基于最简水质综合评价指数的官厅水库上游河流水质评价

doi:10.16258/j.cnki.1674-5906.2023.02.017

生态环境学报 ›› 2023, Vol. 32 ›› Issue (2): 372-380.DOI: 10.16258/j.cnki.1674-5906.2023.02.017

基于最简水质综合评价指数的官厅水库上游河流水质评价

程鹏¹^,^*(), 孙明东², 郝韶楠³

1.山西财经大学资源环境学院，山西太原 030006
2.中国环境科学研究院水生态环境研究所，北京 100012
3.中国电建集团成都勘测设计研究院有限公司，四川成都 610072；

收稿日期:2022-06-20 出版日期:2023-02-18 发布日期:2023-05-11
通讯作者: *
作者简介:程鹏（1989年生），男，副教授，博士研究生，主要研究方向为流域水环境管理。E-mail: pengcheng@sxufe.edu.cn
基金资助:
国家水体污染控制与治理科技重大专项(2018ZX07111-002);山西省教育厅科技创新项目(2019L0482);山西省哲学社会科学规划课题(2020YJ088)

Water Quality Assessment of Upstream Rivers of Guanting Reservoir Based on the Simplest Water Quality Index

CHENG Peng¹^,^*(), SUN Mingdong², HAO Shaonan³

1. College of Resources and Environment, Shanxi University of Finance and Economics, Taiyuan 030006, P. R. China
2. Institute of Water Ecology and Environment, Chinese Research Academy of Environmental Sciences, Beijing 100012, P. R. China
3. Power China Chengdu Engineering Corporation LTD, Chengdu 610072, P. R. China

Received:2022-06-20 Online:2023-02-18 Published:2023-05-11

摘要/Abstract

摘要：

河流水质评价对流域水环境管理具有重要意义。由于具有较强的综合性和可比性，水质综合评价指数（WQI）已成为国内外河流水质评价的主流方法，然而因其需要大量的水质参数，需要耗费大量的物力和财力。采用2018年官厅水库上游河流37个监测点的月度监测数据，运用全子集回归模型分训练和验证两个环节建立了对水质参数数量要求较少的适用于官厅水库上游河流水质评价的最简水质综合评价指数（WQI_min），并采用建立的WQI_min对官厅水库上游河流水质进行了时空分布特征分析。结果显示：在训练环节，水质参数的数量越多，WQI_min的训练效果越好，并且考虑水质参数相对权重的模型效果要优于不考虑相对权重的模型。综合考虑水质评价的准确性和易用性，采用COD_Mn、DO和TN等3个水质参数，且考虑水质参数权重的最简水质综合评价指数模型（WQI_min-b3）是训练环节官厅水库上游流域最佳的WQI_min模型。在验证环节，WQI_min-b3与WQI有较好的线性关系，证实了建立的WQI_min-b3模型可代替WQI模型进行官厅水库上游河流水质评价。官厅水库上游河流水质在秋季最好，夏季次之；由于受到融雪期非点源污染的影响，春季官厅水库上游河流水质最差，因而官厅水库上游流域应对融雪期非点源污染给予充分重视。官厅水库上游各河段WQI_min平均值的最高值出现在清水河，其次是南洋河；桑干河上游由于工业废水和生活污水的大量排放，其水质状态最差。该研究可为官厅水库上游及其他流域河流水质管理提供科学参考。

关键词: 水质综合评价指数, 最简水质综合评价指数, 水质评价, 参数选择, 官厅水库上游, 全子集回归

Abstract:

