Residential firewood use in the United States.

An empirical relation between residential firewood use and population density was developed from survey data for 64 counties in New England and was corroborated by data from other states. The results indicate that usage is concentrated in urbanized areas of the Northeast and north central states and that about 9.0 to 11.0 percent of U.S. space heating input is from firewood. No constraints due to the supply of wood were apparent in 1978-1979. These findings have implications for effects on air quality.

Vehicular traffic effects on survival within the Washington University-EPRI veterans cohort: new estimates and sensitivity studies.

We analyzed survival patterns among approximately 70,000 U.S. male military veterans relative to vehicular traffic density in their counties of residence, by mortality period and type of exposure model. Previous analyses show traffic density to be a better predictor than concentrations of criteria air pollutants. We considered all subjects and also the subset defined by availability of air quality monitoring data from the U.S. EPA PM(2.5) Speciation Trends Network (STN). Traffic density is a robust predictor of mortality in this cohort; statistically significant estimates of deaths associated with traffic range from 1.3% to 4.4%, depending on the method of analysis. This range of uncertainty is larger than the traditional 95% confidence intervals for each estimate (1-2%). Our best estimate of the relative risk for the entire follow-up period is 1.03. These deaths occurred mainly before 1997 in counties with STN air quality data, which tend to be more urban. We identified a threshold in mortality responses to traffic density, corresponding to county-average traffic flow rates of about 4000 vehicles/day. Relative risks were significantly higher in the more urban (STN) counties in the early subperiods, but this gradient appears to have diminished over time. We found larger risks by pooling results from separate portions of the overall follow-up period, relative to considering the entire period at once, which suggests temporal changes in confounding risk factors such as smoking cessation, for example. These results imply that the true uncertainties in cohort studies may exceed those indicated by the confidence intervals from a single modeling approach.

PM2.5 constituents and related air quality variables as predictors of survival in a cohort of U.S. military veterans.

Air quality data on trace metals, other constituents of PM2.5, and criteria air pollutants were used to examine relationships with long-term mortality in a cohort of male U.S. military veterans, along with data on vehicular traffic density (annual vehicle-miles traveled per unit of land area). The analysis used county-level environmental data for the period 1997-2002 and cohort mortality for 1997-2001. The proportional hazards model included individual data on age, race, smoking, body mass index, height, blood pressure, and selected interactions; contextual variables also controlled for climate, education, and income. In single-pollutant models, traffic density appears to be the most important predictor of survival, but potential contributions are also seen for NO2, NO3-, elemental carbon, nickel, and vanadium. The effects of the other main constituents of PM2.5, of crustal particles, and of peak levels of CO, O3, or SO2 appear to be less important. Traffic density is also consistently the most important environmental predictor in multiple-pollutant models, with combined relative risks up to about 1.2. However, from these findings it is not possible to discern which aspects of traffic (pollution, noise, stress) may be the most relevant to public health or whether an area-based predictor such as traffic density may have an inherent advantage over localized measures of ambient air quality. It is also possible that traffic density could be a marker for unmeasured pollutants or for geographic gradients per se. Pending resolution of these issues, including replication in other cohorts, it will be difficult to formulate additional cost-effective pollution control strategies that are likely to benefit public health.

A critical review of studies of the association between demands for hospital services and air pollution.

Studies of the associations between air pollution and hospital admissions and emergency room use are reviewed, including studies of air pollution episodes, time-series analyses, and cross-sectional analyses. These studies encompass a variety of methods of analysis and levels of air quality. Findings from all three types of studies were generally consistent in that almost all of the studies reviewed found statistically significant associations between hospital use and air pollution; this unanimity may have resulted in part from publication bias. These associations were characterized by elasticities of the order of 0.20; i.e., a 100% change in air pollution was associated with a change in hospital use of about 20%, for specific diagnoses. Respiratory diagnoses were emphasized by most studies; cardiac diagnoses were included in five of them. The air pollutants most often associated with changes in hospital use were particulate matter, sulfur oxides, and oxidants. Apart from the major air pollution episodes, there was no obvious link between air pollution level and the significance or magnitudes of the associations. Long-term indicators of hospitalization appeared to also be influenced by medical care supply factors, including the numbers of beds and physicians per capita. These nonpathological causal factors could also have influenced the findings of the time-series studies by introducing extraneous factors in the patterns of admissions. Although consistent associations have been shown between hospital use and air pollution, further research is required to distinguish among potentially responsible pollutants and to deduce specific dose-response relationships of general utility.

