Determining Trends in Values of Concern During a Specified Time Period SCMTSC Determining Trends in Values of Concern During a Specified Time Period Anita Singh, Lockheed-Martin Felicia Barnett, SCMTSC Director August 2, 2016

Why use a Trend Test? Determine if values of an analyte collected during a specified time are exhibiting a statistically significant trend: downward, upward, no trend. Example: Determining trends in Global Warming: Increasing/upward trend in values of greenhouse gases; Increasing trend in water temperatures; Decreasing trend in oxygen content (e.g., during low water summer months). AOCs= areas of concern

Why use a Trend Test? Example: Determine if a remediation treatment (e.g., Pump & Treat) implemented at a site is effective in: Decreasing concentration levels of Contaminants of Concern (CoCs); Initiating and achieving natural attenuation; Meeting remediation goals. MW= Monitoring Wells

Why use a Trend Test? Upward trend in concentrations of a CoC at a monitoring well (MW) or Area of Concern (AOC) suggests continuing and/or new releases. Determine the rate of change in values of a contaminant over time. Determine how long will it take for a CoC to meet remediation goals / cleanup standard? Routinely used in compliance monitoring and corrective action projects at RCRA sites.

Data for Trend Test Trend tests are performed on time-series data collected in chronological order to determine if values in the data set are exhibiting a trend. Typical time-series data set: (time, CoC) = (t, As): (1991,31), (1992, 33), (1993,27), (1994, 25), (1995, 28), (1997, 23),… EPA software, ProUCL has graphical and statistical methods to determine trends in concentrations. Non-Detects in the data set must be handled: When multiple detection limits (DLs) are present Avoid substitution methods of replacing them by their respective DLs or DL/2s. Replacing NDs by their respective DLs or DL/2s values is like performing trend tests on DLs or on DL/2s, especially when % of NDs is high. For data with NDs and varying DLs, replace all NDs by half of the smallest DLSM (= DLSM/2), or by a single value lower than the smallest DLSM.

Accounting for Exogenous/Seasonal Variables Variables such as rainfall, temperature, seasons, streamflow etc. may contribute significantly to changes in concentrations of a CoC. Changes in these seasonal/temporal variables tend to impact actual trends that one wants to determine (due to site activities); In such situations, use methods/models which account (reduce, remove) for impact of these variables on trends present in CoC values. In GW monitoring, impact of different seasons can be a significant source of variation in concentration values. This must be compensated or “removed” to determine actual trend in values over time. Seasonal Kendall test can be used to assess seasonal trends; One can also account for seasonality by computing Mann- Kendall test on each season separately and then combining the results.

Time-Series Trend Graphs No substitute for graphical displays; Graphs Help in visually identifying and understanding a potential trend present; Help in identifying anomalies related to natural occurrences, human errors, changes in analytical methods/laboratories; Perform visual comparison of time-series data: from multiple AOCs/ MWs; collected using different treatments; However, need to use statistical tests to confirm significance/insignificance of trends and outlying conditions. Time series graphs of different treatments (e.g., control/standard versus new more effective and affordable treatment(s)) can be compared graphically. Trend shown on a graphical display is accompanied by a statistical test to determine statistical significance of the trend for some level of significance denoted by α. A commonly used value for α is 0.05. Modern statistical software packages compute and output a p-value associated with a test statistic. Small values of p-values cause the rejection of the hypothesis. When level of significance is 0.05, a p-value <0.05 will lead to the conclusion of rejecting the null hypothesis. Statistical Tests include: Linear Regression Line Trend Test - Parametric Linear Regression Line Test: Assumptions: residuals follow normal distribution with constant variance at all sampling events, and are independently distributed without exhibiting temporal correlation Statistically significant slope of line suggests presence of a trend Negative significant (e.g., at level of significance, α=0.05) slope suggests a decreasing trend; Positive significant slope suggests an increasing/ upward trend Mann-Kendall (M-K) Trend Test - Nonparametric (NP) M-K test does not require normality Better suited for data with nondetects (NDs) M-K statistic is denoted by S; no slope is computed; Significant (e.g., at α = 0.05) positive S indicates an upward trend - observations increase with time; Significant negative S indicates a downward trend; Insignificant S indicates that data set does not provide sufficient evidence of a trend. A small p-value (e.g., <0.05) suggests presence of trend. Theil-Sen Line Trend Test - NP Theil-Sen test does not require normality Better suited for data with NDs, slope and its confidence interval (CI)is computed Inclusion of ‘0’ in CI: (LCL,UCL) of slope suggests data do not provide sufficient evidence of a trend at α (=0.05) level of significance; ‘0’ not in 95% CI, a positive slope suggests an increasing trend at α = 0.05 ‘0’ not in 95% CI, a negative slope suggests a decreasing trend at α = 0.05 Significant negative slope can be used to determine rate of decrease in values over time Like OLS regression, NP Theil-Sen test computes a trend line. Slope and confidence interval of the slope of the trend line are used to determine statistical significance of the trend present in POI data set used.

