Finisterra , LX ( 129 ), 202 5 , e38754 ISSN: 0430 - 5027 doi: 10.18055/38754 Artigo Published under the terms and conditions of an Attribution - NonCommercial - NoDerivatives 4.0 International license. URBAN BIOCLIMATOLOGY IN AN INTERMEDIATE LATIN AMERICAN CITY WITH A TEMPERATE CLIMATE (BAHÍA BLANCA, ARGENTINA) M ARÍA E UGENIA F ERNÁNDEZ 1 J ORGE O SVALDO G ENTILI 1 ABSTRACT – This article aims to characterise the spatio - temporal variability of thermal comfort in Bahía Blanca between 1961 and 2020, including during extreme thermal events (heatwaves and coldwaves). Meteorological records from three stations located in differen t parts of the city were used to calculate the Physiological Equivalent Temperature (PET) index, using the RayMan Pro tool. The results provide new information on the city’s bioclimatology and on the spatio - temporal distribution of thermal comfort conditio ns. Cold stress was more frequent than heat stress in Bahía Blanca. Daily bioclimatic indicators revealed extreme cold stress during the night and early morning hours, and a higher frequency of heat stress between 13:00 and 17:00. During extreme heat event s, PET values exceeded 41°C, while during coldwaves, minimum PET ranged from - 8.3°C to - 18.1°C. In the surrounding suburban area, winter cold stress was more severe than in central and coastal areas. In the urban centre, heat stress was more intense during the central hours of the day and in summer. Coastal areas experienced less cold stress at night and less heat stress during the day.   Keywords: Comfort, physiological equivalent temperature , intermediate cities, urban sustainability . RESUMO – BIOCLIMATOLOGIA URBANA N UMA CIDADE INTERM ÉDIA DA AMÉRICA LATINA DE CLIMA TEMPERADO (BAHÍA BLANCA, ARGENTINA). Este artigo tem como objetivo caracterizar a variabilidade espaço - temporal do conforto térmico em Bahía Blanca entre 1961 e 2020, incluindo durant e eventos térmicos extremos (ondas de calor e ondas de frio). Foram utilizados registos meteorológicos de três estações localizadas em diferentes pontos da cidade para calcular o índice de Temperatura Equivalente Fisiológica (PET), com recurso à ferramenta RayMan Pro . Os resultados fornecem informações novas sobre a bioclimatologia da cidade e a distribuição espaço - temporal das condições de conforto térmico. O stress por frio foi mais frequente do que o stress por calor em Bahía Blanca. Os indicadores biocl imáticos diários revelaram stress térmico extremo por frio durante a noite e as primeiras horas da manhã, e maior frequência de stress por calor entre as 13h e as 17h. Durante eventos extremos de calor, os valores de PET ultrapassaram os 41°C, enquanto dur ante as ondas de frio, o PET mínimo variou entre - 8,3°C e - 18,1°C. Na zona suburbana envolvente, o stress por frio no inverno foi mais severo do que nas áreas centrais e costeiras. No centro urbano, o stress por calor foi mais intenso nas horas centrais do dia e durante o verão. As áreas costeiras registaram menor stress por frio à noite e menor stress por calor durante o dia.   Palavras - chave: Conforto, temperatura equivalente fisiológica , cidades intermédias , sustentabilidade urbana. HIGHLIGHTS Heat stress occurred mainly in summer, and cold stress predominated in winter in Bahía Blanca. Extreme cold stress was common at night and in the early morning; heat stress occurred predominantly in the afternoon. In the last decade, maximum PET values during h eatwaves reached up to 48.7 °C. Coastal areas experienced reduced thermal stress due to the moderating effect of the sea. Suburban areas experienced more cold stress in winter and less heat stress in summer. Recebido : 5/11 /202 4 . Aceite: 21 / 02 /202 5 . Publicado: 21 / 07 /202 5 . 1 Departa mento de Geografía y Turismo, Unive rsidad Nacional del Sur (UNS), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), calle 12 de Octubre 1098, 4° Piso, 8000, Bahía Blanca, Buenos Aires, Argentina. E - mail: eugenia.fernandez@uns.edu.ar , jogentili@uns.edu.ar
Fernández, M. E., Gentili, J. O., Finisterra, LX (129), 2025, e38754 2 1. INTRODU CTION Because of their high energy demand and greenhouse gas (GHG) emissions, cities contribute greatly to climate change (CC), while being particularly vulnerable to its impacts (Barros & Camilloni, 2016; United Nations Habitat, 2020) . Urban geometry (dimensions and arrangement of buildings), construction materials (iron, concrete, cement, bricks, etc.), types of surface coverage (asphalt) and the waste generated by human activities affect climate at different scales (Oke et al ., 2017) . On a global scale, population growth and urban energy consumption modify the natural substrate and emit greenhouse gases (GHG) into the atmospher e, exacerbating CC (IPCC [ Intergovernmental Panel on Climate Change ] , 2014, 2019) . At the local and micro - local scale, cities modify the ver tical and horizontal thermal distribution of temperatures, generating urban cold islands (UCI) and urban heat islands (UHI) (Oke, 1973, 1997, 2011; Oke et al. , 2017) . The increase in urban temperature generates several risks to human hea lth and has a great effect on life quality and comfort conditions. Extreme thermal episodes discourage the development of outdoor activities (Ho et al ., 2023; Jemmett - Smith et al ., 2018; Johansson, 2006; Kotharkar et al. , 2024; Li & Ratti, 2018; Sambrook et al ., 2023; Smith & Lancaster, 2020) and increase energy consumption for indoor cooling or heating (Alnuaimi & Natarajan, 20 21; Añel et al., 2017 ; Doulos et al. , 2004) . Among the most severe extreme thermal events, heatwaves (HW) stand out, which can be defined as a “pervasive natural hazard that can take a heavy toll on human systems, affecting health, livelihoods and infra structure” (WMO [World Meteorological Organization] , 2015b , p. 17 ) . Several studies verified the impact of HW on mortality (Chesini et al. , 2019; D’Ippoliti et al. , 2010; Dimitriadou & Zerefos, 2023; Dimitrova et al. , 2021; Hassan et al. , 2020; López - Bueno et al. , 2020) . Among the extreme cold episodes with the greatest impact, coldwaves (CW) stand out. These are more or le ss prolonged periods in which temperatures are lower than normal. The WMO defines CW as the persistence of cold air over an area (WMO, 2015a) . Extreme cold is associated with respiratory, cardiovascular and infectious diseases (Chen et al. , 2020; Hajat & Haines, 2002; Khanjani & Bahrampour, 2013; Mäkinen et al. , 2009; Medina - Ramón et al. , 2006; Monteiro et al. , 2013; Urban et al. , 2014) and negatively influences human comfort (Basarin et al ., 2016; Roshan et al. , 2018) . With global CC it is expected that the frequency, duration and intensity of extreme weather events will increase (IPCC, 20 19, 2023) , so it is essential to assess the real impact of these events on the quality of life and comfort of citizens. Thermal comfort defines the use and permanence of citizens in urban public spaces, which is why its inclusion in urban development plans is highly recommended (Shooshtarian et al., 2020) . Indeed, one of the main objectives of urban design is to minimize human heat and cold stress ( Oke et al. , 2017) . Thermal comfort is achieved under a set of atmospheric conditions in which self - reg ulation mechanisms are minimal and the body temperature is brought in line with that of the environment (Fernández García, 1996) . According to the American National Standards Institute (ANSI) and the American Society of Heating, Refrigerating and Air - Conditioning Engineers (ASHRAE) , Standard, thermal comfort is defined as “the mental condition which expresses satisfaction with the thermal environment and which is assessed by subjective evaluation” (ANSI/ASHRAE, 2010 , n.p. ) . Acquiring information a bout human comfort in outdoor environments requires information about climatic elements (such as temperature, wind, humidity and radiation) and also about the biophysical and psychological response of the individual, which depends on activity levels, age a nd clothing, among others (Oke et al. , 2017) . A universal index for the assessment of human thermal comfort is the Physiological Equivalent Temperature (PET) (Fröhlich et al. , 2019; Matzarakis et al. , 1999) . PET is defined as the physiological equivalent temperature at any given place (outdoors or indoors) corresponding to the air temperature at which, in a typical indoor setting, the heat balance of the human body (work metabolism 80W of light activity, added to basic metabolism; heat resistance of clothing 0.9clo) is maintained with core and skin temperatures equal to those under the conditions being assessed (Höppe, 1999) . The PET index has been used to descr ibe biometeorological conditions in Europe (Ateş et al. , 2023; Basarin et al. , 2 016; Fernández García et al. , 2012; Fiorillo et al. , 2023; Konstantinov et al. , 2022; Milošević et al ., 2016; Royé et al. , 2012) , North America (Provençal et al. , 2016; Taleghani & Berardi, 2018) , Asia (Hanafi & Dastjerdi, 2014; Kotharkar et al. , 2024; Lin & Tsai, 2017; Yang et al. , 2018) , South America (Helbig et al ., 2007; Mesa et al. , 2009; Ribeiro et al. , 2022; Ruiz & Correa, 2015b ; Puliafito et al ., 2009 ) , among others. At local and micro - local scales, PET has been widely used to determine the intra - urban variability in comfort. Numerous authors (Kotharkar et al. , 2019; Milošević et al. , 2016; Ruiz & Correa,
Fe rn á ndez , M. E., Gentili, J. O. , Finisterra, LX (129), 2025, e38754 3 2015a; Puliafito et al. , 2013; Unger et al. , 2017; Xiang & Ren , 2017) analyzed outdoor thermal comfort based on the Local Climate Zones (LCZ) classification system. In 2015, the United Nations General Assembly adopted the 2030 Agenda for Sustainable Development which incorporates 17 Sustainable Development Goals (Dugarova & Gülasan, 2017; United Nations General Assembly, 2015) , as SDG 11 “Make cities inclusive, safe, resilient and sustainable”. In this cont ext, knowing the spatio - temporal variability of PET in cities is an important step in the definition of climate change adaptation measures and in the planning of thermally comfortable spaces. In Bahía Blanca (Argentina) , several studies have documented the effect of the urban form and function on temperatures (Capelli de Steffens et al. , 2005; Ferrelli, 2016; Gentili & Fernández, 2023) , urban energy balance (Fernández et al. , 2021) and atmospheric pollution (Campo et al. , 2017, 2018; Colman Lerner et al., 2012; Fernández, Gentili, & Campo, 2021; Orte e t al. , 2013; E. Puliafito et al., 2009) . In this context, it is of interest to know the impact these conditions exert on the comfort of the inhabitants of different areas of the city. In this line, the aim of the present work is to characterize the spatio - temporal variability of thermal comfort in Bahía Blanca during the period 1961 - 2020 and during extreme thermal episodes (heatwaves - coldwaves). Since PET has a widely known unit (°C), it is accessible for urban planning decision ma kers (Matzarakis et al. , 1999; Tornero et al. , 2006) . The originality of this research is based on several factors: the study area, its scope and temporal resolution, the multiscale analysis, and the potential for extrapolation to intermediate Latin American cities. Several studies focus on the spatio - temp oral variability of PET in the Northern Hemisphere, while in the Southern Hemisphere, this type of research is still in its early stages (Costa et al., 2024) . In fact, there is a lack of understanding regarding the large - scale and long - term variability and trends of thermal stress on th is continent (Miranda et