A health risk assessment model for homeowners with multiple pathway radon exposure

Environment

Pergamon

International, Vol. 22, Suppl. 1, pp. S739-S747,1996 Copyright 01996 Elsevier Science Ltd Printed in the USA. All rights reserved 0160-4120/96 $15.00+.00

PI1SO160-4120(96)00177-8

A HEALTH RISK ASSESSMENT MODEL FOR HOMEOWNERS WITH MULTIPLE PATHWAY RADON EXPOSURE Alan J. Siniscalchi,

Sarah J. Tibbetts, Regine C. Beakes, and Xaviel Soto

State of Connecticut Department of Public Health, Hartford, CT 06106, USA

Margaret A. Thomas and Nancy W. McHone State of Connecticut Department of Environmental Protection, Hartford, CT 06106, USA

Stanford Rydell U.S. Environmental

Protection Agency, Region I Office, Boston, MA 02203, USA

El 9512-422 M (Received 15 December 1995; accepted 21 December 1995)

The State of Connecticut Department of Public Health and Department of Environmental Protection have conducted statewide measurements of radon exposure in Connecticut homes since 1985. Radon exposure data on over 5000 homes and 700 wells were measured, digitized, and entered into the statewide geographic information system (GIS) database. These data were compared with information on Connecticut bedrock geology, surftcial materials, and aeroradioactivity. The results of these GIS-based analyses revealed important information on the radon potential of various geographic areas in Connecticut. These geographic correlates alone are not sufftcient to predict residential exposures for individual homes. Individual household radon exposure concentrations also vary tremendously by housing characteristics, ventilation/heating/cooling rates, water use, and occupancy behaviors. A computer mode1 was developed that can predict annual and lifetime lung cancer risk from soil gas, water, and other sources of radon exposure. The model incorporates GIS-based correlates together with household/occupancy, smoking, and other lifestyle behavioral factors in its risk assessment calculations. The resulting data, generated with and without Monte Carlo analysis, are useful in: refining statewide risk models, confirming radon exposures in retrospective epidemiology studies, and assisting individuals in assessing radon risks in their homes. Copyright 01996 Elsevier SC;WW Lfd

INTRODUCTION Accurate population health risk assessments require comprehensive characterization of all exposure routes. The State of Connecticut Department of Public Health (DPH) Radon Program completed a number of statewide risk assessment exercises designed to predict statewide radon mortality. These exercises utilized an increasingly comprehensive series of radon exposure data. Most of these data were derived from the three statewide residential radon surveys conducted in Connecticut (Siniscalchi et al. 1990) summarized in Table 1 and the school radon surveys (Siniscalchi et al. 1992a; 1994~)

summarized in Table 2. However, numerous other data sources were incorporated into our geographic information systems (GIS)-based exposure assessment data files. For example, extensive airborne and car-borne aeroradioactivity data from the National Uranium Research Evaluation (NURE) survey and other sources (Popenoe 1966; USAEC 1969; USERDA 1976; Thomas 1989) were included. These various data sources, summarized in Table 3, were used to estimate the radon potential of each of Connecticut’s 169 municipalities. s739

s740

A.J. Siniscalchi et al.

Table 1. Summary of residential radon surveys conducted in Connecticut. Connecticut radon survey (202 homes)

EPA-Conn. radon survey (1157 homes)

Household testing program (3409 homes)

Location

%a

GMb

%

GM

%

GM

Basement

NTC

NT

19

78 (2.1)

21

78 (2.1)

11

48 (1.3)

NT

NT

10

48 (1.3)

Lived-in areas

a = percent of homes with radon concentration > 148 Bq mS3(4 pCi L-‘) b = geometric mean radon concentration in Bq mS3(pCi L-‘) ’ = not tested Table 2. Summary of school radon surveys conducted in Connecticut.

Radon in air

Phase

No. towns (No. schools) Tested

I

8 (63)

II

8 (37) 9 (38) 7 (66)

III

IV

No. & % of schools 2 148 Bq mW3

35 (56%) 21(57%) 26 (68%) 24 (36%)

No.&% rooms 2148 Bq mm3

Radon in water No. towns (schools)

No. & % schools

with wells

211 100Bqm‘3

3 (II)

10 (91%)

258 (10%) 106 (9%) 292 (12%) 103 (5%)

1 (1)

1 (100%)

3 (8) I (2)

8 (100%) 2 (100%)

Table 3. Data parameters used for calculating radon potential of Connecticut municipalities. 1. Connecticut statewide residential survey data 2. Additional residential survey measurements 3. School Testing Program data 4. Residential well measurements 5. Aeroradioactivity data 6. Information on bedrock geology and surficial materials

