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In our study, we present a validated framework to predict the daily PM2.5 distributions, exemplified by a use case of Shijiazhuang City, China, based on daily aerosol optical depth (AOD) datasets. The framework involves obtaining the high-resolution spatiotemporal AOD distributions, estimation of the spatial distributions of PM2.5 and the prediction of these based on a convolutional long short-term memory (ConvLSTM) model. In the estimation part, the eXtreme gradient boosting (XGBoost) model has been determined as the estimation model with the lowest root mean square error (RMSE) of 32.86 \u00b5g\/m3 and the highest coefficient of determination regression score function (R2) of 0.71, compared to other common models used as a baseline for comparison (linear, ridge, least absolute shrinkage and selection operator (LASSO) and cubist). For the prediction part, after validation and comparison with a seasonal autoregressive integrated moving average (SARIMA), which is a traditional time-series prediction model, in both time and space, the ConvLSTM gives a more accurate performance for the prediction, with a total average prediction RMSE of 14.94 \u00b5g\/m3 compared to SARIMA\u2019s 17.41 \u00b5g\/m3. Furthermore, ConvLSTM is more stable and with less fluctuations for the prediction of PM2.5 in time, and it can also eliminate better the spatial predicted errors compared to SARIMA.<\/jats:p>","DOI":"10.3390\/rs12172825","type":"journal-article","created":{"date-parts":[[2020,8,31]],"date-time":"2020-08-31T11:53:49Z","timestamp":1598874829000},"page":"2825","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":37,"title":["A Framework to Predict High-Resolution Spatiotemporal PM2.5 Distributions Using a Deep-Learning Model: A Case Study of Shijiazhuang, China"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5028-2291","authenticated-orcid":false,"given":"Guangyuan","family":"Zhang","sequence":"first","affiliation":[{"name":"IoT Laboratory, School of Electronic Engineering and Computer Science, Queen Mary University of London, London E1 4NS, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haiyue","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Earth Sciences and Engineering, Hohai University, Nanjing 211000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jin","family":"Dong","sequence":"additional","affiliation":[{"name":"College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3156-9609","authenticated-orcid":false,"given":"Stefan","family":"Poslad","sequence":"additional","affiliation":[{"name":"IoT Laboratory, School of Electronic Engineering and Computer Science, Queen Mary University of London, London E1 4NS, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Runkui","family":"Li","sequence":"additional","affiliation":[{"name":"College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5155-122X","authenticated-orcid":false,"given":"Xiaoshuai","family":"Zhang","sequence":"additional","affiliation":[{"name":"IoT Laboratory, School of Electronic Engineering and Computer Science, Queen Mary University of London, London E1 4NS, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7764-4272","authenticated-orcid":false,"given":"Xiaoping","family":"Rui","sequence":"additional","affiliation":[{"name":"School of Earth Sciences and Engineering, Hohai University, Nanjing 211000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,8,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1007\/s11760-019-01574-6","article-title":"A better way to monitor haze through image based upon the adjusted LeNet-5 CNN model","volume":"14","author":"Fan","year":"2019","journal-title":"Signal Image Video Process."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1016\/j.atmosenv.2013.10.010","article-title":"A study of urban pollution and haze clouds over northern China during the dusty season based on satellite and surface observations","volume":"82","author":"Tao","year":"2014","journal-title":"Atmos. 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