The characteristics of the diurnal pattern of biothermal conditions in the summer season in selected European cities

The characteristics of the diurnal pattern of biothermal conditions in the summer season in selected European cities | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article The characteristics of the diurnal pattern of biothermal conditions in the summer season in selected European cities Monika Okoniewska This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6270465/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This paper examines the diurnal pattern of biothermal conditions in three summer months (June, July and August) from 2018 to 2022, in selected European cities and analyses their burden on the human body. The weather data provided the basis for calculating the following indices: Universal Thermal Climate Index, Subjective Temperature Index, Maximal Heart Rate, Insulation Predicted, Water Loos, Overheating Risk. The study determined the daily mean, minimum and maximum values of biothermal and thermophysiological indices and their diurnal patterns, analysed their spatial distribution using spatial interpolation, and determined the incidence of heat load, apparent temperature, acceptable level of physical activity, predicted thermal insulation of clothing, degree of dehydration, and risk of hyperthermia. Studies have shown that, in Europe during the summer, between 12 p.m. and 3 p.m., there may be a burden of very intense heat stress (particularly in July and August), with a subjective sensation described as “very hot”. The risk of dehydration is not significant; however, there is a notable risk of hyperthermia, which can occur even after 20 minutes of exposure in open areas. In southern Europe, from 9 a.m. to 3 p.m., beachwear would be sufficient to maintain thermal comfort, while in the north, especially at night-time, transitional season clothing is necessary. Analyses have also shown that, particularly in cities such as Madrid, Bucharest and Rome, physical exertion should be avoided during midday hours. Climate warming in Europe during the summer is a serious challenge that requires coordinated actions at the local, national and international levels. Earth and environmental sciences/Climate sciences Earth and environmental sciences/Environmental sciences biothermal conditions diurnal pattern summer period biothermal indices Europe Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 1. Introduction Global warming observed worldwide has contributed to increased summer temperature variability throughout Europe, accompanied by more frequent, more prolonged and more intense heat waves, that is, extended periods with temperatures significantly higher than typical for a given area (Wibig, 2020 ). Climate warming in Europe during the summer is therefore becoming an increasingly evident and severe phenomenon. The increase in average temperatures, higher incidence of heat waves, droughts and changes in ecosystems are just some of the effects of global warming. Average summer temperatures in Europe are steadily rising, and the average number of hot days in Europe has tripled over the past 70 years (Lorenz et al., 2019 ; Sulikowska and Wypych, 2023 ). In recent decades, record-high temperatures have been recorded in many regions. For example, during the summer of 2019, many European countries, such as France, Germany and Spain, experienced exceptionally intense heat waves. In Paris, a record temperature of 42.6°C was the highest value in the city's history of measurements (WMO, 2020 ). Earlier, in the summer of 2003, a heat wave also swept across Europe (Twardosz, 2009 ), causing the death of around 40,000 people, primarily elderly individuals (Garc´ia-Herrera et al., 2010 ). The summer of 2010 was exceptionally hot in Eastern Europe and large parts of Russia. Additionally, studies in this area point to an increasing likelihood of mega heat waves in densely populated areas of Europe (Barriopedro et al., 2011 ), and the extreme heat that occurred in Europe during the summer of 2003 may become standard by the end of this century (Beniston, 2004 ). Not only are heat waves more frequent but they are also increasingly intense (Schär et al., 2004; Meehl and Tebaldi, 2004). It is estimated that the number of days with extremely high temperatures has tripled over the past 50 years. Such conditions are not only inconvenient but also pose a hazard to human health. The increase in temperatures can lead to heat strokes, dehydration and the intensification of cardiovascular diseases. In particular, high temperatures during the summer period have a significant impact on human health. Such weather conditions put a heavy strain on the cardiovascular system. Vasodilation contributes to increased heart rates, decreasing blood pressure and increased blood volume, resulting in a significant weakening of the body. Heat waves cause a decline in the level of haemoglobin, which transports oxygen, and an increased respiratory rate, which is dangerous for people suffering from respiratory diseases (Błażejczyk et al., 2014 ). Additionally, increased solar radiation affects a rise in both systolic and diastolic blood pressure (Błażejczyk, 1998 ). In particular, such conditions pose a hazard to the elderly, children, individuals suffering from specific illnesses, and those working in open areas. An increased heart rate in people with atherosclerosis can lead to myocardial hypoxia, as the coronary vessels fill during the diastole of the heart muscle. The loss of water and electrolytes due to sweating leads to a further decrease in blood pressure and an increase in heart rate, which results in decreased peripheral perfusion. Individuals with significant atherosclerotic lesions in the arteries supplying the central nervous system may develop a risk of experiencing stroke-like symptoms (Krzeszkowiak and Pawlak, 2015 ). When the air temperature rises, the human body starts producing sweat droplets on the skin's surface to lower body temperature. If the air humidity is not high, the evaporation of sweat progresses, thereby protecting the body from overheating. However, with the evaporating sweat, the human body loses electrolytes, particularly sodium (Na) (Krzeszkowiak and Pawlak, 2015 ). Sweat evaporation in high-temperature conditions is the primary mechanism of protection against overheating. When this mechanism fails, the body’s internal temperature may increase, posing a risk to health and life. Overheating of the body is accompanied by dehydration and hyponatremia. A deficiency of sodium and water leads to a range of negative health consequences, depending on the severity of the deficiency. The most dramatic ones include muscle cramps, exhaustion, seizures, loss of consciousness, and even death due to pulmonary or cerebral oedema (Bouchama and Knochel, 2002 ). Biothermal conditions worsen when hot days are followed by so-called tropical nights, during which the temperature does not drop below 20°C. Such situations provide no relief and are particularly dangerous for people with respiratory and cardiovascular diseases, while also being quite burdensome for others. There have not been many studies on the bioclimatic conditions in Europe during the summer period (Antonescu et al., 2021 ażejczyk and Błażejczyk, 2014 ; Della-Marta and Beniston, 2007; Katavoutas, 2022). The conducted analyses tend to focus on studies of individual European cities or countries, particularly on the correlation between weather conditions and the number of deaths (An der Heiden, 2019; Bacini et al., 2008; Ballester et al., 2023 ; Dessai, 2002 ; Di Napoli, 2018; Gabriel and Endlicher, 2011 ; Heudorf and Meyer; 2005 ; Kyselý and Kríz, 2003 ). Given the importance of climatic and bioclimatic conditions during the summer period and the simultaneous lack of specific analyses on daily variations of the bioclimatic conditions in Europe, we decided to review the daily variations of biothermal conditions in ten selected European cities, along with their burden on humans. In particular, the analysis focused on determining the degree of heat stress on the human body, estimating the subjective, apparent temperature, assessing the acceptable level of physical activity, and evaluating the clothing insulation required to maintain thermal comfort. It also included calculating water loss from the body and assessing the risk of heat-related conditions such as hyperthermia. 2. Research methods The study determined the daily mean, minimum, maximum, median and upper and lower quartile values of biothermal and thermophysiological indices and their diurnal patterns, analysed the spatial distribution of biothermal and thermophysiological indices using spatial interpolation, and determined the incidence of heat load, apparent temperature, acceptable level of physical activity, thermal insulation of clothing necessary to ensure thermal comfort, degree of dehydration, and risk of hyperthermia. The study utilised the following biothermal and thermophysiological indices: The Universal Thermal Climate Index (UTCI, °C) is defined as the equivalent air temperature at which, in reference conditions, the vital physiological parameters of the human body assume values identical to those in the real environment. The UTCI provides information on real processes regulating the body temperature, which are dependent on the weather conditions and the environment (Błażejczyk et al., 2010 ). The subjective temperature index (STI, °C) where the underlying value is the mean radiant temperature and the “resultant heat balance”, which is determined as a result of the body’s adaptation to the environment (Błażejczyk, 2004 ). The maximal heart rate (MHR, W·m − 2 ) defines the acceptable level of physical activity that does not cause excessive strain on the heart (Błażejczyk and Kunert, 2011 ). The Insulation Predicted (Iclp, clo) allows for determining the clothing insulation required in specific weather to maintain the body's heat balance (Kozłowska-Szczęsna et al., 1997 ). The index determines the acceptable level of physical activity (MHR, W·m⁻²) not causing excessive strain on the heart (Błażejczyk and Kunert, 2011 ). The water loss index (WL, g∙h − 1 ) indicates how much water the body needs to avoid dehydration (Błażejczyk and Kunert, 2011 ). It is particularly useful in the case of active leisure and hiking tourism practised in high ambient temperatures. According to ISO/DIS 7933 , the risk of dehydration occurs at two levels: alarm and danger. The dehydration alarm level for non-acclimatised persons alarm level is 520 < WL < 650 g·h⁻¹, while the danger level is described as WL exceeding 650 g·h⁻¹ (Błażejczyk and Kunert, 2011 ). The overheating risk index (Oh_H, min.) expresses the time after which too much heat is accumulated in the body, leading to potential hyperthermia. This time can be calculated based on the input and output heat transfer values (Błażejczyk and Kunert, 2011 ). All the indices were calculated using BioKlima 2.6 software ( https://www.igipz.pan.pl/Bioklima-zgik.html ). 3. Study material The research material consisted of time-specific meteorological data retrieved from https://www.ogimet.com/home.phtml.en , including air temperature (t, °C), relative humidity (f, %), wind speed (v, km·h⁻¹ converted to m·s⁻¹), and cloud cover (N, oktas) for June, July, and August collected from 2018 to 2022. The data were collected at the following times: 12 a.m., 3 a.m., 6 a.m., 9 a.m., 12 p.m., 3 p.m., 6 p.m. and 9 p.m. The following ten European cities were selected for analysis: Berlin, Bucharest, Kyiv, Copenhagen, Madrid, Paris, Rome, Sofia, Warsaw and Vienna (Table 1 ). The weather stations selected for review are located at the airports of the above-mentioned cities. The cities were selected on the basis of data availability and their uniform distribution throughout the European continent. Table 1 Geographical location of stations under review City latitude longitude height a.s.l. Berlin 52˚22'N 13˚31'E 46 Bucharest 44˚30'N 26˚04'E 90 Copenhagen 55˚36'N 12˚38'E 5 Kyiv 50˚23'N 30˚32'E 167 Madrid 40˚28'N 03˚33'W 609 Paris 49˚00'N 02˚32'E 119 Rome 41˚48'N 12˚35'E 120 Sofia 42˚38'N 23˚22'E 595 Vienna 48˚07'N 16˚34'E 183 Warsaw 52˚09'N 20˚57'E 107 4. Results Table 2 Key meteorological characteristics in the cities under review in June, July and August (2018–22) meteorological element Berlin Bucharest Copenhagen Kyiv Madrid Paris Rome Sofia Vienna Warsaw June t(°C) average 20,6 21,3 17,6 21,8 23,2 19,0 23,7 19,1 21,1 20,4 min 6,3 6,3 7,1 5,7 7,7 8,2 11,8 5,5 8,2 7,5 max 37,7 35,0 29,2 34,7 39,9 35,1 39,0 34,1 34,4 35,2 f (%) average 59,2 74,8 70,2 59,4 42,4 66,9 58,8 71,3 64,3 64,2 min 14,0 20,0 26,0 19,0 9,0 24,0 15,0 26,0 25,0 21,0 max 99,0 100,0 100,0 99,0 97,0 98,0 97,0 98,0 97,0 99,0 N (octants) average 4,4 4,3 2,9 3,8 2,8 5,0 1,9 4,4 4,3 4,0 min 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 max 8,0 8,0 8,0 8,0 8,0 8,0 8,0 8,0 8,0 8,0 v (m·s − 1 ) average 2,8 1,1 2,9 1,5 2,3 2,8 1,9 1,0 3,1 2,4 min 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,7 0,0 max 9,4 5,1 8,2 5,1 8,6 12,3 7,5 5,1 8,7 8,0 July t(°C) average 20,4 23,1 18,6 21,7 27,5 21,3 26,4 21,6 22,0 20,3 min 8,7 10,6 8,6 9,7 11,8 10,4 16,8 7,8 7,7 9,2 max 36,8 38,0 30,2 33,7 41,6 41,1 37,5 35,8 36,7 34,9 f (%) average 61,7 69,2 71,1 64,0 33,1 58,9 56,1 64,8 60,4 68,0 min 16,0 16,0 30,0 27,0 7,0 14,0 20,0 23,0 15,0 22,0 max 99,0 100,0 99,0 97,0 93,0 95,0 96,0 100,0 96,0 99,0 N (octants) average 4,5 3,2 3,2 4,1 1,3 4,0 1,1 3,3 4,1 4,6 min 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 max 8,0 8,0 8,0 8,0 8,0 8,0 8,0 8,0 8,0 8,0 v (m·s − 1 ) average 2,8 1,0 3,3 1,4 2,2 2,8 1,9 1,0 3,0 2,3 min 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,7 0,0 max 10,9 3,6 8,6 4,3 10,8 8,0 8,2 5,8 10,1 9,4 August t(°C) average 20,7 23,7 18,7 21,6 26,6 20,9 26,1 22,0 21,9 20,4 min 9,3 10,4 9,4 11,1 9,4 9,3 15,5 9,7 8,9 8,1 max 36,8 37,3 29,6 34,0 41,9 37,0 37,9 37,0 36,0 32,0 f (%) average 62,6 63,7 74,0 61,8 36,5 61,6 60,3 62,6 64,2 68,5 min 20,0 17,0 31,0 19,0 8,0 19,0 17,0 12,0 23,0 26,0 max 99,0 100,0 100,0 96,0 96,0 96,0 98,0 99,0 96,0 99,0 N (octants) average 4,7 2,4 3,0 3,3 1,7 4,4 1,5 2,7 4,4 4,3 min 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 max 8,0 8,0 8,0 8,0 8,0 8,0 8,0 8,0 8,0 8,0 v (m·s − 1 ) average 2,7 1,1 3,0 1,3 2,0 2,8 1,8 0,9 2,8 2,0 min 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 max 9,4 3,6 8,2 5,1 7,1 7,2 7,1 5,8 8,7 6,5 In June, the mean air temperature in the locations under review oscillated around 20°C. Temperature ranged from 5.5°C (minimum in Sofia) up to nearly 40°C (maximum in Madrid). The weather was the hottest in Madrid and Rome. Relative humidity averaged between approximately 42% and 75% (lowest in Madrid, highest in Bucharest), while cloud cover ranged from 0 to 8 oktas. Wind speed averaged from 0 up to a maximum of 12 m·s⁻¹ measured in Paris. In July, temperatures were slightly higher, both average and maximum. The highest values were recorded in Madrid (maximum of 41.6°C) and Paris (41.1°C). Humidity was comparable to June, with the lowest values again recorded in Madrid at 33.1%. Cloud cover was similar to that of June, with the lowest levels observed in Madrid and Rome. Maximum wind speed reached up to 11 m·s⁻¹ in Berlin, on average fluctuating around 1–2 m·s⁻¹. In August, the mean temperatures in all locations except for Copenhagen were above 20°C. The highest value was recorded in Madrid at 26.6°C, with a maximum of 41.9°C. Humidity, cloud cover and wind speed were similar to those observed in July (Table 2 ). Table 3 Key biometeorological characteristics in the cities under review in June, July and August (2018–22) biometerologicall indices Berlin Bucharest Copenhagen Kyiv Madrid Paris Rome Sofia Vienna Warsaw June UTCI (°C) average 17,4 22,6 15,8 21,8 21,6 15,0 24,2 20,4 18,0 18,6 min -8,2 1,8 -4,9 -1,6 -1,2 -14,9 7,8 -0,5 -4,6 -4,5 max 41,3 42,3 31,6 39,1 44,0 37,9 43,6 37,4 37,2 38,0 STI (°C) average 24,1 28,1 29,0 28,9 28,8 20,8 33,9 26,2 26,3 26,1 min -2,7 -2,6 -1,6 -4,1 -3,9 -3,5 4,3 -3,0 -1,6 -4,3 max 59,5 69,4 60,6 61,2 66,0 62,4 66,8 65,4 64,7 61,0 MHR (W·m − 2 ) average 181,4 160,6 201,7 168,1 174,3 190,3 150,2 185,7 170,7 178,3 min 52,2 41,3 86,1 70,5 59,8 60,5 46,6 63,9 67,4 63,6 max 299,7 296,9 291,2 305,2 302,3 285,4 252,2 306,0 286,9 298,9 Iclp (clo) average 0,5 