# Evaluation of Different Chemical Mechanisms on O3 and PM2.5 Predictions in Alberta, Canada
## 4 Summary and Conclusions  
The three chemical mechanisms (CB6R3, SAPRC07 and RACM2) were implemented in the CMAQ modelling system, and two-month simulations, one in summer and one in winter, were run. The model predictions of various species by each of the mechanisms are compared to one another and to the observed data. Many chemical species concentrations predicted by the three mechanisms differed by relatively large margins. In summer, the simulation with CB6R3 predicted a higher concentration of H\({}_{2}\)O\({}_{2}\) and PAN, while RACM2 predicted higher concentrations of OH, NOx, and O\({}_{3}\). SAPRC07 produced high concentrations of MEPX. The winter ratios, with respect to difference mechanism, were like summer but predicted significantly lower concentrations except for NOx and O\({}_{3}\). The analysis of diurnal variation of O\({}_{3}\) at four stations revealed that SAPRC07 and RACM2 predictions were higher in the early part of the day, while CB6R3 predictions were the highest at noon. In summer, CB6R3 produces higher levels of O\({}_{3}\) in the presence of higher VOCs and NOx levels, while producing low levels of O\({}_{3}\) in the presence of high NOx levels; however, in winter, the difference in the production of O\({}_{3}\) due to the three mechanisms was negligible. An analysis of the spatial and temporal variation of the aerosol components of PM\({}_{2.5}\) shows a significant shift from SO\({}_{4}\)-dominated PM\({}_{2.5}\) in the summer to NO\({}_{3}\)-dominated PM\({}_{2.5}\) in the winter. This shift is probably due to a change in the relative concentration of NH\({}_{4}\) to SO\({}_{4}\) and the temperature. Furthermore, the winter PM\({}_{2.5}\) is probably due to mobile emissions, with some SO\({}_{4}\) coming from oil and gas activity in the region, while summer PM\({}_{2.5}\) is due to non-mobile emissions related to anthropogenic sources and oil and gas activity in the region. Of the three mechanisms tested, RACM2 seems to be marginally better at capturing this variability. A statistical comparison of predicted and observed data from various stations revealed that O\({}_{3}\) performance is dependent on chemical mechanisms and the station. In summer, RACM2 and SAPRC07 performed better at Edson, while CB6R3 was better at the other two stations. No mechanism accurately captures wintertime O\({}_{3}\). When compared to other mechanisms, PM\({}_{2.5}\) predictions with RACM2 were slightly better. The dominant PM\({}_{2.5}\) species in summer were SO\({}_{4}\) and SOA-Bio, while NO\({}_{3}\) and SO\({}_{4}\) were dominant in winter. The NO\({}_{3}\) species were significantly higher in winter. The difference in the model predictions of O\({}_{3}\) and PM\({}_{2.5}\) by the three chemical mechanisms are sufficiently different, so it may impact the categorization of an area for the suitable CAAQS management zone. Moreover, the appropriate choice of KZMIN will produce variation in O\({}_{3}\) and PM\({}_{2.5}\) predictions. When running an air quality model for O\({}_{3}\) control strategies, CB6R3 performs slightly better at the urban stations than the other mechanisms, with RACM2 also showing good performance in rural areas. When modelling for PM\({}_{2.5}\) control strategies, RACM2 appears to be the better mechanism. More modelling is necessary with O\({}_{3}\) and PM\({}_{2.5}\) control scenarios using the three mechanisms, and KZMINsensitivity studies will be required for further understanding of the implications on using one mechanism over other.