{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T18:46:42Z","timestamp":1772131602097,"version":"3.50.1"},"reference-count":49,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2022,6,11]],"date-time":"2022-06-11T00:00:00Z","timestamp":1654905600000},"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":["52079101"],"award-info":[{"award-number":["52079101"]}],"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":["2020HT0011"],"award-info":[{"award-number":["2020HT0011"]}],"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":["2021ZH1CXYD060013"],"award-info":[{"award-number":["2021ZH1CXYD060013"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Zhejiang Ecological Environment Research and Achievement Promotion Project","award":["52079101"],"award-info":[{"award-number":["52079101"]}]},{"name":"Zhejiang Ecological Environment Research and Achievement Promotion Project","award":["2020HT0011"],"award-info":[{"award-number":["2020HT0011"]}]},{"name":"Zhejiang Ecological Environment Research and Achievement Promotion Project","award":["2021ZH1CXYD060013"],"award-info":[{"award-number":["2021ZH1CXYD060013"]}]},{"name":"Ningbo Science and Technology Plan Project","award":["52079101"],"award-info":[{"award-number":["52079101"]}]},{"name":"Ningbo Science and Technology Plan Project","award":["2020HT0011"],"award-info":[{"award-number":["2020HT0011"]}]},{"name":"Ningbo Science and Technology Plan Project","award":["2021ZH1CXYD060013"],"award-info":[{"award-number":["2021ZH1CXYD060013"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>NO2 (nitrogen dioxide) is a common pollutant in the atmosphere that can have serious adverse effects on the health of residents. However, the existing satellite and ground observation methods are not enough to effectively monitor the spatiotemporal heterogeneity of near-surface NO2 concentrations, which limits the development of pollutant remediation work and medical health research. Based on TROPOMI-NO2 tropospheric column concentration data, supplemented by meteorological data, atmospheric condition reanalysis data and other geographic parameters, combined with classic machine learning models and deep learning networks, we constructed an ensemble model that achieved a daily average near-surface NO2 of 0.03\u00b0 exposure. In this article, a meteorological hysteretic effects term and a spatiotemporal term were designed, which considerably improved the performance of the model. Overall, our ensemble model performed better, with a 10-fold CV R2 of 0.89, an RMSE of 5.62 \u00b5g\/m3, and an MAE of 4.04 \u00b5g\/m3. The model also had good temporal and spatial generalization capability, with a temporal prediction R2 and a spatial prediction R2 of 0.71 and 0.81, respectively, which can be applied to a wider range of time and space. Finally, we used an ensemble model to estimate the spatiotemporal distribution of NO2 in a coastal region of southeastern China from May 2018 to December 2020. Compared with satellite observations, the model output results showed richer details of the spatiotemporal heterogeneity of NO2 concentrations. Due to the advantages of using multi-source data, this model framework has the potential to output products with a higher spatial resolution and can provide a reference for downscaling work on other pollutants.<\/jats:p>","DOI":"10.3390\/rs14122807","type":"journal-article","created":{"date-parts":[[2022,6,12]],"date-time":"2022-06-12T23:55:24Z","timestamp":1655078124000},"page":"2807","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["An Ensemble Model-Based Estimation of Nitrogen Dioxide in a Southeastern Coastal Region of China"],"prefix":"10.3390","volume":"14","author":[{"given":"Sicong","family":"He","sequence":"first","affiliation":[{"name":"School of Resources and Environment Engineering, Wuhan University of Technology, Wuhan 430070, China"}]},{"given":"Heng","family":"Dong","sequence":"additional","affiliation":[{"name":"School of Resources and Environment Engineering, Wuhan University of Technology, Wuhan 430070, China"},{"name":"Zhejiang Spatiotemporal Sophon Bigdata Co., Ltd., Ningbo 315101, China"}]},{"given":"Zili","family":"Zhang","sequence":"additional","affiliation":[{"name":"Ecological Environment Monitoring Center of Zhejiang, Hangzhou 310012, China"},{"name":"Zhejiang Key Laboratory of Ecological Environment Monitoring, Early Warning and Quality Control Research, Hangzhou 310012, China"}]},{"given":"Yanbin","family":"Yuan","sequence":"additional","affiliation":[{"name":"School of Resources and Environment Engineering, Wuhan University of Technology, Wuhan 430070, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1111","DOI":"10.5194\/gmd-9-1111-2016","article-title":"OMI NO2 column densities over North American urban cities: The effect of satellite footprint resolution","volume":"9","author":"Kim","year":"2016","journal-title":"Geosci. 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