Assessing heat vulnerability in London care settings: case studies of adaptation to climate change

Abstract

This pilot study aims at testing methods to assess heat vulnerability in London care homes and develop overheating reduction strategies to mitigate temperature exposure and the associated negative health impacts under the warming climate, with a view to scaling up the project on a national scale. It undertakes feasibility work to identify possible causes of overheating across a range of care home types and evaluate the current and future potential of indicative passive solutions. The summertime thermal environments of five case study care homes were monitored and their physical, technical and occupancy profiles were established through surveys. The data was inputed in the EnergyPlus V8.9 dynamic thermal simulations via the DesignBuilder Graphical User Interface. Future overheating risks and their reduction potential through the use of passive strategies were tested under a set of representative climate change scenarios, during a five-day heatwave period. The dynamic thermal simulation analysis indicated that older buildings with higher heat loss and thermal mass capacities are likely to benefit more from the application of high albedo materials rather than external shading methods, whereas newer and highly insulated buildings seem to benefit more from higher ventilation rates and appropriate external shading systems. Night ventilation emerged as the single most impactful passive technique for all building types. This feasibility work has developed novel methods, knowledge and insights that will be helpful in understanding how to enable care settings in the UK to become resilient to rising heat stress. This is one of the first systematic attempts to build a set of dynamic thermal models of care homes in the UK.

Full text

Assessing heat vulnerability in London care settings:
case studies of adaptation to climate change
Eleni Oikonomou1, Anna Mavrogianni1, Nishesh Jain1, Rajat Gupta2, Paul Wilkinson3, Alastair
Howard2, Ai Milojevic3, Mike Davies1
1University College London, London, UK
2Oxford Brookes University, Oxford, UK
3London School of Hygiene and Tropical Medicine, London, UK
Abstract
This pilot study aims at testing methods to assess heat
vulnerability in London care homes and develop
overheating reduction strategies to mitigate temperature
exposure and the associated negative health impacts under
the warming climate, with a view to scaling up the project
on a national scale. It undertakes feasibility work to
identify possible causes of overheating across a range of
care home types and evaluate the current and future
potential of indicative passive solutions.
The summertime thermal environments of five case study
care homes were monitored and their physical, technical
and occupancy profiles were established through surveys.
The data was inputed in the EnergyPlus V8.9 dynamic
thermal simulations via the DesignBuilder Graphical User
Interface. Future overheating risks and their reduction
potential through the use of passive strategies were tested
under a set of representative climate change scenarios,
during a five-day heatwave period. The dynamic thermal
simulation analysis indicated that older buildings with
higher heat loss and thermal mass capacities are likely to
benefit more from the application of high albedo materials
rather than external shading methods, whereas newer and
highly insulated buildings seem to benefit more from
higher ventilation rates and appropriate external shading
systems. Night ventilation emerged as the single most
impactful passive technique for all building types.
This feasibility work has developed novel methods,
knowledge and insights that will be helpful in
understanding how to enable care settings in the UK to
become resilient to rising heat stress. This is one of the
first systematic attempts to build a set of dynamic thermal
models of care homes in the UK.
Introduction
The UK’s ageing population, and particularly people over
65 residing in care homes, are at the highest risk of heat-
related mortality. Understanding the factors that
contribute to high indoor temperatures in care homes is
crucial for developing strategies to avoid summertime
indoor overheating and the associated negative health
impacts, which are expected to intensify as a result of
climate change. The literature regarding overheating in
UK care homes is sparse but there is some previous
evidence to suggest that new-build care settings today are
already overheating even under non-extreme summers
(Gupta et al., 2017). A 2012 study suggested concerns
about overheating were common across all five
participating extra-care schemes in England (Barnes, et
al., 2012).
The aim of this pilot work is to test methods to assess
future overheating risks and to evaluate the effectiveness
of overheating mitigation strategies via detailed building
thermal modelling, with a view to scaling up the project
to a national scale. It has developed a research approach
to assess, understand and address heat vulnerability across
a range of care settings. This is the first time focus is
placed on the specific barriers to overheating mitigation
characterising care settings (e.g. layouts of purpose built
or converted care homes, window opening restrictions
due to security concerns, heat management practices and
decision making practices on behalf of vulnerable
residents etc.). The specific objectives are to identify
possible causes of overheating in five case study care
homes and test the effectiveness of indicative soft- and
hard- engineered passive solutions in reducing the
residents’ temperature exposure, under the current and
future climate.
Dynamic thermal simulations
Empirical work was undertaken in five care homes in
London to monitor the summertime conditions and
understand the associated comfort levels experienced by
residents and staff, model future overheating risks and
investigate the effectiveness of overheating mitigation
strategies on thermal comfort and health outcomes under
a range of current and future climate scenarios. A range
of behaviour change, management practice, building
design, retrofit and operation scenarios were tested.
