Spatiotemporal Analysis of Urban Thermal Effects Caused by Heat Waves through Remote Sensing

Abstract

In recent years there has been an increase in the number of extreme weather events that lead to higher mortality, such as heat waves. This study carries out a new investigation that integrates the environmental quality parameters—the Surface Urban Heat Island (SUHI) and the Terrestrial Surface Temperature (LST)—during these periods of high temperatures and compares them with normal periods. The study of the relationship between these variables will allow improving the quality of life through new mitigation measures that will minimize the effects of climate change in urban areas. This study analyzes eight cities in the south of Spain (Andalusia) to assess environmental quality through gases SO₂, NO₂, CO, O₃ and aerosols, obtained through Sentinel-5P satellite images, and the LST and SUHI obtained through Sentinel-3 images. Next, the results of periods of heat waves are compared with periods of normal environmental conditions during the summers of the years 2020 and 2021. The objective is to determine the possible impact of heat waves on environmental quality, as well as on the LST and SUHI of the investigated cities, which are located in an area identified as highly vulnerable to the effects of global warming. During the period of the heat wave and compared to the periods without a heat wave, a variety of environmental pollutants was found: SO₂ (+165%), NO₂ (+24%), CO (+8%), O₃ (−4%) and aerosols (+193%). Both the LST and the SUHI suffered an average increase of 2.8 K. The results of this document can help to establish pollutant reduction mechanisms in periods prior to heat waves. This could minimize major effects on the population and provide sustainable development.

1. Introduction

Several studies warn that the expansion of urban areas produces an important transformation of the landscape, this being one of the processes that contribute to global warming together with greenhouse gases [1,2,3,4,5]. The construction of new urban areas generates changes in land cover. Namely, there is an increase in the surfaces of waterproof materials for floors and facades (concrete, asphalt, bricks) that reduce evapotranspiration [6]. The absorption of solar radiation increases the net radiation from these surfaces, which is then used to increase sensible heat flux ultimately leading to higher air temperatures [7,8,9]. Urban areas are the places where there is a greater increase in temperatures compared to rural areas, mainly due to the Urban Heat Island (UHI) phenomenon [3,9,10,11,12,13]. Its intensity presents a high variability due to multiple human activities [14,15,16] and heat waves. When remote sensing is used to assess the urban heat island phenomenon, we determine the temperature of the Earth’s surface and not the ambient temperature. Some investigations indicate that the average air temperature will be 1 to 3 K higher in the coming decades (compared to 1961–1990), by the middle of the century (2040–2069) it will be 3 to 5 K higher and at the end of the century (2070–2100) the increase will amount to 3.5 to 7 K [17,18]. These temperatures are not compatible with the sustainable development of the future [17].

Regarding the state of the art of the investigation, the positive correlation between SUHI and heat waves is somewhat debated: in Maryland and Baltimore [18] 2 K increments are anticipated, Beijing [7,19,20,21] may see increases between 0.7 and 8 K, New York [22] would undergo an increment of 1 K, Shanghai and Guangzhou [19,20] increments of 1.3 K, Athens [23] 0.2 K increments per decade, Seoul City [24] increases of 3.3 to 4.5 K between the years 2012 and 2016, Dijon (France) [25] increases of 0.7 to 0.9 K from 2014 to 2019, Karachi [26] increases between 0.1 and 3.2 K, Singapore [27] a 2.5 K increment and Madrid would undergo a 1–3 K rise [28].

In sum, numerous studies illustrate the same trend between SUHI and heat waves in urban areas —during periods of heat waves where the temperature intensifies, the same happens to the SUHI. However, the particular climatic conditions, the type of climate and the geographic and morphological factors of the cities also contribute significantly to this interaction [7,20,29,30,31,32]. Studies on some European cities [33], Oklahoma [34], London [35], Guangzhou and Beijing [20] report strong increases in SUHI at night. On the other hand, studies in Parma, Athens [36] and Shanghai [20,37] found that the highest increase in SUHI occurs during the morning. Further studies do not show notable amplifications of SUHI [13,22]. Taking into account that the data collection method is similar, the array of results could be interpreted in the sense that coastal cities present a greater intensity during the day while inland cities present a greater intensity at night. This is possibly due to the effect of the sea breeze and differs between cold water (e.g., Chile) and warm water (e.g., Japan) coasts [4]. Recent investigations [38,39] report that gases O₃, aerosols, VOCs (volatile organic compounds) and BVOCs (biogenic volatile organic compounds) are considered a major cause of global warming of the planet. Thus, the study of 361 cities in China during the year 2019 reported that the cities with the highest environmental pollution had higher concentrations of O₃ in the atmosphere, intensifying in the summer period. This was positively related to the variable PM2.5 [40]. In this sense, and with the help of remote sensing, the viability of the Modis aerosol optical depth (AOD) algorithm has been demonstrated for the establishment of environmental pollution control programs [40]. This, in turn, affects climate change through the intensification of extreme weather events, such as heat waves, cold waves, droughts, etc. It is important to carry out new research in these situations of climate change that study jointly the three closely related elements (LST, SUHI and environmental pollution).

Numerous investigations indicate that heat waves occur with increasing frequency, duration and intensity [41,42]. Some studies estimate that by the end of the 21st century they will affect larger regions of the planet [41,43,44]. According to the evolution of these, in the next 40 years, Spain could be one of the countries with large surfaces affected by heat waves in summer. Heat waves are known to have a great environmental, social and economic impact [7]. They imply more water and electricity consumption in homes [45], degradation of air quality [46] and higher morbidity and mortality [2,7,47,48]. Proof can be found in the 1995 Chicago (northern USA) heat wave [48]; in Europe in the summer of 2003 when 70,000 people died [49]; the one that occurred in the summer of 2010 in Russia [50] or that of eastern China in 2013 [51]. In Madrid, between 1979 and 2016, deaths from heat waves increased by 10% [52]. Very recently western Canada witnessed temperatures over 318.2 K for consecutive days that caused about 500 deaths [53].