0,3 0,7 0,3 0,3 0,6 0,2 0,4 0,5 0,5 min -0,5 -0,7 0,0 0,0 -0,8 -0,4 -0,6 0,0 -0,4 -0,4 max 1,3 1,3 1,3 1,4 1,3 1,2 1,0 1,2 1,3 1,4 WL (g·h − 1 ) average 220,9 189,6 172,2 208,3 288,1 191,9 249,5 166,6 221,9 204,6 min 90,2 87,8 91,0 90,4 99,4 96,7 106,2 87,0 97,9 90,3 max 1223,7 619,1 524,8 586,3 1565,7 726,7 1422,7 574,4 792,9 658,2 July UTCI (°C) average 17,0 25,0 14,6 21,7 27,2 18,1 27,7 23,1 19,2 18,3 min -2,3 9,7 -3,6 -0,2 7,2 0,4 0,6 4,7 0,1 -3,1 max 39,2 42,9 35,4 37,8 46,0 45,3 42,7 39,5 39,4 37,4 STI (°C) average 23,1 32,3 23,7 27,9 34,9 24,9 38,8 30,7 26,8 24,9 min -0,9 4,2 -0,1 0,7 5,8 0,8 5,2 0,0 -1,5 0,0 max 59,5 68,7 62,1 63,6 66,0 61,6 67,7 64,7 64,3 61,7 MHR (W·m − 2 ) average 181,1 146,5 189,8 164,9 143,7 175,5 123,0 166,3 165,8 176,1 min 78,8 42,2 83,8 70,4 49,7 41,5 42,5 68,0 61,6 53,7 max 281,9 256,2 278,7 267,3 249,7 267,9 213,2 284,4 288,7 270,4 Iclp (clo) average 0,5 0,2 0,6 0,3 0,0 0,5 0,1 0,3 0,4 0,5 min -0,5 -0,8 -0,1 -0,5 -0,9 -0,7 -0,6 -0,8 -0,4 -0,3 max 1,2 1,0 1,2 1,1 0,9 1,2 0,9 1,1 1,3 1,1 WL (g·h − 1 ) average 209,9 220,9 180,8 199,0 400,3 228,4 297,1 195,7 237,4 194,6 min 95,1 96,8 97,7 100,2 101,9 100,8 129,1 90,8 96,0 93,5 max 823,9 853,9 472,6 534,9 1619,6 1811,5 876,6 631,1 1037,8 726,8 August UTCI (°C) average 17,4 25,1 15,3 21,7 26,1 17,3 27,1 23,3 19,0 20,5 min -4,7 11,0 -5,0 7,1 2,3 1,5 7,9 6,2 -0,4 1,4 max 38,0 42,6 35,8 37,7 46,6 40,2 42,6 40,6 39,5 36,6 STI (°C) average 22,6 32,1 22,8 27,3 32,9 22,4 36,6 30,1 25,3 30,8 min -1,6 4,5 1,5 4,2 3,0 1,5 8,4 3,1 -0,3 -0,3 max 61,7 67,3 61,1 62,7 65,0 62,8 66,7 66,5 63,7 62,6 MHR (W·m − 2 ) average 177,9 146,5 187,0 168,6 147,5 176,2 121,7 164,8 162,5 174,7 min 67,4 50,7 95,1 63,3 45,5 57,2 45,8 64,1 65,1 75,8 max 273,8 257,7 271,9 261,9 282,8 279,0 226,3 267,8 276,3 280,6 Iclp (clo) average 0,5 0,2 0,6 0,3 0,1 0,5 0,1 0,2 0,4 0,5 min -0,5 -0,7 -0,2 -0,5 -0,8 -0,5 -0,7 -0,6 -0,5 -0,2 max 1,2 0,9 1,2 1,0 1,2 1,1 0,8 0,9 1,2 1,2 WL (g·h − 1 ) average 214,5 238,0 175,4 200,3 357,8 216,3 279,0 202,3 224,8 199,6 min 98,0 95,8 98,4 101,3 104,9 96,8 125,0 95,2 101,0 92,9 max 848,3 826,5 448,8 631,7 2325,3 916,4 980,1 881,0 750,6 544,6 In June, the average UTCI values indicated no heat stress. The maximum values (ranging from 37.2°C to 44°C) implied very strong heat stress. Subjective thermal sensations, on the other hand, indicated generally comfortable conditions, with warmth experienced in Rome. The maximum STI values suggested very hot sensations (in Bucharest, close to the value of 70, which signifies a heat wave). As regards physical activity, the minimum MHR values pointed to a need for rest, while the maximum values allowed moderate physical activity to be undertaken. The values of Iclp ranged from ˗0.9 (beachwear) to 1.3, which indicates the need to wear clothing suitable for transitional seasons. Water loss averaged around 200 g·h⁻¹, reaching a maximum of over 1500 g in Madrid. The maximum value, which represents the existence of a risk to health (650 g·h⁻¹), was exceeded at almost all stations. In July, the mean UTCI values indicated moderate heat stress in Rome and Madrid, while at the other stations, they signified no heat stress. The maximum value of this index implied very strong heat stress (except at three locations). In the case of subjective thermal sensations, warmth was generally felt in Bucharest, Madrid and Rome, while the maximum values at all stations represented very hot sensations. The minimum MHR values suggested a need for rest and the possibility of engaging in only light physical activity. The values of Iclp were similar to those measured in June. Water loss exceeded 1800 g·h − 1 at maximum in Paris. In August, the maximum UTCI implied very strong heat stress and was almost unbearable in Madrid. In such conditions, cooling the body was necessary. The subjective temperature index indicated sensations ranging from comfort to very hot (at the maximum index values). The maximum Iclp values were lower than in June but suggested the necessity of using transitional clothing. The minimum values of this index, on the other hand, indicated the expected thermal insulation at the level of beachwear. The average water loss in August was slightly higher than in July, typically ranging from 175 to 358 g·h⁻¹, while the maximum value of this indicator exceeded 2000 g·h⁻¹ in Madrid (Table 3 ). 4.1 Universal Thermal Climate Index The minimum values of the Universal Thermal Climate Index occurred at 3 a.m, while the maximum values were observed around midday. At night-time, smaller variations between stations were recorded, while greater differences were observed during the day. In Madrid, Rome, Bucharest and Sofia, the average index values were significantly exceeded during the day, it was also noted that higher median values were recorded, while Kyiv was the station closest to the average values. June exhibited the lowest values of the index. The average across all stations reached 25°C at noon, whereas in July and August, it exceeded this value. The station that stood out among the studied locations was Madrid, which recorded the highest values in the afternoon hours during July and August, with the value of the upper quartile exceeding 30°C (Fig. 1 ). At night-time (3 UTC) across all three months in Europe, the absence of heat stress predominated. However, in June, mild cold stress was observed in the United Kingdom, northern France, the Benelux and western Norway (S1). In contrast, during the midday hours, the absence of heat stress was also observed in northern Europe, while in the rest of the continent (except for Portugal, Spain, Italy, and Greece), moderate heat stress was recorded. In Portugal, Spain, Italy and Greece, heat stress was at a moderate level (Fig. 2 ). In June, moderate heat stress appeared as early as 6 a.m. and persisted until 6 p.m. Between 9 a.m. and 3 p.m., almost all stations recorded strong heat stress, while in Madrid, very strong heat stress occurred with a frequency of approximately 10%. At night, Paris, Berlin, Copenhagen and Vienna experienced mild cold stress, with moderate cold stress occurring sporadically (S2). In July, strong heat stress was recorded much more frequently, and at three stations, very strong heat stress was observed (40% in Madrid at 3 p.m.). The most challenging conditions were observed in Bucharest, Madrid and Rome, where heat stress was absent only during night-time (S3). In August, the frequency of the Universal Thermal Climate Index was similar to that in July. Once again, three stations stood out where very strong heat stress was recorded (Bucharest, Madrid and Rome). The coolest station was Copenhagen, where mild cold stress could occur even during the day (S4). 4.2 Subjective Temperature Index From 6 p.m. to 6 a.m., the values of subjective perceived temperature were more uniform among the stations under review. The highest STI values were recorded in Rome throughout the day, where the value of the upper quartile in July and August exceeded 50°C. Likewise, as with the UTCI index, in Rome, Bucharest, Madrid and Sofia, they were above the mean values. During the day, the differences in the index values between the stations were significantly higher. The differences between the stations with the lowest and highest index values around midday reached approximately 20°C (Fig. 3 ). At night-time (at 3 UTC), in all three months, “cool” sensations were recorded across Europe (S5). At 12 p.m., the prevailing subjective sensation across most of Europe was “warm”. In July and August it was “hot” in the south, while in Spain, Portugal and Italy, it was “very hot” (Fig. 4 ). In June, cool sensations were experienced at night-time. At 6 a.m., the “warm” sensation began, reaching its peak around 6 a.m. and 6 p.m. UTC. Around midday, it was “very hot”, most frequently in Rome, with a 50% frequency at 12 p.m. UTC. At the other stations, it occurred much less frequently (S6). In July, “very hot” sensations were recorded more frequently than in June. In Rome, between 9 a.m. and 3 p.m. UTC, it was recorded in nearly 80% of cases, with a slightly lower frequency in Madrid and Bucharest. Neutral conditions occurred relatively rarely, occasionally recorded at 9 a.m. and 6 p.m. UTC (S7). In August, “very hot” was relatively as frequent a sensation as in July in Rome, Madrid, and Bucharest. The station that showed differences compared to July was Warsaw, where around midday, the sensation of “very hot” was recorded with high frequency. In July, the sensations were “warm” and “hot” (S8). 4.3 Maximal Heart Rate In June, the acceptable level of physical activity was slightly higher than in July and August which is evidenced by the median values and the values of the upper quartile. At night, at most stations, index values exceeded 200 W·m⁻², whereas during the day, it decreased to levels ranging from about 180 W·m⁻² in Copenhagen to around 120 W·m⁻² in Rome. In July and August, the highest values reached 210 W·m⁻², while the lowest were around 91 W·m⁻². The highest median values were in Copenhagen, where they slightly exceeded 180 Wm⁻² (Fig. 5 ). The permissible physical activity during night-time across Europe was a walk at a speed of 8 km·h⁻¹ (S9). Around midday, this type of physical activity also predominated, but in July and August, in southern Europe, it would be advisable to limit the activity to a walk at a speed of 4 km·h⁻¹ (Fig. 6 ). 4.4 Insulation Predicted The Iclp index was varied throughout the day. At night, it increased, implying the need to wear clothing with slightly higher thermal insulation. Values of 0.8 clo, recorded at 3 UTC, for example, in Berlin, Paris or Copenhagen, indicated the need for long sleeves and trousers, whereas, in Rome or Madrid, a T-shirt, shorts or possibly light trousers would suffice. The cooler conditions are also indicated by the elevated median and third quartile values recorded in Copenhagen, Paris and Berlin. During the day, the index values at all stations decreased, and light summer clothing was sufficient for thermal comfort, while in Rome, Bucharest and Madrid, even beachwear would have been enough (Fig. 7 ). In June, at night, clothing such as trousers, a long-sleeved shirt, a sweater and socks are required for thermal comfort. In July and August, in southern Europe, a T-shirt, light trousers, and socks are sufficient, while in Sardinia, shorts would be enough (S10). Around midday, light summer clothing is appropriate. In southern Spain, Italy and Greece, and during July and August, beachwear would suffice (Fig. 8 ). In June, in Bucharest, Madrid and Rome, beachwear is sufficient from 9 a.m. to 3 p.m. This type of clothing can be chosen in 50% of cases at 12 p.m., while at the other stations, at least a T-shirt is necessary. During night-time in Berlin, Kyiv, Warsaw, Paris, Copenhagen and Madrid at 3 a.m., transitional season clothing is necessary – a jacket, sweater and trousers (S11-13). 4.5 Water loss and risk of dehydration In June, water losses were the smallest, ranging between 100 and 300 g·hour⁻¹ at most stations. Only in Madrid did they reach up to 500 g·hour⁻¹, but did not exceed dangerous levels. In July and August, the risk of dehydration was higher, especially in Madrid, where it reached 700 g·hour⁻¹ in August, and in July, it exceeded this value. The value of the upper quartile, significantly exceeding 500 g·hour⁻¹ in July, also indicates Madrid as a station with less favourable conditions in terms of fluid loss (Fig. 9 ). At both 3 a.m. and 12 p.m., the risk of dehydration was absent across Europe. However, at 12 p.m., warning levels were recorded in Spain and Portugal (Fig. 10 , S14). Similarly, the frequency risk charts for each month indicate a low risk of dehydration. The greatest danger in this regard is observed in Madrid in July and August, between 12 p.m. and 9 p.m. (S15-17). 4.6 Risk of hyperthermia The shortest time after which a dangerous increase in internal body temperature may occur was recorded in Madrid during all months, where it did not exceed half an hour. Of all the stations, the shortest time (20 minutes) was recorded in Paris, where the temperature reached 41°C. At the other stations, it was above one hour, with Copenhagen experiencing up to 1.5 hours (Fig. 11 ). Situations in which staying outdoors did not pose a risk of overheating were least common in Madrid and Rome, occurring in only about 20–35% of cases, depending on the month. At the other stations, it was about 50%. In Paris, the frequency of situations where being outdoors was safe was the highest, exceeding 60% in June (Fig. 12 ). In June, the risk of hyperthermia most commonly occurred after two hours, with no such risk appearing during night-time. However, at times, this dangerous condition could occur after just one hour, most often in Madrid and Rome between 9 a.m. and 3 p.m. In the other stations, such situations occur sporadically around midday (S18). In July, the shortest possible time after which hyperthermia could occur did not exceed half an hour – in Madrid, it occasionally occurred before this time (at 3 p.m.). In Bucharest, Madrid and Rome, hyperthermia was much more likely to occur after one hour around midday than in June. During night-time, the conditions were safe in this regard (S19). In August, in Madrid, hyperthermia could occur after half an hour at 3 p.m. In this month, Madrid, Bucharest and Rome were also the stations most exposed to hyperthermia. At the other stations, safer conditions were recorded, and even around midday, the time after which hyperthermia could occur was longer than two hours. At night, as in the other months, it was safe to stay outdoors without any restrictions (S20). 5. Discussion The scientific literature lacks an analysis of the indicators addressed in this study for the European continent. Existing reports indicate the frequent occurrence of heat stress in various cities of Southern Europe (e.g., Błażejczyk and Kunert, 2010 ), but there are no spatial studies. Previous works analyse the indicated parameters based on data up to the end of the 20th century. This study, on the other hand, analyses data from the turn of the second and third decades of the 21st century, during which climate warming is explicitly manifested (IPCC, 2023 ). Researchers believe that heat stress in Southern Europe occurs not only due to high air temperatures but also due to high solar radiation and humidity levels (Błażejczyk, 2004 ; Cegnar and Matzarakis, 2003 ; Zaninovic and Matzarakis, 2003 ; Matzarakis and Mayer, 1991 ). The results of the analysis of biothermal conditions in the analysed cities of Europe during the summer period show significant variability and intensity of heat waves, which is consistent with observations regarding global warming. In particular, average temperatures and biothermal indicators such as UTCI and STI provide valuable information about the impact of high temperatures on the health of urban residents. As shown by the research of Bellester et al. (2023), the summer of 2022 was particularly hot, and the record temperatures that occurred led to the deaths of over 62,000 people. We would like to highlight that in Mediterranean countries, including Italy, Greece, Spain and Portugal, there was a significant increase in heat-related mortality between June and August 2022, close to the record mortality levels observed from June to September 2003. During the studied period, the lowest air temperatures were recorded in June, ranging from 5.5°C in Sofia to nearly 40°C in Madrid. The highest temperatures occurred in August in Madrid, where they reached 41.9°C. Particularly in July and August, which were characterised by higher temperatures than June, Madrid and Rome recorded extreme maxima, confirming the trend of rising average summer temperatures. As shown by the research conducted by di Bernardino et al. ( 2023 ) for Rome in 2022, the city centre is particularly vulnerable to the impact of high air temperatures. In Madrid, studies have shown the intensification of the urban heat island effect, particularly during the night in the central and western parts of the city (Rasilla et al., 2019 ). Regarding relative humidity, it reached its highest value in Copenhagen, while in Madrid, it was the lowest, highlighting the climatic differences between Southern and Northern Europe in this regard. The rise in air temperature is associated