Radon and Its Decay Products - ACS Publications - American

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Chapter 33 Surveys of Radon Levels in Homes in the United States A Test of the Linear-No-Threshold Dose-Response Relationship for Radiation Carcinogenesis Bernard L. Cohen University of Pittsburgh, Pittsburgh, PA 15260 The University of Pittsburgh Radon Project for large scale measurements of radon concentrations in homes is described. Its principal research is to test the linear-no threshold dose-response relationship for radiation carcinogenesis by determining average radon levels in the 25 U.S. counties (within certain population ranges) with highest and lowest lung can cer rates. The theory predicts that the former should have about 3 times higher average radon levels than the latter, under the assumption that any correla tion between exposure to radon and exposure to other causes of lung cancer is weak. The validity of this assumption is tested with data on average radon level vs replies to items on questionnaires; there is little correlation between radon levels in houses and smoking habits, educational attainment, or econ omic status of the occupants, or with urban vs rural environs which is an indicator of exposure to air pollution. The University of Pittsburgh Radon Project makes measurements of radon concentration in indoor air by use of diffusion barrier char coal adsorption collectors (Cohen, 1986). The measurements average radon levels with an integration time constant of 3 days. Exposure times are 7 days. All measurements are handled by mail. After ex posure, collectors are returned to the Laboratory and their radon content is measured by gamma ray counting for 40 minutes with 3-inch diameter Nal (T2,) scintillation detectors to determine the number of counts in the 295, 352, and 609 KeV gamma ray peaks. Currently, 20 of these measuring systems are in use, giving a capacity of about 650 measurements/day. At present, about 300 measurements per day are being completed. About 13,000 collectors are in the field at all times. 0097-6156/87/0331-0462$06.00/0 © 1987 American Chemical Society

Hopke; Radon and Its Decay Products ACS Symposium Series; American Chemical Society: Washington, DC, 1987.

33.

1.


2.

COHEN

Surveys of Radon Levels in U.S. Homes

463

The measurements f a l l into two categories: "$12 measurements" provided to a l l requestors for a $12 charge. About 30,000 of these have been completed. They provide the f i n a n c i a l support for the program and also provide a substantial amount of useful research information. "Random" measurements made at no charge for w i l l i n g participants selected randomly by mailing service computers. They are used for research purposes described below, and to study methods for making data from the $12 measurements useful.

The $12 measurements give higher average radon levels than the random measurements for several reasons, such as a. measurements are more l i k e l y to be requested when a neighboring house i s found to have a high l e v e l , when the house i s i n an area or on a geological formation with high radon l e v e l s , or when a house i s recognized as having construction features associated with high radon levels b. our service i s known to the public p r i n c i p a l l y through media coverage, and there i s generally more media coverage i n high radon areas c. some measurements are for follow-up studies on houses already known to have high radon d. people who purchase measurements are more l i k e l y to know that basements generally have higher levels and to test there, despite the advice i n our instructions not to test in basements . In order to overcome these problems, we include questions i n the questionnaire as: - Do you have any reason to believe that the radon l e v e l i n your house i s higher or lower than the average for your County? - Do you know what factors a f f e c t radon levels i n homes? We form "modified sets" of measurements for which the answer to these two questions i s NO, and for which measurements were not made i n the basement. As an example, our largest f u l l data set — Nov. 1985 to Feb. 1986— includes 9882 measurements with an average radon l e v e l of 5.7 p C i / l i t e r , whereas the modified set from i t contains 1771 measurements with an average of 3.6 p C i / l i t e r . Our random measurements during that period, discounting areas selected because they have high radon l e v e l s , give a modified set with an average 3.5 p C i / l i t e r , i n good agreement with the modified set from $12 measurements . Data from the $12 measurement f u l l set and modified set are shown i n Table I. We see there that the f u l l set exhibits the same general features as the modified set, except that the radon levels are somewhat higher. Since the former have f a r more measurements, they give more detailed information on geographic variations. The $12 measurements are also d i r e c t l y useful for studies of variations of average radon levels with age of house, socioeconomic factors, etc. Test of the Linear-No Threshold Theory The p r i n c i p a l research goal of our project i s to test the linear-no threshold dose-response theory for radon-induced lung cancer. This


