Preliminary Investigation of a New AHI Aerosol Optical Depth (AOD) Retrieval Algorithm and Evaluation with Multiple Source AOD Measurements in China

Satellite-based aerosol optical depth estimates over the continental U.S. during the 2020 wildfire season: Roles of smoke and land cover

Wildfires produce smoke that can affect an area >1000 times the burn extent, with far-reaching human health, ecologic, and economic impacts. Accurately estimating aerosol load within smoke plumes is therefore crucial for understanding and mitigating these impacts. We evaluated the effectiveness of the latest Collection 6.1 MODIS Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm in estimating aerosol optical depth (AOD) across the U.S. during the historic 2020 wildfire season. We compared satellite-based MAIAC AOD to ground-based AERONET AOD measurements during no-, light-, medium-, and heavy-smoke conditions identified using the Hazard Mapping System Fire and Smoke Product. This smoke product consists of maximum extent smoke polygons digitized by analysts using visible band imagery and classified according to smoke density. We also examined the strength of the correlations between satellite- and ground-based AOD for major land cover types under various smoke density levels. MAIAC performed well in estimating AOD during smoke-affected conditions. Correlations between MAIAC and AERONET AOD were strong for medium- (r = 0.91) and heavy-smoke (r = 0.90) density, and MAIAC estimates of AOD showed little bias relative to ground-based AERONET measurements (normalized mean bias = 3 % for medium, 5 % for heavy smoke). During two high AOD, heavy smoke episodes, MAIAC underestimated ground-based AERONET AOD under mixed aerosol (i.e., smoke and dust; median bias = -0.08) and overestimated AOD under smoke-dominated (median bias = 0.02) aerosol. MAIAC most overestimated ground-based AERONET AOD over barren land (mean NMB = 48 %). Our findings indicate that MODIS MAIAC can provide robust estimates of AOD as smoke density increases in coming years. Increased frequency of mixed aerosol and expansion of developed land could affect the performance of the MAIAC algorithm in the future, however, with implications for evaluating wildfire-associated health and welfare effects and air quality standards.

Validation and calibration of aerosol optical depth and classification of aerosol types based on multi-source data over China

A refined classification of aerosol types is essential to identify and control air pollution sources. This study focused on improving the resolution and accuracy of aerosol optical depth (AOD) and further refining the classification of aerosol types in China. We validated the accuracy of the AOD acquired using the Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA2) and Copernicus Atmosphere Monitoring Service (CAMS) by comparing it with that acquired using from the Aeronet Robotic Network (AERONET). We simulated the AOD with high spatial resolution and accuracy based on the extremely randomized trees (ERT), adaptive boosting (AdaBoost), and gradient boosting decision trees (GBDT) models and identified aerosol types based on the Angstrom Exponent (AE) from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the calibrated AOD. The results showed that CAMS overestimates AOD (21.4 %) and MERRA2 underestimates AOD (-17.3 %). Among the three machine learning models, the ERT model performed best, with a determination coefficient (R2) of 0.825 and the root-mean-square error (RMSE) of 0.174. Biomass burning/urban-industrial aerosols dominated China, with the largest contributions to southern, eastern, and central China in spring and summer. Clean continental aerosols contributed the most to southwestern China in fall and winter, whereas desert dust aerosols contributed the most to northwestern and eastern China in spring.

