Source and risk apportionment of selected VOCs and PM₂.₅ species using partially constrained receptor models with multiple time resolution data

Ho-Tang Liao; Charles C-K Chou; Judith C Chow; John G Watson; Philip K Hopke; Chang-Fu Wu

doi:10.1016/j.envpol.2015.05.035

Source and risk apportionment of selected VOCs and PM₂.₅ species using partially constrained receptor models with multiple time resolution data

Environ Pollut. 2015 Oct:205:121-30. doi: 10.1016/j.envpol.2015.05.035. Epub 2015 Jun 6.

Authors

Ho-Tang Liao¹, Charles C-K Chou², Judith C Chow³, John G Watson³, Philip K Hopke⁴, Chang-Fu Wu⁵

Affiliations

¹ Institute of Occupational Medicine and Industrial Hygiene, National Taiwan University, Taipei, Taiwan.
² Research Center for Environmental Changes, Academia Sinica, Taipei, Taiwan.
³ Division of Atmospheric Sciences, Desert Research Institute, Reno, NV, USA.
⁴ Center for Air Resources Engineering and Science and Department of Chemical and Biomolecular Engineering, Clarkson University, Potsdam, NY, USA.
⁵ Institute of Occupational Medicine and Industrial Hygiene, National Taiwan University, Taipei, Taiwan; Department of Public Health, National Taiwan University, Taipei, Taiwan; Institute of Environmental Health, National Taiwan University, Taipei, Taiwan. Electronic address: changfu@ntu.edu.tw.

PMID: 26057474
DOI: 10.1016/j.envpol.2015.05.035

Abstract

This study was conducted to identify and quantify the sources of selected volatile organic compounds (VOCs) and fine particulate matter (PM2.5) by using a partially constrained source apportionment model suitable for multiple time resolution data. Hourly VOC, 12-h and 24-h PM2.5 speciation data were collected during three seasons in 2013. Eight factors were retrieved from the Positive Matrix Factorization solutions and adding source profile constraints enhanced the interpretability of source profiles. Results showed that the evaporative emission factor was the largest contributor (25%) to VOC mass concentration, while the largest contributor to PM2.5 mass concentration was soil dust/regional transport related factor (26%). In terms of risk prioritization, traffic/industry related factor was the major cause for benzene, ethylbenzene, Cr, and polycyclic aromatic hydrocarbons (29-69%) while petrochemical related factor contributed most to the Ni risk (36%). This indicated that a larger contributor to mass concentration may not correspond to a higher risk.

Keywords: Constrained receptor model; Multiple time resolution; Positive matrix factorization; Risk apportionment; Source apportionment.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Air Pollutants / chemistry*
Air Pollution / analysis*
Dust / analysis
Environmental Monitoring
Models, Theoretical
Particulate Matter / analysis*
Seasons
Volatile Organic Compounds / chemistry*

Substances

Air Pollutants
Dust
Particulate Matter
Volatile Organic Compounds