Initial analysis of these data revealed that temporal variation patterns of radon exposure can greatly effect both short-term and long-term assessments. These temporal variation patterns were first characterized in well water(Dupuyetal.1992; McHoneetal. 1992; Siniscalchi et al. 1992b). Other patterns became apparent once the radon in air studies was analyzed (Siniscalchi et al. 1990; 1992a). These temporal variations must be well characterized if accurate exposure assessments are to be generated from existing radon in air and water sampling data. Accurate assessment of risk is a major concern with many callers to the Radon Program Office. Some callers who have recently heard of high radon results inquire about their actual lifetime risk for development of lung cancer. Although literature addressing these risks has

been prepared (Siniscalchi 1990; DPH et al. 1992), callers are confused over what appear to be conflicting newspaper reports which summarized various recent radon residential epidemiology studies (Schoenberg et al. 1990; Alavanja et al. 1994; Letourneau et al. 1994; Pershagen et al. 1995). Other callers ask about their lifetime radon exposure and wish to understand their personal risk. A model that can help homeowners recreate historic radon exposure can assist them in understanding their actual risk. Moreover, a radon exposure prediction model can be extremely useful in both refining our statewide risk assessment exercises and targeting educational programs to areas and population at high risk of radon exposure (USEPA 1993~; Siniscalchi et al. 1994b, 1994e).

Health risk assessment model for homeowners with multiple pathway radon exposure

This paper reviews recent efforts in GIS-assisted exposure assessment and risk assessment modelling. The value of these recent modelling efforts in refining statewide risk assessment projections, predicting historic exposures, and calculating individual risk information is provided. MATERIALS

AND METHODS

The State of Connecticut

Radon Program completed extensive testing of Connecticut homes as part of three statewide surveys. These surveys tested almost 1% of the state’s single-family homes. The results of these surveys are summarized in Table 1. The first survey, the Connecticut Radon Survey, utilized alpha-track (AT) detectors in 202 homes statewide. The devices were placed in the lowest living area during 90 d of the 1985-86 or the 1986-87 heating season. Water samples were collected and analyzed using the standard U.S. Environmental Protection Agency (EPA) method (USEPA 1978) for homes served by private wells (Siniscalchi et al. 1990). In the second survey, known as the EPA/Connecticut Radon Survey, granular activated charcoal (AC) radon testing devices were placed in the lowest livable areas during various 4-d intervals in the 1986-87 heating season. Results were obtained on 1157 homes located in 167 of the 169 Connecticut municipalities. Important information was also gathered on house construction and heating methods during this survey (Siniscalchi et al. 1990). The third statewide survey of 3409 homes was the most comprehensive. This survey, known as the Household Testing Program (HTP), utilized two AC devices as part of screening tests conducted in the 1987-88 heating season. One device was placed in the basement or the lowest living area. A second AC device was concurrently placed in the lowest living area. A detailed questionnaire was administrated to the homeowners to obtain information on drinking water source, house age, construction type, and heating system. Long-term (12 month) AT devices were provided to households where either the lowest living area screening values exceeded the 148 Bq me3 (4 pCi L-‘) guideline, or lowest livable area radon values exceeded 740 Bq mm3(20 pCi I;’ ) (Siniscalchi et al. 1990). An additional statewide survey of Connecticut schools has been underway since 199 1. This survey, known as the School Testing Program (SIP), has conducted radon tests using EPA (USEPA 1989; 1993b) and Radon Program methods (CT DPH 1991; Siniscalchi et al.

s741

1992a). To date, over 2 17 schools in 38 Connecticut municipalities have been evaluated (Table 2). Additional studies of radon in school water have been conducted (Siniscalchi et al. 1994~). Appropriate quality assurance/quality control (QA/ QC) procedures were conducted to assure the accuracy of all residential and school surveys. These procedures were conducted in accordance with DPH and U.S. EPA QA/QC documents (CT DPH 1991; US EPA 1978, 1989, 1992b, 1993a). Results of all three household surveys were digitized and entered into the statewide GIS data system which is primarily based at the State of Connecticut Department of Environmental Protection (DEP) Natural Resources Center (NRC). This system uses Arc Info TMSoftware (Environmental System Research Institute Redlands, CA). This GIS-based radon potential information was used to calculate geometric mean radon in air and water data for all Connecticut municipalities. These GIS-based exposure modelling efforts were previously described (Siniscalchi et al. 1994a; 1995). The radon exposure data, aeroradioactivity data, information on the radon potential of bedrock units, surficial materials, and other parameters are also being utilized by the DEP NRC to provide municipality based radon potential ranking information and a statewide radon potential map. The preferred risk model developed by the U.S. National Academy of Sciences BEIR IV Committee (NRC 1988) and modified by the U.S. EPA (1992a) was used as the basis for the statewide risk assessment modelling. Both the original BEIR IV model and the EPA modifications have undergone extensive review and uncertainty analysis (Puskin 1992; NIH 1994). Various data parameters and other information were used in conducting the statewide risk assessment modelling (Siniscalchi et al. 1994d). These data, listed in Table 4, were obtained from the Connecticut GIS database and statewide residential surveys described earlier, statewide housing information (State of Connecticut Office of Policy and Management 199 l), and smoking prevalence data (CT DPH 1994). Equations used in these statewide modelling exercises are listed below. Radon exposure data obtained in basements or ‘lowest livable area’ during heating season screening studies is converted into living area concentrations using the following equations. These equations were derived from linear regression analysis of the statewide radon surveys described earlier. Linear regression analysis was conducted using SASM (SAS Institute,

