Mobility services and accessibility services could contribute to reduced car-dependency and a more sustainable transport system. However, uncertainty remains regarding what the effects will be and further research is needed. In this paper we examine potential effects on passenger car-travel in an urban context. To do so, we actuate the Avoid-Shift-Improve (ASI) framework using a System Dynamics approach and develop thematic Causal Loop Diagrams. We draw on the findings from a literature study and workshops engaging actors involved in creating visions and planning for the future of mobility and accessibility services in Stockholm, Sweden. The effects discovered are categorized as direct, enabling and structural/systemic, using a retrofitted version of the Three-Levels Model. Contributions include the mapping of mechanisms through which the services can have positive and negative effects in relation to ASI, demonstrating a high degree of interconnectedness. This includes potential synergetic and competitive relations between the services. In addition, the approach gives insight to potential cumulative impact of the services, relatable to Mobility as a Service, including ‘user near’ effects regarding, e.g., commuting and leisure travel, as well as systemic and structural level effects. A discussion is conducted on the implications for actors and policy-makers.

Densifying cities continuously call for new construction, renovation and demolition projects, each generating vast amounts of heavy goods vehicle (HGV) transports. However, how construction transportation affects the urban traffic network remains largely unexplored. This study addresses spatiotemporal network impacts from construction transport, by leveraging traditional traffic and transport simulation. To this end, a framework is presented including (i) a simulation model to compute traffic effects caused by varying off-site construction site transport demands, and (ii) conceptual applications of the simulation model showing construction logistic planning strategies to mitigate congestion disturbances. Simulations are conducted in MATSim using detailed secondary datasets describing site-specific transport arrivals from a case of six projects in Norrköping, Sweden. Subsequently, increasing transport demands were assessed on various time-window arrival scenarios against the baseline schedule, which can be used as decision support in urban planning. Results highlight how rigorous construction transport planning avoiding peak-traffic hours can significantly alleviate traffic congestion. This study also emphasizes the need to combine all simultaneous construction projects’ demands when evaluating disturbances on city-level, alongside the impact on individual links and microenvironments. This study adds knowledge by visualizing the traffic impact during urban transformation.

Densifying cities continuously call for new construction, renovation and demolition projects, each generating vast amounts of heavy goods vehicle (HGV) transports. However, how construction transportation affects the urban traffic network remains largely unexplored. This study addresses spatiotemporal network impacts from construction transport, by leveraging traditional traffic and transport simulation. To this end, a framework is presented including (i) a simulation model to compute traffic effects caused by varying off-site construction site transport demands, and (ii) conceptual applications of the simulation model showing construction logistic planning strategies to mitigate congestion disturbances. Simulations are conducted in MATSim using detailed secondary datasets describing site-specific transport arrivals from a case of six projects in Norrköping, Sweden. Subsequently, increasing transport demands were assessed on various time-window arrival scenarios against the baseline schedule, which can be used as decision support in urban planning. Results highlight how rigorous construction transport planning avoiding peak-traffic hours can significantly alleviate traffic congestion. This study also emphasizes the need to combine all simultaneous construction projects’ demands when evaluating disturbances on city-level, alongside the impact on individual links and microenvironments. This study adds knowledge by visualizing the traffic impact during urban transformation.

There is great potential to apply energy recovery from municipal wastewater treatment plants (WWTP) to achieve better energy efficiency and greenhouse gas emissions abatement. However, in most of the wastewater treatment plants that produce biogas through sludge digestion, there is no thermal energy recovery or electricity generation, and the biogas is flared and wasted, particularly in developing countries. This paper discusses the recent technological advances related to biogas recovery from wastewater treatment besides identifying research gaps and local conditions, which affect implementation. Techniques for enhancement of biogas production such as co-digestion and microalgae systems for energy recovery were reviewed in terms of performance and context for application. This paper provides an assessment of how local conditions promote or hinder biogas recovery from WWTP in megacities of developing countries. The lack of government subsidies and a biogas market that is not well developed and regulated hinder biogas recovery implementation in the studied megacities. Infrastructure and electricity prices are also discussed. Strategies to promote energy recovery are recommended based on successful examples of energy self-sufficient WWTP. The findings may not only support planning on energy recovery but also guide technological development and research on this topic, aligned with the sustainable development goals.

As urban populations continue to grow through the 21st century, more people are projected to be at risk of exposure to climate change-induced extreme events. To investigate the complexity of urban floods, this study applied an interlinked social-ecological-technological systems (SETS) vulnerability framework by developing an urban flood vulnerability index for six US cities. Indicators were selected to reflect and illustrate exposure, sensitivity, and adaptive capacity to flooding for each of the three domains of SETS. We quantified 18 indicators and normalized them by the cities' 500-yr floodplain area at the census block group level. Clusters of flood vulnerable areas were identified differently by each SETS domain, and some areas were vulnerable to floods in more than one domain. Results are provided to support decision-making for reducing risks to flooding, by considering social, ecological, and technological vulnerability as well as hotspots where multiple sources of vulnerability coexist. The spatially explicit urban SETS flood vulnerability framework can be transferred to other regions facing challenging urban floods and other types of environmental hazards. Mapping SETS flood vulnerability helps to reveal intersections of complex SETS interactions and inform policy-making for building more resilient cities in the face of extreme events and climate change impacts.

Urban form can moderate the effects of weather on human movement. As such, the interrelationship between built environment, weather and human movement is a critical component of urban design. This paper explores the impacts of weather on non-motorised human movement (soft-mobility). Throughout we look at soft-mobility from the citizen’s perspective and highlight the barriers to soft-mobility in winter.

The aim of this study was to test the traditional pallet of winter city urban design considerations. Those of solar-access, wind and snow management and explore other weather and terrain conditions that act as barriers to soft-mobility in winter. This study is based on survey responses from 344 citizens in the sub-arctic area of Sweden. Outcomes from the research highlight that rain, icy surfaces and darkness are today’s most significant barriers to soft-mobility in winter.

Results from this study link changing barriers to soft-mobility in winter with climate change. The paper concludes that future urban design and planning for winter cities needs to consider a wider pallet of weather conditions, especially rain.

