Daily estimation of ground-level PM2.5 concentrations at 4km resolution over Beijing-Tianjin-Hebei by fusing MODIS AOD and ground observations

Unmasking the sky: high-resolution PM2.5 prediction in Texas using machine learning techniques

BACKGROUND

Although PM2.5 (fine particulate matter with an aerodynamic diameter less than 2.5 µm) is an air pollutant of great concern in Texas, limited regulatory monitors pose a significant challenge for decision-making and environmental studies.

OBJECTIVE

This study aimed to predict PM2.5 concentrations at a fine spatial scale on a daily basis by using novel machine learning approaches and incorporating satellite-derived Aerosol Optical Depth (AOD) and a variety of weather and land use variables.

METHODS

We compiled a comprehensive dataset in Texas from 2013 to 2017, including ground-level PM2.5 concentrations from regulatory monitors; AOD values at 1-km resolution based on images retrieved from the MODIS satellite; and weather, land-use, population density, among others. We built predictive models for each year separately to estimate PM2.5 concentrations using two machine learning approaches called gradient boosted trees and random forest. We evaluated the model prediction performance using in-sample and out-of-sample validations.

RESULTS

Our predictive models demonstrate excellent in-sample model performance, as indicated by high R2 values generated from the gradient boosting models (0.94-0.97) and random forest models (0.81-0.90). However, the out-of-sample R2 values fall within a range of 0.52-0.75 for gradient boosting models and 0.44-0.69 for random forest models. Model performance varies slightly across years. A generally decreasing trend in predicted PM2.5 concentrations over time is observed in Eastern Texas.

IMPACT STATEMENT

We utilized machine learning approaches to predict PM2.5 levels in Texas. Both gradient boosting and random forest models perform well. Gradient boosting models perform slightly better than random forest models. Our models showed excellent in-sample prediction performance (R2 > 0.9).

Application of satellite remote sensing data and random forest approach to estimate ground-level PM2.5 concentration in Northern region of Thailand

Numerous epidemiological studies have shown that particulate matter with aerodynamic diameter up to 2.5 μm (PM2.5) is associated with many health consequences, where PM2.5 concentration obtained from the monitoring station was normally applied as the exposure level, so that the concentration of PM2.5 in unmonitored areas has not been captured. The satellite-derived aerosol optical depth (AOD) product is then used to spatially predict ground truth of PM2.5 concentration that covers the locations with no air quality monitoring station, but this method has seldom been developed in Thailand. This study aimed at estimating ground-level PM2.5 concentration at 3 km × 3 km spatial resolution over Northern region of Thailand in 2021 using the random forest model integrating the Moderate Resolution Imaging Spectroradiometer (MODIS) AOD products from Terra and Aqua satellites, meteorological factors, and land use data. A random forest model contained 100 decision trees was utilized to train the model, and 10-fold cross-validation approach was implemented to validate the model performance. The good consistency between actual (observed) and predicted concentrations of PM2.5 in Northern region of Thailand was observed, where a coefficient of determination (R2) and root mean square error (RMSE) of the model fitting were 0.803 and 14.30 μg/m3, respectively, and those of 10-fold cross-validation approach were 0.796 and 14.64 μg/m3, respectively. The three most important predictors for estimating the ground-level concentrations of PM2.5 in this study were normalized difference vegetation index (NDVI), relative humidity, and number of fire hotspot, respectively. Findings from this study revealed that integrating the MODIS AOD, meteorological variables, and land use data into the random forest model precisely and accurately estimated ground-level PM2.5 concentration over Northern region of Thailand that can be further used to investigate the effects of PM2.5 exposure on health consequences, even in unmonitored locations, in epidemiological studies.

Spatiotemporally continuous estimates of daily 1-km PM2.5 concentrations and their long-term exposure in China from 2000 to 2020

Monitoring long-term variations in fine particulate matter (PM_2.5) is essential for environmental management and epidemiological studies. While satellite-based statistical/machine-learning methods can be used for estimating high-resolution ground-level PM_2.5 concentration data, their applications have been hindered by limited accuracy in daily estimates during years without PM_2.5 measurements and massive missing values due to satellite retrieval data. To address these issues, we developed a new spatiotemporal high-resolution PM_2.5 hindcast modeling framework to generate the full-coverage, daily, 1-km PM_2.5 data for China for the period 2000-2020 with improved accuracy. Our modeling framework incorporated information on changes in observation variables between periods with and without monitoring data and filled gaps in PM_2.5 estimates induced by satellite data using imputed high-resolution aerosol data. Compared to previous hindcast studies, our method achieved superior overall cross-validation (CV) R² and root-mean-square error (RMSE) of 0.90 and 12.94 μg/m³ and significantly improved the model performance in years without PM_2.5 measurements, raising the leave-one-year-out CV R² [RMSE] to 0.83 [12.10 μg/m³] at a monthly scale (0.65 [23.29 μg/m³] at a daily scale). Our long-term PM_2.5 estimates show a sharp decline in PM_2.5 exposure in recent years, but the national exposure level in 2020 still exceeded the first annual interim target of the 2021 World Health Organization air quality guidelines. The proposed hindcast framework represents a new strategy to improve air quality hindcast modeling and can be applied to other regions with limited air quality monitoring periods. These high-quality estimates can support both long- and short-term scientific research and environmental management of PM_2.5 in China.

PM2.5 concentration assessment based on geographical and temporal weighted regression model and MCD19A2 from 2015 to 2020 in Xinjiang, China

PM2.5 is closely linked to both air quality and public health. Many studies have used models combined with remote sensing and auxiliary data to inverse a large range of PM2.5 concentrations. However, the data's spatial resolution is limited. and better results might have been obtained if higher resolution data had been used. Therefore, this paper establishes a geographical and temporal weighted regression model (GTWR) and estimates the PM2.5 concentration in Xinjiang from 2015 to 2020 using 1 km resolution MCD19A2 (MODIS/Terra+Aqua Land Aerosol Optical Thickness Daily L2G Global 1km SIN Grid V006) data and 9 auxiliary variables. The findings indicate that the GTWR model performs better than the simple linear regression (SLR) and geographically weighted regression (GWR) models in terms of accuracy and feasibility in retrieving PM2.5 concentrations in Xinjiang. Simultaneously, by combining the GTWR model with MCD19A2 data, a spatial distribution map of PM2.5 with better spatial resolution can be obtained. Next, the regional distribution of annual PM2.5 concentrations in Xinjiang is consistent with the terrain from 2015 to 2020. The low value area is primarily found in the mountainous area with higher terrain, while the high value area is primarily in the basin with lower terrain. Overall, the southwest is high and the northeast is low. In terms of time change, the six-year PM2.5 shows a single peak distribution with 2016 as the inflection point. Lastly, from 2015 to 2020, the seasonal average PM2.5 concentration in Xinjiang has a significant difference, thereby showing winter (66.15μg/m3)>spring (52.28μg/m3)>autumn (40.51μg/m3)>summer (38.63μg/m3). The research shows that the combination of MCD19A2 data and GTWR model has good applicability in retrieving PM2.5 concentration.

