{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T00:17:51Z","timestamp":1775693871418,"version":"3.50.1"},"reference-count":64,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2023,7,31]],"date-time":"2023-07-31T00:00:00Z","timestamp":1690761600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Young and Middle-aged Teacher Education Research Project of Fujian Province","award":["JAT220690"],"award-info":[{"award-number":["JAT220690"]}]},{"name":"Young and Middle-aged Teacher Education Research Project of Fujian Province","award":["ZJXF2022056"],"award-info":[{"award-number":["ZJXF2022056"]}]},{"name":"Young and Middle-aged Teacher Education Research Project of Fujian Province","award":["2021A1515010946"],"award-info":[{"award-number":["2021A1515010946"]}]},{"name":"Young and Middle-aged Teacher Education Research Project of Fujian Province","award":["2020KJCX003"],"award-info":[{"award-number":["2020KJCX003"]}]},{"name":"Special Research Project on Innovative Application of Virtual Simulation Technology in Vocational Education Teaching","award":["JAT220690"],"award-info":[{"award-number":["JAT220690"]}]},{"name":"Special Research Project on Innovative Application of Virtual Simulation Technology in Vocational Education Teaching","award":["ZJXF2022056"],"award-info":[{"award-number":["ZJXF2022056"]}]},{"name":"Special Research Project on Innovative Application of Virtual Simulation Technology in Vocational Education Teaching","award":["2021A1515010946"],"award-info":[{"award-number":["2021A1515010946"]}]},{"name":"Special Research Project on Innovative Application of Virtual Simulation Technology in Vocational Education Teaching","award":["2020KJCX003"],"award-info":[{"award-number":["2020KJCX003"]}]},{"name":"Natural Science Foundation of Guangdong Province, China","award":["JAT220690"],"award-info":[{"award-number":["JAT220690"]}]},{"name":"Natural Science Foundation of Guangdong Province, China","award":["ZJXF2022056"],"award-info":[{"award-number":["ZJXF2022056"]}]},{"name":"Natural Science Foundation of Guangdong Province, China","award":["2021A1515010946"],"award-info":[{"award-number":["2021A1515010946"]}]},{"name":"Natural Science Foundation of Guangdong Province, China","award":["2020KJCX003"],"award-info":[{"award-number":["2020KJCX003"]}]},{"name":"Forestry Science and Technology Innovation of Guangdong Province, China","award":["JAT220690"],"award-info":[{"award-number":["JAT220690"]}]},{"name":"Forestry Science and Technology Innovation of Guangdong Province, China","award":["ZJXF2022056"],"award-info":[{"award-number":["ZJXF2022056"]}]},{"name":"Forestry Science and Technology Innovation of Guangdong Province, China","award":["2021A1515010946"],"award-info":[{"award-number":["2021A1515010946"]}]},{"name":"Forestry Science and Technology Innovation of Guangdong Province, China","award":["2020KJCX003"],"award-info":[{"award-number":["2020KJCX003"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Establishing an efficient PM2.5 prediction model and in-depth knowledge of the relationship between the predictors and PM2.5 in the model are of great significance for preventing and controlling PM2.5 pollution and policy formulation in the Yangtze River Delta (YRD) where there is serious air pollution. In this study, the spatial pattern of PM2.5 concentration in the YRD during 2003\u20132019 was analyzed by Hot Spot Analysis. We employed five algorithms to train, verify, and test 17 years of data in the YRD, and we explored the drivers of PM2.5 exposure. Our key results demonstrated: (1) High PM2.5 pollution in the YRD was concentrated in the western and northwestern regions and remained stable for 17 years. Compared to 2003, PM2.5 increased by 10\u201320% in the southeast, southwest, and western regions in 2019. The hot spot for percentage change of PM2.5 was mostly located in the southwest and southeast regions in 2019, while the interannual change showed a changeable spatial distribution pattern. (2) Geographically Weighted Random Forest (GWRF) has great advantages in predicting the presence of PM2.5 in comparison with other models. GWRF not only improves the performance of RF, but also spatializes the interpretation of variables. (3) Climate and human activities are the most important drivers of PM2.5 concentration. Drought, temperature, and temperature difference are the most critical and potentially threatening climatic factors for the increase and expansion of PM2.5 in the YRD. With the warming and drying trend worldwide, this finding can help policymakers better consider these factors for PM2.5 prediction. Moreover, the effect of interference from humans on ecosystems will increase again after COVID-19, leading to a rise in PM2.5 concentration. The strong explanatory power of comprehensive ecological indicators for the distribution of PM2.5 will be a crucial indicator worthy of consideration by decision-making departments.