Real-time atmospheric visibility estimation in foggy and hazy weather plays a crucial role in ensuring traffic safety. Overcoming the inherent drawbacks with traditional optical estimation methods, like limited sampling volume and high cost, vision-based approaches have received much more attention in recent research on atmospheric visibility estimation. Based on the classical Koschmieder's formula, atmospheric visibility estimation is carried out by extracting an inherent extinction coefficient. In this paper we present a variational framework to handle the nature of time-varying extinction coefficient and develop a novel algorithm of extracting the extinction coefficient through a piecewise functional fitting of observed luminance curves. The developed algorithm is validated and evaluated with a big database of road traffic video from Tongqi expressway (in China). The test results are very encouraging and show that the proposed algorithm could achieve an estimation error rate of 10%. More significantly, it is the first time that the effectiveness of Koschmieder's formula in atmospheric visibility estimation was validated with a big dataset, which contains more than two million luminance curves extracted from real-world traffic video surveillance data.

Real-time atmospheric visibility estimation in foggy and hazy weather plays a crucial role in ensuring traffic safety. Overcoming the inherent drawbacks with traditional optical estimation methods, like limited sampling volume and high cost, vision-based approaches have received much more attention in recent research on atmospheric visibility estimation. Based on the classical Koschmieder's formula, atmospheric visibility estimation is carried out by extracting an inherent extinction coefficient. In this paper we present a variational framework to handle the nature of time-varying extinction coefficient and develop a novel algorithm of extracting the extinction coefficient through a piecewise functional fitting of observed luminance curves. The developed algorithm is validated and evaluated with a big database of road traffic video from Tongqi expressway (in China). The test results are very encouraging and show that the proposed algorithm could achieve an estimation error rate of 10%. More significantly, it is the first time that the effectiveness of Koschmieder's formula in atmospheric visibility estimation was validated with a big dataset, which contains more than two million luminance curves extracted from real-world traffic video surveillance data.

Forced migration is not a recent phenomenon, nor is the reality of the amount suffering of the displaced population fleeing from armed conflict. Finding housing for refugees has not only become an acute obligation for hosting countries but it is a situation predicted to continue, and possibly increase, in the future. This study is discussing and showing the results of the first phase of an ongoing project for designing and constructing an eco-cycle refugee shelter. The project discussed how an environmentally low impact shelter could be provided that pays respect to social norms, religious beliefs and cultural traditions of refugees. The study is applying a trans-disciplinary participatory methodology using an occupant centred approach. It is looking at current post conflict housing issues in hosting countries with a focus on Syrian refugees in Sweden, and it depicts a phase of the project where a foundation for subsequent phases – including constructing a physical house prototype through involving refugees in a construction training – was laid. The project aims at fulfilling refugees’ needs and involve them in the design and construction process as well as raising the awareness of a cost efficient and climate responsive way of building back better in the refugee’s home country.

Fast fluid dynamics (FFD) could provide informative and efficient airflow and concentration simulation. The commonly used turbulence model in FFD was Re-Normalization Group (RNG) k-epsilon turbulence model which solved two transport equations to obtain eddy viscosity. To reduce this part of time and further improve computing speed, this investigation implemented no turbulence model, Smagorinsky model and dynamic Smagorinsky model which calculated eddy viscosity without solving equation in FFD in an open-source program, OpenFOAM. By simulating several outdoor cases of varying complexity and comparing with experiment and CFD, this study assessed the accuracy and computing efficiency of FFD with four turbulence models. Compared with CFD, FFD greatly improved the computing speed without reducing accuracy. The simulation of FFD without turbulence model was fast but inaccurate. FFD with Smagorinsky model increased the computing speed while ensuring the same accuracy as RNG k-epsilon turbulence model. FFD with dynamic Smagorinsky model provided accurate results with high efficiency. Computation errors arose mainly from inaccurate prediction of turbulence dispersion. The computing cost was associated with the number of transport equations and calculation method of model coefficient. This investigation recommended the use of FFD with dynamic Smagorinsky model for outdoor airflow and pollutant dispersion studies.

This study delves into the application of deep reinforcement learning (DRL) frameworks for optimizing setpoints in district heating systems, which experience hourly fluctuations in air temperature, customer demand, and fuel prices. The potential for energy conservation and cost reduction through setpoint optimization, involving adjustments to supply temperature and thermal energy storage utilization, is significant. However, the inherent nonlinear complexities of the system render conventional manual methods ineffective. To address these challenges, we introduce a novel learning framework with an expert knowledge module tailored for DRL techniques. The framework leverages system status information to facilitate learning. The training is performed by employing model-free DRL methods and a refined digital twin of the Espoo district heating system. The expert module, accounting for power plant capacities, ensures actionable directives aligned with operational feasibility. Empirical validation through comprehensive simulations demonstrates the efficacy of the proposed approach. Comparative analyses against manual methods and evolutionary techniques highlight the approach's superior ability to curtail fuel costs. This study advances the understanding of DRL in district heating optimization, offering a promising avenue for enhanced energy efficiency and cost savings.

Despite the extensive literature on learning in urban transitions, we still have a limited understanding on how higher-order learning takes place in transition management and is spread within the transition arena. In this paper we analyze the efforts of transferring such embedded knowledge and its interrelation with learning through the examples of three Swedish municipalities engaged in urban transition management. To do so, we developed a framework of learning ripples that conceptualizes learning across social boundaries as an active and two-way process that goes beyond transferring and receiving knowledge, but also requires higher order learning that includes knowledge integration in the form of defining and formulating ones role and contributions to transitions. We found that higher order learning is largely influenced by the quality and frequency of interactions between the transferer and receivers. The further a stakeholder was located from the center of the transition arena in terms of direct interactions, the less chance occurred for higher order learning that resulted in tensions and conflicts in the collaboration. Our results show the problem as a lack of knowledge integration or a lack of conditions which allow stakeholders to articulate their views or develop an idea about their own role in the whole process.

