Indicators, Tracers and Surrogates

Transcription

1 Indicators, Tracers and Surrogates Why Use Them, Probability Analysis, Definitions and Examples AEHS, March 21 st, 2017 Presented by Chris Lutes

2 A Key Term: Reasonable Maximum Exposure: Risk Assessment Guidance for Superfund Part A: 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. 2 Quotes from OSWER A; July 1989

3 Why Do We Need Indicators, Tracers and Surrogates to Guide Sampling? EPA 2015 VI guide definition: reasonable maximum exposure (RME) A semi-quantitative term, referring to the lower portion of the high end of the exposure distribution; conceptually, above the 90th percentile exposure but less than the 98th percentile exposure. Here we discuss the exposure concentration, but don t forget that the exposure frequency and exposure duration are also part of RME. Observing the reasonable maximum exposure concentration with random sampling is hard! 3

4 Required Number of Samples to Observee RME Once Required Number of Unguided Random Samples Per Location/Zone to Observe RME Once at Various Confidence Levels Percentile Defined as RME = Chance of Not Seeing RME With One Sample 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% Prob. Of Underestimating RME 10% Prob. Of Underestimating RME 20% Prob. Of Underestimating RME 30% Prob. Of Underestimating RME

5 Required Number of Samples to Observee RME Once 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 Surrogate Guided True Positive Rate = Chance of Seeing RME (Here defined as 95th Percentile with One Guided Sample)

6 Definition: Indicator Metrics that can indicate elevated potential for chlorinated VOC exposures. Think: An indicator in chemistry is a substance whose color varies with acidity or alkalinity. An indicator in biology is a species an animal or plant species that can be used to infer conditions of a particular habitat. For example amphibians are sensitive to environmental stress. For VI season and differential pressure may be indicators. 6 Images reprinted from:

7 Definition: Tracer Easily observable substances that move physically along with the target compounds of interest for VI Think: In hydrogeology and medicine an identifiable substance, such as a dye or radioactive isotope, that can be followed through the course of a mechanical, chemical, or biological process. For VI radon is a tracer of processes across the slab. Photos reprinted from: 7

8 Definition: Surrogate Metrics with a quantitative relationship to the target compound for VI, sufficiently accurate to be a substitute. In the context of environmental microbiology and health risk assessment, we have defined surrogates as organisms, particles, or substances used to study the fate of a pathogen in a specific environment. The use of surrogates in these scenarios can allow quantification of the degree of exposure (Sinclair, 2012) Images from: s/env295micro/lesson9b.htm on/jackets/amazon/ / jpg 8 Sinclair, Ryan G., Joan B. Rose, Syed A. Hashsham, Charles P. Gerba, and Charles N. Haas. "Criteria for selection of surrogates used to study the fate and control of pathogens in the environment." Applied and environmental microbiology 78, no. 6 (2012):

9 An Analogy TO-15 total cost per sample/data point >$350 leads to very small sample sizes (i.e. three sampling rounds) = A complex population influenced by multiple processes; Rich information content From a subsample, n=3 How much can you tell? Could statistical analysis alone help? 9

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? 10

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. 11

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. 12

13 Examples of Use of Indicators and Surrogates in Other EPA Human Health Risk Management Programs 13 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 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. 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. coliphage.pdf Good combustion practices (time, temperature) are used as indicators of control of air toxics emissions See also 40CFR Part 62, Subpart JJJ

14 Examples of Indicator, Tracer and Surrogate Applications Indianapolis Applied R&D Projects Spatial example from Wheeler Building coauthor Ron Mosley, Dale Greenwell (EPA); Robert Uppencamp (ARCADIS) Temporal examples from Indianapolis Duplex coauthors John Zimmerman and Brian Schumacher (US EPA NERL_; Brian Cosky (ARCADIS), Robert Truesdale, Brenda Munoz and Robert Norberg (RTI International) 420 Not Heated 422 Heated 14

