Periodicity　correlations　between　roadside　PM2.5　concentration　and　traffic　volume　are　difficult　to　explore　due　to　the　complicated　process　of　production　and　diffusion　of　PM2.5:　a　reaction　of　vehicle　emission　with　atmosphere,　dependence　with　wind　speed,　pavement　width,　vehicle　speed,　vehicle　type,　etc.　We　propose　a　framework　to　explore　the　periodicity　correlation　between　roadside　PM2.5　concentration　and　traffic　volume　using　wavelet　transform.　The　framework　utilizes　wavelet　spectrum　to　obtain　the　characteristic　periods　of　PM2.5　concentration　and　traffic　volume,　and　utilizes　cross-wavelet　spectrum　to　explore　the　hysteresis　response　of　PM2.5　concentration　with　respect　to　traffic　volume.　Different　from　traditional　research　work　which　handle　pollutants　and　traffic　data　of　macro　scales,　our　study　examines　themicro-scale　relationship　of　PM2.5　concentration　and　traffic　volume.　We　conduct　experiments　from　four　urban　streets　of　Beijing,　and　reveal　two　micro-scale　rules:　(a)　the　characteristic　period　of　PM2.5　concentration　approximates　the　characteristic　period　of　traffic　volume,　and　both　of　them　are　significantly　affected　by　the　traffic　signal　cycle;　(b)　PM2.5　concentration　exhibits　a　time　delay　within　0.3-0.9　minutes　with　respect　to　traffic　volume.　The　research　will　benefit　the　further　study　on　the　evolution　of　PM2.5　concentration　and　traffic　volume,　and　provide　a　reference　for　the　mitigation　of　PM2.5　pollution　by　traffic　control　as　well　as　practical　traffic　pollutant　regulations　in　metropolises.