PM2.5　can　be　suspended　in　the　air　for　a　long　time,　so　it　has　great　harm　to　human　health　and　atmospheric　environment　quality.　Real-time　and　reliable　estimation　of　PM2.5　concentration　is　of　great　significance.　This　paper　proposes　a　novel　multi-modal　image　information　fusion-based　PM2.5　concentration　estimator.　Specifically,　in　the　depth　domain,　we　estimate　PM2.5　concentration　by　calculating　the　depth　error　between　the　PM2.5　image　and　its　corresponding　dehazing　result.　In　the　texture　domain,　we　measure　PM2.5　concentration　using　the　following　two　modules:　(1)　The　PM2.5　concentration　is　represented　by　calculating　the　entropy　difference　between　the　PM2.5　image　and　its　dehazing　image;　(2)　We　first　compute　the　error　map　between　the　PM2.5　image　and　its　dehazing　image,　and　then　calculate　the　color　similarity　between　the　PM2.5　image　and　the　obtained　error　map　to　measure　PM2.5　concentration.　Finally,　we　use　support　vector　regression　to　perform　regression　learning　on　the　above-mentioned　multi-modal　features　learned　from　the　depth　domain　and　texture　domain　to　obtain　the　final　PM2.5　concentration　estimator.　Experiments　show　that　the　proposed　method　can　measure　PM2.5　concentration　more　efficiently　than　the　existing　PM2.5　concentration　estimation　algorithms.