Mortality and air pollution: Is there a meaningM connection? “We grow weary of speculations about the Air” -Robert Angus Smith“
Frederick W. Lipfert Broowlaven National Laboratory Uoton. N.X 11973
The protection of public health has long been a driving force in air pollution “The Beginning of Chemical Climatology”; Longman, Green and Company: London, England, 1872.
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control, and the specter of people dying mension of the problem-the number in the streets during severe pollution of “excess” deaths-is not apparent unepisodes still remains. The worst epi- til much later, when detailed statistid sodes in terms of percentages of people analysis has been done. The recent affected, the Meuse (1)and Donora (2), tragedy in Bhopal, India, involving were recognized as unfortunate inci- methylisocyanate, a highly toxic gas, is dents, but it was not until the effects of one notable exception. air pollution on major cities were idenDetermining the chronic effects of tified, in London (3)and New York (9, air pollution on health and mortality that control efforts were taken seri- presents an extremely subtle challenge ously. It is the nahlre of the problem to physicians and statisticiansalike. Dithat even in a serious episode the di- rect experiments involving long-term 0013938wByo919076uo1.5On @ lSe5 American Chemical Society
clinical exposures of humans to air pollution are out of the question; analysis must be based on indirect observational studies--“nahwaY experiments. These have become the domain of the statistician, but some important statistical issues have only recently been recognized, despite more than 15 years of published research on the topic. However, as has often been stated, statistical association alone can never establish causality; it is important to invoke physiological factors in considering the reasonableness of hypotheses. Mortality is a useful end point in studying the health effects of air pollution only because of the ready availabdity of data. Studies undoubtedly would be more definitive if an end point were found that was unique to air pollution instead of one that is inevitable for us all. The problem of defining excess deaths is difficult; the problem of assigning cause is even more so. There is no cause of death that is associated only with air pollution, and those studies that have reported large effects due to air pollution have usually found them in broad cause-of-death categories, such as heart disease. This may result in part from undue reliance on examining the underlying ( i e d i a t e ) cause of death rather than on studying the contributing causes. Heart disease is in some sense a catchall cause of death because it is such a large fraction of the total. Three varieties of studies have been used to explore relationships between air pollution and premature mortality: those dealing with air pollution episodes, time-series analyses of daily mortality variations, and cross-sectional analyses of geographic variations in long-term (annual)death rates. Each has its strengths and weaknesses. Most observers would probably agree that taken together, they do indicate a positive association between mortality rates and air quality, usually as represented by some sort of measure of particulate matter. Questions remain, however, about the meaning of the relationship, especially in the context of application to risk assessment and benefit-cost analysis (5).This paper explores various interpretations of the many studies in the literature. It is an exploration that must in part be subjective, and it suggests avenues for future research. There have been reviews of this topic (6, 7), and I will try to minimize the plowing of old ground, even though some understanding of the older studies is required in order to evaluate the newer ones. Indeed, new results continue to be published, based mainly on old data, and it is probably safe to say that the definitive study has not yet appeared. Although advanced statistical
Definitions for nenstatisticians Multiple regression analysis. An analysis that attempts to explain the variance in a dependent variable (v) in terms of linear combinations of a number of independent predictor variables (x;). Rz.The square of the correlation coefficient, relating the independent variable to its predictors. This is numerically equal to the fraction of the variance in y explained by the xi. Regression coefficient. A measure of the magnitude of the effect of a preditor variable x; on the dependent variable roughly analogous to 8y/6xj,. Regression coefficients can be tested to determine whether the effects they represent are statistically significant, provided certain conditions are met. Multicollinearity. The degree to which the predictor variables xi (or linear combinations of predictor variables) resemble one another. If xi is similar to xi, it will not be possible to use linear regression analysis to distinguish their relative effects on y. Dummy variable. A predictor variable that represents the presence or a b sence of a qualitative predictor condition by taking on the value of 0 or 1. Tests of statistical significancecan be used to determine whether the regression coefficientfor such a variable, and therefore the qualitative effect, is statistii cally significant. Spatial autocorrelation analysis. A technique used to determine whether a variable shows a pattern in a geographic space. Its relevance to regression analysis is that the error term from a regression must be randomly distributed in order for the regression tests of significanceto be valid. Pattern in gee graphic space among the errors can thereforeinvalidate the significancetests for a cross-sectional regression. Temporal autocorrelation (serial correlation) analysis. This is the same as spatial autocorrelation analysis, except that temporal persistence (similarity among sequential events) is substituted for spatial patterns.
