Deep　variational　residual　auto-encoder　(ResNet-VAE)　has　shown　promising　outcomes　in　missing　imputation　of　wastewater　quality　data.　Nevertheless,　with　its　large　storage　size　and　computation　overhead,　it　is　of　great　difficulty　to　deploy　wastewater　treatment　plant　(WWTP)　sensors　for　real-time　missing　imputation.　To　address　this　problem,　we　propose　a　novel　approach　called　lightweight-ResNet-VAE　to　compress　classical　ResNet-VAE　by　network　pruning,　weight　quantization,　and　relative　indexing　in　this　article.　First,　we　develop　a　three-step　network　pruning　method　to　sparsify　the　weight　matrices　by　removing　insignificant　weights　to　reduce　the　time　cost　of　model　inference.　Second,　we　develop　weight　quantization　and　use　eight　shared　weights　to　compress　the　size　of　each　weight　from　32-bit　to　3-bit.　Finally,　the　relative　indexing　is　adopted　to　further　compress　the　size　of　the　classical　ResNet-VAE　by　compressed　sparse　row　(CSR),　which　greatly　accelerates　the　model　computation　and　saves　storage　size.　Experiments　on　the　Beipai　IoT　influent　quality　data　set　demonstrate　that　lightweight-ResNet-VAE　compresses　the　size　of　the　classical　ResNet-VAE　from　301.88　to　26.24　kB　with　a　compression　rate　of　11.50　times,　and　outperforms　the　baseline　methods　in　terms　of　computation　acceleration,　storage　saving　and　energy　consumption　with　only　a　slight　decrease　on　accuracy　as　2.74%　in　MAPE　of　missing　imputation　for　wastewater　quality　data　due　to　pruning　less　significant　weights　and　quantizing　the　remaining　weights.