Methodologies to assess mean annual air pollution concentration combining numerical results and wind roses

Abstract

Numerical models are valuable tools to assess air pollutant concentrations in cities which can be used to define new strategies to achieve sustainable cities of the future in terms of air quality. Numerical results are however difficult to be directly compared to air quality standards since they are usually valid only for specific wind speed and direction while some standards are on annual values. The purpose of this paper is to present existing and new methodologies to turn numerical results into mean annual concentrations and discuss their limitations. To this end, methodologies to assess wind speed distribution based on wind rose data are presented first. Then, methodologies are compared to assess mean annual concentrations based on numerical results and on wind speed distributions. According to the results, a Weibull distribution can be used to accurately assess wind speed distribution in France, but the results can be improved using a sigmoid function presented in this paper. It is also shown that using the wind rose data directly to assess mean annual concentrations can lead to underestimations of annual concentrations. Finally, the limitations of discrete methodologies to assess mean annual concentrations are discussed and a new methodology using continuous functions is described.