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天津师范大学学报（自然科学版） >

2026 , Vol. 46 >Issue 2: 26 - 34

DOI: https://doi.org/10.19638/j.issn1671-1114.20260204

地理与环境科学

基于蒙特卡洛的水质综合评价及PMF溯源解析——以木兰溪为例

  • 李光悦 ,
  • 陈锦 ,
  • 刘继辉 ,
  • 石成春 ,
  • 李莉 ,
  • 李家兵 ,
  • 谢蓉蓉
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  • 1.福建师范大学环境与资源学院,福州 350007;
    2.福建省环境科学研究院,福州 350013;
    3.福建省莆田环境监测中心站,福建莆田 351100;
    4.福建师范大学福建省污染控制与资源循环利用重点实验室,福州 350007;
    5.福建师范大学数字福建环境监测物联网实验室,福州 350007
李光悦（2002—）,男,硕士研究生.
谢蓉蓉（1987—）,女,教授,主要从事水环境数学模型及环境管理方面的研究。E-mail:xierr1118@163.com.

收稿日期: 2024-11-27

  网络出版日期: 2026-06-03

基金资助

国家自然科学基金资助项目（42007343）; 福建省自然科学基金资助项目（2021J01195）

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Comprehensive evaluation of water quality and PMF source analysis based on Monte Carlo: A case study of Mulan River

  • LI Guangyue ,
  • CHEN Jin ,
  • LIU Jihui ,
  • SHI Chengchun ,
  • LI Li ,
  • LI Jiabing ,
  • XIE Rongrong
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  • 1. College of Environmental Science and Resource Science, Fujian Normal University, Fuzhou 350007, China;
    2. Fujian Environmental Science Research Institute, Fuzhou 350013, China;
    3. Fujian Putian Environmental Monitoring Center Station, Putian 351100, Fujian Province, China;
    4. Fujian Key Laboratory of Pollution Control and Resource Recycling, Fujian Normal University, Fuzhou 350007, China;
    5. Digital Fujian Environmental Monitoring Internet of Things Laboratory, Fujian Normal University, Fuzhou 350007, China

Received date: 2024-11-27

  Online published: 2026-06-03

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摘要

为了提高水环境评价及污染溯源的准确性和可靠性,基于2015—2019年木兰溪水质监测数据,采用蒙特卡洛模拟方法计算综合污染指数（H）,通过相关性及敏感性分析识别综合污染指数的影响要素,最后采用正定矩阵因子分解法（positive matrix factor,PMF）进行污染溯源. 结果表明:① 时间上,2015—2019年研究区H值范围为0.67~0.81,除2017年和2018年小幅波动外,水质总体呈好转趋势;空间上,水质从上游到下游总体表现为明显恶化,受土地利用类型影响,中游P4和P5断面局部好转.② 相关性分析表明,上游P3断面在污染程度严重的2015年和2018年对区域的H值影响最大,而下游P6断面在污染程度相对低的2016年、2017年和2019年对区域H值影响最大;敏感性分析表明,TN为研究区综合污染指数的主要影响指标.③ PMF溯源结果表明,研究区汛期污染源贡献率排序为农业污染（33.5%）>生活与工业废水（31.0%）>有机污染源（21.2%）>季节效应（14.3%）,非汛期污染源贡献率排序为农业污染（25.0%）>生活污水（22.0%）>季节效应（20.3%）>有机污染源（18.1%）>工业点源（14.6%）.

关键词： 木兰溪; 蒙特卡洛模拟; 水质综合评价; 综合污染指数; 正定矩阵因子分解模型

本文引用格式

李光悦 , 陈锦 , 刘继辉 , 石成春 , 李莉 , 李家兵 , 谢蓉蓉 . 基于蒙特卡洛的水质综合评价及PMF溯源解析——以木兰溪为例[J]. 天津师范大学学报（自然科学版）, 2026 , 46(2) : 26 -34 . DOI: 10.19638/j.issn1671-1114.20260204

Abstract

In order to improve the accuracy and credibility of the assessment of water environment and the traceability of pollution, based on the data from six routine monitoring sections of Mulan River from 2015 to 2019, the Monte Carlo simulation method was used to calculate the comprehensive pollution index (H). The influencing factors of the comprehensive pollution index were identified through correlation and sensitivity analysis. Finally, the positive matrix factor model (PMF) was used to trace the pollution. The results showed that: ① From 2015 to 2019, the H value in the study area was 0.67-0.81, and the water quality showed an overall improvement trend except for slight fluctuations in 2017 and 2018. There was a significant deterioration from upstream to downstream, with partial improvement in P4 section and P5 section in the middle reaches, which were influenced by land use types. ② Correlation analysis showed that P3 section in upstream had the greatest impact on the regional H value in 2015 and 2018, when pollution levels were severe, while P6 section in downstream had the greatest impact on the regional H value in 2016, 2017, and 2019, when pollution levels were relatively low. Sensitivity analysis showed that TN was the main influencing indicator of the comprehensive pollution index in the studied area. ③ PMF traceability results indicated that the order of contribution rates of pollution sources in the studied area during flood season were agricultural pollution (33.5%) > domestic sewage and industrial wastewater (31.0%) > organic pollution sources (21.2%) > seasonal effects (14.3%), and the order of contribution rates of pollution sources during non-flood season were agricultural pollution (25.0%) > domestic sewage (22.0%) > seasonal effects (20.3%) > organic pollution sources (18.1%) > industrial point sources (14.6%).

Key words： Mulan River; Monte Carlo simulation; comprehensive pollution index; comprehensive pollution index; positive matrix factor decomposition model (PMF)

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