Influence of Air Quality Model Resolution on Uncertainty Associated With Health Impacts

Abstract

We evaluate the uncertainty associated with regional air quality modeling grid resolution when calculating the health benefits of proposed air quality regulations. Using a regional photochemical model (CAMx), we ran two modeling episodes (a 2006 basecase and a 2018 attainment demonstration, both for Houston, Texas) at 36, 12, 4 and 2 km resolution. The basecase model performance was evaluated for each resolution for both monitor-based and population-weighted calculations of daily maximum 8-hour averaged ozone. Results from each resolution were more similar to each other than they are to actual measured values. However, the model performance improved when population weighted ozone concentration was used as the metric versus the standard daily maximum ozone concentrations at monitor site locations. Then population-weighted ozone concentrations were used to calculate the estimated health impacts of modeled ozone reduction from the basecase to the attainment demonstration including the 95% confidence intervals associated with each impact from concentrationresponse functions. We found that estimated avoided mortalities were not significantly different using coarse resolution, although 36 km resolution may over predict some potential health impacts. Given the cost/benefit analyses requirements of the Clean Air Act, the uncertainty associated with human health impacts and therefore the results reported in this study, we conclude that population weighted ozone concentrations obtained using regional photochemical models at 36 km resolution are meaningful relative to values obtained using fine (12 km or finer) resolution modeling. This result opens up the possibility for uncertainty analyses on 36 km resolution air quality modeling results, which are on average 10 times more computationally efficient. Contents