{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T08:23:13Z","timestamp":1776241393561,"version":"3.50.1"},"reference-count":63,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2024,6,22]],"date-time":"2024-06-22T00:00:00Z","timestamp":1719014400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Shandong Province Undergraduate Teaching Reform Project","award":["Z20220004"],"award-info":[{"award-number":["Z20220004"]}]},{"name":"Shandong Province Undergraduate Teaching Reform Project","award":["JNSX2023036"],"award-info":[{"award-number":["JNSX2023036"]}]},{"name":"Shandong Province Undergraduate Teaching Reform Project","award":["42201308"],"award-info":[{"award-number":["42201308"]}]},{"name":"Shandong Province Undergraduate Teaching Reform Project","award":["ZR2021QD127"],"award-info":[{"award-number":["ZR2021QD127"]}]},{"name":"Shandong Province Undergraduate Teaching Reform Project","award":["ZR2021ME203"],"award-info":[{"award-number":["ZR2021ME203"]}]},{"name":"Jinan City-School Integration Project","award":["Z20220004"],"award-info":[{"award-number":["Z20220004"]}]},{"name":"Jinan City-School Integration Project","award":["JNSX2023036"],"award-info":[{"award-number":["JNSX2023036"]}]},{"name":"Jinan City-School Integration Project","award":["42201308"],"award-info":[{"award-number":["42201308"]}]},{"name":"Jinan City-School Integration Project","award":["ZR2021QD127"],"award-info":[{"award-number":["ZR2021QD127"]}]},{"name":"Jinan City-School Integration Project","award":["ZR2021ME203"],"award-info":[{"award-number":["ZR2021ME203"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["Z20220004"],"award-info":[{"award-number":["Z20220004"]}],"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":["JNSX2023036"],"award-info":[{"award-number":["JNSX2023036"]}],"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":["42201308"],"award-info":[{"award-number":["42201308"]}],"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":["ZR2021QD127"],"award-info":[{"award-number":["ZR2021QD127"]}],"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":["ZR2021ME203"],"award-info":[{"award-number":["ZR2021ME203"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Foundation of Shandong Province, China","award":["Z20220004"],"award-info":[{"award-number":["Z20220004"]}]},{"name":"Natural Science Foundation of Shandong Province, China","award":["JNSX2023036"],"award-info":[{"award-number":["JNSX2023036"]}]},{"name":"Natural Science Foundation of Shandong Province, China","award":["42201308"],"award-info":[{"award-number":["42201308"]}]},{"name":"Natural Science Foundation of Shandong Province, China","award":["ZR2021QD127"],"award-info":[{"award-number":["ZR2021QD127"]}]},{"name":"Natural Science Foundation of Shandong Province, China","award":["ZR2021ME203"],"award-info":[{"award-number":["ZR2021ME203"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Air quality degradation has triggered a large-scale public health crisis globally. Existing machine learning techniques have been used to attempt the remote sensing estimates of PM2.5. However, many machine learning models ignore the spatial non-stationarity of predictive variables. To address this issue, this study introduces a Flexible Geographically Weighted Neural Network (FGWNN) to estimate PM2.5 based on multi-source remote sensing data. FGWNN incorporates the Flexible Geographical Neuron (FGN) and Geographical Activation Function (GWAF) within the framework of Artificial Neural Network (ANN) to capture the intricate spatial non-stationary relationships among predictive variables. A robust air quality remote sensing estimation model was constructed using remote sensing data of Aerosol Optical Depth (AOD), Normalized Difference Vegetation Index (NDVI), Temperature (TMP), Specific Humidity (SPFH), Wind Speed (WIND), and Terrain Elevation (HGT) as inputs, and Ground-Based PM2.5 as the observation. The results indicated that FGWNN successfully generates PM2.5 remote sensing data with a 2.5 km spatial resolution for the contiguous United States (CONUS) in 2022. It exhibits higher regression accuracy compared to traditional ANN and Geographically Weighted Regression (GWR) models. FGWNN holds the potential for applications in high-precision and high-resolution remote sensing scenarios.