ALBERT

Description: 〈p〉Publication date: October 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 50〈/p〉 〈p〉Author(s): Zhengrong Li, Haowei Xing, Godfried Augenbroe〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉 〈p〉Although there have been numerous studies on the evaluation of models that estimate sky diffuse radiation on inclined surfaces, it is still difficult for investigators to select from available sky diffuse radiation models for urban microclimate and building performance simulation. This is due to the fact that results from different studies are not consistent, or even contradictive, which indicates the fact that the evaluation criterion itself has a great effect on the performance of the model.〈/p〉 〈p〉To explore the effect of different evaluation criteria on the performance rating of the models, four evaluation methods are applied in this paper: diffuse irradiance on facades with respect to sky condition, diffuse irradiance on facades with respect to orientation, diffuse irradiance distribution among sky dome with respect to sky condition and diffuse irradiance on buildings in obstructed environment. Based on a statistical test on available data, Igawa model is considered to be the most accurate and appropriate model for urban and building energy simulation. Besides, an evaluation criterion appropriate for screening sky diffuse models for urban and building energy simulation is proposed. Furthermore, potential errors that may occur in the measurement and the corresponding quality control is presented.〈/p〉 〈/div〉

Description: 〈p〉Publication date: October 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 50〈/p〉 〈p〉Author(s): D. Koteswara Rao, D. Chandrasekharam〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Sustainable conservation of natural resources has become a primary concern for urban cities, globally as they are centers of consumption and economy. Due to population growth, cities depend more on imports of food, energy, water, and services from all over the globe, and consume more virtual water than direct water, because of their food habits and lifestyle. Most of the imported goods are water intensive and pose challenges in tracing the source of virtual water. The goal of this research is to develop a general framework to assess the water footprint (WF) of a typical city in India using existing databases. A consumer-centric approach has been adopted for assessing WF in Hyderabad Metro Development Area (HMDA). The variation of the WF across economic classes of consumers is also analyzed. The WF is estimated based on four broad categories: 1) food consumption, 2) fossil fuels based energy, 3) electric power, and 4) direct water. Average WF of HMDA region is 1041 m〈sup〉3〈/sup〉/cap/year (2852 LPCD), in which 70% (1986 LPCD) of WF was consumed by food, 25% (744 LPCD) by electric power, only 4% (121 LPCD) is from direct water consumption and surprisingly the contribution from fossil fuel WF to total per capita WF of HMDA area is less than 1%.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 5 July 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society〈/p〉 〈p〉Author(s): Shuqin Chen, Xiyong Zhang, Shen Wei, Tong Yang, Jun Guan, Wenxiao Yang, Lijuan Qu, Yunqing Xu〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Accurate grasp of district power demand is of great significance to both sizing of district power supply and its operation optimization. In this study, an index system has been established and visualized through a Geographic Information System, for revealing both temporal and spatial characteristics of district power loads caused by heating/cooling systems, including load level and fluctuation characteristics, spatial distribution of electric loads, and load coupling relationships between individual buildings and the district. Principal component analysis was applied to identify the buildings with significant impact on district load management. Using this method, the spatial-temporal characteristics of electric loads caused by heating in one university campus in China were analyzed. The results showed that building type and the operation modes had great effects on the level and volatility of the district electric load caused by heating. Buildings with high load levels and strong coupling with the peak district electric load, such as academic buildings, often had a major impact on the power demand of the district. Therefore, they were considered as key targets for energy-saving renovation and operation optimization. Buildings with large load fluctuation, such as teaching buildings, could contribute to the peak load shaving by adjusting the heating systems’ operation.〈/p〉〈/div〉

Description: 〈p〉Publication date: October 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 50〈/p〉 〈p〉Author(s): Shuo-Jun Mei, Zhiwen Luo, Fu-Yun Zhao, Han-Qing Wang〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Urban ventilation is important for building a healthy urban living environment. 2-D CFD simulation has been used widely for street canyon ventilation due to its high computational efficiency, but its applicability for a 3-D simulation has never been studied. This paper tried to answer the question: if and under what conditions, the widely-adopted 2-D CFD simulations on street canyon ventilation can represent a 3-D scenarios? 3-D simulations on street canyons with various street lengths and corresponding 2-D simulations are carried out with RNG 〈em〉k〈/em〉-〈em〉ε〈/em〉 model. Our study identified two important ventilation mechanism for controlling ventilation and dispersion in a 3-D street canyon, i.e., canyon vortex on the canyon top and the corner vortices at the street ends. The relative importance of these two driving forces will change with the street length/street width ratio (〈em〉B/W〈/em〉). For isolated street canyon, when 〈em〉B/W〈/em〉 is higher than 20 (for 〈em〉H/W〈/em〉 = 1) and 70 (〈em〉H/W〈/em〉 = 2), the street canyon ventilation will be dominated by canyon vortex, and 3-D street canyon ventilation could be simplified as a 2-D case. For multiple street canyon, the threshold of 〈em〉B/W〈/em〉 will become 20 when 〈em〉H/W〈/em〉 = 1, and 50 when 〈em〉H/W〈/em〉 = 2. The findings in this study could improve our approaches for simulating urban ventilation.〈/p〉〈/div〉

Description: 〈p〉Publication date: October 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 50〈/p〉 〈p〉Author(s): Gaofeng Gu, Dujuan Yang, Tao Feng, Harry Timmermans〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The increasing shift of individuals to use new electric mobility tools like electric cars (EV) and electric bikes has changed household energy expenditure. It may also affect households’ investments in renewable energy equipment, i.e. solar panels, heat pumps. Relatively little research has been conducted on how the decision to purchase electric vehicles affects the decision to invest in home renewable energy equipment. This paper, therefore, aims to examine the effects of mobility tools decisions on the intention to invest in solar panels and heat pumps, based on the data collected through a stated choice experiment. A mixed logit model is estimated to capture unobserved heterogeneity among individuals. Results show that mobility tools significantly influence the choice of home renewable energy equipment. Households who prefer to purchase electric vehicles have a higher probability to invest in solar panels and heat pumps than households who prefer other mobility tools. In addition, EV adopters’ intention to invest in solar panels are stronger than the intention to invest in heat pumps. This suggests that electric vehicle users are likely the early adopters of solar panels.〈/p〉〈/div〉

Description: 〈p〉Publication date: October 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 50〈/p〉 〈p〉Author(s): Heictor Correia Maioli, Raíssa Corrêa de Carvalho, Denise Dumke de Medeiros〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The increase in population concentration in large cities is a trend in the world, which brings several problems. In the context of urban mobility, bicycle sharing systems deserve special mention due to the impact and growth worldwide. The dissemination and success of these systems are also linked to aspects related to quality in their provision. Thus, this paper aims to conduct a study in the context of bicycle sharing to help managers to stimulate the use of this service and contribute to the development of sustainable cities. The SERVPERF tool was adapted and used to assess the quality of bicycle sharing service, in addition to identify which aspects impact on users’ satisfaction. This approach is innovative since there is a gap in the literature about customer satisfaction analyzes and aspects related to the quality of bicycle sharing service. In this way, this study can contribute to the dissemination of this service and to the solution of urban mobility problems by identifying the aspects considered as most important for the customers and thus improving these aspects. Therefore, the bicycle sharing system may have the increasingly use and will help to reduce urban mobility problems.〈/p〉〈/div〉

Description: 〈p〉Publication date: October 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 50〈/p〉 〈p〉Author(s): Zhijian Liu, Di Wu, Yuanwei Liu, Guangya Jin, Qiaomei Wang, Zhonghe Han, Hancheng Yu〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Low energy buildings are an efficient approach to meet space heating requirements and saving energy. Studies of low energy buildings mainly focus on cold climate regions. However, there is less research on the feasibility of low energy buildings on Qinghai-Tibetan Plateau located in a severely cold climatic region. Therefore, a low energy building equipped with an integrated heating system was built on Qinghai-Tibetan Plateau. The performance of the low energy building was quantitatively evaluated by field measurement and dynamic TRNSYS simulation. To be specific, the indoor temperature and relative humidity were measured, and further served as the data to verify the accuracy of the simulated results. The results show that the temperature and humidity were above 20 °C and 40% in the heating season, in accordance with the regulated low energy building standard (DB63/T1682-2018). According to the TRNSYS simulation, the hourly indoor temperature and relative humidity were generally in the range of 20–24 °C and 34–56%, respectively, in the heating season, with an energy demand of 16.1 kW h/(m〈sup〉2〈/sup〉 year). Overall, the building performance meets the local low energy building standard (DB63/T1682-2018). Therefore, a passive building with integrated heating system assistance is feasible for pleasant indoor comfort on Qinghai-Tibetan Plateau. These findings could explore the application potential of low energy buildings in severe cold climate areas.〈/p〉〈/div〉

Description: 〈p〉Publication date: October 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 50〈/p〉 〈p〉Author(s): Abdelkader Dairi, Tuoyuan Cheng, Fouzi Harrou, Ying Sun, TorOve Leiknes〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Wastewater treatment plants (WWTPs) are sustainable solutions to water scarcity. As initial conditions offered to WWTPs, influent conditions (ICs) affect treatment units states, ongoing processes mechanisms, and product qualities. Anomalies in ICs, often raised by abnormal events, need to be monitored and detected promptly to improve system resilience and provide smart environments. This paper proposed and verified data-driven anomaly detection approaches based on deep learning methods and clustering algorithms. Combining both the ability to capture temporal auto-correlation features among multivariate time series from recurrent neural networks (RNNs), and the function to delineate complex distributions from restricted Boltzmann machines (RBM), RNN-RBM models were employed and connected with various classifiers for anomaly detection. The effectiveness of RNN based, RBM based, RNN-RBM based, or standalone individual detectors, including expectation maximization clustering, K-means clustering, mean-shift clustering, one-class support vector machine (OCSVM), spectral clustering, and agglomerative clustering algorithms were evaluated by importing seven years ICs data from a coastal municipal WWTP where more than 150 abnormal events occurred. Results demonstrated that RNN-RBM-based OCSVM approach outperformed all other scenarios with an area under the curve value up to 0.98, which validated the superiority in feature extraction by RNN-RBM, and the robustness in multivariate nonlinear kernels by OCSVM. The model was flexible for not requiring assumptions on data distribution, and could be shared and transferred among environmental data scientists.〈/p〉〈/div〉

Description: 〈p〉Publication date: November 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 51〈/p〉 〈p〉Author(s): Neng Zhu, Daokun Chong〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Hyperthermal environments in public or industrial buildings impose substantial heat stress on the human body. To ensure human health in built environments, research has focused on the interactions between the heat tolerance and environments. This paper summarizes the progress in the literature concerning the evaluation and improvement of heat tolerance in three areas: environmental and physiological parameters, heat tolerance evaluation indexes, and heat acclimation (HA). This review leads to four conclusions: (1) Future indexes should give top priority to subjective perceptions to diminish individual differences; (2) machine learning techniques based on big data should be adopted to deal with complex correlations between hot environments and heat tolerance; (3) an optimal HA training regimen should be determined for workers to improve their heat tolerance and HA should be considered in the design and control strategies of indoor thermal environments for energy conservation; and (4) more field investigations should be conducted to modify laboratory-based findings. This review provides a comprehensive understanding of built environments and health and guidelines for future research.〈/p〉〈/div〉

Description: 〈p〉Publication date: November 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 51〈/p〉 〈p〉Author(s): Sophie A. Nitoslawski, Nadine J. Galle, Cecil Konijnendijk van den Bosc, James W.N. Steenberg〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉 〈p〉Smart cities are increasingly part of urban sustainability discourses. There is a growing interest in understanding how citizen engagement, connected technology, and data analytics can support sustainable development. Evidence has also repeatedly shown that green infrastructure such as urban forests address diverse urban challenges and are critical components of urban sustainability and resilience. Nevertheless, it is unclear whether green space and urban forest management are gaining significant traction in smart city planning. It is thus timely to consider whether and to what extent urban forests and other green spaces can be effectively integrated into smart city planning, to maximize green benefits for all city dwellers.〈/p〉 〈p〉We address this gap by exploring current and emerging smart city trends and technologies, and highlight practical applications for urban forest and green space management. Current “smart urban forest” projects reveal a focus on novel monitoring techniques using sensors and Internet of Things (IoT) technologies, as well as open data and citizen engagement, particularly through the use of mobile devices, applications (“apps”), and open-source mapping platforms. We propose a definition and promising approach to “smart urban forest management”, emphasizing both the potential of digital infrastructure to enhance forest benefits and the facilitation of citizen stewardship and empowerment in green space planning. Cities are getting faster and smarter – can (and should) the trees, and their managers, do the same?〈/p〉 〈/div〉

Description: 〈p〉Publication date: November 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 51〈/p〉 〈p〉Author(s): Jun Bao, Yu Wang, Xinjie Xu, Xiaoyi Niu, Jinxiang Liu, Lanlan Qiu〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The development of big data leads to the increasing heat dissipation of data center chips. As an efficient pattern to remove high heat flux, spray cooling has huge potential for data center cooling. Spray cooling system was established combined with PIV (Particle Image Velocimetry) system. The PIV was used to measure the flow pattern distribution of different nozzle sprays, while the surface heat flux and heat transfer coefficient were obtained by the thermocouples. The results show that as the spray diameter decreases, the outlet pressure and outlet velocity of the droplet increase, and the spray cone angle increases, causing only a small amount of droplets actually participate in the heat exchange, resulting in a higher velocity and a smaller heat transfer coefficient. It is also inferred that better uniformity of droplets velocity is beneficial for the heat transfer performance. Moreover, to further enhance the heat transfer performance, nano-alumina solution with five different fractions was applied to the experimental system. It is found that the heat transfer coefficient of the surface reaches an optimum value with a maximum velocity obtained by the PIV when the mass fraction of the solution is 0.08%.〈/p〉〈/div〉

Description: 〈p〉Publication date: November 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 51〈/p〉 〈p〉Author(s): Peleg Kremer, Annegret Haase, Dagmar Haase〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉In this paper, we introduce the special issue “The future of urban sustainability: Smart, efficient, green or just?” the special issue aims to explore the relationships, conflicts and connections between different approaches to urban sustainability and begin to build a shared understanding of the roles, synergies and tradeoffs between them. Papers in this special issue emphasize the multidimensionality of urban sustainability and its integration of social, ecological, economic, and technological systems.〈/p〉〈/div〉

Description: 〈p〉Publication date: November 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 51〈/p〉 〈p〉Author(s): Sabah Abdul-Wahab, Ziyad Alsubhi〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Despite the small quantities of hydrogen fluoride (HF) that are emitted from aluminum smelters compared with the other gaseous emissions, HF is one of the most dangerous gases to be considered when studying the environmental impact of these smelters. Therefore, modeling HF dispersion is essentially important to make sure that concentrations do not exceed threshold limit, especially for buildings, which are located close to the emission source. The objective of the current study was to examine the dispersion of atmospheric HF emitted from an aluminum smelter located in Oman. The results of the study indicated that the concentrations were found to be well below the allowable concentrations. Results in winter showed that the highest hourly HF concentration (0.104 〈math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si3.svg"〉〈msup〉〈mrow〉〈mtext〉 〈/mtext〉〈mtext〉μ〈/mtext〉〈mtext〉g〈/mtext〉〈mo〉/〈/mo〉〈mtext〉m〈/mtext〉〈/mrow〉〈mn〉3〈/mn〉〈/msup〉〈mo stretchy="false"〉)〈/mo〉〈/math〉 was occurred at 18:00 h local standard time and was found to be at a location −9.5 km west and 2.5 km north of the plant. Conversely, the simulation in summer indicated that the highest hourly HF concentration (0.374 〈math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si4.svg"〉〈msup〉〈mrow〉〈mtext〉μ〈/mtext〉〈mtext〉g〈/mtext〉〈mo〉/〈/mo〉〈mtext〉m〈/mtext〉〈/mrow〉〈mn〉3〈/mn〉〈/msup〉〈mo stretchy="false"〉)〈/mo〉〈/math〉 was obtained at 22:00 h and was found to be at a location -8.5 km west and 3.5 km north of the plant. The results of the study will have significant importance to the residents, stakeholders, and related permitting authorities in the area.〈/p〉〈/div〉