Generating a long-term (2003-2020) hourly 0.25° global PM2.5 dataset via spatiotemporal downscaling of CAMS with deep learning (DeepCAMS)

Generating a long-term high-spatiotemporal resolution global PM_2.5 dataset is of great significance for environmental management to mitigate the air pollution concerns worldwide. However, the current long-term (2003-2020) global reanalysis dataset Copernicus Atmosphere Monitoring Service (CAMS) reanalysis has drawbacks in fine-scale research due to its coarse spatiotemporal resolution (0.75°, 3-h). Hence, this paper developed a deep learning-based framework (DeepCAMS) to downscale CAMS PM_2.5 product on the spatiotemporal dimension for resolution enhancement. The nonlinear statistical downscaling from low-resolution (LR) to high-resolution (HR) data can be learned from the high quality (0.25°, hourly) but short-term (2018-2020) Goddard Earth Observing System composition forecast (GEOS-CF) system PM_2.5 product. Compared to the conventional spatiotemporal interpolation methods, simulation validations on GEOS-CF demonstrate that DeepCAMS is capable of producing accurate temporal variations with an improvement of Root-Mean-Squared Error (RMSE) of 0.84 (4.46 to 5.30) ug/m³ and spatial details with an improvement of Mean Absolute Error (MAE) of 0.16 (0.34 to 0.50) ug/m³. The real validations on CAMS reflect convincing spatial consistency and temporal continuity at both regional and global scales. Furthermore, the proposed dataset is validated with OpenAQ air quality data from 2017 to 2019, and the in-situ validations illustrate that the DeepCAMS maintains the consistent precision (R: 0.597) as the original CAMS (R: 0.593) while tripling the spatiotemporal resolution. The proposed dataset will be available at https://doi.org/10.5281/zenodo.6381600.

[Effect of modification of antihypertensive medications on the association of nitrogen dioxide long-term exposure and chronic kidney disease]

OBJECTIVE

To investigate the potential effect of modification of antihypertensive medications on the association of nitrogen dioxide (NO₂) long-term exposure and chronic kidney disease (CKD).

METHODS

Data of the national representative sample of adult population from the China National Survey of Chronic Kidney Disease (2007-2010) were included in the analyses, and exposure data of NO₂ were collected and matched. Generalized mixed-effects models were used to analyze the associations between NO₂ and CKD, stratified by the presence of hypertension and taking antihypertensive medications. The stratified exposure-response curves of NO₂ and CKD were fitted using the natural spine smoothing function. The modifying effects of antihypertensive medications on the association and the exposure-response curve of NO₂ and CKD were analyzed.

RESULTS

Data of 45 136 participants were included, with an average age of (49.5±15.3) years. The annual average exposure concentration of NO₂ was (7.2±6.4) μg/m³. Altogether 6 517 (14.4%) participants were taking antihypertensive medications, and 4 833 (10.7%) participants were identified as having CKD. After adjustment for potential confounders, in the hypertension population not using antihypertensive medications, long-term exposure to NO₂ was associated with a significant increase risk of CKD (OR: 1.38, 95%CI: 1.24-1.54, P < 0.001); while in the hypertension population using antihypertensive medications, no significant association between long-term exposure to NO₂ and CKD (OR: 0.96, 95%CI: 0.86-1.07, P=0.431) was observed. The exposure-response curve of NO₂ and CKD suggested that there was a non-linear trend in the association between NO₂ and CKD. The antihypertension medications showed significant modifying effects both on the association and the exposure-response curve of NO₂ and CKD (interaction P < 0.001).

CONCLUSION

The association between long-term exposure to NO₂ and CKD was modified by antihypertensive medications. Taking antihypertensive medications may mitigate the effect of long-term exposure to NO₂ on CKD.

Estimating ground-level PM2.5 over Bangkok Metropolitan Region in Thailand using aerosol optical depth retrieved by MODIS

A number of previous studies have shown that statistical model with a combination of satellite-derived aerosol optical depth (AOD) and PM_2.5 measured by the monitoring stations could be applied to predict spatial ground-level PM_2.5 concentration, but few studies have been conducted in Thailand. This study aimed to estimate ground-level PM_2.5 over the Bangkok Metropolitan Region in 2020 using linear regression model that incorporates the Moderate Resolution Imaging Spectroradiometer (MODIS) AOD measurements and other air pollutants, as well as various meteorological factors and greenness indicators into the model. The 12-fold cross-validation technique was used to examine the accuracy of model performance. The annual mean (standard deviation) concentration of observed PM_2.5 was 22.37 (± 12.55) µg/m³ and the mean (standard deviation) of PM_2.5 during summer, winter, and rainy season was 18.36 (± 7.14) µg/m³, 33.60 (± 14.48) µg/m³, and 15.30 (± 4.78) µg/m³, respectively. The cross-validation yielded R ² of 0.48, 0.55, 0.21, and 0.52 with the average of predicted PM_2.5 concentration of 22.25 (± 9.97) µg/m³, 21.68 (± 9.14) µg/m³, 29.43 (± 9.45) µg/m³, and 15.74 (± 5.68) µg/m³ for the year round, summer, winter, and rainy season, respectively. We also observed that integrating NO₂ and O₃ into the regression model improved the prediction accuracy significantly for a year round, summer, winter, and rainy season over the Bangkok Metropolitan Region. In conclusion, estimating ground-level PM_2.5 concentration from the MODIS AOD measurement using linear regression model provided the satisfactory model performance when incorporating many possible predictor variables that would affect the association between MODIS AOD and PM_2.5 concentration.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s11869-022-01238-4.

