High-resolution Aerosol Optical Depth Research Articles

Retrieval of high-resolution aerosol optical depth (AOD) using Landsat 8 imageries over different LULC classes over a city along Indo-Gangetic Plain, India.

Aerosol optical depth (AOD) serves as a crucial indicator for assessing regional air quality. To address regional and urban pollution issues, there is a requirement for high-resolution AOD products, as the existing data is of very coarse resolution. To address this issue, we retrieved high-resolution AOD over Kanpur (26.4499°N, 80.3319°E), located in the Indo-Gangetic Plain (IGP) region using Landsat 8 imageries and implemented the algorithm SEMARA, which combines SARA (Simplified Aerosol Retrieval Algorithm) and SREM (Simplified and Robust Surface Reflectance Estimation). Our approach leveraged the green band of the Landsat 8, resulting in an impressive spatial resolution of 30m of AOD and rigorously validated with available AERONET observations. The retrieved AOD is in good agreement with high correlation coefficients (r) of 0.997, a low root mean squared error of 0.035, and root mean bias of - 4.91%. We evaluated the retrieved AOD with downscaled MODIS (MCD19A2) AOD products across various land classes for cropped and harvested period of agriculture cycle over the study region. It is noticed that over the built-up region of Kanpur, the SEMARA algorithm exhibits a stronger correlation with the MODIS AOD product compared to vegetation, barren areas and water bodies. The SEMARA approach proved to be more effective for AOD retrieval over the barren and built-up land categories for harvested period compared with the cropping period. This study offers a first comparative examination of SEMARA-retrieved high-resolution AOD and MODIS AOD product over a station of IGP.

First retrieval of daily 160 m aerosol optical depth over urban areas using Gaofen-1/6 synergistic observations: Algorithm development and validation

The satellite-based aerosol optical depth (AOD), which can provide continuous spatial observations of aerosol loadings, is widely adopted to estimate atmospheric environmental quality and evaluate its risk for human health. However, current satellite-retrieved AOD products characterized by a comparatively coarse spatial resolution (≥1 km) can hardly analyze the structure of atmospheric pollution or its correlation with urban landscapes over populated urban areas. Existed studies have tried to address this deficiency by retrieving high-resolution AOD using Landsat images, whose long revisit period (16 days nominally), however, largely limits its applications related to urban air pollution research. To achieve both high spatial resolution and expected revisit period from satellite observation, in this study, a comprehensive AOD retrieval framework is developed for Wide-Field-of-View (WFV) satellite sensors to yield a daily AOD dataset over urban areas with 160 m spatial resolution, based on the Gaofen-1 and Gaofen-6 synergistic observations. To address the crucial challenge that the high spatial resolution and complex urban landscape both contribute a dramatic variation of land surface bidirectional reflectance, a Simulated-Annealing-coupled Semiempirical Modified Rahman-Pinty-Verstraete (SAS-MRPV) scheme is proposed to model and estimate the land surface reflectance (LSR) in the AOD retrieval framework, where the SAS-MRPV scheme is implemented using simulated annealing iteration initialized by the bidirectional reflectance distribution function (BRDF) products from the Moderate Resolution Imaging Spectrometer (MODIS) as a priori knowledge. The validation results demonstrate that the Gaofen WFV AOD retrievals exhibit good agreement with the ground-based AErosol RObotic NETwork (AERONET) and SONET (Sun-sky radiometer Observation NETwork) AOD, demonstrating the correlation coefficient and the expected error (EE)±(0.05 + 0.15AODAERONET/SONET) ratio respectively reaching up to 0.97 and over 80 %, and only slight bias fluctuations are found under different land cover types, aerosol loading, and seasonal conditions. Moreover, the validation results of Gaofen WFV AOD retrievals, with LSR estimated based on the SAS-MRPV scheme, the MODIS BRDF Products, and the Minimum Reflectance Technique (MRT) method, demonstrate better accuracy and completeness of AOD retrievals using the SAS-MPRV scheme over both natural and impervious land surfaces, indicating the stability and reliability of proposed SAS-MRPV LSR determination scheme in WFV AOD retrieval. In addition to that, the error analyses and quality control results demonstrate that the SAS-MRPV scheme is effective and necessary to guarantee the reliability and accuracy of land surface bidirectional reflectance estimation by identifying and excluding the land surface pixels unsuitable for LSR modeling, which mitigates the uncertainty and yield better accuracy in the final WFV AOD products. Furthermore, the inter-comparison between WFV-derived AOD against the operational MODIS Deep Blue (10 km), Dark Target (3 km), and Multi-Angle Implementation of Atmospheric Correction (MAIAC, 1 km) AOD products demonstrates that the 160 m WFV AOD retrievals, on the basis of obtaining similar aerosol overall descriptions as MODIS AOD retrievals, possess the capability to characterize the spatial distribution of atmospheric pollution at a finer scale with smoother variation under both clean and polluted conditions. With outstanding accuracy and reliable performance, the developed comprehensive high-resolution AOD retrieval framework exhibits substantial potential for operational AOD retrieval of Gaofen WFV satellite observations, which could be directly applied to other urban areas supporting further studies related to air pollution emission management and health risk assessment at extra-fine spatial scale.

