Long-term ozone (O3) exposure estimates from chemical transport models are frequently paired with exposure-response relationships from epidemiological studies to estimate associated health burdens. Impact estimates using such methods can include biases from model-derived exposure estimates. Data solely from dense ground-based monitoring networks in the United States, Europe, and China for 2015 is used to estimate long-term O3 exposure and calculate premature respiratory mortality using exposure-response relationships derived from two separate analyses of the American Cancer Society Cancer Prevention Study-II (ACS CPS-II) cohort. Using results from the larger, extended ACS CPS-II study, 34 000 (95% CI: 24, 44 thousand), 32 000 (95% CI: 22, 41 thousand), and 200 000 (95% CI: 140, 253 thousand) premature respiratory mortalities are attributable to long-term O3exposure in the USA, Europe and China, respectively, in 2015.
Results are approximately 32%–50% lower when using an older analysis of the ACS CPS-II cohort. Both sets of results are lower (~20%–60%) on a region-by-region basis than analogous prior studies based solely on modeled O3, due in large part to the fact that the latter tends to be high biased in estimating exposure.
This study highlights the utility of dense observation networks in estimating exposure to long-term O3 exposure and provides an observational constraint on subsequent health burdens for three regions of the world. In addition, these results demonstrate how small biases in modeled results of long-term O3exposure can amplify estimated health impacts due to nonlinear exposure-response curves.