Radon and Chemical Soil Gas Vapor Intrusion
R
A
D
O
N
&
C
H
E
M
I
C
A
L
S
O
I
L
-
G
A
S
/
V
A
P
O
R
I
N
T
R
U
S
I
O
N
UPDATE ON TESTING (& Using) ASSOCIATIONS
Henry Schuver*, Chris Lutes, Chase Holton, Jeff Kurtz, Brian Schumacher, John
Zimmerman & Robert Truesdale
*USEPA - Washington DC (schuver.henry@epa.gov)
2019 International Radon Symposium Denver, Colorado
2019 International Radon Symposium Denver, Colorado
1
Outline
2019 International Radon Symposium Denver, Colorado
2019 International Radon Symposium Denver, Colorado
2
W
e
s
h
o
u
l
d
h
a
v
e
c
o
n
t
i
n
u
o
u
s
R
a
d
o
n
d
a
t
a
:
t
o
k
n
o
w
t
h
e
p
e
r
c
e
n
t
i
l
e
o
f
s
o
i
l
-
g
a
s
i
n
t
r
u
s
i
o
n
a
t
t
h
e
t
i
m
e
o
f
o
c
c
a
s
i
o
n
a
l
c
h
e
m
i
c
a
l
s
a
m
p
l
i
n
g
3
2019 International Radon Symposium Denver, Colorado
2019 International Radon Symposium Denver, Colorado
Sun Devil Manor (SDM) UT Vapor Intrusion research house
Background:
Why supplemental measurements? e.g. Radon?
•
Model predictions
of VI have
not
been
validated
•
(e.g., one attempt since 1992 – only works if silt is considered sand …)
•
Only indoor air conc
. –
reflects all variables
•
When collected show
Highly variable
across
Time
(& Space)
•
Chemical measurements –
expensive
*
&
disruptive
to occupants:
1-4
?
•
Confidence in exposures is typically low – so Multiple Lines of ‘Evidence’
•
Simple, lower-cost measurements could add observations &
if
correlated
more
Evidence
for chemical VI over Time (& Space)
•
I
ndicators,
T
racers &
S
urrogates – goal to
minimize chemical samples
(via scheduling/interpretations) to more
confidently
represent RME
2019 International Radon Symposium Denver, Colorado
2019 International Radon Symposium Denver, Colorado
4
*access, clearing ‘background’ sources, collection & analysis
V
I
C
h
a
l
l
e
n
g
e
s
,
e
.
g
.
,
W
h
e
n
t
o
S
a
m
p
l
e
i
n
d
o
o
r
a
i
r
?
(
R
e
a
s
o
n
.
M
a
x
.
E
x
p
o
s
u
r
e
,
~
9
5
t
h
%
i
l
e
(
1
/
2
0
)
)
f
o
r
1
-
d
a
y
t
o
x
i
c
i
t
y
c
o
n
c
e
r
n
s
f
o
r
T
C
E
•
For collecting
Indoor Air
samples, across
Time
:
•
Seasonal – EPA & States …
•
(based on some analyses)
•
Temperature – PA (best I’ve seen), ….
•
(based on some retrospective analyses/testing)
•
Pressure – EPA VIG, States …
•
(based on some few analyses)
•
Radon* – NH, WI, OR, CA, AK, MN?
5
*Using Rn
attenuation factor
. List from presentation by A. Miller, at AEHS Oct. 2018
2019 International Radon Symposium Denver, Colorado
2019 International Radon Symposium Denver, Colorado
Why focus on Radon?
Because supplemental lines of Evidence are Not equal
Summary of conceptual relationships
In
cludes:
Does
Not
include:
•
All factors of VI*
VOC variation over Space & Time
•
All Bldg.
& nearby
factors
Deep/VOC
source
conc. & migrat./path
•
Wind, HVAC
Integrating area (SS), Pulsing, AER
•
Common Driver
Wind, HVAC
6
Temperature
Pressure
Radon
(indoor
conc
.)
