Differences between the evaluation of thermal environment in shaded and sunny position 139Kántor, N. Hungarian Geographical Bulletin 65 (2016) (2) 139–153.DOI: 10.15201/hungeobull.65.2.5 Hungarian Geographical Bulletin 65 2016 (2) 139–153. Introduction The excessive level of urbanization (UNFPA 2011) and the projected challenges due to climate change (IPCC 2014) necessitate deal- ing with urban climate issues all around the world. Correspondingly, the number of stud- ies with focus on the thermal conditions with- in cities is rapidly growing (Chen, L. and Ng, L. 2012; Rupp, R.F. et al. 2015). Several from the earlier investigations applied well-estab- lished human bio-meteorological indices, for example the Physiologically Equivalent Temperature – PET (Höppe, P. 1999), in order to express the physiological and comfort as- pects of small-scale meteorological conditions in outdoor urban spaces. A great portion of these examinations was built on micromete- orological measurements (e.g. Mayer, H. et al. 2008; Lin, T.-P. et al. 2010; Hwang, R.-L. et al. 2011; Gómez, F. et al. 2013), while others ap- plied numerical models in order to simulate the consequences of diff erent landscape de- sign strategies on thermal comfort and human health (e.g. Fröhlich, D. and Matzarakis, A. 2013; Müller, N. et al. 2014). Most of the ex- isting analyzes were based on the original threshold values of the applied index. In the case of the aforementioned PET index the cat- egory benchmarks (Figure 1) are based on the physiological reactions of a ‘typical’ Central European man (Matzarakis, A. and Mayer, Diff erences between the evaluation of thermal environment in shaded and sunny position Noémi KÁNTOR1 Abstract Great att ention has been paid in the last one and a half decade to the subjective evaluation of atmospheric conditions in diff erent outdoor and semi-outdoor urban environments. Several fi eld surveys were conducted all around the world in order to specify those physical and personal factors that infl uence the perception of thermal environment. Many studies reported about seasonal diff erences in the subjective assessment concern- ing thermal sensitivity as well as the so-called neutral temperature. The present investigation aims to reveal these seasonal diff erences in Hungary and to scrutinise the eff ect of solar exposure (staying in shaded position or in the sun) on these patt erns. The analyses are based on a long-term outdoor thermal comfort project with 78 measurements days conducted on six recreational places in Szeged, Hungary. In the frame of the project thousands of people were asked about their actual thermal sensation and about their preference for any change regarding the thermal environment. Parallel to the questionnaire survey, detailed human bio-meteorological measurements were carried out in the vicinity of the questioned individuals. A well-established human bio-me- teorological index was calculated from the measured atmospheric parameters: the Physiologically Equivalent Temperature (PET). Regression analysis was performed between the subjective and objective measures in order to specify neutral and preferred temperatures (nPET, pPET). Furthermore, in the case pPET values a new assignment procedure was also implemented building on probit model technique. The two analytical approaches resulted in very similar pPET values in every case when the sample size was suffi ciently large. The study revealed much higher pPET than nPET values in every season; moreover, signifi cant diff erences depending on the sun exposure of the subjects. Keywords: subjective assessment, Physiologically Equivalent Temperature, neutral and preferred tempera- ture, solar exposure 1 Department of Climatology and Landscape Ecology, University of Szeged. H-6722 Szeged, Egyetem u. 2. E-mail: sztyepp@gmail.com Kántor, N. Hungarian Geographical Bulletin 65 (2016) (2) 139–153.140 H. 1996; Matzarakis, A. et al. 1999). However, adopting the preset threshold values regard- less of the geographical location raises the question of the result’s relevance regarding the thermal perception of local inhabitants. Indeed, numerous studies found consider- able diff erences between the actual thermal perception of people and the predicted evalu- ation based on the well-known objective in- dices (e.g. Lin, T.-P. and Matzarakis, A. 2008; Kántor, N. et al. 2012a; Yin, J.F. et al. 2012; Krüger, E.L. et al. 2013; Lai, D. et al. 2014; Tung, C.-H. et al. 2014). Several papers report- ed that those thermal conditions at which peo- ple feel generally neutral depend on the geo- graphical location and even on the time of the year (e.g. Nikolopoulou, M. and Lykoudis, S. 2006; Hwang, R.-L. and Lin, T.-P. 2007; Lin, T.