Stations In Shanghai Research Articles

Research on multi-energy coupled preheating system of city gate station based on Modelica simulation

Natural gas depressurization at city gate stations can cause hydrate formation, necessitating preheating before pressure regulation. To reduce the high energy consumption of traditional city gate station preheating processes, this study introduces four novel multi-energy coupled systems: photovoltaic-air source heat pump (PV-ASHP), solar thermal-combined heat and power-ASHP (ST-CHP-ASHP), CHP-ASHP, and ASHP system, with a typical city gate station in Shanghai taken as the research case. Dynamic energy simulation systems were developed in Dymola software to evaluate the proposed systems from energy, environmental, and economic perspectives, conducting a comparison with conventional boiler preheating systems, including sensitivity analysis of energy prices on system economics. Results indicate PV-ASHP has the lowest annual total cost (ATC) and shortest discounted payback period, while ATC values for ST-CHP-ASHP, CHP-ASHP, and ASHP increase by 6.4 %, 9.6 %, and 12.6 %, respectively. ST-CHP-ASHP and PV-ASHP demonstrate advantages in primary energy rate and carbon emissions, with ST-CHP-ASHP performing slightly better than PV-ASHP. Currently, PV-ASHP demonstrates the highest development potential. If photovoltaic feed-in tariffs remain constant, and natural gas prices decrease by over 12.5 % or CHP feed-in tariffs increase by over 38.4 %, ST-CHP-ASHP becomes more economically viable. This research provides valuable insights for retrofitting China's existing city gate station preheating systems.

Negotiating difficult (in)tangible heritage: the intricate journey of museum making for the “Dai-ichi Saloon” comfort station in Shanghai

ABSTRACT The heritage of comfort women has always remained on the periphery of authoritative heritage, classified as a “difficult heritage,” facing a predicament of social marginalization, government concealment, and international controversy. This article addresses the negotiations, discussions, and compromises involved in the process of safeguarding the Dai-ichi Saloon, which is on the verge of becoming a museum. It is a priority for the most difficult heritage to be musealized, which is one of the most common methods of “normalizing” to ensure preservation and dissemination. The museumization of Dai-ichi Saloon has involved four stages, through which, scholars, residents, Shanghai citizens, and the government, as the four major stakeholders, have developed different understandings of the Dai-ichi Saloon as a difficult heritage. This article argues that the key reason for this misalignment lies in the misplaced sequence of actions among agencies during the “normalization” process. Through the intricate process of the museum making for Dai-ichi Saloon, this article aims to offer lessons for the museum’s approach to the normalization of difficult heritage.

Multi-energy load forecasting for integrated energy system based on sequence decomposition fusion and factors correlation analysis

Considering the seasonal and cyclical fluctuation of loads and the complexity of multi-energy coupling, this paper proposes a novel load forecasting model based on sequence decomposition fusion and factors correlation analysis. Firstly, the variational mode decomposition (VMD) is used to decompose the highly complex load sequences and the novel influencing factors correlation analysis (ICA) is proposed to select strong factors and remove weak feature variables to construct the input and output set. Secondly, this paper proposes the combined forecasting framework MTL-CNN-BiGRU-Attention to simultaneously forecast the cooling, heat, and electricity loads, along with BiGRU used as the hard sharing layer to deeply explore the coupling information between different types of loads. Meanwhile, the gray wolf algorithm (GWO) is improved to accurately and quickly search for the optimal hyperparameters of the model. Finally, the dataset of a comprehensive energy station in Shanghai is used to test our model, and the results show that the MAPE of the cooling and electricity loads forecasting achieve 5.501% and 5.821% in summer and 5.921%, 7.899% and 7.541% for the cooling, heat and electricity loads in transition season and winter, which confirms the effectiveness and superiority of our model.

