To　avoid　the　adverse　effects　of　severe　air　pollution　on　human　health,　we　need　accurate　real-time　air　quality　prediction.　In　this　paper,　for　the　purpose　of　improve　prediction　accuracy　of　air　pollutant　concentration,　a　deep　neural　network　model　with　multitask　learning　(MTL-DBN-DNN),　pretrained　by　a　deep　belief　network　(DBN),　is　proposed　for　forecasting　of　nonlinear　systems　and　tested　on　the　forecast　of　air　quality　time　series.　MTL-DBN-DNN　model　can　solve　several　related　prediction　tasks　at　the　same　time　by　using　shared　information　contained　in　the　training　data　of　different　tasks.　In　the　model,　DBN　is　used　to　learn　feature　representations.　Each　unit　in　the　output　layer　is　connected　to　only　a　subset　of　units　in　the　last　hidden　layer　of　DBN.　Such　connection　effectively　avoids　the　problem　that　fully　connected　networks　need　to　juggle　the　learning　of　each　task　while　being　trained,　so　that　the　trained　networks　cannot　get　optimal　prediction　accuracy　for　each　task.　The　sliding　window　is　used　to　take　the　recent　data　to　dynamically　adjust　the　parameters　of　the　MTL-DBN-DNN　model.　The　MTL-DBN-DNN　model　is　evaluated　with　a　dataset　from　Microsoft　Research.　Comparison　with　multiple　baseline　models　shows　that　the　proposed　MTL-DBN-DNN　achieve　state-of-art　performance　on　air　pollutant　concentration　forecasting.　©　2019　Jiangeng　Li　et　al.