LINKING URBAN (STREET CANYON) MODELS WITH REGIONAL AIR QUALITY MODELS THROUGH URBAN BOUNDARY CONDITIONS

This contribution addresses the question of how detailed information from the urban canopy can be assimilated into regional models. This detailed information concerns, among others, road transport emissions, specific exchange and turbulence patterns in the built up canopy, and effects of roads and roughness elements on wind direction and wind speed. This information is typically obtained from detailed street canyon models in combination with traffic emission models. In order to integrate the dynamics of the urban canopy into regional air quality models, we propose the formulation of urban boundary conditions. The formulation has been tested and compared with measurements for benzene and NOx in the city of Antwerp, Belgium

Intercomparison of Atmospheric Dispersion Models Applied to an Urban Street Canyon of Irregular Geometry

The NOx concentrations measured at the sampling site of Cordoba Avenue, Buenos Aires City, characterised by an irregular geometry on both sides of the street, are used to intercompare results of five urban street canyon dispersion models, STREET, OSPM, AEOLIUSF, STREET-BOX and SEUS. Three different wind directions with respect to the street axis are considered, i.e., leeward, windward and parallel. Additionally, two wind speed classes are considered, above and below 2 m s–1. In order to evaluate the models performance, observed and calculated concentrations are compared using different statistical measures, i.e., mean values, bias, mean square error and fractional error. In general, all models estimate better leeward conditions and wind speeds above 2 m s–1, with SEUS providing the overall best result.Facultad de Ciencias Astronómicas y Geofísica

Development of a land use regression model for black carbon using mobile monitoring data and its application to pollution-avoiding routing

Black carbon is often used as an indicator for combustion-related air pollution. In urban environments, on-road black carbon concentrations have a large spatial variability, suggesting that the personal exposure of a cyclist to black carbon can heavily depend on the route that is chosen to reach a destination. In this paper, we describe the development of a cyclist routing procedure that minimizes personal exposure to black carbon. Firstly, a land use regression model for predicting black carbon concentrations in an urban environment is developed using mobile monitoring data, collected by cyclists. The optimal model is selected and validated using a spatially stratified cross-validation scheme. The resulting model is integrated in a dedicated routing procedure that minimizes personal exposure to black carbon during cycling. The best model obtains a coefficient of multiple correlation of R = 0.520. Simulations with the black carbon exposure minimizing routing procedure indicate that the inhaled amount of black carbon is reduced by 1.58% on average as compared to the shortest-path route, with extreme cases where a reduction of up to 13.35% is obtained. Moreover, we observed that the average exposure to black carbon and the exposure to local peak concentrations on a route are competing objectives, and propose a parametrized cost function for the routing problem that allows for a gradual transition from routes that minimize average exposure to routes that minimize peak exposure

Future climate of Brussels and Paris for the 2050s under the A1B scenario

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Applications of Models and Tools for Mesoscale and Microscale Thermal Analysis in Mid-Latitude Climate Regions—A Review

Urban analysis at different spatial scales (micro- and mesoscale) of local climate conditions is required to test typical artificial urban boundaries and related climate hazards such as high temperatures in built environments. The multitude of finishing materials and sheltering objects within built environments produce distinct patterns of different climate conditions, particularly during the daytime. The combination of high temperatures and intense solar radiation strongly perturb the environment by increasing the thermal heat stress at the pedestrian level. Therefore, it is becoming common practice to use numerical models and tools that enable multiple design and planning alternatives to be quantitatively and qualitatively tested to inform urban planners and decision-makers. These models and tools can be used to compare the relationships between the micro-climatic environment, the subjective thermal assessment, and the social behaviour, which can reveal the attractiveness and effectiveness of new urban spaces and lead to more sustainable and liveable public spaces. This review article presents the applications of selected environmental numerical models and tools to predict human thermal stress at the mesoscale (e.g., satellite thermal images and UrbClim) and the microscale (e.g., mobile measurements, ENVI-met, and UrbClim HR) focusing on case study cities in mid-latitude climate regions framed in two European research projects.The work leading to these results has received funding from the European Community’s Seventh Framework Programme under Grant Agreement No. 308497, Project RAMSES—Reconciling Adaptation, Mitigation, and Sustainable Development for Cities (2012–2017) and from the European Union’s H2020 Research and Innovation Programme under Grant Agreement No. 73004 (PUCS/Climate-fit.city). The APC was funded by the Research Group of Building and Technology, De partment of Civil and Environmental Engineering, Norwegian University of Science and Technolog