Satellite-based Aerosol Optical Depth Research Articles

Studies on the improvement of modelled solar radiation and the attenuation effect of aerosol using the WRF-Solar model with satellite-based AOD data over north China

Photovoltaic (PV) power generation will become one of the main energy sources to achieve goals of carbon peak and carbon-neutral in China in the future. However, serious haze events have occurred frequently due to massive anthropogenic emissions in autumn and winter in China and resulted in a significant reduction of solar radiation reaching the Earth's surface. In this study, we conducted modifications to modelled solar radiation with the Weather Research and Forecasting (WRF)-Solar model by implementing the satellite-based aerosol optical depth (AOD) data into the model. The satellite-based AOD data have a higher spatial resolution and better accuracy. The studies were performed during the heavy haze period in the winter of 2016/2017 over North China (NC). Comparison with observations shows that the temporal-spatial variations of satellite-based AOD are consistent with those of AERONET and surface PM2.5 concentration, and satellite-based AOD at different sites or some months is slightly underestimated. Satellite-based AOD and surface PM2.5 concentrations in most of the southern areas of NC are significantly larger than in the northern parts. Simulated errors of Global horizontal irradiance (GHI) are reduced remarkably compared with those excluding the AOD data, especially in regions of the BTH (Beijing, Tianjin, and Hebei province), west of Shandong province, and north of Henan province. All RMSE and MAE of simulated GHI under three types of weather conditions declined by more than 20%, and 37%, respectively. Modelled errors of GHI on all-sky and cloudy-sky days are larger than those on clear-sky days due to the complicated aerosol-cloud-radiation interaction and simulated bias of cloud. Reductions of GHI caused by aerosols fall in between -140～-20 W m−2, and the spatial distribution is more refined and perform well compared to observations. Our results show that direct application of the satellite-based AOD data into the WRF-Solar model is very effective for improving the simulation of solar radiation and investigating the attenuation effect of solar radiation caused by aerosol in winter.

Himawari-8/AHI Aerosol Optical Depth Detection Based on Machine Learning Algorithm

Due to the advantage of geostationary satellites, Himawari-8/AHI can provide near-real-time air quality monitoring over China with a high temporal resolution. Satellite-based aerosol optical depth (AOD) retrieval over land is a challenge because of the large surface contribution to the top of atmosphere (TOA) signal and the uncertainty of aerosol modes. Here, by combining satellite TOA reflectance, sun-sensor geometries, meteorological factors and vegetation information, we propose a data-driven AOD detection algorithm based on a deep neural network (DNN) model for Himawari-8/AHI. It is trained by sample data of 2018 and 2019 and is applied to derive hourly AODs over China in 2020. By comparison with ground-based AERONET measurements, R2 for DNN-estimated AOD is up to 0.8702, which is much higher than that for the AHI AOD product with R2 = 0.4869. The hourly AOD results indicate that the DNN model has a good potential in improving the performance of AOD retrieval in the early morning and in the late afternoon, and the spatial distribution is reliable for capturing the variation of aerosol pollution on the regional scale. By analyzing different DNN modeling strategies, it is found that seasonal modeling can hardly increase the accuracy of AOD retrieval to a certain extent, and R2 increases from 0.7394 to 0.8168 when meteorological features, especially air pressure, are involved in the model training.

COVID-19 regulations, culture, and the environment

The economic and social disruptions caused by the COVID-19 pandemic are immense. Unexpectedly, a positive outcome of the stringent Covid restrictions has come in the form of air pollution reduction. Pollution reduction, however, has not happened everywhere at equal rates. Why are lockdown measures not producing this positive externality in all countries? Using satellite-based Aerosol Optical Depth data and panel analysis conducted at the country-day level, we find that the countries that have adopted stringent COVID-19 containment policies have experienced better air quality. Nonetheless, this relationship depends on the cultural orientation of a society. Our estimates indicate that the effect of policy stringency is lower in societies imbued with a collectivistic culture. The findings highlight the role of cultural differences in the successful implementation of policies and the realization of their intended outcomes. It implies that pollution mitigation policies are less likely to yield emission reduction in collectivist societies.

