The wide ranging and diverse microorganisms found within the environment play a central role in maintaining sustainability on our planet. However, their ability to function and the functions themselves have been and are being seriously altered by human activities. One key example of this is the development of antimicrobial resistance in wild bacteria as a result of antibiotics that can be found in waste water. The impact of this is a significant emerging threat to the global economy and health. Increasing evidence shows that variations in the genetic makeup of individual cells, together with the way these are manifest in their physical characteristics plays a critical role in the fate of these microorganisms. Despite this knowledge, most studies of microorganisms currently rely on culturing and analysing them as large groups in laboratories, rather than on the individual level. To further complicate matters, the majority of the naturally occurring microbes (>99%) are not capable of being grown in laboratory conditions. This imposes formidable challenges to understand the activities of microbes in situ and their response to the ever-changing environments. In this project we will establish a novel approach to identify active microbes of interest within complex microbial communities, linking the behaviour and genetic profile of individual cells. Specifically, we will exploit the cutting-edge technology advances to analyse, sort and characterise microbes from a mixture of microorganisms. To achieve this, we will work to develop new devices and protocols for collecting samples on-site. These samples will then be analysed using a range of cutting-edge, lab-based techniques. One key feature of the project is the forging of new international collaborations with the world leading research groups of complementary expertise. This not only provides us with access to a range of world-class tools, but also enables the local collection and handling of samples from sites of interest around the globe. These include the Yanzi river near Shanghai in China, the River Thames near London and the Han River near Seoul in Korea.

The Loess Plateau of China covers an area 2.5x the size of the UK (some 640,000 square km) in the upper and middle reaches of China's Yellow River and is renowned for having the most severe soil erosion in the world; deforestation, over-grazing and poor agricultural practice have resulted in degenerated ecosystems, desertification and unproductive agriculture in the region. To control severe soil erosion on the Loess Plateau, the Chinese government imposed a series of policies for fragile ecosystems, such as the 1999 state-funded "Grain-for-Green" project, which has resulted in significant land use changes. Related programmes have produced beneficial effects on soil erosion and water cycles. However, the impact of these changes in soil and water processes on related ecosystem services is unknown and demands further study. The proposed research will focus on three spatial scales: slope, watershed, and region. It uses a combination of A) experiments to collect environmental, biological and agronomic data; B) remote sensing data and C) modelling approaches. A) Data collection: Four experimental stations located in four main topographical regions of the Plateau are chosen as case studies: 1) Ansai Comprehensive Experimental Station of Soil and Water Conservation; 2) Changwu Agro-ecology Experiment Station; 3) Guyuan Ecological Station; 4) Shenmu Erosion and Environment Station. At each station, treatments of different vegetative covers, slopes, and the practices of soil and water conservation at the plot scale were set up in the 1980s and data collections include: soil water, canopy size, runoff, soil losses and meteorological records. Most of the Chinese members of this project have been involved in prior studies at the stations. At the slope scale, additional environmental, biological and agronomic data will be monitored in a sub-set of the plots. At watershed scale, four watersheds where the stations are located will be monitored. The spatial distribution of the following variables will be measured: precipitation, soil properties, vegetative types, canopy size, runoff and soil loss. B) Remote sensing data collection: At the regional scale, remote sensing combined with ground-truthing data will be used to investigate the spatial variability of vegetation type, land cover, productivity, the components of water balance, soil losses, soil type, etc. C) Modelling approaches: a cascade approach will be used to build an improved model framework applied to different spatial scales. Mechanistic soil-water-plant models will be applied to the slope scale. Their outputs will then be used as inputs for models at watershed level. Spatial empirical/statistical models will be used at the regional level. Observed and collected data from A) and B) will be used to further develop, calibrate and validate our models. Model simulations at the slope level will be used to reveal the dynamic mechanisms in soil and water in different regions and analyse the effects of vegetation type, soil type, slope degree, climatic factors and management practice. Watershed models will estimate soil and water carrying capacity for different vegetation types, predict the effect of land use/cover changes on soil losses, water cycle and ecosystem services and evaluate management scenarios in the practices of soil and water conservation, vegetative changes, and ecosystem services. The soil and water carrying capacity for different vegetation types and the optimal ecosystem services will be addressed at the regional scale. Outreach workshops and demonstrations will disseminate knowledge to farmers and policy makers. The proposed research will elucidate the coupled relationships between soil and water processes and agro-ecosystem services at various scales, and evaluate the effects of vegetation cover and changes in land use on water cycle, soil erosion, and ecosystem services across the Loess Plateau.

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.