River water quality assessment is of great significance to watershed water environment management. Due to its strong comprehensiveness and comparability, the Water Quality Comprehensive Assessment Index (WQI) has become the mainstream standard used for river water quality assessment. However, it requires a large number of water quality parameters, and thus a large amount of material and financial resources. This study used the monthly monitoring data of 37 monitoring points in the upstream of Guanting Reservoir in 2018, and used the full subset regression model to establish the simplest water quality comprehensive assessment index (WQI_min) for the water quality assessment of the upstream rivers of Guanting Reservoir, which required fewer water quality parameters, through training and verification. The established WQI_min was used to analyze the temporal and spatial distribution characteristics of the water quality of the upstream rivers of Guanting Reservoir. The results showed that the more water quality parameters were, the better the training effect of WQImin was, and the model considering the relative weight of water quality parameters was better than the model not considering the relative weight. Considering the accuracy and ease of use of water quality assessment, the simplest water quality comprehensive assessment index model (WQI_min-b3) using three water quality parameters (COD_Mn, DO and TN), and considering the relative weight of water quality parameters was the best WQI_min model for the upstream of Guanting Reservoir during the training period. During the testing period, WQI_min-b3 had a good linear relationship with WQI, which proved that the established WQImin-b3 model could replace the WQI model to evaluate river water quality in the upstream of Guanting Reservoir. The water quality of rivers in the upper reaches of Guanting Reservoir was the best in autumn, followed by that in summer. Due to the impact of non-point source pollution during snowmelt, the water quality of rivers in the upstream of Guanting Reservoir was the worst in spring. Therefore, for the upstream of Guanting Reservoir, attention should be paid to non-point source pollution during snowmelt. The highest value of WQI_min appeared in Qingshui River, followed by that in Nanyang River. Due to the massive discharge of industrial wastewater and domestic sewage in the upstream of Sanggan River, its water quality was the worst, and there was a significant gap with other river sections. This study could provide a scientific reference for river water quality management in the upstream of Guanting Reservoir and other basins.

Key words: WQI, WQI_min, water quality assessment, parameters selection, upstream of Guanting reservoir, full subset regression model

中图分类号:

X824

程鹏, 孙明东, 郝韶楠. 基于最简水质综合评价指数的官厅水库上游河流水质评价[J]. 生态环境学报, 2023, 32(2): 372-380.

CHENG Peng, SUN Mingdong, HAO Shaonan. Water Quality Assessment of Upstream Rivers of Guanting Reservoir Based on the Simplest Water Quality Index[J]. Ecology and Environment, 2023, 32(2): 372-380.

图/表 10

参考文献 39

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水质参数	相对权重 (P)	标准化因子 (C_i)
水质参数	相对权重 (P)	100	90	80	70	60	50	40	30	20	10	0
t/℃	1	16-21	15-16, 21-22	14-15, 22-24	12-14, 24-26	10-12, 26-28	5-10, 28-30	0-5, 30-32	-2-0, 32-36	-4- -2, 36-40	-6- -4, 36-40	<-6, >45
pH	1	7	7-8	8-8.5	8.5-9	6.5-7	6-6.5, 9-9.5	5-6, 9.5-10	4-5, 10-11	3-4, 11-12	2-3, 12-13	1-2, 13-14
Tur/NTU	2	<5	<10	<15	<20	<25	<30	<40	<60	<80	≤100	>100
ρ_(DO)/(mg·L^-1)	4	≥7.5	>7	>6.5	>6	>5	>4	>3.5	>3	>2	≥1	<1
ρ_(TN)/(mg·L^-1)	2	<0.1	<0.2	<0.35	<0.5	<0.75	<1	<1.25	<1.5	<1.75	≤2	>2
ρ_(NH4-N)/(mg·L^-1)	3	<0.01	<0.05	<0.1	<0.2	<0.3	<0.4	<0.5	<0.75	<1	≤1.25	>1.25
ρ_(TP)/(mg·L^-1)	1	<0.01	<0.02	<0.05	<0.1	<0.15	<0.2	<0.25	<0.3	<0.35	≤0.4	>0.4
ρ_(CODMn)/(mg·L^-1)	3	<1	<2	<3	<4	<6	<8	<10	<12	<14	≤15	>15
ρ_(BOD5)/(mg·L^-1)	3	<0.5	<2	<3	<4	<5	<6	<8	<10	<12	≤15	>15

水质参数	相对权重 (P)	标准化因子 (C_i)
水质参数	相对权重 (P)	100	90	80	70	60	50	40	30	20	10	0
t/℃	1	16-21	15-16, 21-22	14-15, 22-24	12-14, 24-26	10-12, 26-28	5-10, 28-30	0-5, 30-32	-2-0, 32-36	-4- -2, 36-40	-6- -4, 36-40	<-6, >45
pH	1	7	7-8	8-8.5	8.5-9	6.5-7	6-6.5, 9-9.5	5-6, 9.5-10	4-5, 10-11	3-4, 11-12	2-3, 12-13	1-2, 13-14
Tur/NTU	2	<5	<10	<15	<20	<25	<30	<40	<60	<80	≤100	>100
ρ_(DO)/(mg·L^-1)	4	≥7.5	>7	>6.5	>6	>5	>4	>3.5	>3	>2	≥1	<1
ρ_(TN)/(mg·L^-1)	2	<0.1	<0.2	<0.35	<0.5	<0.75	<1	<1.25	<1.5	<1.75	≤2	>2
ρ_(NH4-N)/(mg·L^-1)	3	<0.01	<0.05	<0.1	<0.2	<0.3	<0.4	<0.5	<0.75	<1	≤1.25	>1.25
ρ_(TP)/(mg·L^-1)	1	<0.01	<0.02	<0.05	<0.1	<0.15	<0.2	<0.25	<0.3	<0.35	≤0.4	>0.4
ρ_(CODMn)/(mg·L^-1)	3	<1	<2	<3	<4	<6	<8	<10	<12	<14	≤15	>15
ρ_(BOD5)/(mg·L^-1)	3	<0.5	<2	<3	<4	<5	<6	<8	<10	<12	≤15	>15

