A quantitative assessment of the influence of grid resolution on predictions of future-year air quality in North Carolina, USA

Increased focus has been directed at fine-scale modeling for improving the ability of air quality modeling systems to capture local phenomena. While numerous studies have investigated model performance at finer resolution (4–5 km), there is relatively limited information available for choosing the optimum grid resolution for predicting future air quality in attainment demonstration studies. We demonstrate an evaluation of the MM5–SMOKE–MAQSIP modeling system for four 8-h ozone episodes in the summers of 1995, 1996 and 1997 in North Carolina using a one-way nested 36/12/4-km application. After establishing acceptable base-case model performance for ozone predictions during each episode, we developed future-year emissions control scenarios for 2007 and 2012, and finally computed relative reduction factors (RRFs) using model outputs from each of the three grid resolutions. Our analyses, based upon qualitative as well as quantitative approaches like the Student's t-test, indicate that RRFs computed at specific monitoring locations—and hence predicted future-year air quality—are not very different between the 4- and 12-km results, while the differences are slightly larger between the 4- and 36-km results. The results imply that grid resolution contributes to a variability of about 1–3 ppb in the projected future-year design values; this variability needs to be incorporated into policy-relevant decision-making. Since this assessment was performed for four different episodes under diverse meteorological, physical and chemical regimes, one can generalize the results from this study. They are also relevant for regional modeling applications that are currently ongoing for studying PM2.5 nonattainment issues, where the need for annual base-year and future-year simulations for demonstrating attainment may place a large demand on computing resources. Based upon the results from this study, future studies may consider using results from 12-km modeling to address future-year air quality goals for ozone and PM2.5 and its components, and then incorporate grid-resolution uncertainties into the computed results.