S742

A.J. Siniscalchi et al.

Table 4. Data parameters used for conducting risk assessment of radon exposure in Connecticut, 1. Geometric mean radon in residential air concentration per town 2. Geometric mean radon in public well water concentration per town 3. Geometric mean radon in private well water concentration per town 4. Percent of town population on public water 5. Percent of town population on private water 6. Geometric mean school radon in air and water concentration 7. Percent of school population on public water 8. Percent of school population on private well water 9. Number of people per town living on or above the 3rd floor 10. Number of people per town living below the 3rd floor Il. Percent of town population who are current smokers 12. Percent of town population who are former smokers 13. Percent of town population who have never smoked 14. Various house construction characteristics

15. Heating, cooling, and water use behaviors Inc., Gary, NC) and Epi Info Version 5 (USD, Inc., Stone Mountain, GA).

N_IA ] =0.6193

[RnSTBA]

[Rn AALA]=0.49208T[Rn,,,,]

+ 0.675

where, = short-term basement area heating season concentrations; = short-term lowest living area heating season RnLLA concentrations; Rn AALA = annual average living area concentrations.

%TBA

Statewide risk assessment projections can then be calculated by the following four equations. These calculations were modified from equations used in the U.S. EPA Technical Support Document for the 1992 Citizen’s Guide to Radon (USEPA 1992a). Radon exposure data are initially calculated in U.S. units (pCi L“), then converted to International units (Bq m”). The resulting concentration exposure and risk factors are calculated for each Connecticut municipality and totaled using a large spread sheet format. Radon in air concentrations for various housing types served by public or private water supplies are calculated by Eq. 1: ‘public

= [Rn*irl

‘private

=

C x3rd

+ [Rnpubliewater]

‘lo

Ooo

[Rn,iJ + Mprivatewater] / 10 000 floor = [Rnpubliewatcr]

’ * ’ Ooo

(1)

where, = concentration of radon-in-air to which public water users are exposed (from soil gas and water sources); = concentrations of radon-in-air to which Cprivate private water users are exposed (from soil gas and water sources); C >3rd floor = concentrations of radon-in-air to which residents living at or above the third floor are exposed (assuming no exposure to soil gas derived radon but exposure to radon in public water); = radon in air measurements (geographic D%ir] mean by town); [Rnpublic water ] = radon in public water measurements (geographic mean by town or 7.4 kBq me3 (200 pCi L-‘) if town data is not available); P$rivate water] = radon in private well water measure ments (geographic mean by town or 111 kBq mm3(3000 pCi L-l) if town data is not available); = the conversion factor for outgassing from 10 000 radon in water (every 370 kBq mm3(10 000 pCi L-l) contributes 37 Bq rns3 (1 pCi L-l) to the air). ‘public

These derived radon concentrations are converted to radon exposure in Eq. 2, below, by adding both equilibrium and occupancy factors. The equilibrium factor is used to account for the fraction of the alpha radiation dose lost following deposition on .indoor surfaces. The occupancy factor is the estimated time residents spend in their home (75%) or school (25%).

s143

Health risk assessment model for homeowners with multiple pathway radon exposure

Npub*ic = (Rto~*)

(Epublic) (npubljc)

Epublic =

CpU,,JEF(O.O 1 WL/pCi/L)] [OF (5 1.6 WLM/WL -y)] Eprivstc= CP,.,,~[EF(O.O1 WLlpCi/L)] [OF (5 1.6 WLM/WL -y)] E >3rdfloor =

Npri”*te = (Rtown (Epri”ate) (npriv*J

N

r3rd floor = (Rtown)(E>3rd

floor)@

(4)

;?3rd floor)

