Influence of Air Quality Model Resolution on Uncertainty Associated With Health Impacts
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MITJPSPGC_Rpt208.pdf
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Author(s)
Thompson, T.M.
Selin, N.E.
Date Issued
December 2011
Publisher
MIT Joint Program on the Science and Policy of Global Change
Citation
Report no. 208
Series/Report no.
Joint Program Report Series;208
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.
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