Assessing personal exposure using Agent Based Modelling informed by sensors technology

Technology innovations create possibilities to capture exposure-related data at a great depth and breadth. Considering, though, the substantial hurdles involved in collecting individual for whole populations, this study introduces first approach of simulating human movement interaction behaviour, using Agent Based Modelling (ABM). A city scale ABM was developed urban Thessaloniki, Greece that feeds into population-based exposure assessment without imposing prior bias, basing its estimations onto emerging properties behaviour computerised autonomous decision makers (agents) compose city-system. Population statistics, road buildings networks were transformed human, building agents, respectively. Survey outputs with time-use patterns associated agent rules, aiming model representative real-world behaviours. Moreover, time-geography data, derived from local sensors campaign, used inform enhance model. As prevalence an agent-specific decision-making, virtual individuals different sociodemographic backgrounds express spatiotemporal behaviours their trajectories are coupled spatially resolved pollution levels. Personal evaluated by assigning PM concentrations agents based on coordinates, type location intensity encountered activities. Study results indicated PM2.5 inhalation adjusted between housemates can differ 56.5% whereas two neighbours vary as much 87%, due This provides details new methodology permits cost-effective construction refined time-activity diaries daily profiles, taking account microenvironments characteristics. The proposed method leads model, addressing effectively vulnerable subgroups population. It be evaluating probable impacts public health policies implementation reducing, therefore, time expense required identify efficient measures.