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Aehs 03.2017 lutesindicators tracers and surrogates 9 including holton and kurtz_jk0320
- 2. 2 A Key Term: Reasonable Maximum Exposure: Risk Assessment Guidance for Superfund Part A: • 6.4.1 QUANTIFYING THE REASONABLE MAXIMUM EXPOSURE • “There are three categories of variables that are used to estimate intake….exposure concentrations…..exposure frequency and duration……averaging time…… • Each intake variable in the equation has a range of values. For Superfund exposure assessments, intake variable values for a given pathway should be selected so that the combination of all intake variables results in an estimate of the reasonable maximum exposure for that pathway. As defined previously, the reasonable maximum exposure (RME) is the maximum exposure that is reasonably expected to occur at a site. Under this approach, some intake variables may not be at their individual maximum values but when in combination with other variables will result in estimates of the RME. • Exposure concentration. The concentration term in the intake equation is the arithmetic average of the concentration that is contacted over the exposure period. Although this concentration does not reflect the maximum concentration that could be contacted at any one time, it is regarded as a reasonable estimate of the concentration likely to be contacted over time. This is because in most situations, assuming long‐term contact with the maximum concentration is not reasonable. …..Because of the uncertainty associated with any estimate of exposure concentration, the upper confidence limit (i.e., the 95 percent upper confidence limit) on the arithmetic average will be used for this variable.” Quotes from OSWER 9285.701A; July 1989
- 4. 4 28 58 22 45 15 31 11 23 0 20 40 60 80 100 120 140 160 0.88 0.9 0.92 0.94 0.96 0.98 1 Required Number of Samples to Observee RME Once Percentile Defined as RME = Chance of Not Seeing RME With One Sample Required Number of Unguided Random Samples Per Location/Zone to Observe RME Once at Various Confidence Levels 5% Prob. Of Underestimating RME 10% Prob. Of Underestimating RME 20% Prob. Of Underestimating RME 30% Prob. Of Underestimating RME This analysis is just the mathematics of probability. No assumptions about the distribution have been made, only the assumption of random independent sampling. RME defined as percentile.
- 5. 5 58 28 13 8 6 4 3 2 2 10 10 20 30 40 50 60 70 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Required Number of Samples to Observee RME Once Surrogate Guided True Positive Rate = Chance of Seeing RME (Here defined as 95th Percentile with One Guided Sample) Required Number of Surrogate Guided Samples Per Location/Zone to Observe RME with 5% Probabilty of Underestimating A surrogate does not need to be perfect to be very helpful. It just needs to “load the dice” by significantly increasing the odds of observing a sample toward the top of the VOC distribution. Note the 0.05 “guided true positive” is a guide no better then chance
- 10. 10 But Wait, I’ve Always Heard “Only EPA Standard Method Results Can be Used in Risk Assessment” Key elements of an exposure assessment include: • “ Investigate patterns of exposure (e.g., frequency and duration)…. • Consider variability in exposures and appropriate exposure distributions (e.g., the use of Monte Carlo or kriging). • Establish descriptors of exposure, generally including estimates for “average” and “high‐end” exposures, as well as susceptible populations or life stages. “ From US EPA Risk Assessment Forum (2014) “Framework for Human Health Risk Assessment to Inform Decision Making” EPA/100/R‐14/001. Does the current approach do that well?
- 11. 11 EPA Guidance for Data Usability in Risk Assessment (1991) • “Options for combining environmental analytical data of varying levels of quality from different sources and incorporating them into the risk assessment…. • Use data from different data sources together to balance turnaround time, quatity (sic) of data, and cost….. • Three types of analytical data sources can be used during the RI to acquire analytical data for a risk assessment. These include field screening, field analyses, and fixed laboratory analyses.
- 12. 12 US EPA: Guidelines for Exposure Assessment (1992) • “Exposure or dose profiles describe the exposure concentration or dose as a function of time.. Such profiles are very important for use in risk assessment where the severity of effect is dependent on the pattern by which the exposure occurs rather than the total (integrated) exposure…..” • “ Level of Detail: How accurate must the exposure or dose estimate be to achieve the purpose? How detailed must the assessment be to properly account for the biological link between exposure, dose, effect, and risk, if necessary? How is the depth of the assessment limited by resources (time and money), and what is the most effective use of those resources in terms of level of detail of the various parts of the assessment?” • “Surrogate data ‐ Substitute data or measurements on one substance used to estimate analogous or corresponding values of another substance.” • Surrogate data is suggested as one of several methods to be used in addressing data gaps.
