Fine Scale Modeling of Agricultural Air Quality over the Southeastern United States: Application and Evaluation of Two Air Quality Models

Abstract

Three-dimensional air quality models are vital tools for air quality research. The models are under continuous development as the knowledge and understanding of atmospheric processes improve. Once a model has been evaluated retrospectively against available observations, sensitivity studies can be conducted to determine possible causes for discrepancies between the model and observations and to assess potential impact of future emission changes. This research will evaluate the performance of two air quality models, the Community Multiscale Air Quality (CMAQ) model and the Comprehensive Air Quality Model with extensions (CAMx), at a fine scale over the southeastern United States in January and July 2002. The air quality in the southeast is of particular interest because of the high NH3 emissions from agriculture which play a key role in the nutrient and nitrogen cycle, act to neutralize acids in the air, and participate in the formation of PM2.5.

The baseline simulations are completed at a horizontal grid spacing of 4-km using both CMAQ and CAMx. The evaluation of meteorological variables, chemical concentrations, wet and dry deposition, column mass, and visibility is completed using available observations from surface measurements and satellite data. The evaluation protocol involves analysis through domain-wide statistics, spatial distribution, and temporal variations. Additionally, two sensitivity studies are conducted. In order to assess the model sensitivity to horizontal grid spacing, a sensitivity study evaluates the performance of CMAQ at 12-, 4-, and 1.33-km horizontal grid spacings. The second sensitivity study evaluates the sensitivity of CMAQ to emission reductions of SO2, NOx, and agricultural-livestock NH3.

In the baseline simulation, O3 and PM2.5 are overpredicted by both models in January and underpredicted by both models in July, with CAMx predicting higher values than CMAQ in both months. The overprediction by the models in January is likely influenced by simulated weaker vertical mixing than what occurred in the true atmosphere. In July, underestimated emissions of precursor species or overpredicted wet deposition may be contributing to the underpredicted O3 and PM2.5. The spatial distribution of the adjusted gas ratio indicates that the regions of high NH3 emissions in the eastern NC and northeastern GA are NH3-rich and reductions of NH3 alone would do little to reduce PM2.5 pollution, which will be further evaluated in the second sensitivity study. When compared to satellite data, CMAQ shows good agreement for CO and NO2 in both months, with larger biases for O3 and AOD. These discrepancies may be due to uncertainties in the boundary conditions of the model and the calculation of AOD from the model predictions, as well as assumptions made in the satellite retrieval algorithms.

CMAQ shows some improvement for O3 and PM2.5 using finer grid spacings in January, but no improvement in July. One cause for this seasonal variation is the increased mass removed through wet deposition at finer scales in both months. This reduces the overprediction of PM species in January, but exacerbates the underprediction in July. Additional factors impacting the sensitivity to horizontal grid resolution are the sensitivity of the meteorology model and emissions. Through the emission sensitivity study, it was found that NH3 emission reductions have a larger impact than SO2 or NOx emission reductions on PM2.5 in January, while SO2 emission reductions result in the largest decrease in PM2.5 in July. Combining NH3 emission reductions with the projected SO2 and NOx emission reductions act to reduce PM2.5 more than SO2 and NOx emission reductions alone in both January and July.