A high-precision aerosol retrieval algorithm for FY-3D MERSI-II images

The Medium Resolution Spectral Imager-II (MERSI-II) onboard the recently launched Chinese Fengyun-3D (FY-3D) satellite has great capability in detecting global aerosols as it includes aerosol bands similar to Moderate Resolution Imaging Spectroradiometer (MODIS). However, to date, aerosol retrieval based on MERSI-II is still limited to dark target regions and there is no official aerosol products for the MERSI-II. This study focuses on developing a high-precision algorithm to retrieve aerosol optical depth (AOD) suitable for entire land areas (except snow/ice and inland waters) based on MERSI-II measurements. Considering both the accuracy and retrieval efficiency, a new cost function is constructed based on (1) the fact that the AOD (550 nm) retrieved independently from different bands should be the same with the correct aerosol model, and (2) the assumption that the aerosol types are the same in the 5 × 5 km spatial range. The retrieval method based on the new cost function is nearly 50 times faster than most current methods using iterative calculations. To extend the application adaption of the FY-3D MERSI-II AOD retrieval and avoid the errors caused by the surface Lambertian hypothesis, a bidirectional reflectance distribution function (BRDF) database is built using MODIS products. Eight candidate aerosol models in different natural zones of China are constructed based on AERONET aerosol products from 2010 - 2021. The new method is applied to MERSI-II images over China and validated against ground-based measurements at 14 stations from 2020 to 2021. MODIS aerosol products from three operational algorithms are also used for comparison purposes. The results show that MERSI-II AOD retrievals agree well with the ground-based measurements with correlation coefficient (R), root mean square error (RMSE), and relative mean bias (RMB) of 0.913, 0.123, and 0.955, respectively. In addition, 72.19 % of AOD matchups fall within the expected error (EE) envelopes. The MERSI-II retrievals show higher accuracy than that of MODIS dark target (DT) and deep blue (DB) products and comparable accuracy of the MODIS Multi-Angle Implementation of Atmospheric Correction (MAIAC) product. MERSI-II AOD also shows higher stability in terms of spatial and temporal and better performance under heavy aerosol loading conditions than MODIS products. A good AOD agreement with R from 0.777 to 0.863 between MERSI-II and MODIS products is found over the land of China. The new method showing high retrieval efficiency and accuracy has great potential to be operationally applied on AOD retrieval for MERSI-II.

Estimating Full-Coverage PM2.5 Concentrations Based on Himawari-8 and NAQPMS Data over Sichuan-Chongqing

Fine particulate matter (PM2.5) has attracted extensive attention due to its harmful effects on humans and the environment. The sparse ground-based air monitoring stations limit their application for scientific research, while aerosol optical depth (AOD) by remote sensing satellite technology retrieval can reflect air quality on a large scale and thus compensate for the shortcomings of ground-based measurements. In this study, the elaborate vertical-humidity method was used to estimate PM2.5 with the spatial resolution 1 km and the temporal resolution 1 hour. For vertical correction, the scale height of aerosols (Ha) was introduced based on the relationship between the visibility data and extinction coefficient of meteorological observations to correct the AOD of the Advance Himawari Imager (AHI) onboard the Himawari-8 satellite. The hygroscopic growth factor (f(RH)) was fitted site-by-site and month by month (1–12 months). Meanwhile, the spatial distribution of the fitted coefficients can be obtained by interpolation assuming that the aerosol properties vary smoothly on a regional scale. The inverse distance weighted (IDW) method was performed to construct the hygroscopic correction factor grid for humidity correction so as to estimate the PM2.5 concentrations in Sichuan and Chongqing from 09:00 to 16:00 in 2017–2018. The results indicate that the correlation between “dry” extinction coefficient and PM2.5 is slightly improved compared to the correlation between AOD and PM2.5, with r coefficient values increasing from 0.12–0.45 to 0.32–0.69. The r of hour-by-hour verification is between 0.69 and 0.85, and the accuracy of the afternoon is higher than that of the morning. Due to the missing rate of AOD in the southwest is very high, this study utilized inverse variance weighting (IVW) gap-filling method combine satellite estimation PM2.5 and the nested air-quality prediction modeling system (NAQPMS) simulation data to obtain the full-coverage hourly PM2.5 concentration and analyze a pollution process in the fall and winter.

Maternal exposures to fine and ultrafine particles and the risk of preterm birth from a retrospective study in Beijing, China