High Spatial-Temporal PM2.5 Modeling Utilizing Next Generation Weather Radar (NEXRAD) as a Supplementary Weather Source

PM2.5, a type of fine particulate with a diameter equal to or less than 2.5 micrometers, has been identified as a major source of air pollution, and is associated with many health issues. Research on utilizing various data sources, such as remote sensing and in situ sensors, for PM2.5 concentrations modeling remains a hot topic. In this study, the Next Generation Weather Radar (NEXRAD) is used as a supplementary weather data source, along with European Centre for Medium-Range Weather Forecasts (ECMWF), solar angles, and Geostationary Operational Environmental Satellite (GOES16) Aerosol Optical Depth (AOD) to model high spatial-temporal PM2.5 concentrations. PM2.5 concentrations as well as in situ weather condition variables are collected from the 31 sensors that are deployed in the Dallas Metropolitan area. Four machine learning models with different predictor variables are developed based on an ensemble approach. Since in situ weather observations are not widely available, ECMWF is used as an alternative data source for weather conditions in studies. Hence, the four established models are compared in three groups. Both models in this first group use weather variables collected from deployed sensors, but one uses NEXRAD and the other does not. In the second group, the two models use weather variables retrieved from ECMWF, one using NEXRAD and one without. In the third group, one model uses weather variables from ECMWF, and the other uses in situ weather variables, both without NEXRAD. The first two environmental groups investigate how NEXRAD can enhance model performances with weather variables collected from in situ observations and ECMWF, respectively. The third group explores how effective using ECMWF as an alternative source of weather conditions. Based on the results, the incorporation of NEXRAD achieves an R2 score of 0.86 and 0.83 for groups 1 and 2, respectively, for an improvement of 2.8% and 9.6% over those models without NEXRAD. For group three, the use of ECMWF as an alternative source of in situ weather observations results in a 0.13 R2 drop. For PM2.5 estimation, weather variables including precipitation, temperature, pressure, and surface pressure from ECMWF and deployed sensors, as well as NEXRAD velocity, are shown to be significant factors.

A full-coverage estimation of PM2.5 concentrations using a hybrid XGBoost-WD model and WRF-simulated meteorological fields in the Yangtze River Delta Urban Agglomeration, China

In spite of the state-of-the-art performances of machine learning in the PM_2.5 estimation, the high-value PM_2.5 underestimation and non-random aerosol optical depth (AOD) missing are still huge obstacles. By incorporating wavelet decomposition (WD) into the extreme gradient boosting (XGBoost), a hybrid XGBoost-WD model was established to obtain the full-coverage PM_2.5 estimation at 3-km spatial resolution in the Yangtze River Delta Urban Agglomeration (YRDUA). In this study, 3-km-resolution meteorological fields simulated by WRF along with AOD derived from Moderate Resolution Imaging Spectroradiometer (MODIS) were served as explanatory variables. Model MW and Model NW were developed using XGBoost-WD for the areas with and without AOD respectively to obtain a full-coverage PM_2.5 mapping in the YRDUA. The XGBoost-WD model showed good performances in estimating PM_2.5 with R² of 0.80 in the Model MW and 0.87 in the Model NW. Moreover, the K-value of Model MW increased from 0.77 to 0.79 and that of Model NM increased from 0.81 to 0.86 compared with the model without the step of WD, indicating an improvement on the problem of PM_2.5 underestimation. Due to a better ability of capturing abrupt changes in the PM_2.5 concentrations, the spatial evolution of PM_2.5 during a typical pollution event could be mapped more accurately. Finally, the analysis of variable importance showed that the three most important variables in the estimation of the low-frequency coefficients of PM_2.5 (PM_2.5_A4) were temperature at 2 m (T2), day of year (DOY) and longitude (LON), while that in the high-frequency coefficients of PM_2.5 (PM_2.5_D) were CO, AOD and NO₂. This study not only provided an effective solution to the PM_2.5 underestimation and AOD missing problems in the PM_2.5 estimation, but also proposed a new method to further refine the sophisticated correlations between PM_2.5 and some spatiotemporal variables.

Urbanization, ambient air pollution, and prevalence of chronic kidney disease: A nationwide cross-sectional study

An increasing number of studies have linked ambient air pollution to chronic kidney disease (CKD) prevalence. However, its potential effect modification by urbanization has not been investigated. Based on data of 47,204 adults from the China National Survey of Chronic Kidney Disease (CKSCKD) dataset, night light satellite remote sensing data and high-resolution air pollution inversion products, the present cross-sectional study investigated the association between fine particulate matter <2.5 mm in diameter (PM_2.5), nitrogen dioxide (NO₂), night light index (NLI) and CKD prevalence in China, and the effect modification by urbanization characterized by administrative classification and NLI on the pollutant-health associations. Our results showed that a 10-μg/m³ increase in PM_2.5 at 3-year moving average, a 10-μg/m³ increase in NO₂ at 5-year moving average, and a 10-U increase in NLI at 5-year moving average were significantly associated with increased odds of CKD prevalence [OR = 1.24 (95 %CI:1.14, 1.35); OR = 1.12 (95 %CI:1.09, 1.15); OR = 1.05 (95 %CI:1.02, 1.07)]. Meanwhile, the pollutant-health associations were more apparent in medium-urbanized areas compared to low- and high-urbanized areas. For instance, a 10-μg/m³ increase in PM_2.5 concentration at 2-year moving average was associated with increased odds of CKD in the areas with NLI level in the second [OR = 2.78 (95 %CI:1.77, 4.36)] and third quartiles [OR = 1.49 (95 %CI:1.14, 1.95)], compared to the lowest [OR = 0.96 (95% CI: 0.73, 1.26)] and highest [OR = 0.63 (95% CI: 0.39-1.02)] quartiles. PM_2.5 and NO₂ were associated with increased odds of CKD prevalence, especially in areas with medium NLI levels, suggesting the necessity of strengthening environmental management in medium-urbanized regions.

A neural network-based method for modeling PM 2.5 measurements obtained from the surface particulate matter network

Air pollution is a global problem; hence, many countries devoted lots of resources towards its study and possible eradication. The major parameter indicator for air quality is the particulate matter (PM). These particles, especially PM_2.5, are injurious to health either under high concentration levels or after a long-term exposure. PM_2.5 particles are known to cause lung and respiratory diseases, cardiovascular diseases, and even cancer. In this research, artificial neural networks were used to train PM 2.5 measurements obtained from the Surface Particulate Matter Network (SPARTAN). The training was done using inputs that indicate time series of the measurements and the prevailing atmospheric conditions. The developed models were used to estimate PM 2.5 over a sub-Saharan site in Ilorin. Our study considered meteorological parameters and aerosol optical depth (AOD) as inputs for the neural networks. The targets are PM 2.5 measurements obtained from SPARTAN. Our models showed very high correlation with measured data. Apart from the data generated using model p which has a correlation of 0.0009, the correlation R² for other models ranges from 0.59 to 0.95) which has a good performance. The model PRB estimated both low and high PM better while others either under or over predict emission scenarios.

