Rebecca Klemm

Public health and components of particulate matter: The changing assessment of black carbon

In 2012, the WHO classified diesel emissions as carcinogenic, and its European branch suggested creating a public health standard for airborne black carbon (BC). In 2011, EU researchers found that life expectancy could be extended four to nine times by reducing a unit of BC, vs reducing a unit of PM2.5. Only recently could such determinations be made. Steady improvements in research methodologies now enable such judgments. In this Critical Review, we survey epidemiological and toxicological literature regarding carbonaceous combustion emissions, as research methodologies improved over time. Initially, we focus on studies of BC, diesel, and traffic emissions in the Western countries (where daily urban BC emissions are mainly from diesels). We examine effects of other carbonaceous emissions, e.g., residential burning of biomass and coal without controls, mainly in developing countries. Throughout the 1990s, air pollution epidemiology studies rarely included species not routinely monitored. As additional PM2.5. chemical species, including carbonaceous species, became more widely available after 1999, they were gradually included in epidemiological studies. Pollutant species concentrations which more accurately reflected subject exposure also improved models. Natural “interventions” - reductions in emissions concurrent with fuel changes or increased combustion efficiency; introduction of ventilation in highway tunnels; implementation of electronic toll payment systems – demonstrated health benefits of reducing specific carbon emissions. Toxicology studies provided plausible biological mechanisms by which different PM species, e.g., carbonaceous species, may cause harm, aiding interpretation of epidemiological studies. Our review finds that BC from various sources appears to be causally involved in all-cause, lung cancer, and cardiovascular mortality, morbidity, and perhaps adverse birth and nervous system effects. We recommend that the U.S. EPA rubric for judging possible causality of PM2.5. mass concentrations, be used to assess which PM2.5. species are most harmful to public health. Implications: Black carbon (BC) and correlated co-emissions appear causally related with all-cause, cardiovascular, and lung cancer mortality, and perhaps with adverse birth outcomes and central nervous system effects. Such findings are recent, since widespread monitoring for BC is also recent. Helpful epidemiological advances (using many health relevant PM2.5 species in models; using better measurements of subject exposure) have also occurred. “Natural intervention” studies also demonstrate harm from partly combusted carbonaceous emissions. Toxicology studies consistently find biological mechanisms explaining how such emissions can cause these adverse outcomes. A consistent mechanism for judging causality for different PM2.5 species is suggested. A list of acronyms will be found at the end of the article.

Aerosol Research and Inhalation Epidemiological Study (ARIES): Air Quality and Daily Mortality Statistical Modeling— Interim Results

ABSTRACT The Aerosol Research and Inhalation Epidemiological Study (ARIES) is an EPRI-sponsored project to collect air quality and meteorological data at a single site in northwestern Atlanta, GA. Seventy high-resolution air quality indicators (AQIs) are used to examine statistical relationships between air quality and health outcome end points. Contemporaneous mortality data are collected for Fulton and DeKalb counties in Georgia. Currently, 12 months of air quality and weather data are available for analysis, from August 1998 through July 1999. The interim mortality analysis used Poisson regression in generalized additive models (GAMs). The estimated log-linear association of mortality with various AQIs was adjusted for smoothed functions of time and meteorological data. The analysis considered daily deaths due to all nonaccidental causes, deaths to persons 65 years or older, and deaths in each of the two constituent counties. The fine particle effect associated with the four mortality subgroups, using only today (lag 0), yesterday (lag 1), 2-day average (average of today and yesterday), and first difference (today minus yesterday) measurements of the air quality relative to todays number of deaths was positive for lag 0, lag 1, and 2-day average and positive only for decedents at least 65 years of age using first difference. The t values ranged from 0.81 to 1.15 for lag 0, 1.04 to 1.53 for lag 1, 1.10 to 1.66 for 2-day average, and -0.32 to 0.33 for first difference with 346 or 347 days of data. No statistically significant estimate of the linear coefficient was found for the other 14 air quality variables in our interim analysis for the four mortality subgroups. We discuss diagnostics to support these models. These interim analyses did not include an evaluation of sensitivity to a larger set of lag structures, nonlinear model specifications, multipollutant analyses, alternative weather model and smoothing model specifications, air pollution imputation schemes, or cause-specific mortality indicators, nor did they include a full reporting of model selection or goodness-of-fit indicators. No conclusion can be drawn at this time about whether the findings from subsequent studies have sufficiently greater power to detect effects comparable to those found in other U.S. cities including at least 2 or 3 years of data.

