Spatial variability of source contributions to nitrate in regional groundwater based on the positive matrix factorization and Bayesian model

被引:37
作者
Mao, Hairu [1 ,2 ,3 ]
Wang, Guangcai [1 ,2 ,3 ]
Liao, Fu [1 ,2 ,3 ]
Shi, Zheming [1 ,2 ,3 ]
Zhang, Hongyu [1 ,2 ,3 ]
Chen, Xianglong [1 ,2 ,3 ]
Qiao, Zhiyuan [1 ,2 ,3 ]
Li, Bo [1 ,2 ,3 ]
Bai, Yunfei [1 ,2 ,3 ]
机构
[1] China Univ Geosci, State Key Lab Biogeol & Environm Geol, Beijing 100083, Peoples R China
[2] China Univ Geosci, MOE Key Lab Groundwater Circulat & Environm Evolut, Beijing 100083, Peoples R China
[3] China Univ Geosci, Sch Water Resources & Environm, Beijing 100083, Peoples R China
基金
中国国家自然科学基金;
关键词
Groundwater nitrate; Source apportionment; Spatial variability; Bayesian mixing model; Positive matrix factorization; POYANG LAKE BASIN; SHALLOW GROUNDWATER; AGRICULTURAL AREAS; SOURCE APPORTIONMENT; ISOTOPE APPROACH; CLIMATE-CHANGE; SOIL-NITROGEN; WATER; CONTAMINATION; ATTENUATION;
D O I
10.1016/j.jhazmat.2022.130569
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Groundwater nitrate (NO3-) pollution has attracted widespread attention; however, accurately evaluating the sources of NO3 - and their contribution patterns in regional groundwater is difficult in areas with multiple sources and complex hydrogeological conditions. In this study, 161 groundwater samples were collected from the Poyang Lake Basin for hydrochemical and dual NO3 - isotope analyses to explore the sources of NO3 - and their spatial contribution using the Positive Matrix Factorization (PMF) and Bayesian stable isotope mixing (MixSIAR) models. The results revealed that the enrichment of NO3 - in groundwater was primarily attributed to sewage/ manure (SM), which accounted for more than 50 %. The contributions of nitrogen fertilizer and soil organic nitrogen should also be considered. Groundwater NO3 - sources showed obvious spatial differences in contribu-tions. Regions with large contributions of SM (>90 %) were located in the southeastern part of the study area and downstream of Nanchang, which are areas with relatively high population density. Nitrogen fertilizer and soil organic nitrogen showed concentrated contributions in paddy soil in the lower reaches of the Gan and Rao Rivers, and these accumulations were mainly driven by the soil type, land use type, and topography. This study provides insight into groundwater NO3 - contamination on a regional scale.
引用
收藏
页数:10
相关论文
共 50 条
  • [21] A partition computing-based positive matrix factorization (PC-PMF) approach for the source apportionment of agricultural soil heavy metal contents and associated health risks
    Wu, Jin
    Li, Jiao
    Teng, Yanguo
    Chen, Haiyang
    Wang, Yeyao
    JOURNAL OF HAZARDOUS MATERIALS, 2020, 388 (388)
  • [22] Development and assessment of a receptor source apportionment model based on four nonnegative matrix factorization algorithms
    Liu, Haitao
    Tian, Chongguo
    Zong, Zheng
    Wang, Xiaoping
    Li, Jun
    Zhang, Gan
    ATMOSPHERIC ENVIRONMENT, 2019, 197 : 159 - 165
  • [23] Assessing positive matrix factorization model fit: a new method to estimate uncertainty and bias in factor contributions at the measurement time scale
    Hemann, J. G.
    Brinkman, G. L.
    Dutton, S. J.
    Hannigan, M. P.
    Milford, J. B.
    Miller, S. L.
    ATMOSPHERIC CHEMISTRY AND PHYSICS, 2009, 9 (02) : 497 - 513
  • [24] Pinus eldarica (L.) bark as urban atmospheric trace element pollution bioindicator: pollution status, spatial variations, and quantitative source apportionment based on positive matrix factorization receptor model
    Akbarimorad, Shima
    Sobhanardakani, Soheil
    Hosseini, Nayereh Sadat
    Martin, David Bolonio
    ENVIRONMENTAL MONITORING AND ASSESSMENT, 2024, 196 (08)
  • [25] Coupling Chemical Transport Model Source Attributions with Positive Matrix Factorization: Application to Two IMPROVE Sites Impacted by Wildfires
    Sturtz, Timothy M.
    Schichtel, Bret A.
    Larson, Timothy V.
    ENVIRONMENTAL SCIENCE & TECHNOLOGY, 2014, 48 (19) : 11389 - 11396
  • [26] Source apportionment of PAHs in roadside agricultural soils of a megacity using positive matrix factorization receptor model and compound-specific carbon isotope analysis
    Yang, Jing
    Sun, Pei
    Zhang, Xi
    Wei, Xin-Yi
    Huang, Yan-Ping
    Du, Wei-Ning
    Qadeer, Abdul
    Liu, Min
    Huang, Ye
    JOURNAL OF HAZARDOUS MATERIALS, 2021, 403
  • [27] Deep optimization of water quality index and positive matrix factorization models for water quality evaluation and pollution source apportionment using a random forest model
    Zhang, Han
    Ren, Xingnian
    Chen, Sikai
    Xie, Guoqiang
    Hu, Yuansi
    Gao, Dongdong
    Tian, Xiaogang
    Xiao, Jie
    Wang, Haoyu
    ENVIRONMENTAL POLLUTION, 2024, 347
  • [28] Spatial distribution and human health risk assessment of soil heavy metals based on sequential Gaussian simulation and positive matrix factorization model: A case study in irrigation area of the Yellow River
    Shen, Weibo
    Hu, Yue
    Zhang, Jie
    Zhao, Fei
    Bian, Pengyang
    Liu, Yixuan
    ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY, 2021, 225
  • [29] Inhalation cancer risk estimation of source-specific personal exposure for particulate matter–bound polycyclic aromatic hydrocarbons based on positive matrix factorization
    Bin Han
    Yan You
    Yating Liu
    Jia Xu
    Jian Zhou
    Jiefeng Zhang
    Can Niu
    Nan Zhang
    Fei He
    Xiao Ding
    Zhipeng Bai
    Environmental Science and Pollution Research, 2019, 26 : 10230 - 10239
  • [30] Traceability analysis and risk assessment of river antibiotics based on dissolved organic matter spectral features and the positive matrix factorization receptor model
    Xu, Rongle
    Song, Jinqiu
    Li, Denghui
    Song, Xiaowei
    Wang, Xu
    Xiong, Jianhua
    JOURNAL OF CONTAMINANT HYDROLOGY, 2025, 272