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Daily Estimation of Ground-Level PM2.5 Concentrations over Beijing Using 3 km Resolution MODIS AOD
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† Ministry of Education Key Laboratory for Earth System Modeling, Center for Earth System Science, Tsinghua University, Beijing 100084, China
‡ Department of Marine Science, Texas A&M University at Galveston, Galveston, Texas 77553, United States
§ Department of Atmospheric Science, Texas A&M University, College Station, Texas 77853, United States
∥ Department of Epidemiology, Human Genetics and Environmental Sciences, University of Texas School of Public Health, Houston, Texas 77030, United States
*Phone: 86-13701032461. E-mail: [email protected]
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Environmental Science & Technology

Cite this: Environ. Sci. Technol. 2015, 49, 20, 12280–12288
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https://doi.org/10.1021/acs.est.5b01413
Published August 27, 2015

Copyright © 2015 American Chemical Society. This publication is licensed under these Terms of Use.

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Abstract

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Estimating exposures to PM2.5 within urban areas requires surface PM2.5 concentrations at high temporal and spatial resolutions. We developed a mixed effects model to derive daily estimations of surface PM2.5 levels in Beijing, using the 3 km resolution satellite aerosol optical depth (AOD) calibrated daily by the newly available high-density surface measurements. The mixed effects model accounts for daily variations of AOD-PM2.5 relationships and shows good performance in model predictions (R2 of 0.81–0.83) and cross-validations (R2 of 0.75–0.79). Satellite derived population-weighted mean PM2.5 for Beijing was 51.2 μg/m3 over the study period (Mar 2013 to Apr 2014), 46% higher than China’s annual-mean PM2.5 standard of 35 μg/m3. We estimated that more than 19.2 million people (98% of Beijing’s population) are exposed to harmful level of long-term PM2.5 pollution. During 25% of the days with model data, the population-weighted mean PM2.5 exceeded China’s daily PM2.5 standard of 75 μg/m3. Predicted high-resolution daily PM2.5 maps are useful to identify pollution “hot spots” and estimate short- and long-term exposure. We further demonstrated that a good calibration of the satellite data requires a relatively large number of ground-level PM2.5 monitoring sites and more are still needed in Beijing.

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Introduction

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Particulate matter (PM) air pollution in China is a major public health problem. According to the Global Burden of Disease report, ambient PM air pollution is responsible for over 1.2 million premature deaths annually in China, and 50% of its 1.3 billion population is currently exposed to ambient PM2.5 (particulate matter with aerodynamic diameters of less than 2.5 μm) in exceedance of an annual average of 35 μg/m3 (China’s National Ambient Air Quality Standard, or NAAQS, available at http://kjs.mep.gov.cn/). (1) In recent years, extremely high PM2.5 concentrations have been reported over many urban areas in China, notably in Beijing. (2-5) Some of the worst pollution episodes saw PM2.5 concentrations exceeding 500 μg/m3 and lasting multiple days. (6, 7) Accurate assessment of population exposure to such elevated levels of PM2.5 over densely populated urban areas requires surface PM2.5 concentrations at high temporal and spatial resolutions, but ground-level monitors have been sparse in China which hinders our ability to accurately assess the health impact of PM2.5 pollution.
Satellite retrievals of AOD provide larger spatial coverage and have been widely employed as a proxy to infer surface PM2.5 concentrations. (8-13) Early studies obtained the AOD-PM2.5 relationships using simple linear regression models or chemical transport models (CTM) at a global or continental scale, including the United States (US) and Europe. (10, 11, 14-17) Those studies reported R2, a measure of goodness of fit of linear regression, generally between 0.3 and 0.6 and the AOD-PM2.5 relationship was typically assumed to be constant in time and location. (18) Advanced statistical models (e.g., generalized linear model, land use regression model, geographically weighted regression, and generalized additive model) have been applied in order to account for variability in the AOD-PM2.5 relationships associated with meteorology or land use. (13, 19-22) These advanced models yielded higher prediction performance (R2 at 0.6–0.8) for large regions such as the northeastern and southeastern US and China, but the increased complexity of these models requires additional data as inputs. Lee et al. (23) first introduced an AOD daily calibration approach using a mixed effects model to account for day-to-day and location-specific variability in the AOD-PM2.5 relationships over New England. They demonstrated that daily adjustment of the AOD-PM2.5 relationships leads to a significant improvement in model performance with R2 reaching 0.92, as compared to the linear regression (R2 ∼ 0.51) applied to the same region. The mixed effects model provides a valuable tool for estimating PM2.5 concentrations in urban areas where ground monitoring sites are not of sufficient density.
Dense surface networks and high-resolution satellite AOD data are essential for improving the accuracy of exposure assessment models. Since Mar 2013, 35 PM2.5 monitoring sites have been deployed in Beijing Municipality by Beijing Environmental Protection Bureau, providing hourly PM2.5 concentrations at the urban and suburban areas of Beijing. In early 2014, the Moderate Resolution Imaging Spectroradiometer (MODIS) team released the new collection 6 (C006) AOD products at a 3 km resolution. (24) Prior standard AOD products from MODIS have a spatial resolution of 10 km or coarser. Most of the previous studies applying the MODIS AOD to derive surface PM2.5 concentrations over China focused on the regional scale with resolutions ranging from 10 km to 100 km. (12, 13, 22) Utilizing the newly available surface monitoring data in Beijing, this study is the first attempt to develop a mixed effects model to estimate ground-level PM2.5 concentrations using the 3 km resolution AOD data on a daily basis for a heavily polluted urban environment in China. The severe pollution events offer a valuable opportunity to assess the performance of the daily AOD calibration approach through this mixed effects model. The paper is organized as follows: surface and satellite data as well as model development are introduced in the “Materials and Methods” section; the model performance, PM2.5 prediction results, and uncertainties are provided in the “Results and Discussion” section.

Materials and Methods

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Ground-Level PM2.5 Data

Hourly PM2.5 concentrations observed at 35 monitoring stations in Beijing (Figure 1b) were obtained from Beijing Municipal Environmental Monitoring Center (http://zx.bjmemc.com.cn/). Surface PM2.5 mass concentrations are measured by the Tapered Element Oscillating Microbalance (TEOM) method, and the measurements have undergone the calibration processes and quality controls according to the environmental protection standard of China (HJ 618-2011; MEPCN). (25) Beijing Municipality consists of 16 districts with six urban districts located in the center of the city and ten suburban districts surrounding the urban regions (Figure 1c). Among the 35 PM2.5 monitors, 17 are located in the urban districts and 18 in suburban districts. We averaged the surface concentrations measured between 13:00 pm and 14:00 pm local time at each site to derive the daily PM2.5 concentrations that match with the overpassing time of the Aqua satellite. The study period is from March 26th 2013 to April 23th 2014, spanning a total of 394 days.

Figure 1

Figure 1. Temporal and spatial variations of PM2.5 and AOD: (a) Daily time series of the mean PM2.5 concentrations of the 35 monitoring sites and mean site-collocated AOD from the MODIS 3 km product (correlation coefficients r between different portions of the two time series are indicated); (b) Mean PM2.5 concentrations at each site (sites numbered and marked) averaged over the study period; (c) Distribution of the 3 km resolution MODIS AOD averaged during the study period, with the boundaries of 16 districts of Beijing shown in black lines (district names in black character) and the location of the three AERONET sites shown as blue triangles.

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MODIS 3 km AOD Products and Calibration

MODIS 3 km AOD Product

MODIS on board the NASA Aqua satellite has been in operation since 2002, providing retrieval products of aerosol and cloud properties with nearly daily global coverage. (26) The dark target algorithms make use of surface reflectance at three wavelength channels (0.47 μm, 0.66 μm, and 2.12 μm) for AOD retrievals over land. Standard MODIS Level 2 (L2) AOD products are distributed at a 10 km resolution. To fulfill the need for higher resolution pollution detection, the most recently released MODIS Collection 6 product (MYD04_3K) provides aerosol products at a 3 km resolution in addition to the L2 10 km product. The retrieval algorithm of the higher resolution product is similar to that of the 10 km standard product, but averages 6 × 6 pixels in a single retrieval box rather than the 20 × 20 pixels after cloud screening and other surface mask processes. Pixels outside the reflectivity range of the brightest 50% and darkest 20% at 0.66 μm are discarded to reduce uncertainty. Validation against surface sun photometer shows that two-thirds of the 3 km retrievals fall within the expected error on a regional comparison but with a high bias of ∼0.06 especially over urban surface. (27)

MODIS AOD Validation

The ground-based AOD measurements from three AErosol RObotic NETwork (AERONET) sites (http://aeronet.gsfc.nasa.gov/) in Beijing were used to validate the satellite-derived AOD. To be comparable with the spectrum setting of MODIS, AERONET AOD at 550 nm were calculated by interpolating AOD at 440 nm and at 675 nm using the reported angstrom exponent at the respective wavelengths. The 3 km MODIS AOD products from Aqua show high temporal correlations with the AERONET AOD (Pearson correlation coefficient r of 0.93, 0.93, and 0.94 at the three sites respectively, Text S1, Supporting Information (SI)). The mean AOD difference between MODIS and AERONET at the three sites is 0.29. The higher AOD from MODIS could be partly attributed to the reflectance bias over the brighter surface of urban areas, which was also observed over other urban regions. (24, 27) Since this bias is persistent for the full range of AOD values, it can be treated as a systematic bias when deriving the PM2.5 to AOD ratio and is not expected to influence surface PM2.5 estimation in this study because we calibrated MODIS AOD with ground-level PM2.5 measurements on a daily basis.

Model Development and Validation

We selected the site-collocated satellite AOD values for each surface site where it falls within a 3 km grid. If there are more than one site within a single 3 km grid, the PM2.5 values of those sites are averaged. With this process, sites #12 and #14 are averaged, and there remain 34 pairs of AOD and PM2.5 data for model development.
A linear regression model was first applied to the collocated AOD and PM2.5 data sets. The linear regression model for all 34 sites follows the form of(1)where α and β are the fixed intercept and slope, respectively. The intercept and slope do not have spatial and temporal variations since the linear regression model assumes that the AOD-PM2.5 relationship is constant for all the sites throughout the study period. The log transformed AOD and PM2.5 were also tested, and no significant improvement was found in the regression performance. Therefore, we only present the regression results with the original AOD and PM2.5 data sets.
In reality, the AOD-PM2.5 relationship may exhibit spatial and temporal variations due to changing meteorology and other factors. To represent the varying AOD-PM2.5 relationship, we developed a mixed effects model following the approach proposed by Lee et al. (23) which takes into consideration daily variations of the AOD-PM2.5 relationship and derives site-specific parameters for spatial adjustment. The mixed effects model estimates surface PM2.5 concentrations for each site i of day j (PM2.5,ij) from collocated MODIS AOD (AODij) in the form of(2)where α and β are the fixed intercept and slope independent of time and location, and uj and vj are the random intercept and slope for all the sites at each day. When the subscript i is not present for a parameter in eq 2, it indicates that the parameter does not change by site. The random terms (uj and vj) reflect the day-to-day variations of the AOD-PM2.5 relationship influenced by meteorology, satellite retrieval conditions, etc. The sj ∼ N(0, σs2) is a site term which accounts for the spatial difference of the AOD-PM2.5 relationship due to differences in site specific characteristics (i.e., surface reflectivity, topography, PM2.5 emissions, and pollution transported to the observation sites). The comparison between the mixed effects models with and without the site effect si provides a measure of the sensitivity in model-derived day-to-day variations of the AOD-PM2.5 relationship to site locations. εij represents the error term, and ∑ is the variance-covariance matrix for the day-specific random effects. We excluded days with less than two pairs of AOD-PM2.5 data and performed model predictions on the remaining available days. Model performances were evaluated by comparing the predictions to ground measurements using R2, mean prediction error (MPE), and root-mean-square error (RMSE). The correlation coefficient, r, reported in this study is the Pearson correlation coefficient unless stated otherwise.
A cross-validation (CV) method was implemented to test the performance of the linear regression model and mixed effects model. We isolated one site at a time, performed model fitting with the remaining 33 sites, and validated model performances on the isolated site. The process was repeated for each of the 34 sites. The CV statistics are represented with R2, MPE, and RMSE. We also varied the number of isolated sites during cross-validation to 5, 10, and 20 as a way of testing the sensitivity of the mixed effects model to the density of surface monitors needed.

