Abstract. 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. Here, we develop and apply artificial neural networks to
empirically model long-term O3 exposure over the continental United
States from 2000 to 2015, and we generate a measurement-based assessment of
impacts on human-health and crop yields. Notably, we 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.