Professor Susan J Hughes

Objective The aim of this study is to co-create an evidence-based and theoretically informed web-based intervention (RESTORE) designed to enhance self-efficacy to live with cancer-related fatigue (CRF) following primary cancer treatment. Methods A nine-step process informed the development of the intervention: (1) review of empirical literature; (2) review of existing patient resources; (3) establish theoretical framework; (4) establish design team with expertise in web-based interventions, CRF and people affected by cancer; (5) develop prototype intervention; (6) user testing phase 1; (7) refinement of prototype; (8) user testing phase 2; and (9) develop final intervention. Results Key stakeholders made a critical contribution at every step of intervention development, and user testing, which involved an iterative process and resulted in the final intervention. The RESTORE intervention has five sessions; sessions 1 and 2 include an introduction to CRF and goal setting. Sessions 3–5 can be tailored to user preference and are designed to cover areas of life where CRF may have an impact: home and work life, personal relationships and emotional adjustment. Conclusions It is feasible to systematically ‘co-create’ an evidence-based and theory-driven web-based self-management intervention to support cancer survivors living with the consequences of cancer and its treatment. This is the first account of the development of a web-based intervention to support self-efficacy to manage CRF. An exploratory trial to test the feasibility and acceptability of RESTORE is now warranted. Copyright © 2015 John Wiley & Sons, Ltd.

Through the regulatory planning process, UK local government has detailed information on contaminants for most of their development (predominantly brownfield) sites, dating back to 2000. The soil data collected includes information on polycyclic aromatic hydrocarbons, PAH, a group of potentially carcinogenic chemicals. This paper describes how an evidence-based portfolio of PAH contamination has been generated from this site measured dataset and developed into a process route map to facilitate PAH data analyses in other regions. The PAH data, site history and borehole information were extracted from 1990 to 2019 records of 120 brownfield sites in Elmbridge Borough, a suburban region southwest of London. The data were interrogated using spatial and depth analysis, diagnostic ratios and chemical composition analysis. Elevated PAH concentrations were predominantly found within Made Ground and indicated that the major source was anthropogenic deposition. The results from the diagnostic ratios indicated a pyrogenic, non-traffic related source for 90% of the samples. Statistical analysis showed low median concentration values with a few high outliers for all PAHs studied. Most soil samples had a similar PAH composition, irrespective of site history or soil sample description. This PAH composition was found on residential sites, where the likely source was coal-based burning. The data thus suggested a coal-based source for most of the PAH contamination in the study area. The results for PAH contamintation on brownfield sites in suburban Surrey were similar to a UK study for a UK coal-based industrial area. The PAH concentrations found were significantly higher than those reported in other international studies, however this was partly due to different sampling techniques. A process route map is proposed which incorporates both the analytical processes and the evidence-based portfolio of key findings. This route map is portable to different regions and consequently can be used as a platform for the analysis of PAH data to inform developers and regulators of potential sources and distribution in a region. [Display omitted] •Site evidence of UK suburban brownfield PAH concentrations and distribution presented.•Diagnostic ratios indicated pyrogenic, non traffic source.•Anthropological, but non airborne, deposition of PAH contamination determined.•PAH composition was similar on 120 sites, reflecting a probable coal-based source.•Route map of PAH analytical processes proposed for application to any area.

Ensemble methods are increasingly used in data assimilation for numerical weather prediction. These methods utilize sample covariance matrices that are subject to sampling error, which is commonly addressed by application of a localisation. The form of the localisation is usually ad-hoc. This paper presents results from applying a series of theoretically optimal localisations, derived for assimilating a single observation (sparse density), to a Gaussian model state. The theoretical localisations included are optimal localisation for a single true covariance (OSTC), optimal localisation for a variable true covariance (OVTC), which includes knowledge of the climatology and optimal hybrid localisation for a variable true covariance (HOVTC) which damps the difference from the mean covariance as opposed to the covariance itself. The optimal localisations and Gaussian localisation perform similarly for sparse observations. For dense observations, the theoretical assumptions do not hold, and the optimal localisations break down, but the Gaussian, which is retuned, continues to perform well. HOVTC localisation is shown to outperform traditional forms of localisation in the single observation cases. A tuned hybrid localisation is proposed based on the form of the optimal hybrid localisation and this is shown to perform well in all ranges of observation density and assimilation strengths. The paper shows that theoretically derived localisations can produce improved assimilation performance for a range of observation densities and assimilation strengths in a Gaussian model scenario. It provides the proof of concept that studying the optimal localisation can inform the improvement of localisation regimes for more complex models.

