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Aerosol optical depth (AOD) derived from satellite remote sensing is widely used to estimate surface PM2.5 (dry mass concentration of particles with an in situ aerodynamic diameter smaller than 2.5 µm) concentrations. In this research, a two-stage spatio-temporal statistical model for estimating daily surface PM2.5 concentrations in the Guanzhong Basin of China is proposed, using 6 km × 6 km AOD data available from the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument as the main variable and meteorological factors, land-cover, and population data as auxiliary variables. The model is validated using a cross-validation method. The linear mixed effects (LME) model used in the first stage could be improved by using a geographically weighted regression (GWR) model or the generalized additive model (GAM) in the second stage, and the predictive capability of the GWR model is better than that of GAM. The two-stage spatio-temporal statistical model of LME and GWR successfully captures the temporal and spatial variations. The coefficient of determination (R2), the bias and the root-mean-squared prediction errors (RMSEs) of the model fitting to the two-stage spatio-temporal models of LME and GWR were 0.802, −0.378 µg/m3, and 12.746 µg/m3, respectively, and the model cross-validation results were 0.703, 1.451 µg/m3, and 15.731 µg/m3, respectively. The model prediction maps show that the topography has a strong influence on the spatial distribution of the PM2.5 concentrations in the Guanzhong Basin, and PM2.5 concentrations vary with the seasons. This method can provide reliable PM2.5 predictions to reduce the bias of exposure assessment in air pollution and health research.

Aerosol optical depth (AOD) has been widely used to estimate near-surface particulate matter (PM). In this study, ground-measured data from the Campaign on Atmospheric Aerosol Research network of China (CARE-China) and the Aerosol Robotic Network (AERONET) were used to evaluate the accuracy of Visible Infrared Imaging Radiometer Suite (VIIRS) AOD data for different aerosol types. These four aerosol types were from dust, smoke, urban, and uncertain and a fifth “type” was included for unclassified (i.e., total) aerosols. The correlation for dust aerosol was the worst (R2 = 0.15), whereas the correlations for smoke and urban types were better (R2 values of 0.69 and 0.55, respectively). The mixed-effects model was used to estimate the PM2.5 concentrations in Beijing–Tianjin–Hebei (BTH), Sichuan–Chongqing (SC), the Pearl River Delta (PRD), the Yangtze River Delta (YRD), and the Middle Yangtze River (MYR) using the classified aerosol type and unclassified aerosol type methods. The results suggest that the cross validation (CV) of different aerosol types has higher correlation coefficients than that of the unclassified aerosol type. For example, the R2 values for dust, smoke, urban, uncertain, and unclassified aerosol types BTH were 0.76, 0.85, 0.82, 0.82, and 0.78, respectively. Compared with the daily PM2.5 concentrations, the air quality levels estimated using the classified aerosol type method were consistent with ground-measured PM2.5, and the relative error was low (most RE was within ±20%). The classified aerosol type method improved the accuracy of the PM2.5 estimation compared to the unclassified method, although there was an overestimation or underestimation in some regions. The seasonal distribution of PM2.5 was analyzed and the PM2.5 concentrations were high during winter, low during summer, and moderate during spring and autumn. Spatially, the higher PM2.5 concentrations were predominantly distributed in areas of human activity and industrial areas.

Satellite-retrieved aerosol optical depth (AOD) data have been widely used to predict PM2.5 concentrations. Most of their spatial resolutions (~1 km or greater), however, are too coarse to support PM2.5-related studies at fine scales (e.g., urban-scale PM2.5 exposure assessments). Space-time regression models have been widely developed and applied to predict PM2.5 concentrations from satellite-retrieved AOD. Their accuracies, however, are not satisfactory particularly on days that lack a model dataset. The present study aimed to evaluate the effectiveness of recent high-resolution (i.e., ~750 m at nadir) AOD obtained from the Visible Infrared Imaging Radiometer Suite instrument (VIIRS) Intermediate Product (IP) in estimating PM2.5 concentrations with a newly developed nested spatiotemporal statistical model. The nested spatiotemporal statistical model consisted of two parts: a nested time fixed effects regression (TFER) model and a series of geographically weighted regression (GWR) models. The TFER model, containing daily, weekly, or monthly intercepts, used the VIIRS IP AOD as the main predictor alongside several auxiliary variables to predict daily PM2.5 concentrations. Meanwhile, the series of GWR models used the VIIRS IP AOD as the independent variable to correct residuals from the first-stage nested TFER model. The average spatiotemporal coverage of the VIIRS IP AOD was approximately 16.12%. The sample-based ten-fold cross validation goodness of fit (R2) for the first-stage TFER models with daily, weekly, and monthly intercepts were 0.81, 0.66, and 0.45, respectively. The second-stage GWR models further captured the spatial heterogeneities of the PM2.5-AOD relationships. The nested spatiotemporal statistical model produced more daily PM2.5 estimates and improved the accuracies of summer, autumn, and annual PM2.5 estimates. This study contributes to the knowledge of how well VIIRS IP AOD can predict PM2.5 concentrations at urban scales and offers strategies for improving the coverage and accuracy of daily PM2.5 estimates on days that lack a model dataset.

