{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T21:08:32Z","timestamp":1772831312918,"version":"3.50.1"},"reference-count":63,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2022,7,25]],"date-time":"2022-07-25T00:00:00Z","timestamp":1658707200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42005102"],"award-info":[{"award-number":["42005102"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["19ZR1459700"],"award-info":[{"award-number":["19ZR1459700"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["222102110419"],"award-info":[{"award-number":["222102110419"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007219","name":"Shanghai Municipal Natural Science Foundation","doi-asserted-by":"publisher","award":["42005102"],"award-info":[{"award-number":["42005102"]}],"id":[{"id":"10.13039\/100007219","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007219","name":"Shanghai Municipal Natural Science Foundation","doi-asserted-by":"publisher","award":["19ZR1459700"],"award-info":[{"award-number":["19ZR1459700"]}],"id":[{"id":"10.13039\/100007219","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007219","name":"Shanghai Municipal Natural Science Foundation","doi-asserted-by":"publisher","award":["222102110419"],"award-info":[{"award-number":["222102110419"]}],"id":[{"id":"10.13039\/100007219","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Science and Technology Development Project of Henan Province China","award":["42005102"],"award-info":[{"award-number":["42005102"]}]},{"name":"Science and Technology Development Project of Henan Province China","award":["19ZR1459700"],"award-info":[{"award-number":["19ZR1459700"]}]},{"name":"Science and Technology Development Project of Henan Province China","award":["222102110419"],"award-info":[{"award-number":["222102110419"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Owing to a series of air pollution prevention and control policies, China\u2019s PM2.5 pollution has greatly improved; however, the long-term spatial contiguous products that facilitate the analysis of the distribution and variation of PM2.5 pollution are insufficient. Due to the limitations of missing values in aerosol optical depth (AOD) products, the reconstruction of full-coverage PM2.5 concentration remains challenging. In this study, we present a two-stage daily adaptive modeling framework, based on machine learning, to solve this problem. We built the annual models in the first stage, then daily models were constructed in the second stage based on the output of the annual models, which incorporated the parameter and feature adaptive tuning strategy. Within this study, PM2.5 concentrations were adaptively modeled and reconstructed daily based on the multi-angle implementation of atmospheric correction (MAIAC) AOD products and other ancillary data, such as meteorological factors, population, and elevation. Our model validation showed excellent performance with an overall R2 = 0.91 and RMSE = 9.91 \u03bcg\/m3 for the daily models, along with the site-based cross-validation R2s and RMSEs of 0.86\u20130.87 and 12\u201312.33 \u03bcg\/m3; these results indicated the reliability and feasibility of the proposed approach. The daily full-coverage PM2.5 concentrations at 1 km resolution across China during the Three-Year Blue-Sky Action Plan were reconstructed in this study. We analyzed the distribution and variations of reconstructed PM2.5 at three different time scales. Overall, national PM2.5 pollution has significantly improved with the annual average concentration dropping from 33.67\u201328.03 \u03bcg\/m3, which demonstrated that air pollution control policies are effective and beneficial. However, some areas still have severe PM2.5 pollution problems that cannot be ignored. In conclusion, the approach proposed in this study can accurately present daily full-coverage PM2.5 concentrations and the research outcomes could provide a reference for subsequent air pollution prevention and control decision-making.