Although there are numerous methodologies to approach this phenomenon, thermal remote sensing may be most appropriate for large-scale surface temperature studies [5,54]. Studies that rely on Thermal Infrared Sensors (TIRS) have become an important field of research [22]. Among the many investigations include studies of the city of Ganjingzi (China) [54], the Olympic area of Beijing [55], Dwarka river (India) [56], the Chattogram Metropolitan area of Bangladesh [57], the Semarang Metropolitan region [58], Tehran (Iran) [59], Xiamen [60], Wuhan [61] and Changchun (China) [5]. These investigations have allowed researchers to obtain important advances in the studies on UHI. Thus, the laborious task of having environmental data from meteorological stations that are not available or very limited in medium-sized or small cities or located very far from each other has been eliminated. In this way, it has made it possible to significantly increase the spatial variability of UHI studies up to resolutions of 120 m or 10 m if the high-resolution TsHARP algorithm is used. In turn, it has allowed the study of LST in extensive areas of territory, not limited exclusively to urban areas. Finally, due to the free and open access to the images, it has allowed these studies to be opened up to a greater number of researchers.

Sentinel-3 images are a relatively new high-precision product. The Sentinel-3 features three TIRS bands that allow the LST to be determined at a resolution of 1000 m. It is made up of two satellites: Sentinel 3A and 3B. The fact that it, therefore, orbits every point on the planet twice per day. The use of Sentinel is well documented, for example, in the cities of Huazhaizi and Daman (China) [5], Dahra (Senegal) and Oklahoma City (USA) [62]. The use of the Sentinel-5P space satellite as a monitoring tool for environmental pollutants has been widely used in recent literature. Deserving mention are the environmental studies on eight cities in India [54], four cities in Pakistan [63], 19 cities in India [64] and 11 cities in Europe and the US [65], or the small cities of Passo Fundo (Brazil) [66] and Skopje (Macedonia) [67], shedding valuable light on local concentrations of pollutants.

As indicated above, studies on multiple cities have reported increases in SUHI during periods of heat waves. However, in some cities, these increases are greater during the mornings, in other cities, the intensification has occurred at night and, in others, throughout the day. On the other hand, these studies independently explore the links between heat waves and LST, SUHI and environmental pollution. Recent studies [68,69] establish that the relationship between LST, UHI and environmental pollution occurs due to a transfer process of solar radiation and turbulence processes that are increased by the discharge of pollutants into the atmosphere, highlighting the need to study these elements together. The reduction of vegetation and evapotranspiration, the increase in impermeable surfaces and the increase in anthropogenic heat in urban areas together with environmental pollution increase the effect of UHI. Few studies of the Mediterranean Sea basin explore these variables in conjunction. In light of this, our research aims to provide new evidence connecting heat waves and the three variables indicated. So, it is necessary to carry out new studies that complement the existing ones on the possible synergies and consequences of heat waves on three closely related elements: LST, SUHI and environmental pollution. This relationship may provide the basis for future environmental sustainability and resilience to climate change in cities.

Our research aims to provide a complete analysis of the relationship between heat waves and the three variables indicated. The questions we intended to answer with this study are the following: 1. Can heat waves increase pollutant levels? 2. What influence could heat waves have on the LST and the SUHI? 3. Will this variability be different in daytime and nighttime Sentinel-3 LST values? 4. Can the results obtained in this study influence future urban studies?

2. Materials and Methods

2.1. Study Area and Methodology

2.1.1. Area of Study

The eight cities investigated are located in southern Spain, in the region of Andalusia, which has an area of 87,315 km² and a population of 8,329,654. Distinctively, it is the most populated region in Spain and the second largest one (Table 1). According to the Köppen–Geiger climate classification, the region shows different climates. The cities of Cadiz, Granada and Huelva share a Mediterranean Sea climate (Csb), the city of Almeria has a semiarid cold arid climate (Bsh), and the cities of Cordoba, Jaen, Malaga and Seville share a Mediterranean climate (Csa). Hence the region overall has mild, dry summers and humid winters and hot [70]. Andalusia is bordered by the Mediterranean Sea to the south and mountains to the north, which implies that sea and land breezes strongly impact the eight cities studied.

2.1.2. Method

The method undertaken to develop this research work is outlined in Figure 1. Conversely, the LST of the cities was estimated using Sentinel 3 images, and the environmental pollution values (CO, NO₂, SO₂, O₃ and aerosols) were derived from Sentinel-5P images. Some parts of this methodology have been used in other previous studies obtaining good results [71,72]. The VOCs and BVOCs were not taken into account in this investigation, since they cannot be measured by the Sentinel-5P or by the government meteorological stations. Distinctively, to data from the Andalusian Environment Agency, in accordance with the European Environment Agency (EUROSTAT), the investigated cities present an average annual pollution, according to the Air Quality Index (AQI) criteria, of “good” that increases to “moderate” at certain times of the year where prolonged periods of high pressure and low rainfall prevail. The cities of the study were divided into two typologies, inland, and coastal cities. The plane of LST, SUHI and environmental pollutants was obtained during the different periods investigated, without heat waves and with heat waves. Through these, it was possible to obtain the differences between these variables during these periods. The LST and the environmental contamination obtained through satellite images require a verification process that allows the results obtained to be validated. In the last decades, the comparison method of the LST and the contamination obtained by means of satellite images with the air temperatures and the contamination of the meteorological stations it is of great importance as a verification system for the data obtained by means of the satellite [73,74,75,76,77]. This consists of comparing the LST and contamination recovered with the air temperature and environmental contamination obtained from meteorological stations near the ground (1.5 m). Through this system, the statistical variables take on importance including the R² regression coefficient, the mean error bias (MBE) and the root mean square error (RMSE). In our research, and in order to verify the LST and pollution values obtained through Sentinel 3 and 5P satellite images, they have been compared with the air temperature and environmental pollution values obtained by the meteorological stations owned by the State Agency for Meteorology (AEMET) during the hours of passage of the satellites. Each city has a weather station whose data can be consulted free of charge. The Data Panel statistical method allows one to include in the overall result any individual effects that each city may have owing to its particularities. This method has been corroborated by similar studies [78,79,80] involving time series of multiple sites when, in addition, the conditions of the different cities analyzed may vary.

