Air　pollutants　do　much　harm　to　human　safety.　In　particular,　the　fine　particulate　matter　(PM2.5),　a　complex　air　pollutant　which　is　composed　of　the　particles　beneath　the　aerodynamic　diameters　of　2.5　mu　m,　very　possibly　causes　severe　diseases　since　it　is　easy　to　intrude　into　the　lungs.　To　that　end,　in　this　paper,　we　design　a　new　picture-based　predictor　of　PM2.5　concentration　(PPPC),　which　employs　the　pictures　acquired　using　mobile　phones　or　cameras　to　make　a　real-time　estimation　of　PM2.5　concentration.　First,　using　a　large　body　of　pictures　which　were　captured　under　the　good　weather　conditions,　i.e.,　very　low　PM2.5　concentration,　naturalness　statistics　(NS)　models　are　built　upon　entropy　features　in　spatial　and　transform　domains.　Second,　for　a　novel　picture,　we　measure　its　deviation　degree　from　the　above-mentioned　NS　models,　considering　the　fact　that　the　naturalness　of　a　picture　tends　to　reduce　with　the　PM2.5　concentration　increased.　Third,　a　simple　nonlinear　function　is　introduced　to　map　the　deviation　degree　to　the　PM2.5　concentration.　In　comparison　to　existing　relevant　state-of-the-art　predictors,　sufficient　experimental　results　manifest　the　superiority　of　the　proposed　PPPC　model　in　terms　of　prediction　accuracy　and　implementation　efficiency.