Example 1: Trend Evaluations for PCE in GW at a Superfund Site PCE plume present in site groundwater: Pump and treatment system was installed Recent PCE values in MWs are ≤ MCL(= 5 µg/L) - data collected during 2010- 2015 PRP wants to terminate future sampling Region wants to make sure that recent PCE values in MWs are meeting the MCL due to an overall decreasing trend in PCE and not just by chance during recent sampling events. Tetrachloroethylene = PCE µg/L = microgram per liter MCL= Maximum Contaminant Level

Trend TESTs for PCE in MW PCMW-2   Trend TESTs for PCE in MW PCMW-2 Hypothesis for trend tests: Null hypothesis: PCE data set from MW: PCMW-2 does not exhibit any trend vs. Alternative hypothesis: PCE data exhibits a trend All trend tests are supporting the conclusion that there is a statistically significant downward trend in PCE concentrations of MW: PCMW-2. ‘0’ not in 95% CI of the slope of Theil-Sen trend line Slope is negative suggesting significant downward trend P-value ~0 <0.05; S negative suggesting significant downward trend in PCE

Interpretation of Trend Results for PCE in MW PCMW-2 Graph exhibits an obvious downward trend. Mann-Kendall test S is negative, p-value (~0) <0.05 suggesting a significant downward trend. Slope of Theil-Sen trend line is negative, CI of slope does not contain ‘0’ suggesting a significant downward trend. CI= confidence interval S= Letter used for Mann-Kendall trend test statistic

Trend Test for PCE in MW 87-2   Trend Test for PCE in MW 87-2 Null hypothesis: PCE data set from MW 87-2 does not exhibit any trend vs. Alternative hypothesis: PCE data exhibits a trend P-value for slope of OLS regression is 0.407 (large), null hypothesis not rejected. CI for Theil-Sen slope contains zero suggesting no trend. All trend tests are supporting the conclusion PCE concentrations of MW87-2 do not provide sufficient evidence of a trend. ‘0’ in 95% CI of slope of Theil-Sen trend line Insufficient evidence of a significant trend P-value = 0.407 Hypothesis: slope=0 is not rejected at α =0.05 level suggesting trend not significant

Interpretation of Trend Results for PCE in MW 87-2 Graph and trend tests lead to the conclusion that PCE data from MW87-2 does not provide sufficient evidence of a significant trend. Graph exhibits a zig-zag pattern without any trend in PCE. CI for Theil-Sen slope contains zero also suggesting no significant trend. P-value for OLS line slope is 0.407 (large)>0.05

Example 2: Dioxin Contamination at an Air Force Base (AFB) Cleanup of residual contamination from organochlorine pesticides is required at an AFB. Spilled agent solution containing traces of octachlorodibenzodioxin (OCDD) and 2, 3, 7, 8- tetrachlorodibenzodioxin (2, 3, 7, 8-TCDD) remains in soils and in lake sediments. Natural attenuation of herbicides and dioxins has not been effective in detoxifying the soil or sediment.

Bioremediation Treatment Pilot Study Pilot Study was designed by EPA and scientists from another government agency to determine if soils at the AFB can be bio-remediated using aerobic (A) or anaerobic (AN) microbial processes. Study was performed on 11 200mx200m cells using A and AN bio-treatments on: 2 reference (R) cells (no treatment) 2 EPA cells – 1 for each treatment 7 cells used by other agency – 3 for AN and 4 for A treatments Representative samples from the 11 units were collected initially and monthly for 6 months.

Trend Tests on TCDD Biotreatment Data Trend tests were performed to determine if bio-treated soil demonstrate downward trends in dioxin under aerobic or anaerobic conditions. If regression slope (OLS or Theil-Sen) is statistically significantly negative (exhibiting a downward trend): Conclude bioremediation is occurring; and Slope of trend line can be used to determine a degradation rate

Trend in TCDD Data Based Upon Aerobic Treatment (EPA) Negative significant slope, downward trend Aerobic treatment working Time series data set: (t, TCDD) with samples taken at times, t= 0 (initially), 30 day,…180 (last day of study).

Trend in OCDD Data Based Upon Aerobic Treatment (EPA) Negative significant slope, downward trend Aerobic treatment working

Compare TCDD Data Aerobic (EPA) vs Reference Trend in TCDD based upon bio treatment is significantly negative TCDD values from Ref cells do not provide sufficient evidence of a significant trend, p-value >0.05

Dioxin Bioremediation Treatability Test Results Reference data (without any bio-treatment) and anaerobic TCDD and OCDD data do not provide sufficient evidence of a trend. Graphs and statistical tests performed on aerobic TCDD and OCDD data suggest significant downward trends leading to the conclusion that bioremediation is occurring for those treatments.

Contact information Felicia Barnett, Director Site Characterization and Monitoring Technical Support Center (SCMTSC) Ph: 404-562-8659 E-mail: barnett.felicia@epa.gov Jan Szaro, Associate Director, SCMTSC Region 1 Superfund and Technology Liaison Ph: 617-918-1316 E-mail: szaro.jan@epa.gov