Rapid　and　accurate　estimation　of　source　items　was　the　basis　for　environment　emergency　disposal　on　sudden　air　pollution　accidents.　In　order　to　search　for　effective　methods　for　inversing　source　parameters,　we　conducted　a　comparison　study　on　the　performances　of　three　hybrid　algorithms　(e.g.,　GA-PSO,　GA-NM,　PSO-NM)　for　estimating　source　parameters　(strength　and　location).　Three　inversion　models　were　developed　by　combining　GA-PSO,　GA-NM,　PSO-NM　with　Gaussian　dispersion　model,　respectively.　The　study　was　carried　out　based　upon　SO2　leakage　tests　selected　from　1956　Prairie　Grass　emission　experiment.　The　impacts　of　algorithm　structure　and　atmospheric　diffusion　conditions　on　source　term　inversion　were　analyzed.　Results　showed　that　for　source　strength,　the　PSO-NM　algorithm　performed　more　accurate　and　robust,　the　mean　error　and　mean　standard　deviation　were　11.3%　and　0.7g/s,　respectively,　which　were　much　lower　than　those　of　GA-NM　(i.e.,　16.4%,　13.3g/s)　and　GA-PSO　(i.e.,　29.0%,　26.6g/s).　As　for　source　location,　the　performance　of　PSO-NM　was　more　robust,　with　average　standard　deviation　of　0.29m,　which　was　also　much　lower　than　that　of　GA-NM　(3.20m)　and　GA-PSO　(3.03m).　Under　the　unstable　and　neutral　atmospheric　diffusion　conditions,　the　accuracy　of　PSO-NM　algorithm　for　estimating　position　parameter　was　the　best,　with　an　error　of　4.97m;　However,　GA-NM　method　had　the　minimum　error　(7.69m)　under　the　stable　condition.　As　for　computational　efficiency,　PSO-NM　and　GA-PSO　spent　less　time　in　source　item　inversion,　which　were　more　suitable　for　inversing　source　parameters　for　sudden　air　pollution.　©　2019,　Editorial　Board　of　China　Environmental　Science.　All　right　reserved.