Long-term exposure to air pollution, habitual physical activity and risk of non-alcoholic fatty liver disease: A prospective cohort study

BackgroundEmerging studies suggest a positive association between air pollution exposure and risk of non-alcoholic fatty liver disease (NAFLD), however, the combined effects of long-term exposure to air pollution, physical activity (PA), and risk of NAFLD is unclear. MethodsWe included 58,026 Taiwan residents who received a standard medical screening program between 2001 and 2016. Levels of fine particulate matter (PM2.5) at each participant’s residential address were estimated using multiple satellite-based aerosol optical depth data combined with a chemical transport model. PA volume was calculated as hours of metabolic equivalent tasks per week (MET-h/week) based on a standard self-administered questionnaire. Incident NAFLD was defined as the first occurrence of a fatty liver index (FLI) value > 30 or a hepatic steatosis index (HSI) value > 36 in participants without NAFLD at the baseline. Time-varying Cox regression was used to evaluate the combined effects of PA and PM2.5. ResultsExposure to PM2.5 was positively associated with NAFLD. A 1 μg/m3 increase in PM2.5 above 23.5 μg/m3 was associated with a hazard ratio (HR) of 1.06 (95% CI: 1.04, 1.09) and 1.05 (95% CI: 1.03, 1.07) for NAFLD identified by FLI and HSI, respectively. Performing PA was inversely associated with NAFLD. Compared with participants in high PM2.5 [≥ 27.5 μg/m3]–very low PA [< 3.75 MET-h/week] group, low PM2.5 [< 23.5 μg/m3]–very high PA [≥ 25.50 MET-h/week] group had a 57% (95% CI: 50%, 63%) and 42% (95% CI: 33%, 50%) lower risk of NAFLD defined by FLI and HSI, respectively. We found no evidence of any additive or multiplicative interaction between PA and PM2.5. ConclusionLong-term PM2.5 exposure was positively associated with NAFLD, whereas performing PA was inversely associated with NAFLD. The benefits of PA on NAFLD remained stable in participants exposed to various PM2.5 levels.

A novel big data mining framework for reconstructing large-scale daily MAIAC AOD data across China from 2000 to 2020

ABSTRACT Satellite-based aerosol optical depth (AOD) retrieval products are essential in air pollution and climate change research. Unfortunately, cloud contaminations and unfavorable surface conditions result in a considerable proportion of missing AOD data. Numerous studies have been conducted to reconstruct large-scale AOD data gaps by utilizing adjacent spatiotemporal information or modeling AOD data via various external geographical data. However, the erratic variation of AOD and the inconvenience of external data weaken the accuracy and efficiency of reconstruction. To address these issues, a novel big data based iterative variation mining framework (IVMF), utilizing multi-spatiotemporal information on AOD variations, is proposed to reconstruct large-scale AOD data over China from 2000 to 2020. Simulated and real-data experiments are carried out to validate the effectiveness and robustness of the IVMF. Results show that the spatial patterns of the reconstructed AOD are consistent with those of the original AOD in the simulated experiments. The final reconstructed AOD data strongly correlate with in-situ AOD measurements in the real-data experiments (correlation coefficient of 0.91). After reconstruction, the average daily AOD coverage increases from 30.42% to 96.69% (a 218% increase). Besides, results reveal that central China exhibits severe AOD levels, while northwest China presents low AOD levels. Overall, the proposed IVMF can largely resolve the missing AOD data problem with outstanding accuracy and efficiency, and has great potential to be generalized to other regions and remote sensing products.

Long-term spatiotemporal trends in aerosol optical depth and its relationship with enhanced vegetation index and meteorological parameters over South Asia.

