A case study of sulfur dioxide concentrations in Muscatine, Iowa and the ability for AERMOD to predict NAAQS violations Charlene Marie Becka , University of Iowa Follow

[摘要] Sulfur dioxide is a primary pollutant and a known respiratory irritant. While there is a small level of background SO 2 , elevated concentrations are caused by industrial emissions. Muscatine, IA was designated as an area of nonattainment due to the persistent elevated levels of SO 2 in the area. There are currently no available methods for predicting potential SO 2 violations in Muscatine, and very little research was found investigating predictive modeling efforts. This thesis examines atmospheric conditions in Muscatine caused by SO 2 emissions from facilities near the city. The main goals were to examine the plume dispersion model AERMOD for its ability to accurately map pollution levels, and to determine whether AERMOD could be used to predict SO 2 concentrations when using meteorological forecast models as weather inputs. An historical analysis was performed using meteorological records from 2007 and AERMOD. The maximum emission limit was used in AERMOD. The resulting predicted concentrations were compared with concentrations reported at a monitoring site within the city. A forecasting analysis was also completed using two weather model forecasts (WRF and NAM) from March 2012 as meteorological input for AERMOD. Accurate daily SO 2 emissions were obtained from each facility, and the corresponding rates were used in AERMOD. The resulting predicted concentrations were compared with monitored concentrations during the same time period. Overall, the historical analysis showed AERMOD"s tendency to overestimate SO 2 concentrations, particularly on days that also resulted in high monitored levels. The forecasting analysis resulted in favorable results with respect to the WRF weather forecast, but the NAM forecast created concentrations in AERMOD that were poorly correlated with monitored values. AERMOD still was likely to overestimate concentrations, but these overestimations were lessened due to more accurate emission information. Further research will be needed to further advance the prediction of pollution levels.