{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T21:02:44Z","timestamp":1771102964449,"version":"3.50.1"},"reference-count":59,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2022,11,18]],"date-time":"2022-11-18T00:00:00Z","timestamp":1668729600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Strategic Priority Research Program of Chinese Academy of Sciences","award":["XDA20040501"],"award-info":[{"award-number":["XDA20040501"]}]},{"name":"Strategic Priority Research Program of Chinese Academy of Sciences","award":["2022r024"],"award-info":[{"award-number":["2022r024"]}]},{"name":"Startup Foundation for Introducing Talent of NUIST","award":["XDA20040501"],"award-info":[{"award-number":["XDA20040501"]}]},{"name":"Startup Foundation for Introducing Talent of NUIST","award":["2022r024"],"award-info":[{"award-number":["2022r024"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Given the advantages of remote sensing, an increasing number of satellite aerosol optical depths (AOD) have been utilized to evaluate near-ground PM2.5. However, the spatiotemporal relationship between AODs and PM2.5 still lacks a comprehensive investigation, especially in some regions with severe pollution within China. Here, we investigated the spatiotemporal relationships between several satellite AODs and the near-surface PM2.5 concentration across China and its 14 representative regions during 2016\u20132018 using the correlation coefficient (R), the PM2.5\/AOD ratio (\u03b7), the geo-detector (q), and the different aerosol-dominated regimes. The results showed that the MODIS AOD from the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm strongly correlates with PM2.5 (R &gt; 0.6) in China, particularly in the Chengyu (CY), Beijing-Tianjin-Hebei (BTH), and Yangtze River Delta (YRD) regions. The close correlations (R = 0.7) exist between PM2.5 and MODIS and VIIRS AOD from the deep blue (DB) algorithm in the CY, BTH, and YRD regions. Under the key aerosols affecting China (e.g., sulfate and dust), there is a strong correlation (R &gt; 0.5) between the PM2.5 and MODIS and VIIRS AODs from the MAIAC and DB algorithms, with the higher concentration of ground-level PM2.5 per unit of these AODs (\u03b7 &gt; 130). The MAIAC AOD (Terra\/Aqua) can better explain the spatial distribution (q &gt; 0.4) of PM2.5 than those of AODs from the dark target (DT) and DB algorithms applied to the MODIS over China and its specific regions across seasons. The performance of the Advanced Himawari Imager (AHI) AOD (R &gt; 0.5, q &gt; 0.3) was close to that of the MAIAC AOD during the spring and summer; however, it was far less than the MAIAC AOD in the autumn and winter seasons. The investigation provides instructions for estimating the near-surface PM2.5 concentration based on AOD in different regions of China.<\/jats:p>","DOI":"10.3390\/rs14225841","type":"journal-article","created":{"date-parts":[[2022,11,18]],"date-time":"2022-11-18T04:08:40Z","timestamp":1668744520000},"page":"5841","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Evaluation and Comparison of Spatio-Temporal Relationship between Multiple Satellite Aerosol Optical Depth (AOD) and Near-Surface PM2.5 Concentration over China"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4190-137X","authenticated-orcid":false,"given":"Qiangqiang","family":"Xu","sequence":"first","affiliation":[{"name":"State Key Laboratory of Cryospheric Science, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences (CAS), Lanzhou 730000, China"}]},{"given":"Xiaoling","family":"Chen","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China"}]},{"given":"Dipesh","family":"Rupakheti","sequence":"additional","affiliation":[{"name":"Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science & Technology, Nanjing 210044, China"}]},{"given":"Jiadan","family":"Dong","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4505-6684","authenticated-orcid":false,"given":"Linling","family":"Tang","sequence":"additional","affiliation":[{"name":"The School of Geography and Environment and Key Lab of Poyang Lake Wetland and Watershed Research of Ministry of Education, Jiangxi Normal University, Nanchang 330022, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2115-9005","authenticated-orcid":false,"given":"Shichang","family":"Kang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Cryospheric Science, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences (CAS), Lanzhou 730000, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2229","DOI":"10.1175\/1520-0477(1999)080<2229:RSOTAF>2.0.CO;2","article-title":"Remote Sensing of Tropospheric Aerosols from Space: Past, Present, and Future","volume":"80","author":"King","year":"1999","journal-title":"Bull. 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