Satellite-based aerosol optical depth (AOD) is columnar light extinction by aerosol absorption and scattering and has become the most important variable for the assessment of the spatiotemporal distribution of aerosols at a regional and global level. In this paper, we have used AOD observations of multiangle imaging spectroradiometer (MISR) from September 2002 to May 2017, moderate resolution imaging spectroradiometer (MODIS) from September 2002 to December 2020, and sea-viewing wide field-of-view sensor (SeaWiFS) from September 2002 to December 2010 over South Asia. We have observed the association of AOD with enhanced vegetation index (EVI) and meteorological variables (temperature (TEMP), wind speed (WS), and relative humidity (RH)) acquired from Giovanni during the period September 2002-December 2020. The satellite observations of Terra-, MISR-, and SeaWiFS-AOD were also compared with Aqua-AOD. The findings show that AOD in eastern Pakistan is higher than in the western Pakistan due to increase in population density and biomass burning. Mean annual peak AOD (˃ 0.7) has been observed over the IGB region because of the significant increase in economical, industrial, and agricultural activities while AOD of ˃ 0.6 is observed over Bangladesh. The lowest mean annual AOD of ˂ 0.3 is observed over northeastern Afghanistan, western Nepal, and Bhutan whereas the AOD of 0.3 is seen over Sri Lanka. The highest seasonal mean AOD of 0.8 has been seen over Bihar, India, and AOD of ~ 0.7 is observed over Bangladesh while the lowest AOD is observed over Afghanistan, Sri Lanka, Nepal, and Bhutan during the winter season. However, the mean AOD over eastern Pakistan is maximum in both monsoon and post-monsoon season but relatively low in pre-monsoon and winter. The highest positive seasonal AOD anomalies were observed over South Asia in winter season followed by post-monsoon, pre-monsoon, and least being monsoon. The higher mean AOD anomaly value is found to be 0.2 over eastern Pakistan and western India. In northeastern Pakistan and central India, AOD and RH are positively correlated (r ˃ 0.54) while negatively correlated over Afghanistan, southwestern region of Pakistan, eastern India, Nepal, Bhutan, and Bangladesh. AOD is negatively correlated (r = ~ - 0.3) with EVI over eastern Pakistan and western India. The highest correlation coefficient (r) obtained among Terra and Aqua is 0.97, MISR and Aqua is 0.93, and SeaWiFS and Aqua is 0.58 over South Asia.

Geographical and temporal encoding for improving the estimation of PM2.5 concentrations in China using end-to-end gradient boosting

Fine particulate matter with aerodynamic diameters less than 2.5 μm (PM2.5) profoundly affects environmental systems and human health. To dynamically monitor fine particulate matter over large geographic areas, some machine learning methods have been utilized to estimate its concentration using satellite-based aerosol optical depth (AOD). To improve the estimation of PM2.5 concentration across large areas, a geospatial-temporal joint code is proposed in this paper to characterize the influence of spatial-temporal information hidden in satellite-based aerosol products. This encoding method can reveal the relationship between the PM2.5 concentration and its geospatial location and observation time. Instead of aggregating observation data over neighbors, the method directly encodes the spatial-temporal information as features of the end-to-end gradient boosting model for the estimation of PM2.5. Experimental results of PM2.5 concentration in 2019 across China show that the state-of-the-art method is outperformed by the proposed method by a large margin, with R2 from 0.89 to 0.92, RMSE from 10.35 to 7.89μg/m3, and MAE from 6.71 to 5.17μg/m3. In addition, overall partial dependence plots (PDPs) are used for the first time to visualize the complicated relationship between satellite-based aerosol products and PM2.5 concentrations.

Evaluation and comparison of MODIS and VIIRS aerosol optical depth (AOD) products over regions in the Eastern Mediterranean and the Black Sea