Rn
+
CVOC in SG
Starting from the basic factors & move up
* To be tested (not easy so far for SDM)
C
o
n
c
e
p
t
u
a
l
S
i
t
e
M
o
d
e
l
(
C
S
M
)
f
o
r
S
o
i
l
-
G
a
s
/
V
a
p
o
r
I
n
t
r
u
s
i
o
n
(
&
I
T
S
)
M
a
n
y
f
a
c
t
o
r
s
/
s
o
u
r
c
e
s
o
f
V
a
r
i
a
b
i
l
i
t
y
a
c
r
o
s
s
S
p
a
c
e
&
T
i
m
e
7
D
i
s
s
o
l
v
e
d
C
V
O
C
c
o
n
t
a
m
i
n
a
t
i
o
n
L
T
D
i
f
f
u
s
i
o
n
V
a
d
o
s
e
z
o
n
e
B
u
i
l
d
i
n
g
z
o
n
e
o
f
i
n
f
l
u
e
n
c
e
W
i
n
d
e
f
f
e
c
t
s
I
n
d
o
o
r
A
i
r
C
r
a
c
k
s
Q
s
o
i
l
A
i
r
s
t
r
e
a
m
l
i
n
e
s
C
o
n
v
e
c
t
i
o
n
T
o
p
o
f
c
a
p
i
l
l
a
r
y
z
o
n
e
Water Table
S
t
a
c
k
e
f
f
e
c
t
s
M
i
x
i
n
g
i
n
i
n
d
o
o
r
a
i
r
a
n
d
i
n
h
a
l
a
t
i
o
n
A
d
v
e
c
t
i
o
n
D
i
f
f
u
s
i
o
n
P
h
a
s
e
p
a
r
t
i
t
i
o
n
i
n
g
C
g
w
t
o
C
s
o
i
l
g
a
s
Mod. from slide by M. Bolas, Ohio EPA, presented Jan. 2006
C
V
O
C
c
o
n
t
a
m
i
n
a
t
i
o
n
C
V
O
C
c
o
n
t
a
m
i
n
a
t
i
o
n
4 -
Driving Forces
3 -
Building factors
2b
Diffusive Migration
2a
1
1 (x,y,z,t)
2b
(x,y,z,t)
2a
(x,y,z,t)
3
#3 (=100x #2)
1 (x,y,z,t)
Radon (Rn)
Source
, ½ life
limits distance
Rn
&
CVOC
#1
Bottom Line
=
ONLY
indoor
air
Conc
.
of
CVOCs
can represent
ALL
variables/factors
involved
& we want
supplemental
ITS
measures
/metrics that can
represent As Many/Important As Possible
Radon cannot reflect the factors in the 1% of the
attenuation
zone, but does the 99% zone
Bldg. are complex incl.
Pipe Flow pathways,
both Direct & Indirect
CVOC
Source
Term
Metrics (ITS)
Temp -
Condition
Pressure -
Force
Radon
-
Tracer
(
Rn
)
Each has
Strengths &
Limitations
Direct
&
In
direct
non-natural
‘pipe flow’
preferential
pathways
Four
Categories
-of-Variables
for
C
hlorinated
VOC (
CVOCs
)
99% attenuation
between SS & IA
<10%
>90%
O
u
t
l
i
n
e
o
f
P
r
e
v
i
o
u
s
&
C
u
r
r
e
n
t
S
t
u
d
i
e
s
R
a
d
o
n
&
V
O
C
s
i
n
I
n
d
o
o
r
A
i
r
:
O
v
e
r
T
i
m
e
•
Visual comparisons
•
Statistical comparisons
•
Time Series Regression
•
Direction
•
Magnitude
•
Screening 2x2 Tables/Predictive value (of
concentrations
)
•
Correlations of
Percentiles
(Rn:VOC) – w/ continuous Rn measurements
2019 International Radon Symposium Denver, Colorado
2019 International Radon Symposium Denver, Colorado
8
I
n
d
o
o
r
C
V
O
C
&
R
a
d
o
n
&
C
o
n
c
.