Description: 〈p〉Publication date: November 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 51〈/p〉 〈p〉Author(s): Ayman Faza, Amjed Al-Mousa〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Advances in the technologies of smart grids has made it possible to monitor and control the demand side of the power grid in ways that were not possible in the past. Thanks to the advances in information and communication technologies (ICT), Demand Side Management (DSM) is now possible, and promises more efficient and reliable power grids. This paper presents a study of different dynamic pricing techniques that are used as means to shape the demand in ways that minimize generation cost or maximize profit. This is achieved by using a fuzzy logic model that takes into account the different types of customers in the system. The paper further uses Particle Swarm Optimization (PSO) algorithm to generate the parameters that can achieve optimal grid operation. Results show that when customers are actively engaged in DSM schemes, and with the proper Dynamic Pricing scheme, optimal results can be obtained that benefit both the customers and the utility company, not to mention the savings in energy production and its impact on the sustainability of our cities. The results show that savings up to 12% in generation cost and a three times increase in profit can be achieved using an optimized DSM scheme.〈/p〉〈/div〉

Description: 〈p〉Publication date: November 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 51〈/p〉 〈p〉Author(s): Suman Majumder, Krishnarti De, Praveen Kumar, Ramesh Rayudu〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Economic growth in the emerging countries has led to rapid urbanization and expansion of existing cities. As a result, major cities in the emerging nations experience traffic congestion and deteriorating air quality. One of the solutions to overcome these problems is to develop a comprehensive public transportation system based on clean fuel. This work proposes a public transportation system based on electric buses that can be used in the cities. Here, the proposed system is studied and analyzed for Indian scenario. High capacity batteries and distributed solar PV generation has been used to ensure that no additional burden is put on the existing distribution network. These batteries are placed at each bus stop to store energy from the grid during the off-peak hours, and the supercapacitor in the buses will be flash charged from these batteries. In this work, the architecture of the proposed system has been presented along with initial sizing of the subsystems, for understanding the operation of the system for various scenarios, and to investigate its interaction with the grid. The results show that such a system, if carefully designed, is feasible and is capable of meeting the transportation needs without adding additional burden to the grid.〈/p〉〈/div〉

Description: 〈p〉Publication date: November 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 51〈/p〉 〈p〉Author(s): Saghar Mokhtarmanesh, Mohammad Ghomeishi〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Our environment is an essential factor in designing a user-preference-based place. This study investigates the effects of the environment on users in determining their preferences for schools. The school environment/surroundings affect students in terms of their behaviour and achievements. It can be an advantage if students’ take a liking to their school building, as this emotion can motivate them to attend the school eagerly. Consequently, since a child’s view of a space is different from an adult’s view, a school space should be built according to the requirements and factors that are specific to the children and important for them in this environment. In this study, female elementary schools located in District 1 of Tehran, Iran have been investigated qualitatively using card sorting techniques followed by semi-structured interviews in order to determine their preferences regarding their school buildings’ attributes. The results were analyzed using content analysis. It was revealed that students preferred having 〈em〉playgrounds〈/em〉, 〈em〉colourful buildings〈/em〉, 〈em〉big interior spaces〈/em〉, 〈em〉wide windows〈/em〉, 〈em〉curvy shaped facades〈/em〉, 〈em〉articulated facades〈/em〉, and 〈em〉stairs〈/em〉 in school buildings; they did not express a preference for 〈em〉tall buildings〈/em〉 and 〈em〉being near streets〈/em〉. The results reveal that having these spaces could influence the student’s evaluation of the school.〈/p〉〈/div〉

Description: 〈p〉Publication date: November 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 51〈/p〉 〈p〉Author(s): Gholamreza Heravi, Mohammad Mehdi Abdolvand〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The growing global water crisis highlights the importance of the assessment of the amount of virtual water consumption -water consumed during production of material and construction- in residential building projects. The purpose of this research is: (1) to assess the virtual water consumption of residential building projects in Iran; (2) to compare the amount of virtual water consumption in Iran with other countries; and (3) to assess sustainability impacts of the virtual water consumption on the national level. To do so, life cycle analysis method using process analysis for gathering data is implemented. Moreover, the results are generalized to evaluate virtual water consumption on a national level. In this way, the virtual water consumption of six residential buildings in Tehran, the capital of Iran, is assessed. The findings of this study showed that the virtual water consumption of residential buildings is 20.8 〈math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"〉〈msup〉〈mi〉m〈/mi〉〈mn〉3〈/mn〉〈/msup〉〈/math〉 per 〈math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si2.svg"〉〈msup〉〈mi〉m〈/mi〉〈mn〉2〈/mn〉〈/msup〉〈/math〉. Assessing the sustainability impacts of the virtual water consumption on the national level in 2017 resulted in: (1) the energy of supplying water consumed in the residential building construction releases about 13,700,000 Ton of greenhouse gases; and (2) the per capita virtual water consumption is about 20% of the country’s drinking water consumption per capita.〈/p〉〈/div〉

Description: 〈p〉Publication date: November 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 51〈/p〉 〈p〉Author(s): Marjan Ilbeigi, Azadeh Mahmudi KohneRoudPosht, Mohammad Ghomeishi, Emad Behrouzifard〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The aim of this study is to define aesthetic differences between architects’ and non-architects’ perspectives by considering the cognitive properties of residential buildings’ facades located in Iran. Accordingly, in order to observe perceptional similarities and differences between designers and users, the aesthetics of residential facades is investigated in Tehran, Iran, through a comprehensive case study. A quantitative method was used in the form of a questionnaire, and the corresponding data is further analyzed by SPSS software. Although architects’ and non-architects’ perspectives are somewhat similar in selecting and evaluating the chosen facade, corresponding findings reveal significant differences as well. The results further show that the classic architectural style was not appealing for the architects. Moreover, non-architects and architects think that ‘uniqueness’ is the most effective parameter; while architects believe that ‘pleasantness’ has the highest influence among the presumed six parameters in selecting the best facade. Furthermore, the outcomes indicate that the studied groups have some conflicting viewpoints about aesthetics, while there would be some similarities about the unfavorable facades. In this regard, the non-architects’ standpoints, preferences, and satisfaction should be assumed during the design procedure. This research can open a new perspective on the architect’s perception in the early design stage.〈/p〉〈/div〉

Description: 〈p〉Publication date: November 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 51〈/p〉 〈p〉Author(s): Ning Xu, Qiong Zhang, Haoran Zhang, Minsung Hong, Rajendra Akerkar, Yongtu Liang〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Disasters pose a serious threat to people’ lives and urban environment, affecting the sustainable development of society. Then it's crucial to quickly develop an efficient rescue plan for the disaster area. However, disaster rescue is rather difficult due to the requirement to develop the optimal rescue plan as quickly as possible according to the information of trapped people and rescue teams, and the amount of information will continue to increase as the rescue proceeds. At present, most of the rescue plans are manually made based on previous rescue experience. But obviously these plans might be the not optimal one. Considering the real-time location data of trapped people, this paper develops a Mixed Integer Non-linear Programming (MINLP) model to find the highest efficient rescue plan To solve the model accurately and efficiently, a bi-level decomposition (BLD) algorithm is presented to iteratively solve a discretized Mixed Integer Linear Programming (MILP) model and its nonconvex Non-linear Programming (NLP) model until a converged solution is obtained. In addition, since more trapped people could be found over time, the built rescue units should also be considered when making a rescue plan for a new stage. To further improve the solving efficiency, an accelerated bi-level decomposition (ABLD) algorithm is also proposed. Finally, a real-world disaster rescue is given to validate the superiority of the proposed ABLD algorithm relative to particle swarm optimization (PSO) algorithm and BLD algorithm.〈/p〉〈/div〉

Description: 〈p〉Publication date: November 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 51〈/p〉 〈p〉Author(s): Ghaffar Ali, Sawaid Abbas, Yanchun Pan, Zhimin Chen, Jafar Hussain, Muhammad Sajjad, Aqdas Ashraf〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The aim of achieving stable economic growth and development deter the pathways of being a low carbon society in developing countries. Nonetheless, it is imperative to understand and analyze the long-term goals of such societies to upgrade and redirect priorities towards the low-carbon structure. Therefore, this study is carried by applying a novel multi-criteria decision analysis (MCDA) approach and the Bilan Carbone model to examine the different possibilities for cost/benefit analysis from direct reductions in carbon emission levels in the Lahore Metropolitan Area (LMA) of Pakistan. The LMA is a metropolitan city in the developing country and is marked with a huge spike in energy demands and carbon emissions due to increasing population. Three scenarios including one current (2010), and two future (business as usual—2050BaU and low carbon scenario—2050LCS), were developed. Our findings show no evidence of renewable energy use—such as solar, wind, and biomass—in the current energy mix of LMA. Furthermore, an increase in carbon dioxide (CO〈sub〉2〈/sub〉) emissions from 3.5 in 2010 to 14 million tonnes (mt) in 2050 is found. Mitigation potential analysis of different sectors showed that the industrial sector of the city has the biggest mitigation capacity (13%) until the year 2050, whereas, the smallest proportion may come from the commercial sector (4%). The carbon emissions in LMA would be reduced as much as 50% as compared to the BaU scenario until 2050. Moreover, this mitigation potential study would also serve as a ripple effect in the data deficient cities of low-income countries.〈/p〉〈/div〉

Description: 〈p〉Publication date: January 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 52〈/p〉 〈p〉Author(s): Andrea Patricia Cuesta-Mosquera, Matthew Wahl, Jansen Gabriel Acosta-López, José Agustín García-Reynoso, Beatriz Helena Aristizábal-Zuluaga〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Distinct times of sulfur dioxide concentration (SO〈sub〉2〈/sub〉) were observed in the main square of a tropical Andean city, revealing strong meteorological control of SO〈sub〉2〈/sub〉. Concentrations were measured by UV fluorescence at 5-minute intervals, and compared to local meteorological forcing. A consistent morning peak of SO〈sub〉2〈/sub〉 (4.32 μg∙m⁻³ at 7:40 AM) was concurrent with peak traffic, and occurred 10 minutes into an 80-minute period of calm (wind velocities ˂ 0.3 m s⁻1). Instead of accumulating, SO〈sub〉2〈/sub〉 decreased 54%, due an expanding mixing layer. Peak evening SO〈sub〉2〈/sub〉 (5.2 μg∙m⁻³ at 8:15 PM) was observed 2:15 hours after flow reversal, and consistent with travel-times of an air parcel descending from industrial facilities. The consistent anthropogenic emissions formed a pattern of daily SO〈sub〉2〈/sub〉 concentration that aided in identifying more random volcanic SO〈sub〉2〈/sub〉. Manizales (pop. 400000) is located on the western slope of the Cordillera Central in proximity to the Nevado del Ruiz volcano. The complex trajectory necessary for volcanic SO〈sub〉2〈/sub〉 to arrive in the main square was simulated for the highest observed concentration (114 μg•m⁻³), using Hysplit and Aura-OMI information. This type of analysis can be applied to urban planning - from locating industrial zones to managing transportation - particularly in tropical montane cities.〈/p〉〈/div〉

Description: 〈p〉Publication date: November 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 51〈/p〉 〈p〉Author(s): Paola Lassandro, Silvia Di Turi〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Global warming increases the probability of extreme events and heat waves, intensified by Urban Heat Island phenomenon, triggering severe impacts on both human health and economy. The paper aims at developing a new methodological approach for the assessment of building ability to face rising temperatures, also considering the effects on the surrounding urban areas. It focuses on resilient retrofitting strategies need for building envelope according to three macro-categories: reliability, adaptability and mitigation ability. A set of indicators is defined to achieve a Response Index to cope with heat waves. The method is tested on reference building and its neighborhood. The selected strategies are investigated through an integrated and multilevel analysis with EnergyPlus and ENVI_met, in three different cities with increasing summer temperature. The final comparative analysis is carried out through a multi-criteria analysis according to the identified indicators. The best responsive solutions result the green ones combined with high albedo, but the research highlights also some controversial aspects. The method can be a valid tool to support the decisional process about heat waves mitigation in the roadmap towards a more responsive built environment.〈/p〉〈/div〉

Description: 〈p〉Publication date: November 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 51〈/p〉 〈p〉Author(s): Reza Ramyar, Esmaeil Zarghami, Margaret Bryant〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉There is a growing concern in recent years about temperature rise resulting from climate change, especially in high-density urban areas, where it is exacerbated in urban heat islands (UHI). In hot and dry climates, this concern makes it necessary to find a practical solution to adapt to climate change. Rapidly developed cities with large populations, due to ignoring the creation of UHI in planning, mostly have fallen into severe trouble. So, finding the influence of decisions’ impacts on urban climate before implementing them would inform planners about the consequences of their decisions. Advanced 3D-4D numerical models like ENVI-met are becoming a frequent way of investigating climatic impacts of decisions and regulations. However, full-scale modeling of a neighborhood or a city cannot be widely possible at present. Therefore, a practical solution is simplification of small-scale models to calculate the consequences of decisions in developments. In this study, we systematically propose some solutions for cooling urban neighborhoods, and then, after analyzing them, possible scenarios for UHI mitigation are presented. The goal is to find the most effective urban form and design strategies across a typical range of Tehran urban development at a high-density urban fabric. Our results provide insight into the impact of urban form and design on microclimate in hotter and drier future cities by considering water shortage and utilizing mitigation strategies.〈/p〉〈/div〉

Description: 〈p〉Publication date: November 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 51〈/p〉 〈p〉Author(s): Pouya salyani, Mehdi Abapour, Kazem Zare〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉With respect to the penetration of electric vehicles (EVs) due to pursuing environmental targets, EV parking lots establishment for giving charge and discharge services is necessary. Nevertheless, distribution companies (DisCos) seek to reduce the planning cost as much as possible and in this regard, demand response can be one of their effective options. However the customers participated in this program reduce or shift their consumption for the sake of increasing their benefit and accordingly DisCo must provide an incentive like the decrease in energy price which causes a competition between the DisCo and customers decisions. Because selling energy with higher price is preferable for DisCo. Hence what is proposed in this paper is the optimal probabilistic and long term planning of DGs and EV parking lots in the presence of demand response in which the mentioned competition is controlled via implementing Stackelberg game theory. In this game, DisCo plays as the leader and the customers interested in demand response play as the followers and their interaction is conducted though a decentralized distributed algorithm which finally determines the consumption profiles and the related energy selling price for all scenarios through the panning period.〈/p〉〈/div〉

Description: 〈p〉Publication date: November 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 51〈/p〉 〈p〉Author(s): Lei Wang, Guoyuan Ma, Feng Zhou, Yu Liu, Tian Tian〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Ventilation heat recovery devices have been increasingly used in energy-efficient buildings. Many factors, such as the attributes of buildings and the economic conditions of decision makers, should be considered in the installation of ventilation heat recovery devices. Thus, decision makers experience a multicriteria decision-making problem. This study presented a method of combining Analytical Hierarchy Process with the Technique for Order of Preference by Similarity to Ideal Solution to provide guidance in selecting ventilation heat recovery devices on the basis of the attributes of buildings and preferences of decision makers. By using residential house, small shop, and small public service center as examples, four types of ventilation heat recovery devices (total, plate sensible, integral heat pipe (R32), integral heat pipe (R717) heat exchangers) were recommended and sequenced under different background requirements through the proposed method. Results showed that the total heat exchanger obtained the highest recommended indicator value in small load application scenario, indicating its suitability. The proposed method can completely consider the attributes of buildings and psychological preferences of decision makers and combine the characteristics of different types of ventilation heat recovery devices. This approach provides scientific and reasonable recommended results in selecting heat recovery devices.〈/p〉〈/div〉

