A New Prediction Method of Industrial Atmospheric Pollutant Emission Intensity Based on Pollutant Emission Standard Quantification

Industrial emissions are the main source of atmospheric pollutants in China. Accurate and reasonable prediction of the emission of atmospheric pollutants from a single enterprise can determine the exact source of atmospheric pollutants and precisely control atmospheric pollution. Based on China’s coking enterprises in 2020, we proposed a quantitative method of 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. By constructing and comparing the two machine learning models and parameters, the root mean squared error (RMSE) of the linear kernel function in SVR was the lowest (0.045 kiloton (kt)/a), which occurred when the penalty coefficient was 0.01. When the number of trees reached 100, the RMSE of the RFR reached a minimum (0.039 kt/a). After adding QRPES, it was found that the 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 the standards. The forecast 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 a single enterprise and is of great help to the accurate control of air pollutants