2.2. Data Source

2.2.1. Heat Waves

In 2020, according to Spain’s State Meteorological Agency (AEMET), two episodes were classified as heat waves in Andalusia; in 2021, two episodes were also classified. The AEMET defines a heat wave as an episode of at least three consecutive days during which a minimum of 10% of the state stations considered register maximum ambient temperatures above the 95% percentile of their series of maximum daily temperatures for the months of July and August in the period 1971–2000. Table 2 offers the data and characteristics of the heat waves (start date, end date, duration, maximum temperature and thermal anomalies) studied here.

The four heat waves investigated were detected at all AEMET weather stations in Andalusia (Spain). We compared the LST and the SUHI under conditions without heat waves and heat wave conditions. For the period without heat waves, the six days before and after the heat waves were taken into account, giving a characterization of normal periods based on 27 days that are close in time to heat waves.

2.2.2. Sentinel 3—Images

Sentinel-3 satellites have a temperature measurement instrument—the Sea and Land Surface Temperature Radiometer (SLSTR)—with 21 bands, a spatial resolution ranging between 300 and 1000 m and a wavelength from 550 μm to 12,000 μm. Sentinel products feature three levels of processing (0, 1 and 2), although only the latter two are available for scientific use. Those of level 1 present brightness temperatures that require determination of the ground emissivity to derive the LST. As part of the level 2 set of SLSTR operational products, the LST is derived using a split-window formulation.

The area under study lies below the path of both Sentinel 3A and 3B, which respectively pass overhead at around 10:00 am or 09:00 pm. The images selected for our research, corresponding to twenty-five days in July and August 2020 and 2021, present a cloudiness index of less than 10%. Therefore, the time series of the data used was 51 days distributed between the months and years indicated. Of these, 24 days correspond to heat wave conditions and 27 days correspond to non-heat wave conditions. It must be taken into account that day and night images of LST were obtained for each day. This ensures greater visibility and precision in the LST and subsequent calculation of the SUHI. The urban LST and pollution values used in this research correspond to the mean values of the pixels included within the contour that delimits the urban area after a reclassification process at 100 m. The pre-processed level 2 images used were downloaded from the Copernicus Open Access Hub of the European Space Agency (ESA) (https://scihub.copernicus.eu/ (accessed on 10 January 2022)); they give the emissivity of the ground and the water vapor content of the atmospheres recorded by the satellite itself. Copernicus open software environment was used: Sentinel Application Platform (SNAP), version 8.0.0. All images were reclassified and corrected. After this procedure, the LST images, by means of the open-source software QGIS 3.22.10, were exported to Geotiff format.

2.2.3. Environmental Variables CO₂, SO₂, O₃, NO₂ and Aerosols

The values of the environmental pollution variables O₃, SO₂, NO₂, CO and aerosols were collected using the tropospheric monitoring instrument (TROPOMI) integrated into the Sentinel-5P satellite. A total of 51 Sentinel 5P images coinciding with the days of the Sentinel 3 images were used so that the information established between LST, SUHI and contamination coincided in the days. Therefore, a total of 51 days of Sentinel 5P images were used, 24 days without a heat wave and 27 days with a heat wave. Some studies [54,64] have used and calibrated the values of Sentinel 5P, obtaining a high concordance of the contamination data. Sentinel 5P has 7 bands with a minimum spatial resolution of 3500 m and scans the Earth’s surface daily using four high-resolution spectrometers. The time of passage of the Sentinel-5P satellite over the cities of study is between 12:00 noon and 1:00 pm. A total of 150 images were acquired through ESA’s Copernicus Open Access Hub for level 2.

2.2.4. Land Surface Temperature Estimation (LST)

The algorithm that Sentinel uses internally in the products to assess the LST is based on the concept of absorption difference [81]. The molecules of the atmosphere absorb the wave radiation that is captured by the Sentinel bands to which an atmospheric correction is subsequently applied. Previous studies attest to the accuracy of these algorithms in Sentinel-3 images [82]. The SLSTR Sentinel product algorithm internally includes the ground emissivity according to Equation (1) [83,84]:

(1)$\mathrm{LST}={\mathrm{a}}_0+{\mathrm{b}}_{0 }{\left({\mathrm{T}}_{11}-{\mathrm{T}}_{12}\right)}^{\frac{1}{\cos \left(\frac{\mathsf{\theta }}{\mathrm{m}}\right)}}+\left({\mathrm{b}}_0+{\mathrm{c}}_0\right){ \mathrm{T}}_{12}$

where LST is the temperature of the Earth’s surface in K; a₀, b₀ and c₀ are coefficients that depend on land cover type, vegetation fraction (biome), season, time of day (day or night) and is the atmospheric column water vapor content (in cm). T₁₁ and T₁₂ are the brightness temperatures (top-of-the atmosphere) in the upper part of the atmosphere on bands 11 and 12, respectively. In turn, θ is the zenith angle of view of the satellite located in the metadata file, while m is a variable parameter controlling the dependence on view angle [84,85].

2.2.5. SUHI Estimation

SUHI is described in the literature as the temperature difference between an urban area and a rural area taken at the same time [86]. The delimitation between urban and rural areas was carried out manually using the QGIS software with the help of high-resolution Sentinel 2 satellite images. The built-up areas were included within the urban areas and the rest were in rural areas. It can be determined according to Equation (2).

(2)$\mathrm{SUHI} ={\mathrm{LST}}_{\mathrm{urban}}-{ \mathrm{LST}}_{\mathrm{rural}}$

The urban LST values correspond to the mean values of the pixels located within the urban area of the investigated cities. The pixels located between the urban and rural areas were cut taking into account only the part that is within the urban area. The rural areas chosen to derive the SUHI through the temperature difference with urban areas given correspond to the places where AEMET has rural meteorological stations, some 10–30 km away from urban centers. After exporting the LST images obtained from Sentinel 3 to the QGIS software, the SUHI of the cities could be determined with the help of Equation (2). The considerations behind choosing an AEMET rural station were [87]: (1) The percentage of impermeable soils in the station should not be higher than 20%, and the proportion of agricultural land should be higher than 70%; (2) A difference in altitude between the city and the rural station of only approximately 50 m; (3) Rural stations had to be outside urban areas; and (4) The station must be located in an area of approximately equal surface coverage of 1000 × 1000 m².

