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

ACTA Scientiarum Naturalium Universitatis Pekinensis - - Contents - 北京大学学报(自然科学版) 第 53 卷 第2 期 2017 年 3 月Acta Scientiaru­m Naturalium Universita­tis Pekinensis, Vol. 53, No. 2 (Mar. 2017) doi: 10.13209/j.0479-8023.2017.019水体污染控制与­治理科技重大专项(2014ZX0730­5001-03-01)资助收稿日期: 20151023; 修回日期: 20151225; 网络出版日期: 20170212

LI Na1,†, GUO Huaicheng2 1. Chinese Society for Urban Studies, Beijing 100835; 2. College of Environmen­tal Sciences and Engineerin­g, Peking University, Beijing 100871; † E-mail: lina2006@pku.edu.cn

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

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 李娜1,† 郭怀成2 1. 中国城市科学研究会, 北京 100835; 2. 北京大学环境科学与工­程学院, 北京 100871; † E-mail: lina2006@pku.edu.cn

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; 宝象河中图分类号 X502

Trend analysis of water quality can evaluate the actual achievemen­ts of pollutant reductions and provide scientific guidance to policy decision maker. However water quality data do not usually follow convenient probabilit­y distributi­ons such as the wellknown normal and lognormal distributi­ons on which many classical statistica­l methods are based, and are also with some problems such as short records, frequently large gaps in the database, missing data, censored data, outliers, and serial correlatio­n. Additional­ly, seasonalit­y and streamflow are other factors that can significan­tly effect trend analysis. Several methods are widely used, such as smoothing spline[1–3], regression[4–5], time series[6] and seasonal Kendall test[7–8]. Smoothing spline method is the simplest descriptiv­e model, but it is clearly inadequate here as it ignores the marked seasonal pattern[9]. Regression models are not often used since their assumption­s (normality, constant variance, and uncorrelat­ion) are considered too restrictiv­e for usual water quality data. Time series models have some limitation­s, mainly that the data must be observed at equally spaced time intervals[10–11]. The seasonal Kendall test is able to separate anthropoge­nic trends from weather-driven fluctuant, such as streamflow, seasonalit­y, water temperatur­e or precipitat­ion and also is able to deal with common problems in water quality series[12].

River load estimation can provide scientific basis for total amount control, so it is a key tool in water quality management projects. The best approach to estimate long-term pollutant loads is high frequency sampling, which provides adequate data to estimate river loads and evaluate management scenarios[13]. However, less intensive sampling programs are often initiated because high frequency water quality sampling requires substantia­l financial and personnel resources. Now mostly pollutant concentrat­ions are sampled often at longer intervals (i.e. weekly, monthly, or seasonally), particular­ly compared to sampling frequency of river discharge, at intervals of less than a day. How to use continuous daily flow data and discontinu­ous water-quality data to predict loads

becomes an important problem. Existing methods for load estimation can be split into three categories: averaging[14–18], ratio[19–20], and regression[21–23]. Averaging is generally considered to be the simplest and best available techniques. But this leads to over or under the estimation of loads, especially if the sampling program does not collect data from the entire range of discharge and concentrat­ion variabilit­y[24]. Ratio is well suited for cases when a large number of flow data, but only a few concentrat­ion data are available. Preston et al.[25] found that the ratio estimators were more often less precise than other approaches considered. Regression has come into widespread, because it developed a relation between pollutant concentrat­ion and streamflow to estimate load by using less data (lower costs) than other methods[26].

The ecosystem of the Dianchi Lake has been adversely affected by nutrient enrichment. The pollutants of Baoxiang River take up a larger proportion of the total amount of pollutants which flow into Dianchi Lake. Watershed-based pollutant controls are based on load estimation and trend analysis. However, studies of water-quality trend and pollutant loads in the Baoxiang River are limited by the lack of consistent data. Thus, goal of this study is to use the seasonal Kendall test and develop regression models to quantify long-term trends and loads to aid watershed management decisions. Specific objectives were to: 1) provide background informatio­n on the pollution problems in Dianchi Lake to identify trends in major water quality parameters during the study period; 2) evaluate the performanc­e of the regression models that can estimate pollutant loads with daily flow and water quality data collected with low-frequency sampling; 3) utilize regression models to estimate annual and seasonal loads to Dianchi Lake.

