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First, the cloudless TOAR data were matched and modeled with the solar radiation products from the ERA5 dataset to construct and estimate a fully covered TOAR dataset under assumed clear-sky conditions, which increased coverage from 20\u201330% to 100%. Subsequently, this dataset was applied to estimate particulate matter. The analysis demonstrated that the fully covered TOAR dataset (R2 = 0.83) performed better than the original cloudless dataset (R2 = 0.76). Additionally, using feature importance scores and SHAP values, the impact of meteorological factors and air mass trajectories on the increase in PM10 and PM2.5 during dust events were investigated. The analysis of haze events indicated that the main meteorological factors driving changes in particulate matter included air pressure, temperature, and boundary layer height. The particulate matter concentration products obtained using fully covered TOAR data exhibit high coverage and high spatiotemporal resolution. Combined with data-driven interpretable machine learning, they can effectively reveal the influencing factors of particulate matter in China.<\/jats:p>","DOI":"10.3390\/rs16183363","type":"journal-article","created":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T02:34:49Z","timestamp":1726022089000},"page":"3363","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["The Reconstruction of FY-4A and FY-4B Cloudless Top-of-Atmosphere Radiation and Full-Coverage Particulate Matter Products Reveals the Influence of Meteorological Factors in Pollution Events"],"prefix":"10.3390","volume":"16","author":[{"given":"Zhihao","family":"Song","sequence":"first","affiliation":[{"name":"Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China"},{"name":"Collaborative Innovation Center for Western Ecological Safety, Lanzhou University, Lanzhou 730000, China"}]},{"given":"Lin","family":"Zhao","sequence":"additional","affiliation":[{"name":"Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China"},{"name":"Collaborative Innovation Center for Western Ecological Safety, Lanzhou University, Lanzhou 730000, China"}]},{"given":"Qia","family":"Ye","sequence":"additional","affiliation":[{"name":"Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China"},{"name":"Collaborative Innovation Center for Western Ecological Safety, Lanzhou University, Lanzhou 730000, China"}]},{"given":"Yuxiang","family":"Ren","sequence":"additional","affiliation":[{"name":"Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China"},{"name":"Collaborative Innovation Center for Western Ecological Safety, Lanzhou University, Lanzhou 730000, China"}]},{"given":"Ruming","family":"Chen","sequence":"additional","affiliation":[{"name":"Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China"},{"name":"Collaborative Innovation Center for Western Ecological Safety, Lanzhou University, Lanzhou 730000, China"}]},{"given":"Bin","family":"Chen","sequence":"additional","affiliation":[{"name":"Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China"},{"name":"Collaborative Innovation Center for Western Ecological Safety, Lanzhou University, Lanzhou 730000, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,10]]},"reference":[{"key":"ref_1","unstructured":"WHO (2021). Particulate matter (PM2.5 and PM10), ozone, nitrogen dioxide, sulfur dioxide and carbon monoxide. WHO Global Air Quality Guidelines, World Health Organization."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"24463","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_3","doi-asserted-by":"crossref","first-page":"14489","DOI":"10.5194\/acp-22-14489-2022","article-title":"Dust pollution in China affected by different spatial and temporal types of El Ni\u00f1o","volume":"22","author":"Yang","year":"2022","journal-title":"Atmos. Chem. Phys."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"13050","DOI":"10.1002\/2016JD025136","article-title":"Increase in winter haze over eastern China in recent decades: Roles of variations in meteorological parameters and anthropogenic emissions","volume":"121","author":"Yang","year":"2016","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"708","DOI":"10.1289\/ehp.1104049","article-title":"Risk of Nonaccidental and Cardiovascular Mortality in Relation to Long-term Exposure to Low Concentrations of Fine Particulate Matter: A Canadian National-Level Cohort Study","volume":"120","author":"Crouse","year":"2012","journal-title":"Environ. Health Perspect."},{"key":"ref_6","first-page":"E69","article-title":"The impact of PM2.5 on the human respiratory system","volume":"8","author":"Xing","year":"2016","journal-title":"J. Thorac. Dis."