data. Environmental Science & Technology 2022, 56
(20)
, 14284-14295. https://doi.org/10.1021/acs.est.2c03398
- Yu Ting Yu, Sheng Xiang, Tong Zhang, Yan You, Shuchun Si, Shaojun Zhang, Ye Wu. Evaluation of City-Scale Disparities in PM2.5 Exposure Using Hyper-Localized Taxi-Based Mobile Monitoring. Environmental Science & Technology 2022, 56
(19)
, 13584-13594. https://doi.org/10.1021/acs.est.2c02354
- Zhendong Yuan, Jules Kerckhoffs, Gerard Hoek, Roel Vermeulen. A Knowledge Transfer Approach to Map Long-Term Concentrations of Hyperlocal Air Pollution from Short-Term Mobile Measurements. Environmental Science & Technology 2022, 56
(19)
, 13820-13828. https://doi.org/10.1021/acs.est.2c05036
- Junshi Xu, Mingqian Zhang, Arman Ganji, Keni Mallinen, An Wang, Marshall Lloyd, Alessya Venuta, Leora Simon, Junwon Kang, James Gong, Yazan Zamel, Scott Weichenthal, Marianne Hatzopoulou. Prediction of Short-Term Ultrafine Particle Exposures Using Real-Time Street-Level Images Paired with Air Quality Measurements. Environmental Science & Technology 2022, 56
(18)
, 12886-12897. https://doi.org/10.1021/acs.est.2c03193
- Meng Qi, Kuldeep Dixit, Julian D. Marshall, Wenwen Zhang, Steve Hankey. National Land Use Regression Model for NO2 Using Street View Imagery and Satellite Observations. Environmental Science & Technology 2022, 56
(18)
, 13499-13509. https://doi.org/10.1021/acs.est.2c03581
- Lara P. Clark, Maria H. Harris, Joshua S. Apte, Julian D. Marshall. National and Intraurban Air Pollution Exposure Disparity Estimates in the United States: Impact of Data-Aggregation Spatial Scale. Environmental Science & Technology Letters 2022, 9
(9)
, 786-791. https://doi.org/10.1021/acs.estlett.2c00403
- Sheng Xiang, Shaojun Zhang, Hui Wang, Yu Ting Yu, Timothy J. Wallington, Wei Shen, Ulf Kirchner, Ye Deng, Qinwen Tan, Zihang Zhou, Ye Wu. Variability of NO2/NOx Ratios in Multiple Microenvironments from On-Road and Near-Roadway Measurements. ACS ES&T Engineering 2022, 2
(9)
, 1599-1610. https://doi.org/10.1021/acsestengg.2c00027
- Magali N. Blanco, Amanda Gassett, Timothy Gould, Annie Doubleday, David L. Slager, Elena Austin, Edmund Seto, Timothy V. Larson, Julian D. Marshall, Lianne Sheppard. Characterization of Annual Average Traffic-Related Air Pollution Concentrations in the Greater Seattle Area from a Year-Long Mobile Monitoring Campaign. Environmental Science & Technology 2022, 56
(16)
, 11460-11472. https://doi.org/10.1021/acs.est.2c01077
- Ye Tian, Priyanka deSouza, Simone Mora, Xiaobai Yao, Fabio Duarte, Leslie K Norford, Hui Lin, Carlo Ratti. Evaluating the Meteorological Effects on the Urban Form–Air Quality Relationship Using Mobile Monitoring. Environmental Science & Technology 2022, 56
(11)
, 7328-7336. https://doi.org/10.1021/acs.est.1c04854
- Jules Kerckhoffs, Jibran Khan, Gerard Hoek, Zhendong Yuan, Thomas Ellermann, Ole Hertel, Matthias Ketzel, Steen Solvang Jensen, Kees Meliefste, Roel Vermeulen. Mixed-Effects Modeling Framework for Amsterdam and Copenhagen for Outdoor NO2 Concentrations Using Measurements Sampled with Google Street View Cars. Environmental Science & Technology 2022, 56
(11)
, 7174-7184. https://doi.org/10.1021/acs.est.1c05806
- Garima Raheja, Kokou Sabi, Hèzouwè Sonla, Eric Kokou Gbedjangni, Celeste M. McFarlane, Collins Gameli Hodoli, Daniel M. Westervelt. A Network of Field-Calibrated Low-Cost Sensor Measurements of PM2.5 in Lomé, Togo, Over One to Two Years. ACS Earth and Space Chemistry 2022, 6
(4)
, 1011-1021. https://doi.org/10.1021/acsearthspacechem.1c00391
- Haley M. Lane, Rachel Morello-Frosch, Julian D. Marshall, Joshua S. Apte. Historical Redlining Is Associated with Present-Day Air Pollution Disparities in U.S. Cities. Environmental Science & Technology Letters 2022, 9
(4)
, 345-350. https://doi.org/10.1021/acs.estlett.1c01012
- Deo Okure, Joel Ssematimba, Richard Sserunjogi, Nancy Lozano Gracia, Maria Edisa Soppelsa, Engineer Bainomugisha. Characterization of Ambient Air Quality in Selected Urban Areas in Uganda Using Low-Cost Sensing and Measurement Technologies. Environmental Science & Technology 2022, 56
(6)
, 3324-3339. https://doi.org/10.1021/acs.est.1c01443
- Rivkah Gardner-Frolick, David Boyd, Amanda Giang. Selecting Data Analytic and Modeling Methods to Support Air Pollution and Environmental Justice Investigations: A Critical Review and Guidance Framework. Environmental Science & Technology 2022, 56
(5)
, 2843-2860. https://doi.org/10.1021/acs.est.1c01739
- Chun-Ying Chao, Huang Zhang, Melanie Hammer, Yu Zhan, David Kenney, Randall V. Martin, Pratim Biswas. Integrating Fixed Monitoring Systems with Low-Cost Sensors to Create High-Resolution Air Quality Maps for the Northern China Plain Region. ACS Earth and Space Chemistry 2021, 5
(11)
, 3022-3035. https://doi.org/10.1021/acsearthspacechem.1c00174
- Tianjun Lu, Julian D. Marshall, Wenwen Zhang, Perry Hystad, Sun-Young Kim, Matthew J. Bechle, Matthias Demuzere, Steve Hankey. National Empirical Models of Air Pollution Using Microscale Measures of the Urban Environment. Environmental Science & Technology 2021, 55
(22)
, 15519-15530. https://doi.org/10.1021/acs.est.1c04047
- Dana E. Goin, Sylvia Sudat, Corinne Riddell, Rachel Morello-Frosch, Joshua S. Apte, M. Maria Glymour, Deborah Karasek, Joan A. Casey. Hyperlocalized Measures of Air Pollution and Preeclampsia in Oakland, California. Environmental Science & Technology 2021, 55
(21)
, 14710-14719. https://doi.org/10.1021/acs.est.1c02151
- Marshall Lloyd, Ellison Carter, Florencio Guzman Diaz, Kento Taro Magara-Gomez, Kris Y. Hong, Jill Baumgartner, Víctor M. Herrera G, Scott Weichenthal. Predicting Within-City Spatial Variations in Outdoor Ultrafine Particle and Black Carbon Concentrations in Bucaramanga, Colombia: A Hybrid Approach Using Open-Source Geographic Data and Digital Images. Environmental Science & Technology 2021, 55
(18)
, 12483-12492. https://doi.org/10.1021/acs.est.1c01412
- Sofia D. Hamilton, Robert A. Harley. High-Resolution Modeling and Apportionment of Diesel-Related Contributions to Black Carbon Concentrations. Environmental Science & Technology 2021, 55
(18)
, 12250-12260. https://doi.org/10.1021/acs.est.1c03913
- Nikhil Rastogi, H. Christopher Frey. Characterizing Fuel Use and Emission Hotspots for a Diesel-Operated Passenger Rail Service. Environmental Science & Technology 2021, 55
(15)
, 10633-10644. https://doi.org/10.1021/acs.est.1c00273
- Provat K. Saha, Steve Hankey, Julian D. Marshall, Allen L. Robinson, Albert A. Presto. High-Spatial-Resolution Estimates of Ultrafine Particle Concentrations across the Continental United States. Environmental Science & Technology 2021, 55
(15)
, 10320-10331. https://doi.org/10.1021/acs.est.1c03237
- Bu Zhao, Long Yu, Chunyan Wang, Chenyang Shuai, Ji Zhu, Shen Qu, Morteza Taiebat, Ming Xu. Urban Air Pollution Mapping Using Fleet Vehicles as Mobile Monitors and Machine Learning. Environmental Science & Technology 2021, 55
(8)
, 5579-5588. https://doi.org/10.1021/acs.est.0c08034
- Joshua W. Smith, Robert N. O’Meally, Derek K. Ng, Jian-Guo Chen, Thomas W. Kensler, Robert N. Cole, John D. Groopman. Biomonitoring of Ambient Outdoor Air Pollutant Exposure in Humans Using Targeted Serum Albumin Adductomics. Chemical Research in Toxicology 2021, 34
(4)
, 1183-1196. https://doi.org/10.1021/acs.chemrestox.1c00055
- Mei W. Tessum, Lianne Sheppard, Timothy V. Larson, Timothy R. Gould, Joel D. Kaufman, Sverre Vedal. Improving Air Pollution Predictions of Long-Term Exposure Using Short-Term Mobile and Stationary Monitoring in Two US Metropolitan Regions. Environmental Science & Technology 2021, 55
(6)
, 3530-3538. https://doi.org/10.1021/acs.est.0c04328
- Minmeng Tang, Deb A. Niemeier. Using Big Data Techniques to Better Understand High-Resolution Cumulative Exposure Assessment of Traffic-Related Air Pollution. ACS ES&T Engineering 2021, 1
(3)
, 436-445. https://doi.org/10.1021/acsestengg.0c00167
- Meng Qi, Steve Hankey. Using Street View Imagery to Predict Street-Level Particulate Air Pollution. Environmental Science & Technology 2021, 55
(4)
, 2695-2704. https://doi.org/10.1021/acs.est.0c05572
- Lucas R. F. Henneman, Huizhong Shen, Christian Hogrefe, Armistead G. Russell, Corwin M. Zigler. Four Decades of United States Mobile Source Pollutants: Spatial–Temporal Trends Assessed by Ground-Based Monitors, Air Quality Models, and Satellites. Environmental Science & Technology 2021, 55
(2)
, 882-892. https://doi.org/10.1021/acs.est.0c07128
- Kerry E. Kelly, Wei W. Xing, Tofigh Sayahi, Logan Mitchell, Tom Becnel, Pierre-Emmanuel Gaillardon, Miriah Meyer, Ross T. Whitaker. Community-Based Measurements Reveal Unseen Differences during Air Pollution Episodes. Environmental Science & Technology 2021, 55
(1)
, 120-128. https://doi.org/10.1021/acs.est.0c02341
- Xiansheng Liu, Xun Zhang, Jürgen Schnelle-Kreis, Gert Jakobi, Xin Cao, Josef Cyrys, Lanyan Yang, Brigitte Schloter-Hai, Gülcin Abbaszade, Jürgen Orasche, Mohamed Khedr, Michal Kowalski, Marcus Hank, Ralf Zimmermann. Spatiotemporal Characteristics and Driving Factors of Black Carbon in Augsburg, Germany: Combination of Mobile Monitoring and Street View Images. Environmental Science & Technology 2021, 55
(1)
, 160-168. https://doi.org/10.1021/acs.est.0c04776
- Rachel Sousa, Cory M. Simon. Evaluating the Fitness of Combinations of Adsorbents for Quantitative Gas Sensor Arrays. ACS Sensors 2020, 5
(12)
, 4035-4047. https://doi.org/10.1021/acssensors.0c02014
- Derek V. Mallia, Logan E. Mitchell, Lewis Kunik, Ben Fasoli, Ryan Bares, Kevin R. Gurney, Daniel L. Mendoza, John C. Lin. Constraining Urban CO2 Emissions Using Mobile Observations from a Light Rail Public Transit Platform. Environmental Science & Technology 2020, 54
(24)
, 15613-15621. https://doi.org/10.1021/acs.est.0c04388
- Aurélie Laguerre, Linda A. George, Elliott T. Gall. High-Efficiency Air Cleaning Reduces Indoor Traffic-Related Air Pollution and Alters Indoor Air Chemistry in a Near-Roadway School. Environmental Science & Technology 2020, 54
(19)
, 11798-11808. https://doi.org/10.1021/acs.est.0c02792
- Sebastian Nufer, Peter J. Lynch, Matthew J. Large, Sean P. Ogilvie, Jonathan P. Salvage, Mario Pelaez-Fernandez, Thomas Waters, Izabela Jurewicz, Edgar Muñoz, Raul Arenal, Ana M. Benito, Wolfgang K. Maser, Nikos Tagmatarchis, Christopher P. Ewels, Adam Brunton, Alan B. Dalton. Laser-Deposited Carbon Aerogel Derived from Graphene Oxide Enables NO2-Selective Parts-per-Billion Sensing. ACS Applied Materials & Interfaces 2020, 12
(35)
, 39541-39548. https://doi.org/10.1021/acsami.0c09112
- Tanzila Khan, H. Christopher Frey, Nikhil Rastogi, Tongchuan Wei. Geospatial Variation of Real-World Tailpipe Emission Rates for Light-Duty Gasoline Vehicles. Environmental Science & Technology 2020, 54
(14)
, 8968-8979. https://doi.org/10.1021/acs.est.0c00489
