{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T17:14:19Z","timestamp":1767374059660,"version":"build-2065373602"},"reference-count":33,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,2,4]],"date-time":"2022-02-04T00:00:00Z","timestamp":1643932800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"NASA ACCESS","award":["#80NSSC21M0028"],"award-info":[{"award-number":["#80NSSC21M0028"]}]},{"name":"NASA Applied Science","award":["#17-HAQ17-0044"],"award-info":[{"award-number":["#17-HAQ17-0044"]}]},{"name":"NSF Geoinformatics","award":["#EAR-1947893"],"award-info":[{"award-number":["#EAR-1947893"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Effective and precise monitoring is a prerequisite to control human emissions and slow disruptive climate change. To obtain the near-real-time status of power plant emissions, we built machine learning models and trained them on satellite observations (Sentinel 5), ground observed data (EPA eGRID), and meteorological observations (MERRA) to directly predict the NO2 emission rate of coal-fired power plants. A novel approach to preprocessing multiple data sources, coupled with multiple neural network models (RNN, LSTM), provided an automated way of predicting the number of emissions (NO2, SO2, CO, and others) produced by a single power plant. There are many challenges on overfitting and generalization to achieve a consistently accurate model simply depending on remote sensing data. This paper addresses the challenges using a combination of techniques, such as data washing, column shifting, feature sensitivity filtering, etc. It presents a groundbreaking case study on remotely monitoring global power plants from space in a cost-wise and timely manner to assist in tackling the worsening global climate.<\/jats:p>","DOI":"10.3390\/rs14030729","type":"journal-article","created":{"date-parts":[[2022,2,6]],"date-time":"2022-02-06T20:38:40Z","timestamp":1644179920000},"page":"729","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Evaluating Machine Learning and Remote Sensing in Monitoring NO2 Emission of Power Plants"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3608-191X","authenticated-orcid":false,"given":"Ahmed","family":"Alnaim","sequence":"first","affiliation":[{"name":"Center for Spatial Information Science and Systems, College of Science, George Mason University, 4400 University Drive, MSN 6E1, George Mason University, Fairfax, VA 22030, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9810-0023","authenticated-orcid":false,"given":"Ziheng","family":"Sun","sequence":"additional","affiliation":[{"name":"Center for Spatial Information Science and Systems, College of Science, George Mason University, 4400 University Drive, MSN 6E1, George Mason University, Fairfax, VA 22030, USA"},{"name":"Department of Geography and Geoinformation Science, College of Science, George Mason University, 4400 University Drive, MSN 6C3, George Mason University, Fairfax, VA 22030, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4255-4568","authenticated-orcid":false,"given":"Daniel","family":"Tong","sequence":"additional","affiliation":[{"name":"Center for Spatial Information Science and Systems, College of Science, George Mason University, 4400 University Drive, MSN 6E1, George Mason University, Fairfax, VA 22030, USA"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,4]]},"reference":[{"unstructured":"(2021, December 15). 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