<\/jats:p>","DOI":"10.3390\/rs15153826","type":"journal-article","created":{"date-parts":[[2023,8,1]],"date-time":"2023-08-01T09:24:24Z","timestamp":1690881864000},"page":"3826","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Modeling the Effects of Drivers on PM2.5 in the Yangtze River Delta with Geographically Weighted Random Forest"],"prefix":"10.3390","volume":"15","author":[{"given":"Zhangwen","family":"Su","sequence":"first","affiliation":[{"name":"Zhangzhou Institute of Technology, Zhangzhou 363000, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1584-3559","authenticated-orcid":false,"given":"Lin","family":"Lin","sequence":"additional","affiliation":[{"name":"Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD 20740, USA"}]},{"given":"Zhenhui","family":"Xu","sequence":"additional","affiliation":[{"name":"Zhangzhou Institute of Technology, Zhangzhou 363000, China"}]},{"given":"Yimin","family":"Chen","sequence":"additional","affiliation":[{"name":"Zhangzhou Institute of Technology, Zhangzhou 363000, China"}]},{"given":"Liming","family":"Yang","sequence":"additional","affiliation":[{"name":"Zhangzhou Institute of Technology, Zhangzhou 363000, China"}]},{"given":"Honghao","family":"Hu","sequence":"additional","affiliation":[{"name":"Zhangzhou Institute of Technology, Zhangzhou 363000, China"}]},{"given":"Zipeng","family":"Lin","sequence":"additional","affiliation":[{"name":"Zhangzhou Institute of Technology, Zhangzhou 363000, China"}]},{"given":"Shujing","family":"Wei","sequence":"additional","affiliation":[{"name":"Guangdong Academy of Forestry, Guangzhou 510520, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0595-6936","authenticated-orcid":false,"given":"Sisheng","family":"Luo","sequence":"additional","affiliation":[{"name":"Guangdong Academy of Forestry, Guangzhou 510520, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Danesh Yazdi, M., 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_2","doi-asserted-by":"crossref","first-page":"133983","DOI":"10.1016\/j.scitotenv.2019.133983","article-title":"Research on PM2.5 estimation and prediction method and changing characteristics analysis under long temporal and large spatial scale\u2014A case study in China typical regions","volume":"696","author":"Luo","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"116273","DOI":"10.1016\/j.jenvman.2022.116273","article-title":"Spatiotemporal distribution characteristics of PM2.5 concentration in China from 2000 to 2018 and its impact on population","volume":"323","author":"Jin","year":"2022","journal-title":"J. Environ. Manag."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"100155","DOI":"10.1016\/j.envc.2021.100155","article-title":"PM2.5 concentration prediction during COVID-19 lockdown over Kolkata metropolitan city, India using MLR and ANN models","volume":"4","author":"Bera","year":"2021","journal-title":"Environ. Chall."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"134003","DOI":"10.1016\/j.chemosphere.2022.134003","article-title":"Spatiotemporal PM2.5 estimations in China from 2015 to 2020 using an improved gradient boosting decision tree","volume":"296","author":"He","year":"2022","journal-title":"Chemosphere"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"116459","DOI":"10.1016\/j.envpol.2021.116459","article-title":"A spatial-temporal interpretable deep learning model for improving interpretability and predictive accuracy of satellite-based PM2.5","volume":"273","author":"Yan","year":"2021","journal-title":"Environ. Pollut."},{"key":"ref_7","first-page":"100396","article-title":"Evaluation of aerosol optical depth (AOD) and PM2.5 associations for air quality assessment","volume":"20","author":"Yang","year":"2020","journal-title":"Remote Sens. Appl."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"111221","DOI":"10.1016\/j.rse.2019.111221","article-title":"Estimating 1-km-resolution PM2.5 concentrations across China using the space-time random forest approach","volume":"231","author":"Wei","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"6936","DOI":"10.1021\/acs.est.7b01210","article-title":"Estimating PM2.5 concentrations in the conterminous United States using the random forest approach","volume":"51","author":"Hu","year":"2017","journal-title":"Environ. Sci. Technol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/j.atmosenv.2019.04.002","article-title":"Satellite-based PM2.5 estimation directly from reflectance at the top of the atmosphere using a machine learning algorithm","volume":"208","author":"Liu","year":"2019","journal-title":"Atmos. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2595","DOI":"10.1021\/acs.est.8b06392","article-title":"Regional estimates of chemical composition of fine particulate matter using a combined geosciencestatistical method with information from satellites, models, and monitors","volume":"53","author":"Martin","year":"2019","journal-title":"Environ. Sci. Technol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"284","DOI":"10.1007\/s10661-022-09934-5","article-title":"Understanding the distribution and drivers of PM2.5 concentrations in the Yangtze River Delta from 2015 to 2020 using Random Forest Regression","volume":"94","author":"Su","year":"2022","journal-title":"Environ. Monit. Assess."