Lutes, C., C. Holton, J, Kurtz and R. Truesdale “Indicators, Tracers and Surrogates - Why Use Them, Probability Analysis, Definitions and Examples” presented at EPA/AEHS, 21 March 2017 - Workshop: Finding Practical Solutions for the Chlorinated Vapor Intrusion, San Diego.
#3/9 Ornithology monitoring on offshore windfarmsNaturalEngland
Presentation #3 of 9: Mark Trinder of MacArthur Green highlighting issues to do with ornithological monitoring at offshore windfarms, survey design and inference
ndicators, Tracers and Surrogates - Why Use Them, Probability Analysis, Defin...Chris Lutes
Lutes, C., C. Holton, J, Kurtz and R. Truesdale “Indicators, Tracers and Surrogates - Why Use Them, Probability Analysis, Definitions and Examples” presented at EPA/AEHS, 21 March 2017 - Workshop: Finding Practical Solutions for the Chlorinated Vapor Intrusion, San Diego.
#3/9 Ornithology monitoring on offshore windfarmsNaturalEngland
Presentation #3 of 9: Mark Trinder of MacArthur Green highlighting issues to do with ornithological monitoring at offshore windfarms, survey design and inference
ndicators, Tracers and Surrogates - Why Use Them, Probability Analysis, Defin...Chris Lutes
Lutes, C., C. Holton, J, Kurtz and R. Truesdale “Indicators, Tracers and Surrogates - Why Use Them, Probability Analysis, Definitions and Examples” presented at EPA/AEHS, 21 March 2017 - Workshop: Finding Practical Solutions for the Chlorinated Vapor Intrusion, San Diego.
S3A: NOTE FOR GUIDANCE ON TOXICOKINETICS: THE ASSESSMENT OF SYSTEMIC EXPOSURE IN TOXICITY STUDIES
S3B: PHARMACOKINETICS: GUIDANCE FOR REPEATED DOSE TISSUE DISTRIBUTION STUDIES
Sensitivity Analysis, Optimal Design, Population Modeling.pptxAditiChauhan701637
Sensitivity analysis is the study of the unreliability related to output and input of mathematical model or numerical system which can be divided and allocated to various sources.
The process of outcome under possible speculation to find out the impact of a variable under sensitivity analysis can be useful for a range of purpose, consisting -
1. In the existence of unreliability, prefer testing of the results of a model or system.
2. Enhanced understanding of correlation between input and output variables in a model or system.
Sensitivity analysis methods:
There are many number of methods to study the sensitivity analysis, many of which have been developed to address one or more of the limitations discussed above. By the type sensitivity analysis measurement they are differentiate, be it based on variance decompositions, partial derivatives or elementary effects.
Are Industrial Buildings Different? Implication of a Quantitative Vapor Intr...Chris Lutes
Lutes C., K. Hallberg, J. Lowe, L Lund, M. Novak, P. Venable, T. Chaudhry, I. Rivera-Duarte and D. Caldwell Are Industrial Buildings Different? Implication of a Quantitative Vapor Intrusion at DoD Industrial Buildings Nationwide; Presented at Third International Symposium on Bioremediation and Sustainable Environmental Technologies (Battelle Symposium); Miami Florida 2015
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Lutes, C., R. Truesdale, H. Hayes, T. McAlary, H. Dawson, B. Cosky , D. Grossee, B. Schumacher and J. Zimmerman Passive Samplers for Vapor Intrusion Monitoring: Update of EPA’s Technical Support Document and Research Results, Presented at Third International Symposium on Bioremediation and Sustainable Environmental Technologies (Battelle Symposium); Miami Florida 2015
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Sensitivity analysis is the study of the unreliability related to output and input of mathematical model or numerical system which can be divided and allocated to various sources.
The process of outcome under possible speculation to find out the impact of a variable under sensitivity analysis can be useful for a range of purpose, consisting -
1. In the existence of unreliability, prefer testing of the results of a model or system.
2. Enhanced understanding of correlation between input and output variables in a model or system.
Sensitivity analysis methods:
There are many number of methods to study the sensitivity analysis, many of which have been developed to address one or more of the limitations discussed above. By the type sensitivity analysis measurement they are differentiate, be it based on variance decompositions, partial derivatives or elementary effects.
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Lutes C. Wheeler and SEND Buildings In Indianapolis – Radon and Differential Pressure Data in VOC Site Management Presented at US EPA Workshop on: Measurement-Based Methods for Protective & Defensible Chlorinated VI Exposure Determinations; AEHS East Coast Conference, October 22, 2019, Amherst MA.
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Rain, Soil Moisture, Water Table, Snow and Ice” oral presentation at EPA Workshop at 28th Annual AEHS International West Coast Conference on Soils, Water, Energy and Air, March 20, 2018.
