Water quality monitoring—Some practical sampling frequency considerations

Water quality monitoring involves sampling a population, water quality, that is changing over time. Sample statistics (e.g., sample mean) computed from data collected by a monitoring network can be affected by three general factors: (1) random changes due to storms, rainfall, etc.; (2) seasonal changes in temperature, rainfall, etc.; and (3) serial correlation or duplication in information from sample to sample. (Closely spaced samples will tend to give similar information).In general, these effects have been noted, but their specific effects on water quality monitoring network design have not been well defined quantitatively. The purpose of this paper is to examine these effects with a specific data set and draw conclusions relative to sampling frequency determinations in network design.The design criterion adopted for this study of effects due to the above factors is the width of confidence intervals about annual sample geometric means of water quality variables. The data base for the study consisted of a daily record of 5 water quality variables at 9 monitoring stations in Illinois for a period of 1 year.Three general regions of frequencies were identified: (1) greater than approximately 30 samples per year where serial correlation plays a dominant role; (2) between approximately 10 and 30 samples per year where the effects of seasonal variation and serial correlation tended to cancel each other out; and (3) less than approximately 10 samples per year where seasonal variation plays a dominant role. In region 2, either seasonal variation and serial correlation should both be considered or both ignored. To consider only seasonal variation introduces more error than ignoring it. These results are network averages (over variables and stations) from one network, thus results for individual variables may deviate considerably from the average and from those for other networks.Financial support for this study was provided, in part, by the U.S. Environmental Protection Agency, grant number R805759-01-0.     

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.     

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.     

Seasonality of selected surface water constituents in the Indian River Lagoon, Florida

Seasonality is often the major exogenous effect that must be compensated for or removed to discern trends in water quality. Our objective was to provide a methodological example of trend analysis using water quality data with seasonality. Selected water quality constituents from 1979 to 2004 at three monitoring stations in southern Florida were evaluated for seasonality. The seasonal patterns of flow-weighted and log-transformed concentrations were identified by applying side-by-side boxplots and the Wilcoxon signed-rank test (p < 0.05). Seasonal and annual trends were determined by trend analysis (Seasonal Kendall or Tobit procedure) using the U.S. Geological Survey (USGS) Estimate TREND (ESTREND) program. Major water quality indicators (specific conductivity, turbidity, color, and chloride), except for turbidity at Station C24S49, exhibited significant seasonal patterns. Almost all nutrient species (NO(2)-N, NH(4)-N, total Kjeldahl N, PO(4)-P, and total P) had an identical seasonal pattern of concentrations significantly greater in the wet than in the dry season. Some water quality constituents were observed to exhibit significant annual or seasonal trends. In some cases, the overall annual trend was insignificant while opposing trends were present in different seasons. By evaluating seasonal trends separately from all data, constituents can be assessed providing a more accurate interpretation of water quality trends.     

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.     

PARAMETRIC AND NONPARAMETRIC TESTS FOR DEPENDENT DATA1

ABSTRACT: Simulation and analytical results show that ignoring serial dependence can have serious effects on the performance of the t, sign, and Wilcoxen tests. In particular, the true significance levels of these tests are altered significantly from the intended nominal levels. Modifications for these tests are given and shown to have the correct significance levels. Furthermore, an estimate of serial correlation is suggested for binary data and evaluated by simulation. An application to the toxic contaminants data from the Niagara River concludes the paper.     

Detecting acid precipitation impacts on lake water quality

The United States Environmental Protection Agency is planning to expand its long-term monitoring of lakes that are sensitive to acid deposition effects. Effective use of resources will require a careful definition of the statistical objectives of monitoring, a network design which balances spatial and temporal coverage, and a sound approach to data analysis. This study examines the monitoring objective of detecting trends in water quality for individual lakes and small groups of lakes. Appropriate methods of trend analysis are suggested, and the power of trend detection under seasonal (quarterly) sampling is compared to that of annual sampling. The effects of both temporal and spatial correlation on trend detection ability are described.     

Influence of Sampling Frequency on Estimation of Annual Total Phosphorus and Total Suspended Solids Loads1

Abstract: The determination of sediment and nutrient loads is typically based on the collection and analysis of grab samples. The frequency and regularity of traditional sampling may not provide representation of constituent loading, particularly in systems with flashy hydrology. At two sites in the Little Bear River, Utah, continuous, high‐frequency turbidity was used with surrogate relationships to generate estimates of total phosphorus and total suspended solids concentrations, which were paired with discharge to estimate annual loads. The high frequency records were randomly subsampled to represent hourly, daily, weekly, and monthly sampling frequencies and to examine the effects of timing, and resulting annual load estimates were compared to the reference loads. Higher frequency sampling resulted in load estimates that better approximated the reference loads. The degree of bias was greater at the more hydrologically responsive site in the upper watershed, which required a higher sampling frequency than the lower watershed site to achieve the same level of accuracy in estimating the reference load. The hour of day and day of week of sampling impacted load estimation, depending on site and hydrologic conditions. The effects of sampling frequency on the determination of compliance with a water quality criterion were also examined. These techniques can be helpful in determining necessary sampling frequency to meet the objectives of a water quality monitoring program.     