- Sarah E. Chambliss, Chelsea V. Preble, Julien J. Caubel, Troy Cados, Kyle P. Messier, Ramón A. Alvarez, Brian LaFranchi, Melissa Lunden, Julian D. Marshall, Adam A. Szpiro, Thomas W. Kirchstetter, Joshua S. Apte. Comparison of Mobile and Fixed-Site Black Carbon Measurements for High-Resolution Urban Pollution Mapping. Environmental Science & Technology 2020, 54
(13)
, 7848-7857. https://doi.org/10.1021/acs.est.0c01409
- Jianbang Xiang, Elena Austin, Timothy Gould, Timothy Larson, Michael Yost, Jeffry Shirai, Yisi Liu, Sukyong Yun, Edmund Seto. Using Vehicles’ Rendezvous for In Situ Calibration of Instruments in Fleet Vehicle-Based Air Pollution Mobile Monitoring. Environmental Science & Technology 2020, 54
(7)
, 4286-4294. https://doi.org/10.1021/acs.est.0c00612
- David J. Miller, Blake Actkinson, Lauren Padilla, Robert J. Griffin, Katie Moore, P. Grace Tee Lewis, Rivkah Gardner-Frolick, Elena Craft, Christopher J. Portier, Steven P. Hamburg, Ramón A. Alvarez. Characterizing Elevated Urban Air Pollutant Spatial Patterns with Mobile Monitoring in Houston, Texas. Environmental Science & Technology 2020, 54
(4)
, 2133-2142. https://doi.org/10.1021/acs.est.9b05523
- James Adam Mahady, Claudia Octaviano, Oscar Sebastian Araiza Bolaños, Erick Rosas López, Daniel M. Kammen, Sergio Castellanos. Mapping Opportunities for Transportation Electrification to Address Social Marginalization and Air Pollution Challenges in Greater Mexico City. Environmental Science & Technology 2020, 54
(4)
, 2103-2111. https://doi.org/10.1021/acs.est.9b06148
- Ellis
Shipley Robinson, Rishabh Urvesh Shah, Kyle Messier, Peishi Gu, Hugh Z. Li, Joshua Schulz Apte, Allen L. Robinson, Albert A. Presto. Land-Use Regression Modeling of Source-Resolved Fine Particulate Matter Components from Mobile Sampling. Environmental Science & Technology 2019, 53
(15)
, 8925-8937. https://doi.org/10.1021/acs.est.9b01897
- Julien J. Caubel, Troy E. Cados, Chelsea V. Preble, Thomas W. Kirchstetter. A Distributed Network of 100 Black Carbon Sensors for 100 Days of Air Quality Monitoring in West Oakland, California. Environmental Science & Technology 2019, 53
(13)
, 7564-7573. https://doi.org/10.1021/acs.est.9b00282
- Provat
K. Saha, Hugh Z. Li, Joshua S. Apte, Allen L. Robinson, Albert A. Presto. Urban Ultrafine Particle Exposure Assessment with Land-Use Regression: Influence of Sampling Strategy. Environmental Science & Technology 2019, 53
(13)
, 7326-7336. https://doi.org/10.1021/acs.est.9b02086
- Steve Hankey, Peter Sforza, Matt Pierson. Using Mobile Monitoring to Develop Hourly Empirical Models of Particulate Air Pollution in a Rural Appalachian Community. Environmental Science & Technology 2019, 53
(8)
, 4305-4315. https://doi.org/10.1021/acs.est.8b05249
- Ani Wang, Xinxin Pi, Ruiqing Fan, Sue Hao, Yulin Yang. Micromesoporous Nitrogen-Doped Carbon Materials Derived from Direct Carbonization of Metal–Organic Complexes for Efficient CO2 Adsorption and Separation. Inorganic Chemistry 2019, 58
(8)
, 5345-5355. https://doi.org/10.1021/acs.inorgchem.9b00500
- Ani Wang, Ruiqing Fan, Xinxin Pi, Sue Hao, Xubin Zheng, Yulin Yang. N-Doped Porous Carbon Derived by Direct Carbonization of Metal–Organic Complexes Crystal Materials for SO2 Adsorption. Crystal Growth & Design 2019, 19
(3)
, 1973-1984. https://doi.org/10.1021/acs.cgd.8b01925
- Kyle P. Messier, Sarah E. Chambliss, Shahzad Gani, Ramon Alvarez, Michael Brauer, Jonathan J. Choi, Steven P. Hamburg, Jules Kerckhoffs, Brian LaFranchi, Melissa M. Lunden, Julian D. Marshall, Christopher J. Portier, Ananya Roy, Adam A. Szpiro, Roel C. H. Vermeulen, Joshua S. Apte. Mapping Air Pollution with Google Street View Cars: Efficient Approaches with Mobile Monitoring and Land Use Regression. Environmental Science & Technology 2018, 52
(21)
, 12563-12572. https://doi.org/10.1021/acs.est.8b03395
- Ani Wang, Ruiqing Fan, Xinxin Pi, Yuze Zhou, Guangyu Chen, Wei Chen, Yulin Yang. Nitrogen-Doped Microporous Carbons Derived from Pyridine Ligand-Based Metal–Organic Complexes as High-Performance SO2 Adsorption Sorbents. ACS Applied Materials & Interfaces 2018, 10
(43)
, 37407-37416. https://doi.org/10.1021/acsami.8b12739
- Peishi Gu, Hugh Z. Li, Qing Ye, Ellis S. Robinson, Joshua S. Apte, Allen L. Robinson, Albert A. Presto. Intracity Variability of Particulate Matter Exposure Is Driven by Carbonaceous Sources and Correlated with Land-Use Variables. Environmental Science & Technology 2018, 52
(20)
, 11545-11554. https://doi.org/10.1021/acs.est.8b03833
- Ellis
Shipley Robinson, Peishi Gu, Qing Ye, Hugh Z. Li, Rishabh Urvesh Shah, Joshua Schulz Apte, Allen L. Robinson, Albert A. Presto. Restaurant Impacts on Outdoor Air Quality: Elevated Organic Aerosol Mass from Restaurant Cooking with Neighborhood-Scale Plume Extents. Environmental Science & Technology 2018, 52
(16)
, 9285-9294. https://doi.org/10.1021/acs.est.8b02654
- Qing Ye, Peishi Gu, Hugh Z. Li, Ellis S. Robinson, Eric Lipsky, Christos Kaltsonoudis, Alex K.Y. Lee, Joshua S. Apte, Allen L. Robinson, Ryan C. Sullivan, Albert A. Presto, Neil M. Donahue. Spatial Variability of Sources and Mixing State of Atmospheric Particles in a Metropolitan Area. Environmental Science & Technology 2018, 52
(12)
, 6807-6815. https://doi.org/10.1021/acs.est.8b01011
- Georgia Miskell, Jennifer A. Salmond, David E. Williams. Solution to the Problem of Calibration of Low-Cost Air Quality Measurement Sensors in Networks. ACS Sensors 2018, 3
(4)
, 832-843. https://doi.org/10.1021/acssensors.8b00074
- Mei W. Tessum, Timothy Larson, Timothy R. Gould, Christopher D. Simpson, Michael G. Yost, Sverre Vedal. Mobile and Fixed-Site Measurements To Identify Spatial Distributions of Traffic-Related Pollution Sources in Los Angeles. Environmental Science & Technology 2018, 52
(5)
, 2844-2853. https://doi.org/10.1021/acs.est.7b04889
- Ivan C. Hanigan , Grant J. Williamson , Luke D. Knibbs , Joshua Horsley , Margaret I. Rolfe , Martin Cope , Adrian G. Barnett , Christine T. Cowie , Jane S. Heyworth , Marc L. Serre , Bin Jalaludin , Geoffrey G. Morgan . Blending Multiple Nitrogen Dioxide Data Sources for Neighborhood Estimates of Long-Term Exposure for Health Research. Environmental Science & Technology 2017, 51
(21)
, 12473-12480. https://doi.org/10.1021/acs.est.7b03035
- Bijan Yeganeh, Masoud Zarei, Alireza Shakerdonyavi, Soheil Karimi Darmian. Comparative analysis of particle exposure in commuters: Evaluating different modes of transportation in Tehran. Building and Environment 2025, 267 , 112183. https://doi.org/10.1016/j.buildenv.2024.112183
- 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
- Anna C. O'Regan, Henrik Grythe, Stig Hellebust, Susana Lopez-Aparicio, Colin O'Dowd, Paul D. Hamer, Gabriela Sousa Santos, Marguerite M. Nyhan. Data fusion for enhancing urban air quality modeling using large-scale citizen science data. Sustainable Cities and Society 2024, 116 , 105896. https://doi.org/10.1016/j.scs.2024.105896
- Junghwan Kim, Dayoung Kim, Brendan David-John. The role of privacy concerns, perceived benefits, and trust in citizens' acceptance of street-view image collection by local planning agencies. Cities 2024, 154 , 105339. https://doi.org/10.1016/j.cities.2024.105339
- Minmeng Tang, Xinwei Li. Hyper-local black carbon prediction by integrating land use variables with explainable machine learning model. Atmospheric Environment 2024, 336 , 120733. https://doi.org/10.1016/j.atmosenv.2024.120733
- Daniel L. Mendoza, Andres Gonzalez, Alexander A. Jacques, Colin M. Johnson, Peter T. Whelan, John D. Horel. Electric buses as an air pollution and meteorological observation network: Methodology and preliminary results. Science of The Total Environment 2024, 951 , 175327. https://doi.org/10.1016/j.scitotenv.2024.175327
- Russell R. Dickerson, Phillip Stratton, Xinrong Ren, Paul Kelley, Christopher D. Heaney, Lauren Deanes, Matthew Aubourg, Kristoffer Spicer, Joel Dreessen, Ryan Auvil, Gregory Sawtell, Meleny Thomas, Shashawnda Campbell, Carlos Sanchez. Mobile laboratory measurements of air pollutants in Baltimore, MD elucidate issues of environmental justice. Journal of the Air & Waste Management Association 2024, 74
(11)
, 753-770. https://doi.org/10.1080/10962247.2024.2393178
- Zeyin Chen, Siying Li, Chao Liu. Challenges and Potentials: Environmental Assessment of Particulate Matter in Spaces Under Highway Viaducts. Atmosphere 2024, 15
(11)
, 1325. https://doi.org/10.3390/atmos15111325
- Shibao Wang, Yanxu Zhang. An attention-based CNN model integrating observational and simulation data for high-resolution spatial estimation of urban air quality. Atmospheric Environment 2024, 329 , 120921. https://doi.org/10.1016/j.atmosenv.2024.120921
- Paul Morris, Cory M. Simon. Computationally predicting the performance of gas sensor arrays for anomaly detection. Sensors & Diagnostics 2024, 3
(10)
, 1699-1713. https://doi.org/10.1039/D4SD00121D
- Madison Rutherford, Abigail Koss, Joost de Gouw. Mobile VOC measurements in Commerce City, CO reveal the emissions from different sources. Journal of the Air & Waste Management Association 2024, 74
(10)
, 714-725. https://doi.org/10.1080/10962247.2024.2379927
- M. Tarazona Alvarado, J. L. Salamanca-Coy, K. Forero-Gutièrrez, L. A. Núñez, J. Pisco-Guabave, Fr. Escobar-Diaz, D. Sierra-Porta. Assessing and monitoring air quality in cities and urban areas with a portable, modular and low-cost sensor station: calibration challenges. International Journal of Remote Sensing 2024, 45
(17)
, 5713-5736. https://doi.org/10.1080/01431161.2024.2373338
- Peng Wei, Song Hao, Yuan Shi, Abhishek Anand, Ya Wang, Mengyuan Chu, Zhi Ning. Combining Google traffic map with deep learning model to predict street-level traffic-related air pollutants in a complex urban environment. Environment International 2024, 51 , 108992. https://doi.org/10.1016/j.envint.2024.108992
- Jiayao Chen, Anna Mölter, José Pablo Gómez-Barrón, David O’Connor, Francesco Pilla. Evaluating background and local contributions and identifying traffic-related pollutant hotspots: insights from Google Air View mobile monitoring in Dublin, Ireland. Environmental Science and Pollution Research 2024, 31
(44)
, 56114-56129. https://doi.org/10.1007/s11356-024-34903-5
- Provat K. Saha, Albert A. Presto, Allen L. Robinson. Hyper-local to regional exposure contrast of source-resolved PM2.5 components across the contiguous United States: implications for health assessment. Journal of Exposure Science & Environmental Epidemiology 2024, 34
(5)
, 836-844. https://doi.org/10.1038/s41370-023-00623-0
- Sarah E. Chambliss, Mark Joseph Campmier, Michelle Audirac, Joshua S. Apte, Corwin M. Zigler. Local exposure misclassification in national models: relationships with urban infrastructure and demographics. Journal of Exposure Science & Environmental Epidemiology 2024, 34
(5)
, 761-769. https://doi.org/10.1038/s41370-023-00624-z
- Steven Stalder, Michele Volpi, Nicolas Büttner, Stephen Law, Kenneth Harttgen, Esra Suel. Self-supervised learning unveils urban change from street-level images. Computers, Environment and Urban Systems 2024, 112 , 102156. https://doi.org/10.1016/j.compenvurbsys.2024.102156
