Improvements in source apportionment of multiple time-resolved PM2.5 inorganic and organic speciation measurements using constrained Positive Matrix Factorization

Chun-Sheng Huang; Yi-Hsien Liu; Ho-Tang Liao; Chia-Yang Chen; Chang-Fu Wu

doi:10.1007/s11356-024-35476-z

Improvements in source apportionment of multiple time-resolved PM_2.5 inorganic and organic speciation measurements using constrained Positive Matrix Factorization

Environ Sci Pollut Res Int. 2024 Nov 12. doi: 10.1007/s11356-024-35476-z. Online ahead of print.

Authors

Chun-Sheng Huang^#¹, Yi-Hsien Liu^#¹, Ho-Tang Liao¹, Chia-Yang Chen^{1

2

3}, Chang-Fu Wu^{4

5}

Affiliations

¹ Institute of Environmental and Occupational Health Sciences, College of Public Health, National Taiwan University, Taipei, 100, Taiwan.
² Institute of Food Safety and Health, College of Public Health, National Taiwan University, Taipei, 100, Taiwan.
³ Department of Public Health, College of Public Health, National Taiwan University, Taipei, 100, Taiwan.
⁴ Institute of Environmental and Occupational Health Sciences, College of Public Health, National Taiwan University, Taipei, 100, Taiwan. changfu@ntu.edu.tw.
⁵ Department of Public Health, College of Public Health, National Taiwan University, Taipei, 100, Taiwan. changfu@ntu.edu.tw.

^# Contributed equally.

PMID: 39528894
DOI: 10.1007/s11356-024-35476-z

Abstract

The equation of Positive Matrix Factorization (PMF) has been modified to resolve multiple time resolution inputs and applied in numerous field studies. The refined modeling results provide a solution with an increased number of factors and enriched profile features. However, the incorporation of low time-resolved data may retrieve unfavorable mixed factor profiles, introducing high uncertainties into the PMF solution computations. To address this issue, a dual-stage PMF modeling procedure with predefined constraints was proposed. Multiple time-resolved PM_2.5 inorganic and organic speciation measurements were collected from autumn of 2022 to summer of 2023 in Taipei, Taiwan. Without using the proposed approach, a mixed factor of vehicle/biomass burning and an unphysically meaningful factor of sodium ion- and ammonium ion-rich were identified. After implementing the proposed approach, a refined number of eight factors with separated and reasonable profiles were retrieved. Over the sampling period, the largest contributor to PM_2.5 and organic carbon was vehicle (contribution = 26% and 47%, respectively), while those for secondary inorganic aerosols of SO₄^2-, NO₃^-, and NH₄⁺ were industry (27%, 25%, and 31%, respectively), highlighting the importance of regulating these two sources. The low vehicle contribution to NO₃^- may be due to time-lag effects from gas-to-particle conversion, which led to different temporal patterns between NO₃^- and primary species. Addressing this issue is crucial in future studies for better apportionment of secondary aerosols.

Keywords: Constraint; Mixed profiles; Multiple time-resolved data; Organic molecular tracers; Receptor model.

Abstract

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