Air　pollution　is　threatening　human＇s　health　since　the　industrial　revolution,　but　there　are　not　efficient　ways　to　solve　air　pollution,　so　forecasting　air　quality　has　become　an　efficient　measure　to　prevent　citizens　from　hurting　of　heavy　air　pollution.　In　this　paper,　we　proposed　an　advanced　Seq2Seq　(Sequence　to　Sequence)　model　called　attention-based　air　quality　forecasting　model　(ABAFM)　whose　RNN　encoder　is　replaced　by　pure　attention　mechanism　with　position　embedding.　This　improvement　not　only　reduces　the　training　time　of　Seq2Seq　model　with　attention　but　also　enhances　the　robustness　of　Seq2Seq　models.　We　implemented　ABAFM　in　Olympic　center　and　Dongsi　monitoring　stations　in　Beijing　to　forecast　PM2.5　in　future　24　hours.　The　experimental　results　showed　that　the　proposed　model　outperformed　the　related　arts,　especially　in　sudden　changes.