ACS Publications. Most Trusted. Most Cited. Most Read
My Activity
CONTENT TYPES

Figure 1Loading Img

Daily Estimation of Ground-Level PM2.5 Concentrations over Beijing Using 3 km Resolution MODIS AOD

View Author Information
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]
Cite this: Environ. Sci. Technol. 2015, 49, 20, 12280–12288
Publication Date (Web):August 27, 2015
https://doi.org/10.1021/acs.est.5b01413

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

  • Open Access
  • Editors Choice

Article Views

13882

Altmetric

-

Citations

LEARN ABOUT THESE METRICS
PDF (1 MB)
Supporting Info (1)»

Abstract

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.

Introduction

ARTICLE SECTIONS
Jump To

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

ARTICLE SECTIONS
Jump To

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.

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 sjN(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

ARTICLE SECTIONS
Jump To

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).

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).

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.

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

ARTICLE SECTIONS
Jump To

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)

Terms & Conditions

Most electronic Supporting Information files are available without a subscription to ACS Web Editions. Such files may be downloaded by article for research use (if there is a public use license linked to the relevant article, that license may permit other uses). Permission may be obtained from ACS for other uses through requests via the RightsLink permission system: http://pubs.acs.org/page/copyright/permissions.html.

Author Information

ARTICLE SECTIONS
Jump To

  • Corresponding Author
    • Yuxuan Wang - Ministry of Education Key Laboratory for Earth System Modeling, Center for Earth System Science, Tsinghua University, Beijing 100084, ChinaDepartment of Marine Science, Texas A&M University at Galveston, Galveston, Texas 77553, United StatesDepartment 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

ARTICLE SECTIONS
Jump To

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

ARTICLE SECTIONS
Jump To

This article references 36 other publications.

  1. 1
    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
  2. 2
    Guo, S.; Hu, M.; Zamora, M. L.; Peng, J. F.; Shang, D. J.; Zheng, J.; Du, Z. F.; Wu, Z.; Shao, M.; Zeng, L. M. Elucidating severe urban haze formation in China Proc. Natl. Acad. Sci. U. S. A. 2014, 111 (49) 17373 17378 DOI: 10.1073/pnas.1419604111
  3. 3
    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
  4. 4
    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
  5. 5
    Tao, M.; Chen, L.; Wang, Z.; Tao, J.; Su, L. Satellite observation of abnormal yellow haze clouds over East China during summer agricultural burning season Atmos. Environ. 2013, 79, 632 640 DOI: 10.1016/j.atmosenv.2013.07.033
  6. 6
    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
  7. 7
    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
  8. 8
    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
  9. 9
    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
  10. 10
    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
  11. 11
    Schaap, M.; Apituley, A.; Timmermans, R. M. A.; Koelemeijer, R. B. A.; de Leeuw, G. Exploring the relation between aerosol optical depth and PM2.5 at Cabauw, the Netherlands Atmos. Chem. Phys. 2009, 9 (3) 909 925 DOI: 10.5194/acp-9-909-2009
  12. 12
    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
  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
  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
  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
  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
  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
  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
  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
  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
  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
  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
  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
  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
  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) .
  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
  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
  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 .
  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
  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
  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
  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
  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
  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
  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
  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

Cited By

ARTICLE SECTIONS
Jump To

This article is cited by 237 publications.