Contamination information is obtained at considerable expense through site investigations by developers in the UK. This field information is submitted to local government as part of the planning process. Local government, therefore, has a vast resource of site-specific contamination data for developments in their area. These data are focussed on brownfield sites. They are in hard quality format with some data quality issues, particularly for older site investigations. This research presents the results of a study to access and analyse this data. The dataset is evaluated in terms of the information available, its form, quality and the potential use of this information for researchers, regulators and developers. A preliminary analysis of the data is presented in which the main contaminants in the study area were investigated and local anomalies such as elevated lead levels were highlighted. In addition, asbestos was found at low concentrations in 25% of made ground soil samples; a result requiring further study to inform future risk assessment. The potential use of this countrywide dataset is currently limited by the hardcopy format and storage of the dataset. Updating the submission to digital format could result in a vast national resource of brownfield information.

There has been considerable brownfield development in the UK since 2000 due to increasing demand for new housing, combined with local opposition to building on greenbelt land. To facilitate this, extensive site investigations have been carried out and the reports submitted to local government as part of the planning process. This research investigates whether this largely untapped resource of site investigation data can be used to improve understanding of potentially toxic elements (PTE) and persistent organic pollutants (POP) at a local scale. The PTE/POP data were extracted from 1707 soil samples across 120 brownfield sites in an urban/suburban region. The samples were analysed to determine the effect of site location, historical use and site age on PTE/POP concentrations. Box plots indicating statistical results together with GIS maps of PTE/POP sample data provided the optimal visualisation of results. The dataset was shown to be a valuable resource, although further exploitation would be enhanced by digitisation of the submitted data. The paper explores potential applications of this data, including background concentrations and anthropogenic enrichment factors for PTE/POP. The results were summarised in a table for the PTE/POP and a preliminary risk assessment process chart to inform developers/regulators on potential PTE/POP levels on brownfield sites on a local scale. This information could focus design and resources for developers for site investigations and risk assessments and improve planning and regulatory guidance. The lack of predictability in PTE/POP results across sites have emphasised the ongoing need for intrusive site investigation on new brownfield developments.

The development of brownfield sites in the UK requires site investigation for contamination as part of the regulatory planning process. This site investigation includes the collection of soil samples that are analysed for asbestos contamination. This research project analyses this untapped resource of brownfield asbestos data, using site investigation data submitted to a local Borough Council, in Surrey, UK. Over 100 site investigation reports were collected. This paper presents the trends in asbestos testing from 2001 to 2019 and data on the location, concentration, form and type of asbestos found in this Borough. The rate of asbestos detection has increased significantly, reflecting improvements in asbestos measuring techniques, to a rate of 22% for all samples tested between 2016 and 2019. The concentrations of asbestos found were very low, with 74% of samples having concentrations below the limit of detection of the laboratory and were predominantly of fibrous form. A significant proportion of samples contained more carcinogenic amphibole type. Most of the asbestos was found in the top 1 m of Made Ground soil. Site history was found to be important with gas works having the highest asbestos detection rate, though a detection rate of 10% was found in soil samples on former residential sites. This information is important to inform and update risk assessment for site workers and site remediation in relation to asbestos. Hence based upon these results, a qualitative risk chart for asbestos is presented to provide guidance and in-sight into asbestos on brownfield sites for local authorities and developers.

Meeting the 2050 net zero carbon target requires all sectors of civil engineering to include the reduction of carbon emissions within their designs. This paper presents the development and early achievements of a Carbon Reduction Design Tool (CReDiT) for excavation and clean cover remediation of brownfield sites. The tool was developed for this remediation technique as it is the dominant remediation method used on residential redevelopment sites within the UK. CReDiT determines carbon emissions from the complex processes involved in this form of remediation, e.g., excavation of soil, use of raw materials, transport of material and the waste, recycling and reuse of materials. The application of the tool, to evaluate carbon emissions from excavation and cover remediation options applied to a former landfill site, is presented. In this case study, CReDiT quantifies carbon emissions and material volumes for proposed design options. It also demonstrates the carbon savings that can be made by the effective reuse of material onsite and the contribution of waste materials to carbon emissions. Additional carbon savings through the reuse or recycling of carbon-rich or valuable materials are also calculated. The use of CReDiT has led to a rethink of remediation excavation and cover design. Excavated /waste materials are re-evaluated as a resource; material reuse options are assessed and carbon reduction is included in the design process. This leads to more sustainable remediation solutions.

