{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T16:38:59Z","timestamp":1764175139874,"version":"build-2065373602"},"reference-count":53,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,1,26]],"date-time":"2022-01-26T00:00:00Z","timestamp":1643155200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Major project of the National Social Science Fund","award":["19ZDA189"],"award-info":[{"award-number":["19ZDA189"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41301514","41401456"],"award-info":[{"award-number":["41301514","41401456"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science and Technology Project of Nantong","award":["MS12020075","MS12021082"],"award-info":[{"award-number":["MS12020075","MS12021082"]}]},{"name":"2020\u20132022 Transportation Education Scientific Research Projects of the China Institute of Com-munications Education","award":["JTYB20-45"],"award-info":[{"award-number":["JTYB20-45"]}]},{"DOI":"10.13039\/501100013254","name":"National College Students Innovation and Entrepreneurship Training Program","doi-asserted-by":"publisher","award":["202110304041Z","202110304108Y"],"award-info":[{"award-number":["202110304041Z","202110304108Y"]}],"id":[{"id":"10.13039\/501100013254","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Due to the country\u2019s rapid economic growth, the problem of air pollution in China is becoming increasingly serious. In order to achieve a win-win situation for the environment and urban development, the government has issued many policies to strengthen environmental protection. PM2.5 is the primary particulate matter in air pollution, so an accurate estimation of PM2.5 distribution is of great significance. Although previous studies have attempted to retrieve PM2.5 using geostatistical or aerosol remote sensing retrieval methods, the current rough resolution and accuracy remain as limitations of such methods. This paper proposes a fine-grained spatiotemporal PM2.5 retrieval method that comprehensively considers various datasets, such as Landsat 8 satellite images, ground monitoring station data, and socio-economic data, to explore the applicability of different machine learning algorithms in PM2.5 retrieval. Six typical algorithms were used to train the multi-dimensional elements in a series of experiments. The characteristics of retrieval accuracy in different scenarios were clarified mainly according to the validation index, R2. The random forest algorithm was shown to have the best numerical and PM2.5-based air-quality-category accuracy, with a cross-validated R2 of 0.86 and a category retrieval accuracy of 0.83, while both maintained excellent retrieval accuracy and achieved a high spatiotemporal resolution. Based on this retrieval model, we evaluated the PM2.5 distribution characteristics and hourly variation in the sample area, as well as the functions of different input variables in the model. The PM2.5 retrieval method proposed in this paper provides a new model for fine-grained PM2.5 concentration estimation to determine the distribution laws of air pollutants and thereby specify more effective measures to realize the high-quality development of the city.<\/jats:p>","DOI":"10.3390\/rs14030599","type":"journal-article","created":{"date-parts":[[2022,1,27]],"date-time":"2022-01-27T04:49:51Z","timestamp":1643258991000},"page":"599","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Retrieval of Fine-Grained PM2.5 Spatiotemporal Resolution Based on Multiple Machine Learning Models"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2865-9515","authenticated-orcid":false,"given":"Peilong","family":"Ma","sequence":"first","affiliation":[{"name":"School of Geographical Sciences, Nantong University, Nantong 226007, China"}]},{"given":"Fei","family":"Tao","sequence":"additional","affiliation":[{"name":"School of Geographical Sciences, Nantong University, Nantong 226007, China"},{"name":"Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7302-2583","authenticated-orcid":false,"given":"Lina","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Geographical Sciences, Nantong University, Nantong 226007, China"}]},{"given":"Shaijie","family":"Leng","sequence":"additional","affiliation":[{"name":"School of Geographical Sciences, Nantong University, Nantong 226007, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1399-5030","authenticated-orcid":false,"given":"Ke","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Geographical Sciences, Nantong University, Nantong 226007, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3041-6264","authenticated-orcid":false,"given":"Tong","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Geographical Sciences, Nantong University, Nantong 226007, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1038\/nature15371","article-title":"The contribution of outdoor air pollution sources to premature mortality on a global scale","volume":"525","author":"Lelieveld","year":"2015","journal-title":"Nature"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1009","DOI":"10.1016\/j.scitotenv.2017.07.061","article-title":"Forecasting PM2.5 induced male lung cancer morbidity in China using satellite retrieved PM2.5 and spatial analysis","volume":"607\u2013608","author":"Han","year":"2017","journal-title":"Sci. Total Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1374","DOI":"10.1164\/rccm.201106-1011OC","article-title":"Long-term ambient fine particulate matter air pollution and lung cancer in a large cohort of never-smokers","volume":"184","author":"Turner","year":"2011","journal-title":"Am. J. Respir. Crit. Care Med."