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Introduction:
RNA interference (RNAi) or Post-Transcriptional Gene Silencing (PTGS) is an important biological process for modulating eukaryotic gene expression.
It is highly conserved process of posttranscriptional gene silencing by which double stranded RNA (dsRNA) causes sequence-specific degradation of mRNA sequences.
dsRNA-induced gene silencing (RNAi) is reported in a wide range of eukaryotes ranging from worms, insects, mammals and plants.
This process mediates resistance to both endogenous parasitic and exogenous pathogenic nucleic acids, and regulates the expression of protein-coding genes.
What are small ncRNAs?
micro RNA (miRNA)
short interfering RNA (siRNA)
Properties of small non-coding RNA:
Involved in silencing mRNA transcripts.
Called “small” because they are usually only about 21-24 nucleotides long.
Synthesized by first cutting up longer precursor sequences (like the 61nt one that Lee discovered).
Silence an mRNA by base pairing with some sequence on the mRNA.
Discovery of siRNA?
The first small RNA:
In 1993 Rosalind Lee (Victor Ambros lab) was studying a non- coding gene in C. elegans, lin-4, that was involved in silencing of another gene, lin-14, at the appropriate time in the
development of the worm C. elegans.
Two small transcripts of lin-4 (22nt and 61nt) were found to be complementary to a sequence in the 3' UTR of lin-14.
Because lin-4 encoded no protein, she deduced that it must be these transcripts that are causing the silencing by RNA-RNA interactions.
Types of RNAi ( non coding RNA)
MiRNA
Length (23-25 nt)
Trans acting
Binds with target MRNA in mismatch
Translation inhibition
Si RNA
Length 21 nt.
Cis acting
Bind with target Mrna in perfect complementary sequence
Piwi-RNA
Length ; 25 to 36 nt.
Expressed in Germ Cells
Regulates trnasposomes activity
MECHANISM OF RNAI:
First the double-stranded RNA teams up with a protein complex named Dicer, which cuts the long RNA into short pieces.
Then another protein complex called RISC (RNA-induced silencing complex) discards one of the two RNA strands.
The RISC-docked, single-stranded RNA then pairs with the homologous mRNA and destroys it.
THE RISC COMPLEX:
RISC is large(>500kD) RNA multi- protein Binding complex which triggers MRNA degradation in response to MRNA
Unwinding of double stranded Si RNA by ATP independent Helicase
Active component of RISC is Ago proteins( ENDONUCLEASE) which cleave target MRNA.
DICER: endonuclease (RNase Family III)
Argonaute: Central Component of the RNA-Induced Silencing Complex (RISC)
One strand of the dsRNA produced by Dicer is retained in the RISC complex in association with Argonaute
ARGONAUTE PROTEIN :
1.PAZ(PIWI/Argonaute/ Zwille)- Recognition of target MRNA
2.PIWI (p-element induced wimpy Testis)- breaks Phosphodiester bond of mRNA.)RNAse H activity.
MiRNA:
The Double-stranded RNAs are naturally produced in eukaryotic cells during development, and they have a key role in regulating gene expression .
Nutraceutical market, scope and growth: Herbal drug technologyLokesh Patil
As consumer awareness of health and wellness rises, the nutraceutical market—which includes goods like functional meals, drinks, and dietary supplements that provide health advantages beyond basic nutrition—is growing significantly. As healthcare expenses rise, the population ages, and people want natural and preventative health solutions more and more, this industry is increasing quickly. Further driving market expansion are product formulation innovations and the use of cutting-edge technology for customized nutrition. With its worldwide reach, the nutraceutical industry is expected to keep growing and provide significant chances for research and investment in a number of categories, including vitamins, minerals, probiotics, and herbal supplements.
Richard's entangled aventures in wonderlandRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
THE IMPORTANCE OF MARTIAN ATMOSPHERE SAMPLE RETURN.Sérgio Sacani
The return of a sample of near-surface atmosphere from Mars would facilitate answers to several first-order science questions surrounding the formation and evolution of the planet. One of the important aspects of terrestrial planet formation in general is the role that primary atmospheres played in influencing the chemistry and structure of the planets and their antecedents. Studies of the martian atmosphere can be used to investigate the role of a primary atmosphere in its history. Atmosphere samples would also inform our understanding of the near-surface chemistry of the planet, and ultimately the prospects for life. High-precision isotopic analyses of constituent gases are needed to address these questions, requiring that the analyses are made on returned samples rather than in situ.
This presentation explores a brief idea about the structural and functional attributes of nucleotides, the structure and function of genetic materials along with the impact of UV rays and pH upon them.
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
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
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] ‐
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
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
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
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.
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
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)
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
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)
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
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?