- Cristina Ghita. Through the Looking Screen: Exploring Familiar Places Through Google Maps Street View. Postdigital Science and Education 2024, 6
(3)
, 978-997. https://doi.org/10.1007/s42438-024-00484-2
- Abdellatif Bekkar, Badr Hssina, Najib ABEKIRI, Samira Douzi, Khadija Douzi, . Real-time AIoT platform for monitoring and prediction of air quality in Southwestern Morocco. PLOS ONE 2024, 19
(8)
, e0307214. https://doi.org/10.1371/journal.pone.0307214
- Fayma Mushtaq, Majid Farooq, Mili Ghosh Nee Lala, Saswati Banerjee, Anamika Shalini Tirkey, Farzana Shaheen, Gowhar Meraj. Analysis of spatial variability of smog episodes over National Capital Delhi during (2013–2017). Discover Applied Sciences 2024, 6
(8)
https://doi.org/10.1007/s42452-024-06109-4
- Meng-Yi Jin, John Gallagher, Xiao-Bing Li, Kai-Fa Lu, Zhong-Ren Peng, Hong-Di He. Characterizing the distribution pattern of traffic-related air pollutants in near-road neighborhoods. Environmental Monitoring and Assessment 2024, 196
(8)
https://doi.org/10.1007/s10661-024-12917-3
- Gwang-Soon Yong, Gun-Woo Mun, Kyung-Hwan Kwak. Spatial Mapping of Air Pollution Hotspots around Commercial Meat-Cooking Restaurants Using Bicycle-Based Mobile Monitoring. Atmosphere 2024, 15
(8)
, 991. https://doi.org/10.3390/atmos15080991
- Marie Dury, Florent Hozay, Hans Hooyberghs, Fabian Lenartz. Mobile measurements and street-level modelling to assess outdoor and indoor personal exposure to air pollution in urban environment. Air Quality, Atmosphere & Health 2024, 17
(8)
, 1605-1616. https://doi.org/10.1007/s11869-024-01529-y
- Joshua S. Apte, Chirag Manchanda. High-resolution urban air pollution mapping. Science 2024, 385
(6707)
, 380-385. https://doi.org/10.1126/science.adq3678
- Weiwei Pu, Yingruo Li, Di He, Fan Dong, Jin Wu, Xiangxue Liu, Yulu Qiu, Shuangshuang Ge, Zhiqiang Ma. Mobile measurement background quantification methods for determining local traffic emissions and background source contributions to ammonia. Atmospheric Environment 2024, 329 , 120523. https://doi.org/10.1016/j.atmosenv.2024.120523
- David De Ridder, Anaïs Ladoy, Yangji Choi, Damien Jacot, Séverine Vuilleumier, Idris Guessous, Stéphane Joost, Gilbert Greub. Environmental and geographical factors influencing the spread of SARS-CoV-2 over 2 years: a fine-scale spatiotemporal analysis. Frontiers in Public Health 2024, 12 https://doi.org/10.3389/fpubh.2024.1298177
- Provat K. Saha, Tanbhir M. Shovon, Sheikh M. Rahman, Julian D. Marshall, Allen L. Robinson, Albert A. Presto. Contrasting intra-urban variability of ultrafine particle number and fine particle mass concentrations in Dhaka, Bangladesh, and Pittsburgh, USA. Atmospheric Environment 2024, 327 , 120497. https://doi.org/10.1016/j.atmosenv.2024.120497
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- 1Apte, J. S.; Marshall, J. D.; Brauer, M.; Cohen, A. J. Addressing global mortality from ambient PM2.5 Environ. Sci. Technol. 2015, 49, 8057– 8066 DOI: 10.1021/acs.est.5b012361https://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.
- 2Lim, S. S.; Vos, T.; Flaxman, A. D.; Danaei, G.; Shibuya, K.; Adair-Rohani, H.; Amann, M.; Anderson, H. R.; Andrews, K. G.; Aryee, M. A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990–2010: A systematic analysis for the Global Burden of Disease Study 2010 Lancet 2012, 380, 2224– 2260 DOI: 10.1016/S0140-6736(12)61766-82https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BC3s3isFClug%253D%253D&md5=f35c63bad4b58d5266a7ee7c4512569bA comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990-2010: a systematic analysis for the Global Burden of Disease Study 2010Lim Stephen S; Vos Theo; Flaxman Abraham D; Danaei Goodarz; Shibuya Kenji; Adair-Rohani Heather; Amann Markus; Anderson H Ross; Andrews Kathryn G; Aryee Martin; Atkinson Charles; Bacchus Loraine J; Bahalim Adil N; Balakrishnan Kalpana; Balmes John; Barker-Collo Suzanne; Baxter Amanda; Bell Michelle L; Blore Jed D; Blyth Fiona; Bonner Carissa; Borges Guilherme; Bourne Rupert; Boussinesq Michel; Brauer Michael; Brooks Peter; Bruce Nigel G; Brunekreef Bert; Bryan-Hancock Claire; Bucello Chiara; Buchbinder Rachelle; Bull Fiona; Burnett Richard T; Byers Tim E; Calabria Bianca; Carapetis Jonathan; Carnahan Emily; Chafe Zoe; Charlson Fiona; Chen Honglei; Chen Jian Shen; Cheng Andrew Tai-Ann; Child Jennifer Christine; Cohen Aaron; Colson K Ellicott; Cowie Benjamin C; Darby Sarah; Darling Susan; Davis Adrian; Degenhardt Louisa; Dentener Frank; Des Jarlais Don C; Devries Karen; Dherani Mukesh; Ding Eric L; Dorsey E Ray; Driscoll Tim; Edmond Karen; Ali Suad Eltahir; Engell Rebecca E; Erwin Patricia J; Fahimi Saman; Falder Gail; Farzadfar Farshad; Ferrari Alize; Finucane Mariel M; Flaxman Seth; Fowkes Francis Gerry R; Freedman Greg; Freeman Michael K; Gakidou Emmanuela; Ghosh Santu; Giovannucci Edward; Gmel Gerhard; Graham Kathryn; Grainger Rebecca; Grant Bridget; Gunnell David; Gutierrez Hialy R; Hall Wayne; Hoek Hans W; Hogan Anthony; Hosgood H Dean 3rd; Hoy Damian; Hu Howard; Hubbell Bryan J; Hutchings Sally J; Ibeanusi Sydney E; Jacklyn Gemma L; Jasrasaria Rashmi; Jonas Jost B; Kan Haidong; Kanis John A; Kassebaum Nicholas; Kawakami Norito; Khang Young-Ho; Khatibzadeh Shahab; Khoo Jon-Paul; Kok Cindy; Laden Francine; Lalloo Ratilal; Lan Qing; Lathlean Tim; Leasher Janet L; Leigh James; Li Yang; Lin John Kent; Lipshultz Steven E; London Stephanie; Lozano Rafael; Lu Yuan; Mak Joelle; Malekzadeh Reza; Mallinger Leslie; Marcenes Wagner; March Lyn; Marks Robin; Martin Randall; McGale Paul; McGrath John; Mehta Sumi; Mensah George A; Merriman Tony R; Micha Renata; Michaud Catherine; Mishra Vinod; Mohd Hanafiah Khayriyyah; Mokdad Ali A; Morawska Lidia; Mozaffarian Dariush; Murphy Tasha; Naghavi Mohsen; Neal Bruce; Nelson Paul K; Nolla Joan Miquel; Norman Rosana; Olives Casey; Omer Saad B; Orchard Jessica; Osborne Richard; Ostro Bart; Page Andrew; Pandey Kiran D; Parry Charles D H; Passmore Erin; Patra Jayadeep; Pearce Neil; Pelizzari Pamela M; Petzold Max; Phillips Michael R; Pope Dan; Pope C Arden 3rd; Powles John; Rao Mayuree; Razavi Homie; Rehfuess Eva A; Rehm Jurgen T; Ritz Beate; Rivara Frederick P; Roberts Thomas; Robinson Carolyn; Rodriguez-Portales Jose A; Romieu Isabelle; Room Robin; Rosenfeld Lisa C; Roy Ananya; Rushton Lesley; Salomon Joshua A; Sampson Uchechukwu; Sanchez-Riera Lidia; Sanman Ella; Sapkota Amir; Seedat Soraya; Shi Peilin; Shield Kevin; Shivakoti Rupak; Singh Gitanjali M; Sleet David A; Smith Emma; Smith Kirk R; Stapelberg Nicolas J C; Steenland Kyle; Stockl Heidi; Stovner Lars Jacob; Straif Kurt; Straney Lahn; Thurston George D; Tran Jimmy H; Van Dingenen Rita; van Donkelaar Aaron; Veerman J Lennert; Vijayakumar Lakshmi; Weintraub Robert; Weissman Myrna M; White Richard A; Whiteford Harvey; Wiersma Steven T; Wilkinson James D; Williams Hywel C; Williams Warwick; Wilson Nicholas; Woolf Anthony D; Yip Paul; Zielinski Jan M; Lopez Alan D; Murray Christopher J L; Ezzati Majid; AlMazroa Mohammad A; Memish Ziad ALancet (London, England) (2012), 380 (9859), 2224-60 ISSN:.BACKGROUND: Quantification of the disease burden caused by different risks informs prevention by providing an account of health loss different to that provided by a disease-by-disease analysis. No complete revision of global disease burden caused by risk factors has been done since a comparative risk assessment in 2000, and no previous analysis has assessed changes in burden attributable to risk factors over time. METHODS: We estimated deaths and disability-adjusted life years (DALYs; sum of years lived with disability [YLD] and years of life lost [YLL]) attributable to the independent effects of 67 risk factors and clusters of risk factors for 21 regions in 1990 and 2010. We estimated exposure distributions for each year, region, sex, and age group, and relative risks per unit of exposure by systematically reviewing and synthesising published and unpublished data. We used these estimates, together with estimates of cause-specific deaths and DALYs from the Global Burden of Disease Study 2010, to calculate the burden attributable to each risk factor exposure compared with the theoretical-minimum-risk exposure. We incorporated uncertainty in disease burden, relative risks, and exposures into our estimates of attributable burden. FINDINGS: In 2010, the three leading risk factors for global disease burden were high blood pressure (7·0% [95% uncertainty interval 6·2-7·7] of global DALYs), tobacco smoking including second-hand smoke (6·3% [5·5-7·0]), and alcohol use (5·5% [5·0-5·9]). In 1990, the leading risks were childhood underweight (7·9% [6·8-9·4]), household air pollution from solid fuels (HAP; 7·0% [5·6-8·3]), and tobacco smoking including second-hand smoke (6·1% [5·4-6·8]). Dietary risk factors and physical inactivity collectively accounted for 10·0% (95% UI 9·2-10·8) of global DALYs in 2010, with the most prominent dietary risks being diets low in fruits and those high in sodium. Several risks that primarily affect childhood communicable diseases, including unimproved water and sanitation and childhood micronutrient deficiencies, fell in rank between 1990 and 2010, with unimproved water and sanitation accounting for 0·9% (0·4-1·6) of global DALYs in 2010. However, in most of sub-Saharan Africa childhood underweight, HAP, and non-exclusive and discontinued breastfeeding were the leading risks in 2010, while HAP was the leading risk in south Asia. The leading risk factor in Eastern Europe, most of Latin America, and southern sub-Saharan Africa in 2010 was alcohol use; in most of Asia, North Africa and Middle East, and central Europe it was high blood pressure. Despite declines, tobacco smoking including second-hand smoke remained the leading risk in high-income north America and western Europe. High body-mass index has increased globally and it is the leading risk in Australasia and southern Latin America, and also ranks high in other high-income regions, North Africa and Middle East, and Oceania. INTERPRETATION: Worldwide, the contribution of different risk factors to disease burden has changed substantially, with a shift away from risks for communicable diseases in children towards those for non-communicable diseases in adults. These changes are related to the ageing population, decreased mortality among children younger than 5 years, changes in cause-of-death composition, and changes in risk factor exposures. New evidence has led to changes in the magnitude of key risks including unimproved water and sanitation, vitamin A and zinc deficiencies, and ambient particulate matter pollution. The extent to which the epidemiological shift has occurred and what the leading risks currently are varies greatly across regions. In much of sub-Saharan Africa, the leading risks are still those associated with poverty and those that affect children. FUNDING: Bill & Melinda Gates Foundation.