  1. Zhaofeng Tan, Keding Lu, Xuefei Ma, Shiyi Chen, Lingyan He, Xiaofeng Huang, Xin Li, Xiaoyu Lin, Mengxue Tang, Dan Yu, Andreas Wahner, Yuanhang Zhang. Multiple Impacts of Aerosols on O3 Production Are Largely Compensated: A Case Study Shenzhen, China. Environmental Science & Technology 2022, 56 (24) , 17569-17580. https://doi.org/10.1021/acs.est.2c06217
  2. Rong Wang, Yechen Yang, Xiaofan Xing, Lin Wang, Jianmin Chen, Xu Tang, Junji Cao, Lidia Morawska, Yves Balkanski, Didier Hauglustaine, Philippe Ciais, Jianmin Ma. Stringent Emission Controls Are Needed to Reach Clean Air Targets for Cities in China under a Warming Climate. Environmental Science & Technology 2022, 56 (16) , 11199-11211. https://doi.org/10.1021/acs.est.1c08403
  3. Conghong Huang, Jianlin Hu, Tao Xue, Hao Xu, Meng Wang. High-Resolution Spatiotemporal Modeling for Ambient PM2.5 Exposure Assessment in China from 2013 to 2019. Environmental Science & Technology 2021, 55 (3) , 2152-2162. https://doi.org/10.1021/acs.est.0c05815
  4. Alaa Mhawish, Tirthankar Banerjee, Meytar Sorek-Hamer, Muhammad Bilal, Alexei I. Lyapustin, Robert Chatfield, David M. Broday. Estimation of High-Resolution PM2.5 over the Indo-Gangetic Plain by Fusion of Satellite Data, Meteorology, and Land Use Variables. Environmental Science & Technology 2020, 54 (13) , 7891-7900. https://doi.org/10.1021/acs.est.0c01769
  5. Hyung Joo Lee. Benefits of High Resolution PM2.5 Prediction using Satellite MAIAC AOD and Land Use Regression for Exposure Assessment: California Examples. Environmental Science & Technology 2019, 53 (21) , 12774-12783. https://doi.org/10.1021/acs.est.9b03799
  6. Baolei Lyu, Yongtao Hu, Wenxian Zhang, Yunsong Du, Bin Luo, Xiaoling Sun, Zhe Sun, Zhu Deng, Xiaojiang Wang, Jun Liu, Xuesong Wang, Armistead G. Russell. Fusion Method Combining Ground-Level Observations with Chemical Transport Model Predictions Using an Ensemble Deep Learning Framework: Application in China to Estimate Spatiotemporally-Resolved PM2.5 Exposure Fields in 2014–2017. Environmental Science & Technology 2019, 53 (13) , 7306-7315. https://doi.org/10.1021/acs.est.9b01117
  7. Miaomiao Liu, Jun Bi, and Zongwei Ma . Visibility-Based PM2.5 Concentrations in China: 1957–1964 and 1973–2014. Environmental Science & Technology 2017, 51 (22) , 13161-13169. https://doi.org/10.1021/acs.est.7b03468
  8. Jianmin Ma, Staci Simonich, and Shu Tao . New Discoveries to Old Problems: A Virtual Issue on Air Pollution in Rapidly Industrializing Countries. Environmental Science & Technology 2017, 51 (20) , 11497-11501. https://doi.org/10.1021/acs.est.7b04885
  9. Hyung Joo Lee, Robert B. Chatfield, and Anthony W. Strawa . Enhancing the Applicability of Satellite Remote Sensing for PM2.5 Estimation Using MODIS Deep Blue AOD and Land Use Regression in California, United States. Environmental Science & Technology 2016, 50 (12) , 6546-6555. https://doi.org/10.1021/acs.est.6b01438
  10. Baolei Lv, Yongtao Hu, Howard H. Chang, Armistead G. Russell, and Yuqi Bai . Improving the Accuracy of Daily PM2.5 Distributions Derived from the Fusion of Ground-Level Measurements with Aerosol Optical Depth Observations, a Case Study in North China. Environmental Science & Technology 2016, 50 (9) , 4752-4759. https://doi.org/10.1021/acs.est.5b05940
  11. Armita Kar, Mohammed Ahmed, Andrew A. May, Huyen T.K. Le. High spatio-temporal resolution predictions of PM2.5 using low-cost sensor data. Atmospheric Environment 2024, 326 , 120486. https://doi.org/10.1016/j.atmosenv.2024.120486
  12. Kai Zhang, Jeffrey Lin, Yuanfei Li, Yue Sun, Weitian Tong, Fangyu Li, Lung-Chang Chien, Yiping Yang, Wei-Chung Su, Hezhong Tian, Peng Fu, Fengxiang Qiao, Xiaobo Xue Romeiko, Shao Lin, Sheng Luo, Elena Craft. Unmasking the sky: high-resolution PM2.5 prediction in Texas using machine learning techniques. Journal of Exposure Science & Environmental Epidemiology 2024, 10 https://doi.org/10.1038/s41370-024-00659-w
  13. Vasudev Malyan, Vikas Kumar, Manoranjan Sahu, Jai Prakash, Shruti Choudhary, Ramesh Raliya, Tandeep S. Chadha, Jiaxi Fang, Pratim Biswas. Calibrating low-cost sensors using MERRA-2 reconstructed PM2.5 mass concentration as a proxy. Atmospheric Pollution Research 2024, 15 (3) , 102027. https://doi.org/10.1016/j.apr.2023.102027
  14. Shuhui Wu, Yuxin Sun, Rui Bai, Xingxing Jiang, Chunlin Jin, Yong Xue. Estimation of PM2.5 and PM10 Mass Concentrations in Beijing Using Gaofen-1 Data at 100 m Resolution. Remote Sensing 2024, 16 (4) , 604. https://doi.org/10.3390/rs16040604
  15. Pongsakon Punpukdee, Ekbordin Winijkul, Pyae Phyo Kyaw, Salvatore G. P. Virdis, Wenchao Xue, Thi Phuoc Lai Nguyen. Estimation of hourly one square kilometer fine particulate matter concentration over Thailand using aerosol optical depth. Frontiers in Environmental Science 2024, 11 https://doi.org/10.3389/fenvs.2023.1303152
  16. Phan Hong Danh Pham, Vu Hien Phan. Exploring the Impact of Covid-19 on Air Quality Using Sentinel-5P and MODIS Data in Ho Chi Minh City. 2024, 1650-1659. https://doi.org/10.1007/978-981-99-7434-4_178