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"705","DOI":"10.1038\/nature21712","article-title":"Transboundary health impacts of transported global air pollution and international trade","volume":"543","author":"Zhang","year":"2017","journal-title":"Nature"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1132","DOI":"10.1001\/jama.287.9.1132","article-title":"Lung Cancer, Cardiopulmonary Mortality, and Long-term Exposure to Fine Particulate Air Pollution","volume":"287","author":"Burnett","year":"2002","journal-title":"JAMA"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1016\/j.fmre.2021.04.001","article-title":"Unprecedented snow darkening and melting in New Zealand due to 2019\u20132020 Australian wildfires","volume":"1","author":"Pu","year":"2021","journal-title":"Fundam. Res."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"e2021GL095011","DOI":"10.1029\/2021GL095011","article-title":"Atmospheric Circulation Patterns Conducive to Severe Haze in Eastern China Have Shifted Under Climate Change","volume":"48","author":"Yang","year":"2021","journal-title":"Geophys. Res. Lett."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"e2020GL089788","DOI":"10.1029\/2020GL089788","article-title":"Fast Climate Responses to Aerosol Emission Reductions During the COVID-19 Pandemic","volume":"47","author":"Yang","year":"2020","journal-title":"Geophys. Res. Lett."},{"key":"ref_12","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_13","doi-asserted-by":"crossref","first-page":"170493","DOI":"10.1016\/j.scitotenv.2024.170493","article-title":"Impact of COVID-19 emission reduction on dust aerosols and marine chlorophyll-a concentration","volume":"918","author":"Li","year":"2024","journal-title":"Sci. Total Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.envpol.2016.11.043","article-title":"Impact of diurnal variability and meteorological factors on the PM2.5\u2014AOD relationship: Implications for PM2.5 remote sensing","volume":"221","author":"Guo","year":"2017","journal-title":"Environ. Pollut."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"113224","DOI":"10.1016\/j.rse.2022.113224","article-title":"Lidar-based daytime boundary layer height variation and impact on the regional satellite-based PM2.5 estimate","volume":"281","author":"Chen","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"250","DOI":"10.1016\/j.atmosres.2016.06.018","article-title":"Can MODIS AOD be employed to derive PM2.5 in Beijing-Tianjin-Hebei over China?","volume":"181","author":"Ma","year":"2016","journal-title":"Atmos. Res."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"819","DOI":"10.1016\/j.scitotenv.2017.08.209","article-title":"A multidimensional comparison between MODIS and VIIRS AOD in estimating ground-level PM2.5 concentrations over a heavily polluted region in China","volume":"618","author":"Yao","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1156","DOI":"10.1016\/j.scitotenv.2014.11.024","article-title":"Estimating PM2.5 in Xi\u2019an, China using aerosol optical depth: A comparison between the MODIS and MISR retrieval models","volume":"505","author":"You","year":"2015","journal-title":"Sci. Total Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"105877","DOI":"10.1016\/j.atmosres.2021.105877","article-title":"Impact of CALIPSO profile data assimilation on 3-D aerosol improvement in a size-resolved aerosol model","volume":"264","author":"Ye","year":"2021","journal-title":"Atmos. Res."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1016\/j.fmre.2021.04.007","article-title":"Satellite remote sensing of atmospheric particulate matter mass concentration: Advances, challenges, and perspectives","volume":"1","author":"Zhang","year":"2021","journal-title":"Fundam. Res."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"150338","DOI":"10.1016\/j.scitotenv.2021.150338","article-title":"Obtaining vertical distribution of PM2.5 from CALIOP data and machine learning algorithms","volume":"805","author":"Chen","year":"2022","journal-title":"Sci. Total Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"112890","DOI":"10.1016\/j.rse.2022.112890","article-title":"Application of geostationary satellite and high-resolution meteorology data in estimating hourly PM2.5 levels during the Camp Fire episode in California","volume":"271","author":"Vu","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"288","DOI":"10.1016\/j.atmosenv.2018.02.011","article-title":"Assimilating AOD retrievals from GOCI and VIIRS to forecast surface PM2.5 episodes over Eastern China","volume":"179","author":"Pang","year":"2018","journal-title":"Atmos. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"118826","DOI":"10.1016\/j.envpol.2022.118826","article-title":"Combining Himawari-8 AOD and deep forest model to obtain city-level distribution of PM2.5 in China","volume":"297","author":"Song","year":"2022","journal-title":"Environ. Pollut."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"118827","DOI":"10.1016\/j.atmosenv.2021.118827","article-title":"An interpretable deep forest model for estimating hourly PM10 concentration in China using Himawari-8 data","volume":"268","author":"Chen","year":"2022","journal-title":"Atmos. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"120603","DOI":"10.1016\/j.atmosenv.2024.120603","article-title":"Ozone, nitrogen dioxide, and PM2.5 estimation from observation-model machine learning fusion over S. Korea: Influence of observation density, chemical transport model resolution, and geostationary remotely sensed AOD","volume":"331","author":"Tang","year":"2024","journal-title":"Atmos. Environ."},{"key":"ref_27","first-page":"100282","article-title":"Evaluation of WRF-Chem PM2.5 simulations in Thailand with different anthropogenic and biomass-burning emissions","volume":"23","author":"Thongsame","year":"2024","journal-title":"Atmos. Environ. X"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Yang, C., Guan, L., and Sun, X. (2023). Comparison of FY-4A\/AGRI SST with Himawari-8\/AHI and In Situ SST. Remote Sens., 15.","DOI":"10.3390\/rs15174139"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"5516","DOI":"10.1029\/2018JD028599","article-title":"Evaluating Aerosol Optical Depth From Himawari-8 With Sun Photometer Network","volume":"124","author":"Wang","year":"2019","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.apr.2021.02.007","article-title":"Retrieving PM2.5 with high spatio-temporal coverage by TOA reflectance of Himawari-8","volume":"12","author":"Yin","year":"2021","journal-title":"Atmos. Pollut. Res."},{"key":"ref_31","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_32","doi-asserted-by":"crossref","first-page":"116327","DOI":"10.1016\/j.envpol.2020.116327","article-title":"Estimate hourly PM2.5 concentrations from Himawari-8 TOA reflectance directly using geo-intelligent long short-term memory network","volume":"271","author":"Wang","year":"2021","journal-title":"Environ. Pollut."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"e2021JD036393","DOI":"10.1029\/2021JD036393","article-title":"Estimation of Atmospheric PM10 Concentration in China Using an Interpretable Deep Learning Model and Top-of-the-Atmosphere Reflectance Data From China\u2019s New Generation Geostationary Meteorological Satellite, FY-4A","volume":"127","author":"Chen","year":"2022","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"106199","DOI":"10.1016\/j.atmosres.2022.106199","article-title":"High temporal and spatial resolution PM2.5 dataset acquisition and pollution assessment based on FY-4A TOAR data and deep forest model in China","volume":"274","author":"Song","year":"2022","journal-title":"Atmos. Res."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"118898","DOI":"10.1016\/j.atmosenv.2021.118898","article-title":"Performance comparison of Fengyun-4A and Himawari-8 in PM2.5 estimation in China","volume":"271","author":"Hu","year":"2022","journal-title":"Atmos. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Wang, S., Lu, F., and Feng, Y. (2022). An Investigation of the Fengyun-4A\/B GIIRS Performance on Temperature and Humidity Retrievals. Atmosphere, 13.","DOI":"10.3390\/atmos13111830"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"120601","DOI":"10.1016\/j.atmosenv.2024.120601","article-title":"On the added value of satellite AOD for the investigation of ground-level PM2.5 variability","volume":"331","author":"Handschuh","year":"2024","journal-title":"Atmos. Environ."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"3333","DOI":"10.1016\/j.asr.2022.02.032","article-title":"A machine learning-based framework for high resolution mapping of PM2.5 in Tehran, Iran, using MAIAC AOD data","volume":"69","author":"Bagheri","year":"2022","journal-title":"Adv. Space Res."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"120419","DOI":"10.1016\/j.envpol.2022.120419","article-title":"A gap-filling hybrid approach for hourly PM2.5 prediction at high spatial resolution from multi-sourced AOD data","volume":"315","author":"Pu","year":"2022","journal-title":"Environ. Pollut."