analysis techniques are a necessary component of study in this field, I have tried to avoid using statistical jargon and notation.
Short-term effects Studies of acute responses to air pollution (fatalities that occur within a few days of exposure) have generally fallen into two groups. The first is studies of episodes during which air quality deteriorates so markedly that the event (during which a mortality response might be expected) can be clearly delineated. In this case, the excess mortalities can be determined simply by comparing daily death counts before, during, and after the episode. The see ond variety is the time-series analysis of relatively longer periods of routine monitoring that may or may not include episodes. Because episodes usually result in simultaneous, elevated concentrations of all air contaminants (SO,, particulates, NO,, and CO), it is frequently less straightforward to isolate the responsible pollutants. Tim-series analysis for longer periods, of one year or more, also can pose the problem of copollutants with similar temporal patterns and that of confounding due to other t e m p ral influences on mortality rates. These confounding factors can include flu epidemics, holiday and weekend effects of
reporting artifacts, seasonal cycles, and long-term changes in the population under study. Various types of moving averages, or “filtering,” have been used to deal with serial correlation and with cyclical variations unrelated to pollution (8, 9). Even though the severe episodes occurred many years ago, and the air pollution levels recorded then are unlikely to be experienced ever again-at least in the developed countries-statisticians are still grappling with the issue of trying to separate SO2 effects from smokeshade effects. (Smokeshade is a measure of fine particulate matter responding to the degree of staining of fiter paper, roughly equivalent to TSP [total suspended particulate matter] at higher concentration levels. It is measured in units of coefficient of haze [COH].) In the worst days of the London and Paris episodes, there were high levels of both S q and smokeshade, which made the disentanglement quite difficult (8, IO). In addition, the results appear to be somewhat sensitive to the methods used to adjust for seasonal or temperature effects (11). The most consistent statistical association found in studies of episodes and timeseries is with particulate matter (6, 8, 9, 11-13). Some relationships also have been seen in London at very high levels of Sq. Several studies have Environ. Sci. Technol.. MI. 19, NO.9, 1985 765
identified mortality effects due to carbon monoxide, but these findings have been largely overlooked (9, 13, 14). Figure 1 shows excess mortality in relation to particulate matter. Major e p isodes are shown on the right as points (with excess mortality ranges, where available). The results of time-series analyses are displayed as lines indicating the slope of excess mortality with respect to particulate matter. These lines pass through the mean values for each study. Although many different kinds of data from a variety of sources have been combined on this graph, it may be useful to identify common patterns and trends. The time-series coefficients range from 0.6% to 3% excess mortality per COH unit (roughly 100 pg/m3 TSP). They are consistent with a multiple-regression analysis of the episodes, which found a coefficient of 1.1% excess mortality per COH unit (6). Furthermore, this agreement over such a large range of particulate levels indicates that if a threshold of no effect is present, it lies below the ranges shown here. What do these findings mean? lbkey referred to such studies as indicating “the timing of death,” which is certainly true (15). However, those few studies that have examined lag effects found no evidence of death rate s u p pression immediately after the event, indicating the degree of prematurity of these deaths is at least more than a few days (9, 12). However. at current US. oollution levels, the’practical risks enthed are modest. If one million persons were exposed simultaneously to the current 24-h TSP standard (2aO pg/m3), an average of less than one excess death would occur in that population, based on the U.S. average death rate and the average time-series coefficient from Figure 1.
