DATA REQUIREMENTS OF A WATER QUALITY MANAGEMENT PROGRAM1

ABSTRACT: Routine data collection currently consumes a large amount of the total resources devoted to water quality management. All too often data collection becomes an end in itself, with little thought given to the purpose of the data collection. The problem generally stems from a lack of proper routine surveillance system design and a failure on the part of the designers to initially identify the data needs of the management program. This study attempts, in a general way, to delineate the data needs of a water quality management program. This first required an identification of the activities involved in water quality management. The activities were then discussed in terms of the types of information needed to successfully complete their assigned tasks. Several detailed examples are given. The results of the discussion are summarized and several strategies are proposed to relate the results to surveillance system design.     

EVALUTING STREAM STANDARD VIOLATIONS USING A WATER QUALITY DATA BASE1

ABSTRACT: Cumulative density functions (c.d.f.'s) for water quality random variables may be estimated using data from a routine grab sampling program. The c.d.f. may then be used to estimate the probability that a single grab sample will violate a given stream standard and to determine the anticipated number of violations in a given number of samples. Confidence limits about a particular point on the c.d.f. may be used to reflect the accuracy with which the sample estimate represents the true c.d.f. Methods are presented here for calculating such confidence limits using both a normal model and a nonparametric model. Examples are presented to illustrate the usefulness of an estimated c.d.f. and associated confidence limits in assessing whether an observed number of standard violations is the result of natural variability or represents real degradation in water quality.     

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.     

THE ENVIRONMENTAL DISPLAY MANAGER: A TOOL FOR WATER QUALITY DATA INTEGRATION1

ABSTRACT: The Environmental Display Manager, EDM, is a development system on an IBM 3090 mainframe at the U.S. EPA National Computer Center in Research Triangle Park, North Carolina. EDM provides mapping, display, analysis support, and information management capabilities to workstations located across the United States, and connected to EPA through federal, state, academic, and private communications networks. Through interactive software, EDM can quickly support analyses, create maps and graphics, and generate reports that integrate millions of pieces of environmental data. The concept of EDM is to provide easy access to environmental information, to provide automated environmental analyses and reports, and then to provide data, graphics, images, text, and documents that can be used by numerous output devices, software packages, and computers. The mapping cumponent works with an electronic version of the 54,000 7.5 minute quad sheets of the U.S. Geological Survey. The software also works with a hydrographic data base of the surface waters of the United States. With the maps, a user can look at the rivers in any state, can zoom in on a small pond, and can overlay and identify particular features such as industrial waste dischargers and factories. The hydrography allows routing for modeling programs, identification of upstream and downstream components, and linkage of environmental features associated with surface waters. Alternatively, users can query data based on latitude/longitude, city name, EPA permit number, state agency and station code, river name or number, and river cataloging unit. The maps can be overlaid with roads and environmental sites such as: municipal and industrial dischargers, Superfund sites, public drinking water supplies, water quality monitoring stations, stream gages, and city locations. Retrievals from related systems can be performed for selected sites creating graphics showing water quality trends, discharge monitoring reports, and permit discharge limits.     

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.     

USING UNCENSORED TRACE-LEVEL MEASUREMENTS TO DETECT TRENDS IN GROUND WATER CONTAMINATION1

ABSTRACT: Environmental decision making involving trace-levels of contaminants can be complicated by censoring, the practice of reporting concentrations either as less than the limit of detection (LOD) or as not detected (ND) when a test result is less than the LOD. Censoring can result in data series that are difficult to meaningfully summarize, graph, and analyze through traditional statistical methods. In spite of the relatively large measurement errors associated with test results below the LOD, simple and meaningful analyses can be carried out that provide valuable information not available if data are censored. For example, an indication of increasing levels of contamination at the fringe of a plume can act as an early warning signal to trigger further study, an increased sampling frequency, or a higher level of remediation at the source. This paper involves the application of nonparametric trend analyses to uncensored trace-level groundwater monitoring data collected between March 1991 and August 1994 on dissolved arsenic and chromium for seven wells at an industrial site in New York.     