<\/jats:p>","DOI":"10.3390\/rs14153571","type":"journal-article","created":{"date-parts":[[2022,7,26]],"date-time":"2022-07-26T00:17:27Z","timestamp":1658794647000},"page":"3571","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Full-Coverage PM2.5 Mapping and Variation Assessment during the Three-Year Blue-Sky Action Plan Based on a Daily Adaptive Modeling Approach"],"prefix":"10.3390","volume":"14","author":[{"given":"Weihuan","family":"He","sequence":"first","affiliation":[{"name":"College of Surveying and Geo-Informatics, Tongji University, Shanghai 200092, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Songlin","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Surveying and Geo-Informatics, Tongji University, Shanghai 200092, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huan","family":"Meng","sequence":"additional","affiliation":[{"name":"Key Laboratory of Geospatial Technology for Middle and Lower Yellow River Regions, Ministry of Education, College of Environment and Planning, Henan University, Kaifeng 475004, China"},{"name":"Henan Key Laboratory of Earth System Observation and Modeling, Henan University, Kaifeng 475004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Han","sequence":"additional","affiliation":[{"name":"College of Surveying and Geo-Informatics, Tongji University, Shanghai 200092, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gaohui","family":"Zhou","sequence":"additional","affiliation":[{"name":"College of Surveying and Geo-Informatics, Tongji University, Shanghai 200092, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongquan","family":"Song","sequence":"additional","affiliation":[{"name":"Key Laboratory of Geospatial Technology for Middle and Lower Yellow River Regions, Ministry of Education, College of Environment and Planning, Henan University, Kaifeng 475004, China"},{"name":"Henan Key Laboratory of Earth System Observation and Modeling, Henan University, Kaifeng 475004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7895-9348","authenticated-orcid":false,"given":"Shenghui","family":"Zhou","sequence":"additional","affiliation":[{"name":"Key Laboratory of Geospatial Technology for Middle and Lower Yellow River Regions, Ministry of Education, College of Environment and Planning, Henan University, Kaifeng 475004, China"},{"name":"Henan Key Laboratory of Integrated Air Pollution Control and Ecological Security, Kaifeng 475004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hui","family":"Zheng","sequence":"additional","affiliation":[{"name":"Key Laboratory of Geospatial Technology for Middle and Lower Yellow River Regions, Ministry of Education, College of Environment and Planning, Henan University, Kaifeng 475004, China"},{"name":"Henan Key Laboratory of Earth System Observation and Modeling, Henan University, Kaifeng 475004, China"},{"name":"Henan Key Laboratory of Integrated Air Pollution Control and Ecological Security, Kaifeng 475004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"129312","DOI":"10.1016\/j.jclepro.2021.129312","article-title":"Impact of transboundary PM2.5 pollution on health risks and economic compensation in China","volume":"326","author":"Diao","year":"2021","journal-title":"J. Clean Prod."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"491","DOI":"10.1016\/j.apr.2019.11.021","article-title":"Spatiotemporal variation of PM2.5 concentrations and its relationship to urbanization in the Yangtze river delta region, China","volume":"11","author":"Yang","year":"2020","journal-title":"Atmos. Pollut. Res."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"9592","DOI":"10.1073\/pnas.1803222115","article-title":"Global estimates of mortality associated with long-term exposure to outdoor fine particulate matter","volume":"115","author":"Burnett","year":"2018","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.envpol.2013.05.057","article-title":"A five-year study of particulate matter (PM2.5) and cerebrovascular diseases","volume":"181","author":"Leiva","year":"2013","journal-title":"Environ. Pollut."},{"key":"ref_5","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_6","doi-asserted-by":"crossref","first-page":"112063","DOI":"10.1016\/j.ecoenv.2021.112063","article-title":"Long-term exposure to ambient PM2.5 and stroke mortality among urban residents in northern China","volume":"213","author":"Yang","year":"2021","journal-title":"Ecotox. Environ. Safe."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"40711","DOI":"10.1007\/s11356-021-18196-6","article-title":"Rural-urban differences in associations between air pollution and cardiovascular hospital admissions in Guangxi, southwest China","volume":"29","author":"Zhang","year":"2022","journal-title":"Environ. Sci. Pollut. Res."},{"key":"ref_8","unstructured":"(2021, June 01). Chinese State Council Action Plan on Air Pollution Prevention and Control (In Chinese), Available online: http:\/\/www.gov.cn\/zwgk\/2013-09\/12\/content_2486773.htm."},{"key":"ref_9","unstructured":"(2021, June 01). Chinese State Council Three-Year Action Plan on Defending the Blue Sky (In Chinese), Available online: http:\/\/www.gov.cn\/zhengce\/content\/2018-07\/03\/content_5303158.htm."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"443","DOI":"10.1016\/j.atmosenv.2018.11.049","article-title":"Using gap-filled MAIAC AOD and WRF-Chem to estimate daily PM2.5 concentrations at 1 km resolution in the Eastern United States","volume":"199","author":"Goldberg","year":"2019","journal-title":"Atmos. Environ."