An intercomparison of SEMARA high-resolution AOD and MODIS operational AODs

SEMARA is a high-resolution aerosol optical depth (AOD) retrieval algorithm that incorporates two algorithms, Simplified and Robust Surface Reflectance Estimation Method (SREM) for estimating surface reflectance and the Simplified Aerosol Retrieval Algorithm (SARA) for retrieving AOD. This study used SEMARA approach for retrieving AOD utilizing data obtained from Aqua-MODIS in the green channel. The algorithm had good agreement with AERONET data (R = 0.98). Also it was analyzed over a wide area containing five provinces in Iran. SEMARA AOD retrievals were compared with the operational MODIS aerosol retrieval algorithms, including Dark Target (DT), Deep Blue (DB), and Multi-Angle Implementation of Atmospheric Correction (MAIAC) AOD retrievals over different land covers and low to high aerosol loadings. The results show high consistency in SEMARA AOD retrievals with MAIAC AOD retrievals, with an average correlation coefficient of 0.61, while the correlation coefficient with DB and DT was 0.50 and 0.29, respectively. During high AOD loadings and over bright surfaces, the best agreement of SEMARA AOD and DT, DB, and MAIAC AOD was achieved with correlation coefficients of 0.52, 0.75, and 0.80, respectively. SEMARA AOD shows the best spatial coverage, averaging 80%, while MAIAC, DB, and DT had 54%, 32%, and 12% spatial coverage, respectively. This study concluded that SEMARA AOD can retrieve AOD over various land covers and during low to high AOD loadings with acceptable results.

Improved Himawari-8 10-minute scale aerosol optical depth product using deep neural network over Japan

Aerosol Optical Depth (AOD) is critical to air quality research and has a major impact on the Earth's energy budget. Recently, satellite retrieval has been a common data source for large-scale AOD information, for which the new-generation geostationary Himawari-8 satellite, which monitors East Asia and the Pacific region every 10 min, provides an unprecedented opportunity to track its dynamics. However, in the previous evaluation research, the Himawari-8 L2 AOD product has a large deviation from the ground observation. In this study, we proposed a deep neural network (DNN) model to correct the existing Himawari-8 L2 AOD using the synchronous information recorded in the Himawari-8 spectral bands in Japan. The results show that the accuracy has been dramatically improved, as indicated by the random 10-fold cross-validation (CV) (R2: 0.76 vs. 0.24; and RMSE: 0.068 vs. 0.194). Furthermore, many more samples fall in the error lines of the Global Climate Observation System (improved from 24.7% to 52.0% in 10-fold CV). In addition, we also obtained better performance when evaluated using the leave-one-station-out CV (R2 = 0.49 and RMSE = 0.101). The Shapley Additive exPlanations values (SHAP) based feature importance result showed that the Day of Year (DOY) contributed the most to the AOD retrievals, followed by the Himawari-8 L2 AOD. The performance decreased when either DOY or Himawari-8 L2 AOD was removed from the model. Overall, the results of this study will provide a methodology to obtain the high temporal resolution AOD with improved accuracy and thus will help to quantify the dynamics of aerosols in a synchronous manner.

