The Impact Of Elevated Expressway On Vertical PM_2.5 And CO Concentrations In Street Canyon

Due to the increase of private vehicles in cities,high-level pollution is often detected in urban street canyons.There appears extensive evidence that vehicles are a major source of the particulate matter and carbon monoxide,which causes adverse health effects.Because of the limited space in cities,most buildings in areas mixed with industry,resident and commerce are in the form of high-rise towers and close to each other.People living in different floor heights may inhale different amounts of vehicular particulate matter.Hence,it is important to explore the vertical variations of air pollutants such as particulate matter and to investigate how various factors influence such variation of air pollutants.The elevated expressway is a typical roadway pattern which generate serious vehicular emissions and become a special conduit for air pollutants dispersion.Although current literatures have made progress in the dispersion pattern in roadway pattern such as street canyons or intersections,there is just a few studies addressed the relationship between traffic and meteorological factors and pollution vertical variation,and discussed the potential methods for predicting the vertical pollution concentrations on urban roadside.In this study,field measurements were conducted at eight different floor heights outside a building alongside a typical elevated expressway in downtown Shanghai,China.To investigate the relationships between PM_2.5 concentration and influencing factors,the Pearson correlation analysis was performed Furthermore,the back propagation neural network based on principal component analysis?PCA-BPNN?,generalized additive model?GAM?as well as computational fluid dynamics?CFD?were applied to predict the vertical PM_2.5 and CO concentration and examined with the field measurement dataset.Experimental results indicated that both models can obtain accurate predictions,while PCA-BPNN model provides more reliable and accurate predictions as it can reduce the complexity and eliminate data co-linearity.These findings reveal the vertical distribution of PM_2.5 and CO concentration and the potential of the proposed model to be applicable to predict the vertical trends of air pollution in similar situations.