The　scale　of　the　modern　city　has　been　expanding,　which　leads　to　a　lot　of　serious　environmental　pollution　problems.　Among　them,　the　air　pollution　problem　is　the　most　prominent.　In　order　to　control　the　air　pollution　in　urban　cities,　the　government　has　deployed　a　lot　of　air　pollutant　monitoring　equipment　which　produce　massive　multi-dimensional　time　series　data.　Through　the　motif　discovery　and　analysis　of　these　multi-dimensional　time　series,　we　can　find　the　relationships　and　the　rules　between　air　pollutants　to　provide　support　and　suggestions　for　controlling　the　air　pollution.　In　this　paper,　a　novel　method　on　motif　discovery　and　analysis　for　large-scale　multi-dimensional　time　series　data　is　proposed.　The　new　method　can　effectively　find　multi-dimensional　motifs　and　the　correlation　between　them　as　much　as　possible,　which　reveals　the　underlying　rule　of　different　air　pollutants.　It　is　validated　on　practical　historical　data　of　air　pollutants　in　Beijing.　The　experimental　results　show　that　the　proposed　method　could　obtain　better　performance　than　the　related　work.　©　2017　IEEE.