Aerosol loading has direct and indirect impacts on radiative forcing and climate; additionally, exposure to high aerosol concentrations has adverse effects on human health. Several inversion algorithms are used to retrieve aerosol properties, primarily aerosol optical depth (AOD). Satellite aerosol products are available from several moderate spatial resolution sensors and provide information on the spatial and temporal dynamics of aerosol concentration. This study provides an evaluation and inter-comparison of multiple aerosol products from Terra and Aqua Moderate Resolution and Imaging Spectroradiometer (MODIS) and Suomi National Polar-Orbiting Partnership (Suomi NPP) Visible-Infrared Imager Radiometer Suite (VIIRS) between 2014 and 2018 in the Eastern Mediterranean and the Black Sea. This evaluation focuses on four MODIS aerosol products including Dark Target (DT) AOD retrievals at 10-km and 3-km spatial resolutions, Deep Blue (DB) AOD retrievals at10-km spatial resolution, Multi-Angle Implementation of Atmospheric Correction (MAIAC) AOD retrievals at 1-km spatial resolution, and VIIRS DB AOD retrievals at 6-km spatial resolution. Satellite-based AOD retrievals are validated against in-situ AOD measurements from three AERONET ground stations located in urban/land, rural/coastal, and ocean surfaces.The results show that Pearson's correlation coefficient (r) between the satellite AOD retrievals and AERONET AOD measurements range between 0.45 and 0.93 whilst the Root-Mean-Squared Errors (RMSE) varies from 0.047 to 0.159 which highlights the reliability of satellite AOD retrievals' in the region. The best performing products over the urban/land surface are the MODIS MAIAC and VIIRS DB aerosol products with the RMSE of 0.048–0.061 and a higher percentage (81%–89%) of retrievals falling within expected error for land. Over the ocean, the MODIS DT aerosol products have the lowest errors (RMSE 0.047–0.061) and the highest percentage (70%–80%) of retrievals falling within the expected error. The performances of all algorithms over the rural/coastal region are poorer in terms of over- and under-estimations of AOD with a small percentage of retrievals (48%–87%) within expected error. Among the satellite aerosol products, the VIIRS aerosol product performs better over the coastal area in terms of high correlation coefficient (r = 0.88), low error (RMSE = 0.059), and the large percentage of retrievals within expected error (88%). The poor performance found over the more complex coastal regions suggests further improvements are needed in land-water-sediment masks, surface reflectance estimation, and aerosol model assumptions over mixed surface types.The aerosol products show the highest agreement (r ∼ 0.77–0.98) over the ocean surface whilst the coastal site aerosol products verified the greatest dissimilarity in aerosol loading variations (r ∼ 0.50–0.91). Lastly, the good correlation (r > 0.73) between the VIIRS and MODIS AOD products illustrates the potential of VIIRS to provide aerosol data continuity with MODIS.

Contrasting seasonal pattern between ground-based PM2.5 and MODIS satellite-based aerosol optical depth (AOD) at an urban site in Hanoi, Vietnam.

This study investigated the seasonal variations of the ground-based PM2.5 concentration measured at an urban site and Terra MODIS satellite-based aerosol optical depth (AOD) in Hanoi, Vietnam, during the year 2016. The findings showed the large differences in terms of the seasonal variation of both PM2.5 concentration and columnar AOD. Clearly, the ground-based PM2.5 concentrations decreased during the summer period and highly increased during the late fall and winter periods. The Terra MODIS AOD values also decreased in summer, however, significantly increased in spring. Interestingly, the increasing trend of AOD which in contrast to the decreasing trend of PM2.5 concentration was found during the spring period, and vice versa for the winter period. The seasonal analysis of meteorological factors including relative humidity (RH), precipitation, and boundary layer height (BLH) as well as regional fire activities expressed that the increased columnar AOD in spring was largely influenced by the hygroscopic growth of aerosol induced by the increased RH, the larger BLH causing the more effective transport and mixing of atmospheric particles to higher altitudes, and especially the intensive fire activities in the Southeast Asian region during the spring period, whereas the significantly increased surface PM2.5 concentration in winter was partly attributed to the impact from the highly industrialized and polluted source region in the Pearl River Delta of China. Different from the spring and winter periods, the abundant precipitation amount and the dominance of maritime air masses arrived in Hanoi in summer were the main reasons for the low AOD and surface PM2.5 concentration during this period.

Impact of environmental attributes on the uncertainty in MAIAC/MODIS AOD retrievals: A comparative analysis

This work examines the impact of different environmental attributes on the uncertainty in satellite-based Aerosol Optical Depth (AOD) retrieval against the benchmark Aerosol Robotic Network (AERONET) AOD measurements at 21 sites across North Africa, California and Germany, in the years 2007–2017. As a first step, we studied the effects of spatial averaging the Multi-Angle Implementation of Atmospheric Correction (MAIAC) AOD retrievals, and of temporal averaging the AERONET AOD around the satellite (Aqua) overpass, on the agreement between the two products. AERONET AOD averaging over a time-window of ±15 min around the satellite overpass and the 1 × 1 km2 spatial grid of MAIAC were found to provide the best AOD retrieval performance. Next, MAIAC AOD were stratified according to different co-measured environmental attributes (aerosol loading, dominant particle size, vegetation cover, and prevailing particle type) and analyzed against the AERONET AOD. The envelope of the expected retrieval error varied considerably among different environmental attributes categories, with more accurate AOD retrievals obtained over highly vegetated areas (i.e. less surface reflectance) than over arid areas. Moreover, the retrieval accuracy was found to be sensitive to the aerosol loading and particle size, with a large bias between the MAIAC and AERONET AOD during high aerosol loading of coarse particles. In addition, the retrieval accuracy of MAIAC AOD was found to depend on the aerosol type due to the aerosol model assumptions regarding their optical properties.

