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<p>Polycyclic aromatic hydrocarbons (PAHs) in sediments represent a pervasive environmental issue that poses significant ecological risks. This study employed a combination of geographic information systems, diagnostic ratios, correlation analysis, Pb isotope ratios, and positive matrix factorization (PMF) to elucidate the potential sources of 16 priority PAHs in river sediments from a hilly agricultural watershed in Southwestern China. The results indicated that PAHs concentrations ranged from 55.9 to 6083.5&#xa0;ng/g, with a mean value of 1582.1 ± 1528.9&#xa0;ng/g, reflecting high levels of contamination throughout the watershed. The predominant class of PAHs identified was high molecular weight&#xa0;(HMW) PAHs. Diagnostic ratios and correlation analysis suggested that the presence of PHAs is likely attributed primarily to emissions from industrial dust and combustion of coal and petroleum. Furthermore, correlation analysis revealed a significant association between Pb and PAHs, indicating potential shared sources for both pollutants. Additionally, Pb isotopic analysis demonstrated that aerosols may be the primary contributor to Pb accumulation within this environment. Given the similarity in origins between Pb and PAHs, it can be inferred that PAHs predominantly originate from aerosols associated with coal combustion, industrial dust emissions, and vehicle exhaust. This inference is further supported by PMF results which yielded consistent findings with those derived from Pb isotopes analysis. Moreover, PMF estimated three major sources contributing 57.63%, 23.57%, and 18.80%, respectively. These findings provide novel insights into identifying the sources of PAHs in river sediments within hilly agricultural watersheds in Southwest China, thereby establishing a scientific foundation for enhancing environmental quality in agricultural regions.</p>

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Source identification of polycyclic aromatic hydrocarbons (PAHs) in river sediments within a hilly agricultural watershed of Southwestern China: an integrated study based on Pb isotopes and PMF method

  • 期刊论文

  • 发表日期：

    2025年4月15日

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  • Fen Xu,
  • Chunmei Jiang,
  • Qiang Liu,
  • Rui Yang,
  • Weiwei Li,
  • Yao Wei,
  • Linlin Bao,
  • Hongjin Tong

摘要

Polycyclic aromatic hydrocarbons (PAHs) in sediments represent a pervasive environmental issue that poses significant ecological risks. This study employed a combination of geographic information systems, diagnostic ratios, correlation analysis, Pb isotope ratios, and positive matrix factorization (PMF) to elucidate the potential sources of 16 priority PAHs in river sediments from a hilly agricultural watershed in Southwestern China. The results indicated that PAHs concentrations ranged from 55.9 to 6083.5 ng/g, with a mean value of 1582.1 ± 1528.9 ng/g, reflecting high levels of contamination throughout the watershed. The predominant class of PAHs identified was high molecular weight (HMW) PAHs. Diagnostic ratios and correlation analysis suggested that the presence of PHAs is likely attributed primarily to emissions from industrial dust and combustion of coal and petroleum. Furthermore, correlation analysis revealed a significant association between Pb and PAHs, indicating potential shared sources for both pollutants. Additionally, Pb isotopic analysis demonstrated that aerosols may be the primary contributor to Pb accumulation within this environment. Given the similarity in origins between Pb and PAHs, it can be inferred that PAHs predominantly originate from aerosols associated with coal combustion, industrial dust emissions, and vehicle exhaust. This inference is further supported by PMF results which yielded consistent findings with those derived from Pb isotopes analysis. Moreover, PMF estimated three major sources contributing 57.63%, 23.57%, and 18.80%, respectively. These findings provide novel insights into identifying the sources of PAHs in river sediments within hilly agricultural watersheds in Southwest China, thereby establishing a scientific foundation for enhancing environmental quality in agricultural regions.