Photochemical air quality modelling in arid regions

Despite continuous measures to control air pollution, large fractions of the population across the world are still exposed to potentially dangerous pollutant concentration levels. Research continues to increase in the area of air pollution with focus on different pollutants and regions. A particular emphasis is on the understanding of the formation of secondary pollutants as their relationships with primary pollutants are complex. One of the key factors that influences these complex relationships is the regional characteristics, such as temperature, humidity, and solar radiations. Arid regions are of particular concern as the characteristics, especially, extreme temperatures and dust storms, deteriorate air quality significantly. The study area chosen for this research is the Riyadh region in Saudi Arabia. Improving the air quality in this region requires further understanding of the formation of secondary pollutants, particularly ozone (O₃). Generally, Photochemical Air Quality Models (PAQM) are employed to study the formation of secondary pollutants in the atmosphere. This research configured high-resolution Community Multiscale Air Quality (CMAQ) model over the area to study the research objectives. This model could be utilized to study various strategies to mitigate photochemical smog formation in the region. The regional characteristics have significant roles in atmospheric chemical mechanisms, and an effective mitigation plan is important for successful air quality management; hence getting a better understanding of the chemical mechanisms is pivotal. This thesis investigated various chemical mechanisms that are present in PAQM constraining with the observed data resulting in the identification of the most appropriate mechanism for arid regions. The key chemical reactions and corresponding kinetics were also ascertained. The identified chemical mechanism will serve as a benchmark for any future implementation of PAQM in Riyadh as well as similar regions. Conventionally, deterministic PAQMs are applied to evaluate the efficacy of a control strategy to achieve air quality standards. Uncertainties are inherent in any mathematical model, including PAQM, and are specific to regional characteristics. Ignoring model uncertainties might yield a false sense of precision about pollutant response to emission controls. Hence, such uncertainties must be identified and quantified for the selection of control policies. This research identified key factors influencing the O₃ precursor responsiveness and characterized the parametric uncertainties influencing the prediction of O₃ to precursor emissions. Devising an appropriate mitigation plan also requires running PAQM for a number of scenarios, which is computationally challenging. To overcome this computational burden, an efficient Reduced Form Model (RFM) was developed. It characterizes the impact of uncertainties in model input parameters on O₃ response to not only precursor emissions (NOₓ and VOCs) but also to dust emissions. The development of an efficient RFM allowed the use of a probabilistic framework to study the impact of various emission mitigation and dust increase scenarios. This RFM enabled the understanding of the impact of various emission reductions on the formation of O₃. The newly incorporated dust parameter in the RFM revealed that the relationship of dust concentrations with O₃ formation is nonlinear. Initially, O₃ concentration decreased with the increase of dust and later increased. The configured PAQM, the identified atmospheric chemical mechanism, and the developed RFM (incorporating the new dust parameter) would facilitate the responsible authorities in devising appropriate O₃ reduction strategies for the study area and similar regions. The endeavour undertaken in this research to advance the understanding of PAQM in arid conditions opens up several avenues of further research. The developed RFM has a potential to be improved, such as adding more types of uncertainties (structural and meteorological) and further validating with comprehensive observed data. Additionally, the RFM could be integrated with economics and health uncertainty models to study the cost of mitigation plans and health impacts. Moreover, air chambers can be setup to get more insight into chemical kinetics under arid conditions especially the role of heterogeneous reactions of NOₓ with dust particles.