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基于 SI-APSTE 模型的河南省 COVID-19 大流行前后 NO 柱密度的时空演变分析。

Spatiotemporal evolution analysis of NO column density before and after COVID-19 pandemic in Henan province based on SI-APSTE model.

机构信息

Henan Engineering Laboratory of Spatial Information Processing, Henan Key Laboratory of Big Data Analysis and Processing, School of Computer and Information Engineering, Henan University, Kaifeng, 475004, People's Republic of China.

School of Information and Electronics, Beijing Institute of Technology, Beijing, 100081, People's Republic of China.

出版信息

Sci Rep. 2021 Sep 20;11(1):18614. doi: 10.1038/s41598-021-97745-y.

DOI:10.1038/s41598-021-97745-y
PMID:34545111
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8452647/
Abstract

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 products of TROPOMI and OMI were analysed based on SI-APSTE model. The tropospheric NO Vertical Column Densities (VCDs) distribution characteristics of spatiotemporal variation of Henan province before COVID-19 pandemic were studied. Then the tropospheric NO 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.

摘要

空气污染是由排放源、地形、气象和其他环境因素组成的动态复杂系统综合演变的结果。建立时空演变模型对于研究空气污染机制、趋势预测、污染源识别和污染控制具有重要意义。本文基于元胞自动机和限制代理对空气污染系统进行了描述，并构建了基于群体智能的空气污染时空演变（SI-APSTE）模型。然后研究了空气污染的时空演变分析方法。以 COVID-19 疫情前后的河南省为例，基于 SI-APSTE 模型对 TROPOMI 和 OMI 的 NO 产物进行了分析。研究了 COVID-19 疫情前河南省大气 NO 垂直柱浓度（VCDs）时空变化的分布特征。然后利用 TROPOMI 的大气 NO VCDs，研究了河南省 18 个城市在疫情期间、逐月和逐年的变化情况。结果表明，SI-APSTE 模型可以有效地利用环境大数据和群体智能分析空气污染的时空演变，也可以为污染源识别和趋势预测建立理论基础。

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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f5fe/8452647/20c40d2d0d9c/41598_2021_97745_Fig6_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f5fe/8452647/955e5c434168/41598_2021_97745_Fig8_HTML.jpg
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