Experimental investigation on cooling load caused by unorganized ventilation in subway station: A case study

Unorganized ventilation significantly increases the energy consumption of subway stations. However, the quantitative description of cooling load caused by the unorganized ventilation in subway stations with platform screen doors (PSDs) still needs to be explored in depth. This study firstly proposed an experiment-based method to determine the airflow rate of unorganized ventilation, and then the corresponding cooling load is determined. Based upon this method, a series of field tests were conducted at a subway station in Shanghai. By analyzing the experimental data, the characteristic of unorganized ventilation at PSDs and entrance/exit passages was obtained. As for PSDs, the volume of unorganized ventilation in a cycle (the period from the upward train entering the station to the next upward train starting to approach the station) from the tunnel to the platform and from the platform to the tunnel is 2112.24 m3 and 4219.31 m3, respectively. With regard to entrance/exit passages, the volume of unorganized ventilation is 2655 m3 in a cycle. Thereafter, the above-mentioned results were used to determine cooling load. Owing to the difference in the departure density of train on weekdays and weekends, two typical summer days were selected for the purpose of cooling load analysis. The overall cumulative value of the cooling load caused by unorganized ventilation was 1416 kWh on a working day and 1718 kWh for the weekend, which accounted for 16.74 % and 19.19 % of the daily cooling load, respectively.

Analysis of Characteristics and Sensitivity of Photochemical Pollution During High Ozone Season in Shanghai from 2016 to 2020

The sensitivity analysis of ozone generation in key ozone-polluted regions and cities is an important basis for the prevention and control of near-surface ozone （O3） pollution. Based on the five-year data of ozone, VOCs, and NOx from three typical stations in Shanghai, namely Dianshan Lake Station （suburban area）, Pudong Station （urban area）, and Xinlian Station （industrial area） from 2016 to 2020, the nonlinear relationship between ozone and precursors （VOCs and NOx） during the high-ozone season in the five years was quantitatively analyzed using an observation model. The results showed that the peak months of near-surface ozone in Shanghai were from April to September during 2016 to 2020, with the highest values appearing from June to August. The volume fraction of VOCs and NO2 concentration had a strong indicative significance for the O3 concentration at Pudong Station. The O3 concentration at Dianshan Lake Station was mainly influenced by regional environment, meteorological factors, and cross-regional transmission. The ozone concentration at Xinlian Station was a combination of environmental background concentration and industrial area photochemical pollution. Pudong Station and Dianshan Lake Station were in the VOCs control zone. Xinlian Station was gradually closer to the NOx control zone from 2016 to 2019, transitioning to the VOCs control zone since 2020. The L·OH of Pudong Station, Dianshan Lake Station, and Xinlian Station were： NOx control area>collaborative control area>VOCs control area.

A deep learning model incorporating frequency domain information for ultra multi-step air pollutant forecasting: A case study of Shanghai

Multi-step forecasting of air pollutants extends the horizon for individuals and authorities to take informed actions for mitigating potential risks. Due to the instability of air pollutants, current research primarily focuses on relatively short-term forecasting, with achieving ultra multi-step forecasting presenting a significant challenge. In response to this issue, this study proposes a novel model: Frequency Enhanced Decomposed Temporal Convolution Networks (Fed-TCN) to achieve ultra multi-step forecasting. This study applies time-frequency transformation to explore the frequency characteristics of air pollutants and extract long-term patterns. These patterns are then fed into TCN to enhance the accuracy of ultra multi-step forecasting. Extensive experiments were conducted on eight air pollutants at four monitoring stations in Shanghai. The results indicate variations in forecastable ranges for different pollutants. NO and NOx can be forecasted up to one week, while NO2, CO, SO2, and O3 require forecasting within 1–3 days (approximately 24–72 steps ahead). Furthermore, PM2.5 and PM10 can only be forecasted for short-term periods, not exceeding 12 h. When compared to baseline models, Fed-TCN achieves a 4.3%–11% lower Mean Absolute Error. Moreover, Fed-TCN provides insights into the contribution of pollutant composition patterns to forecasting accuracy. In general, daily patterns, semi-commuting patterns, and residuals contribute 68.8%–81.7% to ultra multi-step forecasting. The proposed method is applicable for ultra multi-step forecasts of other regions and different types of air pollutants.

