How to compute long-term averages for air quality assessment at urban areas?

This paper proposes two innovative time-effective approaches to retrieve annual averaged concentrations for air quality assessment in the framework of the AQD. In addition, a traditional method (M1) was applied through numerical simulations for an entire year on an hourly basis to compare the performance of the proposed approaches. The first time-effective approach (M2) is based on the calculation of pollutant concentrations for the full year on an hourly basis through the combination of a set of numerical simulations for 4 typical days weighted by hourly factors obtained from air quality monitoring data. While the second time-effective approach (M3) considers the numerical simulation of pollutant concentrations for a set of typical meteorological conditions. For all the methods, air quality simulations were performed with the second-generation Gaussian model URBAIR. The three methods are applied over two distinct European urban areas, the Aveiro region in Portugal and Bristol in the United Kingdom, for the simulation of NO₂ and PM10 annual concentrations. The main results highlight an underestimation of the NO₂ annual concentrations by M2 and an overestimation of those concentrations by M3 for the Aveiro region, when compared to M1 as the reference method. While, for Bristol the main differences between methods were found for NO₂ concentrations when using M3. M2 underestimates PM10 annual concentrations in the Aveiro Region, while M3 points out underestimation or overestimation of those concentrations for distinct areas of the domain. This study aims to foster the knowledge on air quality assessment under the European policy context, supporting air quality management and urban planning. The innovative nature of this study relies on the proposed time-effective tools, suitable for the fast simulation of complex urban areas applying high spatial resolution. Additionally, these modelling tools may provide key information on air quality to population, particularly where it is not readily available.