United States
Environmental Protection
Agency
Air and Energy Engineering
Research Laboratory
Research Triangle Park, NC 27711
Research and Development
EPA/600/S8-91/210 Dec 1991
w EPA Project Summary
Development of Alternate
Performance Standard for Radon
Resistant Construction Based on
Short-term/Long-term Indoor
Radon Concentrations
Ashley D. Williamson, Susan E. McDonough, and Charles S. Fowler
This report gives results of a study
of short- and long-term variations In
radon concentration In approximately
80 houses In Florida. The study In-
volves year-long comparative sampling
using the most common radon mea-
surement technologies. This study, pro-
viding the most detailed database of
which we are aware, addresses the time
variation of Indoor radon concentra-
tions In a significant number of occu-
pied houses having moderately el-
evated radon concentrations. In these
study houses, the degree of variation
of radon varies roughly in proportion
to the long-term mean concentration,
with a coefficient of variation within a
calendar quarter of approximately 25%
of the quarterly mean, and a coefficient
of variation within a year of approxi-
mately 35% of the annual mean. This
pattern of variability supports the use
of multiplicative models to fit the varia-
tion and to predict intervals of;confi-
dence for long-term averages based on
short-term measurements. This study
Indicates a distinct seasonal effect on
the average radon, with quarterly aver-
ages relative to the annual average In-
creasing in the order of spring (82%)
< summer (93%) < fall (97%) < winter
(123%). These models have been used
to develop threshold values for the per-
formance criteria of the proposed Build-
Ing Standard for Radon-Resistant Con-
struction for the State of Florida.
This Project Summary was developed
by EPA's Air and Energy Engineering
Research Laboratory, Research Tri-
angle Park,, NC, to announce key find-
Ings of the research project that Is fully
documented In a separate report of the
same title (see Project Report ordering
Information at back).
Introduction
Many studies have been conducted na-
tionwide to determine the extent of el-
evated indoor radon concentrations in the
U.S. Most of these studies have employed
short-term screening techniques, ranging
from 1 to 90 days, using either open-
faced or diffusion barrier charcoal canis-
ters or alpha track detectors according to
EPA protocols. Several factors complicate
the relation between short-term measure-
ments and long-term indoor radon con-
centrations. Primarily, radon concentrations
have been shown to vary considerably
with time; diurnal and seasonal variations
are prominent in many houses and sug-
gestions of weekly or other periods have
been made. Some of these variations
clearly correlate with house construction
or occupant behavior patterns, such as
heating and air-conditioning equipment and
usage patterns, and the use of natural or
mechanical ventilation during mild weather.
The physical factors affecting the entry of
radon into buildings are understood, at
least in principle. However, no general
means of computing the effect of these
factors on resulting levels of indoor radon
has been demonstrated. Added to this
uncertainty due to fluctuations in actual
radon concentrations is a smaller mea-
surement of uncertainty due to the radon
measurement devices themselves. Each
major measurement technique has techni-
cal shortcomings and limitations in the
Printed on Recycled Paper
-------
possible sampling periods. This report
gives results of a study of short- and long-
term variations in radon concentration in
about 80 houses in Florida. The study
involves comparative sampling using the
most common radon measurement tech-
nologies during the past year. To our
knowledge, it is the most extensive study
of "its kind.
Conclusions and
Recommendations
This study, providing the most detailed
database of which we are aware, ad-
dresses the time variation of indoor radon
concentrations in a significant number of
occupied houses having moderately el-
evated radon concentrations. In these
study houses, the degree of variation of
radon varies roughly in proportion to the
long-term mean concentration, with a co-
efficient of variation within a calendar
quarter of approximately 25% of the quar-
terly mean, and coefficient of variation
within a year of about 35% of the annual
mean. This pattern of variability supports
the use of multiplicative models to fit the
variation and to predict intervals of confi-
dence for long-term averages based on
short-term measurements. These models
have been used to develop threshold val-
ues for the performance criteria of the
proposed Building Standard for Radon-
Resistant Construction for the State of
Florida.
