Smart environments are designed to react intelligently to the needs of those who visit, live and work in them. For example, the lights can come on when it gets dark in a living room or a video exhibit can play in the correct language when a museum visitor approaches it. However, we lack intuitive ways for users without technical backgrounds to understand and reconfigure the behaviours of such environments, and there is considerable public mistrust of automated environments. Whilst there are tools that let users view and change the rules defining smart environment behaviours without having programming knowledge, they have not seen wide uptake beyond technology enthusiasts. One drawback of existing tools is that they pull attention away from the environment in question, requiring users to translate from real world objects to abstract screen-based representations of them. New programming tools that allow users to harness their understandings of and references to objects in the real world could greatly increase trust and uptake of smart environments. This research will investigate how users understand and describe smart environment behaviours whilst in situ, and use the findings to develop more intuitive programming tools. For example, a tool could let someone simply say that they want a lamp to come on when it gets dark, and point at it to identify it. Speech interfaces are now widely used in intelligent personal assistants, but the functionality is largely limited to issuing immediate commands or setting simple reminders. In reality, there are many challenges with using speech interfaces for programming tasks, and idealised interactions such as the lamp example are not at all simple, in reality. In many cases, research used to design programming interfaces for everyday users is carried out in research labs rather than in the real home or workplace settings, and the people invited to take part in design and evaluation studies are often university students or staff, or people with an existing interest or background in technology. These interfaces often fall down once taken away from the small set of toy usage scenarios in which they have been designed and tested and given to everyday users. This research investigates the challenges with using speech for programming, and evaluates ways to mitigate these challenges, including conversational prompts, use of gesture and proximity data to avoid ambiguity, and providing default behaviours that can be customised. In this project, we focus primarily on smart home scenarios, and we will carry out our studies in real domestic settings. Speech interfaces are increasingly being used in these scenarios, but there is no support for querying, debugging and alternating the behaviours through speech. We will recruit participants with no programming background, including older and disabled users, who are often highlighted as people who could benefit from smart home technology, but rarely included in studies of this sort. We will carry out interviews in people's homes to understand how they naturally describe rules for smart environments, taking into account speech, gesture and location. We will look for any errors or unclear elements in the rules they describe, and investigate how far prompts from researchers can help them to be able to express the rules clearly. We will also explore how far participants can customise default behaviours presented to them. This data will be used to allow us to create a conversational interface that harnesses the approaches that worked with human prompts, and test it in real world settings. Some elements of the system will be controlled by a human researcher, but the system will simulate the experience of interacting with an intelligent conversational interface. This will allow us to identify fruitful areas to pursue in developing fully functional conversational programming tools, which may also be useful in museums, education, agriculture and robotics.

In developed countries such as the UK, we spend 90% of our time indoors with approximately two thirds of this in our homes. Despite this fact, most air pollutant regulation focuses on the outdoor environment. There is increasing evidence that exposure to air pollution causes a range of health effects, but uncertainties on the causal effects of individual pollutants on specific health outcomes still exist partly due to crude exposure metrics. Nearly all studies of health effects to date have used measurements from fixed outdoor air pollution monitoring networks, a procedure that ignores the modification effects of indoor microenvironments where people spend most of their time. There are consequently large uncertainties surrounding human exposure to indoor air pollution, which means we are currently unable to identify the most effective solutions to design, operate and use our homes to minimise our exposure to air pollution within them. In the UK, there are virtually no data to quantify indoor air pollutant emissions, building-to-building variability of these, chemical speciation of indoor pollutants, ingress of outdoor pollution indoors or of indoor generated pollutants outdoors, or the social, economic or lifestyle factors that can lead to elevated pollutant exposures. Without a fundamental understanding of how indoor air pollution is caused, transformed and distributed in UK homes, research aiming to develop behavioural, technical or policy interventions may have little impact, or at worst be counterproductive. For example, energy efficiency measures are broadly designed to make buildings more airtight. However, given that the concentrations of many air pollutants are often higher indoors than outdoors, reducing ventilation rates may increase our exposure to air pollution indoors and to any potentially harmful effects of the resulting pollutant mixture. Further, if interventions are introduced without sufficient consideration of how occupants actually use and behave in a building, they may fail to achieve the desired effect. To understand and improve indoor air quality (IAQ), we must adopt a systems approach that considers both the home and the human. There is a particular paucity of data for the most deprived households in the UK. There is a facile assumption that poorer homes are likely to experience worse IAQ than better off households, although the reality may be considerably more nuanced. Lower quality housing may be leakier than more expensive homes allowing indoor emissions to escape more easily, whilst large, expensive town-houses converted to flats can be badly ventilated following poor retrofitting practices. Differences in cooking practices, smoking rates, internal building materials and the usage of solvent containing products indoors will also be subject to wide variations across populations and hence have differential effects on IAQ and pollutant exposure. In fact, differences in individual behaviour lead to large variations in indoor concentrations of air pollutants even for identical houses, typically driven by the frequency and diversity of personal care product use. The INGENIOUS project will provide a comprehensive understanding of indoor pollution in UK homes, including i) the key sources relevant to the UK ii) the variability between homes in an ethnically diverse urban city, with a focus on deprived areas (using the ongoing Born in Bradford cohort study) iii) the effects of pollutant transformation indoors to generate by-products that may adversely affect health iv) the drivers of behaviours that impact on indoor air pollution (v) recommendations for interventions to improve IAQ that we have co-designed and tested with community members.

