Structural equation models for simultaneous modeling of air pollutants

Abstract

This paper provides a new modeling for air pollution, simultaneously taking into account the six main pollutants (PM10 and PM2.5, Sulphate Dioxide, Nitrogen Dioxide, Carbon Monoxide, ground level Ozone concentrations) and their key determinants, employing Structural Equation Models (SEMs). The model is able to estimate the complex links among air pollutants, often neglected in literature, and identifies specific drivers of air pollution. In literature, indexes of air pollution achieved using a fully statistical methodology have not been proposed yet. Indeed, an added value of this proposal is the statistical procedure itself, which can be applied also to obtain indexes modeling different phenomena. In particular, in this study, the new Air Pollution Index (API) is based on a modeling approach that allows to assess, through statistical criteria, the goodness of fit of the SEM in modeling pollutants and the significance of their determinants. The performance of the new index is assessed using air quality data for municipal European areas, which are characterized by different socioeconomic, geographical, and meteorological features. SEMs are estimated and evaluated in terms of best fit and model complexity. The index resulting by the best SEM is compared with the well-established Air Quality Index (AQI). The new API is validated by means of a sensitivity analysis, performed with a simulation study. Finally, to visualize the meaningfulness of the obtained results, a model-based cluster analysis is estimated on the municipal areas. The proposed SEM contributes to a better understanding of the relationships between air pollutants and their determinants, and this knowledge can inform policy decisions aimed at reducing air pollution and improving public health.