This study indicates a distinct seasonal
effect on the average radon, with quar-
terly averages relative to the annual aver-
age increasing in the order of spring (82%)
< summer (93%) < fall (97%) < winter
(123%). One recommendation for further
study includes follow-up studies on the
seasonal effect. The seasonal trends seen
in these data are clearly beyond experi-
mental uncertainty; they only reflect the
trends within a single year. Follow-on stud-
ies (using only quarterly alpha track de-
tectors) over 1 or more additional years
would allow'replication of the seasonal
data and evaluation of the reproducibility
of the trend in different years.
Background and Approach
The only known significant prior study
of time variability of concentrations in
Florida houses covered the period 1987-
88. This study included a year-long mea-
surement program in 37 houses in the
Gainesville, FL area. Short-term average
radon was measured by charcoal cannis-
ters deployed once a month in each house,
and long-term average concentrations
were measured using alpha-track detec-
tors deployed for the 1- year study. Con-
siderable variability was noted from month
to month, and there were clear sugges-
tions of a seasonal effect, with November-
March elevated, April and May depressed,
and the remaining months intermediate.
On a quarterly basis, these results trans-
lated into the relationship summer < spring
< fall < winter. The data reduction meth-
ods used in this study were developed
from those of the Gainesville study.
Since the goal of this project was to
support a statewide building standard, the
scope of this project was expanded in
several ways beyond that of the Gainesville
study: (1) this study was to be statewide
in scope, so it included four regions ex-
pected to span the climate and geological
variations in the radon-prone portions of
the state; (2) the study was more compre-
hensive in the number of devices em-
ployed, including candidate samples not
in widespread use at the time of most of
the earlier studies; and (3) the study was
structured to provide exploration or con-
trol for house structural and operational
variables which can potentially affect ra-
don entry.
The short-/long-term study was initiated
in November 1989. Originally, the project
work plan called for 40 houses to be se-
lected for the project. The houses were
selected based on the characteristics iden-
tified as common to Florida housing stock
'such as:
- Single-family, single-level, slab-on-
grade housing with forced air heating
and cooling;
- Low to moderate radon levels (2 to
20 pCi/l);
- Unmitigated (although two previously
mitigated houses were selected for
comparison in Polk County);
- Air handler characteristics: split be-
tween houses with air handler inside
building shell (closet) and outside shell
(garage, attic); and
- Natural ventilation: attempt to select
about half of the houses which never
use natural ventilation for cooling.
Candidate houses were screened and
10 study houses selected in each of four
regions in the state, including, Alachua,
Dade, Leon, and Polk Counties. In Febru-
ary 1990 the project increased in scope to
include up to 20 more houses in each
county. The same selection criteria were
employed in identifying the additional
houses for the study. All houses are single-
story, single-family, slab-on-grade houses.
Regional data were collected by the fol-
lowing investigators:
- Alachua County: C. E. Roessler, Uni-
versity of Florida;
- Dade County: Howard Moore, Florida
International University;
- Leon County: James Cowart, Florida
State University; and
- Polk County: Susan McDonough,
Southern Research Institute.
In order to develop a predictive relation-
ship between short-term measurements
and long-term (annual) average concen-
trations, a variety of short- and long-term
sampling devices were deployed in each
study house. The devices selected and
their deployment periods are:
- Alpha-track detector (ATD) (deployed
for 1 year);
- Alpha-track detector (deployed for 3
months each; four per house);
- Low-sensitivity Electret Passive Envi-
ronmental Radon Monitor (EPL) (read
on about 4-week intervals);
- High-sensitivity Electret Passive En-
vironmental Radon Monitor (EPS)
(read on about 1- and 2- week inter-
vals);
- Seven-day Charcoal Canisters (CC7)
(1 week per month per house);
- Two-day Charcoal Canisters (CC2)
(one 2-day deployment per month);
and
- Pylon AB-5 with Passive Radon De-
tector (rotated between houses about
4 weeks per house).