al. , 2024) . I n Argentina, long - term spatio - temporal variability of comfort situations using the PET index has not been developed (Costa et al. , 2024) . Preliminary work in this field focused primarily on the PET index in some Argentine locations, but in shorter periods and very few of these studies e xplore the intra - urban variability of comfort. However, studies conducted in urban environments of the Southern Hemisphere are of great importance for several reasons. First, a continuous rise in the frequency of extreme heat events has been observed in Ba hía Blanca (Gentili & Fernández, 2024) , Arge ntina (Camilloni, 2018; Ferrelli et al ., 2021; Rusticucci et al. , 2015; Santágata et al. , 2017) and South America (Cueto e t al. , 2010; Feron et al ., 2019; Piticar, 2018) . By the end of the 21 st century, numerous countries in South America are likely to face heightened levels of heat - related health stress due to escalating natural hazards and population growth (Hagen et al. , 2022) . In fact, according to Smit (2021) the Southern Hemisphere is rapidly urbanizing, having contributed 94 % of global population growth between 2010 and 2015. The present study includes bioclimatological analysis at various scal es, incorporating a local study spanning a ten - year period. In Bahía Blanca, this study stands out due to its extensive analysis period (60 years in total) and high temporal resolution (hourly analysis), making it a unique contribution. It also sets a prec edent of interest, as its results could potentially be extrapolated to other intermediate cities in Latin America. Crawford et al. (2018) affirm that neighborhoods in the Global South often exhibit different building construction materials and development patterns than those of the Global North and, although the majority of rapidly growing cities occu rs in the Global South, the urban climate research in such cities has been sparse. Therefore, the results of this research will be a useful input for improving the urban habitability of Bahía Blanca as well as a contribution to the knowledge for building a nd generating resilient cities in Latin America (Vecellio & Vanos, 2024) . 2. STUDY AREA Bahía Blanca ( f ig. 1a) is an intermediate city in Argentina located in the south of Buenos Aires province. It is the head city of the homonymous district and has a population of 335.190 inhabitants ( Instituto Nacional de Estadística y Censos [ INDEC ] , 2023) . Bahía Blanca has a transitional climate between the hot and humid of the eastern Buenos Aires province and the cold and dry climate of Patagonia. The regional atmospheric circulation is controlled by the large - scale systems influencing the South of the American continent ( f ig. 2): the semi - permanent anticyclones of the Atlantic ocean (South Atlantic Anticyclone, ASS) and Pacific ocean (South Pacific Anticyclone, APS) (Chiozza & Figueras, 1982; Grimm et al ., 2000) . Bahía Blanca has a mean annual temperature of 15.5 °C with a marked thermal seasonality: 22.3 °C summer mean and 9.5 °C winter mean. Rainfall has an annual mean value of 644.6 mm and summer is the rainiest season in the city, with an average value of 206.2 mm
Fernández, M. E., Gentili, J. O., Finisterra, LX (129), 2025, e38754 4 (Ferre lli, 2016; Zapperi, 2012) . Bahía Blanca has one of the highest average wind speed values in the region, mainly in summer. Its preponderant direction is north and northwest (Campo de Ferreras et al. , 2004) . The regional dynamics of solar radiation is largely determined by the passage of migratory anticyclones over the Argentinean territory and the associated synoptic conditions, as well as by the passage of cold fronts (Fernández, 2020; Fernández, Gentili, Casado, et al. , 2021) . At the local scale, the variable shows a marked seasonal variability and a strong dependence on cloud cover. In all thermal seasons, global solar radiation increases uniformly from sun rise throughout the day, reaching its maximum around 1:00p.m. (the fact that solar noon is at 1:00 p.m. is due to the fact that the time zone in Argentina is - 3 UTC, even when the country is located in the - 4 UTC time zone) (Fernández, 2020; Fernández & Gentili, 2021b) . Fig . 1 – Study area: a) Location , b) Local Climate Zones (LCZ) , c) Spatial distribution of the main energy fluxes . Colour figure available online. Fig . 1 – Área de estudo: a) Localização , b) Zonas Climáticas Locais (ZCL) , c) Distribuição espacial dos principais fluxos de energia . Figura a cores disponível online. Bahía Blanca has shown significant expansion of the urbanized area. Between 2010 and 2016, the urban population increased by 0.5 % annually compared to an increas e in the urbanized area of 2.71 % in the same period ( Centro de Implementación de Políticas Públicas para la Equidad y el Crecimiento , 2017) . High - rise building began to develop in the mid - 20 th century around the central square and in the following years it exceeded the microcenter and macrocenter boundaries (Fittipaldi et al. , 2018; Formiga & Marenco, 2000) . Figure 1b shows the Local Climate Zones (LCZ) classification (Stewart & Oke, 2012) made for Bahía Blanca (Fernández et al. , 2021) . Most of the LCZs built types correspond to LCZ 1, 2, 3, 6 and 9. In the microcente r most of pixels correspond to compact high - rise (LCZ 1) and compact mid - rise (LCZ 2), while in the macrocenter LCZ 3 (compact low rise) prevails. In the peri - urban area, where low - rise residential neighborhoods are common, LCZs 6 and 9 classifications are mainly observed. Regarding land cover types, LCZs B and F are the most observed, mainly in the rural area surrounding the city (Fernández et al. , 2021) . Due to its constant growth, Bahía Blanca is not free from the most common urban problems. Recent studies observed that the city growth between 1985 and 2014 modified the spatial
Fe rn á ndez , M. E., Gentili, J. O. , Finisterra, LX (129), 2025, e38754 5 distribution of temperature and relative humidity while the intensity of the ICU increased in that period as well (Ferrelli, 2016; Ferrelli et al. , 2016) . Fig . 2 – Seasonal patterns of atmospheric circulation and rainfall over South America . Colour figure available online. Fig . 2 – Padrões sazonais de circulação atmosférica e precipitação sobre a América do Sul . Figura a cores disponível online. Source: Sea level pressure, wind vectors, and precipitable water maps were derived from long - term (1981 - 2010) monthly means of the NCEP/NCAR Reanalysis. Fronts and air masses were based on Campo et al. (2004) . Modified from Casado (2013) Figures 1b and 1c synthesize the main results of research related to the urban energy balance (UEB) in the city (Fernández, 2020; Fernández, Picone, et al. , 2021) , which studied the spatial variability of UEB components by calculating indices such as the storage index ( Λ ) and Bowen ratio ( β ), among others (Oke et al. , 2017) . It can be observed that in the central zones of Bahía Blanca (LCZ 1 and 2) sensible heat (QH), anthropogenic heat (QF) and storage heat (QS) fluxes prevail while in the peri - urban area (LCZ 6 and 9) and the coastal area (LCZ F and G) latent heat fluxes were more predominant. In relation to this, Gentili et al . (2020) documented the increase of car parks in the city micro - center (LCZ 1 and 2) and their influence on some urban environmental prob lems, such as ICU and air pollution. Regarding comfort, Fernández et al. (2018) showed that the city center registered a lower percentage of comfortable days than the rural area and a lower percentage of cold stress frequency. 3. MATERIALS AND METHODS Meteorological records from three stations located in different points of the city were used ( f ig. 1a). First, the station “Bahía Blanca Airport” (BBA) which belongs to the National Meteorological
Fernández, M. E., Gentili, J. O., Finisterra, LX (129), 2025, e38754 6 Service (SMN, for its initials in Spanish) and has hourly records of temperature (°C), humidity (%), wind speed (m/s) and cloudiness in Bahía Blanca for the period 1961 - 2020. These data, being the most extensive in time, were used to made the general analy sis of the bioclimatic profile of the city. For its specific location without horizon limitation (10 km away from the city center with no buildings and no higher vegetation), it was considered representative of the geographical factors that define the regio nal climate. On the other hand, for the analysis of the comfort variability at the local scale, we worked with hourly records of temperature (°C), humidity (%), wind speed (m/s) and global solar radiation (W/m2) from two weather stations: “Bahía Blanca Dow ntown” (BBD) and “Bahía Blanca Estuary” (BBE) ( f ig. 1a). Although the period of registration of these stations is limited (2001 - 2010), their data are representative of different LCZs of the city ( f ig. 1b), and it is expected the results of their comparison will provide unprecedented information on the variability of comfort at an intra - urban scale. For the calculations of the PET index, the RayMan Pro tool was used (Fröhlich et al ., 2019; Matzarakis et al ., 2007, 2010, 2021; Matzarakis & Fröhlich, 2018) . Calc ulations were performed using standardized values for age, sex, height and weight (35 years, male, 1.75m and 75kg) and an activity of 80W with a thermal resistance garment of 0.9clo (Höppe, 1999) as reference. The processing results were analyzed with Excel software using techniques associated to descriptive statistics. Threshold values for PET were developed in the form of a graded index (Höppe, 1999; Matzarakis & Mayer, 1996) . The analysis included such categorization following the methodology of Matzarakis and Mayer (1996) and Basarin et al . (2016) ( t able I ). We performed a general bioclimate diagram, which includes 10 - day frequencies of the daily PET va lues for the period 1961 to 2020 (Matza rakis et al ., 2011) . Table I – PET: thermal sensation and grade of physiological stress on humans . Quadro I – PET: sensação térmica e nível de stress fisiológico em seres humanos. PET Thermal sensation Grade of physiological stress < - 10 Very cold Extreme cold stress ≥ - 10 <0 ≥0 <4 ≥4 <8 Cold Strong cold stress ≥8 <13 C ool Moderate cold stress ≥ 13< 18 Slightly cool Slight cold stress ≥ 18 <23 Comfortable No thermal stress ≥ 23 < 29 Slightly warm Slight heat stress ≥ 29 < 35 Warm Moderate heat stress ≥ 35 < 41 Hot Strong heat stress ≥ 41 Very hot Extreme heat stress Source: Basarin et al . (2016), Matzarakis and Mayer (1996) Heat (cold) waves can be defined as more or less prolonged periods of higher (lower) than normal temperatures. In Argentina, the National Meteorological Service (SMN for its initials in Spanish) defines the HW as : the period in which the maxi mum and minimum temperatures equal or exceed, for at least 3 consecutive days and simultaneously, the 90th percentile, calculated from daily data during the months of October to March (warm six - month period in the southern hemisphere) of the period 1961 - 20 10 . (Herrera et al. , 2018 , p. 4 ) For its part, the same organization defines as CW : the period in which the maximum and minimum temperatures equal or are lower, at least during 3 consecutive days and simultaneously, than the 90th percentile, calculated from daily data during the months of April to September (cold six - month period in the southern hemisphere) of the period 1961 - 2010 . (Veiga et al ., 2015 , n.p. ) The identification of HW and CW was based on previous research conducted by the authors using daily maximum and minimum temperature records provided by the SMN for the period 1961 - 2020.