CORRELATION OF ANNUAL PEAK FLOWS FOR PENNSYLVANIA STREAMS1

A common assumption in flood frequency analysis is that annual peak flows are independent events. This study was undertaken to investigate the validity of this assumption with regard to Pennsylvania streams by statistically analyzing the dependence between annual peak flows and to determine if basin carryover effects relate to the degree of dependence. Five tests of dependence, the autocorrelation test, the median crossing test, the turning points test, the rank difference test, and the Spearman rank order serial correlation coefficient test were applied to the series of annual peak flows for 57 streams. Of the 57 streams analyzed, only two exhibited signs of dependence by at least two of the tests performed, and the baseflow component of annual peak flows was found to be unrelated to the degree of dependence exhibited between annual peak flows. It was concluded that the assumption of independence of annual peak flows is valid in flood frequency analysis for Pennsylvania streams.     

Reconstructed Stream Flow for the Salt and Verde Rivers From Tree-Ring Data1

ABSTRACT: The Salt and Verde Rivers of central Arizona provide the water supply for metropolitan Phoenix and a considerable acreage of irrigated agriculture. Rapid urbanization has caused concern over future water supply and aggravated flooding in the already flood-prone Salt River Valley. Tree-ring data were used as a proxy source to extend the annual and seasonal runoff records back to A.D. 1580 and thus to determine whether the period of record for annual discharge adequately represents the long term flow characteristics of the two rivers. Results show that several periods of extended low flow have occurred during the past 400 years, many of which were more severe then any comparable period since 1890. The low flow periods appear to have a recurrence interval of about 22 years. Also the gaged records contain an above average number of high seasonal and annual flows when compared to the entire 400 years. The reconstructions contain important implications for future water supply and flood potentials in the Salt River Valley.     

RELATING TRENDS OF PRINCIPAL COMPONENTS TO TRENDS OF WATER-QUALITY CONSTITUENTS1

ABSTRACT: The seasonal Kendall test is used for detecting water-quality trend or lack of trend for monthly data of 15 water-quality constituents at 15 sampling stations in the Arkansas River, the Neosho River, and the Verdigris River basins. Trends of individual constituents and the trends of the first four principal components for the correlation matrix of water-quality data at each station are determined, and the relationships between the trends of constituents and the trends of principal components are established. Using the principal components not only reduces the high dimensionality of the original data to a few principal components, but also presents an overall picture of water-quality trend of these river basins.     

NONPARAMETRIC TEST PERFORMANCE FOR TRENDS IN WATER QUALITY WITH SAMPLING DESIGN APPLICATIONS1

ABSTRACT: In this paper four nonparametric tests for monotonic trend detection are compared with respect to their power and accuracy. The importance of comparing powers at equal empirical significance levels rather than nominal levels is stressed. Therefore, an appropriate graphical method is presented. The effect of the sampling frequency is also assessed using Monte Carlo simulations and a trajectory representation that visualizes the dynamics of the trade-off between the type I and type II errors. These methods are applied to compare four nonparametrical tests (seasonal Mann. Kendall, modified seasonal Mann-Kendall, covariance eigenvalue and covariance inversion) under several conditions. It is concluded with respect to the power that it is not worthwhile for the modified seasonal Mann-Kendall test applied to the AR(1) process considered in this paper to increase the sampling frequency from monthly to biweekly for detecting a monotonic trend of 5 percent, 10 percent, or 15 percent of the process variance. Under these conditions the seasonal Mann-Kendall test is highly liberal, while the covariance inversion and the covariance eigenvalue test are conservative. This research is situated in the development of an efficient sampling design for the Flemish water quality monitoring network.     

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.     

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.     

