1. Introduction
Over two-thirds of the world’s population are projected to dwell in urban areas by 2050 [
1], reflecting the continuously growing role of cities as centers of human and economic activity. Among other environmental concerns, such as ambient/indoor air pollution and waste management, cities experience elevated air temperatures relative to their surrounding rural areas [
2], which is a phenomenon commonly referred to as the urban heat island (UHI). The thermal effect of a city is mainly related to [
3]: (a) the urban form—the fabric, the surface cover, and the three-dimensional urban structure—and (b) the urban function—the waste heat resulting from human activities. Numerous studies have been conducted in the last decades measuring the urban canopy layer UHI (UHI
UCL) (e.g., [
4,
5,
6]) and estimating the surface UHI (SUHI) (e.g., [
7,
8,
9]). Urban overheating is found to have a strong relation to increased energy consumption for cooling [
10,
11] and heat-stress related morbidity and mortality [
12,
13,
14,
15]. During heat wave events, urban heat has been proven a serious health hazard also for residents of usually cooler mid-latitude and high-latitude cities [
16,
17]. Global climate change further exacerbates the impact of the UHI, as it causes the warming of the climate system and makes the future occurrence of heat waves with higher frequency and duration very likely [
18]. Initiatives for UHI mitigation/adaptation are being interlinked with climate change policies [
19], and cities are gaining a central role in current global agreements, such as the recent Paris Agreement (2015) of the United Nations Framework Convention on Climate Change (UNFCCC).
Climate change [
20,
21] and more generally enhanced heat stress [
22,
23] do not affect all citizens of a city to the same degree, due to the intra-urban diversity of surface microclimates and the varying physiological characteristics of the population (e.g., age, state of health). City planners and policy makers need tools to identify the most thermally vulnerable areas of their city before designing and implementing climate policies [
24]. The Local Climate Zone (LCZ) [
25] categorizes urban areas based on their urban form and function in distinct, neighborhood-scale types that have uniform thermal climates. LCZs can provide valuable insight into the spatial thermal patterns when attributed across a city [
26,
27,
28]. However, the LCZ classification is primarily focused on universally recognized built arrangements in view of UHI field sites’ selection and metadata reporting [
25]—the latter being a significant limitation in the existing UHI literature [
29]. As a consequence, intraclass thermal variability has been reported in several LCZ studies [
6,
30,
31], which can to a great extent be attributed to the considerable range of surface parameters within each LCZ class [
31]. This may hold especially true when LCZ mapping is derived using remote sensing imagery in the World Urban Database and Access Portal Tools (WUDAPT) project [
32]. Furthermore, the LCZ scheme might not be necessarily capable of describing specific cases of the often irregular urban form of European cities [
33].
An indicator that aggregates the driving forces of UHI can be viewed as an alternative representation of the urban thermal environment. Several studies have used thermal environmental indicators, either as a component within the Urban Climatic Map (UCMap) framework (e.g., [
24,
34,
35]) or as an independent metric (e.g., [
22,
36,
37]). Nevertheless, in most of the relevant indicators, the highly important features of urban form and function are not considered. The three-dimensional complexity of the city is reported to be often missing [
38], and dynamic urban processes, such as the turbulent energy exchanges and the anthropogenic heat emissions, are typically not explicitly integrated. Here, a physically based, thermal indicator—the Urban Heat Exposure (UHeatEx) indicator—is proposed. UHeatEx integrates the most important physical processes that have been identified to control local surface climates, modify the surface energy balance, and cause the UHI. These processes are [
2,
25]:
High values of the ratio of building’s height (H) to street width (W)—canyon aspect ratio (H/W)—result in canyon trapping [
39,
40], i.e., the absorption of shortwave radiation in the canopy is stronger via multiple reflections on walls, and in the longwave radiant heat loss being strongly obscured [
41,
42].