Fe rn á ndez , M. E., Gentili, J. O. , Finisterra, LX (129), 2025, e38754 7 4. RESU LTS 4.1. Variability of comfort at different time scales Considering t he interannual and interdecadal variability in comfort conditions (fig. 3), w e found a mean PET of 10.9 °C throughout the year, 16.9 °C in the warm semester and 5.2 °C in the cold semester. During the cold semester, the PET values ranged between 15.5 °C (2007) and 19.9 °C (1984) and during the warm semester between 2.8°C (2007) and 8.3 °C (1970). The interannual and interdecadal variability in comfort conditions showed a c lear seasonal pattern. The first three decades had a major variability in PET conditions. The 10 - year period mean values had a decrease between 1961 - 1970 and 1971 - 1980. In the cold semester this trend was maintained, while in the warm semester a subsequent increase in the decadal mean values (1981 - 1990) was observed. In the first three decades, the decadal mean values showed greater variability, with up to 1 °C of difference with respect to the half - yearly and annual values for the period 1961 - 2020. In the l ast three decades, the variable became more stable (maximum differences of 0.4°C with respect to the half - yearly and annual value for the period 1961 - 2020), although comfort conditions had greater inter - decadal variability in the cold half - year. Fig 3 – Interdecadal and interannual PET (1961 - 2020) . Colour figure available online. Fig . 3 – PET interdec enal e interanual (1961 - 2020) . Figura a cores disponível online. Figure 4 shows the human thermal bioclimatic conditions in Bahía Blanca expressed in perc entages. 26.8% of the records for the period 1961 - 2020 corresponded to extreme cold stress and 2.5% to strong and extreme heat stress. Extreme thermal stress related to hot conditions (PET ≥ 35°C) was observed mainly during December, January and February. Extreme thermal stress linked to cold conditions (PET <4°C) had a higher percentage of occurrence throughout the year, mainly in June, July and August. Between late April and early September more than 50% of the records were associated with a cold and very cold thermal sensation (PET <8°C) and between June and August with a very cold sensation (PET <4°C). Comfortable thermal sensation (PET ≥ 18 <23°C) was minimal during winter months and was more frequent in the intermediate thermal seasons and during the s ummer. The preponderance of cold thermal stress was observed in the city throughout the year with the highest frequency of occurrence during the second half of the year. During the first semester the slight physiological stress (PET ≥ 13 <18°C or ≥ 23 <29°C) was more frequent and the conditions of heat stress due t o cold (PET <8°C) were comparatively less frequent than in the second semester.
Fernández, M. E., Gentili, J. O., Finisterra, LX (129), 2025, e38754 8 Fig . 4 – Bioclimatic diagram for Bahía Blanca in ten - day intervals (1961 - 2020) . Colour figure available online. Fig . 4 – Diagrama bioclimático para Bahía Blanca em intervalos de dez dias (1961 - 2020) . Figura a cores disponível online. Analyzing the annual daily bioclimatic pattern for Bahía Blanca (fig. 5a), between 12:00a.m. and 07:00a.m. over 40% of PET values were < 4°C (extreme cold stress). PET ≥ 13 < 29°C ( slight or no physiological stress) was more frequent between 10:00a.m. and 06:00p.m. (frequencies between 42.2% and 48.4%). PET ≥ 35°C (strong and extreme heat stress) was more frequent between 02:00p.m. and 03:00p.m. The cold semester bioclimatic diagram (Fig. 5b) showed a higher frequency of extreme cold stress conditions (PET < 4°C), mainly during the night and early morning hours (between 10:00p.m. and 08:00a.m.), with percentages as high as 73.4% at 06:00a.m. The diagram also shows a lower frequency o f PET ≥ 13 < 29°C. In fact, comfortable conditions (PET ≥ 18 < 23°C) only reached a 12.4% at 03:00p.m. and the slightly warm (PET ≥ 23 < 29°C) a 5.8%. The most abrupt changes in comfort conditions were recorded after sunrise and with increasing sun altitud e between 08:00a.m. and 12:00p.m. During the night and the middle hours of the day, PET values remained more stable. The diagram of the warm half - year (Fig. 5c) shows the highest frequencies of heat stress situations. PET ≥ 35°C reached the highest frequencies (up to 20.4%) between 01:00p.m. and 05:00p.m. The frequency of cold stress (PET < 8°C) was only visible after 06 :00p.m. and up to 10:00a.m. PET ≥ 8 < 18°C (cool and slightly cool) were the most frequent conditions in that time slot (percentages above 50%). The warm half - year had the highest frequencies of comfortable conditions (PET ≥ 18 < 23°C), with percentages ab ove 20% between 09:00a.m. and 08:00p.m. Figures 5d to 5g show the quarterly variation of the daily bioclimatic diagram in Bahía Blanca. Extreme thermal stress was higher in summer (fig. 5d) and winter (fig. 5f), although the cold season had higher compara tive percentages: extreme cold stress (PET < 4°C) reached up to 86.4% in winter, while in summer strong and extreme heat stress (PET ≥ 35°C) reached 31.8%. Autumn and spring (fig. 5e and fig. 5g) show some differences in comfort situations. Between 09:00p. m. and 08:00a.m., PET < 4°C had lower frequencies in autumn. In fact, up to 78% of the spring nocturnal records corresponded to PET < 8°C, while in autumn the percentages reached 67.5%. Thus, it is observed that cold stress conditions in Bahía Blanca were more frequent in spring than in autumn. During the middle hours of the day, cool and slightly cool (PET ≥ 8 <18°C) were the most frequent conditions both in autumn and spring, with percentages reaching 51.2% and 53.5%, respectively. PET ≥ 18 < 23°C (comfo rtable conditions) was more frequent in spring and both seasons had similar percentages of PET ≥ 23 < 29°C (slightly warm) and PET ≥ 29 < 35°C (warm).
Fe rn á ndez , M. E., Gentili, J. O. , Finisterra, LX (129), 2025, e38754 9 Fig . 5 – Daily bioclimatic diagram for Bahía Blanca: a) 1961 - 2020 , b) Cold semester 1961 - 2020 , c) Warm semester 1961 - 2020 , d) Summer 1961 - 2020 , e) Autumn 1961 - 2020 , f) Winter 1961 - 2020 , g) Spring 1961 - 2020 . Colour figure available online. Fig . 5 – Diagrama bioclimático diário para Bahía Blanca: a) 1961 - 2020 , b) Semestre frio 1961 - 2020 , c) Semestre quente 1961 - 2020 , d) Verão 1961 - 2020 , e) Outono 1961 - 2020 , f) Inverno 1961 - 2020 , g) Primavera 1961 - 2020 . Figura a cores disponível online. 4.2. Local bioclimatic conditions during extreme weather events During most of the HW ( f ig. 6a) PET reached values above 41 °C . In the last decade, all HW had a maximum PET above 43 °C and up to 48.7 °C . These conditions are unique to that period, which shows that discomfort conditions resulting from the occurrence of HW have increased in recent years. During the CW ( f ig. 6b) the minimum PET ranged between - 8.3 °C and - 18.1 °C . The first four 10 - year periods had a greater variability in the minimums found, while , in the period 2001 - 2010 , values were more stable. In the last decade no CW were recorded in Bahía Blanc a. Between 1971 - 1980 and 1991 - 2000 were recorded the most extreme values of thermal discomfort due to cold. Figure 7 shows the daily bioclimatic diagram during extreme weather events in the period 1961 - 2020. Figure 7a shows that during HW extreme heat s tress was highest during the central hours of the day. PET ≥ 41°C reached percentages higher than 60% between 2p.m. and 4p.m. and PET ≥ 35 < 41°C had percentages higher than 55% between 12a.m. and 1p.m.
Fernández, M. E., Gentili, J. O., Finisterra, LX (129), 2025, e38754 10 Fi g. 6 – Bioclimatic conditions during a) HW and b) CW . Colour figure available online. Fig . 6 – Condições bioclimáticas durante a) HW e b) CW . Figura a cores disponível online. Figure 7b shows the daily variability of extreme cold stress situations during CW. Severe (PET ≥ 4 < 8°C) and extreme (PET < 4°C) cold stress situations prevailed throughout the day. From 10p.m. to 10a.m., PET < 4°C had a frequency of 100%. During the middle hours of the day (from 12a.m. to 5p.m.), PET ≥ 0 < 4°C reached a frequency of more than 60% and between 2p.m. and 4p.m., P ET ≥ 4 < 8°C had frequencies of more than 25%.
Fe rn á ndez , M. E., Gentili, J. O. , Finisterra, LX (129), 2025, e38754 11 Fig . 7 - Daily bioclimatic diagram for Bahía Blanca during a) HW b) CW . Colour figure available online. Fig . 7 - Diagrama bioclimático diário para Bahía Blanca durante a) HW b) CW . Figura a cores disponível online. 4.3. Spatio - temporal variability of comfort at a local scale Based on t he mean inter annual variability in the comfort values (1961 - 2020) t hroughout the year, BBA, BBD and BBE have a maximum mean value of 23.4 °C (January) and a minimum of 3 .6 °C and 5.4 °C (June), respectively ( f ig. 8). BBA had lower PET values than BBE and BBD in the whole period and during winter the difference was the highest. During summer (DEF), BBD had similar PET values to BBE. From March to November, BBD showed a highe r PET mean value than BBE. It is evident that the spatial variability in comfort conditions is greater during the cold season. In winter, a mean difference of up to 4 °C was observed between BBA and BBE. During summer, this variation between BBE and BBD decreases. During winter months there is an intense process of heat absorption by the urban surface materials (Oke et al ., 2017) , which explains the lower PET levels in BBD. Fig . 8 – BBD, BBE and BBA : a) monthly and daily mean PET , b) monthly and daily mean amplitude in PET values (2001 - 2010) . Colour figure available online. Fig . 8 – BBD, BBE e BBA : a) média mensal e diária do PET , b) amplitude média mensal e diária nos valores de PET (2001 - 2010) . Figura a cores disponível online.