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1637","DOI":"10.1175\/BAMS-D-16-0065.1","article-title":"Introducing the New Generation of Chinese Geostationary Weather Satellites, Fengyun-4","volume":"98","author":"Yang","year":"2017","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Li, Z., and Li, J. (August, January 28). A Preliminary Layer Perceptible Water Vapor Retrieval Algorithm for Fengyun-4 Advanced Geosynchronous Radiation Imager. Proceedings of the IGARSS 2019\u20142019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan.","DOI":"10.1109\/IGARSS.2019.8900275"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"171541","DOI":"10.1016\/j.scitotenv.2024.171541","article-title":"Improved hourly estimate of aerosol optical thickness over Asian land by fusing geostationary satellites Fengyun-4B and Himawari-9","volume":"923","author":"Cheng","year":"2024","journal-title":"Sci. Total Environ."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"107305","DOI":"10.1016\/j.atmosres.2024.107305","article-title":"Performance of FY-4B GIIRS temperature products under cloudy skies and their enhancement of surface precipitation type forecasting","volume":"302","author":"Gao","year":"2024","journal-title":"Atmos. Res."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"3442","DOI":"10.1109\/TGRS.2018.2800060","article-title":"Improved Hourly Estimates of Aerosol Optical Thickness Using Spatiotemporal Variability Derived From Himawari-8 Geostationary Satellite","volume":"56","author":"Kikuchi","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"708","DOI":"10.1007\/s13351-017-6161-z","article-title":"Developing the science product algorithm testbed for Chinese next-generation geostationary meteorological satellites: Fengyun-4 series","volume":"31","author":"Min","year":"2017","journal-title":"J. Meteorol. Res."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1999","DOI":"10.1002\/qj.3803","article-title":"The ERA5 global reanalysis","volume":"146","author":"Hersbach","year":"2020","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"115775","DOI":"10.1016\/j.envpol.2020.115775","article-title":"Relationship between summertime concurring PM2.5 and O3 pollution and boundary layer height differs between Beijing and Shanghai, China","volume":"268","author":"Miao","year":"2021","journal-title":"Environ. Pollut."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"115358","DOI":"10.1016\/j.ecoenv.2023.115358","article-title":"Urban climate and cardiovascular health: Focused on seasonal variation of urban temperature, relative humidity, and PM2.5 air pollution","volume":"263","author":"Tsao","year":"2023","journal-title":"Ecotoxicol. Environ. Saf."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"123829","DOI":"10.1016\/j.envpol.2024.123829","article-title":"Existence of typical winter atmospheric circulation patterns leading to high PM2.5 concentration days in East Asia","volume":"348","author":"Jeong","year":"2024","journal-title":"Environ. Pollut."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/s10994-006-6226-1","article-title":"Extremely randomized trees","volume":"63","author":"Geurts","year":"2006","journal-title":"Mach. Learn."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"150018","DOI":"10.1016\/j.scitotenv.2021.150018","article-title":"Droughts across China: Drought factors, prediction and impacts","volume":"803","author":"Zhang","year":"2022","journal-title":"Sci. Total Environ."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"810","DOI":"10.1016\/j.jclepro.2018.08.207","article-title":"Predictive modelling for solar thermal energy systems: A comparison of support vector regression, random forest, extra trees and regression trees","volume":"203","author":"Ahmad","year":"2018","journal-title":"J. Clean. Prod."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"527","DOI":"10.1016\/j.fmre.2024.02.006","article-title":"High-resolution short-term prediction of the COVID-19 epidemic based on spatial-temporal model modified by historical meteorological data","volume":"4","author":"Chen","year":"2024","journal-title":"Fundam. Res."},{"key":"ref_54","first-page":"295","article-title":"An overview of the HYSPLIT_4 modelling system for trajectories","volume":"47","author":"Draxler","year":"1998","journal-title":"Aust. Meteorol. Mag."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"100899","DOI":"10.1016\/j.aeolia.2024.100899","article-title":"Combined use of HYSPLIT model and MODIS aerosols optical depth to study the spatiotemporal circulation patterns of Saharan dust events over Central Europe","volume":"67\u201369","author":"Gammoudi","year":"2024","journal-title":"Aeolian Res."