-P. 2009; Lin, T.-P. et al. 2011; Kántor, N. et al. 2012b; Lindner-Cendrowska, K. 2013; Yahia, M.W. and Johansson, E. 2013; Yang, W. et al. 2013a,b; Pearlmutter, D. et al. 2014; Chen, L. et al. 2015; Zeng, Y. and Dong, L. 2015). These results prove that living under various background climates lead to diff er- ent degree of thermal adaptation in people, and even in the case of the same population, there are seasonal diff erences in the subjec- tive evaluation. Hungarian studies contributed also to this extensive research area and revealed obvious seasonal diff erences in the subjective thermal perception and preference patt erns of local people (Kántor, N. et al. 2012a; Kovács, A. et al. 2015). Personal diff erences, time of the day (Pearlmutter, D. et al. 2014) and outdoor or semi-outdoor nature of the physical environ- ment (Hwang, R.-L. and Lin, T.-P. 2007) were also investigated as aff ecting factors. However, to date, no studies have examined the infl u- ence of solar exposure on the subjective assess- ment of thermal environment. Therefore the present study aims to reveal these diff erences using the data of a long-term Hungarian out- door thermal comfort (OTC) project. The main targets of this paper are set as follows: 1. Determining exposure-dependent dif- ferences in the subjective thermal sensation and thermal preference patt erns in diff erent seasons. 2. Specifying the so-called neutral and pre- ferred temperature values of Hungarians ac- cording to season and solar exposure. Methods The city of Szeged Building on the experiences of earlier investi- gations a long-term OTC project was conduct- ed in the city of Szeged (Hungary) (46°15′N, 20°09’E). Szeged is the regional centre of the Southern Hungarian Great Plain with an ur- banized area of about 50 km2. The city off ers ideal study areas for urban climate and hu- man bio-meteorological investigations (e.g. Unger, J. 1996; Gulyás, Á. et al. 2006, 2009) be- cause it is spread on a fl at area without con- siderable topographical diff erences (78–85 m a.s.l.) which allows small-scale meteorologi- cal results to be generalized. Land-use types vary from the densely built-up inner city to the detached housing suburban areas, allow- ing the development of several local climate zone types (Unger, J. et al. 2014). Fig. 1. The original threshold values of the PET index refl ecting the thermal sensation categories and the level of physiological stress 141Kántor, N. Hungarian Geographical Bulletin 65 (2016) (2) 139–153. Szeged has warm temperate climate with uniform annual distribution of precipita- tion. The yearly amount of precipitation is low (489 mm), while the sunshine duration is high (1978 hours). The annual temperature is 10.6 °C. July and August are the hott est months and January is the coldest time of the year. The daily maximum temperature is generally above 10 °C from March to October, therefore these eight months are more suit- able for outdoor activities. On the contrary, the period from November to February is cold when the monthly amount of sunshine remains below 100 hours (HMS 2015). Being already one of the warmest cities in Hungary, the urban climate of Szeged is expected to be aff ected more intensively by the predicted warming tendencies in the Carpathian Basin (Pongrácz, R. et al. 2013). Moreover, Szeged is the third most populat- ed city in the country with more than 170,000 permanent residents. All of these att ributes make it very interesting from the viewpoint of OTC investigations. Outdoor thermal comfort surveys in Szeged Generally, OTC surveys consist of on-site human bio-meteorological measurements as well as transverse questionnaire surveys when great numbers of people are inquired about their subjective assessments regard- ing the actual thermal environment (Chen, L. and Ng, E. 2012; Rupp, R.F. et al. 2015). The Hungarian OTC measurements were carried out in 2011, 2012 and 2015. The investigations took place on six recreational areas, including popular urban squares, parks, playgrounds, and pedestrian zones (Figure 2). Two of the investigated squares (Szent István square and Dugonics square) received an Award of excellence for complete reconstruction from the Hungarian Society for Urban Planning. All survey sites are in the urbanized region of Szeged, allowing large number of visitors att ending on them. The study areas can be characterized with a variety of landscape- design solutions, materials, orientations, vegetation cover, etc. For that reason, a wide range of small-scale human bio-meteorologi- cal conditions may be expected on them. With respect to outdoor activity, summer and the two transient seasons are of particu- lar importance in Hungary. Accordingly, the OTC