[Prediction of PAHs Content in Soil Around Gas Stations in Beijing Based on BP Neural Network].
Huan Jing Ke Xue
; 44(4): 2215-2222, 2023 Apr 08.
Article
in Zh
| MEDLINE
| ID: mdl-37040970
ABSTRACT
With the rapid development of urbanization in China, the number of gas stations in cities is increasing. The composition of oil products in gas stations is complex and diverse, and a series of pollutants will be generated in the process of oil diffusion. Polycyclic aromatic hydrocarbons (PAHs) produced by gas stations can pollute the nearby soil and affect human health. In this study, soil samples (0-20 cm) near 117 gas stations in Beijing were collected, and the contents of seven PAHs were analyzed. Based on the BP neural network model, the contents of PAHs in soil of Beijing gas stations in 2025 and 2030 were predicted. The results showed that the total concentrations of the seven PAHs were 0.01-3.53 mg·kg-1. The concentrations of PAHs were lower than the soil environmental quality risk control standard for soil contamination of development land (Trial) GB 36600-2018. At the same time, the toxic equivalent concentrations (TEQ) of the above seven PAHs were lower than the standard value (1 mg·kg-1) of the World Health Organization (WHO), which they indicate a lower risk to human health. The prediction results showed that the rapid development of urbanization had a positive correlation with the increase in soil PAHs content. By 2030, the content of PAHs in Beijing gas station soil will continue to grow. The predicted concentrations of PAHs in the soil of Beijing gas stations in 2025 and 2030 were 0.085-4.077 mg·kg-1and 0.132-4.412 mg·kg-1, respectively. The contents of seven PAHs were lower than the soil pollution risk screening value of GB 36600-2018; however, the concentration of PAHs increased over time.The contents of PAHs in Chaoyang, Fengtai, and Haidian were relatively higher, which requires further attention.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Type of study:
Prognostic_studies
/
Risk_factors_studies
Language:
Zh
Journal:
Huan Jing Ke Xue
Year:
2023
Document type:
Article
Affiliation country:
China