Monitoring and moderating extreme indoor temperatures in low-income urban communities

Four communities were chosen in Acca and four more in Tamale based on their mix of building types and density, informality, and vulnerability to extreme heat (table 1). Tinytag Transit 2 thermistors were installed within homes to monitor air temperatures encountered by occupants during 2018. According to the manufacturer, the sensors have a precision of 0.01 °C and accuracy of 0.4 °C in the range 0 °C to 50 °C (Gemini Data Loggers 2015). The logging interval was set to 10 min.

Homes were selected to represent different structures, building materials, sizes, and locations (figure 1). This process was guided by the local knowledge of 'community champions' (residents nominated by their communities to liaise between households and the research team). A standard form captured information about building location (coordinates, elevation), number of floors and building height (as a proxy for size/thermal mass), alongside other characteristics (wall, and roof materials, number of doors and windows, amount of shade, and orientation). Any modifications to counter high temperatures, such as ceiling insulation (plywood, plastic sheeting, Polyvinyl chloride, a synthetic plastic polymer tongue and groove panels, plaster), fans (ceiling, free-standing), tree shade or air conditioning, were also recorded. Room occupancy, usage, and artificial heat sources were noted too.

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At each home, one thermistor was fixed outdoors on a self-shaded north-facing wall to measure ambient air temperature, whilst up to four were sited inside rooms with various ceiling types, usage, occupancy, and dimensions (figure S1 (available online at stacks.iop.org/ERL/16/024033/mmedia)). All thermistors were mounted ∼2 m above ground level, away from direct sunlight and artificial heat sources. These sites had to be as unobtrusive as possible and always beyond the reach of children. Spot measurements were taken within several homes to assess any height-dependency of air temperatures using a Testo 610 handheld sensor (Testo 2020). Variations in temperature were always smaller than instrument accuracy (±0.5 °C).

Thermistors were also installed within Stevenson screens at ∼1.25 m above ground level alongside thermometers operated by the Ghana Meteorological Agency (GMA) at three sites: (A) their compound on the University of Ghana (Legon, Accra) campus, (B) Accra airport, and (C) Tamale airport (figure S1). Indoor temperatures are expected to differ from these records because a key purpose of a dwelling is to regulate the thermal environment for occupants. Nonetheless, previous studies show strong positive linear associations between indoor and outdoor temperatures on a home-by-home basis (e.g. Quinn et al 2014, Nguyen and Dockery 2016, Naicker et al 2017). Here, our thermistor records at GMA stations (A and C above) are used as points of reference for our outdoor and indoor temperature measurements. Sensors housed within these official meteorological stations are expected to have more stable environments than outdoor sites within communities.

Initially, a network of 96 thermistors were deployed; after 1 year, 81 were still in place and operating (47 inside homes, 24 outside homes, plus 7 verandas, and 3 at meteorological stations). Data were downloaded every other month and quality assured. Day-to-day changes and basic range checks were performed, with records from GMA stations and homes cross-compared to eliminate outliers/suspect values associated with artificial heat exposure. Percentile-based indicators, rather than absolute extremes, were used for statistical analysis (section 2.3) to reduce possible contamination by erroneous outliers. Abrupt changes in the daily range helped to detect any relocation or tampering with a sensor. The cleaned archive yielded more than 3.5 million data values for analysis. Importantly, the high temporal resolution enabled evaluation of temperature variations within a day, so the effects of location and building factors can be discerned from a range of temperature metrics.

According to multi-decadal thermometer-based records provided by GMA (for sites B and C above), annual mean temperatures in Tamale and Accra for 2018 were 9th and 11th highest since 1960, respectively. Since the 1960s, annual mean temperatures rose by 0.26 °C decade⁻¹ and 0.18 °C decade⁻¹ at the same sites. Hence, we regard our study period as representative of the generally warmer conditions experienced in recent decades. Hereafter, we present results from thermistor data gathered during the 365 d period covering 22 January 2018–21 January 2019.