Results and Discussion

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Descriptive Statistics

Table 1 presents the descriptive statistics of measured PM2.5 concentrations at the 35 surface sites and site-collocated MODIS AOD during the study period. Daily time series of the site-average PM2.5 concentrations and site-collocated MODIS AOD (Figure 1a) indicate an overall good correlation between them (r = 0.6). Average PM2.5 from the ground-based monitors is 81.04 μg/m3 during the study period, more than a factor of 2 higher than China’s NAAQS of 35 μg/m3 for annual-mean PM2.5, and the standard deviation (SD) is 73.23 μg/m3. The average AOD is 0.68 with a SD of 0.49. The maximum site-average daily mean PM2.5 is 385.80 μg/m3, and the maximum single-site daily concentration reaches 500 μg/m3. Many sites have high AOD values larger than 2.0. The data are further divided into the warm season (Apr 15th to Oct 14th) and the cold season (Oct 15th to Apr 14th). The average PM2.5 concentrations does not differ much between the two seasons, but the AOD values are much lower in the cold season than in the warm season. Besides the impact of lower planetary boundary layer (PBL) depths in the cold season as well as other meteorological conditions (e.g., lower relative humidity), a lower percent of available satellite AOD retrievals over snow also explains the lower mean and SD of AOD in the cold season. The detailed statistics for each site and description for seasonal comparisons are shown in Text S2 (SI).
Table 1. Descriptive Statistics of PM2.5 Observations from the 35 Surface Sites and Site-Collocated AOD from the 3 km MODIS Product during the Study Period (March 26th 2013 to Apr 23th 2014)
 Site Average PM2.5 (μg/m3)
averaging periodNameanSDbminmaxmedian
all1112681.0473.233.78385.8057.84
warm season (Apr 15th–Oct 14th)592980.6165.533.94331.4761.68
cold season (Oct 15th–Apr 14th)477381.2680.394.02377.8651.40
 Site-Collocated Average AOD (unitless)
averaging periodNmeanSDminmaxmedian
all28180.680.490.092.390.53
warm season (Apr 15th–Oct 14th)22190.750.510.102.390.61
Cold season (Oct 15th–Apr 14th)5940.340.260.120.980.28
 PM2.5 (AOD) during Periods When Both Data Are Available
averaging periodNmeanSDminmaxmedian
all193349.58 (0.64)c41.06 (0.48)4.80 (0.10)186.44 (2.23)36.10 (0.50)
warm season (Apr 15th–Oct 14th)148252.59 (0.71)42.12 (0.50)5.26 (0.11)184.30 (2.23)39.56 (0.58)
cold season (Oct 15th–Apr 14th)45135.39 (0.33)31.97 (0.23)8.94 (0.13)101.81 (0.85)25.75 (0.28)
a

N denotes the number of valid observations.

b

SD represents the standard deviation of the data.

c

Numbers in parentheses are for the MODIS AOD.

Spatially surface PM2.5 from the ground-level monitors presents a decreasing gradient from south to north due to topography and land use difference in Beijing (Figure 1b). The south to north PM2.5 gradient generally demonstrates the emission difference between suburban and urban regions. In addition, the southern sites are more influenced by pollution transported from the cities south of Beijing, while north of Beijing is surrounded by mountains. Satellite AOD averaged for the entire study period exhibits a similarly strong spatial gradient (Figure 1c), with the highest values over the southeast urban districts. The correlation between the period-mean PM2.5 and site-collocated AOD is 0.64 across the 35 sites, indicating a relatively tight spatial consistency between the two data sets.

Linear Regression Model

The linear regression model gives a regression slope of 58.67 and an intercept of 10.08 between measured PM2.5 and site-collocated MODIS AOD, with an overall R2 of 0.47 (p < 0.0001) (Table 2). MPE and RMSE are 21.49 μg/m3 and 32.09 μg/m3, respectively. The regression performance at each of the 34 sites (sites #12 and #14 were averaged) is shown in Text S2 (SI). The correlation is slightly higher in the cold season (R2 = 0.52) than that in the warm season (R2 = 0.47). The linear regression slope for the warm season is 42.2% lower than that for the cold season due mainly to lower PBL and lower RH in the cold season. The simple linear regression model will be used as a benchmark to assess the improvement in predictability by the mixed effects model.
Table 2. Model Performance Statistics for the Linear Regression and the Mixed Effects Model over All the Surface Sites
model typeNaslopebinterceptcR2MPEd (μg/m3)RMSEe(μg/m3)
MODIS 3 km AOD (Whole Period)
linear regression193358.6710.080.4721.4932.09
mixed effects143553.1320.440.8111.4517.85
mixed effects (w/site effect)143555.6217.740.8310.6916.63
MODIS 3 km AOD (Warm Season Only)
linear regression148257.968.540.4721.8232.44
mixed effects115046.0419.460.7911.5718.14
mixed effects (w/site effect)115049.3317.220.8210.7016.91
MODIS 3 km AOD (Cold Season Only)
linear regression451100.203.930.5218.2327.37
mixed effects285107.0617.110.8710.2415.42
mixed effects (w/site effect)285108.0914.670.8910.2814.48
a

N denotes total available pairs of data.

b

Fixed regression slope derived from the models.

c

Fixed regression intercept derived from the models.

d

MPE is estimated as the absolute differences between predicted and measured PM2.5 concentrations.

e

RMSE is estimated as the root mean squared differences between predicted and measured PM2.5 concentrations.

Mixed Effects Model Fitting and Validation

After the data selection process described above, there are a total of 120 valid days including 1435 pairs of AOD-PM2.5 data available for model fitting. The parameters and performances of the mixed effects model are compared with those from the linear regression model in Table 2. The overall R2 between the predicted and measured PM2.5 is 0.81 from the mixed effects model without a site term, which is a significant improvement compared to the R2 of 0.47 from the linear regression model. The prediction performance at each site is displayed in Text S2 (SI). The site-specific R2 ranges from 0.54 to 0.96 with the average R2 of 0.83 and SD of 0.10 (Figure 2). By adding the daily calibration of the AOD-PM2.5 relationship through the mixed effects model, the 3 km AOD product from MODIS explains on average 83% of the observed surface PM2.5 variability. The overall MPE and RMSE are 11.45 μg/m3 and 17.85 μg/m3, respectively, 47.3% and 44.4% lower than the corresponding values from the simple linear regression. The fixed term of the intercept and slope is 20.44 (p < 0.001) and 53.13 (p < 0.001), with the standard errors being 2.30 and 5.00, respectively. The daily specific intercept and slope have a standard deviation of 13.46 and 32.98, respectively. The mixed effects model has a higher R2 (0.87) and smaller MPE and RMSE for predicting PM2.5 in the cold season than for the warm season. The mean daily specific slope for the warm and cold season is −6.6 (SD = 28.2) and 12.7 (SD = 37.8), respectively. The larger slope for the cold season reflects a higher portion of PM2.5 concentrated near the surface due to lower PBL, consistent with the linear regression model, while the higher SD of the cold season slope indicates the larger day to day variation. The intercepts have less variation between the two seasons.

Figure 2

Figure 2. Box plots of prediction performance of the linear regression model and mixed effects model (with and without site term) at each site for (left) R2 and (right) RMSE (μg/m3).

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The site effect term is incorporated in the mixed effects model to examine if the AOD-PM2.5 relationship can be further improved with spatial variation. The site effect term at each monitoring location varies from −10.55 to 18.71 (SD = 6.03). Larger values are found at sites located in southern (sites #1, 2, 3) and eastern (site #26) Beijing. These southern sites are subject to more frequent regional transport of aerosols from the heavily industrialized cities southward and southwestward of Beijing. The other two sites near traffic (sites #8, 12) also show a higher site effect term, indicating the influence of local pollution. The mixed effects model with a site effect term has an overall R2 of 0.83, with MPE of 10.69 μg/m3, and RMSE of 16.63 μg/m3 (Table 2). Detailed statistics are shown in Text S2 (SI). Compared with the mixed effects model without the site effect, adding the site effect shows only a slight improvement in the model performance with a 6% decrease of MPE and RMSE (Table 2). This comparison further confirms that day-to-day variation of the AOD-PM2.5 relationship is a more important factor than the spatial variation of this relationship. In support of this, we found that exclusion of the two southern sites (#1, 2) or the two traffic sites (#8, 12), which are not represented by the 3 km AOD product, does not affect the performance of the mixed effects model (Text S3, SI).
Given the difference in data statistics between the cold and the warm season, we tested if the linear regression model and the mixed effects model would give better results when fitted separately for each season (Table 2). Based on detailed comparison (Text S2), we found that it is unlikely that the model itself can be improved through separate fitting by season. Therefore, we chose to use the mixed effects model fitted with data from the entire period for PM2.5 predictions over Beijing.
Cross-validation results for different models are shown in Figure 3. The R2 values for the linear regression model and mixed effects models with and without site effects are 0.45, 0.75, and 0.79, respectively. The CV MPE and RMSE of the mixed effects models are reduced by ∼50% compared to the linear regression model. To assess the model dependence on the density of surface monitors needed for calibration, we increased the number of isolated sites during cross-validation of the mixed effects model (without site effect) to 5, 10, and 20 and compared the resulting effects on model agreement. When the number of isolated sites increases from 1 to 10, the CV R2 decreases from 0.75 to 0.71, while MPE and RMSE increase by ∼11%. The CV statistics show little change when the number of isolated sites is less than three. When 20 sites are isolated, the R2 decreases to 0.64 with MPE and RMSE increased by up to 25%. This sensitivity analysis demonstrates the advantage of using more surface monitors for model fitting and calibration. It also suggests that at least 31 sites are needed for a successful calibration of the mixed effects model over Beijing.

Figure 3

Figure 3. Scatter plots for cross-validation results between measured and predicted PM2.5 concentrations from the linear regression model (left), the mixed effects model without site effect (center), and the mixed effects model with site effect (right).

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Predicted Surface PM2.5

Given the high predicting power of the mixed effects model, it is used to derive surface PM2.5 maps for Beijing at 3 km resolution. The site effect is not used when mapping surface PM2.5 for the following reasons: first, the site effect is a site specific term and such information is not available for every 3 km grid; second, while the site effect term can be interpolated spatially onto each grid, this process brings in additional uncertainty in predicted PM2.5 which cannot be well characterized given the sparseness of the existing monitors; third, the mixed effects model without the site effect has similar performance statistics compared to the one with the site effect. The predicted long-term mean surface PM2.5 map is shown in Figure 4a. Here the long-term mean refers to the average from all valid model-days during the entire study period. There is a southeast-to-northwest gradient in the predicted PM2.5 distribution, with higher concentrations over downtown urban areas and southern regions and lower values over north and western suburban districts. The predicted long-term mean PM2.5 for the whole Beijing city is 40.97 μg/m3. The district-mean PM2.5 concentrations, calculated by averaging the predicted PM2.5 from all the satellite grids that fall within the boundaries of individual districts, range from 32.2 μg/m3 for the Mentougou district to 55.5 μg/m3 for the Daxing district. For comparison, we calculated the monitor-derived district-mean PM2.5 by averaging the measured PM2.5 from all the sites that fall within each district (Text S4, SI). The number of available sites for individual districts ranges from one to a maximum of four. For the densely populated Xicheng and Chaoyang districts, the satellite-derived mean PM2.5 is ∼20% and 12% higher than the monitor-derived mean, respectively. This suggests that those urban regions are particularly undersampled by the existing surface monitors.