Satellite-retrieved aerosol optical depth (AOD) data are extensively integrated with ground-level measurements to achieve spatially continuous fine particulate matters (PM2.5). Current satellite-based methods however face challenges in obtaining highly accurate and reasonable PM2.5 distributions due to the inability to handle both spatial non-stationarity and complex non-linearity in the PM2.5–AOD relationship. High-resolution (<1 km) PM2.5 products over the whole of China for fine exposure assessment and health research are also lacking. This study aimed to predict 750 m resolution ground-level PM2.5 in China with the high-resolution Visible Infrared Imaging Radiometer Suite (VIIRS) intermediate product (IP) AOD data using a newly developed geographically neural network weighted regression (GNNWR) model. The performance evaluations demonstrated that GNNWR achieved higher prediction accuracy than the widely used methods with cross-validation and predictive R2 of 0.86 and 0.85. Satellite-derived monthly 750 m resolution PM2.5 data in China were generated with robust prediction accuracy and almost complete coverage. The PM2.5 pollution was found to be greatly improved in 2018 in China with annual mean concentration of 31.07 ± 17.52 µg/m3. Nonetheless, fine-scale PM2.5 exposures at multiple administrative levels suggested that PM2.5 pollution in most urban areas needed further control, especially in southern Hebei Province. This work is the first to evaluate the potential of VIIRS IP AOD in modeling high-resolution PM2.5 over large-scale. The newly satellite-derived PM2.5 data with high spatial resolution and high prediction accuracy at the national scale are valuable to advance environmental and health researches in China.

The visible infrared imaging radiometer suite (VIIRS) environmental data record aerosol product (VIIRS_EDR) and the aqua-moderate resolution imaging spectroradiometer (MYD04) collection 6 (C6) aerosol optical depth (AOD) products are validated against the Cimel sun–photometer (CE318) AOD measurements during different air quality conditions over the Yangtze river basin (YRB) from 2 May 2012 to 31 December 2016. For VIIRS_EDR, the AOD observations are obtained from the scientific data set (SDS) “aerosol optical depth at 550 nm” at 6 km resolution, and for aqua-MODIS, the AOD observations are obtained from the SDS “image optical depth land and ocean” at 3 km (DT3K) and 10 km (DT10K) resolutions, “deep blue aerosol optical depth 550 land” at 10 km resolution (DB10K), and “AOD 550 dark target deep blue combined” at 10 km resolution (DTB10K). Results show that the high-quality (QF = 3) DTB10K performs the best against the CE318 AOD observations, along with a higher R (0.85) and more retrievals within the expected error (EE) ± (0.05 + 15%) (55%). Besides, there is a 10% overestimation, but the positive bias does not exhibit obvious seasonal variations. Similarly, the DT3K and DT10K products overestimate AOD retrievals by 23% and 15%, respectively, all over the year, but the positive biases become larger in spring and summer. For the DB10K AOD retrievals, there is an overestimation (underestimation) in autumn and winter (spring and summer). Compared to the aqua-MODIS AOD products, the VIIRS_EDR AOD retrievals are less correlated (R = 0.73) and only 44% of the retrievals fall within EE. Meanwhile, the VIIRS_EDR shows larger bias than the aqua-MODIS C6 retrievals, and tends to overestimate AOD retrievals in summer and underestimate in winter. Additionally, there is an underestimation for the VIIRS_EDR AOD retrievals over the regions during high aerosol loadings. These indicate that the VIIRS_EDR retrieval algorithm needs to be improved in further applications over the YRB.

