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

文献类型：期刊论文

作者Ju, Tienan1,2; Lei, Mei1,2; Guo, Guanghui1,2; Xi, Jinglun1,2; Zhang, Yang1,2; Xu, Yuan1,2; Lou, Qijia1,2
刊名FRONTIERS OF ENVIRONMENTAL SCIENCE & ENGINEERING
出版日期2023
卷号17期号:1页码:11
关键词Industrial atmospheric pollutants Pollutant emission standards Quantitative method Machine learning Single enterprise
ISSN号2095-2201
DOI10.1007/s11783-023-1608-1
通讯作者Lei, Mei(leim@igsnrr.ac.cn)
英文摘要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 R-2 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. (C) Higher Education Press 2023
WOS关键词AIR-POLLUTION ; POWER-PLANTS ; CHINA ; INVENTORY ; REGION ; IMPACT ; MODEL ; HEBEI ; SO2
资助项目National Key R&D Program of China[2018YFC1800106]
WOS研究方向Engineering ; Environmental Sciences & Ecology
语种英语
WOS记录号WOS:000846961700001
出版者HIGHER EDUCATION PRESS
资助机构National Key R&D Program of China
源URL[http://ir.igsnrr.ac.cn/handle/311030/182260]  
专题中国科学院地理科学与资源研究所
通讯作者Lei, Mei
作者单位1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
推荐引用方式
GB/T 7714		 Ju, Tienan,Lei, Mei,Guo, Guanghui,et al. A new prediction method of industrial atmospheric pollutant emission intensity based on pollutant emission standard quantification[J]. FRONTIERS OF ENVIRONMENTAL SCIENCE & ENGINEERING,2023,17(1):11.
APA Ju, Tienan.,Lei, Mei.,Guo, Guanghui.,Xi, Jinglun.,Zhang, Yang.,...&Lou, Qijia.(2023).A new prediction method of industrial atmospheric pollutant emission intensity based on pollutant emission standard quantification.FRONTIERS OF ENVIRONMENTAL SCIENCE & ENGINEERING,17(1),11.
MLA Ju, Tienan,et al."A new prediction method of industrial atmospheric pollutant emission intensity based on pollutant emission standard quantification".FRONTIERS OF ENVIRONMENTAL SCIENCE & ENGINEERING 17.1(2023):11.

入库方式： OAI收割

来源：地理科学与资源研究所

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