2.2.6. Strategy of Analysis

Panel data refers to a type of statistical analysis that combines a temporal dimension (time) with a cross-sectional dimension (data or values). This method is often cited in the literature nowadays, and it involves the use of multiple regression models [78,79,80], so that a larger amount of data may be included than under traditional methods. It is compatible with three calculation options: ordinary squares method (OSM), generalized least squares (GLS) and intragroup estimation method (IEM) [88]. To know which of them is best, the following steps must be carried out [79]: (1) Using the Hausman test, determine if the effects of the analysis are fixed or random. This allows the method to determine different hypotheses about the behavior of the residuals of statistical analysis. (2) Evaluation of the model using the Wooldridge and Wald tests. Both phases will indicate the most appropriate method to use [89]. In our case, statistical analysis was performed with STATA software, version 16; and after carrying out the indicated tests, the IEM method with random effects was used according to Equation (3):

(3)$Y_{it}=\beta X_{it}+{\alpha }_i+{\mu }_{it}$

where ${\mu }_{it}$ is the error of the model, ${\alpha }_i$ represents the individual effects, X_it are explanatory variables, β is an independent variable.

This method allows for a greater amount of data in the analysis, thereby increasing the degree of freedom, while reducing inconvenient collinearity between the variables. By taking into account individual (each variable analyzed) effects, the final function obtained for the set of individuals (each city analyzed) is totally different from the one that would have been obtained using other statistical techniques. It is assumed that individual effects are not reflected in the explanatory variables of the model; instead, they contribute to the error term. Therefore, the equation determines the results globally for all the cities, but the data is entered individually by city. Obtaining data from satellite images for statistical analysis was performed by randomly determining a certain number of points per image. For the 4 heat waves analyzed between 2020 and 2021, a total of 400,000 data were collected on the LST, SUHI and environmental pollutant variables.

3. Results

3.1. LST Validation and Contamination

The validation results of the LST and environmental contamination values obtained from Sentinel 3 and 5P can be seen in Table 3.

3.2. Land Surface Temperature (LST)

Figure 2 and Figure 3 show the mean differences in daytime and nighttime LST between the periods without and with the heat wave. Figures S1 and S2 in Supplementary Materials show the mean values of daytime and nighttime LSTs without and with a heat wave.

Figure 4 displays the LST values obtained from Sentinel daytime and nighttime images for the periods without a heat wave and with a heat wave. In all eight cities investigated, during the heat wave periods, diurnal LST values were higher than those obtained under conditions without heat waves. The average rise in LST for the eight cities was found to be 3.6 K, the highest increases occurring in the coastal cities of Huelva (8 K) and Malaga (6.1 K). The smallest increases were seen in the cities of Cordoba and Cadiz (1.6 K). The night LST values during the heat wave period were significantly higher than the values recorded at times without heat waves. The average increase in LST in the eight cities was 2 K, the largest increases occurring in Huelva (3.6 K) and Seville (3.5 K). In contrast, the coastal cities of Almería (0.6 K) and Cadiz (0.8 K) gave the smallest increases.

Figure 5 illustrates how the increases in LST generated by heat wave situations present strong spatial and temporal variability. Our results show that the increase in daytime LST was greater in cities located on the coast (an increase of 4.1 K) than in inland cities (a rise of 3.1 K), possibly due to the effects of the breeze. In turn, according to Sentinel 3B data, the increase in LST was greater in inland cities (2.7 K) than in coastal cities (1.7 K). Based on the results obtained, it can be stated that both the day and night LSTs underwent significant increases during the heat wave period; but the mean increase is greater for the daytime reading (3.6 K) than at night (2 K).

3.3. SUHI

The SUHI results indicate average increases of this phenomenon during periods of heat waves, from 0.5 K to 5.3 K, with a mean value of 2.8 K. Figure 6 offers the daytime SUHI values both for conditions without heat waves and for the periods of a heat wave.

The average diurnal intensity of the SUHI under conditions without heat waves was 1.8 K, while during the heat waves it increased to an average of 4.4 K. The greatest SUHI increases were seen for Granada (5.3 K) and Malaga (4.8 K); the smallest increases were seen for Almeria (0.4 K) and Cordoba (0.6 K). In Figure 7 and according to the Sentinel 3B images, the nocturnal SUHI values for different periods are shown.

The average nighttime intensity of the SUHI under conditions without heat waves was 1 K, while during the heat wave period it increased to a mean value of 3.8 K, an average increase of 2.8 K. The largest increases in the intensity of SUHI were seen in the cities of Cadiz (6 K) and Granada (4.5 K), contrasting with the lowest values, those of Malaga (0.7 K) and Cordoba (1.3 K). As illustrated in Figure 8, the SUHI increases in the cities of Andalusia generated by the environmental circumstances of heat waves present strong temporal and geographic variability.

The increase in SUHI is greater in inland cities (2.8 K) than in coastal cities (2.5 K). The data by Sentinel-3B likewise indicate a greater increase in nighttime SUHI for inland cities (3 K) than for coastal ones (2.7 K). It can therefore be affirmed that the heat wave situation causes an increase in the daytime and nighttime SUHI intensities. Indeed, the increase in SUHI presents similar mean values during the day (2.7 K) and at night (2.9 K).

3.4. Environmental Contaminants

The average pollution concentrations that were taken into account for the study cities (SO₂, NO₂, CO and aerosols) suffered significant increases during the heat wave periods compared to the average values for the periods without a heat wave. However, O₃ values showed decreases during the same periods. Figure 9, Figure 10, Figure 11, Figure 12 and Figure 13 show the differences in contaminants found between the different periods. Figures S3–S7 in Supplementary Materials show the average concentrations of pollutants during the periods of heat waves and periods without heat waves. Thus, the figures for the heat wave periods include the mean values of the three waves studied, while the others express the mean values for the days/periods of conditions without heat waves.