Probabilistic assessment of health risks of methylmercury from burning coal.

This paper describes a probabilistic assessment of neurological risks incurred from consuming fish containing methylmercury (MeHg), focusing on the incremental effects of Hg deposited from local coal combustion. A Monte Carlo model is used to simulate a "worst case" scenario in which a population of 5000 fish eaters in the upper midwestern United States derive the freshwater fish portion of their diet from local waters near a hypothetical large coal-fired power plant. This population is characterized by distributions of body mass, half-life of MeHg, and the ratios of blood to body burden and hair to blood MeHg. Each person's diet consists of varying amounts of tuna fish, freshwater sportfish, and marine fish and shellfish, the MeHg contents of which are characterized by national distribution statistics, as are the consumption rates for marine fish. The consumption rates for freshwater fish are specific to the region. The fish portion size is linked to body mass by a variable correlation. Each meal is assumed to be an independent sample; thus, as metabolic equilibrium is approached, each person's body burden of MeHg tends to approach the value corresponding to the mean MeHg intake for the population. Predictions of MeHg levels in hair by this model compared well with an observed distribution of 1437 women. Two neurological endpoints were examined: adult paresthesia, as related to MeHg body burden, and congenital neurological effects, as associated with average concentrations of MeHg in maternal hair during pregnancy. Two exposure scenarios are considered: a "baseline" in which the source of the mercury in fish is from background atmospheric deposition, and an "impact" scenario, in which local Hg deposition and concentrations in fish are roughly doubled to represent additional deposition from the hypothetical nearby power plant. For both scenarios, the 99th percentile of MeHg body burden was more than an order of magnitude below the lowest level at which increased transient adult paresthesia was experienced in an acute MeHg poisoning incident in Iraq. We thus conclude that neurological risks to adults from MeHg resulting from atmospheric Hg deposition are trivial. Based on three epidemiological studies of congenital neurological risks, we find that fetal effects appear to be more critical and that there is a smaller margin of safety for pregnant consumers of freshwater sportfish. However, the margin of safety is still considerable and may have been diminished by uncertainties in the relationships between maternal hair Hg and the actual fetal exposures.

Mortality and air pollution - lessons from statistics.

This paper reviews cross sectional studies which attempt to link persistent geographic differences in mortality rates with air pollution. Some early studies are mentioned and detailed results are given from seven major contemporary studies, two of which are still in the publication process. Differences among the studies are discussed with regard to statistical techniques, trends in the results over time (1959-1974), and interpretation and use of the results. The analysis concludes that there are far too many problems with this technique to allow causality to be firmly established, and thus the results should not be used for cost benefit or policy analysis.

Statistical studies of mortality and air pollution. Multiple regression analyses by cause of death.

Multiple regression analyses are presented relating community air quality, socioeconomic variables, and mortality rates for all cancers, respiratory system cancer, respiratory disease, and external causes, for U.S. cities for 1969-1971. Socioeconomic variables included an index of cigarette smoking (by state), which was usually highly significant. Most air pollution variables were not significant, however, with the exception of the trace metal manganese, which was associated with cancers and respiratory disease. Because of the low ambient concentrations in this study, it is likely that manganese is serving as a surrogate for some other effect, such as occupational influences for example.

Air pollution and mortality: the implications of uncertainties in regression modeling and exposure measurement.

In a previous paper, we showed that the mean effects on daily mortality associated with air pollution are essentially the same for gases and particulate matter (PM) and are invariant with respect to particle size and composition, based on 27 statistical studies that had been published at that time. Since then, a new analysis reported stronger mortality associations for the fine fractions of PM obtained from dichotomous samplers, relative to the coarse fractions. In this paper, we show that differential measurement errors known to be present in dichotomous sampler data preclude reliable determination of such statistical relationships by particle size. Further, it is necessary to consider gaseous pollutants simultaneously with particles to provide robust estimates of the responsibilities for the implied daily mortality gradients. Finally, certain regression model specifications may be sensitive to differences in frequency distribution characteristics according to particle size.

Statistical considerations in determining the health significance of constituents of airborne particulate matter.

