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

Yang Liu,Kun Cai,Shenshen Li,Kunlin Song,Jinhuan Zhao,Cheng Cheng

doi:10.1038/s41598-021-97745-y

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.

Highlights

Air pollution is the result of comprehensive evolution of a dynamic and complex system composed of emission sources, topography, meteorology and other environmental factors
Taking the monitoring of NO2 before and after the COVID-19 pandemic in Henan Province as an example, this paper analysis the inversion products of TROPOspheric Monitoring Instrument (TROPOMI) and Ozone Monitoring Instrument (OMI) N O2, and studies the temporal and spatial evolution of NO2 based on Swarm intelligence (SI)-APSTE model
This paper describes the complex dynamic system of air pollution based on cellular automata and restricted agents, the SI-APSTE model is proposed based on swarm intelligence and spatiotemporal sampling theorem, and further deduces the spatiotemporal evolution analysis method of air pollution

Summary

Introduction

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. The tropospheric NO2 Vertical Column Densities (VCDs) distribution characteristics of spatiotemporal variation of Henan province before COVID-19 pandemic were studied. 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 can establish a theoretical basis for pollution source identification and trend prediction. It is of great significance to build a spatiotemporal evolution model of air pollution to study the mechanism, prediction, pollution sources identification and treatment of air p ollution[4]. Satellite remote sensing observation has the advantages of wide coverage, providing macro change information, reflecting the large-scale of pollutants, etc., which can make up for the lack of observation spatial distribution of ground stations[7]

Methods

Results

Conclusion