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University","award":["RD21006P"],"award-info":[{"award-number":["RD21006P"]}]},{"name":"Introduction of High-Level Talents at Sanming University","award":["KC22020P"],"award-info":[{"award-number":["KC22020P"]}]},{"name":"Introduction of High-Level Talents at Sanming University","award":["KD22028P"],"award-info":[{"award-number":["KD22028P"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The pursuit of higher-resolution and more reliable spatial distribution simulation results for air pollutants is important to human health and environmental safety. However, the lack of high-resolution remote sensing retrieval parameters for gaseous pollutants (sulfur dioxide and ozone) limits the simulation effect to a 1 km resolution. To address this issue, we sequentially generated and optimized the spatial distributions of near-surface PM2.5, SO2, and ozone at a 1 km resolution in China through two approaches. First, we employed spatial sampling, random ID, and parameter convolution methods to jointly optimize a tree-based machine-learning gradient-boosting framework, LightGBM, and improve the performance of spatial air pollutant simulations. Second, we simulated PM2.5, used the simulated PM2.5 result to simulate SO2, and then used the simulated SO2 to simulate ozone. We improved the stability of 1 km-resolution SO2 and ozone products through the proposed sequence of multiple-pollutant simulations. The cross-validation (CV) of the random sample yielded an R2 of 0.90 and an RMSE of 9.62 \u00b5g\u2219m\u22123 for PM2.5, an R2 of 0.92 and an RMSE of 3.9 \u00b5g\u2219m\u22123 for SO2, and an R2 of 0.94 and an RMSE of 5.9 \u00b5g\u2219m\u22123 for ozone, which are values better than those in previous related studies. In addition, we tested the reliability of PM2.5, SO2, and ozone products in China through spatial distribution reliability analysis and parameter importance reliability analysis. The PM2.5, SO2, and ozone simulation models and multiple-air-pollutant (MuAP) products generated by the two optimization methods proposed in this study are of great value for long-term, large-scale, and regional-scale air pollution monitoring and predictions, as well as population health assessments.<\/jats:p>","DOI":"10.3390\/rs15245705","type":"journal-article","created":{"date-parts":[[2023,12,12]],"date-time":"2023-12-12T11:30:14Z","timestamp":1702380614000},"page":"5705","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Spatial Distribution of Multiple Atmospheric Pollutants in China from 2015 to 2020"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3020-670X","authenticated-orcid":false,"given":"Yufeng","family":"Chi","sequence":"first","affiliation":[{"name":"School of Information Engineering, Sanming University, Sanming 365004, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8473-2799","authenticated-orcid":false,"given":"Yu","family":"Zhan","sequence":"additional","affiliation":[{"name":"Department of Environmental Science and Engineering, Sichuan University, Chengdu 610065, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6829-0268","authenticated-orcid":false,"given":"Kai","family":"Wang","sequence":"additional","affiliation":[{"name":"China-UK Low Carbon College, Shanghai Jiaotong University, Shanghai 201308, China"}]},{"given":"Hong","family":"Ye","sequence":"additional","affiliation":[{"name":"Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China"},{"name":"Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China"},{"name":"CAS Haixi Industrial Technology Innovation Center in Beilun, Ningbo 315800, China"},{"name":"Xiamen Key Laboratory of Smart Management of Urban Environment, Xiamen 361021, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1038\/s41586-020-1983-8","article-title":"Premature mortality related to United States cross-state air pollution","volume":"578","author":"Dedoussi","year":"2020","journal-title":"Nature"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"e4","DOI":"10.1016\/S2468-2667(16)30023-8","article-title":"Air pollution and health","volume":"2","author":"Landrigan","year":"2016","journal-title":"Lancet Public Health"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"3405","DOI":"10.1038\/s41467-019-11453-w","article-title":"Impacts of air pollutants from rural Chinese households under the rapid residential energy transition","volume":"10","author":"Shen","year":"2019","journal-title":"Nat. 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