Results and Discussion
Sampling was conducted in the study
house set between November 1989 and
early March 1991. The median quarterly
radon concentration in the study houses
is 3.7 pCi/l, with 35% between 2 and 4
pCi/l, 19% between 4 and 6 pCi/l, and
26% above 6 pCi/l. This distribution is
desirable for the goals of the study in
several ways. First, by minimizing the num-
ber of measurements below 2 pCi/l (about
19% here), all devices were generally able
to operate above their detection limits and
avoid the complications of censored data.
More significantly, most of the houses fall
in the zone near 4 pCi/l in which greatest
uncertainty exists in predicting from a
short-term measurement whether the long-
term average radon will be above or be-
low the 4 pCi/l Department of Health and
Rehabilitation Services (DHRS) standard.
ft is instructive to observe the relation-
ship between the quarterly mean radon
concentrations and the standard deviation
of the corresponding set of EPS measure-
ments in that house and quarter. As is
apparent from plots of these data, the
standard deviation has a clear positive
correlation with the mean and can be fit
(r2 = 0.59) to the linear trend: STD =
mean* (0.2466) with a standard error of
0.0065 for the constant of proportionality,
and the intercept not significantly different
-------
from zero. A corresponding plot using an
annual averaging period gives regression
parameters of r2 = 0.78, slope = 0.357
± 0.013, and intercept not significantly dif-
ferent from zero. The roughly linear corre-
lation of the standard deviation with the
mean is significant. This assumption is
used in multiplicative models such as that
of the Gainesville study and other fitting
techniques that use logarithmic transfor-
mations for stabilization of variance.
Comparison of Study Devices
As might be expected, the correlations
between the data from different devices in
the same house are high. Simple linear
regressions for each pair of data sets were
performed. Standard linear regressions
show r2 values above 0.95, intercepts not
significantly different from zero, and con-
stants of proportionality ranging from 7%
lower (for CC2 measurements) to 8%
higher (ATD measurements) than the EPS
averages. Thus, while some degree of
scatter remains, the comparability of dif-
ferent devices is high and well within the
accuracy objectives for each device indi-
vidually.
Seasonal Trends ,
A key issue in the variability of radon
measurements is the seasonal component
of this variability. To the extent that radon
in a structure varies with a short period
(hours, days, or weeks), multiple short
period measurements (multi-day) or single
medium period measurements (weeks) can
average the fluctuations and give good
predictions of the long-term average. How-
ever, to the extent that a systematic sea-
sonal trend is present, increasing the num-
ber or duration of short-term measure-
ments can reach a point of diminishing
returns unless the general form of the
seasonal effect can be predicted by other
means. Without such a priori knowledge
of the seasonal trend, this trend defines a
minimum level of uncertainty for estimates
of the annual average by any short-term
measurement strategy.
In order to assess the seasonal trends
in this study, quarterly average radon con-
centrations in each house were normal-
ized to the annual average for that house.
The spring quarter radon is tower in es-
sentially all houses, with a mean quarterly
concentration of 82% of the annual aver-
age. The effect is fairly consistent be-
tween houses, with half the houses show-
ing quarterly ratios between 65 and 90%
of the annual. On the other hand, winter
quarter radon was elevated in most houses
(mean concentration 1.28 times the an-
nual average), but the degree of this ef-
fect varied considerably between houses
(for winter the central half of the popula-
tion extended from 1.06 to 1.45 times the
annual mean). Both the elevated radon
and greater house-to-house variability are
evident in two winter seasons a year apart.
The seasonal trends in radon concentra-
tion seen in these data are qualitatively in
accord with the results of the Gainesville
study wrth the same winter/fall elevation
and spring/summer minimum. The only
difference in these results is the reversal
of the spring/summer trend in the present
study.