Maternal exposure to fine particulate matter (PM_2.5) has been associated with increased risk of preterm birth (PTB), but evidence on particles in smaller sizes and PTB risk remains limited. In this retrospective analysis, we included birth records of 24,001 singleton live births from Haidian Maternal and Child Health Hospital in Beijing, China, 2014-2017. Concurrently, number concentrations of size-fractioned particles in size ranges of 5-560 nm (PNC_5-560) and mass concentrations of PM_2.5, black carbon (BC) and gaseous pollutants were measured from a fixed-location monitoring station in central Haidian District. Logistic regression models were used to estimate the odds ratio (OR) of air pollutants on PTB risk after controlling for temperature, relative humidity, and individual covariates (e.g., maternal age, ethnicity, gravidity, parity, gestational weight gain, fetal gender, the year and season of conception). Positive matrix factorization models were then used to apportion the sources of PNC_5-560. Among the 1062 (4.4%) PTBs, increased PTB risk was observed during the third trimester of pregnancy per 10 μg/m³ increase in PM_2.5 [OR = 1.92; 95% Confidence Interval (95% CI): 1.76, 2.09], per 1000 particles/cm³ increase in PNC_25-100 (OR = 1.09; 95% CI: 1.03, 1.15) and PNC_100-560 (OR = 1.22; 95% CI: 1.05, 1.42). Among the identified sources of PNC_5-560, emissions from gasoline and diesel vehicles were significantly associated with increased PTB risk, with ORs of 1.14 (95% CI: 1.01, 1.29) and 1.11 (95% CI: 1.04, 1.18), respectively. Exposures to other traffic-related air pollutants, such as BC and nitrogen dioxide (NO₂) were also significantly associated with increased PTB risk. Our findings highlight the importance of traffic emission reduction in urban areas.

Columnar Water Vapor Retrieval by Using Data from the Polarized Scanning Atmospheric Corrector (PSAC) Onboard HJ-2 A/B Satellites

As the latest members of Chinese Environmental Protection and Disaster Monitoring Satellite Constellation, the first two of HuanjingJianzai-2 (HJ-2) series satellites were launched on 27 September 2020 by China and are usually abbreviated as HJ-2 A/B satellites. The polarized scanning atmospheric corrector (PSAC) is one of main sensors onboard HJ-2 A/B satellites, which is mainly used to monitor atmospheric components such as water vapor and aerosols. In this study, a columnar water vapor (CWV) retrieval algorithm using two bands (865 and 910 nm) is developed for PSAC. The validation results of PSAC CWV data based on ground-based CWV data derived from Aerosol Robotic Network (AERONET) show that PSAC CWV data has a high accuracy, and all statistical parameters of PSAC CWV data are better than those of Moderate-resolution Imaging Spectroradiometer (MODIS) CWV data released by NASA. Overall, there is no obvious overestimation or underestimation in PSAC CWV data. The root mean square error (RMSE), mean absolute error (MAE), relative error (RE), and percentage of CWV data with error within (PER10) of PSAC CWV data are 0.17 cm, 0.13 cm, 0.08, and 78.19%, respectively. The RMSE, MAE, RE, and PER10 of MODIS CWV data are 0.59 cm, 0.48 cm, 0.28, and 16.55%, respectively. Compared with MODIS CWV data, PSAC CWV data shows a 71% decrease in RMSE, a 73% decrease in MAE, a 71% decrease in RE, and a 372% increase in PER10. In addition, the results of day-to-day comparisons between PSAC CWV data and AERONET data show that PSAC CWV data can effectively characterize the change trend of CWV.

Spatiotemporal Variations of Aerosols in China during the COVID-19 Pandemic Lockdown

The widespread nature of the coronavirus disease 2019 (COVID-19) pandemic is gradually changing people’s lives and impacting economic development worldwide. Owing to the curtailment of daily activities during the lockdown period, anthropogenic emissions of air pollutants have greatly reduced, and this influence is expected to continue in the foreseeable future. Spatiotemporal variations in aerosol optical depth (AOD) can be used to analyze this influence. In this study, we comprehensively analyzed AOD and NO2 data obtained from satellite remote sensing data inversion. First, data were corrected using Eidetic three-dimensional-long short-term memory to eliminate errors related to sensors and algorithms. Second, taking Hubei Province in China as the experimental area, spatiotemporal variations in AOD and NO2 concentration during the pandemic were analyzed. Finally, based on the results obtained, the impact of the COVID-19 pandemic on human life has been summarized. This work will be of great significance to the formulation of regional epidemic prevention and control policies and the analysis of spatiotemporal changes in aerosols.