investigations covered the period from the end of March to the end of October, re- sulting, altogether, in 78 measurement days (Table 1). The data collection lasted from 10 a.m. to 6 p.m. except for those days when signifi cant precipitation events interrupted the measurements. Micrometeorological measurements Two special human bio-meteorological sta- tions were used to collect all important atmos- pheric variables that infl uence human thermal sensation. The stations were placed simulta- neously at two signifi cantly diff erent sites of the same study area; typically in sunny and shaded (shaded by tree or building) position. Depending on the specifi c design of the study areas, the stations were placed sometimes on grassy surface, while other times on diff erent types of artifi cial ground cover like asphalt pavement, red-coloured paving stones and light-coloured gravel (Figure 3). The stations recorded one-minute averages of all meteorological variables. Air temperature (Ta), relative humidity (RH) and wind speed (v) were measured by a WXT520 Vaisala weather transmitt er in the case of both stations (Figure 3). Rotatable net radiometers were used to mon- itor the 3D radiant environment, i.e. to record short-wave and long-wave radiation fl ux densi- ties from six perpendicular directions (Ki and Li [W/m2], i: up, down, East, West, South, North). One of the stations was equipped with CNR1, and the other with CNR4 type Kipp & Zonen net radiometer. By means of telescopic tripods the sensors were placed at a height of 1.1–1.2 m above ground level which is suitable for OTC investigations (Mayer, H. et al. 2008). Normally, the arm of the net radiometer points to the South. In this position the two pyranometers and two pyrgeometers faces Kántor, N. Hungarian Geographical Bulletin 65 (2016) (2) 139–153.142 Fi g. 2 . A er ia l p ho to s ab ou t S ze ge d in di ca tin g th e lo ca tio n of th e st ud y ar ea s 143Kántor, N. Hungarian Geographical Bulletin 65 (2016) (2) 139–153. Table 1. Seasonal distribution of OTC measurement days in Szeged Spring (26 days) Summer (28 days) Autumn (24 days) March April May June July August September October 29.03.2011 30.03.2011 26.03.2012 27.03.2012 – – – – – – – – – 12.04.2011 13.04.2011 19.04.2011 20.04.2011 26.04.2011 27.04.2011 02.04.2012 03.04.2012 16.04.2012 23.04.2012 24.04.2012 – – 03.05.2011 04.05.2011 10.05.2011 11.05.2011 08.05.2012 15.05.2012 16.05.2012 08.05.2015 13.05.2015 19.05.2015 20.05.2015 – – 22.06.2011 07.06.2012 08.06.2012 21.06.2012 25.06.2012 08.06.2015 15.06.2015 17.06.2015 18.06.2015 – – – – 04.07.2011 08.07.2011 12.07.2011 02.07.2012 05.07.2012 06.07.2012 09.07.2012 10.07.2012 19.07.2012 20.07.2012 23.07.2012 24.07.2012 – 03.08.2011 04.08.2011 22.08.2011 23.08.2011 02.08.2012 27.08.2012 28.08.2012 – – – – – – 12.09.2011 13.09.2011 19.09.2011 26.09.2011 27.09.2011 17.09.2012 18.09.2012 19.09.2012 21.09.2012 24.09.2012 26.09.2012 – – 03.10.2011 04.10.2011 10.10.2011 17.10.2011 18.10.2011 25.10.2011 01.10.2012 03.10.2012 05.10.2012 08.10.2012 10.10.2012 17.10.2012 19.10.2012 Fig. 3. Human bio-meteorological measurements and questionnaire surveys on popular recreational areas of Szeged. (Accuracy of measured parameters are also indicated). Kántor, N. Hungarian Geographical Bulletin 65 (2016) (2) 139–153.144 upward and downward, allowing the meas- urement of Ki and Li separately from the upper and lower hemisphere (Ku, Kd, Lu, Ld). Aft er 3- minute measurement in this position, the net radio-meters were rotated manually into the second position when the sensors faced to East and West (Ke, Kw, Le, Lw). Again, aft er 3-minute measurement the arms were turned with 90° to measure from South and North (Ks, Kn, Ls, Ln). Considering the 10 a.m. – 6 p.m. meas- urement interval, this procedure required 160 rotations per day in the case of both stations. Taking into account the response time of the sensors as well as the time delay due to the manual rotation, all Ki and Li were deleted that were recorded fi rst time aft er the rotations. Index calculation Mean radiant temperature (Tmrt [°C]) is a pa- rameter with primary importance in the fi eld of human bio-meteorology and OTC surveys. It combines all long-wave and short-wave ra- diant fl ux densities into a single value with °C-dimension. Tmrt is defi ned as the uniform temperature of an imaginary black body-radi- ating surrounding, which results in the same radiant heat exchange for the human body inside this hypothetical environment as the complex 3D-radiant environment in the real- ity (Höppe, P. 1992; Kántor, N. and Unger, J. 2011). Tmrt is usually calculated for a standard- ized standing person. In the case of this study, Tmrt was determined based on six Ki and six