USING HEAT CAPACITY MAPPING MISSION (HCMM) DATA TO ASSESS LAKE WATER QUALITY1

ABSTRACT A water quality investigation on Utah Lake was conducted during the same time period that the Heat Capacity Mapping Mission (HCMM) satellite was collecting thermal infrared and reflectivity data. Relationships were established and evaluated among HCMM data and lake water quality parameters. Although remotely sensed reflective data have been previously utilized, this study was unique in that thermal emitted data were also correlated to algae concentrations and other indicators. Standard statistical evaluations were made along with utilization of color graphics techniques to identify and plot relationships. The emitted thermal energy was found to have high positive correlations with net algal concentrations and with the predominant species, Aphanizomenon flos-aquae, a blue-green alga. No continuous correlation was found for a less abundant red pigment phytoplankton, Ceratium hirundinella. Similar trends, though for negative correlations, were shown for reflectivity data and algal concentrations throughout the spring and summer. Coincidence of areas of warmer emitted energy and darker relfected energy on colorgraphics displays clearly indicate lake areas of high algal concentrations. Night thermal data displayed a strong negative correlation with algal concentration, opposite to day thermal data. Color graphics of warmer day emitted energy and cooler night emitted energy further verify areas of high algal concentrations.     

GROUND WATER QUALITY PREDICTION USING CLIMATIC INDICES1

ABSTRACT: Drought affects the quality of ground water in certain aquifers used by municipalities in Kansas. Water quality changes occur as a function of the amount of water available for recharge and hence to dilute more mineralized ground waters. Several measures of meteorological drought, including the Palmer Index and Eagleman Aridity Index, were correlated with water quality data to determine the degree of association. Several locations showed sharp delinces in water quality as the drought progressed. These relationships can be used to predict possible variations in present and future well-water supplies in locations subject to drought induced water quality deterioration.     

WATER RESOURCES INVESTIGATIONS IN WEST CENTRAL IRAN USING LANDSAT DATA1

ABSTRACT: Extensive investigations have been undertaken to determine the utility of Landsat data for detecting and analyzing hydrologic characteristics of an interior watershed of Iran that drains to Daryachehye-Namak (salt lake). This interior playa serves as the terminus for surface water discharging to it from the Karaj, Shur, Saveh, and Ghom Rivers and ground water from their underlying aquifers. These drainage systems encompass heavily populated and industrial sectors of west central Iran, including Tehran. The result of this investigation demonstrates the applicability of Landsat data for mapping and monitoring water regimen as an aid in interpreting hydrologic conditions throughout this arid region. Fluctuation of water area in a playa lake, occupying the lowest part of this closed basin, was monitored on repetitive Landsat coverage. As the result of field investigations combined with optical and digital analyses of the Landsat data, fluctuating water depths were determined in order to estimate the volume of water present in this lake during various seasons. A comparison between stream discharge rates and the estimated volume of standing water make it possible to quantitatively evaluate the hydrologic regimen and to detect the significance of ground water discharge.     

LAKEMAP: A 2-D AND 3-D MAPPING SYSTEM FOR VISUALIZING WATER QUALITY DATA IN LAKES1

ABSTRACT: The visualization of water quality data in lakes was achieved by integrating the U.S. Environmental Protection Agency's (EPA) STORET water quality database, lake shoreline polygons from EPA's Reach File (version 3), and the UNIMAP 2-D and 3-D interactive mapping and modeling software. Based on lake name (and state abbreviation), a lake shoreline polygon can be accessed from the Reach File. The coordinates of the polygon are portrayed by the U.S. Geological Survey (USGS) 1:100,000 scale Digital Line Graph (DLG) hydrography layer. This polygon is passed, in turn, to the STORET water quality file. Monitoring stations located within the polygon boundary are extracted along with the complete sampling survey. Specific parameters, such as total phosphorus, pH, ammonia, and optional time and depth restrictions can be selected to build a file of x, y, z₁, z₁…, zn data which is imported to UNIMAP. Up to four parameters, including depth, can be selected at a time. Within UNIMAP, the data is gridded and then displayed as a 2-D color contour map, 3-D perspective contour map, or 2-D projected time or depth slices. This system operates on the EPA ES9000 mainframe computer located in Research Triangle Park (RIP), North Carolina. LAKEMAP is the culmination of an effort to bridge the gap between the vast array of environmental data collected by the EPA and the complex analytical and display software resident on the mainframe.     