c
h
a
n
g
i
n
g
s
i
m
i
l
a
r
l
y
o
v
e
r
T
i
m
e
V
i
s
u
a
l
l
y
a
p
p
a
r
e
n
t
i
n
S
D
M
/
A
S
U
’
s
&
E
P
A
-
O
R
D
’
s
V
I
h
o
u
s
e
s
N
o
t
e
:
b
o
t
h
h
o
u
s
e
s
h
a
d
‘
a
t
y
p
i
c
a
l
’
p
r
e
f
e
r
e
n
t
i
a
l
‘
p
i
p
e
’
p
a
t
h
w
a
y
s
9
https://iavi.rti.org/attachments/WorkshopsAndConferences
/02_
Holton
_Weather-Temporal-Variation-3-22-2012.pdf
Naturally-
varying
conditions
in 2012
(EPA 2015b)
10
Sun Devil Manor (SDM), Layton, Utah
1
)
D
i
r
e
c
t
i
o
n
o
f
C
o
n
c
.
c
h
a
n
g
e
.
(
Q
u
a
l
.
)
9
9
%
(
E
P
A
-
I
N
)
9
9
.
9
%
(
S
D
M
-
U
T
)
C
h
a
n
g
i
n
g
c
o
n
c
.
d
i
r
e
c
t
i
o
n
t
o
g
e
t
h
e
r
Note Background (outdoor) Rn
& TCE < Det. Limit)
2
)
M
a
g
n
i
t
u
d
e
Quan
t
itative
‘
p
r
o
p
o
r
t
i
o
n
a
l
i
t
y
’
o
f
c
o
n
c
.
c
h
a
n
g
e
4
0
%
(
E
P
A
-
I
N
)
2
5
%
-
6
0
%
(
S
D
M
-
U
T
)
~
½
o
f
c
h
a
n
g
e
i
n
T
C
E
C
o
n
c
.
‘
e
x
p
l
a
i
n
e
d
b
y
’
t
h
e
c
h
a
n
g
e
i
n
R
n
c
o
n
c
.
(
R
2
)
Statistical Assoc.
of Conc. across
Time
Using Time Series (linear) Regression; results for
Two components
:
Not confident enough for risk decision making
Time Series LR
Not practical
,
computationally
for typical
application
R
a
d
o
n
c
o
n
c
.
(
i
n
d
o
o
r
)
a
s
I
n
d
i
c
a
t
o
r
o
f
T
C
E
R
M
E
;
O
v
e
r
T
i
m
e
40
% of those screened in by
Rn
were truly positive w/
elevated
TCE
[=Positive Predictive Value]
40
% True Positives
60% False Positives
99
%
True
Negatives
Only 1% of those
screened
out
of
concern by
Rn
were found to
have elevated
TCE
1% False Negatives
Diagnostic (Exposure) Screening
of SDM house data, statistics by Kurtz
National outdoor Rn background
Looking for >95
th
% conc. of TCE,
99%
of the data ‘Indicated’ by non-elevated (<90
th
%) Rn were
correctly
‘screened
out
’
[High Rn &
High TCE]
0.48
ug/m3
(0.09
ppbv)
11
84% True Negatives
90% True Positives
10% False Positives
~0.7 pCi/L
90
th
%
Rn
1.5
ug/m3
Not practical
,
timing
chemical
sample
collection
when Rn is
elevated
(>90%ile)
difficult
H
o
w
m
a
n
y
S
a
m
p
l
e
s
N
e
e
d
e
d
t
o
r
e
p
r
e
s
e
n
t
9
5
t
h
(
&
8
2
n
d
%
i
l
e
)
U
s
i
n
g
I
T
S
’
P
o
s
i
t
i
v
e
P
r
e
d
i
c
t
i
v
e
V
a
l
u
e
s
L
o
w
e
r
s
S
a
m
p
l
e
#
N
e
e
d
e
d
w
/
H
i
g
h
(
9
5
%
)
C
o
n
f
i
d
e
n
c
e
-
U
s
i
n
g
I
T
S
-
G
u
i
d
e
d
I
A
Q
s
a
m
p
l
e
s
12
[at Sun Devil Manor – VI research house (formerly ASU), Layton, UT]
(for 95
th
%ile ‘cut point’ (
solid lines
) & dashed lines for * 82
nd
%ile (= lower conc., 0.48 ug/m3 risk #)
Radon
Temp.