Li fl ux densities, which were obtained from three consecutive stands of the net radiometer: where ak and al are absorption coeffi cients of the clothed human body in the short- and long-wave radiation domain (assumed to be 0.7 and 0.97, respectively), σ is the Ste- fan–Boltzmann constant (5.67·10–8 W/m2K4) and Wi is a direction-dependent weighting factor. Assuming standing reference subject, Wi is 0.06 for vertical and 0.22 for horizontal directions (Höppe, P. 1992). PET index was selected for the purpose of this study to describe the thermal envi- ronment along with the possible thermal sensation and degree of physiological stress (Figure 1). PET is regarded to be one of the most comprehensive human bio-meteorologi- cal indices and it has been widely used for dif- ferent OTC studies all around the world (e.g. Gulyás, Á. et al. 2006; Lin, T.-P. 2009; Kántor, N. et al. 2012a; Yahia, M.W. Johansson, E. 2013; Pearlmutter, D. et al. 2014; Kovács, A. et al. 2015; Zeng, Y. and Dong, L. 2015). PET calculations were performed with the RayMan soft ware (Matzarakis, A. et al. 2010) by using the formerly obtained Tmrt values and the directly measured Ta, RH, v values. The evaluation by PET (Figure 1) always refers to a standardized subject (a ‘typical’ 35 years old Central European man perform- ing light activity and wearing light business suit) representing a large group of people (Höppe, P. 1999). Recording subjective assessment of thermal environment The assessment of thermal conditions is highly subjective, meaning that diff erent individuals may evaluate the same thermal environment diff erently (Mayer, H. 2008). In order to reveal these patt erns, structured interviews were car- ried out with thousands of people spending their time in the study areas during the hu- man-meteorological measurements. People who walked, stood or sat near to the stations were invited to fill an OTC questionnaire which could be fi nished within fi ve minutes (Figure 3). Similarly to many international ex- amples (e.g. Nikolopoulou, M. and Lykoudis, S. 2006; Hwang, R.-L. and Lin, T.-P. 2007; Lin, T.-P. 2009; Krüger, E.L. et al. 2013; Yang, W. et al. 2013a,b; Pearlmutter, D. et al. 2014; Chen, L. et al. 2015; Zeng, Y. and Dong, L. 2015), the questionnaire consisted of more question blocks regarding personal factors, area usage, behavioral reactions, evaluation of the area and subjective assessment of the thermal en- vironment (Kántor, N. et al. 2012a). Tmrt = (∑6 =1 Wi · (ak · Ki + al · Li))1/4 –273.15 i al · σ 145Kántor, N. Hungarian Geographical Bulletin 65 (2016) (2) 139–153. This paper focuses on the subjective thermal sensation and thermal preference. Thermal Sensation Votes (TSV) were collected by means of a semantic diff erential scale with 9 main ordered categories: very cold (-4), cold (-3), cool (-2), slightly cool (-1), neutral (0), slightly warm (1), warm (2), hot (3), very hot (4). Selection of intermediate options was also possible. Thermal Preference Votes were recorded by answering the question ‘Would you like any changes in the actual thermal conditions to feel (more) comfortable?’ In this case, people could choose from three options: want cooler (-1), want no change (0), want warmer (+1). From the collected personal information this paper focuses on the solar exposure of subjects – whether they stayed in the sun, or in the shade. Sometimes the ascertainment of the position was not possible because clouds reduced the intensity of global radiation, thus made impossible to distinguish shad- ed and sunny areas. These questionnaires were excluded from the analyses. Additionally, many samples were removed due to missing meteoro- logical data (failure in the record- ings of any micrometeorological parameters which hindered the calculation of PET index). Analysis methods Regression analysis was performed for comparing subjective thermal sensation (and thermal preference) patt erns and specifying neutral (and preferred) temperatures according to the investigated seasons and solar exposure. Besides, probit model was adopted to analyse the thermal prefer- ences of Hungarians among diff erent circumstances. These main analyses were supplemented with simple de- scriptive statistics. The analyses were performed within the PASW Statistics soft ware, and some of the artworks were created within MS Excel. Results and discussion Thermal sensation according to seasons and solar exposure 6,764 questionnaires were obtained during the field surveys, but only 4,700 subjects were selected for the purpose of this study; only those who had valid PET index and so- lar exposure. All interviewees were Hungar- ian citizens, generally they reported about good health conditions and 2/3 of them were