Cellular Automata (CA) models are widely used to study spatial dynamics of urban growth and evolving patterns of land use. One complication across CA approaches is the relatively short period of data available for calibration, providing sparse information on patterns of change and presenting problematic signal-to-noise ratios. To overcome the problem of short-term calibration, this study investigates a novel approach in which the model is calibrated based on the urban morphological patterns that emerge from a simulation starting from urban genesis, i.e., a land cover map completely void of urban land. The application of the model uses the calibrated parameters to simulate urban growth forward in time from a known urban configuration. This approach to calibration is embedded in a new framework for the calibration and validation of a Constrained Cellular Automata (CCA) model of urban growth. The investigated model uses just four parameters to reflect processes of spatial agglomeration and preservation of scarce non-urban land at multiple spatial scales and makes no use of ancillary layers such as zoning, accessibility, and physical suitability. As there are no anchor points that guide urban growth to specific locations, the parameter estimation uses a goodness-of-fit (GOF) measure that compares the built density distribution inspired by the literature on fractal urban form. The model calibration is a novel application of Markov Chain Monte Carlo Approximate Bayesian Computation (MCMC-ABC). This method provides an empirical distribution of parameter values that reflects model uncertainty. The validation uses multiple samples from the estimated parameters to quantify the propagation of model uncertainty to the validation measures. The framework is applied to two UK towns (Oxford and Swindon). The results, including cross-application of parameters, show that the models effectively capture the different urban growth patterns of both towns. For Oxford, the CCA correctly produces the pattern of scattered growth in the periphery, and for Swindon, the pattern of compact, concentric growth. The ability to identify different modes of growth has both a theoretical and practical significance. Existing land use patterns can be an important indicator of future trajectories. Planners can be provided with insight in alternative future trajectories, available decision space, and the cumulative effect of parcel-by-parcel planning decisions.

Motorized traffic discourages cycling, but the relative influence of different aspects of traffic intensity on commuter cycling rates is under-examined. This paper investigates these influences. It employs census data that describes the origins, destinations and mode choices of commuters travelling 2-5km in Surrey, UK (n = 172,665) and derives the shortest cycling route available for each commuter. Observed and modelled transport data is used to characterize traffic on these routes. The relationship between route-level traffic characteristics and the probability a commuter chooses to cycle is then examined using logit models for all commuters, males (42%) and females (58%). Two models consider the following aspects of traffic intensity both along the cycling route and crossing (intersecting) the route at junctions: speed; volume; and the proportion of heavy goods vehicles. The models also consider the influence of separated cycle paths, hilliness and distance. Above-median traffic speeds (>29km/h) along a commuting route is shown to have the greatest negative influence on cycling propensity, followed by above-median traffic volumes (>273 veh/h) and above-median speeds (in combination) crossing the commuting route. Cycle paths have a positive influence on cycling propensity, whereas the proportion of heavy goods vehicles does not show a significant relationship. The results imply that careful management of traffic at the route scale or the provision of separated cycle paths could encourage cycling on key commuting corridors. The relative influence of different characteristics could also identify the optimal locations for intervention. Findings support 30km/h (20mph) speed limits as a mechanism for encouraging cycling.

Characterisation of the urban expansion processes using time series of binary urban/non-urban land cover data is complex due to the need to account for the initial configuration and the rate of urban expansion over the analysed period. Failure to account for these factors makes the interpretation of landscape metrics for compactness, fragmentation, or clumpiness problematic and the comparison between geographical areas and time periods contentious. This paper presents an approach for characterisation using spatio-dynamic modelling which is data-centred using a process based model, Bayesian optimization, cluster identification, and maximum likelihood classification. An application of the approach across 652 functional urban areas in Europe (1975-2014) demonstrates the consistency of the approach and its ability to identify spatial and temporal trends in urban expansion processes.