Description: 〈p〉Publication date: November 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 51〈/p〉 〈p〉Author(s): Jean Engo〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The transport sector represents one of Cameroon's economic sectors with high energy consumption and CO〈sub〉2〈/sub〉 emissions. To this end, it is clear that practical ways to save energy and reduce CO〈sub〉2〈/sub〉 emissions from this sector are needed to enable Cameroon to achieve its greenhouse gas mitigation targets. The Tapio and LMDI methods were applied in this paper, based on an extended Kaya identity, to explore the decoupling relationship among energy-related CO〈sub〉2〈/sub〉 emissions and growth from Cameroon’s transport sector, over the period 1990–2016. The decoupling indicators were broken down into five factors while considering the four fuels consumed in Cameroon's transport sector. Empirical results showed that only four states of decoupling appeared during the study’s period including weak decoupling, strong decoupling, weak negative decoupling, and strong negative decoupling. Meanwhile, scale effects, energy structure effect, and energy intensity effect prevented decoupling, whereas the economic structure effect played an important role in promoting decoupling. Based on the findings of this study, some policies aimed at reducing carbon emissions from Cameroon's transport sector were proposed in this paper.〈/p〉〈/div〉

Description: 〈p〉Publication date: November 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 51〈/p〉 〈p〉Author(s): Yiyang Yang, Dongsheng He, Zhonghua Gou, Ruoyu Wang, Ye Liu, Yi Lu〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Built environment interventions, such as creating green and walkable neighborhoods have increasingly been recognized as an effective approach to promote physical activity and health for older adults. However, evidence of the associations of urban greenery and older adults’ physical activity is still inconclusive, partially due to the difficulty to estimate eye-level urban greenery exposure. To address this gap, we assessed street greenery by Google Street View (GSV) images with machine learning techniques and associated it with walking behavior for 10,700 and 1083 Hong Kong older adults (aged 65 or above) respectively. Neighborhood socioeconomic status, individual factors, and other built environment characteristics were controlled for in the analysis. We found that street greenery assessed by GSV was positively associated with both the odds of engaging in walking and total walking time of the older adults. Our findings suggest that urban planners and policymakers should maximize residents’ greenery exposure by considering the accessibility and visibility of urban greenery from pedestrian and human-scale perspectives.〈/p〉〈/div〉

Description: 〈p〉Publication date: November 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 51〈/p〉 〈p〉Author(s): Mohammad Abujubbeh, Fadi Al-Turjman, Murat Fahrioglu〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Power systems across the globe witness structural challenges because of the increased concerns on climate change and vast growth in energy demand. Purposefully, new technologies and solutions have emerged such as renewable-energy systems, electric vehicles, and micro-grids. With the development of Internet of Things (IoT) and Wireless Sensor Networking (WSN) technologies, Smart Grid (SG) concept is becoming more attractive, whereby it refers to upgrading conventional power-grid infrastructure in order to offer automated control over the resources and emerging technologies in smart and sustainable cities. The implementation of this automated control requires robust and secure bidirectional communication systems. However, legacy-networking paradigms failed to address flexibility in their design to meet SG-networking requirements. Alternatively, Software Defined Networking (SDN) paradigm provides two powerful networking solutions namely, decoupling control from the data layer and network programmability, which can enhance the robustness of communication networks in modern societies. Therefore, the main aim of this article is to present an overview of integrating Software-Defined WSN (SDWSN) concept in SGs in order to solve the aforementioned challenges and improve network robustness. We categorize literature attempts, describe enabling devices and communication technologies, and we suggest open research issues and opportunities associated with integrating SDWSN paradigm into SGs.〈/p〉〈/div〉

Description: 〈p〉Publication date: November 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 51〈/p〉 〈p〉Author(s): Yong-Kang Qiao, Fang-Le Peng, Soheil Sabri, Abbas Rajabifard〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Underground space has been widely used in densely populated cities across the globe, and is attracting increasing attention among academics and practitioners toward further alleviating land use pressure, improving urban resilience and the quality of life. However, few attempts have been made to probe the potential threats posed by underground space use to urban sustainability. Disregarding these threats and the socio-environmental losses accruing to unreasonable underground space use will lead to failure in the decision-making process, particularly the cost-benefit analysis, of underground space development and may to some extent compromise the urban sustainability. This research intends to investigate the potential socio-environmental losses caused by underground space use for urban sustainability from the perspectives of underground assets, including geothermal energy, groundwater, geomaterials, historical heritage, space continuum and organisms, based on their contributions to sustainable development goals (SDGs), and sets up a framework for the monetary valuation of these losses. It is anticipated that the findings of this study will assist the future planning and decision-making process in developing the sustainable urban underground space.〈/p〉〈/div〉

Description: 〈p〉Publication date: November 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 51〈/p〉 〈p〉Author(s): Abdellah Chehri, Hussein T. Mouftah〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Every revolution promises a spirit of optimism and significant changes. Connected and automated vehicles (CAVs) are coming to our roadways. CAVs have a crucial role to play in the future of transportation. These emerging technologies will provide a safe travel mode by eliminating the human driving error. The CAVs are never distracted or tired. In the last five years, autonomous driving has gone from “may be possible” to “inevitable”. Beyond safer and more enjoyable routes for passengers, the autonomous car also represents a significant step forward in terms of sustainable development. However, many questions remaining under investigations, when will autonomous cars be available? What should be improved first, vehicles or infrastructures? What are the requirements that must be satisfied by Intelligent Transportation Systems (ITS) in smart and sustainable cities? How will these modern technologies impact our transportation systems and the social world? What and how the leading technologies can be used in autonomous cars? What type of sensors and embedded devices to be used? In this paper, we address those issues and to try to provide some suggestions for these technologies that are currently emerging.〈/p〉〈/div〉

Description: 〈p〉Publication date: November 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 51〈/p〉 〈p〉Author(s): Jean-Marie Cariolet, Marc Vuillet, Youssef Diab〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Maps and Geographic Information Systems (GIS) are widely used to better understand and manage risks in modern cities. While methods for mapping hazard, vulnerability and risk are well established, mapping resilience in urban areas poses a challenge as there are no agreed-on methodological approaches for doing so. This paper surveys proposed methodologies and approaches for mapping urban resilience to disasters. Our review shows that (1) adaptive resilience is mapped after a disaster mainly through the measure of recovery and inherent resilience is mapped using top-down approaches. Regarding inherent resilience (2), very few methods have been applied at city scale; (3) the limit between resilience and vulnerability mapping is still narrow and may cause confusion for decision makers; (4) the choice of variables and indicators to measure and map resilience is often a function of data availability and reliability; (5) indicators developed in one specific context should not be applied systematically to other contexts as resilience is a context-dependent concept; (6) most resilience maps are based on an analytical approach and do not reflect the systemic property of resilience.〈/p〉〈/div〉

Description: 〈p〉Publication date: November 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 51〈/p〉 〈p〉Author(s): Qiang Wang, Min Su〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Although many studies have explored the effects of urbanization and industrialization on China’s carbon emissions, no work has systematically investigated their impacts on China’s decoupling of economic growth from carbon emissions. To this end, the Tapio model, the Johansen co-integration theory and the Granger causality test are adopted in this paper. According to the results of decoupling analysis, during the period 1990–2015, China’s carbon emissions and economic growth appear to be strongly decoupled only in a limited number of years, and the remaining years are weakly decoupled. It is necessary for China to strengthen carbon emission reduction. The co-integration test results show that, both in the long-term and short-term, urbanization level is the main driving force to promote decoupling elasticity, contributing to a significant increase in carbon emissions. Due to the scale effect, industrialization exerts an inhibitory impact on decoupling elasticity in the early stage. This means that the economic benefits of industrialization are far greater than the environmental pollution it generates. The consumption structure plays a leading role in restraining the increase of carbon emissions. Based on these results, policy recommendations are put forward for policy makers.〈/p〉〈/div〉

Description: 〈p〉Publication date: November 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 51〈/p〉 〈p〉Author(s): Yi Yang, Peipei Huang〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Evaluating the actual progress in the green development of China’s northwestern provinces can play a role in encouraging local governments to promote the reform of the ecological civilization system. By improving the “〈em〉green development indicator system〈/em〉” formulated by the National Development and Reform Commission of China, the green development index (GDI) of Shaanxi Province from 2007 to 2016 was measured and compared with Shaanxi’s ecological footprint (EF) and related indicators. The results showed that (1) the GDI in Shaanxi Province increased from 42.774 in 2007 to 64.971 in 2016, while during the same period, the per capita EF increased from 1.994 ha/cap to 2.719 ha/cap, the ecological deficit increased from 0.782 ha/cap to 1.500 ha/cap, and the ecological pressure index increased from 1.645 to 2.176. These results indicate that the green development of Shaanxi Province is still based on excessive ecological occupation. (2) A comparison between GDI and EF shows that the development of Shaanxi Province has changed from “low GDI, low EF” to “low GDI, high EF” to “high GDI, high EF”. Thus, this study proposes countermeasures to change this trend, such as adjusting the energy structure, reducing carbon emissions, improving environmental capacity, and rigorous managing national land space.〈/p〉〈/div〉

Description: 〈p〉Publication date: November 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 51〈/p〉 〈p〉Author(s): Zhaowen Qiu, Huitao Lv, Fan Zhang, Wazi Wang, Yanzhao Hao〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉To evaluate the exposure of adults and teens to fine particles (PM〈sub〉2.5〈/sub〉), black carbon (BC), and ultrafine particles (UFP) as they walked along urban roads, an investigation was performed using mobile measurement devices on two types of urban roads (arterial and collector) in Xi'an. The inhalation dose model, considering exposure concentration, inhalation rate, and trip time, was employed to estimate the personal inhaled dose of pollutants for adults and teens. Multivariate linear regression was used to explore impact factors that contributed to their exposure variability. Results showed large spatial and temporal pollutant concentration variations along the designed route. Pedestrians experienced higher exposure concentrations on the arterial road than on the collector road. Teens faced higher PM〈sub〉2.5〈/sub〉 and BC inhaled doses than adults on all the streets studied, although sometimes being exposed to lower concentrations than adults. In addition, gender-related inhaled dose differences between teens were more significant than those for adults. The overall background concentrations explained the greatest variability in pollutant exposures, from 18.7% for BC to 40.4% for PM〈sub〉2.5〈/sub〉. Ambient concentrations and traffic volume as well as pollution hotspots (pedestrian cigarette smokers, restaurants, and open burning) were identified as major factors affecting the pollutant concentrations.〈/p〉〈/div〉

Description: 〈p〉Publication date: November 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 51〈/p〉 〈p〉Author(s): Himanshu Kumar Khuntia, Sanjana Chandrashekar, H.N. Chanakya〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Anaerobic treatment of greywater (GW) is challenging due to the presence of recalcitrant household chemical products (HCP) that inhibit the activity and growth of organic pollutant degrading anaerobic microbes. This research attempted to overcome this challenge through a novel, multi-chambered anaerobic biofilm reactor (AnBR) containing fluidized PVC media and packed bed lignocellulosic fiber (〈em〉Cocus nucifera〈/em〉) as biofilm support. The long-term effects of feeding HCP laden GW and effluent recycling on the performance of AnBR were corroborated with the bioconversion data and microbial community dynamics. The results indicated that the composition of wastewater and recycling both determine the rates of COD removal, microbial population, and diversity in AnBR. The inhibitory effects exhibited by GW constituents reduced the COD removal efficiencies by 74–94% in comparison to standard substrates (SS), while simultaneously reducing microbial population and diversity by 30–40%. Effluent recycling in GW and SS fed AnBR enhanced the rates of COD removal from 160 mg/L·day to 214 mg/L·day, and 627 mg/L·day to 3540 mg/L·day respectively, with the selective enrichment of 〈em〉Proteobacteria〈/em〉 sp. and 〈em〉Methanogenic〈/em〉 sp. The GW fed AnBR was dominated by aromatics degrading species of α〈em〉-Proteobacteria, Synergistetes,〈/em〉 etc., whereas, SS fed AnBR were inhabited by fermentative species of 〈em〉δ〈/em〉-〈em〉Proteobacteria, Bacteroidetes,〈/em〉 etc.〈/p〉〈/div〉

Description: 〈p〉Publication date: November 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 51〈/p〉 〈p〉Author(s): Shuo Zhang, Hong Wang, Yufei Zhang, Yingzi Li, Ying Li〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Electric taxi (ET), with zero exhaust emission, plays a vital role in low-carbon sustainable development of urban public traffic to reduce fossil fuel consumption and air pollution. As the key part of ET development, charging station (CS) directly impacts ET’s widespread application by figuring out the charging difficulty of ET. Therefore, to tackle the location issues of electric taxi charging station (ETCS), a novel two-stage location model is built in consideration of ET dynamic distribution and charging requirements in this paper. Firstly, taking the complex features of ET into account, such as dynamic location and dispersed distribution, the dynamic distribution clustering model of ET is formed based on K-means method, by which lots of scattered ET locations are grouped into certain clustering blocks. Secondly, according to aforementioned blocks, the location model of ETCS is established with barycentric method to get the location of ETCS, in view of both the latitude and longitude location and charging demands of each clustering block. Finally, the two-stage location model is employed in the case study of ETCS location in Chengdu, China. The result indicates that the chosen location meets the charging requirements and the characteristic of taxi distribution in Chengdu, proving the validity of the model.〈/p〉〈/div〉

Description: 〈p〉Publication date: November 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 51〈/p〉 〈p〉Author(s): Bin Cheng, Zhonghua Gou, Fan Zhang, Qiushuang Feng, Zefeng Huang〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Urban mountain parks are different from urban parks in that they form a unique microclimate environment with special topographical advantages and natural ecological environment. The study aims to investigate the thermal comfort condition and its related visitations in urban mountain parks in the hot summer and cold winter climate. Particularly, the research conducted thermal comfort measurement and visitation observation in three urban mountain parks in Mianyang City. The measurement and observation covered three months: August, October and December of 2018, which represents summer, autumn and winter respectively. The study included climate measurement, questionnaire survey and observation of the attendance rate in the selected parks. Results showed that temperature and solar radiation are the two most important factors affecting the use of parks. The neutral PET (Physiologically Equivalent Temperature) value is 27.0 °C in summer, 23.2 °C in autumn, and 17.3 °C in winter. This study can help to better plan and design outdoor environments of urban mountain parks to encourage people’s visits and social interaction.〈/p〉〈/div〉

Description: 〈p〉Publication date: November 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 51〈/p〉 〈p〉Author(s): Ali Ghofrani, Seyyed Danial Nazemi, Mohsen A. Jafari〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉In this study, we propose a methodology to assign HVAC operation planning schemes for connected buildings with the objective of energy saving and load leveling. The idea is to use building thermal inertia in a periodic pattern by relaxing the temperature setpoints to an upper bound and setting back to a lower bound to avoid air conditioning while the room is still within human comfort zone. This periodic operation planning in turn facilitates the collaboration across a building community to reduce the aggregate demand. The objective is to assign periodic temperature setpoints for a building cluster so that the aggregate cooling electric demand reduces with the minimum cost and steady aggregate load shape. Ideal operation schedule forms are identified and elaborated in detail. Human comfort level and demand-side management applications are evaluated and incorporated into an optimization framework. The impact of peak demand reduction on the grid and power generation costs and the impact on the society is also investigated. The results demonstrate up to 12.5% savings in electricity consumption and 10% peak demand reduction for a community of 26 buildings.〈/p〉〈/div〉

