| Issue |
E3S Web of Conf.
Volume 406, 2023
2023 9th International Conference on Energy Materials and Environment Engineering (ICEMEE 2023)
|
|
|---|---|---|
| Article Number | 03020 | |
| Number of page(s) | 6 | |
| Section | Pollution Control and Ecosystem Management | |
| DOI | https://doi.org/10.1051/e3sconf/202340603020 | |
| Published online | 31 July 2023 | |
Research on Power IoT System Based City Block Air Pollutant Emission Prediction
1 Yunnan Power Grid Co.,Ltd, 650214 Yunan, China
2 Wiscom System Co., LTD, 211100, Nanjing, China
3 State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chongqing University, 400044, Chongqing, China
* Corresponding author: awang@wiscom.com.cn
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.
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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