EFFECT OF SERIAL CORRECTION ON GROUND WATER QUALITY SAMPLING FREQUENCY1

ABSTRACT: Methods of calculating uncertainty in estimates of serial correlation coefficients, and correcting for bias in short and medium length (less than 50 data point) records, are presented. Uncertainty and bias in the estimation of serial correlation coefficients for ground water quality data is shown to be considerable and to result in inaccurate calculation of the sampling frequencies for monitoring purposes. The methods are applied to a ground water data set consisting of 87 monthly measurements of nitrate concentrations. The variation in serial correlation coefficients with variation of record length is examined. The optimum sampling frequencies for detection of changes in ground water nitrate concentrations are estimated.     

DESIGN CONSIDERATIONS FOR AMBIENT STREAM QUALITY MONITORING1

ABSTRACT: Existing ambient water quality monitoring programs have resulted in data which are often unsuitable for assessment of water quality trends. A primary concern in designing a stream quality monitoring network is the selection of a temporal sampling strategy. It is extremely important that data for trend assessment be collected uniformly in time. Greatly superior trend detection power results for such a strategy as compared to stratified sampling strategies. In general, it is desirable that sampling frequencies be at least monthly but not greater than biweekly; higher sampling frequencies usually result in little additional information. An upper limit on trend detectability exists such that for both five and ten year base periods it is often impossible to detect trends in time series where the ratio of the trend magnitude to time series standard deviation is less than about 0.5. For the same record lengths trends in records with trend to standard deviation ratios greater than about one can usually be detected with very high power when a uniform sampling strategy is followed.     

Optimal water quality monitoring network design for river systems

Typical tasks of a river monitoring network design include the selection of the water quality parameters, selection of sampling and measurement methods for these parameters, identification of the locations of sampling stations and determination of the sampling frequencies. These primary design considerations may require a variety of objectives, constraints and solutions. In this study we focus on the optimal river water quality monitoring network design aspect of the overall monitoring program and propose a novel methodology for the analysis of this problem. In the proposed analysis, the locations of sampling sites are determined such that the contaminant detection time is minimized for the river network while achieving maximum reliability for the monitoring system performance. Altamaha river system in the State of Georgia, USA is chosen as an example to demonstrate the proposed methodology. The results show that the proposed model can be effectively used for the optimal design of monitoring networks in river systems.     

WATER QUALITY SAMPLING SCHEMES FOR VARIABLE FLOW CANALS AT REMOTE SITES1

ABSTRACT: Growing interest in water quality has resulted in the development of monitoring networks and intensive sampling for various constituents. Common purposes are regulatory, source and sink understanding, and trend observations. Water quality monitoring involves monitoring system design; sampling site instrumentation; and sampling, analysis, quality control, and assurance. Sampling is a process to gather information with the least cost and least error. Various water quality sampling schemes have been applied for different sampling objectives and time frames. In this study, a flow proportional composite sampling scheme is applied to variable flow remote canals where the flow rate is not known a priori. In this scheme, historical weekly flow data are analyzed to develop high flow and low flow sampling trigger volumes for auto‐samplers. The median flow is used to estimate low flow sampling trigger volume and the five percent exceedence probability flow is used for high flow sampling trigger volume. A computer simulation of high resolution sampling is used to demonstrate the comparative bias in load estimation and operational cost among four sampling schemes. Weekly flow proportional composite auto‐sampling resulted in the least bias in load estimation with competitive operational cost compared to daily grab, weekly grab sampling and time proportional auto‐sampling.     

EVALUATION OF WATER QUALITY SAMPLING LOCATIONS ON THE YUKON RIVER1

ABSTRACT: This paper presents an approach to the evaluation of water quality sampling locations for their potential use for long term monitoring. This approach was applied to four sites on the Yukon River near the Canada-United States boundary. At three of these sites it was difficult to obtain representative samples due to the presence of extensive lateral heterogeneities. These heterogeneities occur because of a lack of mixing between the Yukon River and the major tributaries upstream from Dawson. Only one of the sampling locations is spatially homogeneous enough to provide representative samples. Concentration variations over the annual cycle are very large, often as much as two orders of magnitude. Estimates are made of the frequency and density with which samples must be collected to be able to detect a 10 percent different between annual mean concentrations. The estimated frequencies are so large that such an undertaking would be impractical. More importantly, the assumptions of this analysis are invalid, and time series analysis of fixed frequency samples is proposed as an alternative that is statistically rigorous and requires fewer samples.     