Particulate matter (PM2.5 and PM10) generation map using MODIS Level-1 satellite images and deep neural network

Most studies about particulate matter (PM) estimation have been done based on satellite-derived optical depth aerosol (AOD) products. But, the use of AOD products having coarse resolution is not possible for PM map generation in small spatial coverage such as local cities. To solve this issue, a PM estimation framework is proposed in this work which accepts the original calibrated radiance of MODIS-Level 1 images as input. There are no intermediate computations for atmospheric reflectance or aerosol thickness calculation. A deep neural network consisting of recurrent layers is proposed to extract the relationship between the grey level values of the satellite image bands and the PM measurements in different days and locations. Two individual networks are trained for PM_2.5 and PM₁₀ concentrations. The PM_2.5 map and PM₁₀ map of Tehran city are generated. The performance of the proposed method is compared with several recently published air pollution studies. The results show that the proposed method is a simple, low cost and efficient approach for PM generation of small-scaled coverage using free available Moderate Resolution Imaging Spectroradiometer (MODIS) images.

Long-Term Exposure to Ambient PM2.5, Sunlight, and Obesity: A Nationwide Study in China

BACKGROUND

Accumulated researches revealed that both fine particulate matter (PM_2.5) and sunlight exposure may be a risk factor for obesity, while researches regarding the potential effect modification by sunlight exposure on the relationship between PM_2.5 and obesity are limited. We aim to investigate whether the effect of PM_2.5 on obesity is affected by sunlight exposure among the general population in China.

METHODS

A sample of 47,204 adults in China was included. Obesity and abdominal obesity were assessed based on body mass index, waist circumference and waist-to-hip ratio, respectively. The five-year exposure to PM_2.5 and sunlight were accessed using the multi-source satellite products and a geochemical transport model. The relationship between PM_2.5, sunshine duration, and the obesity or abdominal obesity risk was evaluated using the general additive model.

RESULTS

The proportion of obesity and abdominal obesity was 12.6% and 26.8%, respectively. Levels of long-term PM_2.5 ranged from 13.2 to 72.1 μg/m³ with the mean of 46.6 μg/m³. Each 10 μg/m³ rise in PM_2.5 was related to a higher obesity risk [OR 1.12 (95% CI 1.09-1.14)] and abdominal obesity [OR 1.10 (95% CI 1.07-1.13)]. The association between PM_2.5 and obesity varied according to sunshine duration, with the highest ORs of 1.56 (95% CI 1.28-1.91) for obesity and 1.66 (95% CI 1.34-2.07) for abdominal obesity in the bottom quartile of sunlight exposure (3.21-5.34 hours/day).

CONCLUSION

Long-term PM_2.5 effect on obesity risk among the general Chinese population are influenced by sunlight exposure. More attention might be paid to reduce the adverse impacts of exposure to air pollution under short sunshine duration conditions.

Estimating PM2.5 with high-resolution 1-km AOD data and an improved machine learning model over Shenzhen, China

Studies on fine particulate matter with an aerodynamic diameter of 2.5 μm or smaller (PM_2.5) are closely related to the atmospheric environment and human activities but are often limited by ground-level in situ observations. Satellite remote sensing techniques have been widely used to estimate the PM_2.5 concentration over large areas where ground-monitoring sites are unavailable. However, satellite-retrieved aerosol optical depth (AOD) products usually feature a coarse resolution, which is insufficient for the estimation of the urban-scale PM_2.5 concentration. We developed a new improved random forest (IRF) model based on machine learning and a newly released AOD product with a high resolution of 1-km, which could more effectively and accurately estimate the PM_2.5 concentration over Shenzhen in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), China. Daily PM_2.5 concentrations from 2016 to 2018 were estimated from ground-level PM_2.5 and meteorological variable data. The popular linear regression model, geographically and temporally weighted regression (GTWR) model and random forest (RF) model without spatiotemporal information were employed for comparison and validation purposes through the 10-fold cross-validation (CV) approach. The IRF model attained an overall R² value of 0.915 and a root mean square error (RMSE) value of 3.66 μg m^-3. This suggests that the IRF model can estimate the urban PM_2.5 concentration with a high spatial resolution at the daily, seasonal and annual scales, and the improved machine learning method is better than the linear model proposed by previous studies in terms of the estimation accuracy of the PM_2.5 concentration. Generally, the IRF model coupled with AOD data with a 1-km resolution can significantly improve the calculation accuracy of the atmospheric PM_2.5 concentration over coastal urban areas in the future.

Comparison of Different Missing-Imputation Methods for MAIAC (Multiangle Implementation of Atmospheric Correction) AOD in Estimating Daily PM2.5 Levels

The immense problem of missing satellite aerosol retrievals (Aerosol Optical Depth, (AOD)) detrimentally affects the prediction ability of ground-level PM2.5 concentrations and may lead to unavoidable biases. An appropriate missing-imputation method has not been well developed to date. This study developed a two-stage approach (AOD-imputation stage and PM2.5-prediction stage) to predict short-term PM2.5 exposure in mainland China from 2013–2018. At the AOD-imputation stage, geostatistical methods and machine learning (ML) algorithms were examined to interpolate 1 km satellite aerosol retrievals. At the PM2.5-prediction stage, the daily levels of PM2.5 were predicted at a resolution of 1 km, based on interpolated AOD and meteorological data. The statistical performances of the different interpolation methods were comprehensively compared at each stage. The original coverage of retrieved AOD was 15.46% on average. For the AOD-imputation stage, ML methods produced a higher coverage (98.64%) of AOD than geostatistical methods (21.43–87.31%). Among ML algorithms, random forest (RF) or extreme gradient boosted (XG-interpolated) AOD produced better interpolated quality (CV R2 = 0.89 and 0.85) than other algorithms (0.49–0.78), but XGBoost required only 15% of the computing time of RF. For the PM2.5 predicted stage, neither RF-AOD nor XG-AOD could guarantee higher accuracy in PM2.5 estimations (CV R2 = 0.88 (RF or XG-AOD) compared to 0.85 (original)), or more stable spatial and temporal extrapolation (spatial, (temporal) CV R2 = 0.83 (0.83), 0.82 (0.82), and 0.65 (0.61) for RF, XG, and original). For the AOD-imputation stage, the missing-filled efficiency depended more on external information, while the missing-filled accuracy relied more on model structure. For the PM2.5 predicted stage, efficient AOD interpolation (or the ability to eliminate the missing data) was a precondition for the stable spatial and temporal extrapolation, while the quality of interpolated AOD showed less significant improvements. It was found that XG-AOD is a better choice to estimate daily PM2.5 exposure in health assessments.

