Statistical Characteristics of Ground‐Water Quality Variables

Montgomery, Robert H.; Loftis, Jim C.; Harris, Jane

doi:10.1111/j.1745-6584.1987.tb02874.x

Cited by 46 publications

(11 citation statements)

References 13 publications

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“…(KEY TERMS: trend detection; water quality; statistics; nonparametnc tests.) skewed distributions (Loftis et al, 1988;Montgomery et al, 1987).…”

mentioning

confidence: 99%

TESTING FOR TREND IN LAKE AND GROUND WATER QUALITY TIME SERIES¹

Taylor¹,

Loftis

1989

J American Water Resour Assoc

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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 intro. duced 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. (KEY TERMS: trend detection; water quality; statistics; nonparametnc tests.) skewed distributions (Loftis et al., 1988;Montgomery et al., 1987).

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“…(KEY TERMS: trend detection; water quality; statistics; nonparametnc tests.) skewed distributions (Loftis et al, 1988;Montgomery et al, 1987).…”

mentioning

confidence: 99%

TESTING FOR TREND IN LAKE AND GROUND WATER QUALITY TIME SERIES¹

Taylor¹,

Loftis

1989

J American Water Resour Assoc

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“…4b. We find that the frequency of the individual realization of the porosity field is close to normal distribution, which conforms to the situation that most characteristics of natural aquifer can be expressed as a normal distribution (Montgomery et al, 1987). Based on the heterogeneous porosity field, the fractal dimension of tortuosity D t , the fractal dimension for pore areas D f and the diameter of capillary tubes in porous media, permeability is obtained by Eq.…”

Section: Site Descriptionmentioning

confidence: 99%

“…4d. The permeability field fits the lognormal distribution well, which has been presented by many studies which show that the parameter of aquifer penetrability follows lognormal distribution (Montgomery et al, 1987;Veneziano and Tabaei, 2004). Compared to the histogram of the porosity field in Fig.…”

Section: Site Descriptionmentioning

confidence: 99%

Effects of microarrangement of solid particles on PCE migration and its remediation in porous media

2018

Hydrol. Earth Syst. Sci.

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Abstract. Groundwater can be stored abundantly in granulacomposed aquifers with high permeability. The microstructure of granular materials has important effect on the permeability of aquifers and the contaminant migration and remediation in aquifers is also influenced by the characteristics of porous media. In this study, two different microscale arrangements of sand particles are compared to reveal the effects of microstructure on the contaminant migration and remediation. With the help of fractal theory, the mathematical expressions of permeability and entry pressure are conducted to delineate granular materials with regular triangle arrangement (RTA) and square pitch arrangement (SPA) at microscale. Using a sequential Gaussian simulation (SGS) method, a synthetic heterogeneous site contaminated by perchloroethylene (PCE) is then used to investigate the migration and remediation affected by the two different microscale arrangements. PCE is released from an underground storage tank into the aquifer and the surfactant is used to clean up the subsurface contamination. Results suggest that RTA can not only cause more groundwater contamination, but also make remediation become more difficult. The PCE remediation efficiency of 60.01-99.78 % with a mean of 92.52 and 65.53-99.74 % with a mean of 95.83 % is achieved for 200 individual heterogeneous realizations based on the RTA and SPA, respectively, indicating that the cleanup of PCE in aquifer with SPA is significantly easier. This study leads to a new understanding of the microstructures of porous media and demonstrates how microscale arrangements control contaminant migration in aquifers, which is helpful to design successful remediation scheme for underground storage tank spill.

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“…For example, Smith The simulation results indicate that, for the levels of autocorrelation studied, such changes would occur fairly often. (Loftis and Ward, 1980, Montgomery et al, 1987, discuss the levels of serial correlation which might be encountered in water quality data.) As we would expect, the frequency of trends of a given magnitude is greater for the 20observation sequences than for the 40-observation sequences and increases with increasing levels of serial correlation in the synthetic data.…”

Section: Analyzing Trendsmentioning

confidence: 99%

CONSIDERATIONS OF SCALE IN WATER QUALITY MONITORING AND DATA ANALYSIS¹

Loftis¹,

McBride²,

Ellis

1991

J American Water Resour Assoc

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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.

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Statistical Characteristics of Ground‐Water Quality Variables

Cited by 46 publications

References 13 publications

TESTING FOR TREND IN LAKE AND GROUND WATER QUALITY TIME SERIES¹

TESTING FOR TREND IN LAKE AND GROUND WATER QUALITY TIME SERIES¹

Effects of microarrangement of solid particles on PCE migration and its remediation in porous media

CONSIDERATIONS OF SCALE IN WATER QUALITY MONITORING AND DATA ANALYSIS¹

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Statistical Characteristics of Ground‐Water Quality Variables

Cited by 46 publications

References 13 publications

TESTING FOR TREND IN LAKE AND GROUND WATER QUALITY TIME SERIES1

TESTING FOR TREND IN LAKE AND GROUND WATER QUALITY TIME SERIES1

Effects of microarrangement of solid particles on PCE migration and its remediation in porous media

CONSIDERATIONS OF SCALE IN WATER QUALITY MONITORING AND DATA ANALYSIS1

Contact Info

Product

Resources

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TESTING FOR TREND IN LAKE AND GROUND WATER QUALITY TIME SERIES¹

TESTING FOR TREND IN LAKE AND GROUND WATER QUALITY TIME SERIES¹

CONSIDERATIONS OF SCALE IN WATER QUALITY MONITORING AND DATA ANALYSIS¹