C z~rdnoor[EF(O.O1 WLlpCi/L)] [OF (5 1.6 WLM/WL -y)] where, N public

(2) where, E public

= radon inhalation exposure for public water user (WLM/y); = radon inhalation exposure for private water Eprivate user (WLM/y); E r3rd floor = radon inhalation exposure for residents living at or above the 3rd floor (WLM/y); = concentration of radon in air for public C public water users (from Eq. 1); = concentration of radon in air for private Cprivate water users (from Eq. 1); C 2 3rd floor = concentration of radon in air for residents living at or above the 3rd floor (fi-om Eq. 1); = equilibrium factor (assume 50%); EF = occupancy factor (75% for residential OF exposure, 25% for school). A risk multiplier was calculated based on smoking prevalence. Both the 1990 national smoking prevalence rates and Connecticut-specific rates were used in Eq. 3, below. Equation and risk factors used are from the Technical Support Document for the 1992 Citizens Guide (USEPA 1992a).

R town=224 [2.33 (PCS) + 1.03 (Pfs) +0.121 (Pns)] (3) where, R town PCS

= risk multiplier per municipality; = proportion of population which are current smokers; = proportion of population which are former Pfs smokers; = proportion of population which never Pns smoked. Finally, the estimated number of expected annual lung cancer deaths statewide can be calculated in Eq. 4 from the above exposure factors and risk multipliers using the GIS database.

= expected annual lung cancer deaths for public water users; = expected annual lung cancer deaths for Nprivate private water users; N r3rd floor = expected annual lung cancer deaths for residents living at or above the 3rd floor; = risk multiplier per municipality % OWIl (from Eq. 3); = radon inhalation exposure for public water Epublic users (from Eq. 2); = radon inhalation exposure for private water Eprivate users (from Eq. 2); E >3rd floor = radon inhalation exposure for residents living at or above the 3rd floor (from Eq. 2); = number of residents supplied by public npublic water per municipality; = number of residents supplied by private nprivate water per municipality; n >3rd floor = number of residents living at or above the 3rd floor in each municipality. Additional information on the contributions of house construction, heating, ventilation, and air conditioning (HVAC) characteristics to occupant radon exposure is obtained by separately analyzing the radon measurement data in each of the construction and HVAC categories. Specifically, separate equations describing the relationship between lowest living area screening measurements and living area annual average radon concentrations can be derived in homes having the housing/ventilation characteristic under study. The equations are also obtained from linear regression analysis using SAS T”(SAS Institute, Inc., Cary, NC 275 13, USA) and Epi Info Version 5 (USD, Inc., Stone Mountain, GA 30087, USA). Radon test data was evaluated for normality and homoscedasticity (homogeneity of variance) using Wilk-Shapiro/Rankit Plot procedures prior to conducting linear regression analysis. Data was transformed using log transformation procedures when the resulting Shapiro Francis statistic indicated evidence of nonnormality (Armitage 197 1; Shapiro 1972; Kleinbaum 1988).

s744

The statewide risk assessment projection was also calculated using a Monte Carlo simulation. The simulation was conducted by one of the authors (Tibbetts) using @ Risk Version 1.5 (Palisade Corporation, Newfield, NY) added to Lotus l-2-3 Release 2.2 (Lotus Development Corporation, Cambridge, MA) (Tibbetts 1995). Simulations of the input variables were selected with Latin Hypercube sampling (LHS). The chosen number of iterations (5000) is considered an acceptable value (Smith 1994). A sensitivity analysis was also performed to examine the variables that contribute to the overall uncertainty of the model prediction. This analysis was performed using probabilistic exposure models (Macintosh et al. 1994). RESULTS AND DISCUSSION The results of the three statewide residential screening surveys are shown in Table 1. These radon in air results are well correlated in both percentage of homes in excess of the 148 Bq rns3 (4 pCi I;’ ) guideline, and geometric mean values. Radon in groundwater sampling revealed a statewide geometric mean level of 105 524 Bq rnw3(2852 pCi L-‘) (McHone 1995). Scatter plots were developed to describe the relationships between lowest livable screening measurements and both short-term and annual radon living area concentrations for the largest survey (HTP) of Connecticut homes. These relationships, used to convert short-term measurements to annual radon concentrations (see Materials and Methods), are similar to those obtained by previous authors in other residential studies (Hull et al. 1990; Hopper et al. 1991; Tommasino et al. 1991; White et al. 1990, 1992). The results of school radon testing are shown in Table 2. These results indicate similar concentrations of radon can be expected in schools and homes in a given municipality. For example, the percent of rooms with radon levels in excess of the 148 Bq mm3guideline (512%) approximates the percentage of homes with living area radon levels in excess of 148 Bq mm3(10-l l%, Table 1). Radon test data were also compared with the additional parameters listed in Table 4. These comparisons revealed a significant (~~0.05) relationship between basement radon measurements and aeroradioactivity data, while private well radon concentrations are more closely related to bedrock unit radon-potential. These and other results were reported in previous papers (Siniscalchi 1990; Siniscalchi et al. 1990,1994a, 1994d, 1995).