Exploring the spatial-temporal characteristics of the aerosol optical depth (AOD) in Central Asia based on the moderate resolution imaging spectroradiometer (MODIS)

Central Asia has become a key node of the belt and road corridor. It is located in arid and semi-arid climate regions, and it is a region where the contribution of global aerosols of sand and dust is continuous. However, few studies have been conducted on the Central Asian aerosol optical depth. Therefore, this paper relied on the belt and road sustainable development dataset to analyze the spatial-temporal variations in the AOD in Central Asia and provide spatial-temporal characteristics of the AOD for environmental services. We analyzed the spatial and temporal variation in the aerosol optical depth (AOD) in Central Asia by using MODIS/AQUA C6 MYD08_M3 images from 2008 to 2017. The results showed that (1) the annual average AOD in Central Asia in the past decade varied from 0.183 to 0.232, which indicated a slow decline starting in 2014. The percentage of average annual decline was approximately 0.18%, and the regular distinct revealed the distribution characteristics of AOD. In different years, the Central Asian region exhibited the similar monthly change characteristics: from July to December, the AOD decreased, and from December to February, it increased. In different seasons, the Central Asian region exhibited the different seasonal change characteristics: the AOD value was higher in the spring and summer. The mean values in the spring, summer, autumn, and winter were 0.273, 0.240, 0.155, and 0.183, respectively, and the standard deviations were 0.036, 0.038, 0.025, and 0.048, respectively. (3) Based on spatial distribution characteristics, the Tarim Basin, Aral Sea region, and Ebinur Lake area were high value areas, and Kazakhstan was a low value area. The AOD of the surrounding area of the Aral Sea had increased in the last 5 years, while that of Kazakhstan, Uzbekistan, and Turkmenistan had decreased. The AOD of the Taklamakan area exhibited an inter-annual change. Sand dust aerosols were the largest contributors to the AOD in the Taklamakan area. The rising trend of the AOD in the Aral Sea area was clear, with an average annual increase of 0.234%, and the contribution of salt dust aerosols to the AOD increased. The average annual AOD in the Ebinur Lake area remained stable.

Estimation of spatially continuous daytime particulate matter concentrations under all sky conditions through the synergistic use of satellite-based AOD and numerical models

Satellite-derived aerosol optical depth (AOD) products are one of main predictors to estimate ground-level particulate matter (PM₁₀ and PM_2.5) concentrations. Since AOD products, however, are only provided under high-quality conditions, missing values usually exist in areas such as clouds, cloud shadows, and bright surfaces. In this study, spatially continuous AOD and subsequent PM₁₀ and PM_2.5 concentrations were estimated over East Asia using satellite- and model-based data and auxiliary data in a Random Forest (RF) approach. Data collected from the Geostationary Ocean Color Imager (GOCI; 8 times per day) in 2016 were used to develop AOD and PM models. Three schemes (i.e. G1, A1, and A2) were proposed for AOD modeling according to target AOD data (GOCI AOD and AERONET AOD) and the existence of satellite-derived AOD. The A2 scheme showed the best performance (validation R² of 0.74 and prediction R² of 0.73 when GOCI AOD did not exist) and the resultant AOD was used to estimate spatially continuous PM concentrations. The PM models with location information produced successful estimation results with R² of 0.88 and 0.90, and rRMSE of 26.9 and 27.2% for PM₁₀ and PM_2.5, respectively. The spatial distribution maps of PM well captured the seasonal and spatial characteristics of PM reported in the literature, which implies the proposed approaches can be adopted for an operational estimation of spatially continuous AOD and PMs under all sky conditions.

Estimating the daily PM2.5 concentration in the Beijing-Tianjin-Hebei region using a random forest model with a 0.01° × 0.01° spatial resolution

High spatiotemporal resolution fine particulate matter (PM_2.5) simulations can provide important exposure data for the assessment of long-term and short-term health effects. Satellite-based aerosol optical depth (AOD) data, meteorological data, and topographic data have become key variables for PM_2.5 estimation. In this study, a random forest model was developed and used to estimate the highest resolution (0.01° × 0.01°) daily PM_2.5 concentrations in the Beijing-Tianjin-Hebei region. Our model had a suitable performance (cv-R² = 0.83 and test-R² = 0.86). The regional test-R² value in southern Beijing-Tianjin-Hebei was higher than that in northern Beijing-Tianjin-Hebei. The model performance was excellent at medium to high PM_2.5 concentrations. Our study considered meteorological lag effects and found that the boundary layer height of the one-day lag had the most important contribution to the model. AOD and elevation factors were also important factors in the modeling process. High spatiotemporal resolution PM_2.5 concentrations in 2010-2016 were estimated using a random forest model, which was based on PM_2.5 measurements from 2013 to 2016.