Figure 14 shows, for all the cities, the mean values of the variables investigated both in periods without heat waves and in periods with heat waves. The increases in pollutant concentrations during periods with heat waves for all the cities studied were aerosols (193%), SO₂ (165%), NO₂ (24%) and CO (8%). A decrease in the O₃ variable of 4% was also registered.

Figure 15 shows the variations suffered by the pollutants by period analyzed and city typology: inland vs. coastal. The average values of the two periods analyzed (with and without heat waves) are given. Clearly, the SO₂ pollutants and aerosols present high spatial variability with regard to the type of city. Pollutants CO, NO₂ and O₃ present similar values, increasing in both types of cities during heat wave periods. Inland, the average increases are: SO₂ (+205.6%), aerosols (+87.4%), NO₂ (+21%) and CO (+8.2%), while the decrease in variable O₃ is −2.8%. The average increases in coastal cities are aerosols (+197.2%), SO₂ (+78.9%), NO₂ (+25.6%) and CO (+7.6%); the decrease in O₃ is −5.6%. It is necessary to indicate that the SO₂ measurements of the coastal cities could be affected by the maritime circulation of the ports. However, taking into account that since the circulation is continuous, it would affect both the values obtained during the periods without a heat wave and during the periods with a heat wave analyzed, it would not alter the differences obtained between both periods.

In sum, the largest increase in environmental aerosols during heat wave periods was found to occur in coastal cities, while the largest increase in SO₂ is seen in inland cities. The pollutant that shows the greatest reductions is O₃, which undergoes a greater drop in coastal cities. Accordingly, the heat wave climatic situation produced a generalized increase in environmental pollutants, whereas O₃ decreased in both city types studied.

3.5. Statistical Analysis of Data

For the statistical analysis of Section 3.5.1 and Section 3.5.2 the dependent variables have been the LST and SUHI while the independent variable has been the environmental conditions of heat waves. On the contrary, and for the analysis of Section 3.5.3 and Section 3.5.4, the dependent variable has been the heat wave conditions while the independent variables have been the environmental pollutants.

3.5.1. LST and SUHI Versus Heat Waves Situation

The Panel Data analysis technique was used to determine if heat waves alter the SUHI and the LST. As indicated in Section 2.2.6, the method followed is the GLS with random effects according to the Hausman test and according to Equation (3). Results can be seen in Table 4 and Table 5. The beta value indicates the number of units that the LST and the SUHI (K) increase for each higher unit of the independent variable (heat wave). The greatest increases in the LST occur during daytime readings; the greatest rises in SUHI occur at nighttime. The LST values convey a statistically positive and significant 95% relationship between the heat wave situation and the LST. Furthermore, the Sentinel daytime results yield a statistically significant and positive relationship of 99% between the SUHI variables and the atmospheric heat wave situation. This relationship is greater than 99% with the values obtained using nighttime data.

The F statistic of an analysis assesses the explanatory power of the independent variable over the dependent variable, while R² and Prob > chi² determine whether there is a significant difference between the expected and observed results. These values are given in Table 6. The first is used to assess the explanatory power of the independent variable with respect to the dependent variable. The second indicates the importance of the relationship between the variables. It can therefore be said that there is a statistically significant relationship between the LST and the SUHI in both periods investigated. Still, this relationship is stronger during periods of heat waves with the SUHI than with the LST. These statistical data are consistent with the data expounded in the previous section. The data distribution assumptions were tested using the Kernel density method. This method and through the study of the histogram allow us to determine if the data present parametric behaviors or not. Kernel results for our data presented a homogeneous distribution.

3.5.2. Variability of the SUHI and LST with the Type of the City Investigated

Next, we calculate the relationship between the SUHI and LST according to the type of city. For this, and through Equation (3), the GLS method was applied. Table 7 shows the results obtained. It is observed that the largest increases in LST and SUHI occur during daytime readings in inland cities, whereas the largest increases in SUHI occur in nighttime readings and in coastal cities. Statistical analysis of daytime data reflects a positive relationship between heat waves and LST in coastal cities, at 95%. Contrariwise, for nighttime data, inland cities show a positive and significant relationship at 95%. With respect to the SUHI, daytime data indicate that in the morning there is a 95% positive relationship between heat waves and SUHI only in inland cities. According to the nocturnal data, the statistically positive relationship at 95% occurs in both types of cities. We may thus affirm that the statistically significant relationship is stronger in inland cities in both daytime and nighttime readings when compared to coastal cities. These statistical data are consistent with the data put forth in the previous section.

The results of statistical analysis, reflected in Table 7 and Table 8, indicate that the daytime LST presents a more intense relationship with heat wave occurrence in coastal cities, whereas the nighttime LST relationship is stronger for inland cities. In the case of SUHI, the daytime and nighttime relationships occur with greater intensity in inland cities. These statistical relationships are consistent with the results described in the analytical calculation section. However, during periods of heat waves, this relationship is stronger with the SUHI than with the LST, in view of the F statistic. Again, these findings would be consistent with the data given in the previous section.

3.5.3. Environmental Pollutants during Heat Waves

In a new phase of statistical analysis, we sought to determine whether the heat wave affected the concentration of pollutants. For this purpose, and following the method described above, Panel Data was performed in conjunction with the GLS method and random effects through equation 3. The results are expressed in Table 9. According to the table, a negative relationship of 95% is seen with the variable O₃ and a positive relationship with the variables SO₂ and aerosols. These statistical data support the data given above. The environmental pollutants data show a regular distribution according to the Kernel test.

3.5.4. Variability of Pollutants by Type of City Investigated

Our statistical analysis aimed to determine the variability of environmental pollution according to the type of city: inland or coastal. After determining that it was necessary to use the Panel Data with random effects, the GLS method was applied through equation 3. The results for each city type are expressed in Table 10. In inland cities, a 95% positive relationship is detected between the variable SO₂ and the heat wave. For coastal cities, a 95% relationship is likewise reported between SO₂ and aerosols with heat waves, and a 95% negative relationship with the variable O₃.

