TESTING FOR TREND IN LAKE AND GROUND WATER QUALITY TIME SERIES1

ABSTRACT: The detection of gradual trends in water quality time series is increasing in importance as concern grows for diffuse sources of pollution such as acid precipitation and agricultural non-point sources. A significant body of literature has arisen dealing with trend detection in water quality variables that exhibit seasonal patterns. Much of the literature has dealt with seasonality of the first moment. However, little has been mentioned about seasonality in the variance, and its effect upon the performance of trend detection techniques. In this paper, eight methods of trend detection that arise from both the statistical literature as well as the water quality literature have been compared by means of a simulation study. Varying degrees of seasonality in both the variances and the means have been introduced into the artificial data, and the performances of these procedures are analyzed. Since the focus is on lake and ground water quality monitoring, quarterly sampling and short to moderate record lengths are examined.     

SELECTING TREND TESTS FOR WATER QUALITY SERIES WITH SERIAL CORRELATION AND MISSING VALUES1

ABSTRACT: The use of nonparametric tests for monotonic trend has flourished in recent years to support routine water quality data analyses. The validity of an assumption of independent, identically distributed error terms is an important concern in selecting the appropriate nonparametric test, as is the presence of missing values. Decision rules are needed for choosing between alternative tests and for deciding whether and how to pre-process data before trend testing. Several data pre-processing procedures in conjunction with the Mann-Kendall tau and the Seasonal Kendall test (with and without serial correlation correction) are evaluated using synthetic time series with generated serial correlation and missing data. A composite test (pre-testing for serial correlation followed by one of two trend tests) is evaluated and was found to perform satisfactorily.     

MULTIVARIATE TREND TESTING OF LAKE WATER QUALITY1

ABSTRACT: Multivariate methods of trend analysis offer the potential for higher power in detecting gradual water quality changes as compared to multiple applications of univariate tests. Simulation experiments were used to investigate the power advantages of multivariate methods for both linear model and Mann-Kendall based approaches. The experiments focused on quarterly observations of three water quality variables with no serial correlation and with several different intervariable correlation structures. The multivariate methods were generally more powerful than the univariate methods, offering the greatest advantage in situations where water quality variables were positively correlated with trends in opposing directions. For illustration, both the univariate and multivariate versions of the Mann-Kendall based tests were applied to case study data from several lakes in Maine and New York which have been sampled as part of EPA's long term monitoring study of acid precipitation effects.     

TECHNIQUES FOR DETECTING TRENDS IN LAKE WATER QUALITY1

ABSTRACT: With the advent of standards and criteria for water quality variables, there has been an increasing concern about the changes of these variables over time. Thus, sound statistical methods for determining the presence or absence of trends are needed. A Trend Detection Method is presented that provides: 1) Hypothesis Formulation - statement of the problem to be tested, 2) Data Preparation - selection of water quality variable and data, 3) Data Analysis - exploratory data analysis techniques, and 4) Statistical Tests - tests for detecting trends. The method is utilized in a stepwise fashion and is presented in a nonstatistical manner to allow use by those not well versed in statistical theory. While the emphasis herein is on lakes, the method may be adopted easily to other water bodies.     

MULTIVARIATE ANALYSIS OF WATER QUALITY IN THE RICHIBUCTO DRAINAGE BASIN (NEW BRUNSWICK,CANADA)1

ABSTRACT: Specific conductivity, pH, dissolved oxygen, carbon, phosphorous, and nitrogen species were measured at 36 stations in the Richibucto River drainage basin, including the estuary, in New Brunswick, Canada, over the six‐year period 1996 through 2001. Each station was sampled between 1 and 26 times (mean = 7.5, standard deviation = 6.0) during the ice free seasons without regard to tide. There was significant variance among stations in most parameters. Principal component analysis (PCA) was used to identify the processes explaining the observed variance in water quality. Because of the high variability in specific conductance, stations were first grouped in a freshwater subset and an estuarine (brackish water) subset. For freshwater stations, most of the variance in water quality was explained by pH and total organic carbon, as well as high nutrient concentrations. These high nutrient concentrations, along with water salinity, which varies with flow and tides, are also important in determining water quality variability in brackish water. It is recommended that water quality parameters that were found to explain most of the variance by PCA be monitored more closely, as they are key elements in understanding the variability in water quality in the Richibucto drainage basin. Cluster analyses showed that high phosphorous and nitrate concentrations were mostly found in areas of peat runoff, tributaries receiving treated municipal effluent, and lentic zones upstream of culverts. Peat runoff was also shown to be acidic, whether it is runoff from a harvested area or a natural bog.     