Primary series of daily minimum (Tn), daily maximum (Tx), daily mean (Tm), and daily range of temperature (DTR; Tx minus Tn) were obtained from thermistor recordings at each site (table 2). These data were then used to derive 12 temperature indices, again for each site. These are the number of very hot nights [when Tn > 30 °C] (Tn30C); the number of very hot days [when Tx > 40 °C] (Tx40C); temperature minima summarised by the very lowest (Tnn), the 1st (Tn01p), 10th (Tn10p), and 50th (Tn50p) percentile, and very highest (Tnx) of the daily distribution of Tn; temperature maxima characterised by the very lowest (Txn), the 50th (Tx50p), 90th (Tx90p) and 99th (Tx99p) percentile, and the very highest (Txx) of the daily distribution of Tx. Note that the temperature thresholds for Tn30C and Tx40C are higher than used elsewhere because the conventional definition of a 'tropical night' (Tn > 20 °C) (Sillmann et al 2013) would be exceeded 100% of the time at all sites (except for the GMA station in Tamale).

We used the above indices to discern variations in temperature associated with building location, type, and modifications that favour lower indoor temperatures. Three tiers of statistical technique were applied: (a) community level averaging of temperature to evaluate local variations in the strength of the UHI; (b) category level averages to show differences in temperature between groups of similar buildings; and (c) building level dummy regression analysis of variables influencing selected indoor temperature indices.

For tier 1, mean daily indoor and outdoor temperatures were stratified by community then compared with thermistor data from GMA sites (A and C). Since these meteorological stations are in open-grassed sites outside the city centre, differences in temperature between them and communities are indicative of local UHI intensity. This reveals ambient temperature variations across each city reflecting local building density, artificial heat sources, proximity to open/green space, water bodies, and distance from the coast (for Accra).

For tier 2, building types were categorized by height/number of floors, wall, and roof material as well as by measures intended to reduce indoor temperatures (table S1). The sampled housing archetypes were as follows. Single-storey homes (⩽5 m high) were comprised of wood walls with metal or asbestos sheet roofs (figure 1(a)), or traditional mud walls with thatch roofs (figure 1(b)), or concrete walls with metal or asbestos roofs (figure 1(c)). Two-storey or large (>5 m high) buildings had concrete walls and asbestos sheet or tiled roofs (figure 1(d)). Shade by vegetation was classified as either present (figure 1(e)) or absent (figure 1(f)). This binary approach was justified given that rapid visual assessment of shade is problematic due to variations by time of day and effect of neighbouring structures.

Building-specific indices (Tn10p, Tx90p and mean Tm) were stratified by city, then averaged by housing archetype, or cooling measure. Two-sample t-tests of differences in the mean (assuming unequal variance) were applied to the groups of data. For example, indoor temperatures may be compared for sub-samples of single-storey (n₁) and two-storey (n₂) housing in Accra, with the null hypothesis of no difference. The number of degrees of freedom (n₁ + n₂ − 2) is sometimes small, in which case the statistical power to identify differences between factors is limited (i.e. there is a higher chance of accepting a null hypothesis that is actually false). Indoor temperatures were also compared for rooms with different modifications within the same house or with a nearby property of similar size, age, and design. Resulting scatterplots reveal daily variations of indoor Tx and Tn depending on outdoor temperatures (at the reference site); roof type (metal versus thatch); ceiling type (insulated versus uninsulated); shade (tree cover versus unshaded); and fans (present or absent).

For tier 3, we apply dummy regression modelling to i temperature indices (Tn30C, Tx40C, Tn10p, Tm, and Tx90p) to quantify the benefit of modifications relative to the effects of building location and characteristics. The influence of n dummy variables (v_j ) were estimated from the coefficients of the following model, fitted via ordinary least squares:

$$\begin{equation}{T_i} = {\alpha _i} + \mathop \sum \limits_{j = 1}^n {\beta _{i,j}}{v_j} + {e_i}\end{equation} \tag{ 1 }$$

where T_i is the indoor temperature index, α_i is the model intercept, β_i,j are the model coefficients, and e_i is the model error. Eight dummy variables were applied, each coded either 0 for the reference case or 1 for the 'treatment' (table S2). The variables were city (0 for Tamale); coastal (0 for sites > 1 km from the sea); thermal mass (0 for buildings ⩽ 5 m high); wall material (0 for wood); roof material (0 for sheet metal or asbestos); ceiling insulation (0 for none); tree shade (0 for none); and ceiling or free-standing fan (0 for none). The coefficients β_i,j are directly comparable (in each temperature index). Some dummy variables could have been sub-classified, such as ceiling insulation (by material), tree shade (by extent), or sheet roofing (by material). However, there is a danger of over-fitting the model given the modest sample size (45 rooms after excluding two homes with air conditioning). The dummy variables allowed us to pool temperature indices reflecting very different building types and locations.