Hopke; Radon and Its Decay Products ACS Symposium Series; American Chemical Society: Washington, DC, 1987. 32 0 0 0 0 193 8 3 0 1 16 0 0 0 0 13 1 0 0 0 31 1 0 0 0 69 6 1 0 0 29 0 2 0 0 16 0 0 0 0

42 3 0 0 0

290 24 12 0 2

41 2 0 0 0

793 30 17 3 1

44 1 0 0 0

100

2.6

4.1 2.2 3.3 3.3 3.8

4.1

2.1

2.6

5.5

1.9

3.2

IODIFI1ED SET

2.2

Aver.

3.5 5.0 2.0 4.0

4.9

4.6

5.1

3.5

6.5

12.0

3.7

126 1 4 1 1 0 134 13 4 0 0 0 44 0 0 0 0 0 67 8 2 0 0 0

95 11 1 1 0 0

219 20 3 1 2 0

4.3

3.6

Aver.

3326 137 48 45 22 8

179 28 5 3 5 0

124 8 3 2 2 0

WA OR CA

AZ CO WY NY UT ID

50 2 1 1 0 0

9

8

TX OK AR LA

7

IL MO NE KS

6

5 ND SD MN IA WI

4 MI OH IN KY

3 TN MS AL GA FL

2

1029 139 51 39 27 12

2

204 6 3 0 2 1

370 18 7 2 0 1

1

PA

NY

0-1

WV NC SC

NJ

0

DC MD VA

100

States

pCi\ per \ liter \

0

ME NH VT MA CT RI

5

Zip (xlO )

Table I. Data from $12 Measurements, Nov. 1985 - Jan. 1986


33.

465


COHEN

theory, i n conjunction with data from studies of miners, makes def i n i t e predictions that are subject to experimental t e s t s . We discuss one of these tests. For each County i n the United States i n a certain population range, we define M = t o t a l mortality rate from lung cancer M = lung cancer mortality rate due to radon M = lung cancer mortality rate due to non-radon causes including smoking and a l l other factors, known or unknown r = average radon l e v e l i n a county Then, from the linear-no threshold theory, M = kr (1) where k i s a constant determined from the studies of miners with due consideration for various other factors. From the d e f i n i t i o n of M , M = M + M (2) We also introduce three simplifying assumptions, the v a l i d i t y of which w i l l be discussed below: A. There i s no c o r r e l a t i o n between M and M ; t h i s means, for ex ample, that the average indoor concentration of radon i n v a r i ous counties i s not correlated with the amount of cigarette smoking i n those counties B. There i s no synergism between r and factors affecting M . Actually synergisms have been treated Cohen, 1986a), and were found not to affect the results appreciably C. The d i s t r i b u t i o n of r values for various counties i s known. F i g . 1 shows schematically the d i s t r i b u t i o n s of values of M, M and M for U.S. counties. They are related through (2), and with Assump tions A and B, any one of these can be mathematically calculated from the other two. For example, i f the d i s t r i b u t i o n of M and M are known, the d i s t r i b u t i o n of M can be determined as follows: (a) Assume that the d i s t r i b u t i o n of M i s gaussian centered at M and with standard deviation S . Pick t r i a l values of M and S . (b) Divide the M and M d i s t r i b u t i o n i n Figure 1 into 100 inter vals of equal area under the curve ( i . e . equal p r o b a b i l i t y ) . (c) For each of the (100 χ 100 =) 10,000 combinations of M and M , calculate M from (2). Each of these 10,000 then has equal probability, and they form the calculated d i s t r i b u t i o n of M values i n Figure 1. (d) Check t h i s calculated M d i s t r i b u t i o n against the known one. If they do not agree, pick d i f f e r e n t values of M and S and repeat (b), ( c ) , (d). (e) Repeat t h i s process u n t i l agreement i s obtained; the values of M and S that give agreement characterize the d i s t r i b u tion of M we are seeking. ( f ) In p r i n c i p l e , one could then i t e r a t e the shape of the M d i s t r i b u t i o n from gaussian to improve the detailed agreement with the known M d i s t r i b u t i o n . From Assumption C and Eq. (1), the d i s t r i b u t i o n of M i s accur ately known. From s t a t i s t i c s on lung cancer mortality, the d i s t r i b u t i o n of M i s accurately known. Thus, the d i s t r i b u t i o n of M can be calculated mathematically, and the problem i s completely solved, allowing us to derive predictions that can be tested. In p a r t i c u l a r , r