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"123887","DOI":"10.1016\/j.jclepro.2020.123887","article-title":"Mapping PM2.5 concentration at high resolution using a cascade random forest based downscaling model: Evaluation and application","volume":"277","author":"Yang","year":"2020","journal-title":"J. Clean. Prod."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"112203","DOI":"10.1016\/j.rse.2020.112203","article-title":"Estimating PM2.5 concentrations in Northeastern China with full spatiotemporal coverage, 2005\u20132016","volume":"253","author":"Meng","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"141093","DOI":"10.1016\/j.scitotenv.2020.141093","article-title":"Estimating PM2.5 with high-resolution 1-km AOD data and an improved machine learning model over Shenzhen, China","volume":"746","author":"Chen","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"133561","DOI":"10.1016\/j.scitotenv.2019.07.367","article-title":"Deep learning-based PM2.5 prediction considering the spatiotemporal correlations: A case study of Beijing, China","volume":"699","author":"Pak","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Li, L. (2020). A robust deep learning approach for spatiotemporal estimation of Satellite AOD and PM2.5. Remote Sens., 12.","DOI":"10.3390\/rs12020264"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"847","DOI":"10.1289\/ehp.0901623","article-title":"Global estimates of ambient fine particulate matter concentrations from satellite-based aerosol optical depth: Development and application","volume":"118","author":"Martin","year":"2010","journal-title":"Environ. Health Perspect."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"44416","DOI":"10.1109\/ACCESS.2019.2908975","article-title":"High Spatial Resolution PM2.5 Retrieval Using MODIS and Ground Observation Station Data Based on Ensemble Random Forest","volume":"7","author":"Chen","year":"2019","journal-title":"IEEE Access"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Yazdi, M.D., Kuang, Z., Dimakopoulou, K., Barratt, B., Suel, E., Amini, H., Lyapustin, A., Katsouyanni, K., and Schwartz, J. (2020). Predicting fine particulate matter (PM2.5) in the greater london area: An ensemble approach using machine learning methods. Remote Sens., 12.","DOI":"10.3390\/rs12060914"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"7436","DOI":"10.1021\/es5009399","article-title":"Estimating ground-level PM2.5 in china using satellite remote sensing","volume":"48","author":"Ma","year":"2014","journal-title":"Environ. Sci. Technol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"727","DOI":"10.1080\/13658816.2015.1095921","article-title":"An optimized spatial proximity model for fine particulate matter air pollution exposure assessment in areas of sparse monitoring","volume":"30","author":"Zou","year":"2016","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1147","DOI":"10.1016\/j.envpol.2017.10.025","article-title":"Modelling daily PM2.5 concentrations at high spatio-temporal resolution across Switzerland","volume":"233","author":"Stafoggia","year":"2018","journal-title":"Environ. Pollut."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1080\/15481603.2020.1712101","article-title":"Spatiotemporal mixed effects modeling for the estimation of PM2.5 from MODIS AOD over the Indian subcontinent","volume":"57","author":"Unnithan","year":"2020","journal-title":"GISci. Remote Sens."},{"key":"ref_16","first-page":"3269","article-title":"Mapping annual mean ground-level PM2.5 concentrations using Multiangle Imaging Spectroradiometer aerosol optical thickness over the contiguous United States","volume":"109","author":"Liu","year":"2004","journal-title":"J. Geophys. Res. D Atmos."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3269","DOI":"10.1021\/es049352m","article-title":"Estimating ground-level PM2.5 in the eastern United States using satellite remote sensing","volume":"39","author":"Liu","year":"2005","journal-title":"Environ. Sci. Technol."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Zeng, Q., Tao, J., Chen, L., Zhu, H., Zhu, S.Y., and Wang, Y. (2020). Estimating ground-level particulate matter in five regions of China using aerosol optical depth. Remote Sens., 12.","DOI":"10.3390\/rs12050881"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"7991","DOI":"10.5194\/acp-11-7991-2011","article-title":"A novel calibration approach of MODIS AOD data to predict PM2.5 concentrations","volume":"11","author":"Lee","year":"2011","journal-title":"Atmos. Chem. Phys."