Figure 4

Figure 4. (a) Mean PM2.5 concentrations (μg/m3) derived from the 3 km resolution MODIS AOD product for Beijing averaged over all the days with valid data; (b) same as left but averaged over the heavily polluted days (daily population-weighted mean PM2.5 larger than 75 μg/m3). The thin black lines indicate major roads in Beijing.

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The population-weighted mean PM2.5 is derived by weighting the satellite-derived PM2.5 by population in each grid. The gridded population data was adopted from the 1 km population data set by Fu et al. (28) The population data were regridded to the same 3 km resolution as the satellite-derived PM2.5. The resident population of Beijing was 19.61 million in 2010, consisting of 11.72 million (59.8%) in the six urban districts and 7.89 million (40.2%) in the ten suburban districts according to the sixth national population census. The population-weighted mean PM2.5 for Beijing as a whole is 51.2 μg/m3, which is 25.0% higher than the area mean of 40.97 μg/m3 and 46.3% higher than China’s annual mean standard of 35 μg/m3. The predicted PM2.5 levels indicate that approximately 58.7% of the area of Beijing Municipality, corresponding to a total of 19.2 million or 98% of the total population, is exposed to risky PM2.5 pollution levels, which is a serious health concern for Beijing. Higher PM2.5 concentrations are predicted near main traffic routes, and several PM2.5 hotspots are located in areas of higher road density, for example over the urban center, near the main expressways extending southward to Fangshan and Daxing districts, and at road conjunctions in Pinggu and Shunyi districts (Figure 4a), (29) suggesting emissions from road transportation as an important contributor to PM2.5 pollution in Beijing. Both AOD-derived PM2.5 and surface measurements indicate lower PM2.5 levels within the second ring road at the urban center where there is lower industry activity and higher percentage of parks and common spaces.
Heavily polluted days were selected as the days with city-average daily population-weighted mean PM2.5 concentrations greater than 75 μg/m3 (China NAAQS level 2 daily standard). Among all the 120 days with valid data, a total of 30 days (25%) are heavily polluted days. As shown in Figure 4, the mean PM2.5 of those heavily polluted days displays a similar spatial pattern with the mean PM2.5 of all the days but with enhanced PM2.5 levels over the whole city of Beijing. The population-weighted mean (area-averaged) PM2.5 during the heavily polluted days is 109.5 μg/m3 (90.02 μg/m3) over the whole Beijing, 46% (20%) higher than the daily standard. During the heavily polluted days, the southern and southeastern edge of Beijing had averaged PM2.5 concentration almost twice the mean concentration of all the days, suggesting that regional pollution transported from south of Beijing contributes to high pollution levels in Beijing during those polluted days. Given the regional influence, those polluted days also saw 123.9% and 85.1% higher PM2.5 levels than the mean conditions in the relatively cleaner northern and northwestern districts, respectively. Under the polluted conditions, more than 58.67% of the city, which includes all the urban districts and 55.01% of the suburban districts, is exposed to PM2.5 levels higher than 75 μg/m3. These areas correspond to a total population of 19.07 million, including 11.72 million in urban districts and 7.35 million in suburban districts. The high PM2.5 concentrations and high population exposures presented here provide clear evidence for the serious health risk of PM pollution for Beijing residents, calling for aggressive emission control measures both within the city and in the surrounding regions.

Model Performance Comparison

To demonstrate the benefit of the fine-resolution AOD product, we averaged the 3 km AOD product onto 6 km and 9 km resolution and used those regridded coarser-resolution AOD to develop the mixed effects model. The details on data processing, model developments, and model performances comparison are provided in the Text S5 (SI). The mixed effects model developed from the 3 km AOD product has better performances than that developed from the 6 km and 9 km products, with R2 higher by up to 0.02 and MPE and RMSE lower by ∼10%.
To compare with previously published studies which used the standard MODIS L2 10 km AOD products as predictors of surface PM2.5, we also tested the change in model performances when using the standard 10 km product (Text S5, SI). (30) The total available pairs of AOD-PM2.5 data decrease by 32.9% when using the 10 km AOD product. Spatially, the 10 km product shows similar patterns with the 3 km products but provides much less details in terms of spatial variability. The model R2 based on the 10 km AOD product is ∼0.02 lower than that based on the 3 km product, with mean MPE and RMSE higher by ∼13% and 17%, respectively. The regression performance based on the 10 km product is also lower than that using the regridded 6 km and 9 km products, further demonstrating the benefit of using finer-resolution AOD. The AOD variation of a single 10 km pixel can be as high as 0.2. Therefore, the 3 km resolution product provides better spatial representation and allows capturing intraurban variations of the PM2.5 concentrations, offering a direct benefit for air quality and pollution exposure assessment.
By considering daily calibration of the AOD-PM2.5 relationship, the mixed effects model gives higher modeled R2 (0.83) and CV R2 (0.79) than previously published studies, e.g., the geographically weighted regression model over the entire China region (R2 = 0.64) and for Pearl River Delta (R2 = 0.74), the empirical nonlinear model for Xi’an (R2 = 0.67). (13, 22, 31) A recent work estimated surface PM2.5 for China using MODIS AOD at a 1 km resolution, but the R2 between the observed and predicted PM2.5 was only 0.49 for Beijing, possibly due to less surface observations used for daily model calibration. (32) The performance statistics of the mixed effects model developed here for Beijing are also comparable with those from the U.S. studies using the 1 km resolution MODIS AOD derived from the Multi-Angle Implementation of Atmospheric Correction (MAIAC) program (R2 = 0.67–0.84). (33, 34)

Prediction Uncertainties

While the satellite-predicted PM2.5 provides larger spatial coverage than the unevenly distributed ground-based monitors, the satellite has less temporal coverage due to its sampling limitation associated with surface conditions, clouds, and other factors especially in the cold season. With only 120 days of model-predicted PM2.5 during the whole study period, the satellite-predicted mean PM2.5 for those model-days is on average 32.0% lower than the monitor-derived mean PM2.5 for the whole study period at the site locations. The difference at individual sites ranges from 11.7% to 41.8%. To account for different data availability during the warm and cold season, we calculated the long-term mean of predicted PM2.5 by weighting the seasonal means by the number of available predictions in each season. The weighted long-term mean of predicted PM2.5 at site locations is 18.9% lower than the monitor-derived long-term mean. This suggests that the sampling bias by the satellite may affect the long-term mean PM2.5 derived from the AOD-based daily calibration model, and the estimation of long-term mean population exposure from this study is probably too low.
Other possible errors of the predicted surface PM2.5 concentrations include those from both satellite AOD products and surface PM2.5 measurements. The 3 km AOD products from MODIS have shown comparable quality with the widely validated 10 km products with an expected error of 0.05 ± 0.25 AOD. (24) However, improper characterization of surface reflectance will adversely impact retrieval accuracy of the higher-resolution products. (27) While this study validated the 3 km MODIS AOD products with ground-based AOD at three urban AERONET sites in Beijing, there are no ground-based AOD measurements to characterize the bias of MODIS AOD over other urban surfaces or the suburban regions in the city. Measurement bias in surface PM2.5 concentrations from the TEOM instrument also contributes to the prediction uncertainty. (35, 36) In addition, different spatial scales could bring uncertainties in the AOD-PM2.5 relationship because the MODIS AOD reflect the average condition of a 3 km grid, while the site PM2.5 is based on point measurement. This partly explains higher PM2.5 at site #1 which is not observed by satellite. The mixed effects model also introduces uncertainties with the assumption of linearity and AOD as the single predictor.
Despite the uncertainties, this study is the first application of the most recent higher-resolution MODIS AOD products to predict surface PM2.5 concentrations over Beijing at 3 km resolution. The advantage of the mixed effects model developed here is that it calibrates AOD-derived PM2.5 models on a daily basis to account for the time varying AOD-PM2.5 relationship. By allowing the AOD-PM2.5 relationship to change temporally, the mixed effects model considers, at least partially, the impact of other factors correlated with time, such as emissions from anthropogenic activities and vegetation as well as weather conditions, because those factors are expected to affect the daily variability of the AOD-PM2.5 relationship. The high prediction performance (R2 = 0.83) of the mixed effects model has demonstrated the value of using the higher-resolution AOD products at the urban scale for Beijing, where population density varies from 23407/km2 in urban center to 958/km2 in suburban developing districts. Nevertheless, the model cannot include the full extent of other explanatory factors. Given the R2 of 0.81–0.83 resulting from the mixed effects model calibrated in this study, the impact of other explanatory factors is unlikely to play a dominant role, but it is worth future investigation to further improve the model. In addition, surface observations from more sites and longer periods are useful to calibrate the AOD-PM2.5 relationships.

Supporting Information

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The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.est.5b01413.

  • Texts S1_S5, Figures S1–S4, and Tables S1–S5 (PDF)

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Author Information

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  • Corresponding Author
    • Yuxuan Wang - Ministry of Education Key Laboratory for Earth System Modeling, Center for Earth System Science, Tsinghua University, Beijing 100084, China;  Department of Marine Science, Texas A&M University at Galveston, Galveston, Texas 77553, United States;  Department of Atmospheric Science, Texas A&M University, College Station, Texas 77853, United States;  Email: [email protected]
  • Authors
    • Yuanyu Xie - Ministry of Education Key Laboratory for Earth System Modeling, Center for Earth System Science, Tsinghua University, Beijing 100084, China
    • Kai Zhang - Department of Epidemiology, Human Genetics and Environmental Sciences, University of Texas School of Public Health, Houston, Texas 77030, United States
    • Wenhao Dong - Ministry of Education Key Laboratory for Earth System Modeling, Center for Earth System Science, Tsinghua University, Beijing 100084, China
    • Baolei Lv - Ministry of Education Key Laboratory for Earth System Modeling, Center for Earth System Science, Tsinghua University, Beijing 100084, China
    • Yuqi Bai - Ministry of Education Key Laboratory for Earth System Modeling, Center for Earth System Science, Tsinghua University, Beijing 100084, China
  • Notes
    The authors declare no competing financial interest.

Acknowledgment

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This research was supported by the National Key Basic Research Program of China (2014CB441302), the CAS Strategic Priority Research Program (Grant No. XDA05100403), and the Beijing Nova Program (Z121109002512052). The authors thank Mr. Yi Zou for providing the photographs shown on the journal cover. The photo series “Beijing: Be Clear at a Glance” features daily photos of Beijing taken from the same location since 2013.

References

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This article references 36 other publications.