Long-term mortality Different methods are required to deduce the potential effects of pollution on long-term mortality rates. Because so many other factors (demography, migration, health care, life-styles) vary slowly with time, spatial differences must be employed. As Evans et al. point out, because annual mortality rates by deffition include the cumulative effects of short-term episodes, cross-sectional analysis is likely to cap ture geographic differences in both long and short terms (16). The ramifications of various outcomes from the comparison of time-series vs. cross-sectional mortality coefficients have been studied and will be considered further (17). There have been many cross-sectional mortality studies; Evans et al. listed more than 50 references on the 766 Environ. Sci. Technol.. Vol. 19.NO.9,1985
FIGURE 1
Relationship of excess mortality to smokeshade (acute exposures)
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Equivalent smoke concentrations topic (7). Table 1 displays results from several of the studies that have received the most attention; some of them form the basis for risk assessments and policy studies. The great variation in the regression coefficients in Table 1 is striking, especially in comparison with the relatively good agreement among timeseries shown in Figure 1. Much of the controversy about crosssectional mortality regression has focused on the results for suspended sulfates. Large regression coefficients are shown in some studies (20, 22, 24); others report Coefficients that are near zero or are negative (I7,23).The large coefficients lead to statements associating tens of thousands of annual deaths with fossil fuel combustion (5). Similar statements have not been issued for TSP, perhaps because of the difficulty in assigning ambient levels to specific types of controllable sources. Because further control of sulfur oxide emissions is being considered as a control strategy for acid rain, there are important policy considerations involved. cross-sectionalstudies
In a recent review of the standardsetting process, Ferris listed some criteria for acceptability of epidemiological studies (27). These are shown in Table 2, where I have evaluated some of the major cross-sectional studies from Table l against Fenis’s criteria. It can be
seen that they all have shortcomings. (Because these are admittedly subjective evaluations, perhaps they should be interpreted as relative rankings rather than as absolutes [in order of desirability, Y > ? > N). Those studies with many different sets of regressions (Table l) are evaluated in the context of the authors’ main conclusions. All of them rely on outdoor measurements of air quality rather than indoor exposures, which can be important both because of the amount of time spent indoors and because indoor concentrations of toxic substances often are higher (except perhaps for sulfur oxides) (28). Perhaps the most difficult methodological issues for cross-sectional studies involve multicollinearity and spatial autocorrelation. With analysis of an air pollution episode, these problems are eased. The population under study serves as its own control, and one simply observes its response to elevated concentrations. To the extent that independent repeat incidents may have occurred, it is not necessary to worry about temporal autocorrelation. There is a limit to the number of conceivable factors that can influence the timing of death; virtually all have been considered in the studies cited, and, in any event, filtering can remove some of them. In analyzing geographically distributed phenomena through cross-sec-
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TABLE 2
Subjective evaluation of Ferris’s c
Control of confoundina or collinear variables
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(7,17,20.23,24). Deleting or including specific locations in the analysis and varying the choice of geographic units (city, county, or Standard Metm politan Statistical Area [SMSA]) also can affect regression Coefficients (7. 17,24). These cross-sectional study design factors account for much of the variabiity of the coefficients shown in Table 1 but do not really explain the phenomenon. The meaning of these results, taken as a group, is not readily apparent. Spatial autocorrelation has not been addressedformally by any of the major cross-sectional studies, but was briefly considered by Evans et al. (7). One requirement of regression analysis is that the residuals be independent. Plots of their distribution can be used to meet this requirement. If the residuals display a spatial pattern, regionality, for example, the significance of regression coefficients may be overstated because