- 13. 13 Examples of Use of Indicators and Surrogates in Other EPA Human Health Risk Management Programs • Date of house construction, plumbing permits, plumbing codes etc. used as surrogate for maximum lead risk in selecting sampling sites for lead in drinking water EPA 816‐R‐10‐004 March 2010 • “Total coliforms are a group of related bacteria that are (with few exceptions) not harmful to humans. A variety of bacteria, parasites, and viruses, known as pathogens, can potentially cause health problems ... EPA considers total coliforms a useful indicator of other pathogens for drinking water. Total coliforms are used to determine the adequacy of water treatment and the integrity of the distribution system.” https://www.epa.gov/dwreginfo/revised‐total‐coliform‐rule‐and‐ total‐coliform‐rule • Total coliform, E coli or coliphages are used as a surrogates for a wide range of pathogens in water. A range of surrogates of different specificity has been used as technology evolves. https://www.epa.gov/sites/production/files/2015‐10/documents/webinar‐10‐15‐2015‐ coliphage.pdf • Good combustion practices (time, temperature) are used as indicators of control of air toxics emissions https://www3.epa.gov/airquality/combustion/docs/20110221mboilersfs.pdf; See also 40CFR Part 62, Subpart JJJ
- 15. 15 Example: Randomized Trial of Radon Tracer for Spatial Sampling Application • Fifty locations within the Wheeler complex were screened for radon • Then two subsets of these sample locations were selected for passive VOC sampling, one randomly and the other based on the radon. • The upstairs radon guided samples were significantly higher in trichloroethene (TCE) than the randomly selected locations • t‐test shows that the two mean concentration values for the guided and randomly collected data are statistically different for the data collected at the 95 percent confidence level. 0 5 10 15 20 25 Unit 106 (Column in Kitchen) 2nd Floor, On North Wall, East Side of Atrium Unit 134 (Column in Center of Unit) Unit 224 (TV Stand near Exterior Wall) On Papertowel Dispenser in Women’s Restroom Unit 154 (Shelf Between Bed and Door) Unit 106 (Column in Kitchen) Unit 148 (Shelf in Kitchen) On Coat Rack in Theater Prop Room Shelf at SE Corner of South File Storage Room On Gate to South Overhead Door in Theater Between Basement & 1s t Floor on West Stairs Radon(pCi/L)andVOCs(µg/m3) Sampling Locations Results forUpstairs Sampling Locations Radon TCE PCE RADON GUIDEDRANDOM Lutes, C.C., R. Uppencamp, L. Abreu , C. Singer, R. Mosley and D.Greenwell. “Radon Tracer as a Multipurpose Tool to Enhance Vapor Intrusion Assessment and Mitigation” poster presentation at AWMA Specialty Conference: Vapor Intrusion 2010, September 28‐30, 2010, Chicago, IL. Available at http://events.awma.org/education/Posters/Final/Lutes_RadonPoster.pdf
- 16. 16 Introduction to Time Series Methods Used in Indianapolis Project • Sequential observations in time series are, in general, time‐correlated and thus, not independent of each other • In this analysis used only consecutive, evenly spaced observations [i.e., daily (GC average) or weekly (passive) observations]. • Auto‐correlation observed = the values at one point in the time series are determined or strongly influenced by values at a previous time. • Given the temporal resolution and length of available data sets we expect to observe causes of change in indoor air concentration from week to week and season to season; not year to year, and not within the diurnal cycle. • Time series is the statistically most correct way to analyze closely spaced observations, but it isn’t likely to be a frequently used tool at “practical” sites
- 18. 18 Example Time Series Results Interpretation Using on‐line GC Data for basement PCE Dec 2012 – March 2013, mitigation off, Exterior Temperature in F, the following equation was significant at the 99% confidence level: Ct – C(t‐1) = 0.748 + ‐0.078*Tt + 0.057*T(t‐1) C= Concentration T = Exterior temperature t= time Note red and green coefficients similar but opposite in sign. Example A: falling temp: 20 F today; 40 F yesterday, on average PCE concentration increased 1.5 µg/m3. Example B: rising temp: 20 F today, 10 F yesterday, on average PCE concentration decreased 0.2 µg/m3
- 19. 19 Key Time Series Conclusions from Indianapolis • The change in the differential temperature and thus stack effect strength was more important than the absolute value of the differential temperature. Indoor air concentrations of VOCs are expected to be high when the weather is getting colder, but would not necessarily be expected to be as high during a period of sustained cold weather. Not all winter sampling times are equivalent. • The most consistent relationship for barometric pressure is that an elevated (greater than 30 inches) and/or rising barometric pressure is associated with increasing vapor intrusion. • No statistically significant effect of rain was seen. • Increasing radon as a predictor was found to be statistically significant at the 1% level and to predict 40‐60% of the variability in indoor air VOC concentrations.