Some of the features of the seasonal
variation can be noted by inspection of
the seasonal variability in individual
houses. While the average radon trend
follows the pattern spring < summer < fall
< winter, most individual houses do not.
Of 65 houses with complete data for four
full quarters only 18 fall into a class which
has a winter maximum and spring mini-
mum. The most common class (25 mem-
bers) shows the winter maximum and sum-
mer minimum which is typical of other
regions of the country (ironically, this pat-
tern is dominant in Dade County). The
third most abundant pattern (eight houses)
shows a summer maximum and spring
minimum. The remaining houses do not
appear to fall into groups of any signifi-
cance. Somewhat surprisingly, the aver-
age coefficient of variance remains es-
sentially constant through the four sea-
sons in the range of 24-28% relative to
the quarterly average.
Prediction of Long-term
Average from Short-term Data
Several slightly different approaches to
the prediction of long-term averages from
short-term concentrations were investi-
gated in the course of this study. The first,
which was adapted from the Gainesville
study, relies on the assumption that the
relative variability of radon concentration
is on the same order in all houses in the
state (at least for houses in the 2-8 pCi/l
range). This assumption is inherent in the
use of radon concentrations only as nor-
malized to the long-term concentration in
the fitting process. Other conventional re-
gression approaches were considered
which incorporate long-term radon explic-
itly as a variable and typically use addi-
tional parameters. These models make
the slightly different assumption that the
absolute variability of radon in the houses
in the 2-8 pCi/1 range in this study is
representative of houses in the state. Since
our data lies in this range, the two as-
sumptions are effectively indistinguishable.
All approaches were found to give similar
results in this case, so the simplest model
was used.
In order to describe the selected ap-
proach, we will use a simplified form of
the linear effects model described above.
First, we assume that we can apply a log-
normal effects model; that is, that all ef-
fects are multiplicative and that short-term
measurements of radon concentration in
each house vary about the long-term av-
erage with a standard deviation propor-
tional to this mean. We define the quanti-
ties
C°,= STi/LT,and
A,j = In (C°|j), where in effect C° be-
comes a dimensionless relative
radon concentration and A is its
logarithm.
Our model becomes
An = U + 3j+ 6|j
where
u = an overall mean of A,,
a, a> a group mean of effect of any
subgroups found to be signifi-
cant, and
e,j = random error (assumed normally
distributed in the log-transformed
variable system).
In terms of measured variables,
where
» In(STj), and
• In (LT,), as described previously.
Thus, our model can also be written in
the form used for the Gainesville study,
X|, - Y, = u + a, + ey
In the event that other groupings are
not treated as significant (which seems
justified except for the possibility of sea-
sonal corrections), the a, term disappears.
The simplest predictive assumption is that
and that the residuals are normally distrib-
uted.
This is in essence the approach used
for the Gainesville study. It can also be
viewed as a very simple regressional ap-
proach where only the intercept is fit.
Using the methodology described above,
the data in the present study were used
for estimation of probability ranges for long-
term average radon, given single short-
term radon measurements. For a given
pool of data of short- and long-term aver-
age radon concentrations, the quantities
u, VAR(u), and VAR(A) are calculated,
where X^ and Y, are defined by house for
each combination of sampler and sample
period, A, is calculated as above,
u is the mean of the quantity (A*)
over all measurements (i and j),
VAR (u) is the square of the standard
error of u given by the variance (s2)
of the sample A, divided by the
number of samples N, and
-------
VAR(A) is the within-house sample
variance of A, as determined from
standard ANOVA methods.
These quantities were then used in a pre-
dictive sense as follows. For any postu-
lated long-term reference value LTR, the
probability p that the long-term average
radon will exceed LTR will fit the relation-
ship
Zp- [IntST/LTn) - U]/[VAR(0)+VAR(A)]1/2
where Zp« is the p quantile of the stan-
dard normal distribution. Rearranging and
redefining the probability, if we wish to
find the short-term average corresponding
to a given probability that the long-term
average will not exceed a given reference
long-term average (that is, an upper confi-
dence limit), we compute the relation
ST- LTn exp[0 - ^ [VARCOH-VARfA)]"2]
Plots analogous to these relationships are
used for the Gainesville data.