Sensible heat refers to the direct warming of air that can be sensed by a thermometer, whereas the latent heat component of the urban energy balance is mostly associated with the energy exchanges during surface moisture evaporation. The dry, impermeable urban facets favor sensible (
QH) over latent (
QE) heat flux; i.e., the Bowen ratio (β =
QH/
QE) has higher values. As a result, the evaporative cooling effect decreases [
43,
44].
High thermal inertia of the construction materials and of the built environment in general results in a large proportion of incoming solar radiation to be stored during the daytime in the urban system—high net heat storage (Δ
QS)—and being released at nighttime, maintaining the urban heating effect [
45].
The anthropogenic heat flux (
QF), e.g., waste heat from vehicular traffic and from space heating/cooling, is an additional energy source into the surface energy balance [
46].
These four decisive parameters for the urban climate (H/W, β, ΔQS, QF) are estimated in this work using meteorological, earth observation, and geographical information system (GIS) data; the different variables are subsequently incorporated in the UHeatEx composite indicator via principal component analysis (PCA). An additional mapping of the study area using the LCZ scheme is applied, and similarities and differences between UHeatEx and LCZs are discussed. Both results are correlated with air temperature measurements from a network of automatic weather stations (AWSs) in the area under examination, for a six-year period (the warm period months). A qualitative and quantitative analysis examines the presence of thermal inequity in Athens and its relationship with socioeconomic characteristics. The growing importance of thermal indicators in sustainable urban planning, due to climate change, is demonstrated by presenting the simulated future conditions of the city. To this end, high-resolution climate projections from an ensemble of regional climate models (RCMs) were used.
3. Results
The LCZ classification map at 400-m resolution (
Figure 5) shows that the dominant morphological type of Athens is compact mid-rise (LCZ 2), covering almost completely the city center areas and a large part of the region around the central municipality. LCZ 2 also prevails at the central areas of Piraeus and can be further found in a few recently overbuilt suburban sites. The generally lower height of buildings at the western periphery of Athens results in a high coverage of the compact low-rise class (LCZ 3); whereas in the lower density districts—the northeastern and southeastern suburbs—the open mid-rise (LCZ 5) and open low-rise (LCZ 6) classes are prevalent. The extended large low-rise (LCZ 8) zone between the city centers of Athens and Piraeus was an active industrial center until the 1970s; nowadays, it serves as a light industrial site, including vacant buildings and brownfield land. The LCZ classification result for the central areas does not manage to describe potential microscale variations of differing surface energy balances, attributing them invariably to LCZ 2. In addition, a clear distinction of the central commercial district of Athens cannot be made. This mainly stems from the historically imposed building height restrictions that result in the compact high-rise class (LCZ 1) (typically corresponding to downtown areas) to be absent from the 400-m scale mapping, with the exception of one pixel. Furthermore, areas of specific land use and associated thermal degradation (e.g., major highways) are not depicted.
The H/W spatial distribution, as shown in
Figure 6a, illustrates part of the previously unrevealed morphology of Athens. While high buildings (above 25 m) are generally scarce, the excessive building density has resulted in disproportionately narrow roads, especially in the central city areas. As a consequence, a large percentage of urban canyons in the inner city of Athens are characterized by H/W ratios over 2, while their adjacent buildings have a mean height fewer than 25 m—which is a combination of morphological parameters that is not covered under the LCZ framework. For the outer urban districts, the H/W patterns generally follow the LCZ classes, since H/W is a primary threshold value in the LCZ mapping procedure. The lower H/W values tend to be found in the LCZ 8 class and in the sparsely built neighbourhoods (e.g., LCZ 9).