河段	水质参数
河段	t/℃	pH	Tur/NTU	ρ_(DO)/(mg·L^-1)	ρ_(TN)/(mg·L^-1)	ρ_(NH4-N)/(mg·L^-1)	ρ_(TP)/(mg·L^-1)	ρ_(CODMn)/(mg·L^-1)	ρ_(BOD5)/(mg·L^-1)
DY	19.90±3.51	8.61±0.14	1010.82±1599.55	8.46±1.40	2.52±1.12	0.12±0.08	0.07±0.05	1.89±0.66	0.53±0.40
NY	19.63±3.53	8.70±0.24	943.74±1495.72	8.88±1.57	4.75±2.41	0.28±0.53	0.10±0.09	2.54±0.89	0.59±0.52
YS	18.74±4.40	8.72±0.30	714.60±1260.35	8.56±1.56	6.46±5.92	1.59±4.01	0.27±0.40	4.38±2.06	2.01±2.73
QS	16.37±5.23	8.79±0.33	647.30±1311.38	9.40±2.52	7.78±3.20	0.32±0.46	0.12±0.13	3.21±2.21	1.60±3.14
YX	20.30±4.98	8.67±0.41	46.08±49.27	9.81±4.85	7.61±4.99	0.43±0.79	0.28±0.39	5.96±4.03	2.71±2.18
SGS	18.29±5.14	8.77±0.44	255.72±584.04	11.39±6.76	10.93±7.29	3.99±6.34	1.10±1.15	14.86±10.00	12.65±22.67
HL	19.15±5.37	8.16±0.72	62.57±73.78	9.88±6.03	4.99±3.54	2.48±4.34	0.36±0.48	7.19±4.53	4.88±8.40
SGX	18.39±4.10	8.74±0.35	57.03±75.61	9.65±4.21	6.96±3.46	0.26±0.27	0.17±0.20	11.45±18.95	6.65±11.75
YD	20.97±4.59	8.60±0.29	17.57±9.98	8.91±1.64	6.38±2.36	0.22±0.11	0.17±0.14	4.08±0.83	1.88±0.85
GS	19.80±4.35	8.71± 0.45	60.29 ±39.59	6.81 ±1.82	3.33 ±3.16	0.19 ±0.17	0.28 ±0.23	5.08 ±3.24	1.78 ±1.91

河段	水质参数
河段	t/℃	pH	Tur/NTU	ρ_(DO)/(mg·L^-1)	ρ_(TN)/(mg·L^-1)	ρ_(NH4-N)/(mg·L^-1)	ρ_(TP)/(mg·L^-1)	ρ_(CODMn)/(mg·L^-1)	ρ_(BOD5)/(mg·L^-1)
DY	19.90±3.51	8.61±0.14	1010.82±1599.55	8.46±1.40	2.52±1.12	0.12±0.08	0.07±0.05	1.89±0.66	0.53±0.40
NY	19.63±3.53	8.70±0.24	943.74±1495.72	8.88±1.57	4.75±2.41	0.28±0.53	0.10±0.09	2.54±0.89	0.59±0.52
YS	18.74±4.40	8.72±0.30	714.60±1260.35	8.56±1.56	6.46±5.92	1.59±4.01	0.27±0.40	4.38±2.06	2.01±2.73
QS	16.37±5.23	8.79±0.33	647.30±1311.38	9.40±2.52	7.78±3.20	0.32±0.46	0.12±0.13	3.21±2.21	1.60±3.14
YX	20.30±4.98	8.67±0.41	46.08±49.27	9.81±4.85	7.61±4.99	0.43±0.79	0.28±0.39	5.96±4.03	2.71±2.18
SGS	18.29±5.14	8.77±0.44	255.72±584.04	11.39±6.76	10.93±7.29	3.99±6.34	1.10±1.15	14.86±10.00	12.65±22.67
HL	19.15±5.37	8.16±0.72	62.57±73.78	9.88±6.03	4.99±3.54	2.48±4.34	0.36±0.48	7.19±4.53	4.88±8.40
SGX	18.39±4.10	8.74±0.35	57.03±75.61	9.65±4.21	6.96±3.46	0.26±0.27	0.17±0.20	11.45±18.95	6.65±11.75
YD	20.97±4.59	8.60±0.29	17.57±9.98	8.91±1.64	6.38±2.36	0.22±0.11	0.17±0.14	4.08±0.83	1.88±0.85
GS	19.80±4.35	8.71± 0.45	60.29 ±39.59	6.81 ±1.82	3.33 ±3.16	0.19 ±0.17	0.28 ±0.23	5.08 ±3.24	1.78 ±1.91