},{"key":"ref_11","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."},{"key":"ref_12","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":"Brauer","year":"2010","journal-title":"Environ. Health Perspect."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.rse.2016.08.027","article-title":"Satellite-based ground PM2.5 estimation using timely structure adaptive modeling","volume":"186","author":"Fang","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.atmosenv.2016.03.040","article-title":"Satellite-derived high resolution PM2.5 concentrations in Yangtze River Delta Region of China using improved linear mixed effects model","volume":"133","author":"Ma","year":"2016","journal-title":"Atmos. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1289\/ehp.1409481","article-title":"Satellite-Based Spatiotemporal Trends in PM2.5 Concentrations: China, 2004\u20132013","volume":"124","author":"Ma","year":"2016","journal-title":"Environ. Health Perspect."},{"key":"ref_16","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_17","doi-asserted-by":"crossref","first-page":"4173","DOI":"10.1021\/acs.est.7b05381","article-title":"Predicting Daily Urban Fine Particulate Matter Concentrations Using a Random Forest Model","volume":"52","author":"Brokamp","year":"2018","journal-title":"Environ. Sci. Technol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1016\/j.envint.2019.01.016","article-title":"Estimation of daily PM10 and PM2.5 concentrations in Italy, 2013\u20132015, using a spatiotemporal land-use random-forest model","volume":"124","author":"Stafoggia","year":"2019","journal-title":"Environ. Int."},{"key":"ref_19","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_20","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.atmosenv.2019.01.027","article-title":"Extreme gradient boosting model to estimate PM2.5 concentrations with missing-filled satellite data in China","volume":"202","author":"Chen","year":"2019","journal-title":"Atmos. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"105801","DOI":"10.1016\/j.envint.2020.105801","article-title":"Construction of a virtual PM2.5 observation network in China based on high-density surface meteorological observations using the Extreme Gradient Boosting model","volume":"141","author":"Gui","year":"2020","journal-title":"Environ. Int."},{"key":"ref_22","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_23","doi-asserted-by":"crossref","first-page":"112136","DOI":"10.1016\/j.rse.2020.112136","article-title":"Reconstructing 1-km-resolution high-quality PM2.5 data records from 2000 to 2018 in China: Spatiotemporal variations and policy implications","volume":"252","author":"Wei","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_24","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_25","doi-asserted-by":"crossref","first-page":"105536","DOI":"10.1016\/j.envint.2020.105536","article-title":"Spatiotemporal trends of PM2.5 concentrations in central China from 2003 to 2018 based on MAIAC-derived high-resolution data","volume":"137","author":"He","year":"2020","journal-title":"Environ. Int."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1016\/j.envint.2018.11.075","article-title":"Spatiotemporal continuous estimates of PM2.5 concentrations in China, 2000\u20132016: A machine learning method with inputs from satellites, chemical transport model, and ground observations","volume":"123","author":"Xue","year":"2019","journal-title":"Environ. Int."},{"key":"ref_27","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_28","doi-asserted-by":"crossref","first-page":"479","DOI":"10.1016\/j.scitotenv.2019.03.480","article-title":"Satellite-based high-resolution mapping of ground-level PM2.5 concentrations over East China using a spatiotemporal regression kriging model","volume":"672","author":"Hu","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"105146","DOI":"10.1016\/j.atmosres.2020.105146","article-title":"Estimation of hourly full-coverage PM2.5 concentrations at 1-km resolution in China using a two-stage random forest model","volume":"248","author":"Jiang","year":"2021","journal-title":"Atmos. Res."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"437","DOI":"10.1016\/j.rse.2017.07.023","article-title":"Full-coverage high-resolution daily PM2.5 estimation using MAIAC AOD in the Yangtze River Delta of China","volume":"199","author":"Xiao","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"998","DOI":"10.1016\/j.envpol.2018.09.052","article-title":"A nonparametric approach to filling gaps in satellite-retrieved aerosol optical depth for estimating ambient PM2.5 levels","volume":"243","author":"Zhang","year":"2018","journal-title":"Environ. Pollut."