Figure 6b presents the average β
m for July 2017, as derived from daily downscaled MODIS-TERRA thermal data and synchronous meteorological measurements. The southwestern neighborhoods tend to have the higher magnitudes of β
m (i.e., channeling more heat into
QH than
QE), which is readily associated with lower values of λ
v and high
Ts. Significantly high midday Bowen ratio values (over 20) are estimated for the 100-meter resolution pixels that consist mainly of dry surfaces (rooftops, roads). Open impervious areas (LCZ 8 and LCZ E) are, as expected, also characterized by high β
m values. In contrast, the lower magnitude of β
m is generally seen in the more vegetated northern suburbs; β
m values as low as 0.6 can be found in a few northern residential districts, where
QE substantially exceeds
QH even at around midday. The central areas of Athens mainly tend to have high β
m (although lower in magnitude in comparison to those in the broader periphery of Piraeus) with the exception of urban parks, which have notably low values. The spatial variation of β
m is generally linked to λ
v; however, wind speed, humidity, and aerodynamic properties play a contributing role in the developing patterns.
The validation of the modeled
QH and
QE values was performed using the turbulent fluxes, which were calculated employing the EC method from the available flux tower measurements during July 2009. The
QH values estimated by the EC techniques ranged here from slightly positive nocturnal values to an average peak of ∼150 W m
−2 at midday, which is in accordance with the results presented in [
79]. In addition, relatively low
QE (EC) values were found, with a daily average maximum value of approximately 30 W m
−2. For the detailed presentation and analysis of the measured energy and momentum fluxes at the Thermopolis campaign site, the reader is referred to the corresponding research papers [
79,
87]. To expand the synchronous remote sensing imagery and EC estimates availability in this study, along with the midday MODIS-TERRA, nighttime MODIS-TERRA (∼23:00 local time), and early afternoon MODIS-AQUA (∼15:00 local time), thermal images were used that were preprocessed as in [
55] (imagery with a sensor viewing angle over 55° were not included).
Figure 7 shows that a moderate level of agreement is obtained between modeled (aerodynamic resistance method) and measured fluxes (EC method). An average root mean square error (RMSE) of 33.1 W m
−2 was found for
QH and of 10.2 W m
−2 for
QE; when considering only daytime results, the RMSE was 41.5 W m
−2 and 15.5 W m
−2 for
QH and
QE, respectively. A sensitivity analysis for the aerodynamic resistance methodology was conducted in order to investigate the response of the modeled turbulent fluxes to perturbations of the most significant input variables. The sensitivity of the different terms was individually tested, i.e., for each examined variable, all the other input values were held fixed; in addition, only the daytime fluxes were considered. As shown from
Table 4, the modeled
QH is highly sensitive to the (
Ts—
Tα) factor of Equation (1). A change of 2 K of the term results in a deviation of ~20% for the estimated
QH, which is an uncertainty of ~30 W m
−2 in the case of a sensible flux in the order of 150 W m
−2. This highlights the high importance of acquiring accurate remotely sensed
Ts for the modeling of surface–atmosphere interactions. The downscaled
Ts used in this study was found to have an uncertainty of ~1.5 K for the Thermopolis site and <2 K for the entire study area [
55]; thus, it is considered to have a relative large impact in the obtained errors. An additional important source of uncertainty in the remotely sensed turbulent fluxes is the derivation of the
u* value. Here, a smaller RMSE (by approximately 10 W m
−2) was obtained for daytime results when the EC-derived
u* value was used in the place of the
u* value modeled from wind speed measurements. The respective change of the Bowen ratio from the perturbed
u* value is to an extent compensated, as both turbulent fluxes change in the same direction.
QH and
QE were found to be rather insensitive with perturbations in the aerodynamic parameters and the Obukhov length (<2% sensitivity). An additional source of uncertainty for
QE is the (
qs—
q) factor and the
rsmin parameter. Their impact in absolute terms is limited in the urban core, as
QE was found to have low values (fewer than 30 W m
−2); however, for the vegetated suburbs, the estimation sensitivity is expected to be higher. The sensitivity analysis findings are in agreement with those reported in [
67,
81] (in terms of percentage variations), where
Ts was also considered to be the most important parameter in the resulting sensitivity of the fluxes.
