Since　Particulate　Matters　(PMs)　are　closely　related　to　people＇s　living　and　health,　it　has　become　one　of　the　most　important　indicator　of　air　quality　monitoring　around　the　world.　But　the　existing　sensor-based　methods　for　PMs　monitoring　have　remarkable　disadvantages,　such　as　low-density　monitoring　stations　and　high-requirement　monitoring　conditions.　It　is　highly　desired　to　devise　a　method　that　can　obtain　the　PMs　concentration　at　any　location　for　the　following　air　quality　control　in　time.　The　prior　works　indicate　that　the　PMs　concentration　can　be　monitored　by　using　ubiquitous　photos.　To　further　investigate　such　issue,　we　gathered　1,500　photos　in　big　cities　to　establish　a　new　AQPDCITY　dataset.　Experiments　conducted　to　check　nine　stateof-the-art　methods　on　this　dataset　show　that　the　performance　of　those　above　methods　perform　poorly　in　the　AQPDCITY　dataset.　To　address　the　above　issue,　we　propose　a　new　photo-based　model　for　PMs　monitoring.　To　be　specific,　we　use　the　Support　Vector　Regression　(SVR)　to　incorporate　four　types　of　18　features　to　obtain　a　high-density　PMs　monitoring　map.　Experiments　show　that　the　newly　proposed　model　has　achieved　superior　performance　than　recently　developed　methods　in　the　AQPDCITY　dataset.　©　2020　Technical　Committee　on　Control　Theory,　Chinese　Association　of　Automation.