The values of R², F statistic and Prob > chi² obtained are represented in Table 11. There is a good concordance between the variables investigated by the method used, with an adjustment level raised to 95% of significance. The values reflected in Table 10 and Table 11 indicate that pollutants present a more intense relationship in coastal cities than in inland cities, as the former have a higher F statistic. These statistical relationships are consistent with the results obtained by means of analytical calculations.

4. Discussion

Overall, the results reported here point to significant increases in the LST, SUHI and environmental pollutants analyzed during periods of heat waves in comparison to periods without heat waves. The LST and SUHI increased by 2.8 K; aerosols, SO₂, NO₂ and CO showed average increases of 193%, 165%, 24% and 8%, respectively. These findings were corroborated by statistical analysis. Heat waves are associated with high pressures that decrease wind speed and cloud cover. This causes more solar radiation to be received, which leads to an increase in temperatures. Such circumstances amplify the SUHI phenomenon and the concentration of environmental pollutants [86,90,91]. The strong warm anticyclonic conditions coming from North Africa are often associated with heat waves in Spain [92,93,94]. Investigations involving other cities/territories report results similar to ours for periods of heat wave [18,22,95,96,97,98]. For instance, in the Netherlands, a heat wave in the summer of 2003 produced an increase in PM₁₀ concentrations and excess mortality [95]. In the city of Moscow, the summer heat wave of 2010 likewise increased ambient temperatures and concentrations of PM₁₀ and CO [98]. Our findings confirm that a heat wave situation can bear an impact on the environment in terms of more pollutants. The fact that O₃ showed an average daily decrease of 4% during the heat wave period is also in line with previous reports [21,99,100,101]. This situation is motivated by various circumstances that occur in the area under study. On the one hand, O₃ in the presence of NO reacts to form NO₂. Therefore, the concentration of O₃ is usually low in urban centers and higher in rural areas. The radiative effect of aerosols is another element of great importance. Higher concentrations of aerosols attenuate O₃ production. Our results have reported increases in NO₂ and aerosols, so a reduction in O₃ is evident. Winds from the south have lower speeds and generate higher O₃ concentrations [101]. In the cities studied, the winds in situations without a heat wave come from the south but in situations of a heat wave, they come from the north. This circumstance justifies this situation of O₃ reduction.

Temporal variability in pollutant increases is more evident in inland cities than in coastal areas. The total mean value, for all study days, is higher in the first (80.50%) than in the second (77.30%). The pollutant that increases the most in inland cities is SO₂, while aerosols experience the greatest increase in coastal cities during a heat wave situation. Statistical analysis pointed to stronger relationships between pollutants and coastal cities during periods of heat waves. This is due to the fact that SO₂ is generated by power plants that operate at higher performance levels in periods of heat waves to guarantee the electricity supply of air conditioning systems. In Andalusia, the generation plants are located in the inland regions, very close to the cities. Therefore, it would be logical to assume that in periods of heat wave, SO₂ is higher in inland cities. This situation has been corroborated by other investigations in metropolitan Kolkata (India) [102] or in the Indo Gangetic Basin (North India) [103].

The largest increase in aerosols observed in coastal cities can be attributed to a major source known as maritime aerosols, which are carried by the wind along coastal areas. This situation has already been studied previously in other similar investigations, reaching the same results obtained here [63,103]. This circumstance allows pollutants CO, NO₂ and O₃ to present similar values, increasing in both types of cities during heat waves. This is due to the fact that in Spain, heat waves are related to strong anticyclonic conditions due to the great height of the ridge, which ensures clear skies and significant solar irradiation, together with high warm advection coming from North Africa. This produces a stagnation of atmospheric pressure and a decrease in wind speed, which implies a higher concentration of pollutants. Similar situations have been reported in other cities or areas of the planet, such as Singapore [104] or in India [105], allowing the results presented here to be validated. The values reported here support previous findings for coastal or inland territories that suffered periods of heat waves [2,33,54,56,95,97,102,103,106].

Our results indicate significant increases in LST during periods of heat waves, with an average value of 2.8 K. Specifically, the average daytime LST of the investigated cities shows an average increase of 3.6 K, while the nighttime LST shows an average increase of 2.0 K. Regarding spatial variability, during the mornings the increase in LST is greater in coastal cities (4.1 K); the largest increase in nocturnal LST occurs in inland cities (2.3 K). The smaller increase in LST detected overnight may be due to cooling rates produced by green areas within cities and areas of abundant vegetation that are often found on the outskirts of cities. These values are in line with the increases reported in the cities of Singapore [27] of 3 K or in the city of Seoul [24] of between 3.3 and 4.5 in heat waves. suffered between 2012 and 2016. The greatest increase in LST in coastal cities could be due, in turn, to an increase in ambient temperature caused by the variation in sea breezes. These values, corroborated by statistical analysis, are in line with the results obtained in the USA [21,107], in China [20,21] or in the city of Athens [23,33]. The results in terms of SUHI also indicate significant increases during heat wave periods, with an average value of 2.8 K. The average daytime SUHI of the cities investigated gives an increase of 2.6 K, while the average increase of night SUHI rises to 2.9 K. As for the geographical area, it should be noted that both in the morning and at night the greatest increase in SUHI is detected in the cities of the interior, with average values of 2.8 K and 3.0 K, respectively. These increases are in line with those reported in similar investigations and determine how the intense anticyclonic situation lowers the intensity of the winds and increases radiation, temperature and SUHI [2,23,54,95,97,102,103,106,108].

An increase in ambient temperatures produces warming of the waterproof materials that line buildings and streets. This heat, even after sunset, is emitted by the materials into the atmosphere, hence greater during periods of heat wave than during normal periods. The reason behind a lower intensity of SUHI in coastal cities would be that, at night, the winds from the sea produce a cooling effect in coastal cities. These results were corroborated by means of statistical analysis and again are in line with results reported for the USA [18,22], China [2,21], or Athens [23,33], further validating the results presented here. Our results and establishments are in agreement with some studies [61,109,110] related to heat flux, heat flux storage and how these lead to greater effects or problems in SUHI during the night. Research carried out in the cities of Athens [23], Seoul city [24] or Singapore [27] report these circumstances as fundamental elements in the intensification of SUHI.