  17. Tan Mi, Die Tang, Jianbo Fu, Wen Zeng, Michael L. Grieneisen, Zihang Zhou, Fengju Jia, Fumo Yang, Yu Zhan. Data augmentation for bias correction in mapping PM2.5 based on satellite retrievals and ground observations. Geoscience Frontiers 2024, 15 (1) , 101686. https://doi.org/10.1016/j.gsf.2023.101686
  18. Bingqing Lu, Xue Meng, Shanshan Dong, Zekun Zhang, Chao Liu, Jiakui Jiang, Hartmut Herrmann, Xiang Li. High-resolution mapping of regional VOCs using the enhanced space-time extreme gradient boosting machine (XGBoost) in Shanghai. Science of The Total Environment 2023, 905 , 167054. https://doi.org/10.1016/j.scitotenv.2023.167054
  19. Bingqing Lu, Chao Liu, Xue Meng, Zekun Zhang, Hartmut Herrmann, Xiang Li. High‐Resolution Mapping of Regional NMVOCs Using the Fast Space‐Time Light Gradient Boosting Machine (LightGBM). Journal of Geophysical Research: Atmospheres 2023, 128 (22) https://doi.org/10.1029/2023JD039591
  20. Yue Jing, Long Pan, Yanling Sun. Estimating PM 2.5 concentrations in a central region of China using a three-stage model. International Journal of Digital Earth 2023, 16 (1) , 578-592. https://doi.org/10.1080/17538947.2023.2175499
  21. Rackhun Son, Dimitris Stratoulias, Hyun Cheol Kim, Jin-Ho Yoon. Estimation of surface PM2.5 concentrations from atmospheric gas species retrieved from TROPOMI using deep learning: Impacts of fire on air pollution over Thailand. Atmospheric Pollution Research 2023, 14 (10) , 101875. https://doi.org/10.1016/j.apr.2023.101875
  22. Pimchanok Wongnakae, Pakkapong Chitchum, Rungduen Sripramong, Arthit Phosri. Application of satellite remote sensing data and random forest approach to estimate ground-level PM2.5 concentration in Northern region of Thailand. Environmental Science and Pollution Research 2023, 30 (38) , 88905-88917. https://doi.org/10.1007/s11356-023-28698-0
  23. Weeberb J. Requia, Ana Maria Vicedo-Cabrera, Evan de Schrijver, Heresh Amini, Antonio Gasparrini. Association of high ambient temperature with daily hospitalization for cardiorespiratory diseases in Brazil: A national time-series study between 2008 and 2018. Environmental Pollution 2023, 331 , 121851. https://doi.org/10.1016/j.envpol.2023.121851
  24. Vu Hien Phan, Danh Phan Hong Pham, Tran Vu Pham, Kashif Naseer Qureshi, Cuong Pham-Quoc. An IoT System and MODIS Images Enable Smart Environmental Management for Mekong Delta. Future Internet 2023, 15 (7) , 245. https://doi.org/10.3390/fi15070245
  25. Meera Goswami, Vinod Kumar, Narendra Singh, Pankaj Kumar. A biochemical and morphological study with multiple linear regression modeling–based impact prediction of ambient air pollutants on some native tree species of Haldwani City of Kumaun Himalaya, Uttarakhand, India. Environmental Science and Pollution Research 2023, 30 (30) , 74900-74915. https://doi.org/10.1007/s11356-023-27563-4
  26. Jiandong Wang, Hang Su, Chao Wei, Guangjie Zheng, Jiaping Wang, Tianning Su, Chengcai Li, Cheng Liu, Jonathan E. Pleim, Zhanqing Li, Aijun Ding, Meinrat O. Andreae, Ulrich Pöschl, Yafang Cheng. Black-carbon-induced regime transition of boundary layer development strongly amplifies severe haze. One Earth 2023, 6 (6) , 751-759. https://doi.org/10.1016/j.oneear.2023.05.010
  27. Kwang Nyun Kim, Seung Hee Kim, Sang Seo Park, Yun Gon Lee. Feasibility analysis of AERONET lunar AOD for nighttime particulate matter estimation. Environmental Research Communications 2023, 5 (5) , 051004. https://doi.org/10.1088/2515-7620/accfe9
  28. Chukwuma Moses Anoruo, Syed Nisar Hussain Bukhari, Okechukwu Kelechi Nwofor. Modeling and spatial characterization of aerosols at Middle East AERONET stations. Theoretical and Applied Climatology 2023, 152 (1-2) , 617-625. https://doi.org/10.1007/s00704-023-04384-6
  29. Zhuldyz Darynova, Milad Malekipirbazari, Daryn Shabdirov, Haider A. Khwaja, Mehdi Amouei Torkmahalleh. Reliability and stability of a statistical model to predict ground-based PM2.5 over 10 years in Karachi, Pakistan, using satellite observations. Air Quality, Atmosphere & Health 2023, 16 (4) , 669-679. https://doi.org/10.1007/s11869-022-01296-8
  30. Jintao Gong, Lei Ding, Yingyu Lu, Qiong Zhang, Yun Li, Beidi Diao. Scientometric and multidimensional contents analysis of PM2.5 concentration prediction. Heliyon 2023, 9 (3) , e14526. https://doi.org/10.1016/j.heliyon.2023.e14526
  31. Yogita Karale, May Yuan. Spatially lagged predictors from a wider area improve PM2.5 estimation at a finer temporal interval—A case study of Dallas-Fort Worth, United States. Frontiers in Remote Sensing 2023, 4 https://doi.org/10.3389/frsen.2023.1041466
  32. Özgür Zeydan, Salman Tariq, Fazzal Qayyum, Usman Mehmood, Zia Ul-Haq. Investigating the long-term trends in aerosol optical depth and its association with meteorological parameters and enhanced vegetation index over Turkey. Environmental Science and Pollution Research 2023, 30 (8) , 20337-20356. https://doi.org/10.1007/s11356-022-23553-0
