Revealing the Influence of the Fine-Scale Built Environment on Urban Rail Ridership with a Semiparametric GWPR Model

There is a causal interaction between urban rail passenger flow and the station-built environment. Analyzing the implicit relationship can help clarify rail transit operations or improve the land use planning of the station. However, to characterize the built environment around the station area, existing literature generally adopts classification factors in broad categories with strong subjectivity, and the research results are often shown to have case-specific applicability. Taking 154 stations on 8 rail transit lines in Xi’an, China, as an example, this paper uses the data sources of multiple open platforms, such as web map spatial data, mobile phone data, and price data on house purchasing and renting, then combines urban land classification in the China Urban Land Classification and Planning and Construction La1d Standard to classify the land use in the station area using structural hierarchy. On the basis of extracting fine-grained factors of the built environment, a semi-parametric Geographically Weighted Poisson Regression (sGWPR) model is used to analyze the correlation and influence between the variation of passenger flow and environmental factors. The results show that the area of Class II residential land (called R2) is the basis for generating passenger flow demand during morning and evening peak periods; The connection intensity between rail transit station area and bus services has a significant impact on commuters’ utilization level of urban rail transit. Furthermore, two scenarios in practical applications will be provided as guidance according to the research results. This study provides a general analytical framework using urban multi-source data to study the internal relationship and impact between the built environment of urban rail transit stations and passenger flow demand

Low-Carbon Impact of Urban Rail Transit Based on Passenger Demand Forecast in Baoji

There are increasing traffic pollution issues in the process of urbanization in many countries; urban rail transit is low-carbon and widely regarded as an effective way to solve such problems. The passenger flow proportion of different transportation types is changing along with the adjustment of the urban traffic structure and a growing demand from passengers. The reduction of carbon emissions brought about by rail transit lacks specific quantitative research. Based on a travel survey of urban residents, this paper constructed a method of estimating carbon emissions from two different scenarios where rail transit is and is not available. This study uses the traditional four-stage model to forecast passenger volume demand at the city level and then obtains the basic target parameters for constructing the carbon emission reduction model, including the trip origin-destination (OD), mode, and corresponding distance range of different modes on the urban road network. This model was applied to Baoji, China, where urban rail transit will be available from 2023. It calculates the changes in carbon emission that rail transit can bring about and its impact on carbon emission reductions in Baoji in 2023

The Synthesis and Application of Nitrogen-Doped Graphene Quantum Dots on Brilliant Blue Detection

Nitrogen-doped graphene quantum dots had been successfully synthesized and characterized by using transmission electron microscope, X-ray photoelectron spectroscopy, absorbance spectrum, fluorescence emission spectrum, and fluorescence decay curve. TEM results indicated that the diameters of the as-prepared nitrogen-doped graphene quantum dots were in the range of 2 - 5 nm and the lattice space is about 0.276 nm; Raman spectrum result indicated that there were two characteristic peaks, generally named D (~1408 cm−1) and G (~1640 cm−1) bands; both TEM and Raman spectrum results indicated that the as-synthesized product was graphene quantum dots. Deconvoluted high resolution XPS spectra for C1s, O1s, and N1s results indicated that there are -NH-, -COOH, and -OH groups on the surface of nitrogen-doped graphene quantum dot. Fluorescence emission spectrum indicated that the maximum fluorescence emission spectrum of nitrogen-doped graphene quantum dots was blue shift about 30.1 nm and the average fluorescence decay time of nitrogen-doped graphene quantum dots increased about 2 ns, compared with graphene quantum dots without doping of nitrogen. Then, the as-prepared nitrogen-doped graphene quantum dots were used to quantitatively analyze brilliant blue based on the fluorescent quenching of graphene quantum dots, and the effect of pH and reaction time on this fluorescent quenching system was also obtained. Under selected condition, the linear regression equations were F0/F=0.0087 (brilliant blue) + 0.9553 and F0/F=0.01205 (brilliant blue) + 0.6695, and low detection limit was 3.776 μmol/L (3.776 nmol/mL). Once more diluted N-GQDs (0.05 mg/mL) were used, the low detection limit could reach 94.87 nmol/L. Then, temperature-dependent experiment, absorbance spectra, and dynamic fluorescence quenching rate constant were used to study the quenching mechanism; all results indicated that this quenching process was a static quenching process based on the formation of complex between nitrogen-doped graphene quantum dots and brilliant blue through hydrogen bond. Particularly, this method was used to quantitatively analyze the wine sample, of which results have a high consistence with the results of the spectrophotometric method; demonstrating this fluorescence quenching method could be used in practical sample application

Lipopolysaccharides of <i>Brucella suis</i> S2 Impaired the Process of Decidualization in Early Pregnancy in Mice

Brucellosis is a notorious zoonotic disease caused by Brucella, which can lead to reproductive diseases in humans and animals, such as infertility and abortion. Lipopolysaccharides (LPS) are the main virulence factor of Brucella. LPS derived from Brucella are different and non-classical and are less toxic and less active than LPS isolated from E. coli. However, the effects and possible mechanisms of Brucella LPS-caused pregnancy loss remain to be revealed. In the present study, we investigated the effects of Brucella suis S2 LPS on early pregnancy loss in mice. The results indicated that embryo implantation failure was induced by Brucella LPS treatment in a dose-dependent manner. The injection of Brucella LPS mainly resulted in fibrinolysis in the decidual area of the uterus on the 6th day post coition (dpc), infiltration of large granular cells among the decidual cells near the embryo on the 8th dpc, a large number of gaps in the decidual area, and cell necrosis around the embryo. In addition, the expression of Cyclin D3 mRNA in the uterus on the 7th and 8th dpc and IGFBP-1 mRNA and the progesterone receptor in the uterus on the 6th and 7th dpc were also inhibited. Moreover, the expression of decidualization marker Cyclin D3 and decidualization prolactin-associated protein (dPRP) in endometrial stromal cells were also inhibited by Brucella LPS treatment in vitro. In summary, Brucella LPS affect the process of endometrial decidualization in mice by affecting the structure of the decidua and the expression of decidual marker factors in endometrial stromal cells

A Study on the Calculation of Platform Sizes of Urban Rail Hub Stations Based on Passenger Behavior Characteristics

The Chinese national rail transit design specification decides the size of urban rail transit platforms in China. This suggested method treats passengers as homogeneous individuals when calculating the walking area within a platform. However, the heterogeneity of passenger behavior in a rail hub station has not been considered. It is not reasonable to see passengers as homogeneous individuals. In this study, by observing passenger behavior characteristics at rail hub platforms, two parameters were obtained, walking speed and luggage size. Passengers were then accordingly put into different groups, and dynamic spatial demands for each passenger group were calculated by parameter fitting functions. Based on the theory of spatiotemporal consumption, the nonlinear constraint model was constructed to determine the space-time consumption of each passenger group, and finally the area demands of different types of passengers were obtained for different time and passenger flows. An application was made to Beikezhan Station on Xi’an Metro line 2. The calculation results show the area demands ranges of four passenger groups with distinct characteristics, and their space-time consumption varied. The study can calculate the space demands for all passenger varieties within a rail hub transit platform and provide suggestions for the determination of the ideal walking area size of rail transit platforms