Investigate Air Pollution Distribution And Its Impact On Slow Traffic Using Distributed Air Sensor Networks

With the continuous development of society,environmental problems have become increasingly prominent.Among them,air quality problems have received widespread attention.Due to the complex built environment and the higher degree of air pollution inside the city,the pollutants varied quickly in a short time and distance.Therefore,in order to explore the spatiotemporal distribution of air pollutants in cities,this dissertation explores the variation patterns of PM_2.5 in cities based on the Distributed Atmospheric Sensor Network（DASN）.First,based on the high spatial-temporal resolution data obtained by DASN,the temporal and spatial variation patterns of pollutants are analyzed.Temporally,the time-series clustering was used to find periods with similar diurnal variations.Finally,four different variation patterns were found,and the cause and distribution of each period were analyzed according to meteorological factors.Spatially,the interpolation method was used to draw the pollution map based on the four periods obtained.The spatial variation patterns were found and the pollution hotspots are identified.Then,on the basis of the pollution map,the general health risk assessments of human exposures and individual path-based microenvironmental inhalation exposure analyses were performed.A path-based inhalation exposure algorithm was designed to calculate the exposure under different paths for different people with different slow-traffic traveling methods.The shortest path and the lowest exposure path are calculated by path planning,and the lowest exposure path planning software has been designed.The research results of this dissertation help to reduce the level of pollution inside the city,and at the same time,provide low-exposure routes for slow-traffic travelers.