Stations In Beijing Research Articles

A Missing Type-Aware Adaptive Interpolation Framework for Sensor Data

Data missing problems often occur on the Internet-of-Things domains. This article proposes a missing type-aware interpolation framework (IMA) for data loss problems in city-wide environmental monitoring systems that contain many scattered stations. To interpolate data as accurately as possible, IMA considers three aspects of information, i.e., spatiotemporal, all attributes of one measurement, and all values and accordingly develop three methods to estimate the missing data. First, we develop an improved multiviewer method, which uses the spatiotemporal correlation of data from neighbor stations to estimate random missing values. Second, we propose a new multi-eXtreme Gradient Boosting (multi-XGBoost) method that uses the values of the co-occurring and correlated correct attributes to predict the value of the missing attribute. Third, we take advantage of matrix factorization to estimate the missing parts if the data of the interpolation matrix are not all missing. To avoid the influence of uncorrelated data, IMA calculates Pearson's correlation coefficient between data of each station and uses those data from its top k highest correlation neighbors to form an interpolation matrix. Furthermore, due to the complexity of missing cases, IMA uses confidence levels in each of the three data prediction methods. For example, if the multiviewer method fails, IMA weights all valid results with confidence levels. We conduct our experiments on two real-world datasets from air quality monitoring stations in Beijing. Both datasets contain numerous missing measurements. Experimental results show that IMA outperforms other counterpart methods in interpolating the missing measurements, in terms of accuracy and effectiveness. Compared with the most related method, IMA improves the interpolation accuracy from 0.818 to 0.849 in a small dataset and from 0.214 to 0.759 in a large one.

Research on Subway Pedestrian Detection Algorithm Based on Big Data Cleaning Technology

The pedestrian detection model has a high requirement on the quality of the dataset. Concerning this problem, this paper uses data cleaning technology to improve the quality of the dataset, so as to improve the performance of the pedestrian detection model. The dataset used in this paper is obtained from subway stations in Beijing and Nanjing. The data images’ quality is subject to motion blur, uneven illumination, and other noisy factors. Therefore, data cleaning is very important for this paper. The data cleaning process in this paper is divided into two parts: detection and correction. First, the whole dataset goes through blur detection, and the severely blurred images are filtered as the difficult samples. Then, the image is sent to DeblurGAN for deblur processing. 2D gamma function adaptive illumination correction algorithm is used to correct the subway pedestrian image. Then, the processed data is sent to the pedestrian detection model. Under different data cleaning datasets, through the analysis of the detection results, it is proved that the data cleaning process significantly improves the detection model’s performance.

Polycyclic aromatic hydrocarbon and n-alkane pollution characteristics and structural and functional perturbations to the microbial community: a case-study of historically petroleum-contaminated soil.

Characterization of the typical petroleum pollutants, polycyclic aromatic hydrocarbons (PAHs) and n-alkanes, and indigenous microbial community structure and function in historically contaminated soil at petrol stations is critical. Five soil samples were collected from a petrol station in Beijing, China. The concentrations of 16 PAHs and 31 n-alkanes were measured by gas chromatography-mass spectrometry. The total concentrations of PAHs and n-alkanes ranged from 973 ± 55 to 2667 ± 183 μg/kg and 6.40 ± 0.38 to 8.65 ± 0.59 mg/kg (dry weight), respectively, which increased with depth. According to the observed molecular indices, PAHs and n-alkanes originated mostly from petroleum-related sources. The levels of ΣPAHs and the total toxic benzo[a]pyrene equivalent (ranging from 6.41 to 72.54 μg/kg) might exert adverse biological effects. Shotgun metagenomic sequencing was employed to investigate the indigenous microbial community structure and function. The results revealed that Proteobacteria and Actinobacteria were the most abundant phyla, and Nocardioides and Microbacterium were the important genera. Based on COG and KEGG annotations, the highly abundant functional classes were identified, and these functions were involved in allowing microorganisms to adapt to the pressure from contaminants. Five petroleum hydrocarbon degradation-related genes were annotated, revealing the distribution of degrading microorganisms. This work facilitates the understanding of the composition, source, and potential ecological impacts of residual PAHs and n-alkanes in historically contaminated soil.

