Estimating the Influence of Housing Energy Efficiency and Overheating Adaptations on Heat-Related Mortality in the West Midlands, UK

Taylor, Jonathon; Symonds, P.; Wilkinson, Paul; Heaviside, Clare; Macintyre, Helen L.; Davies, Martin; Mavrogianni, Anna; Hutchinson, Emma

doi:10.3390/atmos9050190

Cited by 31 publications

(27 citation statements)

References 30 publications

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“…In addition, the EPC data was parameterised a second time to represent the complete retrofit of the housing stock to increase energy efficiency, reflecting changes to building fabric thermal efficiency and airtightening. Fabric U-values were reduced to the minimum possible for dwelling age and fabric type according to SAP (Taylor et al, 2018). Reductions in airtightness were estimated first as changes to the dwelling air change rate (ach) following floor sealing and draught-stripping (using the reductions specified in the SAP model), or cavity wall, solid wall, or loft insulation (using estimated reductions from Hong et al (2004)).…”

Section: Methodsmentioning

confidence: 99%

Application of an indoor air pollution metamodel to a spatially-distributed housing stock

Taylor¹,

Shrubsole²,

Symonds³

et al. 2019

Science of The Total Environment

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Estimates of population air pollution exposure typically rely on the outdoor component only, and rarely account for populations spending the majority of their time indoors. Housing is an important modifier of air pollution exposure due to outdoor pollution infiltrating indoors, and the removal of indoor-sourced pollution through active or passive ventilation. Here, we describe the application of an indoor air pollution modelling tool to a spatially distributed housing stock model for England and Wales, developed from Energy Performance Certificate (EPC) data and containing information for approximately 11.5 million dwellings. First, we estimate indoor/outdoor (I/O) ratios and total indoor concentrations of outdoor air pollution for PM 2.5 and NO 2 for all EPC dwellings in London. The potential to estimate concentration from both indoor and outdoor sources is then demonstrated by modelling indoor background CO levels for England and Wales pre- and post-energy efficient adaptation, including heating, cooking, and smoking as internal sources. In London, we predict a median I/O ratio of 0.60 (99% CIs; 0.53–0.73) for outdoor PM 2.5 and 0.41 (99%CIs; 0.34–0.59) for outdoor NO 2 ; Pearson correlation analysis indicates a greater spatial modification of PM 2.5 exposure by housing (ρ = 0.81) than NO 2 (ρ = 0.88). For the demonstrative CO model, concentrations ranged from 0.4–9.9 ppm (99%CIs)(median = 3.0 ppm) in kitchens and 0.3–25.6 ppm (median = 6.4 ppm) in living rooms. Clusters of elevated indoor concentration are found in urban areas due to higher outdoor concentrations and smaller dwellings with reduced ventilation potential, with an estimated 17.6% increase in the number of living rooms and 63% increase in the number of kitchens exceeding recommended exposure levels following retrofit without additional ventilation. The model has the potential to rapidly calculate indoor pollution exposure across large housing stocks and estimate changes to exposure under different pollution or housing policy scenarios.

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Section: Methodsmentioning

confidence: 99%

Application of an indoor air pollution metamodel to a spatially-distributed housing stock

Taylor¹,

Shrubsole²,

Symonds³

et al. 2019

Science of The Total Environment

Self Cite

View full text Add to dashboard Cite

show abstract

“…18 and Taylor et al. 47 in similar modelling studies of UK dwellings – solar control interventions prevent solar gains from penetrating into the dwelling and being absorbed by building fabric during daytime which is why additional space heating is required to maintain indoor temperatures during winter. Furthermore, ventilation 2 h prior to bedtime and internal curtains are found to be the fourth and fifth most effective intervention, reducing overheating by 64% and 57%, respectively.…”