- 20. 20 Temperature Differential – Another Potential Surrogate Time Series Analysis Results • The strength of the stack effect predicted by the temperature differential between the 422 basement south and outdoors was significant at the 1% level. • Increasing strength of the stack effect was associated with higher VOC concentrations, not primarily high values in and of themselves • Data from EPA/600/R‐15/070 | October 2015 ‘Third Indy Report’ 422 Basement North Temperature (C) 422 Basement South Weekly [PCE] PCE Data from Indy Test House: Jan 2011 to Feb2012 (includes locally weighted scatterplot smoothing line [blue], with a 95% confidence interval [shaded])
- 23. 23
- 24. New Analysis of ASU Sun Devil Manor Data
- 25. ASU Sun Devil Manor Data Reprise 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 170 270 370 470 570 670 Sampling Day Sun Devil Manor 24 hr Ave TCE (ppbv) vs Radon 24 h Average Radon in Indoor Air [pCi/L] 24 h Average TCE in Indoor Air [ppbv] ‐
- 26. ASU SDM Data Reprise
- 27. SDM TCE vs Rn R² = 0.37 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 24hr Ave TCE (ppbv) 24 hr Ave Rn (pCi/L) 24hr Ave TCE vs 24 hr Ave Rn (SDM)
- 28. SDM TCE vs Differential Temperature R² = 0.1607 ‐0.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 ‐10 ‐5 0 5 10 15 20 25 30 35 24 hr TCE (ppbv) Differential T (C) 24 h Average TCE in Indoor Air [ppbv] vs Differential T (SDM)
- 29. SDM Differential Temperature vs Rn R² = 0.2326 ‐10 ‐5 0 5 10 15 20 25 30 35 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 24 hr Differential T 24 hr Rn (pCi/L) 24 hr Differential Temperature [°C] vs 24 hr Rn (SDM)
- 30. A Different Indicator Approach What if we use Radon or Differential Temperature as an Indicator? What criteria work (at least for SDM)? How does this help us with the number of samples required to identify the RME? *Note that this approach is very likely to seriously overestimate the long term average exposure concentration
- 31. Rn Indicator (>90th%) approach for RME 40% True Positives 60% False Positives
- 32. Differential T Indicator (>90th%) approach for RME Differential T>90th percentile, TCE>95th percentile 34% True Positives 66% False Positives
- 33. How Many Samples Are Needed? 58 28 13 8 6 4 3 2 2 10 10 20 30 40 50 60 70 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Required Number of Samples to Observee RME Once Surrogate Guided True Positive Rate = Chance of Seeing RME (Here defined as 95th Percentile with One Guided Sample) Required Number of Surrogate Guided Samples Per Location/Zone to Observe RME with 5% Probabilty of Underestimating
- 34. 95 UCL of Mean vs Number of Samples
- 35. New SDM Sampling Simulation Results & Statistical Applications (N=593 24 hr TCE points, 5000 simulations of random seasonal sampling) Probability of 1 or more indoor air sample exceeding the Target Concentration and 95UCL of Mean compared to percentile of total dataset for various sampling strategies Seasons Sampled 4 season s Summer& Winter 2 Winter 3 Winter 4 Winter 5 Winter 6 Winter 7 Winter 8 Winter Total Samples 4 2 2 3 4 5 6 7 8 1 or more sample>90th% 34% 27% 47% 62% 73% 81% 86% 90% 93% 1 or more sample>95th% 19% 11% 26% 36% 45% 52% 59% 65% 70% 95 UCL OF MEAN=% OF DISTRIBUTION 90TH% >95.5 % >95TH % >95TH % >94.5T H% >94TH % >94TH %
- 36. Implications Indicators can provide guide for WHEN to sample Indicators can limit the NUMBER of samples Statistics for limited number of indicator guided samples can provide a protective estimate of the RME
- 38. 38 Outline 1. Introduction 2. Indicators, Tracers, and Surrogates for Vapor Intrusion Pathway Assessment 3. Related Guidance and Supporting Information 4. Conclusions