tional analysis, there are many factors in addition to air pollution that must be taken into account because of possible multicollimearity. No one has a theoretical model that delineates these factors, and because of similarity in geographic patterns, it is difficult to decide whicb variables are truly independent. The forces that create air pollution, such as industry and urban agglomeration, also could conceivably affect longevity or health through pathways unrelated to inhalation. These include exercise, the quality of drinking water, overcrowded living conditions, income, and other vagaries of life-style (29). Cigarette smoking is a prime candidate variable for collinearity considerations because it damages the same organs as air pollution. In addition, the percentage of sensitive individuals may be related to factors such as migration and ethnic backpund, which tend tc be regionally distributed. Leamer pr+ vides some guidance in making inferences h m data of this kind, including fmding the “tm” model, selecting data sets, and using proxy variables (30). The distinction between finding the true model and evaluating its coefficients must be kept in micd. When all of these factors are combiwd in multiple regressions, independence and collinearity become matters of dem.It is necessary to use multiple repssions with many difirent combinations of variables (or models) to explore these interrelationships. The extent to which this has been accom. plished in studies in the literature is var iable. Several studies have shown explicitly that inclusion of these factors of lifestyle can drastically influence pollution coefficients, especially that for sulfate 768 Envimn. sci. Technci..MI. 19, NO.9, 1905
Studies ol smoking and hea There are some similarities and some important diierences between stud‘ of chronic air pollution health effects and the effects of smoking. Both depe heavily on statistical associat’m and suffer from our imprecise understandi of detailed causal mechanisms. One of the early indicators of the effects smoking on lung cancer was a cross-sectional study of tobacco use and Iu cancer rates in 11 different countries, with R2 0.5. However, unlike the chronic air pollution studies, studies of smoking gone on to characterize differential mortality rates between exposed nonexposed persons, to establish dose-response relationships,and to e lish viable causal hypotheses for specific causes of death. Smoking is i cated as the cause of 85% of all lung cancer deaths, 70% of deaths chronic obstructive lung disease, and 30% of coronary heart disease, which comes to about 15% of all deaths. Although there are thousands of different compounds in cigarette s m including “tars,” various polycyclic hydrocarbons. CO, and NO2,any of w could be responsible. some general causal Cancers (principally lung cancer) seem to b late phase in smoke. Other lung diseases and,impaired respiratory appear to be associated with the gas phase. Smokers of low-tar cigarettes do not show a lower incidence of heart disease than smokers of regular cigarettes do, leading to the h that gas-phase pollutants may be more important than particulates. such findingsare complicated by the tendency of low-tar smokers to compel sate for nicotine loss by smoking more or by inhaling more deeply Recent studies have gone on to associate sidestream smoke (p smoking) with certain health effects. This may have important implication community air pollution health studies. Siestream smoke tends to h smaller particles and more NO2; it has been linked to lung cancer and paired lung function but not to cardiwascular disease. How do these facts relate to community air pollution? First, compare doses. A onepack-perday smoker receives dosages equivalent to 24 breathing air wRh 10,000 pglm3 of inhalable particles, 20 ng/m3 benzo[a rene (Elalp), 6000 pg/m3 CO, 15 pglm3 NO2, and 2 pg/m3 SO2 (more if H S is oxidized). Sidestream smoke will have considerably higher conce tion levels, especially of NO2, but it becomes diluted in a ventilated room.
