Groundwater Statistics and Interpretation at Landfills It can be a - - PowerPoint PPT Presentation

▶
groundwater statistics and interpretation at landfills it
SMART_READER_LITE
LIVE PREVIEW

Groundwater Statistics and Interpretation at Landfills It can be a - - PowerPoint PPT Presentation

Jun 26, 2023 318 likes •558 views

Groundwater Statistics and Interpretation at Landfills It can be a useful tool . . . honest! Paula Leier-Engelhardt, P.G., C.P.G. Principal Geologist HydroGeo Solutions LLC How do you get the data in the first place Create and

slide-1
SLIDE 1

Groundwater Statistics and Interpretation at Landfills It can be a useful tool . . . honest!

Paula Leier-Engelhardt, P.G., C.P.G. Principal Geologist HydroGeo Solutions LLC

slide-2
SLIDE 2

4/26/2017 U.P. Solid Waste Forum, Marquette MI 2

  • How do you get the data in the first place
  • Create and maintain a database
  • What is the purpose for the statistics, and what

methods should be used

slide-3
SLIDE 3

The Groundwater Data

  • Your statistics will only be as good as the data you are using
  • Sampling team
  • Proper training and equipment
  • Laboratory
  • QA/QC
  • Review detection limits; ask questions if they change

4/26/2017 U.P. Solid Waste Forum, Marquette MI 3

slide-4
SLIDE 4

The Groundwater Data (cont.)

  • Upon receiving analyses, review and compare to historical

results

  • Can catch the obvious things
  • Detection limits
  • Were all the parameters analyzed?
  • Is there a sampling point missing?
  • Pay special attention to the field data

4/26/2017 U.P. Solid Waste Forum, Marquette MI 4

slide-5
SLIDE 5

Create and Maintain a Database

  • Should be in a program separate from a statistical software

package, such as Excel or Access

  • What goes in it?

Everything!

  • Do not change anything from the original lab submittal

(except to correct obvious errors)

4/26/2017 U.P. Solid Waste Forum, Marquette MI 5

slide-6
SLIDE 6

The PQL vs. MDL vs. Reporting Limit Dilemma

Site Identifier Client Sample ID Collection Date CAS Registry Number Substance Name Result Units Reporting Limit Qualifier Code Analytical Method Date Analyzed Prep Date Analysis Comments XXXXX MW-2 2/14/2017 ALK-CACO3 alkalinity (as caco3) 330000 ug/L 2000 2320 B-1997 2/17/2017 2/17/2017 973577 XXXXX MW-21 2/14/2017 SW301 chlorides 20000 ug/L 2500 SW846 9056 2/15/2017 2/15/2017 973569 XXXXX MW-4A 2/14/2017 SW301 chlorides 2600 ug/L 2500 J SW846 9056 2/15/2017 2/15/2017 973574 4/26/2017 U.P. Solid Waste Forum, Marquette MI 6

Reporting limit: Constituent concentration that, when processed through the complete method, produces a signal that is statistically different from a blank.

slide-7
SLIDE 7

What is the purpose of the statistical analyses?

  • R 299.4908 -- Landfill groundwater monitoring; statistical

procedures

  • Release detection
  • Are concentrations greater than background concentrations, or

above/below a criterion?

  • Is the sampling frequency appropriate?
  • Is the monitoring network appropriate?

4/26/2017 U.P. Solid Waste Forum, Marquette MI 7

slide-8
SLIDE 8

Statistical Methods for Release Detection

  • Tolerance Intervals
  • Prediction Intervals
  • Shewhart/CUSUM Control Charts

4/26/2017 U.P. Solid Waste Forum, Marquette MI 8

slide-9
SLIDE 9

Tolerance Intervals

  • Statistical ranges constructed from on-site background

data.

  • Tolerance limits define the range of data that fall within a

specified percentage with a specified level of confidence.

  • An upper tolerance limit (UTL) is designed to contain, but

not exceed, a large fraction (that is, 95%, 99%) of the possible background concentrations, thus providing a reasonable upper limit on what is likely to be observed in background.

4/26/2017 U.P. Solid Waste Forum, Marquette MI 9

slide-10
SLIDE 10

Tolerance Intervals

  • Assumptions

Data are stable; no trends; no seasonal variation Little spatial variation between background and compliance monitoring wells Background data are normally distributed, or transformed normally distributed

4/26/2017 U.P. Solid Waste Forum, Marquette MI 10

slide-11
SLIDE 11

Tolerance Intervals

Strength

  • Fairly easy to calculate
  • Best used where you can pool

all the data from the upgradient wells to create your tolerance limits

Weakness

  • Built-in ‘failure rate’
  • The more non-detects you have,

the more data you need

  • It can be used for intrawell

analyses, but the statistics start to fall apart at a sample size greater than 50

4/26/2017 U.P. Solid Waste Forum, Marquette MI 11

slide-12
SLIDE 12

Statistical Methods for Release Detection

  • Tolerance Intervals
  • Prediction Intervals
  • Shewhart/CUSUM Control Charts

4/26/2017 U.P. Solid Waste Forum, Marquette MI 12

slide-13
SLIDE 13

Prediction Intervals

  • A prediction limit (or interval) is used to determine whether

a single observation is statistically representative of a group of observations.

