PM2.5　concentration　prediction　is　of　great　significance　to　environ-mental　protection　and　human　health.　Achieving　accurate　prediction　of　PM2.5　concentration　has　become　an　important　research　task.　However,　PM2.5　pollu-tants　can　spread　in　the　earth???s　atmosphere,　causing　mutual　influence　between　different　cities.　To　effectively　capture　the　air　pollution　relationship　between　cities,　this　paper　proposes　a　novel　spatiotemporal　model　combining　graph　attention　neural　network　(GAT)　and　gated　recurrent　unit　(GRU),　named　GAT-GRU　for　PM2.5　concentration　prediction.　Specifically,　GAT　is　used　to　learn　the　spatial　dependence　of　PM2.5　concentration　data　in　different　cities,　and　GRU　is　to　extract　the　temporal　dependence　of　the　long-term　data　series.　The　proposed　model　integrates　the　learned　spatio-temporal　depen-dencies　to　capture　long-term　complex　spatio-temporal　features.　Considering　that　air　pollution　is　related　to　the　meteorological　conditions　of　the　city,　the　knowledge　acquired　from　meteorological　data　is　used　in　the　model　to　enhance　PM2.5　prediction　performance.　The　input　of　the　GAT-GRU　model　consists　of　PM2.5　concentration　data　and　meteorological　data.　In　order　to　verify　the　effectiveness　of　the　proposed　GAT-GRU　prediction　model,　this　paper　designs　experiments　on　real-world　datasets　compared　with　other　baselines.　Experimental　results　prove　that　our　model　achieves　excellent　performance　in　PM2.5　concentration　prediction.