Despite the extensive literature on learning in urban transitions, we still have a limited understanding on how higher-order learning takes place in transition management and is spread within the transition arena. In this paper we analyze the efforts of transferring such embedded knowledge and its interrelation with learning through the examples of three Swedish municipalities engaged in urban transition management. To do so, we developed a framework of learning ripples that conceptualizes learning across social boundaries as an active and two-way process that goes beyond transferring and receiving knowledge, but also requires higher order learning that includes knowledge integration in the form of defining and formulating one's role and contributions to transitions. We found that higher order learning is largely influenced by the quality and frequency of interactions between the transferer and receivers. The further a stakeholder was located from the center of the transition arena in terms of direct interactions, the less chance occurred for higher order learning that resulted in tensions and conflicts in the collaboration. Our results show the problem as a lack of knowledge integration or a lack of conditions which allow stakeholders to articulate their views or develop an idea about their own role in the whole process.

This study addresses the emergence of new actors and their roles in the transformation of smart cities. By building on a Service-Dominant logic perspective, the study capture how smart city transformation is closely related to a smart market transformation. While prior conceptualizations of markets have followed a linear supply-demand structure, the new market conceptualization can be described as a service ecosystem. The study empirically follows the increased use of renewable energy, such as photovoltaic (PV) systems and their related services, as they are incorporated into smart cities. The results reveal that the overall interaction level among the involved actors increases as the energy market changes from a linear to a networked logic. This transition impacts the market's information quality and, subsequently, the actors’ level of required knowledge. The study shows that even if the prevailing actors become more informed, information needs to be ‘translated’ into ‘knowledge-in-context’ to become a valuable resource. Thus, the resulting service ecosystem demands a complementary actor that requires the role of a knowledge broker to function. The paper describes the mechanisms behind this smart city transformation and clarifies the broker functions.

Urban water and energy systems are crucial for sustainably meeting basic service demands in cities. This paper proposes and applies a technology-independent “reference resource-to-service system” framework for concurrent evaluation of urban water and energy system interventions and their ‘nexus’ or ‘interlinkages’. In a concrete application, data that approximate New York City conditions are used to evaluate a limited set of interventions in the residential sector, spanning from low-flow toilet shifts to extensive green roof installations. Results indicate that interventions motivated primarily by water management goals can considerably reduce energy use and contribute to mitigation of greenhouse gas emissions. Similarly, energy efficiency interventions can considerably reduce water use in addition to lowering emissions. However, interventions yielding the greatest reductions in energy use and emissions are not necessarily the most water conserving ones, and vice versa. Useful further research, expanding the present analysis should consider a broader set of resource interactions, towards a full climate, land, energy and water (CLEW) nexus approach. Overall, assessing the impacts, trade-offs and co-benefits from interventions in one urban resource system on others also holds promise as support for increased resource efficiency through integrated decision making.

The authors regret two instances of misinterpretation of input data and one formatting error in the previously published paper as titled above. First, the numerical estimates for water use in NYC electricity and natural gas supply were found to be incorrect due to a conversion error in a data file. This error has now been corrected and the estimates have been changed to correctly correspond to the references on which they are based on. These changes have led to a recalculation of indirect water use reduction potentials in the interventions studied in the paper. Second, two errors due to primary data misinterpretation related to the studied green roof intervention have been found and corrected. The first led to an overestimation of the green roofs’ energy use reduction potential in the previously published paper. The second led to an underestimation of their installation cost. These errors have also been corrected and all numerical results for the green roof intervention have been recalculated. In the updated sections 3 and 4 of the original publication (below), Table 2, Table 3, Fig. 2 and Fig. 3 are updated with the new results related to both indirect water use reductions and green roof performance and costs. The text in the below sections have been given minor adjustments to clarify this update. These changes make green roofs a less economically favourable intervention in comparison to the previously published results. It also makes indirect water use reductions relatively smaller compared to direct water use reductions. All other results as well as the conclusions of this paper are still valid and unchanged. Lastly, a typo in writing of Eq. (7) in the manuscript text has been corrected. There was no error in the equation used in the analysis; hence, no numerical results have been effected by this correction. The authors would like to apologise for any inconvenience caused. Corrected writing of Eq. (7), section 2.3.1: [Formula presented] Updated sections of the original publication.

Urban water and energy systems are crucial for sustainably meeting basic service demands in cities. This paper proposes and applies a technology-independent “reference resource-to-service system” framework for concurrent evaluation of urban water and energy system interventions and their ‘nexus’ or ‘interlinkages’. In a concrete application, data that approximate New York City conditions are used to evaluate a limited set of interventions in the residential sector, spanning from low-flow toilet shifts to extensive green roof installations. Results indicate that interventions motivated primarily by water management goals can considerably reduce energy use and contribute to mitigation of greenhouse gas emissions. Similarly, energy efficiency interventions can considerably reduce water use in addition to lowering emissions. However, interventions yielding the greatest reductions in energy use and emissions are not necessarily the most water conserving ones, and vice versa. Useful further research, expanding the present analysis should consider a broader set of resource interactions, towards a full climate, land, energy and water (CLEW) nexus approach. Overall, assessing the impacts, trade-offs and co-benefits from interventions in one urban resource system on others also holds promise as support for increased resource efficiency through integrated decision making.

Decisions made at early design stage have major impacts on buildings’ life-cycle environmental performance. However, when only a few parameters are determined in early design stages, the detailed design decisions may still vary significantly. This may cause same early design to have quite different environmental impacts. Moreover, default settings for unknown detailed design parameters clearly cannot cover all possible variations in impact, and Monte Carlo analysis is sometimes not applicable as parameters’ probability distributions are usually unknown. Thus, uncertainties about detailed design make it difficult for existing environmental assessment methods to support early design decisions.

Thus, this study developed a quantitative method using parametric design technology and machine learning algorithms for assessing buildings’ environmental performance in early decision stages, considering uncertainty associated with detailed design decisions. The parametric design technology creates design scenarios dataset, then associated environmental performances are assessed using environmental assessment databases and building performance simulations. Based on the generated samples, a machine learning algorithm integrating fuzzy C-means clustering and extreme learning machine extracts the case-specific knowledge regarding designed buildings’ early design associated with environmental uncertainty. Proposed method is an alternative but more generally applicable method to previous approaches to assess building's environmental uncertainty in early design stages.

