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A new prediction method of industrial atmospheric pollutant emission intensity based on pollutant emission standard quantification

A new prediction method of industrial atmospheric pollutant emission intensity based on pollutant... Industrial emissions are the main source of atmospheric pollutants in China. Accurate and reasonable prediction of the emission of atmospheric pollutants from single enterprise can determine the exact source of atmospheric pollutants and control atmospheric pollution precisely. Based on China’s coking enterprises in 2020, we proposed a quantitative method for pollutant emission standards and introduced the quantification results of pollutant emission standards (QRPES) into the construction of support vector regression (SVR) and random forest regression (RFR) prediction methods for SO2 emission of coking enterprises in China. The results show that, affected by the types of coke ovens and regions, China’s current coking enterprises have implemented a total of 21 emission standards, with marked differences. After adding QRPES, it was found that the root mean squared error (RMSE) of SVR and RFR decreased from 0.055 kt/a and 0.059 kt/a to 0.045 kt/a and 0.039 kt/a, and the R2 increased from 0.890 and 0.881 to 0.926 and 0.945, respectively. This shows that the QRPES can greatly improve the prediction accuracy, and the SO2 emissions of each enterprise are highly correlated with the strictness of standards. The predicted result shows that 45% of SO2 emissions from Chinese coking enterprises are concentrated in Shanxi, Shaanxi and Hebei provinces in central China. The method created in this paper fills in the blank of forecasting method of air pollutant emission intensity of single enterprise and is of great help to the accurate control of air pollutants.[graphic not available: see fulltext] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Frontiers of Environmental Science & Engineering Springer Journals

A new prediction method of industrial atmospheric pollutant emission intensity based on pollutant emission standard quantification

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References (58)

Publisher
Springer Journals
Copyright
Copyright © Higher Education Press 2023
ISSN
2095-2201
eISSN
2095-221X
DOI
10.1007/s11783-023-1608-1
Publisher site
See Article on Publisher Site

Abstract

Industrial emissions are the main source of atmospheric pollutants in China. Accurate and reasonable prediction of the emission of atmospheric pollutants from single enterprise can determine the exact source of atmospheric pollutants and control atmospheric pollution precisely. Based on China’s coking enterprises in 2020, we proposed a quantitative method for pollutant emission standards and introduced the quantification results of pollutant emission standards (QRPES) into the construction of support vector regression (SVR) and random forest regression (RFR) prediction methods for SO2 emission of coking enterprises in China. The results show that, affected by the types of coke ovens and regions, China’s current coking enterprises have implemented a total of 21 emission standards, with marked differences. After adding QRPES, it was found that the root mean squared error (RMSE) of SVR and RFR decreased from 0.055 kt/a and 0.059 kt/a to 0.045 kt/a and 0.039 kt/a, and the R2 increased from 0.890 and 0.881 to 0.926 and 0.945, respectively. This shows that the QRPES can greatly improve the prediction accuracy, and the SO2 emissions of each enterprise are highly correlated with the strictness of standards. The predicted result shows that 45% of SO2 emissions from Chinese coking enterprises are concentrated in Shanxi, Shaanxi and Hebei provinces in central China. The method created in this paper fills in the blank of forecasting method of air pollutant emission intensity of single enterprise and is of great help to the accurate control of air pollutants.[graphic not available: see fulltext]

Journal

Frontiers of Environmental Science & Engineering – Springer Journals

Published: Jan 1, 2023

Keywords: Industrial atmospheric pollutants; Pollutant emission standards; Quantitative method; Machine learning; Single enterprise

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