With numerous governments, cities, and organisations declaring climate emergencies and net-zero emissions targets, greenhouse gases (GHGs) are now the focus of international geopolitics and UK domestic policies. Furthermore, with the recent identification of violations of the Montreal Protocol, ozone depleting substances (ODS), are receiving renewed attention. It is therefore critically important to be able to analyse GHG and ODS emissions trends, examine spatial patterns, estimate future trajectories, and explore mitigation options in an open, transparent and publicly accessible way. Our proposed project will enable this, using state-of-the-art computing technology to create a platform, "OpenGHG". The estimation of GHG and ODS emissions requires close collaboration between a diverse group of scientists and stakeholders: "bottom-up" methods rely on statistical information collected by governments and industries, combined with scientific studies of the emissions intensity of particular activities, or the development of computer models that describe how human or natural processes produce or absorb GHGs. Complementary "top-down" techniques rely on instruments developed by spectroscopists and analytical chemists, the data from which are analysed along with outputs from meteorological models using advanced statistical methods. The data that is being generated by these diverse research and stakeholder communities is growing rapidly. However, the development of computational tools to help researchers aggregate data from such a wide range of sources and carry out and share analyses has not kept pace. Furthermore, given the sensitive nature of, for example, the inference of national GHG or ODS emissions, these communities must urgently take steps to make their analyses more transparent and reproducible. OpenGHG meets these needs, by providing an open, cloud-based, platform for researchers to share data and analysis methods and publish workflows. Furthermore, we have co-designed with our stakeholders, a range of tools that will facilitate the sharing of research outputs with governments, private companies and the public. The OpenGHG platform will: - Continuously incorporate and standardise up to date GHG and ODS measurements, bottom-up emission estimates, and a range of ancillary information related to GHG and ODS emissions. This data will be pulled automatically, or on demand, from a range of public archives, or pushed to the platform by data providers seeking to analyse or share their own data - Provide a wide range of analysis options, including the ability to design, publish and share custom workflows - Allow production of new top-down and bottom-up emissions estimates by accessing pre-existing and newly developed models and methods incorporated into the platform - Provide users with lower levels of computational expertise an easy-to-use interface for the most useful data analysis and visualisation. This will include comparisons of top-down and bottom-up estimates of emissions from different sectors of the economy, and potential future warming from different emissions scenarios.

