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

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

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...

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Autores principales: Yang Liu, Jinhuan Zhao, Kunlin Song, Cheng Cheng, Shenshen Li, Kun Cai
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2021
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Science
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Acceso en línea:https://doaj.org/article/c92905bc45424c5ca703c658639c8c77
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spelling oai:doaj.org-article:c92905bc45424c5ca703c658639c8c772021-12-02T17:27:11ZSpatiotemporal evolution analysis of NO2 column density before and after COVID-19 pandemic in Henan province based on SI-APSTE model10.1038/s41598-021-97745-y2045-2322https://doaj.org/article/c92905bc45424c5ca703c658639c8c772021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-97745-yhttps://doaj.org/toc/2045-2322Abstract 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 NO2 products of TROPOMI and OMI were analysed based on SI-APSTE model. The tropospheric NO2 Vertical Column Densities (VCDs) distribution characteristics of spatiotemporal variation of Henan province before COVID-19 pandemic were studied. Then the tropospheric NO2 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.Yang LiuJinhuan ZhaoKunlin SongCheng ChengShenshen LiKun CaiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-15 (2021)
institution DOAJ
collection DOAJ
language EN
topic Medicine
R
Science
Q
spellingShingle Medicine
R
Science
Q
Yang Liu
Jinhuan Zhao
Kunlin Song
Cheng Cheng
Shenshen Li
Kun Cai
Spatiotemporal evolution analysis of NO2 column density before and after COVID-19 pandemic in Henan province based on SI-APSTE model
description 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 NO2 products of TROPOMI and OMI were analysed based on SI-APSTE model. The tropospheric NO2 Vertical Column Densities (VCDs) distribution characteristics of spatiotemporal variation of Henan province before COVID-19 pandemic were studied. Then the tropospheric NO2 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.
format article
author Yang Liu
Jinhuan Zhao
Kunlin Song
Cheng Cheng
Shenshen Li
Kun Cai
author_facet Yang Liu
Jinhuan Zhao
Kunlin Song
Cheng Cheng
Shenshen Li
Kun Cai
author_sort Yang Liu
title Spatiotemporal evolution analysis of NO2 column density before and after COVID-19 pandemic in Henan province based on SI-APSTE model
title_short Spatiotemporal evolution analysis of NO2 column density before and after COVID-19 pandemic in Henan province based on SI-APSTE model
title_full Spatiotemporal evolution analysis of NO2 column density before and after COVID-19 pandemic in Henan province based on SI-APSTE model
title_fullStr Spatiotemporal evolution analysis of NO2 column density before and after COVID-19 pandemic in Henan province based on SI-APSTE model
title_full_unstemmed Spatiotemporal evolution analysis of NO2 column density before and after COVID-19 pandemic in Henan province based on SI-APSTE model
title_sort spatiotemporal evolution analysis of no2 column density before and after covid-19 pandemic in henan province based on si-apste model
publisher Nature Portfolio
publishDate 2021
url https://doaj.org/article/c92905bc45424c5ca703c658639c8c77
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AT jinhuanzhao spatiotemporalevolutionanalysisofno2columndensitybeforeandaftercovid19pandemicinhenanprovincebasedonsiapstemodel
AT kunlinsong spatiotemporalevolutionanalysisofno2columndensitybeforeandaftercovid19pandemicinhenanprovincebasedonsiapstemodel
AT chengcheng spatiotemporalevolutionanalysisofno2columndensitybeforeandaftercovid19pandemicinhenanprovincebasedonsiapstemodel
AT shenshenli spatiotemporalevolutionanalysisofno2columndensitybeforeandaftercovid19pandemicinhenanprovincebasedonsiapstemodel
AT kuncai spatiotemporalevolutionanalysisofno2columndensitybeforeandaftercovid19pandemicinhenanprovincebasedonsiapstemodel
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