Magnitude, trends, and impacts of ambient long-term ozone exposure in the United States from 2000 to 2015

Long-term exposure to ambient ozone (O₃) is associated with a variety of impacts, including adverse human-health effects and reduced yields in commercial crops. Ground-level O₃ concentrations for assessments are typically predicted using chemical transport models; however such methods often feature biases that can influence impact estimates.

This paper describes how artificial neural networks are developed and applied to empirically model long-term O₃ exposure over the continental United States from 2000 to 2015, and a measurement-based assessment of impacts on human-health and crop yields is produced. The authors found that two commonly used human-health averaging metrics, based on separate epidemiological studies, differ in their trends over the study period. The population-weighted, April–September average of the daily 1 h maximum concentration peaked in 2002 at 55.9 ppb and decreased by 0.43 [95 % CI: 0.28, 0.57] ppb yr⁻¹ between 2000 and 2015, yielding an ∼18 % decrease in normalized human-health impacts. In contrast, there was little change in the population-weighted, annual average of the maximum daily 8 h average concentration between 2000 and 2015, which resulted in a ∼5 % increase in normalized human-health impacts. In both cases, an aging population structure played a substantial role in modulating these trends. Trends of all agriculture weighted crop-loss metrics indicated yield improvements, with reductions in the estimated national relative yield loss ranging from 1.7 % to 1.9 % for maize, 5.1 % to 7.1 % for soybeans, and 2.7 % for wheat.

Overall, these results provide a measurement-based estimate of long-term O₃ exposure over the United States, quantify the historical trends of such exposure, and illustrate how different conclusions regarding historical impacts can be made through the use of varying metrics.