with an increasing health risk, particularly among vulnerable individuals (Błażejczyk et al., 2014 ). The results show that, during heat waves, particularly in Madrid and Bucharest, there is intense heat stress, which can lead to serious health problems such as heat strokes and dehydration. This is due to, among other factors, the absence of cooling winds (Rasilla et al., 2019 ). Extreme heat stress is reflected in the subjective temperature index, which signals the onset of the “very hot” sensation during this period. Previous studies analysing and comparing the impact of high temperatures on daily mortality rates in urban and rural populations in Madrid have shown that, in particular, the urban population of the Madrid province is more vulnerable to heat waves than the rural population. Authors attribute this fact to socio-economic status, the percentage of population over 64 years of age, and heat acclimation (Lopez-Bueno et al., 2021). The values of water loss indicate that, under extreme conditions in Madrid, they can exceed 2000 g·hr⁻¹, placing at high risk the health and even life of city centre residents. The analysis of diurnal patterns of biothermal indicators reveals that the greatest heat stress occurs around midday, which is consistent with physiological expectations. The UTCI values reach their maxima during the day, suggesting that intense physical exertion should be avoided during this time. By contrast, at night, in most of the analysed cities, the indicators suggest the absence of heat stress, which may be beneficial for the body's recovery. Simultaneously, the STI signals “very hot” sensations around midday in Southern Europe and “hot” and “warm” sensations in the rest of the continent. In cities such as Berlin, Copenhagen, Paris and Warsaw, heat stress is often absent around midday, although there are instances where “moderate” or even “strong” heat stress may occur. Studies indicate that cities in the temperate zone, particularly the central areas of large metropolises, are increasingly vulnerable to heat stress during the summer (Iqbal et al., 2024 ; Tomczyk and Owczarek, 2020 ; Wichmann et al., 2009 ). This is particularly dangerous because the residents of these areas are not accustomed to such high air temperatures daily basis. Based on the conducted studies, several key recommendations can be made regarding the type of clothing required depending on the time of day, especially in the context of extremely hot cities such as Madrid, Bucharest and Rome. In the morning hours (until 9 a.m.), when temperatures are still relatively low, lightweight, breathable clothing such as cotton T-shirts or linen blouses is recommended. This will allow for comfortable outdoor movement without the risk of overheating. Around midday (from 9 a.m. to 3 p.m.), beachwear can be an option, although, at the same time, it would be advisable to protect the skin from the harmful effects of solar radiation. It is also important to consider clothing made from breathable materials that allow ventilation while also protecting against UV radiation. In the evening hours, to maintain thermal comfort, clothing similar to that worn in the morning would be most appropriate, such as a T-shirt, light trousers and shoes. During the night hours, in some stations such as Warsaw, Copenhagen, Berlin or Paris, transitional season clothing will be necessary, such as trousers, a sweater and even a jacket. In the context of physical activity limitations during heat waves, the results highlight the necessity of adjusting activity levels to prevailing conditions. Błażejczyk and Kunert ( 2011 ) highlighted the explicit need to limit intense physical activities during high-temperature periods, a finding corroborated by the analysed data. In cities such as Madrid, Bucharest and Rome, physical exertion should be avoided around midday. The most suitable activity during this time is short, moderate walks, preferably in the shade. Physical activity can be intensified at night-time, particularly in the north of the continent. The studies also included analyses of safe sun exposure time, that is, the period after which hyperthermia may occur. An alarming fact is the results obtained, which indicate that in cities in southern Europe, the body can overheat in as little as 20 minutes. The analysis of the diurnal pattern of biothermal conditions in selected European cities explicitly demonstrates the impact of global warming on local climate conditions, especially in summer. Phenomena such as rising temperatures and more frequent and intense heat waves have significant consequences for human health, as confirmed by studies conducted by Błażejczyk ( 2000 ), Wibig ( 2020 ) and Weilnhammer et al. ( 2021 ). Their analyses demonstrate that extreme temperatures adversely affect the cardiovascular system, which becomes particularly alarming in the context of an ageing European population. The research results support the observations of other authors pointing to an increase in the number of days with extremely high temperatures in recent years (Hansen et al., 2012 ; Meehl and Tebaldi, 2004). Błażejczyk et al. ( 2014 ) highlight that heat waves contribute to increasing heart rate and the risk of heat strokes, which is particularly dangerous for the elderly and children. This is also confirmed by Boucham and Knochel (2002), who describe the mechanisms leading to hyperthermia due to high temperatures. When analysing the variability of biothermal conditions, it is essential to note the differences between cities. The studies noted that Madrid, Bucharest and Rome exhibited the highest heat stress, which is consistent with previous research highlighting that Southern Europe is more vulnerable to extreme climate conditions (Antonescu et al., 2021 ; Della-Marta and Beniston, 2007). Di Napoli et al. ( 2018 ) identified two types of bioclimate in Europe: one, located at lower latitudes, characterised by a predominant presence of heat stress, and the other, at higher latitudes, where heat stress is mostly absent. These bioclimates influence the intensity and frequency of heat stress in the capital cities of Europe, depending on their location within European bioclimates. The explicit increase in summer temperatures across Europe, particularly in cities like Madrid, Rome and Bucharest, highlights the need for effective adaptive strategies. It is essential to consider informational campaigns aimed at residents, particularly the elderly and children, regarding the health risks associated with heat waves, as well as promoting actions to mitigate their effects, such as drinking more water and avoiding the sun during peak hours. To sum up, the presented research results fit into the broader context of climate change in Europe, showing how changing biothermal conditions can affect human health and what actions should be taken to protect the most vulnerable social groups. It is crucial to continue research in this area to improve the understanding of climate change effects and design appropriate adaptive strategies (Heudorf and Meyer, 2005 ; Kyselý and Kríz, 2003 ). Monitoring long-term climate trends and their impact on human health is also significant, as this will aid in planning health policies and managing health crises related to heat waves. 6. Conclusions Our research leads to the following conclusions: The most strenuous biothermal conditions for the human body occur between 12 p.m. and 3 p.m., with the least stressful conditions at 3 a.m. In the morning hours, biothermal conditions across Europe are balanced, with no heat stress and a subjective “cool” sensation. Around midday, in the northern part of the continent, heat stress is absent, and the subjective sensation is “warm”, while in the south, moderate heat stress occurs with a sensation of “hot”. Simultaneously, in Madrid, Rome and Bucharest, between 12 p.m. and 3 p.m., very strong heat stress may occur (especially in July and August), while at other stations, only strong and moderate heat stress is present. Additionally, at all stations, between 9 a.m. and 3 p.m., from June to August, a subjective sensation described as “very hot” may arise. The MHR index indicates the possibility of engaging in walking activities at a speed of 8 km/h during the night across Europe and, in July and August, only in the northern part of the continent. In the south, during this time, physical activity should be limited to slow walking. Between 9 a.m. and 3 p.m., physical activity should be restricted, especially in July and August. During the night, the required clothing to maintain thermal comfort includes underwear, sweatpants, long socks and sports shoes. In the northern part of the continent, especially in June, it is necessary to include a long-sleeved shirt, trousers a sweater and a jacket (i.e., transitional season clothing). Around midday, summer underwear, a T-shirt, shorts and sandals are sufficient to ensure thermal comfort, while in the far north, light trousers should be added to the outfit. In Bucharest, Madrid and Rome, between 9 a.m. and 3 p.m., beachwear would generally be enough to maintain thermal comfort, while in other stations (except Copenhagen), a T-shirt and shorts are the most suitable garments. The risk of dehydration in summer in Europe is not high, with Madrid being the most exposed station, where dangerous water loss can occur between 12 p.m. and 3 p.m., reaching even above 2000 g·hour⁻¹. Hyperthermia can occur as soon as in about 20 minutes, with the fastest onset likely in Madrid and Paris in July. However, in approximately 50% of cases (except for Madrid), conditions are favourable enough to remain outdoors without restrictions. Declarations CRediT authorship contribution statement Conceptualisation, M.O.; methodology, M.O.; formal analysis, M.O.; investigation, M.O.; resources, M.O.; software, M.O.; validation, M.O. ; data curation, M.O.; writing—original draft, M.O.; writing – review and editing, M.O.; visualisation, M.O.; supervision, M.O.; project administration, M.O. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgements I would like to thank Tim Brombley for proofreading the work. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. 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R. de Freitas, D. Scott (eds.) Advances in Tourism Climatology, Berichte des Meteorologischen Institutes der Universität Freiburg, 12. https://www.ogimet.com/home.phtml.en (accesed on 30-05-2023). Additional Declarations No competing interests reported. Supplementary Files S1.jpg S3.jpg S2.jpg S4.jpg S5.jpg S6.jpg S7.jpg S9.jpg S14.jpg S16.jpg S15.jpg S17.jpg S8.jpg S20.jpg S19.jpg S18.jpg S11.jpg S10.jpg S12.jpg S13.jpg listoffiguresLegds.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6270465","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":442734569,"identity":"c07b586d-e1c8-46dc-be2f-6763e62e2ab3","order_by":0,"name":"Monika Okoniewska","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABE0lEQVRIie3PMUvDQBTA8RcCulx1vSCtn0C48iBT8LMkBDplc3GQ0CkuSecGoZ8hImS+48Ash3PFpR/AwS7ioK0XqQ4lV4qTyP3hHY8Hv+EAbLY/mhjrh+rhehgcjt326uirw/cjhP8QMBHnm8AXoeFucnZdCDG9goGnF77MUjwunxEJBP2K93gX8dVDKKp7wBOilzKTPn1KMCYwwoofhZ1knjCxOIBoRhMmezUPQBOZgIwqTpiZrDbko06D00fVkvVucptBdNMSp3Z9NicYJ8DNRCkmyglFL9dLsZI4VKMLfGcxltLwlybHZf4aDKheXt5UOpw18s6bXp73J00hFh1kE90+MD0uMQNTvyA2m832H/sEJD9y2MeLB9kAAAAASUVORK5CYII=","orcid":"","institution":"Kazimierz Wielki University in Bydgoszcz","correspondingAuthor":true,"prefix":"","firstName":"Monika","middleName":"","lastName":"Okoniewska","suffix":""}],"badges":[],"createdAt":"2025-03-20 14:23:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6270465/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6270465/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":81183858,"identity":"9cc2e7f7-6881-40eb-9cf5-3913fd0c607c","added_by":"auto","created_at":"2025-04-23 08:02:21","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":840738,"visible":true,"origin":"","legend":"\u003cp\u003eDiurnal patterns of the Universal Thermal Climate Index (UTCI, °C) in June, July and August (2018–22)\u003c/p\u003e","description":"","filename":"fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6270465/v1/7617ba7b120c1e4bebd1cc5f.jpg"},{"id":81183526,"identity":"e8a3303a-d20f-4f35-a79a-c4418ac5b015","added_by":"auto","created_at":"2025-04-23 07:54:22","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1061752,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of UTCI index at 12 UTC in June, July and August (2018–22)\u003c/p\u003e","description":"","filename":"fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6270465/v1/9307ae9b88f68635ec5bcebb.jpg"},{"id":81183874,"identity":"43e7632e-defa-4717-b319-bbf2f8d96004","added_by":"auto","created_at":"2025-04-23 08:02:26","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":913483,"visible":true,"origin":"","legend":"\u003cp\u003eDiurnal patterns of Subjective Temperature Index (STI, °C) in June, July and August (2018–22)\u003c/p\u003e","description":"","filename":"fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6270465/v1/5a397fe81e4deb4b93f62fc7.jpg"},{"id":81183862,"identity":"695bfa65-c8e4-4329-942f-f3c781137c62","added_by":"auto","created_at":"2025-04-23 08:02:24","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1036478,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of STI index at 12 UTC in June, July and August (2018–22)\u003c/p\u003e","description":"","filename":"fig4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6270465/v1/d1bfe0c4e0851aa600dd61b0.jpg"},{"id":81183507,"identity":"9a27c959-b3c3-4b3c-8f8d-2cc73195697f","added_by":"auto","created_at":"2025-04-23 07:54:19","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":852055,"visible":true,"origin":"","legend":"\u003cp\u003eDaily patterns of acceptable level of physical activity (MHR. W·m⁻²) in June, July and August (2018–22)\u003c/p\u003e","description":"","filename":"fig5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6270465/v1/66806a3684fb11a8befd3d38.jpg"},{"id":81184723,"identity":"0b867ab6-37c2-4b74-bfb9-032a9d322e29","added_by":"auto","created_at":"2025-04-23 08:10:21","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1115536,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of MHR index at 12 UTC in June, July and August (2018–22)\u003c/p\u003e","description":"","filename":"fig6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6270465/v1/32eb28ebe1fe41809e992577.jpg"},{"id":81183547,"identity":"fbcaca45-672c-4a22-b28a-b659b215cbfe","added_by":"auto","created_at":"2025-04-23 07:54:24","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":920716,"visible":true,"origin":"","legend":"\u003cp\u003eDiurnal patterns of predicted thermal insulation of clothing (Iclp, clo) in June, July and August (2018–22)\u003c/p\u003e","description":"","filename":"fig7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6270465/v1/55230b88131c5178f7158e01.jpg"},{"id":81183524,"identity":"adf5e8b3-e879-4730-a226-c930f17d68ba","added_by":"auto","created_at":"2025-04-23 07:54:21","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":1312890,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of Iclp index at 12 UTC in June, July and August (2018–22)\u003c/p\u003e","description":"","filename":"fig8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6270465/v1/e23ed7cbe140433882fda4d6.jpg"},{"id":81183520,"identity":"4b55707e-545d-4c94-bce3-93409748b6f7","added_by":"auto","created_at":"2025-04-23 07:54:21","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":828645,"visible":true,"origin":"","legend":"\u003cp\u003eDiurnal patterns of Water Loss (WL, g·hour\u003csup\u003e-1\u003c/sup\u003e) in June, July and August (2018–22)\u003c/p\u003e","description":"","filename":"fig9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6270465/v1/9c0ff783cf9d5a9bf268f7e4.jpg"},{"id":81183514,"identity":"a6c9ff5c-2f6b-4a93-a6ef-62b6a558e84b","added_by":"auto","created_at":"2025-04-23 07:54:20","extension":"jpg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":879847,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of WL index at 12 UTC in June, July and August (2018–22)\u003c/p\u003e","description":"","filename":"fig10.