No abstract

[1] Modeling systems that are designed to investigate tropospheric air quality concerns must address several issues simultaneously: ozone, particulate matter, deposition and visibility. These modeling systems consist of three components: meteorology, emissions and air quality. A simulation was conducted for the July 1999 Southern Oxidants Study to evaluate one such modeling system, Models-3. Performance of the meteorological component, MM5, was evaluated against observations. Consistency of two emissions models, SMOKE and EPS 2.5, was evaluated by comparing their outputs. For comparison, the performance of CMAQ and three additional models (CMAQ-MADRID 1, CMAQ-MADRID 2, and REMSAD) was evaluated for the same time period. Nested simulations for a 32-km and an 8-km grid were conducted for CMAQ and CMAQ-MADRID 1. Results for CMAQ-MADRID 2 and REMSAD are available only for the 8-km grid. Performance was evaluated for PM and its components, ozone and wet deposition. Differences in model performance for PM 2.5 and its components were greatest for OC and total PM 2.5 ; performance was more consistent for the other components. Model performance was generally better on the 32-km grid than the 8-km grid for PM 2.5 and its components. R 2 values ranged from 20 to 50% for NH 4 + and EC and were lower for other PM 2.5 components, indicating that the predictive capabilities of the models for PM 2.5 are limited. Model performance for ozone met EPA guidance for MNB and MNE when a 60 ppb cutoff was used and was better on the 8-km grid than on the 32-km grid. Performance for wet deposition was good.

Three mathematical models of air quality (CMAQ, CMAQ-MADRID, and REMSAD) are applied to simulate the response of atmospheric fine particulate matter (PM2.5) concentrations to reductions in the emissions of gaseous precursors for a 10 day period of the July 1999 Southern Oxidants Study (SOS) in Nashville. The models are shown to predict similar directions of the changes in PM2.5 mass and component (sulfate, nitrate, ammonium, and organic compounds) concentrations in response to changes in emissions of sulfur dioxide (SO2), nitrogen oxides (NO(x)), and volatile organic compounds (VOC), except for the effect of SO2 reduction on nitrate and the effect of VOC reduction on PM2.5 mass. Furthermore, in many cases where the directional changes are consistent, the magnitude of the changes are significantly different among models. Examples are the effects of SO2 and NO(x) reductions on nitrate and PM2.5 mass and the effects of VOC reduction on organic compounds, sulfate and nitrate. The spatial resolution significantly influences the results in some cases. Operational model performance for a PM2.5 component appears to provide some useful indication on the reliability of the relative response factors (RRFs) for a change in emissions of a direct precursor, as well as for a change in emissions of a compound that affects this component in an indirect manner, such as via oxidant formation. However, these results need to be confirmed for other conditions and caution is still needed when applying air quality models for the design of emission control strategies. It is advisable to use more than one air quality model (or more than one configuration of a single air quality model) to span the full range of plausible scientific representations of atmospheric processes when investigating future air quality scenarios.