SEVIRI Aerosol Optical Depth Validation Using AERONET and Intercomparison with MODIS in Central and Eastern Europe

This paper presents the validation results of Aerosol Optical Depth (AOD) retrieved from the Spinning Enhanced Visible Infrared Radiometer (SEVIRI) data using the near-real-time algorithm further developed in the frame of the Satellite-based Monitoring Initiative for Regional Air quality (SAMIRA) project. The SEVIRI AOD was compared against multiple data sources: six stations of the Aerosol Robotic Network (AERONET) in Romania and Poland, three stations of the Aerosol Research Network in Poland (Poland–AOD) and Moderate Resolution Imaging Spectroradiometer (MODIS) data overlapping Romania, Czech Republic and Poland. The correlation values between a four-month dataset (June–September 2014) from SEVIRI and the closest temporally available data for both ground-based and satellite products were identified. The comparison of the SEVIRI AOD with the AERONET AOD observations generally shows a good correlation (r = 0.48–0.83). The mean bias is 0.10–0.14 and the root mean square error RMSE is between 0.11 and 0.15 for all six stations cases. For the comparison with Poland–AOD correlation values are 0.55 to 0.71. The mean bias is 0.04–0.13 and RMSE is between 0.10 and 0.14. As for the intercomparison to MODIS AOD, correlations values were generally lower (r = 0.33–0.39). Biases of −0.06 to 0.24 and RMSE of 0.04 to 0.28 were in good agreement with the ground–stations retrievals. The validation of SEVIRI AOD with AERONET results in the best correlations followed by the Poland–AOD network and MODIS retrievals. The average uncertainty estimates are evaluated resulting in most of the AOD values falling above the expected error range. A revised uncertainty estimate is proposed by including the observed bias form the AERONET validation efforts.

An Improved Method for Retrieving Aerosol Optical Depth Using Gaofen-1 WFV Camera Data

The four wide-field-of-view (WFV) cameras aboard the GaoFen-1 (GF-1) satellite launched by China in April 2013 have been applied to the studies of the atmospheric environment. To highlight the advantages of GF-1 data in the atmospheric environment monitoring, an improved deep blue (DB) algorithm using only four bands (visible–near infrared) of GF-1/WFV was adopted to retrieve the aerosol optical depth (AOD) at ~500 m resolution in this paper. An optimal reflectivity technique (ORT) method was proposed to construct monthly land surface reflectance (LSR) dataset through converting from MODIS LSR product according to the WFV and MODIS spectral response functions to make the relationship more suitable for GF-1/WFV. There is a good spatial coincidence between our retrieved GF-1/WFV AOD results and MODIS/Terra or Himawari-8/AHI AOD products at 550 nm, but GF-1/WFV AOD with higher resolution can better characterized the details of regional pollution. Additionally, our retrieved GF-1/WFV AOD (2016–2019) results showed a good agreement with AERONET ground-based AOD measurements, especially, at low levels of AOD. Based on the same LSR dataset transmitted from 2016–2018 MODIS LSR products, RORT of 2016–2018 and 2019 GF-1/WFV AOD retrievals can reach up to 0.88 and 0.94, respectively, while both of RMSEORT are smaller than 0.13. It is indicated that using the ORT method to deal with LSR information can make GF-1/WFV AOD retrieval algorithm more suitable and flexible.

Himawari-8 Aerosol Optical Depth (AOD) Retrieval Using a Deep Neural Network Trained Using AERONET Observations

Spectral aerosol optical depth (AOD) estimation from satellite-measured top of atmosphere (TOA) reflectances is challenging because of the complicated TOA-AOD relationship and a nexus of land surface and atmospheric state variations. This task is usually undertaken using a physical model to provide a first estimate of the TOA reflectances which are then optimized by comparison with the satellite data. Recently developed deep neural network (DNN) models provide a powerful tool to represent the complicated relationship statistically. This study presents a methodology based on DNN to estimate AOD using Himawari-8 Advanced Himawari Imager (AHI) TOA observations. A year (2017) of AHI TOA observations over the Himawari-8 full disk collocated in space and time with Aerosol Robotic Network (AERONET) AOD data were used to derive a total of 14,154 training and validation samples. The TOA reflectance in all six AHI solar bands, three TOA reflectance ratios derived based on the dark-target assumptions, sun-sensor geometry, and auxiliary data are used as predictors to estimate AOD at 500 nm. The DNN AOD is validated by separating training and validation samples using random k-fold cross-validation and using AERONET site-specific leave-one-station-out validation, and is compared with a random forest regression estimator and Japan Meteorological Agency (JMA) AOD. The DNN AOD shows high accuracy: (1) RMSE = 0.094, R2 = 0.915 for k-fold cross-validation, and (2) RMSE = 0.172, R2 = 0.730 for leave-one-station-out validation. The k-fold cross-validation overestimates the DNN accuracy as the training and validation samples may come from the same AHI pixel location. The leave-one-station-out validation reflects the accuracy for large-area applications where there are no training samples for the pixel location to be estimated. The DNN AOD has better accuracy than the random forest AOD and JMA AOD. In addition, the contribution of the dark-target derived TOA ratio predictors is examined and confirmed, and the sensitivity to the DNN structure is discussed.