Press.
*
Rn
Note ~3x (20) # random chance, for conf.
C
o
n
t
i
n
u
o
u
s
D
i
s
t
r
i
b
u
t
i
o
n
o
f
R
n
C
o
n
c
e
n
t
r
a
t
i
o
n
s
*
H
i
s
t
o
r
y
&
p
i
c
t
u
r
e
o
f
a
b
u
i
l
d
i
n
g
’
s
(
s
o
i
l
g
a
s
/
v
a
p
o
r
)
I
n
t
r
u
s
i
o
n
b
e
h
a
v
i
o
r
C
o
n
t
i
n
u
o
u
s
R
n
l
e
v
e
l
s
a
r
e
p
o
s
s
i
b
l
e
/
p
r
a
c
t
i
c
a
l
P
r
o
v
i
d
e
s
c
o
n
t
e
x
t
(
%
i
l
e
s
)
f
o
r
f
e
w
c
h
e
m
i
c
a
l
g
r
a
b
s
a
m
p
l
e
e
v
e
n
t
s
*Rn & TCE each plotted on their
respective
(Y-axis)
ranges
observed during baseline study –
research
house
Hypothetical chemical grab sample event at scheduled interval
Rn curve provides context for chemical results and how ¾ where Not from periods of concern for RME
Indoor Rn & TCE at
SDM-UT
2011-2012 (naturally-varying conditions)
TCE conc. above reporting limit (0.011 ppbv) and
Rn conc. above the lower confidence limit of the RAD7 (0.5 pCi/L)
13
Max. Rn & TCE peak
We also tried std. Linear Regres. of various avg-periods & %iles
R
2
- Proportion of the variance in TCE conc. that is predictable from variance in Rn
conc., Indoor Rn & TCE at
SDM-UT
2011-2012
~90
th
%ile
-24 hr
14
Appears higher R
2
values are found for
longer
(7-d) and
higher percentiles
of Rn
Appears the
highest TCE
(24-hr & 7-d) values are associated with
high Rn (>90
th
%ile)
,
but
Not the very highest Rn
.
Elevated
Rn (e.g., >90
th
%ile) appears important for
knowing when VI is turned ON
Look for more
work in this
area in the
future, e.g.,
lagged time
effects
Sampling for TCE when the Rn level is <80
th
%ile gives an
40% probability (~1/2) of finding a ND TCE value!
You
need to know the building’s %ile of Radon conc. when chem. sample is collected
to understand chemical conc. found
When sampling when Rn was > 80
th
%, or even better >90
th
%, you could find much higher TCE levels
When >90
th
%ile Rn,
almost all TCE levels
>70
th
%ile & up to
100
th
%ile
(Highest TCE levels)
At >80
th
%ile Rn near-
lack of ND levels;
Calculated
Percentiles
(%iles),
including
No-Detected
(ND) values
~ 40% of
the TCE
levels are
Non
Detected
15
Calculated Percentiles
Rn %ile can document
*
… Probability of finding
TCE in levels of interest –
for regulatory decision making
*If all samples have the same probability of finding
a
sample w/ TCE above the given target %ile (w/ 95% conf.)
16
*
And guide/help samplers decision to analyze chemical samples or not
Having Rn percentiles can allow
probability of multiple samples to be combined for a (higher) total probability
of having one or more samples from within the Exposure Levels of Interest – for regulatory decision making
S
u
m
m
a
r
y
o
f
P
r
e
v
i
o
u
s
&
C
u
r
r
e
n
t
S
t
u
d
i
e
s
:
R
a
d
o
n
&
V
O
C
s
i
n
I
n
d
o
o
r
A
i
r
–
O
v
e
r
T
i
m
e
•
Visual comparisons
•
Statistical comparisons
•
Time Series Regression –
Not practical
, computationally for typical application
•
Direction – 99 & 99.9%
•
Magnitude – ~50%
•
Screening 2x2 Tables/Predictive value (of
conc
.