Cities around the world are facing the challenge of reducing greenhouse gas emissions and promoting urban sustainability. If comprehensive low-carbon development of cities is to be possible, transportation, as a major contributor of greenhouse gas emissions, is a particularly important sector to address. Major changes to transport behaviour and systems will be required in order to secure a shift to sustainable modes of transport and removal of infrastructure that reinforces unsustainable patterns of transportation. This paper examines the links between low-carbon urban development and sustainable mobility, with reference to the case of Odense, Denmark. With a reputation as a city of cyclists, Odense has recently closed a highway that divided central districts of the city, and is constructing a new tramway in its place. Results from a qualitative study, including interviews with individuals active in local policy processes, provide insights into Odense’s experiences and inform analysis of the influences on municipal strategies and plans for sustainable mobility, questioning if climate change need be the primary focus when designing policies to enable low-carbon urban development.

Among the construction related transports, the transport of soil and rock materials stand for a major part. The purpose of this study is to develop an approach enabling scenario analysis of the relation between storage location and soil and rock material transport planning. The study follows a sequential exploratory mixed methods design. First a literature review and a qualitative case study identifies the problem. Second a quantitative optimization method is used to evaluate possible scenarios showing the interdependence between storage location costs and transport impact. The study has two main contributions, the mixed methods approach to evaluate economic and environmental considerations in an infrastructure project and the scenario analysis of different options for inventory control and transport. The presented study adds knowledge to transport efficiency of rock and soil materials.

Human movement is a significant factor in extensive spatial-transmission models of contagious viruses. The proposed COUNTERACT system recognizes infectious sites by retrieving location data from a mobile phone device linked with a particular infected subject. The proposed approach is computing an incubation phase for the subject's infection, backpropagation through the subjects’ location data to investigate a location where the subject has been during the incubation period. Classifying to each such site as a contagious site, informing exposed suspects who have been to the contagious location, and seeking near real-time or real-time feedback from suspects to affirm, discard, or improve the recognition of the infectious site. This technique is based on the contraption to gather confirmed infected subject and possibly carrier suspect area location, correlating location for the incubation days. Security and privacy are a specific thing in the present research, and the system is used only through authentication and authorization. The proposed approach is for healthcare officials primarily. It is different from other existing systems where all the subjects have to install the application. The cell phone associated with the global positioning system (GPS) location data is collected from the COVID-19 subjects.

Electromobility has gained momentum in the last years following the efforts to reduce transportation-related emissions. In this context, efficient charging infrastructure is vital to foster the expansion of electric vehicles. This paper presents a standardized framework for micro-scale analysis of potential charging locations for electric buses aiming at easing the analysis process and promoting the expansion of electric buses. The framework is tailor-made for the Municipality of Stockholm and tested in two city-centre public transport hubs, namely Odenplan and Slussen. However, the framework can be used in other locations by implementing minimum changes. Connecting charging stations to the power grid is identified as the main drawback in city-centre locations due to their high impact on the grid. Lack of available capacity at nearby connection points results in long distance connections, reaching up to 1 km in some cases. Such connections impact the overall cost of electrification directly, as they may account for up to 63% of the total cost. Although other issues regarding space availability and operational efficiency also need to be addressed, such as the lack of enough dwell time to charge the batteries, the framework proves the suitability of these inner-city locations as charging points.

A pressing issue for mankind is how to combine urban expansion and food production for present and future generations. Using a case study example -the Stockholm County in Sweden-we illustrate how incorporating an ecosystem service perspective into urban planning may help us rethink the urban-rural divide in order to facilitate a sustainable development of the urban agricultural landscape of Stockholm. In our case study we show that semi-natural pollinator habitats will be 12 times as affected by the planned urban expansion than farmland. Hence, the fate and management of semi-natural pollinator habitats need to be prioritized at least as much as saving productive areas for farming in the urban expansion process. We also show that urban green areas, through their potential to act as semi-natural habitats, provide a tangible link between the pollination service and the urban planning process, contributing to a better grounding of the urban expansion in an ecosystem service reality. Also, acknowledging that land use types typically classified as urban, such as urban green areas, can ecologically support many rural ecosystem services, like pollination and food production, contributes to overcoming the, often unconstructive, urban-rural divide. We conclude that beneath the apparent direct trade-offs between finding suitable land for urban expansion and preserving land for food production, there is potential for compromises, opportunities and synergies.

A pressing issue for mankind is how to combine urban expansion and food production for present and future generations. Using a case study example -the Stockholm County in Sweden-we illustrate how incorporating an ecosystem service perspective into urban planning may help us rethink the urban-rural divide in order to facilitate a sustainable development of the urban agricultural landscape of Stockholm. In our case study we show that semi-natural pollinator habitats will be 12 times as affected by the planned urban expansion than farmland. Hence, the fate and management of semi-natural pollinator habitats need to be prioritized at least as much as saving productive areas for farming in the urban expansion process. We also show that urban green areas, through their potential to act as semi-natural habitats, provide a tangible link between the pollination service and the urban planning process, contributing to a better grounding of the urban expansion in an ecosystem service reality. Also, acknowledging that land use types typically classified as urban, such as urban green areas, can ecologically support many rural ecosystem services, like pollination and food production, contributes to overcoming the, often unconstructive, urban-rural divide. We conclude that beneath the apparent direct trade-offs between finding suitable land for urban expansion and preserving land for food production, there is potential for compromises, opportunities and synergies.

The sustainable and continuous development of public transport systems is crucial to ensuring robust and resilient transport and economic activity whilst improving the urban environment. Through technological improvement, cities can increase the competitiveness of public transport, promote equality and pursue a multimodal shift to greener solutions. The introduction of vehicle automation technology into existing public transport systems has potential impacts on mobility behaviours and may replace conventional bus service in the future. This study examines travellers? preferences for automated buses versus conventional buses, using a contextdependent stated choice experiment. This experiment measured the effects of context variables (such as trip purpose, travel distance, time of day, weather conditions and travel companion) on the choice of automated buses versus conventional buses. The results were analysed using mixed logit models, and the findings indicate that, in general, choice behaviours do not diverge much between the choice of automated bus and conventional bus. However, individuals? choices are more elastic towards the changes in automated bus service levels compared to conventional bus service. The results show that poor weather conditions may lower the quality and reliability of public transport service, and the probability of choosing an automated bus over a conventional bus is reduced due to such disruptions. In addition, passengers travelling for work purposes, covering long distances, or travelling with companions are more likely to choose conventional buses than automated buses.

