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Inicio  /  Atmósfera  /  Vol: 32 Núm: 4 Par: 0 (2019)  /  Artículo
ARTÍCULO
TITULO

PM2.5 concentrations in the greenbelt near the Lin?an toll station of the Hang Rui expressway and related influencing factors

Feng Shao    
Haitang Wu    
Guo Li    
Fengbin Sun    
Lu Yu    
Yinke Zhang    
Li Dong    
Zhiyi Bao    

Resumen

Expressways in China are developing rapidly, as is traffic pollution, which is one of the major sources of urban pollution. In this study, we chose the greenbelt in the idle zone near the Lin?an toll station along the Hang Rui expressway as our sampling area. Five points in the sampling area along Qianjin road were marked vertically at distances of 0, 15, 30, 45 and 60 m to monitor concentrations of PM2.5 and learn the varying patterns of these concentrations and influencing factors. The results showed that in spring (March, April and May), the average PM2.5 concentrations in the greenbelt were 32.56 ± 22.51, 77.71 ± 32.11 and 64.15 ± 29.00 µg m?3, respectively. The ranking of concentrations at different monitoring points in the same period was 0 > 15 > 60 > 30 > 45 m. The average concentrations in winter (November and December 2017, and February 2018) were 33.56 ± 9.34, 60.78 ± 17.67 and 124.71 ± 43.19 µg m?3, respectively. However, the ranking of concentrations at different monitoring points in the same period revealed some differences. Except at 0 m, the concentrations of PM2.5 in the other four positions were higher in winter than in spring. The reduction rate at 45 m reached its maximum in both spring and winter. PM2.5 concentrations were significantly correlated with meteorological factors, the structure of the plant community and traffic flow. PM2.5 concentrations were negatively correlated with temperature, positively correlated with relative humidity and was not significantly correlated with wind speed. The correlations of PM2.5 concentrations with the canopy density and degree of porosity differed greatly due to different seasons, and concentrations were significantly correlated with the amount of traffic flow, especially when there were large trucks. 

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