) –
Not practical
, sample timing difficult
•
Percentiles
correlations (Rn:VOC)
•
Getting a
building-specific baseline/history
for indoor
Rn
is
somewhat practical
/
possible
•
Knowing the percentile
(%
ile
) of Rn intrusion
(from baseline)
occurring when
a chemical (VOC)
sample is collected;
•
Could give us
context
&
indication
for knowing
how probable
the chemical sample(s)
collected are representing exposure levels of interest (e.g., RME, ~95
th
%ile)
•
Can
add the probabilities
of multiple samples – for
•
probability/confidence > any individual sample
2019 International Radon Symposium Denver, Colorado
2019 International Radon Symposium Denver, Colorado
17
C
o
n
c
l
u
s
i
o
n
s
:
W
e
n
e
e
d
c
o
n
t
i
n
u
o
u
s
R
n
d
a
t
a
t
o
k
n
o
w
t
h
e
p
e
r
c
e
n
t
i
l
e
o
f
i
n
t
r
u
s
i
o
n
a
n
d
m
e
a
n
i
n
g
o
f
o
c
c
a
s
i
o
n
a
l
c
h
e
m
i
c
a
l
s
a
m
p
l
e
r
e
s
u
l
t
s
*
•
Grab sampling 1-day indoor air for chemical VI assessments, at:
•
Some random
Time
is:
•
Unlikely to find RME (i.e., >95
th
%ile) - &
Would Not know it if you did
•
The
meaning
and
context
of even multi. grab sample results will be
unclear
•
We need
to
know When
VI is turned ‘ON’
•
Pre-screening with Rn before VI chemical samples will
maximize
the
meaning
•
by placing the chemical sample within the
soil gas intrusion
history of the building
Makes possible
Quantitative probability/
confidence levels
for small sample #s
18
*based on SDM house, more buildings being studied in Oct. workshop
C
o
n
t
i
n
u
o
u
s
R
n
i
s
a
n
u
n
p
a
r
a
l
l
e
l
e
d
S
u
p
p
l
e
m
e
n
t
a
l
/
M
u
l
t
i
p
l
e
L
i
n
e
o
f
E
v
i
d
e
n
c
e
–
f
o
r
o
c
c
a
s
i
o
n
a
l
c
h
e
m
i
c
a
l
V
I
s
a
m
p
l
i
n
g
•
Questions?
•
THANK YOU
•
1-day Workshop (w/ webinar access)
Oct. 22
, 2019
•
https://www.aehsfoundation.org/East-Coast-
Conference.aspx
•
For email list contact Robert Truesdale
rst@rti.org
19
This presentation at the 2019 International Radon Symposium in Denver, Colorado explores the association between radon, chemical soil gas, and vapor intrusion. It discusses the challenges in testing, analyzing differences, and the need for supplemental measurements like radon to validate models. The goal is to minimize chemical sampling while confidently representing risks associated with soil gas intrusion.
Download Presentation
Please find below an Image/Link to download the presentation.
The content on the website is provided AS IS for your information and personal use only. It may not be sold, licensed, or shared on other websites without obtaining consent from the author. Download presentation by click this link. If you encounter any issues during the download, it is possible that the publisher has removed the file from their server.
