Background Because ambient air pollution exposure occurs as mixtures, consideration of joint effects of multiple pollutants may advance our understanding of air pollution health effects. Methods We assessed the joint effect of air pollutants in selected combinations (representative of oxidant gases, secondary, traffic, power plant, and criteria pollutants; constructed using combinations of criteria pollutants and fine particulate matter (PM2.5) components) on pediatric asthma emergency department (ED) visits in Atlanta during 1998–2004. Joint effects were assessed using multi-pollutant Poisson generalized linear models controlling for time trends, meteorology and daily non-asthma upper respiratory ED visit counts. Rate ratios (RR) were calculated for the combined effect of an interquartile-range increment in each pollutant’s concentration. Results Increases in all of the selected pollutant combinations were associated with increases in warm-season pediatric asthma ED visits [e.g., joint effect rate ratio=1.13 (95% confidence interval 1.06–1.21) for criteria pollutants (including ozone, carbon monoxide, nitrogen dioxide, sulfur dioxide, and PM2.5)]. Cold-season joint effects from models without non-linear effects were generally weaker than warm-season effects. Joint effect estimates from multi-pollutant models were often smaller than estimates calculated based on single-pollutant model results, due to control for confounding. Compared with models without interactions, joint effect estimates from models including first-order pollutant interactions were largely similar. There was evidence of non-linear cold-season effects. Conclusions Our analyses illustrate how consideration of joint effects can add to our understanding of health effects of multi-pollutant exposures, and also illustrate some of the complexities involved in calculating and interpreting joint effects of multiple pollutants.

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Objective This study describes associations of ozone and fine particulate matter with Parkinson’s disease observed among farmers in North Carolina and Iowa. Methods We used logistic regression to determine the associations of these pollutants with self-reported, doctor-diagnosed Parkinson’s disease. Daily predicted pollutant concentrations were used to derive surrogates of long-term exposure and link them to study participants’ geocoded addresses. Results We observed positive associations of Parkinson’s disease with ozone (OR=1.39; 95% CI: 0.98, 1.98) and fine particulate matter (OR=1.34; 95% CI: 0.93, 1.93) in North Carolina but not in Iowa. Conclusion The plausibility of an effect of ambient concentrations of these pollutants on Parkinson’s disease risk is supported by experimental data demonstrating damage to dopaminergic neurons at relevant concentrations. Additional studies are needed to address uncertainties related to confounding and to examine temporal aspects of the associations we observed.