Retrieval and Validation of AOD from Himawari-8 Data over Bohai Rim Region, China

The geostationary satellite Himawari-8, possessing the Advanced Himawari Imager (AHI), which features 16 spectral bands from the visible to infrared range, is suitable for aerosol observations. In this study, a new algorithm is introduced to retrieve aerosol optical depth (AOD) over land at a resolution of 2 km from the AHI level 1 data. Considering the anisotropic effects of complex surface structures over land, Moderate Resolution Imaging Spectroradiometer (MODIS) bidirectional reflectance distribution function (BRDF) model parameters product (MCD19A3) is used to calculate the surface reflectance for Himawari-8’s view angle and band. In addition, daily BRDF model parameters are calculated in areas with dense vegetation, considering the rapid variation of surface reflectance caused by vegetation growth. Moreover, aerosol models are constructed based on long duration Aerosol Robotic Network (AERONET) single scattering albedo (SSA) values to stand for aerosol types in the retrieval algorithm. The new algorithm is applied to AHI images over Bohai Rim region from 2018 and is evaluated using the newest AERONET version 3 AOD measurements and the latest MODIS collection 6.1 AOD products. The AOD retrievals from the new algorithm show good agreement with the AERONET AOD measurements, with a correlation coefficient of 0.93 and root mean square error (RMSE) of 0.12. In addition, the new algorithm increases AOD retrievals and retrieval accuracy compared to the Japan Aerospace Exploration Agency (JAXA) aerosol products. The algorithm shows stable performance during different seasons and times, which makes it possible for use in climate or diurnal aerosol variation studies.

Evaluation of JAXA Himawari-8-AHI Level-3 Aerosol Products over Eastern China

A novel geostationary satellite, the H8/AHI (Himawari-8/Advanced Himawari Imager), greatly improved the scan times per day covering East Asia, and the operational products have been stably provided for a period of time. Currently, atmospheric aerosol pollution is a major concern in China. H8/AHI aerosol products with a high temporal resolution are helpful for real-time monitoring of subtle aerosol variation. However, the H8/AHI aerosol optical thickness (AOT) product has been updated three times since its launch, and the evaluation of this dataset is currently rare. In order to validate its accuracy, this study compared the H8/AHI Level-3 (L3) hourly AOT products of all versions with measurements obtained from eleven sunphotometer sites located in eastern China from 2015 to 2018. Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 AOT products from the same period were also used for inter-comparison. Although the H8/AHI AOT retrievals in version 010 show a moderate agreement with ground-based observations (correlation coefficient (R): 0.66–0.85), and the time series analysis shows that it can effectively monitor hourly variation, it suffers from an obvious underestimation of 0.3 compared to ground-based and MODIS observations. After the retrieval algorithm updated the predefined aerosol model, the overall underestimation of AHI AOTs was solved (version 010 slope: 0.43–0.62, version 030 slope: 0.75–1.02), and the AOTs in version 030 show a high agreement with observations from ten sites (R: 0.73–0.91). In addition, the surface reflectance dataset derived from the minimum reflectivity model in version 010 is inaccurate in parts of eastern China, for both “bright” and “dark” land surfaces, which leads to the overestimation of the AOT values under low aerosol loads at the Beijing and Xianghe sites. After the update of the surface dataset in version 030, this phenomenon was alleviated, resulting in no significant difference in scatterplots under different surface conditions. The AOTs of H8/AHI version 030 show a significant improvement compared to the previous two versions, but the spatial distribution of AHI is still different from MODIS AOT products due to the differences in sensors and algorithms. Therefore, although the evaluation in this study demonstrates the effectiveness of H8/AHI AOT products for aerosol monitoring at fine temporal resolutions, the performance of H8/AHI AOT products needs further study by considering more conditions.