Fernández, M. E., Gentili, J. O., Finisterra, LX (129), 2025, e38754 12 Figure 9 shows the mean daily distribution of PET values in BBA, BBD and BBE throughout the year, in the summer (DEF) and winter months (JJA). The daily pattern was determined by sunrise and sunset. During the night and early morning hours, BBE had higher PET values than BBD and BBA had the lowest. After sunrise, the pattern changed. The difference between the curves decreased and during central hours of the day BBD had higher PET values than BBE and BBA. In the twelve - month period and throughout the day, B BA had lower PET values than BBD and BBE. The maximum differences (up to 4.6°C) were between BBA and BBE during the night hours. BBD had lower PET values than BBE between 7p.m. and 9a.m., with a mean difference of 2.08°C. Fig . 9 – Distribution of PET da ily in BBA, BBD and BBE (2001 - 2010) . Colour figure available online. Fig . 9 – Distribuição diária do PET em BBA, BBD e BBE (2001 - 2010) . Figura a cores disponível online. The seasonal analysis showed that during winter (JJA) the difference in comfort conditions was greater: between 5p.m. to 10a.m. BBD had lower PET values than BBE, with differences up to of 2.6 °C and during the night ( 10p.m. to 6 a.m. ) the difference between BBE and BBA was higher and up to 5.2 °C . On summer nights, the maximum difference in comfort conditions was found between BBE and BBA, amounting to more than 3 °C . At midday and up to 5p.m. , BBD had the highest PET values and BBA the lowest, with difference s of up to 3.5 °C . The central areas of the city had greater hourly variability in comfort conditions, with lower PET values at night (due to night cooling) and higher values during the day. In contrast, the coastal areas (BBE) showed less hourly variabilit y in PET, with relatively higher values during the day. 5. DISCUSSION We analyzed the interannual and interdecadal variability in comfort conditions. The seasonal pattern observed in this variability aligns well with previous investigations. For instance, Pecelj et al . (2021) reported annual fluctuations of PET in Belgrade (Serbia), with maximum values in July and minimum values in January, characteristic of a moderate continental climate in the Northern Hemisphere. Other studies have also identified season ality in comfort values (Cinar et al. , 2023; Irmak et al. , 2020; Toy et al. , 2023) . Despite its coastal location, Bahía Blanca exhibits a continental climate influenced by dry air masses from the N - NW (Campo de Ferreras et al. , 2004; Capelli de Steffens et al. , 2005) . This results in high daily temperature amplitude, low relative humidity, and low cloudiness compared to other cities in the region (Campo de Ferreras et al. , 2004; Fernández, Gentili, et al. , 2018) , all of which are factors influen cing urban comfort. The annual and seasonal daily bioclimatic pattern for Bahía Blanca showed extreme cold stress mainly during the night and early morning (between 00 a.m. and 7 a.m. ). Slight or no physiological stress was found in the annual distribution during the centr al hours of the day (between 10a.m. and 6p.m. ), and mainly during t he warm semester between 9a.m. and 8p.m . The highest frequencies of heat stress situations were recorded in the warm semester between 1p.m. and 5p.m . This daily annual distr ibution is in good agreement with results found by Taleghani and Berardi (2018) , who stated that
Fe rn á ndez , M. E., Gentili, J. O. , Finisterra, LX (129), 2025, e38754 13 the minimum PET for all scenarios in the city of Toronto occurred before sunrise and the maximum PET around 3p.m . Similarly, this pattern aligns with the summer daily pattern found by Helbig et al . (2007) in their PET analysis conducted in eight Argentinean locations. Our results are also consistent with those of Puliafito et al. (2013) , who found acceptable thermal comfort values in the early mornings of summer days in downtown Mendoza (Argentina). The analysis showed that HW and CW have e xtreme negative consequences on human comfort in Bahía Blanca, as corroborated by previous findings in the literature (Konstantinov et al. , 2014; Kotharkar et al. , 2019; Matzarakis & Mayer, 1996) . The PET values observe d during HW y CW are consistent with those reported by Basarin et al. (2016) for PET conditions during HW in Northern Serbia. The PET values found during CW in Bahía Bla nca are barely distinguishable from those found by Basarin et al. (2016) . Additionally, our analysis confirms previous findings regarding HW in Bahía Blanca, showing an increasing frequency and intensity between 1961 and 2020, with a peak between 2011 and 2020 (Fernández et al ., 2022; Gentili & Fernández, 2024) . This trend is consistent with other studies conducted in Argentina (Camilloni, 2018; Ferrelli et al. , 2021; Rusticucci et al ., 2015; Santágata et al. , 2017) and Latin America (Cueto et al. , 2010; Feron et al ., 2019; Piticar, 2018) . Regarding extreme heat events and thermal comfort in South America, Miranda et al. (2024) reported a significant increase in the annual number of hours under heat stress between 1979 and 2020, noting that the past 20 years (fro m 2000 onward) experienced not only more consecutive hours under heat stress than the previous two decades but also a higher persistence of such conditions. The intra - urban variability of comfort aligns is in line with what was stated by Capelli de Steffen s et al. (2005) in their previous analysis of comfort in Bahía Blanca. The moderating influence of the oceanic surface (associated with higher thermal inertia, higher evaporation and the land - sea breeze) (Royé et al. 2012) decreases the daily PET thermal a mplitude in the estuary (Capelli de Steffens et al. 2005). The greater thermal comfort of coastal environments justifies their promotion as leisure spaces within the urban fabric, as previously stated by Fernández and Gentili (2021a) . Several authors analyzing comfort variability in urban areas worldwide have reached similar conclusions, observing differential comfort values across rural areas, peri - urban zones, urban cente rs, green areas, and areas adjacent to water bodies (Azimi et al ., 2024; Çağlak & Toy, 2023; Cinar et al. , 2023; Irmak et al. , 2020; Matzarakis et al. , 1999; Metin et al. , 2024; Pecelj et al ., 2021; Puliafito et al. , 2013; Toy et al. , 2023; Unger et al. , 2017) . This is consistent with previous studies that have verified the cooling effects of trees, grass and water on urban climate (Arabi et al. , 2015; Gill et al. , 2007; Middel et al ., 2015; Norton et al. , 2015; Pfautsch et al. , 2020; Pfautsch & Tjoelker, 2020; Shahidan et al. , 2012; Vásquez, 2016; Zhen et al. , 2022) . As stated by Oke et al . (2017) , urban morphology and construction materials favor heat absorption. Buildings in urban central areas (where BBD is located) can be two or three times higher than the natural substrate, which generates a greater uptake of short - wave radiation between urban canyons and reduces the loss of long - wave radiation. These urban configurations also increase wind shelter, reducing sensib le heat loss from turbulent flows (Oke et al. , 2017) . Consequently, soils and buildings heat up during the day, increasing both the stored heat and temperature in the city. Eleasing the stored heat and causing temperatures to drop. This mechanism explains the variability of comfort found in the city. The analysis of diurnal comfort distribution across different seasons revealed that central a reas of the city exhibit greater hourly variability in comfort conditions, characterized by lower PET values at night (night cooling) and higher values during the day. The prolonged duration of insolation during the summer season accentuates this phenomeno n exacerbating thermal discomfort due to heat in central areas. In winter, longer nighttime duration and consequent nocturnal heat loss, increase thermal discomfort due to cold in BBD. These findings are consistent with previous studies on the urban heat i sland and cold island effect in the city (Capelli de Steffens et al. , 2005) . Furthermore, they relate to the prevalence of anthropogenic and stored heat in these areas (Fig. 1c) and the urban heat island phenomenon, as previously discussed by Fernández et al . (2021b) in their analysis of the Bahía Blanca UEB and supported by various researchers globally (Alexander et al. , 2016; Christen & Vogt, 2004; Grimmond & Oke, 1995; Moreno et al. , 2012; Offerle et al. , 2003; Spronken - Smith, 2002) . On the other hand, the coastal areas (BBE) present a lower hourly PET variability of PET, with comparatively higher values during the day. This is in good agreement with Fernández et al . (2021b) , who stated that the Bahía Blanca estuary had the lowest magn itude of anthropogenic heat and the highest magnitudes of latent flux and stored sensitive heat. Similarly, findings by Ferrelli (2016) and Ferrelli et al. (2016) , indicate that more densely built - up areas in Bahía Blanca were warmer and drier than the peri - urban and coastal areas.