Description: 〈p〉Publication date: November 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 51〈/p〉 〈p〉Author(s): Yijie Cao, Dan Shen〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉In recent years, the popularity of sharing economy has been growing worldwide. Therefore, its features must be understood to adapt the economic development for the enterprises. The performance of Mobike in Beijing is used as a case in this study. Mobike is the largest shared bike company in the world and plays a prominent role in the sharing economy. Based on data obtained, online reports, and actual situations, the factors affecting the environment from shared bikes and the usage of bikes have been analyzed qualitatively and quantitatively. From the qualitative analysis, the model of the contribution of carbon dioxide emission reduction to the society has been constructed with the significant variables of registered rate, riding distance, and usage rate for shared bikes. The influence degree of each variable and their interactions are evaluated through response surface method and Minitab. The influencing extent of factors on CO〈sub〉2〈/sub〉 emission reduction is in the following order: riding distance 〉 proportion of registered users 〉 usage rate of shared bikes. The results show that shared bike plays comprehensive and positive roles for economy and environment. The study will provide a significant help for policymakers and business supervisors on development of shared bikes.〈/p〉〈/div〉

Description: 〈p〉Publication date: January 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 44〈/p〉 〈p〉Author(s): Hye Soo Suh, Daeung Danny Kim〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The study demonstrated a systematic approach to achieve the nearly zero energy community building through the assessment of energy performance of the combined passive and active design solutions and renewable energy systems. Among 16 community buildings, four buildings were selected and the parameters that had an impact on energy consumption were identified through the analysis of electricity and gas consumption. After the validation with the measured data of electricity and gas, the energy simulation was used to model the newly constructed community building. A combined passive and active design strategy was applied to improve the energy performance. To offset the energy use for the domestic hot water, possible renewable energy systems such as the PV system, the solar thermal system, and the geothermal heat pump system were reviewed and their efficiency was analyzed. As a result, the combination of the PV system with additional PV modules and the geothermal system was chosen for the achievement of the nearly zero energy target.〈/p〉〈/div〉

Description: 〈p〉Publication date: January 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 44〈/p〉 〈p〉Author(s): Zhiqiang John Zhai, Jacob Michael Helman〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Climate change is a widely acknowledged major environmental problem whose impacts on building energy use however are not fully understood. Various climate models have been developed and used to project climate changes; however most of existing studies on climate change impacts only use few model outcomes, providing narrow predictions on the influences. This study analyzed a large number (i.e., 56) of models and scenarios. The obtained future climate data showed a wider range of potential changes and thus impacts on building energy. Four reference climate models were identified to cover the full range of the 56 models for three time periods and seven climate zones. The accuracy of model projections was validated using historical data. The study predicted the potential energy implications of climate changes to a campus building stock. Influences of these changes on utility and economics were analyzed. The study further explored the potential impacts of climate change to the current climate zones that directly relate to building energy consumption.〈/p〉〈/div〉

Description: 〈p〉Publication date: January 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 44〈/p〉 〈p〉Author(s): Jorge Navarro-Rubio, Paloma Pineda, Antonio García-Martínez〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Eco-efficiency and building optimization potential of prefabricated structures to be used in new buildings are studied, focusing on the analysis of a novel dry precast beam-column connection under different durability and re-using scenarios. The analyses include structural response (via numerical models), optimization potential (material take off, building schedule and economic cost) and environmental impact (via Life Cycle Assessment, LCA). The connection is applied to a case study which is representative of common buildings: a seven-story concrete structure with frames and deck slabs. The structural response of the connection accomplishes the Eurocode safety prescriptions. Man-hours and task duration decrease around 80%, and the global schedule undergoes 60% diminution. Focusing on economic issues, when the prefabricated structural elements are reused the accumulated economic cost significantly decreases. LCA shows that durability, in terms of service life, is directly related to the environmental impact. However, other design options, such us re-using, have less repercussion in the impact categories (i.e. Global Warming Potential and Embodied Energy), and in the global cost. Results from this research could contribute to the implementation of prefabricated elements in the building stock promoting eco-efficiency.〈/p〉〈/div〉

Description: 〈p〉Publication date: January 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 44〈/p〉 〈p〉Author(s): Fabiana Silvero, Fernanda Rodrigues, Sergio Montelpare, Enrico Spacone, Humberto Varum〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Nowadays, energy efficiency (EE) is presented as a reliable strategy towards sustainable development, but its application has not been developed equitably worldwide, since most EE policies have been implemented in industrialised nations, and developing countries are still in the process of improving their EE levels. This paper provides a contextual framework to analyse the EE situation in countries of Latin American Southern Cone, such as Argentina, Brazil, Chile and Uruguay, with the objective of describing the policies and projects implemented, highlighting the results achieved and outlining the path followed towards EE. A focus was placed on the building sector, where thermal performance standards and the programs to achieve EE in this sector were briefly described. Furthermore, a comparison between these standards was developed, to identify the country with the most demanding standard. As a result, it was found that all the countries are implementing several EE programs and recognising the importance of this issue within their policies. Through this paper, energy policy implications of the region can be identified, which can be useful for energy policymakers of other countries, who are beginning to consider EE policies and can take as an example the path followed by the countries analysed.〈/p〉〈/div〉

Description: 〈p〉Publication date: February 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 45〈/p〉 〈p〉Author(s): Jinsoo Park, Dong Ho Shin, Seung Joo Lee, Youhwan Shin, Sarng Woo Karng〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Thermal energy storage is an essential technology for using renewable energy to reduce building energy consumption. Among the various energy technologies, much attention has been paid to latent thermal energy storage system using phase change materials because of its large capacity of thermal energy. However, due to the low thermal conductivity of the phase change materials, the system has disadvantages at low charging and discharging rates. To address this issue, a new latent heat thermal energy storage system using a flexible and thin pouch is proposed. The effectively designed pouch arrangement and thin pouch shape provide better heat transfer performance. The real product is tested at various flow rates of heat transfer fluids and the thermal and fluid characteristics of the system are analyzed by the computational fluid dynamics model. As a result, the proposed system has 1.4 times larger amount of discharged energy than that of the water storage system. Moreover, the system can reduce charging, discharging, and total process times by 1.7, 2.5, and 2.2 times, respectively, compared with the conventional sphere capsule piling system.〈/p〉〈/div〉

Description: 〈p〉Publication date: January 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 44〈/p〉 〈p〉Author(s): Logaraj Ramakreshnan, Nasrin Aghamohammadi, Chng Saun Fong, Amirhosein Ghaffarianhoseini, Li Ping Wong, Nik Meriam Sulaiman〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉A very few studies have evaluated and understood the temporal dynamics of UHI in many expanding tropical cities. Hence, this study investigated the temporal variations of canopy-level UHI in selected urban stations, namely Petaling Jaya (PJ) and Subang (SUB), of Greater Kuala Lumpur (GKL) using 2016’s hourly data set obtained from meteorological observatories. The association between meteorological factors and UHI Intensity (UHII) is evaluated using linear regression models and Pearson correlation analysis. The findings revealed positive thermal contrasts between urban and sub-urban stations with maximum UHII during dry, southwest monsoon season in PJ (June: 1.68 °C) and SUB (August: 1.29 °C) stations respectively. PJ station exhibited a distinct diurnal cycle with the maximum nocturnal UHII of 1.71 °C at about 8 p.m. after sunset under ideal meteorological conditions. The results also demonstrated that UHI events occurred more frequently at nights in urban stations in the magnitude range of 0–2 °C. Cooling at all urban sites starts around 2–3 p.m. with the highest rate of 0.73 °C/h and 0.96 °C/h in PJ and SUB stations. Meanwhile, relative humidity displayed a low positive correlation (r = 0.37, p ≥ 0.05) and a high negative correlation (r = −0.79, p 〈 0.05) with UHII in PJ and SUB stations respectively. The influence of wind speed on UHII is weak (r = −0.44, p 〈 0.05) in PJ station and strong (r = 0.83, p 〈 0.05) in SUB station. Overall, this study can be regarded as one of the comprehensive observational investigations of canopy-level UHI in a tropical city that provide vital inputs to enrich the tropical urban climate literacy.〈/p〉〈/div〉

Description: 〈p〉Publication date: January 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 44〈/p〉 〈p〉Author(s): Bao-Jie He, Zi-Qi Zhao, Li-Du Shen, Hong-Bo Wang, Li-Guang Li〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Numerous studies on urban heat island (UHI) effects have examined relationships between land use/land cover (LULC) and land surface temperature (LST), while impacts of environmental temperatures on these relationships have received little attention. This paper has investigated how background temperature affects performances of cool/hot sources in either enhancing or mitigating LST in Shenyang, China based on Landsat 8 image. In specific, according to the method of standard classification method with an interval of 1.0 standard deviation, LST was classified into seven temperature levels: highest, higher, high, medium, low, lower and lowest. Afterwards, the seven levels were merged into three temperature zones (TZ), namely cold TZ (CTZ, highest, higher and high), neutral TZ (NTZ, medium) and hot TZ (HTZ, low, lower and lowest), for investigating relationships between LST and LULC (including building (BU), road (RO), bare land (BA), green land (GR), agricultural land (AG) and water bodies (WA)). LST generally follows the order of BU 〉 RO 〉 BA 〉 AR 〉 GR 〉 WA, but temperature differences between cool/hot sources reduce as background temperature increases. LULC-LST relationships vary greatly with background temperature. Overall, BU and RO have significant positive relationships with LST, while negative relationships are found among GR, AR, WA and LST. However, in CTZ, RO is in negative relationships with LST, GR and WA are no longer significant in NTZ, and GR and BA play significant positive roles in aggravating LST in HTZ. Generally, this study can help orientate scientific decisions in urban environmental governance.〈/p〉〈/div〉

Description: 〈p〉Publication date: January 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 44〈/p〉 〈p〉Author(s): Xiaojun Liu, Wei Hu〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Energy conservation and consumption reduction in the field of construction are the keys to achieving the target global temperature growth of the Paris Agreement. However, the current promotion of 〈strong〉green〈/strong〉 buildings is still stuck in the rut of government excessive intervention, market less participation. In order to explore the status of the Chinese public’s attention, changing trends, sentiment orientation, and focus toward 〈strong〉green〈/strong〉 buildings, this paper collected and analyzed information of Weibo users and posts and comments of popular posts related to 〈strong〉green〈/strong〉 buildings. We used the Sina Weibo platform with web crawler technology and a text mining method. The results showed that: the public’s attention toward 〈strong〉green〈/strong〉 buildings has enhanced significantly with the change of government governance ideas, but still needs to be improved. Although vertical greening houses possess good heat preservation and thermal insulation, 46.32% of the Chinese public has negative sentiments toward vertical greening houses mainly due to worries about the increase in snakes, and mosquitoes and other insects caused by the increased vegetation cover. Price is not the main reason why the public has negative sentiments toward vertical greening houses.〈/p〉〈/div〉

Description: 〈p〉Publication date: January 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 44〈/p〉 〈p〉Author(s): Charles Peprah, Owusu Amponsah, Charles Oduro〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Urbanisation in the cities of Sub-Saharan Africa has been associated with urban sprawl and increased vehicular traffic, which undermine the agglomeration effects of the cities on socioeconomic development. These adverse effects will exacerbate as the human population of the region continues to increase. Some scholars argue that making the cities mobility-smart could mitigate the adverse effects of urbanisation in the region. However, the conventional literature on smart mobility in Sub-Saharan Africa does little to demonstrate how the concept can be applied. In this regard, the purpose of this study is two pronged: a) to assess the mobility smartness of Ghanaian cities and b) to demonstrate how the concept can be operationalised to mitigate some of the adverse effects of urbanisation in the cities. The research process began with a review of relevant literature to conceptualise smart mobility. The conceptual framework was then used to assess the mobility-smartness of Ghanaian cities, which was followed by a discussion of how the concept of smart cities can be operationalised in Ghanaian cities. The results show that the cities in Ghana, like many cities in Sub-Saharan Africa, depend mainly on the road sub-sector for the movement of people and freight. The rapid increases in vehicular population without a commensurate expansion in road infrastructure have undermined the cities’ productivity. The conclusion from this is that the cities are not mobility-smart and for this to change, the paper argues that efforts should be made to improve people mobility, information logistic mobility and information mobility. These require technological literate and receptive population and investments in transport infrastructure.〈/p〉〈/div〉

Description: 〈p〉Publication date: January 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 44〈/p〉 〈p〉Author(s): Ning Wang, Linhao Tang, Huizhong Pan〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Environmental issues and energy security have led governments to introduce lots of incentive policies on electric vehicle promotion. Benefiting from policy dividend, the global threshold of 1 million electric cars on the road had been exceeded in 2015, closing at 1.26 million. Among these incentive policies, subsidy scheme was regarded as the most important and effective. However, many governments intend to abrogate subsidy for electric vehicle, such as China, America and Germany. It`s worth finding out the key factors including incentive measures and additional socio-economic factors that promote electric vehicle adoption. Utilizing multiple linear regression method, we explore the relationship between those variables and 30 national electric vehicle market shares for the year 2015. The results show that chargers` density, fuel price and road priority are significantly positive factors correlated with a country`s electric vehicle market share. Nevertheless, fiscal incentives are no longer the reasons for the huge differences of electric vehicle promotion over countries. This paper is helpful for policymakers to adjust and improve their policy for electric mobility.〈/p〉〈/div〉

Description: 〈p〉Publication date: May 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 47〈/p〉 〈p〉Author(s): Louise Krog〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Radical changes in energy production, going from sector-based energy systems based on fossil energy resources to smart energy systems based on renewable resources are starting to occur worldwide. This transition is a matter of balancing technical solutions with societal needs and possibilities, which requires a large effort in terms of policymaking and energy planning. This paper investigates how Danish municipalities are currently dealing with these issues through strategic energy planning. Through a case study of a Danish municipality, strategic and practical barriers to municipal strategic energy planning are identified. The paper ends by presenting recommendations on how to eliminate the identified barriers. In doing so, this paper brings new knowledge into further research of national and local framework conditions for strategic energy planning and thereby, the green transition of the energy system.〈/p〉〈/div〉

Description: 〈p〉Publication date: May 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 47〈/p〉 〈p〉Author(s): Meng Yuan, Qiong Zhang, Bohong Wang, Yongtu Liang, Haoran Zhang〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉This paper investigates optimal planning of a bicycle sharing system (BSS) from an integrated and long-term perspective, namely by combining strategic design and operation decisions in an integrated way while considering the stochastic demand and service level. The methodological contribution of the current research effort is to propose a unified mixed integer linear programming (MILP) model, in which several sub-problems such as the number, location, and capacity of bicycle stations; total fleet size design; depot location design; and rebalancing and maintenance plans are combined and can be solved together. A scenario-based approach is applied to deal with stochastic demand. Also, the concept of subjective distance is proposed to characterize the coverage area of bicycle stations, which together with the availability rate for bicycles are set as metrics of the service level. A case in Beijing, China is studied and sensitivity analyses are performed for key parameters. To illustrate the practical value of the proposed approach, a comparison between the planning results solved by the proposed MILP model with the BSS in real life regarding station layout is also undertaken. From the results, we observe that a balance between the costs borne by the operator and the service level for users can be achieved. The stations are distributed evenly across the study area, which could increase the coverage and thus enhance the convenience of the service. The model developed can be employed by BSS operators for the planning decisions.〈/p〉〈/div〉