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exchange rates and extrapolate downwa community air polluti the gaseous doses Because B[a]P is on
variances are underestimated. Such regionality may derive from demographic factors not accounted for by the model, such as ethnic background, genetic heritabiity, dietary patterns, occupational patterns, or other regional factors of life-style. Real spatial autocorrelation, such as disease transmission, must be considered unlikely for the causes of death important to air pollution. Sulfates tend to be regionally distrib uted, and thus more subject to problems of spatial autocorrelation and multicoll i t y that results from regional factors than are more localized pollutants. For example, in Lave and Seskin’s analysis of 1960 mortality rates (2@, use of dummy regional variables re.sulted ina loss of statistical significance for the sulfate variable but not for the TSP coefficient. A reanalysis (23) of the Lave and Seskin 1%9 data set ac-
Acute mortality effects of sulfate Is it possible that the relatively large and somewhat variable mortality-sulfate regression coefficients derived from cross-sectional studies (Table 1) are actually short-term rather than chronic effects? The infrequent sampling schedule used in most particulate networks has thus far precluded an adequate timeseries analysis of daily mortality for sulfate. A crude way to test this hypothesis would be to compare seasonal cycles of mortality in high-sulfate locations against those found in lowsulfate locations, using the seasonal sulfate gradient the independent variable. The variables of interest would be as follows: July deaths vs. (July S04i2- January SO4-? January deaths Use of this mortality ratio eliminates the usual confounding variables of crosssectional analysis (smoking, diet, etc.), but may introduce weather-related causes of death instead. If a relationship were found, it could be expressed as the percentage of excess deaths per pglm3 of sulfate. Table 1 shows that previous estimates rangefrom essentially zero to 125% per pg/m3(expressed in the table as 125% per 1W pglm3).Two data sets were used to evaluate this acute sulfate response hypothesis. They define acute response as a seasonal relative increase in mortality They were 1960 JulylJanuary mortality vs. 1957-61 quarterly measured sulfate difference (23states) ( I ) and 1979 July/January mortality vs. computed sulfate aerosol concentrations based on seasonal ASTRAP model runs for 1980 (48 states and Washington, D.C.) (2). Neither study showed a statistically significant association between mortality id sulfate. The 1960 data gave a negative result (r = -0.26, slope = -0.6% cess deaths/pg/m3). The 1979 data showed a positive relationship of 0.35% cess deathsluolm3fr = +0.1141. I noted that the seasonal cvcle in mortalitv has evened oui iconsiherablj behbeen the two periods (1960{ju&/January 2 0.814; 1970: JulyNanuary = 0.860,1979: JulyNanuary = 0,902). This shift aeems to rule out temperature effects as a major factor in the seasonal variabilof mortality. .&alt should also be noted that for the Table 1 sulfate coefficients to represent r u t e rather than chronic effects, sulfate aerosol (which is usually mostly innoe wus (NH&SO,) would have to be 100 times more irritating than SO2 (gas) Amdur’s studies on guinea pigs show only a factor of lour greater irritancy fo &SOI over SO2.and the more commonly found ammonium sulfate salts werc *ss irritating than SOz (3). If the acute mortality effects of S04-2 are to bt waluated seriouslK deaths must be segregated by age and cause and perhap! averaged over several years in order to reduce variability. Even at that. it will bc difficult to rule out the confounding effects of ozone or fine particles, which ma! beve similar seasonal profiles. ~~
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“Air Pollution Measurements 01 the National Air Sampling Network-Anal ses 01 Suspended Particulates, 1957-1961 ,” PHS publication 978; Public Health zervice Department 01 Health, Education and Welfare: Wasnington, D.C.. 1962 I J. Shannon, Argonne National Laboratory, personal communication. 1984. 1 Lewis, T. R. et 81. “Toxicology of Atmospheric Sulfur Dioxide Decay Producl- ” 111, Environmental Protection Agency. Research Triangle Park, N.C., 1972.
complished the same result using specific variables, such as diet and water hardness, rather than dummy variables. In contrast, however, Mendelsohn and Orcutt achieved statistical significance for sulfate in their analysis with dummy regional variables included (22). As a result, there is no consensus in the literature. Critiques on cross-sectional regressions discuss these practical and statistical difficulties (7, 23).