- 3Pope, C. A.; Dockery, D. W. Health effects of fine particulate air pollution: Lines that connect J. Air Waste Manage. Assoc. 2006, 56, 709– 742 DOI: 10.1080/10473289.2006.104644853https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD28Xmt1ygs7k%253D&md5=7aaf80b762054e234b73d653096e18f2Health effects of fine particulate air pollution: lines that connectPope, C. Arden, III; Dockery, Douglas W.Journal of the Air & Waste Management Association (2006), 56 (6), 709-742CODEN: JAWAFC; ISSN:1096-2247. (Air & Waste Management Association)A review. Efforts to understand and mitigate the health effects of participate matter (PM) air pollution have a rich and interesting history. This review focuses on six substantial lines of research that have been pursued since 1997 that have helped elucidate our understanding about the effects of PM on human health. There has been substantial progress in the evaluation of PM health effects at different time-scales of exposure and in the exploration of the shape of the concn.-response function. There has also been emerging evidence of PM-related cardiovascular health effects and growing knowledge regarding interconnected general pathophysiol. pathways that link PM exposure with cardiopulmonary morbidity and mortality. Despite important gaps in scientific knowledge and continued reasons for some skepticism, a comprehensive evaluation of the research findings provides persuasive evidence that exposure to fine particulate air pollution has adverse effects on cardiopulmonary health. Although much of this research has been motivated by environmental public health policy, these results have important scientific, medical, and public health implications that are broader than debates over legally mandated air quality stds.
- 4Brook, R. D.; Rajagopalan, S.; Pope, C. A., III; Brook, J. R.; Bhatnagar, A.; Diez-Roux, A. V.; Holguin, F.; Hong, Y.; Luepker, R. V.; Mittleman, M. A. Particulate matter air pollution and cardiovascular disease: An update to the scientific statement from the American Heart Association Circulation 2010, 121, 2331– 2378 DOI: 10.1161/CIR.0b013e3181dbece14https://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.
- 5Carvalho, H. The air we breathe: differentials in global air quality monitoring Lancet Respir. Med. 2016, 4, 603– 605 DOI: 10.1016/S2213-2600(16)30180-15https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BC2s3ht1yitA%253D%253D&md5=1aa08dd6124df8bd2e5543312cccb104The air we breathe: differentials in global air quality monitoringCarvalho HelotonioThe Lancet. Respiratory medicine (2016), 4 (8), 603-605 ISSN:.There is no expanded citation for this reference.
- 6Brauer, M.; Freedman, G.; Frostad, J.; van Donkelaar, A.; Martin, R. V.; Dentener, F.; Dingenen, R. v.; Estep, K.; Amini, H.; Apte, J. S. Ambient air pollution exposure estimation for the Global Burden of Disease 2013 Environ. Sci. Technol. 2016, 50, 79– 88 DOI: 10.1021/acs.est.5b037096https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXhvVyit7bM&md5=0ff17c54d051acef99c5a703b91d4c2fAmbient Air Pollution Exposure Estimation for the Global Burden of Disease 2013Brauer, Michael; Freedman, Greg; Frostad, Joseph; van Donkelaar, Aaron; Martin, Randall V.; Dentener, Frank; Dingenen, Rita van; Estep, Kara; Amini, Heresh; Apte, Joshua S.; Balakrishnan, Kalpana; Barregard, Lars; Broday, David; Feigin, Valery; Ghosh, Santu; Hopke, Philip K.; Knibbs, Luke D.; Kokubo, Yoshihiro; Liu, Yang; Ma, Stefan; Morawska, Lidia; Sangrador, Jose Luis Texcalac; Shaddick, Gavin; Anderson, H. Ross; Vos, Theo; Forouzanfar, Mohammad H.; Burnett, Richard T.; Cohen, AaronEnvironmental Science & Technology (2016), 50 (1), 79-88CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)Ambient air pollution exposure is a major risk factor for global disease. Assessing the impact of air pollution on population health and evaluating trends relative to other major risk factors requires regularly updated, accurate, spatially resolved exposure ests. This work combined satellite-based ests., chem. transport model simulations, and ground measurements from 79 countries to produce global ests. of annual av. fine particle (PM2.5) and O3 concns. at 0.1° × 0.1° spatial resoln. for 5-yr intervals from 1990 to 2010 and year 2013. These ests. were used to assess population-weighted mean concns. for 1990-2013 for 188 countries. In 2013, 87% of the world population lived in areas exceeding the World Health Organization air quality guideline (10 μg/m3 PM2.5 annual av.). From 1990 to 2013, global population-weighted PM2.5 increased 20.4%, driven by trends in southern and southeastern Asia and China. Decreases in population-weighted mean PM2.5 concns. were evident in most high income countries. Population-weighted mean O3 concns. increased globally 8.9% from 1990 to 2013, with increases in most countries; modest decreases occurred in North America, parts of Europe, and several southeastern Asia countries.
- 7Zhang, K. M.; Wexler, A. S.; Zhu, Y. F.; Hinds, W. C.; Sioutas, C. Evolution of particle number distribution near roadways. Part II: the ‘Road-to-Ambient’ process Atmos. Environ. 2004, 38, 6655– 6665 DOI: 10.1016/j.atmosenv.2004.06.0447https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2cXptV2nsrg%253D&md5=aec367aa36e1d526fa688ac01341854fEvolution of particle number distribution near roadways. Part II. The 'Road-to-Ambient' processZhang, K. Max; Wexler, Anthony S.; Zhu, Yi Fang; Hinds, William C.; Sioutas, ConstantinosAtmospheric Environment (2004), 38 (38), 6655-6665CODEN: AENVEQ; ISSN:1352-2310. (Elsevier B.V.)The 'road-to-ambient' evolution of particle no. distributions near the 405 and 710 freeways in Los Angeles, California, in both summer and winter, were analyzed and then simulated by a multi-component sectional aerosol dynamic model. Condensation/evapn. and diln. were demonstrated to be the major mechanisms in altering aerosol size distribution, while coagulation and deposition play minor roles. Seasonal effects were significant with winters generally less dynamic than summers. A large no. of particles grew into the >10 nm range around 30-90 m downwind of the freeways. Beyond 90 m some shrink to <10 nm range and some continued growing to >100 nm as a result of competition between partial pressure and vapor pressure. Particle compns. probably change dramatically as components adapt to decreasing gas-phase concn. due to diln., so no. distribution evolution is also an evolution of compn. As a result, people who live within about 90 m of roadways are exposed to particle sizes and compns. that others are not.
- 8Karner, A. A.; Eisinger, D. S.; Niemeier, D. A. Near-roadway air quality: Synthesizing the findings from real-world data Environ. Sci. Technol. 2010, 44, 5334– 5344 DOI: 10.1021/es100008x8https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3cXns1Wrtb4%253D&md5=ac6e9c38c90964168688cc4094594073Near-Roadway Air Quality: Synthesizing the Findings from Real-World DataKarner, Alex A.; Eisinger, Douglas S.; Niemeier, Deb A.Environmental Science & Technology (2010), 44 (14), 5334-5344CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)Despite increasing regulatory attention and literature linking roadside air pollution to health outcomes, studies on near roadway air quality have not yet been well synthesized. We employ data collected from 1978 as reported in 41 roadside monitoring studies, encompassing more than 700 air pollutant concn. measurements, published as of June 2008. Two types of normalization, background and edge-of-road, were applied to the obsd. concns. Local regression models were specified to the concn.-distance relationship and anal. of variance was used to det. the statistical significance of trends. Using an edge-of-road normalization, almost all pollutants decay to background by 115-570 m from the edge of road; using the more std. background normalization, almost all pollutants decay to background by 160-570 m from the edge of road. Differences between the normalization methods arose due to the likely bias inherent in background normalization, since some reported background values tend to under-predict (be lower than) actual background. Changes in pollutant concns. with increasing distance from the road fell into one of three groups: at least a 50% decrease in peak/edge-of-road concn. by 150 m, followed by consistent but gradual decay toward background (e.g., carbon monoxide, some ultrafine particulate matter no. concns.); consistent decay or change over the entire distance range (e.g., benzene, nitrogen dioxide); or no trend with distance (e.g., particulate matter mass concns.).
- 9Zhu, Y. F.; Hinds, W. C.; Kim, S.; Shen, S.; Sioutas, C. Study of ultrafine particles near a major highway with heavy-duty diesel traffic Atmos. Environ. 2002, 36, 4323– 4335 DOI: 10.1016/S1352-2310(02)00354-09https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD38Xms1Gru7k%253D&md5=7815ecf468fb4a5a9c63d3f8dad5714fStudy of ultrafine particles near a major highway with heavy-duty diesel trafficZhu, Yifang; Hinds, William C.; Kim, Seongheon; Shen, Si; Sioutas, ConstantinosAtmospheric Environment (2002), 36 (27), 4323-4335CODEN: AENVEQ; ISSN:1352-2310. (Elsevier Science Ltd.)Motor vehicle emissions usually constitute the most significant source of ultrafine particles (diam. <0.1 μm) in an urban environment. Y. Zhu, et al. (2002, accepted for publication) conducted systematic measurements of ultra-fine particle concn. and size distribution near a highway dominated by gasoline vehicles. The reported study compared these measurements with those made on Interstate 710 in Los Angeles, California. The 710 freeway was selected because >25% of vehicles are heavy-duty diesel trucks. Particle no. concn. and size distribution from 6 to 220 nm were measured by a condensation particle counter and scanning mobility particle sizer, resp. Measurements were made 17, 20, 30, 90, 150, and 300 m downwind and 200 m upwind from the center of the freeway. At each sampling site, CO and black carbon (BC) concns. were also measured with a Dasibi CO monitor and an aethalometer, resp. The range of av. CO, BC, and total particulate no. concns. at 17 m was 1.9-2.6 ppm, 20.3-24.8 μg/m3, and 1.8×105-3.5×105/cm3, resp. Relative CO, BC, and particle no. concns. decreased exponentially and tracked each other well as distance from the freeway increased. Atm. dispersion and coagulation appeared to contribute to the rapid decrease in particle no. concn. and change in particle size distribution with increasing distance from the freeway. Av. traffic flow during sampling was 12,180 vehicles/h with >25% of vehicles heavy-duty diesel trucks. Ultra-fine particle no. concn. measured 300 m downwind from the freeway was indistinguishable from the upwind background concn. Data may be used to est. exposure to ultra-fine particles near major highways.
