Multi-criteria selection of an Air Quality Model configuration based on quantitative and linguistic evaluations

This study presents the application of multi-criteria evaluation in the selection of an optimal configuration for an Air Quality Model. The simulation domains focus on the Mexico City Metropolitan Area. A set of 10 different configurations were considered as alternatives. These configurations included convective parameterization, 6th order diffusion and exclusion of data assimilation within the Planetary Boundary Layer. In addition, model integration in a continuous setup and in a segmented setup was also considered. The modeling variables were surface temperature, wind speed, wind direction, and sulfur dioxide. The performance of the meteorological fields was evaluated with statistical metrics together with the Local Trend Association measure and further used as criteria. The air pollution field was evaluated qualitatively with five expert-based linguistic criteria and further converted into numerical terms. Meteorological variables and sulfur dioxide were aggregated into two single arrays, one for representing meteorology and the other for representing transport of a large industrial plume. Pareto Fronts were constructed for these two arrays under different weights scenarios. Results suggested that a model with no parameterizations in continuous integration setup and a model with segmented integrations using 6th order diffusion were the optimal configurations to conduct future air quality studies. It has long been recognized the negative impacts of air pollution on human health and ecosystems. The role of anthropogenic emissions is at the forefront of these impacts (WMO, 2012). To effectively address the air pollution at several spatial scales, air quality modeling is of utmost relevance. Air quality forecasting uses source and receptor models to study the degree of pollution in future events. There are several approaches to model air pollution , and all of them rely on the scientific goals under study which include the spatial scale of interest. In this respect, expert-based approaches are important in air quality studies. For example: in forecasting air pollution with fuzzy time series methods (Dom-anska & Wojtylak, 2012); classifying concentrations of criteria pol-, identification of geographical distribution of particle pollution (Li & Shue, 2004); or in suggesting a system for air quality management at regional scale based on interval programming with stochastic variables (Cao, Huang, & He, 2011). Another approach consists in the application of deterministic models which solves the conservation equations of different physical and chemical processes (Zhang, Bocquet, Mallet, Seigneur, & Baklanov, 2012). These models are known as Air Quality Models (AQM) or Chemical Transport Models (CTM). This kind of models …