Section: Discussionmentioning

confidence: 99%

Indoor overheating and mitigation of converted lofts in London, UK

Taylor

Symonds

2019

Building Services Engineering Research and Technology

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In the UK, there has been an increase in the number of loft conversions in properties, driven by demands for increased floor areas of dwellings to accommodate more individuals or increase property values. While rooms directly underneath roofs are known to have increased overheating risks, there is little research available that quantifies this risk, and how to mitigate it cost-effectively. This paper seeks to evaluate overheating risks in loft conversions, using Integrated Environmental Solutions Virtual Environment to dynamically simulate indoor temperatures in a semi-detached dwelling in London, UK, under current and future (2050s and 2080s medium and high emissions) climate scenarios. Adaptive overheating risk and energy consumption is calculated with and without passive overheating adaptations that reduce solar gains, increase ventilation, or add thermal insulation. Marginal Abatement Cost Curves (MACC) are then used to select the most cost-effective adaptations based on installation and ongoing energy consumption costs. Results estimate 11,340 -12,210 more summertime Category I overheating degree hours for the loft than conventional bedrooms in the dwelling under the current climate; total Category I loft overheating degree hours may increase to 20,319 by 2080. While external shutters and night purge ventilation were the most effective at reducing overheating degree hours (96% and 89%, respectively), the most cost-effective solutions considering capital and ongoing costs are ventilation strategies, including nighttime purge ventilation, advance ventilation, and cross ventilation. Passive adaptations are not capable of eliminating overheating entirely, and by the 2080s active cooling is likely to be required to maintain comfortable indoor conditions in lofts. Practical applicationsConverted lofts -present in 5.8% of English and 10.8% of London dwellings -are at significantly elevated risk of high indoor temperatures relative to conventional rooms. Passive adaptations such as ventilation and shading can effectively mitigate loft overheating until around 2080, after which active measures become necessary. When capital and ongoing costs are considered, the most costeffective heat mitigating adaptations are night and advance ventilation and internal curtains/blinds. Heat mitigating adaptations for converted lofts should become mandatory, and such spaces should not be occupied by the vulnerable or elderly during hot weather.

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“…According to Jessel et al [10], symptoms of arthritis, pulmonary, cardiovascular, and respiratory illnesses deteriorate in houses that are not adequately cool. Similarly, Taylor et al [13] show that increased mortality rates in the UK are directly related to severe weather conditions and, consequently, to extremely high temperatures inside the house. In this respect, a warmer world will imply that more people will need access to cooling systems worldwide to live in a healthy indoor environment [14].…”

Section: Introductionmentioning

confidence: 92%

Cooling Degree Models and Future Energy Demand in the Residential Sector. A Seven-Country Case Study

et al. 2021

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The intensity and duration of hot weather and the number of extreme weather events, such as heatwaves, are increasing, leading to a growing need for space cooling energy demand. Together with the building stock’s low energy performance, this phenomenon may also increase households’ energy consumption. On the other hand, the low level of ownership of cooling equipment can cause low energy consumption, leading to a lack of indoor thermal comfort and several health-related problems, yet increasing the risk of energy poverty in summer. Understanding future temperature variations and the associated impacts on building cooling demand will allow mitigating future issues related to a warmer climate. In this respect, this paper analyses the effects of change in temperatures in the residential sector cooling demand in 2050 for a case study of nineteen cities across seven countries: Cyprus, Finland, Greece, Israel, Portugal, Slovakia, and Spain, by estimating cooling degree days and hours (CDD and CDH). CDD and CDH are calculated using both fixed and adaptive thermal comfort temperature thresholds for 2020 and 2050, understanding their strengths and weaknesses to assess the effects of warmer temperatures. Results suggest a noticeable average increase in CDD and CDH values, up to double, by using both thresholds for 2050, with a particular interest in northern countries where structural modifications in the building stock and occupants’ behavior should be anticipated. Furthermore, the use of the adaptive thermal comfort threshold shows that the projected temperature increases for 2050 might affect people’s capability to adapt their comfort band (i.e., indoor habitability) as temperatures would be higher than the maximum admissible values for people’s comfort and health.

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Estimating the Influence of Housing Energy Efficiency and Overheating Adaptations on Heat-Related Mortality in the West Midlands, UK

Cited by 31 publications

References 30 publications

Application of an indoor air pollution metamodel to a spatially-distributed housing stock

Application of an indoor air pollution metamodel to a spatially-distributed housing stock

Indoor overheating and mitigation of converted lofts in London, UK

Cooling Degree Models and Future Energy Demand in the Residential Sector. A Seven-Country Case Study

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