E N D
Presentation Transcript
RADON & CHEMICAL RADON & CHEMICAL SOIL SOIL- -GAS/VAPOR INTRUSION GAS/VAPOR INTRUSION UPDATE ON TESTING (& Using) ASSOCIATIONS Henry Schuver*, Chris Lutes, Chase Holton, Jeff Kurtz, Brian Schumacher, John Zimmerman & Robert Truesdale *USEPA - Washington DC (schuver.henry@epa.gov) 2019 International Radon Symposium Denver, Colorado 1
Outline Rn & CVOCs Conceptually move together Differences in Analysis & Evidence to-date Two hazardous components of soil gas (& indoor air) source material (from 2 data-rich studies) in soil gas as areal extent Tracer of each other half-lives New Analyses & Applications mass storage flux rates 2019 International Radon Symposium Denver, Colorado 2
We should have We should have continuous percentile of soil-gas intrusion at the time of occasional continuous Radon data: Radon data: to know the occasional chemical sampling Sun Devil Manor (SDM) UT Vapor Intrusion research house 2019 International Radon Symposium Denver, Colorado 3
Background: Why supplemental measurements? e.g. Radon? Model predictions of VI have not been validated (e.g., one attempt since 1992 only works if silt is considered sand ) Only indoor air conc. reflects all variables When collected show Highly variable across Time (& Space) Chemical measurements expensive*& disruptive to occupants: 1-4? Confidence in exposures is typically low so Multiple Lines of Evidence Simple, lower-cost measurements could add observations & if correlated more Evidence for chemical VI over Time (& Space) Indicators, Tracers & Surrogates goal to minimize chemical samples (via scheduling/interpretations) to more confidently represent RME *access, clearing background sources, collection & analysis 2019 International Radon Symposium Denver, Colorado 4
VI Challenges, e.g., When VI Challenges, e.g., When to Sample indoor air? (R Reason. M Max. E Exposure, ~95th%ile (1/20)) for 1 for 1- -day toxicity concerns for TCE day toxicity concerns for TCE For collecting Indoor Air samples, across Time: Seasonal EPA & States (based on some analyses) Temperature PA (best I ve seen), . (based on some retrospective analyses/testing) Pressure EPA VIG, States (based on some few analyses) Radon* NH, WI, OR, CA, AK, MN? *Using Rn attenuation factor. List from presentation by A. Miller, at AEHS Oct. 2018 2019 International Radon Symposium Denver, Colorado 5
Why focus on Radon? Because supplemental lines of Evidence are Not equal Summary of conceptual relationships Includes: Does Not include: Rn + All factors of VI* CVOC in SG VOC variation over Space & Time All Bldg. & nearby factors Radon Deep/VOC source conc. & migrat./path (indoor conc.) Wind, HVAC Integrating area (SS), Pulsing, AER Pressure Common Driver Wind, HVAC Temperature * To be tested (not easy so far for SDM) Starting from the basic factors & move up 6
Conceptual Site Model (CSM) for Soil-Gas/Vapor Intrusion (& ITS) Many factors/sources of Variability across Space & Time Many factors/sources of Variability across Space & Time Bldg. are complex incl. Pipe Flow pathways, both Direct & Indirect 99% attenuation between SS & IA Four Categories-of-Variables for Chlorinated VOC (CVOCs ) 1 (x,y,z,t) Stack effects Wind effects Mixing in indoor air and inhalation Metrics (ITS) 4 - Driving Forces Radon (Rn) Source, life limits distance Temp - Condition Indoor Air 3 - Building factors #3 (=100x #2) >90% Cracks Rn & CVOC Qsoil Advection Pressure - Force Building zone of influence Air streamlines 3 2b CVOC contamination Convection Radon-Tracer (Rn) Vadose zone 2b (x,y,z,t) Diffusive Migration Direct & Indirect non-natural pipe flow preferential pathways Diffusion LT Top of capillary zone Diffusion CVOC contamination 2a 2a (x,y,z,t) <10% Each has Strengths & Limitations Water Table Phase partitioning Cgwto Csoil gas 1 1 (x,y,z,t) Dissolved CVOC contamination CVOC Source Term Mod. from slide by M. Bolas, Ohio EPA, presented Jan. 2006 #1 Bottom Line = ONLY indoor air Conc. of CVOCs can represent ALL variables/factors involved & we want supplemental ITS measures/metrics that can represent As Many/Important As Possible Radon cannot reflect the factors in the 1% of the attenuation zone, but does the 99% zone 7