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6270465/v1/ef06efe464427fd08e92c5c8.jpg"},{"id":81184724,"identity":"52a84c97-794b-4243-8c5a-cbdc8c006f78","added_by":"auto","created_at":"2025-04-23 08:10:25","extension":"jpg","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":507668,"visible":true,"origin":"","legend":"\u003cp\u003eShortest possible time after which hyperthermia may occur in June, July and August (2018–22)\u003c/p\u003e","description":"","filename":"fig11.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6270465/v1/a6d38b533685631a3872004c.jpg"},{"id":81183569,"identity":"ced87b3f-56c4-4c8f-848a-015f9f1c65ac","added_by":"auto","created_at":"2025-04-23 07:54:26","extension":"jpg","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":492249,"visible":true,"origin":"","legend":"\u003cp\u003eFrequency of situations in which being outdoors does not carry the risk of hyperthermia in June, July and August (2018–22)\u003c/p\u003e","description":"","filename":"fig12.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6270465/v1/0027888de5a2aa588e873080.jpg"},{"id":86537370,"identity":"31b08f92-3de6-47cd-8912-3a09c0d90c2e","added_by":"auto","created_at":"2025-07-11 19:01:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":12104305,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6270465/v1/edaf0a8d-a0e0-4835-95e1-4ae2ae05724f.pdf"},{"id":81183541,"identity":"8b9808e8-071f-42b4-8c85-b816d73c66fe","added_by":"auto","created_at":"2025-04-23 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08:02:25","extension":"jpg","order_by":19,"title":"","display":"","copyAsset":false,"role":"supplement","size":1460782,"visible":true,"origin":"","legend":"","description":"","filename":"S12.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6270465/v1/e8361738bfaf45ecff64ac70.jpg"},{"id":81183866,"identity":"bedb5546-266c-49f5-99cf-c35d6ced393b","added_by":"auto","created_at":"2025-04-23 08:02:25","extension":"jpg","order_by":20,"title":"","display":"","copyAsset":false,"role":"supplement","size":1447093,"visible":true,"origin":"","legend":"","description":"","filename":"S13.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6270465/v1/ff2308d15ac168457fa3f0bf.jpg"},{"id":81183528,"identity":"25a731a4-ae1e-414c-b0f0-a6cb7cca514b","added_by":"auto","created_at":"2025-04-23 07:54:22","extension":"docx","order_by":21,"title":"","display":"","copyAsset":false,"role":"supplement","size":12685,"visible":true,"origin":"","legend":"","description":"","filename":"listoffiguresLegds.docx","url":"https://assets-eu.researchsquare.com/files/rs-6270465/v1/728284f4354e7e7e3f6a0fc0.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The characteristics of the diurnal pattern of biothermal conditions in the summer season in selected European cities","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eGlobal warming observed worldwide has contributed to increased summer temperature variability throughout Europe, accompanied by more frequent, more prolonged and more intense heat waves, that is, extended periods with temperatures significantly higher than typical for a given area (Wibig, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Climate warming in Europe during the summer is therefore becoming an increasingly evident and severe phenomenon. The increase in average temperatures, higher incidence of heat waves, droughts and changes in ecosystems are just some of the effects of global warming. Average summer temperatures in Europe are steadily rising, and the average number of hot days in Europe has tripled over the past 70 years (Lorenz et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Sulikowska and Wypych, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In recent decades, record-high temperatures have been recorded in many regions. For example, during the summer of 2019, many European countries, such as France, Germany and Spain, experienced exceptionally intense heat waves. In Paris, a record temperature of 42.6\u0026deg;C was the highest value in the city's history of measurements (WMO, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Earlier, in the summer of 2003, a heat wave also swept across Europe (Twardosz, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), causing the death of around 40,000 people, primarily elderly individuals (Garc\u0026acute;ia-Herrera et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The summer of 2010 was exceptionally hot in Eastern Europe and large parts of Russia. Additionally, studies in this area point to an increasing likelihood of mega heat waves in densely populated areas of Europe (Barriopedro et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), and the extreme heat that occurred in Europe during the summer of 2003 may become standard by the end of this century (Beniston, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Not only are heat waves more frequent but they are also increasingly intense (Sch\u0026auml;r et al., 2004; Meehl and Tebaldi, 2004). It is estimated that the number of days with extremely high temperatures has tripled over the past 50 years. Such conditions are not only inconvenient but also pose a hazard to human health. The increase in temperatures can lead to heat strokes, dehydration and the intensification of cardiovascular diseases.\u003c/p\u003e \u003cp\u003eIn particular, high temperatures during the summer period have a significant impact on human health. Such weather conditions put a heavy strain on the cardiovascular system. Vasodilation contributes to increased heart rates, decreasing blood pressure and increased blood volume, resulting in a significant weakening of the body. Heat waves cause a decline in the level of haemoglobin, which transports oxygen, and an increased respiratory rate, which is dangerous for people suffering from respiratory diseases (Błażejczyk et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Additionally, increased solar radiation affects a rise in both systolic and diastolic blood pressure (Błażejczyk, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). In particular, such conditions pose a hazard to the elderly, children, individuals suffering from specific illnesses, and those working in open areas. An increased heart rate in people with atherosclerosis can lead to myocardial hypoxia, as the coronary vessels fill during the diastole of the heart muscle. The loss of water and electrolytes due to sweating leads to a further decrease in blood pressure and an increase in heart rate, which results in decreased peripheral perfusion. Individuals with significant atherosclerotic lesions in the arteries supplying the central nervous system may develop a risk of experiencing stroke-like symptoms (Krzeszkowiak and Pawlak, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhen the air temperature rises, the human body starts producing sweat droplets on the skin's surface to lower body temperature. If the air humidity is not high, the evaporation of sweat progresses, thereby protecting the body from overheating. However, with the evaporating sweat, the human body loses electrolytes, particularly sodium (Na) (Krzeszkowiak and Pawlak, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Sweat evaporation in high-temperature conditions is the primary mechanism of protection against overheating. When this mechanism fails, the body\u0026rsquo;s internal temperature may increase, posing a risk to health and life. Overheating of the body is accompanied by dehydration and hyponatremia. A deficiency of sodium and water leads to a range of negative health consequences, depending on the severity of the deficiency. The most dramatic ones include muscle cramps, exhaustion, seizures, loss of consciousness, and even death due to pulmonary or cerebral oedema (Bouchama and Knochel, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2002\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBiothermal conditions worsen when hot days are followed by so-called tropical nights, during which the temperature does not drop below 20\u0026deg;C. Such situations provide no relief and are particularly dangerous for people with respiratory and cardiovascular diseases, while also being quite burdensome for others.\u003c/p\u003e \u003cp\u003eThere have not been many studies on the bioclimatic conditions in Europe during the summer period (Antonescu et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003eażejczyk and Błażejczyk, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Della-Marta and Beniston, 2007; Katavoutas, 2022). The conducted analyses tend to focus on studies of individual European cities or countries, particularly on the correlation between weather conditions and the number of deaths (An der Heiden, 2019; Bacini et al., 2008; Ballester et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Dessai, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Di Napoli, 2018; Gabriel and Endlicher, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Heudorf and Meyer; \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Kysel\u0026yacute; and Kr\u0026iacute;z, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Given the importance of climatic and bioclimatic conditions during the summer period and the simultaneous lack of specific analyses on daily variations of the bioclimatic conditions in Europe, we decided to review the daily variations of biothermal conditions in ten selected European cities, along with their burden on humans. In particular, the analysis focused on determining the degree of heat stress on the human body, estimating the subjective, apparent temperature, assessing the acceptable level of physical activity, and evaluating the clothing insulation required to maintain thermal comfort. It also included calculating water loss from the body and assessing the risk of heat-related conditions such as hyperthermia.\u003c/p\u003e"},{"header":"2. Research methods","content":"\u003cp\u003eThe study determined the daily mean, minimum, maximum, median and upper and lower quartile values of biothermal and thermophysiological indices and their diurnal patterns, analysed the spatial distribution of biothermal and thermophysiological indices using spatial interpolation, and determined the incidence of heat load, apparent temperature, acceptable level of physical activity, thermal insulation of clothing necessary to ensure thermal comfort, degree of dehydration, and risk of hyperthermia.\u003c/p\u003e \u003cp\u003eThe study utilised the following biothermal and thermophysiological indices:\u003c/p\u003e \u003cp\u003eThe Universal Thermal Climate Index (UTCI, \u0026deg;C) is defined as the equivalent air temperature at which, in reference conditions, the vital physiological parameters of the human body assume values identical to those in the real environment. The UTCI provides information on real processes regulating the body temperature, which are dependent on the weather conditions and the environment (Błażejczyk et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe subjective temperature index (STI, \u0026deg;C) where the underlying value is the mean radiant temperature and the \u0026ldquo;resultant heat balance\u0026rdquo;, which is determined as a result of the body\u0026rsquo;s adaptation to the environment (Błażejczyk, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2004\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe maximal heart rate (MHR, W\u0026middot;m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e) defines the acceptable level of physical activity that does not cause excessive strain on the heart (Błażejczyk and Kunert, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe Insulation Predicted (Iclp, clo) allows for determining the clothing insulation required in specific weather to maintain the body's heat balance (Kozłowska-Szczęsna et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). The index determines the acceptable level of physical activity (MHR, W\u0026middot;m⁻\u0026sup2;) not causing excessive strain on the heart (Błażejczyk and Kunert, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe water loss index (WL, g∙h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) indicates how much water the body needs to avoid dehydration (Błażejczyk and Kunert, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). It is particularly useful in the case of active leisure and hiking tourism practised in high ambient temperatures. According to \u003cem\u003eISO/DIS 7933\u003c/em\u003e, the risk of dehydration occurs at two levels: alarm and danger. The dehydration alarm level for non-acclimatised persons alarm level is 520\u0026thinsp;\u0026lt;\u0026thinsp;WL\u0026thinsp;\u0026lt;\u0026thinsp;650 g\u0026middot;h⁻\u0026sup1;, while the danger level is described as WL exceeding 650 g\u0026middot;h⁻\u0026sup1; (Błażejczyk and Kunert, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe overheating risk index (Oh_H, min.) expresses the time after which too much heat is accumulated in the body, leading to potential hyperthermia. This time can be calculated based on the input and output heat transfer values (Błażejczyk and Kunert, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAll the indices were calculated using BioKlima 2.6 software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.igipz.pan.pl/Bioklima-zgik.html\u003c/span\u003e\u003cspan address=\"https://www.igipz.pan.pl/Bioklima-zgik.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e"},{"header":"3. Study material","content":"\u003cp\u003eThe research material consisted of time-specific meteorological data retrieved from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ogimet.com/home.phtml.en\u003c/span\u003e\u003cspan address=\"https://www.ogimet.com/home.phtml.en\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, including air temperature (t, \u0026deg;C), relative humidity (f, %), wind speed (v, km\u0026middot;h⁻\u0026sup1; converted to m\u0026middot;s⁻\u0026sup1;), and cloud cover (N, oktas) for June, July, and August collected from 2018 to 2022. The data were collected at the following times: 12 a.m., 3 a.m., 6 a.m., 9 a.m., 12 p.m., 3 p.m., 6 p.m. and 9 p.m.\u003c/p\u003e \u003cp\u003eThe following ten European cities were selected for analysis: Berlin, Bucharest, Kyiv, Copenhagen, Madrid, Paris, Rome, Sofia, Warsaw and Vienna (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The weather stations selected for review are located at the airports of the above-mentioned cities. The cities were selected on the basis of data availability and their uniform distribution throughout the European continent.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGeographical location of stations under review\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003elatitude\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003elongitude\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eheight a.s.l.