5. Conclusions

In recent decades, episodes of high temperatures and heat waves have become more persistent over diverse parts of the planet Earth. In coming decades these exceptional situations will affect larger land surfaces, compounding their social, economic and environmental impact. A better knowledge of these conditions is needed in order to establish resilience measures that might improve the climatic conditions of cities.

Our results indicate that environmental pollutants (aerosols, SO₂, NO₂ and CO) increase during periods of heat waves in the cities of southern Spain investigated here. On the contrary, the pollutant O₃ shows a decrease. High temporal and geographical variability of the pollutants analyzed is observed, accentuated in inland cities.

Associated with this increase in pollutants, the LST and SUHI of cities rise during heat wave situations. The average increase for the first variable was 2.8 K and was greater in the morning than in the evening. The largest daytime increase was recorded in coastal cities. However, the highest overnight increase was observed in inland cities, not coastal ones. The mean increase in the SUHI of the surveyed cities, 2.8 K, was greater in the nighttime readings than in the daytime ones. Inland cities suffer the greatest increase in LST during the day and at night; for coastal cities, the increase is smaller.

It is deemed necessary that further research endeavors should increase the number of days of analysis without heat waves versus with a heat wave. This research contemplates just three heat wave periods. New studies should also explore the differences in the nature of heat waves depending on the proximity to the sea. Although some heat transfer can be considered as a return to normal, this is not the case for latent heat transfer. Heat waves generally accompany or induce prolonged periods of extreme dryness that disrupt the water balance of permeable and impervious surfaces. It would be necessary for future studies, to establish the degree of effect of maritime traffic on the pollution of coastal cities. Cities such as Cadiz and Malaga are very close to the Strait of Gibraltar, the passageway for the vast majority of maritime traffic in the Mediterranean Sea.

Our findings here, and in the scope of practice, can form a comprehensive basis that will improve our understanding of heat waves in conjunction with increased environmental pollutants and heightened effects on the LST and SUHI of cities. Studies along these lines should help the persons responsible for city planning when tackling new areas of growth. Minimizing the effects of the LST and SUHI, in view of the geographic situation of the city, calls for implementing efficient and sustainable measures. Key decisions could be made in light of relevant environmental studies such as the ones cited here. Furthermore, our findings could be extrapolated to other Mediterranean cities that share the general characteristics of Andalusia, contributing to future research efforts of this type, especially taking into account that the Sentinel-3 satellite images are available to the entire scientific community.

Author Contributions

Conceptualization, D.H.G. and J.A.D.; methodology, D.H.G.; software, D.H.G.; validation, D.H.G., J.A.D., A.M.M. and E.G.C.; formal analysis, D.H.G., J.A.D., A.M.M. and E.G.C.; investigation, D.H.G., J.A.D., A.M.M. and E.G.C.; resources, D.H.G., J.A.D., A.M.M. and E.G.C.; data curation, D.H.G.; writing—original draft preparation, D.H.G., J.A.D., A.M.M. and E.G.C.; writing—review and editing, D.H.G., J.A.D., A.M.M. and E.G.C.; visualization, D.H.G., J.A.D., A.M.M. and E.G.C.; supervision, D.H.G., J.A.D., A.M.M. and E.G.C.; project administration, D.H.G., J.A.D., A.M.M. and E.G.C. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

In this section, please provide details regarding where data supporting reported results can be found, including links to publicly archived datasets analyzed or generated during the study. Please refer to suggested Data Availability Statements in section “MDPI Research Data Policies” at https://www.mdpi.com/ethics. You might choose to exclude this statement if the study did not report any data.

Conflicts of Interest

The authors declare no conflict of interest.

Abbreviations

AEMET	Meteorology Statal Agency
AOD	Aerosol Optical Depth
AQI	Air Quality Index
BVOCs	Biogenic Volatile Organic Compounds
ESA	European Space Agency
EUROSTAT	European Statistical Office
GLS	Generalized Least Squares
IEM	Intragroup Estimation Method
LST	Land Surface Temperature
MBE	Mean Error Bias
OMS	ordinary squares method
RMSE	Root Mean Square Error
SLSTR	Sean and Land Surface Temperature Radiometer
SUHI	Surface Urban Heat Island
TIRS	Thermal Infrared Sensors
TROPOMI	Tropospheric Monitoring Instrument
UHS	Urban Heat Island
VOCs	Volatile Organic Compounds

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Figures and Tables

Figure 1. Description of the methodology.

Figure 2. Daytime LST differences between periods with and without heat wave.

Figure 3. Nighttime LST differences between periods with and without heat wave.

Figure 4. Average day and night LST by city.

Figure 5. Average increases in LST according to city group.

Figure 6. Daytime SUHI averages by city.

Figure 7. Nighttime SUHI averages by city.

Figure 8. Average increases in SUHI according to type of city.

Figure 9. SO2 Sentinel 5P differences between periods with and without heat wave.

Figure 10. NO2 Sentinel 5P differences between periods with and without heat wave.

Figure 11. CO Sentinel 5P differences between periods with and without heat wave.

Figure 12. Aerosol Sentinel 5P differences between periods with and without heat wave.

Figure 13. O3 Sentinel 5P differences between periods with and without heat waves.

Figure 14. Variability of the average concentration of global pollutants, taking into account all the cities.

Figure 15. Variation (%) of pollutants by city type and period analyzed.

Table 1

Characteristics of cities.

