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Frontiers of Environmental Science & Engineering

ISSN 2095-2201

ISSN 2095-221X(Online)

CN 10-1013/X

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Front. Environ. Sci. Eng.    2023, Vol. 17 Issue (1) : 8    https://doi.org/10.1007/s11783-023-1608-1
RESEARCH ARTICLE
A new prediction method of industrial atmospheric pollutant emission intensity based on pollutant emission standard quantification
Tienan Ju1,2, Mei Lei1,2(), Guanghui Guo1,2, Jinglun Xi1,2, Yang Zhang1,2, Yuan Xu1,2, Qijia Lou1,2
1. Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
2. University of Chinese Academy of Sciences, Beijing 100049, China
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Abstract

● Established a quantification method of pollutant emission standard.

● Predicted the SO2 emission intensity of single coking enterprises in China.

● Evaluated the influence of pollutant discharge standard on prediction accuracy.

● Analyzed the SO2 emissions of Chinese provincial and municipal coking enterprises.

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 theR2 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.

Keywords Industrial atmospheric pollutants      Pollutant emission standards      Quantitative method      Machine learning      Single enterprise     
Corresponding Author(s): Mei Lei   
About author:

Tongcan Cui and Yizhe Hou contributed equally to this work.
Issue Date: 26 August 2022
 Cite this article:   
Tienan Ju,Mei Lei,Guanghui Guo, et al. A new prediction method of industrial atmospheric pollutant emission intensity based on pollutant emission standard quantification[J]. Front. Environ. Sci. Eng., 2023, 17(1): 8.
 URL:  
https://academic.hep.com.cn/fese/EN/10.1007/s11783-023-1608-1
https://academic.hep.com.cn/fese/EN/Y2023/V17/I1/8
Fig.1  Proportion of data crawling process and basic information of coking enterprises in production.
Fig.2  Quantification results and quantities of pollution emission standards of Chinese coking enterprises.
Fig.3  Optimal parameters of SVR and RFR models and changes in RMSE and R2 under different parameters.
Fig.4  Comparison of predicted and observed SO2 emissions of coking enterprises with and without QRPES in SVR and RFR models under optimal parameters. (a) SVR with QRPES, (b) RFR with QRPES, (c) SVR without QRPES, (d) RFR without QRPES, (e) The absolute error of single coking enterprises.
Fig.5  SO2 emission statistics of coking enterprises in production in China in 2020. (a) SO2 emission statistics of coking enterprises in various provinces in China. (b) Statistics of SO2 emissions from coking enterprises in different cities in Shanxi, Shaanxi and Hebei provinces.
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