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.rse.2018.06.030","article-title":"Estimation of ultrahigh resolution PM2.5 concentrations in urban areas using 160 m Gaofen-1 AOD retrievals","volume":"216","author":"Zhang","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Sun, L., Wei, J., Bilal, M., Tian, X., Jia, C., Guo, Y., and Mi, X. (2016). Aerosol optical depth retrieval over bright areas using Landsat 8 OLI images. Remote Sens., 8.","DOI":"10.3390\/rs8010023"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"116909","DOI":"10.1016\/j.atmosenv.2019.116909","article-title":"On the opposite seasonality of MODIS AOD and surface PM2.5 over the Northern China plain","volume":"215","author":"Xu","year":"2019","journal-title":"Atmos. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"526","DOI":"10.1016\/j.envpol.2019.02.071","article-title":"The relationships between PM2.5 and aerosol optical depth (AOD) in mainland China: About and behind the spatio-temporal variations","volume":"248","author":"Yang","year":"2019","journal-title":"Environ. Pollut."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Zhang, B., Zhang, M., Kang, J., Hong, D., Xu, J., and Zhu, X. (2019). Estimation of PMx Concentrations from Landsat 8 OLI Images Based on a Multilayer Perceptron Neural Network. Remote Sens., 11.","DOI":"10.3390\/rs11060646"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Yun, G., Zuo, S., Dai, S., Song, X., Id, C.X., Liao, Y., Zhao, P., Chang, W., Id, Q.C., and Li, Y. (2018). Individual and Interactive Influences of Anthropogenic and Ecological Factors on Forest PM2.5 Concentrations at an Urban Scale. Remote Sens., 10.","DOI":"10.3390\/rs10040521"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Liu, J., Weng, F., Li, Z., and Cribb, M.C. (2019). Hourly PM2.5 estimates from a geostationary satellite based on an ensemble learning algorithm and their spatiotemporal patterns over central East China. Remote Sens., 11.","DOI":"10.3390\/rs11182120"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1027","DOI":"10.1016\/j.envpol.2018.01.053","article-title":"Satellite-based high-resolution PM2.5 estimation over the Beijing-Tianjin-Hebei region of China using an improved geographically and temporally weighted regression model","volume":"236","author":"He","year":"2018","journal-title":"Environ. Pollut."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1400","DOI":"10.4209\/aaqr.2018.12.0450","article-title":"Evaluation of different machine learning approaches to forecasting PM2.5 mass concentrations","volume":"19","author":"Kaimian","year":"2019","journal-title":"Aerosol. Air Qual. Res."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1098","DOI":"10.1016\/j.atmosenv.2007.10.073","article-title":"Quality and performance of a PM10 daily forecasting model","volume":"42","author":"Stadlober","year":"2008","journal-title":"Atmos. Environ."},{"key":"ref_30","first-page":"779","article-title":"Support Vector Regression Machines","volume":"28","author":"Drucker","year":"1996","journal-title":"Neural Inf. Process."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.atmosenv.2018.04.004","article-title":"PM2.5 forecasting using SVR with PSOGSA algorithm based on CEEMD, GRNN and GCA considering meteorological factors","volume":"183","author":"Zhu","year":"2018","journal-title":"Atmos. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1266","DOI":"10.1016\/j.scitotenv.2010.12.039","article-title":"Intercomparison of air quality data using principal component analysis, and forecasting of PM10 and PM2.5 concentrations using artificial neural networks, in Thessaloniki and Helsinki","volume":"409","author":"Voukantsis","year":"2011","journal-title":"Sci. Total Environ."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Li, L., Chen, B., Zhang, Y., Zhao, Y., Xian, Y., Xu, G., Zhang, H., and Guo, L. (2018). Retrieval of daily PM2.5 concentrations using nonlinear methods: A case study of the Beijing-Tianjin-Hebei Region, China. Remote Sens., 10.","DOI":"10.3390\/rs10122006"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Bai, Y., Wu, L., Qin, K., Zhang, Y., Shen, Y., and Zhou, Y. (2016). A geographically and temporally weighted regression model for ground-level PM2.5 estimation from satellite-derived 500 m resolution AOD. Remote Sens., 8.","DOI":"10.3390\/rs8030262"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Yang, H., Chen, W., and Liang, Z. (2017). Impact of land use on PM2.5 pollution in a representative city of middle China. Int. J. Environ. Res. Public Health, 14.","DOI":"10.3390\/ijerph14050462"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"117113","DOI":"10.1016\/j.atmosenv.2019.117113","article-title":"Traffic contribution to PM2.5 increment in the near-road environment","volume":"224","author":"Askariyeh","year":"2020","journal-title":"Atmos. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.atmosres.2018.04.002","article-title":"Do volatile organic compounds (VOCs) emitted from petrochemical industries affect regional PM2.5?","volume":"209","author":"Han","year":"2018","journal-title":"Atmos. Res."},{"key":"ref_38","first-page":"1414","article-title":"FLAASH, a MODTRAN4-based atmospheric correction algorithm, its applications and validation","volume":"3","author":"Cooley","year":"2002","journal-title":"Int. Geosci. Remote Sens. Symp."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"234","DOI":"10.2307\/143141","article-title":"A Computer Movie Simulating Urban Growth in the Detroit Region","volume":"46","author":"Tobler","year":"1970","journal-title":"Econ. Geogr."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Pal, M., and Bharati, P. (2019). Introduction to correlation and linear regression analysis. Applications of Regression Techniques, Springer.","DOI":"10.1007\/978-981-13-9314-3"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"153","DOI":"10.3390\/rs70100153","article-title":"Comparing machine learning classifiers for object-based land cover classification using very high resolution imagery","volume":"7","author":"Qian","year":"2015","journal-title":"Remote Sens."},{"key":"ref_42","first-page":"247","article-title":"Application of SVR optimized by modified simulated annealing (MSA-SVR) air conditioning load prediction model","volume":"15","author":"Tao","year":"2019","journal-title":"J. Ind. Inf. Integr."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"126169","DOI":"10.1016\/j.chemosphere.2020.126169","article-title":"Hybrid decision tree-based machine learning models for short-term water quality prediction","volume":"249","author":"Lu","year":"2020","journal-title":"Chemosphere"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"13788","DOI":"10.1038\/s41598-019-50177-1","article-title":"Estimation of PM2.5 concentrations in China using a spatial back propagation neural network","volume":"9","author":"Wang","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_45","first-page":"2079","article-title":"On over-fitting in model selection and subsequent selection bias in performance evaluation","volume":"11","author":"Cawley","year":"2010","journal-title":"J. Mach. Learn. Res."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"e2020EA001599","DOI":"10.1029\/2020EA001599","article-title":"Daily and Hourly Surface PM2.5 Estimation From Satellite AOD","volume":"8","author":"Zhang","year":"2021","journal-title":"Earth Space Sci."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"123742","DOI":"10.1016\/j.jclepro.2020.123742","article-title":"Spatiotemporal PM2.5 variations and its response to the industrial structure from 2000 to 2018 in the Beijing-Tianjin-Hebei region","volume":"279","author":"Xue","year":"2021","journal-title":"J. Clean. Prod."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"123622","DOI":"10.1016\/j.jclepro.2020.123622","article-title":"Have traffic restrictions improved air quality? A shock from COVID-19","volume":"279","author":"Chen","year":"2021","journal-title":"J. Clean. Prod."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1016\/j.apgeog.2018.07.018","article-title":"Effects of urban form on haze pollution in China: Spatial regression analysis based on PM2.5 remote sensing data","volume":"98","author":"Yuan","year":"2018","journal-title":"Appl. Geogr."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Li, X., Wu, C., Meadows, M.E., Zhang, Z., Lin, X., Zhang, Z., Chi, Y., Feng, M., Li, E., and Hu, Y. (2021). Factors underlying spatiotemporal variations in atmospheric pm2.5 concentrations in zhejiang province, china. Remote Sens., 13.","DOI":"10.3390\/rs13153011"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"101314","DOI":"10.1016\/j.jth.2021.101314","article-title":"A short-distance healthy route planning approach","volume":"24","author":"Gao","year":"2022","journal-title":"J. Transp. Health"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"3887","DOI":"10.1021\/es505846r","article-title":"Spatiotemporal prediction of fine particulate matter during the 2008 Northern California wildfires using machine learning","volume":"49","author":"Reid","year":"2015","journal-title":"Environ. Sci. Technol."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Wang, Y., Wang, M., Huang, B., Li, S., and Lin, Y. (2021). Estimation and analysis of the nighttime PM2.5 concentration based on lj1-01 images: A case study in the pearl river delta urban agglomeration of china. Remote Sens., 13.","DOI":"10.3390\/rs13173405"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/3\/599\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:08:25Z","timestamp":1760134105000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/3\/599"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,26]]},"references-count":53,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2022,2]]}},"alternative-id":["rs14030599"],"URL":"https:\/\/doi.org\/10.3390\/rs14030599","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2022,1,26]]}}}