Description: 〈p〉Publication date: May 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 47〈/p〉 〈p〉Author(s): Byoung Hoon Ha, Yoon-shin Kim, Pyoung Won Kim〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The smart city has become the key factor of the progression of the future city, and the smart device sensors are widely used in real-time applications to achieve smart city. Especially, super-resolution techniques have resulted in embedded video surveillance turning into a principal method of protecting public security. In this paper, we present a super-resolution method for embedded system which interpolates unknown pixel components at the zero-padded signals. This work is for a smart global village by applying real-time embedded monitoring system (such as traffic purpose) using visual sensor networks. In general, images are obtained from a number of visual sensor nodes and cameras. The visual sensor node conveys them to the computer devices for processing. To this end, a process called image super-resolution is needed. In this work, a filter design method is researched by using a regression approach. There are two main stages of this work. First, we study filter designing method training dataset. Second, we implement super-resolution process by applying pre-designed filters. Experimental results are given to show the superiority of the proposed method compared to the conventional methods. This paper also demonstrates super-resolution results obtained by designed filter and its implementation.〈/p〉〈/div〉

Description: 〈p〉Publication date: May 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 47〈/p〉 〈p〉Author(s): M.A. Tavakoli Ghazi Jahani, P. Nazarian, A. Safari, M.R. Haghifam〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Distribution networks are energy sectors that usually deal with residential energy consumers whose expectations associated with voltage quality and other relevant issues should be taken into account. There are various factors that might threaten reliable/economic performance of the distribution networks. In this paper, optimal reconfiguration of distribution systems is studied under a multi-objective optimization model to enhance reliability and minimize power losses of distribution networks. The developed multi-objective model is solved with epsilon-constrained (EPC) method and the trade-off conditions are achieved with min-max fuzzy satisfying (MMFF) technique. Demand response services (DRSs) including time-of-use (TOU) mechanism are proposed to make the energy consumers capable of revising their energy usage pattern. The proposed framework is investigated regarding the IEEE 33-bus test system in general algebraic modeling systems (GAMS) and the results validating the effectiveness of the proposed model are presented and compared. According to the obtained results, after reconfiguring the distribution system under the proposed model, loss of power in the system is reduced and reliability of a system associated with energy not supplied (ENS) index is improved. After proposing DRS and having the consumers motivated to change their consumption pattern, the obtained values for the mentioned targets are more suitable.〈/p〉〈/div〉

Description: 〈p〉Publication date: May 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 47〈/p〉 〈p〉Author(s): Sadaf Feyzi, Mehrdad Khanmohammadi, Niloofar Abedinzadeh, Mehdi Aalipour〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Site selecting for installing municipal solid waste incineration power plant with sustainable development perspectives is a multi-criteria decision. Identifying the factors that affect it will lead to more advantages including reducing costs and eliminating community dissatisfaction. In this research, in order to provide the most optimal model, the criteria for installing MSWI power plant were developed in the form of three main environmental, economic, and socio-cultural criteria. First, the decision making trial and evaluation laboratory (DEMATEL) technique has been applied to identify the interrelations between factors. Then, they were ranked by importance by fuzzy analytic network process (FANP). Based on the results, the highest and least significance have been assigned to the main economic, socio-cultural criteria, and sub-criteria of land use and railway respectively. Finally, a case study was conducted in Rasht County in order to evaluate the efficiency of the obtained model. Using geographic information system, GIS, we applied the final weights to the layers of each of the sub-criteria and overlay them. The results of the output map indicated the capability of the obtained model based on FANP and GIS to select the proper place for installing MSWI power plant and its application in other similar studies.〈/p〉〈/div〉

Description: 〈p〉Publication date: January 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 44〈/p〉 〈p〉Author(s): Reza Sedaghati, Mahmoud Reza Shakarami〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉This paper proposes a new control and power management strategy for a grid-connected microgrid, which includes a hybrid renewable energy sources (HRES) system and a three-phase load. The HRES system consists of a photovoltaic (PV), a battery storage system (BSS), a super-capacitor (SC) and a solid oxide fuel cell (SOFC). The dynamic model of each of these units is described. The PV is the main energy source, while the SC and the BSS due to their various power densities are considered to provide a steady and transient load demand, respectively. For increasing the reliability of the system, SOFC source is selected to keep the BSS completely charged. All these units with different DC-DC converters are connected in parallel to a common DC bus. Then, a three-phase voltage source inverter (VSI) is employed to convert the DC voltage to AC. To maintain the power balance and appropriate load-sharing, an adaptive fractional fuzzy sliding mode control (AFFSMC) strategy for VSI-based HRES system is presented. The controller is able to track the pre-defined instruction precisely and quickly in the microgrid. For stable performance of the control strategy under load variation, a fractional order-based sliding surface is considered. Moreover, fractional adaptive rules-based fuzzy sets are employed to accurately estimate the uncertain parameters in the microgrid. The simulation results demonstrate the effectiveness and capability of an AFFSMC strategy under various faults and different loading conditions. Moreover, the proposed control strategy is compared with the conventional PI controller.〈/p〉〈/div〉

Description: 〈p〉Publication date: February 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 45〈/p〉 〈p〉Author(s): Rupali Zope, N. Vasudevan, Shriniwas S. Arkatkar, Gaurang Joshi〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Increasing number of issues related to transport system around the world has raised concern about sustainable development. For developing country like India, the role of transportation sector has become critical in creating supporting growth of diverse sectors of its urban economy. The key issues of present state of transport system are higher level of motorization with dismally low share of public transport. Both of these issues are intertwined with number of social, economic and environmental aspects of sustainable development. Hence, there is a need for an evaluation approach which will help to analyze ‘macro-level’ system to give ‘micro-level’ analysis of the transport system. Benchmarking is one such method of comparing performances and practices and to learn from best. This paper is an attempt to study the use of benchmarking in performance improvement of the transport system. Results obtained through the study shows that the cities having a better modal share of sustainable modes occupy a higher rank with greater values of the sustainability index. In the current study, a software tool for monitoring the sensitivity of composite index of transport system towards performance indicators of sustainability is also developed. The comparative analysis of current study facilitates city-to-city learning.〈/p〉〈/div〉

Description: 〈p〉Publication date: January 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 44〈/p〉 〈p〉Author(s): Xueqin Chen, Hehua Zhu, Xiaojun Li, Xiaodong Lin, Xiuzhi Wang〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉To investigate the inherent uncertain and dynamic deterioration of metro shield tunnels in operation, the Bayesian ordered probit model, a data mining method, was used in this study. Through Markov Chain Monte Carlo (MCMC) simulation, the uncertainty in parameter estimation was significantly reduced, and the confidence in the results was improved. The effects of influencing variables on the deterioration process were evaluated. It was found that the tunnel sections with greater burial depths were more likely to deteriorate than the shallow ones. Crossing below a river or near a station or cross passage accelerated the deterioration rate. The deterioration probability increased as the tunnel became older. Finally, the model was applied to a tunnel section. It was shown that the probability of the best state decreased while that of the worst state increased with age. For states between the best and the worst, the probability would first increase, reach a peak, and then decrease. This study found that the ordered probit model with MCMC was a valuable probabilistic method for performance prediction, which is crucial for cost-effective decision-making in future work.〈/p〉〈/div〉

Description: 〈p〉Publication date: January 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 44〈/p〉 〈p〉Author(s): Muhammad Waseem Khan, Jie Wang, Meiling Ma, Linyun Xiong, Penghan Li, Fei Wu〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉At present, energy generation is evolving into a smart distribution system that assimilates several green energy resources at a distributed level assuring to generate clean energy without producing any harmful gases, to have consistent operational procedures, and to improve energy management and supervision arrangements. The renewable energy resources (RERs) are considered the best suitable approach to generate electrical power at the distributed level since they offer benefits to the power systems as well as to the environment. Therefore, this paper presents the recent research work on multi-agents-based coordination for the optimal management of electrical energy and its proper controlling at the distributed level exploiting RERs. The multi-agent system (MAS) technique is discussed in detail along with the storage and protection system that solve the microgrid (MG) control and management issues efficiently. Several platforms to develop the MASs are addressed including those that empower the MG to control its configuration, generation capacity, power flow, and fault control. There are several controlling approaches used on distributed generation systems to efficiently operate the whole system comprising of centralized, distributed, and hybrid control techniques are discussed. A comprehensive description about different optimization techniques applied to the energy system have also been highlighted, particularly to identify the most common and effective technique that is currently applied to hybrid energy system at the distributed level. The analysis shows that the particle swarm optimization (PSO) is the most useful and effective technique that has been applied since it can minimize the interruption costs, maximize the reliability, and optimize the operational schedules at the MG level.〈/p〉〈/div〉

Description: 〈p〉Publication date: January 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 44〈/p〉 〈p〉Author(s): Yi Yang, Guanfei Meng〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉China plans to pursue low-carbon development in its western megacities to control greenhouse gas emissions. Using the 〈em〉“〈/em〉2006 IPCC and the 〈em〉Chinese Guidelines”〈/em〉, the carbon footprint (CF), carbon carrying capacity, carbon deficit and carbon deficit pressure index of Xi’an were measured from 2007 to 2016. Then, the decoupling indicator was used to analyse the dynamic relationship between the urban CF and economic growth using partial least squares to explore the driving factors of CF. The results show that: (1) the CF increased from 23.34 million t CO〈sub〉2〈/sub〉eq to 32.25 million t CO〈sub〉2〈/sub〉eq, with average annual growth being 4.01%. The energy consumption accounts contributed 69.51%, far exceeding other accounts. (2) The carbon carrying capacity of Xi’an decreased from 7.78 million t CO〈sub〉2〈/sub〉eq to 7.45 million t CO〈sub〉2〈/sub〉eq, with an average annual reduction of 0.47%. The carbon deficit index fluctuated in the interval [−2.57%, 17.09%], indicating that Xi’an was in the ecological stress relief and the mildly enhanced zone. The total CF and the CF of each account exhibit a relationship of “connecting-decoupling-re-hook-re-decoupling” with economic growth. (3) The most important driver of CF growth is the per capita residential building area, while the urbanization rate restrains the growth of the CF.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 2 December 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society〈/p〉 〈p〉Author(s): Nuno D. Cortiços〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The building sector — following tech's development — favor buildings' short-term replacement. This demands substantial construction operations, especially if implies the demolition of superstructures, before reaching their potential lifespan: up to 120 years for modern solutions. With high costs for societies, due to the complex logistics and the risks involved: public health, GHG emissions, energy waste, noise, light particles pollution, excessive water consumption, urban restrictions, delays in traffic, et cetera. This research aims to extend building’s life with desirable gains in performance, considering the superstructure longevity, supported by technologies on renovation to meet the requirements set for new buildings, as nZEBs. Supported by maintenance methodology to assess actual performance and a Tool to simulate each underperforming system renovation, limited to passive solutions of the building envelope, quantifying the energy savings to favor the shortest investment reimbursement in decennial periods. Buildings were assessed, and a renovation plan simulated, concluding that the rehabilitation is only justified if under 10% (decennial period) of renovation cost, and if able to recover the initial performance. Renovation showed the potential to increase building performance 〉10% and energy savings 〈45% or 811 kWh/m〈sup〉2〈/sup〉.year for acclimatization only, under 9,6 kWh/m〈sup〉2〈/sup〉.year, set for Lisbon’s nZEBs.〈/p〉〈/div〉

Description: 〈p〉Publication date: February 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 45〈/p〉 〈p〉Author(s): Wangtu (Ato) Xu, Linchuan Yang〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉This study proposes a measure to evaluate the urban land use plan with transit accessibility, more specifically, the spatial accessibility of transit stations. The spatial accessibility of transit stations is measured with the number of effective reachable grids, and the influence of transfer on reduction in spatial accessibility is considered. A geographically weighted regression model is used to determine the correlation between transit accessibility and urban land use characteristics. Moreover, the methodology is applied to Xiamen, China and the corresponding results demonstrate the usefulness and effectiveness of the proposed methodology. Researchers can adopt the proposed approach to evaluate urban land use plan, particularly in transit-dominated and car-lite contexts.〈/p〉〈/div〉

Description: 〈p〉Publication date: February 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 45〈/p〉 〈p〉Author(s): Eduard Oró, Paolo Taddeo, Jaume Salom〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The heating and cooling sector in Europe aims to increase the share of thermal energy provided by district heating (DH) and cooling networks, from the current 13% to 50% by 2050. To reach this ambitious goal, it is needed to create the scientific evidence required to support the decarbonisation of the sector in Europe by combining energy efficiency and local waste heat. A vast amount of waste heat is produced in urban areas from a range of local sources such as data centres. These unique infrastructures are a rapidly growing industry generating heat that could potentially be recovered for heating applications, and in particular for DH networks. This manuscript evaluates numerically the energy and economic feasibility of the implementation of heat reuse solutions in air-cooled data centres to increase the energy efficiency of DH networks. Even though the primary energy reused in such installations can be above 50%, the economic analysis performed for a specific 1000 kW data centre located in Barcelona (Spain) demonstrates the non-viability of heat recovery integration in most of the conventional air-cooled data centres. However, for some cooling configurations, in particular in rear door cooling technology, the results prove the economic viability of heat reuse options.〈/p〉〈/div〉

Description: 〈p〉Publication date: February 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 45〈/p〉 〈p〉Author(s): Pritam Bhowmik, Sheetal Chandak, Pravat Kumar Rout〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉A conventional microgrid comprising both ac and dc components undergoes a significant power loss during the multistage conversion process. To overcome the problem of reduced efficiency in a conventional microgrid, a significant attention has been drawn towards the structure of the hybrid ac/dc microgrid. The precise power management in a hybrid microgrid is relatively complicated due to the presence of the dc sub-grids in the architecture. In dc sub-grids, the converter power can only be controlled by regulating the output voltage. However, the computation of the reference voltage turns out to be a challenging task due to the system resistance. The complexity further increases as the line resistances associated with the interlinking converter differs. Addressing this issue, to implement an efficient power management strategy in a dc sub-grid, a superimposed virtual frequency based control variable is proposed in this work. The proposed control variable remains unaffected by the system impedance which helps to attain an efficient power bifurcation. The study extensively evaluates the maximum exchanged power, storage utilisation factor, and circulating power among the multiple interlinking converters. The proposed strategy efficiently manages the bidirectional power flow between the ac and dc sub-grids.〈/p〉〈/div〉

Description: 〈p〉Publication date: February 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 45〈/p〉 〈p〉Author(s): Triluck Koossalapeerom, Thaned Satiennam, Wichuda Satiennam, Watis Leelapatra, Atthapol Seedam, Thana Rakpukdee〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉This study compared the real-world driving patterns of electric and gasoline motorcycles. The developed onboard system was installed on test motorcycles to collect real-world driving patterns in a mixed traffic and congested urban corridor. The driving cycles of both electric and gasoline motorcycles were developed, and their driving parameters, energy consumption, CO〈sub〉2〈/sub〉 emissions, and energy cost were compared. The results reveal that under the same driving behaviors and traffic conditions, certain driving parameters of the electric motorcycle, including average speed, average running speed, and proportion of time spent idling, were close to those of the gasoline motorcycle. However, the maximum speed and proportion of time spent in acceleration and deceleration were lower for the electric motorcycle than those of the gasoline motorcycle. In contrast, average acceleration, average deceleration, and proportion of time spent cruising were higher for the electric motorcycle than the gasoline motorcycle. The energy consumption, CO〈sub〉2〈/sub〉 equivalent emissions, and energy cost of the electric motorcycle was approximately eight, two, and six times lower than those of the gasoline motorcycle, respectively, when driving in a congested urban corridor.〈/p〉〈/div〉

