The　interpolation　of　fine-grained　air　quality　has　significant　prospects　in　the　area　of　air　quality　monitoring.　The　solution　to　this　problem　can　effectively　monitor　the　air　quality　of　the　areas　by　sparse　air　quality　monitoring　stations,　so　as　to　reduce　the　monitoring　cost.　Most　of　the　existing　researches　are　to　solve　the　problem　of　air　quality　monitoring　in　the　areas　without　stations　by　different　interpolation　methods.　However,　most　of　them　are　unable　to　verify　the　reliability　of　the　proposed　interpolation　methods　and　can＇t　provide　a　feasible　range　of　interpolation　methods　at　spatial　resolutions.　Therefore,　this　paper　proposes　an　effectively　unsupervised　PM2.5　estimation　method　based　on　time　distributed　convolutional　gated　recurrent　unit　(TCGRU)　and　an　interpolation　method　based　on　k-nearest　neighbor　inverse　distance　weighted　(KIDW)　to　solve　these　problems.　The　model　is　trained　by　the　data　obtained　by　the　interpolation　method　with　missing　one　station　in　turn　to　get　the　monitoring　capability　of　the　areas　without　monitoring　stations.　Inverse　distance　weighted　is　combined　with　k-nearest　neighbor　to　improve　the　performance　of　interpolation.　In　addition,　time　distributed　convolutional　neural　network　extracts　the　spatial　features　of　air　quality　and　storage　time　information　for　extracting　temporal　features　by　the　gated　recurrent　units.　A　large　number　of　experiments　are　carried　out　to　evaluate　the　performance　of　the　method　by　using　the　air　quality　dataset　of　Hubei　Province,　China.　The　experimental　results　show　that　the　proposed　model　is　effective　for　the　monitoring　of　PM2.5　in　the　unsupervised　areas　and　the　performance　(i.e.　MAE=8.23,　RMSE=11.27　and　MAPE=19.14%)　of　estimating　PM2.5　concentration　is　better　than　the　comparative　methods.