  33. Yinchi Ma, , , , . Spatiotemporal dynamic interpolation simulation and prediction method of fine particulate matter based on multi-source pollution model. E3S Web of Conferences 2023, 393 , 03008. https://doi.org/10.1051/e3sconf/202339303008
  34. Phan Hong Danh Pham, Dang Khoa Le, Thi Minh Trang Nguyen, Vu Hien Phan. Estimating PM2.5 Mass Concentration from MODIS AOD Products in Ho Chi Minh City, Vietnam. 2023, 579-588. https://doi.org/10.1007/978-981-19-3303-5_51
  35. Ruonan Fan, Yingying Ma, Shikuan Jin, Wei Gong, Boming Liu, Weiyan Wang, Hui Li, Yiqun Zhang. Validation, analysis, and comparison of MISR V23 aerosol optical depth products with MODIS and AERONET observations. Science of The Total Environment 2023, 856 , 159117. https://doi.org/10.1016/j.scitotenv.2022.159117
  36. Lizhi Miao, Sheng Tang, Yanhui Ren, Mei-Po Kwan, Kai Zhang. Estimation of daily ground-level PM2.5 concentrations over the Pearl River Delta using 1 km resolution MODIS AOD based on multi-feature BiLSTM. Atmospheric Environment 2022, 290 , 119362. https://doi.org/10.1016/j.atmosenv.2022.119362
  37. Renzhen Peng, Wenhui Yang, Wenpu Shao, Bin Pan, Yaning Zhu, Yubin Zhang, Haidong Kan, Yanyi Xu, Zhekang Ying. Deficiency of interleukin-6 receptor ameliorates PM2.5 exposure-induced pulmonary dysfunction and inflammation but not abnormalities in glucose homeostasis. Ecotoxicology and Environmental Safety 2022, 247 , 114253. https://doi.org/10.1016/j.ecoenv.2022.114253
  38. Bussayaporn Peng-in, Peeyaporn Sanitluea, Pimnapat Monjatturat, Pattaraporn Boonkerd, Arthit Phosri. Estimating ground-level PM2.5 over Bangkok Metropolitan Region in Thailand using aerosol optical depth retrieved by MODIS. Air Quality, Atmosphere & Health 2022, 15 (11) , 2091-2102. https://doi.org/10.1007/s11869-022-01238-4
  39. Bhupal Shrestha, Jerald A. Brotzge, Junhong Wang. Observations and Impacts of Long‐Range Transported Wildfire Smoke on Air Quality Across New York State During July 2021. Geophysical Research Letters 2022, 49 (19) https://doi.org/10.1029/2022GL100216
  40. Pascoal M.D. Campos, José C.M. Pires, Anabela A. Leitão. Assessment of aerosols over five cities of Angola based on MERRA–2 reanalysis data. Atmospheric Pollution Research 2022, 13 (10) , 101569. https://doi.org/10.1016/j.apr.2022.101569
  41. Qiangqiang Guo, Mengjuan Ren, Shouyuan Wu, Yajia Sun, Jianjian Wang, Qi Wang, Yanfang Ma, Xuping Song, Yaolong Chen. Applications of artificial intelligence in the field of air pollution: A bibliometric analysis. Frontiers in Public Health 2022, 10 https://doi.org/10.3389/fpubh.2022.933665
  42. Lei Yao, Shuo Sun, Yixu Wang, Chaoxue Song, Ying Xu. New insight into the urban PM2.5 pollution island effect enabled by the Gaussian surface fitting model: A case study in a mega urban agglomeration region of China. International Journal of Applied Earth Observation and Geoinformation 2022, 113 , 102982. https://doi.org/10.1016/j.jag.2022.102982
  43. Wenhao Chu, Chunxiao Zhang, Yuwei Zhao, Rongrong Li, Pengda Wu. Spatiotemporally Continuous Reconstruction of Retrieved PM2.5 Data Using an Autogeoi-Stacking Model in the Beijing-Tianjin-Hebei Region, China. Remote Sensing 2022, 14 (18) , 4432. https://doi.org/10.3390/rs14184432
  44. Weijie Fu, Xu Yue, Zhengqiang Li, Chenguang Tian, Hao Zhou, Kaitao Li, Yuwen Chen, Xu Zhao, Yuan Zhao, Yihan Hu. Decoupling between PM2.5 concentrations and aerosol optical depth at ground stations in China. Frontiers in Environmental Science 2022, 10 https://doi.org/10.3389/fenvs.2022.979918
  45. Padmavati Kulkarni, V. Sreekanth, Adithi R. Upadhya, Hrishikesh Chandra Gautam. Which model to choose? Performance comparison of statistical and machine learning models in predicting PM2.5 from high-resolution satellite aerosol optical depth. Atmospheric Environment 2022, 282 , 119164. https://doi.org/10.1016/j.atmosenv.2022.119164
  46. Xiaohui Yang, Dengpan Xiao, Lihang Fan, Fuxing Li, Wei Wang, Huizi Bai, Jianzhao Tang. Spatiotemporal estimates of daily PM2.5 concentrations based on 1-km resolution MAIAC AOD in the Beijing–Tianjin–Hebei, China. Environmental Challenges 2022, 8 , 100548. https://doi.org/10.1016/j.envc.2022.100548
  47. Seohui Park, Jungho Im, Jhoon Kim, Sang-Min Kim. Geostationary satellite-derived ground-level particulate matter concentrations using real-time machine learning in Northeast Asia. Environmental Pollution 2022, 306 , 119425. https://doi.org/10.1016/j.envpol.2022.119425
  48. B. Mahesh, Venkataraman Sivakumar, Padmavati Kulkarni, V. Sreekanth. Particulate air pollution in Durban: Characteristics and its relationship with 1 km resolution satellite aerosol optical depth. Advances in Space Research 2022, 70 (2) , 371-382. https://doi.org/10.1016/j.asr.2022.04.053
  49. Jia Xu, Zhenchun Yang, Bin Han, Wen Yang, Yusen Duan, Qingyan Fu, Zhipeng Bai. A unified empirical modeling approach for particulate matter and NO2 in a coastal city in China. Chemosphere 2022, 299 , 134384. https://doi.org/10.1016/j.chemosphere.2022.134384