Because of the U.S. Environmental Protection Agency's (EPA) new ambient air quality standard for fine particles, the need is likely to continue for more detailed scientific investigation of various types of particles and their effects on human health. Epidemiology studies have become the method of choice for investigating health responses to such particles and to other air pollutants in community settings. Health effects have been associated with virtually all of the gaseous criteria pollutants and with the major constituents of airborne particulate matter (PM), including all size fractions less than about 20 microns, inorganic ions, carbonaceous particles, metals, crustal material, and biological aerosols. In many of the more recent studies, multiple pollutants or agents (including weather variables) have been significantly associated with health responses, and various methods have been used to suggest which ones might be the most important. In an ideal situation, classical least-squares regression methods are capable of performing this task. However, in the real world, where most of the pollutants are correlated with one another and have varying degrees of measurement precision and accuracy, such regression results can be misleading. This paper presents some guidelines for dealing with such collinearity and model comparison problems in both single- and multiple-pollutant regressions. These techniques rely on mean effect (attributable risk) rather than statistical significance per se as the preferred indicator of importance for the pollution variables.

Air pollution and mortality: issues and uncertainties.

Results from 31 epidemiology studies linking air pollution with premature mortality are compared and synthesized. Consistent positive associations between mortality and various measures of air pollution have been shown within each of two fundamentally different types of regression studies and in many variations within these basic types; this is extremely unlikely to have occurred by chance. In this paper, the measure of risk used is the elasticity, which is a dimensionless regression coefficient defined as the percentage change in the dependent variable associated with a 1% change in an independent variable, evaluated at the means. This metric has the advantage of independence from measurement units and averaging times, and is thus suitable for comparisons within and between studies involving different pollutants. Two basic types of studies are considered: time-series studies involving daily perturbations, and cross-sectional studies involving longer-term spatial gradients. The latter include prospective studies of differences in individual survival rates in different locations and studies of the differences in annual mortality rates for various communities. For a given data set, time-series regression results will vary according to the seasonal adjustment method used, the covariates included, and the lag structure assumed. The results from both types of cross-sectional regressions are highly dependent on the methods used to control for socioeconomic and personal lifestyle factors and on data quality. A major issue for all of these studies is that of partitioning the response among collinear pollution and weather variables. Previous studies showed that the variable with the least exposure measurement error may be favored in multiple regressions; assigning precise numerical results to a single pollutant is not possible under these circumstances. We found that the mean overall elasticity as obtained from time-series studies for mortality with respect to various air pollutants entered jointly was about 0.048, with a range from 0.01 to 0.12. This implies that about 5% of daily mortality is associated with air pollution, on average. The corresponding values from population-based cross-sectional studies were similar in magnitude, but the results from the three recent prospective studies varied from zero to about five times as much. Long-term responses in excess of short-term responses might be interpreted as showing the existence of chronic effects, but the uncertainties inherent in both types of studies make such an interpretation problematic.

Infant mortality and air pollution: a comprehensive analysis of U.S. data for 1990.

This paper uses U.S. linked birth and death records to explore associations between infant mortality and environmental factors, based on spatial relationships. The analysis considers a range of infant mortality end points, regression models, and environmental and socioeconomic variables. The basic analysis involves logistic regression modeling of individuals; the cohort comprises all infants born in the United States in 1990 for whom the required data are available from the matched birth and death records. These individual data include sex, race, month of birth, and birth weight of the infant, and personal data on the mother, including age, adequacy of prenatal care, and smoking and education in most instances. Ecological variables from Census and other sources are matched on the county of usual residence and include ambient air quality, elevation above sea level, climate, number of physicians per capita, median income, racial and ethnic distribution, unemployment, and population density. The air quality variables considered were 1990 annual averages of PM10, CO, SO2, SO4(2-), and "non-sulfate PM10" (NSPM10--obtained by subtracting the estimated SO4(2-) mass from PM10). Because all variables were not available for all counties (especially maternal smoking), it was necessary to consider various subsets of the total cohort. We examined all infant deaths and deaths by age (neonatal and postneonatal), by birth weight (normal and low [< 2500 g]), and by specific causes within these categories. Special attention was given to sudden infant death syndrome (SIDS). For comparable modeling assumptions, the results for PM10 agreed with previously published estimates; however, the associations with PM10 were not specific to probable exposures or causes of death and were not robust to changes in the model and/or the locations considered. Significant negative mortality associations were found for SO4(2-). There was no indication of a role for outdoor PM2.5, but possible contributions from indoor air pollution sources cannot be ruled out, given higher SIDS rates in winter, in the north and west, and outside of large cities.