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    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.
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    Epidemiol. and health impact studies of fine particulate matter with diam. < 2.5 μm (PM2.5) are limited by the lack of monitoring data, esp. in developing countries. Satellite observations offer valuable global information about PM2.5 concns. In this study, we developed a technique for estg. surface PM2.5 concns. from satellite observations. We mapped global ground-level PM2.5 concns. using total column aerosol optical depth (AOD) from the MODIS (Moderate Resoln. Imaging Spectroradiometer) and MISR (Multiangle Imaging Spectroradiometer) satellite instruments and coincident aerosol vertical profiles from the GEOS-Chem global chem. transport model. We detd. that global ests. of long-term av. (1 Jan. 2001 to 31 Dec. 2006) PM2.5 concns. at approx. 10 km × 10 km resoln. indicate a global population-weighted geometric mean PM2.5 concn. of 20 μg/m3. The World Health Organization Air Quality PM2.5 Interim Target-1 (35 μg/m3 annual av.) is exceeded over central and eastern Asia for 38% and for 50% of the population, resp. Annual mean PM2.5 concns. exceed 80 μg/m3 over eastern China. Our evaluation of the satellite-derived est. with ground-based in situ measurements indicates significant spatial agreement with North American measurements (r = 0.77; slope = 1.07; n = 1057) and with noncoincident measurements elsewhere (r = 0.83; slope = 0.86; n = 244). The 1 SD of uncertainty in the satellite-derived PM2.5 is 25%, which is inferred from the AOD retrieval and from aerosol vertical profile errors and sampling. The global population-weighted mean uncertainty is 6.7 μg/m3. Satellite-derived total-column AOD, when combined with a chem. transport model, provides ests. of global long-term av. PM2.5 concns.
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    Ma, Z.; Hu, X.; Huang, L.; Bi, J.; Liu, Y. Estimating ground-level PM2.5 in China using satellite remote sensing Environ. Sci. Technol. 2014, 48 (13) 7436– 44 DOI: 10.1021/es5009399
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    Estimating Ground-Level PM2.5 in China Using Satellite Remote Sensing
    Ma, Zongwei; Hu, Xuefei; Huang, Lei; Bi, Jun; Liu, Yang
    Environmental Science & Technology (2014), 48 (13), 7436-7444CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)
    Estg. ground-level PM2.5 from satellite-derived aerosol optical depth (AOD) using a spatial statistical model is a promising new method to evaluate the spatial and temporal characteristics of PM2.5 exposure in a large geog. region. However, studies outside North America have been limited due to the lack of ground PM2.5 measurements to calibrate the model. Taking advantage of the newly established national monitoring network, we developed a national-scale geog. weighted regression (GWR) model to est. daily PM2.5 concns. in China with fused satellite AOD as the primary predictor. The results showed that the meteorol. and land use information can greatly improve model performance. The overall cross-validation (CV) R2 is 0.64 and root mean squared prediction error (RMSE) is 32.98 μg/m3. The mean prediction error (MPE) of the predicted annual PM2.5 is 8.28 μg/m3. Our predicted annual PM2.5 concns. indicated that over 96% of the Chinese population lives in areas that exceed the Chinese National Ambient Air Quality Std. (CNAAQS) Level 2 std. Our results also confirmed satellite-derived AOD in conjunction with meteorol. fields and land use information can be successfully applied to extend the ground PM2.5 monitoring network in China.
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    Wang, J.; Christopher, S. A. Intercomparison between satellite-derived aerosol optical thickness and PM2.5 mass: Implications for air quality studies Geophys. Res. Lett. 2003, 30 (21) 2095 DOI: 10.1029/2003GL018174
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    Engel-Cox, J. A.; Holloman, C. H.; Coutant, B. W.; Hoff, R. M. Qualitative and quantitative evaluation of MODIS satellite sensor data for regional and urban scale air quality Atmos. Environ. 2004, 38 (16) 2495– 2509 DOI: 10.1016/j.atmosenv.2004.01.039
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    Qualitative and quantitative evaluation of MODIS satellite sensor data for regional and urban scale air quality
    Engel-Cox, Jill A.; Holloman, Christopher H.; Coutant, Basil W.; Hoff, Raymond M.
    Atmospheric Environment (2004), 38 (16), 2495-2509CODEN: AENVEQ; ISSN:1352-2310. (Elsevier Science B.V.)
    Advances in satellite sensors have provided new datasets for monitoring air quality at urban and regional scales. Qual. true color images and quant. aerosol optical depth data from the Moderate Resoln. Imaging Spectroradiometer (MODIS) sensor on the Terra satellite were compared with ground-based particulate matter data from US Environmental Protection Agency (EPA) monitoring networks covering the period from 1 Apr. to 30 Sept., 2002. Using both imagery and statistical anal., satellite data enabled the detn. of the regional sources of air pollution events, the general type of pollutant (smoke, haze, dust), the intensity of the events, and their motion. Very high and very low aerosol optical depths were found to be eliminated by the algorithm used to calc. the MODIS aerosol optical depth data. Correlations of MODIS aerosol optical depth with ground-based particulate matter were better in the eastern and Midwest portion of the United States (east of 100°W). Data were patchy and had poorer correlations in the western US, although the correlation was dependent on location. This variability is likely due to a combination of the differences between ground-based and column av. datasets, regression artifacts, variability of terrain, and MODIS cloud mask and aerosol optical depth algorithms. Preliminary anal. of the algorithms indicated that aerosol optical depth measurements calcd. from the sulfate-rich aerosol model may be more useful in predicting ground-based particulate matter levels, but further anal. would be required to verify the effect of the model on correlations. Overall, the use of satellite sensor data such as from MODIS has significant potential to enhance air quality monitoring over synoptic and regional scales.
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    Liu, Y.; Park, R. J.; Jacob, D. J.; Li, Q. B.; Kilaru, V.; Sarnat, J. A. Mapping annual mean ground-level PM2.5 concentrations using Multiangle Imaging Spectroradiometer aerosol optical thickness over the contiguous United States J. Geophys. Res. 2004, 109 (D22) D22206 DOI: 10.1029/2004JD005025
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    Boys, B. L.; Martin, R. V.; van Donkelaar, A.; MacDonell, R. J.; Hsu, N. C.; Cooper, M. J.; Yantosca, R. M.; Lu, Z.; Streets, D. G.; Zhang, Q.; Wang, S. W. Fifteen-Year Global Time Series of Satellite-Derived Fine Particulate Matter Environ. Sci. Technol. 2014, 48 (19) 11109– 11118 DOI: 10.1021/es502113p
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    Fifteen-Year Global Time Series of Satellite-Derived Fine Particulate Matter
    Boys, B. L.; Martin, R. V.; van Donkelaar, A.; MacDonell, R. J.; Hsu, N. C.; Cooper, M. J.; Yantosca, R. M.; Lu, Z.; Streets, D. G.; Zhang, Q.; Wang, S. W.
    Environmental Science & Technology (2014), 48 (19), 11109-11118CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)
    Ambient fine particulate matter (PM2.5) is a leading environmental risk factor for premature mortality. This work used aerosol optical depth (AOD) measurements from 2 satellite instruments, multi-angle imaging spectroradiometer and sea viewing wide field of vision sensor, to produce a unified 15-yr global time series (1998-2012) of ground-level PM2.5 concns. at a 1° x 1° resoln. The GEOS-chem chem. transport model related each individual AOD retrieval to ground-level PM2.5 concn. Four broad areas displaying significant, spatially coherent, annual trends were examd. in detail: eastern USA (-0.39 ± 0.10 μg/m3-yr), Arabian Peninsula (0.81 ± 0.21 μg/m3-yr), southern Asia (0.93 ± 0.22 μg/m3-yr), and eastern Asia (0.79 ± 0.27 μg/m3-yr). Over the dense in-situ observation period, 1999-2012, the linear tendency for the eastern USA (-0.37 ± 0.13 μg/m3-yr) agreed well with in-situ measurements (-0.38 ± 0.06 μg/m3-yr). A GEOS-Chem simulation showed secondary inorg. aerosols largely explained the obsd. PM2.5 trend over the eastern USA and southern and eastern Asia; mineral dust largely explained the obsd. trend over the Arabian Peninsula.
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    Hoff, R. M.; Christopher, S. A. Remote Sensing of Particulate Pollution from Space: Have We Reached the Promised Land? J. Air Waste Manage. Assoc. 2009, 59 (6) 645– 675 DOI: 10.3155/1047-3289.59.6.645
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    Remote sensing of particulate pollution from space: have we reached the promised land?
    Hoff, Raymond M.; Christopher, Sundar A.
    Journal of the Air & Waste Management Association (2009), 59 (6), 645-675CODEN: JAWAFC; ISSN:1096-2247. (Air & Waste Management Association)
    A review. The recent literature on satellite remote sensing of air quality is reviewed. 2009 Is the 50th anniversary of the first satellite atm. observations. For the first 40 of those years, atm. compn. measurements, meteorol., and atm. structure and dynamics dominated the missions launched. Since 1995, 42 instruments relevant to air quality measurements have been put into orbit. Trace gases such as ozone, nitric oxide, nitrogen dioxide, water, oxygen/tetraoxygen, bromine oxide, sulfur dioxide, formaldehyde, glyoxal, chlorine dioxide, chlorine monoxide, and nitrate radical have been measured in the stratosphere and troposphere in column measurements. Aerosol optical depth (AOD) is a focus of this review and a significant body of literature exists that shows that ground-level fine particulate matter (PM2.5) can be estd. from columnar AOD. Precision of the measurement of AOD is ±20% and the prediction of PM2.5 from AOD is order ±30% in the most careful studies. The air quality needs that can use such predictions are examd. Satellite measurements are important to event detection, transport and model prediction, and emission estn. It is suggested that ground-based measurements, models, and satellite measurements should be viewed as a system, each component of which is necessary to better understand air quality.
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    Liu, Y.; Paciorek, C. J.; Koutrakis, P. Estimating Regional Spatial and Temporal Variability of PM2.5 Concentrations Using Satellite Data, Meteorology, and Land Use Information Environ. Health Perspect. 2009, 117 (6) 886– 892 DOI: 10.1289/ehp.0800123
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    Estimating regional spatial and temporal variability of PM(2.5) concentrations using satellite data, meteorology, and land use information
    Liu Yang; Paciorek Christopher J; Koutrakis Petros
    Environmental health perspectives (2009), 117 (6), 886-92 ISSN:.
    BACKGROUND: Studies of chronic health effects due to exposures to particulate matter with aerodynamic diameters <or= 2.5 microm (PM(2.5)) are often limited by sparse measurements. Satellite aerosol remote sensing data may be used to extend PM(2.5) ground networks to cover a much larger area. OBJECTIVES: In this study we examined the benefits of using aerosol optical depth (AOD) retrieved by the Geostationary Operational Environmental Satellite (GOES) in conjunction with land use and meteorologic information to estimate ground-level PM(2.5) concentrations. METHODS: We developed a two-stage generalized additive model (GAM) for U.S. Environmental Protection Agency PM(2.5) concentrations in a domain centered in Massachusetts. The AOD model represents conditions when AOD retrieval is successful; the non-AOD model represents conditions when AOD is missing in the domain. RESULTS: The AOD model has a higher predicting power judged by adjusted R(2) (0.79) than does the non-AOD model (0.48). The predicted PM(2.5) concentrations by the AOD model are, on average, 0.8-0.9 microg/m(3) higher than the non-AOD model predictions, with a more smooth spatial distribution, higher concentrations in rural areas, and the highest concentrations in areas other than major urban centers. Although AOD is a highly significant predictor of PM(2.5), meteorologic parameters are major contributors to the better performance of the AOD model. CONCLUSIONS: GOES aerosol/smoke product (GASP) AOD is able to summarize a set of weather and land use conditions that stratify PM(2.5) concentrations into two different spatial patterns. Even if land use regression models do not include AOD as a predictor variable, two separate models should be fitted to account for different PM(2.5) spatial patterns related to AOD availability.
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    Kloog, I.; Nordio, F.; Coull, B. A.; Schwartz, J. Incorporating local land use regression and satellite aerosol optical depth in a hybrid model of spatiotemporal PM2.5 exposures in the Mid-Atlantic states Environ. Sci. Technol. 2012, 46 (21) 11913– 11921 DOI: 10.1021/es302673e
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    Incorporating Local Land Use Regression And Satellite Aerosol Optical Depth In A Hybrid Model Of Spatiotemporal PM2.5 Exposures In The Mid-Atlantic States
    Kloog, Itai; Nordio, Francesco; Coull, Brent A.; Schwartz, Joel
    Environmental Science & Technology (2012), 46 (21), 11913-11921CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)
    Satellite-derived aerosol optical depth (AOD) measurements have the potential to provide spatiotemporally resolved predictions of both long and short-term exposures, but previous studies have generally shown moderate predictive power and lacked detailed high spatio- temporal resoln. predictions across large domains. We aimed at extending our previous work by validating our model in another region with different geog. and metrol. characteristics, and incorporating fine scale land use regression and nonrandom missingness to better predict PM2.5 concns. for days with or without satellite AOD measures. We start by calibrating AOD data for 2000-2008 across the Mid-Atlantic. We used mixed models regressing PM2.5 measurements against day-specific random intercepts, and fixed and random AOD and temp. slopes. We used inverse probability weighting to account for nonrandom missingness of AOD, nested regions within days to capture spatial variation in the daily calibration, and introduced a penalization method that reduces the dimensionality of the large no. of spatial and temporal predictors without selecting different predictors in different locations. We then take advantage of the assocn. between grid-cell specific AOD values and PM2.5 monitoring data, together with assocns. between AOD values in neighboring grid cells to develop grid cell predictions when AOD is missing. Finally to get local predictions (at the resoln. of 50 m), we regressed the residuals from the predictions for each monitor from these previous steps against the local land use variables specific for each monitor. "Out-of-sample" 10-fold cross-validation was used to quantify the accuracy of our predictions at each step. For all days without AOD values, model performance was excellent (mean "out-of-sample" R2 = 0.81, year-to-year variation 0.79-0.84). Upon removal of outliers in the PM2.5 monitoring data, the results of the cross validation procedure was even better (overall mean "out of sample" R2 of 0.85). Further, cross validation results revealed no bias in the predicted concns. (Slope of obsd. vs predicted = 0.97-1.01). Our model allows one to reliably assess short-term and long-term human exposures in order to investigate both the acute and effects of ambient particles, resp.
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    Hu, X.; Waller, L. A.; Al-Hamdan, M. Z.; Crosson, W. L.; Estes, M. G., Jr.; Estes, S. M.; Quattrochi, D. A.; Sarnat, J. A.; Liu, Y. Estimating ground-level PM2.5 concentrations in the southeastern US using geographically weighted regression Environ. Res. 2013, 121, 1– 10 DOI: 10.1016/j.envres.2012.11.003
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    Song, W.; Jia, H.; Huang, J.; Zhang, Y. A satellite-based geographically weighted regression model for regional PM2.5 estimation over the Pearl River Delta region in China Remote. Sens. Environ. 2014, 154, 1– 7 DOI: 10.1016/j.rse.2014.08.008
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    Lee, H. J.; Liu, Y.; Coull, B. A.; Schwartz, J.; Koutrakis, P. A novel calibration approach of MODIS AOD data to predict PM2.5 concentrations Atmos. Chem. Phys. 2011, 11 (15) 7991– 8002 DOI: 10.5194/acp-11-7991-2011
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    A novel calibration approach of MODIS AOD data to predict PM2.5 concentrations
    Lee, H. J.; Liu, Y.; Coull, B. A.; Schwartz, J.; Koutrakis, P.
    Atmospheric Chemistry and Physics (2011), 11 (15, Pt. 2), 7991-8002CODEN: ACPTCE; ISSN:1680-7316. (Copernicus Publications)
    Epidemiol. studies studying the human health effects of PM2.5 are susceptible to exposure measurement errors, a form of bias in exposure ests., since they rely on data from a limited no. of PM2.5 monitors within their study area. Satellite data can be used to expand spatial coverage, potentially enhancing the authors' ability to est. location-sp. or subject-sp. exposures to PM2.5, but some have reported poor predictive power. A new methodol. was developed to calibrate aerosol optical depth (AOD) data obtained from the Moderate Resoln. Imaging Spectroradiometer (MODIS). Subsequently, this method was used to predict ground daily PM2.5 concns. in the New England region. 2003 MODIS AOD data corresponding to the New England region were retrieved, and PM2.5 concns. measured at 26 US Environmental Protection Agency (EPA) PM2.5 monitoring sites were used to calibrate the AOD data. A mixed effects model which allows day-to-day variability in daily PM2.5-AOD relations was used to predict location-specific PM2.5 levels. PM2.5 concns. measured at the monitoring sites were compared to those predicted for the corresponding grid cells. Both cross-sectional and longitudinal comparisons between the obsd. and predicted concns. suggested that the proposed new calibration approach renders MODIS AOD data a potentially useful predictor of PM2.5 concns. Also, the estd. PM2.5 levels within the study domain were examd. in relation to air pollution sources. The authors' approach made it possible to study the spatial patterns of PM2.5 concns. within the study domain.
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    Ji, W.; Wang, Y.; Zhuang, D.; Song, D.; Shen, X.; Wang, W.; Li, G. Spatial and temporal distribution of expressway and its relationships to land cover and population: A case study of Beijing, China Transport. Res. Part D: Trans. Environ. 2014, 32, 86– 96 DOI: 10.1016/j.trd.2014.07.010
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    Levy, R. C.; Mattoo, S.; Munchak, L. A.; Remer, L. A.; Sayer, A. M.; Patadia, F.; Hsu, N. C. The Collection 6 MODIS aerosol products over land and ocean Atmos. Meas. Tech. 2013, 6 (11) 2989– 3034 DOI: 10.5194/amt-6-2989-2013
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    You, W.; Zang, Z.; Pan, X.; Zhang, L.; Chen, D. Estimating PM2.5 in Xi’an, China using aerosol optical depth: a comparison between the MODIS and MISR retrieval models Sci. Total Environ. 2015, 505, 1156– 65 DOI: 10.1016/j.scitotenv.2014.11.024
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    Lin, C.; Li, Y.; Yuan, Z.; Lau, A. K. H.; Li, C.; Fung, J. C. H. Using satellite remote sensing data to estimate the high-resolution distribution of ground-level PM2.5 Remote. Sens. Environ. 2015, 156, 117– 128 DOI: 10.1016/j.rse.2014.09.015
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    Hu, X. F.; Waller, L. A.; Lyapustin, A.; Wang, Y. J.; Al-Hamdan, M. Z.; Crosson, W. L.; Estes, M. G.; Estes, S. M.; Quattrochi, D. A.; Puttaswamy, S. J.; Liu, Y. Estimating ground-level PM2.5 concentrations in the Southeastern United States using MAIAC AOD retrievals and a two-stage model Remote. Sens. Environ. 2014, 140, 220– 232 DOI: 10.1016/j.rse.2013.08.032
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    Chudnovsky, A. A.; Koutrakis, P.; Kloog, I.; Melly, S.; Nordio, F.; Lyapustin, A.; Wang, Y.; Schwartz, J. Fine particulate matter predictions using high resolution Aerosol Optical Depth (AOD) retrievals Atmos. Environ. 2014, 89, 189– 198 DOI: 10.1016/j.atmosenv.2014.02.019
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    Fine particulate matter predictions using high resolution Aerosol Optical Depth (AOD) retrievals
    Chudnovsky, Alexandra A.; Koutrakis, Petros; Kloog, Itai; Melly, Steven; Nordio, Francesco; Lyapustin, Alexei; Wang, Yujie; Schwartz, Joel
    Atmospheric Environment (2014), 89 (), 189-198CODEN: AENVEQ; ISSN:1352-2310. (Elsevier Ltd.)
    To date, spatial-temporal patterns of particulate matter (PM) within urban areas have primarily been examd. using models. On the other hand, satellites extend spatial coverage but their spatial resoln. is too coarse. In order to address this issue, here we report on spatial variability in PM levels derived from high 1 km resoln. AOD product of Multi-Angle Implementation of Atm. Correction (MAIAC) algorithm developed for MODIS satellite. We apply day-specific calibrations of AOD data to predict PM2.5 concns. within the New England area of the United States. To improve the accuracy of our model, land use and meteorol. variables were incorporated. We used inverse probability weighting (IPW) to account for nonrandom missingness of AOD and nested regions within days to capture spatial variation. With this approach we can control for the inherent day-to-day variability in the AOD-PM2.5 relationship, which depends on time-varying parameters such as particle optical properties, vertical and diurnal concn. profiles and ground surface reflectance among others. Out-of-sample "ten-fold" cross-validation was used to quantify the accuracy of model predictions. Our results show that the model-predicted PM2.5 mass concns. are highly correlated with the actual observations, with out-of-sample R2 of 0.89. Furthermore, our study shows that the model captures the pollution levels along highways and many urban locations thereby extending our ability to investigate the spatial patterns of urban air quality, such as examg. exposures in areas with high traffic. Our results also show high accuracy within the cities of Boston and New Haven thereby indicating that MAIAC data can be used to examine intra-urban exposure contrasts in PM2.5 levels.
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    Schwab, J. J.; Felton, H. D.; Rattigan, O. V.; Demerjian, K. L. New York state urban and rural measurements of continuous PM2.5 mass by FDMS, TEOM, and BAM J. Air Waste Manage. Assoc. 2006, 56 (4) 372– 383 DOI: 10.1080/10473289.2006.10464523
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    New York State urban and rural measurements of continuous PM2.5 mass by FDMS, TEOM, and BAM
    Schwab James J; Felton Henry D; Rattigan Oliver V; Demerjian Kenneth L
    Journal of the Air & Waste Management Association (1995) (2006), 56 (4), 372-83 ISSN:1096-2247.
    Field evaluations and comparisons of continuous fine particulate matter (PM2,5) mass measurement technologies at an urban and a rural site in New York state are performed. The continuous measurement technologies include the filter dynamics measurement system (FDMS) tapered element oscillating microbalance (TEOM) monitor, the stand-alone TEOM monitor (without the FDMS), and the beta attenuation monitor (BAM). These continuous measurement methods are also compared with 24-hr integrated filters collected and analyzed under the Federal Reference Method (FRM) protocol. The measurement sites are New York City (the borough of Queens) and Addison, a rural area of southwestern New York state. New York City data comparisons between the FDMS TEOM, BAM, and FRM are examined for bias and seasonality during a 2-yr period. Data comparisons for the FDMS TEOM and FRM from the Addison location are examined for the same 2-yr period. The BAM and FDMS measurements at Queens are highly correlated with each other and the FRM. The BAM and FDMS are very similar to each other in magnitude, and both are approximately 25% higher than the FRM filter measurements at this site. The FDMS at Addison measures approximately 9% more mass than the FRM. Mass reconstructions using the speciation trends network filter data are examined to provide insight as to the contribution of volatile species of PM2.5 in the FDMS mass measurement and the fraction that is likely lost in the FRM mass measurement. The reconstructed mass at Queens is systematically lower than the FDMS by approximately 10%.
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    Engel-Cox, J.; Nguyen Thi Kim, O.; van Donkelaar, A.; Martin, R. V.; Zell, E. Toward the next generation of air quality monitoring: Particulate Matter Atmos. Environ. 2013, 80, 584– 590 DOI: 10.1016/j.atmosenv.2013.08.016
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Cite this: Environ. Sci. Technol. 2015, 49, 20, 12280–12288
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https://doi.org/10.1021/acs.est.5b01413
Published August 27, 2015