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBerlin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52˚22'N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13˚31'E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBucharest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44˚30'N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26˚04'E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCopenhagen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55˚36'N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12˚38'E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKyiv\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50˚23'N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30˚32'E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e167\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMadrid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40˚28'N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e03˚33'W\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e609\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49˚00'N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e02˚32'E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e119\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41˚48'N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12˚35'E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSofia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42˚38'N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23˚22'E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e595\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVienna\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48˚07'N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16˚34'E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e183\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWarsaw\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52˚09'N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20˚57'E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e107\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"4. Results","content":"\u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eKey meteorological characteristics in the cities under review in June, July and August (2018\u0026ndash;22)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003emeteorological element\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBerlin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBucharest\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCopenhagen\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eKyiv\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMadrid\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eParis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eSofia\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eVienna\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eWarsaw\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"12\" nameend=\"c12\" namest=\"c1\"\u003e \u003cp\u003eJune\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003et(\u0026deg;C)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eaverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e23,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e19,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e23,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e19,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e21,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e20,4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e11,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e8,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e7,5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emax\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e34,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e39,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e35,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e39,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e34,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e34,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e35,2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ef (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eaverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e70,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e59,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e42,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e66,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e58,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e71,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e64,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e64,2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e24,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e15,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e26,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e25,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e21,0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emax\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e99,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e99,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e97,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e98,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e97,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e98,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e97,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e99,0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eN (octants)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eaverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e4,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e4,0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0,0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emax\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e8,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e8,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e8,0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ev (m\u0026middot;s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eaverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e3,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2,4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0,0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emax\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e12,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e8,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e8,0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"12\" nameend=\"c12\" namest=\"c1\"\u003e \u003cp\u003eJuly\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003et(\u0026deg;C)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eaverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e27,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e21,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e26,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e21,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e22,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e20,3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e16,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e7,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e7,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e9,2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emax\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e33,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e41,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e41,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e37,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e35,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e36,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e34,9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ef (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eaverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e71,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e64,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e33,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e58,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e56,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e64,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e60,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e68,0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e27,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e14,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e20,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e23,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e15,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e22,0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emax\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e99,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e99,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e97,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e93,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e95,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e96,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e100,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e96,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e99,0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eN (octants)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eaverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e4,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e4,6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0,0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emax\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e8,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e8,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e8,0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ev (m\u0026middot;s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eaverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e3,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2,3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0,0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emax\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e10,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e9,4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"12\" nameend=\"c12\" namest=\"c1\"\u003e \u003cp\u003eAugust\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003et(\u0026deg;C)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eaverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e26,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e20,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e26,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e22,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e21,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e20,4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e15,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e9,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e8,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e8,1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emax\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e34,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e41,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e37,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e37,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e37,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e36,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e32,0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ef (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eaverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e74,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e61,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e36,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e61,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e60,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e62,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e64,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e68,5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e19,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e17,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e12,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e23,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e26,0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emax\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e99,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e96,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e96,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e96,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e98,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e99,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e96,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e99,0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eN (octants)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eaverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e4,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e4,3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0,0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emax\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e8,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e8,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e8,0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ev (m\u0026middot;s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eaverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2,0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0,0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emax\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e8,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e6,5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn June, the mean air temperature in the locations under review oscillated around 20\u0026deg;C. Temperature ranged from 5.5\u0026deg;C (minimum in Sofia) up to nearly 40\u0026deg;C (maximum in Madrid). The weather was the hottest in Madrid and Rome. Relative humidity averaged between approximately 42% and 75% (lowest in Madrid, highest in Bucharest), while cloud cover ranged from 0 to 8 oktas. Wind speed averaged from 0 up to a maximum of 12 m\u0026middot;s⁻\u0026sup1; measured in Paris.\u003c/p\u003e \u003cp\u003eIn July, temperatures were slightly higher, both average and maximum. The highest values were recorded in Madrid (maximum of 41.6\u0026deg;C) and Paris (41.1\u0026deg;C). Humidity was comparable to June, with the lowest values again recorded in Madrid at 33.1%. Cloud cover was similar to that of June, with the lowest levels observed in Madrid and Rome. Maximum wind speed reached up to 11 m\u0026middot;s⁻\u0026sup1; in Berlin, on average fluctuating around 1\u0026ndash;2 m\u0026middot;s⁻\u0026sup1;.\u003c/p\u003e \u003cp\u003eIn August, the mean temperatures in all locations except for Copenhagen were above 20\u0026deg;C. The highest value was recorded in Madrid at 26.6\u0026deg;C, with a maximum of 41.9\u0026deg;C. Humidity, cloud cover and wind speed were similar to those observed in July (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eKey biometeorological characteristics in the cities under review in June, July and August (2018\u0026ndash;22)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebiometerologicall indices\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBerlin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBucharest\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCopenhagen\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eKyiv\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMadrid\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eParis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eSofia\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eVienna\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eWarsaw\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"12\" nameend=\"c12\" namest=\"c1\"\u003e \u003cp\u003eJune\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eUTCI (\u0026deg;C)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eaverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e21,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e15,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e24,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e20,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e18,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e18,6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-8,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-4,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-1,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-14,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-4,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-4,5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emax\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e39,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e44,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e37,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e43,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e37,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e37,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e38,0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSTI (\u0026deg;C)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eaverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e28,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e20,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e33,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e26,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e26,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e26,1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-4,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-3,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-3,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-3,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-1,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-4,3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emax\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e60,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e61,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e66,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e62,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e66,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e65,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e64,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e61,0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eMHR (W\u0026middot;m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eaverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e181,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e160,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e201,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e168,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e174,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e190,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e150,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e185,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e170,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e178,3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e86,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e70,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e59,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e60,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e46,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e63,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e67,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e63,6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emax\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e299,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e296,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e291,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e305,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e302,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e285,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e252,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e306,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e286,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e298,9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eIclp (clo)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eaverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0,5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0,4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emax\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1,4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eWL (g\u0026middot;h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eaverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e220,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e189,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e172,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e208,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e288,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e191,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e249,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e166,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e221,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e204,6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e87,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e91,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e90,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e99,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e96,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e106,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e87,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e97,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e90,3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emax\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1223,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e619,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e524,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e586,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1565,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e726,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1422,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e574,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e792,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e658,2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"12\" nameend=\"c12\" namest=\"c1\"\u003e \u003cp\u003eJuly\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eUTCI (\u0026deg;C)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eaverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e27,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e18,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e27,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e23,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e19,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e18,3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-3,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-3,1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emax\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e37,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e46,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e45,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e42,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e39,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e39,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e37,4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSTI (\u0026deg;C)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eaverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e27,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e34,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e24,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e38,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e30,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e26,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e24,9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-1,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0,0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emax\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e62,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e63,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e66,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e61,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e67,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e64,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e64,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e61,7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eMHR (W\u0026middot;m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eaverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e181,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e146,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e189,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e164,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e143,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e175,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e123,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e166,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e165,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e176,1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e83,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e70,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e49,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e41,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e42,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e68,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e61,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e53,7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emax\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e281,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e256,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e278,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e267,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e249,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e267,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e213,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e284,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e288,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e270,4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eIclp (clo)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eaverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0,5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0,3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emax\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1,1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eWL (g\u0026middot;h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eaverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e209,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e220,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e180,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e199,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e400,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e228,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e297,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e195,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e237,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e194,6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e96,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e97,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e101,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e100,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e129,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e90,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e96,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e93,5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emax\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e823,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e853,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e472,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e534,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1619,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1811,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e876,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e631,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1037,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e726,8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"12\" nameend=\"c12\" namest=\"c1\"\u003e \u003cp\u003eAugust\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eUTCI (\u0026deg;C)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eaverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e26,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e17,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e27,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e23,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e19,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e20,5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-4,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-5,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1,4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emax\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e37,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e46,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e40,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e42,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e40,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e39,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e36,6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSTI (\u0026deg;C)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eaverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e27,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e32,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e22,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e36,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e30,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e25,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e30,8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0,3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emax\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e67,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e61,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e62,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e65,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e62,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e66,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e66,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e63,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e62,6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eMHR (W\u0026middot;m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eaverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e177,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e146,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e187,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e168,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e147,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e176,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e121,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e164,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e162,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e174,7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e63,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e45,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e57,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e45,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e64,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e65,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e75,8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emax\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e273,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e257,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e271,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e261,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e282,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e279,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e226,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e267,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e276,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e280,6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eIclp (clo)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eaverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0,5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0,2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emax\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1,2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eWL (g\u0026middot;h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eaverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e214,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e238,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e175,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e200,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e357,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e216,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e279,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e202,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e224,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e199,6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e98,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e101,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e104,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e96,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e125,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e95,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e101,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e92,9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emax\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e848,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e826,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e448,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e631,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2325,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e916,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e980,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e881,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e750,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e544,6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn June, the average UTCI values indicated no heat stress. The maximum values (ranging from 37.2\u0026deg;C to 44\u0026deg;C) implied very strong heat stress. Subjective thermal sensations, on the other hand, indicated generally comfortable conditions, with warmth experienced in Rome. The maximum STI values suggested very hot sensations (in Bucharest, close to the value of 70, which signifies a heat wave). As regards physical activity, the minimum MHR values pointed to a need for rest, while the maximum values allowed moderate physical activity to be undertaken. The values of Iclp ranged from ˗0.9 (beachwear) to 1.3, which indicates the need to wear clothing suitable for transitional seasons. Water loss averaged around 200 g\u0026middot;h⁻\u0026sup1;, reaching a maximum of over 1500 g in Madrid. The maximum value, which represents the existence of a risk to health (650 g\u0026middot;h⁻\u0026sup1;), was exceeded at almost all stations.