Estimation of high-resolution aerosol optical depth (AOD) from Landsat and Sentinel images using SEMARA model over selected locations in South Asia

The study investigates a high-resolution retrieval of AOD over complex surfaces and various aerosol regimes of South Asian countries such as China, Bangladesh and Nepal from Landsat 7/8/9 and Sentinel 2 images. To estimate AOD from these satellite images, unlike the conventional radiative transfer-based look-up-table (LUT) approach, this study uses SEMARA coupled model which combines a simplified and robust surface reflection estimation (SREM) model and a simplified aerosol retrieval algorithm (SARA). The SEMARA model uses top-of-atmospheric reflectance (TOA) and land surface reflectance (LSR), solar and sensors viewing geometries, including the in-situ AOD data from the Aerosol Robotic Network (AERONET) station to simulate AOD at 30 m and 10 m spatial resolutions. The results are compared with AERONET and MODIS-based AOD for the validation sites in the South Asian countries. A high degree of agreement with AERONET AOD is obtained over Beijing in China (R2 > 0.9) for all sensors, while it varies for the validation AERONET stations over Nepal and Bangladesh (R2 = 0.40–0.86). The accuracy metrices suggest a low uncertainty over China Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) for Beijing: 0.07 and 0.13), while it remains higher for stations over Nepal and Bangladesh (MAE and RMSE: 0.33, and 0.41; 0.15–0.27, respectively). >85% of the modelled observations fall within expected error (EE) for China, while 20–75% of observations fall within EE for Nepal and Bangladesh. We have also obtained a proximity effect for the accuracy of the estimated AOD from the SEMARA model. The findings, in general, suggest that the complexity in surface cover and aerosol type produce a higher offset with respect to AERONET observations over Bangladesh and Nepal, and therefore further validation studies are needed for other parts of the globe to measure the consistency of the model.

Integration of Landsat 8 (OLI) and MODIS images to monitor suspended particles and evaluate the spatial pattern of air pollution

Air pollution has become one of the disturbances of daily life in urban areas due to its negative and repulsive effect in current conditions. On the other hand, it has a long-term negative impact on the behavior and quantity of climate change scenarios at the local, national and global levels. In this research, the integration of Landsat 8 (OLI) satellite images and Moderate Resolution Imaging Spectroradiometer (MODIS) sensor (to estimate the ozone permeability and the water vapor permeability coefficients) using the Second Simulation of a Satellite Signal in the Solar Spectrum (6S) radiation transfer model was used to determine Aerosol Optical Depth (AOD). For this purpose, instead of using the look-up table and approximating the AOD values, the components of the 6S radiative transfer model were estimated with a combined approach and the AOD values were retrieved without using the look-up table approach. One of the main problems of past research is to identify a suitable conversion model to connect the refractive index to the composition of aerosols. Therefore, the main innovation of this research is focused on a simplified, high resolution AOD retrieval method for Landsat 8 (OLI) images over mixed surfaces which does not require a LUT. Considering the relationship between AOD values and various indicators of air quality measurement, in this work, AOD estimation has been done with the aim of helping to predict these indicators. In this research, 24,569 square kilometers from a part of two states of Maryland and Virginia in the United States of America were investigated in order to estimate AOD. In order to evaluate the results of AOD retrieval, 5 Aerosol Robotic Network (AERONET) ground stations were used for comparison and their accuracy was evaluated. The results show that the proposed method has been able to extract the amount of aerosol with acceptable accuracy. According to the results obtained in this research, the root mean square errors (RMSE) is equal to 0.1036, the adjusted R2 coefficient (AdjR2) is equal to 0.8032 and the coefficient of determination (R2) is equal to 0.8079. It can be concluded that the integration of Landsat 8 (OLI) images with MODIS images has been able to provide more suitable results.