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  • Abstract

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    Figure 1

    Figure 1. Temporal and spatial variations of PM2.5 and AOD: (a) Daily time series of the mean PM2.5 concentrations of the 35 monitoring sites and mean site-collocated AOD from the MODIS 3 km product (correlation coefficients r between different portions of the two time series are indicated); (b) Mean PM2.5 concentrations at each site (sites numbered and marked) averaged over the study period; (c) Distribution of the 3 km resolution MODIS AOD averaged during the study period, with the boundaries of 16 districts of Beijing shown in black lines (district names in black character) and the location of the three AERONET sites shown as blue triangles.

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    Figure 2

    Figure 2. Box plots of prediction performance of the linear regression model and mixed effects model (with and without site term) at each site for (left) R2 and (right) RMSE (μg/m3).

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    Figure 3

    Figure 3. Scatter plots for cross-validation results between measured and predicted PM2.5 concentrations from the linear regression model (left), the mixed effects model without site effect (center), and the mixed effects model with site effect (right).

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    Figure 4

    Figure 4. (a) Mean PM2.5 concentrations (μg/m3) derived from the 3 km resolution MODIS AOD product for Beijing averaged over all the days with valid data; (b) same as left but averaged over the heavily polluted days (daily population-weighted mean PM2.5 larger than 75 μg/m3). The thin black lines indicate major roads in Beijing.

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  • References

    This article references 36 other publications.