<\/jats:p>","DOI":"10.3390\/ijgi13070217","type":"journal-article","created":{"date-parts":[[2024,6,24]],"date-time":"2024-06-24T03:53:43Z","timestamp":1719201223000},"page":"217","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A Novel Flexible Geographically Weighted Neural Network for High-Precision PM2.5 Mapping across the Contiguous United States"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3362-0631","authenticated-orcid":false,"given":"Dongchao","family":"Wang","sequence":"first","affiliation":[{"name":"College of Geography and Environment, Shandong Normal University, Jinan 250358, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9395-0317","authenticated-orcid":false,"given":"Jianfei","family":"Cao","sequence":"additional","affiliation":[{"name":"College of Geography and Environment, Shandong Normal University, Jinan 250358, China"}]},{"given":"Baolei","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Geography and Environment, Shandong Normal University, Jinan 250358, China"}]},{"given":"Ye","family":"Zhang","sequence":"additional","affiliation":[{"name":"Shandong Provincial Territorial Spatial Ecological Restoration Center, Jinan 250014, China"}]},{"given":"Lei","family":"Xie","sequence":"additional","affiliation":[{"name":"Shandong Provincial Territorial Spatial Ecological Restoration Center, Jinan 250014, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Jia, N., Li, Y., Chen, R., and Yang, H. (2023). A Review of Global PM2.5 Exposure Research Trends from 1992 to 2022. Sustainability, 15.","DOI":"10.3390\/su151310509"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Christopher, S., and Gupta, P. (2020). Global Distribution of Column Satellite Aerosol Optical Depth to Surface PM2.5 Relationships. Remote Sens., 12.","DOI":"10.3390\/rs12121985"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.atmosenv.2017.05.009","article-title":"A Method to Predict PM2.5 Resulting from Compliance with National Ambient Air Quality Standards","volume":"162","author":"Kelly","year":"2017","journal-title":"Atmos. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"19990","DOI":"10.1021\/acs.est.3c05143","article-title":"Wildland Fires Worsened Population Exposure to PM2.5 Pollution in the Contiguous United States","volume":"57","author":"Zhang","year":"2023","journal-title":"Environ. Sci. Technol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"139942","DOI":"10.1016\/j.scitotenv.2020.139942","article-title":"Chemical Characterization of PM2.5 Emissions and Atmospheric Metallic Element Concentrations in PM2.5 Emitted from Mobile Source Gasoline-Fueled Vehicles","volume":"739","author":"Lin","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"e2022GH000603","DOI":"10.1029\/2022GH000603","article-title":"Nationwide and Regional PM2.5 -Related Air Quality Health Benefits from the Removal of Energy-Related Emissions in the United States","volume":"6","author":"Mailloux","year":"2022","journal-title":"GeoHealth"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"162614","DOI":"10.1016\/j.scitotenv.2023.162614","article-title":"Quantifying the Premature Mortality and Economic Loss from Wildfire-Induced PM2.5 in the Contiguous U.S","volume":"875","author":"Pan","year":"2023","journal-title":"Sci. Total Environ."},{"key":"ref_8","first-page":"1","article-title":"Air Pollution, Weather, and Agricultural Worker Productivity","volume":"Early View","author":"Hill","year":"2023","journal-title":"Am. J. Agric. Econ."},{"key":"ref_9","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_10","doi-asserted-by":"crossref","first-page":"128599","DOI":"10.1016\/j.jclepro.2021.128599","article-title":"Google Earth Engine Based Spatio-Temporal Analysis of Air Pollutants before and during the First Wave COVID-19 Outbreak over Turkey via Remote Sensing","volume":"319","author":"Ghasempour","year":"2021","journal-title":"J. Clean. Prod."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Shen, L., Hu, W., Zhao, T., Bai, Y., Wang, H., Kong, S., and Zhu, Y. (2021). Changes in the Distribution Pattern of PM2.5 Pollution over Central China. Remote Sens., 13.","DOI":"10.3390\/rs13234855"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"134640","DOI":"10.1016\/j.chemosphere.2022.134640","article-title":"Spatiotemporal Variations of PM2.5 Pollution and Its Dynamic Relationships with Meteorological Conditions in Beijing-Tianjin-Hebei Region","volume":"301","author":"Deng","year":"2022","journal-title":"Chemosphere"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"107719","DOI":"10.1016\/j.envint.2022.107719","article-title":"A Novel Ensemble-Based Statistical Approach to Estimate Daily Wildfire-Specific PM2.5 in California (2006\u20132020)","volume":"171","author":"Aguilera","year":"2023","journal-title":"Environ. Int."