- 10Marshall, J. D.; Nethery, E.; Brauer, M. Within-urban variability in ambient air pollution: Comparison of estimation methods Atmos. Environ. 2008, 42, 1359– 1369 DOI: 10.1016/j.atmosenv.2007.08.01210https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1cXhtlGjurc%253D&md5=11eaeb0b662cb0a671027d6eabb023f7Within-urban variability in ambient air pollution: Comparison of estimation methodsMarshall, Julian D.; Nethery, Elizabeth; Brauer, MichaelAtmospheric Environment (2008), 42 (6), 1359-1369CODEN: AENVEQ; ISSN:1352-2310. (Elsevier Ltd.)An important component of air quality management and health risk assessment is improved by understanding of spatial and temporal variability in pollutant concns. We compare, for Vancouver, Canada, three approaches for estg. within-urban spatiotemporal variability in ambient concns.: spatial interpolation of monitoring data; an empirical/statistical model based on geog. analyses ("land-use regression"; LUR); and an Eulerian grid model (community multiscale air quality model, CMAQ). Four pollutants are considered-nitrogen oxide (NO), nitrogen dioxide (NO2), carbon monoxide, and ozone-represent varying levels of spatiotemporal heterogeneity. Among the methods, differences in central tendencies (mean, median) and variability (std. deviation) are modest. LUR and CMAQ perform well in predicting concns. at monitoring sites (av. abs. bias: <50% for NO; <20% for NO2). Monitors (LUR) offer the greatest (least) temporal resoln.; LUR (monitors) offers the greatest (least) spatial resoln. Of note, the length scale of spatial variability is shorter for LUR (units: km; 0.3 for NO, 1 for NO2) than for the other approaches (3-6 for NO, 4-6 for NO2), indicating that the approaches offer different information about spatial attributes of air pollution. Results presented here suggest that for investigations incorporating spatiotemporal variability in ambient concns., the findings may depend on which estn. method is employed.
- 11Morello-Frosch, R.; Pastor, M.; Sadd, J. Environmental justice and Southern California’s “riskscape”: the distribution of air toxics exposures and health risks among diverse communities Urban Affairs Review 2001, 36, 551– 578 DOI: 10.1177/10780870122184993There is no corresponding record for this reference.
- 12Apte, J. S.; Kirchstetter, T. W.; Reich, A. H.; Deshpande, S. J.; Kaushik, G.; Chel, A.; Marshall, J. D.; Nazaroff, W. W. Concentrations of fine, ultrafine, and black carbon particles in auto-rickshaws in New Delhi, India Atmos. Environ. 2011, 45, 4470– 4480 DOI: 10.1016/j.atmosenv.2011.05.02812https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3MXotlCgu7w%253D&md5=87209d4751f23f18d941714ed1a7669cConcentrations of fine, ultrafine, and black carbon particles in auto-rickshaws in New Delhi, IndiaApte, Joshua S.; Kirchstetter, Thomas W.; Reich, Alexander H.; Deshpande, Shyam J.; Kaushik, Geetanjali; Chel, Arvind; Marshall, Julian D.; Nazaroff, William W.Atmospheric Environment (2011), 45 (26), 4470-4480CODEN: AENVEQ; ISSN:1352-2310. (Elsevier Ltd.)Concns. of air pollutants from vehicles are elevated along roadways, indicating that human exposure in transportation microenvironments may not be adequately characterized by centrally located monitors. Results are reported from ∼180 h of real-time measurements of fine particle and black carbon mass concn. (PM2.5, BC) and ultrafine particle no. concn. (PN) inside a common vehicle, the auto-rickshaw, in New Delhi, India. Measured exposure concns. are much higher in this study (geometric mean for ∼60 trip-averaged concns.: 190 μg m-3 PM2.5, 42 μg m-3 BC, 280 × 103 particles cm-3; GSD ∼1.3 for all three pollutants) than reported for transportation microenvironments in other megacities. In-vehicle concns. exceeded simultaneously measured ambient levels by 1.5× for PM2.5, 3.6× for BC, and 8.4× for PN. Short-duration peak concns. (averaging time: 10 s), attributable to exhaust plumes of nearby vehicles, were greater than 300 μg m-3 for PM2.5, 85 μg m-3 for BC, and 650 × 103 particles cm-3 for PN. The incremental increase of within-vehicle concn. above ambient levels-which is attributed to in- and near-roadway emission sources-accounted for 30%, 68% and 86% of time-averaged in-vehicle PM2.5, BC and PN concns., resp. Based on these results, it is established that one's exposure during a daily commute by auto-rickshaw in Delhi is as least as large as full-day exposures experienced by urban residents of many high-income countries. This study illuminates an environmental health concern that may be common in many populous, low-income cities.
- 13Boogaard, H.; Kos, G. P. A.; Weijers, E. P.; Janssen, N. A. H.; Fischer, P. H.; van der Zee, S. C.; de Hartog, J. J.; Hoek, G. Contrast in air pollution components between major streets and background locations: Particulate matter mass, black carbon, elemental composition, nitrogen oxide and ultrafine particle number Atmos. Environ. 2010, 45, 650– 658 DOI: 10.1016/j.atmosenv.2010.10.033There is no corresponding record for this reference.
- 14Jerrett, M.; Arain, A.; Kanaroglou, P.; Beckerman, B.; Potoglou, D.; Sahsuvaroglu, T.; Morrison, J.; Giovis, C. A review and evaluation of intraurban air pollution exposure models J. Exposure Anal. Environ. Epidemiol. 2005, 15, 185– 204 DOI: 10.1038/sj.jea.750038814https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2MXitV2nsLo%253D&md5=0496867b19128403143544dad76fa16dA review and evaluation of intraurban air pollution exposure modelsJerrett, Michael; Arain, Altaf; Kanaroglou, Pavlos; Beckerman, Bernardo; Potoglou, Dimitri; Sahsuvaroglu, Talar; Morrison, Jason; Giovis, ChrisJournal of Exposure Analysis and Environmental Epidemiology (2005), 15 (2), 185-204CODEN: JEAEE9; ISSN:1053-4245. (Nature Publishing Group)A review. The development of models to assess air pollution exposures within cities for assignment to subjects in health studies was identified as a priority area for future research. This paper reviews models for assessing intra-urban exposure under 6 classes, including: (i) proximity-based assessments, (ii) statistical interpolation, (iii) land use regression models, (iv) line dispersion models, (v) integrated emission-meteorol. models, and (vi) hybrid models combining personal or household exposure monitoring with one of the preceding methods. The modeling procedures and results are enriched with applied examples from Hamilton, Canada. In addn., we qual. evaluate the models based on key criteria important to health effects assessment research. Hybrid models appear well suited to overcoming the problem of achieving population representative samples while understanding the role of exposure variation at the individual level. Remote sensing and activity-space anal. will complement refinements in pre-existing methods, and with expected advances, the field of exposure assessment may help to reduce scientific uncertainties that now impede policy intervention aimed at protecting public health.
- 15Hankey, S.; Marshall, J. D. Land use regression models of on-road particulate air pollution (particle number, black carbon, PM2.5, particle size) using mobile monitoring Environ. Sci. Technol. 2015, 49, 9194– 9202 DOI: 10.1021/acs.est.5b0120915https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXhtFSrtLnP&md5=4f4460e84f8c5ed99412e423fae75624Land Use Regression Models of On-Road Particulate Air Pollution (Particle Number, Black Carbon, PM2.5, Particle Size) Using Mobile MonitoringHankey, Steve; Marshall, Julian D.Environmental Science & Technology (2015), 49 (15), 9194-9202CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)Land use regression (LUR) models typically use fixed-site monitoring; this work used mobile monitoring as a cost-effective alternative for LUR model development. Bicycle-based, mobile measurements (∼85 h) during rush-hour in Minneapolis, Minnesota, was used to build LUR models for particulate concns. (particle no. [PN], black carbon [BC], fine particulate matter [PM2.5], particle size). A total of 1224 sep. LUR models were developed and examd. by varying pollutant, time-of-day, and time-series data spatiotemporal smoothing method. Base-case LUR models had modest goodness-of-fit (adjusted R2, ∼0.5 PN; ∼0.4 PM2.5; 0.35 BC; ∼0.25 particle size), low bias (<4%) and abs. bias (2-18%), and included predictor variables which captured proximity to and d. of emission sources. Measurement spatial d. resulted in a large model-building dataset (n = 1101 concn. ests.); ∼25% of buffer variables were selected at spatial scales <100 m, suggesting on-road particle concns. change on small spatial scales. LUR model R2 improved as sampling runs were completed, with diminishing benefits after ∼40 h data collection. Spatial auto-correlation of model residuals indicated the models performed poorly where emission source spatiotemporal resoln. (i.e., traffic congestion) was poor. Results suggested LUR modeling from mobile measurements is possible, but more work could usefully inform best practices.
- 16Reyes, J. M.; Serre, M. L. An LUR/BME framework to estimate PM2.5 explained by on road mobile and stationary sources Environ. Sci. Technol. 2014, 48, 1736– 1744 DOI: 10.1021/es404052816https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXisVaqsg%253D%253D&md5=a13dfd85a5b418ac2f12867af9219191An LUR/BME Framework to Estimate PM2.5 Explained by on Road Mobile and Stationary SourcesReyes, Jeanette M.; Serre, Marc L.Environmental Science & Technology (2014), 48 (3), 1736-1744CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)Knowledge of particulate matter concns. <2.5 μm in diam. (PM2.5) across the United States is limited due to sparse monitoring across space and time. Epidemiol. studies need accurate exposure ests. to properly investigate potential morbidity and mortality. Previous works have used geostatistics and land use regression (LUR) sep. to quantify exposure. This work combines both methods by incorporating a large area variability LUR model that accounts for on road mobile emissions and stationary source emissions along with data that take into account incompleteness of PM2.5 monitors into the modern geostatistical Bayesian Maximum Entropy (BME) framework to est. PM2.5 across the United States from 1999 to 2009. A cross-validation was done to det. the improvement of the est. due to the LUR incorporation into BME. These results were applied to known diseases to det. predicted mortality coming from total PM2.5 as well as PM2.5 explained by major contributing sources. This method showed a mean squared error redn. of over 21.89% oversimple kriging. PM2.5 explained by on road mobile emissions and stationary emissions contributed to nearly 568 090 and 306 316 deaths, resp., across the United States from 1999 to 2007.
- 17Kerckhoffs, J.; Hoek, G.; Messier, K. P.; Brunekreef, B.; Meliefste, K.; Klompmaker, J. O.; Vermeulen, R. Comparison of ultrafine particle and black carbon concentration predictions from a mobile and short-term stationary land-use regression model Environ. Sci. Technol. 2016, 50, 12894– 12902 DOI: 10.1021/acs.est.6b0347617https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC28Xhslyju7bO&md5=8e0e286336f842bda652f98cf77574a3Comparison of Ultrafine Particle and Black Carbon Concentration Predictions from a Mobile and Short-Term Stationary Land-Use Regression ModelKerckhoffs, Jules; Hoek, Gerard; Messier, Kyle P.; Brunekreef, Bert; Meliefste, Kees; Klompmaker, Jochem O.; Vermeulen, RoelEnvironmental Science & Technology (2016), 50 (23), 12894-12902CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)Mobile and short-term monitoring campaigns are increasingly used to develop land use regression (LUR) models for ultra-fine particles (UFP) and black carbon (BC). It is not yet established whether LUR models based on mobile or short-term stationary measurements result in comparable models and concn. predictions. This work compared LUR models based on stationary (30 min) and mobile UFP and BC measurements in a single campaign. An elec. car collected repeated stationary and mobile measurements in Amsterdam and Rotterdam, Netherlands. A total of 2964 road segments and 161 stationary sites were sampled over two seasons. The main comparison was based on predicted concns. of mobile and stationary monitoring LUR models at 12,682 residential addresses in Amsterdam. Predictor variables in the mobile and stationary LUR model were comparable, resulting in highly correlated predictions at external residential addresses (R2 = 0.89 for UFP and 0.88 for BC). Mobile model predictions were, on av., 1.41 and 1.91 times higher than stationary model predictions for UFP and BC, resp. LUR models based on mobile and stationary monitoring predicted highly correlated UFP and BC concn. surfaces; predicted concns. based on mobile measurements were systematically higher.
