Real-time　monitoring　and　timely　warning　of　air　quality　are　vital　components　of　building　livable　cities　and　implementing　the　＂Healthy　China＂　strategy.　Real-time,　efficient,　and　accurate　detection　of　air　quality　anomalies　holds　great　significance.　However,　almost　all　existing　methods　for　air　quality　anomaly　detection　often　overlook　the　imbalanced　distribution　of　data.　In　addition,　many　traditional　methods　cannot　learn　both　pointwise　representation　and　pairwise　association,　so　they　cannot　solve　complex　features.　This　study　proposes　an　anomaly　detection　method　for　air　quality　monitoring　based　on　Deep　Smooth　Random　Sampling　and　Association　Attention　in　Transformer　(DSRS-AAT).　Firstly,　based　on　the　third　geographical　law,　the　more　similar　the　geographical　environment,　the　closer　the　geographical　target　features　are.　We　cluster　sites　according　to　the　surrounding　geographic　features　to　fully　explore　latent　feature　associations.　Then,　we　employ　Deep　Smooth　Random　Sampling　to　rebalance　the　air　quality　datasets.　Meanwhile,　the　Transformer　with　association　attention　considers　both　prior　associations　and　series　associations　to　distinguish　anomaly　patterns.　Experiments　are　carried　out　with　real　data　from　95　monitoring　stations　in　Haikou　City,　China.　Final　results　demonstrate　that　the　proposed　DSRS-AAT　improves　the　effectiveness　of　anomaly　detection　and　provides　interpretability　analysis　for　traceability,　owing　to　a　significant　improvement　with　the　baselines　(OmniAnomaly,　THOC,　etc.).　The　proposed　method　effectively　enhances　the　effectiveness　of　air　quality　anomaly　detection　and　provides　a　reference　value　for　real-time　monitoring　and　early　warning　of　urban　air　quality.