Identifying and quantifying PM2.5 pollution episodes with a fusion method of moving window technique and constrained Positive Matrix Factorization

Chun-Sheng Huang; Ho-Tang Liao; Shao-Hao Lu; Chang-Chuan Chan; Chang-Fu Wu

doi:10.1016/j.envpol.2022.120382

Identifying and quantifying PM_2.5 pollution episodes with a fusion method of moving window technique and constrained Positive Matrix Factorization

Environ Pollut. 2022 Dec 15:315:120382. doi: 10.1016/j.envpol.2022.120382. Epub 2022 Oct 9.

Authors

Chun-Sheng Huang¹, Ho-Tang Liao¹, Shao-Hao Lu², Chang-Chuan Chan³, Chang-Fu Wu⁴

Affiliations

¹ Institute of Environmental and Occupational Health Sciences, College of Public Health, National Taiwan University, Taipei, Taiwan.
² LE & DER Instrument Co. Ltd., Taipei, Taiwan.
³ Institute of Environmental and Occupational Health Sciences, College of Public Health, National Taiwan University, Taipei, Taiwan; Department of Public Health, College of Public Health, National Taiwan University, Taipei, Taiwan.
⁴ Institute of Environmental and Occupational Health Sciences, College of Public Health, National Taiwan University, Taipei, Taiwan; Department of Public Health, College of Public Health, National Taiwan University, Taipei, Taiwan. Electronic address: changfu@ntu.edu.tw.

PMID: 36220571
DOI: 10.1016/j.envpol.2022.120382

Abstract

PM_2.5 pollution episodes rapidly and significantly deteriorate the air quality and are a critical concern worldwide. This study developed a fusion method based on the moving window dataset technique and constrained Positive Matrix Factorization (PMF) to differentiate and characterize potential factors in a PM_2.5 episode case assuming having one new contributor. The hourly PM_2.5 compositions of elements, ions and carbonaceous components, were collected from September to December 2020 in Taipei, Taiwan. Constraint targets based on the bootstrap analysis result of a PMF model using a long-term input dataset were imposed on the modeling of each moving window to ensure similar features of the retrieved factors. The constituents of an additionally differentiated factor to the episode, which was identified as regional transport, were stable among each moving window that covered the occurrence of the episode as revealed by the profile matching index. The results showed that the largest contributor to the PM_2.5 mass during the episode period of 12/12/2020 was regional transport (61%), whereas that of 12/13 was the regular pollution of industry/ammonium sulfate related (43%). According to our review of the literature, this study is the first to apply both the moving window technique and constrained PMF to characterize the episode. The findings provide valuable information that can be used to explore the causes of PM_2.5 episodes and implement air pollution control strategies.

Keywords: Factor profile; Moving window technique; Online measurements; Source apportionment.

Publication types

Review

MeSH terms

Air Pollutants* / analysis
Air Pollution* / analysis
Environmental Monitoring / methods
Ions
Particulate Matter / analysis
Vehicle Emissions / analysis

Substances

Air Pollutants
Particulate Matter
Ions
Vehicle Emissions