This　article　devises　a　photograph-based　monitoring　model　to　estimate　the　real-time　PM2.5　concentrations,　overcoming　currently　popular　electrochemical　sensor-based　PM2.5　monitoring　methods＇　shortcomings　such　as　low-density　spatial　distribution　and　time　delay.　Combining　the　proposed　monitoring　model,　the　photographs　taken　by　various　camera　devices　(e.g.,　surveillance　camera,　automobile　data　recorder,　and　mobile　phone)　can　widely　monitor　PM2.5　concentration　in　megacities.　This　is　beneficial　to　offering　helpful　decision-making　information　for　atmospheric　forecast　and　control,　thus　reducing　the　epidemic　of　COVID-19.　To　specify,　the　proposed　model　fuses　Information　Abundance　measurement　and　Wide　and　Deep　learning,　dubbed　as　IAWD,　for　PM2.5　monitoring.　First,　our　model　extracts　two　categories　of　features　in　a　newly　proposed　DS　transform　space　to　measure　the　information　abundance　(IA)　of　a　given　photograph　since　the　growth　of　PM2.5　concentration　decreases　its　IA.　Second,　to　simultaneously　possess　the　advantages　of　memorization　and　generalization,　a　new　wide　and　deep　neural　network　is　devised　to　learn　a　nonlinear　mapping　between　the　above-mentioned　extracted　features　and　the　groundtruth　PM2.5　concentration.　Experiments　on　two　recently　established　datasets　totally　including　more　than　100　000　photographs　demonstrate　the　effectiveness　of　our　extracted　features　and　the　superiority　of　our　proposed　IAWD　model　as　compared　to　state-of-the-art　relevant　computing　techniques.