Identification of Aerosol Pollution Hotspots in Jiangsu Province of China

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.

Spatiotemporal changes in aerosols over Bangladesh using 18 years of MODIS and reanalysis data

In this study, combined Dark Target and Deep Blue (DTB) aerosol optical depth at 550 nm (AOD_{550 nm}) data the Moderate Resolution Imaging Spectroradiometer (MODIS) flying on the Terra and Aqua satellites during the years 2003-2020 are used as a reference to assess the performance of the Copernicus Atmosphere Monitoring Services (CAMS) and the second version of Modern-Era Retrospective analysis for Research and Applications (MERRA-2) AOD over Bangladesh. The study also investigates long-term spatiotemporal variations and trends in AOD, and determines the relative contributions from different aerosol species (black carbon: BC, dust, organic carbon: OC, sea salt: SS, and sulfate) and anthropogenic emissions to the total AOD. As the evaluations suggest higher accuracy for CAMS than for MERRA-2, CAMS is used for further analysis of AOD over Bangladesh. The annual mean AOD from both CAMS and MODIS DTB is high (>0.60) over most parts of Bangladesh except for the eastern areas of Chattogram and Sylhet. Higher AOD is observed in spring and winter than in summer and autumn, which is mainly due to higher local anthropogenic emissions during the winter to spring season. Annual trends from 2003-2020 show a significant increase in AOD (by 0.006-0.014 year^-1) over Bangladesh, and this increase in AOD was more evident in winter and spring than in summer and autumn. The increasing total AOD is caused by rising anthropogenic emissions and accompanied by changes in aerosol species (with increased OC, sulfate, and BC). Overall, this study improves understanding of aerosol pollution in Bangladesh and can be considered as a supportive document for Bangladesh to improve air quality by reducing anthropogenic emissions.

Identification of NO2 and SO2 Pollution Hotspots and Sources in Jiangsu Province of China

Nitrogen dioxide (NO2) and sulfur dioxide (SO2) are important atmospheric trace gases for determining air quality, human health, climate change, and ecological conditions both regionally and globally. In this study, the Ozone Monitoring Instrument (OMI), total column nitrogen dioxide (NO2), and sulfur dioxide (SO2) were used from 2005 to 2020 to identify pollution hotspots and potential source areas responsible for air pollution in Jiangsu Province. The study investigated the spatiotemporal distribution and variability of NO2 and SO2, the SO2/NO2 ratio, and their trends, and potential source contribution function (PSCF) analysis was performed to identify potential source areas. The spatial distributions showed higher values (>0.60 DU) of annual mean NO2 and SO2 for most cities of Jiangsu Province except for Yancheng City (<0.50 DU). The seasonal analyses showed the highest NO2 and SO2 in winter, followed by spring, autumn, and summer. Coal-fire-based room heating and stable meteorological conditions during the cold season may cause higher NO2 and SO2 in winter. Notably, the occurrence frequency of NO2 and SO2 of >1.2 was highest in winter, which varied between 9.14~32.46% for NO2 and 7.84~21.67% for SO2, indicating a high level of pollution across Jiangsu Province. The high SO2/NO2 ratio (>0.60) indicated that industry is the dominant source, with significant annual and seasonal variations. Trends in NO2 and SO2 were calculated for 2005–2020, 2006–2010 (when China introduced strict air pollution control policies during the 11th Five Year Plan (FYP)), 2011–2015 (during the 12th FYP), and 2013–2017 (the Action Plan of Air Pollution Prevention and Control (APPC-AC)). Annually, decreasing trends in NO2 were more prominent during the 12th FYP period (2011–2015: −0.024~−0.052 DU/year) than in the APPC-AC period (2013–2017: −0.007~−0.043 DU/year) and 2005–2020 (−0.002 to −0.012 DU/year). However, no prevention and control policies for NO2 were included during the 11th FYP period (2006–2010), resulting in an increasing trend in NO2 (0.015 to 0.031) observed throughout the study area. Furthermore, the implementation of China’s strict air pollution control policies caused a larger decrease in SO2 (per year) during the 12th FYP period (−0.002~−0.075 DU/year) than in the 11th FYP period (−0.014~−0.071 DU/year), the APPC-AC period (−0.007~−0.043 DU/year), and 2005–2020 (−0.015~−0.032 DU/year). PSCF analysis indicated that the air quality of Jiangsu Province is mainly influenced by local pollution sources.