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Toxicity source apportionment of fugitive dust PM2.5-bound polycyclic aromatic hydrocarbons using multilayer perceptron neural network analysis in Guanzhong Plain urban agglomeration, China

  • Qian Zhang
  • , Ziyi Zhao
  • , Zhichun Wu
  • , Xinyi Niu
  • , Yuhang Zhang
  • , Qiyuan Wang
  • , Steven Sai Hang Ho
  • , Zhihua Li
  • , Zhenxing Shen
  • Xi'an University of Architecture and Technology
  • CAS - Institute of Earth Environment
  • Xi'an Jiaotong University
  • Desert Research Institute

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

Polycyclic aromatic hydrocarbons (PAHs) in urban fugitive dust, known for their toxicity and ability to generate reactive oxygen species (ROS), are a major public health concern. This study assessed the spatial distribution and health risks of 15 PAHs in construction dust (CD) and road dust (RD) samples collected from June to November 2021 over the cities of Tongchuan (TC), Baoji (BJ), Xianyang (XY), and Xi'an (XA) in the Guanzhong Plain, China. The average concentration of ΣPAHs in RD was 39.5 ± 20.0 μg g−1, approximately twice as much as in CD. Four-ring PAHs from fossil fuels combustion accounted for the highest proportion of ΣPAHs in fugitive dust over all four cities. Health-related indicators including benzo(a)pyrene toxic equivalency factors (BAPTEQ), oxidative potential (OP), and incremental lifetime cancer risk (ILCR) all presented higher risk in RD than those in CD. The multilayer perceptron neural network algorithm quantified that vehicular and industrial emissions contributed 86 % and 61 % to RD and CD BAPTEQ, respectively. For OP, the sources of biomass and coal combustion were the key generator which accounted for 31–54 %. These findings provide scientific evidence for the direct efforts toward decreasing the health risks of fugitive dust in Guanzhong Plain urban agglomeration, China.

Original languageEnglish
Article number133773
JournalJournal of Hazardous Materials
Volume468
DOIs
StatePublished - 15 Apr 2024

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being
  2. SDG 11 - Sustainable Cities and Communities
    SDG 11 Sustainable Cities and Communities

Keywords

  • Artificial neural network
  • Health risk assessment
  • Oxidative potential
  • Source apportionment
  • Urban dust PM

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