Spatiotemporal Variations of Aerosol Optical Depth and the Spatial Heterogeneity Relationship of Potential Factors Based on the Multi-Scale Geographically Weighted Regression Model in Chinese National-Level Urban Agglomerations

Investigating the spatiotemporal variation characteristics of aerosol optical depth (AOD) and its driving factors is essential for assessing atmospheric environmental quality and alleviating air pollution. Based on a 22-year high-resolution AOD dataset, the spatiotemporal variations of AOD in mainland China and ten national urban agglomerations were explored based on the Mann–Kendall trend test and Theil–Sen median method. Random forest (RF) and multiscale geographically weighted regression (MGWR) were combined to identify the main driving factors of AOD in urban agglomerations and to reveal the spatial heterogeneity of influencing factors. The results showed that areas with high annual average AOD concentrations were mainly concentrated in the Chengdu–Chongqing, Central Plains, Shandong Peninsula, and Middle Yangtze River urban agglomerations. Southern Beijing–Tianjin–Hebei and its surrounding areas revealed the highest AOD pollution during summer, whereas the worst pollution during the remaining three seasons occurred in the Chengdu–Chongqing urban agglomeration. Temporally, except for the Ha-Chang and Mid-Southern Liaoning urban agglomerations, where the average annual AOD increased, the other urban agglomerations showed a decreasing trend. Among them, the Central Plains, Middle Yangtze River, Guanzhong Plain, and Yangtze River Delta urban agglomerations all exhibited a decline greater than 20%. According to the spatial trends, most urban agglomerations encompassed much larger areas of decreasing AOD values than areas of increasing AOD values, indicating that the air quality in most areas has recently improved. RF analysis revealed that PM2.5 was the dominant factor in most urban clusters, followed by meteorological factors. MGWR results show that the influencing factors have different spatial scale effects on AOD in urban agglomerations. The socioeconomic factors and PM2.5 showed strong spatial non-stationarity with regard to the spatial distribution of AOD. This study can provide a comprehensive understanding of AOD differences among urban agglomerations, and it has important theoretical and practical implications for improving the ecological environment and promoting sustainable development.

Direct aerosol optical depth retrievals using MODIS reflectance data and machine learning over East Asia

Anthropogenic aerosols have rapidly increased since the industrial revolution and are harmful to human health. Moderate Resolution Imaging Spectroradiometer (MODIS) data are critical for retrieving aerosol properties worldwide. However, current MODIS aerosol optical depth (AOD) products require extensive computations and a precalculated lookup table. This study proposes assumption-free high-resolution AOD retrieval models based on the light gradient boosting machine method using MODIS data and ground-based observations over East Asia. The models were developed with three spatial resolutions: 250 m, 500 m, and 1 km. The results showed that 77.8% of the 250 m AOD values were within the MODIS expected error (EE) range, while 76.5%, 76.3%, and 70.08% of the 500 m, 1 km, and Multi-Angle Implementation of Atmospheric Correction (MAIAC) AOD values were within the EE range, respectively. Furthermore, an analysis of the time series and detailed spatial distribution of the proposed model-derived AOD based on data from the Korea–United States Air Quality campaign demonstrated the excellent quality of the 250 m AOD via further validation using a spatially independent dataset. The Shapley Additive exPlanations analysis identified the sensor zenith angle and top-of-atmosphere reflectance of the blue band as the key contributors to the models. In addition, while MAIAC has limited spatial coverage, the spatial frequency of the proposed direct AOD retrieval was nearly 1.5-times higher than that of the MAIAC AOD. Our findings confirmed that machine learning-based high-resolution AOD estimates can be obtained using only satellite data.

An algorithm for retrieving aerosol optical depth from Landsat 8 Operational Land Imager in Vietnam

High-resolution aerosol products offer advantages over lower spatial-resolution products for monitoring pollution in urban areas and industrial zones. Therefore, in this study we develop a Landsat 8 algorithm to retrieve aerosol optical depth (AOD) based on the MODIS Dark Target (DT) algorithm. This algorithm uses the direct surface reflectance of Landsat 8 to build a surface reflectance relationship between the visible (BLUE, RED) and short-wave infrared bands (SWIR2). AOD data from AERONET stations in the study area and AOD data from MODIS 3 km were used for validation of the estimated Landsat 8 AOD. The estimated AOD was highly correlated at most AERONET AOD, with =0.98, (RMSE = 0.3), except Bac Lieu, but only had a moderate correlation with MODIS AOD which was attributed to the difference in data retrieval time and associated changes in climate conditions. This is the first high-resolution AOD retrieval in Vietnam, leading to more practical applications for air quality monitoring.

