{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T18:09:54Z","timestamp":1775066994629,"version":"3.50.1"},"reference-count":29,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2024,8,8]],"date-time":"2024-08-08T00:00:00Z","timestamp":1723075200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["2023YFC3709500"],"award-info":[{"award-number":["2023YFC3709500"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["2020SF-434"],"award-info":[{"award-number":["2020SF-434"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Key R&amp;D Program of Shaanxi Province (China)","award":["2023YFC3709500"],"award-info":[{"award-number":["2023YFC3709500"]}]},{"name":"Key R&amp;D Program of Shaanxi Province (China)","award":["2020SF-434"],"award-info":[{"award-number":["2020SF-434"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Monitoring air pollution is important for human health and the environment. Previous studies on the prediction of air pollutants from satellite images have employed machine learning, yet there are few enhancements to the constructure of model. Moreover, the existing models have been successful in predicting pollutants like PM2.5, PM10, and O3. They have not been as effective in predicting other primary air pollutants. To improve the overall prediction performance of the existing model, a novel residual learning backpropagation model, abs. as BresNet, has been proposed in this research. This model has revealed the availability to precisely predict the ground-surface concentration of the six primary air pollutants, PM2.5, PM10, O3, NO2, CO, and SO2, based on the satellite imagery of MODIS AOD. Two of the most commonly used machine learning models so far, viz. the multilayer backpropagation neural network (MLBPN) and random forest (RF), were employed as the control. In the conducted experiments, the proposed BresNet model demonstrated significant improvements of 18.75%\/31.94%, 33.82%\/85.71%, 15.00%\/35.29%, 39.06%\/134.21%, 23.23%\/68.00%, and 137.14%\/260.87% in terms of R2 for the six primary air pollutants, compared to the RF\/MLBPN model. Moreover, the convergence speed and loss function of the BresNet model compared to that of the MLBPN decreased by 55.15%, revealing superior convergence speed with the lower loss function.<\/jats:p>","DOI":"10.3390\/rs16162897","type":"journal-article","created":{"date-parts":[[2024,8,8]],"date-time":"2024-08-08T12:13:14Z","timestamp":1723119194000},"page":"2897","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["BresNet: Applying Residual Learning in Backpropagation Neural Networks to Predict Ground Surface Concentration of Primary Air Pollutants"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-5532-3763","authenticated-orcid":false,"given":"Zekai","family":"Shi","sequence":"first","affiliation":[{"name":"School of Human Settlements and Civil Engineering, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8744-2922","authenticated-orcid":false,"given":"Meng","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Human Settlements and Civil Engineering, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}]},{"given":"Mei","family":"Han","sequence":"additional","affiliation":[{"name":"School of Human Settlements and Civil Engineering, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"},{"name":"Investigation and Ecological Assessment Center of Shaanxi Province, Xi\u2019an 710054, China"}]},{"given":"Yaowei","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Human Settlements and Civil Engineering, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}]},{"given":"Guodong","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Human Settlements and Civil Engineering, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}]},{"given":"Haoyuan","family":"Ren","sequence":"additional","affiliation":[{"name":"School of Human Settlements and Civil Engineering, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1146\/annurev-psych-032720-042905","article-title":"Psychology of Climate Change","volume":"74","author":"Steg","year":"2023","journal-title":"Annu. 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