Description: 〈p〉Publication date: February 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 45〈/p〉 〈p〉Author(s): Li Li, Xin Li, Yuan Yang, Jia Dong〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Building energy consumption is influenced not merely by the energy-saving performance of hardware but also by the occupants’ indoor behaviours. Advances in indoor positioning technologies can generate large volumes of spatial trajectory data on the occupants, which can reveal the distribution of the occupants or be interpreted to reflect the occupants’ behaviours. This calls for systematic research on new computing technologies to identify information from trajectory data rather than from visualizations or statistics. Due to the imperfections and complexities of trajectory data, few robust techniques are available for similarity comparisons, which are critical for further clustering and pattern mining. In this work, we propose a novel means of evaluating similarities in occupant trajectory data based on the use of a convolutional autoencoder (CAE). Trajectory data can be compared and their feature vectors extracted with the CAE in an unsupervised manner. We applied this approach to high-precision tracking data collected from an ultra-wide band (UWB) indoor positioning system (IPS) installed in an exhibition hall. The calculated results show that our approach offers great advantages in terms of its application, robustness, and flexibility.〈/p〉〈/div〉

Description: 〈p〉Publication date: February 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 45〈/p〉 〈p〉Author(s): Amir Hossein Sharifi, Pouria Maghouli〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The construction of smart homes under the infrastructure of smart grids (or micro grids) is currently one of the most important topics in the field of smart energy systems. Demand-side management confers many benefits on both users and utility companies. This paper proposes a novel scheduling procedure for power consumption in homes equipped with energy storage devices. The proposed optimal power scheduling method can reduce electricity bills and improve peak-to-average ratio (PAR) while taking into account the comfort of residents. Real-time pricing combined with an inclining block rate (IBR) tariff is used to prevent high power consumption at times of low cost, and to simultaneously improve the PAR index. Because erroneous determination of the state of the charge of storage systems can have deleterious effects on PAR, a revised formulation is therefore applied to attain the best solution of the problem. Home devices and batteries are modelled in a practical and efficient manner. The proposed multi-objective optimization problem is solved with non-dominated sorting genetic algorithm (NSGA-II) using MATLAB software. Then fuzzy decision is applied to obtain the most preferred solution.〈/p〉〈/div〉

Description: 〈p〉Publication date: April 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 46〈/p〉 〈p〉Author(s): Jinyao Lin, Xia Li〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Ecological red line (ERL) planning is an effective measure for ecological protection and management. Although many attempts have been made in this field, most have targeted a single city. The task of ERL planning will become particularly difficult in large regions, given the complexity of city networks. Another concern is that ERL schemes cannot be easily coordinated with pre-existing land-use plans and may even conflict with one another, since every plan has its own boundaries, management policies, and other constraints. To address these problems, this study presents a novel framework for large-scale ERL planning whose contributions are twofold. First, we developed a top-down planning strategy that could ensure both the consistency of ecological management and equality for every city within large regions. Second, we propose a semi-automatic intelligent zoning method based on a modified genetic algorithm. The difference between “semi-automatic” and “automatic” is that the former allows users to manually identify some key protected areas. This integrated framework was applied to ERL planning in an urban agglomeration. Three groups of evaluations indicated that the planning schemes generated via the proposed framework are more practical and closer to reality than the results of traditional methods. For example, the comparison between semi-automatic and automatic methods suggested that the former can generate zoning schemes that are easily coordinated with previous land-use plans. Therefore, this comprehensive framework could facilitate large-scale ecological management and planning and will be helpful for the design of environmentally sustainable cities.〈/p〉〈/div〉

Description: 〈p〉Publication date: February 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 45〈/p〉 〈p〉Author(s): Kasra Saberi, Hamed Pashaei-Didani, Ramin Nourollahi, Kazem Zare, Sayyad Nojavan〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉In this article, an improved system in micro energy grid is investigated composing of energy hub system as combined cooling, heating and power (CCHP) based microgrid integrated with renewable energies like photovoltaic (PV) and wind turbine (WT). The main goal is to solve a multi-objective model that includes reducing carbon emission and operation cost in the presence of real time demand response program (DRP). The flexibility of suggested energy hub system is the result of utilizing three hubs, storage devices and optimal energy flow. The multi-objective model has been solved using weighted sum approach and max-min fuzzy to make the optimal Pareto solutions and select the trade-off solution, respectively. This paper implemented the priced based real time DRP to flatten the load curve by shifting some amounts of loads from peak periods to off-peak periods. Two cases studied in the article that indicates the effects of real time DRP on optimization of energy hub systems. Finally, the energy hub system model is formulated and solved using CPLEX solver in GAMS optimization software. The results show that the operation cost and carbon emission are reduced 3.97% and 2.26%, respectively due to implementation of real time DRP.〈/p〉〈/div〉

Description: 〈p〉Publication date: February 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 45〈/p〉 〈p〉Author(s): Yong-Kang Qiao, Fang-Le Peng, Soheil Sabri, Abbas Rajabifard〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Urbanization is progressing rapidly, while problems raised by climate change are occurring across the globe. Cities are at the center of both of the two tendencies therefore, low carbon or zero carbon cities are being promoted. Urban underground space (UUS) is a key component in the process of urbanization and plays a significant role in creating low carbon cities, as highlighted in this paper. Meanwhile, UUS also involves unavoidable drawbacks regarding energy consumption in terms of lighting, ventilation and dehumidification. In this paper, the advantages of UUS in creating low carbon cities are analyzed, and a framework for calculating the positive low carbon effects derived from UUS use is established. Additionally, some alleviating measures in response to the potential low carbon disadvantages of UUS are proposed. Based on the analysis, some planning and design implications for the development of urban underground space in order to fulfill its role as a contributor to urban sustainability are discussed.〈/p〉〈/div〉

Description: 〈p〉Publication date: February 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 45〈/p〉 〈p〉Author(s): Tanveer Ahmad, Huanxin Chen〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The capability to forecast how differences in patterns of utilization in various kinds of loads can influence energy usage is an essential effort to decrease carbon emissions and demand-side energy management. The difference in weather change starts as the first step to change the energy consumption pattern in the domestic, commercial and industrial sector. To find the change in climate and their impact on energy usage, this study examines the medium-term (MT) and long-term (LT) energy prediction for utilities, independent power producers and industrial customers to estimate the energy usage requirement of large-scale city-wide by means of using the nonlinear autoregressive model (NARM), linear model using stepwise regression (LMSR) and random forest (least square boosting) (LSBoost) approaches, based on actual environmental as well as energy consumption data. The irregular load pattern recognition to remove the abnormal trend in actual energy usage is performed by applying the outlier detection and clustering analysis. The coefficient of variation (CV) of LSBoost model is 5.019%, 3.159%, 3.292% and 3.184% in summer, autumn, winter and spring season respectively. The machine learning (ML) techniques are validated and compared based on performance and accuracy with the previously existing Gaussian process regression (GPR) model. The optimal modeling of city-wide energy demand prediction using ML-based models are guaranteed the accurate operation and design of distributed energy systems.〈/p〉〈/div〉

Description: 〈p〉Publication date: February 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 45〈/p〉 〈p〉Author(s): Qingwu Hu, Guikai Bai, Shaohua Wang, Mingyao Ai〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Urban hot spot detection and commercial area analysis for city economic planning and dynamic urban planning is of vital importance. However, it is difficult to obtain a more accurate commercial area boundary in the past. Check-in data obtained by a social networking service (SNS) and/or a location-based service (LBS) is a type of crowd-sourced geographic data that can reveal mass daily life activities, which provides a new big data source for urban hot spot detection and commercial area analysis. In this paper, a dynamic urban commercial area extraction and monitoring approach is proposed using SINA Weibo (a social network) check-in data. First, a check-in data pre-process model is proposed to simplify the amount of check-in data and improve the efficiency of cluster analysis. The spatial autocorrelation validation is implemented to validate the significant patterns of the spatial clustering of check-in data. Then, an exploratory spatial analysis and hot spot clustering method based on check-in data is proposed to detect urban hot spots and extract commercial areas using a geographic distribution metric with urban commercial hot spots. Second, the hot spot cluster analysis results are taken to determine the center of the commercial area and calculate the distribution of an ellipse, which is adopted to obtain the rough boundary of the commercial area. A planar Delaunay iterative triangulation algorithm is presented to determine the exact boundary of the commercial area. Then, the time sequence extraction result of the commercial area is presented to analyze the evolutionary trend in the city business space. Finally, the Weibo check-in data from 2012 to 2014 of Wuhan city are taken as an example dataset for the commercial area extraction and detection with the proposed approach. The results show that the method can accurately determine the boundary of and changes within the commercial area in Wuhan city. This study provides a new method for the monitoring of hot spots and the geographical situations of city commercial areas.〈/p〉〈/div〉

Description: 〈p〉Publication date: February 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 45〈/p〉 〈p〉Author(s): Mostafa Sedighizadeh, Amirhosein Mohammadpour, Seyed Mohammad Mahdi Alavi〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The design of optimal energy management systems has been an important problem, in particular for daytime plug-in electric vehicles’ (PEVs’) parking lots at workplaces and commercial buildings, where the number of vehicles, their arrival and departure times are typically unknown and time-varying. The paper addresses this problem by introducing a two-stage optimization based on Approximate Dynamic Programing (ADP) and Hybrid Big Bang Big Crunch (HBB-BC) algorithm considering a Multi-Layer Perceptron Artificial Neural Network (MLP-ANN) which predicts the electricity price. The proposed optimal energy management minimizes the cost of parking lot owner with respect to the Time of Use (TOU) Demand Response (DR) program without reducing the welfare of EV owners. The two-stage optimization manages the charge scheduling subject to short and long term data coming from the MLP-ANN and intelligent transportation system. The stochastic features of the commercial parking are fully addressed into the problem. The effectiveness of the proposed charging technology is assessed and discussed by using real electricity data from ERCOT, under various stochastic conditions. The results demonstrate optimal energy management during peak and off time periods at the minimum cost.〈/p〉〈/div〉

Description: 〈p〉Publication date: February 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 45〈/p〉 〈p〉Author(s): Prateek Jain, Ashutosh Das, Trapti Jain〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The objective of this paper is to present a mechanism to determine the two-way energy storage capacity of a large pool of electric vehicles (EV) which can be contracted in ancillary services market on a long-term basis to provide the regulation up (RU) and regulation down (RD) to the grid. The proposed mechanism uses a scheme which delivers the schedule of power supplied to or drawn from the grid by treating the mobility attributes dependent electrical parameters. Two operational places, the workplace, and the home were identified as per driving pattern of customers for the provision of regulation ancillary service. An illustrative model considering a fleet of representative battery electric vehicle (BEV) is presented based on the mechanism, to obtain the minute-wise storage capacity that can be contracted in the market to provide frequency regulation to the grid. Results demonstrate that two major mobility traits namely, driven distance and arrival pattern, as well as the charging and discharging power standards directly influences the regulation schedule. Further, it has been realized that even though the charging cost is associated with energy consumption, the regulation provision will yield a notable revenue stream on a long-term capacity commitment basis. This supplements the concept of EVs participation in power markets by virtue of their high ramp up ramp down speed capabilities.〈/p〉〈/div〉

Description: 〈p〉Publication date: February 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 45〈/p〉 〈p〉Author(s): Xiaofang Shan, Wei Xu, Yi-Kuen Lee, Wei-Zhen Lu〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉HVAC systems are utilized to construct a thermally comfortable environment for occupants. As people spend more than 90% of time indoors, thermal conditions of indoor environment constructed by HVAC systems demand precise assessment. Predicted mean vote (PMV), a synthesized index, can reveal thermal conditions by evaluating occupants’ thermal sensations. Four environmental parameters affecting PMV: air temperature, air speed, radiant temperature and relative humidity. This study integrates CFD simulations and wireless-sensor measurements to assess distributions of PMV considering radiation models. The distributions of environmental parameters: velocity, temperature, radiant temperature, inside an office room with fan coil unit (FCU) are firstly presented. Based on these distributions, spatial profiles of PMV are obtained to intuitively illustrate thermal conditions. Combined with experimental database collected by thermal-flow wireless-sensors, CFD simulations offer detailed predictions of indoor airflow and thermal parameters. The mean temperature at occupied zone is 23.3 °C agreeing well with set-point temperature 23 °C. Furthermore, velocity values are below draft sensation limitations. The distribution of PMV indicates the cooling system is capable to construct thermally comfortable environment for occupants as well as the draft sensation conforming the satisfactory status. The research outputs provide useful information for designers of cooling system to build a comfortable indoor environment.〈/p〉〈/div〉 〈h5〉Graphical abstract〈/h5〉 〈div〉〈p〉〈figure〉〈img src="https://ars.els-cdn.com/content/image/1-s2.0-S2210670718322455-ga1.jpg" width="215" alt="Graphical abstract for this article" title=""〉〈/figure〉〈/p〉〈/div〉

Description: 〈p〉Publication date: February 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 45〈/p〉 〈p〉Author(s): Cecilia Tortajada, Francisco González-Gómez, Asit K. Biswas, Joost Buurman〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉This paper provides an overview of pricing and non-pricing measures to reduce domestic water consumption at the household levels in five urban areas in Spain. Analyses are based on questionnaires sent to water utilities that provide water services in the metropolitan areas of Barcelona and Seville, the cities of Malaga and Saragossa and the region of Madrid. Our main contribution is that, compared to studies that are based on estimates of the water demand function, we asked directly the managers of the utilities on the effectiveness of the measures implemented. We found that all areas studied have implemented pricing and non-pricing measures to encourage the efficient use of water and that reduction in per capita water consumption has been the result of periods of drought, accompanied in certain cases by water restrictions and pricing and non-pricing measures. In all five areas studied, the utilities believe that non-pricing measures have had a greater impact on water consumption decisions compared to pricing measures.〈/p〉〈/div〉

Description: 〈p〉Publication date: February 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 45〈/p〉 〈p〉Author(s): Gareth Davies, Nicholas Boot-Handford, Daniel Curry, William Dennis, Abayomi Ajileye, Akos Revesz, Graeme Maidment〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The paper concerns the recovery and use of secondary/waste heat and identifies secondary heat sources in London of 71 TWh i.e. c10% more than demand. London Underground (LU) railway tunnels generate significant quantities of low grade heat. There is also a requirement for active cooling to reverse the long term trend of rising tunnel temperatures. A novel combined cooling and heat recovery scheme for LU tunnels, utilising existing ventilation shafts, was proposed. Recovered heat was upgraded using a heat pump, for reuse in a local district heating network (DHN). A new model was developed to investigate the thermodynamic performance, economic feasibility and potential carbon savings available. It was concluded that the system could simultaneously provide 900 kW of cooling to the tunnel, and after upgrading to 70 °C, approximately 1.1 MW of heat, which could be used as a source for a DHN. The model predicted that this could be carried out economically and could deliver massive carbon and cost savings. With 200 ventilation shafts in London, 〉360,000 tonnes of carbon could be saved annually, while generating revenues of £40,000,000. Applying this technology to 50% of 150 metro systems worldwide would enable more than 27 million tonnes of carbon savings.〈/p〉〈/div〉

Description: 〈p〉Publication date: February 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 45〈/p〉 〈p〉Author(s): Ming Hu〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Developers, building owners, and design teams often point to initial capital costs as the primary obstacle hindering the uptake of net-zero buildings. In-depth research and an understanding of whether net-zero buildings cost more to design and construct are still scattered and non-systemic. Accordingly, this study provides the first comprehensive investigation into actual net-zero building construction costs in the United States, based on qualitative and quantitative research. The aims of this research are to: (1) provide a comprehensive survey of the existing body of literature to aggregate the findings and identify the consensus and pattern, (2) compare the results and analyze the evidence with a focus on quantitative studies, and (3) conduct a quantitative comparative analysis of twelve built zero energy buildings (ZEB) in order to understand whether there is enough evidence of cost differences between ZEB, conventional building (CB) and green building (GB). Statistical tests were performed, with the results showing no significant differences between actual ZEB costs and modeled CB costs. Further details investigated the cost difference between actual ZEB and modeled GB. The findings of this research provide initial detailed insights into net-zero building costs in the United States, which may benefit the promotion of ZEB practices.〈/p〉〈/div〉