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. 15 Radon (pci/l) and VOCs (µg/m3) Unit 106 (Column in Kitchen) Results for Upstairs Sampling Locations 2nd Floor, On North Wall, East Side of Atrium RANDOM 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) Sampling Locations RADON GUIDED 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 & 1 st Floor on West Stairs 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 Radon TCE PCE

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 16

17 Visualization of Time Series Data Fit Change in Exterior Temperature vs. Change in PCE Increasing PCE Read more in Simple, Efficient, and Rapid Methods to Determine the Potential for Vapor Intrusion into the Home: Temporal Trends, Vapor Intrusion Forecasting, Sampling Strategies, and Contaminant Migration Routes. U.S. Environmental Protection Agency, Washington, DC, EPA/600/R-15/070, c_record_report.cfm?direntryid = Decreasing PCE 17 Cooling Warming

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: C t C (t-1) = *T t *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/m 3. Example B: rising temp: 20 F today, 10 F yesterday, on average PCE concentration decreased 0.2 µg/m 3 18

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. 19

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 South Weekly [PCE] 422 Basement North Temperature (C) PCE Data from Indy Test House: Jan 2011 to Feb2012 (includes locally weighted scatterplot smoothing line [blue], with a 95% confidence interval [shaded]) 20

21 Visualization of Time Series Data: Change in indoor radon vs. change in indoor PCE Increasing PCE Decreasing PCE Decreasing 21 Radon Increasing Radon

22 What if This Time Series Stuff is Too Complicated for Me?.What If I Just Look at the Thermometer or the Radon Instrument Today and Use that To Guide Sampling? 22 Image reprinted from and

23 23

24 New Analysis of ASU Sun Devil Manor Data

25 ASU Sun Devil Manor Data Reprise 2.0 Sun Devil Manor 24 hr Ave TCE (ppbv) vs Radon Sampling Day 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 24hr Ave TCE vs 24 hr Ave Rn (SDM) hr Ave TCE (ppbv) R² = hr Ave Rn (pci/l)

28 SDM TCE vs Differential Temperature 24 h Average TCE in Indoor Air [ppbv] vs Differential T (SDM) 24 hr TCE (ppbv) R² = Differential T (C)

29 SDM Differential Temperature vs Rn hr Differential Temperature [ C] vs 24 hr Rn (SDM) R² = hr Differential T hr Rn (pci/l)

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 (>90 th %) approach for RME 40% True Positives 60% False Positives

32 Differential T Indicator (>90 th %) approach for RME Differential T>90 th percentile, TCE>95 th percentile 34% True Positives 66% False Positives

33 How Many Samples Are Needed? Required Number of Samples to Observee RME Once Required Number of Surrogate Guided Samples Per Location/Zone to Observe RME with 5% Probabilty of Underestimating Surrogate Guided True Positive Rate = Chance of Seeing RME (Here defined as 95th Percentile with One Guided Sample)

34 95 UCL of Mean vs Number of Samples

35 New SDM Sampling Simulation Results & Statistical Applications (N= 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 4 seasonsummer& Seasons Sampled s Winter Winter Winter Winter Winter Winter Winter Winter Total Samples 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

37 Outline Practical Methods for Using Indicators, Tracers, and Surrogates in Vapor Intrusion Pathway Assessment Presented by Chase Holton AEHS, March 21 st, 2017

38 Outline 1. Introduction 2. Indicators, Tracers, and Surrogates for Vapor Intrusion Pathway Assessment 3. Related Guidance and Supporting Information 4. Conclusions 38

39 1. Introduction 1.0 Introduction 39 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-thescience 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 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, SF 6, differential temperature, differential pressure, and other methods/parameters that have shown greatest utility in VI assessments 40

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. 41

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 postmitigation (e.g. VIMS OMM) and long-term stewardship approaches 42

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 43

44 4. Conclusions Current assessment approaches rely on small number of pointin-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 44

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 45

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 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. 46 Temperature logger image reprinted from

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 w.c. (0.5 Pa) Image reprinted from 47

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 48 Graphic reprinted from: 015/07/when-weather-apps-go-bad/