Retrieval and Validation of XCO2 from TanSat Target Mode Observations in Beijing

Satellite observation is one of the main methods used to monitor the global distribution and variation of atmospheric carbon dioxide (CO2). Several CO2 monitoring satellites have been successfully launched, including Japan’s Greenhouse Gases Observing SATellite (GOSAT), the USA’s Orbiting Carbon Observatory-2 (OCO-2), and China’s Carbon Dioxide Observation Satellite Mission (TanSat). Satellite observation targeting the ground-based Fourier transform spectrometer (FTS) station is the most effective technique for validating satellite CO2 measurement precision. In this study, the coincident observations from TanSat and ground-based FTS were performed numerous times in Beijing under a clear sky. The column-averaged dry-air mole fraction of carbon dioxide (XCO2) obtained from TanSat was retrieved by the Department for Eco-Environmental Informatics (DEEI) of China’s State Key Laboratory of Resources and Environmental Information System based on a full physical model. The comparison and validation of the TanSat target mode observations revealed that the average of the XCO2 bias between TanSat retrievals and ground-based FTS measurements was 2.62 ppm, with a standard deviation (SD) of the mean difference of 1.41 ppm, which met the accuracy standard of 1% required by the mission tasks. With bias correction, the mean absolute error (MAE) improved to 1.11 ppm and the SD of the mean difference fell to 1.35 ppm. We compared simultaneous observations from GOSAT and OCO-2 Level 2 (L2) bias-corrected products within a ±1° latitude and longitude box centered at the ground-based FTS station in Beijing. The results indicated that measurements from GOSAT and OCO-2 were 1.8 ppm and 1.76 ppm higher than the FTS measurements on 20 June 2018, on which the daily observation bias of the TanSat XOC2 results was 1.87 ppm. These validation efforts have proven that TanSat can measure XCO2 effectively. In addition, the DEEI-retrieved XCO2 results agreed well with measurements from GOSAT, OCO-2, and the Beijing ground-based FTS.

Integrated Governance of Scenarized Space and Community — Reform of Beijing Qianggen Community Service Station and Enlightenment

Community governance is significant for the grass-roots governance in China. Micro-governance and micro-reform starting from community service station is a meaningful measure to explore the improvement of grass-roots governance. Focusing on the reform of community service stations in Beijing, this paper, in consideration to the background of service station reform, describes the history, content and characteristics of the reform of comprehensive setting of Qianggen Community on G Subdistrict of Xicheng District, Beijing, in details, and conducts in-depth analysis based on “The Theory of Scenes” and “The Theory of Governance”. The author holds that community service stations, with new roles taken, new scenarios created and new mechanisms shaped after transformation and upgrading, are turned into governance centers that connect multiple parties, respond to needs of residents better and improve the effectiveness of community governance. The reform practice is committed to the generating of scenarized social space, promoting the manifestation of the integrated governance pattern. The author is inspired to consider the issues related to grassroots governance further and to put forward several suggestions for deepening reform.

Copula-based seasonal rainfall simulation considering nonstationarity

In recent years, anthropogenic and climate changes have greatly impacted the probabilistic behavior of hydrometeorological variables, and have challenged the stationarity assumption of marginal distributions of individual characteristics of variables as well as their temporal dependence structure inherent in copula-based modelling. This study developed a dynamic copula-based simulation model (DCSM) for single-site seasonal rainfall generation, observed at Beijing Station in Haihe River basin, China. The model entailed four phases: (1) nonstationary modelling of the margins by the Generalized Additive Models for Location, Scale and Shape (GAMLSS); (2) dynamical copula method to describe the nonstationary temporal dependence structure of rainfall observed at adjacent two seasons; (3) a dynamic copula-based conditional quantile function to generate simulated series; (4) performance assessment of the simulated series by the proposed DCSM model with the preservation of basic statistics of each sequence and simulation accuracy at each data point. Comparison of the nonstationary and stationary models to simulate the seasonal rainfall series shows that the incorporation of nonstationarity in hydrometeorological simulation would improve the performance of simulation accuracy.

