Characterising the English school stock using a unified national on-site survey and energy database

The recent commitment towards a net-zero target by 2050 will require considerable improvement to the UK’s building stock. Accounting for over 10% of the services energy consumption of the United Kingdom, the education sector will play an important role. This study aims to improve the understanding of English primary and secondary schools, using national on-site survey data with several large-scale disaggregate data sources. Property Data Survey Programme (PDSP) data on 18,970 schools collected between 2012 and 2014, Display Energy Certificate (DEC) and school census data from the same period were linked and processed to form a unified schools dataset. Statistical analyses were undertaken on 10,392 schools, with a focus on energy performance, and the relationship to several building and system characteristics. The analyses may point to the possibility of assessing operational energy use of schools in a more disaggregate manner. New datasets with detailed and accurate disaggregate information on characteristics of buildings, such as those used in this study, provide opportunities to develop more robust models of the building stock. Such data would provide an opportunity to identify pathways for reducing carbon emissions effectively and provide lessons for other organisations seeking to achieve significant reductions for achieving climate change goals

Developing a Data-driven School Building Stock Energy and Indoor Environmental Quality Modelling Method

The school building sector has a pivotal role to play in the transition to a low carbon UK economy. School buildings are responsible for 15% of the country’s public sector carbon emissions, with space heating currently making up the largest proportion of energy use and associated costs in schools. Children spend a large part of their waking life in school buildings. There is substantial evidence that poor indoor air quality and thermal discomfort can have detrimental impacts on the performance, wellbeing and health of schoolchildren and school staff. Maintaining high indoor environmental quality whilst reducing energy demand and carbon emissions in schools is challenging due to the unique operational characteristics of school environments, e.g. high and intermittent occupancy densities or changes in occupancy patterns throughout the year. Furthermore, existing data show that 81% of the school building stock in England was constructed before 1976. Challenges facing the ageing school building stock may be exacerbated in the context of ongoing and future climate change. In recent decades, building stock modelling has been widely used to quantify and evaluate the current and future energy and indoor environmental quality performance of large numbers of buildings at the neighbourhood, city, regional or national level. Building stock models commonly use building archetypes, which aim to represent the diversity of building stocks through frequently occurring building typologies. The aim of this paper is to introduce the Data dRiven Engine for Archetype Models of Schools (DREAMS), a novel, data-driven, archetype-based school building stock modelling framework. DREAMS enables the detailed representation of the school building stock in England through the statistical analysis of two large scale and highly detailed databases provided by the UK Government: (i) the Property Data Survey Programme (PDSP) from the Department for Education (DfE), and (ii) Display Energy Certificates (DEC). In this paper, the development of 168 building archetypes representing 9,551 primary schools in England is presented. The energy consumption of the English primary school building stock was modelled for a typical year under the current climate using the widely tested and applied building performance software EnergyPlus. For the purposes of modelling validation, the DREAMS space heating demand predictions were compared against average measured energy consumption of the schools that were represented by each archetype. It was demonstrated that the simulated fossil-thermal energy consumption of a typical primary school in England was only 7% higher than measured energy consumption (139 kWh/m2/y simulated, compared to 130 kWh/m2/y measured). The building stock model performs better at predicting the energy performance of naturally ventilated buildings,which constitute 97% of the stock, than that of mechanically ventilated ones. The framework has also shown capabilities in predicting energy consumption on a more localised scale. The London primary school building stock was examined as a case study. School building stock modelling frameworks such as DREAMS can be powerful tools that aid decision-makers to quantify and evaluate the impact of a wide range of building stock-level policies, energy efficiency interventions and climate change scenarios on school energy and indoor environmental performance

Modelling platform for schools (MPS): The development of an automated One-By-One framework for the generation of dynamic thermal simulation models of schools