ESTABLISHING STATISTICAL DESIGN CRITERIA FOR WATER QUALITY MONITORING SYSTEMS: REVIEW AND SYNTHESIS1

ABSTRACT: Regulatory water quality management has placed fairly extensive information expectations on routine, fixed-station monitoring without a corresponding emphasis being placed on the need to design monitoring systems to meet these expectations. To correct the situation there is increasing interest in developing more quantitative monitoring system design procedures which incorporate the statistical nature of sampling. In examining the development of such quantitative criteria, this paper describes the roles of statistics in a systematic approach to monitoring - initial design and routine reporting of results - and reviews the use of statistics in each. The paper emphasizes the need to tie the two together, via statistical design criteria, in order for the identified information expectations to be met in a statistically sound manner. However, the use of statistics in water quality monitoring is noted as currently being as much an art as it is a science.     

CONSOLIDATION OF A STREAM QUALITY MONITORING NETWORK1

ABSTRACT A method for systematic consolidation of a fixed station water quality monitoring network using dynamic programming is described. The approach utilizes a hierarchical structure; stations are allocated to what are termed primary basins on the basis of a weighted attribute score, and specific station locations within each primary basin are specified using a criterion based on stream order numbers. The method has been applied to the Municipality of Metropolitan Seattle (Metro) stream and river quality monitoring network. The results aided in a 1982 metro decision to reduce the scope of its fixed station monitoring from 81 to 47 stations, at an annual savings of approximately $33,000 per year exclusive of equipment depreciation and indirect costs.     

Network design for water quality monitoring of surface freshwaters: a review

To date, many water quality monitoring networks for surface freshwaters have been rather haphazardly designed without a consistent or logical design strategy. Moreover, design practices in recent years indicate a need for cost-effective and logistically adaptable network design approaches. There are many variables that need to be included in a comprehensive yet practical monitoring network: a holistic appraisal of the monitoring objectives, representative sampling locations, suitable sampling frequencies, water quality variable selection, and budgetary and logistical constraints are examples. In order to investigate the factors which affect the development of an effective water quality monitoring network design methodology, a review of past and current approaches is presented.     

STATISTICAL METHODS AND SAMPLING DESIGN FOR ESTIMATING STEP TRENDS IN SURFACE-WATER QUALITY1

ABSTRACT: This paper addresses two components of the problem of estimating the magnitude of step trends in surface water quality. The first is finding a robust estimator appropriate to the data characteristics expected in water-quality time series. The Hodges-Lehmann class of estimators is found to be robust in comparison to other nonparametric and moment-based estimators. A seasonal Hodges-Lehmann estimator is developed and shown to have desirable properties. Second, the effectiveness of various sampling strategies are examined using Monte Carlo simulation coupled with application of this estimator. The simulation is based on a large set of total phosphorus data from the Potomac River. To assure that the simulated records have realistic properties, the data are modeled in a multiplicative fashion incorporating flow, hysteresis, seasonal, and noise components. The results demonstrate the importance of balancing the length of the two sampling periods and balancing the number of data values between the two periods. The inefficiency of sampling at frequencies much in excess of 12 samples per year is demonstrated. Rotational sampling designs are discussed, and efficient designs, at least for this river and constituent, are shown to involve more than one year of active sampling at frequencies of about 12 per year.     

SAMPLING FREQUENCY SELECTION FOR REGULATORY WATER QUALITY MONITORING1

ABSTRACT: The selection of sampling frequencies in order to achieve reasonably small and uniform confidence interval widths about annual sample means or sample geometric means of water quality constituents is suggested as a rational approach to regulatory monitoring network design. Methods are presented for predicting confidence interval widths at specified sampling frequencies while considering both seasonal variation and serial correlation of the quality time series. Deterministic annual cycles are isolated and serial dependence structures of the autoregressive, moving average type are identified through time series analysis of historic water quality records. The methods are applied to records for five quality constituents from a nine-station network in Illinois. Confidence interval widths about annual geometric means are computed over a range of sampling frequencies appropriate in regulatory monitoring. Results are compared with those obtained when a less rigorous approach, ignoring seasonal variation and serial correlation, is used. For a monthly sampling frequency the error created by ignoring both seasonal variation and serial correlation is approximately 8 percent. Finally, a simpler technique for evaluating serial correlation effects based on the assumption of AR(1) type dependence is examined. It is suggested that values of the parameter p₁, in the AR(1) model should range from 0.75 to 0.90 for the constituents and region studied.     