Field Monitoring of the Deformation and Internal Forces of the Surrounding Rock and Support Structures in the Construction of a Super-Span High-Speed Railway Tunnel—A Case Study

A super-span tunnel that has the characteristics of a large excavation area, a small high-span ratio and a significant spatial effect exhibits a complex mechanical response during the excavation process. In this paper, taking the Badaling Great Wall station in Beijing, China as the engineering background, a case study of field monitoring a super-span tunnel has been presented. A typical monitoring section was selected in the super-span transition section of the tunnel and the deformation and forces of both the surrounding rock and the support structures were systematically monitored. The dynamic evolution and the spatial distribution characteristics of the monitoring data, including the internal displacement of the surrounding rock, the tunnel displacement, the contact pressure between the surrounding rock and the primary supports, the contact pressure between the primary and secondary supports, the axial forces in the bolts and cables, the internal forces in both the steel arches and the secondary supports and the internal stresses of the surrounding rock, were analyzed. The results of the monitoring and the analyses have shown that the deformation and the forces acting on both the surrounding rock and the tunnel’s lining are directly related to the construction procedures, the geological conditions and the locations in the super-span tunnel. According to the results, a few suggestions to improve the construction of the tunnel have been proposed.

Application of a genetic algorithm to groundwater pollution source identification

The accurate identification of groundwater pollution sources is the key to site remediation and management, which directly relates to the cost and effect of remediation during later periods. Studying the identification of some small-area site pollution sources by using a small amount of groundwater monitoring data is difficult and is currently a hot topic in the field of groundwater management. In this study, a method combining the Advection-Dispersion Equation (ADE) of contaminants in groundwater with Genetic Algorithm (GA) was proposed to identify groundwater pollution sources, that is, the location of the pollution source, the time of pollution release, and the intensity of pollution release. A sandbox experiment for the migration and transformation of deterministic petroleum hydrocarbons in groundwater is designed to verify the method. The results show that the method code program has basically the same identification parameters as the deterministic experimental pollution source parameters. The ADE-GA method was applied to an actual site polluted by a gas station in Beijing, China. The parameters of the pollution source obtained from the detailed site investigation are basically consistent with the calculated values. Therefore, the source code established in this work is a reliable tool for identifying the parameters of groundwater pollution sources in small areas.

Weather Forecasting Using Ensemble of Spatial-Temporal Attention Network and Multi-Layer Perceptron

Weather forecasting is a challenging task, which is especially suited for artificial intelligence due to the large amount of data involved. This paper proposed an end-to-end hybrid regression model, called Ensemble of Spatial-Temporal Attention Network and Multi-Layer Perceptron (E-STAN-MLP), to forecast surface temperature, humidity, wind speed, and wind direction at 24 automatic weather stations in Beijing. Combining the data from historical observations with the data from the numerical weather prediction (NWP) system, our proposed model give better results than the NWP system or previously reported algorithms. Our E-STAN-MLP model consists of two parts. One is to use the spatial-temporal attention based recurrent neural network to model the time series of meteorological elements. The other is a simple but efficient multi-layer perceptron architecture forecasts the regression value while ignoring time dependence. Results at each time stamp are integrated together using a step-wise fusion strategy. Moreover, we use a joint loss step integrating both the regression loss function and the classification loss function to simultaneously forecast the wind speed and direction. Experiments demonstrate that our proposed E-STAN-MLP model achieves state-of-the-art results in weather forecasting.

An empirical analysis of public transit networks using smart card data in Beijing, China

Most existing studies on public transit network (PTN) rely on either small-scale passenger flow data or small PTN, and only traditional network parameters are used to calculate the correlation coefficient. In this work, the real smart card data (SCD) (when passenger tap in and tap out a station) of over eight million users is used as a proxy of passenger flow to dynamically explore and evaluate the structure of large-scale PTNs with tens of thousands of stations in Beijing, China. Three types of large-scale PTNs are generated, and the overall network structure of PTNs are examined and found to follow heavy-tailed distributions (mostly power law). Further, three traditional centrality measures (i.e. degree, betweenness and closeness) are adopted and modified to dynamically explore the relationship between PTNs and passenger flow. Our findings show that, the modified centrality measures outperform the traditional centrality measures in estimating passenger flow.

The Functional Spatio-Temporal Statistical Model with Application to O3 Pollution in Beijing, China.

