Monitoring PCDD/Fs emissions from municipal solid waste incinerations (MSWIs) is of paramount importance, yet it can be time-consuming and labor-intensive. Predictive models offer an alternative approach for estimating their levels. However, robust models specific to PCDD/Fs were lacking. In this study, we collected 190 PCDD/Fs samples from 4 large-scale MSWIs in China, with the average PCDD/Fs levels and TEQ levels of 0.987 ng/m3 and 0.030 ng TEQ/m3, respectively. We developed and evaluated predictive models, including traditional statistical methods, e.g., linear regression (LR) as well as machine learning models such as back propagation-artificial neural networks (BP ANN) and random forest (RF). Correlation analysis identified 2,3,4,7,8-PeCDF, 1,2,3,6,7,8-HxCDF, 2,3,4,6,7,8-HxCDF were better indicator congeners for PCDD/Fs estimation (R2 > 0.9, p < 0.001). The predictive results favored the RF model, exhibiting a high R2 value and low root mean square error (RMSE) and mean absolute error (MAE). Additionally, the RF model showed excellent prediction ability during external validation, with low absolute relative error (ARE) of 10.9 %-12.6 % for the three indicator congeners in the normal PCDD/F TEQ levels group (<0.1 ng TEQ/m3) and slightly higher ARE values (13.8 %-17.9 %) for the high PCDD/F TEQ levels group (>0.1 ng TEQ/m3). In conclusion, our findings strongly support the RF model's effectiveness in predicting PCDD/Fs TEQ emission from MSWIs.
Keywords: Emissions; Indicator; MSWI; Machine learning method; Traditional statistical method.
Copyright © 2023. Published by Elsevier Ltd.