RESERVOIR WATER QUALITY SAMPLING DESIGN1

ABSTRACT: The design of monitoring programs often serves as one of the major sources of error or uncertainty in water quality data. Properly designed programs should minimize uncertainty or at least provide a means by which variability can be partitioned into recognizable components. While the design of sampling programs has received recent attention, commonly employed strategies for limnological sampling of lakes may not be completely appropriate for many reservoirs. Based on NES data, reservoirs are generally larger, deeper, and morphologically more complex than natural lakes. Reservoirs also receive a majority of their inflow from a single tributary located a considerable distance from the point of outflow. The result is the establishment of marked physical, biological, and chemical gradients from headwater to dam. The existence of horizontal as well as vertical gradients, and their importance in water quality sampling design were the subject of intensive transect sampling efforts at DeGray Lake, a U.S. Army Corps of Engineers reservoir in southern Arkansas. Data collected were used to partition Variance, identify areas of similarity, and demonstrate how an equitable sampling program might be designed.     

ELEMENTS OF A WATER QUALITY INFORMATION SYSTEM FOR THE STATE OF ILLINOIS1

ABSTRACT: A great deal of information can be derived from study of standard stream monitoring data, if these are properly ordered and organized. This information may then be used to make decisions about water quality management. Among critical information items are evaluation of performance to standards, determination of seasonality and time-trends of water quality conditions, and estimation of the effects on water quality to be expected from load reductions or standards modifications. Additional information on the magnitude of individual pollution sources is also critical to water quality management. Each of these items can be derived within the water quality information system which is currently under development for the State of Illinois.     

KALMAN FILTER CALCULATION OF SAMPLING FREQUENCY WHEN DETERMINING ANNUAL MEAN SOLUTE CONCENTRATIONS

ABSTRACT: A linear filter (Kalman filter) technique was used with a Streamflow-concentration model the minimize surface water quality sampling frequencies when determining annual mean solute concentrations with a predetermined allowable error. The Kalman filter technique used the stream discharge interval as a replacement for the more commonly used time interval. Using filter computations, the measurement error variance was minimized within the sample size constraints. The Kalman filter application proposed here is applicable only under several conditions including: monitoring is solely to estimate annual mean concentration; discharge measurement errors are negligible; the Streamflow-concentration model is valid; and monthly samples reflect the total variance of the solute in question.     

断面污染物分布均匀度及影响因素研究

水质监测在跨行政区、跨水域功能区、排污总量控制区的水质控制与管理方面有着非常重要的作用,而断面污染物分布均匀与否是水环境监测断面采样是否具备可靠性、合理性和代表性的关键。本文根据监测采样要求提出了断面污染物分布均匀度的概念,即断面污染物分布均匀度是指在污染带内任一垂直于水流方向断面上某一污染物最小浓度值与最大浓度值之比。并就污染物排放量、河流环境水文特征、河流背景浓度、排污方式等断面污染物分布均匀度影响因素进行了分析研究,借助水质模拟技术,反演在上游有排污口情况下如何布置排污方式以满足下游监测采样的要求。这对跨界河流的水质交接、流域水环境质量管理、排污总量控制、水环境监测断面设置等方面的应用具有重要指导和借鉴意义。     

MONITORING STRATEGIES TO DETERMINE COMPLIANCE WITH WATER QUALITY OBJIECTIVES1

ABSTRACT: Two sampling strategies designed to test for compliance with water quality objectives are examined. For objectives based on long-term mean requirements, fixed frequency sampling at frequent intervals is most advantageous regardless of the underlying distribution of the data. For objectives that are based on maximum allowable concentrations, effective sampling strategies increase the likelihood of detecting noncompliance. If data are highly autocorrelated or sharply seasonal in distribution, an exceedance-driven sampling strategy is more effective and efficient for detecting violations than fixed frequency sampling. However, data generated by exceedance-driven sampling provide biased estimates of mean and standard deviation.     

YX-WQMS在左卫国控点水质自动监测站的应用

官厅水系左卫国控点水质自动监测站的YX-WQMS系统，由采水系统、配水系统、在线水质分析仪器、PLC控制和数据采集举统、数据处理和传输系统，监测站房和配套设备六个部分组成。表文重点介绍了该自动监测系统的关键技术和分析权的特点。     