These relationships can also be applied
to any homogeneous subsets of the study
pool. If quarterly data are evaluated, in
principle the seasonal factor would be ab-
sorbed into the bias factor Q and the VAR
(A) for the quarterly population would ap-
ply (recall that this value is smaller for our
quarterly averages). These analyses are
not included here, partly due to the arbi-
trariness in defining quarterly boundaries.
In order to compare the difference in
the predictive strength of the different
short-term sampling techniques used in
this study, these calculations were ap-
plied to the data for all short-term sam-
plers. The EPS data were further subdi-
vided, since these samplers were oper-
ated over different time periods. Likewise,
averages of continuous radon monitor data
from a subset of the houses over three
different data averaging periods were com-
puted as a comparison. The data from the
non-continuous samplers were further sub-
divided into three sets based on house
ventilation characteristics. The first analy-
sis was performed on all 65 unmitigated
houses which had complete data over the
period from February 1990 to February
1991. A second calculation was run on
the subset of 26 houses which never use
natural ventilation (open windows) for cool-
ing. A third calculation was performed with
the closed houses and the eight houses
which "rarely" opened their windows
(nominally < 5% of the time). Table 1
contains upper confidence limit calcula-
tions for these data sets for several prob-
ability values.
Comparison of the data shows very little
difference between the three groups of
houses. This suggests that the variability
due to the use of natural ventilation status
is relatively minor compared to the vari-
Table 1. Threshold Short-term Radon Concentrations (in pCi/1) Corresponding to Differing Levels
of Confidence That Long-term Average Does Not Exceed 4 pCi/l
Device/days* 0.5 0.6
All Houses (70), Last 4 Qtrs
Confidence
0.7 0.75 0.8
0.85
0.9
Device/days* 0.5
Closed Houses (26), Last 4 Qrtrs
Confidence
0.6 0.7 0.75 0.8
0.85
0.9
Device/days* 0.5
Mostly Closed Houses (39), Last 4 Qrtrs
Confidence
0.6 0.7 0.75 0.8 0.85
0.9
(*) crm = continuous radon monitor
eps = short-term EPerm
epl = long-term EPerm
cc2 s open face charcoal canister (2 day)
cc7 = diffusion barrier charcoal canister (7 day)
0.95
crm-1
crm-7
crm-1 4
eps-7
eps-14
epl-14
epl-28
cc2
cc7
3.82
3.98
4.00
3.81
3.46
3.75
3.62
3.66
3.77
3.54
3.79
3.83
3.44
3.12
3.34
3.32
3.27
3.42
3.25
3.58
3.66
3.08
2.78
2.95
3.02
2.90
3.08
3.10
3.48
3.57
2.90
2.61
2.75
2.87
2.72
2.91
2.95
3.36
3.46
2.71
2.44
2.55
2.71
2.52
2.73
2.77
3.23
3.35
2.51
2.25
2.33
2.53
2.32
2.53
2.57
3.07
3.21
2.27
2.03
2.08
2.32
2.08
2.30
2.30
2.86
3.02
1.96
1.75
1.77
2.05
1.77
2.01
0.95
eps-7
eps-14
epl-14
epl-28
cc2
cc7
3.81
3.58
3.67
3.48
3.67
3.84
3.43
3.26
3.29
3.18
3.30
3.52
3.08
2.94
2.92
2.89
2.95
3.20
2.90
2.78
2.74
2.75
2.77
3.04
2.71
2.61
2.55
2.59
2.58
2.86
2.50
2.43
2.34
2.42
2.38
2.68
2.26
2.21
2.11
2.21
2.15
2.46 I
.96
.93
.80
.95
.85
2.17
0.95
eps-7
eps-14
epl-14
epl-28
cc2
cc7
3.81
3.55
3.67
3.64
3.68
3.83
3.46
3.22
3.29
3.36
3.31
3.52
3.13
2.90
2.92
3.08
2.95
3.21
2.95
2.74
2.74
2.94
2.77
3.05
2.77
2.56
2.55
2.78
2.58
2.89
2.58
2.38
2.34
2.62
2.38
2.71
2.35
2.16
2.11
2.42
2.14
2.49
2.05
1.88
1.80
2.16
1.84
2.21
ability from other causes. If this is gener-
ally true, these results may be generally
applicable to houses with a wide range of
ventilation practices.