GeographicInformation	Seville	Granada	Cordoba	Jaen	Cadiz	Malaga	Huelva	Almeria
UTM	37.375 N, −6.025 W	37.111 N, −3.362 W	37.891 N, −4.819 W	37.780 N, −3.831 W	36.516 N, −6.317 W	36.765 N, −4.564 W	37.270 N, −6.974 W	36.841 N, −2.492 W
Köppen-Geiger climate	Csa	Csb	Csa	Csa	Csb	Csa	Csb	Bsh
Mean annual T. (K)	291.7	288.7	290.9	290.1	291.5	291.6	290.9	291.1
Average annual rainfall (mm)	576	450	612	552	597	520	467	228
Area (km2)	141.8	89.8	1263	434	12.3	388	153.3	286.2
Urban (km2)	69.69	21.88	32.35	9.63	7.44	59.6	14.97	15.95
Population (hab)	687592	234562	324701	111999	115927	573954	143563	198933
Urban mean elevation (masl)	12	681	107	571	14	9	25	17

Climate Zones: Csb: Mediterranean Sea climate; Csa: Mediterranean climate; Bsh: Semiarid cold climate. Source: State Meteorological Agency (AEMET) and National Statistics Institute (NIS).

Table 2

Characteristics of heat waves.

Heat Waves	1st (2020)	2nd (2020)	3rd (2021)	4th (2021)
Start date	25 July 2020	5 August 2020	11 August 2021	21 July 2021
End date	2 August 2020	10 August 2020	16 August 2021	23 July 2021
Duration (days)	9	6	6	3
Air thermal anomaly (K)	3.1	2.5	4	1.9
Maximum air temperature reached (K)	310.15	312.15	312.65	310.7

Source: State Meteorological Agency (AEMET).

Table 3

Sentinel 3 and 5P validation data.

Heat Waves	R2	RMSE	MBE
LST	0.93	2.34 K	−0.12 K
O3	0.92	0.02 µm/m3	0.01 µm/m3
CO	0.91	0.01 µm/m3	0.02 µm/m3
SO2	0.94	0.0001 µm/m3	0.0004 µm/m3
NO2	0.91	2 × 10−6 µm/m3	2 × 10−7µm/m3
Aerosol	0.95	0.02 µm/m3	−0.003 µm/m3

Table 4

Panel data statistical analysis of heat waves influence on LST.

	Daytime			Nighttime
	β	p	sd	β	p	sd
LST	3.627	0.042 *	2.329	0.120	0.049 *	0.057

β: Regression coefficient; p value: * p < 0.05; sd: standard deviation.

Table 5

Panel data statistical analysis of heat waves influence on SUHI.

	Daytime			Nighttime
	β	p	sd	β	p	sd
SUHI	0.175	0.004 **	0.050	0.223	0.000 ***	0.044

β: Regression coefficient; p value: ** p < 0.01 and *** p < 0.001; sd: standard deviation.

Table 6

R² and F statistical analysis of heat waves situation.

	Daytime			Nighttime
	R2	F	Prob > chi2	R2	F	Prob > chi2
LST	0.383	2.434	0.002	0.451	4.484	0.003
SUHI	0.463	12.146	0.004	0.649	24.771	0.000

R²: Linear regression coefficient; F: F statistic.

Table 7

LST and SUHI Sentinel 3A and 3B results by city type.

	Daytime						Nighttime
	Inland Cities			Coastal Cities			Inland Cities			Coastal Cities
	β	p	sd	β	p	Sd	β	p	sd	β	p	sd
LST	0.121	0.103	0.629	0.069	0.015 *	0.045	0.210	0.045 *	0.088	0.191	0.141	0.113
SUHI	0.197	0.036 *	0.073	0.158	0.094	0.080	0.215	0.016 *	0.065	0.234	0.018 *	0.072

β: Regression coefficient; p: p value: * p < 0.05; sd: standard deviation.

Table 8

Statistical analysis: R² and F, Sentinel 3A and 3B, by type of city.

	Daytime						Nighttime
	Inland Cities			Coastal Cities			Inland Cities			Coastal Cities
	R2	F	Prob > chi2	R2	F	Prob > chi2	R2	F	Prob > chi2	R2	F	Prob > chi2
LST	0.38	3.68	0.1034	0.28	2.37	0.1749	0.48	5.65	0.0551	0.33	2.87	0.141
SUHI	0.54	7.28	0.0357	0.39	3.95	0.0941	0.64	10.96	0.0162	0.63	10.41	0.018

R²: Linear regression coefficient; F: F statistic.

Table 9

Environmental pollutants under heat waves.

Variables	Sentinel 5P
	β	p	sd
CO	60.4375	0.233	47.5893
NO2	10,664.9	0.421	12,699.14
O3	−42.922	0.013 *	14.2939
SO2	182.793	0.038 *	76.3555
Aerosol	0.4559	0.037 *	0.19013

β: Regression coefficient; p: p value: * p < 0.05; sd: standard deviation.

Table 10

Statistical analysis of environmental variables by type of city.

Variables	Inland Cities			Coastal Cities
	β	p	sd	β	p	sd
CO	113.92	0.310	84.44	10.066	0.830	41.259
NO2	12,824	0.494	15,466	−4874	0.771	14,541
O3	−28.32	0.191	14.566	−84.08	0.029 *	14.690
SO2	232.75	0.043 *	61.53	111.85	0.035 *	115.36
Aerosol	0.5940	0.255	0.376	0.4156	0.022 *	0.1266

β: Regression coefficient; p: p value: * p < 0.05; sd: standard deviation.

Table 11

R² and F Sentinel 5P statistical analysis by city type.

	Inland Cities			Coastal Cities
	R2	F	Prob > chi2	R2	F	Prob > chi2
Variables	0.97	13.10	0.072	0.98	20.10	0.048

R²: Linear regression coefficient; F: F statistic.

Supplementary Materials

The following are available online at https://www.mdpi.com/article/10.3390/su141912262/s1, Figure S1. Daytime LST (a) without heat wave (b) with heat wave. Figure S2. Nighttime LST (a) without heat wave (b) with heat wave. Figure S3. SO₂ concentration Sentinel 5P (a) without heat wave (b) with heat wave. Figure S4. NO₂ concentration Sentinel 5P (a) without heat wave (b) with heat wave. Figure S5. CO concentration Sentinel 5P (a) without heat wave (b) with heat wave. Figure S6. Aerosol concentration Sentinel 5P (a) without heat wave (b) with heat wave. Figure S7. O₃ concentration Sentinel 5P (a) without heat wave (b) with heat wave.

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