Estimation of Long-Term Trends and Loads with Low-Frequency Water Quality Sampling in the Baoxiang River, One Tributary to Dianchi Lake

doi:10.13209/j.0479-8023.2017.019

Abstract

Abstract:

Studies of water quality trends and pollutant loads in the Baoxiang River, a tributary to Dianchi Lake were limited by the lack of consistent data. This study evaluated long-term trends and loads using ESTREND and LOADEST with water quality data collected with low-frequency sampling and continuous daily flow data calculated by Muskingum method. Significantly increasing trends in nutrient (NH₃-N, TN, and TP) concentrations were detected at the 0.05 probability level. TSS concentration showed a significant decreasing trend of 12.34 percent per year. The similar results of unadjusted and flow-adjusted concentration indicated that these trends were caused by variation in pollutant emission rather than in river discharge. Regression models within LOADEST performed very well. Most of pollutants great loaded in the wet season in comparison to the dry and normal season, due to increased transports of nonpoint source pollution. The results indicate that it is the effective way to evaluation for low-frequency sampling, and methodology can be used in other watersheds.

Key words: trend analysis, load estimation, seasonal Kendall, regression model, Baoxiang River

摘要：

鉴于河流污染通量估算和水质趋势分析受到水质、流量数据缺乏的限制, 基于ESTREND和LOADEST模型, 利用低频采样获得离散型水质数据, 对滇池宝象河进行水质趋势分析和污染通量估算。结果表明: 1) 营养物质(NH₃-N, TN和TP)在0.05概率水平下呈显著上升趋势, 氮已经成为制约宝象河水质的重要因素; 2) TSS浓度呈现显著下降趋势, 年均下降率达到 12.34%; 3) 流量调节水质和非流量调节水质出现相同的趋势, 表明水质变化受流量的影响很小, 主要由污染物排放量变化引起; 4) 通过方程的系列检验, 利用离散水质数据和连续的日流量数据建立回归方程是有效的, 可以用于污染入湖通量的估算; 5) 由于非点源污染的增加,大多数污染物雨季的入湖负荷高于旱季; 6) ESTREND和LOADEST模型对于解决低频、离散型水质数据的水质趋势分析和通量估算是一个有效的方法, 可以推广应用于其他流域, 其分析结果能够为流域总量控制方案的制订和评估提供有力的科学依据。

关键词: 趋势分析, 污染通量, 季节Kendall检验, 回归模型, 宝象河

CLC Number:

X502

Na LI, Huaicheng GUO. Estimation of Long-Term Trends and Loads with Low-Frequency Water Quality Sampling in the Baoxiang River, One Tributary to Dianchi Lake[J]. Acta Scientiarum Naturalium Universitatis Pekinensis, 2017, 53(2): 378-386.

李娜, 郭怀成. 基于低频水质采样估算滇池宝象河的长期水质趋势和污染通量[J]. 北京大学学报自然科学版, 2017, 53(2): 378-386.

Figures/Tables 8

References 36

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Constituent	FAC data			RC data
Constituent	Trend/%	p	Sig (p<0.05)	Trend/%	p	Sig (p<0.05)
NO₃-N	0	0.98	None	-2.48	0.17	None
NH₃-N	24.11	0	+	24.92	0	+
TN	13.23	0	+	13.98	0	+
TP	16.74	0	+	17.57	0	+
TSS	-12.43	0	-	-13.12	0	-
COD	13.07	0.02	+	16.65	0.03	+

Constituent	FAC data			RC data
Constituent	Trend/%	p	Sig (p<0.05)	Trend/%	p	Sig (p<0.05)
NO₃-N	0	0.98	None	-2.48	0.17	None
NH₃-N	24.11	0	+	24.92	0	+
TN	13.23	0	+	13.98	0	+
TP	16.74	0	+	17.57	0	+
TSS	-12.43	0	-	-13.12	0	-
COD	13.07	0.02	+	16.65	0.03	+

Constituent	Best equation	R²	AIC	PPCC
NO₃-N	lnL=3.208+1.4233lnQ	0.82	3.29	0.98
NH₃-N	lnL=4.7785+0.9705lnQ+0.2287dtime	0.68	3.33	0.93
TN	lnL=5.6225+0.9684lnQ+0.1323dtime	0.86	2.26	0.95
TP	lnL=3.3252+1.1367lnQ+0.0142lnQ²+0.0488sin(2πdtime)+ 0.2554cos(2πdtime)+0.0183dtime-0.0632dtime²	0.90	2.42	0.95
TSS	lnL=6.5867+1.505lnQ+0.2555lnQ²-0.1342dtime	0.95	2.35	0.96
COD	lnL=7.7395+1.1332lnQ-0.0138lnQ²-0.2882sin(2πdtime)+ 0.5cos(2πdtime-0.1116dtime-0.0744dtime²)	0.86	2.68	0.95

Constituent	Best equation	R²	AIC	PPCC
NO₃-N	lnL=3.208+1.4233lnQ	0.82	3.29	0.98
NH₃-N	lnL=4.7785+0.9705lnQ+0.2287dtime	0.68	3.33	0.93
TN	lnL=5.6225+0.9684lnQ+0.1323dtime	0.86	2.26	0.95
TP	lnL=3.3252+1.1367lnQ+0.0142lnQ²+0.0488sin(2πdtime)+ 0.2554cos(2πdtime)+0.0183dtime-0.0632dtime²	0.90	2.42	0.95
TSS	lnL=6.5867+1.505lnQ+0.2555lnQ²-0.1342dtime	0.95	2.35	0.96
COD	lnL=7.7395+1.1332lnQ-0.0138lnQ²-0.2882sin(2πdtime)+ 0.5cos(2πdtime-0.1116dtime-0.0744dtime²)	0.86	2.68	0.95