Scenarios of future urban expansion are expected to be plausible: they must be diverse to reflect future uncertainty, yet realistic in their depiction of urban expansion processes. We investigated the plausibility of scenarios derived from a novel data-driven simulation approach. In a Turing-like test, experts completed a quiz in which they were asked to identify the map showing true urban expansion amidst three model-generated scenarios. Across diverse expansion patterns, ranging from compact to dispersed, the experts had no significant ability to identify the true pattern. The results support the hypothesis that the investigated scenarios are plausible and hence that cluster analysis of estimated dynamic models is a viable method for producing scenarios of future urban expansion.

When strong rock masses, with discontinuity patterns parallel and perpendicular to the ground surface, are subjected to normal loads, linear or concave stress-deformation curves are produced. In contrast, chalk rock masses with the same discontinuity pattern, produce convex curves. This paper investigates the underlying mechanisms, which may be responsible for such differences. Experimental results are presented for profiled chalk specimens in which the contact area at the discontinuity boundary is approximately 15% of the specimen cross-sectional area. It was found that these low contact area specimens exhibited both concave and convex behavior. This behavior was attributed to discontinuity closure and yielding of the intact material, respectively. The overall trend in behavior was found to be a function of the contact area at the discontinuity boundary, the initial discontinuity aperture, and the yield stress. © 2007 ASCE.

The processes of urban growth vary in space and time. There is a lack of model transferability, which means that models estimated for a particular study area and period are not necessarily applicable for other periods and areas. This problem is often addressed through scenario analysis, where scenarios reflect different plausible model realisations based typically on expert consultation. This study proposes a novel framework for data-driven scenario development which, consists of three components - (i) multi-area, multi-period calibration, (ii) growth mode clustering, and (iii) cross-application. The framework finds clusters of parameters, referred to as growth modes: within the clusters, parameters represent similar spatial development trajectories; between the clusters, parameters represent substantially different spatial development trajectories. The framework is tested with a stochastic dynamic urban growth model across European functional urban areas over multiple time periods, estimated using a Bayesian method on an open global urban settlement dataset covering the period 1975–2014. The results confirm a lack of transferability, with reduced confidence in the model over the validation period, compared to the calibration period. Over the calibration period the probability that parameters estimated specifically for an area outperforms those for other areas is 96%. However, over an independent validation period, this probability drops to 72%. Four growth modes are identified along a gradient from compact to dispersed spatial developments. For most training areas, spatial development in the later period is better characterized by one of the four modes than their own historical parameters. The results provide strong support for using identified parameter clusters as a tool for data-driven and quantitative scenario development, to reflect part of the uncertainty of future spatial development trajectories. A promising further application is to use the growth modes to characterize past spatial development patterns. A trend of increasingly dispersed patterns could be identified over the studied functional urban areas which calls for more detailed explorations.

Women are under-represented in commuter cycling in England and Wales. Consequently, women miss out on the health benefits of active commuting over distances where walking is less practical. Similarly, where cycling could replace motorised forms of transport, society is missing out on the wider health benefits associated with reductions in air pollution, road noise and social severance. This paper uses aggregate (ecological) models to investigate the reasons behind the gender gap in cycling. The relative attractiveness of cycling in different areas is described using a set of 17 determinants of commuter cycling mode share: distance, population density, cycle paths, cycle lanes, traffic density, hilliness, temperature, sun, rain, wind, wealth, lower social status, children, green votes, bicycle performance, traffic risk and parking costs. The correlation between these determinants and census-recorded cycling mode share is examined in logit models for commuters who work 2-5 km from home. The models explain a large share of the variation in cycling levels. There are small but significant differences in the importance of individual determinants between men and women. However, the gender gap is largely explained by a differentiated response to the relative attractiveness of an area for cycling, the sum effect of all determinants. The ratio of male to female cycling rates is greatest in areas that are less attractive for cycling, whereas in the most attractive areas the ratio approaches parity. On average, women require a more conducive environment for cycling than men. Since the typical environment in England and Wales is not conducive for cycling, women are under-represented in commuter cycling rates and miss out on the health dividend. The results suggest improvements to the cycling environment may be moderated by the existing attractiveness of the environment for cycling, with improvements in less attractive areas having a smaller absolute effect on cycling rates.