- 18Snyder, E. G.; Watkins, T. H.; Solomon, P. A.; Thoma, E. D.; Williams, R. W.; Hagler, G. S. W.; Shelow, D.; Hindin, D. A.; Kilaru, V. J.; Preuss, P. W. The changing paradigm of air pollution monitoring Environ. Sci. Technol. 2013, 47, 11369– 11377 DOI: 10.1021/es402260218https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3sXhtlektr3L&md5=04be15e19300cd103b92f730f8d805f3The Changing Paradigm of Air Pollution MonitoringSnyder, Emily G.; Watkins, Timothy H.; Solomon, Paul A.; Thoma, Eben D.; Williams, Ronald W.; Hagler, Gayle S. W.; Shelow, David; Hindin, David A.; Kilaru, Vasu J.; Preuss, Peter W.Environmental Science & Technology (2013), 47 (20), 11369-11377CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)The air pollution monitoring paradigm is rapidly changing due to recent advances in: development of portable, lower-cost air pollution sensors which report data in near-real time at high-time resoln.; increased computational and visualization capabilities; and wireless communication/infrastructure. It is possible these advances can support traditional air quality monitoring by supplementing ambient air monitoring and enhancing compliance monitoring. Sensors are beginning to provide individuals and communities necessary tools to understand their environmental exposure; these individual and community-based data strategies can be developed to reduce pollution exposure and to understand links to health indicators. Topics discussed include: current state of sensor science; supplementing routine ambient air monitoring networks; expanding the conversation with communities and citizens; enhancing source compliance monitoring; monitoring personal exposure; challenges; and opportunities for solns.: a changing role for government.
- 19West, J. J.; Cohen, A.; Dentener, F.; Brunekreef, B.; Zhu, T.; Armstrong, B.; Bell, M. L.; Brauer, M.; Carmichael, G.; Costa, D. L. What we breathe impacts our health: Improving understanding of the link between air pollution and health Environ. Sci. Technol. 2016, 50, 4895– 4904 DOI: 10.1021/acs.est.5b0382719https://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.
- 20Whitby, K. T.; Clark, W. E.; Marple, V. A.; Sverdrup, G. M.; Sem, G. J.; Willeke, K.; Liu, B. Y. H.; Pui, D. Y. H. Characterization of California aerosols--I. Size distributions of freeway aerosol Atmos. Environ. 1975, 9, 463– 482 DOI: 10.1016/0004-6981(75)90107-9There is no corresponding record for this reference.
- 21Westerdahl, D.; Fruin, S.; Sax, T.; Fine, P. M.; Sioutas, C. Mobile platform measurements of ultrafine particles and associated pollutant concentrations on freeways and residential streets in Los Angeles Atmos. Environ. 2005, 39, 3597– 3610 DOI: 10.1016/j.atmosenv.2005.02.03421https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2MXlt1Wms7g%253D&md5=7c710956781e16dff612f20883c4af3eMobile platform measurements of ultrafine particles and associated pollutant concentrations on freeways and residential streets in Los AngelesWesterdahl, Dane; Fruin, Scott; Sax, Todd; Fine, Philip M.; Sioutas, ConstantinosAtmospheric Environment (2005), 39 (20), 3597-3610CODEN: AENVEQ; ISSN:1352-2310. (Elsevier B.V.)Recent health studies have reported that ultrafine particles (UFP) (<0.1 μm in diam.) may be responsible for some of the adverse health effects attributed to particulate matter. In urban areas UFP are produced by combustion sources such as vehicle exhaust, and by secondary formation in the atm. While UFP can be monitored, few studies have explored the impact of local primary sources in urban areas (including mobile sources on freeways) on the temporal and spatial distribution of UFP. This paper describes the integration of multiple monitoring technologies on a mobile platform designed to characterize UFP and assocd. pollutants, and the application of this platform in a study of UFP no. concns. and size distributions in Los Angeles. Monitoring technologies included 2 condensation particle counters (TSI Model 3007 and TSI 3022A) and scanning mobility particle sizers for UFP. Real-time measurements made of NOx (by chemiluminescence), black C (BC) (by light absorption), particulate matter-phase PAH (by UV ionization), and particle length (by diffusional charging) showed high correlations with UFP nos., (r2 = 0.78 for NO, 0.76 for BC, 0.69 for PAH, and 0.88 for particle length). Av. concns. of UFP and related pollutants varied by location, road type, and truck traffic vols., suggesting a relation between these concns. and truck traffic d.
- 22Hudda, N.; Gould, T.; Hartin, K.; Larson, T. V.; Fruin, S. A. Emissions from an international airport increase particle number concentrations 4-fold at 10 km downwind Environ. Sci. Technol. 2014, 48, 6628– 6635 DOI: 10.1021/es500156622https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXoslOrs70%253D&md5=ba41b31e733c463edaf5b8e44bf1a912Emissions from an International Airport Increase Particle Number Concentrations 4-fold at 10 km DownwindHudda, Neelakshi; Gould, Tim; Hartin, Kris; Larson, Timothy V.; Fruin, Scott A.Environmental Science & Technology (2014), 48 (12), 6628-6635CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)This work measured the spatial pattern of particle no. (PN) concns. downwind from the Los Angeles International Airport (LAX) with an instrumented vehicle which enabled coverage of larger areas than allowed by traditional stationary measurements. LAX emissions adversely affected air quality much farther than reported in previous studies. At least a 2-fold increase in PN concns. over un-impacted baseline PN concns. was measured for most daytime hours in an ∼60 km2 area which extended 16 km (10 mi) downwind, and a 4- to 5-fold increase 8-10 km (5-6 mi) downwind. Locations of max. PN concns. were aligned to eastern, downwind jet trajectories during prevailing westerly winds; 8 km downwind concns. exceeded 75,000 particles/cm3, more than the av. freeway PN concn. in Los Angeles. During infrequent northerly winds, the impact area remained large, but shifted to south of the airport. A freeway length which would cause an impact equiv. to that measured in this work (i.e., PN concn. increases weighted by impacted area) was estd. to be 280-790 km. The total freeway length in Los Angeles is 1500 km. Results suggested airport emissions are a major source of PN in Los Angeles and are of the same general magnitude as the entire urban freeway network. They also indicated major airport air quality impact areas may be seriously underestimated.
- 23Larson, T.; Henderson, S. B.; Brauer, M. Mobile monitoring of particle light absorption coefficient in an urban area as a basis for land use regression Environ. Sci. Technol. 2009, 43, 4672– 4678 DOI: 10.1021/es803068e23https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1MXktFemu7Y%253D&md5=2272c625e697e1b1f41abb4c1b3ed6eeMobile Monitoring of Particle Light Absorption Coefficient in an Urban Area as a Basis for Land Use RegressionLarson, Timothy; Henderson, Sarah B.; Brauer, MichaelEnvironmental Science & Technology (2009), 43 (13), 4672-4678CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)Land use regression (LUR) is used to map air pollutant concn. spatial variability for risk assessment, epidemiol., and air quality management. Conventional LUR requires long-term measurements at multiple sites, so application to particulate matter has been limited. Mobile monitoring characterized spatial variability in carbon black concns. for LUR modeling. A particle soot absorption photometer in a moving vehicle measured the absorption coeff. (σap) in summer during peak afternoon traffic at 39 sites. LUR modeled the mean and 25th, 50th, 75th, and 90th percentile values of the distribution of 10-s measurements for each site. Model performance (detd. by R2) was higher for the 25th and 50th percentiles (0.72 and 0.68, resp.) than for the mean, 75th, and 90th percentiles (0.51, 0.55, and 0.54, resp.). Performance was similar to that reported for conventional LUR models of NO2 and NO in this region (116 sites) and better than that for mean σap from fixed-location samplers (25 sites). Models of the mean, 75th, and 90th percentiles favored predictors describing truck, rather than total, traffic. This approach is applicable to other urban areas to facilitate development of LUR models for particulate matter.
- 24Hasenfratz, D.; Saukh, O.; Walser, C.; Hueglin, C.; Fierz, M.; Arn, T.; Beutel, J.; Thiele, L. Deriving high-resolution urban air pollution maps using mobile sensor nodes Pervasive and Mobile Computing 2015, 16 (Part B) 268– 285 DOI: 10.1016/j.pmcj.2014.11.008There is no corresponding record for this reference.
- 25Bukowiecki, N.; Dommen, J.; Prevot, A. S. H.; Richter, R.; Weingartner, E.; Baltensperger, U. A mobile pollutant measurement laboratory-measuring gas phase and aerosol ambient concentrations with high spatial and temporal resolution Atmos. Environ. 2002, 36, 5569– 5579 DOI: 10.1016/S1352-2310(02)00694-5There is no corresponding record for this reference.
- 26Pirjola, L.; Parviainen, H.; Hussein, T.; Valli, A.; Hameri, K.; Aaalto, P.; Virtanen, A.; Keskinen, J.; Pakkanen, T. A.; Makela, T. ″Sniffer″ - a novel tool for chasing vehicles and measuring traffic pollutants Atmos. Environ. 2004, 38, 3625– 3635 DOI: 10.1016/j.atmosenv.2004.03.047There is no corresponding record for this reference.
- 27Kolb, C.; Herndon, S. C.; McManus, J. B.; Shorter, J. H.; Zahniser, M. S.; Nelson, D. D.; Jayne, J. T.; Canagaratna, M. R.; Worsnop, D. R. Mobile laboratory with rapid response instruments for real-time measurement of urban and regional trace gas and particulate distributions and emissions source characteristics Environ. Sci. Technol. 2004, 38, 5694– 5703 DOI: 10.1021/es030718pThere is no corresponding record for this reference.
- 28Brantley, H. L.; Hagler, G. S. W.; Kimbrough, E. S.; Williams, R. W.; Mukerjee, S.; Neas, L. M. Mobile air monitoring data-processing strategies and effects on spatial air pollution trends Atmos. Meas. Tech. 2014, 7, 2169– 2183 DOI: 10.5194/amt-7-2169-2014There is no corresponding record for this reference.
- 29Hagemann, R.; Corsmeier, U.; Kottmeier, C.; Rinke, R.; Wieser, A.; Vogel, B. Spatial variability of particle number concentrations and NOx in the Karlsruhe (Germany) area obtained with the mobile laboratory ‘AERO-TRAM’ Atmos. Environ. 2014, 94, 341– 352 DOI: 10.1016/j.atmosenv.2014.05.05129https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXhtFehtrfP&md5=c48d3e76d6180add350d3eb5523b9b32Spatial variability of particle number concentrations and NOx in the Karlsruhe (Germany) area obtained with the mobile laboratory 'AERO-TRAM'Hagemann, Rowell; Corsmeier, Ulrich; Kottmeier, Christoph; Rinke, Rayk; Wieser, Andreas; Vogel, BernhardAtmospheric Environment (2014), 94 (), 341-352CODEN: AENVEQ; ISSN:1352-2310. (Elsevier Ltd.)For the first time in Germany, we obtained high-resoln. spatial distributions of particle nos. and nitrogen oxides in an urban agglomeration using a tram system. In comparison to particle nos. the NOx concn. decreased much faster with a significantly steeper gradient when going from the inner city to the surrounding area. In case of NOx the decrease was 70% while for particle no. concn. it was only 50%. We found an area in the rural surrounding with a second increase of particle nos. without simultaneous enhanced NOx levels. The source of the high particle nos. could be ascribed to industry emissions about 5-10 km away. The mean spatial distribution of particle no. concn. depended on wind direction, wind velocity and boundary layer stability. The dependency was particularly strong in the rural area affected by industrial emissions, where individual wind directions led to concn. differences of up to 25%. The particulate concn. was 40% higher during low wind velocities (1-5 m s-1) than during high wind velocities (>5 m s-1). We obsd. similar findings for the impact of boundary layer stability on particle nos. concn. Particle pollution was 40% higher for stable stratification compared to neutral or unstable cases.