Care home selection and characterisation
Five London-based care homes case studies (CS1, CS2,
CS3, CS4 and CS5) were purposively recruited either
directly via the Care Quality Commission’s (CQC)
database (CQC, 2020) or indirectly through the assistance
of CQC. All five offer both residential and nursing care
and are located in various parts of central, west and north-
east London. They present a range of characteristics in
terms of occupant capacity, building typology, age and
construction, as shown in Table 1. Their occupants fall
into two main categories: (a) those not independently
mobile, i.e. bedbound or requiring more intense
nursing/care and (b) those more independent and able-
bodied that spend a significant amount of time in common
rooms during the day.
A survey was undertaken to establish each building’s
physical, technical and occupancy profile to be used as

input to the dynamic thermal simulation models. A ‘walk-
through’ was arranged in each case, where one member
of staff accompanied the visiting researcher. Data
collection was implemented via observation,
photographic evidence, architectural drawings, technical
paperwork and verbal communication with the
accompanying member of staff and informed a database
containing information including building configuration,
structure type, internal conditions, equipment installed
and their operation. The data collection protocol was
informed by the Standard Assessment Procedure (BRE,
2014) and the Carbon Trust Survey framework (The
Carbon Trust, 2011).
Table 1: Case study characteristics
ID
Occupancy/
max capacity
Year built
Typology &
construction
CS1
115/115
2013
Purpose built, 5-
storey modern
building, flat roof,
block and beam
built
CS2
8/11
1348 (2004
conversion)
Converted, 2-
storey, unoccupied
pitched roof, stone
built
CS3
38/40
1980s (1993
conversion)
Converted, 3-
storey, partly
pitched/ partly flat
roof, brick built
CS4
36/44
1714-1830
Converted, 3-
storey, partly
pitched/ partly flat
roof, brick built
CS5
34/42
1956
Purpose built, 3-
storey, partly
pitched/ partly flat
roof, brick built
Each case study’s local external and internal
environments were monitored between the start of June
2019 and the 19th September 2019. Data loggers recorded
dry bulb temperature and relative humidity at 5-minute
intervals in selected resident rooms, communal spaces,
offices and outdoor temperatures in close proximity to the
buildings.
As there is currently no universally accepted overheating
criterion that sets the indoor temperature threshold posing
health risks and/or causing significant discomfort
(Anderson et al., 2013; Lomas & Porritt, 2017; Zero
Carbon Hub, 2015), this study utilises an overheating air
temperature threshold of 26 ℃. Public Health England
(PHE) states that care home residents experiencing
temperatures higher than 26 ℃ should be moved to a
cooler room or take actions to cool them down, as they
may be physiologically unable to cool themselves
efficiently beyond this threshold (PHE, 2015). The same
temperature is suggested by CIBSE Guide A (CIBSE,
2015) as a bedroom upper operative temperature
threshold, as well as a summer overheating temperature
threshold for residential spaces of sedentary use. CIBSE’s
Design methodology for the assessment of overheating
risk in homes (TM59) (CIBSE, 2017) also states that
operative temperatures in naturally ventilated bedrooms
should not exceed 26 ℃ for more than 1% of annual hours
to maintain nighttime comfort.
Baseline data input to the dynamic thermal model
The study used the widely tested and validated dynamic
building performance software EnergyPlus V8.9 via the
DesignBuilder Graphical User Interface to simulate the
case studies’ summer thermal performance and quantify
current and future overheating risks under a representative
set of future climate scenarios. The dynamic thermal
modelling utilised the Chartered Institution of Building
Services Engineers (CIBSE) design summer year 1
(DSY1) weather files, based on UK Climate Projections
2009 (UKCP09) since future weather files using the more
recent UKCP18 are not yet available in a format that is
tailored for building performance simulation. The
following weather files represent a year of moderately
warm summer for the available locations closest to the
case study care homes, i.e. one urban (London Weather
Centre, LWC) and one suburban (London Heathrow,
LHR) for different timescales and emissions scenarios:
• 2020s high emissions, 50th percentile
• 2080s low emissions, 50th percentile
• 2080s high emissions, 50th percentile
The 2020s weather file represents current climate and the
low- and high- emissions 2080s weather files represent
the different scenarios of global warming. Specifically,
the 2°C and 4°C increase in Global Mean Surface
Temperature (GMST) above pre-industrial levels
(DEFRA, 2018) correlate with the selected 2080s CIBSE
weather files and identify with the corresponding
UKCP18 probabilistic projections, in the form of four
Representative Concentration Pathway (RCP) emissions
scenarios. Table 2 shows when the four RCP scenarios
(RCP26, RCP45, RCP60 and RCP85) are set to reach the
2 °C and 4 °C of global mean warming based on the 10th,
50th and 90th percentiles.