Fernández, M. E., Gentili, J. O., Finisterra, LX (129), 2025, e38754 14 6. CONCLUSIONS The spatio - temporal variation of thermal comfort in Bahía Blanca during the period 1961 - 2020 and during extreme thermal episodes (HW - CW) at different scales was analyzed. Extreme heat stress could be observed mainly during December, January and February and extreme cold stress mainly in June, July and August. Due to its specific location and climatology, cold stress was more common in Bahía Blanca than heat stress. The thermal comfort sensation was lowest during the winter months and highest in the intermediate thermal seasons as well as during the summer. The daily bioclimatic diagram of Bahía Blanca showed a direct relationship with sunris e and sunset. The cold semester had a higher frequency of extreme cold stress conditions during the night and the early morning hours (between 10p.m. and 8 a.m. ). The warm half - year diagram showed the highest frequencies of hot s tress conditions between 1p. m. and 5p.m . During most of the HW, PET reached values above 41 °C (extreme heat stress). In the last decade, all HW had a maximum PET above 43 °C and up to 48.7 °C . These conditions are unique to that period, showing that discomfort conditions resulting from the occurrence of HW have increased in recent years. During CW the minimum PET ranged between - 8.3 °C and - 18.1 °C (extreme cold stress). Between 1971 - 1980 and 19 91 - 2000 the most extreme values of CW - related thermal cold discomfort were recorded. At the local level, the variability of comfort in the city was tested. In the suburban area (BBA), thermal stress due to cold in winter was more severe than in coastal are as and the central area of the city and during summer, the thermal stress due to heat was less pronounced. In BBD, heat stress was more severe than in BBE and BBA in the central hours of the day and during summer. Coastal areas (BBE) experienced less cold stress during the night and less heat stress during the day. In the central areas, a more pronounced daily variability in comfort conditions was evident: citizens experience more cold stress at night and more heat stress during the day, a factor generated by urban building materials. Coastal areas show less diurnal variability, a factor defined by the moderating effect of the sea surface. These results are an interesting contribution to the planning of thermally comfortable spaces. Among the possible measur es to be implemented are highlighted the importance of promoting the use of coastal public green spaces and of improving urban green infrastructure to reduce mainly the frequency of heat stress during the day in the central areas of the city. ACKNOWLEDGM ENTS To the following research projects: “ Climatología y planificación urbana: aportes para la construcción de ciudades sostenibles y resilientes [24/ZG33] and “Geografía Física Aplicada al estudio de la interacción sociedad - naturaleza. Problemáticas ambientales a diferentes escalas témporo - espaciales” research projects, [ 24/G092 ] , both with the subsidy of the Secretaría General de Ciencia y Tecnología, Universidad Nacional del Sur. To the “Playas de estacionamiento y problemáticas ambientales urbanas: estudio para la definición y propuesta de medidas sustentables en ciudades medias” [ PIP 11220200100032 ] research proyect, with the subsidy of the Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET). To the Servicio Meteorológico Nacional (SMN) of Argentina for providing the official records to perform this work. AUTHOR CONTRIBUTIONS María Eugenia Fernández : Conceptualization ; Data curation ; Methodology ; Formal analysis and investigation ; Visualization ; Writing – original draft. Jorge Osvaldo Gentili : Conceptualization ; Methodology ; Formal analysis and investigation ; Visualization ; Writing – review and editing ; Supervision ; Project administration; F unding acquisition. ORCID María Eugenia Fernández https://orcid.org/0000 - 0002 - 6335 - 7774 Jorge Osvaldo Gentili https://orcid.org/0000 - 0002 - 4787 - 4667
Fe rn á ndez , M. E., Gentili, J. O. , Finisterra, LX (129), 2025, e38754 15 REFERENCES Alexander, P. J., Fealy, R., & Mills , G. M. (2016). Simulating the impact of urban development pathways on the local climate: a scenario - based analysis in the greater Dublin region, Ireland. Landscape and Urban Planning , 152, 72 – 89. https://doi.org/10.1016/j.landurbplan.2016.02.006 Alnuaimi, A., & Natarajan, S. (2021). Extreme Cold Discomfort in Extreme Hot Climates, a Study of Building Overcooling in Office Buildings in Qatar. Journal of Engineering Research , 18 (2), 101 – 113. https://doi.org/10.53540/TJER.VOL18ISS2PP101 - 113 Añel, J. A., Fernández - González, M., Labandeira, X., López - Otero, X., & de la Torre, L. (2017). Impact of cold waves and heat waves on the energy production sector. Atmosphere , 8 (11), 209. https://doi.org/10.3390/atmos8110209 American National Standards Institute/American Society of Heating, Refrigerating and Air - Conditioning Engineers . (2010). ANSI/ASHRAE Standard 55 - 2010 . Thermal Environmental Conditions for Human Occupancy. www.ashrae.org Arabi, R., Shahidan, M. F., Kam al, M. S. M., Fakri Zaky Bin Ja ’afar, M., & Rakhshandehroo, M. (2015). Mitigating Urban Heat Island through green roofs. Current World Environment , 10 (1), 918 – 927. https://doi.org/10.12944/CWE.10.Special - Issue1.11 1 Ateş, O., Kiper, T., & Uzun, O. (2023). The Relationship between Tourism Planning and Bioclimatic Comfort in Rural Areas: The Case of Kofçaz/Kirklareli/Türkiye. Turkish Journal of Agriculture - Food Science and Technology , 11 (4), 883 – 896. https://doi.org/10.24925/turjaf.v11i4.883 - 896.5063 Azimi, Z., Kashfi, S. S., Semiari, A., & Shafaat, A. (2024). Outdoor thermal comfort in open transitional spaces with limited greenery in h ot summer/cold winter climates. Discover Environment , 2 (1), 31. https://doi.org/10.1007/s44274 - 024 - 00062 - 0 Barros, V., & Camilloni, I. (2016). La Argentina y el Cambio Climático : de la física a la política [Argentina and Climate Change: From Physics to Politics] . Eudeba. Basarin, B., Lukić, T., & Matzarakis, A. (2016). Quantification and assessment of heat and cold waves in Novi Sad, Northern Serbia. International Journal of Biometeor ology , 60 (1), 139 – 150. https://doi.org/10.1007/s00484 - 015 - 1012 - z Çağlak, S., & Toy, S. (2023). The Effect of Urban Areas on Human Bioclimatic Comfort Conditions; Sample of Amasya City. Journal of Science and Technology , 16 (1), 184 – 195. https://doi.org/10.18185/erzifbed.1103828 Camilloni, I. (2018). Argentina y el Cambio Climático [Argentina and Climate Change] . Ciencia e Investigación , 68 (5 ), 5 – 10. https://aargentinapciencias.org/wp - content/uploads/2018/11/1 - Camilloni - cei68 - 5 - 2.pdf Campo, A. M., Fernández, M. E., & Gentili, J. O. (2017). Variabilidad temporal del PM10 en Bahía Blanca (Argentina) y su relación con variables climáticas [Temporal variability of PM10 in Bahía Blanca (Argentina) and its relationship with climatic variables] . Cuadernos Geográficos , 56 (3), 6 – 25. https://revistaseug.ugr.es/index.php/cuadgeo/article/view/5084 Campo, A. M., Fernández, M. E., & Gentili, J. O. (2018). Relación entre CO, NOX, SO2, O3 y factores naturales y antropogénicos en Bahía Blanca (Argentina) [ Relationship between CO, NOX, SO2, O3 and natural and anthropogenic factors in Bahía Blanca (Argentina)] . Pesquisas Em Geociências , 45 (1), e0661. https://doi.org/10.22456/18 07 - 9806.85645 Campo de Ferreras, A. M., Capelli de Steffens, A. M., & Diez, P. (2004). Clima del Suroeste bonaerense [Climate of the Southwest of Buenos Aires] . Departamento de Geografía y Turismo, Universidad Nacional del Sur. Capelli de Steffens , A. M., Piccolo, M. C., & Campo de Ferreras, A. M. (2005). Clima urbano de Bahía Blanca [Urban climate of Bahía Blanca] . Dunken. Casado, A. (2013). Human impacts and fluvial metamorphosis. The effects of flow regulation on the hydrology, morphology and wa ter temperature of the Sauce Grande River, Argentina . Université Blaise Pascal Clermont - Ferrand II - Universidad Nacional del Sur, Clermont - Ferrand. Chen, J., Zhou, M., Yang, J., Yin, P., Wang, B., Ou, C. Q., & Liu, Q. (2020). The modifying effects of heat and cold wave characteristics on cardiovascular mortality in 31 major Chinese cities. Environmental
Fernández, M. E., Gentili, J. O., Finisterra, LX (129), 2025, e38754 16 Research Letters , 15 (10) , 105009 . https://doi.org/10.1088/1748 - 9326/abaea0 Chesini, F., Abrutzky , R., & De Titto, E. (2019). Mortality from heat waves in the city of Buenos Aires, Argentina (2005 - 2015). Cadernos de Saúde P ú blica , 35 (9), e00165218. Fundação Oswaldo Cruz. https://doi.org/10.1590 /0102 - 311X00165218 Chiozza, E., & Figueras, R. (1982). Atlas total de la República Argentina [Total Atlas of the Argentine Republic] . Tomo II. Centro Editor de América Latina. Christen, A., & Vogt, R. (2004). Energy and radiation balance of a central European City. International Journal of Climatology , 24 (11), 1395 – 1421. https://doi.org/10.1002/joc.1074 Cinar, İ., Karakus, N., & Toy, S. (2023). Analysing daytime summer thermal comfort conditions for Tur key’s third largest tourism destination. Environmental Science and Pollution Research , 30 (17), 50046 – 50056. https://doi.org/10.1007/s11356 - 023 - 25719 - w Centro de Implementación de Políticas Pública s para la Equidad y el Crecimiento . (2017). Hacia el desarrollo urbano integral de Bahía Blanca. Una propuesta de co - creación de políticas públicas y planificación [Toward the Comprehensive Urban Development of Bahía Blanca: A Proposal for Co - Creation of Public Policy and Planning] . https://www.cippec.org/wp - content/uploads/2017/09/Hacia - un - plan - de - desarrollo - urbano - integral - para - Bahia - Blanca2.pdf Colman Lerner, J. E., Morales, A., Aguilar, M., Sánchez, E. Y., Giuliani, D., Ditondo, J., Massolo, L., Dodero, V. I., & Porta, A. A. (2012). Concentración de compuestos orgánicos volátiles y material particulado en ambientes urbano s e industriales de dos regiones bonaerenses [Concentration of volatile organic compounds and particulate matter in urban and industrial environments in two regions of Buenos Aires] . In VII Congreso Del Medio Ambiente AUGM [AUGM Environmental Congress] , 22 a 24 de Maio, Buenos Aires, Argentina . Costa, I. T., Wollmann, C. A., Writzl, L., Iensse, A. C., da Silva, A. N., de Freitas Baumhardt, O., Gobo, J. P. A., Shooshtarian, S., & Matzarakis, A. (2024). A Systematic Review on Human Thermal Comfort and Methodo logies for Evaluating Urban Morphology in Outdoor Spaces. Climate , 12 (3) , 30 . https://doi.org/10.3390/cli12030030 Crawford, B., Krayenhoff, E. S., & Cordy , P. (2018). The urban energy balance of a lightweight low - rise neighborhood in Andacollo, Chile. Theoretical and Applied Climatology , 131 (1 – 2), 55 – 68. https://doi.org/10.1007/s00704 - 016 - 1922 - 7 Cue to, R. O. G., Martínez, A. T., & Ostos, E. J. (2010). Heat waves and heat days in an arid city in the northwest of México: Current trends