A.J. Siniscalchi et al.

The results of statewide annual radon mortality predictions are shown in Table 5. These predictions are based on measurement data obtained from the residential and school radon exposure studies as modified by characteristics listed in Table 4, using the equations described in this paper. This initial risk assessment modelling exercise reveals a predicted annual lung cancer mortality incidence ranging from 80 to 143. The predicted excess lung cancer deaths differs based on smoking prevalence using former or current smoking data, addition of school exposure data, and use of Monte Carlo simulation. Additional refinement of these predictions is possible if the mathematical relationships between screening data and annual radon levels in homes with various heating/ construction parameters are obtained. Linear regression analysis performed on screening and long-term follow-up data from the HTP reveals significant associations in lowest living level screening values for various household parameters. For example, mathematical relationships have been identified between the results of radon screening values and long-term results in households with private wells, for HTP households using central forced air heating, and households with soil foundation floors. Scatter plots and linear regression analysis comparing basement screening and annual living area radon concentration, among HTP households with a central forced air heating system and with dirt foundations, respectively, were examined to reveal other mathemathical relationships. Other household characteristics affecting living area radon concentrations are shown in Table 4. CONCLUSIONS

AND RECOMMENDATIONS

The risk assessment modelling work conducted to date has proven useful in refining statewide radon risk assessment predictions. This exercise also revealed the value of these GIS-based exposure and risk assessment modelling efforts in identifying high risk areas in Connecticut (USEPA 1993~). These efforts will continue to be the focus of targeted educational campaigns to increase radon awareness (Siniscalchi et al. 1994b, 1994e) and promote radon-resistant construction techniques (USEPA 1994b). Initial runs using individual home parameters have suggested that the model can also provide useful information to supplement existing techniques such as glass track etch lead 210 or polonium 2 10 methods (Lively et al. 1987, 1993; Mahaffey et al. 1993) to recreate historic radon exposures in individual homes.

Health risk assessment model for homeowners with multiple pathway radon exposure

s745

Table 5. Predicted annual lung cancer deaths from residential and school exposure to radon in air and water as modified by former and current smoking usage in Connecticut.

Predicted Radon and smoking (current rates) ’ Residential radon exposure Carlo analysis

only using Monte

Residential

only

radon exposure

lung cancer deaths Radon and smoking (former rates) b NCd

(678.R)

(82%4)

140 (117-163)’

100 143 (84-116) ’ (120-166)’ a = 1992-1993 rates; b = pre-1985 rates; c = 95% confidence limit; d = not calculated. Residential

and school radon exposure

Moreover, these retrospective exposure assessments can be useful in estimating or verifying radon dose in large epidemiological studies. Use of Monte Carlo analysis in one of the modelling exercises proved useful in further refining the risk predictions. For example, an uncertainty analysis for the exposure assessment portion revealed additional information on population-weighted residential exposure by municipality. The sensitivity analysis revealed that the stochastic variation of radon concentration in the studied municipalities accounted for the greatest (0.7% of the total variation) overall uncertainty (Tibbetts 1995). Additional studies are planned to test the effectiveness of these modelling techniques. For example, predicted exposures will be compared with measured radon exposures in test homes to evaluate the model’s value in predicting historic measurements. Use of linear regression analysis to evaluate the impact of various housing characteristics that may effect annual radon concentrations emphasized the importance of evaluating radon data for normality and making appropriate transformations to achieve homoscedasticity. Additional evaluations of the radon data are planned to evaluate non-linear relationships between shortterm and long-term data sets. These evaluations will determine if a non-linear relationship, also known as regression toward the mean or ‘regression effect’, exists (Price 1995). Finally, the authors note the recent analysis conducted by Maillie et al. (1994) which suggested different factors such as the influence of former and current smoking on both males and females exposed to radon. The model described in this paper will be modified to utilize both these factors and the new radon risk factor being

prepared by the BEIR VI Committee (NRC 1994). An evaluation of the impact of both smoking, environmental tobacco smoke (ETS), and other pulmonary carcinogens on the equilibrium and radon risk factors is also planned. Additional work will be conducted to characterize the radon in wat& ingestion risk, which the U.S. EPA has characterized as contributing 52% of the total exposure from radon in water sources (USEPA 1994a). Acknowledgmenr-The authors gratefUlly acknowledge the work of Mr. Amjad Mahmood, Mr. Gary Perlman, Mr. Robert Pokrinchak, and Dr. Hari Rao in conducting statistical analysis and data plots to support this paper, and Ms. Theresa Williams and Ms. Kathy Graff in preparing the manuscript.