Table 2: Year when the projected 2 °C / 4 °C increase of
global temperature in relation to the preindustrial period is set
to occur in the UKCP18 probabilistic projection scenarios
90th
percentile
50th
percentile
10th
percentile
RCP26
2037 / ≥2099
≥2099 /
≥2099
≥2099 / ≥2099
RCP45
2036 / ≥2099
2056 / ≥2099
2083 / ≥2099
RCP60
2041 / 2085
2059 / ≥2099
2075 / ≥2099
RCP85
2031 / 2065
2043 / 2081
2059 / ≥2099
Input data for the thermal models of the five case study
care homes was established primarily through physical
surveys. Where needed, e.g. for data often unobtainable
in existing buildings, such as construction characteristics
and U-values, these were complemented and/or
triangulated with widely available databases. The

building fabric characteristics were inferred from
Reduced SAP (RdSAP) (DECC, 2017) for dwellings of
relevant age and construction type. Building age was
cross-examined with readily available geospatial data
sources (EDINA, 2020; Google, 2020). Table 3
summarises the building element construction type and U-
value associated with each case study.
Table 3: Building fabric data input
ID
Construction
element
Construction type
U-value
(W/m2K)
CS1
Roof
Flat, block and beam,
outmost layer
insulation
0.1
Floor
Concrete, innermost
layer insulation
0.2
External wall
Concrete block,
cavity wall insulation
0.2
Glazing
Double
1.4
CS2
Roof
Pitched, unoccupied,
joist insulation
0.5
Ground floor
Solid, floor boards
and covering,
uninsulated
1.2
External wall
Stone wall,
uninsulated
2.0
Glazing
Single
4.7
CS3
Roof
Pitched, unoccupied,
joist insulation/
pitched, occupied,
rafter insulation/ flat
roof, outermost layer
insulation
0.6/ 0.6/
0.6
Ground floor
Solid, uninsulated
1.2
External wall
Brick, cavity wall
insulation
0.4
Glazing
Double
3.0
CS4
Roof
Pitched, occupied,
rafter insulation/ flat
roof, outermost layer
insulation
0.6/ 0.6
Ground floor
Suspended timber,
uninsulated
1.2
External wall
Brick, solid wall,
innermost layer
insulation
0.5
Glazing
Double
3.0
CS5
Roof
Pitched, unoccupied,
uninsulated/ flat roof,
outermost layer
insulation
2.3/ 0.6
Ground floor
Solid, uninsulated
1.2
External wall
Brick, cavity wall
insulation
0.7
Glazing
Double
3.0
Ventilation temperature thresholds were sourced from
TM59 (CIBSE, 2017), which has also informed the case
studies’ operational schedules when relevant data was not
available through the data collected on site. Windows
were assumed to be open throughout the day and night,
whenever internal temperature exceeded 22 ℃ and was
higher than the external. Window openable area was
calculated on the basis of window geometry and/or
restrictor configuration and was assumed between 10%
and 12.5% in all buildings with restrictors in place (CS1,
CS3-CS5) and 25% in CS2, where there are no restrictors
present. Resident rooms, common rooms and office doors
were set to be open 80% of the time and all other internal
doors (e.g. to storarge rooms, bathrooms and utility
rooms) were assumed to be closed. Infiltration was
assumed to be the same for all care homes (0.7 ac/h).
Occupancy varies per zone type, i.e. resident rooms were
assumed to be continuously occupied by a single person,
as per TM59 guidance. According to the information
provided by care home staff, common room occupancy
(lounges, dining rooms etc.) varies considerably per room
and time of the day, i.e. from a few residents to up about
20 at peak times. For the purposes of the dynamic thermal
simulation, an average occupancy was calculated per
room and assigned during the daytime only. The TM59
guidance was also utilised in assigning internal heat gains
from equipment and lighting in different zones. Lighting
was assumed to be proportional to floor area and on
between 6 pm and 11 pm (CIBSE, 2017). Lighting heat
gain density is assumed to be 2 W/m2 (CIBSE, 2017),
where energy efficient lighting is present throughout the
building (CS1, CS4 and CS5) and 12.7 W/m2
(Suszanowicz, 2017) where the majority of lights are non-
energy efficient (CS2 and CS3). Additional gains were
incorporated in the corridors of CS1 due to the space
heating hot water circulation, where the bypass is not
utilised to avoid leakages from pipework joints. These
were based on the simplified method provided by the
Domestic Building Services Compliance Guide (HMG,
2013).
Validation and calibration
The thermal simulation outputs were tested against the
monitored data to provide confidence in the models,
during the period of a ‘heatwave’ with at least three-day
moving average external temperatures above 21.5 ℃
(Hajat et al., 2002). The cross tabulation of the 2020s
DSY CIBSE weather files for the locations closest to the
case study care homes and the monitoring data available,
identified a common 5-day heatwave period that also
presented the highest average summertime daily
temperatures for the duration of the monitoring period, i.e.
