Search Results (2)

Black carbon is one of the riskiest particle matter pollutants that is harmful to human health. Although it has been increasingly investigated, factors that depend on black carbon distribution and concentration are still insufficiently researched. Variables, such as traffic density, wind speeds, and ground levels can lead to substantial variations of black carbon concentrations and potential exposure, which is even riskier for people living in less-airy sites. Therefore, this paper “fills the gaps” by studying black carbon distribution variations, concentrations, and oscillations, with special emphasis on traffic density and road segments, at multiple locations, in a small city located in a basin, with frequent temperature inversions and infrequent low wind speeds. As wind speed has a significant impact on black carbon concentration trends, it is critical to present how low wind speeds influence black carbon dispersion in a basin city, and how black carbon is dependent on traffic density. Our results revealed that when the wind reached speeds of 1 ms⁻¹, black carbon concentrations actually increased. In lengthy wind periods, when wind speeds reached 2 or 3 ms⁻¹, black carbon concentrations decreased during rush hour and in the time of severe winter biomass burning. By observing the results, it could be concluded that black carbon persists longer in higher altitudes than near ground level. Black carbon concentration oscillations were also seen as more pronounced on main roads with higher traffic density. The more the traffic decreases and becomes steady, the more black carbon concentrations oscillate. Full article

Research on spatial cluster detection of traffic crash (TC) at the city level plays an essential role in safety improvement and urban development. This study aimed to detect spatial cluster pattern and identify riskier road segments (RRSs) of TC constrained by network with a two-step integrated method, called NKDE-GLINCS combining density estimation and spatial autocorrelation. The first step is novel and involves in spreading TC count to a density surface using Network-constrained Kernel Density Estimation (NKDE). The second step is the process of calculating local indicators of spatial association (LISA) using Network-constrained Getis-Ord Gi* (GLINCS). GLINCS takes the smoothed TC density as input value to identify locations of road segments with high risk. This method was tested using the TC data in 2007 in Wuhan, China. The results demonstrated that the method was valid to delineate TC cluster and identify risk road segments. Besides, it was more effective compared with traditional GLINCS using TC counting as input. Moreover, the top 20 road segments with high-high TC density at the significance level of 0.1 were listed. These results can promote a better identification of RRS, which is valuable in the pursuit of improving transit safety and sustainability in urban road network. Further research should address spatial-temporal analysis and TC factors exploration. Full article