Current district heating networks are undergoing a sustainable transition towards the 4th and 5th generation of district heating networks, characterized by the integration of different types of renewable energy sources (RES) and low operational temperatures, i.e., 55 ◦C or lower. Due to the lower temperature difference between supply and return, it is necessary to develop novel methods to understand the loads accurately and provide operation scenarios to anticipate demand peaks and increase flexibility in the energy network, both for long- and short- term horizons. In this study, a hybrid machine-learning (ML) method is developed, combining a clustering pre-processing step with a multi-input artificial neural network (ANN) model to predict heat loads in buildings cluster-wise. Specifically, the impact of time-series data clustering, as a pre-processing step, on the performance of ML models was investigated. It was found that data clustering contributes effectively to the reduction of data training costs by limiting the training processes to representative clusters only instead of all datasets. Additionally, low-quality data, including outliers and large measurement gaps, are excluded from the training to enhance the overall prediction performance of the models.

An occupant's window opening and closing behaviour can significantly influence the level of comfort in the indoor environment. Such behaviour is, however, complex to predict and control conventionally. This paper, therefore, proposes a novel reinforcement learning (RL) method for the advanced control of window opening and closing. The RL control aims at optimising the time point for window opening/closing through observing and learning from the environment. The theory of model-free RL control is developed with the objective of improving occupant comfort, which is applied to historical field measurement data taken from an office building in Beijing. Preliminary testing of RL control is conducted by evaluating the control method’s actions. The results show that the RL control strategy improves thermal and indoor air quality by more than 90 % when compared with the actual historically observed occupant data. This methodology establishes a prototype for optimally controlling window opening and closing behaviour. It can be further extended by including more environmental parameters and more objectives such as energy consumption. The model-free characteristic of RL avoids the disadvantage of implementing inaccurate or complex models for the environment, thereby enabling a great potential in the application of intelligent control for buildings.

Research communities, engagement campaigns, and administrative agents are increasingly valuing low-cost air-quality monitoring technologies, despite data quality concerns. Mobile low-cost sensors have already been used for delivering a spatial representation of pollutant concentrations, though less attention is given to their uncertainty quantification. Here, we perform static/on-bike inter-comparison tests to assess the performance of the Snifferbike sensor kit in measuring outdoor PM2.5 (Particulate Matter < 2.5 μm). We build a network of citizen-operated Snifferbike sensors in Kristiansand, Norway, and calibrate the measurements using Machine

Learning techniques to estimate the concentrations of PM2.5 along the city roads. We also propose a method to estimate the minimum number of PM2.5 measurements required per road segment to assure data representativeness. The co-location of three Snifferbike kits (Sensirion SPS30) at the monitoring station showed a RMSD of 7.55 μg m−3. We approximate that one km h−1 increase in the speed of the bikes will add 0.03 - 0.04 μg m−3 to the Standard Deviation of the Snifferbike PM2.5 measurements. We estimate that at least 27 measurements per road segment are required (50 m here) if the data are sufficiently dispersed over time. We recommend calibrating the mobile sensors when they coincide with reference monitoring stations.

As cities worldwide implement strategies to accelerate the transition toward a circular economy (CE), there is an increasing need for tools to monitor progress. However, a standardised metric for CE monitoring in urban areas is lacking. This study examines the potential of the EU Circular Economy Monitoring Framework (CEMF), an established indicator-based framework for measuring national- and EU-level circularity performance, as a monitoring tool for urban areas. For this purpose, available data sources that can support the framework's application at the urban level are mapped, and data quality is assessed following the pedigree matrix approach. Next, the CEMF indicators are computed for the urban area of Umeå, Sweden. The mapping showed limited availability of urban-level data, necessitating the downscaling of national-level data using proxy factors. Most available urban-level data are of high quality, while the quality of national-level data is reduced when used to compute indicators at the urban level. The application of the CEMF in Umeå indicates that there are areas where the municipality performs well, though further improvements are needed. We conclude that the CEMF has potential as a monitoring tool for urban areas. However, improvements in CEMF...s scope and data availability are recommended.

The influence of different ventilation levels on indoor air quality (IAQ) and energy savings were studied experimentally and analytically in a single-family house occupied by two adults and one infant, situated in Borlänge, Sweden. The building studied had an exhaust ventilation system with a range of air flow rate settings. In order to find appropriate ventilation rates regarding CO2, relative humidity (RH) and temperature as indicators of IAQ, four ventilation levels were considered, as follows: (I) A very low ventilation rate of 0.10 L s-1 m-2; (II) A low ventilation rate of 0.20 L s-1 m-2; (III) A normal ventilation rate of 0.35 L s-1 m-2; (IV) A high ventilation rate of 0.70 L s-1 m-2. In all cases, the sensor was positioned in the exhaust duct exiting from habitable spaces. Measurements showed that, for case I, the CO2 concentration reached over 1300 ppm, which was higher than the commonly referenced threshold for ventilation control, i.e. 1000 ppm, showing unacceptable IAQ. In case II, the CO2 level was always below 950 ppm, indicating that 0.20 L s-1 m-2 is a sufficient ventilation rate for the reference building. The case III showed that the ventilation rate of 0.35 L s-1 m-2 caused a maximum CO2 level of 725 ppm; showing the level recommended by Swedish regulations was high with respect to CO2 level. In addition, measurements showed that the RH and temperature were within acceptable ranges in all cases. An energy savings calculation showed that, in case II, the comparative savings of the combined energy requirement for ventilation fan and ventilation heating were 43% compared with case III.

