Impact of single and combined local air pollution mitigation measures in an urban environment.

Urban air pollution is one of the most important environmental problems for human health and several strategies have been developed for its mitigation. The objective of this study is to assess the impact of single and combined mitigation measures on concentrations of air pollutants emitted by traffic at pedestrian level in the same urban environment. The effectiveness of different scenarios of green infrastructure (GI), the implementation of photocatalytic materials and traffic low emission zones (LEZ) are investigated, as well as several combinations of LEZ and GI. A wide set of scenarios is simulated through Computational Fluid Dynamics (CFD) modelling for two different wind directions (perpendicular (0°) and 45° wind directions). Wind flow for the BASE scenario without any measure implemented was previously evaluated using wind-tunnel measurements. Air pollutant concentrations for this scenario are compared with the results obtained from the different mitigation scenarios. Reduction of traffic emissions through LEZ is found to be the most effective single measure to improve local air quality. However, GI enhances the effects of LEZ, which makes the combination of LEZ + GI a very effective measure. The effectiveness of this combination depends on the GI layout, the intensity of emission reduction in the LEZ and the traffic diversion in streets surrounding the LEZ. These findings, in line with previous literature, suggest that the implementation of GI may increase air pollutant concentrations at pedestrian level for some cases. However, this study highlights that this negative effect on air quality can turn into positive when used in combination with reductions of local traffic emissions.

Estimates of population exposure to atmospheric pollution and health-related externalities in a real city: The impact of spatial resolution on the accuracy of results.

Health impacts of atmospheric pollution is an important issue in urban environments. Its magnitude depends on population exposure which have been frequently estimated by considering different approaches relating pollutant concentration and population exposed to it. However, the uncertainties due to the spatial resolution of the model used to estimate the pollutant concentration or due to the lack of representativeness of urban air quality monitoring station (AQMS) have not been evaluated in detail. In this context, NO2 annual average concentration at pedestrian level in the whole city of Pamplona (Spain) modelled at high spatial resolution (~1 m) by Computational Fluid Dynamic (CFD) simulations is used to estimate the total population exposure and health-related externalities by using different approaches. Air pollutant concentration and population are aggregated at different spatial resolutions ranging from a horizontal grid cell size of 100 m × 100 m to a coarser resolution where the whole city is covered by only one cell (6 km × 5 km). In addition, concentrations at AQMS locations are also extracted to assess the representativeness of those AQMS. The case with a spatial resolution of 100 m × 100 m for both pollutant-concentration distribution and population data is used as a reference (Base case) and compared with those obtained with the other approaches. This study indicates that the spatial resolution of concentration and population distribution in the city should be 1 km × 1 km or finer to obtain appropriate estimates of total population exposure (underestimations <13%) and health-related externalities (underestimations <37%). For the cases with coarser resolutions, a strong underestimation of total population exposure (>31%) and health-related externalities (>76%) was found. On the other hand, the use of AQMS concentrations can induce important errors due to the limited spatial representativeness, in particular in terms of population exposure.

Estimates of pedestrian exposure to atmospheric pollution using high-resolution modelling in a real traffic hot-spot.

Atmospheric pollution is a very relevant risk for the human health, in particular in urban environments, where most people lives and high levels of pollution are found. Population exposure is traditionally estimated through concentration recorded at air quality monitoring stations (AQMS) or modelled at a spatial resolution of the order of 1 km2. However, these methodologies have limitations in urban areas where strong gradients of concentration, even in the same street, exist. In addition, the movements of pedestrians make difficult to compute reliable estimates of pollutant concentration to which people are exposed to. In this context, the main objective of this study is to estimate the exposure of pedestrians to ambient nitrogen oxides (NOx) concentrations with high spatial resolution in a real urban traffic hot-spot under different methodologies. To achieve this objective, a novel methodology which combines high-resolution NOx concentrations from computational fluid dynamic (CFD) simulations with the pedestrian flows obtained by pedestrian mobility microsimulations is applied to an urban area of Madrid, Spain. High-resolution maps show pedestrian exposure peaks, at bus stops and crosswalks, that cannot be captured by the simpler methods based on spatial average concentration (SAC) or concentration measured in an AQMS. Total daily exposure obtained is 1.19 · 109 person s µg m-3, while SAC and AQMS concentration methods yielded 9-23% and 30-40% lower values. In conclusion, the proposed methodology allows to determine the areas with higher exposure in order to design local strategies to reduce the impact on human health. In addition, from a more general point of view, the total exposure in the studied area is better estimated by using spatial average concentration than through concentration recorded by AQMS. The assessment of the spatial representative of AQMS becomes necessary to use AQMS concentration to evaluate air quality and population exposure of an urban area.

Evaluation of a CFD-based approach to estimate pollutant distribution within a real urban canopy by means of passive samplers.

The distribution of pollutants is spatially heterogeneous within urban streets making difficult to build a realistic concentration map. In this paper, a methodology based on computational fluid dynamic modeling with Reynolds-averaged Navier-Stokes approach is used to compute maps of concentration for a period of several weeks. The methodology is evaluated by comparing simulation results against experimental data from two different campaigns where a large number of passive samplers deployed in an area with heavy vehicular traffic in Madrid (Spain). The evaluation shows that the methodology is able to reproduce the general pattern of several-week averaged pollutant distribution in an urban area with heavy vehicular traffic, resolving the spatial variability up to a resolution of 1-2m. In addition, the model results fit satisfactorily the time evolution of the pollutant concentration measured at an air quality station deployed in the studied area. However, problems were detected close to zones with complex emissions patterns (tunnels, street forks, etc.), where the model compared poorly against passive sampler measurements. A preliminary assessment of the uncertainties induced in the numerical methodology due to consider NO2 as non-reactive pollutant under winter conditions indicates that it would be an acceptable approach for this particular case study. Overall, our analysis contributes to raise the confidence in that approached similar to the one presented in this study can be adopted for dealing with several aspects of the air quality management such as air quality assessment, optimization of the location of measurement stations, and the evaluation of air pollution reduction strategies.