Reanalysis and satellite retrieval are two primary approaches for obtaining large-scale and long-term Aerosol Optical Depth (AOD) datasets. This study evaluates and compares the accuracy, long-term stability, and error characteristics of the MERRA-2, MODIS combined Dark Target and Deep Blue (DT&DB), and VIIRS DB AOD products globally and regionally. The results indicate that the MERRA-2 AOD exhibits the highest accuracy with an expected error (EE, ±0.05 ± 20%) of 83.24% and mean absolute error (MAE) of 0.056, maintaining a stability of 0.010 per decade. However, since the MERRA-2 AOD ceased assimilating observations other than the MODIS AOD in 2014, its accuracy decreased by approximately 5.6% in the EE metric after 2014. The VIIRS Deep Blue (DB) AOD product, with an EE of 79.43% and stability of 0.016 per decade, is slightly less accurate and stable compared to the MERRA-2 AOD. The MODIS DT&DB AOD demonstrates an EE of 76.75% and stability of 0.011 per decade. Regionally, the MERRA-2 AOD performs acceptably in most areas, especially in low-aerosol-loading regions, with an EE > 86% and stability ~0.02 per decade. The VIIRS DB AOD excels in high-aerosol-loading regions, such as the Indian subcontinent, with an EE of 69.14% and a stability of 0.049 per decade. The performance of the MODIS DT&DB AOD falls between that of VIIRS DB and MERRA-2 across most regions. Overall, each product meets the accuracy and stability metrics globally, but users need to select the appropriate product for analysis based on the validation results of the accuracy and stability in different regions.

National Oceanic and Atmospheric Administration (NOAA) and United States Environmental Protection Agency (USEPA) have been deriving surface particulate matter with a median diameter of 2.5 µm or less (PM2.5) from satellite aerosol optical depth (AOD) over Continental United States (CONUS) using a climatological PM2.5‐AOD regression relation. However, because PM2.5‐AOD relation can change over time, this method can have large errors when the relation deviates from the climatological values. In this work, the geographically weighted regression (GWR) model is used to estimate surface PM2.5 from AOD. The parameters of the regression model are derived dynamically in a daily or hourly manner using surface PM2.5 measurements and AOD from satellite. The method is evaluated using Visible Infrared Imaging Radiometer Suite (VIIRS) AOD and Advanced Baseline Imager (ABI) AOD to estimate daily and hourly PM2.5 over CONUS, and Advanced Himawari Imager AOD to estimate hourly PM2.5 over Taiwan. The algorithm performs much better than using simple climatological relationships. The estimated daily PM2.5 from VIIRS AOD has a cross validation (CV) R2 of 0.59 with surface measured PM2.5, bias of 0.09 µg/m3 and Root mean square error (RMSE) of 5.66 µg/m3. The hourly PM2.5 estimates from ABI AOD has a CV R2 of 0.44, bias of 0.04 µg/m3, and RMSE of 4.53 µg/m3. The algorithm will run in near‐real‐time at NOAA to provide air quality community PM2.5 estimates over CONUS. Key Points Daily and hourly PM2.5 are estimated from Suomi National Polar‐Orbiting Partnership Visible Infrared Imaging Radiometer Suite and Geostationary Operational Environmental Satellites‐16 Advanced Baseline Imager PM2.5 and aerosol optical depth (AOD) relationships are revised dynamically using geographically weighted regression The PM2.5 estimates are improved over those using climatological relationships between PM2.5 and AOD

