Long-term exposure to ambient ozone is associated with a variety of impacts, including adverse human-health effects and reduced commercial crop yields. We apply machine learning to empirically model long-term O₃ exposure over the continental United States from 2000 to 2015 and generate a measurement-based assessment of impacts on human health and crop yields. Notably, our results illustrate how different conclusions regarding historical impacts can be drawn through the use of varying metrics.