SECURE is a network of statisticians, modellers and environmental scientists and our aim is to grow a shared vision of how to describe and quantify environmental change to assist in decision making. Understanding and forecasting environmental changes are crucial to the development of strategies to mitigate against the impacts of future events. Communications and decision making around environmental change are sometimes troubled by issues concerning the weight of evidence, the nature and size of uncertainties and how both are described. Evidence for environmental change comes from a number of sources, but key to this proposal is the optimal use of data (from observational, regulatory monitoring and earth observations platforms such as satellites and mobile sensors) and models (process and statistical). A robust and reliable evidence base is key in the decision making process, informed by powerful statistical models and the best data. This proposal will deliver the statistical tools to support decision making. Many environmental challenges related to change require statistical modelling and inferential tools to be developed to understand the drivers and system responses which may be direct or indirect and linked by feedback and lags. The character of environmental data is changing as new technologies (e.g. sensor networks offering high resolution data streams) are developed and become more widely accessible. Emerging sensor technology is able to deliver enhanced dynamic detail of environmental systems at unprecedented scale and . There is also an increasing public engagement with environmental science, through citizen science. Increasing use of citizen science observatories will present new statistical challenges, since the sampling basis of such observations will most likely be preferential and not directed, be of varying quality and collected with different effort. Fusion of the different streams of data will be challenging but essential in terms of informing society and regulators alike about change. Linkage of the different data sources, and the challenges of dealing with big data, in the environmental sphere lie in drawing together diverse, high-throughput data sources, analysing, aggregating and integrating the signals with models and then ultimately using the data-model system to address complex and shifting environmental change issues in support of decision making. Key to success lies in generating digestible outputs which can be disseminated and critiqued across academia, policy-makers and other stakeholders. In climate change, food security, ecosystem resilience, sustainable resource use, hazard warning and disaster management there are new high-volume data sources, including crowd sourced streams, which present problems and untapped opportunities around data management, synthesis, communication and real-time decision-support. Our research will involve: improving modelling and communication tools concerning uncertainty and variability, which are ubiquitous in many environmental data sources; developing and extending modelling capabilities to deal with multi-scale issues, specifically integrating over the different spatial and temporal scales of the data streams, and the derived timescales of model outputs; exploring the power and limitations of recent statistical innovations applied to environmental change issues and finally reflecting on new technologies for visualisation and communication.

Beijing suffers from very high concentrations of airborne pollutants, leading to adverse health and wellbeing for over twenty million people. The pollutants likely to have the greatest effects upon human health are particulate matter, nitrogen dioxide and ozone. Both particulate matter and nitrogen dioxide are emitted directly from individual sources (primary contributions, many of which are not well quantified); and are formed in the atmosphere (secondary contributions, which are highly complex). Ozone is entirely secondary in nature, formed from reactions of precursor gases, whose sources and abundance are also challenging to constrain. These uncertainties hinder understanding of the causes of air pollution in Beijing, which is needed to deliver effective and efficient strategies for pollution reduction and health improvement. AIRPOLL-Beijing project will address this challenge, through identification and quantification of the sources and emissions of air pollutants in Beijing. The project sits within the NERC/MRC-NSFC China megacity programme, which includes projects addressing the atmospheric processes affecting air pollutants, human exposure and health effects, and solutions / mitigation strategies to reduce air pollution and health impacts. The project exploits the combined experience and expertise of leading UK and Chinese scientists, applying multiple complementary approaches. The project deploys multiple atmospheric measurement and analysis strategies to characterise pollutant abundance and sources, develop novel emissions inventories, and integrate these to produce new modelling tools for use in policy development. We adopt a range of state-of-the-science approaches: -Receptor Modelling, where detailed composition measurements are used to infer pollutant sources from their chemical signatures, combining world-leading UK and Chinese capability. -Flux Measurements, where the total release of pollutants from all sources is measured, providing a key metric to refine emission inventories. We will combine near-ground measurements (using the unique Institute of Atmospheric Physics 325m tower in central Beijing), ground-based observations and fluxes derived from satellite observations. -3D spatial analysis, in which a novel sensor network will be deployed around central Beijing to measure pollutant fields. -Development of novel emissions inventories, which will predict the temporally- and spatially- resolved emissions of air pollutants from all sources, enhancing existing capability. -Development of new online modelling tools, within which to integrate emissions, atmospheric processing and meteorology to predict primary and secondary pollutant concentration fields. AIRPOLL-Beijing will integrate these approaches to provide thorough understanding of the sources and emissions of air pollutants in Beijing, at unprecedented detail and accuracy. While the project is a self-contained activity, key deliverables feed into Processes, Health and Solutions themes of the programme. This proposal seeks Newton fund support, part of the UK's Official Development Assistance (ODA) commitment. The project will directly address ODA objectives, in the categories of (i) people (through the joint development of novel scientific approaches to the understanding of megacity air pollution), (ii) programmes (as all aspects of the project are joint UK-Chinese research endeavours) and (iii) translation (through provision of detailed air pollution source assessments, in support of assessment of health impacts and development of mitigation strategies). More generally, the project will leave a legacy of improved air pollution understanding and research capacity of the Chinese teams, and, through integration with other themes of the Megacities programme, underpin improvements in the health and welfare of the population of Beijing, and across China more widely - ultimately benefitting more than a billion people.