This　study　aims　to　develop　a　second　order　self-organizing　fuzzy　neural　network　(SOFNN)　to　predict　the　hourly　concentrations　of　fine　particulate　matter　(PM2.5)　for　the　next　24　h　at　a　regional　background　station　called　Shangdianzi　(SDZ)　in　China　from　14　to　23　January　2010.　The　structure　of　the　SOFNN　was　automatically　adjusted　according　to　the　sensitivity　analysis　(SA)　of　model　output　and　the　parameter-learning　phase　was　performed　applying　a　second　order　gradient　(SOG)　algorithm.　Principal　component　analysis　(PCA)　was　employed　to　select　the　dominating　factors　for　PM2.5　concentrations　as　the　input　variables　for　the　SOFNN.　It　was　found　that　the　dominating　variables　(relative　humidity　(RH),　pressure　(Pre),　aerosol　optical　depth　(AOD),　wind　speed　(WS)　and　wind　direction　(WD))　extracted　by　PCA　agreed　well　with　the　characteristics　of　PM2.5　at　SDZ　where　the　PM2.5　concentrations　were　heavily　affected　by　meteorological　parameters　and　were　closely　related　to　AOD.　The　forecasting　results　showed　that　the　proposed　SOG-SASOFNN　performed　better　than　other　models　with　higher　coefficient　of　determination　(R-2)　during　both　training　phase　and　test　phase　(0.89　and　0.84,　respectively)　in　predicting　PM2.5　concentrations　at　SDZ.　In　conclusion,　the　developed　SOG-SASOFNN　provided　satisfying　results　for　modeling　the　hourly　distribution　of　PM2.5　at　SDZ　during　the　studied　period.