Sensitivity analysis of a mobility – emissions – air quality – exposure modelling chain to assess individuals’ exposure in metropolitan areas

Exposure to air pollution contributes to chronic cardiovascular and respiratory diseases as well as mortality, particularly in urban areas. For assessing the health impacts of air pollution, integrated mobility – emissions – air quality – exposure modelling chains have been developed in recent years (Gurram, Stuart, et Pinjari 2019). Numerous studies have highlighted the importance of considering daily mobility when assessing air pollution exposure (Dias et Tchepel 2018). However, the question of uncertainties associated with these modelling chains remains little studied, in particular uncertainties related to models’ spatiotemporal resolution. This work aims to perform a sensitivity analysis of individual exposure to ambient air pollution with an agent-based mobility model coupled with emission, air quality and exposure models.

This study is based on a modelling chain to assess individuals’ exposure with an agent-based approach. Individuals’ daily mobility and car traffic within the region are simulated with MATSim. As urban air quality is both affected by long-range pollution transport and local pollution sources within the urban canyon layer, spatial resolution of air quality was addressed. To this end, we developed novel methodology to generate a disaggregated car fleet attributing car types (i.e. fuel and Euro norm) to households depending on their socioeconomic characteristics, instead of the state-of-the-art average emitting car. This car fleet model aims to better represent spatial heterogeneities in car traffic emissions. Moreover, air quality is simulated at the street scale with the MUNICH street-network model (Kim et al. 2022) while urban background concentrations are simulated with the Polair3D Eulerian chemical transport model (CTM). The exposure model, at last, combines individual travel patterns and street-level pollution concentrations to assess individuals’ exposure, taking into account ambient air pollution infiltration and exposure in transportation.

To study the modelling chain sensitivity, three scenarios comparisons will be performed to assess the impact of the spatiotemporal resolution of car emissions, air quality and individual activities. First, we compare individuals’ exposure when implementing emissions based on a disaggregated car fleet versus a homogenous car fleet composed of an average emitting car. Secondly, we explore the impact of air quality spatial representation on exposure by comparing the use of the background CTM model alone (Polair3D) and the combined CTM and street air quality model. The third test will compare an exposure model incorporating mobility with a traditional static approach, where individuals stay at home.

 

References

Dias, Daniela, et Oxana Tchepel. 2018. « Spatial and Temporal Dynamics in Air Pollution Exposure Assessment ». International Journal of Environmental Research and Public Health 15 (3): 558. https://doi.org/10.3390/ijerph15030558.

Gurram, Sashikanth, Amy Lynette Stuart, et Abdul Rawoof Pinjari. 2019. « Agent-Based Modeling to Estimate Exposures to Urban Air Pollution from Transportation: Exposure Disparities and Impacts of High-Resolution Data ». Computers, Environment and Urban Systems 75 (mai):22‑34. https://doi.org/10.1016/j.compenvurbsys.2019.01.002.

Kim, Youngseob, Lya Lugon, Alice Maison, Thibaud Sarica, Yelva Roustan, Myrto Valari, Yang Zhang, Michel André, et Karine Sartelet. 2022. « MUNICH v2.0: A Street-Network Model Coupled with SSH-Aerosol (v1.2) for Multi-Pollutant Modelling ». Geoscientific Model Development 15 (19): 7371‑96. https://doi.org/10.5194/gmd-15-7371-2022.