Temporal Refinement of Major Primary Air Pollutant Emissions Based on Electric Power Big Data: A Case of the Cement Industry in Tangshan City

Abstract

High-temporal resolution and timely emission estimates are essential for developing refined air quality management policies. Considering the advantages of extensive coverage, high reliability, and near real-time capabilities, in this work, electric power big data (EPBD) was first employed to obtain accurate hourly resolved facility-level air pollutant emissions information from the cement industries in Tangshan City, China. Then, the simulation optimization was elucidated by coupling the data with the weather research and forecasting (WRF)-community multiscale air quality (CMAQ) model. Simulation results based on estimated emissions effectively captured the hourly variation, with the NMB within ±50% for NO2 and PM2.5 and R greater than 0.6 for SO2. Hourly PM2.5 emissions from clinker production enterprises exhibited a relatively smooth pattern, whereas those from separate cement grinding stations displayed a distinct diurnal variation. Despite the remaining underestimation and/or overestimation of the simulation concentration, the emission inventory based on EPBD demonstrates an enhancement in simulation results, with RMSE, NMB, and NME decreasing by 9.6%, 15.8%, and 11.2%, respectively. Thus, the exploitation of the vast application potential of EPBD in the field of environmental protection could help to support the precise prevention and control of air pollution, with the possibility of the early achievement of carbon peaking and carbon neutrality targets in China and other developing countries.

Keywords

• temporal refined emission inventory
• electric power big data
• cement industry
• hourly emission