Outline of Previous & Current Studies Radon & VOCs in Indoor Air: Over Time Time Visual comparisons Statistical comparisons Time Series Regression Direction Magnitude Screening 2x2 Tables/Predictive value (of concentrations) Correlations of Percentiles (Rn:VOC) w/ continuous Rn measurements 2019 International Radon Symposium Denver, Colorado 8
Indoor CVOC & Radon & Conc. changing similarly over Time Visually apparent in SDM/ASU s & EPA SDM/ASU s & EPA- -ORD s VI houses Note: both houses had atypical preferential pipe ORD s VI houses pipe pathways Naturally-varying conditions in 2012 https://iavi.rti.org/attachments/WorkshopsAndConferences /02_Holton_Weather-Temporal-Variation-3-22-2012.pdf (EPA 2015b) 9
Statistical Assoc. of Conc. across Time Using Time Series (linear) Regression; results for Two components: 1) Direction Direction of Conc. change. (Qual.) 99% 99% (EPA-IN) 99.9% 99.9% (SDM-UT) Conc. Changing conc. direction together Changing conc. direction together Time Series LR Not practical, computationally for typical application Note Background (outdoor) Rn & TCE < Det. Limit) Magnitude Quantitative proportionality proportionality of conc 2) Magnitude conc. change 40% 40% (EPA-IN) 25% 25%- -60% 60% (SDM-UT) Sun Devil Manor (SDM), Layton, Utah ~ of change in ~ of change in TCE explained by the change in Rn Rn conc. (R conc. (R2 2 ) ) TCE Conc. Conc. the change in Not confident enough for risk decision making 10
Radon Radon conc conc. (indoor) as Indicator of TCE RME; Over Time Time ~0.7 pCi/L Only 1% of those screened out of concern by Rn were found to have elevated TCE [High Rn & High TCE] Not practical, timing chemical sample collection when Rn is elevated (>90%ile) difficult 40% of those screened in by Rn were truly positive w/ elevated TCE [=Positive Predictive Value] 1% False Negatives 1.5 ug/m3 40% True Positives 60% False Positives 0.48 ug/m3 (0.09 ppbv) 99% True Negatives 90% True Positives 10% False Positives 90th% Rn 84% True Negatives National outdoor Rn background Diagnostic (Exposure) Screening of SDM house data, statistics by Kurtz Looking for >95th% conc. of TCE, 99%of the data Indicated by non-elevated (<90th%) Rn were correctly screened out 11
How many Samples Needed How many Samples Needed to represent 95 Using ITS Positive Positive Predictive Values Lowers Sample # Needed w/ High (95% 95%) Confidence - Using ITS 95th th (& 82nd%ile) ITS- -Guided Guided IAQ samples Convenience-timed/~random Note ~3x (20) # random chance, for conf. Radon Temperature Pressure Press. Winter Temp. Radon Winter + 95%UCL Rn * (for 95th %ile cut point (solid lines) & dashed lines for * 82nd %ile (= lower conc., 0.48 ug/m3 risk #) 12 [at Sun Devil Manor VI research house (formerly ASU), Layton, UT]
Continuous Distribution of Rn Concentrations* History & picture History & picture of a building s (soil gas/vapor) Intrusion behavior Continuous Rn Rn levels are possible/practical Provides context (%iles) for few chemical grab sample events possible/practical Max. Rn & TCE peak *Rn & TCE each plotted on their respective (Y-axis) ranges observed during baseline study research house Hypothetical chemical grab sample event at scheduled interval Rn curve provides context for chemical results and how where Not from periods of concern for RME Indoor Rn & TCE at SDM-UT 2011-2012 (naturally-varying conditions) TCE conc. above reporting limit (0.011 ppbv) and Rn conc. above the lower confidence limit of the RAD7 (0.5 pCi/L) 13