High-Resolution Aerosol Optical Depth Retrieval in Urban Areas Based on Sentinel-2

In this paper, an improved aerosol optical depth (AOD ) retrieval algorithm is proposed based on Sentinel-2 and AErosol RObotic NETwork (AERONET ) data. The surface reflectance for AOD retrieval was estimated from the image that had minimal aerosol contamination in a temporal window determined by AERONET data. Validation of the Sentinel-2 AOD retrievals was conducted against four Aerosol Robotic Network (AERONET ) sites located in Beijing. The results show that the Sentinel-2 AOD retrievals are highly consistent with the AERONET AOD measurements (R = 0.942), with 85.56% falling within the expected error. The mean absolute error and the root-mean-square error are 0.0688 and 0.0882, respectively. In addition, the AOD distribution map obtained by this algorithm well reflects the fine-spatial-resolution changes in AOD distribution. These results suggest that the improved high-resolution AOD retrieval algorithm is robust and has the potential advantage of retrieving high-resolution AOD over urban areas.

Window-Based Filtering Aerosol Retrieval Algorithm of Fine-Scale Remote Sensing Images: A Case Using Sentinel-2 Data in Beijing Region

The satellite-based Aerosol Optical Depth (AOD) retrieval algorithms are generally needed to construct Land Surface Reflectance (LSR) database. However, errors are unavoidable due to the surface complexity, especially for the short observation period and high-resolution images, such as Sentinel-2 Multi-Spectral Instrument (MSI) data. To address this, reference day images are used instead of the LSR database. The surface is assumed to be Lambertian; however, the fact is that not all pixels meet it well. Therefore, we proposed a window-based AOD retrieval algorithm, which can ignore the unreliable/non-Lambertian pixels in a retrieval window based on two main filtering processes. Finally, using Sentinel-2 Band 1 (60 m), the AODs (120 m) of 134 reference images to 43 reference images were retrieved by this algorithm from 2017 to 2021 in Beijing region, China. The results show that the retrieved AOD with the proposed algorithm exhibits good agreement with the ground-based measured AOD (R > 0.97). The high-resolution AOD presents comparable spatial distributions to the Multiangle Implementation of Atmospheric Correction (MAIAC) algorithm AOD (1 km) products. Moreover, the very little noise and very high spatial continuity of retrieval AOD imply that this algorithm could be ported to other algorithms as part of improving AOD quality.

Spatially lagged predictors from a wider area improve PM2.5 estimation at a finer temporal interval—A case study of Dallas-Fort Worth, United States

Fine particulate matter, also known as PM2.5, has many adverse impacts on human health. However, there are few ground monitoring stations measuring PM2.5. Satellite data help fill the gaps in ground measurements, but most studies focus on estimating daily PM2.5 levels. Studies examining the effects of environmental exposome need accurate PM2.5 estimates at fine temporal intervals. This work developed a Convolutional Neural Network (CNN) to estimate the PM2.5 concentration at an hourly average using high-resolution Aerosol Optical Depth (AOD) from the MODIS MAIAC algorithm and meteorological data. Satellite-acquired AOD data are instantaneous measurements, whereas stations on the ground provide an hourly average of PM2.5 concentration. The current work aimed to refine PM2.5 estimates at temporal intervals from 24-h to 1-h averages. Our premise posited the enabling effects of spatial convolution on temporal refinements in PM2.5 estimates. We trained a CNN to estimate PM2.5 corresponding to the hour of AOD acquisition in the Dallas-Fort Worth and surrounding area using 10 years of data from 2006–2015. The CNN accepts images as input. For each PM2.5 station, we strategically subset temporal MODIS images centering at the PM2.5 station. Hence, the resulting image-patch size represented the size of the area around the PM2.5 station. It thus was analogous to spatial lag in spatial statistics. We systematically increased the image-patch size from 3 × 3, 5 × 5, … , to 19 × 19 km2 and observed how increasing the spatial lag impacted PM2.5 estimation. Model performance improved with a larger spatial lag; the model with a 19 × 19 km2 image-patch as input performed best, with a correlation coefficient of 0.87 and a RMSE of 2.57 g/m3 to estimate PM2.5 at in situ stations corresponding to the hour of satellite acquisition time. To overcome the problem of a reduced number of image-patches available for training due to missing AOD, the study employed a data augmentation technique to increase the number of samples available to train the model. In addition to avoiding overfitting, data augmentation also improved model performance.

