In　order　to　improve　the　prediction　accuracy　of　PM2.5　concentration,　a　method　based　on　the　principal　component　analysis　and　online　sequential　extreme　learning　machine　(PCA-OS-ELM)　was　proposed　to　predict　PM2.5　concentration　in　this　paper.　Firstly,　principal　component　analysis　(PCA)　was　used　to　extract　the　key　variables　affecting　air　quality　in　high-dimensional　atmospheric　data,　and　remove　unnecessary　redundant　variables.　Secondly,　an　online　sequential　extreme　learning　machine　(OS-ELM)　network　prediction　model　was　established　by　using　the　extracted　key　variables.　Finally,　the　training　data　and　network　parameters　were　continuously　updated　to　realize　the　rapid　prediction　of　PM2.5　concentration　by　combining　batch　processing　with　successive　iteration.　The　results　show　that,　taking　different　batches　of　training　data　to　update　the　model,　the　PCA-OS-ELM　prediction　method　can　quickly　realize　the　prediction　of　atmospheric　PM2.5　concentration,　proving　the　effectiveness　of　the　proposed　method.　Compared　with　other　methods,　this　method　shows　little　prediction　error,　higher　prediction　accuracy　and　better　practical　value.　©　2021,　Editorial　Department　of　Transaction　of　Beijing　Institute　of　Technology.　All　right　reserved.