Sensitivity analysis of Look-up table for satellite-based aerosol optical depth retrieval

Due to the high variation of aerosol in space and time, remote sensing has become an effective tool in aerosol optical depth (AOD) retrieval. Most of the satellite-based AOD retrieval methods using the radiative transfer model (RTM). RTMs help to establish a relation between AOD and the observed top-of-atmosphere (TOA) reflectance. Using look-up table-based approaches is among the commonly used methods for reducing the computational complexity in RTMs. Tuning steps for parameters in the LUTs influences computational cost and accuracy of retrieved satellite-based AOD. However, a comprehensive and quantitative assessment for the construction of LUT based on sensitivity analysis does not exist. In this paper, we present a sensitivity analysis approach called Morris one-at-a-time (MOAT) for identifying parameters that dominate model behaviour in the AOD retrieval process. We also use 8 cases to tune steps for each parameter in the look-up table. We investigate the influence of geometrical and physical parameters in the 6S RT code on radiance and reflectance. The result indicated that ground reflectance, aerosol model, and single scattering albedo are dominant parameters in LUT construction. Other geometrical and physical parameters have less impact on the simulated radiance and reflectance. Finally, we tuned proper steps for LUT construction based on the trend and behaviour of each parameter. The proposed method in this study can improve the accuracy of satellite-based AOD by constructing a LUT with a short processing time.

Air pollution and hospitalization in megacities: empirical evidence from Pakistan.

Air pollution has become a threat to human health in urban settlements, ultimately leading to negative impacts on overall economic system as well. Already developed nations and still developing countries both are at the risk of air pollution globally. In this scenario, this work aims to investigate the associations of asthma (AS) and acute upper respiratory infection (ARI) patients with satellite-based aerosol optical depth (AOD) and meteorological factors, i.e., relative humidity (RH), temperature (TEMP), and wind speed (WS). We applied second-generation unit root tests to provide empirical evidence. Two sets of unit root tests confirmed mix order of integration, and the other Westerlund co-integration test further showed strong linkages between estimated variables. Fully modified ordinary least square (FMOLS) and dynamic ordinary least square (DOLS) tests were applied, only to explore that TEMP and WS lower the number of AS and ARI patients, but RH and AOD increase the number of patients. Therefore, in accordance with these findings, our study provides some important policy instruments to improve the health status in megacities of Pakistan.

Estimation of ambient PM2.5 in Iraq and Kuwait from 2001 to 2018 using machine learning and remote sensing

