Abstract
In order to constantly improve city environmental air quality, it is necessary to accurately control the major pollutants emissions such as air fine particulate matter. By adopting the proposed iterative update framework of air pollutant emission inventory, combined with block-level real-time electricity consumption data acquired by the smart city power IoT, and utilizing station-level and hourly environmental air quality monitoring data in specific areas of Yuxi and Dali in Yunnan Province from 2020 to 2021, the iterative update of emission inventory and prediction of air pollutant emission are studied. The experimental results shows that the prediction of the monthly average major air pollutants emissions such as NO2/PM10/PM2.5 in specific neighbourhoods of the two cities mentioned above reaches the same accuracy level as using numerical simulation prediction methods, but the prediction computational power requirements are greatly reduced, making it more suitable for the application requirements of the power IoT. This study provides a new idea for improving the regulatory capacity of intelligent environment and achieving higher urban air quality based on the smart city power IoT.
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