22nd – 26th July. This period, of which the hourly external
dry bulb temperature distribution is shown in Figure 1 for
different locations and weather data sources, was utilised
in the calibration of the dynamic thermal modelling
output. The local measurements indicate a large spread of
external temperatures between sites.
The hourly indoor modelled temperatures and the on-site
monitored data were compared for all rooms monitored in
the five case studies. This included four or five different
rooms in each building, i.e. one staff office, two resident
rooms and one or two common rooms on different floors,
where applicable.

Figure 1: Hourly external dry bulb temperature distribution
Figure 2 shows that the majority of indoor average
modelled temperatures broadly match the indoor average
monitored temperatures and, with a few exceptions (CS1
ground floor office, CS3 first floor lounge, CS4 first floor
lounge and second floor ensuite), remain within a one to
two degrees temperature difference from the latter. Under
both datasets, average temperatures during the five-day
heatwave period remained significantly higher than the 26
℃ threshold during the day and just two of them (CS1
ground floor, CS2 first floor dining area) maintained
temperatures just under 26 ℃ during the night. The
highest discrepancy between the two is noted in the first
floor common room of CS3, however this can be
attributed to the use of two portable air-conditioning units
in this zone, which were not taken into consideration in
the simulation as this work focuses on the evaluation of
the building’s overheating reduction potential based on
passive measures alone. A comparison of the average day-
and night- time temperatures between the modelled and
monitored data revealed a significantly higher diurnal
temperature variation in the former. Average
temperatures tended to increase with higher floor level
(except the first floor of CS5, where a lower cross-
ventilation capacity was reported) and resident rooms
presented higher temperatures than common rooms of the
same floor level.
Figure 2: Monitored and modelled average internal
temperatures during the five-day heatwave period
(floor level indicated above bars)
Overall, the comparison of the modelled and monitored
data indicates that the case study dynamic thermal models
are adequate to be used as a useful basis for the prediction
of internal temperatures under a range of future climates
and overheating reduction interventions.
Overheating quantification and mitigation strategies
Following the testing and calibration of the models, future
overheating risks were quantified. The effectiveness of a
range of passive climate change adaptation and
overheating mitigation strategies were tested under the
aforementioned climate change scenarios, during the
same five-day heatwave period that was utilised for
testing purposes. The dynamic thermal analysis software
provided individual output for each room in each case
study at an hourly time interval. The key metrics used in
the quantification of overheating represent the
temperature exposure of the two types of residents
identified during the site surveys, i.e. average resident
room temperatures for bedbound residents (area
weighted) and interzone average temperatures (area
weighted) for active residents. The latter refers to the day-
and night- time average for common rooms and resident
rooms respectively and is obtained by averaging the 9am
– 9pm or 9pm – 9pm hourly temperature per zone type
during the five day heatwave period.
Table 4: Dynamic thermal simulation test cases
Category
ID
Test Case target area
A. Baseline
TC0
Base case scenario
B. Minimise
internal heat
generation
TC1
Space heating circulation
bypass
TC2a,b*
Passive infrared sensors in
corridors/ staircases
TC3
Energy efficient lighting
C. Keep the
heat out
TC4a,b
Roof and wall albedo
TC5
Curtain rules
TC6a,b,c
Glazing types
TC7a,b
Roof and wall insulation
TC8a,b
External window shading
D. Manage
heat
TC9*
Window opening rules
TC10a,b
Increased thermal mass
E. Passive
ventilation
TC11*
Night ventilation
TC12*
Internal door rules
TC13a
Increased ventilation
TC13b,c
Increased ventilation coupled
with increased thermal mass
F. Cumulative
impact of
selected
measures
TC14
Cumulative soft-engineered
solutions
TC15
Cumulative soft- and hard-
engineered solutions
*Soft-engineered measures incorporated in all or part of the baseline
case study models, representing additional tests, whose impact was
quantified by removing them from the base case scenarios.
The interventions tested include both non-structural
(‘soft’) and structural (‘hard’) engineering solutions as
these are defined in Coley et al. (2012); they range from
behaviour change to management practices, building
design, retrofit and operational variations. These were
grouped according to Greater London Authority’s cooling
hierarchy (GLA, 2016), i.e. prioritising in ascending

order: (a) the minimisation of internal heat generation, (b)
keeping the heat out, (c) the management of the building’s
heat and (d) the use of passive ventilation. The
interventions were tested selectively for each case study
building, according to its individual characteristics. The
generic test areas are presented in Table 4 (TC0-TC13)
and the specific underlying assumptions per case study
are listed in Table 5. The cumulative effect of two (where
applicable) of the most impactful soft- and/or hard-
engineered solutions depending on the characteristics of
each case study were also tested (TC14-TC15). Apart
from the impact of the individual measures on average
internal temperatures, the selection criteria also took into
consideration the potential conflicts resulting from their
concurrent implementation. Where certain measures were
assumed to be already incorporated in the building’s
original design or operation, the effect of removing them
was also tested and their average impact was quantified
alongside other measures.
