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Article

Evaluating Machine Learning and Remote Sensing in Monitoring NO2 Emission of Power Plants

by 1, 1,2,* and 1
1
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
2
Department of Geography and Geoinformation Science, College of Science, George Mason University, 4400 University Drive, MSN 6C3, George Mason University, Fairfax, VA 22030, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Luke Knibbs
Remote Sens. 2022, 14(3), 729; https://doi.org/10.3390/rs14030729
Received: 18 December 2021 / Revised: 24 January 2022 / Accepted: 2 February 2022 / Published: 4 February 2022
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. View Full-Text
Keywords: emission; coal-fired power plant; remote sensing; machine learning; NO2 emission; coal-fired power plant; remote sensing; machine learning; NO2
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MDPI and ACS Style

Alnaim, A.; Sun, Z.; Tong, D. Evaluating Machine Learning and Remote Sensing in Monitoring NO2 Emission of Power Plants. Remote Sens. 2022, 14, 729. https://doi.org/10.3390/rs14030729

AMA Style

Alnaim A, Sun Z, Tong D. Evaluating Machine Learning and Remote Sensing in Monitoring NO2 Emission of Power Plants. Remote Sensing. 2022; 14(3):729. https://doi.org/10.3390/rs14030729

Chicago/Turabian Style

Alnaim, Ahmed, Ziheng Sun, and Daniel Tong. 2022. "Evaluating Machine Learning and Remote Sensing in Monitoring NO2 Emission of Power Plants" Remote Sensing 14, no. 3: 729. https://doi.org/10.3390/rs14030729

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