Time-series vs. cross-sectional Several interesting hypotheses can be tested by comparing regression results from timeseries and cross-sectional studies. As mentioned, cross-sectional studies include chronic and acute effects alike. I f two cities show different mean values for a given pollutant and similar temporal distributions (e.g., lognormal), then it will be impossible to tell whether the effect i s long term or short term. In general, we would expect the long-term effects (or regression coefficients, &) to exceed the short-term ones @Ir,), because the timeseries analyses usually deliberately fiiter out the slowly varying components of mortality. Suppose that p,, i s found to equal @,* for a given pollutant. TSP comes closest to satisfyiig this condition, because the median TSP coefficient @Ixs, Table 1) i s near the upper end of the timeseries data shown in Figure 1. The implication would be that there are no long-term TSP effects and that there is no noeffect threshold for short-term TSP effects. The mechanismleading to premature mortality would be seen as a short-term insult, presumably leading to stress on the cardiovascular system, as opposedto a long-term accumulation of lung deposits and progressive cardiovascular deterioration. Parity in & vs. 8, by age group and cause of death Envimn. Scl Techrml., MI. 19, No. 9, 1985 769
should be sought as well, in order to verify this hypothesis. Alternatively, it is possible that 0, > This would imply truly longterm effects caused by previous e x p sures rather than current-year air quality. Lave and Seskin indeed found somewhat better relationships between 1969 mortality and 1960 air quality than with 1969 air quality (2@, although this finding must be tempered by consideration of the flawed 1960 aerometric d a t a used (31). Conflicting results were found in another study, in part because of insufficient observations for cities with adequate data for both periods (17). It is also possible that the uncertainty with regard to the effects of related pollutants (TSP vs. sulfate, for example) is partly the result of confounding between long-term and short-term effects. Although the comparison of such completely different kinds of studies (cross-sectional vs. time-series) is a speculative endeavor, a more complete investigation of the effects of truly long-term exposures is clearly needed. What can he concluded from 20 years Of study? we can be fairly confident that some portion o f the SO2-particulate mixture affects daily mortality at high concentrations. There may be other important pollutants, such as carbon monoxide, that have not yet been fully investigated. H ~ hecause ~ we me unlikely to =e high concentrations occurring again, at least in the u,s,, our concerns may be tempered somewhat. For chronic effects, we must be less sanguine. It is noteworthy that experts disagree and that the recent s?2narticulate criteria document (32)relies .~~~~~~~~ heavily on the same studies cited 15 years previously in the first such documen1 and on few of the studies cited here. People do tend to live longer in ~~~~~
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the western parts o f this country than in the East. Is this the result of life-style, ethnic stock, self-selection, or clean air? Most likely, all of the above. Better data and statistical methods are now in hand to help us try once more to resolve this problem; let‘s hope the resources can be found as well.
Acknowledgment I am indebted to the reviewers for their helpful comments. particularly for reminding me of the relationships between crosssectional and time-series studies. I also benefited from discussions with many colleagues on the ideas presented here; any errors or omissions are mine alone. The manuscript was typed by Donna Cange and Elizabeth Seubert. Financial support was provided by the Department of Energy under contract DE-AC02-76CH00016. Before publication this article was reviewed for suitability as an ES&T feature by Robert Harris, University of North Carolina School of Public Health, Chapel Hill, N.C. 27514; lulian Andelman, University of Pittsburgh, School o f Public Health, Pinsburgh, Pa. 15261; and John Evans, Harvard School of Public Health, Boston. Mass. 02115.