2 Note: Calculated 7-day rolling using current day and values from previous 6 days; only included complete 7-day periods for analysis We also tried std. Linear Regres. of various avg-periods & %iles R2 - Proportion of the variance in TCE conc. that is predictable from variance in Rn conc., Indoor Rn & TCE at SDM-UT 2011-2012 1.8 1.6 Appears higher R2 values are found for longer (7-d) and higher percentiles of Rn 1.4 Appears the highest TCE (24-hr & 7-d) values are associated with high Rn (>90th%ile), but Not the very highest Rn. Elevated Rn (e.g., >90th%ile) appears important for knowing when VI is turned ON 24-h Daily Average 1.2 7-d Rolling Average TCE [ppbv] 7-d Rolling 90th% 1 7-d Rolling 95% R = 0.5785 Linear (24-h Daily Average) R = 0.565 0.8 Linear (7-d Rolling Average) R = 0.4482 Linear (7-d Rolling 90th%) 0.6 Linear (7-d Rolling 95%) 0.4 Look for more work in this area in the future, e.g., lagged time effects R = 0.5488 0.2 0 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 ~90th%ile -24 hr 14 Rn [pCi/L]
Calculated Percentiles Calculated Percentiles (%iles), including No-Detected (ND) values When >90th%ile Rn, almost all TCE levels >70th%ile & up to 100th%ile (Highest TCE levels) ~ 40% of the TCE levels are Non Detected At >80th%ile Rn near- lack of ND levels; Sampling for TCE when the Rn level is <80th%ile gives an 40% probability (~1/2) of finding a ND TCE value! You need to know the building s %ile of Radon conc. when chem. sample is collected to understand chemical conc. found When sampling when Rn was > 80th%, or even better >90th%, you could find much higher TCE levels 15
Rn %ile can document* Probability of finding TCE in levels of interest for regulatory decision making Prob TCE > 90%ile Prob TCE > 95%ile Rn %ile ~# Samples Needed* ~# Samples Needed* 50th % 16% 20 10% 28 75th % 31% 8 19% 13 90th % 56% 3 41% 6 95th % 75% 2 55% 4 Total Probability for all four samples 94% 81% Having Rn percentiles can allow probability of multiple samples to be combined for a (higher) total probability of having one or more samples from within the Exposure Levels of Interest for regulatory decision making *And guide/help samplers decision to analyze chemical samples or not *If all samples have the same probability of finding a sample w/ TCE above the given target %ile (w/ 95% conf.) 16
Summary Summary of Previous & Current Studies: Radon & VOCs in Indoor Air Over Time Visual comparisons Statistical comparisons Time Series Regression Not practical, computationally for typical application Direction 99 & 99.9% Magnitude ~50% Screening 2x2 Tables/Predictive value (of conc.) Not practical, sample timing difficult Percentiles correlations (Rn:VOC) Getting a building-specific baseline/history for indoor Rn is somewhat practical/possible Knowing the percentile (%ile) of Rn intrusion (from baseline) occurring when a chemical (VOC) sample is collected; Could give us context & indication for knowing how probable the chemical sample(s) collected are representing exposure levels of interest (e.g., RME, ~95th%ile) Can add the probabilities of multiple samples for probability/confidence > any individual sample Time 2019 International Radon Symposium Denver, Colorado 17
Conclusions: We need continuous Rn data We need continuous Rn data to know the percentile of intrusion and meaning of occasional chemical sample results* Grab sampling 1-day indoor air for chemical VI assessments, at: Some random Time is: Unlikely to find RME (i.e., >95th%ile) - & Would Not know it if you did The meaning and context of even multi. grab sample results will be unclear We need to know WhenVI is turned ON Pre-screening with Rn before VI chemical samples will maximize the meaning by placing the chemical sample within the soil gas intrusion history of the building Makes possible Quantitative probability/confidence levels for small sample #s *based on SDM house, more buildings being studied in Oct. workshop 18
Continuous Rn Continuous Rn is an unparalleled Supplemental/M Multiple L Line of E Evidence for occasional chemical VI sampling unparalleled Questions? THANK YOU 1-day Workshop (w/ webinar access) Oct. 22, 2019 https://www.aehsfoundation.org/East-Coast- Conference.aspx For email list contact Robert Truesdale rst@rti.org 19
