Size distribution, meteorological influence and uncertainty for source-specific risks: PM2.5 and PM10-bound PAHs and heavy metals in a Chinese megacity during 2011-2021

Yingze Tian; Bin Jia; Peng Zhao; Danlin Song; Fengxia Huang; Yinchang Feng

doi:10.1016/j.envpol.2022.120004

Size distribution, meteorological influence and uncertainty for source-specific risks: PM_2.5 and PM₁₀-bound PAHs and heavy metals in a Chinese megacity during 2011-2021

Environ Pollut. 2022 Nov 1:312:120004. doi: 10.1016/j.envpol.2022.120004. Epub 2022 Aug 19.

Authors

Yingze Tian¹, Bin Jia², Peng Zhao², Danlin Song³, Fengxia Huang³, Yinchang Feng⁴

Affiliations

¹ State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, China; CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research, Tianjin, 300350, China. Electronic address: tianyingze@hotmail.com.
² State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, China.
³ Chengdu Research Academy of Environmental Sciences, Chengdu, 610015, China.
⁴ State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, China; CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research, Tianjin, 300350, China.

PMID: 35995293
DOI: 10.1016/j.envpol.2022.120004

Abstract

This study aims at exploring size distribution, meteorological influence and uncertainty for source-specific risks of atmospheric particulate matter (PM), which can improve risk-mitigation strategies for health protection. Heavy metals (HMs) and polycyclic aromatic hydrocarbons (PAHs) in PM_2.5 and PM₁₀ were detected in a Chinese megacity during 2011-2021. A new method named as PMFBMR, which combines the Positive Matrix Factorization, Bootstrapping, Mote Carlo and Risk assessment model, was developed to estimate uncertainty of source-specific risks. It was found that PAH risks concentrated in fine PM, while HMs showed high risks in both fine and coarse PMs. For PM_2.5, HQ (non-cancer risk hazard quotient) of gasoline combustion (GC), diesel and heavy oil combustion (DC), coal combustion (CC), industrial source (IS), resuspended dust (RD) and secondary and transport PM (ST) were 0.6, 1.4, 0.9, 1.6, 0.3, and 0.3. ILCR (lifetime cancer risk) of sources were IS (9.2E-05) > DC (2.6E-05) = CC (2.6E-05) > RD (2.2E-05) > GC (1.7E-05) > ST (6.4E-06). PM_2.5 from GC, DC, CC and IS caused higher risks than coarse PM, while coarse PM from RD caused higher risks. Source-specific risks were influenced not only by emissions, but also by meteorological condition and dominant toxic components. Risks of GC and DC were usually high during stable weather. Some high risks of CC, IS and RD occurred at strong WS due to transport or wind-blown resuspension. GC and DC risks (influenced by both PAHs and HMs) showed strong relationship with T, while IS and RD risks (dominated by HMs) showed weak link with meteorological conditions. For uncertainty of source-specific risks, HQ and ILCR were sensitive for different variables, because they were dominated by components with different uncertainties. When using source-specific risks for risk-mitigation strategies, the focused toxic components, used toxic values, PM sizes and uncertainty are necessary to be considered.

Keywords: Heavy metals; PM; Polycyclic aromatic hydrocarbons; Source-specific risks; Uncertainty.

MeSH terms

Air Pollutants* / analysis
China
Coal
Dust / analysis
Environmental Monitoring / methods
Gasoline
Metals, Heavy* / analysis
Particulate Matter / analysis
Polycyclic Aromatic Hydrocarbons* / analysis
Risk Assessment
Uncertainty

Substances

Air Pollutants
Coal
Dust
Gasoline
Metals, Heavy
Particulate Matter
Polycyclic Aromatic Hydrocarbons