In　this　paper　we　propose　a　novel　model-data　jointly　driven　(MDJD)　method　from　a　single　picture　for　airborne　particulate　matter　(APM)　monitoring,　towards　assisting　the　decision-making　for　government　and　reducing　the　health　risks　for　individuals.　The　MDJD　method　is　mainly　composed　of　three　steps.　First,　we　create　a　vector　of　.distance.　as　the　model　driven　natural　scene　statistic　(NSS)　features　through　comparing　the　sparsity　features　that　are　extracted　from　one　picture　in　five　transform　domains　with　their　corresponding　benchmark　features　that　are　derived　by　using　a　huge　number　of　pictures　with　the　extremely　low　APM　concentrations　in　advance.　Second,　we　produce　a　vector　of　.distance.　as　the　data-driven　NSS　features　through　comparing　the　contrast-sensitive　features　that　are　chosen　from　hundreds　of　deep　features　with　their　associated　benchmark　features　that　are　derived　based　on　the　same　feature　generation　method　as　used　in　model-driven　NSS　features.　Lastly,　we　fuse　the　aforesaid　model-　and　data-driven　NSS　features　by　introducing　a　nonlinear　regressor　to　estimate　the　APM　concentration.　Extensive　experiments　conducted　on　two　large-size　APM　picture　datasets　validate　the　superiority　of　our　proposed　MDJD　method　over　the　state-of-the-art　model-driven　methods　and　data-driven　methods　by　a　sizable　gain　of　7.4%　in　terms　of　peak　signal　to　noise　ratio.　Via　a　series　of　ablation　studies,　we　can　observe　that　fusing　model-　and　data-driven　NSS　features　is　beneficial　to　improving　the　model＇s　generalization　and　fitting　abilities　and　leads　to　the　gains　of　over　15.1%　compared　with　using　either　type　of　features　in　isolation.