n

r

n

r

n

r

n

n

r

n

r

n

n

n

n

n

n

n

r

n

r

n

n

n

n

n

n

r

r


RADON AND ITS DECAY PRODUCTS


466

Total lung CA rate

Lung CA rate due to radon

Lung CA rate due to other causes than radon

Figure 1. Relationship between frequency d i s t r i b u t i o n of lung mortality rates for various U.S. counties. M i s the t o t a l rate, M i s the rate due to radon, and M i s the rate due to causes other than radon. They are related by (2). r

n


33.

COHEN

467


we can calculate the average values of r i n the upper and lower ends of the d i s t r i b u t i o n of M values — that i s , in counties which have very high or very low lung cancer rates. This i s a by-product of the calculation outlined above: as we know the M and M contribut i o n to each of the 10,000 M values, we need only average the M contributions from the 100 highest and 100 lowest M values, and apply (2) to obtain the average r values for these. The results can then be tested experimentally by measuring r for those counties. I f the experimental measurements do not agree with the results of the c a l culation, t h i s can only mean either that our assumptions are not v a l i d , or that Eq.(l) i s not correct, which means that the linear-no threshold theory f a i l s . It i s extremely important to note that t h i s process requires no knowledge of smoking practices or of the other factors that influence M. If the linear-no threshold theory i s correct and our assumptions A, B, C are v a l i d , the d i s t r i b u t i o n of M i s calculated s t r i c t l y mathematically from the known d i s t r i b u t i o n s of M and M , and there i s no need to understand i t s causal factors. Since smoking cigarettes i s an important contributor to lung cancer, counties with high lung cancer rates (large M-values) undoubtedly have a high incidence of smoking, and vice versa. But that i s taken into account i n the calculation. In fact i f we assume some simple relationship between M and smoking frequency, we could do a completely analagous calcul a t i o n to determine the r e l a t i v e smoking incidence i n high lung cancer vs low lung cancer counties. But these matters are irrelevant. The theory makes d e f i n i t e predictions about radon levels i n counties with large-M and small-M, and these predictions can be experimentally tested. The above discussion depends on our three assumptions, A, B, and C. We next consider t h e i r v a l i d i t y : - Assumption B, that there i s no synergism, i s not necessary. The calculation has been carried out with the largest synergism consistent with miner data, and the results are l i t t l e changed. - Assumption C, that the d i s t r i b u t i o n of r values i s known, i s not l i t e r a l l y correct, but there i s enough information available to bound the types of d i s t r i b u t i o n that are plausible, and calculations can be made for various d i s t r i b u t i o n s spanning these bounds. They then span the range of plausible r e s u l t s . r

n


r

n

n

r

n

- Assumption A i s the p r i n c i p a l matter considered i n t h i s paper. Results of Calculation and Comparison with Data The test we are discussing i s more sensitive i f we go back to the time before lung cancer s t a t i s t i c s were dominated by cigarette smoking. We therefore consider white females who died i n 1950-1969. When the d i s t r i b u t i o n of M values from that population i s used i n the above calculation, i t i s found (Cohen,1986a) that r should be about 3 times larger for the counties with the highest lung cancer rates than for those with the lowest lung cancer rates. This result i s not much changed i f we assume a strong synergism between smoking cigarettes and radon i n causing lung cancer. We have been testing t h i s prediction by determining average radon levels i n the 20 U.S. counties within certain population




