甘肃省黄河干支流水质差异及其影响因素分析

张晋博; 陆志翔; 罗艳; 冯起

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中国环境科学 ›› 2025, Vol. 45 ›› Issue (5) : 2767-2779.

环境生态

甘肃省黄河干支流水质差异及其影响因素分析

张晋博^1,2, 陆志翔¹, 罗艳^1,2, 冯起¹

作者信息 +

Analysis of the water quality difference and its influencing factors in the main stream and tributaries of the Yellow River in Gansu Province

ZHANG Jin-bo^1,2, LU Zhi-xiang¹, LUO Yan^1,2, Feng Qi¹

Author information +

文章历史 +

摘要

以黄河流域甘肃段为研究对象,基于DEM数据、土地利用数据、水质监测数据和社会经济数据,采用Fragstats 3.3软件和Pearson相关分析方法,计算河道缓冲区景观格局指数,分析水质指标与景观格局的相关性,探讨影响甘肃省黄河干支流水质的因素.结果表明:(1)2018~2021年,除洮河水质指标浓度总体趋于增长外,甘肃省黄河干流和其他支流水质整体好转,但超过80%的监测点的总氮(TN)指标处于Ⅴ类以上,且渭河、泾河和庄浪河氮源污染水平仍然较高;总氮(TN)和氨氮(NH₄⁺-N)高值区主要分布在流域中部和东部局部地区,大部分地区的总磷(TP)很高,化学需氧量(COD)值空间差异大,且高值区主要集中在兰州市、临夏回族自治州中部的局部地区.(2)景观格局与水质指标存在显著相关性,景观斑块聚集度和连通性越强,水质越好;景观斑块破碎化程度和离散程度越高,水质污染风险越高;(3)水质指标具有较强的空间差异,水质驱动因素结果表明氨氮(NH₄⁺-N)、总磷(TP)、高锰酸钾指数和化学需氧量(COD)指标主要受农村生活和农业生产影响,总氮和溶解氧指标主要受城镇生活和工业生产影响.

Abstract

Using the Gansu section of the Yellow River Basin as the research object, Fragstats 3.3 software and Pearson correlation analysis method were used to calculate the landscape pattern index of the river buffer zone, analyze the correlation between water quality and landscape pattern indicators, and investigate the factors influencing the water quality of the Yellow River’s main stream and tributaries in Gansu Province. The data included the land use data with the resolution of 30m in 2020, water quality monitoring data, and socio-economic data from 2018 to 2021. The results indicated that: (1) From 2018 to 2021, the water quality of the main stream and tributaries of the Yellow River in Gansu Province were improving, except the Taohe River, where the concentration of water quality indicators was in the increasing trend; however, TN at more than 80% of monitoring sites remained above Class V water quality standards, with significant nitrogen pollution persisting in the Weihe River, Jinghe River, and Zhuanglang River. High concentrations of TN and NH₄⁺-N were mainly distributed in the central and eastern parts of the basin, TP levels were elevated across most areas, and COD was dispersed, with hotspots concentrated in parts of Lanzhou City and central Linxia Hui Autonomous Prefecture. (2) A significant correlation was observed between landscape patterns and water quality indicators. Higher aggregation and connectivity of landscape patches were associated with better water quality, whereas higher levels of fragmentation and dispersion increased the risk of water pollution. (3) Water quality indicators exhibited strong spatial heterogeneity. The driving factor analysis revealed that NH₄⁺-N, TP, permanganate index, and COD were primarily influenced by rural activities, while TN and DO were mainly affected by urban living and industrial production.

导出引用

张晋博, 陆志翔, 罗艳, 冯起. 甘肃省黄河干支流水质差异及其影响因素分析[J]. 中国环境科学. 2025, 45(5): 2767-2779

ZHANG Jin-bo, LU Zhi-xiang, LUO Yan, Feng Qi. Analysis of the water quality difference and its influencing factors in the main stream and tributaries of the Yellow River in Gansu Province[J]. China Environmental Science. 2025, 45(5): 2767-2779

中图分类号： X522

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