and in climate change scenarios. International Journal of Biometeorology , 54 (4), 335 – 345. https://doi.org/10.1007/s00484 - 009 - 0283 - 7 D’Ippoliti, D., Michelozzi, P., Marino, C., De’Donato, F., Menne, B., Katsouyanni, K., … & Perucci, C. A. (2010). The impact of heat waves on mortality in 9 European cities: Results f rom the EuroHEAT project. Environmental Health: A Global Access Science Source , 9 , 37 . https://doi.org/10.1186/1476 - 069X - 9 - 37 Dimitriadou, L., & Zerefos, C. (2023). Heatwaves and Mortality in Spain an d Greece: a comparative analysis. Atmosphere , 14 (5) , 766 . https://doi.org/10.3390/atmos14050766 Dimitrova, A., Ingole, V., Basagaña, X., Ranzani, O., Milà, C., Ballester , J., & Tonne, C. (2021). Association between ambient temperature and heat waves with mortality in South Asia: systematic review and meta - analysis. Environment International , 146, 106170. https:/ /doi.org/10.1016/j.envint.2020.106170 Doulos, L., Santamouris, M., & Livada, I. (2004). Passive cooling of outdoor urban spaces. The role of materials. Solar Energy , 77 (2), 231 – 249. https://doi .org/10.1016/j.solener.2004.04.005 Dugarova, E., & Gülasan, N. (2017). Global Trends : Challenges and opportunities in the implementation of the Sustainable Development Goals. UNDP. https://www.undp.org/publications/global - trends - challenges - and - opportunities - implementation - sdgs Fernández García, F. (1996). Manual de climatol ogía aplicada. Clima, medio ambiente y planificación [Handbook of Applied Climatology. Climate, Environment, and Planning] . Editorial Síntesis. Fernández García, F., Galán, E., & Cañada, R. (2012). Caracterización del régimen bioclimático medio del área m etropolitana de Madrid, mediante la aplicación de la temperatura fisiológica (PET) [ Characterization of the average bioclimatic regime of the metropolitan area of Madrid, through the
Fe rn á ndez , M. E., Gentili, J. O. , Finisterra, LX (129), 2025, e38754 17 application of physiological temperature (PET)] . Territoris , 8, 83 – 101. https://raco.cat/index.php/Territoris/article/view/259755 Fernández, M. E. (2020). Radiación Solar en Bahía Blanca [Solar Radiation in Bahía Blanca] . [PhD tesis - Universidad Naciona l del Sur ] . Repositório digital UNS. http://repositoriodigital.uns.edu.ar/handle/123456789/5454 Fernández, M. E., Buscarini, J., Pellejero, J., & Gentili, J. O. (2022). Olas de Frí o y Calor en Bahía Blanca (Argentina): impactos en el ambiente urbano analizados a través de la prensa escrita local [Cold and Heat Waves in Bahía Blanca (Argentina): Impacts on the Urban Environment Analyzed Through the Local Print Media] . Geográfica Digital , 19 (37), 80 – 101. https://doi.org/10.30972/geo.19375834 Fernández, M. E., Campo, A. M., & Gentili, J. O. (2018). Análisis bioclimatológico en la ciudad de Bahía Blanca, Argentina [Bioclimatologi cal analysis in the city of Bahía Blanca, Argentina] . In XII Jornadas Nacionales de Geografía Física “Contribuciones de La Geografía Física a La Gestión de Los Territorios” [XII National Conference on Physical Geography "Contributions of Physical Geography to Territorial Management "], 11,12 y 13 de abril, Trelew, Chubut, Argentina, 280 – 285. Fernández, M. E., & Gentili, J. O. (2021a). Radiación solar en entornos urbanos: un recurso, un peligro y un derecho. Análisis desde la percepción en Bahía Blanca, Argen tina [Solar radiation in urban environments: a resource, a hazard, and a right. A perspective analysis in Bahía Blanca, Argentina] . Estudios Geográficos , 82 (291), e076. https://doi.org/10.3989/estgeogr.202187.087 Fernández, M. E., & Gentili, J. O. (2021b). Radiación solar y planeamiento urbano: factores e interacciones en Bahía Blanca, Argentina. Revista de Urbanismo , 45, 4 – 24. https://doi.org/10.5354/0717 - 5051.2021.58824 Fernández, M. E., Gentili, J. O., & Campo, A. M. (2018). Sunshine duration analysis as a first step to estimate solar resource for photovoltaic electricity production in middle latitudes. Envi ronmental Processes , 5 (2), 313 – 328. https://doi.org/10.1007/s40710 - 018 - 0298 - 3 Fernández, M. E., Gentili, J. O., & Campo, A. M. (2021). Air Pollutants in an Intermediate City: Variability and Interactions with Weather and Anthropogenic Elements in Bahía Blanca, Argentina. Environmental Processes , 8 (1), 349 – 375. https://doi.org/10.1 007/s40710 - 021 - 00502 - 6 Fernández, M. E., Gentili, J. O., Casado, A., & Campo, A. M. (2021). Global horizontal irradiation: spatio - temporal variability on a regional scale in the south of the Pampeana region (Argentina). AUC Geogr a phica , 56(2), 220 – 233. https://doi.org/10.14712/23361980.2021.14 Fernández, M. E., Picone, N., Gentili, J. O., & Campo, A. M. (2021). Analysis of the Urban Energy Balance in Bahía Blanca (Argentina). Urban Climate , 37, 100 856. https://doi.org/10.1016/j.uclim.2021.100856 Feron, S., Cordero, R. R., Damiani, A., Llanillo, P. J., Jorquera, J., Sepulveda, E., Asencio, V., Laroze, D., Labbe , F., Carrasco, J., & Torres, G. (2019). Observations and Projections of Heat Waves in South America. Scientific Reports , 9 (1), 8173 . https://doi.org/10.1038/s41598 - 019 - 44614 - 4 Ferrelli, F. (2016) . Análisis del clima local y micro - local de la ciudad de Bahía Blanca [PhD tesis - Universidad Nacional del Sur]. Repositório digital UNS. http://repositoriodigital.uns.edu.ar/bitstream/123456789/2698/1/Tesis_Ferrelli_2016.pdf Ferrelli, F., Brendel, A. S., Perillo, G. M. E., & Piccolo, M. C. (2021). Warming signals emerging from the analysis of daily changes in extreme temperature events ove r Pampas (Argentina). Environmental Earth Sciences , 80, 422. https://doi.org/10.1007/S12665 - 021 - 09721 - 4 Ferrelli, F., Luján Bustos, M., & Piccolo, M. C. (2016). La expansión urbana y sus impactos sobre el clima y la sociedad de la ciudad de Bahía Blanca, Argentina [Urban expansion and its impacts on the climate and society of Bahía Blanca, Argentina] . Estudios Geográficos , 77, 469 – 489. https:/ /doi.org/10.3989/estgeogr.201615 Fiorillo, E., Brilli, L., Carotenuto, F., Cremonini, L., Gioli, B., Giordano, T., & Nardino, M. (2023). Diurnal Outdoor Thermal Comfort Mapping through Envi - Met Simulations, Remotely Sensed and In Situ Measurements. Atmosp here , 14 (4). https://doi.org/10.3390/atmos14040641 Fittipaldi, R. Á., Espasa, L., Masrandrea, A., & Michalijos, M. P. (2018). Geografía de Bahía Blanca. La conformación del espacio urbano en el siglo XX [Geography of Bahía Blanca. The formation of urban
Fernández, M. E., Gentili, J. O., Finisterra, LX (129), 2025, e38754 18 space in the 20th century] . In M. N. Cernadas & J. B. Marcilese (Eds.), Bahía Blanca siglo XX: historia política, económica y sociocultural [Bahía Blanca in the 20th Century: Political, Economic, and So cio - Cultural History] (pp. 15 – 36). EdiUNS. Formiga, N., & Marenco, S. (2000). La dinámica urbana. El proceso de desarrollo vertical y la problemática de la marginalidad urbana en Bahía Blanca [Urban dynamics. The process of vertical development and the pro blem of urban marginalization in Bahía Blanca] . EdiUNS. Fröhlich, D., Gangwisch, M., & Matzarakis, A. (2019). Effect of radiation and wind on thermal comfort in urban environments - Application of the RayMan and SkyHelios model. Urban Climate , 27, 1 – 7. https://doi.org/10.1016/j.uclim.2018.10.006 Gentili, J. O., & Fernández, M. E. (2023). Influencia de la forma y función urbana en la intensidad de las noches tropicales y las noches tórridas en Bahía Blanca (República Argentina) [Influence of urban form and function on the intensity of tropical and torrid nights in Bahía Blanca (Argentina)] . Contribuciones Cientificas GÆA , 35, 26 – 36. https://gaea.org.ar/contribuciones/ContribCientificasVol35No2.pdf Gentili, J. O., & Fernández, M. E. (2024). Eventos de calor extremo en una ciudad suramericana de clima templado. El caso de Bahía Blanca (Argentina) [Extreme heat events in a temperate South American city. The case of Bahía Blanca (Argentina)] . Estudios Geográficos [ IN PRESS ] . Gentili, J. O., Fernández, M. E., Ortuño Cano, M. de los Á., & Campo, A. M. (2020). Assessment of the sustainable potential of parking lots in Bahí a Blanca City, Argentina. GeoJournal , 85, 1257 – 1275. https://doi.org/10.1007/s10708 - 019 - 10021 - 5 Gill, S. E., Handley, J. F., Ennos, A. R., & Pauleit , S. (2007). Adapting cities for climate change: the role of the green infrastructure. Built Environment , 33 (1), 155 – 133. https://doi.org/10.2148/benv.33.1.115 Grimm, A. M., Barros, V. R., & Doyle, M. E. (2000). Climate Variability in Southern South America Associated with El Niño and La Niña Events. Journal of Climate , 13 (1), 35 – 58. https://doi.org/10.1175/1520 - 0442(2000)013<0035:CVISSA>2.0.CO;2 Grimmond, C. S. B., & Oke, T. R. (1995). Comparison of heat fluxes from summertime observations in the suburbs of four North American cities. Journal of Applied Meteorology , 34 ( 4 ) , 873 – 889. https://doi.org/10.1175/1520 - 0450(1995)034<0873:COHFFS>2.0.CO;2 Hagen, I., Huggel, C., Ramajo, L., Chacón, N., Ometto, J. P., Postigo, J. C., & Castellanos, E. J. (2022). Climate change - related risks and adaptation potential in Central and South America during the 21st century. Environmental Research Letters , 17 (3) , 033002. https://doi.org/10.1088/1748 - 9326/ac5271 Hajat, S., & Haines, A. (2002). Associations of cold temperatures with GP consultations for respiratory and cardiovascular disease amongst the elderly in London. International Journal of Epidemiology , 31 (4), 825 – 830. https://doi.org/10.1093/ije/31.4.825 Hanafi, A., & Dastjerdi, J. K. (2014). An Assessment of Bioclimatic Conditions for Tourists In the Southwest of Iran. Bulletin of Environment, Pharmacology and Life Sciences , 3 (10), 109 – 118. Hassan, Z. M. , Al - Jiboori, M. H., & Al - Abassi, H. M. (2020). The Effect of The Extremes Heat Waves on Mortality Rates in Baghdad During the Period (2004 - 2018). Al - Mustansiriyah Journal of Science , 31 (2), 15 - 23 . htt ps://doi.org/10.23851/mjs.v31i2.753 Helbig, A., Matzarakis, A., & Piacentini, E. (2007). North - South variation of bioclimatic parameters in Argentina during summer months. In A. Matzarakis, C. R. Freitas & D. Scott (Eds.), Developments in Tourism Climatol ogy ( 66 – 73 ) . CCTR. Herrera, N., De Los, M., Skansi, M., Ángel Berón, M., Campetella, C., Cejas, A., Chasco, J., … & Suaya, M. (2018). Sistema de Alerta Temprana por Olas de Calor y Salud (SAT - OCS) [Early Warning System for Heatwaves and Health (SAT - OCS)] . Nota Técnica SMN 2018 - 50. www.smn.gov.ar Ho, J. Y., Lam, H. Y. C., Huang, Z., Liu, S., Goggins, W. B., Mo, P. K. H., & Chan, E. Y. Y. (2023). Factors affecting outdoor physical activity in extreme temperatures in a s ub - tropical Chinese urban population: an exploratory telephone survey. BMC Public Health , 23 (1), 101. https://doi.org/10.1186/s12889 - 022 - 14788 - 0 Höppe , P. (1999). The physiological equivalent temperature - a universal index for the biometeorological assessment of the thermal environment. International Journal of Biometeorology , 43 (2), 71 – 75.