Himawari-8 High Temporal Resolution AOD Products Recovery: Nested Bayesian Maximum Entropy Fusion Blending GEO With SSO Satellite Observations

High temporal resolution aerosol optical depth (AOD) observations derived from new-generation geostationary (GEO) satellite possess unique advantages in analyzing aerosol fast variation processes and thereby providing more accurate assessments on their climate effects and health risks. Unfortunately, the expected advantages and values are dramatically limited by relatively large proportion of data missing in the GEO AOD products due to cloud obscuration and intrinsic retrieval algorithm. Although several data recovery algorithms have been proposed in recent years to improve the spatial coverage for GEO AOD products, yet most of them aims at filling up the data blanks rather than reconstructing the temporally continuous variation of aerosol. Accordingly, in this study, a novel framework of nested spatiotemporal fusion blending GEO with sun-synchronous orbit (SSO) satellite observations based on Bayesian maximum entropy (BME) theorem is developed for GEO Advanced Himawari-8 Imager (AHI) AOD recovery with the sufficient excavation of complementary information from GEO and SSO satellite observations, where the minute-stage and hour-stage BME fusion are jointly employed to reconcile temporal inconsistency and data discrepancies between GEO and SSO observations. The results demonstrate that the AOD spatial coverage is dramatically increased by 240.9% (from 20.5% to 70%) with ensured accuracy after Nested-BME fusion. Additionally, two case analyses, during the development and dispersion processes of haze respectively, both demonstrate that the proposed Nested-BME fusion framework could reconstruct the reliable aerosol diurnal variation trends on the basis of recovering missing data for Himawari-8 AHI AOD datasets, while the AHI official level-2 and level-3 AOD products fail to capture these key trends. Furthermore, the developed Nested-BME AOD fusion framework is also applicable for other geostationary satellites over other regions, which could substantially enhance the availability and value of high temporal resolution AOD products for better scientific applications.

Impact of satellite AOD data on top-down estimation of biomass burning particulate matter emission

Particulate matter emitted from open biomass burning affects climate, air quality and public health. In the development of remote sensing techniques, top-down methods using satellite observations have become an effective way to estimate particulate matter emissions, but different spatial resolution and coverage of satellite aerosol optical depth (AOD) products introduce great uncertainties. In this work, we assess the differences in total particulate matter (TPM) emission when calculated using different satellite AOD data. To do this, we derive top-down biomass burning TPM emission coefficients (Ce) of Australia based on 1 km and 10 km MODIS AOD products, and compare the results of emission estimation. Using high spatial resolution AOD data, a 90 % decrease of Ce mean value is found in Australia, and the quality of Ce improves by 290 %. When we use 1 km AOD data in place of 10 km AOD data, the estimation of biomass burning TPM emissions in Australia during 2012–2020 drops from 1.08Tg to 0.11Tg; the temporal trends of the two products remain the same. The TPM emission estimates for biome based on higher spatial resolution AOD data in this study are lower than Fire Energetics and Emissions Research (FEERv1) inventories by factors of 0.08–0.2. Our work shows that using satellite AOD products of higher spatial resolution avoids overestimation of biomass burning TPM emissions, and improves the quality of final estimates, providing a possible way to understand biomass burning emission more accurately.