NONPARAMETRIC APPROACHES TO ENVIRONMENTAL IMPACT ASSESSMENT1

ABSTRACT: The interesting developments in non-parainetric testing and estimation methods presented in the upcoming sequence of nine papers are evaluated, compared, and put into proper perspective. Because a deterioration in water quality constitutes a direct threat to human health, it is of utmost importance to have flexible non-parametric methods available for detecting and describing trends in water quality time series. A distinct advantage of nonparametric tests is that they are usually very effective when applied to “messy” environmental data which may, for example, contain many missing observations and not be normally distributed. By applying their enhanced approaches for nonparametric methods to water quality time series, as well as employing well designed simulation experiments, the authors of the papers clearly demonstrate the efficacy of utilizing nonparametric tests in environmental impact assessment.     

AN ANTIDEGRADATION POLICY FOR PRESERVING SURFACE WATER QUALITY IN FLORIDA1

ABSTRACT: As part of its overall system for protecting aquatic systems from unnecessary degradation, the State of Florida provides special protection for water bodies of unusual importance. Such water bodies are designated as “Outstanding Florida Waters” (OFW5). New discharges to OFWs are possible only if certain stringent criteria are met. A new point source direct discharge to an OFW is usually not allowed if it would cause any lowering of ambient water quality. A new indirect discharge (upstream from an OFW boundary) may be allowed only if it would not significantly degrade the OFW. To date, the advantages of the OFW system have clearly outweighed the disadvantages, and OFW designations are helping to protect Florida's most valuable waters from additional degradation. Florida's system could be a useful model for other jurisdictions wanting to provide special protection to special water bodies.     

GOALS AND DATA COLLECTION DESIGNS FOR WATER QUALITY MONITORING1

ABSTRACT: Water quality monitoring cannot address every information need through one data collection procedure. This paper discusses the goals and related procedures for designing water quality monitoring programs. The discussion focuses on the broad information needs of those agencies operating water quality networks. These information needs include the ability to assess trends and environmental impacts, determine compliance with objectives or standards, estimate mass transport, and perform general surveillance. Each of these information needs has different data requirements. This paper outlines these goals and discusses factors to consider in developing a monitoring plan on a site by site basis.     

COST-EFFECTIVE REGULATORY OPTIONS FOR WATER QUALITY LIMITED STREAMS1

With the pressure from industries and municipalities to reduce the waste water treatment costs associated with the permit limits needed to attain the goals of the Clean Water Act, attention has turned ways of introducing flexibility into the regulations without sacrificing the water quality goals. Wisconsin is the first state to have adopted a variety of options from which dischargers may choose when meeting their water quality requirements. These options were developed for the express purpose of minimizing the costs and maximizing the flexibility of the point source water quality regulations while ensuring that permitted discharge would not violate the water quality standards. This paper presents five options that the state has made available to dischargers, as well as one the state did not adopt. The conclusion is that a mix of options can substantially increase the flexibility and reduce the costs of meeting water quality standards on effluent limited streams.     

PUBLIC PERCEPTIONS IN WATER QUALITY EVALUATION1

ABSTRACT: A Visual Perception Test, consisting of photographic slides of water sites, was designed to examine laymen's water quality perceptions. The slides were taken at five water sites where the level of visual pollution was artifically altered by the investigator. Analysis of variance indicated that the water sites were evaluated differently for each of five pollution levels. Increases in water discoloration and the quality of litter were viewed as increases in level of pollution. Laymen not only evaluated visually polluted sites lower for uses such as picnicking, but they also evaluated the quality of the actual water lower. Stepwise multiple regression indicated that a combination of water color, scenic beauty appreciation, quality of the surrounding environment and industry as a pollution source explained 73 percent of the variance in predicting Overall Pollution. Application of factor analysis simplified the variables to an Overall Pollution Factor and a Boating Use Factor.     