468

r e s t r i c t i o n s with the highest and lowest lung cancer rates using random selection of i n v i t a t i o n s to p a r t i c i p a t e i n our measurement program at no charge. The studies to date have suffered from poor response and from marginal s t a t i s t i c s , and the results cannot be taken very seriously, but we exhibit them i n Table I I . We see that these data suggest that average radon levels may be lower i n high lung cancer counties than i n low lung cancer counties, in gross disagreement with the prediction that they should be about 3 times higher! I f t h i s trend continues and i s confirmed, there can be two explanations: (1) Our Assumption A i s f a l s e and there i s a strong negative correlation between radon levels i n houses and exposure to other things that cause lung cancer (2) Eq.(l) i s not v a l i d , which means that the linear-no threshold theory f a i l s , grossly over-predicting the cancer r i s k at low doses. Explanation (2) i s what our project i s intended to investigate, but before that investigation becomes meaningful, the p o s s i b i l i t y of explanation (1) must be explored. We next consider that problem. Possible Negative Correlations Between Radon and Other Lung Carcinogens Ab i n i t i o , there i s l i t t l e reason to expect a strong correlation between radon levels and exposure to other factors that cause lung cancer; for example, i t i s d i f f i c u l t to see why houses of cigarette smokers should have substantially lower radon levels than houses of non-smokers. By f a r , the most important factor i n determining radon levels i s geology, and i t i s d i f f i c u l t to see how that can correlate with smoking or a i r p o l l u t i o n i n any consistent way. The most plausible explanations for a possible correlation are through ven" t i l a t i o n rates (e.g. smokers may keep windows open more, which reduces radon), but radon levels have been found to have surprisingly l i t t l e correlation with v e n t i l a t i o n rates (Nero, et a l . , 1983). In t h i s Section, we explore some possible correlations using data from our studies.

1.

Houses of cigarette smokers may have lower radon levels than houses of non-smokers.

An item i n our questionnaire asks how many cigarettes per day are smoked i n the house. Results to date are: cig./day

0 1-5 6-19 >20

$12 measurements Number

Av. p C i / l i t e r

9484 467 623 1014

4.6 3.7 3.9 3.7

Random measurements Number 838 39 109 167

Av. p C i / l i t e r 4.0 2.0 2.4 2.6

Present indications are that there i s some correlation which w i l l have to be taken into account i n the analysis.



Hi lung CA aver.

Average

Salt Lake, UT S c h u y l k i l l , PA Franklin, PA Oneida, NY Northumb'l'd, PA Marion, OR Kalamazoo, MI Linn, IA Chatauqua, NY St. C l a i r , MI Lycoming, PA Madison, IN Cambria, PA Washtenau, MI Berrien, MI

County

3.5 3.6 3.7 4.0 4.1 4.2 4.5 4.7 4.7 4.7 4.8 4.8 4.8 4.8 4.9

CA rate

2.8 1.2

3.4 1.9

3.2 4.8 2.8 1.5

1.5 2.4 1.1

2.0 5.4 5.2 1.6

0.90

2.5 4.4 1.6

5.8 0.9

2.5

2.1

5.5

Mar-Apr •86

1.33

1.1 1.9

1.3 1.1

1.8 1.8 0.7 1.4 1.0

Jan-Feb Summer •86 '85 [2. 3]

Low Lung Cancer Counties

Average

Charleston, SC Clark, NV Newport, RI Solano, CA Jefferson, MO Campbell, KY Humboldt, CA F a i r f a x , VA Harrison, MS Somerset, ΝJ Monmouth, ΝJ San Mateo, CA Ventura, CA Sarasota, FL A t l a n t i c , NJ Prince Geo.,MD Contra Costa, CA Sebastien, AR Duval, FL Tulsa, OK Chatham, GA

County 11.0 9.6 9.2 9.1 9.1 9.0 8.8 8.8 8.7 8.6 8.5 8.5 8.4 8.4 8.1 8.1 8.1 8.1 8.0 8.0 8,0

CA rate

0.90

0.9 0.9 0.8 0.7 1.1 1.0 0.6 0.8 0.8 0.9 0.6 1.2 1.1

1.1

1.0 1.1 0.7 0.9

Summer »85

1.9

3.2

1.3

1.0

f

Jan-Feb 86

High Lung Cancer Counties

Table I I . Average Radon Levels i n Counties with Very High and Very Low Lung Cancer Rates from Random Studies i n Various Time Periods


470 2.