female. The individuals’ age varied between 5 to 95 years, and most of them belonged to the young age group (14 to 30 years). People selected generally from the main thermal sensation votes (Figure 4). Nevertheless, more than 20% of the ques- tioned individuals selected intermediates (votes of 1.5 and 2.5 occurred most frequent- ly). The most frequent vote was slightly warm (1) in both transient seasons. In summer how- ever, in accordance with the warmer thermal Fig. 4. Distribution of TSV according to seasons and subjects’ solar exposure Kántor, N. Hungarian Geographical Bulletin 65 (2016) (2) 139–153.146 conditions, people reported most frequently warm (2) thermal sensation. In each season, mean TSV values were lower if subjects stayed in the ‘shade’. The greatest diff erence between the two exposure groups was found in spring (Figure 4). Neutral temperature and neutral PET zone Neutral temperature (nPET) refers to those thermal conditions at which people feel nei- ther cool, nor warm, i.e. which are perceived as neutral. Several studies determined nPET by regression analysis between TSV and PET (e.g. Lin, T.-P. 2009; Kántor, N. et al. 2012a,b; Yang, W. et al. 2012b; Krüger, E.L. et al. 2013; Yahia, M.W. and Johansson, E. 2013; Kovács, A. et al. 2015). Since thermal sensation varies greatly among subjects even in the same ther- mal conditions (i.e. at the same PET value), mean thermal sensation votes (MTSV) were calculated according to 1 °C wide PET inter- vals. Considerably diff erent thermal percep- tion patt erns were revealed among the inves- tigated groups by plott ing mean TSV values against PET index, and weighting them with the case numbers per PET bin (Figure 5). Quadratic regression fi t the TSV–PET data pairs well with considerable statistical sig- nifi cance (Table 2). According to the deter- mination coeffi cients (R2) at least 92% of the variability in Hungarians’ subjective thermal sensation can be explained by the PET index in every season if the subjects stay in shaded position. The worst R2 value was found in the summertime ‘sunny’ group; probably due to the small sample size (N = 151). Neutral temperature (nPET) can be deter- mined by solving the regression equation for TSV = 0, or reading its value from the re- gression chart. The fi tt ed quadratic functions intersect the TSV = 0 line at diff erent PET values, indicating sometimes considerably diff erent nPET (Figure 5). Substituting -0.5 and 0.5 into the quadratic equations assign the lower and upper PET thresholds of the neutral category. Worth noting that for the ‘sunny’ group in summer the values of nPET and the lower boundary of neutrality could be determined only by extrapolation, i.e. out- side from the covered PET range (Figure 5). Preferred temperature and preferred PET zone Two analysis techniques were adopted aiming to allocate the preferred thermal conditions in diff erent seasons and diff erent exposure groups. First, the above presented regression procedure was repeated for thermal prefer- ence votes (Kovács, A. et al. 2015; Table 3). Compared to the case of TSV, the obtained R2 values were lower in this case, probably due to people had only three preference options to choose. However, the signifi cance level was 0.000 in every group and the quadratic func- tions fi tt ed the TPV–PET data pairs fairly well. Preferred temperature (pPET), as well as the boundaries of the preferred PET zone were calculated by substituting 0, 0.125 and -0.125 into the quadratic equations. The obtained results will be demonstrated later. The other way of pPET determination is based on probit model (Ballantyne, E.R. et al. 1977). Generally, probit analysis is used to in- vestigate many kinds of dichotomous response variables in a variety of research fi elds. In this study, two binomial response variables were investigated as a function of PET: the relative frequency of TPV>0 (want warmer) and TPV<0 (want cooler) votes per PET bin. 1 °C wide PET intervals were utilized for both models. The occurrence probability of the mentioned preference votes depends on PET according to a sigmoid function (a mathematical function having an ‘S’ shape), and with the increment of PET the probability of TPV>0 votes decreases, while the probability of TPV<0 votes increases (Figure 6). Pearson goodness of fi t test (building on χ2 statistics) was utilized to check the ob- tained probit models (Table 4). The signifi cance level was generally below 0.150, indicating that the fi t was suffi cient in most of the cases. Researchers from East Asia assumed that the intersection point of the fi tt ed probit mod- els TPV<0 and TPV>0 indicates the preferred temperature (Hwang, R.-L. and Lin, T.