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"148535","DOI":"10.1016\/j.scitotenv.2021.148535","article-title":"Full-coverage spatiotemporal mapping of ambient PM2.5 and PM10 over China from Sentinel-5P and assimilated datasets: Considering the precursors and chemical compositions","volume":"793","author":"Wang","year":"2021","journal-title":"Sci. Total Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.ecoenv.2019.02.070","article-title":"Estimate annual and seasonal PM1, PM2.5 and PM10 concentrations using land use regression model","volume":"174","author":"Miri","year":"2019","journal-title":"Ecotox. Environ. Saf."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2152","DOI":"10.1021\/acs.est.0c05815","article-title":"High-Resolution Spatiotemporal Modeling for Ambient PM2.5 Exposure Assessment in China from 2013 to 2019","volume":"55","author":"Huang","year":"2021","journal-title":"Environ. Sci. Technol."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1016\/j.atmosenv.2015.05.009","article-title":"A method to estimate missing AERONET AOD values based on artificial neural networks","volume":"113","author":"Olcese","year":"2015","journal-title":"Atmos. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.isprsjprs.2019.08.017","article-title":"Large-scale MODIS AOD products recovery: Spatial-temporal hybrid fusion considering aerosol variation mitigation","volume":"157","author":"Wang","year":"2019","journal-title":"ISPRS-J. Photogramm. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.scitotenv.2018.04.251","article-title":"A machine learning method to estimate PM2.5 concentrations across China with remote sensing, meteorological and land use information","volume":"636","author":"Chen","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"675","DOI":"10.1016\/j.envpol.2018.07.016","article-title":"Predicting monthly high-resolution PM2.5 concentrations with random forest model in the North China Plain","volume":"242","author":"Huang","year":"2018","journal-title":"Environ. Pollut."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"118930","DOI":"10.1016\/j.atmosenv.2021.118930","article-title":"Deriving hourly full-coverage PM2.5 concentrations across China\u2019s Sichuan Basin by fusing multisource satellite retrievals: A machine-learning approach","volume":"271","author":"Liu","year":"2022","journal-title":"Atmos. Environ."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"5876","DOI":"10.1016\/j.atmosenv.2009.08.026","article-title":"Correlation between PM concentrations and aerosol optical depth in eastern China","volume":"43","author":"Guo","year":"2009","journal-title":"Atmos. Environ."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"6301","DOI":"10.5194\/acp-14-6301-2014","article-title":"10-year spatial and temporal trends of PM(2.5) concentrations in the southeastern US estimated using high-resolution satellite data","volume":"14","author":"Hu","year":"2014","journal-title":"Atmos. Chem. Phys."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1016\/j.atmosres.2013.11.001","article-title":"The empirical relationship between the PM2.5 concentration and aerosol optical depth over the background of North China from 2009 to 2011","volume":"138","author":"Xin","year":"2014","journal-title":"Atmos. Res."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.rse.2019.01.033","article-title":"Comparison and evaluation of MODIS Multi-angle Implementation of Atmospheric Correction (MAIAC) aerosol product over South Asia","volume":"224","author":"Mhawish","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Knowledge Discovery and Data Mining, Association for Computing Machinery.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_45","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_46","doi-asserted-by":"crossref","first-page":"13260","DOI":"10.1021\/acs.est.8b02917","article-title":"An Ensemble Machine-Learning Model to Predict Historical PM2.5 Concentrations in China from Satellite Data","volume":"52","author":"Xiao","year":"2018","journal-title":"Environ. Sci. Technol."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1417","DOI":"10.1016\/j.envpol.2018.08.029","article-title":"Evaluation of machine learning techniques with multiple remote sensing datasets in estimating monthly concentrations of ground-level PM2.5","volume":"242","author":"Xu","year":"2018","journal-title":"Environ. Pollut."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"12106","DOI":"10.1021\/acs.est.1c01863","article-title":"Tracking Air Pollution in China: Near Real-Time PM2.5 Retrievals from Multisource Data Fusion","volume":"55","author":"Geng","year":"2021","journal-title":"Environ. Sci. Technol."