Daily mortality in the Philadelphia metropolitan area and size-classified particulate matter.

Time-series of daily mortality data from May 1992 to September 1995 for various portions of the seven-county Philadelphia, PA, metropolitan area were analyzed in relation to weather and a variety of ambient air quality parameters. The air quality data included measurements of size-classified PM, SO4(2-), and H+ that had been collected by the Harvard School of Public Health, as well as routine air pollution monitoring data. Because the various pollutants of interest were measured at different locations within the metropolitan area, it was necessary to test for spatial sensitivity by comparing results for different combinations of locations. Estimates are presented for single pollutants and for multiple-pollutant models, including gaseous pollutants and mutually exclusive components of PM (PM2.5 and coarse particles, SO4(2-) and non-SO4(2-) portions of total suspended particulate [TSP] and PM10), measured on the day of death and the previous day. We concluded that associations between air quality and mortality were not limited to data collected in the same part of the metropolitan area; that is, mortality for one part may be associated with air quality data from another, not necessarily neighboring, part. Significant associations were found for a wide variety of gaseous and particulate pollutants, especially for peak O3. Using joint regressions on peak O3 with various other pollutants, we found that the combined responses were insensitive to the specific other pollutant selected. We saw no systematic differences according to particle size or chemistry. In general, the associations between daily mortality and air pollution depended on the pollutant or the PM metric, the type of collection filter used, and the location of sampling. Although peak O3 seemed to exhibit the most consistent mortality responses, this finding should be confirmed by analyzing separate seasons and other time periods.

Air pollution and human health: perspectives for the '90s and beyond.

This paper considers the health effects of air pollution from three perspectives: historical, statistical, and public policy, and also as depicted by the recent epidemiology, primarily mortality studies. The historical perspectives establish the reality of population-based health effects, and they provide data with which to evaluate more recent evidence. Statistical perspectives imply that, while there is strong evidence that associations between air quality and health persist, many details of these relationships remain obscure, especially as to the existence of concentration thresholds that might define safe exposure levels. Additional major questions include the effects of uncertainties in actual pollution exposures, the degree of prematurity of "excess" deaths, and whether the development of new cases of chronic disease is associated with air pollution. Public policy issues center around interpreting the new epidemiological studies in the light of these uncertainties and the analysis and management of the concomitant health risks.

Air pollution and mortality: specification searches using SMSA-based data.

"A series of cross-sectional multiple regressions of 1969 and 1970 U.S. mortality rates is presented for up to 112 SMSAs, against various demographic, environmental, and lifestyle variables. The basic data set is the same as that used by L. B. Lave and E. P. Seskin...for 1969, except that many more independent explanatory variables have been added. Since not all of these variables were available for all of the SMSAs, there were several data sets analyzed, depending on the selection of independent variables. The regression coefficients for air pollution tended to be quite sensitive to both the inclusion of the new independent variables and the selection of data sets."

Temporal patterns in air pollution and hospital admissions.

A 6-year data set of daily counts of admissions to 79 acute care hospitals in Southern Ontario was analyzed in relation to concurrent measurements of air pollution and weather pooled over the same regions, using progressively more sophisticated statistical techniques. The diagnoses studied included a group of respiratory causes and two control diagnoses: accidents and gastrointestinal causes. The 6-year period (1979-1985) was subdivided into six 2-month "seasons" and the area of study was divided into three subregions. Bivariate correlations were found to be significant more often than expected due to chance for all three admissions variables, but accounting for the temporal variation within the 60-day seasons greatly reduced the significance of the control diagnoses. Twenty-four-hour averages for air quality were found to yield more significant associations than peak hourly concentrations. July-August was the only period not having important within-season temporal trends and also had the lowest daily counts for respiratory admissions. Based on a model which accounted for serial correlation, SO2, ozone, and sulfate aerosol were found to be significant predictors of respiratory admissions during July-August. Using cumulative lags increased the magnitude of the estimated response to about 20% of summer respiratory admissions, but no consistent relationships were found which could identify the "responsible" pollutant(s) with certainty. Average pollutant concentrations were generally within U.S. ambient standards.