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.