  50. Hongbo Zhao, Yaxin Liu, Tianshun Gu, Hui Zheng, Zheye Wang, Dongyang Yang. Identifying Spatiotemporal Heterogeneity of PM2.5 Concentrations and the Key Influencing Factors in the Middle and Lower Reaches of the Yellow River. Remote Sensing 2022, 14 (11) , 2643. https://doi.org/10.3390/rs14112643
  51. Travis D. Toth, Jianglong Zhang, Mark A. Vaughan, Jeffrey S. Reid, James R. Campbell. Retrieving particulate matter concentrations over the contiguous United States using CALIOP observations. Atmospheric Environment 2022, 274 , 118979. https://doi.org/10.1016/j.atmosenv.2022.118979
  52. Jana Handschuh, Thilo Erbertseder, Martijn Schaap, Frank Baier. Estimating PM2.5 surface concentrations from AOD: A combination of SLSTR and MODIS. Remote Sensing Applications: Society and Environment 2022, 26 , 100716. https://doi.org/10.1016/j.rsase.2022.100716
  53. Kaixu Bai, Ke Li, Jianping Guo, Ni-Bin Chang. Multiscale and multisource data fusion for full-coverage PM2.5 concentration mapping: Can spatial pattern recognition come with modeling accuracy?. ISPRS Journal of Photogrammetry and Remote Sensing 2022, 184 , 31-44. https://doi.org/10.1016/j.isprsjprs.2021.12.002
  54. Qiaolin Zeng, Tianshou Xie, Songyan Zhu, Meng Fan, Liangfu Chen, Yu Tian. Estimating the Near-Ground PM2.5 Concentration over China Based on the CapsNet Model during 2018–2020. Remote Sensing 2022, 14 (3) , 623. https://doi.org/10.3390/rs14030623
  55. Moorthy Nair, Sagnik Dey, Hemant Bherwani, Ashok Kumar Ghosh. Long-term changes in aerosol loading over the ‘BIHAR’ State of India using nineteen years (2001–2019) of high-resolution satellite data (1 × 1 km2). Atmospheric Pollution Research 2022, 13 (1) , 101259. https://doi.org/10.1016/j.apr.2021.101259
  56. Eunhwa Jang, Minkyeong Kim, Woogon Do, Geehyeong Park, Eunchul Yoo. Real-time estimation of PM2.5 concentrations at high spatial resolution in Busan by fusing observational data with chemical transport model outputs. Atmospheric Pollution Research 2022, 13 (1) , 101277. https://doi.org/10.1016/j.apr.2021.101277
  57. Tingting Xie, Ye Yuan. The Unintended Effects of Environmental Information on Mental Health: Evidence from Pollution Disclosure in China. SSRN Electronic Journal 2022, 113 https://doi.org/10.2139/ssrn.4007688
  58. Qingyang Xiao, Guannan Geng, Shigan Liu, Jiajun Liu, Xia Meng, Qiang Zhang. Spatiotemporal continuous estimates of daily 1 km PM 2.5 from 2000 to present under the Tracking Air Pollution in China (TAP) framework. Atmospheric Chemistry and Physics 2022, 22 (19) , 13229-13242. https://doi.org/10.5194/acp-22-13229-2022
  59. Bhupal Shrestha, Jerald A. Brotzge, Junhong Wang. Evaluation of the New York State Mesonet Profiler Network data. Atmospheric Measurement Techniques 2022, 15 (20) , 6011-6033. https://doi.org/10.5194/amt-15-6011-2022
  60. Zhenyu Tan, Xinghua Li, Meiling Gao, Liangcun Jiang. The Environmental Story During the COVID-19 Lockdown: How Human Activities Affect PM2.5 Concentration in China?. IEEE Geoscience and Remote Sensing Letters 2022, 19 , 1-5. https://doi.org/10.1109/LGRS.2020.3040435
  61. Qingzhi Zhao, Jing Su, Zufeng Li, Pengfei Yang, Yibin Yao. Adaptive Aerosol Optical Depth Forecasting Model Using GNSS Observation. IEEE Transactions on Geoscience and Remote Sensing 2022, 60 , 1-9. https://doi.org/10.1109/TGRS.2021.3129159
  62. Yulei Chi, Meng Fan, Chuanfeng Zhao, Lin Sun, Yikun Yang, Xingchuan Yang, Jinhua Tao. Ground-level NO2 concentration estimation based on OMI tropospheric NO2 and its spatiotemporal characteristics in typical regions of China. Atmospheric Research 2021, 264 , 105821. https://doi.org/10.1016/j.atmosres.2021.105821
  63. Shan Xu, Bin Zou, Ying Xiong, Neng Wan, Huihui Feng, Chenxia Hu, Yan Lin. High spatiotemporal resolution mapping of PM2.5 concentrations under a pollution scene assumption. Journal of Cleaner Production 2021, 326 , 129409. https://doi.org/10.1016/j.jclepro.2021.129409
  64. Prem Maheshwarkar, Ramya Sunder Raman. Population exposure across central India to PM2.5 derived using remotely sensed products in a three-stage statistical model. Scientific Reports 2021, 11 (1) https://doi.org/10.1038/s41598-020-79229-7
  65. Ian Hough, Ron Sarafian, Alexandra Shtein, Bin Zhou, Johanna Lepeule, Itai Kloog. Gaussian Markov random fields improve ensemble predictions of daily 1 km PM2.5 and PM10 across France. Atmospheric Environment 2021, 264 , 118693. https://doi.org/10.1016/j.atmosenv.2021.118693
  66. Huaping Li, Ming Zhang, Lunche Wang, Yingying Ma, Wenmin Qin, Wei Gong. The effect of aerosol on downward diffuse radiation during winter haze in Wuhan, China. Atmospheric Environment 2021, 265 , 118714. https://doi.org/10.1016/j.atmosenv.2021.118714
  67. Guanna Pan, Yuan Xu, Bo Huang. Evaluating national and subnational CO2 mitigation goals in China’s thirteenth five-year plan from satellite observations. Environment International 2021, 156 , 106771. https://doi.org/10.1016/j.envint.2021.106771