Our thermistor data reveal considerable variations of indoor temperature linked to site. In Accra, the lowest recorded indoor temperature (Tn) was 22.4 °C for a small asbestos-roofed, wood-walled room near the coast; the highest indoor temperature (Tx) was 45.9 °C for a small metal-roofed, wood-walled room in a densely built-up area. As expected, equivalent values (Tn = 20.7 °C, Tx = 37.0 °C) at Accra meteorological station were lower. The average number of very hot nights (Tn30C) within homes ranged between 7 (single-storey concrete houses) and 92 (two-storey or large concrete houses) per year, whereas none were recorded at the meteorological station (table 3). Likewise, the number of very hot days (Tx40C) was on average 40 inside single-storey wood homes but zero at the nearest meteorological station.

In Tamale, the lowest indoor temperature (Tn) was 22.2 °C and the highest (Tx) was 43.0 °C (both for metal-roofed homes). Temperatures at Tamale airport ranged between 17.4 °C and 41.4 °C. Average Tn30C within metal and thatch roof homes were 96 and 102 nights per year respectively, compared with none at the GMA station (table 3). In contrast, average Tx40C was zero in thatch roof rooms, 7 in metal roof rooms, and 10 at the GMA station.

We also stratified our temperature data by community to elucidate variations across the two cities. In Accra, average indoor Tn were higher for all communities than at GMA stations (by up to +3.3 °C in Alajo) (figure 2). Likewise, indoor Tx was higher in all communities (by up to +3.1 °C in Alajo) except Odawna (−0.7 °C), an area with some two-storey houses (figure 1(d)). In Tamale, Tn were higher than the GMA station in all communities, whereas Tx were lower on average than the meteorological station (by as much as −1.3 °C in Ward K).

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Outdoor temperatures in communities were consistently higher than those at GMA stations (figure 2), as official sites (A and C) are located away from the city centre, in open, well-ventilated areas. Hence, by comparing outdoor temperature differences between communities and reference stations, we estimated local UHI intensity. Ambient Tx (i.e. daytime UHI) in communities were on average +2.5 to +4.3 °C higher in Accra and +0.7 to +3.0 °C higher in Tamale. Outdoor Tn (i.e. night-time UHI) in communities differed from reference sites by +1.1 °C to +2.4 °C in Accra, and by +1.2 °C to +2.4 °C in Tamale. These findings are consistent with previous studies of UHIs in tropical Africa (Ojeh et al 2016, Giridharan and Emmanuel 2018, Simwanda et al 2019).

Home type and community-level averages mask considerable temperature variations between and within building archetypes, especially during very hot days and nights. Such differences are generally obscured by daily mean statistics (table 4). In Accra, significant (p < 0.01) variations were found in high indoor temperatures (Tx90p) between rooms with and without ceiling insulation (−6.9 °C). Rooms in two-story residences were also significantly cooler on average than those in single-storey homes (−4.4 °C). However, night temperatures (Tn10p) were higher in insulated versus uninsulated rooms (+1.4 °C) and in two- versus single-storey homes (+1.5 °C). The effect of shade (−0.2 °C) or fans (+0.4 °C) was insignificant (p > 0.1). In Tamale, the largest difference in Tx90p was between homes with insulated thatch roofs compared with uninsulated metal roofs (−4.5 °C) (table 4). Similarly, Tx90p was lower in rooms with insulated ceilings (−2.8 °C). Overall, rooms with thatch roofs had lower Tx90p than those with metal roofs (−2.2 °C) but only marginally lower Tm (−0.6 °C). There were weakly significant (p < 0.1) differences in Tn10p (−0.8 °C) for shaded rooms and in Tx90p (+1.3 °C) for rooms with fans.

Differences also emerge when comparing daily Tx (−4.9 °C) and Tn (+1.3 °C) for rooms with thatch or metal roofs, in this case, within the same house (figures 3(a) and (b)). During the hottest day sampled in Tamale, the smallest daily temperature range was recorded in a room with thatch underlain by plastic sheeting (installed to protect occupants from dust and insects rather than for insulation) (figure 4(a)). Variations within the diel regime were likewise recorded amongst rooms with metal roofs (figure 4(b)). The room with the smallest daily variation was in a large, single-storey house inhabited by one family, with partial shade to the rear and PVC cladding on high ceilings. Conversely, the metal-roofed room with greatest daily temperature range had no shade and a low, uninsulated ceiling.