In recent years, with rapid industrialization and massive energy consumption, ground-level ozone () has become one of the most severe air pollutants. In this paper, we propose a functional spatio-temporal statistical model to analyze air quality data. Firstly, since the pollutant data from the monitoring network usually have a strong spatial and temporal correlation, the spatio-temporal statistical model is a reasonable method to reveal spatial correlation structure and temporal dynamic mechanism in data. Secondly, effects from the covariates are introduced to explore the formation mechanism of ozone pollution. Thirdly, considering the obvious diurnal pattern of ozone data, we explore the diurnal cycle of pollution using the functional data analysis approach. The spatio-temporal model shows great applicational potential by comparison with other models. With application to pollution data of 36 stations in Beijing, China, we give explanations of the covariate effects on ozone pollution, such as other pollutants and meteorological variables, and meanwhile we discuss the diurnal cycle of ozone pollution.

Development and research on energy performance assessment method of heat-exchanging stations based on real data

In this paper the author collected heat consumption data of 170 heat-exchanging stations in Beijing and the influencing factors such as heating time, heating area, equipment information, outdoor weather and so on. By means of sample selection, influencing factors selection, regression analysis, statistical validation (R2 test, F test, t-test, student residuals test, etc.), and model comparison (six regression models are established in this paper), a heat consumption benchmark model for Beijing thermal power station was established. At last the author drew lessons from the method of Energy-Star, by fitting the GAMMA curve established the evaluation system. In this evaluation system a percentage system (1–100 points) was drawn up, any thermal station could directly find out whether its operation was efficient or not by scoring.The heat consumption benchmark model established in this paper could calculate the heat consumption datum of any heat-exchanging station according to its own characteristics, and realize “one station one benchmark”. The evaluation system could make the evaluation of heat Consumption of heat-exchanging station more objective and scientific.The heat consumption benchmark model and evaluation system established in this paper provided theoretical and technical support in promoting the efficient operation of the heating system and discovering the potential of energy-saving transformation of the heat-exchanging station.The results of this paper would be applied to the operation and management system of the thermal station, and could be demonstrated and popularized in China in the future.

Similarities and Differences in the Temporal Variability of PM2.5 and AOD Between Urban and Rural Stations in Beijing

Surface particulate matter with an aerodynamic diameter of <2.5 μm (PM2.5) and column-integrated aerosol optical depth (AOD) exhibits substantial diurnal, daily, and yearly variabilities that are regionally dependent. The diversity of these temporal variabilities in urban and rural areas may imply the inherent mechanisms. A novel time-series analysis tool developed by Facebook, Prophet, is used to investigate the holiday, seasonal, and inter-annual patterns of PM2.5 and AOD at a rural station (RU) and an urban station (UR) in Beijing. PM2.5 shows a coherent decreasing tendency at both stations during 2014–2018, consistent with the implementation of the air pollution action plan at the end of 2013. RU is characterized by similar seasonal variations of AOD and PM2.5, with the lowest values in winter and the highest in summer, which is opposite that at UR with maximum AOD, but minimum PM2.5 in summer and minimum AOD, but maximum PM2.5 in winter. During the National Day holiday (1–7 October), both AOD and PM2.5 holiday components regularly shift from negative to positive departures, and the turning point generally occurs on October 4. AODs at both stations steadily increase throughout the daytime, which is most striking in winter. A morning rush hour peak of PM2.5 (7:00–9:00 local standard time (LST)) and a second peak at night (23:00 LST) are observed at UR. PM2.5 at RU often reaches minima (maxima) at around 12:00 LST (19:00 LST), about four hours later (earlier) than UR. The ratio of PM2.5 to AOD (η) shows a decreasing tendency at both stations in the last four years, indicating a profound impact of the air quality control program. η at RU always begins to increase about 1–2 h earlier than that at UR during the daytime. Large spatial and temporal variations of η suggest that caution should be observed in the estimation of PM2.5 from AOD.

Reducing turnover intention: perceived organizational support for frontline employees

Frontline employees are generally under great pressure, and carry out repetitive and mundane daily tasks, leading to burnout and a high turnover intention among them. To identify ways to reduce this turnover intention, this study examines the effect of perceived organizational support (POS) on burnout and turnover intention in the Chinese context and adds to the literature on frontline employee burnout. Using data from a survey of the frontline employees of a gas station in Beijing, we examine the mediating effects of frontline employee burnout on their POS and turnover intention. This study shows that POS has a significant negative impact on burnout and turnover intention, and that job resources cannot substitute POS.

