{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T03:52:24Z","timestamp":1784001144684,"version":"3.55.0"},"reference-count":59,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2022,3,29]],"date-time":"2022-03-29T00:00:00Z","timestamp":1648512000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>As an aggregate of suspended particulate matter in the air, atmospheric aerosols can affect the regional climate. With the help of satellite remote sensing technology to retrieve AOD (aerosol optical depth) on a global or regional scale, accurate estimation of PM2.5 concentration has become an important task to quantify the spatiotemporal distribution of AOD and PM2.5. However, due to the limitations of satellite platforms, sensors, and inversion algorithms, the spatiotemporal resolution of current major AOD products is still relatively low. Meanwhile, for the impact of cloud, the AOD products often have a serious data gap problem, which also objectively limits the spatiotemporal coverage of predicted PM2.5 concentration. Therefore, how to effectively improve the spatiotemporal resolution and coverage of PM2.5 concentration under the requisite accuracy is still a grand challenge. In this study, the fused high spatial-temporal resolution AOD data in our previous study were used to estimate the ground PM2.5 concentration through machine learning algorithms, the deep belief network (DBN). The PM2.5 data had spatiotemporal autocorrelation in geostatistics and followed the Gaussian kernel distribution. Hence, the autocorrelation model modified by Gaussian kernel function integrated with DBN algorithm, named Geoi-DBN, was used to estimate PM2.5 concentration. The cross-validation results showed that the Geoi-DBN (R2 = 0.86, RMSE = 6.84 \u00b5g m\u22123) performed better than the original DBN (R2 = 0.67, RMSE = 10.46 \u00b5g m\u22123). The final high quality PM2.5 concentration data can be applied for urban air quality monitoring and related PM2.5 exposure risk assessment such as wildfire.<\/jats:p>","DOI":"10.3390\/rs14071635","type":"journal-article","created":{"date-parts":[[2022,3,29]],"date-time":"2022-03-29T21:45:51Z","timestamp":1648590351000},"page":"1635","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["High Spatiotemporal Resolution PM2.5 Concentration Estimation with Machine Learning Algorithm: A Case Study for Wildfire in California"],"prefix":"10.3390","volume":"14","author":[{"given":"Qian","family":"Cui","sequence":"first","affiliation":[{"name":"College of Atmospheric Sciences, Nanjing University of Information Science & Technology, Nanjing 210044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4373-4058","authenticated-orcid":false,"given":"Feng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Atmospheric and Oceanic Sciences, Institute of Atmospheric Sciences, Fudan University, Shanghai 200433, China"},{"name":"Shanghai Qi Zhi Institute, Shanghai 200232, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shaoyun","family":"Fu","sequence":"additional","affiliation":[{"name":"Jiading District Meteorological Bureau, Shanghai Meteorological Service, Shanghai 201800, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoli","family":"Wei","sequence":"additional","affiliation":[{"name":"Jiading District Meteorological Bureau, Shanghai Meteorological Service, Shanghai 201800, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yue","family":"Ma","sequence":"additional","affiliation":[{"name":"Jiading District Meteorological Bureau, Shanghai Meteorological Service, Shanghai 201800, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kun","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Atmospheric Sciences, Nanjing University of Information Science & Technology, Nanjing 210044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"6267","DOI":"10.1016\/j.atmosenv.2011.08.066","article-title":"Assessing Temporally and Spatially Resolved PM2.5 Exposures for Epidemiological Studies Using Satellite Aerosol Optical Depth Measurements","volume":"45","author":"Kloog","year":"2011","journal-title":"Atmos. 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