Hybrid cellular automata-based air pollution model for traffic scenario microsimulations

Scenario microsimulations like agent-based models can account for feedbacks and spatio-temporal and social heterogeneity when projecting future intervention impacts. Addressing air pollution exposure requires traffic scenario models ( i.e . of car-free zones). Traditional air pollution models do not meet all requirements for traffic scenario microsimulation: isolating traffic emission, integrating relevant dispersion moderators, while computationally efficient, interoperable and valid. We propose a hybrid model of land use regression-based baseline concentrations and on-road emissions in conjunction with cellular automata-based off-road dispersion. The model efficiently assesses air pollution, while accounting for meteorological and morphological dispersion processes. We calibrate using genetic algorithms and externally validate the model based on mobile measurements and fixed-site routine monitoring data of NO2 concentrations across Amsterdam. Our model achieves an external validation R2 of 0.60 and 0.48 s computation time in a 50 m × 50 m raster. Further, we successfully projected the NO2 reduction of the first Covid-19 lockdown traffic scenario (R2 0.57). • Current FAIR air pollution models are unsuited for traffic scenario microsimulations. • Our hybrid model combining land use regression with cellular automata can fill the gap. • Model efficiently simulates key meteorological and morphological dispersion moderators. • We construct, calibrate and externally validate the hybrid model in step-wise manner. • Our model accurately projects the NO2 reduction in Amsterdam during the 2020 lockdown.