Tai-Yi Yu1, How-Ran Chao This email address is being protected from spambots. You need JavaScript enabled to view it.2,3,4,5, Ming-Hsien Tsai6, Chih-Chung Lin2, I-Cheng Lu2, Wei-Hsiang Chang7, Chih-Cheng Chen8, Liang-Jen Wang9, En-Tzu Lin2, Ching-Tzu Chang3, Chunneng Chen10, Cheng-Chih Kao11, Wan Nurdiyana Wan Mansor12,13, Kwong-Leung J. Yu11,14 

1 Department of Risk Management and Insurance, Ming Chuan University, Shilin District, Taipei 111, Taiwan
2 Department of Environmental Science and Engineering, College of Engineering, National Pingtung University of Science and Technology, Pingtung 912, Taiwan
3 Institute of Food Safety Management, College of Agriculture, National Pingtung University of Science and Technology, Pingtung 912, Taiwan
4 Emerging Compounds Research Center, General Research Service Center, National Pingtung University of Science and Technology, Pingtung 912, Taiwan
5 School of Dentistry, College of Dental Medicine, Kaohsiung Medical University, Kaohsiung 80708, Taiwan
6 Department of Child Care, College of Humanities and Social Sciences, National Pingtung University of Science and Technology, Pingtung 912, Taiwan
7 Department of Food Safety/Hygiene and Risk Management, National Cheng Kung University, Tainan 70101, Taiwan
8 Section of Neonatology, Department of Pediatrics, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung 83347, Taiwan
9 Department of Child and Adolescent Psychiatry, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung 83347, Taiwan
10 JS Environmental Technology and Energy Saving Co. Ltd., Kaohsiung 806, Taiwan
11 Superintendent Office, Pingtung Christian Hospital, Pingtung 90053, Taiwan
12 Faculty of Ocean Engineering Technology & Informatics, Universiti Malaysia Terengganu, 21300, Malaysia
13 Air Quality and Environment Research Group, Universiti Malaysia Terengganu, 21300, K. Nerus, Malaysia
14 Department of Anesthesiology, College of Medicine, Kaohsiung Medical University, Kaohsiung 80708, Taiwan


Received: January 30, 2021
Revised: April 21, 2021
Accepted: April 24, 2021

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.


Download Citation: ||https://doi.org/10.4209/aaqr.210020  


Cite this article:

Yu, T.Y., Chao, H.R., Tsai, M.H., Lin, C.C., Lu, I.C., Chang, W.H., Chen, C.C., Wang, L.J., Lin, E.T., Chang, C.T., Chen, C., Kao, C.C., Wan Mansor, W.N., Yu, K.L.J. (2021). Big Data Analysis for Effects of the COVID-19 Outbreak on Ambient PM2.5 in Areas that Were Not Locked Down. Aerosol Air Qual. Res. 21, 210020. https://doi.org/10.4209/aaqr.210020


HIGHLIGHTS

  • Big data analysis used to examine PM2.5 during pre-COVID-19 and post-COVID-19.
  • Low-cost PM2.5 sensors investigating PM2.5 patterns during pre- and post-COVID-19 situation.
  • A slight reduction of PM2.5 from January to March in 2020 compared with 2019.
  • Similar PM2.5 patterns observed in the industrial areas in north and south Taiwan in 2019 and 2020.
  • PM2.5 decline during COVID-19 due to decreased domestic emissions of PM2.5 and its precursors.
 

ABSTRACT


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 and southern Taiwan during 2020 resembled those from the previous year. Finally, controlling local PM2.5 emission sources critically contributes to reducing the number of COVID-19 cases.


Keywords: COVID-19, SAS-cov-2, PM2.5, Low-cost sensors, Domestic emission, Big data




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