A modified receptor model for source apportionment of sediment polycyclic aromatic hydrocarbons

Yan Li; Ning Li; Xiangling Zhang; Ke Liu; Zhenyi Jia; Genmei Wang

doi:10.1016/j.jenvman.2022.115637

A modified receptor model for source apportionment of sediment polycyclic aromatic hydrocarbons

J Environ Manage. 2022 Sep 15:318:115637. doi: 10.1016/j.jenvman.2022.115637. Epub 2022 Jul 1.

Authors

Yan Li¹, Ning Li², Xiangling Zhang², Ke Liu³, Zhenyi Jia⁴, Genmei Wang²

Affiliations

¹ Collaborative Innovation Center of Sustainable Forestry, Nanjing Forestry University, Nanjing, Jiangsu, China; Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, Jiangsu, China; Key Laboratory of Geographic Information Science of the Ministry of Education, School of Geographic Sciences, East China Normal University, Shanghai, China. Electronic address: lyle@njfu.edu.cn.
² Collaborative Innovation Center of Sustainable Forestry, Nanjing Forestry University, Nanjing, Jiangsu, China.
³ School of Geography and Ocean Science, Nanjing University, 163 Xianlin Road, Nanjing, Jiangsu, China.
⁴ College of Geography and Environmental Sciences, Zhejiang Normal University, 688 Yingbin Road, Jinhua, Zhejiang, China.

PMID: 35949089
DOI: 10.1016/j.jenvman.2022.115637

Abstract

Polycyclic aromatic hydrocarbons (PAHs) have become a serious threat to human health and ecological security due to their persistence and high toxicity. Lake sediments are in a relatively closed environment, so PAHs and other pollutants can be preserved for a long time. Accurate analysis of the sources of PAHs in sediments is an important prerequisite for PAH pollution control. However, the existing PAHs source resolution receptor model (the absolute principal component analysis - multilinear regression (APCA-MLR) and positive matrix factorization (PMF)) has many defects, such as great uncertainty in the process of matrix rotation. In this study, we collected sediment samples from Taihu Lake and tested their PAH content, and the existing receptor model was improved. High PAH contents were distributed in Meiliang Bay, Zhushan Bay, Gonghu Bay and areas close to the shore. "High-High" areas were distributed in Meiliang Bay, Gonghu Bay and areas close to the shore. "Low-Low" areas appeared in the central and southern parts of Taihu Lake. The results show that the improved positive matrix factorization partition computing (PMF-PC) model is significantly better than the APCA-MLR and PMF models in terms of both numerical simulation accuracy and the spatial distribution consistency of PAHs. The correlations (R²) between the measured and simulated values of low-molecular-weight PAHs (L-PAHs), high-molecular-weight PAHs (H-PAHs) and PAHs were 0.992, 0.989 and 0.993, respectively. The contributions of biomass sources, coal combustion sources and petroleum sources to PAHs in Taihu Lake sediments reached 16.7%, 31.7% and 51.6%, respectively. Fossil fuel sources were mainly concentrated in areas near the shore, and the contribution was lower in areas far from the shore. Although the algorithm still needs to be improved, the PMF-PC model may become a useful tool for the source apportionment of PAHs in sediments.

Keywords: Polycyclic aromatic hydrocarbons; Receptor model; Sediments; Source apportionment.

MeSH terms

China
Environmental Monitoring / methods
Geologic Sediments / analysis
Humans
Lakes
Polycyclic Aromatic Hydrocarbons*
Water Pollutants, Chemical* / analysis

Substances

Polycyclic Aromatic Hydrocarbons
Water Pollutants, Chemical