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The Environmental Story During the COVID-19 Lockdown: How Human Activities Affect PM2.5 Concentration in China?
At the end of 2019, the very first COVID-19 coronavirus infection was reported and then it spread across the world just like wildfires. From late January to March 2020, most cities and villages in China were locked down, and consequently, human activities decreased dramatically. This letter presents...
Formato: | Online Artículo Texto |
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Lenguaje: | English |
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IEEE
2020
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8843045/ https://www.ncbi.nlm.nih.gov/pubmed/35582473 http://dx.doi.org/10.1109/LGRS.2020.3040435 |
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collection | PubMed |
description | At the end of 2019, the very first COVID-19 coronavirus infection was reported and then it spread across the world just like wildfires. From late January to March 2020, most cities and villages in China were locked down, and consequently, human activities decreased dramatically. This letter presents an “offline learning and online inference” approach to explore the variation of PM2.5 pollution during this period. In the experiments, a deep regression model was trained to establish the complex relationship between remote sensing data and in situ PM2.5 observations, and then the spatially continuous monthly PM2.5 distribution map was simulated using the Google Earth Engine platform. The results reveal that the COVID-19 lockdown truly decreased the PM2.5 pollution with certain hysteresis and the fine particle pollution begins to increase when advancing resumption of work and production gradually. |
format | Online Article Text |
id | pubmed-8843045 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | IEEE |
record_format | MEDLINE/PubMed |
spelling | pubmed-88430452022-05-13 The Environmental Story During the COVID-19 Lockdown: How Human Activities Affect PM2.5 Concentration in China? IEEE Geosci Remote Sens Lett Atmosphere At the end of 2019, the very first COVID-19 coronavirus infection was reported and then it spread across the world just like wildfires. From late January to March 2020, most cities and villages in China were locked down, and consequently, human activities decreased dramatically. This letter presents an “offline learning and online inference” approach to explore the variation of PM2.5 pollution during this period. In the experiments, a deep regression model was trained to establish the complex relationship between remote sensing data and in situ PM2.5 observations, and then the spatially continuous monthly PM2.5 distribution map was simulated using the Google Earth Engine platform. The results reveal that the COVID-19 lockdown truly decreased the PM2.5 pollution with certain hysteresis and the fine particle pollution begins to increase when advancing resumption of work and production gradually. IEEE 2020-12-08 /pmc/articles/PMC8843045/ /pubmed/35582473 http://dx.doi.org/10.1109/LGRS.2020.3040435 Text en This article is free to access and download, along with rights for full text and data mining, re-use and analysis. |
spellingShingle | Atmosphere The Environmental Story During the COVID-19 Lockdown: How Human Activities Affect PM2.5 Concentration in China? |
title | The Environmental Story During the COVID-19 Lockdown: How Human Activities Affect PM2.5 Concentration in China? |
title_full | The Environmental Story During the COVID-19 Lockdown: How Human Activities Affect PM2.5 Concentration in China? |
title_fullStr | The Environmental Story During the COVID-19 Lockdown: How Human Activities Affect PM2.5 Concentration in China? |
title_full_unstemmed | The Environmental Story During the COVID-19 Lockdown: How Human Activities Affect PM2.5 Concentration in China? |
title_short | The Environmental Story During the COVID-19 Lockdown: How Human Activities Affect PM2.5 Concentration in China? |
title_sort | environmental story during the covid-19 lockdown: how human activities affect pm2.5 concentration in china? |
topic | Atmosphere |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8843045/ https://www.ncbi.nlm.nih.gov/pubmed/35582473 http://dx.doi.org/10.1109/LGRS.2020.3040435 |
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