UK economic growth, security, and sustainability are in danger of being compromised due to insufficient infrastructure supply. This partly reflects a recognised skills shortage in Engineering and the Physical Sciences. The proposed EPSRC funded Centre for Doctoral Training (CDT) aims to produce the next generation of engineers and scientists needed to meet the challenge of providing Sustainable Infrastructure Systems critical for maintaining UK competitiveness. The CDT will focus on Energy, Water, and Transport in the priority areas of National Infrastructure Systems, Sustainable Built Environment, and Water. Future Engineers and Scientists must have a wide range of transferable and technical skills and be able to collaborate at the interdisciplinary interface. Key attributes include leadership, the ability to communicate and work as a part of a large multidisciplinary network, and to think outside the box to develop creative and innovative solutions to novel problems. The CDT will be based on a cohort ethos to enhance educational efficiency by integrating best practices of traditional longitudinal top-down / bottom-up learning with innovative lateral knowledge exchange through peer-to-peer "coaching" and outreach. To inspire the next generation of engineers and scientists an outreach supply chain will link the focal student within his/her immediate cohort with: 1) previous and future cohorts; 2) other CDTs within and outside the University of Southampton; 3) industry; 4) academics; 5) the general public; and 6) Government. The programme will be composed of a first year of transferable and technical taught elements followed by 3 years of dedicated research with the opportunity to select further technical modules, and/or spend time in industry, and experience international training placements. Development of expertise will culminate in an individual project aligned to the relevant research area where the skills acquired are practiced. Cohort building and peer-to-peer learning will be on-going throughout the programme, with training in leadership, communication, and problem solving delivered through initiatives such as a team building residential course; a student-led seminar series and annual conference; a Group Design Project (national or international); and industry placement. The cohort will also mentor undergraduates and give outreach presentations to college students, school children, and other community groups. All activities are designed to facilitate the creation of a larger network. Students will be supported throughout the programme by their supervisory team, intensively at the start, through weekly tutorials during which a technical skills gap analysis will be conducted to inform future training needs. Benefitting from the £120M investment in the new Engineering Campus at the Boldrewood site the CDT will provide a high class education environment with access to state-of-the-art computer and experimental facilities, including large-scale research infrastructure, e.g. hydraulics laboratories with large flumes and wave tanks which are unparalleled in the UK. Students will benefit from the co-location of engineering, education, and research alongside industry users through this initiative. To provide cohort, training, inspiration and research legacies the CDT will deliver: 1) Sixty doctoral graduates in engineering and science with a broad understanding of the challenges faced by the Energy, Water, and Transport industries and the specialist technical skills needed to solve them. They will be ambitious research, engineering, industrial, and political leaders of the future with an ability to demonstrate creativity and innovation when working as part of teams. 2) A network of home-grown talent, comprising of several CDT cohorts, with a greater capability to solve the "Big Problems" than individuals, or small isolated clusters of expertise, typically generated through traditional training programmes.

In the past decades, great progresses have been made in tackling disaster risk around the world especially since the Hyogo Framework for Action in 2005. However, there are still many challenging issues to be solved, and the disasters over recent years have clearly demonstrated the inadequate resilience in our highly interconnected and interdependent systems, including well-known devastating disasters such as the 2008 Wenchuan, 2011 Tohoku and 2015 Nepal Earthquakes. RESIST have identified the following weaknesses and knowledge gaps in the current disaster risk assessment and management that are in need of urgent research: 1) although our understanding in individual hazards has been greatly improved, there is a lack of sound knowledge about mechanism and processes of interacting multi-hazards (cascading, concurring and altering). Therefore, the resultant multi-hazard risk are often significantly underestimated with severe consequences (e.g., the cascading disasters of 2011 Tohoku Earthquake). It is also poorly understood about the spatial and temporal changes in hazards and vulnerability during successive hazards; 2) hazard monitoring, forecasting and early warning systems have not fully utilised the domain knowledge of physical processes and the statistical information of the observations; 3) uncertainties have not been well recognised in the current risk management practice, and ignorance of uncertainties could lead to major threat to the society and poor consideration with inefficient or unsustainable preferences of options; 4) the current hazard and risk assessments are fragmented with a weakness in holistically combining quantitative and qualitative information from a variety of sources; 5) there is an urgent need for the holistic (i.e., systems) thinking framework and decision support system (DSS) tools in adequate scenario assessment and resilience development from a harmonised and transdisciplinary perspective. It is our ambition for RESIST to deliver a research project that tackles the unsolved issues with a joint effort from a multidisciplinary team in social science, natural science, engineering and systems. The overall goal of RESIST is set to develop a holistic thinking framework and the next generation systems modelling platform for sustainable economic development considering social welfare and well-being to increase resilience to natural hazards in earthquake-prone regions in China. To achieve this goal, the following objectives are targeted: 1) to develop a Disaster Risk Information System (DRIS) by literature review, field trips, and new observations; 2) to study mechanisms and processes of interacting multi-hazards influenced by earthquakes, climate change and human activities; 3) to analyse holistic disaster risk assessment and mitigation measures; 4) to develop a whole systems resilience modelling platform for sustainable economic development and social welfare. China is a large country suffering from nearly all natural hazards of varying magnitudes, and the economic and social costs of disasters resulting from earthquakes, landslides, debris flows, and floods are immense. RESIST will contribute to increasing resilience to natural hazards in earthquake prone regions in China by a research partnership between UK and Chinese scientists. The developed new knowledge and models will improve our understanding about disaster risks which will benefit many countries around the world including the UK that suffer from natural hazard threats.