Fe rn á ndez , M. E., Gentili, J. O. , Finisterra, LX (129), 2025, e38754 19 https:// doi.org/10.1007/s004840050118 Instituto Nacional de Estadística y Censos . (2023). Censo Nacional de Población, Hogares y Viviendas 2022: resultados provisionales [ 2022 National Population, Households and Housing Census: Provisional Results ] . Instituto Nacional de Estadística y Censos - INDEC. Intergovernmental Panel on Climate Change . ( 2023). Climate Change 2023: Synthesis Report. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (Vol. 13). IPCC. Intergovernmental Panel on Climate Change . (2019). Informe especial sobre los impactos de un calentamiento global de 1,5oC y las sendas de emisión relacionadas. E spañol [Special report on the impacts of 1.5°C global warming and related emissions pathways. Spanish] i n Intergovernmental Panel on Climate Change. Grupo Intergubernamental de Expertos sobre el Cambio Climático. https://www.ipcc.ch/sr15/ Intergovernmental Panel on Climate Change . (2014 ). Climate Change 2014. Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change Core. Gian - Kasper Plattner. IPCC. Irmak, M. A., Yilmaz, S., Mutlu, E., & Yilmaz, H. (2020 ). Analysis of different urban spaces on thermal comfort in cold regions: a case from Erzurum. Theoretical and Applied Climatology , 141 (3 – 4), 1593 – 1609. https://doi.org/10.1007/s00704 - 020 - 03289 - y Jemmett - Smith, B., Ross, A. N., & Sheridan, P. (2018). A short climatological study of cold air pools and drainage flows in small valleys. Weather , 73 (8), 256 – 262. https://doi.org/10.1002/wea.3281 Johansson , E. (2006). Influence of urban geometry on outdoor thermal comfort in a hot dry climate: A study in Fez, Morocco. Building and Environment , 41 (10), 1326 – 1338. https://doi.org/10.1016/J.BUILDEN V.2005.05.022 Khanjani, N., & Bahrampour, A. (2013). Temperature and cardiovascular and respiratory mortality in desert climate. A case study of Kerman, Iran. Iranian Journal of Environmental Health Science and Engineering , 10 (11), 2 – 7. https://doi.org/10.1186/1735 - 2746 - 10 - 11 Konstantinov, P. I., M.I. Varentsov, M. I., & Malinina, E. P. (2014). Modeling of thermal comfort conditions inside the urban boundary layer during Moscow’s 2010 summer heat wav e (case - study). Urban Climate , 10 (part. 3) , 563 – 572. https://doi.org/10.1016/j.uclim.2014.05.002 Konstantinov, P. I., Varentsov, M. I., & Shartova , N. V. (2022). North Eurasian thermal comfort indices dataset (NETCID): new gridded database for the biometeorological studies. Environmental Research Letters , 17 (8), 085006. https://doi.org/10.1088 /1748 - 9326/ac7fa9 Kotharkar, R., Bagade, A., & Agrawal, A. (2019). Investigating local climate zones for outdoor thermal comfort assessment in an Indian city. Geographica Pannonica , 23 (4), 318 – 328. https: //doi.org/10.5937/GP23 - 24251 Kotharkar, R., Dongarsane, P., & Ghosh, A. (2024). Quantification of summertime thermal stress and PET range in a tropical Indian city. Urban Climate , 53, 101758. htt ps://doi.org/10.1016/j.uclim.2023.101758 Li, X., & Ratti, C. (2018). Mapping the spatio - temporal distribution of solar radiation within street canyons of Boston using Google Street View panoramas and building height model. Landscape and Urban Planning , 191, 103387. https://doi.org/10.1016/J.LANDURBPLAN.2018.07.011 Lin, Y. H., & Tsai, K. T. (2017). Screening of tree species for improving outdoor human thermal comfort in a Taiwanese City. Sustainability , 9 (3), 340. https://doi.org/10.3390/su9030340 López - Bueno, J. A., Díaz, J., Sánchez - Guevara, C., Sánchez - Martínez, G., Franco, M., Gullón, P., … & Linares, C. (2020). The impact of heat wav es on daily mortality in districts in Madrid: The effect of sociodemographic factors. Environmental Research , 190, 109993. https://doi.org/10.1016/j.envres.2020.109993 Mäkinen, T. M., Juvonen, R ., Jokelainen, J., Harju, T. H., Peitso, A., Bloigu, A., Silvennoinen - Kassinen, S., Leinonen, M., & Hassi, J. (2009). Cold temperature and low humidity are associated with increased occurrence of respiratory tract infections. Respiratory Medicine , 103 (3), 456 – 462. https://doi.org/10.1016/j.rmed.2008.09.011
Fernández, M. E., Gentili, J. O., Finisterra, LX (129), 2025, e38754 20 Matzarakis, A., & Fröhlich, D. (2018). Influence of urban green on human thermal bioclimate - Application of thermal indices and micro - scale models. Acta Horticulturae , 1215, 1 – 9. https://doi.org/10.17660/ACTAHORTIC.2018.1215.1 Matzarakis, A., Gangwisch, M., & Fröhlich, D. (2021). RayMan and SkyHelios Model. In M. Palme & A. Salvati (Eds.), Urban Microclimate Modelling for Comfort and Energy Studies (pp. 339 – 361). Springer. Matzarakis, A., & Mayer, H. (1996). Another Kind of Environmental Stress: Thermal Stress. WHO Newsletter , 18, 7 – 10. Matzarakis, A., Mayer, H., & Iziomon, M. G. (1999). Applications of a universal thermal index: physiological equivalent temperature. International Journal of Biometeorology , 43, 76 – 84. https://doi.org/10.1007/s004840050119 Matzarakis, A., Muthers, S., & Koch, E. (2011). Human biometeorological evaluation of heat - related mortality in Vienna. Theoretical and Applied Climatology , 105 (1), 1 – 10. https://doi.org/10.1007/s00704 - 010 - 0372 - x Matzarakis, A., Rutz, F., & Mayer, H. (2007). Modelling radiation fluxes in simple and complex environments — application of the RayMan model. International Journal of Biometeorology , 51 (4), 323 – 334. https://doi.org/10.1007/S00484 - 006 - 0061 - 8 Matzarakis, A., Rutz, F., & Mayer, H. (2010). Modelling radiation fluxes in simple and complex environments: basics of the RayMan model. International Journ al of Biometeorology , 54 (2), 131 – 139. https://doi.org/10.1007/S00484 - 009 - 0261 - 0 Medina - Ramón, M., Zanobetti , A., Cavanagh, D. P., & Schwartz, J. (2006). Extreme temperatures and mortality: Assessing effect modification by personal characteristics and specific cause of death in a multi - city case - only analysis. Environmental Health Perspectives , 114 (9), 1331 – 1336 . https://doi.org/10.1289/ehp.9074 Mesa, A., Arboit, M., & De Rosa, C. (2009). Modelos de cálculo de los rangos del confort térmico. Verificación de su aplicabilidad y la incidencia de las variables determi nantes [Models for calculating thermal comfort ranges. Verification of their applicability and the impact of determining variables] . Avances en Energías Renovables y Medio Ambiente , 13, 61 - 68. Metin, A. E., Çağlak, S., & Toy, S. (2024). Bioclimatic comfort difference with the effect of urbanisation: the case of Uşak city, Turkey. Theoretical and Applied Climatology , 155, 2399 – 2414. https://doi.org/10.1007/s00704 - 023 - 04813 - 6 Midde l, A., Chhetri, N., & Quay, R. (2015). Urban forestry and cool roofs: Assessment of heat mitigation strategies in Phoenix residential neighborhoods. Urban Forestry & Urban Greening , 14 (1), 178 – 186. https://doi.org/10.1016/J.UFUG.2014.09.010 Milošević, D. D., Savić, S. M., Marković, V., Arsenović, D., & Šećerov, I. (2016). Outdoor human thermal comfort in local climate zones of Novi Sad (Serbia) during heat wave period. Hungarian Geographical Bullet in , 65 (2), 129 – 317. https://doi.org/10.15201/hungeobull.65.2.4 Miranda, V. F. V. V., dos Santos, D. M., Peres, L. F., Salvador, C., Nieto, R., Müller, G. V., Thielen, D., & Libonati, R. (2024). Heat stress in South America over the last four decades: a bioclimatic analysis. Theoretical and Applied Climatology , 155 (2), 911 – 928. https://doi.org/10.1007/s00704 - 023 - 04668 - x Monteiro, A., Carv alho, V., Góis, J., & Sousa, C. (2013). Use of ‘Cold Spell’ indices to quantify excess chronic obstructive pulmonary disease (COPD) morbidity during winter (November to March 2000 - 2007): Case study in Porto. International Journal of Biometeorology , 57 (6), 857 – 870. https://doi.org/10.1007/s00484 - 012 - 0613 - z Moreno, M. C., Jáuregui, E., & Tejeda, A. (2012). Valores de las componentes del balance de energía en la superficie - atmósfera en el centro de Bar celona en verano [Values of the components of the surface - atmosphere energy balance in the center of Barcelona in summer] . Boletín de La Asociación de Geógrafos Españoles , 60, 427 – 429. https://doi.org/10.21138/bage.1514 Norton, B. A., Coutts, A. M., Livesley, S. J., Harris, R. J., Hunter, A. M., & Williams, N. S. G. (2015). Planning for cooler cities: A framework to prioritise green infrastructure to mitigate high temperatures in urb an landscapes. Landscape and Urban Planning , 134, 127 – 138.
