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

doi:10.13209/j.0479-8023.2017.019

摘要/Abstract

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

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

中图分类号:

X502

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.

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.

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链接本文: https://xbna.pku.edu.cn/CN/10.13209/j.0479-8023.2017.019

https://xbna.pku.edu.cn/CN/Y2017/V53/I2/378

图/表 8

参考文献 36

[1]	Hastie T J, Tibshirani R J.Generalized additive models. London: Chapman and Hall, 1990
[2]	Miller J D, Hirst D.Trends in concentrations of solutes in an upland catchment in Scotland. Science of the Total Environment, 1998, 216(1/2): 77-88
[3]	Ferrier R C, Edwards A C, Hirst D, et al.Water quality of Scottish rivers: spatial and temporal trends. The Science of the Total Environment, 2001, 265: 327-342
[4]	Pastres R, Solidoro C, Ciavatta S, et al.Long-term changes of inorganic nutrients in the Lagoon of Venice (Italy). Journal of Marine Systems, 2004, 51(1): 179-189
[5]	Abaurrea J, Asín J, Cebrián A C, et al. Trend analysis of water quality series based on regression models with correlated errors. Journal of Hydrology, 2011, 400: 341-352
[6]	Johnson H O, Gupta S C, Vecchia A V, et al.Assessment of water quality trends in the Minnesota River using non-parametric and parametric methods. Journal of Environment Quality, 2009, 38(3): 1018-1030
[7]	Hirsch R M, Slack J R, Smith R A.Techniques of trend analysis for monthly water quality data. Water Resources Research, 1982, 18(1): 107-121
[8]	Carey R O, Migliaccio K W, Brown M T.Nutrient discharges to Biscayne Bay, Florida: trends, loads, and a pollutant index. Science of the Total Environment, 2011, 409: 530-539
[9]	Hirst D.Estimating trend in stream water quality with a time-varing flow relationship. Austrian Journal of Statistics, 1998, 27(1/2): 39-48
[10]	Hipel K W, McLeod A I. Time series modelling of water resources and environmental systems. Amster- dam: Elsevier Science, 1994
[11]	Mattikalli N M.Times series analysis of historical surface water quality data of river Glen catchment UK. Journal of Environmental Management, 1996, 48: 149-172
[12]	Esterby S R.Review of methods for the detection and estimation of trends with emphasis on water quality application. Hydrological Processes, 1996, 10: 127-149
[13]	Harmel R D, King K W, Slade R.Automated storm water sampling on small watersheds. Appl Eng Agric, 2003, 19: 667-674
[14]	Walling D E, Webb B W.Estimating the discharge of contaminants to coastal waters by rivers: some cautionary comments. Marine Pollution Bulletin, 1985, 16: 488-492
[15]	Webb B W, Phillips J M, Walling D E, et al. Load estimation methodologies for British rivers and their relevance to the LOIS RACS (R) Programme. The Science of the Total Environment, 1997, 194/195: 379-389
[16]	Littlewoods I G, Watts C D, Custance J M. Systematic application of United Kingdom river flow and quality databases for estimating annual river mass loads. Science of the Total Environment, 1998, 210/211: 21-40
[17]	Phillips J M, Webb B W, Walling D E, et al.Estimating the suspended sediment loads of rivers in the LOIS study area using infrequent samples. Hydrological Processes, 1999, 13: 1035-1050
[18]	Quilbe R, Rousseau A N, Duchemin M, et al.Selecting a calculation method to estimate sediment and nutrient loads in streams: application to the Beaurivage River (Quebec, Canada). Journal of Hydrology, 2006, 326: 295-310
[19]	Dolan D M, Yui A K, Geist R D.Evaluation of river load estimation methods for total phosphorus. Journal of Great Lakes Research, 1981, 7(3): 207-214
[20]	Rekolainen S, Posch M, Kämäri J, et al.Evaluation of the accuracy and precision of annual phosphorus load estimates from two agricultural basins in Finland. Journal of Hydrology, 1991, 128: 237-255
[21]	Ferguson R I.Accuracy and precision of methods for estimating river loads. Earth Surface Processes and Landforms, 1987, 12: 95-104
[22]	Thomas R, Meybeck M.The use of particulate matter // Chapman D. Water quality assessments. London: Chapman & Hall, 1992: 121-122
[23]	Kronvang B, Bruhn A J.Choice of sampling strategy and estimation method for calculating nitrogen and phosphorus transport in small lowland streams. Hydrologic Processes, 1996, 10: 1483-1501
[24]	Harmel R D, King K W, Haggard B E, et al.Practical guidance for discharge and water quality data collection on small watersheds. Transactions of the ASABE, 2006, 49(4): 937-948
[25]	Preston S D, Bierman Jr V J, Silliman S E. An evaluation of methods for the estimation of tributary mass load. Water Resources Research, 1989, 25(6): 1379-1389
[26]	Letcher R A, Jakeman A J, Calfas M, et al.A comparison of catchment water quality models and direct estimation techniques. Environmental Model- ling & Software, 2002, 17: 77-85
[27]	Schertz T W, Alexander R B, Ohe D J.The computer program estimate trend (ESTREND), a system for the detection of trends in water-quality data. US Geological Survey Water Resources Investigations Report, 1991: 91-4040
[28]	Sen P K.Estimates of the regression coefficient based on Kendall’s tau. Journal of the American Statistical Association, 1968, 63: 1379-1389
[29]	Helsel D R, Hirsch R M.Statistical methods in water resources: US Geological Survey Techniques of Water-Resources Investigations. (2002-03) [2014- 07-12].
[30]	Runkel R L, Crawford C G, Cohn T A.Load estimator (LOADEST): a FORTRAN program for estimating constituent loads in streams and rivers. US Geological Survey Techniques and Methods book. (2004-03) [2014-07-12].
[31]	Akaike H.Information theory and an extension of the maximum likelihood principle//2nd. International Symposium on Information Theory. Budapest, 1973: 267-281
[32]	Akaike H.A new look at the statistical model identification. IEEE Trans Automat Control, 1974, 19(6): 716-723
[33]	Cohn T A, Delong L L, Gilroy E J.Estimating constituent loads. Water Resources Research, 1989, 25(5): 937-942
[34]	Cohn T A, Gilroy E J, Baier W G.Estimating fluvial transport of trace constituents using a regression model with data subject to censoring // Proceedings of the Joint Statistical Meeting. Boston, 1992: 142-151
[35]	Powell J L. Least absolute deviations estimation for the censored regression model. Journal of Econometrics, 1984, 25: 303-325
[36]	Vogel R M.The probability plot correlation coefficient test for the normal, lognormal and Gumbel distributional hypotheses. Water Resource Research, 1986, 22(4): 587-590

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