High Resolution Assessment of Pedestrian Exposure to Air Pollution in a Real Urban Hot-Spot

Nowadays, urban air quality is considered one of the most important environmental challenges and the largest environmental health risk in Europe. A high percentage of population lives in cities (e.g. in Europe more than 70% and it is expected that this percentage increases in the next years) where high pollution levels are found. Therefore, part of population is exposed to pollutant concentrations exceeding the air quality standards and this consequently induces an impact on human health. For example, the Environmental European Agency estimated these impacts as 400 000 premature deaths in EU-28 in 2013. Taking into account these issues, the population exposure must be reduced. However it is difficult to address this problem because the interaction between atmosphere and urban surfaces induces complex air flow patterns in the city and this fact, linked with the heterogeneities of traffic emissions, produces high pollution levels with strong gradient of concentrations within the streets. Then, we need to investigate this issue at street scale and high spatial resolution is needed. Several methodologies to estimate the health impacts and population exposure to urban air pollution have been proposed so far, among others: a) air quality monitoring stations (AQMS) and population data (horizontal resolution of order of few km²); b) mesoscale models (horizontal resolution of order of few km²) and population data (horizontal resolution of order of few km²); c) mesoscale models (horizontal resolution of order of few km²) and population dynamics based on mobile phone data (Picornell et al., 2018); d) CFD models (horizontal resolution of order of few m²) + population data (horizontal resolution of order of 100 m x 100 m) (Rivas et al., 2019). In this study, the main objective is to quantify the exposure of pedestrians to NO_X (one of the most important pollutant in cities) in a real urban hot-spot with high spatial resolution. To achieve this aim, we present a novel approach to compute the exposure of pedestrians based on:

• High resolution concentration maps (µg m^-3) obtained by CFD simulations.
• Pedestrian flows throughout the study area (persons) with a resolution of 5 m x 5 m computed by pedestrian microsimulations.

The studied zone is a highly polluted area in southern Madrid. CFD simulations were performed using high resolution traffic emissions and have been previously evaluated with measurements from a three weeks experimental campaign (Sanchez et al., 2017). Traffic and pedestrian fluxes were simulated with the microscale modelling system VISSIM-VISWALK. Pedestrian routes were computed based on data collected in experimental campaigns. Hourly exposures during an average day of the experimental campaign and the daily total exposure are computed with a resolution of 5 m x 5 m. Results show that pedestrian positions (e.g. bus stop) have an important influence on total daily exposure. In addition, daily total exposure in the whole area is computed in different ways using:

1. High resolution maps
2. spatial average concentration (similar to a mesoscale resolution) + total number of pedestrians
3. concentration data from an AQMS located in the studied zone + total number of pedestrians.

Differences of 8.5% were found comparing the detailed maps results with the computation described in point 2. However, higher differences (up to 30%) are found when the exposure is calculated with concentrations at one location (AQMS). This result indicates the importance of knowing the spatial representativeness of an AQMS to correctly determine the air quality from the zone and the potential health impacts.

We can conclude that this methodology, using CFD simulations and pedestrian microsimulations, can provide pedestrian exposure with high resolution. Additionally, it helps to quantify the spatial representativeness of AQMS in terms of concentration and exposure. Furthermore, this study shows that spatial variability in concentration and pedestrian position make difficult to assess air quality with only one AQMS.

References:

• Picornell M., Ruiz T., Borge R., García-Albertos P., de la Paz D., Lumbreras J. (2019). Journal of Exposure Science & Environmental Epidemiology 29, 278–291.
• Rivas E., Santiago J.L., Lechón Y., Martín F., Ariño A., Pons J.J., Santamaría J.M. (2019). Science of The Total Environment 649, 1362-1380.
• Sanchez B., Santiago J.L., Martilli A., Martin F., Borge R., Quaassdorff C., de la Paz D. (2017). Atmospheric Environment 163, 155-165.