4.7 Article

Investigation of polycyclic aromatic hydrocarbons in soils from Caserta provincial territory, southern Italy: Spatial distribution, source apportionment, and risk assessment

期刊

JOURNAL OF HAZARDOUS MATERIALS
卷 383, 期 -, 页码 -

出版社

ELSEVIER
DOI: 10.1016/j.jhazmat.2019.121158

关键词

Polycyclic aromatic hydrocarbons; Soils; Spatial distribution; Source apportionment; Risk assessment

资金

  1. National Natural Science Foundation of China [41807342]
  2. Istituto Zooprofilattico Sperimentale del Mezzogiorno (IZSM)
  3. Dipartimento di Scienze della Terra, dell'Ambientee delle Risorse (DiSTAR), University of Napoli Federico II [497/2013]

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The concentrations of polycyclic aromatic hydrocarbons (PAHs) in soils from Caserta provincial territory, southern Italy, were systematically investigated along with their correlations with soil properties and health risk. The concentrations of Sigma(16)PAHs ranged from 10.0 to 4191 ng/g, with a median (1 st quartile, Qi; 3rd quartile, Q3) of 28.5 (17.5-65.0) ng/g; Four-ring PAHs were the most abundant and contributed an average of similar to 50.2% of the Sigma(16)PAHs. Significant differences in the spatial distributions of PAHs in soil were observed, with higher levels of PAH contamination found in Caserta city and the surrounding areas. According to the positive matrix factorization (PMF) model, three sources were identified: chemical production and metal smelting, vehicle emissions, and coal/biomass combustion. Soil total organic carbon was significantly correlated with the concentration of total PAHs and the concentrations of PAHs with three-, four-, and five-rings. In contrast, only the concentration of Sigma(4)DBPs (dibenzo(a,e)pyrene, dibenzo(a,h)pyrene, dibenzo(a,i)pyrene, dibenzo(a,l)pyrene) was well correlated with population density. The soil mass inventory of Sigma(16)PAHs was estimated to be 6.87 metric tons (geometric mean). The ecological risks posed by PAHs in the study are negligible; however, health risks of exposure to soil-borne PAHs were identified based on a probabilistic risk model.

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