Table 5: Description of test cases
ID
Description / Soft (S) or Hard (H) engineered
TC0
Base case
TC1
Enabling space heating circulation bypass and
removing associated heat gains (S)
TC2a*
Corridor and staircase lights assumed to be on for
longer periods than in the base case, i.e. 12hrs per
day if zone naturally lit and 24/7 if not (S)
TC2b*
Passive infrared sensors assumed to be present in
corridors and staircases (S)
TC3
Replacing non-energy efficient lighting (halogen)
with energy efficient (fluorescent) (S)
TC4a
High roof albedo (H)
TC4b
High wall albedo (H)
TC5
High reflectivity curtains closed when exposed to
the sun (H)
TC6a
Highly insulative, low-e, argon-filled double
glazing replacing simple double/single glazing (H)
TC6b
Solar window film application on the external pane
of air-filled, double-glazed window (H)
TC6c
Spectrally selective, low-e double glazing (H)
TC7a
Super-insulated roof (externally insulated) (H)
TC7b
Super-insulated wall (externally insulated) (H)
TC8a
Window louvres/side fins (0.5m projection) (H)
TC8b
Movable shutters with high reflectivity slats (H)
TC9*
Keep windows closed when hotter outside (S)
TC10a
Increase thermal mass through the use of
traditional heavyweight materials (50mm) on either
side of internal partitions (H)
TC10b
Increase thermal mass through the use of
traditional heavyweight materials (25mm) on either
side of internal partitions (H)
TC11*
Enabling night ventilation (S)
TC12*
Keeping internal doors open (S)
TC13a
Increasing window openable areas to 30%, e.g.
through the use of ventilation panels (H)
TC13b
TC13a + TC10a (H)
TC13c
TC13a + TC10b (H)
TC14
Cumulative soft-engineered solutions
TC15
Cumulative soft- and hard- engineered solutions
Analysis of results
The five base case models were tested under the projected
current (2020s) and future (2080s) weather climate during
the selected five-day heatwave period (22nd – 26th July),
with external temperatures ranging from about 18 ℃ to
34 ℃ in 2020s and increasing by 2 ℃ for each of the
2080s emissions scenarios, respectively, as shown in
Figure 3. During this period, the projected average
outdoor temperature increase is 1.5 ℃ and 3.9 ℃, under
the 2080s low- and high- emissions scenario respectively,
in comparison to the 2020s climate data. The dynamic
thermal simulation analysis of the five case study care
homes indicated that the average internal building
temperatures will increase by approximately the same
degree, both under the base case and intervention
scenarios (Figure 4 - Figure 8). Results from the baseline
simulations for all case studies show that internal
temperatures remain above the 26 ℃ threshold most of
the time, under all climate scenarios. They follow external
temperature fluctuations but, overall, remain at
significantly higher levels than external, particularly
during the night.
Figure 3: Hourly external dry bulb temperature distribution
under the 2020s and 2080s weather scenarios for two locations
Figure 4 - Figure 8 present the impact of a range of
intervention measures on the baseline CS1-CS5 dynamic
thermal models, based on the average temperatures
experienced by active and bedbound occupants.
Individual results for active and bedbound occupants
show that the latter are more likely to experience higher
temperatures by approximately 0.6 ℃ in CS1 and CS4,
whereas the opposite effect is noticed in CS3, CS2 and
CS5, where active occupants are exposed to higher
temperatures by 0.7 ℃, 0.26 ℃ and 0.16 ℃, respectively.
Among all intervention groups, i.e. minimise internal heat
generation (B), keep heat out (C), manage heat (D) and
passive ventilation (E), the most impactful in lowering
internal temperatures in all case studies is passive
ventilation, with an average temperature reduction impact
range of 1.4 ℃ - 3.2 ℃, in the form of increased
ventilation rates, except for CS2 (impact of 0.3 ℃), where
a relatively high ventilation rate already applies. This has
been tested individually, as well as coupled with increased
thermal mass, however, the thermal mass application
yielded a borderline negative impact to the average

internal temperatures, both when applied as a standalone
measure (compared to the base case) and coupled with
increased ventilation rates (compared to the individual
application of increased ventilation). The thermal mass
application in this case may have been too high, i.e.
retaining more heat during the night than could have been
removed through the ventilation available. The optimal
balance between thermal mass capacity and ventilation
(and night ventilation in particular) needs to be
investigated further.