REFERENCES Alavanja, M.C.R.; Brownson, RX.; Lubin, J.H.; Berger, E.; Chang, J.; Boice, J.D., Jr. Residential radon exposure and lung cancer among nonsmoking woman. J. Nat. Cancer Inst. 86(24): 18291837; 1994. Armitage, P. Statistical methods in medical research. Oxford, U.K.: Blackwell Scientific Publishers; 197 1. CT DPH (State of Connecticut Department of Public Health). Radon measurement quality assurance plan. Hartford, CT: CT DPH; 1991. CT DPH (State of Connecticut Department of Public Health); University of Connecticut Cooperative Extension System; American Lung Association of Connecticut. Radon and you: Testing and reducing radon levels in Connecticut homes. Storrs, CT: University of Connecticut; 92-12: 1992. CT DPH (State of Connecticut Department of Public Health). Smoking prevalence by county. 1994. Hartford, CT: CT DPH Dupuy, C.J.; Healy, D.; Thomas, M.A.; Brown, D.R.; Siniscalchi, A.J.; Dembek, Z.F. A survey of naturally occurring radionuclides in groundwater in selected bedrock aquifers in Connecticut and implications for public health policy. In: Gilbert, C.; Calabrese, E., eds. Regulating drinking water quality. Chap. 10. Boca Raton, FL: C.R.C. Press; 1992: 95-119.

S746

Hopper, R.D.; Levy, R.A.; Rankin, R.C.; Boyd, M.A. National ambient radon study. In: Proc. 1991 international symposium of radon and radon reduction technology, Philadelphia, PA.. Washington, D.C.: US EPA; 1991. Hull, D.A.; Reddy, T.A. Study on the reliability of short-term measurements to predict long-term basement radon levels in a residence. In: Proc. 1990 international symposium on radon and radon reduction technology, Atlanta, GA. Research Triangle Park, N.C.: US EPA; 1990: 11 I-P2-4. Kleinbaum, D.G.; Kupper, L.L.; Muller, K.E. Applied regression analysis and other multivariable methods. Belmont, CA: Duxbury Press; 1988. Letoumeau, E.G.; Krewski, D.; Choi, N.W.; Goddard, M.J.; McGregor, R.G.; Zielinski, Z.M.; Du, J. Case-control study of residential radon and lung cancer in Winnipeg, Manitoba, Canada. Am. J. Epidemiol. 140(4): 3 10-322; 1994. Lively, R.S.; Ney, E.P. Surface radioactivity resulting from the deposition of ‘*‘Rn daughter products. Health Phys. 52: 4 1 l-4 15; 1987. Lively, R.S.; Steck, D.J. Long-term radon concentrations estimated from *“PO in glass. Health Phys. 64: 485-490; 1993. Macintosh, D.L.; Suter II, G.W.; Hoffman, O.W. Uses of probabilistic exposure models in ecological risk assessment of contaminated sites. Risk Anal. 14: 405-419; 1994. Mahaffey, J.A.; Parkhurst, M.A.; James, A.C.; Cross, F.T.; Alavanja, M.C.R.; Boice, J.D.; Ezrine, S.; Henderson, P.; Brownson, R.C. Estimating past exposure to indoor radon from household glass. Health Phys. 64(4): 381-391; 1993. Maillie, H.D.; Simon, W.; Greenspan, B.S.; Watts, R.J.; Quinn, B.R. The influence of life table corrections for smokers and nonsmokers on the health effects of radon using the BEIR IV method. Health Phys. 66(6): 615-620; 1994. McHone,N.W.; Thomas, M.A.; Siniscalchi, A.J. Temporal variations in bedrock well water radon and radium and water radon’s effect on indoor air radon. In: Proc. 1992 international symposium on radon and radon reduction technology, Minneapolis, MN; 5(XIIP-5). Washington, D.C.: US EPA; 1992: 443-463. McHone, N.W. Radon in water values by bedrock unit and municipality. In: State of Connecticut Department of Environmental Protection Memorandum. Hartford, Ct ; 1995. NIH (National Institutes of Health). U.S. Department of Health and Human Services. Public Health Service. Radon and lung cancer risk: A joint analysis of 11 underground miners studies. NIH No. 94-3644. Bethesda, MD: National Cancer Institute; 1994. NRC (National Research Council). Health risks of radon and other internally deposited alpha-emitters BEIR IV. Washington, D.C.: National Academy Press; 1988. NRC (National Research Council). Health effects of exposure to radon: Time for reassessment? BEIR VI. Part A. Washington, DC.: National Academy Press; 1994. Pershagen, G.; Akerbolm, G.; Axelson, 0.; Clavensjo, B.; Damber, L.; DeSai, G.; E&lo, A.; Lagarde, F.; Mellander, H.; Svartengren, M.; Swedjemark, G.A. Residential radon exposure and lung cancer in Sweden. New England J. Med. 330: 159164; 1994. Popenoe, P. Aeroradioactivity and generalized geologic maps of parts of New York, Connecticut, Rhode Island, and Massachusetts. Scale 1:250 000. US GS Geophysics Inventory Map GP-359. Denver, CO: U.S. Geological Survey; 1966. Price, P.N. Regression effect as a cause of non-linear relationship between short-term and long-term radon concentration measurement. Health Phys. 69(l): 11 l-l 14; 1995.

