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Urban air quality poses a significant public health challenge, particularly in complex urban areas like street canyons, where traffic emissions intensify the issue. The limitations of traditional monitoring methods, marked by a narrow scope and poor visualisation, hinder our understanding of pollutant dispersion dynamics, impeding effective air quality management. Consequently, this study explores the synergy of 3D city models and Computational Fluid Dynamics (CFD) simulation to analyse air pollutant dispersion in complex urban settings. To enhance geo-visualisation, the research employs the next generation of Open Geospatial Consortium (OGC) APIs for data delivery. The integration of 3D city models with CFD simulations and geo-visualisation techniques aims to develop an urban digital twin. This digital twin is envisioned to offer valuable insights into the dynamics of air pollutant dispersion within street canyons, thereby informing more effective air quality management strategies. [ABSTRACT FROM AUTHOR]

Both citizens and authorities play a crucial role in the successful implementation of Positive Energy Districts (PEDs). However, the current public participation methods often fail to facilitate interactive knowledge production in the context of energy flexibilisation and PEDs for the citizens. To close this gap, the DigiTwins4PEDs project will use an innovative public participation process and further develop energy simulation tools using urban digital twins. These tools could address issues such as energy flexibility strategies, including demand-side management, sector coupling, and energy storage options, among others. To support the public participation process, the urban digital twin platform will be integrated into a living lab concept and implemented in different case study regions to co-design energy flexibilisation strategies with citizens. The ultimate goal is to deliver a best-practice catalogue and an action plan for other cities to implement a community-driven transition to PEDs. [ABSTRACT FROM AUTHOR]

This paper presents a concept and first glimpse at the development of an urban digital twin framework to estimate and forecast the carbon footprints of urban neighbourhoods, with a focus on household consumption choices, specifically in buildings, food, and transportation sectors, as key emission contributors. Despite constituting nearly three-quarters of global carbon emissions, the influence of household consumption choices on a region's carbon footprint is often neglected. While assessments at a regional or city scale may prove too broad for targeted mitigation strategies, estimating carbon emissions at the neighbourhood scale can foster sustainable and resilient urban areas. However, challenges arise in estimating emissions at this scale due to the availability of aggregated data, insufficient cross-sectoral data integration, and a lack of practical visualisation tools, causing policymakers to overlook the impact of household choices on neighbourhood carbon footprints. Therefore, the present article provides insights into the ongoing early-stage development of using urban digital twins to model, simulate, analyse, and visualise the impact of household consumption choices on neighbourhood-scale consumption-based carbon emissions. By exploring "what-if" scenarios, this research also seeks to forecast emission profiles based on how household consumption choices influence a neighbourhood's carbon emissions under future climatic and demographic conditions. [ABSTRACT FROM AUTHOR]

The effective tracking and analysis of moving objects within urban environments presents a complex challenge that necessitates robust geospatial data integration. Open Geospatial Consortium (OGC) APIs offer standardized approaches to managing dynamic geospatial information. This paper presents a case study of real-time moving object tracking including buses and trains in the city of Munakata, Japan, utilizing two prominent OGC APIs: OGC API Moving Features and OGC SensorThings API. The study explores the implementation of both APIs, examining their strengths and limitations in handling real-time location updates and associated sensor data generated by moving buses. The research provides insights into the practical suitability of each API model for dynamic object tracking, offering valuable guidance for practitioners seeking to optimize geospatial data integration within smart cities and intelligent transportation systems. [ABSTRACT FROM AUTHOR]