A more uniform spatial distribution than H/W and β
m was found for the estimated ΔQ
sm (
Figure 6c). Generally, major highways and other open paved zones tend to be those with the larger values, which was a consequence of the lower surface reflectance and high OHM parameters for asphalt. A large ΔQ
sm value is also obtained for central areas, due to the higher building density and lower vegetation fraction. Northern suburbs, which are covered to a large extent by natural surfaces, tend to absorb and store a smaller amount of heat. An average ΔQ
sm of 220 W m
−2 at the Thermopolis flux tower site indicates that the OHM estimates are in good agreement with the average midday value of approximately 250 W m
−2, which was derived as an energy balance residual during the Thermopolis campaign [
87].
The average daily
QF value in Athens for July 2012 is presented in
Figure 6d. It can be seen that a concentric pattern of heat emissions develops around the city center of Athens. Generally, the higher
QF values are associated with high commercial energy usage; the residential
QF value is relatively lower, despite the high population density and a continuous space cooling demand for the discussed period. Daily average
QF values up to 108 W m
−2 were estimated for the commercial city center. The average
QF releases for residential areas do not exceed 60 W m
−2, including the most densely built neighborhoods. Peak average heat emissions tend also to appear in the major highways, with values generally (up to ∼80 W m
−2). A generally moderate
QFT can be found for the secondary traffic network, resulting from the contrasting features of Athens as a compact city with a well-established public transport network and with high vehicle ownership. The heat released by anthropogenic activities presented in
Figure 6d correspond to averaged values throughout daytime and nighttime hours.
In order to outline the daily
QF dynamics and the local spatial variations, the median daily temporal profiles of
QF (for July 2012) are calculated for an extended area (∼530,000 m
2) covering the city center of Athens (“Athens Commercial Triangle”) (Site 01 in
Figure 8) and five representative grid cells (10,000 m
2) corresponding: (i) to an anthropogenic emissions hot spot inside the above-mentioned area (located in the vicinity of the Omonoia Square), (ii) the Thermopolis flux tower site (Patission Street), (iii) a main traffic corridor site (Kifissou Avenue), (iv) a high population density residential site (Zografou), and (v) a low population density residential site (Vrilissia) (sites 02–06 in
Figure 8). For the city center (
Figure 9a), it can be seen that
QF follows a bell-shaped curve profile with high values between (9:00–17:00), corresponding to the combined effect of heat emissions from air conditioning usage, vehicular traffic, and the various office/shop electricity needs. A late evening increase reflects increased
QF values from recreational venues in the examined area. The average daytime heat emissions for the Athens Commercial Triangle are about 75 W m
−2, with a peak value over 100 W m
−2. The local commercial site presented in
Figure 9b follows a similar daily cycle; however, it is characterized by much higher anthropogenic heat releases with average daytime
QF values of 125 W m
−2 and a maximum of 170 W m
−2. The peak surpassed 185 W m
−2 in the hottest day of the month. With increasing distance from the city center core, the anthropogenic emissions are becoming lower in magnitude (
Figure 9c). For Patission Street (Thermopolis site), an estimation for
QF of approximately 50 W m
−2 has been previously derived in [
79]—for a coincident period under similar meteorological conditions (July 2009)—using the assumption that during the daylight hours, anthropogenic heat emissions are included in the sum of turbulent fluxes and upwelling longwave radiation, and subsequently employing a fluxes time lag between normal working days and Sunday. Despite the differences in methodologies in [
79] and in this study, a reasonable level of agreement is found, with estimated daytime values of ∼40 W m
−2 (
Figure 9c). The temporal profile shown in this figure presents differences from the profile in
Figure 9a, due to the stronger relative contributions of vehicular, residential, and metabolic emissions in the area compared to the commercial buildings’
QF component. The daily evolution of
QF is also significantly different for the Kifissou Avenue site (
Figure 9d); the two daily maxima correspond to morning/evening traffic rush hours. During these high vehicular traffic moments,
QF reaches up to 100 W m
−2, whereas heat emissions during the nighttime get closer to zero. The primarily residential site of
Figure 9e shows higher evening
QF values, reflecting the hours that most people spend at home, rather than at their workplace. The average daytime anthropogenic heat flux is 56 W m
−2, while an absolute maximum for the month was found to be over 75 W m
−2. In the early nighttime hours, a relative high
QF value is sustained as a result of space-cooling energy usage. Finally, in the low-density residential site (
Figure 9f), anthropogenic heat emissions are significantly lower, and daytime variations are relatively small.