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      Lim, 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)61766-8
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      Lancet (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.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BC3s3isFClug%253D%253D&md5=f35c63bad4b58d5266a7ee7c4512569b
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      Guo, Song; Hu, Min; Zamora, Misti L.; Peng, Jianfei; Shang, Dongjie; Zheng, Jing; Du, Zhuofei; Wu, Zhijun; Shao, Min; Zeng, Limin; Molina, Mario J.; Zhang, Renyi
      Proceedings of the National Academy of Sciences of the United States of America (2014), 111 (49), 17373-17378CODEN: PNASA6; ISSN:0027-8424. (National Academy of Sciences)
      As the world's 2nd largest economy, China has experienced severe haze pollution, with fine particulate matter (PM) recently reaching unprecedentedly high levels across many cities, and an understanding of the PM formation mechanism is crit. in the development of efficient mediation policies to minimize its regional to global impacts. We demonstrate a periodic cycle of PM episodes in Beijing that is governed by meteorol. conditions and characterized by 2 distinct aerosol formation processes of nucleation and growth, but with a small contribution from primary emissions and regional transport of particles. Nucleation consistently precedes a polluted period, producing a high no. concn. of nano-sized particles under clean conditions. Accumulation of the particle mass concn. exceeding several hundred micrograms per cubic meter is accompanied by a continuous size growth from the nucleation-mode particles over multiple days to yield numerous larger particles, distinctive from the aerosol formation typically obsd. in other regions worldwide. The particle compns. in Beijing, on the other hand, exhibit a similarity to those commonly measured in many global areas, consistent with the chem. constituents dominated by secondary aerosol formation. Our results highlight that regulatory controls of gaseous emissions for volatile org. compds. and NOx from local transportation and SO2 from regional industrial sources represent the key steps to reduce the urban PM level in China.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXhvFKmu77J&md5=49a94fd850a57f15618f38c78d8d68f2
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      Che, H.; Xia, X.; Zhu, J.; Li, Z.; Dubovik, O.; Holben, B.; Goloub, P.; Chen, H.; Estelles, V.; Cuevas-Agulló, E. Column aerosol optical properties and aerosol radiative forcing during a serious haze-fog month over North China Plain in 2013 based on ground-based sunphotometer measurements Atmos. Chem. Phys. 2014, 14 (4) 2125– 2138 DOI: 10.5194/acp-14-2125-2014
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      Andersson, A.; Deng, J.; Du, K.; Zheng, M.; Yan, C.; Skold, M.; Gustafsson, O. Regionally-Varying Combustion Sources of the January 2013 Severe Haze Events over Eastern China Environ. Sci. Technol. 2015, 49 (4) 2038– 43 DOI: 10.1021/es503855e
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      Regionally-Varying Combustion Sources of the January 2013 Severe Haze Events over Eastern China
      Andersson, August; Deng, Junjun; Du, Ke; Zheng, Mei; Yan, Caiqing; Skoeld, Martin; Gustafsson, Oerjan
      Environmental Science & Technology (2015), 49 (4), 2038-2043CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)
      Thick haze plagued northeastern China in Jan. 2013, strongly affecting both regional climate and human respiratory health. Here, we present dual carbon isotope constrained (Δ14C and δ13C) source apportionment for combustion-derived black carbon aerosol (BC) for three key hotspot regions (megacities): North China Plain (NCP, Beijing), the Yangtze River Delta (YRD, Shanghai), and the Pearl River Delta (PRD, Guangzhou) for Jan. 2013. BC, here quantified as elemental carbon (EC), is one of the most health-detrimental components of PM2.5 and a strong climate warming agent. The results show that these severe haze events were equally affected (∼30%) by biomass combustion in all three regions, whereas the sources of the dominant fossil fuel component was dramatically different between north and south. In the NCP region, coal combustion accounted for 66% (46-74%, 95% C.I.) of the EC, whereas, in the YRD and PRD regions, liq. fossil fuel combustion (e.g., traffic) stood for 46% (18-66%) and 58% (38-68%), resp. Taken together, these findings suggest the need for a regionally-specific description of BC sources in climate models and regionally-tailored mitigation to combat severe air pollution events in East Asia.
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      Bi, J. R.; Huang, J. P.; Hu, Z. Y.; Holben, B. N.; Guo, Z. Q. Investigating the aerosol optical and radiative characteristics of heavy haze episodes in Beijing during January of 2013 J. Geophys. Res. Atmos. 2014, 119 (16) 9884– 9900 DOI: 10.1002/2014JD021757
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      Wang, Y.; Zhang, Q.; Jiang, J.; Zhou, W.; Wang, B.; He, K.; Duan, F.; Zhang, Q.; Philip, S.; Xie, Y. Enhanced sulfate formation during China’s severe winter haze episode in January 2013 missing from current models J. Geophys. Res. Atmos. 2014, 119 (17) 10425– 10440 DOI: 10.1002/2013JD021426
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      Chu, D. A.; Kaufman, Y. J.; Zibordi, G.; Chern, J. D.; Mao, J.; Li, C. C.; Holben, B. N. Global monitoring of air pollution over land from the Earth Observing System-Terra Moderate Resolution Imaging Spectroradiometer (MODIS) J. Geophys. Res. 2003, 108 (D21) 4661 DOI: 10.1029/2002JD003179
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      Koelemeijer, R. B. A.; Homan, C. D.; Matthijsen, J. Comparison of spatial and temporal variations of aerosol optical thickness and particulate matter over Europe Atmos. Environ. 2006, 40 (27) 5304– 5315 DOI: 10.1016/j.atmosenv.2006.04.044
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      Comparison of spatial and temporal variations of aerosol optical thickness and particulate matter over Europe
      Koelemeijer, R. B. A.; Homan, C. D.; Matthijsen, J.
      Atmospheric Environment (2006), 40 (27), 5304-5315CODEN: AENVEQ; ISSN:1352-2310. (Elsevier Ltd.)
      To mitigate the harmful effect of airborne particulate matter on human health, European Union-wide concn. limits were established; however, particulate matter (PM) measurements suffer from substantial uncertainty because PM is difficult to measure routinely, which is necessary for compliance monitoring. Different measurement and calibration methods are used in the many European air quality networks, consequently, understanding PM concns. over Europe as a whole is limited. This situation will be improved by using addnl. information from satellite observations. As a first step, a European comparison of spatiotemporal PM variations with aerosol optical thickness (AOT) measured by the MODIS satellite instrument for 2003, is discussed. MODIS measurements clearly showed major aerosol source regions in northern Italy, southern Poland, the Belgium/Netherlands/Ruhr area, and individual large cities and industrialized valleys (Rhone, Danube). The spatial correlation between annual av. PM10 and AOT was 0.6 for rural background sites; however, seasonal AOT and PM variations are distinctly different. Throughout most of Europe, MODIS-measured AOT showed a clear min. in winter. PM seasonal variations differ across Europe; at many sites, the seasonal variation is less marked than the AOT. Consequently, correlations between 1-yr AOT time-series with PM10/PM2.5 were low (0.3). Correlations between PM and AOT improved when AOT was divided by the boundary layer height, and, to a lesser extent, when it was cor. for aerosol growth with relative humidity. In that case, the av. correlation was 0.5 (PM10) and 0.6 (PM2.5), averaged over rural and (sub)urban background sites. Results indicated AOT measurements can be useful to improve PM distribution monitoring over Europe.
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      Zhang, H.; Hoff, R. M.; Engel-Cox, J. A. The Relation between Moderate Resolution Imaging Spectroradiometer (MODIS) Aerosol Optical Depth and PM2.5 over the United States: A Geographical Comparison by U.S. Environmental Protection Agency Regions J. Air Waste Manage. Assoc. 2009, 59 (11) 1358– 1369 DOI: 10.3155/1047-3289.59.11.1358
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      The relation between Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth and PM2.5 over the United States: a geographical comparison by U.S. Environmental Protection Agency regions
      Zhang Hai; Hoff Raymond M; Engel-Cox Jill A
      Journal of the Air & Waste Management Association (1995) (2009), 59 (11), 1358-69 ISSN:1096-2247.
      Aerosol optical depth (AOD) acquired from satellite measurements demonstrates good correlation with particulate matter with diameters less than 2.5 microm (PM2.5) in some regions of the United States and has been used for monitoring and nowcasting air quality over the United States. This work investigates the relation between Moderate Resolution Imaging Spectroradiometer (MODIS) AOD and PM2.5 over the 10 U.S. Environmental Protection Agency (EPA)-defined geographic regions in the United States on the basis of a 2-yr (2005-2006) match-up dataset of MODIS AOD and hourly PM2.5 measurements. The AOD retrievals demonstrate a geographical and seasonal variation in their relation with PM2.5. Good correlations are mostly observed over the eastern United States in summer and fall. The southeastern United States has the highest correlation coefficients at more than 0.6. The southwestern United States has the lowest correlation coefficient of approximately 0.2. The seasonal regression relations derived for each region are used to estimate the PM2.5 from AOD retrievals, and it is shown that the estimation using this method is more accurate than that using a fixed ratio between PM2.5 and AOD. Two versions of AOD from Terra (v4.0.1 and v5.2.6) are also compared in terms of the inversion methods and screening algorithms. The v5.2.6 AOD retrievals demonstrate better correlation with PM2.5 than v4.0.1 retrievals, but they have much less coverage because of the differences in the cloud-screening algorithm.
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      van Donkelaar, A.; Martin, R.; Brauer, M.; Kahn, R.; Levy, R.; Verduzco, C.; Villeneuve, P. Global estimates of ambient fine particulate matter concentrations from satellite-based aerosol optical depth: development and application Environ. Health Perspect. 2010, 118 (6) 847– 855 DOI: 10.1289/ehp.0901623
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      van Donkelaar, Aaron; Martin, Randall V.; Brauer, Michael; Kahn, Ralph; Levy, Robert; Verduzco, Carolyn; Villeneuve, Paul J.
      Environmental Health Perspectives (2010), 118 (6), 847-855CODEN: EVHPAZ; ISSN:0091-6765. (U. S. Department of Health and Human Services, Public Health Services)
      Epidemiol. and health impact studies of fine particulate matter with diam. < 2.5 μm (PM2.5) are limited by the lack of monitoring data, esp. in developing countries. Satellite observations offer valuable global information about PM2.5 concns. In this study, we developed a technique for estg. surface PM2.5 concns. from satellite observations. We mapped global ground-level PM2.5 concns. using total column aerosol optical depth (AOD) from the MODIS (Moderate Resoln. Imaging Spectroradiometer) and MISR (Multiangle Imaging Spectroradiometer) satellite instruments and coincident aerosol vertical profiles from the GEOS-Chem global chem. transport model. We detd. that global ests. of long-term av. (1 Jan. 2001 to 31 Dec. 2006) PM2.5 concns. at approx. 10 km × 10 km resoln. indicate a global population-weighted geometric mean PM2.5 concn. of 20 μg/m3. The World Health Organization Air Quality PM2.5 Interim Target-1 (35 μg/m3 annual av.) is exceeded over central and eastern Asia for 38% and for 50% of the population, resp. Annual mean PM2.5 concns. exceed 80 μg/m3 over eastern China. Our evaluation of the satellite-derived est. with ground-based in situ measurements indicates significant spatial agreement with North American measurements (r = 0.77; slope = 1.07; n = 1057) and with noncoincident measurements elsewhere (r = 0.83; slope = 0.86; n = 244). The 1 SD of uncertainty in the satellite-derived PM2.5 is 25%, which is inferred from the AOD retrieval and from aerosol vertical profile errors and sampling. The global population-weighted mean uncertainty is 6.7 μg/m3. Satellite-derived total-column AOD, when combined with a chem. transport model, provides ests. of global long-term av. PM2.5 concns.
