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A Big Data Analysis of PM2.5 and PM10 from Low Cost Air Quality Sensors near Traffic Areas

Category: Urban Air Quality

Volume: 19 | Issue: 8 | Pages: 1721-1733
DOI: 10.4209/aaqr.2019.06.0328

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Shida Chen1, Kangping Cui 1, Tai-Yi Yu 2, How-Ran Chao3,4,5, Yi-Chyun Hsu6, I-Cheng Lu3, Rachelle D. Arcega3, Ming-Hsien Tsai7, Sheng-Lun Lin8,9,10, Wan-Chun Chao11, Chunneng Chen11, Kwong-Leung J. Yu12

  • 1 School of Resources and Environmental Engineering, Hefei University of Technology, Hefei 246011, China
  • 2 Department of Risk Management and Insurance, Ming Chuan University, Taipei 11103, Taiwan
  • 3 Emerging Compounds Research Center, Department of Environmental Science and Engineering, College of Engineering, National Pingtung University of Science and Technology, Pingtung 91201, Taiwan
  • 4 Institute of Food Safety Management, College of Agriculture, National Pingtung University of Science and Technology, Pingtung 91201, Taiwan
  • 5 Emerging Compounds Research Center, General Research Service Center, National Pingtung University of Science and Technology, Pingtung 91201, Taiwan
  • 6 Department of Environmental Engineering, Kun Shan University, Tainan 71003, Taiwan
  • 7 Department of Child Care, College of Humanities and Social Sciences, National Pingtung University of Science and Technology, Pingtung 91201, Taiwan
  • 8 Department of Civil Engineering and Geomatics, Cheng Shiu University, Kaohsiung 83347, Taiwan
  • 9 Center for Environmental Toxin and Emerging-Contaminant Research, Cheng Shiu University, Kaohsiung 83347, Taiwan
  • 10 Super Micro Mass Research and Technology Center, Cheng Shiu University, Kaohsiung 83347, Taiwan
  • 11 JS Environmental Technology and Energy Saving Co. Ltd., Kaohsiung 80661, Taiwan
  • 12 Superintendent Office, Pingtung Christian Hospital, Pingtung 90059, Taiwan


  • A good correlation between PM and relative humidity was observed at low PM levels.
  • Both PM were concentrated in the heavily industrialized areas of Kaohsiung City.
  • PM2.5 displayed higher peaks during daytime than in nighttime.
  • PM2.5 diurnal pattern was affected by industrial and human transport activities.


Particulate matter (PM) pollution (including PM2.5 and PM10), which is reportedly caused primarily by industrial and vehicular emissions, has become a major global health concern. In this study, we aimed to reveal spatiotemporal characteristics and diurnal patterns of PM2.5 and PM10 data obtained from 50 air quality sensors situated in public bike sites in Kaohsiung City on June and November 2018 using principal component analysis (PCA). Results showed that PM concentrations in the study were above the standard World Health Organization criteria and were found to be associated, although complicated, with relative humidity. Specifically, the relationship between PM concentrations and relative humidity suggest a clear association at lower PM concentrations. Temporal analysis revealed that PM2.5 and PM10 occurred at higher concentrations in winter than in summer, which could be explained by the long-range transport of pollutants brought about by the northeast monsoon during the winter season. Both PM fractions displayed similar spatial distribution, wherein PM2.5 and PM10 were found to be concentrated in the heavily industrialized areas of the city, such as near petrochemical factories in Nanzih and Zuoying districts in north Kaohsiung and near the shipbuilding and steel manufacturing factories in Xiaogang district in south Kaohsiung. A pronounced diurnal variation was found for PM2.5, which generally displayed higher peaks during the daytime than in the nighttime. Peaks generally occurred at 7:00–9:00 a.m., noontime, and 5:00–7:00 p.m., while minima generally appeared at nighttime. The diurnal pattern of PM was greatly influenced by a greater number of industrial and human transportation activities during the day than at night. Overall, a number of factors such as relative humidity and type of season, transboundary pollution from neighboring countries, and human activities, such as industrial operations and vehicle use, affects the PM quality in Kaohsiung City, Taiwan.


Particulate matter Public bike sites Principal component analysis Internet of things Low-cost air sensor

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