Accurate　and　rapid　predictions　of　air　pollutant　dispersion　are　important　for　effective　emergency　responses　after　sudden　air　pollution　accidents　(SAPA).　Notably,　dispersion　parameters　(s)　are　the　key　variables　that　influence　the　simulation　accuracy　of　dispersion　models.　Empirical　dispersion　schemes　based　on　power-law　formulas　are　probably　appropriate　choices　for　simulations　in　SAPA　because　of　the　requirement　for　only　routine　meteorological　data.　However,　performance　comparisons　of　different　schemes　are　lacking.　In　this　study,　the　performances　during　simulations　of　air　pollutant　dispersion　of　four　typical　empirical　parameterised　schemes,　i.e.　BRIGGS,　SMITH,　Pasquill-Gifford,　and　Chinese　National　Standard　(CNS),　were　investigated　based　on　the　GAUSSIAN　plume　model　with　datasets　for　the　classic　Prairie　Grass　experiments,　1956.　The　performances　when　simulating　peak　and　overall　concentrations　in　different　Pasquill　atmospheric　stability　classes　(A,　B,　C,　D,　E,　F)　were　quantitatively　analysed　through　different　statistical　approaches.　Results　showed　that　the　performances　of　four　schemes　for　peak　and　overall　concentrations　were　basically　consistent.　Scheme　CNS　in　unstable　atmospheric　conditions　(A,　B,　and　C)　performed　significantly　better　than　the　others　according　to　performance　criteria,　which　included　the　lowest　mean　of　absolute　value　of　fractional　biases,　lowest　normalised　mean　square　errors,　and　largest　mean　values　of　the　fraction　within　a　factor　of　two　when　predicting　peak　and　overall　concentrations,　respectively.　Schemes　BRIGGS　and　P-G　exhibited　slightly　better　performances　during　the　neutral　condition　(D)　followed　by　scheme　CNS.　Schemes　SMITH　and　CNS　demonstrated　slight　merits　in　predicting　concentrations　compared　to　the　other　schemes　during　stable　conditions　(E　and　F).　As　a　whole,　scheme　CNS　generally　performed　well　for　the　different　atmospheric　stability　classes.　These　analysis　results　can　help　to　fill　in　the　data　gaps　and　improve　our　understanding　of　the　influence　of　typical　power-law　function　schemes　on　simulations　of　air　pollutant　dispersion.　The　results　are　expected　to　provide　scientific　support　for　air　pollution　predictions,　especially　during　emergency　responses　to　SAPA.