The UK Government has recently committed to achieve net zero carbon status by year 2050. Schools are responsible for around 2% of the UK’s total energy consumption, and around 15% of the UK public sector’s carbon emissions. A detailed analysis of the English school building stock’s performance can help policymakers improve its energy efficiency and indoor environmental quality. Building stock modelling is a technique commonly used to quantify current and future energy demand or indoor environmental quality performance of large numbers of buildings at the neighbourhood, city, regional or national level. ‘Building-by-building’ stock modelling is a modelling technique whereby individual buildings within the stock are modelled and simulated, and performance results are aggregated and analysed at stock level. This paper presents the development of the Modelling Platform for Schools (MPS) – an automated generation of one-by-one thermal models of schools in England through the analysis and integration of a range of data (geometry, size, number of buildings within a school premises etc.) from multiple databases and tools (Edubase/Get Information About Schools, Property Data Survey Programme, Ordanance Survey and others). The study then presents an initial assessment and evaluation of the modelling procedure of the proposed platform. The model evaluation has shown that out of 15,245 schools for which sufficient data were available, nearly 50% can be modelled in an automated manner having a high level of confidence of similarity with the actual buildings. Visual comparison between automatically-generated models and actual buildings has shown that around 70% of the models were, indeed, geometrically accurate

Indoor Air Quality and Overheating in UK Classrooms – an Archetype Stock Modelling Approach

Children spend a large part of their waking lives in school buildings. There is substantial evidence that poor indoor air quality (IAQ) and thermal discomfort can have detrimental impacts on the performance, wellbeing and health of schoolchildren and staff. Maintaining good IAQ while avoiding overheating in classrooms is challenging due to the unique occupancy patterns and heat properties of schools. Building stock modelling has been extensively used in recent years to quantify and evaluate performance of large numbers of buildings at various scales. This paper builds on an archetype stock modelling approach which represents the diversity of the school stock in England through an analysis of The Property Data Survey Programme (PDSP) and the Display Energy Certificates (DEC) databases. The model was used for simulating Indoor-to-Outdoor pollution ratios to estimate indoor air pollution levels (NO2, PM2.5 and CO2) and thermal comfort (overheating) in two climate areas in England: London and the West Pennines. analysis highlighted variations in classrooms' indoor CO2 levels in different seasons and explored the risk of overheating in relation to a classroom's orientation

Reducing emissions in London schools with photovoltaics

This paper examines the potential for PV to improve the performance of primary schools in London. Disaggregate data including energy use is compared with modelled PV generation, showing that electricity demand could theoretically be met in 59% of the schools investigated. The impact of several key factors is then considered, including architectural heritage, building age and form. The results show that the greatest PV potential exists in newer schools, as well as those that are shorter and with less dense forms

Chemotaxis When Bacteria Remember: Drift versus Diffusion

{\sl Escherichia coli} ({\sl E. coli}) bacteria govern their trajectories by switching between running and tumbling modes as a function of the nutrient concentration they experienced in the past. At short time one observes a drift of the bacterial population, while at long time one observes accumulation in high-nutrient regions. Recent work has viewed chemotaxis as a compromise between drift toward favorable regions and accumulation in favorable regions. A number of earlier studies assume that a bacterium resets its memory at tumbles -- a fact not borne out by experiment -- and make use of approximate coarse-grained descriptions. Here, we revisit the problem of chemotaxis without resorting to any memory resets. We find that when bacteria respond to the environment in a non-adaptive manner, chemotaxis is generally dominated by diffusion, whereas when bacteria respond in an adaptive manner, chemotaxis is dominated by a bias in the motion. In the adaptive case, favorable drift occurs together with favorable accumulation. We derive our results from detailed simulations and a variety of analytical arguments. In particular, we introduce a new coarse-grained description of chemotaxis as biased diffusion, and we discuss the way it departs from older coarse-grained descriptions.Comment: Revised version, journal reference adde

