Ju Tienan, Lei Mei, Guo Guanghui, Xi Jinglun, Zhang Yang, Xu Yuan, Lou Qijia
Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101 China.
University of Chinese Academy of Sciences, Beijing, 100049 China.
Front Environ Sci Eng. 2023;17(1):8. doi: 10.1007/s11783-023-1608-1. Epub 2022 Aug 28.
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 SO 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 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 SO emissions of each enterprise are highly correlated with the strictness of standards. The predicted result shows that 45% of SO 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.
Supplementary material is available in the online version of this article at 10.1007/s11783-023-1608-1 and is accessible for authorized users.
工业排放是中国大气污染物的主要来源。准确合理地预测单个企业的大气污染物排放，能够确定大气污染物的确切来源，从而精准控制大气污染。基于2020年中国的焦化企业，我们提出了一种污染物排放标准的量化方法，并将污染物排放标准量化结果（QRPES）引入到中国焦化企业SO排放的支持向量回归（SVR）和随机森林回归（RFR）预测方法的构建中。结果表明，受焦炉类型和地区的影响，中国目前的焦化企业共实施了21种排放标准，差异显著。加入QRPES后发现，SVR和RFR的均方根误差（RMSE）分别从0.055 kt/a和0.059 kt/a降至0.045 kt/a和0.039 kt/a， 分别从0.890和0.881增至0.926和0.945。这表明QRPES能大幅提高预测精度，且各企业的SO排放与标准的严格程度高度相关。预测结果显示，中国焦化企业45%的SO排放集中在中部的山西、陕西和河北省。本文创建的方法填补了单个企业空气污染物排放强度预测方法的空白，对空气污染物的精准控制有很大帮助。
补充材料可在本文的在线版本中获取，链接为10.1007/s11783-023-1608-1，授权用户可访问。