-P. 2007; 147Kántor, N. Hungarian Geographical Bulletin 65 (2016) (2) 139–153. Table 2. Quadratic regression between ‘weighted mean TSV’ and PET Conditions N R2 Sig. Equation Spring shade sunny 940 1,163 0.919 0.899 0.000 0.000 -2.28⋅10-3 ⋅PET2 + 0.215⋅PET - 2.715 -2.50⋅10-3 ⋅PET2 + 0.227⋅PET - 3.152 Summer shade sunny 815 151 0.928 0.645 0.000 0.000 -3.56⋅10-3 ⋅PET2 + 0.342⋅PET - 5.294 -0.53⋅10-3 ⋅PET2 + 0.119⋅PET - 1.921 Autumn shade sunny 1,260 371 0.967 0.811 0.000 0.000 -2.50⋅10-3 ⋅PET2 + 0.234⋅PET - 3.218 -0.83⋅10-3 ⋅PET2 + 0.144⋅PET - 2.735 Fig. 5. Determination of neutral temperature (nPET) and neutral PET zone according to seasons and exposure groups Kántor, N. Hungarian Geographical Bulletin 65 (2016) (2) 139–153.148 Table 3. Quadratic regression between ‘weighted mean TPV’ and PET Conditions N R2 Sig. Equation Spring shade sunny 940 1,163 0.935 0.827 0.000 0.000 -1.77⋅10-3⋅PET2 + 0.015⋅PET + 0.893 -0.52⋅10-3⋅PET2 - 0.010⋅PET + 1.033 Summer shade sunny 815 151 0.912 0.593 0.000 0.000 2.90⋅10-3⋅PET2 - 0.235⋅PET + 3.918 -0.87⋅10-3⋅PET2 + 0.019⋅PET + 0.288 Autumn shade sunny 1,260 371 0.925 0.755 0.000 0.000 -0.04⋅10-3⋅PET2 - 0.052⋅PET + 1.159 -0.51⋅10-3⋅PET2 - 0.004⋅PET + 0.645 Fig. 6. Determination of preferred temperature (pPET) and thermal preference zone based on probit technique 149Kántor, N. Hungarian Geographical Bulletin 65 (2016) (2) 139–153. Lin, T.-P. 2009; Lin, T.-P. et al. 2011; Yang, W. et al. 2013a,b). However, TPV<0 and TPV>0 curves intersect each other always below 50% level of probability, and considering a vertical axis, the two transition curves are usually not symmetrical to each other (Figure 6). That is, the rate of decline in the probability of ‘want warmer’ votes does not equal generally to the rate of incline in the probability of ‘want cooler’ votes. Therefore the intersection point does not necessarily coincide with the maxi- mum probability of ‘want no change’ votes. Consequently, it seemed reasonable to depict the probability of TPV = 0 votes (calculated by substracting the probabilities of TPV<0 and TPV>0 from 100%) against the PET index and assign pPET where this curve reaches its maximum. Besides, preferred PET zone could be defi ned on those PET range where the oc- currence probability of TPV = 0 vote exceeds the probability of the other votes (Figure 6). Summary of the results Figure 7 off ers a graphical summary about the pPET values obtained through the diff er- ent analysis approaches. The outcomes are very close to each other in almost every case. Moreover, the match is perfect in the cases of the sunny-spring, and the shade-autumn groups. The greatest diff erence (3.5 °C) was found in the sunny-summer group. Note that the probit-fi t was less signifi cant (sig = 0.301 for TPV<0 and 0.159 for TPV>0) in this group, moreover, this group had the lowest R2 (0.593) value in quadratic regression. Figure 8 off ers a graphical overview about the neutral and preferred thermal conditions (based on the results obtained through the re- gression technique). One can observe fi rst of all the striking diff erence between the nPET and pPET values. Hungarians’ nPET values ranged from 15.1 °C (spring-shade) to 21.6 °C (autumn-sunny), thereby falling into the origi- nal PET-zones of ‘slight cold stress’ and ‘no thermal stress’. On the contrary, pPET values scatt ered from the ‘no thermal stress’ to ‘strong heat stress’ categories. The lowest pPET (22 °C) occurred in the autumn-shade group and the highest (36 °C) in the spring-sunny group. The second most important feature is the remarkable diff erence between the sunny and shaded groups (Figure 8). Indeed, sunny nPET and pPET values were always higher than the corresponding shaded values. In terms of nPET the diff erence was only 2 °C in spring, Table 4. Goodness of fi t of the probit models Conditions Number of PET bins model TPV<0 model TPV>0 χ2 Sig. χ2 Sig. Spring shade sunny 30 35 51.618 43.645 0.004 0.102 31.534 48.044 0.294 0.044 Summer shade sunny 32 29 89.711 30.301 0.000 0.301 13,894.862 34.233 0.000 0.159 Autumn shade sunny 31 41 57.067 53.028 0.001 0.066 350.176 40.031 0.000 0.424 Fig. 7. Comparison of pPET values obtained through diff erent analysis techniques Kántor, N. Hungarian Geographical Bulletin 65 (2016) (2) 139–153.150 Fig. 8. Thermal conditions assessed as ‘neutral’ and ‘preferred’ by Hungarians according to their solar exposure in diff erent seasons (all results are based on regression technique; level of thermal stress is indicated according to the original PET scale) while it was almost 5 °C in autumn. The diff er- ences in pPET values were even greater: they were close to 10 degrees in every season. Finally, different seasonal order can be observed in nPET and pPET values. (Merely the