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Similarly, diel temperature profiles for the hottest day in Accra were different for single-storey houses with insulated ceilings compared to those with uninsulated ceilings (figure 4(c)). The sample size is not large enough to discern the effect of floor-level (in two-storey buildings) or roof type; however, the data reveal a narrower indoor daily temperature range for insulated than uninsulated rooms, with a diel regime similar to thatch (figure 4(a)). Although Tx was lower, temperatures in rooms with insulated metal/asbestos and thatch roofs were consistently higher at night than uninsulated metal-roofed rooms (see below).

Modifications to homes, such as ceiling insulation, shade, and fans are intended to improve thermal comfort. However, consequences for indoor temperatures were mixed, depending on time of day and building archetype. Rooms with insulated ceilings were up to 7 °C cooler in the day than ones without insulated ceilings within the same home (figures 3(c) and (d)) but during one night, the insulated room did not fall below 32.2 °C. Hence, insulation reduces Tx but at the penalty of higher Tn.

Tree shade reduces indoor temperatures most during the day (Tx) in the dry season (figure S2(a)) and, as expected, there is no difference between shaded and unshaded rooms at night (figure S2(b)). Although lower indoor temperatures were recorded in rooms with some shade (table 4), the difference in Tx peaked during April–July (2.2 °C) for the two exemplar rooms. On some days, Tx in a shaded room was at least 8 °C cooler than the meteorological station (figure 3(e)).

Discerning the effect of (ceiling) fans on indoor temperatures is problematic as their presence or absence tends to coincide with that of insulation. Moreover, even when present, fans may not be used. Overall, fans are not expected to reduce indoor temperatures unless by promoting ventilation of cooler air from outside. This was confirmed by the absence of any statistically significant temperature differences in our pairwise analysis of all rooms with, and without fans (table 4). Likewise, there was no differences in temperature due to fans in the subset of rooms with no ceiling insulation (figure S3). Although Tn was higher in some rooms with PVC ceiling cladding and a fan (figures 3(g) and (h)), this may be explained by a greater number of electrical appliances (i.e. artificial heat sources) and fewer doors.

We used dummy regression to evaluate the relative effects of geographic location (city, distance from sea), building archetype (number of floors/thermal mass, wall, and roof materials), and modifications (ceiling insulation, tree shade, and fans) on indoor temperature indices (figure 5 and table S2). The amount of variance explained by these factors ranged from 17% (Tx40C) to 73% (Tx90p). Statistically significant (p = 0.05) model coefficients (with standard errors) emerge for rooms with:

• (a)  
  Thatch roof versus metal roof. This changes Tx40C by −38 (±19) d, Tm by −1.0 (±0.4) °C, Tn10p by +1.0 (±0.4) °C, and Tx90p by −3.7 (±0.8) °C.
• (b)  
  Ceiling insulation versus no insulation. This changes Tm by −0.8 (±0.3) °C and Tx90p by −2.6 (±0.6) °C.
• (c)  
  Concrete/mud walls versus wood walls. This changes Tx90p by −2.3 (±1.0) °C.
• (d)  
  Tree shade versus no shade. This changes Tn10p by −0.7 (±0.3) °C.
• (e)  
  Large (>5 m high) versus small (⩽5 m high) buildings. This changes Tx90p by −2.6 (±1.0) °C.

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Dummy variable coefficients can be summed to give combined effects of several measures. For example, traditional homes in Tamale are always ⩽5 m high, have thatch roofs and mud walls but are not located by the coast. Hence, to reduce Tx90p in such homes, the modelled measures (i.e. ceiling insulation −2.6 °C, tree shade −0.1 °C, and a fan −0.2 °C) would yield a benefit of −2.9 °C relative to the reference building (table S2). However, the model also indicates that this set of modifications would increase the number of very hot nights by 28 per year. Adding ceiling insulation, tree shade and a fan to a concrete walled and metal roofed home would change Tx90p by +0.8 °C and Tn30C by −7 nights per year.

Day/night trade-offs are also predicted by the models for modifications to low-income homes in Accra. For example, upgrading a reference building (⩽5 m high, wooden wall, metal roof, with no insulation) to one with concrete walls, insulation, and fan, could change Tx90p by −5.2 °C but Tn30C by +57 nights per year. Overall, choice of roof material and ceiling insulation are the most heavily weighted variables for reducing Tx90p. Addition of tree shade reduces the frequency of hot nights by 15 per year.