跳至主導覽 跳至搜尋 跳過主要內容

Big data analysis for effects of the covid-19 outbreak on ambient PM2.5 in areas that were not locked down

  • Tai Yi Yu
  • , How Ran Chao*
  • , Ming Hsien Tsai
  • , Chih Chung Lin
  • , I. Cheng Lu
  • , Wei Hsiang Chang
  • , Chih Cheng Chen
  • , Liang Jen Wang
  • , En Tzu Lin
  • , Ching Tzu Chang
  • , Chunneng Chen
  • , Cheng Chih Kao
  • , Wan Nurdiyana Wan Mansor
  • , Kwong Leung J. Yu
  • *此作品的通信作者
  • Ming Chuan University
  • National Pingtung University of Science and Technology
  • Kaohsiung Medical University
  • National Cheng Kung University
  • Chang Gung University
  • JS Environmental Technology and Energy Saving Co. Ltd.
  • Ping Tung Christian Hospital, Taiwan
  • Universiti Malaysia Terengganu

研究成果: 期刊稿件文章同行評審

7 引文 斯高帕斯(Scopus)

摘要

COVID-19, which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), first broke out at the end of 2019. Despite rapidly spreading around the world during the first half of 2020, it remained well controlled in Taiwan without the implementation of a nationwide lockdown. This study aimed to evaluate the PM2.5 concentrations in this country during the 2020 COVID-19 pandemic and compare them with those during the corresponding period from 2019. We obtained measurements (taken every minute or every 3 minutes) from approximately 1,500 PM2.5 sensors deployed in industrial areas of northern and southern Taiwan for the first quarters (January–March) of both years. Our big data analysis revealed that the median hourly PM2.5 levels decreased by 3.70% (from 16.3 to 15.7 µg m–3 ) and 10.6% (from 32.4 to 29.3 µg m–3 ) in the north and south, respectively, between these periods owing to lower domestic emissions of PM2.5 precursors (viz., nitrogen dioxide and sulfur dioxide) and, to a lesser degree, smaller transported emissions of PM2.5, e.g., from China. Additionally, the spatial patterns of the PM2.5 in both northern.

原文英語
文章編號210020
期刊Aerosol and Air Quality Research
21
發行號8
DOIs
出版狀態已出版 - 08 2021

文獻附註

Publisher Copyright:
© The Author(s).

UN SDG

此研究成果有助於以下永續發展目標

  1. SDG3 健康與福祉
    SDG3 健康與福祉

指紋

深入研究「Big data analysis for effects of the covid-19 outbreak on ambient PM2.5 in areas that were not locked down」主題。共同形成了獨特的指紋。

引用此