A Characterization of Scale Invariant Responses in Enzymatic Networks

An ubiquitous property of biological sensory systems is adaptation: a step increase in stimulus triggers an initial change in a biochemical or physiological response, followed by a more gradual relaxation toward a basal, pre-stimulus level. Adaptation helps maintain essential variables within acceptable bounds and allows organisms to readjust themselves to an optimum and non-saturating sensitivity range when faced with a prolonged change in their environment. Recently, it was shown theoretically and experimentally that many adapting systems, both at the organism and single-cell level, enjoy a remarkable additional feature: scale invariance, meaning that the initial, transient behavior remains (approximately) the same even when the background signal level is scaled. In this work, we set out to investigate under what conditions a broadly used model of biochemical enzymatic networks will exhibit scale-invariant behavior. An exhaustive computational study led us to discover a new property of surprising simplicity and generality, uniform linearizations with fast output (ULFO), whose validity we show is both necessary and sufficient for scale invariance of enzymatic networks. Based on this study, we go on to develop a mathematical explanation of how ULFO results in scale invariance. Our work provides a surprisingly consistent, simple, and general framework for understanding this phenomenon, and results in concrete experimental predictions

Comparison of T-Cell Interferon-γ Release Assays for Mycobacterium tuberculosis-Specific Antigens in Patients with Active and Latent Tuberculosis

Through the use of QuantiFERON-TB Gold, a commercial IFN-γ assay, we compared differences in quantitative T-cell responses to Mycobacterium tuberculosis (MTB)-specific antigens [QuantiFERON TB-2G (QFT-2G)] between patients with active tuberculosis (TB) disease and those with latent TB infection (LTBI). The patient group consisted of 180 patients with active TB disease (culture-positive for MTB) and 50 screening contacts with LTBI-positive response to the QFT-2G test. We prospectively performed a tuberculin skin test (TST) and a QFT-2G test for all subjects. The median IFN-γ levels upon the application of both antigens, ESAT-6 and CFP-10, were significantly higher in patients with active TB disease than in those with LTBI. A combined positive response to both antigens occurred at a higher rate in patients with active TB disease than in those with LTBI. There were no significant relationships between the quantitative responses of IFN-γ to both antigens and the maximum induration on TST in both patient groups. We demonstrated significant differences in the quantitative responses of IFN-γ to MTB between patients with active TB disease and those with LTBI in this study. However, there was an overlap in the IFN-γ levels between active TB disease and LTBI groups. Therefore, it would be difficult to use the QFT-2G test to completely discriminate active TB disease from LTBI

IFNβ Protects Neurons from Damage in a Murine Model of HIV-1 Associated Brain Injury.

Infection with human immunodeficiency virus-1 (HIV-1) causes brain injury. Type I interferons (IFNα/β) are critical mediators of any anti-viral immune response and IFNβ has been implicated in the temporary control of lentiviral infection in the brain. Here we show that transgenic mice expressing HIV-1 envelope glycoprotein 120 in their central nervous system (HIVgp120tg) mount a transient IFNβ response and provide evidence that IFNβ confers neuronal protection against HIVgp120 toxicity. In cerebrocortical cell cultures, neuroprotection by IFNβ against gp120 toxicity is dependent on IFNα receptor 1 (IFNAR1) and the β-chemokine CCL4, as IFNAR1 deficiency and neutralizing antibodies against CCL4, respectively, abolish the neuroprotective effects. We find in vivo that IFNβ mRNA is significantly increased in HIVgp120tg brains at 1.5, but not 3 or 6 months of age. However, a four-week intranasal IFNβ treatment of HIVgp120tg mice starting at 3.5 months of age increases expression of CCL4 and concomitantly protects neuronal dendrites and pre-synaptic terminals in cortex and hippocampus from gp120-induced damage. Moreover, in vivo and in vitro data suggests astrocytes are a major source of IFNβ-induced CCL4. Altogether, our results suggest exogenous IFNβ as a neuroprotective factor that has potential to ameliorate in vivo HIVgp120-induced brain injury