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"527","DOI":"10.1016\/j.scitotenv.2014.11.072","article-title":"A comparison of HYSPLIT backward trajectories generated from two GDAS datasets","volume":"506\u2013507","author":"Su","year":"2015","journal-title":"Sci. Total Environ."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"101197","DOI":"10.1016\/j.dynatmoce.2020.101197","article-title":"Determining the source of dust storms with use of coupling WRF and HYSPLIT models: A case study of Yazd province in central desert of Iran","volume":"93","author":"Iraji","year":"2021","journal-title":"Dyn. Atmos. Ocean."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"17707","DOI":"10.1021\/acs.est.2c06800","article-title":"Attribution of Air Quality Benefits to Clean Winter Heating Policies in China: Combining Machine Learning with Causal Inference","volume":"57","author":"Song","year":"2023","journal-title":"Environ. Sci. Technol."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"578","DOI":"10.1016\/j.scitotenv.2018.10.344","article-title":"Using meteorological normalisation to detect interventions in air quality time series","volume":"653","author":"Grange","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"6223","DOI":"10.5194\/acp-18-6223-2018","article-title":"Random forest meteorological normalisation models for Swiss PM10 trend analysis","volume":"18","author":"Grange","year":"2018","journal-title":"Atmos. Chem. Phys."},{"key":"ref_61","unstructured":"Lundberg, S.M., and Lee, S.-I. A Unified Approach to Interpreting Model Predictions. Proceedings of the Neural Information Processing Systems."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"102133","DOI":"10.1016\/j.compenvurbsys.2024.102133","article-title":"How can SHAP (SHapley Additive exPlanations) interpretations improve deep learning based urban cellular automata model?","volume":"111","author":"Yang","year":"2024","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"100178","DOI":"10.1016\/j.acags.2024.100178","article-title":"Machine Learning model interpretability using SHAP values: Application to Igneous Rock Classification task","volume":"23","author":"Antonini","year":"2024","journal-title":"Appl. Comput. Geosci."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"104789","DOI":"10.1016\/j.jwpe.2024.104789","article-title":"Evaluation of water quality indexes with novel machine learning and SHapley Additive ExPlanation (SHAP) approaches","volume":"58","author":"Aldrees","year":"2024","journal-title":"J. Water Process Eng."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"142442","DOI":"10.1016\/j.jclepro.2024.142442","article-title":"Toward better atmospheric polycyclic aromatic hydrocarbons pollution control in the Northern Hemisphere: Process analysis based on interpretable deep learning models","volume":"457","author":"Tao","year":"2024","journal-title":"J. Clean. Prod."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"120714","DOI":"10.1016\/j.atmosenv.2024.120714","article-title":"Combined PMF modelling and machine learning to identify sources and meteorological influencers of volatile organic compound pollution in an industrial city in eastern China","volume":"334","author":"Chen","year":"2024","journal-title":"Atmos. Environ."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"158271","DOI":"10.1016\/j.scitotenv.2022.158271","article-title":"Using machine learning approach to reproduce the measured feature and understand the model-to-measurement discrepancy of atmospheric formaldehyde","volume":"851","author":"Yin","year":"2022","journal-title":"Sci. Total Environ."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"168588","DOI":"10.1016\/j.scitotenv.2023.168588","article-title":"Application of machine learning in atmospheric pollution research: A state-of-art review","volume":"910","author":"Peng","year":"2024","journal-title":"Sci. Total Environ."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"851","DOI":"10.1021\/acs.estlett.3c00505","article-title":"Revealing the Covariation of Atmospheric O2 and Pollutants in an Industrial Metropolis by Explainable Machine Learning","volume":"10","author":"Liu","year":"2023","journal-title":"Environ. Sci. Technol. Lett."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Yang, P., and Meyer, K. (2024). Satellites and Satellite Remote Sensing|Remote Sensing: Cloud Properties. Reference Module in Earth Systems and Environmental Sciences, Elsevier.","DOI":"10.1016\/B978-0-323-96026-7.00009-6"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"107316","DOI":"10.1016\/j.atmosres.2024.107316","article-title":"Global characteristics of cloud macro-physical properties from active satellite remote sensing","volume":"302","author":"Chi","year":"2024","journal-title":"Atmos. Res."