How the completeness of spatial knowledge influences the evacuation behavior of passengers in metro stations: A VR-based experimental study

Emergency evacuation in metro stations could be highly challenging due to complex underground environments, dynamic hazards and high-density crowds. There are different types of metro passengers, such as first-time visitors and daily commuters, who may possess different levels of prior spatial knowledge about the metro station. While spatial knowledge is known as an influential factor on people's indoor wayfinding behavior, it has largely remained unknown whether and how the level of completeness of prior spatial knowledge would affect people's wayfinding behavior during emergency evacuation in underground environments such as metro stations. Motivated by this gap, this study aimed to examine the effect of completeness of spatial knowledge on the evacuation behavior of passengers in metro stations, as they observed different patterns of crowd flow. An evacuation experiment was conducted in an immersive virtual environment, which was a virtual representation of a real metro station in Beijing. Participants were divided into six groups according to a 3 × 2 between-participants design. Participants could possess none, partial or complete spatial knowledge of the metro station, acquired from a spatial exploration task, prior to evacuation. Meanwhile, they could observe two patterns of crowd flow, modeled by having virtual avatars evacuating from the metro station evenly or unevenly split at every decision point (DP) along the evacuation routes. Five wayfinding performance measures, as well as the emotional responses, sense of direction, wayfinding anxiety, simulator sickness and sense of presence of all participants were collected and analyzed. The results showed that the completeness of spatial knowledge significantly impacted all measures of participants' evacuation performance, and that the pattern of crowd flow significantly impacted participants' evacuation speed, route choice, and directional choices at two DPs. Significant interaction effect between completeness of spatial knowledge and pattern of crowd flow was observed, which negatively impacted participants' evacuation time, distance and speed. The behavioral explanations underlying these findings, their practical implications and future work are also discussed in the paper.

Raindrop size distribution and microphysical characteristics of a great rainstorm in 2016 in Beijing, China

This paper analyzes the characteristics of rain drop size distributions (DSDs) and vertical profiles of hydrometeors for a severe rainstorm event that occurred in Beijing, northern China, from 19 to 21 July 2016 using in situ measurements from two disdrometers (OTT-Parsivel2 and 2D-Video-Distrometer) and remote sensing data collected by a second-generation Micro Rain Radar (MRR2). This rainstorm was characterized by stratiform precipitation with embedded strong convection on 19 July and widespread, intense mixed convective-stratified rainfall resulting in persistent rainfall between 20 and 21 July. The temporal and spatial variations in rainfall rate R, liquid water content W, reflectivity Z, DSDs and the gamma DSD parameters in different precipitation episodes of this event are derived and compared between two meteorological stations in Beijing. Rainfall throughout the entire event was characterized by large concentrations of small- to medium-sized raindrops (diameters between 2 and 4 mm) and few larger particles (D > 4 mm), resulting in small values of reflectivity Z (<40 dBZ) and liquid water content W (<1.0 g m−3). In addition, the log10Nw-Dm scatter pairs recorded during the convective phase of this rainstorm indicate maritime-like convection. The time evolution of DSDs shows that most single peak DSDs occurred during stratiform rainfall, and bimodal DSDs were observed when the convective rainfall cells passed by and collisional breakups occurred. The gamma DSD model with a single peak in the stratiform phase of the storm and the bimodal DSD in the convective episode of the storm are quantitatively evaluated. In addition, in each precipitation episode, the differences in exponent b and coefficient A of the Z-R relationship between the two stations are smaller for stratiform rainfall than for convective rainfall due to the more evident variability of drop size and number concentration in convective rain. The physical nature of this relationship and the possible mechanisms leading to diverse characteristics in different phases of this heavy rainstorm are discussed.