The physical and mental health benefits of cycling are well established. During the COVID-19 pandemic cycling has also presented additional health benefits by enabling social distancing compared to public transport modes. In low-cycling countries these benefits are unevenly realised, with substantial differences in cycling mode share by age and gender. In England and Wales women are four times less likely to commute by bicycle than men; and commuters aged 35–49 cycle more than other age categories. This paper explores these demographic effects and their interactions. It uses logit models to examine the relationship between 17 determinants of cycling mode share and cycling rates for six demographic groups (males and females in age categories of 18-34, 35–49 and 50–74) across 29,694 small geographic units in England and Wales. The determinants comprise: distance; population density; cycle paths; cycle lanes; traffic density; hilliness; temperature; sun; rain; wind; wealth; lower social status; children; green votes; bicycle performance; traffic risk and parking costs. Determinants associated with physical effort (hilliness and distance) and traffic (traffic density and cycle lanes) are more important in the older age groups for both men and women. More important than the qualitative mix of determinants is their combined effect, or utility. Women require a higher threshold of utility to start cycling than men; and in higher utility environments gender differences are almost non-existent. Differences in cycling rates by age-group also reduce in higher utility environments, although the effects are less pronounced and older commuters still cycle less than other age-groups even in the highest utility environemnts. The results provide insight into the relative importance of gender versus age, and illustrate that cycling rates are more strongly associated with gender than age. For both dimensions, better cycling environments are shown to be more equal cycling environments. •The analysis examines interactions between age, gender and determinants of cycling.•Gender has a greater influence on commuter cycling behaviours than age.•Physical and risk factors may be more important for older commuters.•More supportive cycling environments are more equal in terms of both age and gender.

The design of low to medium-rise buildings is based on quasi-static analysis of wind loading. Such procedures do not fully address issues such as interference from other structures, wind directionality, across-wind response and dynamic effects including acceleration, structural stiffness and damping which influence comfort criteria of the occupants. This paper studies wind loads on a prototype, rectangular cross-section building, 80 m high. Computational Wind Tunnel (CWT) tests were performed using Autodesk Flow Design with the buildings located in London and New York City. The analysis included tests with and without the surrounding structures and manual computation of wind loads provided data for comparison. Comfort criteria (human response to building motion) were assessed from wind-induced horizontal peak accelerations on the top floor. As expected, analytical methods proved conservative, with wind pressures significantly larger than those from the CWT tests. Surrounding structures reduced the mean component of the wind action. As for comfort criteria, across-wind direction governed the horizontal accelerations with wind targeted on the building’s narrow face. CWT tests provide a cheaper alternative to experimental wind tunnel tests and can be used as preliminary design tools to aid civil engineers, architects and designers with high-rise developments in urban environments.

Satellite infrared sounders are invaluable tools for making observations of the structure of the atmosphere. They provide much of the observational data used to initialize atmospheric models, especially in regions that do not have extensive surface-based observing systems, such as oceans. However, information is lacking in the presence of cloud, as the cloud layer is opaque to infrared radiation. This means that where information is most desired (such as in a developing storm) it is often in the shortest supply. In order to explore the mathematics of assimilating data from cloudy radiances, a study has been performed using an idealized single-column atmospheric model. The model simulates cloud development in an atmosphere with vertical motion, allowing the characteristics of a 2D-Var data assimilation system using a single simulated infrared satellite observation taken multiple times to be studied. The strongly nonlinear nature of cloud formation poses a challenge for variational methods. The adjoint method produces an accurate gradient for the cost function and minimization is achieved using preconditioned conjugate gradients. The conditioning is poor and varies strongly with the atmospheric variables and the cost function has multiple minima, but acceptable results are achieved. The assimilation system is provided with a prior forecast simulated by adding random correlated Gaussian error to the truth. Assimilating observations comparable to those available from current geostationary satellites allows vertical motion to be retrieved with an error of less than a centimetre per second in most conditions. Moreover, evaluating the second derivative of the cost function at the minimum provides an estimate of the uncertainty in the retrieval. This allows atmospheric states that do not provide sufficient information for retrieval of vertical motion to be detected (such as a cloudless atmosphere or a non-moving opaque cloud layer in the upper troposphere). Retrieval is most accurate with upwards motion.