Poor air quality is widely recognised to affect human health and wellbeing. Cumulative exposure to pollutants throughout the life course is a determinant for numerous long term health conditions including dementia, heart disease and diabetes, Short term high exposures are shown to exacerbate conditions such as asthma and COPD, increase risks of heart attacks and stroke and influence respiratory infections. The very young, very old and those with pre-existing conditions are most at risk and inequality further increases this; the poorest in society often live in the lowest quality housing in the most polluted areas. Human exposure to air pollutants occurs in both indoor and outdoor environments. Urban air pollution results from a combination of local outdoor sources (e.g. transport, combustion, industry) and regional and large scale atmospheric transport of pollutants. We spend up to 90% of our time indoors and indoor air quality is therefore a significant part of human exposure. Indoor air quality is influenced by the climate, weather and air quality in the external environment in addition to local indoor sources (e.g. microorganisms, chemicals cleaning and personal care, cooking, industry processes, emissions from building materials, heating and mechanical systems) and the building design and operation. In all cases it is the airflows within and between indoor and outdoor locations that enables the transport of pollutants and ultimately determines human exposures. Understanding airflows is therefore at the heart developing effective mitigating actions, particularly in cases where there is limited ability to remove a pollutant source. Being able to predict the influence of airflows enables understanding of how pollutants are likely to move within and between buildings in a city, both under normal day-to-day conditions and in response to emergencies such as heatwaves or wildfires. With the right computational and measurement tools it is then possible to change the design or management of city neighbourhoods enabling better urban flows to reduce exposure to pollutants and also to innovate new ventilation solutions to control the indoor environment in buildings. While there are a number of approaches that already enable assessment of urban flows and indoor flows, these aspects are not currently considered together in an integrated way or focused on optimising environments for health. The Future Urban Ventilation Network (FUVN) aims to address this by defining a new holistic methodology - the Breathing City. This will define a new integrated assessment approach that considers coupled indoor-outdoor flows together to minimise exposure for people within a neighbourhood who are most at risk from the effects of poor air quality. The network will bring together people from a range of disciplines and areas of application with a common interest in improving urban and indoor airflows to improve health. Through small scale research and workshop activities we will advance the understanding of the fluid dynamics that determines the physics of this indoor-outdoor exchange. The network will develop a research programme to address technical gaps in modelling and measuring pollutant transport and how we can use this to determine long and short term exposures to a range of pollutants. We will work collaboratively with industry, policy makers and the public to understand how this approach could change city planning, building design guidance and community actions to enable health based future urban ventilation design and to "design out" health risks for people who are most vulnerable.

The Innovative Manufacturing and Construction Research Centre (IMCRC) will undertake a wide variety of work in the Manufacturing, Construction and product design areas. The work will be contained within 5 programmes:1. Transforming Organisations / Providing individuals, organisations, sectors and regions with the dynamic and innovative capability to thrive in a complex and uncertain future2. High Value Assets / Delivering tools, techniques and designs to maximise the through-life value of high capital cost, long life physical assets3. Healthy & Secure Future / Meeting the growing need for products & environments that promote health, safety and security4. Next Generation Technologies / The future materials, processes, production and information systems to deliver products to the customer5. Customised Products / The design and optimisation techniques to deliver customer specific products.Academics within the Loughborough IMCRC have an internationally leading track record in these areas and a history of strong collaborations to gear IMCRC capabilities with the complementary strengths of external groups.Innovative activities are increasingly distributed across the value chain. The impressive scope of the IMCRC helps us mirror this industrial reality, and enhances knowledge transfer. This advantage of the size and diversity of activities within the IMCRC compared with other smaller UK centres gives the Loughborough IMCRC a leading role in this technology and value chain integration area. Loughborough IMCRC as by far the biggest IMRC (in terms of number of academics, researchers and in funding) can take a more holistic approach and has the skills to generate, identify and integrate expertise from elsewhere as required. Therefore, a large proportion of the Centre funding (approximately 50%) will be allocated to Integration projects or Grand Challenges that cover a spectrum of expertise.The Centre covers a wide range of activities from Concept to Creation.The activities of the Centre will take place in collaboration with the world's best researchers in the UK and abroad. The academics within the Centre will be organised into 3 Research Units so that they can be co-ordinated effectively and can cooperate on Programmes.