Iraq and Kuwait are in a region of the world known to be impacted by high levels of fine particulate matter (PM2.5) attributable to sources that include desert dust and ambient pollution, but historically have had limited pollution monitoring networks. The inability to assess PM2.5 concentrations have limited the assessment of the health impact of these exposures, both in the native populations and previously deployed military personnel. As part of a Department of Veterans Affairs Cooperative Studies Program health study of land-based U.S. military personnel who were previously deployed to these countries, we developed a novel approach to estimate spatially and temporarily resolved daily PM2.5 exposures 2001–2018. Since visibility is proportional to ground-level particulate matter concentrations, we were able to take advantage of extensive airport visibility data that became available as a result of regional military operations over this time period. First, we combined a random forest machine learning and a generalized additive mixed model to estimate daily high resolution (1 km × 1 km) visibility over the region using satellite-based aerosol optical depth (AOD) and airport visibility data. The spatially and temporarily resolved visibility data were then used to estimate PM2.5 concentrations from 2001 to 2018 by converting visibility to PM2.5 using empirical relationships derived from available regional PM2.5 monitoring stations. We adjusted for spatially resolved meteorological parameters, land use variables, including the Normalized Difference Vegetation Index, and satellite-derived estimates of surface dust as a measure of sandstorm activity. Cross validation indicated good model predictive ability (R2 = 0.71), and there were considerable spatial and temporal differences in PM2.5 across the region. Annual average PM2.5 predictions for Iraq and Kuwait were 37 and 41 μg/m3, respectively, which are greater than current U.S. and WHO standards. PM2.5 concentrations in many U.S. bases and large cities (e.g. Bagdad, Balad, Kuwait city, Karbala, Najaf, and Diwaniya) had annual average PM2.5 concentrations above 45 μg/m3 with weekly averages as high as 150 μg/m3 depending on calendar year. The highest annual PM2.5 concentration for both Kuwait and Iraq were observed in 2008, followed by 2009, which was associated with extreme drought in these years. The lowest PM2.5 values were observed in 2014. On average, July had the highest concentrations, and November had the lowest values, consistent with seasonal patterns of air pollution in this region. This is the first study that estimates long-term PM2.5 exposures in Iraq and Kuwait at a high resolution based on measurements data that will allow the study of health effects and contribute to the development of regional environmental policies. The novel approach demonstrated may be used in other parts of the world with limited monitoring networks.

An interpretable self-adaptive deep neural network for estimating daily spatially-continuous PM2.5 concentrations across China

Accurate estimation of daily spatially-continuous PM2.5 (fine particulate matter) concentration is a prerequisite to address environmental public health issues, and satellite-based aerosol optical depth (AOD) products have been widely used to estimate PM2.5 concentrations using statistical-based or machine learning-based models. However, statistical-based models oversimplify the AOD-PM2.5 relationships, whereas complex machine learning technologies ignore the spatiotemporal heterogeneity of the predictors and demonstrate shortage in interpretation. Besides, large AOD data gaps resulting in PM2.5 estimation biases have been seldom imputed in previous studies, especially at national scales. To fill the above research gaps, this study attempts to present a feasible methodology to estimate daily spatially-continuous PM2.5 concentrations in China. The AOD data gaps across China were first imputed via a random forest (RF) model. Then, an interpretable self-adaptive deep neural network (SADNN) model, incorporating AOD, meteorological and other auxiliary predictors, was developed to estimate daily spatially-continuous PM2.5 concentrations from 2017 to 2018. Five-fold sample (site)-based cross-validation results showed a high accuracy of the SADNN model, with coefficient of determination and root mean square error values equal to 0.86 (0.84) and 13.07 (14.30) μg/m3, respectively, outperforming the standard DNN and the RF model. Furthermore, the SADNN model identified the spatiotemporal patterns of predictor importance, and demonstrated that the boundary layer height, elevation and AOD were the most important predictors both spatially and temporally. And the predictor importance in the Qinghai-Tibet Plateau was different from that in the rest of China. These results enhance our understanding of AOD-PM2.5 relationships and elucidate the estimated PM2.5 datasets with complete coverage are applicable for related air pollution studies and epidemiological cohort studies. Moreover, considering the effective nonlinear model capability and interpretability, the SADNN model is beneficial for not only PM2.5 estimation but also other earth data and scenarios.

Robust prediction of hourly PM2.5 from meteorological data using LightGBM.

Retrieving historical fine particulate matter (PM2.5) data is key for evaluating the long-term impacts of PM2.5 on the environment, human health and climate change. Satellite-based aerosol optical depth has been used to estimate PM2.5, but estimations have largely been undermined by massive missing values, low sampling frequency and weak predictive capability. Here, using a novel feature engineering approach to incorporate spatial effects from meteorological data, we developed a robust LightGBM model that predicts PM2.5 at an unprecedented predictive capacity on hourly (R2 = 0.75), daily (R2 = 0.84), monthly (R2 = 0.88) and annual (R2 = 0.87) timescales. By taking advantage of spatial features, our model can also construct hourly gridded networks of PM2.5. This capability would be further enhanced if meteorological observations from regional stations were incorporated. Our results show that this model has great potential in reconstructing historical PM2.5 datasets and real-time gridded networks at high spatial-temporal resolutions. The resulting datasets can be assimilated into models to produce long-term re-analysis that incorporates interactions between aerosols and physical processes.