Figure 4: Weighted average of resident exposure during the
five-day heatwave period in CS1
Figure 5: Weighted average of resident exposure during the
five-day heatwave period in CS2
Figure 6: Weighted average of resident exposure during the
five-day heatwave period in CS3
The group containing the next most impactful measures is
C - keeping the heat out, in particular through external
window shading (impact range of 0.7 ℃ - 1.4 ℃), except
for CS2 (impact of 0.25 ℃) that seems to benefit more
from the application of high albedo materials on the
exterior of its thick stone walls (impact of 0.7 ℃),
followed closely by external window shading and
increased ventilation. Further increasing its already high
window ventilation capacity may still offer some benefit.
The application of external wall insulation is also one of
the measures that appears to have a noticeable beneficial
effect on CS2, however its effectiveness should be
carefully considered when coupled with other measures,
such as external wall albedo, as its effectiveness might be
compromised when the two are combined (Arumugam et
al., 2015). In the remaining cases, the application of
additional insulation on roofs and walls that are already
insulated to some extent is only marginally beneficial and
the same applies to the application of high albedo coatings
on insulated walls and roofs.
Figure 7: Weighted average of resident exposure during the
five-day heatwave period in CS4
Figure 8: Weighted average of resident exposure during the
five-day heatwave period in CS5
The consistent application of curtain rules, i.e. keeping the
high reflectivity indoor shading closed whenever a
window is exposed to the sun, lowers the average
temperature experienced in CS1 by approximately 0.7 ℃,
in CS2 by 0.1 ℃ and between 0.4 ℃ and 0.5 ℃ in the
remaining three cases. However, this is likely to have an
impact on indoor lighting levels and daylight access,

which may be critical for the mental health of bedridden
occupants. Between different applications of double
glazing, the best performing are those incorporating some
type of coating, blocking in part solar radiation, but they
are still not performing as well as external shading
devices. Replacing air-filled double glazed windows with
highly insulative argon-filled, low-emissivity double
glazing has a marginally positive effect of approximately
0.3 ℃ in cases CS1 and CS2-CS5. However, replacing
the single-glazed windows of CS2 with the same type of
highly insulative double-glazing leads an average
increase of the temperature experienced by residents of
approximately 0.2 ℃ to 0.3 ℃, which is only slightly
reduced with the application of solar radiation filters.
However, the combination of highly insulative glazing
with proper shading (not tested as part of this study) has
the potential to offer better protection.
The measures designed to minimise internal heat
generation (B) have a fairly small impact on the overall
temperature experience of the residents in comparison to
other interventions but are worth considering as their
implementation is usually straightforward, involves
minimal costs and disruption and has a positive impact on
energy consumption. The relevant measures tested are
turning off any unnecessary hot water circulation in CS1,
replacing non-energy efficient lighting with energy
efficient in CS2 and CS3 and using passive infrared
sensors in staircases and corridors in all cases.
The cumulative effect of the most impactful/appropriate
soft- and/or hard- engineered solutions in each case is
depicted in section F of Figure 4 - Figure 8. The soft-
engineered measures include the implementation of
curtain rules for all case studies, the installation of energy
efficient lighting in CS2 and CS3 and enabling the space
heating bypass in CS1. Where only curtain rules are
implemented, the impact ranges beween approximately
0.4 ℃ to 0.5 ℃. The implementation of two soft measures
leads to an overall temperature reduction of over 1.1 ℃,
with the exception of CS2, where a temperature difference
of just under 0.3 ℃ is noted, possibly due to the high
building heat losses dispersing any high internal gains.
The hard measures were tested together with the
aforementioned soft measures and include the addition of
louvres and side fins to all case studies, as well as
enhanced ventilation for all except CS2, where increased
wall albedo was the most impactful measure. The
cumulative impact of the soft- and hard- measures
combined resulted in an overall temperature reduction of
between 1.3 ℃ and 4.4 ℃. The case study benefiting the
most is the highly insulated CS1, whereas the oldest
among all buildings (CS2) benefited the least, with CS2,
CS3 and CS4 lying in between.
Figure 9 presents the average impact on the reduction of
residents’ temperature exposure of a set of interventions
applied to the case study buildings, expanding the original
list of measures presented in Figures 4 - 8. The additional
tests concern soft-engineered measures that were already
incorporated in all or part of the baseline case study
models and whose impact is quantified alongside other
measures examined previously by removing them from
the base case scenarios. These relate to the testing of
extreme scenarios in some cases, such the complete
elimination of night ventilation. The impact of individual
measures is reported as the mean temperature resident
exposure during the five-day heatwave period, averaged
for the 2020s high- and 2080s low- and high- emissions
scenarios.