A.J. Siniscalchi et al.

Puskin, J.S. Analysis of uncertainties in estimates of radon-induced lung cancer. Risk Anal. 12: 227-285; 1992. Schoenberg, J.B.; Klotz, J.B.; Wilcox, H.B.; et al. Case-control study of residential radon and lung cancer in Swedish women. Cancer Res. 50: 6520-6524; 1990. Shapiro, S.S.; Francis, R.S. Approximate analysis of variance, test of normality. J. Am. Stat. Assoc. 67: 215-216; 1972. Siniscalchi, A.J. Assessment of radon exposure in Connecticut. CASE Reports. Conn. Acad. Sci. Eng. 5(3): 1-4; 1990. Siniscalchi, A.J.; Rothney, L.M.; Toal, B.F.; Thomas, M.A.; Brown, D.R.; van der Werff, M.C.; Dupuy, C.J. Radon exposure in Connecticut: Analysis of three statewide studies of nearly one percent of single family homes. In: Proc. 1990 international symposium on radon and radon reduction technology, Atlanta, GA. EPA/600/9-90/005c. 3(IV-1). Research Triangle Park, N.C.: US EPA; 1990: 3-16. Siniscalchi, A.J.; Dembek, Z.F.; Weiss, B.S.; Pokrinchak, R.K., Jr.; Gaudio, M.-M.; Kertanis, J.C.; Gokey, L.A.; Schur, P.M. Radon in schools: The Connecticut experience. In: Proc. 1992 international symposium on radon and radon reduction technology. 2(111-l). Minneapolis, MN: U.S. EPA, Washington, D.C.; 1992a: 3-22. Siniscalchi, A.J.; Dembek, Z.F.; Thomas, M.A.; Pokrinchak, R.P., Jr.; Dupuy, C.J.; Brown, D.R.; van der Werff, M.C. Risk assessment implications of tempera1 variation of radon and radium well water concentrations. In: Proc. 1992 international symposium on radon and radon reduction technology. 4(X11-1). Minneapolis, MN. Washington, D.C.: US EPA; 1992b: 75-87. Siniscalchi, A.J.; Mahmood, A.; Dembek, Z.F.; Thomas, M.A.; McHone, N.W.; Toal, B.F.; Dupuy, C.J. Use of geographic information systems (GIS) in the assessment of radon exposure in Connecticut. In: Proc. GIS applications in public health and risk analysis: An ATSDR workshop, Atlanta, GA. (Abstract); 1994a. Siniscalchi, A.J.; Mahmood, A.; Beakes, R.; Soto, X.; Dembek, Z.; Ferree, G.D., Jr. Radon awareness in Connecticut: Comparison of two statewide residential surveys and their use in setting numerical target goals for increasing radon awareness, testing and mitigation for the years 1995 and 2000. In: Proc. 1994 international radon symposium. I(II), Atlantic City, NJ. Madison, WI: Omnipress, Inc.; 1994b: 5. l- 5.10. Siniscalchi, A.J.; Dembek, Z.F.; Tibbetts, S.J.; Soto, X.; Beakes, R.C.; Mahmood, A. Radon in school well water: Case studies and mitigation implications. In: Proc. 1994 international radon symposium. I(VII), Atlantic City, NJ. Madison, WI: Omnipress, Inc.; 1994~: 2.1-2.9. Siniscalchi, A.J.; Tibbetts, S.J.; Mahmood, A.; Soto, X.; Dembek, Z.F.; Beakes, R.C.; McHone, M.A. Multicomponent health risk assessment of Connecticut homes with multiple pathway radon exposure. In: Morwska, L.; Botinger, N.D.; Maroni, M., eds. Health risk and assessment in indoor air - An integrated approach. Chap. 3. Gold Coast, Australia: Elsevier Science, Ltd., Oxford, U.K.; 19946 Siniscalchi, A.J.; Beakes, R.; Soto, X. A discussion of various risk communications and outreach techniques used to increase awareness of radon and other indoor air pollutants. In: Morwska, L.; Bolinger, N.D.; Maroni, M., eds. Public policy and information in indoor air - An integrated approach. Chap. 5. Gold Coast, Australia. Oxford, UK: Elsevier Science; 1994e. Siniscalchi, A.J.; Mahmood, A.; Thomas, M.A.; McHone, N.W.; Dembek, Z.F.; Beakes, R.C.; Soto, X. Use of geographic information systems (GIS) in the assessment of radon exposure in

