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Comprehensive prediction of soil benzo[a]pyrene content in Chinese coking enterprises from 2020-2040: an innovative full production cycle approach based on interpretable machine learning

文献类型：期刊论文

作者Ju, Tienan2,3; Lei, Mei2,3,4; Li, Hu-an1; Xing, Andrew Zi Feng5; Guo, Guanghui2,3; Wang, Shaobin2,3
刊名JOURNAL OF CLEANER PRODUCTION
出版日期2025-08-25
卷号521页码:146223
关键词Coking enterprises Soil benzo[a]pyrene content Full production cycle Emission standards Interpretable machine learning
ISSN号0959-6526
DOI10.1016/j.jclepro.2025.146223
产权排序1
文献子类Article
英文摘要Given the intricate and variable causes of site soil pollution, accurately predicting specific soil pollutant content over extended periods remains a challenging task. This study proposes an innovative approach to quantify the degree of enterprise full production cycle management and construct an interpretable machine learning model with dynamic optimization, thereby addressing this challenge. By incorporating 12 soil pollution influencing factors, we predicted the soil benzo[a]pyrene (BaP) content of coking enterprises in China from 2020 to 2040. Additionally, we employed SHAP and partial dependence plots techniques to conduct an in-depth analysis of the relationships between each influencing factor and soil BaP content. The random forest algorithm was identified as the optimal model for predicting soil BaP content in coking enterprises, yielding an R2 value of 0.771 and an RMSE value of 2.1. Among various influencing factors, full production cycle emission standard quantification result exhibited the most significant impact with an importance score of 24.4 %. Compared with natural environmental factors (such as sunshine, rainfall, and temperature), the production activities of enterprises themselves (such as production time and output) have a more significant impact on the accumulation of soil pollutants. The highest soil BaP content among coking enterprises in China in 2020 was 231.1 mg/kg. Assuming that the unpredictable variables such as output and the number of environmental violations remain unchanged, by 2040, the average content of BaP in the soil of coking enterprises is expected to increase from 6.1 mg/kg to 7.38 mg/kg, and the over-standard rate is expected to increase by approximately 12.24 %. This study underscores the crucial role of stringent pollutant emission standards in reducing soil contamination in industrial environments.
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WOS关键词POLYCYCLIC AROMATIC-HYDROCARBONS ; SUSTAINABILITY ; INDUSTRY ; SITE
WOS研究方向Science & Technology - Other Topics ; Engineering ; Environmental Sciences & Ecology
语种英语
WOS记录号WOS:001543094100002
出版者ELSEVIER SCI LTD
源URL[http://ir.igsnrr.ac.cn/handle/311030/215658]  
专题资源利用与环境修复重点实验室_外文论文
通讯作者Lei, Mei
作者单位1.North China Univ Technol, Beijing 100144, Peoples R China;
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China;
3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China;
4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, 11A Datun Rd, Beijing 100101, Peoples R China
5.Beijing Acad, Beijing 100018, Peoples R China;
推荐引用方式
GB/T 7714		 Ju, Tienan,Lei, Mei,Li, Hu-an,et al. Comprehensive prediction of soil benzo[a]pyrene content in Chinese coking enterprises from 2020-2040: an innovative full production cycle approach based on interpretable machine learning[J]. JOURNAL OF CLEANER PRODUCTION,2025,521:146223.
APA Ju, Tienan,Lei, Mei,Li, Hu-an,Xing, Andrew Zi Feng,Guo, Guanghui,&Wang, Shaobin.(2025).Comprehensive prediction of soil benzo[a]pyrene content in Chinese coking enterprises from 2020-2040: an innovative full production cycle approach based on interpretable machine learning.JOURNAL OF CLEANER PRODUCTION,521,146223.
MLA Ju, Tienan,et al."Comprehensive prediction of soil benzo[a]pyrene content in Chinese coking enterprises from 2020-2040: an innovative full production cycle approach based on interpretable machine learning".JOURNAL OF CLEANER PRODUCTION 521(2025):146223.

入库方式： OAI收割

来源：地理科学与资源研究所

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