- 39. 39 1. Introduction • 1.0 Introduction – Describe general vapor intrusion (VI) pathway, issues associated with conventional assessment approaches, long‐term stewardship – Define worst case conditions (spatially and temporally) based on current state‐of‐the‐ science – Describe current state‐of‐the‐science of conventional sampling approaches, as well as real‐time/continuous monitoring approaches – Short summary to define/differentiate indicators, tracers, and surrogates; describe rationale for using alternative methods and need for document • 1.1 Objectives – Provide simple, efficient, and economically viable approaches for supporting VI investigations (as a line of evidence), assessing the potential for VI, estimating/projecting/forecasting potential risks/exposure, – Provide insight on when conventional approaches should be applied • 1.2 Document Scope – Companion to statistical toolbox document – Applicability to different building types, exposure scenarios
- 40. 40 2. Indicators, Tracers, and Surrogates for Vapor Intrusion Pathway Assessment • 2.1 Characteristics of Potential Indicators, Tracers, and Surrogates – Define/differentiate indicators, tracers, and surrogates using examples from related fields to explain differences (examples shown in presentation outline) – Provide list of indicators, tracers, and surrogates included in past studies (e.g., ~80 studied at USEPA research house in Indianapolis, not all significant); highlight those that have shown greatest utility in VI assessments – Highlight best practices for use of radon, SF6, differential temperature, differential pressure, and other methods/parameters that have shown greatest utility in VI assessments
- 41. 41 2. Indicators, Tracers, and Surrogates for Vapor Intrusion Pathway Assessment (cont’d) • 2.2 Uses and Utility within the Screening Process – Define the screening process; building/site selection, prioritization, VI susceptibility; discuss in context of conventional approaches – Identify indicators, tracers, and/or surrogates applicable to screening process and relative benefit (e.g., lower cost, higher confidence) • 2.3 Uses and Utility within Detailed Assessments – Define detailed VI assessments and provide a short summary of conventional approaches – Discuss approaches/tools/methods where indicators, tracers, and/or surrogates can be applied and the relative value compared to other lines of evidence.
- 42. 42 2. Indicators, Tracers, and Surrogates for Vapor Intrusion Pathway Assessment • 2.4 Uses and Utility within Post‐Mitigation/Remediation Phase – Define mitigation/remediation, institutional controls, long‐term stewardship and provide a short summary of conventional approaches – Identify indicators, tracers, and/or surrogates that can be applied to post‐ mitigation (e.g. VIMS OMM) and long‐term stewardship approaches
- 43. 43 3. Related Guidance and Supporting Information • 3.1 Incorporating Indicators, Tracers, and Surrogates into the Risk Assessment Process – Discuss purpose of risk assessments, estimation and projection of risks/exposure, and how other approaches can be applied – Discuss EPA guidance on data quality and usability requirements for risk assessment and exposure assessment – Present examples where indicators, tracers, and/or surrogates have been used in HHRA, site management, decision‐making
- 44. 44 4. Conclusions • Current assessment approaches rely on small number of point‐ in‐time samples analyzed by off‐site laboratories, can result in false‐negatives; costly if implemented frequently • Currently available continuous/real‐time analytical instruments and sensors have some limitations (costs, selectivity, sensitivity, accuracy) • Previous applications have demonstrated that certain variables (e.g., differential temperature, radon) can be used to understand variability, appropriate sampling periods, etc. • Other USEPA guidance and/or studies supports use of indicators, tracers, and/or surrogates for protection of public health
- 45. 45 What Can You Potentially Do With a Good Tracer or Surrogate? • Guide sampling times and locations • Find soil gas entry points to buildings • Track mitigation system effectiveness • Distinguish subslab from indoor sources of VOCs