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"101238","DOI":"10.1016\/j.apr.2021.101238","article-title":"A Global-Scale Analysis of the MISR Level-3 Aerosol Optical Depth (AOD) Product: Comparison with Multi-Platform AOD Data Sources","volume":"12","author":"Gui","year":"2021","journal-title":"Atmos. Pollut. Res."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"114465","DOI":"10.1016\/j.envres.2022.114465","article-title":"Evaluation of Different Machine Learning Approaches and Aerosol Optical Depth in PM2.5 Prediction","volume":"216","author":"Karimian","year":"2023","journal-title":"Environ. Res."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"578","DOI":"10.1080\/17538947.2023.2175499","article-title":"Estimating PM2.5 Concentrations in a Central Region of China Using a Three-Stage Model","volume":"16","author":"Jing","year":"2023","journal-title":"Int. J. Digit. Earth"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"118145","DOI":"10.1016\/j.jenvman.2023.118145","article-title":"Spatiotemporally Continuous Estimates of Daily 1-Km PM2.5 Concentrations and Their Long-Term Exposure in China from 2000 to 2020","volume":"342","author":"He","year":"2023","journal-title":"J. Environ. Manag."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"157910","DOI":"10.1016\/j.scitotenv.2022.157910","article-title":"Influence and Prediction of PM2.5 through Multiple Environmental Variables in China","volume":"849","author":"Jin","year":"2022","journal-title":"Sci. Total Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"136892","DOI":"10.1016\/j.scitotenv.2020.136892","article-title":"Evaluating the Contributions of Changed Meteorological Conditions and Emission to Substantial Reductions of PM2.5 Concentration from Winter 2016 to 2017 in Central and Eastern China","volume":"716","author":"Zhang","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"118944","DOI":"10.1016\/j.envpol.2022.118944","article-title":"Impact of Deep Basin Terrain on PM2.5 Distribution and Its Seasonality over the Sichuan Basin, Southwest China","volume":"300","author":"Shu","year":"2022","journal-title":"Environ. Pollut."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Hua, Z., Sun, W., Yang, G., and Du, Q. (2019). A Full-Coverage Daily Average PM2.5 Retrieval Method with Two-Stage IVW Fused MODIS C6 AOD and Two-Stage GAM Model. Remote Sens., 11.","DOI":"10.3390\/rs11131558"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"117121","DOI":"10.1016\/j.atmosenv.2019.117121","article-title":"Retrieval of Surface PM2.5 Mass Concentrations over North China Using Visibility Measurements and GEOS-Chem Simulations","volume":"222","author":"Li","year":"2020","journal-title":"Atmos. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"117729","DOI":"10.1016\/j.jclepro.2019.117729","article-title":"A Temporal-Spatial Interpolation and Extrapolation Method Based on Geographic Long Short-Term Memory Neural Network for PM2.5","volume":"237","author":"Ma","year":"2019","journal-title":"J. Clean. Prod."},{"key":"ref_24","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_25","doi-asserted-by":"crossref","first-page":"118302","DOI":"10.1016\/j.atmosenv.2021.118302","article-title":"Review of Satellite-Driven Statistical Models PM2.5 Concentration Estimation with Comprehensive Information","volume":"256","author":"Xu","year":"2021","journal-title":"Atmos. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Feng, Y., Fan, S., Xia, K., and Wang, L. (2022). Estimation of Regional Ground-Level PM2.5 Concentrations Directly from Satellite Top-of-Atmosphere Reflectance Using A Hybrid Learning Model. Remote Sens., 14.","DOI":"10.3390\/rs14112714"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Tang, Y., Xie, S., Huang, L., Liu, L., Wei, P., Zhang, Y., and Meng, C. (2022). Spatial Estimation of Regional PM2.5 Concentrations with GWR Models Using PCA and RBF Interpolation Optimization. Remote Sens., 14.","DOI":"10.3390\/rs14215626"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"582","DOI":"10.1080\/13658816.2020.1775836","article-title":"Geographically and Temporally Neural Network Weighted Regression for Modeling Spatiotemporal Non-Stationary Relationships","volume":"35","author":"Wu","year":"2021","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.envres.2012.11.003","article-title":"Estimating Ground-Level PM2.5 Concentrations in the Southeastern U.S. Using Geographically Weighted Regression","volume":"121","author":"Hu","year":"2013","journal-title":"Environ. Res."