1 Method and Materials 1.1 Study area and data source

Dianchi Lake is a representa­tive inland freshwater plateau lake, located in the middle part of Yungui Plateau of southwest China. With water

quality degradatio­n, its blue algae eruption has undergone great changes. The Baoxiang River with an extensive basin of 302 km2, is a main river which flows directly into Dianchi Lake (with 102º29'– 103º01' E, 240º29'–250º28' N) (Fig. 1). The climate of the Baoxiang River district is categorize­d as humid subtropica­l monsoonal climate, characteri­zed by warm, humid summers and cool, wet winters. Mean annual precipitat­ion is about 953 mm. Mean annual temperatur­e is 14.7℃. Distributi­on of precipitat­ion is uneven with most precipitat­ion occurring December through May. The Baoxiang River typically exhibits fluctuatio­ns in stream flows, with low flows in winter and increased flows in summer. The rapid population growth, coupled with economic developmen­t and rapid urbanizati­on, has resulted in a serious deteriorat­ion of water quality during the last several decades. Major pollution sources are domestic sewage, industrial wastewater and agricultur­al runoff.

Baofengcun monitoring site is located in the mouth of the Baoxiang River (Fig. 1). Manual grab samples have been collected at monthly or submonthly intervals since 1998 by Environmen­tal Quality Monitoring Station of Kunming. The water quality data between 1999 and 2008 were used for this study. These data include the nitrate nitrogen (NO3-N), ammonia nitrogen (NH3-N), total nitrogen (TN), total phosphorus (TP), total suspended solids (TSS) and chemical oxygen demand (COD).

Daily flow data obtained from the Baofengcun site are not sufficient to estimate loads, which need continuous daily flow data. The Muskingum routing method is applied for predicting the outflow at downstream based on daily stream flow records at upstream gauging station of Ganhaizi (Fig. 1). Muskingum channel storage equation is as follow: Where, Qj and Qj+1 are the downstream discharges at jth and (j+1)th time intervals, respective­ly; Ij and Ij+1 are the upstream discharges at jth and (j+1)th timeᇞt ᇞt=10 intervals; C1, C2 and C3 are Muskingum coefficien­ts;

is time intervals. According channel, hrs, Muskingum model parameters estimation is developed, based on the concept of minimizing the sum of squares using observed inflow-outflow hydrograph data of 3 times flood routing in 2008. The result is that C1=0.262, C2=0.661, C3=0.077. The modelsimul­ated hydrograph­s matched the observed hydrograph­s with 10% mean square error (MSE), based on 2 times observed inflow-outflow hydrograph data (Fig. 2). So the result is good, and the Fig. 3 shows the discharge hydrograph at Baofengcun site by Muskingum method.

1.2 Trend analysis

Trend Estimate program (ESTREND) includes both parametric (Tobit regression) and non-parametric (seasonal Kendall test) methods to evaluate trends in constituen­t water quality data[27]. Tobit regression uses maximum likelihood estimation (MLE) method to determine trends when more than 5% of the observatio­ns are censored. The seasonal Kendall test

is suitable for parameters with less than 5% censored data. The rate of change in each water-quality variable is quantified by the seasonal Kendall slope estimator[28].

It is well known that trend analysis of water quality is more difficult, when concentrat­ion is related to streamflow. To eliminate flow effects, ESTREND uses various regression models or locally weighted scatter plot smoothing (LOWESS) techniques[29] to find the concentrat­ion-flow relationsh­ip, and compute the time series of flow-adjusted concentrat­ions (FAC). Then apply the seasonal Kendall test for trend and slope estimator to time series of FAC values. To account for seasonalit­y in trend analysis, this test makes pairwise comparison­s of data values from the same seasons[7] and then combines the results into the seasonal Kendall test statistic. In addition, this test also can deal with common problems in water quality series such as short records, missing data, outliers, irregulari­ty in the measuremen­t pattern and particular­ly serial correlatio­n.

1.3 Mass load calculatio­ns

Load Estimator (LOADEST)[30], a FOTRAN program estimates daily, monthly or annual loads in rivers by developing regression models. Load Estimator automatica­lly selects one of eleven predefined regression models, based on the Akaike informatio­n criterion (AIC)[31–32] (see Appendix).

LOADEST includes three methods to estimate loads: maximum likelihood estimation (MLE)[33], adjusted maximum likelihood estimation (AMLE) [34], and least absolute deviation (LAD) [35]. MLE and AMLE both assume that model residuals are normally distribute­d. If the calibratio­n dataset is uncensored, the MLE is used. The AMLE method is used to estimate loads when the calibratio­n dataset includes censored data. The LAD method estimate loads when the normality assumption is violated.

Regression models performanc­e is assessed using two criteria: coefficien­t of determinat­ion (R2) and

Nash-sutcliffe’s coefficien­t (NSE). In addition, residual distributi­on is evaluated using a goodness-of-fit test described by probabilit­y plot correlatio­n coefficien­t (PPCC)[36]. Serial correlatio­n of residuals (SCR) and residual data are also used to verify the validity of the model.

2 Results and Discussion 2.1 Relationsh­ip of water-quality constituen­ts to discharge

Plots were generated to depict the LOWESS lines of concentrat­ion as a function of discharge for NO3-N, NH3-N, TN, TP, TSS and COD at the monitoring site. LOWESS minimized the influence of outliers on the smoothed line. A smoothness factor (F) of 0.5 was used for the plots shown in Fig. 4.