Evaluation and uncertainty estimate of next-generation geostationary meteorological Himawari-8/AHI aerosol products

The next-generation geostationary meteorological Himawari-8 satellite carrying the Advanced Himawari Imager (AHI) allows frequent observations of the atmosphere, the surface, and oceans every 10 min. With its retrieval algorithms recently updated, Himawari-8/AHI Version 2 Level 2 aerosol products are now available. However, these retrievals have not yet undergone a quality assessment. This study aims to comprehensively validate the official aerosol optical properties derived from Himawari-8/AHI over land and ocean. Aerosol Robotic Network and Sun-Sky Radiometer Observation Network ground-based measurements at 98 stations in the Himawari-domain region are used to validate aerosol optical depth (AOD, or τ) retrievals at 500 nm and Ångström exponent (AE) retrievals at 440-675 nm from the year 2016. The AOD retrievals agree well with surface observations (i.e., from linear regression, slope = 0.876, intercept = 0.076, and correlation coefficient = 0.756) with a mean absolute error and a root-mean-square error of 0.168 and 0.293, respectively. On site and regional scales, large uncertainties are seen, especially in Australia (significant overestimation) and South Asia (significant underestimation). The AOD retrievals can correctly capture daily variations and show the best (worst) performance in summer (spring). The AE performance is poorer on all scales, showing overall underestimations, especially in Australia, Southeast Asia, and China. The data quality of AOD retrievals improves as the vegetation coverage and the AE increases. This suggests that the official aerosol retrieval algorithm still faces great challenges over bright surfaces and under coarse-particle-dominated conditions. In general, approximately 61% and 64% of the AOD matchups meet the newly defined expected errors of [0.330 × τ + 0.024; -0.132 × τ - 0.125] and [0.519 × τ + 0.005; -0.007 × τ - 0.194] determined by ground measurements and aerosol retrievals, respectively. The highly variable accuracy of aerosol retrievals raises a concern about the reliability of the current product under different environmental conditions and underlying surfaces. It also sheds light on what future improvements need implementing to the aerosol retrieval algorithm.

Spatio-Temporal Variation Characteristics of PM2.5 in the Beijing-Tianjin-Hebei Region, China, from 2013 to 2018

Air pollution, including particulate matter (PM_2.5) pollution, is extremely harmful to the environment as well as human health. The Beijing–Tianjin–Hebei (BTH) Region has experienced heavy PM_2.5 pollution within China. In this study, a six-year time series (January 2013–December 2018) of PM_2.5 mass concentration data from 102 air quality monitoring stations were studied to understand the spatio-temporal variation characteristics of the BTH region. The average annual PM_2.5 mass concentration in the BTH region decreased from 98.9 μg/m³ in 2013 to 64.9 μg/m³ in 2017. Therefore, China has achieved its Air Pollution Prevention and Control Plan goal of reducing the concentration of fine particulate matter in the BTH region by 25% by 2017. The PM_2.5 pollution in BTH plain areas showed a more significant change than mountains areas, with the highest PM_2.5 mass concentration in winter and the lowest in summer. The results of spatial autocorrelation and cluster analyses showed that the PM_2.5 mass concentration in the BTH region from 2013–2018 showed a significant spatial agglomeration, and that spatial distribution characteristics were high in the south and low in the north. Changes in PM_2.5 mass concentration in the BTH region were affected by both socio-economic factors and meteorological factors. Our results can provide a point of reference for making PM_2.5 pollution control decisions.

Modeling Wildland Fire-Specific PM2.5 Concentrations for Uncertainty-Aware Health Impact Assessments

Wildland fire is a major emission source of fine particulate matter (PM_2.5), which has serious adverse health effects. Most fire-related health studies have estimated human exposures to PM_2.5 using ground observations, which have limited spatial/temporal coverage and could not separate PM_2.5 emanating from wildland fires from other sources. The Community Multiscale Air Quality (CMAQ) model has the potential to fill the gaps left by ground observations and estimate wildland fire-specific PM_2.5 concentrations, although the issues around systematic bias in CMAQ models remain to be resolved. To address these problems, we developed a two-step calibration strategy under the consideration of prediction uncertainties. In a case study of the eastern U.S. in 2014, we evaluated the calibration performance using three cross-validation methods, which consistently indicated that the prediction accuracy was improved with an R² of 0.47-0.64. In a health impact study based on the wildland fire-specific PM_2.5 predictions, we identified regions with excess respiratory hospital admissions due to wildland fire events and quantified the estimation uncertainty propagated from multiple components in health impact function. We concluded that the proposed calibration strategy could provide reliable wildland fire-specific PM_2.5 predictions and health burden estimates to support policy development for reducing fire-related risks.

Predicting gestational personal exposure to PM2.5 from satellite-driven ambient concentrations in Shanghai

BACKGROUND

It has been widely reported that gestational exposure to fine particulate matters (PM_2.5) is associated with a series of adverse birth outcomes. However, the discrepancy between ambient PM_2.5 concentrations and personal PM_2.5 exposure would significantly affect the estimation of exposure-response relationship.

OBJECTIVE

Our study aimed to predict gestational personal exposure to PM_2.5 from the satellite-driven ambient concentrations and analyze the influence of other potential determinants.

METHOD

We collected 762 72-h personal exposure samples from a panel of 329 pregnant women in Shanghai, China as well as their time-activity patterns from Feb 2017 to Jun 2018. We established an ambient PM_2.5 model based on MAIAC AOD at 1 km resolution, then used its output as a major predictor to develop a personal exposure model.

RESULTS

Our ambient PM_2.5 model yielded a cross-validation R² of 0.96. Personal PM_2.5 exposure levels were almost identical to the corresponding ambient concentrations. After adjusting for time-activity patterns and meteorological factors, our personal exposure has a CV R² of 0.76.

CONCLUSION

We established a prediction model for gestational personal exposure to PM_2.5 from satellite-based ambient concentrations and provided a methodological reference for further epidemiological studies.

Dynamic assessment of PM2.5 exposure and health risk using remote sensing and geo-spatial big data

In the past few decades, extensive epidemiological studies have focused on exploring the adverse effects of PM_2.5 (particulate matters with aerodynamic diameters less than 2.5 μm) on public health. However, most of them failed to consider the dynamic changes of population distribution adequately and were limited by the accuracy of PM_2.5 estimations. Therefore, in this study, location-based service (LBS) data from social media and satellite-derived high-quality PM_2.5 concentrations were collected to perform highly spatiotemporal exposure assessments for thirteen cities in the Beijing-Tianjin-Hebei (BTH) region, China. The city-scale exposure levels and the corresponding health outcomes were first estimated. Then the uncertainties in exposure risk assessments were quantified based on in-situ PM_2.5 observations and static population data. The results showed that approximately half of the population living in the BTH region were exposed to monthly mean PM_2.5 concentration greater than 80 μg/m³ in 2015, and the highest risk was observed in December. In terms of all-cause, cardiovascular, and respiratory disease, the premature deaths attributed to PM_2.5 were estimated to be 138,150, 80,945, and 18,752, respectively. A comparative analysis between five different exposure models further illustrated that the dynamic population distribution and accurate PM_2.5 estimations showed great influence on environmental exposure and health assessments and need be carefully considered. Otherwise, the results would be considerably over- or under-estimated.

