Spatio-Temporal Variation Characteristics of Water Quality and Pollutant Source Apportionment in Xiaoqing River Mainstream
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摘要:
河流作为人类及其他生物赖以生存的重要自然生态系统，其水质状况直接关系到生态安全与可持续发展.为探究小清河干流水质时空变化特征，基于2019—2022年的水质监测数据，采用综合水质指数法（water quality index，WQI）分析小清河干流水质时空变化特征，并利用绝对主成分-多元线性回归模型解析干流污染物来源及其贡献率.结果表明：2019—2022年小清河干流水质年均WQI值呈现增加趋势，水质等级从“中等”转变为“良好”；水质状况在季节尺度上未呈现显著波动，但丰水期WQI均值相对较低.空间上，小清河干流水质WQI值总体上表现为中游＜上游＜下游，水质较差的区域主要分布在中上游，尤其是干流沿岸建设用地和耕地面积占比较大的地区.源解析结果显示，干流污染物来源较为复杂，主要为工业废水排放源（25.81%）、农业种植活动源（24.92%）、生活污水排放和畜禽养殖源（17.60%）；干流各监测断面的污染物来源组成相似，然而，其污染源的贡献结构存在差异，这种差异与沿岸的土地利用方式及人类活动强度密切相关.研究结果可为小清河流域的污染防控与治理决策提供关键科学依据.
Abstract:As a critical natural ecosystem supporting the survival of humans and other organisms, the water quality of rivers directly influences ecological security and sustainable development. To investigate the spatiotemporal variations in water quality of the Xiaoqing River mainstream, this study analyzed and evaluated these characteristics using the comprehensive water quality index (WQI) method based on monitoring data from 2019 to 2022. Additionally, an absolute principal component score-multiple linear regression (APCS-MLR) model was employed to identify pollution sources and quantify their contribution rates in the mainstream. The results indicate that the annual average WQI of the Xiaoqing River mainstream showed an increasing trend, with the water quality grade improving from "moderate" to "good" from 2019 to 2022. No significant seasonal variation in water quality grade was observed, though the WQI during the wet season was slightly higher than in other periods. Spatially, the WQI values along the Xiaoqing River mainstream generally exhibited a pattern of midstream < upstream < downstream, with the best water quality observed in the downstream section. Poorer water quality was primarily distributed in the midstream and upstream regions, particularly in areas with a high proportion of built-up and agricultural land along the main channel. Source apportionment results revealed that pollutant sources in the Xiaoqing River mainstream were diverse, with primary contributions from industrial wastewater discharge (25.81%), agricultural activities (24.92%), and domestic sewage coupled with livestock farming (17.60%). The predominant pollution sources were generally consistent across all monitoring sections, though dominant types varied spatially, which was closely associated with land use patterns and anthropogenic activities along the river. The findings of this study can provide an important scientific basis for pollution control decision-making in the Xiaoqing River basin.
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图 2 2019—2022年干流各监测断面WQI值(a)及其各水质等级百分比的年际变化(b)
Fig. 2. Interannual variation in WQI (a) and interannual in percentage of each water quality grade (b) at various monitoring sites from 2019 to 2022
图 3 2019—2022年干流各监测断面WQI值及其各水质等级百分比季节变化
Fig. 3. Seasonal variation in WQI and seasonal variation in percentage of each water quality grade at various monitoring sites from 2019 to 2022
图 4 两类分类指数空间分布玫瑰图
Fig. 4. Rose chart of spatial distribution of two groups of classification indexes
图 5 各断面处不同土地利用类型面积占比
Fig. 5. Proportion of land use type area at various monitoring sites
表 1 WQI中各水质指标权重值和标准化因子
Table 1. The normalization values and weights of water quality parameters used in the WQI calculation
权重(Pi) 参数 地表水质量标准 Ⅰ Ⅱ Ⅲ Ⅳ Ⅴ Ii, 1=20b Ii, 2=40b Ii, 3=60b Ii, 4=80b Ii, 5=100b 4 DO (mg/L) ≥ 7.5 6 5 3 2 3 NH4-N (mg/L) ≤ 0.15 0.5 1 1.5 2 4 TP (mg/L) ≤ 0.02 0.1 0.2 0.3 0.4 - TN (mg/L) ≤ 0.2 0.5 1 1.5 2 2 F- (mg/L) ≤ 1 1 1 1.5 1.5 2 CODCr (mg/L) ≤ 15 15 20 30 40 注：根据《地表水环境质量标准》（2002年），在评价河流地表水时，总氮不作为基本项目，不参与评价，因此在本研究中，水质指数（WQI）的计算将不包括总氮. 
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表 2 水质指标与土地利用类型的相关性
Table 2. Relationship between landscape pattern and water quality
土地利用类型 TP CODCr DO NH4⁃N F- TN NO3⁃N CI1 CI2 WQI 耕地 0.351 -0.354 0.346 0.014 -0.203 0.809* 0.472 -0.265 0.346 0.248 林地 -0.389 -0.096 0.053 -0.171 -0.216 0.414 -0.066 -0.163 -0.343 -0.367 草地 0.167 -0.654* -0.438 -0.270 -0.500 0.026 -0.106 0.282 0.124 0.097 水域 -0.374 0.188 -0.304 -0.563 -0.267 -0.443 -0.717* -0.143 -0.014 -0.448 建设用地 0.313 0.217 0.113 0.891* 0.737* 0.464 0.312 0.537 0.010 0.303 未利用土地 -0.528 0.295 -0.437 -0.537 -0.199 -0.516 -0.512 -0.056 -0.394 -0.402 注：*表示在0.05水平上显著相关.
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表 3 研究区主成分总方差解释(%)
Table 3. Interpretation of total variance of principal components in the study area
成分 初始特征值 初始提取载荷平方和 旋转后载荷平方和 合计 方差贡献率 累积方差贡献率 合计 方差贡献率 累积方差贡献率 合计 方差贡献率 累积方差贡献率 F1 2.36 29.44 29.44 2.36 29.44 29.44 2.07 25.81 25.81 F2 1.83 22.88 52.32 1.83 22.88 52.32 1.99 24.92 50.74 F3 1.28 16.01 68.33 1.28 16.01 68.33 1.41 17.60 68.33
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