The　emerging　paradigm　of　urban　computing　aims　to　infer　latent　patterns　from　various　aspects　of　a　city＇s　environment　and　possibly　identify　their　hidden　correlations　by　analyzing　urban　big　data.　This　article　provides　a　fine-grained　analysis　of　air　quality　from　diverse　sensor　data　streams　retrieved　from　regions　in　the　city　of　London.　The　analysis　derives　spatio-temporal　patterns,　that　is,　across　different　location　categories　and　time　spans,　and　also　reveals　the　interplay　between　urban　phenomena　such　as　human　commuting　behavior　and　the　built　environment,　with　the　observed　air　quality　patterns.　The　findings　have　important　implications　for　the　health　of　ordinary　citizens　and　for　city　authorities　who　may　formulate　policies　for　a　better　environment.　©　2018　IEEE.