序号	线性模型	Adjusted R²	P
1	1.357+0.221*lg(p₉+1)	0.600	<0.001
2	1.092+0.164lg(p₈+1)+0.204lg(p₄+1)	0.801	<0.001
3	1.092+0.162lg(p₈+1)+0.203lg(p₄+1)+ 0.019lg*(p₅+1)	0.807	<0.001
4	1.022+0.200lg(p₄+1)+0.108lg(p₉+1)+0.011lg(p₃+1)+0.097lg(p₆+1)	0.888	<0.001
5	1.027+0.075lg(p₈+1)+0.184lg(p₄+1)+0.078lg(p₉+1)+0.006lg(p₅+1)+0.073*lg(p₆+1)	0.924	<0.001
6	0.921+0.054lg(p₂+1)+0.071lg(p₈+1)+0.185lg(p₄+1)+0.077lg(p₉+1)+ 0.007lg(p₅+1)+0.074lg(p₆+1)	0.925	<0.001
7	0.925+0.060lg(p₁+1)+0.056lg(p₈+1)+0.175lg(p₄+1)+0.084lg(p₉+1)+0.030lg(p₇+1)+0.005lg(p₅+1)+0.056*lg(p₆+1)	0.934	<0.001
8	0.686+0.057lg(p₁+1)+0.134lg(p₂+1)+0.049lg(p₈+1)+0.179lg(p₄+1)+0.079lg(p₉+1)+0.035lg(p₇+1)+0.005lg(p₅+1)+ 0.057lg(p₆+1)	0.937	<0.001

基于最简水质综合评价指数的官厅水库上游河流水质评价

Water Quality Assessment of Upstream Rivers of Guanting Reservoir Based on the Simplest Water Quality Index

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水质参数	WQI_min-a			WQI_min-b
水质参数	模型	Adjusted R²	P	模型	Adjusted R²	P
COD_Mn, DO	a2	0.715	<0.001	b2	0.744	<0.001
COD_Mn, DO, TN	a3	0.734	<0.001	b3	0.803	<0.001
DO, BOD₅, Tur, NH₄-N	a4	0.892	<0.001	b4	0.909	<0.001
COD_Mn, DO, BOD₅, TN, NH₄-N	a5	0.903	<0.001	b5	0.922	<0.001

季节	水质分级
季节	优秀	好	中等	差	极差
春季	0	0	3/37 (8.1%)	22/37 (59.5%)	12/37 (32.4%)
夏季	0	4/37 (10.8%)	25/37 (67.6%)	6/37 (16.2%)	2/37 (5.4%)
秋季	0	8/37 (21.6%)	25/37 (67.6%)	4/37 (10.8%)	0
全部季节	0	12/111 (10.8%)	53/111 (47.7%)	32/111 (28.8%)	14/111 (12.6%)

[1]	姜永伟, 丁振军, 袁俊斌, 张峥, 李杨, 问青春, 王业耀, 金小伟. 辽宁省主要河流底栖动物群落结构及水质评价研究[J]. 生态环境学报, 2023, 32(5): 969-979.
[2]	嵇晓燕, 王姗姗, 杨凯, 任蓓. 2016—2020年中国地表水中总氮浓度时空变化特征分析[J]. 生态环境学报, 2022, 31(6): 1184-1192.