Influence of observation angle change on satellite retrieval of aerosol optical depth

Use of multi-angle sensors, which can perform ground observations in different view directions, provides an important technical means to acquire more comprehensive and detailed remote sensing information. This study explored the effect of angle changes on satellite-based aerosol optical depth (AOD) retrieval using 2016–2018 Multi-angle Imaging SpectroRadiometer (MISR) data in the Yangtze River Delta. The Moderate resolution Imaging Spectroradiometer (MODIS) surface reflectance determined by the MODIS V5.2 algorithm and MODIS BRDF data are used to obtain MISR surface reflectance at different angles after a spectral conversion between MODIS and MISR, and then MISR AOD with different angles were retrieved based on the MISR surface reflectance at different angles. Moreover, the errors and their formation mechanism in retrieving AOD using MISR remote sensing data at different angles were analyzed based on the Global Aerosol Robotic Network (AERONET) ground observations. We used six indicators, including correlation coefficient (R), significant level (P value), mean absolute error (MAE), mean relative error (MRE) root mean square error (RMSE) and relative mean bias (RMB), and AERONET AOD is used to verify the MISR AOD. The results show that at Taihu and Xuzhou stations, the highest R values are 0.81 (Aa AOD) and 0.75 (An AOD), and the minimum RMSE values at Taihu and Xuzhou-CUMT stations are 0.11 (Aa AOD) and 0.20 (An AOD), respectively. Incorrect surface reflectance measurement is the root cause of the angle effect in MISR AOD retrieval. With the same surface reflectance deviation, the larger the MISR observation angle, the closer the retrieved MISR AOD is to the AOD observed by AERONET. The AOD retrieval of the forward observation angle is closer to AERONET AOD than that of backward observation angle, and the retrieved error is greatest at the vertical angle (nadir). Therefore, sensors for vertical ground observation are not suitable for AOD retrieval, and sensors in non-vertical directions, especially forward to the ground, are more favorable to MISR AOD retrieval.

Satellite-based ground PM2.5 estimation using a gradient boosting decision tree

Fine particulate matter with an aerodynamic diameter less than 2.5 μm (PM2.5) is one of the major air pollutants risks to human health worldwide. Satellite-based aerosol optical depth (AOD) products are an effective metric for acquiring PM2.5 information, featuring broad coverage and high resolution, which compensate for the sparse and uneven distribution of existing monitoring stations. In this study, a gradient boosting decision tree (GBDT) model for estimating ground PM2.5 concentration directly from AOD products across China in 2017, integrating human activities and various natural variables was proposed. The GBDT model performed well in estimating temporal variability and spatial contrasts in daily PM2.5 concentrations, with relatively high fitted model (10-fold cross-validation) coefficients of determination of 0.98 (0.81), low root mean square errors of 3.82 (11.57) μg/m3, and mean absolute error of 1.44 (7.45) μg/m3. Seasonal examinations revealed that summer had the cleanest air with the highest estimation accuracies, whereas winter had the most polluted air with the lowest estimation accuracies. The model successfully captured the PM2.5 distribution pattern across China in 2017, showing high levels in southwest Xinjiang, the North China Plain, and the Sichuan Basin, especially in winter. Compared with other models, the GBDT model showed the highest performance in the estimation of PM2.5 with a 3-km resolution. This algorithm can be adopted to improve the accuracy of PM2.5 estimation with higher spatial resolution, especially in summer. In general, this study provided a potential method of improving the accuracy of satellite-based ground PM2.5 estimation.