Full-coverage 250 m monthly aerosol optical depth dataset (2000–2019) amended with environmental covariates by an ensemble machine learning model over arid and semi-arid areas, NW China

Abstract. Aerosols are complex compounds that greatly affect the global radiation balance and climate system and even human health; in addition, aerosols are currently a large source of uncertainty in the numerical simulation process. The arid and semi-arid areas have fragile ecosystems with abundant dust but lack related high-accuracy aerosol data. To solve these problems, we use the bagging trees ensemble model, based on 1 km aerosol optical depth (AOD) data and multiple environmental covariates, to produce a monthly advanced-performance, full-coverage, and high-resolution (250 m) AOD product (named FEC AOD, fusing environmental covariates AOD) covering the arid and semi-arid areas. Then, based on the FEC AOD products, we analyzed the spatiotemporal AOD pattern and further discussed the interpretation of environmental covariates to AOD. The results show that the bagging trees ensemble model has a good performance, with its verification R2 values always remaining at 0.90 and the R2 being 0.79 for FEC AOD compared with AERONET AOD product. The high-AOD areas are located in the Taklimakan Desert and on the Loess Plateau, and the low-AOD areas are concentrated in southern Qinghai province. The higher the AOD, the stronger the interannual variability. Interestingly, the AOD reflected a dramatic decrease on the Loess Plateau and an evident increase in the south-eastern Taklimakan Desert, while the southern Qinghai province AODs showed almost no significant change between 2000 and 2019. The annual variation characteristics show that the AOD was largest in spring (0.267±0.200) and smallest in autumn (0.147±0.089); the annual AOD variation pattern showed different features, with two peaks in March and August over Gansu province but only one peak in April in other provinces/autonomous regions. Farmlands and construction lands have high AOD levels compared to other land cover types. Meteorological factors demonstrate the maximum interpretation ability of the AODs on all set temporal scales, followed by the terrain factors, while surface properties have the smallest explanatory abilities; the corresponding average contributions are 77.1 %, 59.1 %, and 50.4 %, respectively. The capability of the environmental covariates to explain the AOD varies seasonally in the following sequence: winter (86.6 %) > autumn (80.8 %) > spring (79.9 %) > summer (72.5 %). In this research, we provide a pathbreaking high spatial resolution (250 m) and long time series (2000–2019) FEC AOD dataset covering arid and semi-arid regions to support atmospheric and related studies in northwest China; the full dataset is available at https://doi.org/10.5281/zenodo.5727119 (Chen et al., 2021b).

Spatial Variation and Relation of Aerosol Optical Depth with LULC and Spectral Indices

In the current study area (Faridabad, Gurugram, Ghaziabad, and Gautam Buddha Nagar), the aerosol concentration is very high, adversely affecting the environmental conditions and air quality. Investigating the impact of Land Use Land Cover (LULC) on Aerosol Optical Depth (AOD) helps us to develop effective solutions for improving air quality. Hence, the spectral indices derived from LULC ((Normalized difference vegetation index (NDVI), Soil adjusted vegetation index (SAVI), Enhanced vegetation index (EVI), and Normalized difference build-up index (NDBI)) with Moderate Resolution Imaging Spectroradiometer (MODIS) Multiangle Implementation of Atmospheric Correction (MAIAC) high spatial resolution (1 km) AOD from the years 2010–2019 (less to high urbanized period) has been correlated. The current study used remote sensing and Geographical Information System (GIS) techniques to examine changes in LULC in the current study region over the ten years (2010–2019) and the relationship between LULC and AOD. A significant increase in built-up areas (12.18%) and grasslands (51.29%) was observed during 2010–2019, while cropland decreased by 4.42%. A positive correlation between NDBI and SAVI (0.35, 0.27) indicates that built-up soils play an important role in accumulating AOD in a semi-arid region. At the same time, a negative correlation between NDVI and EVI (−0.24, −0.15) indicates the removal of aerosols due to an increase in vegetation. The results indicate that SAVI can play an important role in PM2.5 modeling in semi-arid regions. Based on these findings, urban planners can improve land use management, air quality, and urban planning.

Improving Clear-Sky Solar Power Prediction over China by Assimilating Himawari-8 Aerosol Optical Depth with WRF-Chem-Solar

Although the Weather Research and Forecasting model with solar extensions (WRF-Solar) is tailed for solar energy applications, its official version lacks the consideration of the online aerosol-radiation process. To overcome this limitation, we have coupled the aerosol module online with the radiation module, then assimilated the high-resolution aerosol optical depth (AOD) from the Himawari-8 next-generation geostationary satellite using a three-dimensional variational (3DVAR) AOD data assimilation system to optimize the irradiance predictions with the better aerosol–radiation interaction. The results show that data assimilation can significantly eliminate the AOD underestimations and reasonably reproduce the AOD temporal distributions, improving 51.63% for biases and 61.29% for correlation coefficients. Compared with the original WRF-Solar version, coupled online with an advanced aerosol module minifies the bias value of global horizontal irradiance (GHI) up to 44.52%, and AOD data assimilation contributes to a further reduction of 17.43%.