\u003c/p\u003e \u003cp\u003eIn July, the mean UTCI values indicated moderate heat stress in Rome and Madrid, while at the other stations, they signified no heat stress. The maximum value of this index implied very strong heat stress (except at three locations). In the case of subjective thermal sensations, warmth was generally felt in Bucharest, Madrid and Rome, while the maximum values at all stations represented very hot sensations. The minimum MHR values suggested a need for rest and the possibility of engaging in only light physical activity. The values of Iclp were similar to those measured in June. Water loss exceeded 1800 g\u0026middot;h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e at maximum in Paris.\u003c/p\u003e \u003cp\u003eIn August, the maximum UTCI implied very strong heat stress and was almost unbearable in Madrid. In such conditions, cooling the body was necessary. The subjective temperature index indicated sensations ranging from comfort to very hot (at the maximum index values). The maximum Iclp values were lower than in June but suggested the necessity of using transitional clothing. The minimum values of this index, on the other hand, indicated the expected thermal insulation at the level of beachwear. The average water loss in August was slightly higher than in July, typically ranging from 175 to 358 g\u0026middot;h⁻\u0026sup1;, while the maximum value of this indicator exceeded 2000 g\u0026middot;h⁻\u0026sup1; in Madrid (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e4.1 Universal Thermal Climate Index\u003c/h2\u003e\n \u003cp\u003eThe minimum values of the Universal Thermal Climate Index occurred at 3 a.m, while the maximum values were observed around midday. At night-time, smaller variations between stations were recorded, while greater differences were observed during the day. In Madrid, Rome, Bucharest and Sofia, the average index values were significantly exceeded during the day, it was also noted that higher median values were recorded, while Kyiv was the station closest to the average values. June exhibited the lowest values of the index. The average across all stations reached 25\u0026deg;C at noon, whereas in July and August, it exceeded this value. The station that stood out among the studied locations was Madrid, which recorded the highest values in the afternoon hours during July and August, with the value of the upper quartile exceeding 30\u0026deg;C (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eAt night-time (3 UTC) across all three months in Europe, the absence of heat stress predominated. However, in June, mild cold stress was observed in the United Kingdom, northern France, the Benelux and western Norway (S1). In contrast, during the midday hours, the absence of heat stress was also observed in northern Europe, while in the rest of the continent (except for Portugal, Spain, Italy, and Greece), moderate heat stress was recorded. In Portugal, Spain, Italy and Greece, heat stress was at a moderate level (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eIn June, moderate heat stress appeared as early as 6 a.m. and persisted until 6 p.m. Between 9 a.m. and 3 p.m., almost all stations recorded strong heat stress, while in Madrid, very strong heat stress occurred with a frequency of approximately 10%. At night, Paris, Berlin, Copenhagen and Vienna experienced mild cold stress, with moderate cold stress occurring sporadically (S2).\u003c/p\u003e\n \u003cp\u003eIn July, strong heat stress was recorded much more frequently, and at three stations, very strong heat stress was observed (40% in Madrid at 3 p.m.). The most challenging conditions were observed in Bucharest, Madrid and Rome, where heat stress was absent only during night-time (S3).\u003c/p\u003e\n \u003cp\u003eIn August, the frequency of the Universal Thermal Climate Index was similar to that in July. Once again, three stations stood out where very strong heat stress was recorded (Bucharest, Madrid and Rome). The coolest station was Copenhagen, where mild cold stress could occur even during the day (S4).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e4.2 Subjective Temperature Index\u003c/h2\u003e\n \u003cp\u003eFrom 6 p.m. to 6 a.m., the values of subjective perceived temperature were more uniform among the stations under review. The highest STI values were recorded in Rome throughout the day, where the value of the upper quartile in July and August exceeded 50\u0026deg;C. Likewise, as with the UTCI index, in Rome, Bucharest, Madrid and Sofia, they were above the mean values. During the day, the differences in the index values between the stations were significantly higher. The differences between the stations with the lowest and highest index values around midday reached approximately 20\u0026deg;C (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eAt night-time (at 3 UTC), in all three months, \u0026ldquo;cool\u0026rdquo; sensations were recorded across Europe (S5). At 12 p.m., the prevailing subjective sensation across most of Europe was \u0026ldquo;warm\u0026rdquo;. In July and August it was \u0026ldquo;hot\u0026rdquo; in the south, while in Spain, Portugal and Italy, it was \u0026ldquo;very hot\u0026rdquo; (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eIn June, cool sensations were experienced at night-time. At 6 a.m., the \u0026ldquo;warm\u0026rdquo; sensation began, reaching its peak around 6 a.m. and 6 p.m. UTC. Around midday, it was \u0026ldquo;very hot\u0026rdquo;, most frequently in Rome, with a 50% frequency at 12 p.m. UTC. At the other stations, it occurred much less frequently (S6).\u003c/p\u003e\n \u003cp\u003eIn July, \u0026ldquo;very hot\u0026rdquo; sensations were recorded more frequently than in June. In Rome, between 9 a.m. and 3 p.m. UTC, it was recorded in nearly 80% of cases, with a slightly lower frequency in Madrid and Bucharest. Neutral conditions occurred relatively rarely, occasionally recorded at 9 a.m. and 6 p.m. UTC (S7).\u003c/p\u003e\n \u003cp\u003eIn August, \u0026ldquo;very hot\u0026rdquo; was relatively as frequent a sensation as in July in Rome, Madrid, and Bucharest. The station that showed differences compared to July was Warsaw, where around midday, the sensation of \u0026ldquo;very hot\u0026rdquo; was recorded with high frequency. In July, the sensations were \u0026ldquo;warm\u0026rdquo; and \u0026ldquo;hot\u0026rdquo; (S8).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e4.3 Maximal Heart Rate\u003c/h2\u003e\n \u003cp\u003eIn June, the acceptable level of physical activity was slightly higher than in July and August which is evidenced by the median values and the values of the upper quartile. At night, at most stations, index values exceeded 200 W\u0026middot;m⁻\u0026sup2;, whereas during the day, it decreased to levels ranging from about 180 W\u0026middot;m⁻\u0026sup2; in Copenhagen to around 120 W\u0026middot;m⁻\u0026sup2; in Rome. In July and August, the highest values reached 210 W\u0026middot;m⁻\u0026sup2;, while the lowest were around 91 W\u0026middot;m⁻\u0026sup2;. The highest median values were in Copenhagen, where they slightly exceeded 180 Wm⁻\u0026sup2; (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe permissible physical activity during night-time across Europe was a walk at a speed of 8 km\u0026middot;h⁻\u0026sup1; (S9). Around midday, this type of physical activity also predominated, but in July and August, in southern Europe, it would be advisable to limit the activity to a walk at a speed of 4 km\u0026middot;h⁻\u0026sup1; (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e4.4 Insulation Predicted\u003c/h2\u003e\n \u003cp\u003eThe Iclp index was varied throughout the day. At night, it increased, implying the need to wear clothing with slightly higher thermal insulation. Values of 0.8 clo, recorded at 3 UTC, for example, in Berlin, Paris or Copenhagen, indicated the need for long sleeves and trousers, whereas, in Rome or Madrid, a T-shirt, shorts or possibly light trousers would suffice. The cooler conditions are also indicated by the elevated median and third quartile values recorded in Copenhagen, Paris and Berlin. During the day, the index values at all stations decreased, and light summer clothing was sufficient for thermal comfort, while in Rome, Bucharest and Madrid, even beachwear would have been enough (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eIn June, at night, clothing such as trousers, a long-sleeved shirt, a sweater and socks are required for thermal comfort. In July and August, in southern Europe, a T-shirt, light trousers, and socks are sufficient, while in Sardinia, shorts would be enough (S10). Around midday, light summer clothing is appropriate. In southern Spain, Italy and Greece, and during July and August, beachwear would suffice (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eIn June, in Bucharest, Madrid and Rome, beachwear is sufficient from 9 a.m. to 3 p.m. This type of clothing can be chosen in 50% of cases at 12 p.m., while at the other stations, at least a T-shirt is necessary. During night-time in Berlin, Kyiv, Warsaw, Paris, Copenhagen and Madrid at 3 a.m., transitional season clothing is necessary \u0026ndash; a jacket, sweater and trousers (S11-13).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e4.5 Water loss and risk of dehydration\u003c/h2\u003e\n \u003cp\u003eIn June, water losses were the smallest, ranging between 100 and 300 g\u0026middot;hour⁻\u0026sup1; at most stations. Only in Madrid did they reach up to 500 g\u0026middot;hour⁻\u0026sup1;, but did not exceed dangerous levels. In July and August, the risk of dehydration was higher, especially in Madrid, where it reached 700 g\u0026middot;hour⁻\u0026sup1; in August, and in July, it exceeded this value. The value of the upper quartile, significantly exceeding 500 g\u0026middot;hour⁻\u0026sup1; in July, also indicates Madrid as a station with less favourable conditions in terms of fluid loss (Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eAt both 3 a.m. and 12 p.m., the risk of dehydration was absent across Europe. However, at 12 p.m., warning levels were recorded in Spain and Portugal (Fig. \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e, S14).\u003c/p\u003e\n \u003cp\u003eSimilarly, the frequency risk charts for each month indicate a low risk of dehydration. The greatest danger in this regard is observed in Madrid in July and August, between 12 p.m. and 9 p.m. (S15-17).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e4.6 Risk of hyperthermia\u003c/h2\u003e\n \u003cp\u003eThe shortest time after which a dangerous increase in internal body temperature may occur was recorded in Madrid during all months, where it did not exceed half an hour. Of all the stations, the shortest time (20 minutes) was recorded in Paris, where the temperature reached 41\u0026deg;C. At the other stations, it was above one hour, with Copenhagen experiencing up to 1.5 hours (Fig. \u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eSituations in which staying outdoors did not pose a risk of overheating were least common in Madrid and Rome, occurring in only about 20\u0026ndash;35% of cases, depending on the month. At the other stations, it was about 50%. In Paris, the frequency of situations where being outdoors was safe was the highest, exceeding 60% in June (Fig. \u003cspan class=\"InternalRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eIn June, the risk of hyperthermia most commonly occurred after two hours, with no such risk appearing during night-time. However, at times, this dangerous condition could occur after just one hour, most often in Madrid and Rome between 9 a.m. and 3 p.m. In the other stations, such situations occur sporadically around midday (S18).\u003c/p\u003e\n \u003cp\u003eIn July, the shortest possible time after which hyperthermia could occur did not exceed half an hour \u0026ndash; in Madrid, it occasionally occurred before this time (at 3 p.m.). In Bucharest, Madrid and Rome, hyperthermia was much more likely to occur after one hour around midday than in June. During night-time, the conditions were safe in this regard (S19).\u003c/p\u003e\n \u003cp\u003eIn August, in Madrid, hyperthermia could occur after half an hour at 3 p.m. In this month, Madrid, Bucharest and Rome were also the stations most exposed to hyperthermia. At the other stations, safer conditions were recorded, and even around midday, the time after which hyperthermia could occur was longer than two hours. At night, as in the other months, it was safe to stay outdoors without any restrictions (S20).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eThe scientific literature lacks an analysis of the indicators addressed in this study for the European continent. Existing reports indicate the frequent occurrence of heat stress in various cities of Southern Europe (e.g., Błażejczyk and Kunert, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), but there are no spatial studies. Previous works analyse the indicated parameters based on data up to the end of the 20th century. This study, on the other hand, analyses data from the turn of the second and third decades of the 21st century, during which climate warming is explicitly manifested (IPCC, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Researchers believe that heat stress in Southern Europe occurs not only due to high air temperatures but also due to high solar radiation and humidity levels (Błażejczyk, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Cegnar and Matzarakis, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Zaninovic and Matzarakis, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Matzarakis and Mayer, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1991\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe results of the analysis of biothermal conditions in the analysed cities of Europe during the summer period show significant variability and intensity of heat waves, which is consistent with observations regarding global warming. In particular, average temperatures and biothermal indicators such as UTCI and STI provide valuable information about the impact of high temperatures on the health of urban residents. As shown by the research of Bellester et al. (2023), the summer of 2022 was particularly hot, and the record temperatures that occurred led to the deaths of over 62,000 people. We would like to highlight that in Mediterranean countries, including Italy, Greece, Spain and Portugal, there was a significant increase in heat-related mortality between June and August 2022, close to the record mortality levels observed from June to September 2003.