ESTIMATING MEAN AND VARIANCE FOR ENVIRONMENTAL SAMPLES WITH BELOW DETECTION LIMIT OBSERVATIONS1

ABSTRACT: Left-censoring of data sets complicates subsequent statistical analyses. Generally, substitution or deletion methods provide poor estimates of the mean and variance of censored samples. These substitution and deletion methods include the use of values above the detection limit (DL) only, or substitution of 0, DL/2 or the DL for the below DL values during the calculation of mean and variance. A variety of statistical methods provides better estimators for different types of distributions and censoring. Maximum likelihood and order statistics methods compare favorably to the substitution or deletion methods. Selected statistical methods applicable to left-censoring of environmental data sets are reviewed with the purpose of demonstrating the use of these statistical methods for coping with Type I (and Type II) left-censoring of normally and log-normally distributed environmental data sets. A PC program (UNCENSOR) is presented that implements these statistical methods. Problems associated with data sets with multiple DLs are discussed relative to censoring methods for life and fatigue tests as recently applied to water quality data sets.     

EVALUATION OF METHODS FOR ESTIMATING STREAM WATER QUALITY PARAMETERS IN A TRANSIENT MODEL FROM STOCHASTIC DATA1

ABSTRACT: In this study the estimation of parameters in water quality models represented by linear first order partial differential equations is investigated. Two sets of simulated input-output data, one with input noise and the other with output measurement error, were used. The parameters were estimated by a gradient technique (Bard's method) and a pattern search technique. The results indicate that the output measurement error significantly affects the values of parameter estimates as compared to the noise added to the input. Bard's method consistently gave results with a smaller sum of square value.     

WATER USE DATA – WHO NEEDS IT?1

ABSTRACT: This paper addresses problems in the design of a water use data base and valuation of water use data. An examination of the economic efficiency and distributional aspects of selected water management problems indicates that there are benefits to be obtained from water use information and, thus, there is a potential value to water use data. Water use information is viewed as being produced by combining water use data with other data in a modeling context suggested by theory relevant to the problem at hand, and a well designed water use data base will facilitate this synthesis. Data “needs” in the production of water use information via economic modeling are described to illustrate the relationship between theory and data. Some of the desirable characteristics of a water use data base and inadequacies of the existing water use data “base” are described. The fugitive nature of the water use data resource and a lack of understanding of both the information “production function” and the role of water use information in decisionmaking complicates the water use data valuation problem.     

REGIONAL DETECTION OF CHANGE IN WATER QUALITY VARIABLES1

ABSTRACT: The detection of change in a hydrologic varaible, particularly water quality, is a current problem. A method is presented for testing whether there has been a shift in the mean of a hydrologic variable based on the well established bivariate normal distribution theory. In this technique, the dependent, or target, and the independent, or control, variables are formed as weighted linear combinations of the mean values at a number of locations in a selected target and control area. The weighting factors are determined based on a mathematical programming technique which minimizes the conditional coefficient of variation thereby minimizing the number of observations required to detect a change of a preselected magnitude in the mean of the target area. The result is a situation where a savings in the number of observations required to detect a change is a consequence of adding more stations: the space-time tradeoff. Two applications of the technique are presented, the first using electrical conductivity (EC) data from two sets of river basins and the second using EC data from a set of basins as the target variable and annual discharge as the control. The results indicate that a significant savings in time can be achieved by using this method.     

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.     

IMPACTS OF WATER QUALITY IMPROVEMENT ON SITE VISITATION: A PROBABILISTIC MODELING APPROACH1

ABSTRACT: The influence of perceptions of water quality on the likelihood of recreating at a particular recreation site is investigated using a logit model. The model is estimated for St. Albans Bay, Vermont. A hypothetical improvement in water quality was shown to greatly increase the probability of at least one visit to the bay during a season. This implies that many recreationists who do not use the bay at present will do so if water quality improves. A demand model for predicting site visitation must, therefore, include current nonusers in the sample. Failure to do so would result in misleading predictions about future demand for the bay.     

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.     

A MODELING APPROACH FOR STORM WATER QUANTITY AND QUALITY CONTROL VIA DETENTION BASINS1

ABSTRACT: Storm water detention basins have historically been employed for quantity (i.e., flooding) control only. However, recently it has been suggested that these basins may also provide a practical means of storm water quality control. This paper presents the formulation of a mathematical modeling approach which may be used by professionals to simultaneously design detention basins for the dual purpose of storm water quantity and quality control. Model simulations demonstrate that for a given basin, pollutant removal increases as storm frequency increases. The importance of particle size distribution and settling velocity for net pollutant removal is illustrated, The design procedure is demonstrated, and pollutant loading diagrams for estimating pollutant removal as a function of storm size are developed.