In
Figure 10, the annual variations of citywide total
QF and of its individual components are presented, relative to the built-up land area (as outlined in
Figure 1). The citywide aggregated
QF values vary from 7.8 to 29.6 W m
−2, with an average value of 13.9 W m
−2 (9 W m
−2 when the total land area is considered), including both daylight and nocturnal emissions. The seasonal cycles of
QFB (electricity and oil/gas) were well reproduced (
Figure 10) following the varying heating and cooling demands; interestingly,
QFB for the cold period is vastly higher in comparison to the summer months. This finding is consistent with a recent energy consumption survey for Greek households (ELSTAT) [
129] and can be attributed to the poor construction standards and lack of insulation for a large proportion of buildings in Athens, a potential relative higher tolerance of the population to warmer than colder conditions, and furthermore, the lower number of city dwellers in July and August, as a result of summer holidays.
Then, PCA was applied to the images of the four examined thermal parameters (H/W, β
m, ΔQ
sm, and
QF) (
Figure 6); the obtained results of the analysis are shown in
Table 5 and
Figure 11. As can be observed, the first principal component (PC1) integrates a large amount of the spatial variation that characterizes the initial, individual variables (50.6%). Critically, PC1 is accompanied with positive component coefficients (scores) for all the parameters. Thus, it can be regarded as the composite image for which the pixel values correspond to the aggregated thermal effect of the heat-related parameters, while preserving the maximum possible proportion of the total variance in the original dataset. The degree of variation that is not feasible to be accounted through PC1 is to a significant extent expected, as the urban climate may be viewed as the mix of different thermal effects that often exhibit contrasting results. For instance, high H/W values—which are in general associated with higher thermal stress—may result in lower midday
Ts values due to the shading of the buildings and the canopy surface. PC2 contains a considerable amount of variance (23.7%); however, it can be directly linked to more specific cases areas that can be distinguished from a microclimatic point of view. Specifically, PC2 is mainly associated with regions characterized by the contrasting combination of high ΔQ
sm estimates and low H/W values, as it can be inferred from the high values and opposite signs of component scores for the two parameters in
Table 5 and from the patterns in
Figure 11b. This situation coincides with city areas of high impervious ground fraction (λ
i) (primarily light industrial and port areas) that tend to have lower reflectance and high OHM coefficients (see
Table 1). In addition, PC2 is considered to be directly affected by a limitation of the OHM scheme, which tends to overestimate the net heat storage from impervious ground surfaces in comparison with high-rise buildings configurations [
130]. Finally, the strong heat storage release in the PC2 hot spots is expected to have less influence on the UHI intensity, as low H/W values will result in higher cooling rates. Likewise, as indicated from
Table 5 and
Figure 11, PC3 and PC4 correspond to particular cases of high β
m and
QF estimates. However, the amount of explained variance for these components is lower, and furthermore, the corresponding parameters have already been, to a great extent, incorporated in PC1.