  68. Qiao Ma, Qianqian Zhang, Qingsong Wang, Xueliang Yuan, Renxiao Yuan, Congwei Luo. A comparative study of EOF and NMF analysis on downward trend of AOD over China from 2011 to 2019. Environmental Pollution 2021, 288 , 117713. https://doi.org/10.1016/j.envpol.2021.117713
  69. Lianfa Li. Geographic Graph Network for Robust Inversion of Particulate Matters. Remote Sensing 2021, 13 (21) , 4341. https://doi.org/10.3390/rs13214341
  70. Junchen He, Zhili Jin, Wei Wang, Yixiao Zhang. Mapping Seasonal High-Resolution PM2.5 Concentrations with Spatiotemporal Bagged-Tree Model across China. ISPRS International Journal of Geo-Information 2021, 10 (10) , 676. https://doi.org/10.3390/ijgi10100676
  71. Wei Guo, Bo Zhang, Qiang Wei, Yuanxi Guo, Xiaomeng Yin, Fuxing Li, Liyan Wang, Wei Wang. Estimating ground-level PM2.5 concentrations using two-stage model in Beijing-Tianjin-Hebei, China. Atmospheric Pollution Research 2021, 12 (9) , 101154. https://doi.org/10.1016/j.apr.2021.101154
  72. Lianfa Li, Ying Fang, Jun Wu, Jinfeng Wang, Yong Ge. Encoder–Decoder Full Residual Deep Networks for Robust Regression and Spatiotemporal Estimation. IEEE Transactions on Neural Networks and Learning Systems 2021, 32 (9) , 4217-4230. https://doi.org/10.1109/TNNLS.2020.3017200
  73. Chau-Ren Jung, Wei-Ting Chen, Shoji F. Nakayama. A National-Scale 1-km Resolution PM2.5 Estimation Model over Japan Using MAIAC AOD and a Two-Stage Random Forest Model. Remote Sensing 2021, 13 (18) , 3657. https://doi.org/10.3390/rs13183657
  74. Yusi Huang, Tianhao Zhang, Zhongmin Zhu, Wei Gong, Xinghui Xia. PM2.5 concentration estimation with 1-km resolution at high coverage over urban agglomerations in China using the BPNN-KED approach and potential application. Atmospheric Research 2021, 258 , 105628. https://doi.org/10.1016/j.atmosres.2021.105628
  75. Nurul Amalin Fatihah Kamarul Zaman, Kasturi Devi Kanniah, Dimitris G. Kaskaoutis, Mohd Talib Latif. Evaluation of Machine Learning Models for Estimating PM2.5 Concentrations across Malaysia. Applied Sciences 2021, 11 (16) , 7326. https://doi.org/10.3390/app11167326
  76. Johana M. Carmona, Pawan Gupta, Diego F. Lozano-García, Ana Y. Vanoye, Iván Y. Hernández-Paniagua, Alberto Mendoza. Evaluation of MODIS Aerosol Optical Depth and Surface Data Using an Ensemble Modeling Approach to Assess PM2.5 Temporal and Spatial Distributions. Remote Sensing 2021, 13 (16) , 3102. https://doi.org/10.3390/rs13163102
  77. Xinghan Xu, Chengkun Zhang, Yi Liang. Review of satellite-driven statistical models PM 2.5 concentration estimation with comprehensive information. Atmospheric Environment 2021, 256 , 118302. https://doi.org/10.1016/j.atmosenv.2021.118302
  78. Bin Guo, Dingming Zhang, Lin Pei, Yi Su, Xiaoxia Wang, Yi Bian, Donghai Zhang, Wanqiang Yao, Zixiang Zhou, Liyu Guo. Estimating PM2.5 concentrations via random forest method using satellite, auxiliary, and ground-level station dataset at multiple temporal scales across China in 2017. Science of The Total Environment 2021, 778 , 146288. https://doi.org/10.1016/j.scitotenv.2021.146288
  79. Saeed Sotoudeheian, Mohammad Arhami. Estimating ground-level PM2.5 concentrations by developing and optimizing machine learning and statistical models using 3 km MODIS AODs: case study of Tehran, Iran. Journal of Environmental Health Science and Engineering 2021, 19 (1) , 1-21. https://doi.org/10.1007/s40201-020-00509-5
  80. Nikolaos Kanellopoulos, Ioannis Pantazopoulos, Maria Mermiri, Georgios Mavrovounis, Georgios Kalantzis, Georgios Saharidis, Konstantinos Gourgoulianis. Effect of PM2.5 Levels on Respiratory Pediatric ED Visits in a Semi-Urban Greek Peninsula. International Journal of Environmental Research and Public Health 2021, 18 (12) , 6384. https://doi.org/10.3390/ijerph18126384
  81. Ying Zhang, Zhengqiang Li, Kaixu Bai, Yuanyuan Wei, Yisong Xie, Yuanxun Zhang, Yang Ou, Jason Cohen, Yuhuan Zhang, Zongren Peng, Xingying Zhang, Cheng Chen, Jin Hong, Hua Xu, Jie Guang, Yang Lv, Kaitao Li, Donghui Li. Satellite remote sensing of atmospheric particulate matter mass concentration: Advances, challenges, and perspectives. Fundamental Research 2021, 1 (3) , 240-258. https://doi.org/10.1016/j.fmre.2021.04.007
  82. Binjie Chen, Shixue You, Yang Ye, Yongyong Fu, Ziran Ye, Jinsong Deng, Ke Wang, Yang Hong. An interpretable self-adaptive deep neural network for estimating daily spatially-continuous PM2.5 concentrations across China. Science of The Total Environment 2021, 768 , 144724. https://doi.org/10.1016/j.scitotenv.2020.144724
  83. QianJun Mao, ChunLin Huang, HengXing Zhang, QiXiang Chen, Yuan Yuan. Performance of MODIS aerosol products at various timescales and in different pollution conditions over eastern Asia. Science China Technological Sciences 2021, 64 (4) , 774-784. https://doi.org/10.1007/s11431-018-9462-5