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1016\/j.atmosenv.2019.05.004","article-title":"Meteorological parameters and gaseous pollutant concentrations as predictors of daily continuous PM2.5 concentrations using deep neural network in Beijing\u2013Tianjin\u2013Hebei, China","volume":"211","author":"Wang","year":"2019","journal-title":"Atmos. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1413","DOI":"10.1021\/acs.est.8b06038","article-title":"Performance of prediction algorithms for modeling outdoor air pollution spatial surfaces","volume":"53","author":"Kerckhoffs","year":"2019","journal-title":"Environ. Sci. Technol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"128801","DOI":"10.1016\/j.chemosphere.2020.128801","article-title":"Satellite-based ground PM2.5 estimation using a gradient boosting decision tree","volume":"268","author":"Zhang","year":"2021","journal-title":"Chemosphere"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"112772","DOI":"10.1016\/j.ecoenv.2021.112772","article-title":"Estimating PM2.5 concentration using the machine learning GA-SVM method to improve the land use regression model in Shaanxi, China","volume":"225","author":"Zhang","year":"2021","journal-title":"Ecotox. Environ. Safe."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Dai, H., Huang, G., Wang, J., Zeng, H., and Zhou, F. (2022). Spatio-temporal characteristics of PM2.5 concentrations in china based on multiple sources of data and LUR-GBM during 2016\u20132021. Int. J. Environ. Res. Public Health, 19.","DOI":"10.3390\/ijerph19106292"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.jes.2020.04.042","article-title":"PM2.5 concentration estimation using convolutional neural network and gradient boosting machine","volume":"98","author":"Luo","year":"2020","journal-title":"J. Environ. Sci."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"144516","DOI":"10.1016\/j.scitotenv.2020.144516","article-title":"PM2.5 concentration forecasting at surface monitoring sites using GRU neural network based on empirical mode decomposition","volume":"768","author":"Huang","year":"2021","journal-title":"Sci. Total Environ."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Su, Z., Xu, Z., Lin, L., Chen, Y., Hu, H., Wei, S., and Luo, S. (2022). Exploration of the Contribution of Fire Carbon Emissions to PM2.5 and Their Influencing Factors in Laotian Tropical Rainforests. Remote Sens., 14.","DOI":"10.3390\/rs14164052"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.rse.2017.12.018","article-title":"Satellite-based mapping of daily high-resolution ground PM2.5 in China via space-time regression modeling","volume":"206","author":"He","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"6955","DOI":"10.1038\/s41598-021-85381-5","article-title":"Geographically weighted machine learning model for untangling spatial heterogeneity of type 2 diabetes mellitus (T2D) prevalence in the USA","volume":"11","author":"Goyal","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"e5518","DOI":"10.7717\/peerj.5518","article-title":"Random Forest as a Generic Framework for Predictive Modeling of Spatial and Spatio-Temporal Variables","volume":"6","author":"Hengl","year":"2018","journal-title":"PeerJ"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1080\/10106049.2019.1595177","article-title":"Geographical random forests: A spatial extension of the random forest algorithm to address spatial heterogeneity in remote sensing and population modelling","volume":"36","author":"Georganos","year":"2019","journal-title":"Geocarto Int."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Santos, F., Graw, V., and Bonilla, S. (2019). A geographically weighted random forest approach for evaluate forest change drivers in the Northern Ecuadorian Amazon. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0226224"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Khan, S.N., Li, D., and Maimaitijiang, M. (2022). A geographically weighted random forest approach to predict corn yield in the US corn belt. Remote Sens., 14.","DOI":"10.3390\/rs14122843"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1016\/j.envint.2018.10.029","article-title":"Space-time trends of PM2.5 constituents in the conterminous United States estimated by a machine learning approach, 2005\u20132015","volume":"121","author":"Meng","year":"2018","journal-title":"Environ. Int."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"18881","DOI":"10.1007\/s11356-017-9500-9","article-title":"Evaluation of air pollution tolerance index and anticipated performance index of plants and their application in development of green space along the urban areas","volume":"24","author":"Kaur","year":"2017","journal-title":"Environ. Sci. Pollut. Res."