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PM2.5 and microbial aerosols in cold climates: Biological and chemical characteristics, correlation, and source identification during the heating period

  • Heilongjiang University
  • School of Environment, Harbin Institute of Technology
  • Harbin Centre for Disease Control and Prevention

Research output: Contribution to journalArticlepeer-review

Abstract

PM2.5 pollution in cold, dry regions demonstrates heightened cumulative effects through dual roles as chemical and microbial carriers. This study investigated PM2.5 and associated microbial aerosols in Harbin (October 2023–March 2024) to track temporal trends and identify biological component drivers. PM2.5 concentrations ranged from 0.020–0.101 mg/m3, with mean concentrations of: heavy metals (699.42 ng/m3), secondary ions (16.51 μg/m3), PAHs (20.99 ng/m3), and culturable microbes (129.57 CFU/m3). PAH health risk assessments indicate a low risk of cancer. Using enrichment factor analysis, scanning electron microscopy (SEM), and principal component analysis-multiple linear regression (PCA-MLR), we identified coal combustion and vehicle emissions as dominant sources. Microbial aerosols peaked within 1.1–2.1 μm particles (21.04 %–51.31 %), demonstrating greater activity on coarse particles. Genomic sequencing revealed Achromobacter (44.68 %) as the predominant bacterial genus and Epicoccum (33.13 %) as the dominant fungal genus. Functional profiling indicated bacterial dominance in metabolic processes and fungal specialization in saprotrophy. Spearman correlation analysis further demonstrated distinct chemical-microbial interactions: elements like Sb, Se, Ni, Al, As, Cr, PAHs, and inorganic ions, along with meteorological factors such as atmospheric pressure and wind speed, exhibited varying positive or negative correlations with microbial concentrations, activity levels, and community composition.

Original languageEnglish
Article number107103
JournalProcess Safety and Environmental Protection
Volume198
DOIs
StatePublished - Jun 2025
Externally publishedYes

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

Keywords

  • 16S rRNA
  • PM
  • Physicochemical properties
  • Source apportionment

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