{"title":"2019冠状病毒病暴发期间伊朗大城市二氧化氮和夜间灯光数据集的时空变化","authors":"S. Sangi, S. Falahatkar, M. Gholamalifard","doi":"10.3808/jei.202300488","DOIUrl":null,"url":null,"abstract":"COVID-19 lockdown has caused a reduction in traffic volume and industrial activities which are the main sources of air pollution in whole of the world. As tropospheric NO2 pollutant and nighttime light (NTL) are the representative of human activities, this study focused to quantify the annual and monthly change of NO2 concentration and NTL in 14 metropolises of Iran before, during and after the lockdown months such as March, April, October and November. TROPOMI images of Sentinel-5p were used for investigation of NO2 column density in 2019, 2020 and 2021, and the variation of NTL was monitored by VIIRS images. The findings showed the majority of metropolises have an increase of NO2 concentration in March and October and a decrease in April and November in 2020 but a significant increase in 2021. The similar pattern of NTL change as NO2 was observed in the most metropolises. The correlation coefficient between NO2 concentration and NTL was calculated from 0.66 to 0.75. So, in majority of metropolises, the reduction of NO2 was observed with reduction of NTL. According to the results, reducing traffic volume as mobile source does not has an effective contribution in NO2 emission in some metropolises of Iran which the stationary sources are dominant such as Isfahan. Tehran as the capital of Iran showed the highest annual mean NO2 reduction in lockdown, this finding showed the important role of traffic volume on air quality of Tehran compared to industrial activities. The integrated application of TROPOMI and NTL data will help to better decision making for controlling and managing of air quality in country's urban area.","PeriodicalId":54840,"journal":{"name":"Journal of Environmental Informatics","volume":"5 1","pages":""},"PeriodicalIF":6.0000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Spatiotemporal Variation of Nitrogen Dioxide and Nighttime Light Dataset of Iranian Metropolises in the COVID-19 Outbreak\",\"authors\":\"S. Sangi, S. Falahatkar, M. Gholamalifard\",\"doi\":\"10.3808/jei.202300488\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"COVID-19 lockdown has caused a reduction in traffic volume and industrial activities which are the main sources of air pollution in whole of the world. As tropospheric NO2 pollutant and nighttime light (NTL) are the representative of human activities, this study focused to quantify the annual and monthly change of NO2 concentration and NTL in 14 metropolises of Iran before, during and after the lockdown months such as March, April, October and November. TROPOMI images of Sentinel-5p were used for investigation of NO2 column density in 2019, 2020 and 2021, and the variation of NTL was monitored by VIIRS images. The findings showed the majority of metropolises have an increase of NO2 concentration in March and October and a decrease in April and November in 2020 but a significant increase in 2021. The similar pattern of NTL change as NO2 was observed in the most metropolises. The correlation coefficient between NO2 concentration and NTL was calculated from 0.66 to 0.75. So, in majority of metropolises, the reduction of NO2 was observed with reduction of NTL. According to the results, reducing traffic volume as mobile source does not has an effective contribution in NO2 emission in some metropolises of Iran which the stationary sources are dominant such as Isfahan. Tehran as the capital of Iran showed the highest annual mean NO2 reduction in lockdown, this finding showed the important role of traffic volume on air quality of Tehran compared to industrial activities. The integrated application of TROPOMI and NTL data will help to better decision making for controlling and managing of air quality in country's urban area.\",\"PeriodicalId\":54840,\"journal\":{\"name\":\"Journal of Environmental Informatics\",\"volume\":\"5 1\",\"pages\":\"\"},\"PeriodicalIF\":6.0000,\"publicationDate\":\"2023-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Environmental Informatics\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://doi.org/10.3808/jei.202300488\",\"RegionNum\":1,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENVIRONMENTAL SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Environmental Informatics","FirstCategoryId":"93","ListUrlMain":"https://doi.org/10.3808/jei.202300488","RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
Spatiotemporal Variation of Nitrogen Dioxide and Nighttime Light Dataset of Iranian Metropolises in the COVID-19 Outbreak
COVID-19 lockdown has caused a reduction in traffic volume and industrial activities which are the main sources of air pollution in whole of the world. As tropospheric NO2 pollutant and nighttime light (NTL) are the representative of human activities, this study focused to quantify the annual and monthly change of NO2 concentration and NTL in 14 metropolises of Iran before, during and after the lockdown months such as March, April, October and November. TROPOMI images of Sentinel-5p were used for investigation of NO2 column density in 2019, 2020 and 2021, and the variation of NTL was monitored by VIIRS images. The findings showed the majority of metropolises have an increase of NO2 concentration in March and October and a decrease in April and November in 2020 but a significant increase in 2021. The similar pattern of NTL change as NO2 was observed in the most metropolises. The correlation coefficient between NO2 concentration and NTL was calculated from 0.66 to 0.75. So, in majority of metropolises, the reduction of NO2 was observed with reduction of NTL. According to the results, reducing traffic volume as mobile source does not has an effective contribution in NO2 emission in some metropolises of Iran which the stationary sources are dominant such as Isfahan. Tehran as the capital of Iran showed the highest annual mean NO2 reduction in lockdown, this finding showed the important role of traffic volume on air quality of Tehran compared to industrial activities. The integrated application of TROPOMI and NTL data will help to better decision making for controlling and managing of air quality in country's urban area.
期刊介绍:
Journal of Environmental Informatics (JEI) is an international, peer-reviewed, and interdisciplinary publication designed to foster research innovation and discovery on basic science and information technology for addressing various environmental problems. The journal aims to motivate and enhance the integration of science and technology to help develop sustainable solutions that are consensus-oriented, risk-informed, scientifically-based and cost-effective. JEI serves researchers, educators and practitioners who are interested in theoretical and/or applied aspects of environmental science, regardless of disciplinary boundaries. The topics addressed by the journal include:
- Planning of energy, environmental and ecological management systems
- Simulation, optimization and Environmental decision support
- Environmental geomatics - GIS, RS and other spatial information technologies
- Informatics for environmental chemistry and biochemistry
- Environmental applications of functional materials
- Environmental phenomena at atomic, molecular and macromolecular scales
- Modeling of chemical, biological and environmental processes
- Modeling of biotechnological systems for enhanced pollution mitigation
- Computer graphics and visualization for environmental decision support
- Artificial intelligence and expert systems for environmental applications
- Environmental statistics and risk analysis
- Climate modeling, downscaling, impact assessment, and adaptation planning
- Other areas of environmental systems science and information technology.