Figure 9: Average impact of individual interventions on the
reduction of residents’ temperature exposure during the five-day
heatwave period (TC0* represents the baseline simulation of
CS1, CS4 and CS5 without energy efficient lighting)
The results show that the most impactful measures lie in
the area of passive ventilation and heat management. The
single most impactful measure for all case studies is the
implementation of night ventilation (I-15), with an impact
range of between 2.5 ℃ and 4.4 ℃, followed by the
implementation of increased ventilation rates (I-17, 0.3 ℃
- 3.3 ℃) and the application of internal door rules (I-16,
0.2 ℃ - 3.6 ℃). The impact of these measures is greater
when they are applied to the most recently constructed
building (CS1) of the case study group and lower when
applied to the oldest one (CS2), an outcome likely to be
linked to the buildings’ individual heat loss and thermal
mass characteristics. Of the remaining scenarios tested,
the application of window rules (I-14) led a significant
average temperature decrease only in the oldest building
(CS2, 0.7 ℃), presumably due to its high ventilation rates
allowing more warm air to enter the building when
windows are allowed to open even when external
temperatures are higher. On the contrary, testing the effect
of utilising energy efficient in the place of non-energy
efficient lighting throughout the building (I-3) for all case
studies presented the lowest temperature reduction in CS2
(0.2 ℃), with the remaining cases ranging between 0.5 ℃
to 0.9 ℃. The use of passive infrared sensors in corridors
and staircases tested against keeping the lights on half the
day for naturally-lit zones and 24/7 for artificially-lit only
zones (I-2) was linked to a negligible impact for those
cases utilising energy efficient lights. Between CS2 and
CS3, whose lights are non-energy efficient, a noticeable
impact of 0.5 ℃ was indicated only in CS3. The thermal
and heat loss characteristics of CS2 are likely to
effectively disperse any light-associated heat gains.

Testing all possible interventions and their combinations
is beyond the scope of this study. However, the combined
impact of different measures for the identification of key
interventions with maximum overheating reduction
potential and the avoidance of combinations that may
compromise each other’s impact is of key importance.
Conclusion
The testing of various modelling scenarios, as part of this
pilot study, quantified overheating risks and temperature
exposure of the care home residents with a view to
informing the feasibility assessment for the promotion of
passive cooling systems and overheating mitigation
behaviour change measures. Average internal
temperatures in the five case study care homes during the
five-day heatwave period remained predominantly above
the 26 ℃ threshold and were projected to remain at
significantly higher levels under the future climate
scenarios. This is likely to increase challenges both for
care home residents and staff, as higher temperatures are
linked with compromised human comfort, performance
and health, particularly for the most vulnerable.
The combination of soft- and hard- engineered passive
strategies tested in this study appear to be capable of
reducing the residents’ indoor temperature exposure from
approximately 1.3 ℃ to 4.4 ℃, depending on building
type. They were not able to reduce average temperatures
below the 26 ℃ threshold, under any of the climate
scenarios, however, a very limited combination of
strategies has been tested.
Overheating reduction strategies should be carefully
considered according to the buildings’ individual
characteristics. Older, heavyweight, single-glazed and
well ventilated buildings were found to benefit more from
the application of high albedo materials rather than
external shading methods, whereas newer and well
insulated buildings seem to benefit more from higher
ventilation rates and appropriate external shading
systems. Modern buildings are more likely to benefit from
passive interventions for the reduction of overheating
rather than older buildings, with the latter maintaining
slightly lower temperatures at all times. Night ventilation,
which was reported to be implemented in all case study
care homes to some extent, emerged as the single most
impactful measure, irrespective of building type.
Future work should include a detailed parametric analysis
to facilitate a robust investigation on the integration of key
interventions contributing to overheating reduction on
different types of buildings. The interventions should also
be tested under periods with different types of hot
weather, e.g. longer, less intense warm spells and
throughout the whole summer. Another aspect to be taken
into consideration is the feasibility of the selected
measures, which vary from simple, easy-to-implement,
incurring minimal or no cost (e.g. behavioural changes) to
highly efficient but more complex, disruptive and/or
expensive solutions that could be implemented in the long
term. These will be explored further as part of the
project’s ongoing work, which will also explore the
implication of these findings for guidelines and
regulations and the potential for scaling up the project to
a national scale. The work will be of interest to relevant
stakeholders from the built environment, social care,
public health and policy development.
Acknowledgements
This work was supported by the Natural Research Council
(grant number NE/S016767) and DesignBuilder Software
Ltd., UCL and Innovate UK KTP project (Partnership
number 11616).
References
Anderson, M., Carmichael, C., Murray, V., Dengel, A.,
& Swainson, M. (2013). Defining indoor heat
thresholds for health in the UK. Perspectives in
Public Health, 133(3), 158–164.
https://doi.org/10.1177/1757913912453411
Arumugam, R. S., Garg, V., Ram, V. V., & Bhatia, A.
