Black carbon (BC) is one of the major components of the atmospheric particulate matter (PM), which can cause adverse health impacts and contribute significantly to climate change. Poor understanding of BC sources and concentrations is the main obstacles to reduce BC emissions. Current commercial BC sensors remain too costly to deploy widely. A fast, cost-effective, and easily accessible deployment of smartphone camera was used to quantify colour information of PM collected on filters to estimate BC and elemental carbon (EC) loading. When applied to 1266 PM2.5 ambient samples collected from six sites across Ontario, Canada, the RGB-based BC model showed powerful predictability with R2=0.95 between predicted and measured BC concentrations from an aethalometer. The RGB-based EC model was trained using 478 personal PM2.5 samples collected from pre-diabetic subjects in Beijing with an R2=0.91 between predicted and measured EC concentrations from OC/EC analyzer.