{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T20:36:23Z","timestamp":1773520583240,"version":"3.50.1"},"reference-count":63,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2022,3,29]],"date-time":"2022-03-29T00:00:00Z","timestamp":1648512000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The Innovation Development Program of Anhui Meteorology Bureau","award":["CXM202102"],"award-info":[{"award-number":["CXM202102"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No.41705014"],"award-info":[{"award-number":["No.41705014"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017668","name":"Key Research and Development Program of Anhui Province","doi-asserted-by":"publisher","award":["202004b11020012"],"award-info":[{"award-number":["202004b11020012"]}],"id":[{"id":"10.13039\/501100017668","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Having a high-quality historical air pollutant dataset is critical for environmental and epidemiological research. In this study, a novel deep learning model based on convolutional neural network architecture was developed to estimate ground-level ozone concentrations across eastern China. A high-resolution maximum daily average 8-h (MDA8) surface ground ozone concentration dataset was generated with the support of the total ozone column from the satellite Tropospheric Monitoring Instrument, meteorological data from the China Meteorological Administration Land Data Assimilation System, and simulations of the WRF-Chem model. The modeled results were compared with in situ measurements in five cities that were not involved in model training, and the mean R2 of predicted ozone with observed values was 0.9, indicating the good robustness of our model. In addition, we compared the model results with some widely used machine learning techniques (e.g., random forest) and recently published ozone datasets, showing that the accuracy of our model is higher and that the spatial distributions of predicted ozone are more coherent. This study provides an efficient and exact method to estimate ground-level ozone and offers a new perspective for modeling spatiotemporal air pollutants.<\/jats:p>","DOI":"10.3390\/rs14071640","type":"journal-article","created":{"date-parts":[[2022,3,29]],"date-time":"2022-03-29T21:45:51Z","timestamp":1648590351000},"page":"1640","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["A High-Performance Convolutional Neural Network for Ground-Level Ozone Estimation in Eastern China"],"prefix":"10.3390","volume":"14","author":[{"given":"Sichen","family":"Wang","sequence":"first","affiliation":[{"name":"School of Resources and Environmental Engineering, Anhui University, Hefei 230601, China"},{"name":"Anhui Institute of Meteorological Sciences, Key Laboratory for Atmospheric Sciences & Remote Sensing of Anhui Province, Hefei 230031, China"},{"name":"Shouxian National Climate Observatory, Huainan 232200, China"},{"name":"Huaihe River Basin Typical Farmland Ecological Meteorological Field Science Experiment Base of China Meteorological Administration, Huainan 232200, China"}]},{"given":"Yanfeng","family":"Huo","sequence":"additional","affiliation":[{"name":"Anhui Institute of Meteorological Sciences, Key Laboratory for Atmospheric Sciences & Remote Sensing of Anhui Province, Hefei 230031, China"},{"name":"Shouxian National Climate Observatory, Huainan 232200, China"},{"name":"Huaihe River Basin Typical Farmland Ecological Meteorological Field Science Experiment Base of China Meteorological Administration, Huainan 232200, China"}]},{"given":"Xi","family":"Mu","sequence":"additional","affiliation":[{"name":"School of Resources and Environmental Engineering, Anhui University, Hefei 230601, China"}]},{"given":"Peng","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Resources and Environmental Engineering, Anhui University, Hefei 230601, China"},{"name":"Information Materials and Intelligent Sensing Laboratory of Anhui Province, Hefei 230601, China"}]},{"given":"Shangpei","family":"Xun","sequence":"additional","affiliation":[{"name":"Anhui Institute of Meteorological Sciences, Key Laboratory for Atmospheric Sciences & Remote Sensing of Anhui Province, Hefei 230031, China"}]},{"given":"Binfang","family":"He","sequence":"additional","affiliation":[{"name":"Anhui Institute of Meteorological Sciences, Key Laboratory for Atmospheric Sciences & Remote Sensing of Anhui Province, Hefei 230031, China"}]},{"given":"Wenyu","family":"Wu","sequence":"additional","affiliation":[{"name":"Anhui Institute of Meteorological Sciences, Key Laboratory for Atmospheric Sciences & Remote Sensing of Anhui Province, Hefei 230031, China"}]},{"given":"Lin","family":"Liu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Severe Weather (LASW) and Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, China"}]},{"given":"Yonghong","family":"Wang","sequence":"additional","affiliation":[{"name":"Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100029, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,29]]},"reference":[{"key":"ref_1","first-page":"28","article-title":"Tropospheric Ozone Assessment Report: Global Ozone Metrics for Climate Change, Human Health, and Crop\/Ecosystem Research","volume":"6","author":"Lefohn","year":"2018","journal-title":"Elem. 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