shaded exposure group is discussed here, because the sunny results are more uncer- tain due to the lower sample size.) The nPET values follow the increasing order of spring, autumn and summer (Figure 8), which cor- responds both to the general seasonal back- ground temperature diff erences, both to the diff erences in the recorded actual thermal conditions. This seasonal pattern proves that Hungarian people adapt themselves to the seasonally diff erent climate conditions. In summer they perceive neutral between 17 °C and 22 °C (with a neutral temperature of 19.4 °C), while in the cooler transient seasons they feel neutral at lower PET values which can be assessed even as slight cool stress. Regarding the preferred thermal conditions, the seasonal order is diff erent: pPET is around 22 °C in autumn and summer, while it is above 27 °C in spring. (The sunny-springtime value is even higher: it is 36 °C.) The highest pPET in springtime, as well as the greatest diff erence between the nPET and pPET values in spring may be explained as follows. Aft er the cold and dark winter pe- riod Hungarian people are looking for any environmental opportunities to feel warm. Although they are adapted to cooler temper- atures during winter, they wish for warmer thermal sensation, even if this behaviour connotes certain degree of heat stress. The longing for warmer thermal conditions make most people to expose them to the intense direct sunlight in springtime (see the great portion of subjects in the spring-sunny group; Figure 4), which may have serious consequences regarding their sensitive skin and sensitive thermoregulation system at the end of the long winter-period. 151Kántor, N. Hungarian Geographical Bulletin 65 (2016) (2) 139–153. Relevance of the research fi ndings Although PET and many other well-estab- lished indices are suffi ciently and frequently used in the fi eld of human-biometeorology for the objective assessment of the thermal envi- ronment, the outcomes of this study prove the importance of questionnaire-supported OTC investigations. Having knowledge about the subjective thermal assessment of local peo- ple allows explaining such ‘illogical’ outdoor behaviours which are reported by Kántor, N. and Unger, J. (2010). By comparing the number of visitors and their outdoor behav- iours in a small urban park in the transient seasons they revealed that in spite of the strong heat stress, most people tend to sit or lie in sunny places in springtime. At the same level of thermal stress in autumn, people rather choose shady places to spend their time outdoors (Kántor, N. and Unger, J. 2010). This investigation demonstrated clear seasonal diff erences in neutral temperature and preferred temperature, similarly to pre- vious studies (e.g. Spagnolo, J. and de Dear, R. 2003; Nikolopoulou, M. and Lykoudis, S. 2006; Lin, T.-P. 2009). Additionally, this pa- per evinced that subjective thermal sensa- tion may greatly diff er from the original PET categorization system which was established for Central European people (Matzarakis, A. and Mayer, H. 1996). Indeed, except the shady-summer group, the neutral zone of Hungarian people (Figure 8) was consider- ably wider than the original 5 °C-wide zone (18–23 °C; Figure 1) indicating greater toler- ance against the changes of outdoor thermal environment. The width of the neutral zone exceeded 7 °C in the transient seasons, being more than 9 °C wide in the sunny-autumn group. This fi nding may be explained by the multifarious and changeable weather condi- tions in the transient seasons, which make people less sensitive against the variations of outdoor thermal conditions. As most important achievement, this paper revealed that solar exposure has signifi cant infl uence on the subjective evaluation of ther- mal conditions. This fi nding may have inter- national signifi cance. As mentioned above, several studies reported about seasonal diff erences in neutral temperature without scrutinizing the role of exposure on the ob- tained results. However, people choose dif- ferent positions in diff erent seasons: seeking to expose them to direct sunlight aft er the cold and dark winter months, and stay in the shade during summer and the warm months of autumn. The diff erent seasonal exposure- patt erns mean that the neutral temperature refl ects more the assessment of sun-exposed subjects in spring, and the assessment of people in the shade in summer and autumn. Thorough analysis of the seasonal neutral (and preferred) temperature values accord- ing to exposure-groups sheds more light on the real assessment patt erns of local people, which explains bett er the seasonal diff erences in their outdoor behaviour. Acknowledgement: The author would like to acknowl- edge Lilla Égerházi, Ágnes Takács and Att ila Kovács for their valuable help in the course of fi eld measure- ments or later in the period of data processing. REFERENCES Ballantyne, E.R., Hill, R.K. and Spencer, J.W. 1977. Probit analysis of thermal sensation assessments. International Journal of Biometeorology 21. 