The　cloud　computing　and　microsensor　technology　has　greatly　changed　environmental　monitoring,　but　it　is　difficult　for　cloud-computing　based　monitoring　system　to　meet　the　computation　demand　of　smaller　monitoring　granularity　and　increasing　monitoring　applications.　As　a　novel　computing　paradigm,　edge　computing　deals　with　this　problem　by　deploying　resource　on　edge　network.　However,　the　particularity　of　environmental　monitoring　applications　is　ignored　by　most　previous　studies.　In　this　paper,　we　proposed　a　resource　allocation　algorithm　and　a　task　scheduling　strategy　to　reduce　the　average　completion　latency　of　environmental　monitoring　application,　when　considering　the　characteristic　of　environmental　monitoring　system　and　dependency　among　task.　Simulations　are　conducted,　and　the　results　show　that　compared　with　the　traditional　algorithms.　With　considering　the　emergency　task,　the　proposed　methods　decrease　the　average　completion　latency　by　21.6%　in　the　best　scenario.