Research of Power IoT System Based Air Pollutant Emission Inventory Compilation Optimization Technology

Current methods for compiling inventories of air pollutant emissions may introduce spatial and temporal uncertainty. Data on electricity consumption is a spatially and temporally precise objective evaluation of the level of activity in modern national industry and life. This study investigates and applies a definition of activity level based on energy consumption data to the production of an inventory of air pollutant emissions. In the meantime, the estimation of the ideal activity level emission coefficient is described, and a framework for the iterative update of air pollutant emission inventory compilation based on the power IoT system is proposed. From 2020 to 2022, verification experiments are being conducted using historical electricity consumption data from a Yunnan company and daily observation data from the relevant national monitoring stations. Compared to the traditional emission inventory based on coal-fired consumption data, the correlation of the reactive power-based emission inventory is increased by 58%, the Normalised Mean Bias (NMB) is reduced by 60 times, and the Normalised Mean Error (NME) is reduced by 30%, as determined by experiment results.