Influence of architectural visual access on emergency wayfinding: A cross-cultural study in China, United Kingdom and United States

This study examines the effects of architectural visual access on people's wayfinding behavior and evacuation performance during building emergencies using virtual reality. Fire evacuation experiments were conducted in an immersive virtual metro station, which was based on a real metro station in Beijing, China. A total of 226 participants, positioned among evenly or unevenly distributed crowd, were asked to evacuate the station that was designed with low or high visual access, manipulated through building design features (e.g., changing wall materials, removing columns in hallways). Crowd was presented in the virtual metro station by incorporating non-player characters assigned to different evacuation routes. To explore the possible influence of cultural background on participants' wayfinding behavior, experiments were conducted in London, Beijing, and Los Angeles. The results showed that improving architectural visual access could improve participants' virtual evacuation performance during emergencies; it could also influence participants' directional choices during evacuation, depending on the design strategy used and the spatial characteristics of the building. In addition, participants' tendency of following the crowd was reduced when there was an alternative route with high architectural visual access.

A multi-criteria decision method for performance evaluation of public charging service quality

Public electric vehicle charging infrastructure provides an essential support for sustainable electric transportation systems. However, the current development model for such infrastructure tends to emphasize quantity over quality and cannot meet the charging needs of electric vehicle users. Addressing this situation requires further guidance from governments, which should be based on performance evaluation systems. This study therefore developed a multi-criteria evaluation framework to assess the performance of public charging infrastructure in terms of planning rationality, operational efficiency, service capacity, charging safety, and sustainable development. After defining individual charging station attributes through numerical data and user/expert input, a modified Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) model was used, with vague sets to standardize, weight, and process the data. The assessed stations were subsequently ranked. The model was applied to three public charging stations in Beijing, China to verify its effectiveness and robustness. The resulting rankings can facilitate regulatory assessment of these stations’ performance and guide improvements in the quality of their charging services. The results further indicated that the sustainable development value of charging facilities is often undervalued, and relevant incentive strategies should thus be implemented by policymakers.

Simulation of Daily Diffuse Solar Radiation Based on Three Machine Learning Models

Solar radiation is an important parameter in the fields of computer modeling, engineering technology and energy development. This paper evaluated the ability of three machine learning models, i.e., Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM) and Multivariate Adaptive Regression Splines (MARS), to estimate the daily diffuse solar radiation (Rd). The regular meteorological data of 1966-2015 at five stations in China were taken as the input parameters (including mean average temperature (Ta), theoretical sunshine duration (N), actual sunshine duration (n), daily average air relative humidity (RH), and extra-terrestrial solar radiation (Ra)). And their estimation accuracies were subjected to comparative analysis. The three models were first trained using meteorological data from 1966 to 2000. Then, the 2001-2015 data was used to test the trained machine learning model. The results show that the XGBoost had better accuracy than the other two models in coefficient of determination (R2 ), root mean square error (RMSE), mean bias error (MBE) and normalized root mean square error (NRMSE). The MARS performed better in the training phase than the testing phase, but became less accurate in the testing phase, with the R2 value falling by 2.7-16.9% on average. By contrast, the R2 values of SVM and XGBoost increased by 2.9-12.2% and 1.9-14.3%, respectively. Despite trailing slightly behind the SVM at the Beijing station, the XGBoost showed good performance at the rest of the stations in the two phases. In the training phase, the accuracy growth is small but observable. In addition, the XGBoost had a slightly lower RMSE than the SVM, a signal of its edge in stability. Therefore, the three machine learning models can estimate the daily Rd based on local inputs and the XGBoost stands out for its excellent performance and stability.

Study on an on-site radon-in-water measurement system based on degassing membrane

Radon-in-water is an important tracer in hydrology, oceanography and geology, where a demand for fast response and long-term continuous measurement of radon-in-water concentration on-site is necessary. In this paper, an on-site radon-in-water measurement system based on a degassing membrane, an electrostatic radon monitor and a water temperature sensor is presented, and calibration experiment is carried out. The relative degassing efficiency of the degassing membrane was studied using self-made radon-in-water standard, which gives the result of (97.1 ± 7.3) %. The sensitivity of the measurement system is (3730 ± 190) cph/(Bq/L), and the lower limit of detection is 0.005 Bq/L, with 10 min cycle at 25 °C water temperature. The response time of the measurement system is about 26 min, which is independent of the water flowrate within the range of 0.5–2.5 L/min. Using this measurement system, field measurements were carried out on tap water and deep well water. And the measurement system has been successfully used in the continuous monitoring of radon-in-water concentration at a seismic station in Beijing.