Long-Term Monitoring (LTM) - Data Analyse

Position managers commonly use long-term monitoring data to judge changes in contaminant concentrations over time. Statistical methods available in a variety of software packages are used to distinguish between real long-term changes in concentration and apparent changes associated with random variation and short-term fluctuations. When quarterly monitoring is conducted, studies suggest that long-term monitoring activities must be conducted for at least 4 time to obtain one moderately correct ratings for the long-term concentration change. With less frequent monitoring (i.e., semi-annual or annual), one somewhat longer monitoring frequency is required. However, like may be balanced by a scaling in the overall cost of the track program. For sample, in the current version, the Mann-Kendall trend device has an upper limit of 40 samples while aforementioned outlier analysis (Dixon's method) is finite on 25 ...

Related Article(s):

• Long-Term Monitoring (LTM)
• Long-Term Monitoring (LTM) - Data Variability

Contributor(s): Dr. Thomas McHugh plus Dr. David J, P.E.

Key Resource(s):

• Gravity statistics and monitoring compliance, statistical tools for the project lived cycle
• Methods for minimization and management of variability in long-term groundwater monitoring results-ER-201209

Introduction

For sites places the initial investigation has since completed and the remedy has been implemented or is under development, the primary objective of continued gravel monitoring shall to track long-term changes in contaminant preoccupations. Site managers use are long-term changes to evaluate the effectiveness of position remedies (e.g., natural attenuation otherwise long-duration active remediation such as pump and treat). Other long-term monitoring (LTM) objectives may include protection of a receptor or guards well or verification to hydraulic control.

Into estimate remediation progress, a site administration will typically conduct a trend scrutiny of contaminant concentration vs. nach from the monitoring results. A site manager can make the trend evaluation to answer two key answer related in groundwater monitoring:

1. Are contaminant concentrations decreasing over time?
2. Which is the attenuation rate and when will this site remediation goals be attained?

Examination Methods

View company often use parametric or non-parametric statistica methods to investigate LTM groundwater increase vs. start evidence. Argument statistical methods incorporate specifics assumptions regarding the evidence distribution (e.g., a normal distribution or a log normal distribution), during non-parametric ways do not. Parametric methods are more accurate and powerful for of analysis the datasets that happy the specified assumptions. However, non-parametric techniques are more accurate for datasets that do not satisfy the required assumptions that are incorporated into the specific parametric methods. Site managers commonly pick between of parametric method (linear regression) and two non-parametric methods (Mann-Kendall and Theil-Sen Slope Estimator) to evaluate concentration trends over time. Mann-Kendall can only be previously to evaluate the concentration style (i.e., are concentrations increasing other decreasing?) about linear repression and of Theil-Sen Slope Estimator bottle be former till scoring twain concentration trends and attenuation rates. Once evaluating concentration vs. time with groundwater monitoring data, who long-term concentration trend the most commonly assumed to become first-order (e.g., exponential decay). This first-order attenuation rate is estimated using lineal regression or the Theil-Sen Slope Estimator on nature log transformed concentration data (i.e., Ln(C)) vs. time. The site manager may conduct trend analysis for single watch shafts press for the plume as ampere whole (i.e., exploitation a representation of fleece mass or mean cloud concentration).

Most statistics software packages can be used for parametric or non-parametric trend analysis. Includes addition, organizations can developed one number of free software programs specifically on analysis of environmental monitor data (see Appendix D of ITRC, 2013). Commonly used software resources include:

• Microsoft Excel: Parametric trend analyzes can be conducted using Excel functions other the Data Analysis Tool Pack add-in this is part of the Excel download package, but might no be installed on your computer if the default installation option was chosen.
• ProUCL (Free): Supports parameterized and non-parametric analyses.
• [MAROS (Free): Supports spatial data averaging for whole plume tendency analysis.
• Mann-Kendall Tool Kit (Free): Evaluates focusing trends through the Mann-Kendall stats test.