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"10482","DOI":"10.1021\/acs.est.5b02076","article-title":"High-Resolution Satellite-Derived PM2.5 from Optimal Estimation and Geographically Weighted Regression over North America","volume":"49","author":"Martin","year":"2015","journal-title":"Environ. Sci. Technol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"904","DOI":"10.1016\/j.scitotenv.2018.02.255","article-title":"Predicting Daily PM2.5 Concentrations in Texas Using High-Resolution Satellite Aerosol Optical Depth","volume":"631\u2013632","author":"Zhang","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"113395","DOI":"10.1016\/j.envpol.2019.113395","article-title":"Estimating PM2.5 Concentration of the Conterminous United States via Interpretable Convolutional Neural Networks","volume":"256","author":"Park","year":"2020","journal-title":"Environ. Pollut."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Lu, D., Mao, W., Zheng, L., Xiao, W., Zhang, L., and Wei, J. (2021). Ambient PM2.5 Estimates and Variations during COVID-19 Pandemic in the Yangtze River Delta Using Machine Learning and Big Data. Remote Sens., 13.","DOI":"10.3390\/rs13081423"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1353","DOI":"10.1080\/13658816.2019.1707834","article-title":"Geographically Neural Network Weighted Regression for the Accurate Estimation of Spatial Non-Stationarity","volume":"34","author":"Du","year":"2020","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"112514","DOI":"10.1016\/j.rse.2021.112514","article-title":"Geographically and Temporally Weighted Neural Network for Winter Wheat Yield Prediction","volume":"262","author":"Feng","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1016\/j.isprsjprs.2020.06.019","article-title":"Geographically and Temporally Weighted Neural Networks for Satellite-Based Mapping of Ground-Level PM2.5","volume":"167","author":"Li","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"e13203","DOI":"10.7717\/peerj.13203","article-title":"Machine Learning Driven by Environmental Covariates to Estimate High-Resolution PM2.5 in Data-Poor Regions","volume":"10","author":"Jin","year":"2022","journal-title":"PeerJ"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Yu, X., Xi, M., Wu, L., and Zheng, H. (2023). Spatiotemporal Weighted for Improving the Satellite-Based High-Resolution Ground PM2.5 Estimation Using the Light Gradient Boosting Machine. Remote Sens., 15.","DOI":"10.3390\/rs15164104"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"141813","DOI":"10.1016\/j.scitotenv.2020.141813","article-title":"Using Kriging Incorporated with Wind Direction to Investigate Ground-Level PM2.5 Concentration","volume":"751","author":"Zhang","year":"2021","journal-title":"Sci. Total Environ."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"2305","DOI":"10.1002\/nag.2979","article-title":"Bayesian Model Selection for Sand with Generalization Ability Evaluation","volume":"43","author":"Jin","year":"2019","journal-title":"Num. Anal. Meth. Geomech."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"e3476","DOI":"10.1093\/cid\/ciaa857","article-title":"Model-Based Cost-Effectiveness of State-Level Latent Tuberculosis Interventions in California, Florida, New York, and Texas","volume":"73","author":"Jo","year":"2021","journal-title":"Clin. Infect. Dis."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"e2021GL094908","DOI":"10.1029\/2021GL094908","article-title":"Dominance of Wildfires Impact on Air Quality Exceedances During the 2020 Record-Breaking Wildfire Season in the United States","volume":"48","author":"Li","year":"2021","journal-title":"Geophys. Res. Lett."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"119859","DOI":"10.1016\/j.foreco.2021.119859","article-title":"Past and Future of Wildfires in Northern Hemisphere\u2019s Boreal Forests","volume":"504","author":"Soon","year":"2022","journal-title":"For. Ecol. Manag."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"104461","DOI":"10.1016\/j.earscirev.2023.104461","article-title":"Global Synthesis of Two Decades of Research on Improving PM2.5 Estimation Models from Remote Sensing and Data Science Perspectives","volume":"241","author":"Bai","year":"2023","journal-title":"Earth-Sci. Rev."},{"key":"ref_45","unstructured":"(2024, March 21). US-EPA AirData Website File Download Page, Available online: https:\/\/aqs.epa.gov\/aqsweb\/airdata\/download_files.html."},{"key":"ref_46","unstructured":"Lyapustin, A., and Wang, Y. (2022). MODIS\/Terra+Aqua Land Aerosol Optical Depth Daily L2G Global 1 km SIN Grid V061."},{"key":"ref_47","unstructured":"Didan, K. (2021). MODIS\/Aqua Vegetation Indices 16-Day L3 Global 250m SIN Grid V061."