Predicting air pollutant emissions of the foundry industry: Based on the electricity big data.

Industrial enterprises are one of the largest sources of air pollution. However, the existing means of monitoring air pollutant emissions are narrow in coverage, high in cost, and low in accuracy. To bridge these gaps, this study explored a predicting model for air pollutant emissions from foundry industries based on high-accuracy electricity consumption data and continuous emission monitoring system (CEMS). The model has then been applied to the calculation of air pollutant emissions from foundries without CEMS and the optimization of air pollutant emission temporal allocation factors. The results reveal that electricity consumption and PM emissions during the 2022 Beijing Winter Olympics have the same ascending and descending relationship. Furthermore, a cubic polynomial model between electricity consumption and flue gas flow is established based on the whole year data of 2021 (R ²  = 0.85). The relative errors between the PM emissions calculated by the model and the emission factor method are small (-17.09-24.12 %), and the results from the two methods revealed a strong correlation (r = 0.93, p < 0.01). In addition, the monthly PM emissions from foundries are mainly concentrated in spring and winter, and the daily emissions on weekends are significantly lower than those on workdays. These results can be useful for environmental regulation and optimization of air pollutant emission inventories of foundry industry.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024. Published by Elsevier B.V.)