Land Use and Land Cover Influence on Sentinel-2 Aerosol Optical Depth below City Scales over Beijing

Atmospheric aerosols can impact human health, necessitating the understanding of their distribution determinants, especially in urban areas. The study discusses the relationships between five major land cover types and aerosol optical depth (AOD) within a city combining the high-resolution satellite-derived AOD products (derived from Sentinel-2) and land cover products (60 m and 100 m, respectively) for Beijing and its surroundings from 2017 to 2019. Contribution analysis is performed to quantitatively evaluate the influences of land cover on regional AOD over the study area. Patterns of aerosol distribution remarkably vary in time and space. Statistics of seasonal average AOD peak in spring and then progressively decline from summer through autumn to winter. High AOD values coincide with a low normalized difference vegetation index (NDVI) and a high normalized difference built-up index (NDBI). Urban and built-up land is a major contributor to regional AOD in the study area, especially in spring; forest and grassland always reduce AOD. Anthropogenic activities have a non-negligible influence on AOD and can even reverse the contribution of a land cover type to aerosols. Insights of the study promote the comprehension of the impacts of land cover on aerosols and air pollution and contribute to the planning of land use within a city.

Bayesian Aerosol Retrieval-Based PM2.5 Estimation through Hierarchical Gaussian Process Models

Satellite-based aerosol optical depth (AOD) data are widely used to estimate land surface PM2.5 concentrations in areas not covered by ground PM2.5 monitoring stations. However, AOD data obtained from satellites are typically at coarse spatial resolutions, limiting their applications on small or medium scales. In this paper, we propose a new two-step approach to estimate 1-km-resolution PM2.5 concentrations in Shanghai using high spatial resolution AOD retrievals from MODIS. In the first step, AOD data are refined to a 1×1km2 resolution via a Bayesian AOD retrieval method. In the second step, a hierarchical Gaussian process model is used to estimate PM2.5 concentrations. We evaluate our approach by model fitting and out-of-sample cross-validation. Our results show that the proposed approach enjoys accurate predictive performance in estimating PM2.5 concentrations.

Which model to choose? Performance comparison of statistical and machine learning models in predicting PM2.5 from high-resolution satellite aerosol optical depth

The mathematical solution to estimate surface fine particulate matter (PM2.5) from columnar aerosol optical depth (AOD) includes complex variables and involves a bunch of assumptions. Hence, researchers tend to use training-based models to predict PM2.5 from AOD. Here, we integrated regulatory composite PM2.5 measurements, high-resolution satellite AOD, reanalysis meteorological parameters, and a few other auxiliary parameters to train ten different regression models. The performance of these (seven statistical and three machine learning) models was evaluated and inter-compared to identify the best performing model. The accuracies of the model predicted PM2.5 were quantified based on the coefficient of determination (R2), mean absolute bias (MAB), normalized root mean square error (NRMSE), and other relevant regression coefficients. The model's performance on unseen data was investigated in terms of 10-fold cross-validation (CV) and Leave-one station-out CV (LOOCV). For this exercise, we considered the case of NCT-Delhi due to: (i) the availability of dense regulatory PM2.5 measurements, (ii) the possibility of understanding the model performance over a large range of PM2.5 (the daily mean PM2.5 values ranged between ∼ 4 and 492 μg m−3 during the study period), and (iii) the scope of better understanding the influence of extreme meteorological conditions (e.g. the ambient surface temperature varies between ∼5 and 40 °C during a calendar year) on the AOD-PM2.5 relationship. All the models were trained using data collected for the year 2019 (a non-COVID year). Among models under investigation, Machine Learning (ML) models performed better with R2, MAB, and NRMSE values for the CV exercises ranging between 0.88 and 0.93, 14.1 and 18.2 μg m−3, and 0.18 and 0.23, respectively. The generalizability of the results obtained in this study was discussed.