The influence of different ventilation levels on indoor air quality (IAQ) and energy savings were studied experimentally and analytically in a single-family house occupied by two adults and one infant, situated in Borlänge, Sweden. The building studied had an exhaust ventilation system with a range of air flow rate settings. In order to find appropriate ventilation rates regarding CO2, relative humidity (RH) and temperature as indicators of IAQ, four ventilation levels were considered, as follows: (I) A very low ventilation rate of 0.10 L s−1 m−2; (II) A low ventilation rate of 0.20 L s−1 m−2; (III) A normal ventilation rate of 0.35 L s−1 m−2; (IV) A high ventilation rate of 0.70 L s−1 m−2. In all cases, the sensor was positioned in the exhaust duct exiting from habitable spaces. Measurements showed that, for case I, the CO2 concentration reached over 1300 ppm, which was higher than the commonly referenced threshold for ventilation control, i.e. 1000 ppm, showing unacceptable IAQ. In case II, the CO2 level was always below 950 ppm, indicating that 0.20 L s−1 m−2 is a sufficient ventilation rate for the reference building. The case III showed that the ventilation rate of 0.35 L s−1 m−2 caused a maximum CO2 level of 725 ppm; showing the level recommended by Swedish regulations was high with respect to CO2 level. In addition, measurements showed that the RH and temperature were within acceptable ranges in all cases. An energy savings calculation showed that, in case II, the comparative savings of the combined energy requirement for ventilation fan and ventilation heating were 43% compared with case III.

The use of electric vehicles (EVs) has been on the rise during the past decade, and the number is expected to rapidly increase in the future. At aggregated level, the large EV charging loads, if not well regulated, will cause great stress on the existing grid infrastructures. On the other hand, considered as a resource-efficient and cost-effective demand response resource, EV fleet smart charging control methods have been developed and applied to mitigate power issues of the grid while avoiding expensive upgrade of power grid infrastructure. Until now, there is no systematic study on how different coordination mechanisms affecting the EV fleet's charging demand response performance. Thus, it is still unclear which one may perform better in the increasingly common solar-powered building communities, especially as demand response is increasingly concerned. Aiming to fill in such knowledge gaps, this study conducted systematic comparative studies of three representative control methods selected from the non-coordinated, bottom-up coordinated, and top-down coordinated control categories. Their power regulation performances have been comparatively investigated in two perspectives: minimizing peak power exchanges with the grid and maximizing PV self-utilization, based on a real building community in Sweden. Meanwhile, their computational performances have also been investigated. The study results show that due to the ability to schedule and coordinate all the EVs simultaneously, the top-down coordinated control is superior to the other two control methods in the considered demand response performances. Note that its better performance is realized with a higher computational load, leading to possible convergence difficulties in practice. The study results will help improve understanding of how coordination affect the EV smart charging control performances. It will pave the way for developments of more sophisticated control methods for EV smart charging in more complex scenarios. © 2022 Elsevier Ltd

The use of electric vehicles (EVs) has been on the rise during the past decade, and the number is expected to rapidly increase in the future. At aggregated level, the large EV charging loads, if not well regulated, will cause great stress on the existing grid infrastructures. On the other hand, considered as a resource-efficient and cost-effective demand response resource, EV fleet smart charging control methods have been developed and applied to mitigate power issues of the grid while avoiding expensive upgrade of power grid infrastructure. Until now, there is no systematic study on how different coordination mechanisms affecting the EV fleet's charging demand response performance. Thus, it is still unclear which one may perform better in the increasingly common solar-powered building communities, especially as demand response is increasingly concerned. Aiming to fill in such knowledge gaps, this study conducted systematic comparative studies of three representative control methods selected from the non-coordinated, bottom-up coordinated, and top-down coordinated control categories. Their power regulation performances have been comparatively investigated in two perspectives: minimizing peak power exchanges with the grid and maximizing PV self-utilization, based on a real building community in Sweden. Meanwhile, their computational performances have also been investigated. The study results show that due to the ability to schedule and coordinate all the EVs simultaneously, the top-down coordinated control is superior to the other two control methods in the considered demand response performances. Note that its better performance is realized with a higher computational load, leading to possible convergence difficulties in practice. The study results will help improve understanding of how coordination affect the EV smart charging control performances. It will pave the way for developments of more sophisticated control methods for EV smart charging in more complex scenarios.

This study gives emphasis to the techno-economic analysis of renovating the energy supply system of a grid-connected large office building through a Hybrid Renewable Energy System (HRES). The study is focused on how to minimize electricity consumption from the grid by producing as much as possible renewable energy, and in addition to that it also observes the economic impacts of integrating green vehicles, such as hydrogen cars, electric cars etc. which are indispensable elements of a sustainable city, in the proposed system. The work initiated collecting the sites monthly electrical load data, climate data and associated monetary data with the aim of investigating a renewable energy supply system feasibility study. Three alternative scenarios are developed according to the project needs and these scenarios are modelled by a hybrid renewable energy system design tool. The study concludes with a direct comparison of the economic feasibility, renewable energy fraction, and emissions among all systems, looking for the more appropriate and sustainable solution. It is found that integrating solar photovoltaic (PV) curtails more than 43% electricity consumption of the office building from the utility grid. The result also shows that per unit cost of electricity of PV/Grid system to satisfy the load demand is around 10% lower compared to the utility grid tariff and furthermore, it minimizes over 90% emission compared to the total emission in the study site. This study will provide helpful insights to the relevant stakeholders and policy makers in the development of grid connected HRES system.

Urban building energy models (UBEMs) are considered as applicable tools for urban energy planning. Model developers use different strategies to simulate urban building energy use appropriately, and yet they are often doing so in the absence of high-quality data. While data collection is challenging in many cases, in Sweden, the availability of national databases is relatively good and is expected to facilitate the modeling procedure considerably. This study aims to develop, calibrate and validate an UBEM using available national data, including GIS-based property maps and energy performance certificates (EPCs). The developed UBEM offers an automated framework for constructing simple building-level models from open data and conducting energy simulations in EnergyPlus. The developed UBEM was calibrated and validated for two case study cities in Sweden, Borlänge and Uppsala, where the mean absolute percentage error (MAPE) between simulated results and EPC data was 26% and 22%, respectively. Furthermore, a downward trend was observed in the MAPE with increasing spatial aggregation from building to district and city levels (from 26% to 21% and 10%), which highlights the performance of the UBEM in this study to support accurate urban-scale energy analyses for buildings.

