Establishment and application of electricity-based industrial supervision indicators for heavy pollution emergencies.

Current assessment of industrial air pollution emissions in China faces challenges such as high costs and poor real-time performance. This study proposes a supervisory system based on electricity consumption to facilitate the real-time evaluation of air pollution emergency control measures. The system introduces three key indicators: the hourly electricity consumption limiting value, response time, and response depth. The system was validated using electricity consumption data from eight industries and real-time emissions data from two industries in Weifang City, and was subsequently applied to analyze eight heavy pollution emergency episodes across 36 enterprises from 2020 to 2023. Verification results demonstrated that the diurnal variation of the electricity consumption limiting value closely aligned with actual enterprise consumption patterns. During two emergency episodes, two enterprises were found to exceed the electricity consumption limit, and their SO₂ and NOx emissions increased by approximately 50 %, confirming the indicator's effectiveness in identifying violations. In the application of the system in Weifang City, 10 enterprises (27.7 %) were initially identified as exceeding electricity consumption limits when response time and depth were not considered. By setting the response time limit to 0.9 and the response depth limit to 0.5, the number of enterprises with inadequate responses was reduced to 3 (8.3 %), thereby minimizing false positives and relaxing regulatory pressure. Both response time and depth can be dynamically adjusted based on regulatory stringency and regional conditions. By leveraging electricity data, this study provides an innovative and practical method to enhance the efficiency of environmental supervision.