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Abstract: This paper presents a multi-pollutant sensitivity study of an air quality model over Europe with a focus on aerosols. Following the evaluation presented in the companion paper, the aim here is to study the sensitivity of the model to input data, mathematical parameterizations and numerical approximations. To that end, 30 configurations are derived from a reference configuration of the model by changing one input data set, one parameterization or one numerical approximation at a time. Each of these configurations is compared to the same reference simulation over two time periods of the year 2001, one in summer and one in winter. The sensitivity of the model to the different configurations is evaluated through a statistical comparison between the simulation results and through comparisons to available measurements. The species studied are ozone (O3), nitrogen dioxide (NO2), sulfur dioxide (SO2), ammonia (NH3), coarse and fine aerosol particles (PM c and PM2.5), sulfate, nitrate, ammonium, chloride and sodium. For all species, the modeled concentrations are very sensitive to the parameterization used for vertical turbulent diffusion and to the number of vertical levels. For the other configurations considered in this work, the sensitivity of the modeled concentration to configuration choice varies with the species and the period of the year. O3 is impacted by options related to boundary conditions. PM c is sensitive to sea-salt related options, to options influencing deposition and to options related to mass transfer between gas and particulate phases. PM2.5 is sensitive to a larger number of options than PM c : sea-salt, boundary conditions, heterogeneous reactions, aqueous chemistry and gas/particle mass transfer. NO2 is strongly influenced by heterogeneous reactions. Nitrate shows the highest variability of all species studied. As with NO2, nitrate is strongly sensitive to heterogeneous reactions but also to mass transfer, thermodynamic related options, aqueous chemistry and computation of the wet particle diameter. While SO2 is mostly sensitive to aqueous chemistry, sulfate is also sensitive to boundary conditions and, to a lesser extent, to heterogeneous reactions. As with nitrate, ammonium is largely impacted by the different configuration choices, although the sensitivity is slightly lower than for nitrate. NH3 is sensitive to aqueous chemistry, mass transfer and heterogeneous reactions. Chloride and sodium are impacted by sea-salt related options, by options influencing deposition and by options concerning the aqueous-phase module. [Copyright &y& Elsevier]

This paper reports on the development and evaluation of a new modelling system for studying air quality on local scales. A multi-model approach has been adopted to develop the OSCAR Air Quality Modelling System to conduct assessments at different levels of complexity. This work focuses on the evaluation of the performance of two of the models, CAR II (semi-empirical, annual averaged model) and CAR-FMI (line-source Gaussian model for hourly predictions), as integrated into the OSCAR System and employing consistent emissions and meteorological inputs. The models have been evaluated using measured NOx, NO2, PM10 and PM2.5 data for 2003 from the Vallila (Helsinki) and Cromwell Road (London) monitoring stations; both of these stations are located in urban roadside environments. The evaluation consists of comparisons of annual averaged and hourly predicted and measured data, as well as detailed statistical analysis of the model performance. Overall, closer agreement between the modelled and measured data is observed for the Vallila site, where traffic levels are medium, but the models show significant underestimation for Cromwell Road which is subject to high traffic levels. The paper also discusses possible reasons for this underestimation. The wider implications of the work are also stated for development and evaluation of environmental models. [Copyright &y& Elsevier]

Abstract: The contribution of vehicular traffic to air pollutant concentrations is often difficult to establish. This paper utilizes both time-series and simulation models to estimate vehicle contributions to pollutant levels near roadways. The time-series model used generalized additive models (GAMs) and fitted pollutant observations to traffic counts and meteorological variables. A one year period (2004) was analyzed on a seasonal basis using hourly measurements of carbon monoxide (CO) and particulate matter less than 2.5 μm in diameter (PM2.5) monitored near a major highway in Detroit, Michigan, along with hourly traffic counts and local meteorological data. Traffic counts showed statistically significant and approximately linear relationships with CO concentrations in fall, and piecewise linear relationships in spring, summer and winter. The same period was simulated using emission and dispersion models (Motor Vehicle Emissions Factor Model/MOBILE6.2; California Line Source Dispersion Model/CALINE4). CO emissions derived from the GAM were similar, on average, to those estimated by MOBILE6.2. The same analyses for PM2.5 showed that GAM emission estimates were much higher (by 4–5 times) than the dispersion model results, and that the traffic-PM2.5 relationship varied seasonally. This analysis suggests that the simulation model performed reasonably well for CO, but it significantly underestimated PM2.5 concentrations, a likely result of underestimating PM2.5 emission factors. Comparisons between statistical and simulation models can help identify model deficiencies and improve estimates of vehicle emissions and near-road air quality. [Copyright &y& Elsevier]