- 46. 46 Practical Aspects – Availability of Baseline Comparison Data • Differential temperature – almost always available, because – Inside temperature often as easy as “where do you set your thermostat?” – Historical outside temperature data for thousands of locations cataloged either for specific period or normal values https://www.ncdc.noaa.gov/cdo‐web/ https://www.wunderground.com/history/ https://www.ncdc.noaa.gov/cdo‐ web/datatools/selectlocation • Radon – baseline comparison very building dependent, so needs to be acquired • Differential pressure: o Beneficial to get some background data o But upper end of range fairly similar for many structures (5 to 20 Pa) so may be able to make useful decisions with limited background. Temperature logger image reprinted from https://www.microdaq.com
- 47. 47 Practical Aspects – Capital Cost of Monitoring Equipment (not including data transmission) • Differential temperature o Range is from free to <$100 per location • Radon – as low as $129 for a device that reads out the average of 48 hours to 7 days $250 for four hours time resolution. Some have audible alarms. • Differential pressure ‐ $405 for resolution to 0.002” w.c. (0.5 Pa) Image reprinted from www.homedepot.com
- 48. 48 Practical Aspects – Advanced Predictability • Temperature – readily available forecasts, probably sufficient short term accuracy for purpose • Radon – not easily forecasted • Differential pressure – may be partially forecastable if in a given structure controlled by differential temperature rather then wind Graphic reprinted from: http://blog.extension.uga.edu/climate/2 015/07/when‐weather‐apps‐go‐bad/
- 50. 50 Introduction: Chlorinated VOCs in Indoor Air • Challenging to assess at ‘low’ levels • Difficult to predict • Concentrations result from complex interaction of many variables • Data‐rich residential studies show significant variability: – Spatial (x, y, z) – across neighborhood and within building scales – Temporal (t) – diurnal, seasonal and climatic scales Graphic adapted from: http://www.nature.com/ncomms/2014/140708/ncomms5 344/images/ncomms5344‐f2.jpg
- 52. 52 Current Practice • Heavy reliance on extractive samples for laboratory analysis – Costly (>$350 per sample) & disruptive to measure – Only provide single ‘Points of Evidence’ (at a specific location & time period) within spatial and temporal distributions • The accuracy of inferences/extrapolations between & beyond the ‘points of evidence’ (samples) have rarely been tested (but ‘data‐rich’ studies informative) • Statistics is the scientific tool to provide defensible inferences from the sample to the population. But statistical methods are rarely applied to VI even though the variability in VI is more than for groundwater • Analysis of performance of typical sampling strategies suggests safety factor of at least 3X may be needed (Holton, 2013; Weinberg, 2014)
- 53. 53 Reprise from Last Session Why Study Statistics? If we: • don’t have guidance on when to sample; • don’t have the basis for an assumption about the distribution, • don’t want to use an additional safety factor and • do want to see a single example of the RME We will be sampling a long time! 58 28 13 8 6 4 3 2 2 10 10 20 30 40 50 60 70 0 0.2 0.4 0.6 0.8 1 Required Number of Samples to Observee RME Once Surrogate Guided True Positive Rate = Chance of Seeing RME (Here defined as 95th Percentile with One Guided Sample) Required Number of Surrogate Guided Samples Per Location/Zone to Observe RME with 5% Probabilty of Underestimating
- 54. 54 Occupational Methods for Estimating Exposure a/ • Identify similar exposure groups (SEGs) – Based on similarity/frequency of tasks and types of materials/processes • Number of samples needed for baseline assessment – Based on statistical estimates/methods and SEG profile – General AIHA recommendation: 6 – 10 samples where variability is expected to be low to moderate – Fewer than 6 samples can be used with caution; 3 is generally a minimum a/ AIHA. 2015. A Strategy for Assessing and Managing Occupational Exposure. 4th Edition. Ratio True 95th/OEL Low Variability (GSD=1.5) GSD = 2 Moderate Variability (GSD=2.5) GSD = 3 High Variability (GSD=3.5) 0.75 53 138 231 326 418 0.5 13 30 47 65 82 0.25 6 10 16 20 25 0.1 4 6 8 10 12 Sample Size Estimates (95% confidence; Power of 90%) If goal is for 95th to be 10X <OEL GSDs at Industrial Buildings Shown/Expected