Dust deposition in a sub-tropical opencast coalmine area, India

This paper provides baseline information about the total annual dust fall, and its constituents and seasonal variation, from a sub-tropical opencast coalmine area in Bina, India. Dust samples were collected monthly for 2 years (June 2002-May 2004) from five sampling sites in the region and analyzed in the laboratory for water-soluble and -insoluble matter. Water-insoluble components constituted the major fraction of the total annual dust fall. Two-way ANOVA indicated significant variations in dust fall at different sites, over the months and in their interactions. The dust deposition rate was highest during summer (March-June), followed by winter (November-February) and lowest in the rainy season (July-October). Maximum dust fall was observed near the coal handling plant (at site 2) followed by the receiving pit of the coal handling plant (site 3), near the main sub-station (site 4), Jawahar colony (site 1) and Gharasari village (site 5). An inverse and significant relation was observed between dust fall and precipitation. Our studies have shown that the main residential areas are experiencing higher levels of dust fall which makes them unsuitable for living. We suggest that residential areas should be moved farther away from the mining area in the opposite direction of prevalent winds.     

TEMPORAL VARIABILITY OF WATER QUALITY IN THE ST. LUCIE ESTUARY,SOUTH FLORIDA1

ABSTRACT: Four years of monthly freshwater discharge and constituent concentration data from three tributaries were related to a concurrent series of data for three segments of the St. Lucie Estuary in South Florida using multiple regression and time-series analysis techniques. Water quality parameters examined were dissolved inorganic and total nitrogen and phosphorus, chlorophyll a, total suspended solids, turbidity, and color. On monthly time scales, a multiple regression, which included freshwater discharge, freshwater constituent concentration, and dilution with ocean water (salinity) as independent variables, explained 50 percent or less of the variability in estuarine constituents. No single independent variable explained more variation than another. By contrast, on seasonal (wet, dry) time scales, freshwater discharge explained the bulk of variation in estuarine water quality (up to 93 percent). On monthly time scales, variability in concentrations of nutrients and other constituents may be largely controlled by processes internal to the system. At seasonal time scales, freshwater discharge appears to drive variability in most estuarine water quality parameters examined. Results indicate that management of tributary input on a seasonal basis, with the expectation of achieving seasonal concentration goals in the estuary, would have a higher probability of success than managing on a monthly basis.     

CONSIDERATIONS OF SCALE IN WATER QUALITY MONITORING AND DATA ANALYSIS1

ABSTRACT: An assumption of scale is inherent in any environmental monitoring exercise. The temporal or spatial scale of interest defines the statistical model which would be most appropriate for a given system and thus affects both sampling design and data analysis. Two monitoring objectives which are strongly tied to scale are the estimation of average conditions and the evaluation of trends. For both of these objectives, the time or spatial scale of interest strongly influences whether a given set of observations should be regarded as independent or serially correlated and affects the importance of serial correlation in choosing statistical methods. In particular serial correlation has a much different effect on the estimation of long-term means than it does on the estimation of specific-period means. For estimating trends, a distinction between serial correlation and trend is scale dependent. An explicit consideration of scale in monitoring system design and data analysis is, therefore, most important for producing meaningful statistical information.     

吐曼河近5a水质变化浅析

通过单因子评价法对吐曼河水质现状及近期变化趋势进行了分析,揭示了近几年来吐曼河水质年内及年际变化的特点,以期对吐曼河水污染治理提供参考。     

Nitrate and sediment fluxes from a California rangeland watershed

Long-term water quality records for assessing natural variability, impact of management, and that guide regulatory processes to safeguard water resources are rare for California oak woodland rangelands. This study presents a 20-yr record (1981-2000) of nitrate-nitrogen (NO(3)-N) and suspended sediment export from a typical, grazed oak woodland watershed (103 ha) in the northern Sierra Nevada foothills of California. Mean annual precipitation over the 20-yr period was 734 mm yr(-1) (range 366-1205 mm yr(-1)). Mean annual stream flow was 353 mm y(-1) (range 87-848 mm yr(-1)). Average annual stream flow was 48.1 +/- 16% of precipitation. Mean annual NO(3)-N export was 1.6 kg ha(-1) yr(-1) (range 0.18-3.6 kg ha(-1) yr(-1)). Annual NO(3)-N export significantly (P < 0.05) increased with increasing annual stream flow and precipitation. Mean daily NO(3)-N export was 0.004 kg ha(-1) d(-1) (range 10(-5) to 0.55 kg ha(-1) d(-1)). Mean annual suspended sediment export was 198 kg ha(-1) yr(-1) (range 23-479 kg ha(-1) yr(-1)). There was a positive relationship (P < 0.05) between annual suspended sediment export, annual stream flow and precipitation. Mean daily suspended sediment export was 0.54 kg ha(-1) d(-1) (range 10(-4) to 155 kg ha(-1) d(-1)). Virtually no sediment was exported during the dry season. The large variation in daily and annual fluxes highlights the necessity of using long-term records to establish quantitative water quality targets for rangelands and demonstrates the difficulty of designing a water quality monitoring program for these ecosystems.