Description: 〈p〉Publication date: February 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 45〈/p〉 〈p〉Author(s): Adeel Anjum, Nayma Farooq, Saif Ur Rehman Malik, Abid Khan, Mansoor Ahmed, Moneeb Gohar〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Privacy-preserving data publishing (PPDP) aims at providing an anonymized view of a private microdata to the recipients, e.g., researchers, pharmaceutical companies etc. This private data contains sensitive information about individuals that needs to be protected. In the literature, it is generally assumed that there exists a single record for one individual in any given microdata (1:1 dataset). However, more practically, there are many instances in which an individual can have multiple records in microdata (termed as 1: M datasets). Several techniques have been proposed for the 1:1 microdata but, a few researchers paid attention towards 1:M microdata problems, that perhaps led to new privacy disclosures. A novel privacy model (〈em〉k, l〈/em〉)-diversity was proposed to cater such disclosure risks and based on this model, an algorithm named 1: M generalization was proposed. Although it was efficient than several other techniques; still has a drawback of huge information loss. In this paper, we propose a hybrid approach named as 〈em〉l〈/em〉-anatomy for 1: M microdata and prove that 〈em〉l〈/em〉-anatomy ensures the privacy of given individuals. Also, experiments performed on two real-world datasets (〈em〉namely INFORMS and YOUTUBE〈/em〉) reveal that the proposed scheme exhibits higher efficiency and effectiveness as compared to its counterpart.〈/p〉〈/div〉

Description: 〈p〉Publication date: February 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 45〈/p〉 〈p〉Author(s): Yang Zhou, Yang Yang, Zhanli Mao, Rongwei Bu, Junhui Gong, Yixiao Wang, Liang Yi〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Natural ventilation plays an important role in reducing air pollution caused by idle automobile exhaust in traffic-blocking city tunnels, especially in sloping ones. In order to investigate the effects of natural ventilation in both single- and gable-slope city tunnels, a set of simplified analytical models was proposed in this work based on the equations of continuity, energy, and pressure, and the theoretical values of airflow temperature and velocity were obtained by iterative calculations. The influences of thermal pressure, tunnel slope, tunnel geometry, and shaft on tunnel ventilation performance were discussed. In the single-slope-shaftless, single-slope-single-shaft, and gable-slope-single-shaft tunnels, the calculated ranges of average temperature and velocity of airflow were obtained and analyzed, respectively. Moreover, the computational fluid dynamic (CFD) method employing the software FLUENT was selected to simulate the temperature and airflow velocity distributions in different types of tilted city tunnels. The variation tendencies of both analytical and CFD results were consistent. The iterated values of airflow temperature and velocity fitted the simulation results well. Results in this work can provide a reference for the optimal design and management of natural ventilation system and contribute to improving the air quality and controlling the pollutant concentration in single and gable sloping city tunnels.〈/p〉〈/div〉

Description: 〈p〉Publication date: November 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 51〈/p〉 〈p〉Author(s): Dong Yang, Rui Shi, Haibin Wei, Jinhui Du, Jilibo Wang〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉This paper proposes the use of a cylindrical annulus composed of phase change material (PCM), referred to as a PCM–air heat exchanger (PAHE), to regulate the supplied air temperature in ventilated buildings exposed to fluctuating thermal environments. The forced convection inside the PAHE tube enhances the heat transfer between the air and PCM, and thus both the sensible and latent heat transfer contribute to reducing the air temperature fluctuations and peak cooling/heating loads. Both experiments and numerical simulations are performed to investigate the performance of a PAHE exposed to fluctuating ambient temperatures. The effects of three PAHE parameters, i.e., the flow rate through the PAHE tube, melting temperature of the PCM, external diameter of the PAHE, and the tube length on the PAHE performance were investigated numerically. Maintaining a stable inner surface temperature in the PAHE is crucial for maximizing its ability to reduce air temperature fluctuation amplitude. For this purpose, these key parameters should be appropriately selected. The cooling performance of the PAHE in a region with hot summers and cold winters region was evaluated. The simulation incorporated real meteorological data of two transitional seasons and a summer, and demonstrated a maximum reduction in air temperature of 5.4 °C.〈/p〉〈/div〉

Description: 〈p〉Publication date: January 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 52〈/p〉 〈p〉Author(s): Liu Yang, Lufeng Zhang, Marc E.J. Stettler, Manlika Sukitpaneenit, Dunhui Xiao, Koen H. van Dam〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Traditional urban and transport infrastructure planning that emphasized motorized transport has fractured public space systems and worsened environmental quality, leading to a decrease in active travel. A novel multiscale simulation method for supporting an integrated transportation infrastructure and public space design is presented in this paper. This method couples a mesoscale agent-based traffic prediction model, traffic-related emission calculation, microclimate simulations, and human thermal comfort assessment. In addition, the effects of five urban design strategies on traffic pollution and pedestrian level microclimate are evaluated (i.e., a “two-fold” evaluation). A case study in Beijing, China, is presented utilizing the proposed urban modeling-design framework to support the assessment of a series of transport infrastructure and public space scenarios, including the Baseline scenario, a System-Internal Integration scenario, and two External Integration scenarios. The results indicate that the most effective way of achieving an environmentally- and pedestrian- friendly urban design is to concentrate on both the integration of transport infrastructure and public space to mitigate system externalities (e.g., air pollution and heat exhaustion). It also demonstrates that the integrated blue-green approach is a promising way of improving local air quality, micro-climatic conditions, and human comfort.〈/p〉〈/div〉

Description: 〈p〉Publication date: January 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 52〈/p〉 〈p〉Author(s): Mingyang Du, Lin Cheng, Xuefeng Li, Jingzong Yang〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉This study investigated the travel characteristics and influential factors of travel mode choice for healthcare activity by the elderly in core area and suburb. The results of descriptive analysis and multinomial logistic models show that: (1) Bus and walking are main modes for the elderly to seek medical treatment. The elderly in suburb travel longer distance and are more dependent on bus than those in core area. (2) The service efficiency improvements of hospitals could promote the elderly in core area to choose green modes, while those in suburb are more likely to utilize cars to obtain high-quality medical resources. (3) For long-distance travel, the influence of family economic backgrounds on the mode choice is more significant in suburb, and the difference of family economic will further aggravate the internal differentiation in long-distance travel among the elderly in suburb. (4) Interestingly, the elderly in core area tend to utilize cars to seek for healthcare when they live with their offspring, while those in suburb tend to use cars and taxis when they have serious illness and require companion. Finally, relevant policies and suggestions were proposed to improve the accessibility and fairness of healthcare travel for the elderly.〈/p〉〈/div〉

Description: 〈p〉Publication date: January 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 52〈/p〉 〈p〉Author(s): Harold J. Rickenbacker, William O. Collinge, Vaclav Hasik, Angelica Ciranni, Isaac Smith, Paige Colao, Aurora L. Sharrard, Melissa M. Bilec〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉While several national and international organizations offer standards for pollution levels and techniques to measure ambient air, there are no consistent metrics or methods for assessing and monitoring indoor air quality (IAQ) for 〈em〉an entire community〈/em〉. In this paper, we develop a framework for monitoring and addressing indoor air pollution in the context of an energy conservation district in Pittsburgh, Pennsylvania, comprised of 518 buildings. This district-based IAQ framework has two major components. The first component is based on an IAQ sampling protocol developed from the U.S. Environmental Protection Agency BASE study. IAQ assessments were performed in eight pilot buildings to evaluate concentrations of particulate matter, volatile organic compounds, carbon dioxide, relative humidity, and temperature. The second component was a communications strategy designed to help building professionals understand IAQ science and translate results into actionable procedures. Both the sampling data and feedback from building stakeholders, informed the development of an IAQ survey, which was used to establish a performance baseline and guide the future operation and maintenance of buildings in the district. This research has far-reaching impacts as there is a need for a replicable framework and actionable program that monitors and assesses IAQ in a wide range of buildings.〈/p〉〈/div〉

Description: 〈p〉Publication date: January 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 52〈/p〉 〈p〉Author(s): Xi Luo, Yanfeng Liu, Jiaping Liu, Xiaojun Liu〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉With the rapid development of information and energy generation technologies, the multi-level integrated energy system (IES) with multiple energy suppliers and end users has been vigorously promoted globally. In this study, the energy scheduling for a three-level IES is investigated by applying the hierarchical Stackelberg game approach. The IES is composed of one electricity utility company and one natural gas utility company (upper level), multiple same-structured smart energy hubs (S.E. hubs) that can produce electricity and heat simultaneously (middle level), and multiple users (lower level). By applying the Lagrange’s function, the operation strategies of all market participants are derived with analytical solutions, which are verified by a decentralized algorithm developed in this study. Simulation results show that the increase in the number of S.E. hubs decreases the energy prices received by users, increases the energy demands, and decreases the profit of each S.E. hub; therefore, each S.E. hub strives to crowd out other S.E. hubs as much as possible. Technological advancement is an effective strategy for S.E. hubs to rise above the market competition; therefore, S.E. hubs whose technological levels are lower than those of others are at an obvious disadvantage.〈/p〉〈/div〉

Description: 〈p〉Publication date: January 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 52〈/p〉 〈p〉Author(s): K. Fabbri, V. Costanzo〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉 〈p〉Outdoor microclimate modelling is getting popular for estimating comfort conditions in urban environments. Current calibration approaches usually rely on measurements of air temperature for a limited number of points in the study domain, in spite of the fact that it is the Mean Radiant Temperature (Tmrt) the parameter mostly affecting comfort. However, the direct measurement of Tmrt outdoors via the globe thermometer or using the six direction radiation method is prone to errors.〈/p〉 〈p〉To overcome these issues, the present paper proposes a novel method to indirectly improve its estimate based on the use of infrared thermal pictures gathered by drone flights to measure surface temperatures, and then use these values for calibrating numerical models. This novel approach has been demonstrated for a 400 × 400 m〈sup〉2〈/sup〉 wide area in the city of Medicina (Italy) and showed a good agreement with ENVI-met simulations, with average and standard deviation values difference between measured and simulated surface temperatures of 1.53 °C and 2.22 °C respectively. The highest differences (up to 19 °C) are found for areas densely covered by vegetation. Further studies are planned to evaluate the goodness of the calibration pixel wise and to propose calibration thresholds based on Tmrt sensitivity.〈/p〉 〈/div〉

Description: 〈p〉Publication date: January 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 52〈/p〉 〈p〉Author(s): Huakun Huang, Yijun Zhou, Renda Huang, Huijun Wu, Yongjun Sun, Gongsheng Huang, Tao Xu〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Implementing thermal insulation on the building has been acknowledged as a highly effective way to achieve cost-effective energy conservation. For implementing thermal insulation, a key problem is the proper selection of wall insulation in terms of material types and optimum thicknesses because it is challenged to balance energy-saving effects and economic benefits. To address this problem, we investigated the new aerogel super-insulation material for building-energy-conservation application in this paper. Specifically, we took a typical office building of humid subtropical climate as a model, established a whole-life-cycle-assessment model to exploit the optimum economic thickness, and further evaluated the energy-saving rate, economic benefits, the greenhouse-gas emissions, etc. Besides, we compared the super-insulated aerogel with four commonly-used insulation materials, i.e., expanded polystyrene, extruded polystyrene, foamed polyurethane, and glass fibers. Experimental results showed that aerogel had the minimum optimum thickness of 3.7 mm. When aerogel was implemented with the optimum thickness, the annual cooling and the heating load for the hollow shale brick building were reduced by 7.5% and 18.2%, respectively. Additionally, compared with the other materials, aerogel achieved a faster reduction for greenhouse-gas emissions as the thickness increased. The aerogel insulation could lead to lower carbon emissions, e.g., CO〈sub〉2〈/sub〉 (8.169 kg/(m〈sup〉2〈/sup〉yr)) emissions with LPG fuel, and thus would be more preferable for environmental protection. The building energy simulations employed can be further strengthened in the future by considering the effect of urban microclimates and actual internal heat gains in offices.〈/p〉〈/div〉

Description: 〈p〉Publication date: February 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 53〈/p〉 〈p〉Author(s): Clément Nicolas, Jinwoo Kim, Seokho Chi〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉In recent years, smart city projects have drawn significant attention as initiatives for enhancing urban development. Many studies have incorporated technical and non-technical enablers to better control the growth of smart cities. However, despite considerable achievements, the direct and indirect effects of smart city enablers on urban performances have not been quantified comprehensively. Thus, due to this lack of in-depth understanding, smart city leaders encounter difficulties in establishing proper development strategies. To address this issue, the present study has used Structural Equation Modeling (SEM) to identify the critical enablers of smart cities and to quantify their dynamic effects (i.e., direct and indirect effects) on the performances of such cities. More specifically, the authors applied SEM to test and estimate the relationships between four enabler clusters (i.e., technological infrastructure, open governance, intelligent community, and innovative economy) and four performance objectives (i.e., efficiency, sustainability, livability, and competitiveness) using the actual data of 50 smart cities. The statistical results demonstrated that non-technical enabler clusters, as well as the technical drivers, have significant impacts on the performances of smart cities with their highly interrelated, synergetic dynamics. Based on those findings, urban leaders can enhance strategic planning for smart city transitions through proper policy management.〈/p〉〈/div〉

Description: 〈p〉Publication date: February 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 53〈/p〉 〈p〉Author(s): Yameng Chen, Chen Wang, Heng Li, Jeffrey Boon Hui Yap, Rui Tang, Bin Xu〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉A basic cellular automaton model does not consider social forces interaction in building evacuation thus could not explain complex crowd evacuation processes. This study developed a cellular automaton model on social forces interaction in building evacuation, where the positive and negative impacts of social forces on evacuation time were simulated and analyzed. The model was verified using experimental data. The clustering attraction between people always increased the evacuation time regardless of the number of evacuees, the building size, the number of exits, and the people distribution, thus the clustering attraction is always to be avoided. When people in a room were randomly and evenly distributed, the repulsive force of obstacles, the attractiveness of evacuation direction, and the repulsive force of exit congestion played negligible roles on the evacuation time thus could be ignored. The evacuation path could be optimised by adjusting the influence coefficients of social forces to reduce the evacuation time.〈/p〉〈/div〉