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    13. 13
      Ma, Z.; Hu, X.; Huang, L.; Bi, J.; Liu, Y. Estimating ground-level PM2.5 in China using satellite remote sensing Environ. Sci. Technol. 2014, 48 (13) 7436– 44 DOI: 10.1021/es5009399
      13
      Estimating Ground-Level PM2.5 in China Using Satellite Remote Sensing
      Ma, Zongwei; Hu, Xuefei; Huang, Lei; Bi, Jun; Liu, Yang
      Environmental Science & Technology (2014), 48 (13), 7436-7444CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)
      Estg. ground-level PM2.5 from satellite-derived aerosol optical depth (AOD) using a spatial statistical model is a promising new method to evaluate the spatial and temporal characteristics of PM2.5 exposure in a large geog. region. However, studies outside North America have been limited due to the lack of ground PM2.5 measurements to calibrate the model. Taking advantage of the newly established national monitoring network, we developed a national-scale geog. weighted regression (GWR) model to est. daily PM2.5 concns. in China with fused satellite AOD as the primary predictor. The results showed that the meteorol. and land use information can greatly improve model performance. The overall cross-validation (CV) R2 is 0.64 and root mean squared prediction error (RMSE) is 32.98 μg/m3. The mean prediction error (MPE) of the predicted annual PM2.5 is 8.28 μg/m3. Our predicted annual PM2.5 concns. indicated that over 96% of the Chinese population lives in areas that exceed the Chinese National Ambient Air Quality Std. (CNAAQS) Level 2 std. Our results also confirmed satellite-derived AOD in conjunction with meteorol. fields and land use information can be successfully applied to extend the ground PM2.5 monitoring network in China.
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    14. 14
      Wang, J.; Christopher, S. A. Intercomparison between satellite-derived aerosol optical thickness and PM2.5 mass: Implications for air quality studies Geophys. Res. Lett. 2003, 30 (21) 2095 DOI: 10.1029/2003GL018174
      There is no corresponding record for this reference.
    15. 15
      Engel-Cox, J. A.; Holloman, C. H.; Coutant, B. W.; Hoff, R. M. Qualitative and quantitative evaluation of MODIS satellite sensor data for regional and urban scale air quality Atmos. Environ. 2004, 38 (16) 2495– 2509 DOI: 10.1016/j.atmosenv.2004.01.039
      15
      Qualitative and quantitative evaluation of MODIS satellite sensor data for regional and urban scale air quality
      Engel-Cox, Jill A.; Holloman, Christopher H.; Coutant, Basil W.; Hoff, Raymond M.
      Atmospheric Environment (2004), 38 (16), 2495-2509CODEN: AENVEQ; ISSN:1352-2310. (Elsevier Science B.V.)
      Advances in satellite sensors have provided new datasets for monitoring air quality at urban and regional scales. Qual. true color images and quant. aerosol optical depth data from the Moderate Resoln. Imaging Spectroradiometer (MODIS) sensor on the Terra satellite were compared with ground-based particulate matter data from US Environmental Protection Agency (EPA) monitoring networks covering the period from 1 Apr. to 30 Sept., 2002. Using both imagery and statistical anal., satellite data enabled the detn. of the regional sources of air pollution events, the general type of pollutant (smoke, haze, dust), the intensity of the events, and their motion. Very high and very low aerosol optical depths were found to be eliminated by the algorithm used to calc. the MODIS aerosol optical depth data. Correlations of MODIS aerosol optical depth with ground-based particulate matter were better in the eastern and Midwest portion of the United States (east of 100°W). Data were patchy and had poorer correlations in the western US, although the correlation was dependent on location. This variability is likely due to a combination of the differences between ground-based and column av. datasets, regression artifacts, variability of terrain, and MODIS cloud mask and aerosol optical depth algorithms. Preliminary anal. of the algorithms indicated that aerosol optical depth measurements calcd. from the sulfate-rich aerosol model may be more useful in predicting ground-based particulate matter levels, but further anal. would be required to verify the effect of the model on correlations. Overall, the use of satellite sensor data such as from MODIS has significant potential to enhance air quality monitoring over synoptic and regional scales.
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    16. 16
      Liu, Y.; Park, R. J.; Jacob, D. J.; Li, Q. B.; Kilaru, V.; Sarnat, J. A. Mapping annual mean ground-level PM2.5 concentrations using Multiangle Imaging Spectroradiometer aerosol optical thickness over the contiguous United States J. Geophys. Res. 2004, 109 (D22) D22206 DOI: 10.1029/2004JD005025
      There is no corresponding record for this reference.
    17. 17
      Boys, B. L.; Martin, R. V.; van Donkelaar, A.; MacDonell, R. J.; Hsu, N. C.; Cooper, M. J.; Yantosca, R. M.; Lu, Z.; Streets, D. G.; Zhang, Q.; Wang, S. W. Fifteen-Year Global Time Series of Satellite-Derived Fine Particulate Matter Environ. Sci. Technol. 2014, 48 (19) 11109– 11118 DOI: 10.1021/es502113p
      17
      Fifteen-Year Global Time Series of Satellite-Derived Fine Particulate Matter
      Boys, B. L.; Martin, R. V.; van Donkelaar, A.; MacDonell, R. J.; Hsu, N. C.; Cooper, M. J.; Yantosca, R. M.; Lu, Z.; Streets, D. G.; Zhang, Q.; Wang, S. W.
      Environmental Science & Technology (2014), 48 (19), 11109-11118CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)
      Ambient fine particulate matter (PM2.5) is a leading environmental risk factor for premature mortality. This work used aerosol optical depth (AOD) measurements from 2 satellite instruments, multi-angle imaging spectroradiometer and sea viewing wide field of vision sensor, to produce a unified 15-yr global time series (1998-2012) of ground-level PM2.5 concns. at a 1° x 1° resoln. The GEOS-chem chem. transport model related each individual AOD retrieval to ground-level PM2.5 concn. Four broad areas displaying significant, spatially coherent, annual trends were examd. in detail: eastern USA (-0.39 ± 0.10 μg/m3-yr), Arabian Peninsula (0.81 ± 0.21 μg/m3-yr), southern Asia (0.93 ± 0.22 μg/m3-yr), and eastern Asia (0.79 ± 0.27 μg/m3-yr). Over the dense in-situ observation period, 1999-2012, the linear tendency for the eastern USA (-0.37 ± 0.13 μg/m3-yr) agreed well with in-situ measurements (-0.38 ± 0.06 μg/m3-yr). A GEOS-Chem simulation showed secondary inorg. aerosols largely explained the obsd. PM2.5 trend over the eastern USA and southern and eastern Asia; mineral dust largely explained the obsd. trend over the Arabian Peninsula.
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    18. 18
      Hoff, R. M.; Christopher, S. A. Remote Sensing of Particulate Pollution from Space: Have We Reached the Promised Land? J. Air Waste Manage. Assoc. 2009, 59 (6) 645– 675 DOI: 10.3155/1047-3289.59.6.645
      18
      Remote sensing of particulate pollution from space: have we reached the promised land?
      Hoff, Raymond M.; Christopher, Sundar A.
      Journal of the Air & Waste Management Association (2009), 59 (6), 645-675CODEN: JAWAFC; ISSN:1096-2247. (Air & Waste Management Association)
      A review. The recent literature on satellite remote sensing of air quality is reviewed. 2009 Is the 50th anniversary of the first satellite atm. observations. For the first 40 of those years, atm. compn. measurements, meteorol., and atm. structure and dynamics dominated the missions launched. Since 1995, 42 instruments relevant to air quality measurements have been put into orbit. Trace gases such as ozone, nitric oxide, nitrogen dioxide, water, oxygen/tetraoxygen, bromine oxide, sulfur dioxide, formaldehyde, glyoxal, chlorine dioxide, chlorine monoxide, and nitrate radical have been measured in the stratosphere and troposphere in column measurements. Aerosol optical depth (AOD) is a focus of this review and a significant body of literature exists that shows that ground-level fine particulate matter (PM2.5) can be estd. from columnar AOD. Precision of the measurement of AOD is ±20% and the prediction of PM2.5 from AOD is order ±30% in the most careful studies. The air quality needs that can use such predictions are examd. Satellite measurements are important to event detection, transport and model prediction, and emission estn. It is suggested that ground-based measurements, models, and satellite measurements should be viewed as a system, each component of which is necessary to better understand air quality.
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    19. 19
      Liu, Y.; Paciorek, C. J.; Koutrakis, P. Estimating Regional Spatial and Temporal Variability of PM2.5 Concentrations Using Satellite Data, Meteorology, and Land Use Information Environ. Health Perspect. 2009, 117 (6) 886– 892 DOI: 10.1289/ehp.0800123
      19
      Estimating regional spatial and temporal variability of PM(2.5) concentrations using satellite data, meteorology, and land use information
      Liu Yang; Paciorek Christopher J; Koutrakis Petros
      Environmental health perspectives (2009), 117 (6), 886-92 ISSN:.
      BACKGROUND: Studies of chronic health effects due to exposures to particulate matter with aerodynamic diameters <or= 2.5 microm (PM(2.5)) are often limited by sparse measurements. Satellite aerosol remote sensing data may be used to extend PM(2.5) ground networks to cover a much larger area. OBJECTIVES: In this study we examined the benefits of using aerosol optical depth (AOD) retrieved by the Geostationary Operational Environmental Satellite (GOES) in conjunction with land use and meteorologic information to estimate ground-level PM(2.5) concentrations. METHODS: We developed a two-stage generalized additive model (GAM) for U.S. Environmental Protection Agency PM(2.5) concentrations in a domain centered in Massachusetts. The AOD model represents conditions when AOD retrieval is successful; the non-AOD model represents conditions when AOD is missing in the domain. RESULTS: The AOD model has a higher predicting power judged by adjusted R(2) (0.79) than does the non-AOD model (0.48). The predicted PM(2.5) concentrations by the AOD model are, on average, 0.8-0.9 microg/m(3) higher than the non-AOD model predictions, with a more smooth spatial distribution, higher concentrations in rural areas, and the highest concentrations in areas other than major urban centers. Although AOD is a highly significant predictor of PM(2.5), meteorologic parameters are major contributors to the better performance of the AOD model. CONCLUSIONS: GOES aerosol/smoke product (GASP) AOD is able to summarize a set of weather and land use conditions that stratify PM(2.5) concentrations into two different spatial patterns. Even if land use regression models do not include AOD as a predictor variable, two separate models should be fitted to account for different PM(2.5) spatial patterns related to AOD availability.
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    20. 20
      Kloog, I.; Nordio, F.; Coull, B. A.; Schwartz, J. Incorporating local land use regression and satellite aerosol optical depth in a hybrid model of spatiotemporal PM2.5 exposures in the Mid-Atlantic states Environ. Sci. Technol. 2012, 46 (21) 11913– 11921 DOI: 10.1021/es302673e
      20
      Incorporating Local Land Use Regression And Satellite Aerosol Optical Depth In A Hybrid Model Of Spatiotemporal PM2.5 Exposures In The Mid-Atlantic States
      Kloog, Itai; Nordio, Francesco; Coull, Brent A.; Schwartz, Joel
      Environmental Science & Technology (2012), 46 (21), 11913-11921CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)