- 55. 55 Occupational Approach to Exposure Assessment: Exceedance Fraction • The exceedance fraction (f) is the probability that a concentration in a distribution is higher than the screening level (AIHA, 2015). • One property of a log‐normal distribution is that the exceedance fraction can be related to the mean, and more specifically the ratio of the screening level (SL) to the mean, where Z is the value from a normal distribution table corresponding to the desired exceedance fraction (Rappaport, 1991). For planning purposes, Z is used in lieu of the standard deviation which generally is not known prior to sampling. • This can provide a risk manager an indication of how far below a SL the mean concentration needs to be to keep exposures below the desired exceedance fraction. For example if the exceedance fraction is 5% (0.05), the mean should be maintained ~4‐fold below the screening level. • How much sampling is needed to evaluate this situation with confidence? That will depend on the variability of the distribution of concentrations in air in addition to the ratio of the screening level to the mean concentration. Rappaport, S.M. 1991. Assessment of Long‐Term Exposure to Toxic Substances in Air. Annals of Occupational Hygiene. 35(1): 61‐121. See also Lowe, AEHS Conference 2016,
- 57. 57 Daily Average Concentration Data Set* Fall FallSummer SummerWinter Spring Winter August 14, 2012Aug 15, 2010 (08:00) Spring Spring Summer 25 of 723 days (3.5%) contribute 50% of total exposure over this time frame 0.01 0.1 1 10 -180 -120 -60 0 60 120 180 240 300 360 420 480 540 600 660 720 TCEinIndoorAir[ppbv] Time [d] Daily Average Concentrations Average (0.078 ppbv) Median (<0.01 ppbv) 50% of Exposure (25 days >0.6 ppbv)
- 58. 58 Statistical Toolbox Outline – Basic Questions • Is VI pathway complete & significant at a site (that has GW conc. >levels of concern & undergoing GW remediation)? • Given that GW remediation can extend for decades, how can we ensure, with a known degree of confidence, that VI exposures are under control during the cleanup period? Objectives • Provide sound statistical methodologies to determine appropriate frequencies & approaches for verifying protection from CVI over long‐term periods (with a known degree of confidence) • primary applicability is to RCRA VI decisions, but could be useful for other types of VI sites
- 59. 59 Organization and Scope • Follows 2009 RCRA Statistical Analysis of Groundwater Monitoring Data at RCRA Facilities (Unified Guidance) and supporting ITRC document, but applies applicable methods to VI, structured by same or similar study questions. • Also draws on other technical resources when Unified Guidance does not address an important topic. Include the NIOSH Occupational Exposure Sampling Strategy Manual, AIHA “A Strategy for Assessing and Monitoring Occupational Exposures”, and ITRC’s “Groundwater Statistics and Monitoring Compliance: Statistical Tools for the Project Life Cycle”. The web link to some very useful and relevant Superfund Groundwater guidance for groundwater statistics and their Excel‐based groundwater statistics tool is: • https://www.epa.gov/superfund/superfund‐groundwater‐groundwater‐ response‐completion • Limits and Applicability: Not comprehensive, but does focus on many decisions/questions a RCRA practitioner is likely to encounter during an extended groundwater cleanup period at typical CVI sites.
- 60. 60 Statistical Toolbox for RCRA VI – Considerations • Background comparison • Comparison to Screening Level • Population Distribution • Seasonality • Trends • Required sampling frequency • Closure Comparisons to RCRA/CERCLA Groundwater Statistical Tools
- 61. 61 ARE THE INDOOR AIR CONCENTRATIONS ABOVE BACKGROUND? • Start with lit values (mean or %); but background indoor air is highly variable • Are there indoor VOC sources? If so, can they be removed? • Are indoor air concentrations above outdoor air concentrations? • Are indoor air concentrations above background? ‐ Probably a “bright line” test against the selected background value. • NAVFAC User’s Guide UG2‐2091‐ENV “Interim Final: Guidance for Environmental Background Analysis; Volume IV: Vapor Intrusion Pathway” has useful material on lines of evidence evaluation and statistical tests for this purpose.
