Research on spatio-temporal distribution characteristics of urban river water quality based on principal component analysis: a case study of Cangzhou City
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摘要:
客观、综合评价城市河流水质的污染状况对城市河流水污染精准防治具有重要意义。以2022年沧州市13条重要河流的pH、溶解氧（DO）、高锰酸盐指数（CODMn）、化学需氧量（CODCr）、总磷（TP）、氨氮（NH3-N）、氟化物（F−）共7项水质指标数据为基础，采用主成分分析法，提取引起河流水质变化的主导指标，诊断河流污染状况，再运用水质指标权重计算各河流监测断面和不同季节综合得分，分析河流水质时空分布特征。结果表明：1）2022年沧州市13条河流水质整体较好，大部分河流水质为GB 3838―2002《地表水环境质量标准》Ⅲ类，少数河流CODMn、CODCr达到Ⅳ类水质标准；2）使用主成分分析法，可将7个水质指标转化为2个主成分，累计方差贡献率达78.492%，其中与第一主成分显著相关的水质指标CODMn、CODCr、TP和F−主导着研究区域水质变化，且4个水质指标之间呈显著正相关；3）空间分析表明，沧浪渠为13条监测河流中污染程度最高的河流，且沧州市东北区域河流污染程度高于西北区域和中南区域；4）季节分析表明，13条河流不同季节水质污染严重程度表现为夏季>春季>冬季>秋季。研究结果可为沧州市城市河流水污染控制策略的制定提供参考。
Abstract:Comprehensive and objective evaluation of the pollution status of urban river water quality is of great significance to the precise prevention and control of urban river water pollution. Based on the data of 7 water quality indicators of pH, dissolved oxygen (DO), permanganate index (CODMn), chemical oxygen demand (CODCr), total phosphorus (TP), ammonia nitrogen (NH3-N) and fluoride (F−) of 13 important rivers in Cangzhou City in 2022, the principal component analysis (PCA) method was employed to extract the leading indicators causing changes in river water quality and to diagnose the pollution status of the rivers. Subsequently, the weights of water quality indicators were used to calculate the comprehensive scores for each river monitoring section and different seasons and analyze the spatial and temporal distribution characteristics of river water quality. The results showed that : (1) The overall water quality of the 13 rivers in Cangzhou City was relatively good in 2022, with most water bodies meeting the Class Ⅲ water standards of Environmental Quality Standards for Surface Water (GB 3838-2002), while a few rivers reached the Class Ⅳ water standards in terms of CODMn and CODCr indicators. (2) The application of the PCA method allowed the transformation of the 7 water quality indicators into 2 principal components, with a cumulative variance contribution rate of 78.492%. Among them, the water quality indicators, CODMn, CODCr, TP, and F−, were significantly correlated with the first principal component and dominated the water quality changes in the study area. Moreover, these four indicators showed a significant positive correlation between each other. (3) The spatial analysis revealed that Canglang Channel was the most polluted among the 13 monitored rivers, and the river pollution in the northeast of Cangzhou City was worse than that in the northwestern and southern areas. (4) The seasonal analysis indicated that the seasonal variation for water pollution in urban rivers was in the following order: summer>spring>winter>autumn. The analysis results could provide reference for the control strategy of urban river water pollution in Cangzhou City.
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图 1 沧州市13条河流水质监测断面
Figure 1. Water quality monitoring sections of 13 rivers in Cangzhou City
图 2 2022年13条河流各监测断面7个水质指标的月度分布
注：橙色代表春季；绿色代表夏季；紫色代表秋季；黄色代表冬季。
Figure 2. Monthly distribution of 7 water quality indicators in 13 river monitoring sections in 2022
图 3 2022年13条河流各监测断面7个水质指标的空间分布
Figure 3. Spatial distribution of 7 water quality indicators in 13 river monitoring sections in 2022
图 4 PCA水质指标主成分载荷分析
Figure 4. PCA water quality index principal component load analysis diagram
图 5 PCA水质指标主成分季节载荷分析
注：不同颜色椭圆代表数据点在主成分空间中不同季节的分布，椭圆的形状和大小反映数据点在2个主成分方向上的分散程度; 圆圈内部数据点位于95%置信区间内，圆圈外部数据点无统计学意义。
Figure 5. PCA water quality index principal component seasonal load analysis diagram
表 1 相关系数矩阵
Table 1. Correlation coefficient matrix
水质指标 pH DO CODMn CODCr TP NH3-N F− pH 1 DO 0.535 1 CODMn 0.354 0.294 1 CODCr 0.506 0.473 0.908** 1 TP 0.110 0.288 0.761* 0.741** 1 NH3-N −0.018 −0.198 0.458 0.158 0.345 1 F− 0.143 0.180 0.915* 0.709** 0.719* 0.596 1 注：*表示P<0.05，**表示P<0.01。 
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表 2 旋转之后的主成分载荷矩阵
Table 2. Principal component load matrix after rotation
指标 PC1 PC2 pH 0.438 0.669 DO 0.441 0.737 CODMn 0.969 −0.103 CODCr 0.918 0.226 TP 0.834 −0.149 NH3-N 0.458 −0.666 F− 0.890 −0.333 特征值 3.866 1.629 方差百分比/% 55.226 23.266 累计方差贡献率/% 55.226 78.492 注：黑体数值为因子载荷大于0.7的值。
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表 3 2022年沧州市13条河流监测断面水质状况评价综合得分
Table 3. Comprehensive scores of water quality status of 13 river monitoring sections in Cangzhou City in 2022
监测断面 F1 F2 F 排序 杨官庄 2.414 1.784 1.748 1 四埝村桥 1.959 1.368 1.400 2 李家堡桥 2.149 −0.861 0.987 3 何老营 1.385 −0.429 0.665 4 大口河口 1.260 −2.108 0.205 5 伊庄子闸 0.071 −0.233 −0.015 6 永红桥 −0.025 −0.705 −0.178 7 南运河北街 −0.501 −0.105 −0.301 8 阎辛庄 −0.526 −0.129 −0.321 9 小王庄 −1.094 −0.948 −0.384 10 朱庄闸 −0.781 0.088 −0.411 11 小泊头桥 −1.376 0.277 −0.696 12 大浪淀水库 −4.935 0.105 −2.701 13
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表 4 13条河流监测断面不同季节水质PCA评价综合得分
Table 4. Comprehensive scores of PCA evaluation of water quality of 13 river monitoring sections in different seasons
季节 F1 F2 F 排序 春季 1.583 −1.121 0.483 2 夏季 2.842 −1.219 0.863 1 秋季 −0.655 0.437 −0.205 4 冬季 −0.712 0.595 −0.199 3
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