Accurate　and　fine-grained　individual　Air　Quality　Index　(IAQI)　prediction　is　the　basis　of　Air　Quality　Index　(AQI),　which　is　of　great　significance　for　air　quality　control　and　human　health.　Traditional　approaches　such　as　time　series　modeling,　Recurrent　Neural　Network　(RNN)　or　Graph　Convolutional　Network　(GCN)　cannot　effectively　integrate　spatial-temporal　and　meteorological　factors　and　manage　dynamic　edge　relationship　among　scattered　monitoring　stations.　In　this　paper,　a　ST-CCN-IAQI　model　is　proposed　based　on　spatial-temporal　causal　convolution　networks.　Firstly,　both　the　spatial　effects　of　multi-source　air　pollutants　and　meteorological　factors　are　considered　via　spatial　attention　mechanism.　Secondly,　time-dependent　features　in　causal　convolution　network　are　extracted　by　stacked　dilated　convolution　and　time　attention.　Finally,　multiple　parameters　in　ST-CCN-IAQI　are　tuned　by　Bayesian　optimization.　In　this　paper,　the　Individual　Air　Quality　Index　(IAQI-　PM2.5)　data　of　Shanghai　air　monitoring　station　are　used　to　carry　out　the　experiment,　and　a　series　of　baseline　models　(AR,　MA,　ARMA,　ANN,　SVR,　GRU,　LSTM,　and　ST-GCN)　are　employed　to　compare　with　ST-CCN-IAQI.　Our　results　show　that:　(1)　In　the　single　station　test,　RMSE　and　MAE　values　of　ST-　CCN-　IAQI　are　9.873　and　7.469,　respectively,　which　decreases　by　24.95%　and　16.87%　on　average,　respectively;　R2　is　0.917,　about　5.69%　higher　than　that　of　the　baselines;　(2)　The　prediction　of　IAQI-PM2.5,　IAQI-PM10,　and　IAQI-NO2　of　all　stations　proves　that　ST-CCN-IAQI　has　strong　generalization　ability　and　stability;　(3)　Shapley　analysis　shows　IAQI-PM10,　humidity,　and　IAQI-NO2　have　a　great　impact　on　the　prediction　of　IAQI-PM2.5.　Friedman　test　under　different　data　sampling　conditions　proves　that　ST-CCN-IAQI　has　significant　performance　improvement　by　comparisons　with　baselines.　The　ST-CCN-IAQI　method　provides　a　robust　and　feasible　solution　for　accurate　prediction　of　fine-grained　IAQI.　©　2023　Journal　of　Geo-Information　Science.　All　rights　reserved.