Socioeconomic factors may correlate with both radon levels and lung cancer rates f o r unrelated reasons.

Our questionnaire asks about d o l l a r value of the house and annual family income. Results to date for average radon levels in p C i / l i t e r are:


Value of house

$130K Income/year

$45K

$12 measurements Number

pCi/liter

159 1102 3639 5398

2.2 5.2 4.3 4.4

Random measurements Number

pCi/liter 2.2 3.6 3.5 3.6

152 414 278 223

$12 measurements

Random measurements

Number

pCi/liter

Number

pCi/liter

169 724 2789 5991

4.0 4.5 4.0 4.5

116 228 369 310

2.4 3.4 3.3 3.0

One problem with these data i s that they are not well distributed geographically; over half of these measurements are from the Reading Prong region of New Jersey, and i t i s quite possible that i n that region, poor people happen to l i v e i n a geological area with low radon while wealthier people l i v e i n a geological area with high radon. However, when large numbers of regions are considered, t h i s should average out. There i s no indication i n these data of a consistent monotonie relationship between radon levels and wealth. There i s a consistent indication that very poor people have lower radon levels than others, but t h i s indication disappears rapidly for incomes above $15,000/yr and for houses valued above $40,000. The data on very poor people may be dominated by students and young people rather than by poor families. 3.

Education l e v e l may somehow correlate with both radon levels and lung cancer rates for unrelated reasons.

Some data on t h i s are l i s t e d below, as average Rn l e v e l vs years of education beyond 8th grade for the head of household; i n each square, the upper figure i s number of measurements and the lower figure i s average p C i / l i t e r . years $12 measurements random measurements

8

1377 4.7 192 3.2

2599 4.3 177 2.4

4473 4.5 263 3.8


33. COHEN

All


There i s l i t t l e indication here of any correlation between radon exposure and educational l e v e l .


4.

There may be correlations, for some unknown reason, between radon levels and exposure to a i r p o l l u t i o n .

Air p o l l u t i o n i s normally an urban problem, i s less of a problem i n suburban areas, and i s almost never a problem i n r u r a l areas. Some data on t h i s from a national survey of physics professors (Cohen, 1986b) follows: Environs urban suburban rural

number

Av. p C i / l i t e r

108 248 84

1.63 1.37 1.53

There i s no support here for the premise being studied. data on t h i s question w i l l soon be available.

Much more

Problems i n Inferring Past Radon Exposure from Present Measurements Any study of lung cancer-radon exposure correlations, including both case-control studies and the one considered here, must infer radon exposures many decades ago from measurements made at present. The most obvious reason why there might be differences are as follows: 1.

Differences i n house construction practices

It i s commonly believed that new houses are tighter and therefore have higher radon l e v e l s . Our data on average radon l e v e l vs age of house are l i s t e d i n Table III. They seem to indicate no very large e f f e c t , but some correction f o r t h i s w i l l be necessary. 2.

Recent weatherization a c t i v i t i e s may have increased radon levels

Nero et a l . (Nero et a l . , 1983) report l i t t l e correlation between radon l e v e l and v e n t i l a t i o n rate. Our data on this are l i s t e d in Table IV. The data i n Table IVa indicate a negative correlation, but those i n Table IVb and IVc indicate a rather strong p o s i t i v e correlation. Hopefully t h i s matter w i l l be c l a r i f i e d when much more data become available. Typical estimates are that recent weatherization a c t i v i t i e s have reduced v e n t i l a t i o n rates by about 20%, presumably increasing radon levels that much. Since only about half of a l l houses have been weatherized, this i s only about a 10% correction. 3.

In e a r l i e r times windows were kept open i n summer, whereas now many homes keep windows closed f o r a i r conditioning purposes.