},{"key":"ref_48","unstructured":"(2024, March 21). NCEP Central Operations NCEP Data Products RTMA\/URMA, Available online: https:\/\/www.nco.ncep.noaa.gov\/pmb\/products\/rtma\/."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1016\/S0198-9715(01)00009-6","article-title":"Geographically Weighted Summary Statistics\u2014A Framework for Localised Exploratory Data Analysis","volume":"26","author":"Brunsdon","year":"2002","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Li, M., Zhang, T., Chen, Y., and Smola, A.J. (2014, January 24). Efficient Mini-Batch Training for Stochastic Optimization. Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, NY, USA.","DOI":"10.1145\/2623330.2623612"},{"key":"ref_51","unstructured":"Dozat, T. (2016, January 2\u20134). Incorporating Nesterov Momentum into Adam. Proceedings of the 4th International Conference on Learning Representations, Workshop Track, San Juan, Puerto Rico."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1007\/s11222-009-9153-8","article-title":"Estimation of Prediction Error by Using K-Fold Cross-Validation","volume":"21","author":"Fushiki","year":"2011","journal-title":"Stat. Comput."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1538","DOI":"10.1214\/009053605000000255","article-title":"Boosting with Early Stopping: Convergence and Consistency","volume":"33","author":"Zhang","year":"2005","journal-title":"Ann. Statist."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"8349","DOI":"10.1038\/s41467-023-43862-3","article-title":"First Close Insight into Global Daily Gapless 1 Km PM2.5 Pollution, Variability, and Health Impact","volume":"14","author":"Wei","year":"2023","journal-title":"Nat. Commun."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"565","DOI":"10.1007\/s00376-023-3241-0","article-title":"Severe Global Environmental Issues Caused by Canada\u2019s Record-Breaking Wildfires in 2023","volume":"41","author":"Wang","year":"2024","journal-title":"Adv. Atmos. Sci."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"11605","DOI":"10.1021\/acs.est.3c02780","article-title":"Change of Composition, Source Contribution, and Oxidative Effects of Environmental PM2.5 in the Respiratory Tract","volume":"57","author":"Liu","year":"2023","journal-title":"Environ. Sci. Technol."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1007\/BF00048036","article-title":"Spatial Pattern and Ecological Analysis","volume":"80","author":"Legendre","year":"1989","journal-title":"Vegetatio"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1111\/j.1538-4632.1996.tb00936.x","article-title":"Geographically Weighted Regression: A Method for Exploring Spatial Nonstationarity","volume":"28","author":"Brunsdon","year":"1996","journal-title":"Geogr. Anal."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1109\/MGRS.2021.3063465","article-title":"Change Detection from Very-High-Spatial-Resolution Optical Remote Sensing Images: Methods, Applications, and Future Directions","volume":"9","author":"Wen","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"106818","DOI":"10.1016\/j.envint.2021.106818","article-title":"A Global Observational Analysis to Understand Changes in Air Quality during Exceptionally Low Anthropogenic Emission Conditions","volume":"157","author":"Sokhi","year":"2021","journal-title":"Environ. Int."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Tian, Z., Wei, J., and Li, Z. (2023). How Important Is Satellite-Retrieved Aerosol Optical Depth in Deriving Surface PM2.5 Using Machine Learning?. Remote Sens., 15.","DOI":"10.3390\/rs15153780"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Yang, X., Yang, Y., Xu, S., Han, J., Chai, Z., and Yang, G. (2023). A New Algorithm for Large-Scale Geographically Weighted Regression with K-Nearest Neighbors. IJGI, 12.","DOI":"10.3390\/ijgi12070295"},{"key":"ref_63","first-page":"102086","article-title":"A Multi-Level Context-Guided Classification Method with Object-Based Convolutional Neural Network for Land Cover Classification Using Very High Resolution Remote Sensing Images","volume":"88","author":"Zhang","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/13\/7\/217\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:02:55Z","timestamp":1760108575000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/13\/7\/217"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,22]]},"references-count":63,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2024,7]]}},"alternative-id":["ijgi13070217"],"URL":"https:\/\/doi.org\/10.3390\/ijgi13070217","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,22]]}}}