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Characterisation of individual time-activity-location patterns is critical for accurate estimations of personal exposure and dose because pollutant concentrations and inhalation rates vary significantly by location and activity.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We developed and evaluated an automated model to classify major exposure-related microenvironments (<jats:italic>home, work, other static, in-transit<\/jats:italic>) and separated them into indoor and outdoor locations,<jats:italic>sleeping activity<\/jats:italic>and five modes of transport (<jats:italic>walking, cycling, car, bus, metro\/train<\/jats:italic>) with multidisciplinary methods from the fields of movement ecology and artificial intelligence. As input parameters, we used GPS coordinates, accelerometry, and noise, collected at 1 min intervals with a validated Personal Air quality Monitor (PAM) carried by 35 volunteers for one week each. The model classifications were then evaluated against manual time-activity logs kept by participants.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Overall, the model performed reliably in classifying home, work, and other indoor microenvironments (F1-score&gt;0.70) but only moderately well for sleeping and visits to outdoor microenvironments (F1-score=0.57 and 0.3 respectively). Random forest approaches performed very well in classifying modes of transport (F1-score&gt;0.91). We found that the performance of the automated methods significantly surpassed those of manual logs.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Automated models for time-activity classification can markedly improve exposure metrics. 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