Comparison and study of different seismic calculation methods for underground stations in Shanghai

In terms of seismic design methods for subway stations in the Shanghai area, both national and local standards have presented several pseudo-static methods, including response displacement, response acceleration, equivalent earthquake acceleration, and equivalent inertial force methods. However, certain issues are encountered when these methods are applied to the seismic design of real subway projects. The load input and results obtained from different calculation methods differ from each other. To address such discrepancies and compare the diversity of different methods, this study performs a comparative analysis based on the project case of a ground station in Shanghai. The seismic design is conducted using five different calculation methods. By referring to the results of the dynamic time history analysis, the causes of errors in the calculation results of the displacement and internal force are analyzed. The applicability of the methods is comprehensively explored in terms of a calculation model, determination of the equivalent load, error in the results, and project application. The results show that the response acceleration method exhibits the least error compared to the dynamic time history method. Therefore, the response acceleration method is recommended as the preferred method for the seismic design of underground stations in Shanghai.

The heterogeneous effects of dockless bike-sharing usage intensity on house prices near subway stations

Dockless bike-sharing (DBS) systems have emerged as a popular mode of transportation in urban areas. While existing literature has explored the potential effects of DBS on urban systems, there is limited research on its impact on housing markets. This study addresses this gap by investigating the heterogeneous effects of DBS usage intensity on house prices at various distances from subway stations in Shanghai. Utilizing Mobike trip data and a dataset of 50,837 second-hand houses sold between May 2016 and December 2018, the analysis reveals that DBS usage intensity positively impacts house prices in areas outside 800 m and within 3000 m from subway stations, resulting in a 1.4 % increase in house prices for every 1,000 DBS rides within a 500 m radius. The study also finds that the marginal effect of DBS usage intensity on house prices is contingent on the distance from subway stations. For distances shorter than 2.33 km, the marginal effect rises with increasing distance. Conversely, for distances exceeding 2.33 km, the marginal effect declines and turns insignificant beyond 3 km. These findings imply that the positive influence of DBS on house prices is more pronounced in areas that are neither too close nor too far from subway stations, where people are more likely to use DBS to connect to subway networks. The findings of this study contribute to a better understanding of the complex relationship between DBS and real estate markets.

Prediction of Buildings’ Settlement Induced by Metro Station Deep Foundation Pit Construction

The construction of deep foundation pits in subway stations can affect the settlement of existing buildings adjacent to the pits to varying degrees. In this paper, the Long Short-Term Memory neural network prediction model of building settlement caused by deep foundation pit construction was established using the monitoring data of building settlement around a deep foundation pit project in a metro station in Shanghai, and appropriate hyperparameters including batch size and training set ratio were determined. The accuracy of settlement prediction for single-point and multi-point monitoring of buildings was analyzed. Meanwhile, the effects of construction parameters, engineering geological parameters, and spatial parameters on the accuracy of building settlement prediction were investigated. The results show that the batch size and training set proportion can be taken as 16 and 60%, respectively, when using the Long Short-Term Memory neural network prediction model. The proposed Long Short-Term Memory network model can stably predict the settlement of buildings adjacent to deep foundation pits. The accuracy of settlement prediction at a single point of a building (80%) is lower than the accuracy of coordinated prediction at multiple points (88%). More accurate settlement prediction is achieved with the total reverse construction method. The more detailed the consideration of working conditions, geological parameters, and spatial parameters, the better. The evaluation metrics of the prediction model, RMSE, MAE, and R2, were 0.57 mm, 0.65 mm, and 0.91, respectively. The results of this paper have some practical reference value for analyzing the settlement of buildings caused by foundation pit works.

Research on Chinese Fire Station Optimal Location Model Based on Fire Risk Statistics: Case Study in Shanghai

With the rapid development of urbanization in China, the gap between urban and rural areas is decreasing. The traditional approach of constructing fire stations based on urban built-up areas is no longer suitable for the needs of modern fire rescue. Therefore, a comprehensive fire station location model is proposed based on fire risk assessment. This method divides the protected area units based on the urban road network. By evaluating different regions based on spatial position, land attributes, population density, floor area ratio, and fire incident indicators, the fire rescue risk levels and categorize regions into four risk levels are assessed. Corresponding response times were determined, and an objective model was developed to maximize the coverage area for fire response. The Baidu API was utilized to accurately calculate driving distances and times, and Gurobi optimization software was used to solve the model. Taking Shanghai as an example, the fire station location and layout from two perspectives—re-planning based on overall station placement and re-planning based on existing stations—were analyzed. The results suggest that constructing around 150 fire stations in Shanghai would effectively meet the fire rescue needs, which aligns with the actual situation in Shanghai and demonstrates the strong applicability of this model. This approach enables the meeting of new demand for fire station construction due to the significant increase in the coverage area while effectively utilizing firefighting resources.

