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 PM 2.5. 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 PM 2.5. 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 PM 2.5. mass concentrations, be used to assess which PM 2.5. 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 PM 2.5 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 PM 2.5 species is suggested.A list of acronyms will be found at the end of the article. Aims of Critical ReviewThis critical review (CR) consists of three main sections. First, we review recent major regulatory and scien...

In 1996, Schwartz, Dockery, and Neas 1 reported that daily mortality was more strongly associated with concentrations of PM 2.5 than with concentrations of larger particles (coarse mass [CM]) in six U.S. cities ("original paper"/"original analyses"). Because of the public policy implications of the findings and the uniqueness of the concentration data, we undertook a reanalysis of these results. This paper presents results of the reconstruction of these data and replication of the original analyses using the reconstructed data. The original investigators provided particulate air pollution data for this paper. Daily weather and daily counts of total and causespecific deaths were reconstructed from original public records. The reconstructed particulate air pollution and weather data were consistent with the summaries presented in the original paper. Daily counts of deaths in the reconstructed data set were lower than in the original paper because of restrictions on residence and place of death. The reconstruction process identified an administrative change in county codes that led to higher IMPLICATIONSThe reported association of daily mortality with fine particles has been cited as central in the debate over control of particulate air pollution. Replication of results and validation of data and methods are cornerstones of scientific inquiry. This data reconstruction and reanalysis using the reconstructed data produced results similar to those presented in the original publication important to that debate. Validation of the original Schwartz, Dockery, and Neas 1 findings would require replication and analyses of independently constructed datasets and procedures.numbers of deaths in St. Louis. Despite these differences in daily counts of deaths, the estimated effects of particulate air pollution from the reconstructed dataset, using analytic methods as described in the original paper, produced combined effect estimates essentially equivalent to the originally published results. For example, the estimated association of a 10 µg/m 3 increase in 2-day mean particulate air pollution on total mortality was 1.3% (95% confidence interval [CI] 0.9-1.7%, t = 6.53) for PM 2.5 based on the reconstructed dataset, compared to the originally reported association of 1.5% (95% CI 1.1-1.9%, t = 7.41). For coarse particles, the estimated association from the reconstructed dataset was 0.4% (95% CI -0.2-0.9%, t = 1.43) compared to the originally reported association of 0.4% (95% CI -0.1-1.0%, t = 1.48). These results from the reconstructed data suggest that the original results reported by Schwartz, Dockery, and Neas 1 were essentially replicated.

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 IMPLICATIONS Numerous papers have purported to show a statistically significant positive association of mortality with air pollution levels. However, no statistically significant estimate of the linear coefficient was found in our analysis. Model diagnostics provide little evidence for further investigation of the model behavior or inconsistencies of the estimates. These results occurred with average PM 2.5 levels similar to those in prior studies. Additional modeling using other variables in the ARIES epidemiological analysis data set must be completed to fully assess the relationship(s) between daily mortality counts and AQIs. Additional meteorological variables will also be examined to model the effects of other weather variables. A better understanding of the ambient environmental exposure in targeted populations during the period immediately preceding death may also provide clues on our lack of statistically significant interim results. air quality relative to today's 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-fi...

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 < or =2.5 microm [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.

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 Atlanta's 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, PM(2.5) was partitioned into its three major constituents: SO(2-)(4), carbon (EC + 1.4 OC), and the remainder; sulfate was assumed to be (NH(4))(2)SO(4) 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--PM(2.5) and its 3 major constituents, coarse PM mass [CM], 1-h maximum CO, 8-h maximum O(3)--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, NO(2), PM(2.5), CM, SO(2), and O(3), 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...