(2015). Optimizing roof insulation for roofs with
high albedo coating and radiant barriers in India.
Journal of Building Engineering, 2, 52–58.
https://doi.org/10.1016/j.jobe.2015.04.004
Barnes, S., Torrington, J., Darton, R., Holder, J., Lewis,
A., McKee, K., Netten, A., & Orrell, A. (2012).
Does the design of extra-care housing meet the
needs of the residents? A focus group study.
Ageing & Society, 32(7), 1193-1214.
https://doi:10.1017/S0144686X11000791
BRE. (2014). SAP 2012 The Government ’ s Standard
Assessment Procedure for Energy Rating of
Dwellings. In Energy (Issue March, p. 174).
https://doi.org/10.1007/s13398-014-0173-7.2
CIBSE. (2015). CIBSE Guide A: Environmental Design.
https://www.cibse.org/knowledge/knowledge-
items/detail?id=a0q20000008I79JAAS
CIBSE. (2017). TM59: Design Methodology for the
Assessment of Overheating Risk in Homes.
https://www.cibse.org/knowledge/knowledge-
items/detail?id=a0q0O00000DVrTdQAL
Coley, D., Kershaw, T., & Eames, M. (2012). A
comparison of structural and behavioural
adaptations to future proofing buildings against
higher temperatures. Building and Environment,
55, 159-166.
https://doi.org/10.1016/j.buildenv.2011.12.011
CQC. (2020). CQC care directory.
https://www.cqc.org.uk/files/cqc-care-directory-
csv-format
DECC. (2017). Appendix S: Reduced Data SAP for
existing dwellings. In The Government’s Standard
Assessment Procedure for Energy Rating of
Dwellings, Appendix S: Reduced SAP for existing
dwellings (RdSAP) (Vol. 91, Issue April).
https://www.bre.co.uk/filelibrary/SAP/2012/RdSA
P-9.93/RdSAP_2012_9.93.pdf
DEFRA. (2018). The National Adaptation Programme
and the Third Strategy for Climate Adaptation
Reporting : Making the country resilient to a

changing climate.
https://www.gov.uk/government/publications/clim
ate-change-second-national-adaptation-
programme-2018-to-2023
EDINA. (2020). DIgimap: Historical Ordnance Survey
maps of Great Britain.
https://digimap.edina.ac.uk/historic
GLA. (2016). The London Plan - The spatial
development strategy or London consolidated with
alterations since 2011.
https://www.london.gov.uk/what-we-
do/planning/london-plan/current-london-
plan/london-plan-chapter-five-londons-
response/poli-8
Google. (2020). Google Earth: Historical Maps from
Around the World.
https://earth.google.com/web/data=CiQSIhIgY2Nj
OGJhYWVhYjg5MTFlODg4N2U3MWQ2MDFi
MTRkNzU
Gupta, R., Barnfield, L., & Gregg, M. (2017).
Overheating in care settings: magnitude, causes,
preparedness and remedies. Building Research &
Information, 45(1-2): 83-101.
https://doi.org/10.1080/09613218.2016.1227923
Hajat, S., Kovats, R. S., Atkinson, R. W., & Haines, A.
(2002). Impact of hot temperatures on death in
London: a time series approach. Journal of
Epidemiology and Community Health, 56(5), 367–
372. https://doi.org/10.1136/jech.56.5.367
HMG. (2013). Domestic Building Services Compliance
Guide.
https://www.mendeley.com/viewer/?fileId=1951bb
52-83d6-5045-372b-
9e18e9d99a72&documentId=c7af9c00-13a4-
3806-918e-cac429a02e3a
Lomas, K. J., & Porritt, S. M. (2017). Overheating in
buildings: lessons from research. Building
Research & Information, 45(1–2), 1–18.
https://doi.org/10.1080/09613218.2017.1256136
PHE. (2015). Heatwave Plan for England: supporting
vulnerable people before and during a heatwave -
advice for care home managers and staff.
https://assets.publishing.service.gov.uk/governmen
t/uploads/system/uploads/attachment_data/file/429
600/Heatwave-Care_Home_Managers.pdf
Suszanowicz, D. (2017). Internal heat gain from
different light sources in the building lighting
systems. E3S Web of Conferences, 19.
https://doi.org/10.1051/e3sconf/20171901024
The Carbon Trust. (2011). Energy surveys. In A
practical guide to identifying energy saving
opportunities.
papers3://publication/uuid/B6E2C50D-1773-4649-
A774-2241B1BE9588
Zero Carbon Hub. (2015). Assessing Overheating Risk -
Evidence Review.
http://www.zerocarbonhub.org/resources/reports/a
ssessing-overheating-risk