PM2.5　and　PM10　could　increase　the　risk　for　cardiovascular　and　respiratory　diseases　in　the　general　public　and　severely　limit　the　sustainable　development　in　urban　areas.　Land　use　regression　models　are　effective　in　predicting　the　spatial　distribution　of　atmospheric　pollutants,　and　have　been　widely　used　in　many　cities　in　Europe,　North　America　and　China.　To　reveal　the　spatial　distribution　characteristics　of　PM2.5　and　PM10　in　Xi＇an　during　the　heating　seasons,　the　authors　established　two　regression　prediction　models　using　PM2.5　and　PM10　concentrations　from　181　monitoring　stations　and　87　independent　variables.　The　model　results　are　as　follows:　for　PM2.5,　R2　=　0.713　and　RMSE　=　8.355　μg/m3;　for　PM10,　R2　=　0.681　and　RMSE　=　14.842　μg/m3.　In　addition　to　the　traditional　independent　variables　such　as　area　of　green　space　and　road　length,　the　models　also　include　the　numbers　of　pollutant　discharging　enterprises,　restaurants,　and　bus　stations.　The　prediction　results　reveal　the　spatial　distribution　characteristics　of　PM2.5　and　PM10　in　the　heating　seasons　of　Xi＇an.　These　results　also　indicate　that　the　spatial　distribution　of　pollutants　is　closely　related　to　the　layout　of　industrial　land　and　the　location　of　enterprises　that　generate　air　pollution　emissions.　Green　space　can　mitigate　pollution,　and　the　contribution　of　traffic　emission　is　less　than　that　of　industrial　emission.　To　our　knowledge,　this　study　is　the　first　to　apply　land　use　regression　models　to　the　Fenwei　Plain,　a　heavily　polluted　area　in　China.　It　provides　a　scientific　foundation　for　urban　planning,　land　use　regulation,　air　pollution　control,　and　public　health　policy　making.　It　also　establishes　a　basic　model　for　population　exposure　assessment,　and　promotes　the　sustainability　of　urban　environments.　©　2020