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"380","DOI":"10.1016\/j.atmosenv.2015.03.046","article-title":"Characteristics and source apportionment of PM2.5 during a fall heavy haze episode in the Yangtze River Delta of China","volume":"123","author":"Hua","year":"2015","journal-title":"Atmos. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"201907956","DOI":"10.1073\/pnas.1907956116","article-title":"Drivers of improved PM2.5 air quality in China from 2013 to 2017","volume":"116","author":"Zhang","year":"2019","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"100989","DOI":"10.1016\/j.uclim.2021.100989","article-title":"A review of the inter-correlation of climate change, air pollution and urban sustainability using novel machine learning algorithms and spatial information science","volume":"40","author":"Balogun","year":"2021","journal-title":"Urban Clim."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Xue, T., Zheng, Y., Geng, G., Zheng, B., Jiang, X., Zhang, Q., and He, K. (2017). Fusing observational, satellite remote sensing and air quality model simulated data to estimate spatiotemporal variations of PM2.5 exposure in China. Remote Sens., 9.","DOI":"10.20944\/preprints201702.0059.v1"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"7879","DOI":"10.1021\/acs.est.0c01764","article-title":"Global estimates and long-term trends of fine particulate matter concentrations (1998\u20132018)","volume":"54","author":"Hammer","year":"2020","journal-title":"Environ. Sci. Technol."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"4695","DOI":"10.1021\/acs.est.8b04852","article-title":"Observing severe drought influences on ozone air pollution in California","volume":"53","author":"Demetillo","year":"2019","journal-title":"Environ. Sci. Technol."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1038\/s41558-021-01007-8","article-title":"No projected global drylands expansion under greenhouse warming","volume":"11","author":"Berg","year":"2021","journal-title":"Nat. Clim. Chang."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1038\/s41597-020-0453-3","article-title":"Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset","volume":"7","author":"Harris","year":"2020","journal-title":"Sci. Data"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"115155","DOI":"10.1016\/j.envres.2022.115155","article-title":"Demystifying normalized difference vegetation index (NDVI) for greenness exposure assessments and policy interventions in urban greening","volume":"220","author":"Martinez","year":"2023","journal-title":"Environ. Res."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1071\/WF21129","article-title":"The role of drought conditions on the recent increase in wildfire occurrence in the high Andean regions of Peru","volume":"32","author":"Zubieta","year":"2023","journal-title":"Int. J. Wildland Fire"},{"key":"ref_39","unstructured":"Du, X. (2006). Research on Vegetation Leaf Water Monitoring by Remote Sensing and Spatio-Temporai Character Analysis, Institute of Remote Sensing and Digital Earth Chinese Academy of Sciences. (In Chinese)."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1317","DOI":"10.1007\/s11069-021-04733-6","article-title":"Modeling wildfire drivers in Chinese tropical forest ecosystems using global logistic regression and geographically weighted logistic regression","volume":"108","author":"Su","year":"2021","journal-title":"Nat. Hazards"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1038\/s41893-021-00843-y","article-title":"A large but transient carbon sink from urbanization and rural depopulation in China","volume":"5","author":"Zhang","year":"2022","journal-title":"Nat. Sustain."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"100786","DOI":"10.1016\/j.ssmph.2021.100786","article-title":"Comparing denominator sources for real-time disease incidence modeling: American Community Survey and WorldPop","volume":"14","author":"Nethery","year":"2021","journal-title":"SSM Popul. Health"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"480","DOI":"10.1093\/jpe\/rtu041","article-title":"Historic distribution and driving factors of human-caused fires in the Chinese boreal forest between 1972 and 2005","volume":"8","author":"Guo","year":"2015","journal-title":"J. Plant. Ecol."},{"key":"ref_44","first-page":"6797","article-title":"Quantitative evaluation of human activity intensity on the regional ecological impact studies","volume":"38","author":"Liu","year":"2018","journal-title":"Acta Ecol. Sin."},{"key":"ref_45","first-page":"28","article-title":"Investigation of hemeroby degree of vegetation in urban transport areas: The case of izmit (Kocaeli)","volume":"1","author":"Beyhan","year":"2020","journal-title":"Front. Life Sci. Relat. Technol."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1007\/s10584-020-02654-0","article-title":"New estimates of greenhouse gas emissions from biomass burning and peat fires using MODIS Collection 6 burned areas","volume":"161","author":"Prosperi","year":"2020","journal-title":"Clim. Chang."