The　distribution　of　PM2.5　concentration　has　obvious　spatial　heterogeneity　in　the　inner　city.　However,　traditional　analysis　methods　based　on　remote　sensing　data　or　monitoring　station　data　are　difficult　to　reveal　the　distribution　characteristics　of　PM2.5　concentration　in　the　inner　city　at　high　spatial-temporal　resolution,　and　there　is　also　a　lack　of　analysis　on　the　complex　nonlinear　effect　of　urban　scenes　(e.g.,　roads,　industrial　areas,　residential　areas,　etc.)　on　PM2.5　concentration.　In　this　study,　we　installed　the　mobile　monitoring　sensor　on　the　express　van　to　collect　PM2.5　concentration　in　different　urban　scenes　in　the　south　of　Fuzhou　main　urban　area.　The　PM2.5　simulation　and　scene　analysis　model　based　GWR-GBDT　method　was　proposed　by　fusing　Geographical　Weighted　Regression　(GWR)　and　Gradient　Boosting　Decision　Tree　(GBDT).　The　model　can　fit　the　nonlinear　relationship　between　meteorological　factors,　scene　factors,　and　PM2.5　concentration,　and　enhance　the　fine-scale　monitoring　ability　of　PM2.5　pollution　in　city.　Combined　with　partial　dependency　plot,　the　nonlinear　effect　of　different　urban　scenes　on　PM2.5　concentration　in　different　periods　was　analyzed　to　provide　support　for　urban　PM2.5　pollution　control.　The　results　show　that:　(1)　Based　on　the　mobile　PM2.5　concentration　monitoring　data,　the　GWR-GBDT　model　can　well　simulate　the　nonlinear　relationship　between　urban　scene　factors,　meteorological　factors,　and　PM2.5　concentration,　and　simulate　the　fine　spatial　distribution　of　PM2.5　concentration.　The　results　of　cross-validation　R2　was　between　0.52　and　0.94;　(2)　The　heterogeneity　of　the　response　of　the　same　scene　to　PM2.5　concentration　in　different　time　periods　was　analyzed　by　the　partial　dependence　plots,　and　we　found　that　the　effect　of　various　scenes　on　PM2.5　concentration　was　different;　(3)　By　analyzing　the　interaction　of　human　activities　and　urban　scenes　on　PM2.5　concentration　in　different　periods,　we　found　that　the　effect　of　urban　scenes　on　PM2.5　concentration　was　related　to　human　commuting　between　schools,　hospital,　and　residential　areas.　As　the　high　pollution　scene,　construction　site　can　effectively　reduce　PM2.5　pollution　in　several　hours　after　taking　watering　measures.　In　the　park　and　sports　service　area,　PM2.5　concentration　was　low　in　most　periods.　For　industrial　area　and　roads,　PM2.5　concentration　was　high　in　most　periods;　(4)　For　the　spatial　distribution　of　PM2.5　concentration,　PM2.5　concentration　in　the　south　of　Fuzhou　main　urban　area　presented　a　general　trend　of　high　pollution　in　the　southeast　and　low　pollution　in　the　northwest.　The　proportion　of　slightly　polluted　areas　in　construction　sites,　roads,　and　industrial　areas　was　significantly　higher　than　that　in　other　scenes.　The　overall　PM2.5　concentration　in　the　park　scene　was　low,　however,　the　park　in　mountains　was　affected　by　the　surrounding　industrial　areas　at　nightfall,　resulting　in　increased　PM2.5　concentration.　The　urban　outer　waters　have　mitigation　effect　on　PM2.5　concentration　around　them.　This　study　can　provide　support　for　fine-scale　PM2.5　pollution　treatment,　urban　planning,　and　PM2.5　pollution　exposure　risk　prevention　of　high-risk　groups　such　as　the　elderly　and　children　in　different　scenarios.　©　2022,　Science　Press.　All　right　reserved.