The SEMARA approach, an integration of the Simplified and Robust Surface Reflectance Estimation (SREM) and Simplified Aerosol Retrieval Algorithm (SARA) methods, was used to retrieve aerosol optical depth (AOD) at 550 nm from a Landsat 8 Operational Land Imager (OLI) at 30 m spatial resolution, a Terra-Moderate Resolution Imaging Spectroradiometer (MODIS) at 500 m resolution, and a Visible Infrared Imaging Radiometer Suite (VIIRS) at 750 m resolution over bright urban surfaces in Beijing. The SEMARA approach coupled (1) the SREM method that is used to estimate the surface reflectance, which does not require information about water vapor, ozone, and aerosol, and (2) the SARA algorithm, which uses the surface reflectance estimated by SREM and AOD measurements obtained from the Aerosol Robotic NETwork (AERONET) site (or other high-quality AOD) as the input to estimate AOD without prior information on the aerosol optical and microphysical properties usually obtained from a look-up table constructed from long-term AERONET data. In the present study, AOD measurements were obtained from the Beijing AERONET site. The SEMARA AOD retrievals were validated against AOD measurements obtained from two other AERONET sites located at urban locations in Beijing, i.e., Beijing_RADI and Beijing_CAMS, over bright surfaces. The accuracy and uncertainties/errors in the AOD retrievals were assessed using Pearson’s correlation coefficient (r), root mean squared error (RMSE), relative mean bias (RMB), and expected error (EE = ± 0.05 ± 20%). EE is the envelope encompassing both absolute and relative errors and contains 68% (±1σ) of the good quality retrievals based on global validation. Here, the EE of the MODIS Dark Target algorithm at 3 km resolution is used to report the good quality SEMARA AOD retrievals. The validation results show that AOD from SEMARA correlates well with AERONET AOD measurements with high correlation coefficients (r) of 0.988, 0.980, and 0.981; small RMSE of 0.08, 0.09, and 0.08; and small RMB of 4.33%, 1.28%, and −0.54%. High percentages of retrievals, i.e., 85.71%, 91.53%, and 90.16%, were within the EE for Landsat 8 OLI, MODIS, and VIIRS, respectively. The results suggest that the SEMARA approach is capable of retrieving AOD over urban areas with high accuracy and small errors using high to medium spatial resolution satellite remote sensing data. This approach can be used for aerosol monitoring over bright urban surfaces such as in Beijing, which is frequently affected by severe dust storms and haze pollution, to evaluate their effects on public health.

Determining background aerosol optical depth threshold value (BAOD) is critical to aerosol type identification and air pollution control. This study presents a statistical method to select the best BAOD threshold value using the VIIRS DB AOD products at 1 × 1 degree resolution from 2012 to 2019 as a major testbed. A series of multiple lognormal distributions with 1 to 5 peaks are firstly applied to fit the AOD histogram at each grid point, and the distribution with the highest correlation coefficient (R) gives preliminary estimations of BAOD, which is defined as either the intersection point of the first two normal distribution curves when having multiple peaks, or the midpoint between the peak AOD and the first AOD with non-zero probability when the mono peak is the best fit. Then, the lowest 1st to 100th percentile AOD distributions are compared with the preliminary BAOD distribution on a global scale. The final BAOD is obtained from the best cutoff percentile AOD distributions with the lowest bias compared with preliminary BAOD. Results show that the lowest 30th percentile AOD is the best estimation of BAOD for different AOD datasets and different seasons. Analysis of aerosol chemical information from MERRA-2 further supports this selection. Based on the BAOD, we updated the VIIRS aerosol type classification scheme, and the results show that the updated scheme is able to achieve reliable detection of aerosol type change in low aerosol loading conditions.

Much of the population is exposed to PM2.5 (particulate matter) pollution in China, and establishing a high-precision PM2.5 grid dataset will be very valuable for air pollution and related studies. However, limited by the traditional models themselves and input data sources, PM2.5 estimations are of low accuracy with narrow spatial coverage. Therefore, we develop a new spatiotemporally weighted random forest (SWRF) model to improve the estimation accuracy and expand the spatial coverage of PM2.5 concentrations using the latest release of the Visible infrared Imaging Radiometer (VIIRS) Deep Blue (DB) aerosol product, along with meteorological variables, and socioeconomic data. Compared with traditional methods and the results of previous similar studies, our satellite-derived PM2.5 distribution shows better consistency with surface-measured records, having a high out-of-sample (out-of-station) cross-validation (CV) coefficient of determination (CV-R2), root mean squared error (RMSE), and mean absolute error (MAE) of 0.87 (0.85), 11.23 (11.53) μg m−3 and 8.25 (8.78) μg m−3, respectively. The monthly, seasonal, and annual mean PM2.5 were also successfully captured (CV-R2 = 0.91–0.92, RMSE = 4.35–6.72 μg m−3). Then, the spatial characteristics of PM2.5 pollution in 2018 were investigated, showing that although air pollution has diminished in recent years, China still faces a high PM2.5 pollution risk overall, especially in winter (average = 50.43 + 16.81 μg m−3). In addition, 19 provinces or administrative regions have annual PM2.5 concentrations >35 μg m−3, particularly the Xinjiang Uygur Autonomous Region (~55.25 μg m−3), Tianjin (~49.65 μg m−3), and Henan Province (~48.60 μg m−3). Our estimated surface PM2.5 concentrations are accurate, which could benefit further research on air pollution in China.