TREND ASSESSMENT OF WATER QUALITY TIME SERIES1

ABSTRACT A general methodology is described for identifying and statistically modeling trends which may be contained in a water quality time series. A range of useful exploratory data analysis tools are suggested for discovering important patterns and statistical characteristics of the data such as trends caused by external interventions. To estimate the entries in an evenly spaced time series when data are available at irregular time intervals, a new procedure based upon seasonal adjustment is described. Intervention analysis is employed at the confirmatory data analysis stage to rigorously model changes in the mean levels of a series which are identified using exploratory data analysis techniques. Furthermore, intervention analysis can be utilized for estimating missing observations when they are not too numerous. The effects of cutting down a forest upon various water quality variables and also the consequences of acid rain upon the alkalinity in a stream provide illustrative applications which demonstrate the effectiveness of the methodology.     

SUPPLEMENTATION OF MISSING VALUES IN WATER QUALITY DATA1

ABSTRACT The problem of estimating missing values in water quality data using linear interpolation and harmonic analysis is studied to see which one of these two methods yields better estimates for the missing values. The data used in this study consisted of midnight values of dissolved oxygen from the Ohio River collected over a period of one year at Stratton station. Various hypothetical cases of missing data are considered and the two methods of supplementing missing values are evaluated using statistical tests. The results indicate that when the percentage of missed data points exceeded ten percent of the total number in the original sample, harmonic analysis usually yielded better estimates for both the regularly and irregularly missed cases. For data that exhibit cyclic variation, examples of which are dissolved oxygen concentration and water temperature, harmonic analysis as a data generation technique appears to be superior to linear interpolation.     

CONTINUOUS SIMULATION FOR WATER QUALITY PLANNING1

ABSTRACT: This paper describes a methodology for the evaluation of water quality plans analogous to procedures used in flood control planning, where flood damage frequency curves provide the basis for determining flood control benefits. The proposed method uses continuous water quality simulation to develop long term information from which water quality frequency curves can be obtained. This frequency information allows the evaluation of the impact of proposed water quality control plans taking into consideration the variable nature of the water resource. Using treatment costs and other economic indicators of water quality, the frequency information can be used to estimate the cost-effectiveness and economic efficiency of alternative plans. The method is demonstrated in a semi-hypothetical environment; real hydrologic and climatic characteristics are assigned to a hypothetical watershed configuration. Alternative management plans are simulated and analyzed for both physical and economic impacts. The advantages of continuous simulation and its use in water quality planning are explored.     

ANTIDEGRADATION WATER QUALITY CRITERIA FOR THE DELAWARE RIVER: A DISTRIBUTION-FREE STATISTICAL APPROACH1

ABSTRACT: Existing water quality for the Middle Delaware Scenic and Recreational River is significantly better than is required by current standards, leaving a potential for degradation. A method is presented for deriving candidate antidegradation water quality criteria for this segment of the Delaware River using statistical analysis of historic (ambient) water quality data. Data for 34 water quality parameters are first evaluated for data density, serial correlation, trend, seasonality, and other factors. These preliminary analyses are based on observation of data plots and application of distribution-free statistical techniques that are insensitive to outliers and are robust to relatively mild violations of basic assumptions. Data for 12 of the parameters have sufficient density for further analysis and can reasonably be modeled as independent and identically distributed over time (either seasonally or for the entire data sets). For these 12 parameters, distribution-free statistical methods are developed and used to derive intervals within which there is high confidence (usually greater than 95 percent) that the quantiles with potential use as anti-degradation criteria (the 0.85^th, 0.90^th, and 0.95^th quantiles in this study) for a particular parameter lie.     