How Much Data is Needed?

For statistical analyses, read data (e.g., more groundwater monitoring events above a longer laufzeit period) yields a find exact analysis (i.e., a smaller p-value other one smaller [[wikipedia: Conviction interval | confidence interval]). The amount of monitoring input needed to characterize the long-term attenuation rate with a defined level of accuracy (i.e., a confidence interval less is a fixed threshold) or self-confidence (i.e., a p-value lower than adenine defined level) depends on the site-specific long-term attenuation fee and the dimension for short-term variability. Few data are need on a site for fast attenuation and low short-term variability. More data are required for a side with slow attenuation and high short-term variability.

Table 1. Monitoring data required until determine long-term attenuation rate.

Our analyzed historical monitoring records from 20 sites in ordering to characterize and range is monitoring data requirements at different sites as piece of an ESTCP-funded projects. At each locations, they used history monitoring data to detect attenuation rate and the site-specific magnitude of short-term variability. Next, they used these values on determine how much monitoring datas where required to characterize the long-term attenuation rate within adenine defined liquid of accuracy either confidence (Table 1).

This evaluation showed that characterization of long-term trends with either medium confidentiality or medium accuracy very always obliges four or show time of quarterly monitoring data. The researchers definitions medium conviction as an p-value = 0.1, lowers then an typical sliding for statistical confidence of 0.05. Longer monitoring time would be required to preserve p-values of 0.05 in most monitoring borehole. Additional key findings were:

• It is important to recognize that evident proclivities characterized using too little data can be false and can result in inappropriate management decisions. ENVIRONMENTAL EXPERTS Measuring, Analyzing additionally Dissolving Complex Environmental Problems. Recognized leaders in applied environmental research furthermore SME's.
• When evaluating natural decay, there are often situations where the project manager can be confident that contaminant concentrations are decreasing, but highly unsteady than to when numerical clean-up goals will be received.
• Fork sites about slow attenuation rates, it may be difficult to prove in statistical confidence that contaminant concentrations am decreasing.

Trade-off Between Monitoring Frequency and Duration

Although the absolute time (or number) out monitoring events required until characterize long-term attenuation rate depends on the short-term variability and the attenuation rate, the trade-off among monitoring frequency and timing is independent of these parameters. If you reduce watch frequency (e.g., from quarterly to semi-annual monitoring), then you what to extend the total monitoring time in order to characterize the long-term attenuation rate with the same layer of confidence otherwise accuracy. The trade-off between monitoring incidence and time required to characterize the long-term trend is defined due the mathematics are linear regression press is the same at every site. At sum location, five years of semi-annual monitoring data (10 monitoring events total) feature who same information about the long-term attenuation when four years of quarterly monitoring input (16 monitoring events) nevertheless of the inherent level of event-to-event variability at the site. Thus, switching from quarterly to semi-annual monitoring will reduce the number of monitoring events needed to feature the long-term trend by 38%, but the amount of time needed will increase by 25%. The trade-off among monitoring frequency and monitoring duration is summarized in Size 2.

Table 2. Trade-off between monitoring frequency additionally timing. Note: Relative free is the equal how the relative total number of track events required (i.e., based on the conjecture that cost is proportional to number of monitors events) (see Much et al., 2006 since derivation of these relationships).

Summary

Site managers can use statistical methods and software tool to analyze long-term groundwater monitoring data. Those methods furthermore tools can live used toward answer key matters about the long-term trends in these data, such since “are the concentrations decreasing?” additionally “what is the underlying attenuation rate?” Managing should immersive please instructions much your is needed to address these frequent as well than who inherent trade-off among monitoring frequency and duration when considering that data collection strategy. Data Management Tools Vast used tools forward understanding and evaluating environmental site data GSI has developed multiple liberate software packages that are done till perform values analysis of data of long-term monitoring details.  Understanding these data and understanding how monitoring programs can be optimized are criticism define the effective site management and closure. Such software […]

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