  84. Mudasir Ahmad Bhat, Shakil Ahmad Romshoo, Gufran Beig. Measurement and Modelling of Particulate Pollution over Kashmir Himalaya, India. Water, Air, & Soil Pollution 2021, 232 (3) https://doi.org/10.1007/s11270-021-05062-x
  85. Qingqing He, Ming Zhang, Yimeng Song, Bo Huang. Spatiotemporal assessment of PM2.5 concentrations and exposure in China from 2013 to 2017 using satellite-derived data. Journal of Cleaner Production 2021, 286 , 124965. https://doi.org/10.1016/j.jclepro.2020.124965
  86. Mojgan Mirzaei, Stefania Bertazzon, Isabelle Couloigner, Babak Farjad. Assessing the Potential of Artificial Intelligence (Artificial Neural Networks) in Predicting the Spatiotemporal Pattern of Wildfire-Generated PM2.5 Concentration. Geomatics 2021, 1 (1) , 18-33. https://doi.org/10.3390/geomatics1010003
  87. Lingyu Wang, Baolei Lyu, Yuqi Bai. Global aerosol vertical structure analysis by clustering gridded CALIOP aerosol profiles with fuzzy k-means. Science of The Total Environment 2021, 761 , 144076. https://doi.org/10.1016/j.scitotenv.2020.144076
  88. Binjie Chen, Yi Lin, Jinsong Deng, Zheyu Li, Li Dong, Yibo Huang, Ke Wang. Spatiotemporal dynamics and exposure analysis of daily PM2.5 using a remote sensing-based machine learning model and multi-time meteorological parameters. Atmospheric Pollution Research 2021, 12 (2) , 23-31. https://doi.org/10.1016/j.apr.2020.10.005
  89. Masoud Ghahremanloo, Yunsoo Choi, Alqamah Sayeed, Ahmed Khan Salman, Shuai Pan, Meisam Amani. Estimating daily high-resolution PM2.5 concentrations over Texas: Machine Learning approach. Atmospheric Environment 2021, 247 , 118209. https://doi.org/10.1016/j.atmosenv.2021.118209
  90. Bin Wang, Qiangqiang Yuan, Qianqian Yang, Liye Zhu, Tongwen Li, Liangpei Zhang. Estimate hourly PM2.5 concentrations from Himawari-8 TOA reflectance directly using geo-intelligent long short-term memory network. Environmental Pollution 2021, 271 , 116327. https://doi.org/10.1016/j.envpol.2020.116327
  91. Bin Guo, Xiaoxia Wang, Lin Pei, Yi Su, Dingming Zhang, Yan Wang. Identifying the spatiotemporal dynamic of PM2.5 concentrations at multiple scales using geographically and temporally weighted regression model across China during 2015–2018. Science of The Total Environment 2021, 751 , 141765. https://doi.org/10.1016/j.scitotenv.2020.141765
  92. Lizhi Miao, Yi Wen, Yanhui Ren. Analysing PM2.5 concentrations using MODIS and monitoring measurements for Shanghai area. Journal of Physics: Conference Series 2021, 1732 (1) , 012104. https://doi.org/10.1088/1742-6596/1732/1/012104
  93. Ruixue Lei, Sha Feng, Thomas Lauvaux. Country-scale trends in air pollution and fossil fuel CO 2 emissions during 2001–2018: confronting the roles of national policies and economic growth. Environmental Research Letters 2021, 16 (1) , 014006. https://doi.org/10.1088/1748-9326/abc9e1
  94. Nengcheng Chen, Meijuan Yang, Wenying Du, Min Huang. PM2.5 Estimation and Spatial-Temporal Pattern Analysis Based on the Modified Support Vector Regression Model and the 1 km Resolution MAIAC AOD in Hubei, China. ISPRS International Journal of Geo-Information 2021, 10 (1) , 31. https://doi.org/10.3390/ijgi10010031
  95. Yang Zhang, Zhengqiang Li, Zhihong Liu, Yongqian Wang, Lili Qie, Yisong Xie, Weizhen Hou, Lu Leng. Retrieval of aerosol fine-mode fraction over China from satellite multiangle polarized observations: validation and comparison. Atmospheric Measurement Techniques 2021, 14 (2) , 1655-1672. https://doi.org/10.5194/amt-14-1655-2021
  96. Hyung Joo Lee. Advancing Exposure Assessment of PM2.5 Using Satellite Remote Sensing: A Review. Asian Journal of Atmospheric Environment 2020, 14 (4) , 319-334. https://doi.org/10.5572/ajae.2020.14.4.319
  97. Lianglin Zhang, Jinghu Pan. Estimation of PM2.5 Mass Concentrations in Beijing–Tianjin–Hebei Region Based on Geographically Weighted Regression and Spatial Downscaling Method. Journal of the Indian Society of Remote Sensing 2020, 48 (12) , 1691-1703. https://doi.org/10.1007/s12524-020-01193-6
  98. Lianfa Li, Mariam Girguis, Frederick Lurmann, Nathan Pavlovic, Crystal McClure, Meredith Franklin, Jun Wu, Luke D. Oman, Carrie Breton, Frank Gilliland, Rima Habre. Ensemble-based deep learning for estimating PM2.5 over California with multisource big data including wildfire smoke. Environment International 2020, 145 , 106143. https://doi.org/10.1016/j.envint.2020.106143
  99. Xuechen Zhang, Huanfeng Shen, Tongwen Li, Liangpei Zhang. The Effects of Fireworks Discharge on Atmospheric PM2.5 Concentration in the Chinese Lunar New Year. International Journal of Environmental Research and Public Health 2020, 17 (24) , 9333. https://doi.org/10.3390/ijerph17249333
  100. Zhiming Yang, Cristian Zdanski, Dipatrimarki Farkas, John Bang, Harris Williams. Evaluation of Aerosol Optical Depth (AOD) and PM2.5 associations for air quality assessment. Remote Sensing Applications: Society and Environment 2020, 20 , 100396. https://doi.org/10.1016/j.rsase.2020.100396
Load more citations
  • Abstract