Electrical power generation across the world is facing dramatic changes for a variety of reasons related to reliability, economics and environmental concerns. Over recent years a significant increase has been observed in installed capacity of photovoltaic systems. Due to their typical seasonal and diurnal energy conversion patterns their integration into power systems creates new opportunities as well as threats. This paper intends to show how photovoltaics can contribute to reducing peak load in office buildings and thereby minimise expenditure on electricity during time- and peak-load-dependent energy prices/tariffs. An additional benefit is also provided to the national power system by reducing the need for peaking power stations. The calculations are performed for energy tariffs commonly used for commercial buildings in Poland. The simulation relies on climatic and price data for 2016. The results show significant potential for photovoltaics to reduce the peak load (from almost 60 kW to slightly over 44 kW) whilst simultaneously minimising energy costs to the building (from 1.2% up to 5.8% depending on the selected tariff). This study demonstrates the economic benefits of using PV system for reducing peak loads. A sensitivity analysis with regard to photovoltaics investment costs is carried out showing that the increasing investment costs have different impact on total energy cost depending on the considered energy tariff.

Electrical power generation across the world is facing dramatic changes for a variety of reasons related to reliability, economics and environmental concerns. Over recent years a significant increase has been observed in installed capacity of photovoltaic systems. Due to their typical seasonal and diurnal energy conversion patterns their integration into power systems creates new opportunities as well as threats. This paper intends to show how photovoltaics can contribute to reducing peak load in office buildings and thereby minimise expenditure on electricity during time- and peak-load-dependent energy prices/tariffs. An additional benefit is also provided to the national power system by reducing the need for peaking power stations. The calculations are performed for energy tariffs commonly used for commercial buildings in Poland. The simulation relies on climatic and price data for 2016. The results show significant potential for photovoltaics to reduce the peak load (from almost 60 kW to slightly over 44 kW) whilst simultaneously minimising energy costs to the building (from 1.2% up to 5.8% depending on the selected tariff). This study demonstrates the economic benefits of using PV system for reducing peak loads. A sensitivity analysis with regard to photovoltaics investment costs is carried out showing that the increasing investment costs have different impact on total energy cost depending on the considered energy tariff.

Climate change impacts can escalate the deteriorating rate of infrastructures and impact the infrastructure’s functionality, safety, operation and maintenance (O&M). This research explores climate change’s influence on urban railway infrastructure. Given the geographical diversity of Sweden, the railway network is divided into different climate zones utilizing the K-means algorithm. Reliability analysis using the Cox Proportional Hazard Model is proposed to integrate meteorological parameters and operational factors to predict the degree of impacts of different climatic parameters on railway infrastructure assets. The proposed methodology is validated by selecting a number of switches and crossings (S&Cs), which are critical components in railways for changing the route, located in different urban railway stations across various climate zones in Sweden. The study explores various databases and proposes a climatic feature to identify climate-related risks of S&C assets. Furthermore, different meteorological covariates are analyzed to understand better the dependency between asset health and meteorological parameters. Infrastructure asset managers can tailor suitable climate adaptation measures based on geographical location, asset age, and other life cycle parameters by identifying vulnerable assets and determining significant covariates. Sensitivity analysis of significant covariates at one of the urban railway stations shows precipitation increment reveal considerable variation in the asset reliability.

The large-scale penetration of electric vehicles (EV) in road transport brings a challenging task to ensure the balance between supply and demand from urban districts. EVs, being shiftable loads can provide system flexibility. This work investigates the potential role of smart charging of EVs in mitigating the impact of the integration of a mix of residential and public EV charging infrastructure on power networks. Furthermore, the impact of integrating solar photo-voltaic (PV) and battery energy storage systems (BESS) has been explored where BESS improves PV self-consumption and helps in peak shaving during peak load hours. Annual losses, transformer congestion, and cost of electricity import assessment are detailed by considering the power network of Stockholm as a case study. Smart charging with loss-optimal and cost-optimal charging strategies are compared to uncoordinated charging. The cost-optimal charging strategy is more favorable as compared to the loss-optimal charging strategy as it provides more incentives to the DSOs. The loss-optimal charging strategy reduces 35.5 % of losses in the network can be reduced while the cost-optimal solution provides a 4.3 % reduction in the electricity cost. The combined implementation of smart charging, PV, and BESS considerably improves energy and economic performance and is more effective than EV smart charging alone.

Cities in the 'New Urban World' display an enormous diversity in appearance, growth and performance. The awareness is growing that the urban development potential ('magnetism') of cities is closely related to safety and security conditions in these cities. This paper develops a new analytical framework based on a wealth of empirical data on both safety/security and socio-economic magnetism achievements of many world cities, by combining two comprehensive relevant global urban data bases. The aim of the study is to offer a comparative analysis of the combined safety/security data and socio-economic performance data of 30 global cities, through the use of an advanced sequential cluster dynamics analysis that is (partly) inspired by a novel machine learning approach (using Python software). In this way, cities can be categorized according to their quantitative characteristic features represented by the relevant clusters. It appears that city safety/security features are an important predictor of the variability in overall urban performance regarding magnetism. This study allows also for drawing relevant policy lessons.

Recent advances in numerical tools and data for the study of urban microclimates have helped to evaluate countermeasures for urban heat in heterogeneous and high-rise cities such as Hong Kong. Thus, two ventilation strategy designs, point ('oases') and linear ('corridors') features, were numerically simulated during a typical heatwave using the multi-layer coupled MesoNH-SURFEX-TEB mesoscale atmospheric model.

