Spatial Variability of PM_(2.5) Pollution in Imbalanced Natural and Socioeconomic Processes: Evidence from the Beijing-Tianjin-Hebei Region of China

Accurately identifying and quantifying the factors influencing PM_(2.5) pollution is of great significance for the prevention and control of pollution. However, the redundancy among potential factors of PM_(2.5) may be overlooked. Meanwhile, the inconsistent spatial distribution of the natural and socioeconomic conditions brings unique implications for the cities within a region, which may lead to an uncertain understanding of the relationship between pollution and environmental factors. This study focused on the Beijing-TianjinHebei(BTH) Region, China, which presents complex and varied background conditions. Potential impact factors on PM_(2.5) were firstly screened by combining systematic cluster analysis with a random forest recursive feature elimination algorithm. Then, the representative multi-factor responsible for PM_(2.5) pollution in the region during the key period of 2014–2018(when the strict national air pollution control policy was implemented). The results showed that the key driving factors of PM_(2.5) pollution in the BTH cities are different, indicating that the uniqueness of a city will have an impact on the leading causes of pollution. Further discussion shows that air control policy provides an effective way to improve air quality. This study aims to deepen the understanding of the risk drivers of air pollution within the BTH Region. In the future, it is recommended that more attention should be paid to the specific differences between the cities when formulating PM_(2.5) concentration control measures.

Capturing driving behavior Heterogeneity based on trajectory data

Driving behavior is heterogeneous for various drivers due to the different influencing factors as reaction time,gender,driving years and so on.Some existing works tried to reproduce some of the complex characteristics of real traffic flow by taking into account the heterogeneous driving behavior,and the drivers are generally divided into two classes(including aggressive drivers and careful drivers)or three classes(including aggressive drivers,normal drivers and careful drivers).Nevertheless,the classification approaches have not been verified,and the rationality of the classifications has not been confirmed as well.In this study,the trajectory data of drivers is extracted from the NGSIM datasets.By combining the K-Means method and Silhouette measure index,the drivers are classified into four clusters(named as clusters A,B,C and D,respectively)in accordance with the acceleration and time headway.The two-dimensional approach is applied to analyze the characteristics of different clusters.Here,one dimension consists of“Cautious”and“Aggressive”behaviors in terms of velocity and acceleration,and the other dimension consists of“Sensitive”and“Insensitive”behaviors in terms of reaction time.Finally,the fuel consumption and emissions for different clusters are calculated by using the VT-Micro model.A surprising result indicates that overly“cautious”and“sensitive”behaviors may result in more fuel consumption and emissions.Therefore,it is necessary to find the balance between the driving characteristics.