      Satellite-derived aerosol optical depth (AOD) measurements have the potential to provide spatiotemporally resolved predictions of both long and short-term exposures, but previous studies have generally shown moderate predictive power and lacked detailed high spatio- temporal resoln. predictions across large domains. We aimed at extending our previous work by validating our model in another region with different geog. and metrol. characteristics, and incorporating fine scale land use regression and nonrandom missingness to better predict PM2.5 concns. for days with or without satellite AOD measures. We start by calibrating AOD data for 2000-2008 across the Mid-Atlantic. We used mixed models regressing PM2.5 measurements against day-specific random intercepts, and fixed and random AOD and temp. slopes. We used inverse probability weighting to account for nonrandom missingness of AOD, nested regions within days to capture spatial variation in the daily calibration, and introduced a penalization method that reduces the dimensionality of the large no. of spatial and temporal predictors without selecting different predictors in different locations. We then take advantage of the assocn. between grid-cell specific AOD values and PM2.5 monitoring data, together with assocns. between AOD values in neighboring grid cells to develop grid cell predictions when AOD is missing. Finally to get local predictions (at the resoln. of 50 m), we regressed the residuals from the predictions for each monitor from these previous steps against the local land use variables specific for each monitor. "Out-of-sample" 10-fold cross-validation was used to quantify the accuracy of our predictions at each step. For all days without AOD values, model performance was excellent (mean "out-of-sample" R2 = 0.81, year-to-year variation 0.79-0.84). Upon removal of outliers in the PM2.5 monitoring data, the results of the cross validation procedure was even better (overall mean "out of sample" R2 of 0.85). Further, cross validation results revealed no bias in the predicted concns. (Slope of obsd. vs predicted = 0.97-1.01). Our model allows one to reliably assess short-term and long-term human exposures in order to investigate both the acute and effects of ambient particles, resp.
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    21. 21
      Hu, X.; Waller, L. A.; Al-Hamdan, M. Z.; Crosson, W. L.; Estes, M. G., Jr.; Estes, S. M.; Quattrochi, D. A.; Sarnat, J. A.; Liu, Y. Estimating ground-level PM2.5 concentrations in the southeastern US using geographically weighted regression Environ. Res. 2013, 121, 1– 10 DOI: 10.1016/j.envres.2012.11.003
      There is no corresponding record for this reference.
    22. 22
      Song, W.; Jia, H.; Huang, J.; Zhang, Y. A satellite-based geographically weighted regression model for regional PM2.5 estimation over the Pearl River Delta region in China Remote. Sens. Environ. 2014, 154, 1– 7 DOI: 10.1016/j.rse.2014.08.008
      There is no corresponding record for this reference.
    23. 23
      Lee, H. J.; Liu, Y.; Coull, B. A.; Schwartz, J.; Koutrakis, P. A novel calibration approach of MODIS AOD data to predict PM2.5 concentrations Atmos. Chem. Phys. 2011, 11 (15) 7991– 8002 DOI: 10.5194/acp-11-7991-2011
      23
      A novel calibration approach of MODIS AOD data to predict PM2.5 concentrations
      Lee, H. J.; Liu, Y.; Coull, B. A.; Schwartz, J.; Koutrakis, P.
      Atmospheric Chemistry and Physics (2011), 11 (15, Pt. 2), 7991-8002CODEN: ACPTCE; ISSN:1680-7316. (Copernicus Publications)
      Epidemiol. studies studying the human health effects of PM2.5 are susceptible to exposure measurement errors, a form of bias in exposure ests., since they rely on data from a limited no. of PM2.5 monitors within their study area. Satellite data can be used to expand spatial coverage, potentially enhancing the authors' ability to est. location-sp. or subject-sp. exposures to PM2.5, but some have reported poor predictive power. A new methodol. was developed to calibrate aerosol optical depth (AOD) data obtained from the Moderate Resoln. Imaging Spectroradiometer (MODIS). Subsequently, this method was used to predict ground daily PM2.5 concns. in the New England region. 2003 MODIS AOD data corresponding to the New England region were retrieved, and PM2.5 concns. measured at 26 US Environmental Protection Agency (EPA) PM2.5 monitoring sites were used to calibrate the AOD data. A mixed effects model which allows day-to-day variability in daily PM2.5-AOD relations was used to predict location-specific PM2.5 levels. PM2.5 concns. measured at the monitoring sites were compared to those predicted for the corresponding grid cells. Both cross-sectional and longitudinal comparisons between the obsd. and predicted concns. suggested that the proposed new calibration approach renders MODIS AOD data a potentially useful predictor of PM2.5 concns. Also, the estd. PM2.5 levels within the study domain were examd. in relation to air pollution sources. The authors' approach made it possible to study the spatial patterns of PM2.5 concns. within the study domain.
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    24. 24
      Remer, L. A.; Mattoo, S.; Levy, R. C.; Munchak, L. MODIS 3 km aerosol product: algorithm and global perspective Atmos. Meas. Tech. 2013, 6 (7) 1829– 1844 DOI: 10.5194/amt-6-1829-2013
      There is no corresponding record for this reference.
    25. 25
      MEPCN. Determination of atmospheric articles PM10 and PM2.5 in ambient air by gravimetric method. Available at http://english.mep.gov.cn/standards_reports/ (accessed Dec 10, 2014) .
      There is no corresponding record for this reference.
    26. 26
      Remer, L. A.; Kaufman, Y. J.; Tanre, D.; Mattoo, S.; Chu, D. A.; Martins, J. V.; Li, R. R.; Ichoku, C.; Levy, R. C.; Kleidman, R. G. The MODIS aerosol algorithm, products, and validation J. Atmos. Sci. 2005, 62 (4) 947– 973 DOI: 10.1175/JAS3385.1
      There is no corresponding record for this reference.
    27. 27
      Munchak, L. A.; Levy, R. C.; Mattoo, S.; Remer, L. A.; Holben, B. N.; Schafer, J. S.; Hostetler, C. A.; Ferrare, R. A. MODIS 3 km aerosol product: applications over land in an urban/suburban region Atmos. Meas. Tech. 2013, 6 (7) 1747– 1759 DOI: 10.5194/amt-6-1747-2013
      There is no corresponding record for this reference.
    28. 28
      Fu, J. Y.; Jiang, D.; Huang, Y. H. 1 KM Grid Population Dataset of China (PopulationGrid_China). Global Change Research Data Publishing & Repository. 2014. http://www.geodoi.ac.cn/ (accessed Aug 1, 2015). DOI: DOI: 10.3974/geodb.2014.01.06.V1 .
      There is no corresponding record for this reference.
    29. 29
      Ji, W.; Wang, Y.; Zhuang, D.; Song, D.; Shen, X.; Wang, W.; Li, G. Spatial and temporal distribution of expressway and its relationships to land cover and population: A case study of Beijing, China Transport. Res. Part D: Trans. Environ. 2014, 32, 86– 96 DOI: 10.1016/j.trd.2014.07.010
      There is no corresponding record for this reference.
    30. 30
      Levy, R. C.; Mattoo, S.; Munchak, L. A.; Remer, L. A.; Sayer, A. M.; Patadia, F.; Hsu, N. C. The Collection 6 MODIS aerosol products over land and ocean Atmos. Meas. Tech. 2013, 6 (11) 2989– 3034 DOI: 10.5194/amt-6-2989-2013
      There is no corresponding record for this reference.
    31. 31
      You, W.; Zang, Z.; Pan, X.; Zhang, L.; Chen, D. Estimating PM2.5 in Xi’an, China using aerosol optical depth: a comparison between the MODIS and MISR retrieval models Sci. Total Environ. 2015, 505, 1156– 65 DOI: 10.1016/j.scitotenv.2014.11.024
      There is no corresponding record for this reference.
    32. 32
      Lin, C.; Li, Y.; Yuan, Z.; Lau, A. K. H.; Li, C.; Fung, J. C. H. Using satellite remote sensing data to estimate the high-resolution distribution of ground-level PM2.5 Remote. Sens. Environ. 2015, 156, 117– 128 DOI: 10.1016/j.rse.2014.09.015
      There is no corresponding record for this reference.
    33. 33
      Hu, X. F.; Waller, L. A.; Lyapustin, A.; Wang, Y. J.; Al-Hamdan, M. Z.; Crosson, W. L.; Estes, M. G.; Estes, S. M.; Quattrochi, D. A.; Puttaswamy, S. J.; Liu, Y. Estimating ground-level PM2.5 concentrations in the Southeastern United States using MAIAC AOD retrievals and a two-stage model Remote. Sens. Environ. 2014, 140, 220– 232 DOI: 10.1016/j.rse.2013.08.032
      There is no corresponding record for this reference.
    34. 34
      Chudnovsky, A. A.; Koutrakis, P.; Kloog, I.; Melly, S.; Nordio, F.; Lyapustin, A.; Wang, Y.; Schwartz, J. Fine particulate matter predictions using high resolution Aerosol Optical Depth (AOD) retrievals Atmos. Environ. 2014, 89, 189– 198 DOI: 10.1016/j.atmosenv.2014.02.019
      34
      Fine particulate matter predictions using high resolution Aerosol Optical Depth (AOD) retrievals
      Chudnovsky, Alexandra A.; Koutrakis, Petros; Kloog, Itai; Melly, Steven; Nordio, Francesco; Lyapustin, Alexei; Wang, Yujie; Schwartz, Joel
      Atmospheric Environment (2014), 89 (), 189-198CODEN: AENVEQ; ISSN:1352-2310. (Elsevier Ltd.)
      To date, spatial-temporal patterns of particulate matter (PM) within urban areas have primarily been examd. using models. On the other hand, satellites extend spatial coverage but their spatial resoln. is too coarse. In order to address this issue, here we report on spatial variability in PM levels derived from high 1 km resoln. AOD product of Multi-Angle Implementation of Atm. Correction (MAIAC) algorithm developed for MODIS satellite. We apply day-specific calibrations of AOD data to predict PM2.5 concns. within the New England area of the United States. To improve the accuracy of our model, land use and meteorol. variables were incorporated. We used inverse probability weighting (IPW) to account for nonrandom missingness of AOD and nested regions within days to capture spatial variation. With this approach we can control for the inherent day-to-day variability in the AOD-PM2.5 relationship, which depends on time-varying parameters such as particle optical properties, vertical and diurnal concn. profiles and ground surface reflectance among others. Out-of-sample "ten-fold" cross-validation was used to quantify the accuracy of model predictions. Our results show that the model-predicted PM2.5 mass concns. are highly correlated with the actual observations, with out-of-sample R2 of 0.89. Furthermore, our study shows that the model captures the pollution levels along highways and many urban locations thereby extending our ability to investigate the spatial patterns of urban air quality, such as examg. exposures in areas with high traffic. Our results also show high accuracy within the cities of Boston and New Haven thereby indicating that MAIAC data can be used to examine intra-urban exposure contrasts in PM2.5 levels.
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    35. 35
      Schwab, J. J.; Felton, H. D.; Rattigan, O. V.; Demerjian, K. L. New York state urban and rural measurements of continuous PM2.5 mass by FDMS, TEOM, and BAM J. Air Waste Manage. Assoc. 2006, 56 (4) 372– 383 DOI: 10.1080/10473289.2006.10464523
      35
      New York State urban and rural measurements of continuous PM2.5 mass by FDMS, TEOM, and BAM
      Schwab James J; Felton Henry D; Rattigan Oliver V; Demerjian Kenneth L
      Journal of the Air & Waste Management Association (1995) (2006), 56 (4), 372-83 ISSN:1096-2247.
      Field evaluations and comparisons of continuous fine particulate matter (PM2,5) mass measurement technologies at an urban and a rural site in New York state are performed. The continuous measurement technologies include the filter dynamics measurement system (FDMS) tapered element oscillating microbalance (TEOM) monitor, the stand-alone TEOM monitor (without the FDMS), and the beta attenuation monitor (BAM). These continuous measurement methods are also compared with 24-hr integrated filters collected and analyzed under the Federal Reference Method (FRM) protocol. The measurement sites are New York City (the borough of Queens) and Addison, a rural area of southwestern New York state. New York City data comparisons between the FDMS TEOM, BAM, and FRM are examined for bias and seasonality during a 2-yr period. Data comparisons for the FDMS TEOM and FRM from the Addison location are examined for the same 2-yr period. The BAM and FDMS measurements at Queens are highly correlated with each other and the FRM. The BAM and FDMS are very similar to each other in magnitude, and both are approximately 25% higher than the FRM filter measurements at this site. The FDMS at Addison measures approximately 9% more mass than the FRM. Mass reconstructions using the speciation trends network filter data are examined to provide insight as to the contribution of volatile species of PM2.5 in the FDMS mass measurement and the fraction that is likely lost in the FRM mass measurement. The reconstructed mass at Queens is systematically lower than the FDMS by approximately 10%.
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    36. 36
      Engel-Cox, J.; Nguyen Thi Kim, O.; van Donkelaar, A.; Martin, R. V.; Zell, E. Toward the next generation of air quality monitoring: Particulate Matter Atmos. Environ. 2013, 80, 584– 590 DOI: 10.1016/j.atmosenv.2013.08.016
      There is no corresponding record for this reference.

  • Supporting Information

    Supporting Information

    The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.est.5b01413.

    • Texts S1_S5, Figures S1–S4, and Tables S1–S5 (PDF)


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