    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.

    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).

    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).

    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.

  • References

    ARTICLE SECTIONS
    Jump To

    This article references 36 other publications.

    1. 1
      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
    2. 2
      Guo, S.; Hu, M.; Zamora, M. L.; Peng, J. F.; Shang, D. J.; Zheng, J.; Du, Z. F.; Wu, Z.; Shao, M.; Zeng, L. M. Elucidating severe urban haze formation in China Proc. Natl. Acad. Sci. U. S. A. 2014, 111 (49) 17373 17378 DOI: 10.1073/pnas.1419604111
    3. 3
      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
    4. 4
      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
    5. 5
      Tao, M.; Chen, L.; Wang, Z.; Tao, J.; Su, L. Satellite observation of abnormal yellow haze clouds over East China during summer agricultural burning season Atmos. Environ. 2013, 79, 632 640 DOI: 10.1016/j.atmosenv.2013.07.033
    6. 6
      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
    7. 7
      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
    8. 8
      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
    9. 9
      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
    10. 10
      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
    11. 11
      Schaap, M.; Apituley, A.; Timmermans, R. M. A.; Koelemeijer, R. B. A.; de Leeuw, G. Exploring the relation between aerosol optical depth and PM2.5 at Cabauw, the Netherlands Atmos. Chem. Phys. 2009, 9 (3) 909 925 DOI: 10.5194/acp-9-909-2009
    12. 12
      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
    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
    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
    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
    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
    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
    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
    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
    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
    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
    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
    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
    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
    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) .
    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
    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
    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 .
    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
    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
    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
    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
    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
    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
    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
    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
  • Supporting Information

    Supporting Information

    ARTICLE SECTIONS
    Jump To

    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)


    Terms & Conditions

    Most electronic Supporting Information files are available without a subscription to ACS Web Editions. Such files may be downloaded by article for research use (if there is a public use license linked to the relevant article, that license may permit other uses). Permission may be obtained from ACS for other uses through requests via the RightsLink permission system: http://pubs.acs.org/page/copyright/permissions.html.

Pair your accounts.

Export articles to Mendeley

Get article recommendations from ACS based on references in your Mendeley library.

Pair your accounts.

Export articles to Mendeley

Get article recommendations from ACS based on references in your Mendeley library.

You’ve supercharged your research process with ACS and Mendeley!

STEP 1:
Click to create an ACS ID

Please note: If you switch to a different device, you may be asked to login again with only your ACS ID.

Please note: If you switch to a different device, you may be asked to login again with only your ACS ID.

Please note: If you switch to a different device, you may be asked to login again with only your ACS ID.

MENDELEY PAIRING EXPIRED
Your Mendeley pairing has expired. Please reconnect