Survey of state water quality monitoring programs

Budget changes, whether positive or negative, in water quality management agencies often mean a change in resources available for water quality monitoring. Many state agencies are currently facing monitoring budget cuts and, as a result, are reevaluating their monitoring programs. Such evaluations make use of a number of information sources, not the least of which are monitoring activities in other states. This article reports results of a survey of all fifty state water quality monitoring programs. Twenty questions were asked in the general areas of fixed-station monitoring, special studies, and biological monitoring. Each state was contacted by telephone at least twice during the survey. Fixed-station monitoring is conducted by 48 of 50 states. An average of 75 stations per state are sampled, generally on a monthly basis. There is a large variation in the way data are analyzed by the states; water quality indices and plots of concentration or loading over time are the most common methods. All but three states conduct special studies, but only seven repeat the studies on a regular basis. Special studies are generally problem specific as opposed to basin oriented. Biological monitoring is performed by 33 states; however, this is an area in which budget cuts are having a noticeable impact. In some cases, biological monitoring is being completely eliminated or suspended. Macroinvertebrate sampling is performed quarterly to biannualiy by 50% of the states; 75% of the states that sample macroinvertebrates do so annually. Periphyton sampling is performed by 33% of the states. Over 50% of the states are in the process of revising, or have revised, their monitoring program during the past five years. However, only four states had a detailed rationale and operating procedure for the entire monitoring system. Results of the survey are, therefore, averages of existing monitoring programs. Average results do not necessarily represent ideal situations, but do give an indication of how states are coping with their monitoring responsibilities.     

Estimating upper percentiles of surface water monitoring data with sparse samples

The estimation of upper percentiles of chemical concentrations in surface water systems within sites and regions may be necessary for the assessment of potential risk to ecosystems and human health. Limited sample sizes at monitoring sites often limit the use of direct methods to estimate upper percentiles. In such cases, upper percentiles within regions within a time frame may be estimated by pooling data across sites and years, and then deriving percentile estimates from the pooled dataset. The method uses the observations resulting from either a known probability-sampling design or a sampling design treated like one because its observations come close to matching that of a probability-sample. These observations are then weighted to ensure that estimates are representative of a target population across all the sites within the region and the range of years in the time frame. This method of estimating upper percentiles of annual site concentration profiles is demonstrated using atrazine and validated using the monitoring data from both sparsely sampled and high-frequency water monitoring programs, where point and interval estimates of the 90th, 95th, and 99th pooled population percentiles are provided. This method shows that the pooled data from multiple sparse datasets can be used to provide estimates of near-peak concentrations with greater certainty, which are consistent with those generated by high-frequency sampling monitoring programs.     

MODELING DRINKING WATER QUALITY VIOLATIONS WITH BAYESIAN NETWORKS1

ABSTRACT: Compliance violations at community water systems are rare but represent significant human health risks. These risks are mediated by the decision schema of human operators at water treatment facilities. However, causal uncertainty among physical and human factors involved in water quality problems complicates assessment of their probability and severity. This study uses a probabilistic Bayesian network modeling approach to explore the causes of compliance violations in a sample of water treatment systems in Pennsylvania. The model presented here is one of several created by treatment system operators during an expert elicitation process. The expert model alone predicts violations poorly, suggesting that experts make inaccurate quantitative estimates. However, Bayesian networks are capable of combining the subjective expertise of treatment system operators with the objective compliance histories of the facilities they manage, and the expert model accurately predicts violations when trained with historical compliance data. Analysis of the trained network reveals those components of the treatment process, including environmental and system characteristics as well as operator decisions, that play the greatest role in determining the likelihood of major violation types. Among operator decisions, coagulant dosing and filter backwash frequency are the most important determinants of violation likelihood.     

A DECISION SUPPORT SYSTEM FOR WATER QUALITY DATA AUGMENTATION: A CASE STUDY1

ABSTRACT: Recent developments in water quality monitoring have generated interest in combining non-probability and probability data to improve water quality assessment. The Interagency Task Force on Water Quality Monitoring has taken the lead in exploring data combination possibilities. In this paper we take a developed statistical algorithm for combining the two data types and present an efficient process for implementing the desired data augmentation. In a case study simulated Environmental Protection Agency (EPA) Environmental Monitoring and Assessment Program (EMAP) probability data are combined with auxiliary monitoring station data. Auxiliary stations were identified on the STORET water quality database. The sampling frame is constructed using ARC/INFO and EPA's Reach File-3 (RF3) hydrography data. The procedures for locating auxiliary stations, constructing an EMAP-SWS sampling frame, simulating pollutant exposure, and combining EMAP and auxiliary stations were developed as a decision support system (DSS). In the case study with EMAP, the DSS was used to quantify the expected increases in estimate precision. The benefit of using auxiliary stations in EMAP estimates was measured as the decrease in standard error of the estimate.