Considering the above, it was determined to associate UHeatEx only with the first principal component (PC1). This decision is further justified considering that employing a different weighting technique—e.g., equal weights or assignment of weights through expert opinion—would lead to even lower explained variance, since PC1 corresponds by definition to the linear combination that accounts for the largest amount of variability. Nevertheless, in case where there is differentiation with respect to the sign of component scores for PC1, either both PC1 and PC2 should be regarded as UHeatEx, or an alternative weighting procedure may be followed for the four components (H/W, β
m, ΔQ
sm, and
QF). Furthermore, as UHeatEx is a physically based indicator, it is suggested that these four initial parameters should be presented alongside the indicator. In this way, the distinct features of the urban form and function can be identified for different regions, thus enabling the identification of tailor-made climate adaptation responses. The final results for UHeatEx—after being normalized to a 0 to 10 scale—are presented in
Figure 12. As stated above, all the heat-related parameters exhibit positive scores for PC1 (and correspondingly UHeatEx); therefore, the scale of
Figure 12 can be regarded as classes of Very Low/Low (0–4), Moderate (4–6), and High/Very High (6–10) heat exposure. The spatial distribution of UHeatEx illustrates the diversity of Athens microclimates and surface energy balance modifications. The higher UHeatEx values are found in the city center of Athens, where all the driving forces of excess urban heat have high magnitudes, in particular H/W and
QF. Low thermal environmental quality is also estimated for a secondary pole of high commercial concentration northeast of the city central area, for the overbuilt residential districts at the central municipality, the city center of Piraeus, and the southwestern districts. The busiest highways are also clearly outlined, following extensive vehicular heat emissions and distinct surface cover characteristics. Northern and southeastern suburbs tend to have the lowest UHeatEx values; low magnitudes are also found in the light industrial zone, reflecting its stronger surface cooling potential, due to a high sky view factor.
The average minimum
Tα (
Tαmin) during the warm period (May–September) for the years 2013 to 2018 was calculated for five weather stations in the central neighborhoods of Athens (sites 07–11 in
Figure 8). All the station sites correspond to the LCZ 2 class (both at 100-m and at 400-m scale); however, they differ in the derived UHeatEx scores.
Tαmin was selected to be examined, as UHI
UCL is predominantly a nocturnal phenomenon [
43,
131] due to slower cooling rates for the denser urban areas. Since long-term canopy layer temperatures for the study area were not available, measurements in the RSL were used (
Section 2.3), corresponding to a combined effect of microscale and local-scale processes [
89]. The results presented in
Table 6 indicate that UHeatEx values follow the
Tαmin measurements well, reflecting both the stored warmth during the day and the subsequent nighttime release. Especially for the northern station (Site 11), the higher amount of green space and the associated cooling effects led to significantly lower temperatures; while it belonging to the morphological type of densely built LCZs.
UHeatEx demonstrates that the urban thermal landscape of Athens is characterized by a high spatial variability, both among different districts and for nearby urban blocks. At first sight, this thermal inequity seems to be associated with the socioeconomic status of residents. Low UHeatEx values are found in the affluent north and southeastern areas and high values are found in the lower-income and medium-income west and central areas. A more clear view can be obtained by examining the relationship between the mean household income per postal code for year 2011 (Greek Ministry of Finance;
http://www.gsis.gr/gsis/info/gsis_site/PublicIssue/) and the corresponding average UHeatEx values (non-residential land use zones were excluded from consideration). From
Figure 13, it follows that a correlation is present between household income and UHeatEx (R
2 = 0.45), albeit moderate in strength; a significant spread of the indicator values is found for medium incomes, with higher UHeatEx for the areas of central Athens.
Climate change is expected to augment the impacts of the current urban overheating. As follows from
Figure 14, a positive trend of 0.24 °C per decade for the annual mean temperature and of 0.28 °C per decade for the mean temperature of summer months was found to be projected for the next 50 years in Athens, according to the multi-model mean of the EURO-CORDEX simulations.
4. Discussion
City planners and policy makers need to consider the local-scale vulnerability in climate change adaptation actions. UHeatEx provides a map-based planning tool that can help the identification of the most sensitive urban climatic areas across a city. Furthermore, the physical basis of the indicator—integrating the urban form, function, and surface energy exchanges—can assist planners in deciding appropriate, site-specific actions that can improve the urban thermal environment. Generally, UHeatEx (
Figure 12) better presents the urban inner spatial heterogeneity of Athens compared to the LCZ mapping (
Figure 5)—it highlights the city-specific characteristics of urban form and allows a physical explanation of the derived values. This was also demonstrated by examining the
Tαmin of five LCZ 2 stations (
Table 6); UHeatEx managed, to a great extent, to match the thermal variability of these cases. Further validation of the indicator is planned for future research, focusing on
Tα measurements inside the UCL.
