It　is　imperative　for　the　students＇　future　health　to　ensure　the　students　in　good　physical　levels.　Recent　years　have　witnessed　the　increasingly　serious　harm　to　student　health　caused　by　the　continually　growing　concentration　of　Particulate　Matters　(PMs).　Consequently,　the　task　of　preventing　and　controlling　PM　concentrations　in　the　campus　is　eagerly　required.　A　well-designed　model　for　the　monitoring　of　PM　(as　the　basis　for　PM　prevention　and　control)　has　posed　a　big　challenge.　Prior　works　have　revealed　that　photo-based　methods　are　available　for　the　monitoring　of　PM.　Towards　validating　the　effectiveness　of　existing　methods　for　PM　monitoring　in　the　campus,　we　construct　a　novel　dataset　that　involves　1,500　photos　collected　in　the　Beijing　University　of　Technology.　Results　confirm　that　stated-of-the-art　methods　are　far　from　ideal　for　the　monitoring　of　PM　in　the　campus.　To　solve　the　aforesaid　issue,　this　paper　further　proposes　a　novel　photo-based　PM　monitoring　model　by　using　the　weighted　average　method　solved　by　LASSO　regression　to　fuse　the　above　methods＇　outputs　tested　to　infer　the　PM　values.　Results　demonstrate　the　superiority　of　our　proposed　model　as　compared　to　state-of-the-art　methods　on　the　large-scale　AQPDBJUT　dataset.　©　Published　under　licence　by　IOP　Publishing　Ltd.