Spatiotemporal Variability and Influencing Factors of Aerosol Optical Depth over the Pan Yangtze River Delta during the 2014-2017 Period

Large amounts of aerosol particles suspended in the atmosphere pose a serious challenge to the climate and human health. In this study, we produced a dataset through merging the Moderate Resolution Imaging Spectrometers (MODIS) Collection 6.1 3-km resolution Dark Target aerosol optical depth (DT AOD) with the 10-km resolution Deep Blue aerosol optical depth (DB AOD) data by linear regression and made use of it to unravel the spatiotemporal characteristics of aerosols over the Pan Yangtze River Delta (PYRD) region from 2014 to 2017. Then, the geographical detector method and multiple linear regression analysis were employed to investigate the contributions of influencing factors. Results indicate that: (1) compared to the original Terra DT and Aqua DT AOD data, the average daily spatial coverage of the merged AOD data increased by 94% and 132%, respectively; (2) the values of four-year average AOD were high in the north-east and low in the south-west of the PYRD; (3) the annual average AOD showed a decreasing trend from 2014 to 2017 while the seasonal average AOD reached its maximum in spring; and that (4) Digital Elevation Model (DEM) and slope contributed most to the spatial distribution of AOD, followed by precipitation and population density. Our study highlights the spatiotemporal variability of aerosol optical depth and the contributions of different factors over this large geographical area in the four-year period, and can, therefore, provide useful insights into the air pollution control for decision makers.

Hourly PM2.5 Estimates from a Geostationary Satellite Based on an Ensemble Learning Algorithm and Their Spatiotemporal Patterns over Central East China

Satellite-derived aerosol optical depths (AODs) have been widely used to estimate surface fine particulate matter (PM2.5) concentrations over areas that do not have PM2.5 monitoring sites. To date, most studies have focused on estimating daily PM2.5 concentrations using polar-orbiting satellite data (e.g., from the Moderate Resolution Imaging Spectroradiometer), which are inadequate for understanding the evolution of PM2.5 distributions. This study estimates hourly PM2.5 concentrations from Himawari AOD and meteorological parameters using an ensemble learning model. We analyzed the spatial agglomeration patterns of the estimated PM2.5 concentrations over central East China. The estimated PM2.5 concentrations agree well with ground-based data with an overall cross-validated coefficient of determination of 0.86 and a root-mean-square error of 17.3 μg m−3. Satellite-estimated PM2.5 concentrations over central East China display a north-to-south decreasing gradient with the highest concentration in winter and the lowest concentration in summer. Diurnally, concentrations are higher in the morning and lower in the afternoon. PM2.5 concentrations exhibit a significant spatial agglomeration effect in central East China. The errors in AOD do not necessarily affect the retrieval accuracy of PM2.5 proportionally, especially if the error is systematic. High-frequency spatiotemporal PM2.5 variations can improve our understanding of the formation and transportation processes of regional pollution episodes.

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

Atmospheric chemical transport models (CTMs) have been widely used to simulate spatiotemporally resolved PM_2.5 concentrations. However, CTM results are usually prone to bias and errors. In this study, we improved the accuracy of PM_2.5 predictions by developing an ensemble deep learning framework to fuse model simulations with ground-level observations. The framework encompasses four machine-learning models, i.e., general linear model, fully connected neural network, random forest, and gradient boosting machine, and combines them by stacking approach. This framework is applied to PM_2.5 concentrations simulated by the Community Multiscale Air Quality (CMAQ) model for China from 2014 to 2017, which has complete spatial coverage over the entirety of China at a 12-km resolution, with no sampling biases. The fused PM_2.5 concentration fields were evaluated by comparing with an independent network of observations. The R² values increased from 0.39 to 0.64, and the RMSE values decreased from 33.7 μg/m³ to 24.8 μg/m³. According to the fused data, the percentage of Chinese population residing under the level II National Ambient Air Quality Standards of 35 μg/m³ for PM_2.5 has increased from 46.5% in 2014 to 61.7% in 2017. The method is readily adapted to utilize near-real-time observations for operational analyses and forecasting of pollutant concentrations and can be extended to provide source apportionment forecasts as well.

Satellite-based high-resolution mapping of ground-level PM2.5 concentrations over East China using a spatiotemporal regression kriging model

Statistical modeling using ground-based PM_2.5 observations and satellite-derived aerosol optical depth (AOD) data is a promising means of obtaining spatially and temporally continuous PM_2.5 estimations to assess population exposure to PM_2.5. However, the vast amount of AOD data that is missing due to retrieval incapability above bright reflecting surfaces such as cloud/snow cover and urban areas challenge this application. Furthermore, most previous studies cannot directly account for the spatiotemporal autocorrelations in PM_2.5 distribution, impacting the associated estimation accuracy. In this study, fixed rank smoothing was adopted to fill the data gaps in a semifinished 3 km AOD dataset, which was a combination of the Moderate Resolution Imaging Spectroradiometer (MODIS) 3 km Dark Target AOD data and MODIS 10 km Deep Blue AOD data from the Terra and Aqua satellites. By matching the gap-filled 3 km AOD data, ground-based PM_2.5 observations, and auxiliary variable data, sufficient samples were screened to develop a spatiotemporal regression kriging (STRK) model for PM_2.5 estimation. The STRK model achieved notable performance in a cross-validation experiment, with a R square of 0.87 and root-mean-square error of 16.55 μg/m³ when applied to estimate daily ground-level PM_2.5 concentrations over East China from March 1, 2015 to February 29, 2016. Using the STRK model, daily PM_2.5 concentrations with full spatial coverage at a resolution of 3 km were generated. The PM_2.5 distribution pattern over East China can be identified at a relatively fine spatiotemporal scale. Thus, the STRK model with gap-filled high-resolution AOD data can provide reliable full-coverage PM_2.5 estimations over large areas for long-term exposure assessment in epidemiological studies.