\u003c/p\u003e \u003cp\u003eDuring the studied period, the lowest air temperatures were recorded in June, ranging from 5.5\u0026deg;C in Sofia to nearly 40\u0026deg;C in Madrid. The highest temperatures occurred in August in Madrid, where they reached 41.9\u0026deg;C. Particularly in July and August, which were characterised by higher temperatures than June, Madrid and Rome recorded extreme maxima, confirming the trend of rising average summer temperatures. As shown by the research conducted by di Bernardino et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) for Rome in 2022, the city centre is particularly vulnerable to the impact of high air temperatures. In Madrid, studies have shown the intensification of the urban heat island effect, particularly during the night in the central and western parts of the city (Rasilla et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Regarding relative humidity, it reached its highest value in Copenhagen, while in Madrid, it was the lowest, highlighting the climatic differences between Southern and Northern Europe in this regard.\u003c/p\u003e \u003cp\u003eThe rise in air temperature is associated with an increasing health risk, particularly among vulnerable individuals (Błażejczyk et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The results show that, during heat waves, particularly in Madrid and Bucharest, there is intense heat stress, which can lead to serious health problems such as heat strokes and dehydration. This is due to, among other factors, the absence of cooling winds (Rasilla et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Extreme heat stress is reflected in the subjective temperature index, which signals the onset of the \u0026ldquo;very hot\u0026rdquo; sensation during this period. Previous studies analysing and comparing the impact of high temperatures on daily mortality rates in urban and rural populations in Madrid have shown that, in particular, the urban population of the Madrid province is more vulnerable to heat waves than the rural population. Authors attribute this fact to socio-economic status, the percentage of population over 64 years of age, and heat acclimation (Lopez-Bueno et al., 2021). The values of water loss indicate that, under extreme conditions in Madrid, they can exceed 2000 g\u0026middot;hr⁻\u0026sup1;, placing at high risk the health and even life of city centre residents.\u003c/p\u003e \u003cp\u003eThe analysis of diurnal patterns of biothermal indicators reveals that the greatest heat stress occurs around midday, which is consistent with physiological expectations. The UTCI values reach their maxima during the day, suggesting that intense physical exertion should be avoided during this time. By contrast, at night, in most of the analysed cities, the indicators suggest the absence of heat stress, which may be beneficial for the body's recovery. Simultaneously, the STI signals \u0026ldquo;very hot\u0026rdquo; sensations around midday in Southern Europe and \u0026ldquo;hot\u0026rdquo; and \u0026ldquo;warm\u0026rdquo; sensations in the rest of the continent. In cities such as Berlin, Copenhagen, Paris and Warsaw, heat stress is often absent around midday, although there are instances where \u0026ldquo;moderate\u0026rdquo; or even \u0026ldquo;strong\u0026rdquo; heat stress may occur. Studies indicate that cities in the temperate zone, particularly the central areas of large metropolises, are increasingly vulnerable to heat stress during the summer (Iqbal et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Tomczyk and Owczarek, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Wichmann et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). This is particularly dangerous because the residents of these areas are not accustomed to such high air temperatures daily basis.\u003c/p\u003e \u003cp\u003eBased on the conducted studies, several key recommendations can be made regarding the type of clothing required depending on the time of day, especially in the context of extremely hot cities such as Madrid, Bucharest and Rome. In the morning hours (until 9 a.m.), when temperatures are still relatively low, lightweight, breathable clothing such as cotton T-shirts or linen blouses is recommended. This will allow for comfortable outdoor movement without the risk of overheating. Around midday (from 9 a.m. to 3 p.m.), beachwear can be an option, although, at the same time, it would be advisable to protect the skin from the harmful effects of solar radiation. It is also important to consider clothing made from breathable materials that allow ventilation while also protecting against UV radiation. In the evening hours, to maintain thermal comfort, clothing similar to that worn in the morning would be most appropriate, such as a T-shirt, light trousers and shoes. During the night hours, in some stations such as Warsaw, Copenhagen, Berlin or Paris, transitional season clothing will be necessary, such as trousers, a sweater and even a jacket.\u003c/p\u003e \u003cp\u003eIn the context of physical activity limitations during heat waves, the results highlight the necessity of adjusting activity levels to prevailing conditions. Błażejczyk and Kunert (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) highlighted the explicit need to limit intense physical activities during high-temperature periods, a finding corroborated by the analysed data. In cities such as Madrid, Bucharest and Rome, physical exertion should be avoided around midday. The most suitable activity during this time is short, moderate walks, preferably in the shade. Physical activity can be intensified at night-time, particularly in the north of the continent.\u003c/p\u003e \u003cp\u003eThe studies also included analyses of safe sun exposure time, that is, the period after which hyperthermia may occur. An alarming fact is the results obtained, which indicate that in cities in southern Europe, the body can overheat in as little as 20 minutes.\u003c/p\u003e \u003cp\u003eThe analysis of the diurnal pattern of biothermal conditions in selected European cities explicitly demonstrates the impact of global warming on local climate conditions, especially in summer. Phenomena such as rising temperatures and more frequent and intense heat waves have significant consequences for human health, as confirmed by studies conducted by Błażejczyk (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), Wibig (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and Weilnhammer et al. (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Their analyses demonstrate that extreme temperatures adversely affect the cardiovascular system, which becomes particularly alarming in the context of an ageing European population.\u003c/p\u003e \u003cp\u003eThe research results support the observations of other authors pointing to an increase in the number of days with extremely high temperatures in recent years (Hansen et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Meehl and Tebaldi, 2004). Błażejczyk et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) highlight that heat waves contribute to increasing heart rate and the risk of heat strokes, which is particularly dangerous for the elderly and children. This is also confirmed by Boucham and Knochel (2002), who describe the mechanisms leading to hyperthermia due to high temperatures.\u003c/p\u003e \u003cp\u003eWhen analysing the variability of biothermal conditions, it is essential to note the differences between cities. The studies noted that Madrid, Bucharest and Rome exhibited the highest heat stress, which is consistent with previous research highlighting that Southern Europe is more vulnerable to extreme climate conditions (Antonescu et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Della-Marta and Beniston, 2007). Di Napoli et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) identified two types of bioclimate in Europe: one, located at lower latitudes, characterised by a predominant presence of heat stress, and the other, at higher latitudes, where heat stress is mostly absent. These bioclimates influence the intensity and frequency of heat stress in the capital cities of Europe, depending on their location within European bioclimates.\u003c/p\u003e \u003cp\u003eThe explicit increase in summer temperatures across Europe, particularly in cities like Madrid, Rome and Bucharest, highlights the need for effective adaptive strategies. It is essential to consider informational campaigns aimed at residents, particularly the elderly and children, regarding the health risks associated with heat waves, as well as promoting actions to mitigate their effects, such as drinking more water and avoiding the sun during peak hours.\u003c/p\u003e \u003cp\u003eTo sum up, the presented research results fit into the broader context of climate change in Europe, showing how changing biothermal conditions can affect human health and what actions should be taken to protect the most vulnerable social groups. It is crucial to continue research in this area to improve the understanding of climate change effects and design appropriate adaptive strategies (Heudorf and Meyer, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Kysel\u0026yacute; and Kr\u0026iacute;z, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Monitoring long-term climate trends and their impact on human health is also significant, as this will aid in planning health policies and managing health crises related to heat waves.\u003c/p\u003e"},{"header":"6. Conclusions","content":"\u003cp\u003eOur research leads to the following conclusions:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe most strenuous biothermal conditions for the human body occur between 12 p.m. and 3 p.m., with the least stressful conditions at 3 a.m.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eIn the morning hours, biothermal conditions across Europe are balanced, with no heat stress and a subjective \u0026ldquo;cool\u0026rdquo; sensation. Around midday, in the northern part of the continent, heat stress is absent, and the subjective sensation is \u0026ldquo;warm\u0026rdquo;, while in the south, moderate heat stress occurs with a sensation of \u0026ldquo;hot\u0026rdquo;. Simultaneously, in Madrid, Rome and Bucharest, between 12 p.m. and 3 p.m., very strong heat stress may occur (especially in July and August), while at other stations, only strong and moderate heat stress is present. Additionally, at all stations, between 9 a.m. and 3 p.m., from June to August, a subjective sensation described as \u0026ldquo;very hot\u0026rdquo; may arise.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe MHR index indicates the possibility of engaging in walking activities at a speed of 8 km/h during the night across Europe and, in July and August, only in the northern part of the continent. In the south, during this time, physical activity should be limited to slow walking. Between 9 a.m. and 3 p.m., physical activity should be restricted, especially in July and August.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eDuring the night, the required clothing to maintain thermal comfort includes underwear, sweatpants, long socks and sports shoes. In the northern part of the continent, especially in June, it is necessary to include a long-sleeved shirt, trousers a sweater and a jacket (i.e., transitional season clothing). Around midday, summer underwear, a T-shirt, shorts and sandals are sufficient to ensure thermal comfort, while in the far north, light trousers should be added to the outfit. In Bucharest, Madrid and Rome, between 9 a.m. and 3 p.m., beachwear would generally be enough to maintain thermal comfort, while in other stations (except Copenhagen), a T-shirt and shorts are the most suitable garments.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe risk of dehydration in summer in Europe is not high, with Madrid being the most exposed station, where dangerous water loss can occur between 12 p.m. and 3 p.m., reaching even above 2000 g\u0026middot;hour⁻\u0026sup1;.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eHyperthermia can occur as soon as in about 20 minutes, with the fastest onset likely in Madrid and Paris in July. However, in approximately 50% of cases (except for Madrid), conditions are favourable enough to remain outdoors without restrictions.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCRediT authorship contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualisation, M.O.; methodology, M.O.; formal analysis, M.O.; investigation, M.O.; resources, M.O.; software, M.O.; validation, M.O. ; data curation, M.O.; writing\u0026mdash;original draft, M.O.; writing \u0026ndash; review and editing, M.O.; visualisation, M.O.; supervision, M.O.; project administration, M.O.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of competing interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eI would like to thank Tim Brombley for proofreading the work.\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data used in this study are available at https://www.ogimet.com/home.phtml.en\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAn der Heiden, M., Muthers, S., Niemann, H., Buchholz, U., Grabenhenrich, L., Matzarakis, A., 2019. 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Advances in Tourism Climatology, Berichte des Meteorologischen Institutes der Universit\u0026auml;t Freiburg, 12. https://www.ogimet.com/home.phtml.en (accesed on 30-05-2023).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"biothermal conditions, diurnal pattern, summer period, biothermal indices, Europe","lastPublishedDoi":"10.21203/rs.3.rs-6270465/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6270465/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis paper examines the diurnal pattern of biothermal conditions in three summer months (June, July and August) from 2018 to 2022, in selected European cities and analyses their burden on the human body. The weather data provided the basis for calculating the following indices: Universal Thermal Climate Index, Subjective Temperature Index, Maximal Heart Rate, Insulation Predicted, Water Loos, Overheating Risk. The study determined the daily mean, minimum and maximum values of biothermal and thermophysiological indices and their diurnal patterns, analysed their spatial distribution using spatial interpolation, and determined the incidence of heat load, apparent temperature, acceptable level of physical activity, predicted thermal insulation of clothing, degree of dehydration, and risk of hyperthermia. Studies have shown that, in Europe during the summer, between 12 p.m. and 3 p.m., there may be a burden of very intense heat stress (particularly in July and August), with a subjective sensation described as \u0026ldquo;very hot\u0026rdquo;. The risk of dehydration is not significant; however, there is a notable risk of hyperthermia, which can occur even after 20 minutes of exposure in open areas. In southern Europe, from 9 a.m. to 3 p.m., beachwear would be sufficient to maintain thermal comfort, while in the north, especially at night-time, transitional season clothing is necessary. Analyses have also shown that, particularly in cities such as Madrid, Bucharest and Rome, physical exertion should be avoided during midday hours. Climate warming in Europe during the summer is a serious challenge that requires coordinated actions at the local, national and international levels.\u003c/p\u003e","manuscriptTitle":"The characteristics of the diurnal pattern of biothermal conditions in the summer season in selected European cities","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-23 07:53:49","doi":"10.21203/rs.3.rs-6270465/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"aba2fc85-6985-4f48-ab5c-9f1f685585a2","owner":[],"postedDate":"April 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":47132628,"name":"Earth and environmental sciences/Climate sciences"},{"id":47132629,"name":"Earth and environmental sciences/Environmental sciences"}],"tags":[],"updatedAt":"2025-07-11T18:53:22+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-23 07:53:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6270465","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6270465","identity":"rs-6270465","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}