- 30Van den Bossche, J.; Peters, J.; Verwaeren, J.; Botteldooren, D.; Theunis, J.; De Baets, B. Mobile monitoring for mapping spatial variation in urban air quality: Development and validation of a methodology based on an extensive dataset Atmos. Environ. 2015, 105, 148– 161 DOI: 10.1016/j.atmosenv.2015.01.01730https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXmtFWrtA%253D%253D&md5=57ec6fa6edf6c110e23bb2fe9c35cc0bMobile monitoring for mapping spatial variation in urban air quality: Development and validation of a methodology based on an extensive datasetVan den Bossche, Joris; Peters, Jan; Verwaeren, Jan; Botteldooren, Dick; Theunis, Jan; De Baets, BernardAtmospheric Environment (2015), 105 (), 148-161CODEN: AENVEQ; ISSN:1352-2310. (Elsevier Ltd.)Mobile monitoring is increasingly used as an addnl. tool to acquire air quality data at a high spatial resoln. However, given the high temporal variability of urban air quality, a limited no. of mobile measurements may only represent a snapshot and not be representative. In this study, the impact of this temporal variability on the representativeness is investigated and a methodol. to map urban air quality using mobile monitoring is developed and evaluated.A large set of black carbon (BC) measurements was collected in Antwerp, Belgium, using a bicycle equipped with a portable BC monitor (micro-aethalometer). The campaign consisted of 256 and 96 runs along two fixed routes (2 and 5 km long). Large gradients over short distances and differences up to a factor of 10 in mean BC concns. aggregated at a resoln. of 20 m are obsd. Mapping at such a high resoln. is possible, but a lot of repeated measurements are required. After computing a trimmed mean and applying background normalization, depending on the location 24-94 repeated measurement runs (median of 41) are required to map the BC concns. at a 50 m resoln. with an uncertainty of 25%. When relaxing the uncertainty to 50%, these nos. reduce to 5-11 (median of 8) runs. We conclude that mobile monitoring is a suitable approach for mapping the urban air quality at a high spatial resoln., and can provide insight into the spatial variability that would not be possible with stationary monitors. A careful set-up is needed with a sufficient no. of repetitions in relation to the desired reliability and spatial resoln. Specific data processing methods such as background normalization and event detection have to be applied.
- 31Peters, J.; Theunis, J.; Van Poppel, M.; Berghmans, P. Monitoring PM10 and ultrafine particles in urban environments using mobile measurements Aerosol Air Qual. Res. 2013, 13, 509– 522 DOI: 10.4209/aaqr.2012.06.0152There is no corresponding record for this reference.
- 32Efron, B. Bootstrap methods: another look at the jackknife Ann. Stat. 1979, 7, 1– 26 DOI: 10.1214/aos/1176344552There is no corresponding record for this reference.
- 33Dons, E.; Int Panis, L.; Van Poppel, M.; Theunis, J.; Wets, G. Personal exposure to black carbon in transport microenvironments Atmos. Environ. 2012, 55, 392– 398 DOI: 10.1016/j.atmosenv.2012.03.02033https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC38Xot1ejt7g%253D&md5=bb216b05539090ae7a11ed46d88287bfPersonal exposure to Black Carbon in transport microenvironmentsDons, Evi; Int Panis, Luc; Van Poppel, Martine; Theunis, Jan; Wets, GeertAtmospheric Environment (2012), 55 (), 392-398CODEN: AENVEQ; ISSN:1352-2310. (Elsevier Ltd.)We evaluated personal exposure of 62 individuals to the air pollutant Black Carbon, using 13 portable aethalometers while keeping detailed records of their time-activity pattern and whereabouts. Concns. encountered in transport are studied in depth and related to trip motives. The evaluation comprises more than 1500 trips with different transport modes. Measurements were spread over two seasons. Results show that 6% of the time is spent in transport, but it accounts for 21% of personal exposure to Black Carbon and approx. 30% of inhaled dose. Concns. in transport were 2-5 times higher compared to concns. encountered at home. Exposure was highest for car drivers, and car and bus passengers. Concns. of Black Carbon were only half as much when traveling by bike or on foot; when incorporating breathing rates, dose was found to be twice as high for active modes. Lowest in transport' concns. were measured in trains, but nevertheless these concns. are double the concns. measured at home. Two thirds of the trips are car trips, and those trips showed a large spread in concns. In-car concns. are higher during peak hours compared to off-peak, and are elevated on weekdays compared to Saturdays and even more so on Sundays. These findings result in significantly higher exposure during car commute trips (motive Work'), and lower concns. for trips with motive Social and leisure'. Because of the many factors influencing exposure in transport, travel time is not a good predictor of integrated personal exposure or inhaled dose.
- 34Zhou, Y.; Levy, J. I. Factors influencing the spatial extent of mobile source air pollution impacts: a meta-analysis BMC Public Health 2007, 7, 89 DOI: 10.1186/1471-2458-7-8934https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BD2szksV2itQ%253D%253D&md5=dd3d92c929cbb36909dd1c04a8d7b900Factors influencing the spatial extent of mobile source air pollution impacts: a meta-analysisZhou Ying; Levy Jonathan IBMC public health (2007), 7 (), 89 ISSN:.BACKGROUND: There has been growing interest among exposure assessors, epidemiologists, and policymakers in the concept of "hot spots", or more broadly, the "spatial extent" of impacts from traffic-related air pollutants. This review attempts to quantitatively synthesize findings about the spatial extent under various circumstances. METHODS: We include both the peer-reviewed literature and government reports, and focus on four significant air pollutants: carbon monoxide, benzene, nitrogen oxides, and particulate matter (including both ultrafine particle counts and fine particle mass). From the identified studies, we extracted information about significant factors that would be hypothesized to influence the spatial extent within the study, such as the study type (e.g., monitoring, air dispersion modeling, GIS-based epidemiological studies), focus on concentrations or health risks, pollutant under study, background concentration, emission rate, and meteorological factors, as well as the study's implicit or explicit definition of spatial extent. We supplement this meta-analysis with results from some illustrative atmospheric dispersion modeling. RESULTS: We found that pollutant characteristics and background concentrations best explained variability in previously published spatial extent estimates, with a modifying influence of local meteorology, once some extreme values based on health risk estimates were removed from the analysis. As hypothesized, inert pollutants with high background concentrations had the largest spatial extent (often demonstrating no significant gradient), and pollutants formed in near-source chemical reactions (e.g., nitrogen dioxide) had a larger spatial extent than pollutants depleted in near-source chemical reactions or removed through coagulation processes (e.g., nitrogen oxide and ultrafine particles). Our illustrative dispersion model illustrated the complex interplay of spatial extent definitions, emission rates, background concentrations, and meteorological conditions on spatial extent estimates even for non-reactive pollutants. Our findings indicate that, provided that a health risk threshold is not imposed, the spatial extent of impact for mobile sources reviewed in this study is on the order of 100-400 m for elemental carbon or particulate matter mass concentration (excluding background concentration), 200-500 m for nitrogen dioxide and 100-300 m for ultrafine particle counts. CONCLUSION: First, to allow for meaningful comparisons across studies, it is important to state the definition of spatial extent explicitly, including the comparison method, threshold values, and whether background concentration is included. Second, the observation that the spatial extent is generally within a few hundred meters for highway or city roads demonstrates the need for high resolution modeling near the source. Finally, our findings emphasize that policymakers should be able to develop reasonable estimates of the "zone of influence" of mobile sources, provided that they can clarify the pollutant of concern, the general site characteristics, and the underlying definition of spatial extent that they wish to utilize.
- 35Zhu, Y.; Hinds, W. C.; Kim, S.; Sioutas, C. Concentration and size distribution of ultrafine particles near a major highway J. Air Waste Manage. Assoc. 2002, 52, 1032– 1042 DOI: 10.1080/10473289.2002.1047084235https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BD38votlyhtA%253D%253D&md5=94f97350aaf59e808cd7770bbe874111Concentration and size distribution of ultrafine particles near a major highwayZhu Yifang; Hinds William C; Kim Seongheon; Sioutas ConstantinosJournal of the Air & Waste Management Association (1995) (2002), 52 (9), 1032-42 ISSN:1096-2247.Motor vehicle emissions usually constitute the most significant source of ultrafine particles (diameter <0.1 microm) in an urban environment, yet little is known about the concentration and size distribution of ultrafine particles in the vicinity of major highways. In the present study, particle number concentration and size distribution in the size range from 6 to 220 nm were measured by a condensation particle counter (CPC) and a scanning mobility particle sizer (SMPS), respectively. Measurements were taken 30, 60, 90, 150, and 300 m downwind, and 300 m upwind, from Interstate 405 at the Los Angeles National Cemetery. At each sampling location, concentrations of CO, black carbon (BC), and particle mass were also measured by a Dasibi CO monitor, an aethalometer, and a DataRam, respectively. The range of average concentration of CO, BC, total particle number, and mass concentration at 30 m was 1.7-2.2 ppm, 3.4-10.0 microg/m3, 1.3-2.0 x 10(5)/cm3, and 30.2-64.6 microg/m3, respectively. For the conditions of these measurements, relative concentrations of CO, BC, and particle number tracked each other well as distance from the freeway increased. Particle number concentration (6-220 nm) decreased exponentially with downwind distance from the freeway. Data showed that both atmospheric dispersion and coagulation contributed to the rapid decrease in particle number concentration and change in particle size distribution with increasing distance from the freeway. Average traffic flow during the sampling periods was 13,900 vehicles/hr. Ninety-three percent of vehicles were gasoline-powered cars or light trucks. The measured number concentration tracked traffic flow well. Thirty meters downwind from the freeway, three distinct ultrafine modes were observed with geometric mean diameters of 13, 27, and 65 nm. The smallest mode, with a peak concentration of 1.6 x 10(5)/cm3, disappeared at distances greater than 90 m from the freeway. Ultrafine particle number concentration measured 300 m downwind from the freeway was indistinguishable from upwind background concentration. These data may be used to estimate exposure to ultrafine particles in the vicinity of major highways.
- 36Both, A. F.; Balakrishnan, A.; Joseph, B.; Marshall, J. D. Spatiotemporal aspects of real-time PM2.5: Low- and middle-income neighborhoods in Bangalore, India Environ. Sci. Technol. 2011, 45, 5629– 5636 DOI: 10.1021/es104331wThere is no corresponding record for this reference.
- 37Watson, J. G.; Chow, J. C. Estimating middle-, neighborhood-, and urban-scale contributions to elemental carbon in Mexico City with a rapid response aethalometer J. Air Waste Manage. Assoc. 2001, 51, 1522– 1528 DOI: 10.1080/10473289.2001.1046437937https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD38Xks1KnsQ%253D%253D&md5=21da24f0a76a9af744f4fa06c5ab3630Estimating middle-, neighborhood-, and urban-scale contributions to elemental carbon in Mexico City with a rapid response aethalometerWatson, John G.; Chow, Judith C.Journal of the Air & Waste Management Association (2001), 51 (11), 1522-1528CODEN: JAWAFC; ISSN:1096-2247. (Air & Waste Management Association)A successive moving av. subtraction method was developed and applied to black carbon measured over 5-min intervals at a downtown site near many small emitters and at a suburban residential site within the urban plume, but distant from specific emitters. Short-duration pulses assumed to originate from nearby sources were subtracted from concns. at each site and were summed to est. middle-scale (0.1-1 km) contributions. The difference of the remaining baselines at urban and suburban monitors was interpreted as the contribution to the downtown monitor from source emissions mixed over a neighborhood scale (1-5 km). Baseline at the suburban site was interpreted as the contribution of the mixt. of black carbon sources for the entire city. When applied to 24-day periods from Feb. and Mar. 1997 in Mexico City, the anal. showed 65% of the 24-h black carbon was part of the urban mixt.; 23% originated in the neighborhood surrounding the monitor and 12% was contributed from nearby sources. Results indicate a fixed-site monitor can reasonably represent exposure in the surrounding neighborhood, even when many local sources, e.g., diesel vehicle exhaust, affects the monitor.
- 38Seinfeld, J. H.; Pandis, S. N. Atmospheric Chemistry and Physics: From Air Pollution to Climate Change, 2nd ed.; Wiley-Interscience: Hoboken, NJ, 2006.There is no corresponding record for this reference.
- 39Dallmann, T. R.; Harley, R. A.; Kirchstetter, T. W. Effects of diesel particle filter retrofits and accelerated fleet turnover on drayage truck emissions at the Port of Oakland Environ. Sci. Technol. 2011, 45, 10773– 10779 DOI: 10.1021/es202609q39https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3MXhtlyltL7O&md5=fb48254e37adb98cb05613b0599be899Effects of Diesel Particle Filter Retrofits and Accelerated Fleet Turnover on Drayage Truck Emissions at the Port of OaklandDallmann, Timothy R.; Harley, Robert A.; Kirchstetter, Thomas W.Environmental Science & Technology (2011), 45 (24), 10773-10779CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)Heavy-duty diesel drayage trucks have a disproportionate impact on the air quality of communities surrounding major freight-handling facilities. In an attempt to mitigate this impact, the state of California has mandated new emission control requirements for drayage trucks accessing ports and rail yards in the state beginning in 2010. This control rule prompted an accelerated diesel particle filter (DPF) retrofit and truck replacement program at the Port of Oakland. The impact of this program was evaluated by measuring emission factor distributions for diesel trucks operating at the Port of Oakland prior to and following the implementation of the emission control rule. Emission factors for black carbon (BC) and oxides of nitrogen (NOx) were quantified in terms of grams of pollutant emitted per kg of fuel burned using a carbon balance method. Concns. of these species along with carbon dioxide were measured in the exhaust plumes of individual diesel trucks as they drove by en route to the Port. A comparison of emissions measured before and after the implementation of the truck retrofit/replacement rule shows a 54 ± 11% redn. in the fleet-av. BC emission factor, accompanied by a shift to a more highly skewed emission factor distribution. Although only particulate matter mass redns. were required in the first year of the program, a significant redn. in the fleet-av. NOx emission factor (41 ± 5%) was obsd., most likely due to the replacement of older trucks with new ones.