Daily Mortality and Air Pollution in Atlanta: Two Years of Data from ARIES

Associations between daily mortality and air pollution were investigated in Fulton and DeKalb Counties, Georgia, for the 2-yr period beginning in August 1998, as part of the Aerosol Research and Inhalation Epidemiological Study (ARIES). Mortality data were obtained directly from county offices of vital records. Air quality data were obtained from a dedicated research site in central Atlanta; 15 separate air quality indicators (AQIs) were selected from the 70 particulate and gaseous air quality parameters archived in the ARIES ambient air quality database. Daily meteorological parameters, comprising 24-h average temperatures and dewpoints, were obtained from Atlantas Hartsfield International Airport. Effects were estimated using Poisson regression with daily deaths as the response variable and time, meteorology, AQI, and days of the week as predictor variables. AQI variables entered the model in a linear fashion, while all other continuous predictor variables were smoothed via natural cubic splines using the generalized linear model (GLM) framework in S-PLUS. Knots were spaced either quarterly, monthly, or biweekly for temporal smoothing. A default model using monthly knots and AQIs averaged for lags 0 and 1 was postulated, with other models considered in sensitivity analyses. Lags up to 5 days were considered, and multipollutant models were evaluated, taking care to avoid overlapping (and thus collinear) AQIs. For this reason, PM2.5 was partitioned into its three major constituents: SO42−, carbon (EC + 1.4 OC), and the remainder; sulfate was assumed to be (NH4)2SO4 for this purpose. Initial AQI screening was based on all-cause (ICD-9 codes < 800) mortality for those aged 65 and over. For the (apparently) most important pollutants—PM2.5 and its 3 major constituents, coarse PM mass [CM], 1-h maximum CO, 8-h maximum O3—we investigated 15 mortality categories in detail. (The 15 categories result from three age groups [all ages, < 65, 65+] and five cause-of-death groups [all disease causes, cardiovascular, respiratory, cancer, and other “remainder” disease causes]). The GLM model outputs that were considered included mean AQI effects and their standard errors, and two indicators of relative model performance (deviance and deviance adjusted for the number of observations and model parameters). The latter indicator was considered to account for variations in the number of observations created by varying amounts of missing AQI data, which were not imputed. The single-AQI screening regressions on all-cause 65+ mortality show that CO, NO2, PM2.5, CM, SO2, and O3, followed by EC and OC, consistently have the best model fits, after adjusting for the number of observations. Their relative rankings, however, vary according to the smoothing knots used, and there is no correspondence between mean AQI effect and overall model fit. (Other regression runs often show that the best model fits are obtained with no AQI in the model.) There is no correspondence between mean AQI effect and statistical significance or between mean effect and serial correlation. There is a highly significant (.001 level) relationship between overall model fit and serial correlation; the best fitting models have the most frequent knot spacing and the most negative serial correlation. The regression analyses by cause of death find elderly circulatory deaths to be consistently associated with CO for all models.

The Impact of Frequency and Duration of Air Quality Monitoring: Atlanta, GA, Data Modeling of Air Pollution and Mortality

ABSTRACT The purpose of this analysis is threefold. We first examine the extent to which a longer series of data improves our understanding of air pollution on human mortality in the Atlanta, GA, area by updating the findings presented in Klemm and Mason (J. Air Waste Manage. Assoc. 2000, 50, 1433-1439) and Klemm et al. (Inhal. Toxicol. 2004, 16 (Suppl 1), 131-141) with 7.5 additional years of data. We explore estimated effects on two age groups (<65 and 65+) and four categories of cause of death. Second, we investigate how enlarging the geographic area of inquiry influences the estimated effects. Third, because some air quality (AQ) measures are monitored less frequently than daily, we investigate the extent to which AQ measurement frequency can influence estimates of relationships with human mortality. Our analytical approach employs a Poisson regression model using generalized linear modeling in S-Plus to estimate the relationship between daily AQ measures and daily mortality counts. We show that the estimated effects and their associated t values vary by year for nine AQ measures (particulate matter with aerodynamic diameter ≤2.5 μm [PM2.5], elemental carbon [EC], organic carbon [OC], NO3, SO4, O3, NO2, CO, and SO2). Several of the estimated AQ effects show downward trends during the 9-year period of study. The estimated effects tend to be strongest for the AQ measurement during the day of death and tend to decrease with additional lags. Enlarging the geographic area from two to four counties in the metropolitan area decreased the estimated effects, perhaps partly due to the fact that the measurement site is located in one of the two original counties. Estimated effects utilizing data as if the AQ were only measured every 3rd or every 6th day each week or twice per week vary from lower to higher than that estimated with daily measurements, although the t values are lower, as expected. IMPLICATIONS The ranges of published estimated effects of air quality measures on mortality suggest both underlying complexities and statistical variability. Decision-makers may be uncertain as to how to best use subgroup estimates; however, they should be aware of random fluctuations and underlying trends. Results from three scenarios of reduced frequencies of air quality measure monitoring show that estimated effects may differ substantially from those estimated from daily measurements. Although individual annual estimates will fluctuate, analysis of their trends provides information not available from analyzing the entire period as a whole. Daily measurements are essential for precise estimates of health effects.

The effect of imputation of exposure estimates on the association between fine particulate matter and mortality.

PURPOSE: The Harvard Six Cities Study (HSCS) found a small but significant association between daily PM2.5 and daily mortality count. The HSCS findings have been used as the basis for new EPA regulations, requiring lower levels of PM2.5. We feel that there are unresolved issues regarding the HSCS that should be fully evaluated prior to its findings being used as the basis of new regulation, including how the extent and method of imputing exposure data affect the association with daily mortality counts.METHODS: We examined the association between PM2.5 levels and daily mortality count, comparing the results from the HSCS methods with results based on an alternate imputation method, and with non-missing data.RESULTS: Overall, approximately 30% of the data points used in the HSCS were imputed. The method of imputation affected the association between particulate matter and mortality to a substantial degree in most of the cities. When the model using the HSCS method was compared to the model using the alternate method, in two areas the coefficients decreased substantially and lost significance. In two areas they changed little; in one area it rose substantially and became significant; and in one area it declined substantially but remained significant. When compared to the model based on the non-missing data, somewhat different patterns were observed. In both comparisons there were some large changes in the magnitude of the effect, but these were not consistent with the model used.CONCLUSIONS: We are concerned about the degree of data imputation and the effect that the method of imputation has on the association between particulate matter levels and mortality. In the case of the HSCS it appears that the imputed data are more strongly associated with the outcome than other methods of imputation and than the non-missing data. The reasons for these observations are not readily apparent, but the differences in effect should be explored and explained.