Economic Analysis of a Photovoltaic Hydrogen Refueling Station Based on Hydrogen Load

With the goal of achieving “carbon peak in 2030 and carbon neutrality in 2060”, as clearly proposed by China, the transportation sector will face long–term pressure on carbon emissions, and the application of hydrogen fuel cell vehicles will usher in a rapid growth period. However, true “zero carbon” emissions cannot be separated from “green hydrogen”. Therefore, it is of practical significance to explore the feasibility of renewable energy hydrogen production in the context of hydrogen refueling stations, especially photovoltaic hydrogen production, which is applied to hydrogen refueling stations (hereinafter referred to “photovoltaic hydrogen refueling stations”). This paper takes a hydrogen refueling station in Shanghai with a supply capacity of 500 kg/day as the research object. Based on a characteristic analysis of the hydrogen demand of the hydrogen refueling station throughout the day, this paper studies and analyzes the system configuration, operation strategy, environmental effects, and economics of the photovoltaic hydrogen refueling station. It is estimated that when the hydrogen price is no less than 6.23 USD, the photovoltaic hydrogen refueling station has good economic benefits. Additionally, compared with the conventional hydrogen refueling station, it can reduce carbon emissions by approximately 1237.28 tons per year, with good environmental benefits.

How can trees protect us from air pollution and urban heat? Associations and pathways at the neighborhood scale

With rapidly increasing urbanization, cities frequently suffer from the development of urban pollution and heat islands, the co-occurrence of which increases the vulnerability of cities and threatens public health. Greening practices, particularly the plantation of trees, have become an effective strategy for enhancing the quality of urban environments. However, extensive studies examining the effects of urban greening tend to focus on the singular contribution of trees to mitigating either urban air pollution or heat. In this study, the metropolis of Shanghai, China was selected as a study case to explore associations between particulate matters with aerodynamic diameters ≤10 μm (PM10) and heat mitigation by urban trees. During the summer months, we obtained hourly PM10 concentration and meteorological data from environmental monitoring stations in Shanghai, and land cover data were derived from 0.8-m-resolution GF-2 satellite images. Regression analysis indicated a negative non-linear logarithmic pattern between canopy cover and PM10 or heat indicators within neighborhoods. Mediation analysis confirmed the significant mediatory role of heat mitigation pathways, particularly extreme heat mitigation on PM10 reduction from urban trees. The proportion of indirect PM10mean mitigation effects of urban trees associated with reductions in average temperature, maximum temperature, extreme heat days, and extreme heat hours amounted to 27.11%, 27.59%, 30.61%, and 39.76%, respectively. Different results were obtained when using PM10max as the dependent variable, with only extreme heat events (EHEs) pathways being significant, whereas the proportion of indirect effects associated with extreme heat days and extreme heat hours accounted for 25.89% and 37.08%, respectively. Overall, mitigating extreme heat events makes a larger contribution to air purification than temperature regulation. Our findings enrich and extend the current understanding of associations between the air purification and cooling effects of urban trees, and highlight the potential co-benefits for effectively dealing with air pollution and urban heat in high-density cities.

Analysis and research on the characteristics of pollutants discharged from the separate storm pump station

The separate storm pump station can reduce the overflow pollution in rainy days and reduce the impact on the water environment. However, before or after rainfall, the impact of the discharge of diversion rainwater pump station on the water environment is still prominent, which has become one of the important reasons restricting the improvement of water environment pollution. This paper investigates the diversion rainwater pump stations in Shanghai, analyzes the discharge flow and pollution law of the diversion rainwater pump stations according to the operation data, and studies the characteristics, causes and effects of discharge pollution. Therefore, to provide support for the pollutant discharge control of the diversion rainwater pump station.