},{"key":"ref_47","unstructured":"ESRI (2019). ArcGIS Desktop, Release 10.6.1., Environmental Systems Research Institute."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1465","DOI":"10.2147\/CCID.S373534","article-title":"Differential Diagnosis of Rosacea Using Machine Learning and Dermoscopy","volume":"15","author":"Ge","year":"2022","journal-title":"Clin. Cosmet. Inv. Derm."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"2783","DOI":"10.1890\/07-0539.1","article-title":"Random forests for classifcation in ecology","volume":"88","author":"Cutler","year":"2007","journal-title":"Ecology"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.patrec.2021.01.008","article-title":"Large group activity security risk assessment and risk early warning based on random forest algorithm","volume":"144","author":"Chen","year":"2021","journal-title":"Pattern. Recogn. Lett."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"4180","DOI":"10.1021\/acs.est.7b05669","article-title":"Satellite-based estimates of daily NO2 exposure in China using hybrid random forest and spatiotemporal Kriging model","volume":"52","author":"Zhan","year":"2018","journal-title":"Environ. Sci. Technol."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"858","DOI":"10.1089\/thy.2018.0380","article-title":"Machine Learning-Assisted System for Thyroid Nodule Diagnosis","volume":"29","author":"Zhang","year":"2019","journal-title":"Thyroid"},{"key":"ref_54","first-page":"102353","article-title":"Modeling tree canopy height using machine learning over mixed vegetation landscapes","volume":"101","author":"Wang","year":"2021","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.apgeog.2014.01.011","article-title":"Modeling the spatial variation of the explanatory factors of human-caused wildfires in Spain using geographically weighted logistic regression","volume":"48","author":"Rodrigues","year":"2014","journal-title":"Appl. Geogr."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Yan, J., Tao, F., Zhang, S., Lin, S., and Zhou, T. (2021). Spatiotemporal distribution characteristics and driving forces of PM2.5 in three urban agglomerations of the Yangtze River Economic Belt. Int. J. Environ. Res. Public Health, 18.","DOI":"10.3390\/ijerph18052222"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1000","DOI":"10.1016\/j.scitotenv.2016.11.025","article-title":"A review of biomass burning: Emissions and impacts on air quality, health and climate in China","volume":"579","author":"Chen","year":"2017","journal-title":"Sci. Total Environ."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1016\/j.ecolind.2016.12.045","article-title":"Air quality and its response to satellite-derived urban form in the Yangtze River Delta, China","volume":"75","author":"She","year":"2017","journal-title":"Ecol. Indic."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"652","DOI":"10.1016\/j.apr.2016.12.014","article-title":"Recursive neural network model for analysis and forecast of PM10 and PM2.5","volume":"8","author":"Biancofiore","year":"2017","journal-title":"Atmos. Pollut. Res."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1214\/aos\/1013203451","article-title":"Greedy function approximation: A gradient boosting machine","volume":"29","author":"Friedman","year":"2001","journal-title":"Ann. Stat."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"7863","DOI":"10.5194\/acp-21-7863-2021","article-title":"Himawari-8-derived diurnal variations in ground-level PM2.5 pollution across China using the fast space-time Light Gradient Boosting Machine (LightGBM)","volume":"21","author":"Wei","year":"2021","journal-title":"Atmos. Chem. Phys."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"12827","DOI":"10.5194\/acp-17-12827-2017","article-title":"Adverse effects of increasing drought on air quality via natural processes","volume":"17","author":"Wang","year":"2017","journal-title":"Atmos. Chem. Phys."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"138579","DOI":"10.1016\/j.scitotenv.2020.138579","article-title":"Temperature inversions in the atmospheric boundary layer and lower troposphere over the Sichuan Basin, China: Climatology and impacts on air pollution","volume":"726","author":"Feng","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1016\/j.rse.2015.05.016","article-title":"Estimating long-term PM2.5 concentrations in China using satellite-based aerosol optical depth and a chemical transport model","volume":"166","author":"Geng","year":"2015","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/15\/3826\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:23:34Z","timestamp":1760127814000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/15\/3826"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,31]]},"references-count":64,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2023,8]]}},"alternative-id":["rs15153826"],"URL":"https:\/\/doi.org\/10.3390\/rs15153826","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,31]]}}}