- 62. 62 ARE INDOOR AIR CONCENTRATIONS ABOVE OR BELOW A CRITERION? • What is the criterion, ACUTE OR CHRONIC exposure? • Specify certainty: e.g., max, 95th percentile , or 95% UCL of mean? Selected certainty dependent on number of samples. Maximum or 95th percentile typically used for small number of samples. • When will criteria be met? (base on rate of change over time) • AIHA exposure assessment strategy, similar exposure group concept & exceedance fraction concept from Rappaport provide useful tools. • Controls & engineering modeling can constrain variability of indoor air due to VI.
- 64. 64 IS THERE A TREND? WHAT IS DIRECTION AND RATE OF CHANGE OVER TIME? • Trends can be used to predict future exposure, remediation success, etc. • Must account for seasonality, which can invalidate a trend test • Start with scatter plot, linear regression • Steady increase = increasing VI • Confidence bands usually increase over (future) time unless GW is being remediated & source term is decreasing, which is likely to be common for RCRA sites under MNA • Once trend is detected, rate of change over time can be determined • Trends & rates can be determined at the VOC source, indoor air, or in between (subslab)
- 65. 65 IS THE SAMPLING FREQUENCY APPROPRIATE (TEMPORAL OPTIMIZATION)? • Optimization and design of the monitoring program must assure sample independence while covering site sufficiently & collecting adequate data over an appropriate time period for proposed statistical evaluations. • In early stages, statistical design options should be considered such that adequate number of samples are collected. • For sites with existing long term monitoring data, sampling frequency can often be reduced while still providing adequate data • Required sampling frequency can be evaluated with statistical methods that assess redundancy of sample results. • How often do I need to sample to capture a trend? • Cost effective sampling (CES) ‐ rate of change (linear regression) vs Trend (Mann‐Kendal)
- 66. 66 IS THE SPATIAL COVERAGE APPROPRIATE (SPATIAL OPTIMIZATION)? • Optimization and design of monitoring program must assure sample independence while providing adequate spatial coverage of site. Optimization can lead to decreasing or increasing number of sampling points. Expected to result in decrease in locations over time with MNA. • Are sample locations redundant or should new locations be added? • Geostatistics, other spatial statistical methods applicable. – unlikely to be applicable to indoor air, but might apply to GW, external soil gas, and subslab soil gas.
- 67. Panel discussion – draft toolbox outlines – specific questions 1. We will begin the panel session with a review of the existing outlines and any panel comments. a) Indicators Toolbox Outline _ from Chase Holton Presentation Slides b) Statistics Tool box Outline _ from Jeff Kurtz Presentation Slides
- 68. 68 Indicators, Tracers and Surrogates Toolbox 1.0 Introduction 1.1 Objectives 1.2 Document Scope 2.1 Characteristics of Potential Indicators, Tracers, and Surrogates 2.2 Uses and Utility within the Screening Process 2.3 Uses and Utility within Detailed Assessments 2.4 Uses and Utility within Post‐Mitigation/Remediation Phase 3.1 Incorporating Indicators, Tracers, and Surrogates into the Risk Assessment Process 4.0 Conclusions
- 69. 69 Statistical Toolbox for RCRA VI – Considerations • Background comparison • Comparison to Screening Level • Population Distribution • Seasonality • Trends • Required sampling frequency • Closure Comparisons to RCRA/CERCLA Groundwater Statistical Tools
- 70. Panel discussion – draft toolbox outlines – general questions 1. Are there any community benefits or concerns for the Statistics or Indicators approaches? How should they be addressed? 2. Would each product (Statistics or Indicators Toolboxes) be useful? a) What are the best parts? b) What would you add or change? c) What would you not include? 3. Do you think indicators, tracers, and/or surrogates have promise for greater use in the VI field? Why or why not? 4. What is your view on the utility of statistical methods for VI decision making? At what stages of the project/types of sites? 5. What is your opinion on the directed sampling approach and its ability to efficiently capture RME? a) Is it statistically valid? b) Where should it not be applied?