Spatial Analysis And Assessment Of CO And PM_2.5 Exposure Concentrations Under Different Transport Microenvironments In Shanghai

Transportation has become a significant source of urban air pollution,leading to various problems of personal health and urban sustainability. InShanghai, the percentage of vehicle emission-related CO exceeds70%ofthe totalambient CO. Recent days, the continuous haze weather because ofPM2.5also aroused people’s great attention. Thus, accurately measuring thepersonal exposure to air pollutants under different transportation micro-environments has great importance to understanding the impacts of urbantransportation on residents. However, traditional fixed-site stations weregenerally used for macro area pollutants detection and could not accuratelyget accessto human exposuresunder different microenvironments.This studyinvestigated commuters’exposure to carbonmonoxide(CO)and PM2.5under different commuting microenvironments with mobiledetectors (i.e., LanganModelT15nCO Measurer and TSI SidePakAM510)in Shanghai, China. Three travel modes (bus, subway and walk) wereexamined in field studies. The average concentrations and variation in eachtravelmode were analyzed with thecollected data, and a comparisonamongdifferent modes in different cities and discussion of affecting factors wereconducted. The results showed that average CO concentrations were1.51±0.37ppm,0.80±0.13ppm,1.06±0.30ppm and PM2.5concentrations of138.80±34.60,52.10±12.86,114.82±26.91ug/m^3for bus, subway andwalk trips respectively, bothindicatingthat thebus passengers havethemostexposureto CO and subwayriders have the lowest.In order to explore the possible affecting factors for pollutants distribution and variation. This study carried out an experiment at aneighborhood scale (2km*2km) in Minhang District, Shanghai, China withmobile measurements of PM2.5and CO at25locations. Land useregression(LUR) model was developed to analyze the relationship between PM2.5andCO concentrations and traffic volume, meteorological conditions and landuse factors such as building areas, areas of open spaces and green spaces.The results showed that thesevariables explained87.8%of spatialvariationsof PM2.5concentration and89.4%variations of CO concentration whentaking the regional reference concentrations into consideration. This studydemonstrated the utility for applying LUR to a small scale area andevaluating personal exposure to PM2.5and CO under different micro-environments. Humidity, wind speed, traffic flow, areas of open spaces andgreen spaces have the most effects on the spatial variation of PM2.5; whiletemperature, traffic flow volume, areas of open spaces and green spacesshowed most effect on spatial variations of CO. Among all the factors, thereference concentration contributed most to the variability of PM2.5whiletraffic volume contributed most to CO variation. Based on the LUR results,a concentration distribution map was generated using Kriging Interpolation.The results in this study provided an intuitive presentation and explanationfor people’s understanding of their exposure level to pollutants underdifferent microenvironments. Meanwhile, this study laid a foundation forthe approachescoming up to reduce pollutants level.