In the literature, several studies have modeled the urban energy fluxes using a remotely-sensed approach [
66,
67,
68,
132,
133,
134,
135,
136,
137,
138]; however, only few of them made synchronous measurements of the turbulent fluxes available for validation purposes. Here, the obtained accuracy is considered to be consistent with the reported errors in recent studies of long-term, remotely-sensed turbulent fluxes [
68,
81,
86]. Furthermore, the present work extends the current knowledge of the applicability of the downscaled satellite
Ts in the urban
QH estimation, which to the authors’ knowledge has only be examined in [
68]. Downscaled thermal data are necessary in the estimation of high-resolution daily fluxes, due to the trade-off between spatial and temporal resolutions of current thermal imagery [
139].
High anthropogenic heat emissions were obtained for the city center of Athens (∼100 W m
−2 at summer midday, with local peaks up to 185 W m
−2) and the major highways. On the other hand, the average
QF for the greater urban area of the city was considerably lower (city-scale average magnitude under 30 W m
−2 for all dates), which was mostly due to the reduced anthropogenic heat emissions in areas that were at a great distance from the city center. These findings confirm the importance of scale in the anthropogenic heat emissions estimation [
97,
98] and follow the general premise of [
140], which concludes that values in the central city areas can be up to 5–10 times higher than the citywide values. A typical example of the
QF scale and temporal dependence is the case of the city Tokyo, where in [
141], a significantly high daytime anthropogenic heat flux value—over 400 W m
−2—was derived on several 25 m × 25 m grid cells in central areas, whereas for the greater Tokyo urban area, average
QF values in the range of 20 to 40 W m
−2 were later estimated [
99,
142]. The relatively low average city-scale anthropogenic flux that was found in this study for Athens (annual average of 14 W m
−2) is in agreement with reported
QF aggregates for a few metropolitan areas worldwide: for instance, 13.2 W m
−2 in Istanbul [
118], 10.9 W m
−2 for London [
103], 6.2 W m
−2 in Manchester [
102], and 16.8 W m
−2 in Mexico City [
118].
As shown from
Figure 13, UHeatEx displayed a moderate correlation with household income. The distribution of the indicator can be interpreted by considering the main aspects of post-war urban development and spatial socioeconomic segregation in Athens. In the 1950s–1970s, the at the time affluent center was massively built through a flats-for-land system [
143] that led to the unregulated replacement of low-rise housing stock with multi-story apartments [
144] for relative narrow streets—i.e., forming urban canyons of high H/W. The working class dwelled in lower height, often initially informal, settlements at the western periphery of the agglomeration [
145,
146]. As a result, these western areas, especially in the northwestern districts, have lower building density, H/W, and anthropogenic heat emissions (
QF). The deterioration of living conditions in the central areas due to overbuilding—including overheating, as demonstrated by UHeatEx—led to a portion of the middle and higher social classes gradually relocating to the vegetated northern and southeastern suburbs [
147], with the exception of the residents of some historically important central neighborhoods. In the 1990s, the majority of the arriving immigrants settled around the city center, where they mixed with the remaining lower and middle-class residents [
48]. Considering the above, it follows that the east–west social segregation of Athens [
148,
149] is less pronounced in terms of unequally distributed extra warmth; however, an income-based stratification of the thermal environmental quality is generally present in the agglomeration. The synergy of the UHI and the global climate change (
Figure 14) will further exacerbate the existing overheating hazards in Athens. Increased energy for cooling is expected to outweigh the decreased heating consumption for Greece [
150,
151] and exert additional stress on current UHI health implications in Athens [
152].