Spatiotemporal evolution analysis of NO2 column density before and after COVID-19 pandemic in Henan province based on SI-APSTE model

• Published in: Scientific reports Vol. 11; no. 1; p. 18614
• Main Authors: Liu, Yang, Zhao, Jinhuan, Song, Kunlin, Cheng, Cheng, Li, Shenshen, Cai, Kun
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 20.09.2021; Nature Publishing Group; Nature Portfolio
• Subjects: 704/106/35; 704/172; Air pollution; Coronaviruses; COVID-19; Environmental factors; Evolution; Humanities and Social Sciences; Intelligence; multidisciplinary; Nitrogen dioxide; Pandemics; Pollution control; Pollution sources; Pollution studies; Science; Science (multidisciplinary); Troposphere; Urban areas; China; Henan China
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-021-97745-y

Air pollution is the result of comprehensive evolution of a dynamic and complex system composed of emission sources, topography, meteorology and other environmental factors. The establishment of spatiotemporal evolution model is of great significance for the study of air pollution mechanism, trend prediction, identification of pollution sources and pollution control. In this paper, the air pollution system is described based on cellular automata and restricted agents, and a Swarm Intelligence based Air Pollution SpatioTemporal Evolution (SI-APSTE) model is constructed. Then the spatiotemporal evolution analysis method of air pollution is studied. Taking Henan Province before and after COVID-19 pandemic as an example, the NO 2 products of TROPOMI and OMI were analysed based on SI-APSTE model. The tropospheric NO 2 Vertical Column Densities (VCDs) distribution characteristics of spatiotemporal variation of Henan province before COVID-19 pandemic were studied. Then the tropospheric NO 2 VCDs of TROPOMI was used to study the pandemic period, month-on-month and year-on-year in 18 urban areas of Henan Province. The results show that SI-APSTE model can effectively analyse the spatiotemporal evolution of air pollution by using environmental big data and swarm intelligence, and also can establish a theoretical basis for pollution source identification and trend prediction.