Description: 〈p〉Publication date: February 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 53〈/p〉 〈p〉Author(s): Jialin Wu, Zhiwei Lian, Zhuling Zheng, Huibo Zhang〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Appropriate management of energy consumption provides great support for the sustainable development of buildings and even the whole cities. Nowadays, comprehensive energy consumption monitoring index is widely used in many practical projects to make horizontal comparisons among buildings. However, problems may occur when this method is applied in comprehensive buildings. This paper proposed a method to evaluate the overall energy consumption of buildings based on the energy index obtained from different functional sectors. A linear regression model was developed to predict energy consumption index in multifunctional areas. The model was based on online monitoring data from 30 single functional buildings and 20 multifunctional buildings. The sub-item energy consumption intensity difference between pure hotel buildings and hotel sector in comprehensive buildings was within 4.2%. The deviation of sub-item energy consumption intensity was less than 12% between pure shopping malls and shopping mall sector in comprehensive buildings. As for the pure office buildings and office sector in comprehensive buildings, except for heat pumps and chilled water pumps, the deviations of energy intensity of other sub-items ranged from 3.9% to 16.7%. The results from on-site experiments show that the sub-item energy consumption index obtained from multifunctional buildings had lower error than that of single functional buildings. Therefore, the sub-item energy consumption index can also be used to evaluate energy consumption in pure functional buildings. The model proposed in this paper provides a good reference for the prediction of energy consumption index in large public buildings.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 28 December 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society〈/p〉 〈p〉Author(s): Marina Bonomolo, Simone Ferrari, Gaetano Zizzo〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Building automation systems are able to influence significantly buildings’ energy performance. The BAC factors method, presented for the first time by the International Standard EN 15232, is the most used and simple instrument for evaluating the impact of such systems in terms of reduction of the electrical and thermal energy consumption of buildings services. Nevertheless, Standard EN 15232 does not take into consideration the automation of lighting systems serving buildings’ external areas like passages, gardens, entrance walkways, etc. In some cases, such areas can be very extended and, as a consequence, responsible for a not negligible percentage of the overall electricity consumption. Taking this last consideration as a basis, in this paper, a method is proposed for the definition and the evaluation of a new BAC factor for outdoor lighting systems, named OL-BAC factor. The method can be considered complementary to the one proposed by the Standard EN 15232 for the indoor lighting energy consumption estimation and proposes some improvements in the implementation of the latter. In particular, in this paper, the OL-BAC factors are calculated showing the importance of two parameters that are not taken into account by the Standard EN 15232: the latitude of the installation site and the energy absorbed by the devices for the luminaires’ automatic control.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 23 December 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society〈/p〉 〈p〉Author(s): Wei Liu, Jiao Cai, Chen Huang, Zhijun Zou, Chanjuan Sun, Baizhan Li〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉During 2013–2014, we collected bed dust samples from 453 children’s bedclothes in Shanghai, China, and analyzed two mite types (Der p 1 and Der f 1) in these dusts, using the enzyme-linked immune-sorbent assay (ELISA). In this study, we explored associations between dust mite concentrations and different family lifestyle aspects and dwelling characteristics. Der p 1 concentration was positively dose-related with cumulative numbers of dampness-related indicators in bedrooms. Use of household air conditioners often had significant associations with increased likelihood of above median concentrations of both Der f 1 (AOR, 95% CI: 3.71, 2.07–6.63) and Der p 1 (1.93, 1.03–3.61). Changing bed sheets with a frequency of less than once per week was significantly associated with an increased likelihood of higher concentration of Der f 1 (2.42, 1.23–4.77). No significant dust mite concentration differences were found between families with different habits in relation to exposing bedclothes to sunshine, using household humidifiers, or pet-keeping. Our results indicate that Der p 1 in bedding dust was associated with home dampness-related indicators, such that controlling home dampness could effectively reduce Der p 1 numbers. Bedclothes treatments alone were insufficient to avoid the presence of Der p 1 and Der f 1 in bedding.〈/p〉〈/div〉 〈h5〉Graphical abstract〈/h5〉 〈div〉〈p〉〈figure〉〈img src="https://ars.els-cdn.com/content/image/1-s2.0-S2210670719335486-ga1.jpg" width="301" alt="Graphical abstract for this article" title=""〉〈/figure〉〈/p〉〈/div〉

Description: 〈p〉Publication date: March 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 54〈/p〉 〈p〉Author(s): S. Metia, Q.P. Ha, H.N. Duc, Y. Scorgie〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The increasing rate of urbanization requires effective and reliable techniques for air quality monitoring and control. For this, the Air Pollution Model and Chemical Transport Model (TAPM-CTM) has been developed and used in Australia with emissions inventory data, synoptic data and terrain data used as its input parameters. Since large uncertainties exist in the emissions inventory (EI), further refinements and improvements are required for accurate air quality prediction. This study evaluates the performance of urban air quality forecasting, using TAPM-CTM, and improves accuracy of air pollution estimation by using a two-stage optimization technique to upgrade EI with validation from monitoring data. The first stage is based on statistical analysis for EI correction and the second stage is based on the unscented Kalman filter (UKF) to take into account the spatio-temporal distributions of air pollutant levels utilizing a Matérn covariance function. The predicted nitrogen monoxide (NO) and nitrogen dioxide (NO〈math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si2.svg"〉〈msub〉〈mrow〉〈/mrow〉〈mn〉2〈/mn〉〈/msub〉〈/math〉) concentrations with 〈math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si22.svg"〉〈mi〉a〈/mi〉〈/math〉 〈math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si23.svg"〉〈mi mathvariant="italic"〉priori〈/mi〉〈/math〉 emissions are first compared with observations at monitoring stations in the New South Wales (NSW). Ozone (O〈math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si3.svg"〉〈msub〉〈mrow〉〈/mrow〉〈mn〉3〈/mn〉〈/msub〉〈/math〉) is also considered since at the ground level it represents a major air pollutant affecting human health and the environment. In the second stage, with the improved EI, TAPM-CTM model errors are reduced further by using the UKF to calibrate EI. Results obtained show effectiveness of the proposed technique, which is promising for air quality inverse modeling, an important aspect of air pollution control in smart cities to achieve environmental sustainability.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 23 December 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society〈/p〉 〈p〉Author(s): Lei Wang, Xiaoyun Fan, Jiahao Chen, Jun Cheng, Jun Tan, Xiaoliang Ma〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉People in cities are suffering from traffic congestion and air pollution in daily life partly due to a great number of private cars, and always face the danger of accidents, so autonomous driving is developed by many institutes and companies especially in recent years. Autonomous driving will play an import role in the future smart cities, reduce the time and economic cost of the whole society, and be helpful for the sustainability of the city and society. A significant task for autonomous driving is to detect surrounding objects accurately in real-time, including car, pedestrian, cyclist, etc. In this paper, we propose one end-to-end three dimensional (3D) object detection method based on voxelization, sparse convolution, and feature fusion. The proposed method exploits only point cloud as input, and it has two key components—small voxels and efficient feature fusion. Instead of utilizing extra networks to transform voxels, we directly average the points within each voxel as their feature representation. To enrich features for prediction, we have designed a two-step feature fusion method called 〈em〉fusion of fusion network〈/em〉 that can combine information of multiple scales and 3D space. We have submitted to the official test server of the 3D detection benchmark—KITTI, and achieved state-of-the-art performance especially in the Cyclist class. Besides, detection speed of our method achieves 0.05 seconds per frame with a 2〈math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"〉〈mo〉∼〈/mo〉〈/math〉4 fold runtime improvement against state-of-the-art methods due to its simple and compact architecture.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 23 December 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society〈/p〉 〈p〉Author(s): Komal Shukla, Prashant Kumar, Gaurav S. Mann, Mukesh Khare〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Anthropogenic airborne particulates are among the major contributors to urban air pollution and pose a significant health risk. Particulate matter has emerged as a serious pollution threat in India, specifically to the capital—New Delhi. The objective of this study is to map PM〈sub〉2.5〈/sub〉 profile using two widely used spatial interpolation techniques (Kriging and IDW) by predicting their concentrations at distinct unmonitored locations. The implemented methodology has a wide-scoped utility in the field of air pollution; especially in Low-Middle Income Countries where setting up new monitoring stations include financial/logistical/location problems. The generated maps can help in policy formulation and decision making by providing aid in PM〈sub〉2.5〈/sub〉 visualisation of spatial and temporal variability. First phase of study involves prediction of concentrations at two sites (reinforcing the need for sustainable development of the city) using concentrations for 2015-2017.In the second phase, pollutant mixing ratios were obtained for four winter months between Nov-2017 to Feb-2018 at 17 monitoring stations. In this phase, predictions were made for 11 supersites (zones of important land-use). The average error of Kriging and IDW (taking both phases) was ∼22% and 24%, respectively. The magnitude of change in the daily concentration was relatively negligible and annual trend can be identified.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 23 December 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society〈/p〉 〈p〉Author(s): Wenwei Che, H. Christopher Frey, Jimmy C.H. Fung, Zhi Ning, Huamin Qu, Hong-Kam Lo, Lei Chen, Tze-Wai Wong, Michelle K.M. Wong, Ophelia C.W. Lee, David Carruthers, Freeman Cheung, Jimmy W.M. Chan, David W. Yeung, Yik Him Fung, Xuguo Zhang, Jenny Stocker, Christina Hood, Tilman Leo Hohenberger, King Wai Leung〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Exposure to air pollutants causes a range of adverse health effects. These harmful effects occur whenever and wherever people come into direct contact with air pollution. Therefore, individual actions that reduce the frequency, duration, and severity of personal contact with air pollution can reduce health risks. We developed a system that empowers the public with personalized information on air quality and exposure health risk. This system, the Personalised Real-Time Air Quality Informatics System for Exposure – Hong Kong (PRAISE-HK, 〈a href="http://praise.ust.hk/" target="_blank"〉http://praise.ust.hk/〈/a〉), is embodied in an interactive mobile application. PRAISE-HK is based on real-time data on emissions, high resolution urban morphology, meteorology, physical and chemical processes affecting pollutant transport and transformations, extensive measurements of air pollution concentrations in typical locations such as homes, schools, offices, and transportation, and big data integration of sensor monitoring to accurately estimate current and short-term forecasted street-level air quality. The street-level air quality simulation has been validated against reference monitoring data. Ongoing and planned future enhancements to PRAISE-HK include prediction of personal exposure and health response. PRAISE-HK is an example of the use of collective intelligence in a smart city to engage citizens in learning about and managing their own exposure to air pollution.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 23 December 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society〈/p〉 〈p〉Author(s): Farkhondeh Jabari, Hadi Ghaebi, Behnam Mohammadi-ivatloo, Mousa Mohammadpourfard, Mohammad-Bagher Bannae-Sharifian〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉This paper presents a trigeneration facility based on a biogas driven gas turbine cycle for electricity production and an air to water heat pump for simultaneous extraction of cooling and potable water, and evaluates its robustness and effectiveness under different climatic conditions and variable demands. The air to water heat pump, which consists of an inside evaporator fan, expansion device, condenser, evaporator, and a compressor, is installed for supplying the cooling load of a hospital building in summer. In the refrigeration cycle, R134a absorbs heat from the inside warm air for space cooling and releases it to the saline water for driving a desalination process. Maximum values of cooling and electrical demands of a standard hospital building, where is located in south of Iran, over the hottest day of 2018 are considered for the worst case implementation. Engineering equation solver and general algebraic mathematical modeling system are employed to develop the non-linear optimization strategy, find the best operating point of the air to water heat pump based trigeneration plant, and minimize its energy cost under the greatest load level. Moreover, the sensitivity of the gained output ratio of desalination cycle to the relative humidity of the air entering and leaving the humidifier, the dehumidifier and humidifier effectiveness, and the top and bottom temperatures are comprehensively analyzed. The relative humidity of air entering the humidifier and the larger dehumidifier effectiveness have the significant effects on increasing the gained output ratio of the desalination cycle. Due to intense solar irradiation in summer, the seawater temperature naturally increases, which results in larger gained output ratio and higher mass flow rate of pure water.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 23 December 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society〈/p〉 〈p〉Author(s): Zhuangbo Feng, Junyan Yang, Jie Zhang〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Airborne particulate matters hinder the development of environmentally sustainable cities/towns due to the adverse effects on human health. Because of their small size, ultra-fine particles (〈 100 nm) are believed to exert higher toxicity and cause adverse health effects. For ultra-fine particle removal, widely-used high efficient particulate filter (HEPA) leads to high energy consumption and economic cost. Electrostatic enhanced air filter (EEAF), integrating electrostatic field and fibrous filter, proves to be an efficient and energy-saving tool for fine particle (〉 100 nm) removal. For ultra-fine particles, available experimental research found that filtration efficiency of EEAF was not satisfied (〈 90%). Thus, the current study adopted numerical method to optimize EEAF by adjusting filtration velocity and electric field in filter. Due to that the traditional Lagrangian-based model failed to predict ultra-fine particles, an Eulerian-based numerical method was developed and validated by experiments. For 50 nm particles, efficiency of optimized EEAF was enhanced to 96%, which was much higher than that of electrostatic precipitator (10%). The optimized EEAF could save 1/3 of energy consumed by HEPA without sacrificing filtration efficiency. Finally, the coupling design/control strategy of EEAF, urban and indoor environments were analyzed.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 23 December 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society〈/p〉 〈p〉Author(s): Chonggang Zhou, Zhaosong Fang, Xiaoning Xu, Xuelin Zhang, Yunfei Ding, Xiangyang Jiang, Ying ji〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The prediction of energy consumption is important for the efficient operation of building air-conditioning systems. Most predicted models are based on historical energy consumption data and the factors influencing air conditioning systems, including weather, time of day, and previous consumption. However, the traditional prediction models, such as the Autoregressive Integrated Moving Average (ARIMA) time series model and back propagation (BP) neural network model, show large errors in their prediction of the energy consumption of air-conditioning systems. To achieve better prediction, the Long Short-Term Memory (LSTM) model of deep learning is adopted in this study based on an air-conditioning system of a University Library in Guangzhou. The results demonstrate that the LSTM model can produce more reliable predictions. The daily energy consumption forecast reduced by 11.2% compared to that of the Autoregressive Moving Average model (MAPE). The hourly energy consumption forecast reduced by 16.31%. In addition, compared with the BP neural network model, the MAPE’s daily energy consumption prediction reduced by 49% and the hourly energy consumption prediction reduced by 36.61%.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 23 December 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society〈/p〉 〈p〉Author(s): Khalid Haseeb, Naveed Islam, Tanzila Saba, Amjad Rehman, Zahid Mehmood〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Wireless Sensor Network (WSN) gains a lot of research devotion because of their unique features as compared to traditional networks. The resource constraints next-generation sensors are interconnected in billions for the foundation of the large-sized Internet of Things (IoT). Due to the large-sized deployment of IoT devices and their dynamic conditions, existing security mechanisms do not meet the requirements of protection against malicious adversaries. Moreover, because of the unreliable and open-access Internet medium, traditional routing protocols are vulnerable to a variety of malicious threats and compromised to data security. Thus, the aim of this research article is to present a novel light-weight structure based Data Aggregation Routing (LSDAR) protocol for IoT integrated Next-generation Sensor Networks for the improvement of energy routing performance with node level data protection against malicious threats. Firstly, the network nodes are decomposed into independent clusters based on varying radiuses and preventing energy holes around the locality of the base station (BS). Secondly, based on the A-star heuristics algorithm, efficient and loop-free routing paths are constructed. Furthermore, the end-to end communication links are protected against malicious nodes by using mathematically unbreakable one time pad (OTP) encryption scheme to provide data security. The simulation results indicate significant improvements with the comparison of the state-of-the-art in terms of energy consumption, network lifetime, an end to end delay and packet drop ratio.〈/p〉〈/div〉

Description: 〈p〉Publication date: February 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 Sustainable Cities and Society, Volume 53〈/p〉 〈p〉Author(s): Yaolin Liu, Feiguo Fang, Ying Jing〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Commuting is an essential part of our urban daily life. Understanding of commuting flows is crucial for urban and traffic planning. Urban land use plays a fundamental role of urban studies, given that its verified impact on commuting pattern in previous studies. This paper explores whether urban land use influences the commuting flows and how it impacts the relationship between commuting flows and origin-destination distances (i.e., the accessibility). In this study, the basic gravity model is extended with land use variables. And both multiple regression and moderated multiple regression models are applied to analyze the relationship between commuting flows and land use (i.e., industrial, commercial, residential, educational land etc.), as well as the interaction effect between land use variables and the accessible distance. Commuting data is extracted from Wuhan’s mobile phone signaling data in 2016, particularly incorporated in the empirical analysis. The results show that industrial and residential origins are likely to send more commuters while industrial and commercial destinations attract more. An interaction effect between land use and distance is revealed that commuting flows with industrial origins (destinations) decrease fastest with distance increasing while those with commercial origins (destinations) decrease slowest. These findings imply that: 1) Urban land use of commuters’ origins and destinations significantly influences commuting flows; and 2) Urban land use moderates the relationship between the distance and commuting flows as well. This study contributes to policies-makings of urban and traffic planning through understanding commuting flows from the mobile phone signaling data perspective〈/p〉〈/div〉