Our vision is that humans can attenuate and control positively the impact of their buildings on the environment and mitigate the effects of climate change. This can be achieved by a new generation of life cycle assessment methods and tools that are model-based, continuously learn from real-time data, while informing effective operation and management strategies of buildings and districts. In that respect, current LCA methods present important limitations and gaps, including: (a) Lack of reasoning and decision support capabilities, such as exploring "what if" scenarios for the evaluation of alternative design options and devising adapted strategies, thus promoting active control of buildings and districts. (b) Lack of alignment with domain models, e.g. BIM (Building Information Modelling), GIS (Geographical Information Systems), and LCA data structures. (c) Lack of support of temporal information. There is a need to factor in temporal information in the life cycle inventory (LCI) and Impact Assessment (LCIA) phases to address maintenance, operation, deconstruction, disposal and recycling stages. The proposed research addresses the challenge of leveraging digital built environment resources by using semantic web technologies to deliver life cycle assessment solutions to our built assets. Our hypothesis is that: life cycle assessment underpinned by semantics and informed by dynamic data paves the way to more accurate life cycle impact assessment while supporting life cycle decision making and active control of buildings and districts. In a nutshell, the aim of SemanticLCA is the development of a (near) real-time semantic capability that exploits a wide range of digital data sources and leverages artificial intelligence to assess the whole-life cycle environmental impacts of built assets. The following research questions are posited: RQ1: Can the use of semantics, including BIM (IFC) and GIS (CityGML), to integrate and contextualise existing life cycle inventory databases, provide a sound basis to streamline the life cycle assessment process of buildings and districts? RQ2: Can access to dynamic data, managed in a BIM and GIS friendly time series database, provide more accurate accounts of environmental impacts during the construction and operation stages? RQ3: Can the resulting SemanticLCA environment assist in decision making by non-experts by exploring a wide range of options and scenarios with the least environmental impact, while also advising on corrective plans? Our work programme involves three Work-Packages (WP), each addressing one of our posited research questions, and a fourth cross-cutting WP addressing demonstration and validation activities. The evaluation will be carried out in two demonstration sites: Cardiff (UK) and Belval (Luxembourg). The Cardiff demonstration will be carried out in the Queen's building (School of Engineering) and scaled up to the 130 buildings owned and managed by Cardiff university, majority of which are located in the city centre. The LIST demonstration will be carried out in the Maison de l'Innovation in Belval and scaled up to the entire district of Belval (managed by Fonds Belval). Given the complexity of LCA at district level, validation will utilise a simulation based approach with a subset of use cases demonstrated and validated in real operation conditions. The validation work will leverage ongoing developments of city platforms for Cardiff and Belval, as illustrated on the CUSP website: www.cuspplatform.com. SemanticLCA is supported by 10 partners and an experienced team of investigators from Cardiff University and LIST bringing together complementary expertise in: a) AI applications in the built environment, b) semantic contextualisation of multi-scale built environment data, c) intelligent cloud/edge computing, d) Life cycle assessment methods and tools, e) Building Information Modelling for asset modelling and energy efficiency.