Health risk assessment model for homeowners with multiple pathway radon exposure

Connecticut municipalities. (Abstract) In: Proc. international symposium on computer mapping in epidemiology and environmental health. Tampa, FL: World Computer Graphics Foundation; 1995. Smith, R.L. Use of Monte Carlo simulation for human exposure at a superfund site. Risk Anal. 14(4): 433-439; 1994. State of Connecticut Offtce of Policy and Management. Connecticut housing characteristics: 1990 census complete count data - Part B. Hartford, CT: Connecticut State Data Center; 199 1. Thomas, M.A. Results of car-borne radiation survey of Connecticut. Hartford, CT: State of Connecticut Department of Environmental Protection: 1989. Tibbetts, S.J. Radon risk assessment using Monte Carlo analysis. MPH Thesis. Yale University, New Haven, CT; 1995. Tommasino, L.; Torri, G. Results of short and long-term radon measurements in soil and dwellings by alpha-track detectors. In: Proc. 1991 international symposium on radon and radon reduction technology. Vol. II. Philadelphia, PA. Washington, DC.: US EPA; 1991: III-P2-5. USAEC (U.S. Atomic Energy Commission). US GS Preliminary reconnaissance for uranium in Connecticut, Maine, Massachusetts, New Jersey, New York, and Vermont, 1950-1959. RNE-4106, TID UC-51. U.S. AEC (now DOE). Denver, CO: U.S. Geological Survey; 1969. USEPA (U.S. Environmental Protection Agency). Radon in water sampling program. EPA/EERF-Manual78-1. Montgomery, AL: EPA; 1978. USEPA (U.S. Environmental Protection Agency). Radon measurement in schools: An interim report. U.S. EPA 520/l -89-O 10. Washington, D.C.: USEPA; 1989. USEPA (U.S. Environmental Protection Agency). Technical support document for the 1992 citizens guide to radon. EPA 400-R92-011. U.S. Washington, D.C.: Government Printing Office; 1992a.

s747

USEPA (U.S. Environmental Protection Agency). Indoor radon and radon decay product measurement device protocols. EPA 402-R92-004. Washington, D.C.: U.S. Government Printing Office; 1992b. USEPA (U.S. Environmental Protection Agency). Protocols for radon and radon decay products measurement in homes. EPA 402-R-93-003. Washington, DC.: U.S. Government Printing Office; 1993a. USEPA (U.S. Environmental Protection Agency). Radon measurement in schools (rev. ed.). EPA 402-R-92-0 14. Washington, D.C.: U.S. Government Printing Office; 1993b. USEPA (U.S. Environmental Protection Agency). EPA’s map of radon zones: Connecticut. 402-R-93-027. Washington, DC.: U.S. Government Printing Office; 1993c. USEPA (U.S. Environmental Protection Agency). Report to the United States Congress on radon in drinking water: Multimedia risk and cost assessment of radon. EPA 81 l-R-94-001. Washington, D.C.: U.S. Government Printing Office; 1994a. USEPA (U.S. Environmental Protection Agency). Model standards and techniques for control of radon in new residential buildings. EPA 402-R-94-009. Washington, D.C.: U.S. Government Printing Offtce; 1994b. USERDA (U.S. Energy Research and Development Administration). Airborne geophysical survey of a portion of New England. GJO-1666-1; ERDA (now DOE). Washington, D.C.: U.S. Department of Energy; 1976. White, S.B.; Clayton, C.A.; Alexander, B.U.; Clifford, M.A. A statistical analysis: Predicting annual “‘Rn concentrations from 2-day screening tests. In: Proc. 1990 international symposium on radon and radon reduction technology, Atlanta, GA. Vol. II. Research Triangle Park, N.C.: US EPA; 1990: III-P2-6. White, S.B.; Bergsten, J.W.; Alexander, B.V.; Rodman, N.F.; Philips, J.L. Indoor 222 radon concentrations in a probability sample of 43,000 houses across 30 states. Health Phys. 62: 4150; 1992.