ANTIDEGRADATION WATER QUALITY CRITERIA FOR THE DELAWARE RIVER: A DISTRIBUTION-FREE STATISTICAL APPROACH1

ABSTRACT: Existing water quality for the Middle Delaware Scenic and Recreational River is significantly better than is required by current standards, leaving a potential for degradation. A method is presented for deriving candidate antidegradation water quality criteria for this segment of the Delaware River using statistical analysis of historic (ambient) water quality data. Data for 34 water quality parameters are first evaluated for data density, serial correlation, trend, seasonality, and other factors. These preliminary analyses are based on observation of data plots and application of distribution-free statistical techniques that are insensitive to outliers and are robust to relatively mild violations of basic assumptions. Data for 12 of the parameters have sufficient density for further analysis and can reasonably be modeled as independent and identically distributed over time (either seasonally or for the entire data sets). For these 12 parameters, distribution-free statistical methods are developed and used to derive intervals within which there is high confidence (usually greater than 95 percent) that the quantiles with potential use as anti-degradation criteria (the 0.85^th, 0.90^th, and 0.95^th quantiles in this study) for a particular parameter lie.     

EVALUATING THE OPTIONS FOR WATER QUALITY MANAGEMENT1

ABSTRACT: The agricultural revolution occurred because the un-managed environment was not providing food in either the quantity or quality that society desired. The “environmental revolution” is developing because the unmanaged environment is clearly not capable of assimilating societal wastes without being seriously degraded. Effective environmental management will require regional and site-specific modification of general principles and practices that can be used at a national or international level. An environmental management system can be operated in the same manner as any industrial quality control system with three basic components: (a) sensors at appropriate locations, (b) rapid generation and feedback of information, and (c) a quality control group capable of taking immediate effective action when system performance is outside predetermined boundary conditions. This discussion focuses primarily on three areas: (a) management options available to regulate intrusion of societal wastes into natural systems, (b) types of methods available for predicting and validating effects on natural systems, and (c) modifications of present legislation that would permit the most flexibility in selecting from the various management options. Also considered are multispecies toxicity tests using species with cosmopolitan distribution in test systems with a high degree of environmental realism. Among the many values of such tests is the ability to exchange information from all parts of the world effectively because the test organisms are not restricted to a particular geographic region.     

A MODELLING METHOD FOR EVALUATING WATER QUALITY POLICIES IN NONSERIAL RIVER SYSTEMS1

ABSTRACT: This paper describes a modelling method which simplifies the evaluation of water quality policies for nonserial (branching) river systems. The method introduces dummy facilities at the junctions of the major tributary branches with the mainstream as replacements for the facilities and activities on the tributaries. The cost functions for the dummy facilities and the DO and BOD concentrations at the junctions as determined for each tributary are introduced into the mainstream serial system model which is then solved for the optimal values of the mainstream treatment plant efficiencies, the dummy facility effeciencies, and the tributary system DO and BOD concentrations using nonlinear programming. Given the optimum values for the dummy facility efficiencies and the values for the tributary system DO and BOD concentrations, the optimum values for the tributary treatment plant efficiencies are found using nonlinear programming. The method is applied to a river system with a mainstream and one major tributary which contain industrial and municipal organic and thermal polluters and their associated wastewater treatment plants.     

NUTRIENT BALANCES FOR THE EVALUATION OF NUTRIENT SOURCES IN WATER QUALITY MANAGEMENT1

ABSTRACT. This paper describes the methodology for a nutrient balance to evaluate the sources and distribution of nutrients in a small river basin. Loadings for total nitrogen and phosphorus are calculated from measured nutrient concentration and river discharge data. Using a special retrieval program and a data storage and processing system, loadings are accumulated over a given time period to allow for time of passage through the basin and seasonal changes in nutrient distribution. Nutrient balances are made with the accumulated loadings to obtain the relative contribution of each nutrient source and the retention of nutrients within the basin through sedimentation and aquatic growth. The methodology has been used to study nutrients in the Qu'Appelle River Basin, Saskatchewan, Canada.