The data collected and analyzed to date
in the FRRP Alternate Performance Stan-
dard project have been incorporated into
thresholds in the recommended code cur-
rently in the rule making process. In sum-
mary, the assumptions and philosophy that
have been used to develop the standard
are:
(1) The goal of a building standard is to
reduce the long-term average (annual or
longer) radon concentration in the build-
ing to be occupied.
(2) Short-term measurements in the
building will have uncertainty due to (a)
measurement accuracy of the device used
and (b) variability of the indoor radon con-
centration with time. Uncertainty due to
the second effect can be reduced by in-
creasing the measurement time.
(3) A performance test must be com-
pleted and the results known prior to oc-
cupancy for practical enforcement of a
construction performance standard. In view
of the time pressures on the construction
industry, the measurement period in a
workable performance standard will prob-
ably be a compromise between the sched-
ule needs of the builder and the uncer-
tainty of the radon measurement.
(4) The radon standard set by DHRS is
assumed to remain at 4 pCi/l.
(5) The threshold for passing a short-
term performance test should be conser-
vative; i.e., low enough to ensure that
-------
(within a confidence level to be deter-
mined by the state) the building will not
have a long-term average radon concen-
tration in excess of the MRS standard if a
short-term performance test gives results
less than the threshold.
(6) Thresholds of this type are being
developed for several device/measurement
period combinations, so that the builder
may elect to use a shorter duration test
with a lower pass/fail threshold in order to
achieve the same confidence that the
building will comply with the standard.
(7) Similarly, the project data have been
analyzed to allow the state to choose
thresholds based on different levels of con-
fidence according to its regulatory priori-
ties and the standard ultimately to be set
byHRS.
(8) If the effects of the time of year on
indoor radon concentration can be quanti-
fied, an algorithm to account for seasonal
effects can be built into the threshold cri-
teria. If such an algorithm cannot be de-
veloped, the variability due to season must
be included in the total variability of radon
measurements in determining the thresh-
olds for all times of the year. (This ap-
proach has been taken in the current rec-
ommendations.)
The code language incorporates the
possibility of several combinations of de-
vice and measurement period. No provi-
sion for incorporating average seasonal
variations in radon data is included, due
to lack of sufficient data on the form of
systematic seasonal differences.
A. Williamson, S. McDonough, and C. Fowler are with Southern Research
Institute, Birmingham, AL 35255-5305
David C. Sanchez is the EPA Project Officer (see below).
The complete report consists of two volumes, entitled "Development of
Alternate Performance Standard for Radon Resistant Construction Based
on Short-term/Long-term Indoor Radon Concentrations":
"Volume 1" (Order No. PB92-115 211/AS; Cost: $19.00, subject to change)
is the technical report.
"Volume 2" (Order No. PB92-115 229/AS; Cost: $19.00, subject to change)
contains the appendices.
Both volumes of this report will be available only from:
National Technical Information Service
5285 Port Royal Road
Springfield, VA 22161
Telephone: 703-487-4650
The EPA Project Officer can be contacted at:
Air and Energy Engineering Research Laboratory
U.S. Environmental Protection Agency
Research Triangle Park, NC 27711
&U.S. GOVERNMENT PRINTING OFFICE: 1992 - 648-080/40118
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