  • The interval is constructed from a background set of
  • bservations such that it will contain K future compliance
  • bservations with stated confidence.
  • If any observation exceeds the bounds of the prediction

limit, this is statistically significant evidence that that

  • bservation is not representative of the background group.

4/26/2017 U.P. Solid Waste Forum, Marquette MI 13

slide-14
SLIDE 14

Prediction Intervals

  • Assumptions

Background data are normally distributed, or transformed normally distributed Data sets with seasonal variations, or spatial variations, can be used, but adjustments must be made At historically contaminated compliance wells, establishing a proper baseline for a prediction limit is problematic, since uncontaminated concentration data cannot be collected.

4/26/2017 U.P. Solid Waste Forum, Marquette MI 14

slide-15
SLIDE 15

Prediction Intervals

Strength

  • Quite flexible – EPA describes

nine different prediction limit test

  • ptions
  • A prediction limit estimates a

firm ‘cap’ on the background population for a specified number of future sampling events (comparisons). Allows for clear interpretation of when background levels have been exceeded.

Weakness

  • Re-testing must be done
  • Nonparametric prediction limits

typically require a much larger sample size than parametric prediction limits to achieve the desired confidence level.

4/26/2017 U.P. Solid Waste Forum, Marquette MI 15

slide-16
SLIDE 16

Statistical Methods for Release Detection

  • Tolerance Intervals
  • Prediction Intervals
  • Shewhart/CUSUM Control Charts

4/26/2017 U.P. Solid Waste Forum, Marquette MI 16

slide-17
SLIDE 17

Shewhart/CUSUM Control Charts

  • Commonly used to monitor the stability of groundwater

data and to detect changes in data trends that may require further investigation.

  • The Shewhart control limit tests for and flags a sudden

spike or change in trend of the data, which may indicate an event such as a new release at the site. The CUSUM control limit tests for and flags a gradual, but significant, increase or decrease over time, which may, for example, indicate plume migration.

4/26/2017 U.P. Solid Waste Forum, Marquette MI 17

slide-18
SLIDE 18

Shewhart/CUSUM Control Charts

  • Assumptions

Background data are normally distributed, or transformed normally distributed No trends are present in the background data

4/26/2017 U.P. Solid Waste Forum, Marquette MI 18

slide-19
SLIDE 19

4/26/2017 U.P. Solid Waste Forum, Marquette MI 19

slide-20
SLIDE 20

Shewhart/CUSUM Control Charts

Strength

  • Can be constructed as either

interwell or intrawell tests.

  • Provides a visual representation
  • f compliance data over time

and allow for better identification

  • f gradual, long term trends.

Weakness

  • Should update the background

data, and the baseline statistics, every two years

  • If a release occurs, that data can

not be used in future background calculations

  • Adjustments to certain

calculations need to be made if non-detects are >25%

4/26/2017 U.P. Solid Waste Forum, Marquette MI 20

slide-21
SLIDE 21

4/26/2017 U.P. Solid Waste Forum, Marquette MI 21

  • Statistics will only be as good as the data that

are used to create them.

  • Have a data base that has everything, with

no changes made to the original results.

  • What is the purpose of the statistical

analyses?

  • Don’t be afraid to change statistical methods

(in fact, you probably should).

slide-22
SLIDE 22

References

  • ASTM. 2010b. Standard Guide for Applying

Statistical Methods for Assessment and Corrective Action Environmental Monitoring

  • Programs. D7048-04(2010). West

Conshohocken, PA: ASTM International.

  • ASTM. 2012. Standard Guide for Developing

Appropriate Statistical Approaches for Groundwater Detection Monitoring Programs. D6312-98(2012)e1. West Conshohocken, PA: ASTM International.

  • USEPA. 2009. "Statistical Analysis of

Groundwater Monitoring Data at RCRA Facilities." Unified Guidance EPA 530/R-09-

  • 007. Washington DC: United States

Environmental Protection Agency. http://www3.epa.gov/epawaste/hazard/correct iveaction/resources/guidance/sitechar/gwstats /unified-guid.pdfguid.pdf.

  • Interstate Technology Regulatory Council

(ITRC) – Web based training course, Groundwater Statistics for Monitoring and Compliance

  • Gibbons, R.D. 1994. Statistical Methods for

Groundwater Monitoring. New York: John Wiley & Sons.

  • Dr. Kirk Cameron, MacStat Consulting, Ltd. –

Applied Groundwater Statistics (2 day workshop)

4/26/2017 U.P. Solid Waste Forum, Marquette MI 22

slide-23
SLIDE 23

Contact Information

Paula Leier-Engelhardt, P.G., C.P.G. Principal Geologist HydroGeo Solutions LLC 5951 Allen Road | Little Suamico, Wisconsin paula@hydrogeosolutionswi.com 920-737-9811

4/26/2017 U.P. Solid Waste Forum, Marquette MI 23

Thank you