Improving Intra-Urban Prediction of Atmospheric Fine Particles Using a Hybrid Deep Learning Approach

Growing evidence links intra-urban gradients in atmospheric fine particles (PM2.5), a complex and variable cocktail of toxic chemicals, to adverse health outcomes. Here, we propose an improved hierarchical deep learning model framework to estimate the hourly variation of PM2.5 mass concentration at the street level. By using a full-year monitoring data (including meteorological parameters, hourly concentrations of PM2.5, and gaseous precursors) from multiple stations in Shanghai, the largest city in China, as a training dataset, we first apply a convolutional neural network to obtain cross-domain and time-series features so that the inherent features of air quality and meteorological data associated with PM2.5 can be effectively extracted. Next, a Gaussian weight calculation layer is used to determine the potential interaction effects between different regions and neighboring stations. Finally, a long and short-term memory model layer is used to efficiently extract the temporal evolution characteristics of PM2.5 concentrations from the previous output layer. Further comparative analysis reveals that our proposed model framework significantly outperforms previous benchmark methods in terms of the stability and accuracy of PM2.5 prediction, which has important implications for the intra-urban health assessment of PM2.5-related pollution exposures.

Concentrations and Community Structures of Culturable Bacteria in Aerosols of Household Garbage Stations in Shanghai

The airborne microorganism has attracted increasing attention, and household garbage carries various pathogenic bacteria that affect the surrounding environment and public health. In this study, the culturable bacteria in the air were collected by using a six-level Anderson sampler, and the temperature, relative humidity, PM2.5, and PM10 in the garbage stations and their surrounding environment were recorded. The relationships between environmental factors and culturable bacterial pollution in the air were also analyzed. The results showed that the culturable bacterial concentrations in five sampling sites (the garbage station of a villa and the area downwind, the garbage station of a campus, the roof of an office building, and the garbage station of a residential area) were (1254±92), (280±123), (172±47), (84±18), and (175±174) CFU·m-3, respectively. The concentrations of the culturable bacteria in the garbage station of the villa were significantly higher than those of other sampling sites, mainly because there were biochemical treatment facilities for the on-site treatment of wet garbage in the garbage house. The sizes of the culturable bacteria in the garbage station of the villa mainly ranged from 1.1-4.7 μm, and the bacterial sizes at the other four sampling sites were primarily larger than 7 μm, with a few bacteria ranging from 1.1-2.1 μm. In this study, Proteobacteria and Firmicutes were the dominant phyla, and Corynebacterium and Bacillus were the dominant genera. More importantly, some opportunistic pathogens such as Corynebacterium, Staphylococcus, and Acinetobacter were also detected. The concentrations of the culturable bacteria in the garbage station of the villa were highly correlated with temperature, relative humidity, PM2.5, and PM10. Exiguobacterium in the air was highly correlated with PM10, temperature, and relative humidity. The health hazard quotient (HQ) values of the five sampling sites were all less than 1; however, the results of microbial quantitative risk assessment showed that the health risks of the male and female staff in the three garbage houses were all higher than the corresponding reference values. This study revealed the influence of garbage stations on the bioaerosol in the surrounding environment and provided references for the evaluation of air quality in and around garbage stations.

Solar Radiation Characteristics in Shanghai

Based on the solar radiation and meteorological data of five meteorological stations of Baoshan, Chongming, Minhang, Fengxian, and Songjiang meteorological stations in Shanghai from 1982 to 2021, the suitable climatological formula for calculating solar radiation in the Shanghai area was selected as Qg = Qt (a+bS) by applying the climatological calculation principle of total solar radiation. The empirical coefficients for each month of the equations were fitted using the least squares method based on the total radiation and sunshine percentages for each month of the calendar year at five meteorological stations. The results show that the average total radiation of Shanghai in 1982-2021 is 4688.69 MJ/m2, among which the total radiation is the most in May, the least in January and December, and although the noon solar altitude angle is the largest in June and July, the total radiation is less than in May and August due to the new influence of the rainy season sky shading than in May. Due to the small area of the Shanghai region, the difference in total solar radiation between the five stations for each month is insignificant. In terms of the spatial distribution of total solar radiation, the northeastern and coastal areas of Shanghai have the most, and the Baoshan area has relatively less; the northern part has the most in spring; the coastal area has the most in summer; the spatial distribution of total solar radiation in winter and autumn is similar, both having the least in the northeast.