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"106726","DOI":"10.1016\/j.envint.2021.106726","article-title":"Satellite-derived 1-km estimates and long-term trends of PM2.5 concentrations in China from 2000 to 2018","volume":"156","author":"He","year":"2021","journal-title":"Environ. Int."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1214","DOI":"10.1080\/10962247.2012.701193","article-title":"Winter and Summer PM2.5 Chemical Compositions in Fourteen Chinese Cities","volume":"62","author":"Cao","year":"2012","journal-title":"J. Air Waste Manag. Assoc."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1007\/s11430-013-4773-4","article-title":"Mechanism for the formation of the January 2013 heavy haze pollution episode over central and eastern China","volume":"57","author":"Wang","year":"2014","journal-title":"Sci. China-Earth Sci."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Chen, W., Meng, H., Song, H., and Zheng, H. (2022). Progress in Dust Modelling, Global Dust Budgets, and Soil Organic Carbon Dynamics. Land, 11.","DOI":"10.3390\/land11020176"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"477","DOI":"10.1016\/j.atmosenv.2017.01.004","article-title":"Point-surface fusion of station measurements and satellite observations for mapping PM2.5 distribution in China: Methods and assessment","volume":"152","author":"Li","year":"2017","journal-title":"Atmos. Environ."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"150792","DOI":"10.1016\/j.scitotenv.2021.150792","article-title":"Responses of surface O3 and PM2.5 trends to changes of anthropogenic emissions in summer over Beijing during 2014\u20132019: A study based on multiple linear regression and WRF-Chem","volume":"807","author":"He","year":"2022","journal-title":"Sci. Total Environ."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1175\/BAMS-D-16-0301.1","article-title":"PM2.5 Pollution in China and How It Has Been Exacerbated by Terrain and Meteorological Conditions","volume":"99","author":"Wang","year":"2018","journal-title":"Bull. Amer. Meteorol. Soc."},{"key":"ref_56","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_57","doi-asserted-by":"crossref","first-page":"3273","DOI":"10.5194\/acp-20-3273-2020","article-title":"Improved 1 km resolution PM2.5 estimates across China using enhanced space time extremely randomized trees","volume":"20","author":"Wei","year":"2020","journal-title":"Atmos. Chem. Phys."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/j.isprsjprs.2021.12.002","article-title":"Multiscale and multisource data fusion for full-coverage PM2.5 concentration mapping: Can spatial pattern recognition come with modeling accuracy?","volume":"184","author":"Bai","year":"2022","journal-title":"ISPRS-J. Photogramm. Remote Sens."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"117357","DOI":"10.1016\/j.atmosenv.2020.117357","article-title":"Evaluation of Himawari-8 version 2.0 aerosol products against AERONET ground-based measurements over central and northern China","volume":"224","author":"Wang","year":"2020","journal-title":"Atmos. Environ."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"122926","DOI":"10.1016\/j.jclepro.2020.122926","article-title":"Hysteretic effects of meteorological conditions and their interactions on particulate matter in Chinese cities","volume":"274","author":"Wang","year":"2020","journal-title":"J. Clean Prod."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.envpol.2018.11.103","article-title":"Particulate matter pollution in Chinese cities: Areal-temporal variations and their relationships with meteorological conditions (2015\u20132017)","volume":"246","author":"Li","year":"2019","journal-title":"Environ. Pollut."},{"key":"ref_62","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_63","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1016\/j.isprsjprs.2020.05.018","article-title":"Mapping PM2.5 concentration at a sub-km level resolution: A dual-scale retrieval approach","volume":"165","author":"Yang","year":"2020","journal-title":"ISPRS-J. Photogramm. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/15\/3571\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:56:12Z","timestamp":1760140572000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/15\/3571"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,25]]},"references-count":63,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2022,8]]}},"alternative-id":["rs14153571"],"URL":"https:\/\/doi.org\/10.3390\/rs14153571","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,25]]}}}