- 40Jenkin, M. E. Analysis of sources and partitioning of oxidant in the UK—Part 2: contributions of nitrogen dioxide emissions and background ozone at a kerbside location in London Atmos. Environ. 2004, 38, 5131– 5138 DOI: 10.1016/j.atmosenv.2004.05.055There is no corresponding record for this reference.
- 41Riley, E. A.; Schaal, L.; Sasakura, M.; Crampton, R.; Gould, T. R.; Hartin, K.; Sheppard, L.; Larson, T.; Simpson, C. D.; Yost, M. G. Correlations between short-term mobile monitoring and long-term passive sampler measurements of traffic-related air pollution Atmos. Environ. 2016, 132, 229– 239 DOI: 10.1016/j.atmosenv.2016.03.00141https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC28XktVyjtrk%253D&md5=595bd2a2aa3520becfd5caf66b17c2beCorrelations between short-term mobile monitoring and long-term passive sampler measurements of traffic-related air pollutionRiley, Erin A.; Schaal, LaNae; Sasakura, Miyoko; Crampton, Robert; Gould, Timothy R.; Hartin, Kris; Sheppard, Lianne; Larson, Timothy; Simpson, Christopher D.; Yost, Michael G.Atmospheric Environment (2016), 132 (), 229-239CODEN: AENVEQ; ISSN:1352-2310. (Elsevier Ltd.)Mobile monitoring has provided a means for broad spatial measurements of air pollutants that are otherwise impractical to measure with multiple fixed site sampling strategies. However, the larger the mobile monitoring route the less temporally dense measurements become, which may limit the usefulness of short-term mobile monitoring for applications that require long-term avs. To investigate the stationarity of short-term mobile monitoring measurements, we calcd. long term medians derived from a mobile monitoring campaign that also employed 2-wk integrated passive sampler detectors (PSD) for NOx, Ozone, and nine volatile org. compds. at 43 intersections distributed across the entire city of Baltimore, MD. This is one of the largest mobile monitoring campaigns in terms of spatial extent undertaken at this time. The mobile platform made repeat measurements every third day at each intersection for 6-10 min at a resoln. of 10 s. In two-week periods in both summer and winter seasons, each site was visited 3-4 times, and a temporal adjustment was applied to each dataset. We present the correlations between eight species measured using mobile monitoring and the 2-wk PSD data and observe correlations between mobile NOx measurements and PSD NOx measurements in both summer and winter (Pearson's r = 0.84 and 0.48, resp.). The summer season exhibited the strongest correlations between multiple pollutants, whereas the winter had comparatively few statistically significant correlations. In the summer CO was correlated with PSD pentanes (r = 0.81), and PSD NOx was correlated with mobile measurements of black carbon (r = 0.83), two ultrafine particle count measures (r = 0.8), and intermodal (1-3 μm) particle counts (r = 0.73). Principal Component Anal. of the combined PSD and mobile monitoring data revealed multipollutant features consistent with light duty vehicle traffic, diesel exhaust and crankcase blow by. These features were more consistent with published source profiles of traffic-related air pollutants than features based on the PSD data alone. Short-term mobile monitoring shows promise for capturing long-term spatial patterns of traffic-related air pollution, and is complementary to PSD sampling strategies.
- 42Hu, S.; Fruin, S.; Kozawa, K.; Mara, S.; Paulson, S. E.; Winer, A. M. A wide area of air pollutant impact downwind of a freeway during pre-sunrise hours Atmos. Environ. 2009, 43, 2541– 2549 DOI: 10.1016/j.atmosenv.2009.02.03342https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1MXks1ygurc%253D&md5=af0ed9dd4bfdf88b7756281991965ab6A wide area of air pollutant impact downwind of a freeway during pre-sunrise hoursHu, Shishan; Fruin, Scott; Kozawa, Kathleen; Mara, Steve; Paulson, Suzanne E.; Winer, Arthur M.Atmospheric Environment (2009), 43 (16), 2541-2549CODEN: AENVEQ; ISSN:1352-2310. (Elsevier Ltd.)We have obsd. a wide area of air pollutant impact downwind of a freeway during pre-sunrise hours in both winter and summer seasons. In contrast, previous studies have shown much sharper air pollutant gradients downwind of freeways, with levels above background concns. extending only 300 m downwind of roadways during the day and up to 500 m at night. In this study, real-time air pollutant concns. were measured along a 3600 m transect normal to an elevated freeway 1-2 h before sunrise using an elec. vehicle mobile platform equipped with fast-response instruments. In winter pre-sunrise hours, the peak ultrafine particle (UFP) concn. (∼95 000 cm-3) occurred immediately downwind of the freeway. However, downwind UFP concns. as high as ∼40 000 cm-3 extended at least 1200 m from the freeway, and did not reach background levels (∼15 000 cm-3) until a distance of about 2600 m. UFP concns. were also elevated over background levels up to 600 m upwind of the freeway. Other pollutants, such as NO and particle-bound polycyclic arom. hydrocarbons, exhibited similar long-distance downwind concn. gradients. In contrast, air pollutant concns. measured on the same route after sunrise, in the morning and afternoon, exhibited the typical daytime downwind decrease to background levels within ∼300 m as found in earlier studies. Although pre-sunrise traffic vols. on the freeway were much lower than daytime congestion peaks, downwind UFP concns. were significantly higher during pre-sunrise hours than during the daytime. UFP and NO concns. were also strongly correlated with traffic counts on the freeway. We assoc. these elevated pre-sunrise concns. over a wide area with a nocturnal surface temp. inversion, low wind speeds, and high relative humidity. Observation of such wide air pollutant impact area downwind of a major roadway prior to sunrise has important exposure assessment implications since it demonstrates extensive roadway impacts on residential areas during pre-sunrise hours, when most people are at home.
- 43Brinkhoff, T. Major agglomerations of the world. http://citypopulation.de/world/Agglomerations.html 2016, (accessed February 14, 2017).There is no corresponding record for this reference.
- 44Zeger, S. L.; Thomas, D.; Dominici, F.; Samet, J. M.; Schwartz, J.; Dockery, D.; Cohen, A. Exposure measurement error in time-series studies of air pollution: concepts and consequences Environ. Health Persp. 2000, 108, 419– 426 DOI: 10.1289/ehp.00108419There is no corresponding record for this reference.
- 45Sheppard, L.; Burnett, R. T.; Szpiro, A. A.; Kim, S.-Y.; Jerrett, M.; Pope, C. A.; Brunekreef, B. Confounding and exposure measurement error in air pollution epidemiology Air Qual., Atmos. Health 2012, 5, 203– 216 DOI: 10.1007/s11869-011-0140-945https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BC2srmslSrtQ%253D%253D&md5=d90a61b600c6cd21d96484a718aa7c5cConfounding and exposure measurement error in air pollution epidemiologySheppard Lianne; Burnett Richard T; Szpiro Adam A; Kim Sun-Young; Jerrett Michael; Pope C Arden 3rd; Brunekreef BertAir quality, atmosphere, & health (2012), 5 (2), 203-216 ISSN:1873-9318.Studies in air pollution epidemiology may suffer from some specific forms of confounding and exposure measurement error. This contribution discusses these, mostly in the framework of cohort studies. Evaluation of potential confounding is critical in studies of the health effects of air pollution. The association between long-term exposure to ambient air pollution and mortality has been investigated using cohort studies in which subjects are followed over time with respect to their vital status. In such studies, control for individual-level confounders such as smoking is important, as is control for area-level confounders such as neighborhood socio-economic status. In addition, there may be spatial dependencies in the survival data that need to be addressed. These issues are illustrated using the American Cancer Society Cancer Prevention II cohort. Exposure measurement error is a challenge in epidemiology because inference about health effects can be incorrect when the measured or predicted exposure used in the analysis is different from the underlying true exposure. Air pollution epidemiology rarely if ever uses personal measurements of exposure for reasons of cost and feasibility. Exposure measurement error in air pollution epidemiology comes in various dominant forms, which are different for time-series and cohort studies. The challenges are reviewed and a number of suggested solutions are discussed for both study domains.
- 46de Nazelle, A.; Seto, E.; Donaire-Gonzalez, D.; Mendez, M.; Matamala, J.; Nieuwenhuijsen, M. J.; Jerrett, M. Improving estimates of air pollution exposure through ubiquitous sensing technologies Environ. Pollut. 2013, 176, 92– 99 DOI: 10.1016/j.envpol.2012.12.03246https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3sXktFWntb8%253D&md5=7ab98c38bc3da1c2fecc5e5553574d92Improving estimates of air pollution exposure through ubiquitous sensing technologiesde Nazelle, Audrey; Seto, Edmund; Donaire-Gonzalez, David; Mendez, Michelle; Matamala, Jaume; Nieuwenhuijsen, Mark J.; Jerrett, MichaelEnvironmental Pollution (Oxford, United Kingdom) (2013), 176 (), 92-99CODEN: ENPOEK; ISSN:0269-7491. (Elsevier Ltd.)Traditional methods of exposure assessment in epidemiol. studies often fail to integrate important information on activity patterns, which may lead to bias, loss of statistical power, or both in health effects ests. Novel sensing technologies integrated with mobile phones offer potential to reduce exposure measurement error. We sought to demonstrate the usability and relevance of the CalFit smartphone technol. to track person-level time, geog. location, and phys. activity patterns for improved air pollution exposure assessment. We deployed CalFit-equipped smartphones in a free-living population of 36 subjects in Barcelona, Spain. Information obtained on phys. activity and geog. location was linked to space-time air pollution mapping. We found that information from CalFit could substantially alter exposure ests. For instance, on av. travel activities accounted for 6% of time and 24% of their daily inhaled NO2. Due to the large no. of mobile phone users, this technol. potentially provides an unobtrusive means of enhancing epidemiol. exposure data at low cost.
- 47Nyhan, M.; Grauwin, S.; Britter, R.; Misstear, B.; McNabola, A.; Laden, F.; Barrett, S. R. H.; Ratti, C. “Exposure Track”—the impact of mobile-device-based mobility patterns on quantifying population exposure to air pollution Environ. Sci. Technol. 2016, 50, 9671– 9681 DOI: 10.1021/acs.est.6b0238547https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC28XhtlaktbvF&md5=5d34bf4e28f0d8ce3a9ba21b8f01fdce"Exposure Track"-The Impact of Mobile-Device-Based Mobility Patterns on Quantifying Population Exposure to Air PollutionNyhan, Marguerite; Grauwin, Sebastian; Britter, Rex; Misstear, Bruce; McNabola, Aonghus; Laden, Francine; Barrett, Steven R. H.; Ratti, CarloEnvironmental Science & Technology (2016), 50 (17), 9671-9681CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)Air pollution is recognized as the single largest environmental and human health threat. Many environmental epidemiol. studies have quantified the health impacts of population exposure to pollution. In previous studies, exposure ests. at the population level have not considered spatial and temporal varying populations in the study regions. Thus, in the first study of it is kind, measured population activity patterns representing several million people were used to evaluate population-weighted exposure to air pollution on a city-wide scale. Mobile and wireless devices yielded information concerning where and when people are present, these collective activity patterns were detd. using counts of connections to cellular networks. Population-weighted exposure to PM2.5 in New York City (NYC), i.e., Active Population Exposure, was evaluated using population activity patterns and spatiotemporal PM2.5 concns. vs. Home Population Exposure, which assumed a static population distribution using Census data. Areas of relatively higher population-weighted exposure were concd. in different districts within NYC in both scenarios, but were more centralized for the Active Population Exposure scenario. Population-weighted exposure computed in each NYC district for the Active scenario were statistically significantly (p <0.05) different from the Home scenario for most districts. Temporal variability of the Active population-weighted exposure detd. in districts were significantly different (p <0.05) during the day and at night. Evaluating population exposure to air pollution using spatiotemporal population mobility patterns warrants consideration in future environmental epidemiol. studies linking air quality and human health.
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