Analyzing the Passenger Flow of Urban Rail Transit Stations by Using Entropy Weight-Grey Correlation Model: A Case Study of Shanghai in China

In this paper, the factors influencing the passenger flow of rail transit stations in Shanghai of China are studied by using the entropy weight-grey correlation model. The model assumptions and the corresponding variables are proposed, including traffic accessibility, built environment, regional characteristics of the district to which the rail transit station belongs, conditions of the station and spatial location, which affect the passenger flow of rail transit stations. Based on the assumptions and the variables, the entropy weight-grey correlation model for analyzing the passenger flow of urban rail transit stations is presented. By collecting the data of passenger flow of rail transit stations and corresponding influencing factors in Shanghai, the results of the entropy weight-grey correlation model are obtained. It is shown that the influencing factors, such as the distances from the rail transit station to the adjacent third-class hospital and the adjacent large commercial plazas, district committees, parking areas and the transaction price of important plots, and the gross output value of the tertiary industry, have significant impacts on the passenger flow of a subway station. Finally, some suggestions are proposed for the local governments to formulate improved policies for rail transit development. The conclusions can provide a reference for the development of rail transit in other large cities and countries.

Electric Vehicle Charging Station Load Prediction Based on IWOA-LSSVM Combined Model with Improved Variational Mode Decomposition

Accurately predicting the electric vehicle charging station load is the basis for the optimal design of power supply planning. Aiming at the problem that the load data of electric vehicle charging station has strong randomness and volatility due to the interaction of various factors. The electric vehicle charging load prediction model is constructed, which considers the highest temperature, day type and weather type factors to select similar days. Firstly, an algorithm for optimizing variational modal decomposition parameters with relative entropy is proposed to decompose the extracted similar daily charging load data into multiple sub-sequences. Then, the decomposed data are respectively input into the combined prediction model of the least squares support vector machine optimized by the whale algorithm improved by the adaptive probability threshold and the random difference mutation strategy, and finally the predicted value of each subsequence is superimposed and summed to obtain the final predicted value. Based on the measured charging load data and weather forecast data of an electric vehicle charging station in Shanghai, simulation experiments are carried out to compare and verify the accuracy and effectiveness of the model in this paper.

A Multiobjective Land Use Design Framework with Geo-Big Data for Station-Level Transit-Oriented Development Planning

Transit-oriented development (TOD) is among the most feasible strategies for relieving urban issues caused by the unbalanced development of transportation and land use. This study proposes a multiobjective TOD land use design framework for the optimization of the land use layout in station catchments. Given the high density and diverse development in Chinese megacities, a planning model that considers nonlinear impacts on ridership, land use efficiency, quality of life, and the environment is constructed. The model applies fine-grained geo-big data to fill gaps in the empirical and statistical data and improve practicability. A genetic multiobjective optimization approach without reliance on objective weighting is used to generate alternative land use schemes. A metro station in Shanghai is applied as a case study. The results indicate that the proposed ridership objective outperforms the commonly used linear function, and the optimization method has superior extreme optima and convergence to baseline models. We also discuss the consistencies and conflicts in the objectives and provide a balanced land use scheme considering local policies. This work provides suggestions for sustainable urban design with coordinated land use and transportation.

Influence of ambient air pollution on successful pregnancy with frozen embryo transfer: A machine learning prediction model

Numerous air pollutants have been reported to influence the outcomes of in vitro fertilization (IVF). However, whether air pollution affects implantation in frozen embryo transfer (FET) process is under debate. We aimed to find the association between ambient air pollution and implantation potential of FET and test the value of adding air pollution data to a random forest model (RFM) predicting intrauterine pregnancy. Using a retrospective study of a 4-year single-center design，we analyzed 3698 cycles of women living in Shanghai who underwent FET between 2015 and 2018. To estimate patients’ individual exposure to air pollution, we computed averages of daily concentrations of six air pollutants including PM2.5, PM10, SO2, CO, NO2, and O3 measured at 9 monitoring stations in Shanghai for the exposure period (one month before FET). Moreover, A predictive model of 15 variables was established using RFM. Air pollutants levels of patients with or without intrauterine pregnancy were compared. Our results indicated that for exposure periods before FET, NO2 were negatively associated with intrauterine pregnancy (OR: 0.906, CI: 0.816–0.989). AUROC increased from 0.712 to 0.771 as air pollutants features were added. Overall, our findings demonstrate that exposure to NO2 before transfer has an adverse effect on clinical pregnancy. The performance to predict intrauterine pregnancy will improve with the use of air pollution data in RFM.