Some of the data presented above indicate that average radon levels are now only half as large i n summer as i n winter. Thus the summer a i r conditioning season contributes only 10-15% of our annual radon exposure, and the correction due to t h i s factor i s less than 10%.



9/4.0 13/3.9 35/11.1

40/9.8

43/10.8

25/7.7

'84-'85

Cumberland-Winter

15/3.3 50/2.0 31/2.3 26/2.3

33/2.3

12/2.7

Pittsburgh - 1 yr

54/1.37 44/0.88 75/1.77 94/1.69

101/1.52

85/1.29

101/2.8 179/2.1

Univ. Profs. - 1 yr

55/3.2 103/4.2 185/5.4 267/5.6

151/4.7 267/3.5

176/5.5

188/5.1

Random-Spring '86

259/3.0

126/3.4

Random-Winter '85-'86

110/1.4 151/1.5 168/1.5

159/2.0

183/1.7

139/2.5

Random-Summer '85

100/6.7 169/10.1

168/15.1

185/7.5

127/14.3

189/12.4

257/2.1

308/2.9 530/3.0

521/3.0

621/2.3

854/3.8

Other

119/2.4

$12-Fall '85

150/5.3 68/17.6

105/3.7

184/5.1 465/2.2

195/10.5

613/3.6

1036/3.7

474/2.8

266/7.7

408/8.4

Pennsylvania

DC-MD-VA

317/4.9 437/2.1

946/2.8

1080/4.9

1215/6.2

1204/4.8

$12-Spring '86 New Jersey

168/3.1 234/3.1

354/3.9

350/4.3

491/3.5

772/5.1

Other

11/1.6

70/5.8

198/5.5

18/7.2

106/2.5

108/2.5

DC-MD-VA

191/6.4

193/29.7

308/6.9

250/10.2

318/2.7

394/11.4

Pennsylvania

AGE >80 236/3.9

AGE 50-79 510/2.2

AGE 30-49

Figures are

1063/3.3

1236/3.7

AGE 20-29

1264/5.8

!

AGE 10-19

1325/5.6

!

AGE 1-9

$12-Winter 85- 86 New Jersey

SURVEY

Table I I I . Average Radon Level vs Age of House from Various Data Sets. the Number of measurements/Average p C i / l i t e r .


COHEN


473


Table IV. Replies to Questions about Weatherization from Various Studies. Figures are Number of Measurements-Average pCi/liter.

a.

b.

How much weatherization i n past 10 yr?

much

$12-Feb, Mar '86 550-4.0 pCi/fc

iSS.

£:i:5

How much has been done i n

Weatherstripping around doors and windows

Random, Summer '85 ., A S f t

: 10 years? Random, Nov.85-Jan.86 249--6. 2 much 273·-4. 3 little 147--3. 3 nothing

Closing gaps under doors to outside

much little nothing

256 -5. 6 263--5. 1 145·-2. 9

Caulking, glazing around windows

much little nothing

269 -5. 5 220 -4. 1 169 -4. 2

How much has been done to weatherize i n past 10 years (MarchA p r i l 1986)?

much little nothing

$12 2270-6.0 2635-4.6 965-3.7

Random 422-3.7 502-3.2 168-4.1


474


Questions on window opening practices have been added to our questionnaire. The results for winter indicate that nearly all windows are kept closed. Results for summer will give information on the question under discussion. Literature Cited Cohen, B.L., A Diffusion Barrier Charcoal Adsorption Collector for Measuring R Concentrations in Indoor Air, Health Phys. 50:457 (1986). Cohen, B.L., Expected Indoor Radon Levels in Counties with Very High or Very Low Lung Cancer Rates, Health Phys. (submitted) (1986a). Cohen, B.L., A National Survey of Radon in Homes and Correlating Factors, Health Phys. (in press) (1986b). Nero, Α.V., M.L. Baegel, C.D. Hollowell, J.G. Ingersoll, and W.W. Nazaroff, Radon Concentrations and Infiltration Rates Measured in Conventional and Energy-efficient Houses, Health Phys. 45:401 (1983). RECEIVED August 4, 1986


n


Radon and Its Decay Products - ACS Publications - American

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