Fe rn á ndez , M. E., Gentili, J. O. , Finisterra, LX (129), 2025, e38754 21 https://doi.org/10.1016/j.landurbplan.2014.10.018 Offerle, B., Grimmond, C. S. B., Fortuniak, K., Oke, T. R., & Klysik , K. (2003). Temporal variability in heat fluxes over a northern european downtown. ICUC - 5. https://www.researchgate.net/publication/224001561 Oke, T. R. (2011). Urban heat island. In I. Douglas, D. Goode, M. C. Houck & R. Wang (Eds.), The Routledge Handbook of Urban Ecology (pp. 120 – 131). Routledge Abingdon. Oke, T. R. (1997). Urban climates and global environmental change. In R. D. Thompson & A. Perry (Eds.), Applied Climatology: Princip les & Practices (pp. 273 – 287). Routledge. Oke, T. R., Mills, G., Christen, A., & Voogt, J. A. (2017). Urban Climates . Cambridge University Press. https://doi.org/10.1017/9781139016476 Orte, M., Colman Lerner, J. E., Morales, A., Barrionuevo, P., Aguilar, M., Giulani, D., … & Porta, A. (2013). Estudio del material particulado inhalable y contaminantes asociados en las ciudades de La Plata y Bahía Blanca [Study of inhalable particulate matter and associated pollutants in the cities of La Plata and Bahía Blanca] . In IV Congreso del Proyecto Integrador para la Mitigación de la Contaminación Atmosférica (PROIMCA) y II Congreso del Proyecto Integrador para la Determinación de la Calidad del Agua (PRODE CA) [IV Congress of the Integrative Project for the Mitigation of Air Pollution (PROIMCA) and II Congress of the Integrative Project for the Determination of Water Quality (PRODECA)] (pp. 595 – 602) , Córdoba, Argentina . Pecelj, M., Matzarakis, A., Vujadinov ić, M., Radovanović, M., Vagić, N., Đurić, D., & Cvetkovic, M. (2021). Temporal analysis of urban - suburban PET, mPET and UTCI indices in Belgrade (Serbia). Atmosphere , 12 (7), 916. https://doi.org/10.339 0/atmos12070916 Pfautsch, S., & Tjoelker, A. R. (2020). The impact of surface cover and tree canopy on air temperature in Western Sydney . Western Sydney University. https://doi.org/10.26183/bk6d - 1466 Pfautsch, S., Wujeska - Krause, A., & Rouillard, S. (2020). Benchmarking Summer Heat Across Penrith, New South Wales . Western Sydney University. https://doi.org/10.26183/44va - ck37 Piticar, A. (2018). Change s in heat waves in Chile. Global and Planetary Change , 169, 234 – 246. https://doi.org/10.1016/j.gloplacha.2018.08.007 Provençal, S., Bergeron, O., Leduc, R., & Barrette, N. (2016). Thermal com fort in Quebec City, Canada: sensitivity analysis of the UTCI and other popular thermal comfort indices in a mid - latitude continental city. International Journal of Biometeorology , 60 (4), 591 – 603. https://doi.org/10.1007/s00484 - 015 - 1054 - 2 Puliafito, E., Rey Saravia, F., Pereyra, M., & Pagani, M. (2009). Calidad del aire en el Polo Petroquímico de Bahía Blanca [Air Quality in the Petrochemical Complex of Bahía Blanca] . In II Reunión Anual del Proyec to Integrador para la Mitigación de la Contaminación Atmosférica (PROIMCA) [2nd Annual Meeting of the Integrative Project for the Mitigation of Air Pollution (PROIMCA)] (pp. 113 – 122) , Mendoza, Argentina . Puliafito, S. E., Bochaca, F. R., Allende, D. G., & Fernandez, R. (2013). Green Areas and Microscale Thermal Comfort in Arid Environments: A Case Study in Mendoza, Argentina. Atm ospheric and Climate Sciences, 3 (03), 372 – 384. https://doi.org/10.4236/acs.2013.33039 Puliafito, S. E., Ortiz, G., & Puliafito, C. (2009). Evaluación del confort térmico urbano por medio de la Temperatura Fisiológica Equivalente (PET), en la ciudad de Mendoza [Assessment of urban thermal comfort using Physiological Equivalent Temperature (PET) in the city of Mendoza] . Energías Renovables y Medio Ambiente , 24, 57 – 63. Ribeiro, K. F. A., Justi, A. C. A., Novais, J. W. Z., Santos, F. M. de M., Nogueira, M. C. de J. A., Miranda, S. A. De, & Marques, J. B. ( 2022). Calibration of the Physiological Equivalent Temperature (PET) index range for outside spaces in a tropical climate city. Urban Climate , 44, 101196. https://doi.org/10.1016/J.UCLIM.2022.1011 96 Roshan, G. R., Ghanghermeh, A. A., & Kong, Q. (2018). Spatial and temporal analysis of outdoor human thermal comfort during heat and cold waves in Iran. Weather and Climate Extremes , 19, 58 – 67. https://doi.org/10.1016/j.wace.2018.01.005 Royé, D., Martí Ezpeleta, A., & Cabalar Fuentes, M. (2012). Aproximación al comportamiento espacial del estrés térmico en Galicia mediante el uso del índice bioclimático PET [An approach to the spatial
Fernández, M. E., Gentili, J. O., Finisterra, LX (129), 2025, e38754 22 behavior of thermal stress in Galicia using the PET bioclimatic index] . Publicaciones de La Asociación Española de Climatología , 8, 941 – 949. Ruiz, M. A., & Correa, E. N. (2015a). Adaptive model for outdoor thermal comfort assessment in an Oasis city of arid climat e. Building and Environment , 85, 40 – 51. https://doi.org/10.1016/j.buildenv.2014.11.018 Ruiz, M. A., & Correa, E. N. (2015b). Suitability of different comfort indices for the prediction of ther mal conditions in tree - covered outdoor spaces in arid cities. Theoretical and Applied Climatology , 122 (1 – 2), 69 – 83. https://doi.org/10.1007/s00704 - 014 - 1279 - 8 Rusticucci, M., Kyselý, J., Almeira , G., & Lhotka, O. (2015). Long - term variability of heat waves in Argentina and recurrence probability of the severe 2008 heat wave in Buenos Aires. Theoretical and Applied Climatology , 124, 679 – 689. https://doi.org/10.1007/s00704 - 015 - 1445 - 7 Sambrook, K., Russell, S., Okan, Y., & Konstantinidis, E. (2023). Outdoor Sport in Extreme Heat: Capturing the Personal Experiences of Elite Athletes. Weather, Climate, and Society , 15 (3), 619 – 631. https://doi.org/10.1175/WCAS - D - 22 - 0107.1 Santágata, D. M., Castesana, P., Rössler, C. E., & Gómez, D. R. (2017). Extreme temperature events affecting the electricity distribution system of the metropolitan area of Buenos Aires (1971 – 2013). Energy Policy , 106, 404 – 414. https://doi.org/10.1016/j.enpol.2017.04.006 Shahidan, M. F., Jones, P. J., Gwilliam, J., & Salleh, E. (2012). An evaluation of outdoor a nd building environment cooling achieved through combination modification of trees with ground materials. Building and Environment , 58, 245 – 257. https://doi.org/10.1016/J.BUILDENV.2012.07.012 Shooshtarian, S., Lam, C. K. C., & Kenawy, I. (2020). Outdoor thermal comfort assessment: A review on thermal comfort research in Australia. Building and Environment , 177, 106917. https://doi.o rg/10.1016/j.buildenv.2020.106917 Smit, W. (2021). Urbanization in the Global South. In Oxford Research Encyclopedia of Global Public Health (pp. 1 – 22). Oxford University Press. https: //doi.org/10.1093/acrefore/9780190632366.013.251 Smith, L. A., & Lancaster, L. T. (2020). Increased duration of extreme thermal events negatively affects cold acclimation ability in a high - latitude, freshwater ectotherm (Ischnura elegans; Odonata: Coenagrionidae). European Journal of Entomology , 117, 93 – 100. https://doi.org/10.14411/eje.2020.010 Spronken - Smith, R. A. (2002). Comparison of summer - and winter - time suburban energy fluxes in Christc hurch, New Zealand. International Journal of Climatology , 22 (8), 979 – 992. https://doi.org/10.1002/joc.767 Stewart, I. D., & Oke, T. R. (2012). Local climate zones for urban temperature studies. Bulletin of t he American Meteorological Society , 93, 1879 – 1900. https://doi.org/10.1175/BAMS - D - 11 - 00019.1 Taleghani, M., & Berardi, U. (2018). The effect of pavement characteristics on pedestrians’ thermal comf ort in Toronto. Urban Climate , 24, 449 – 459. https://doi.org/10.1016/j.uclim.2017.05.007 Tornero, J., Pérez Cueva, A., & Gómez Lopera, F. (2006). Ciudad y confort ambiental: estado de la cuestión y aportaciones recientes. Cuadernos de Geografía , 80, 147 – 182. https://di alnet.unirioja.es/servlet/articulo?codigo=2750257 Toy, S., Kejanli, D. T., Koç, A., & Koç, C. (2023). Temporal distribution of human thermal comfort conditions in and around Diyarbakır city, Turkey. GeoJournal , 88 (4), 4389 – 4402. https://doi.org/10.1007/s10708 - 023 - 10872 - z Unger, J., Skarbit, N., & Gál, T. (2017). Evaluation of outdoor human thermal sensation of local climate zones based on long - term database. International Journal of Biometeorology , 62 (2), 183 – 193. https://doi.org/10.1007/S00484 - 017 - 1440 - Z United Nations General Assembly. (2015). Transforming our world: the 2030 Agenda for Sustainable Development . UN. United Nations Habitat. (2020). Annual Progress Report 2019. UNH. https://unhabitat.org/sites/default/files/2020/03/annual_report_2019.pdf Urban, A., Davídkovová, H., & Kyselý, J. (2014). Heat - and cold - stress effects on cardiovascular mortality and morbidity among urban and rural populations in the Czech Republic. International Journal of
Fe rn á ndez , M. E., Gentili, J. O. , Finisterra, LX (129), 2025, e38754 23 Biometeorology , 58 (6), 1057 – 1068. https://doi.org/10.1007/s00484 - 013 - 0693 - 4 Vásquez, A. E. (2016). Infraestructura verde, servicios ecosistémicos y sus aportes para enfrentar el cambio climático en ciudades: el caso del corredor ribereño del río Mapocho en Santiago de Chile [G reen infrastructure, ecosystem services, and their contributions to addressing climate change in cities: the case of the Mapocho River riparian corridor in Santiago, Chile] . Revista de Geografía Norte Grande , 63, 63 – 86. http://dx.doi.org/10.4067/S0718 - 34022016000100005 Vecellio, D. J., & Vanos, J. K. (2024). Aligning thermal physiology and biometeorological research for heat adaptation and resilience in a ch anging climate. Journal of applied physiology , 136 (6), 1322 - 1328 https://doi.org/10.1152/JAPPLPHYSIOL.00098.2024 Veiga, H., Stella, J. L., Herrera, N., Gatto, M., Garay, N., & Skans, M. de lo s M. (2015). Monitoreo operativo de olas de calor y de frío en el Servicio Meteorológico Nacional [Operational monitoring of heat and cold waves at the National Meteorological Service] . CONGREMET XII. World Meteorological Organization . (2015a). Guidelines on the Definition and Monitoring of Extreme Weather and Climate Events . WMO . World Meteorological Organization . (2015b). Heatwaves and Health: Guidance on Warning - System Development (Issue 1142) . WMO. http://www.who.int/globalchange/publications/WMO_WHO_Heat_Health_Guidance_2015.pdf Xiang, L., & Ren, C. (2017). Effects of the building typology on PET value in different local climate zones - A case study in Beijing, China. In PLEA 2017 , Edimburg. Yang, S., Lin, T., & Matzarakis, A. (2018). Defining the thermal comfort range of UTCI, PET, and mPET thermal indices. In 10th International Conference on Urban Clim ate (ICUC 10) , Tainan, Taiwan . Zapperi, P. A. (2012). Hidrografía Urbana de Bahía Blanca [Urban Hydrography of Bahía Blanca]. [PhD thesis - Universidad Nacional del Sur , Argentina ] . Repositório digital UNS. http://repositoriodigital.uns.edu.ar/handle/123456789/485 Zhen, M., Zou, W., Zheng, R., & Lu, Y. (2022). Urban outdoor thermal environment and adaptive thermal comfort during the summ er. Environmental Science and Pollution Research , 29 (51), 77864 – 77883. https://doi.org/10.1007/s11356 - 022 - 21162 - 5