PM2.5　emissions　from　heavy-duty　diesel　trucks　(HDDTs)　have　a　significant　impact　on　air　quality,　human　health,　and　climate　change,　and　seriously　threaten　the　UN　Sustainable　Development　Goals.　Globally,　a　series　of　emission　control　measures　have　been　implemented　to　reduce　pollution　emissions　from　HDDTs.　Current　studies　assessing　the　impact　of　these　measures　on　air　quality　and　human　health　have　mainly　used　coarse-grained　emission　data　as　input　to　dispersion　model,　resulting　in　the　inability　to　capture　the　spatiotemporal　variability　of　pollutant　concentrations　and　tending　to　increase　the　uncertainty　of　health　impact　assessment　results.　In　this　study,　we　quantified　the　impact　of　pollution　control　policies　for　HDDTs　in　Beijing　on　PM2.5　concentrations,　human　health,　and　economic　losses　by　integrating　policy　scenario　analysis,　pollution　dispersion　simulation,　public　health　impact　and　economic　benefit　assessment　models,　supported　by　high　spatiotemporal　resolution　emission　data　from　HDDTs.　The　results　show　that　PM2.5　concentrations　from　HDDTs　exhibit　significant　spatial　aggregation　characteristics,　with　the　intensity　of　aggregation　at　night　being　about　twice　as　high　as　that　during　the　day.　The　emission　hotspots　are　mainly　concentrated　in　the　sixth,　fifth　and　fourth　rings　and　major　highways.　Compared　to　the　＂business　as　usual＂　scenario　in　2018,　the　current　policy　of　updating　the　fuel　standard　to　China　VI　and　the　emission　standard　to　China　6　can　reduce　PM2.5　concentrations　by　96.72%,　thereby　avoiding　612　premature　deaths,　which　is　equivalent　to　obtaining　economic　benefits　of　1.65　billion　CNY.　This　study　further　emphasizes　the　importance　of　high　spatiotemporal　resolution　emission　data　during　traffic　dispersion　modeling.　The　results　can　help　improve　the　understanding　of　the　effectiveness　of　emission　reduction　measures　for　HDDTs　from　a　health　benefit　perspective.