(12) Schimmel, H. Bull. N . P Acod. Med. 1978.54, 1052-1109. (13) Buechley. R . W. “SO1 Levels. 19671972. and Perturbations in Mortality,” report to National Institutes of Health. September 1975. (14) Hater, A. C.; Goldsmith. J. R. Science 1971, 172, 265-67. (15) Tukey. J. Bull. N . P Acad. Med. 1978, 54. 1 1 1 1 . (16) Evans. J. S. et al. J. Air Pollui. Control Assoc. 19234.34, 551-53. (17) Lipfert, F. W. Ph.D. Dissertation. Union Graduate School. Cincinnati, Ohio, 1978. (18) Gregor, J. J. “Mortality and Air Quality, the 1968-1972 Allegheny County Experience.” Report No. 3 0 Center far the Study of Environmental Policy, Pennsylvania State University: University Park. Pa.. 1977. (19) Kitagawa, E. J.; Hauser. !I M. ”Differential Mortality in the United States: A Study in Socio-economic Epidemiology”; Harvard University Press: Cambridge, Mass., 1973. (20) Lave. L. B.; Seskin. E. P “Air Pollution and Human Health, Resources for the Future”; Johns Hopkins University Press: Baltimore, Md.. 1978. (21) Crocker. T. D. et SI. “Methcds Development for Assessing Air Pollution Control Benefits,” EPA-600/5-79-001a: Environmental Protection Agency: Washington. D.C., 1979; Vol. 1. (22) Mendelsohn, R.; Orcutt, G. J. Emiron. Econ. Manag. 1979.6. 85-106. (23) Lipfert. F. W. J. Environ. Econ. Monog. 1984, 1 1 . 208-43. (24) Chappie. M.; Lave, L. 1. Urbon Econ. 1982, 12. 346-76. (25) G6ttinger. H. W. Environ. Int. 1983, 9, 207-20. (26) Lipfert. E W. J. Emiron. Econ. Manag. 1983, IO. 184-86. (27) Ferris, B. 1..Jr., presented at APCA Specialty Conference on the Proposed SO, and Particulate Standard. Atlanta, Ga., 1980. (28) Spengler. J. D.; Ferris. B. G.; McCarthy, 1. Paper 78-60.2, presented at 7151 Annual Meeting of the Air Pollution Control Asrocation, 1978 (29) Marks. J.S. et al., presented at 19th Annual Meeting of the Society of Prospective Medicine, Atlanta, Ga.. 1983. (30) Learner. E. E. “Specification Searches. Ad HK Inference with Non-experimental Data”; Wiley: New York, N.Y.. 1978. (31) Lipfert, F. W. Energy Sysi. Pol. 19330. 3 , 4. (32) Environmental Criteria and Assessment Office. “Air Quality Criteria for Particulate Matter and Sulfur Oxides.” EPA-60018-82029c; Environmental Protection Agency: Research Triangle Park. N.C.. 1982; Vol. 111.
References (1) Firket. J. Trans. Forndoy Soc. 1936, 32.
1192-97. (2) Schrenk. H. H. et al. U S . Public Health Service Bulletin 306. 1946. (3) Logan. W.PD. Loncei 1953,264, 336-38. (4) MCC~WOII.J.; Bradley. w. Am. J. Public Healrh 196656. 1933-42. ( 5 ) “Acid Rain and Transported Air Pollu~ Implications ~ ~ , for~Public Policy,” OTAtants: 0-204; U.S. Congress. Office of Technology Assessment: Wafhington. D.c., 1984. (6) Lipfert. F. W. J. Air Pollur. Conirol Asroc. .M”
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(7) Evans, J. S.; Tosteron. T; Kinney, P L. Environ. hi. 1984.10, 55-83. (8) Mazumdar, S . ; Schimmel, H.; Higgins, I.T.T. Arch. Environ. Heolrh 1982.37, 213. (9) Wyzga, R. E. J. Am. Stor. ASSOC. 1978,
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.-.
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(IO) Lcewenstein. J.C.; Bourdel, M.C.; Bertin, M. Rev. Epidemiol. Med. Soc. Sonre
Publiqur 1983.31. 143-61. (11) Schimmel. H; Murawski, T.J. J. Med. 1976.18, 316-33.
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Frederick W IipJert i i (I p l u p leader in Brookhaven Nurional Laboratory k Department oJApplird Science. He holds degrees in engineering from the University of Cincinnati and a Ph.D. in environmental studies from the Union Graduate School, also in Cincinnati. At Brookhaven, his research focuses on acid rain and airpollution issues, especially materials damage and dispersion modeling and ana1ysi.s.