These strategies proved to be effective at night with respect to thermal comfort but caused a localised increase in heat stress during the day in the ventilated areas, which were less shaded. There was no significant deterioration in the wind performance around the developments that were redesigned to accommodate the displaced population due to the construction of the ventilation features; however, an improvement was observed in thermal comfort during the daytime. The simulated impacts were relatively localised, suggesting the importance of increasing porosity across the entire urban fabric. The corridors, especially when built along the axis of the prevailing winds, exhibited better ventilation at the pedestrian level than the oases. Nevertheless, the oases remain interesting features in the context of progressive urban ventilation planning that involve the implementation of isolated, connected, and eventually a network of features to provide benefits at the megalopolis scale.

This work aims to effectively resolve the problem of waterlogging in cities and manage water resources in sustainable cities. Digital Twins (DTs) technology was applied to the Urban Drainage System (UDS) and solves the modeling and scheduling problems of emergency drainage through the core model construction method of DTs. Firstly, the components of the UDS that are necessary in the process of building the model were listed according to the entity elements of the five-dimensional (5D) DTs model. Then, this work analyzed the essential data of the DTs of UDS and the Data Collection method according to the data elements of the 5D DTs model. Finally, the Multi-Level Dynamic Priority and Importance Scheduling (MDPIS) algorithm was proposed based on the Fixed Priority Scheduling (FPS) algorithm, which was verified by the simulation experiment. The experimental results indicated that the MDPIS algorithm showed significant performance in the rainfall scene with large fluctuations compared with the FPS algorithm. Specifically, the average improvement ratio was the highest, reaching 49.81%; the overall improvement rate was constant at about 48%. The operation results showed an apparent correlation between the catchment parameters and the overflow loss of the pumping station. The improved MDPIS algorithm can effectively utilize the water storage capacity and drainage capacity of the pumping station and reduce overflow losses during rainfall by dynamically adjusting the priority to solve the problem of urban inland inundation. The DTs-based UDS proposed here can effectively mitigate the overflow loss and improve the working efficiency of the pumping station cluster, promoting the development of Substainable Cities.

Urban parks have been found to provide mental health benefits. Some empirical studies have tested natural features and perceptual measures respectively, announcing their contribution to psychological restoration. However, inconsistent findings were occasionally reported, whereas few attempts have been made to combine both observed and perceptual factors for validation. Little is known about the variation of restorative drivers and their spatial patterns. To address these problems, this study combined public participation geographic information system (PPGIS) and deep learning method to capture visual qualities of landscape features along with several important perceptual measures. A typical urban park in Wuhan, China, was selected for a pilot study, and 1560 crowdsourced on-site images were collected, with thematic and geographic information being integrated. A series of statistical models, e.g., OLS, QRM, and MGWR, were employed successively for validation. The results showed that landscape preference, place attachment, greenery and water were validated as the global explanatory factors to estimate the conditional mean of psychological restoration. The variation of influential effects of these factors were detected at different restoration levels. There exist spatial heterogeneity for these influential factors on restorative effects. Findings provided new knowledge on a deeper understanding of the subtlety of restoration drivers and their spatial patterns. The findings offered useful insights and guidance for urban planners in creating high-quality green parks with restorative values.

The energy performance of heritage buildings is attracting growing interest in research and practice. Accordingly, as shown by our literature review, increasing numbers of articles on energy-efficiency measures for heritage buildings are being published in peer-reviewed journals. However, there is no overview of how energy efficiency and heritage conservation have been approached in the studies. To address this gap we categorized and assessed the identified studies in terms of two key elements of such investigations: energy analysis and analysis of cultural heritage values. Most of the studies evaluate and propose measures to reduce the operational energy use of single heritage buildings, and fewer have applied a broader system perspective. Moreover, the underlying notion of the buildings’ cultural heritage values seems to have been derived mainly from international conventions and agreements, while potentially significant architectural, cultural and historical factors have been rarely discussed. Our findings highlight that, when considering energy improvements, cultural heritage values should be more explicitly articulated and analysed in relation to established conservation principles or methodologies. Besides further scientific study, this point to the need of designing best-practice approaches that allow transparency and knowledge sharing about the complex relationships between energy efficiency and heritage conservation of buildings.

Sustainable and clean multi-energy systems have been hybrid coordinated in microgrid (MG) to fulfill regional multi-energy users economically, but shortages and surpluses still exist. The growth of win-win energy sharing between MGs attracts attention to smart information center (SIC) concept in MGs community, which facilitates sustainable solutions across three key objectives: lower carbon emission, sufficient utilization, and long-term cooperation. This paper designs a two-stage decision framework for day-ahead operations of SIC, where the first stage minimizes the total cost (i.e. operational cost and carbon emission cost) by the proposed multi-energy intra-scheduling and inter-sharing model (MIIM) and the second stage implements the smart cost-saving redistribution model (SCRM) to stimulate MGs in long-term joining of energy sharing. The extended alternating direction multipliers method algorithm (E-ADMM) is developed to protect the privacy of MGs data while approximating the optimal solution of MIIM. The case study proves that a) demand response and energy sharing in MIIM result in cost savings of 2.01% for carbon emissions and 7.25% for total operations; b) renewable energy has been fully utilized and the total multi-energy loss has been avoided nearly 92.22% in MIIM; c) the asymmetric Nash bargaining approach fits the SCRM with incentive cooperation, whereby those who contribute more gain a higher share; d) the optimal gap of E-ADMM is less than 0.2% by iterated insensitive decision result from each MG, which computes in polynomial time when the scale of MG increases; e) the two-stage framework performs better with more heterogeneity of MGs. Furthermore, we provide valuable insights for guidance and models with worldwide generalization potential.

Urbanization increased population density in cities and consequently leads to severe indoor air pollution. As a result of these trends, the issue of sustainable and healthy indoor environment has received increasing attention. Various air filtration techniques have been adopted to optimize indoor air quality. Air filtration technique can remove air pollutants and effectively alleviate the deterioration of indoor air quality. This paper presents a comprehensive review on the synergistic effect of different air purification technologies, air filtration theory, materials and standards. It evaluated different air filtration technologies by considering factors such as air quality improvement, filtering performance, energy and economic behaviour, thermal comfort and acoustic impact. Current research development of air filtration technologies along with their advantages, limitations and challenges are discussed. This paper aims to drive the future of air filtration technology research and development in achieving sustainable and healthy building ventilation.