The ability for communities to "bounce back" from major disasters is essential for poverty alleviation and economic development. Termed "disaster resilience", this process is of particular importance in China as rapid economic expansion and urbanization has increased Chinese susceptibility to a number of major disasters, including the 2008 Wenchuan Earthquake. Earthquake-induced landslides represent a particular challenge to resilience as increased rates of landslide hazard may persist for many decades. The proposed research seeks to understand what controls this persistent landslide hazard and the processes that cause landslides to jeopardise recovery. To understand the recovery process and how it affects resilience, we will investigate the role of "social vulnerability" in modifying the response to earthquakes and their related hazards. We will assess the underlying drivers of social vulnerability and the spatio-temporal differences across Sichuan province. We will combine our estimates of landslide hazard and social vulnerability across the decade after the Wenchuan Earthquake, investigating both the spatial patterns of risk and how these change with time. To achieve these goals, we will focus our work on the areas affected by the Wenchuan Earthquake, where the Chengdu Institute of Technology-State Key Laboratory of Geohazard Prevention and Geoenvironment Protection has created an incredibly large dataset of landslide hazards since the earthquake. In collaboration with landslide scientists and social scientists at Cardiff University's Sustainable Places Research Institute, we will expand this dataset in two ways; (1) increasing the resolution of landslide hazard mapping to understand the relative role of aftershocks and rainfall in controlling hazard, and (2) using local census data to understand social vulnerability and how the interaction between social vulnerability and landslide hazards has changed in space and through time. The unprecedented detail of our data will enable us to develop a new probabilistic landslide hazard model that incorporates landslides caused by both aftershocks and rainfall events that can be applied across earthquake-prone China and perhaps even globally. Field data collected as part of this effort will help to constrain threshold values and so help support the construction of a landslide early warning system for Sichan. Finally, we will model the resilience of the built environment and key infrastructure through state of the art machine learning algorithms. As evidence of our commitment to improve the welfare of earthquake-prone China through better planning for disasters we will engage with an extensive network of governmental and non-governmental institutions. From the first day of the grant we will engage with organisations with interests in both science and policy to achieve this goal. We will also model resilience under different demographic and policy scenarios, using this as a tool to understand and communicate the challenges of building resilient communities.

It is well known that climate change will have a significant impact on UK building design and energy use. It is also known, that the current standard reference year and design summer year (these are the weather files used by industry-standard computer models of buildings), being assembled from data collected only up to 1995, do not represent even the current UK climate. The building design community is therefore highly exposed to the possibility of occupant dissatisfaction and possible litigation. In addition, most buildings are not being designed to cope with increased variability in a warming climate. The desire to use probabilistic scenarios will not solve this unless either new reference years are created, made widely available and guidance given on which ones to use and when/or, totally new methods are developed. Even this is likely to be unsuccessful in driving adaptation decisions unless a full understanding of how designers might use such data is gained and a consistent way found of examining any changes in costs. There is therefore a need to simultaneously study not only probabilistic data sets for the built environment, but also how such information can be used to drive adaptation decisions. In many ways the move to probabilistic outputs by such groups as UKCIP presents an opportunity. The ability to create bespoke probabilistic reference years using, for example a weather generator, changes the way problems can be tackled and even how the client or architect thinks about such issues.An interdisciplinary approach is envisaged with the project separated into seven work packages:1. It has been identified that high resolution climate information has many practical applications for building design/(for example the BETWIXT project). However, the best way to downscale climate model information for any particular application is not clear. We will agree a process for the creation of new reference years for the period 2010 to 2080, with hourly time steps. This will make use of the UKCIP08 probability distribution functions and UKCIP08's weather generator, but with the addition of wind direction estimates.2. Consider how in theory, probabilistic climate data is best used to produce useful and accurate predictions of internal environments and energy use. 3. Create a large set of reference years compatible with common building simulation codes based on the latest probabilistic results. 4. Given the complex decision-making context of future proofing, an additional aim of the project is to better understand the organisational, social, and psychological factors that might influence the willingness of the industry to adopt new technologies/practices. Research will focus on how engineers work in practice, the time and knowledge constraints they work under, and the motivational factors that are likely to influence decisions about using future-proofing technology. 5. There is the need to fully understand the range of possible results in building performance that can be generated by UKCIP08 and then to finalise a much smaller sub-set of probabilistic reference years (PRYs), that reflect the needs and practices of design teams working within a commercial environment. (These files would be delivered in a format consistent with the requirements of common building simulation codes.) 6. Examination of the effect of climate change on UK building design and refurbishment. The smaller PRY subset would be used to examine how parameters such as thermal mass and glazed fraction can be used most effectively to improve thermal comfort and reduce energy demand for a range of built forms and uses, and produce case studies. 7. The economic costs of various design strategies will also need to be examined, for example the increased cost of cooling, as will the cost to architectural practices of altering their working practices in order to make use of probabilistic data.