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"118768","DOI":"10.1016\/j.atmosenv.2021.118768","article-title":"Modeling for the source apportionments of PM10 during sand and dust storms over East Asia in 2020","volume":"267","author":"Wang","year":"2021","journal-title":"Atmos. Environ."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"947","DOI":"10.1016\/j.scitotenv.2018.12.412","article-title":"Contribution of dust in northern China to PM10 concentrations over the Hexi corridor","volume":"660","author":"Guan","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_74","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_75","doi-asserted-by":"crossref","first-page":"111731","DOI":"10.1016\/j.envres.2021.111731","article-title":"Spatiotemporal variation in residential PM2.5 and PM10 concentrations in China: National on-site survey","volume":"202","author":"Zhu","year":"2021","journal-title":"Environ. Res."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"113901","DOI":"10.1016\/j.rse.2023.113901","article-title":"Retrieving hourly seamless PM2.5 concentration across China with physically informed spatiotemporal connection","volume":"301","author":"Ding","year":"2024","journal-title":"Remote Sens. Environ."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"120231","DOI":"10.1016\/j.atmosenv.2023.120231","article-title":"Influence of meteorological reanalysis field on air quality modeling in the Yangtze River Delta, China","volume":"318","author":"Wang","year":"2024","journal-title":"Atmos. Environ."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"120163","DOI":"10.1016\/j.atmosenv.2023.120163","article-title":"Impacts of coal use phase-out in China on the atmospheric environment: Emissions, surface concentrations and exceedance of air quality standards","volume":"315","author":"Ge","year":"2023","journal-title":"Atmos. Environ."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"119297","DOI":"10.1016\/j.atmosenv.2022.119297","article-title":"Accuracy assessment of CAMS and MERRA-2 reanalysis PM2.5 and PM10 concentrations over China","volume":"288","author":"Ali","year":"2022","journal-title":"Atmos. Environ."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"101762","DOI":"10.1016\/j.gsf.2023.101762","article-title":"An analysis of air pollution associated with the 2023 sand and dust storms over China: Aerosol properties and PM10 variability","volume":"15","author":"Filonchyk","year":"2024","journal-title":"Geosci. Front."},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Li, Y., and Wang, W. (2024). Long-Range Transport of a Dust Event and Impact on Marine Chlorophyll-a Concentration in April 2023. Remote Sens., 16.","DOI":"10.3390\/rs16111883"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"107342","DOI":"10.1016\/j.atmosres.2024.107342","article-title":"Climate factors influencing springtime dust activities over Northern East Asia in 2021 and 2023","volume":"303","author":"Liu","year":"2024","journal-title":"Atmos. Res."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"107204","DOI":"10.1016\/j.envint.2022.107204","article-title":"Multiannual assessment of the desert dust impact on air quality in Italy combining PM10 data with physics-based and geostatistical models","volume":"163","author":"Barnaba","year":"2022","journal-title":"Environ. Int."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"107456","DOI":"10.1016\/j.atmosres.2024.107456","article-title":"Study of boundary layer parameterization simulation uncertainties of sand-dust storm windfield using high-resolution three-dimensional Doppler wind lidar data","volume":"306","author":"Zhang","year":"2024","journal-title":"Atmos. Res."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"106600","DOI":"10.1016\/j.atmosres.2022.106600","article-title":"Dust radiation effect on the weather and dust transport over the Taklimakan Desert, China","volume":"284","author":"Chen","year":"2023","journal-title":"Atmos. Res."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/18\/3363\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:53:21Z","timestamp":1760111601000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/18\/3363"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,10]]},"references-count":85,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2024,9]]}},"alternative-id":["rs16183363"],"URL":"https:\/\/doi.org\/10.3390\/rs16183363","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,10]]}}}