Assessing personal exposure to air pollutants using agent based modelling and wearable sensors

An individual exposure model was developed using Agent Based Modelling (ABM) to simulate human movement and interaction behaviour, informed by wearable sensors data. This model can feed into a population-based exposure assessment without imposing prior bias, but rather basing its estimations onto emerging properties of the agent system behaviour. This method integrates Socioeconomic Status (SES) data and provides the capacity for aggregation at various levels of population size. It is a novel approach that permits the computationally efficient identification of refined time-activity diaries and exposure profiles throughout the day, for different sociodemographic groups of population.