Mapping daily PM2.5 at 500 m resolution over Beijing with improved hazy day performance

The application of satellite-derived aerosol optical depth (AOD) to infer surface PM_2.5 has significantly increased the spatial coverage and resolutions (1-10 km) of ground-level PM_2.5 mapping as required for accurate exposure estimation. The remaining challenge is to further increase the mapping resolution to the sub-km level with improved algorithms to minimize misrepresentation of severe haze as clouds. In this study, we provide the first daily PM_2.5 estimation over Beijing at a 500 m resolution using AOD from the Simplified Aerosol Retrieval Algorithm (SARA) and linear mixed effects model. A novel cloud screen method is developed which significantly improves data availability during hazy days. The cross-validation R² for PM_2.5 estimations is 0.82 with the cloud-screened SARA AOD. Based on the satellite-predicted high-resolution PM_2.5 map, all-day population-weighted PM_2.5 is estimated to be 81.4 μg m^-3 over Beijing (2.3 times higher than China's NAAQS of 35 μg m^-3). Compared to the standard MODIS Dark Target 3 km product which presents a significant percentage of missing data, the 500 m resolution PM_2.5 mapping derived from SARA AOD reveals distinct pollution patterns and population exposure conditions during severe hazy days, thereby providing valuable information for pollution control and epidemiological studies.

Estimating PM2.5 Concentrations Based on MODIS AOD and NAQPMS Data over Beijing⁻Tianjin⁻Hebei

Accurately estimating fine ambient particulate matter (PM_2.5) is important to assess air quality and to support epidemiological studies. To analyze the spatiotemporal variation of PM_2.5 concentrations, previous studies used different methodologies, such as statistical models or neural networks, to estimate PM_2.5. However, there is little research on full-coverage PM_2.5 estimation using a combination of ground-measured, satellite-estimated, and atmospheric chemical model data. In this study, the linear mixed effect (LME) model, which used the aerosol optical depth (AOD) from the Moderate Resolution Imaging Spectroradiometer (MODIS), meteorological data, normalized difference vegetation index (NDVI), and elevation data as predictors, was fitted for 2017 over Beijing–Tianjin–Hebei (BTH). The LME model was used to calibrate the PM_2.5 concentration using the nested air-quality prediction modeling system (NAQPMS) simulated with ground measurements. The inverse variance weighting (IVW) method was used to fuse satellite-estimated and model-calibrated PM_2.5. The results showed a strong agreement with ground measurements, with an overall coefficient (R²) of 0.78 and a root-mean-square error (RMSE) of 26.44 μg/m³ in cross-validation (CV). The seasonal R² values were 0.75, 0.62, 0.80, and 0.78 in the spring, summer, autumn, and winter, respectively. The fusion results supplement the lack of satellite estimates and can capture more detailed information than the NAQPMS model. Therefore, the results will be helpful for pollution process analyses and health-related studies.

A review on health cost accounting of air pollution in China

Over the last three decades, rapid industrialization in China has generated an unprecedentedly high level of air pollution and associated health problems. Given that China accounts for one-fifth of the world population and suffers from severe air pollution, a comprehensive review of the indicators accounting for the health costs in relation to air pollution will benefit evidence-based and health-related environmental policy-making. This paper reviews the conventional static and the new dynamic approach adopted for air pollution-related health cost accounting in China and analyzes the difference between the two in estimating GDP loss. The advantages of adopting the dynamic approach for health cost accounting in China, with conditions guaranteeing its optimal performance are highlighted. Guidelines on how one can identify an appropriate approach for health cost accounting in China are put forward. Further, we outline and compare the globally-applicable and China-specific indicators adopted by different accounting methodologies, with their pros and cons being discussed. A comprehensive account of the available databases and methodologies for health cost accounting in China are outlined. Future directions to guide health cost accounting in China are provided. Our work provides valuable insights into future health cost accounting research in China. Our study has strengthen the view that the dynamic approach is comparatively more preferred than the static approach for health cost accounting in China, if more data is available to train the dynamic models and improve the robustness of the parameters employed. In addition, future dynamic model should address the socio-economic impacts, including benefits or losses of air pollution polices, to provide a more robust policy picture. Our work has laid the key principles and guidelines for selecting proper econometric approaches and parameters. We have also identified a proper estimation method for the Value of Life in China, and proposed the integration of engineering approaches, such as the use of deep learning and big data analysis for health cost accounting at the fine-grained level (city-district or sub-regional level). Our work has also identified the gap for more accurate health cost accounting at the fine-grained level in China, which will subsequently affect the quality of health-related air pollution policy decision-making at such levels, and the health-related quality of life of the citizens in China.

Critical Review of Methods to Estimate PM2.5 Concentrations within Specified Research Region

Obtaining PM2.5 data for the entirety of a research region underlies the study of the relationship between PM2.5 and human spatiotemporal activity. A professional sampler with a filter membrane is used to measure accurate values of PM2.5 at single points in space. However, there are numerous PM2.5 sampling and monitoring facilities that rely on data from only representative points, and which cannot measure the data for the whole region of research interest. This provides the motivation for researching the methods of estimation of particulate matter in areas having fewer monitors at a special scale, an approach now attracting considerable academic interest. The aim of this study is to (1) reclassify and particularize the most frequently used approaches for estimating the PM2.5 concentrations covering an entire research region; (2) list improvements to and integrations of traditional methods and their applications; and (3) compare existing approaches to PM2.5 estimation on the basis of accuracy and applicability.

Synergy of AERONET and MODIS AOD products in the estimation of PM2.5 concentrations in Beijing

Satellite aerosol optical depth (AOD) is widely used to estimate particulate matter with aerodynamic diameter ≤2.5 µm (PM2.5) mass concentrations. Polar orbiting satellite retrieval 1-2 times each day is frequently affected by cloud, snow cover or misclassification of heavy pollution. Novel methods are therefore required to improve AOD sampling. Sunphotometer provides much more AODs than satellite at a fixed point. Furthermore, much of the aerosol pollution is regional. Both factors indicate that sunphotometer has great potential for PM2.5 concentration estimation. The spatial representativeness of the Aerosol Robotic Network (AERONET) AOD at Beijing site is investigated by linear regression analysis of 13-year daily paired AODs at each grid from Moderate Resolution Imaging Spectroradiometer (MODIS) on Aqua and Beijing AERONET. The result suggests a good correlation for the whole Beijing Administrative region, with regional mean correlation coefficient exceeding 0.73. Pixel AODs are then estimated from AERONET AOD using linear equations, which are verified to have the same accuracy as that of MODIS AOD. Either AOD from MODIS retrieval or estimation from AERONET AOD in the absence of MODIS pixel AOD is finally used to predict PM2.5 concentration. Daily AOD sampling in average is enhanced by 59% in winter when MODIS AODs are very limited. More importantly, synergy of AERONET and MODIS AOD is able to improve the estimation of regional mean PM2.5 concentrations, which indicates this method would play a significant role in monitoring regional aerosol pollution.