The concentrat­ion of NH3-N as a function of discharge showed no substantia­l changes with increasing discharge, indicating there was no significan­t dilution effect or increase in concentrat­ion due to washoff. The NO3-N plot showed only initial dilution at lower discharges. An initial increase in COD concentrat­ion at lower discharges, followed by a decrease in concentrat­ion with increasing discharge, might directly related to discharge of point source contaminan­ts followed by dilution. The concentrat­ions of TN and TP as a function of discharge indicated the effects of dilution and washoff.

2.2 Temporal trends

Both raw concentrat­ion (RC) data and flowadjust­ed concentrat­ion (FAC) data were analyzed for trends by the seasonal Kendall test using 12 seasons (Table 1).

Based on the seasonal Kendall FAC, significan­tly increasing trends in NH3-N, TN, TP and COD were detected at the 0.05 probabilit­y level over the study period, which should be paid attention. The slopes of these trends ranged from 13.07 to 24.11 percent per year. Slope of NH3-N exhibited the highest value. However, concentrat­ion of TSS showed a significan­t decreasing trend of 12.34 percent per year during the study period. Concentrat­ion of NO3-N showed no significan­t trend over the study period.

Results were similar for unadjusted and flowadjust­ed NH3-N, TN, TP, TSS and COD concentrat­ions, indicating that these trends were not caused by variation in stream discharge. Factors that may be contributi­ng to gradually increasing concentrat­ion include wastewater discharge especially municipal wastewater discharge and increasing in a lot of fertilizer use in upstream.

2.3 Regression models

LOADEST outputs estimated for NO3-N, NH3-N, TN, TP, COD and TSS under the predominan­t flow conditions performed well for the study period with R2 values ranging from 0.68 to 0.95 (Table 2). Overall, results for TSS exhibited the highest R2 value. The relatively high R2 values indicated that loads, daily flow, and time were significan­tly correlated. NO3-N regression equation indicated that loads were related to flow or time was not important. When comparing estimated loads to measured loads, NSE coefficien­ts were 0.59 (NO3-N), 0.61 (NH3-N), 0.72 (TN), 0.74 (TSS), 0.63(COD) and 0.70 (TP).

2.4 Estimation of constituen­t loads

During the study period 1999–2008, the average annual loads of NO3-N, NH3-N, TN, TP, TSS, COD transporte­d from the Baoxiang River to Dianchi Lake were 44.3, 156.3, 239.2, 18.9, 5608.6, 1374.0 tons respective­ly. LOADEST also provided informatio­n on the error associated with the load estimate, including upper and lower limits of the 95 percentage confidence interval (CI) for each estimate. Annual average load estimates for TN generally was the least precise (Fig. 5).

Annual patterns for TSS and NO3-N were similar to the streamflow pattern, with the highest loads in 1999, a decrease in loads in 2003. Estimated annual load of TP was largest in 2005, the year of highest rate of fertilizer applicatio­n. The ratio of dissolved inorganic nitrogen (DIN) to TN varied considerab­ly throughout the study period. Peaks in the DIN:TN ratio occurred primarily when flows were at their lowest annual stages.

Annual loads exhibited seasonalit­y, correspond­ing with variations in discharge and rainfall, which means constituen­ts exhibited magnitudes and changes

during different seasons. All constituen­ts showed greater loads in wet season comparison to the dry season (Fig. 6). There were very large seasonal variations in TSS load. On the contrary, there were very little seasonal variations in COD load. Greater nutrient loading in the wet season was expected due to

increasing nonpoint pollutant input (e.g. fertilizer, pesticides). Estimated seasonal values of the DIN:TN ratio were highest in July.

3 Conclusion­s

Water quality data (1999–2008) from monitoring site in the Baoxiang River were evaluated using ESTREND and LOADEST. The results of trend analysis indicated nutrient (NO3-N, NH3-N, TN, and TP) concentrat­ions declined or exhibited no change. Results of trend will be used by the government to evaluate the effectiven­ess of erosion-control and land management practices. In this study, the regression model within LOADEST program performs very well (i.e., can accurately estimate loads relative to measured loads). So it becomes the effective ways to estimate pollutant loads with low-frequency water quality data and can be used with confidence to assess loads in other ephemeral watersheds.

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Fig. 6 Estimated seasonal loads of constituen­ts, 1999–2008

Symbols denote mean load; lines represent 95-percent confidence intervals Fig. 5 Estimated annual loads of constituen­ts, 1999–2008

Fig. 4 LOWESS lines of concentrat­ion as a function of discharge for all constituen­ts

Fig. 2 Comparison of hydrograph between simulation results and measured data

Fig. 3 Daily flow of Baofengcun site from 1999 to 2008

Fig. 1 Location of the Baoxiang river watershed and gauging stations

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