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

Abstract

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...

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

“…This study updates the findings presented in Klemm and Mason 4 and Klemm et al 5 on the relationship between air quality measures and daily mortality. In so doing, it discusses the impact of approximately 7.5 years (7 years and 5 months) of additional data on the analytical results.…”

“…5,6 Previous studies have identified associations between ambient (outdoor) PM and adverse health outcomes using a 2-day average of fine particulates and daily mortality. 7,8 We continue that approach during this analysis.…”

Air Quality Measurements for the Aerosol Research and Inhalation Epidemiology Study

“…In 1999, 24-hr integrated measurements of PM 2.5 mass, SO 4 ϭ , EC, and OC and gaseous O 3 , SO 2 , and NO 2 were made. In 2000, the same measurements plus PM 10 were made. These data have been used not only to examine associations of indicators related to COPD and MI with exposures but also residential infiltration rates, relations among microenvironmental indoor and outdoor pollutant concentrations, and spatial variability over the domain spanned by the residences.…”

Modification of the Versatile Aerosol Concentration Enrichment System (VACES) for conducting inhalation exposures to semi-volatile vapor phase pollutants

“…For example, polar organic compounds such as quinones are reported to act as catalysts to directly produce reactive oxygen species (ROS), resulting in oxidative stress (Kumagai et al, 1997;Squadrito et al, 2001); polycyclic aromatic hydrocarbons (PAHs) can induce oxidative stress indirectly, through their biotransformation to generate redox active quinones (Penning et al, 1999). VOCs concentration levels in the ambient air have been linked to the incidence of chronic respiratory symptoms and the frequency of hospital admissions due to ischemic heart disease and myocardial infarctions (Gordon et al, 1998;Klemm et al, 2004;Tolbert et al, 2001). In a recent exposure study of freeway traffic emissions on the cardiovascular effects of rats, vapor-phase components, rather than PM, were associated with a decrease in heart rate variability (Elder et al, 2007).…”