Abstract: Over the past few years, the national/provincial/municipal Ministry of Ecology and Environment has actively signed strategic cooperation agreements with State Grid Corporation of China to promote the application of electric power big data in environmental management. In this study, based on the analysis of the application of electric power big data in air pollution prevention and control, taking the steel enterprises in Tangshan as an example, a high-temporal-resolution emission estimation model of air pollutants was established. The results showed that: (1) The estimated total air pollutant emissions in 2019 are well consistence with the traditional emission inventory in Tangshan, and the emissions of SO₂, NO_x and PM_2.5 are 1017.90, 2047.75, 1141.81 t, respectively, with the relative errors ranging between −0.46% and 4.27%. (2) Based on the process, taking PM_2.5 as an example, the relative error between the model prediction result and the Tangshan emission inventory is −17.34% -10.60%. (3) The characteristics of monthly fluctuating emissions (affected by the steel price), uniform daily emissions, and hourly differentiated emissions (affected by the electricity price) are revealed by estimating air pollutant emissions in 2022 from a typical steel enterprise based on the electric power big data in corresponding year, with the highest emission in January and the lowest emission in June. The results show that the accounting model based on electric power big data builds the dynamic response relationship between electric power big data and air pollutant emissions, which can partly improve the temporal resolution of emission accounting, reflecting the research significance and feasibility of accounting air pollutant emission based on electric power big data.