The　spatiotemporal　variability　in　air　pollutant　concentrations　raises　challenges　in　linking　air　pollution　exposure　to　individual　health　outcomes.　Thus,　understanding　the　spatiotemporal　patterns　of　human　mobility　plays　an　important　role　in　air　pollution　epidemiology　and　health　studies.　With　the　advantages　of　massive　users,　wide　spatial　coverage　and　passive　acquisition　capability,　mobile　phone　data　have　become　an　emerging　data　source　for　compiling　exposure　estimates.　However,　compared　with　air　pollution　monitoring　data,　the　temporal　granularity　of　mobile　phone　data　is　not　high　enough,　which　limits　the　performance　of　individual　exposure　estimation.　To　mitigate　this　problem,　we　present　a　novel　method　of　estimating　dynamic　individual　air　pollution　exposure　levels　using　trajectories　reconstructed　from　mobile　phone　data.　Using　the　city　of　Shanghai　as　a　case　study,　we　compared　three　different　types　of　exposure　estimates　using　(1)　reconstructed　mobile　phone　trajectories,　(2)　recorded　mobile　phone　trajectories,　and　(3)　residential　locations.　The　results　demonstrate　the　necessity　of　trajectory　reconstruction　in　exposure　and　health　risk　assessment.　Additionally,　we　measure　the　potential　health　effects　of　air　pollution　from　both　individual　and　geographical　perspectives.　This　helped　reveal　the　temporal　variations　in　individual　exposures　and　the　spatial　distribution　of　residential　areas　with　high　exposure　levels.　The　proposed　method　allows　us　to　perform　large-area　and　long-term　exposure　estimations　for　a　large　number　of　residents　at　a　high　spatiotemporal　resolution,　which　helps　support　policy-driven　environmental　actions　and　reduce　potential　health　risks.