Quantitative Soil Wind Erosion Potential Mapping for Central Asia Using the Google Earth Engine Platform

A lack of long-term soil wind erosion data impedes sustainable land management in developing regions, especially in Central Asia (CA). Compared with large-scale field measurements, wind erosion modeling based on geospatial data is an efficient and effective method for quantitative soil wind erosion mapping. However, conventional local-based wind erosion modeling is time-consuming and labor-intensive, especially when processing large amounts of geospatial data. To address this issue, we developed a Google Earth Engine-based Revised Wind Erosion Equation (RWEQ) model, named GEE-RWEQ, to delineate the Soil Wind Erosion Potential (SWEP). Based on the GEE-RWEQ model, terabytes of Remote Sensing (RS) data, climate assimilation data, and some other geospatial data were applied to produce monthly SWEP with a high spatial resolution (500 m) across CA between 2000 and 2019. The results show that the mean SWEP is in good agreement with the ground observation-based dust storm index (DSI), satellite-based Aerosol Optical Depth (AOD), and Absorbing Aerosol Index (AAI), confirming that GEE-RWEQ is a robust wind erosion prediction model. Wind speed factors primarily determined the wind erosion in CA (r = 0.7, p &lt; 0.001), and the SWEP has significantly increased since 2011 because of the reversal of global terrestrial stilling in recent years. The Aral Sea Dry Lakebed (ASDLB), formed by shrinkage of the Aral Sea, is the most severe wind erosion area in CA (47.29 kg/m2/y). Temporally, the wind erosion dominated by wind speed has the largest spatial extent of wind erosion in Spring (MAM). Meanwhile, affected by the spatial difference of the snowmelt period in CA, the wind erosion hazard center moved from the southwest (Karakum Desert) to the middle of CA (Kyzylkum Desert and Muyunkum Desert) during spring. According to the impacts of land cover change on the spatial dynamic of wind erosion, the SWEP of bareland was the highest, while that of forestland was the lowest.

Spatiotemporal dynamics and exposure analysis of daily PM2.5 using a remote sensing-based machine learning model and multi-time meteorological parameters

Identifying spatiotemporal characteristics of daily fine particulate matter (PM2.5) concentrations is essential for assessing air quality and understanding the environmental consequences of urbanization. More importantly, exposure analysis can provide basic information for appropriate decision making. This paper demonstrates an integrated method incorporating satellite-based aerosol optical depth, machine learning models and multi-time meteorological parameters to analyze spatiotemporal dynamics and exposure levels of daily PM2.5 in the economically developed Yangtze River Delta (YRD) from 2016 to 2018. Ten-fold cross validation (CV) was implemented to evaluate the model performance. Compared to the models with daily means of meteorological fields, the models with multi-time meteorological parameters had higher CV coefficient of determination (R2) and lower CV root mean square error (RMSE) values. The model with the best performance achieved sample- (site-) based CV R2 values of 0.88 (0.88) and RMSE values of 10.33 (10.35) μg/m3. The YRD region was seriously polluted (exceeding the World Health Organization Interim Targets-1 standard of 35 μg/m3) during our study period, especially in Jiangsu Province, but with an improving trend. The residents in Zhejiang Province suffered the least from exposure, with 39 days (4% of the total days) characterized as over polluted (daily average > 75 μg/m3) in our study period. Air pollution in Shanghai Municipality mitigated the most from 2016 to 2018. With the advantages of high-accuracy and high-resolution (daily and 0.01 ° × 0.01 ° resolutions), the proposed method can guide for environmental policy planning.

A Review on Estimation of Particulate Matter from Satellite-Based Aerosol Optical Depth: Data, Methods, and Challenges

Detailed, reliable, and continuous monitoring of aerosol optical depth (AOD) is essential for air quality management and protection of human health. The satellite-based AOD datasets have been typically used in many studies for the estimation of particulate matter (PM2.5 and PM10) concentration in the tropospheric region. The prime focus of this study is to review the past studies to analyze the performance of various satellite-based AOD datasets and models used for PM estimation. The review results suggest that every satellite sensors data have some specific capabilities as well as some drawbacks. Multi-angle imaging spectroradiometer (MISR) and visible infrared imaging radiometer suite (VIIRS) datasets showed better consistency in AOD and PM estimation in comparison to the moderate resolution imaging spectroradiometer (MODIS) datasets. In the context of PM estimation models’ accuracy, the mixed-effect model (MEM) has been extensively used and found to be more consistent in general, whereas, geographically weighted regression (GWR) model outperforms other statistical regression models in regional scale. Incorporation of land use parameters along with meteorological parameters improves the PM estimation accuracy at various spatial scale. The review suggests that in the near future, high resolution (spatial and temporal) satellite data with the improved algorithms will be highly appreciable for accurate estimation of AOD and PM.