29–43. Chen, L. and Ng, E. 2012. Outdoor thermal comfort and outdoor activities: a review of research in the past decade. Cities 29. 118–125. Chen, L., Wen, Y., Zhang, L. and Xiang, W.N. 2015. Studies of thermal comfort and space use in an urban park square in cool and cold seasons in Shanghai. Building and Environment 94. 644–653. Fröhlich, D. and Matzarakis, A. 2013. Modelling of changes in thermal bioclimate: examples based on urban spaces in Freiburg, Germany. Theoretical and Applied Climatology 111. 547–558. Gómez, F., Pérez Cueva, A., Valcuende, M. and Matzarakis, A. 2013. Research on ecological design to enhance comfort in open spaces of a city (Valencia, Spain). Utility of the physiological equivalent tem- perature (PET). Ecological Engineering 57. 27–39. Gulyás, Á., Matzarakis, A. and Unger, J. 2009. Diff erences in the thermal bioclimatic conditions on the urban and rural areas in a southern Hungarian city (Szeged). Berichte des Meteorologischen Institutes der Universität Freiburg 18. 229–234. Kántor, N. Hungarian Geographical Bulletin 65 (2016) (2) 139–153.152 Gulyás, Á., Unger, J. and Matzarakis, A. 2006. Assessment of the microclimatic and human comfort conditions in a complex urban environ- ment: modelling and measurements. Building and Environment 41. 1713–1722. HMS 2015. Climate characteristics of Szeged. htt p:// www.met.hu/eghajlat/magyarorszag_ eghajlata/ varosok_jellemzoi/Szeged/ Höppe, P. 1992. Ein neues Verfahren zur Bestimmung der mittleren Strahlungstemperatur im Freien. Wett er und Leben 44. 147–151. Höppe, P. 1999. The physiological equivalent tem- perature – a universal index for the biomete- orological assessment of the thermal environment. International Journal of Biometeorology 43. 71–75. Hwang, R-L. and Lin, T-P. 2007. Thermal comfort requirements for occupants of semi-outdoor and outdoor environments in hot-humid regions. Architectural Science Review 50. 357–364. Hwang, R-L., Lin, T-P. and Matzarakis, A. 2011. Seasonal eff ects of urban street shading on long- term outdoor thermal comfort. Building and Environment 46. 863–870. IPCC 2014. Climate Change 2014: Synthesis Report, 2014. Contribution of Working Groups I, II and III to the Fift h Assessment Report of the Intergovernmental Panel on Climate Change (Core Writing Team, eds. Pachauri, R.K. and Meyer, L.A.), Geneva, IPCC. Kántor, N. and Unger, J. 2010. Benefi ts and oppor- tunities of adopting GIS in thermal comfort studies in resting places: An urban park as an example. Landscape and Urban Planning 98. 36–46. Kántor, N. and Unger, J. 2011. The most problematic variable in the course of human biometeorologi- cal comfort assessment – the mean radiant tem- perature. Central European Journal of Geosciences 3. 90–100. Kántor, N., Égerházi, L.A. and Unger, J. 2012a. Subjective estimation of thermal environment in recreational urban spaces. Part 1: investiga- tions in Szeged, Hungary. International Journal of Biometeorology 56. 1075–1088. Kántor, N., Unger, J. and Gulyás, Á. 2012b. Subjective estimations of thermal environment in recreational urban spaces – Part 2: international comparison. International Journal of Biometeorology 56. 1089–1101. Kovács, A., Unger, J., Gál, C.V. and Kántor, N. 2015. Adjustment of the thermal component of two tourism climatological assessment tools using thermal perception and preference surveys from Hungary. Theoretical and Applied Climatology. Doi: 10.1007/s00704-015-1488-9. Krüger, E.L., Drach, P., Emmanuel, R. and Corbella, O. 2013. Assessment of daytime outdoor comfort levels in and outside the urban area of Glasgow, UK. International Journal of Biometeorology 57. 521–533. Lai, D., Guo, D., Hou, Y., Lin, C. and Chen, Q. 2014. Studies of outdoor thermal comfort in northern China. Building and Environment 77. 110–118. Lin, T.-P. 2009. Thermal perception, adaptation and att endance in a public square in hot and humid regions. Building and Environment 44. 2017–2026. Lin, T.-P. and Matzarakis, A. 2008. Tourism climate and thermal comfort in Sun Moon Lake, Taiwan. International Journal of Biometeorology 52. 281–290. Lin, T.-P., de Dear, R. and Hwang. R.-L. 2011. Eff ect of thermal adaptation on seasonal outdoor ther- mal comfort. International Journal of Climatology 31. 302–312. Lin, T.-P., Matzarakis, A. and Hwang, R.-L. 2010. Shading eff ect on long-term outdoor thermal com- fort. Building and Environment 45. 213–221. Lindner-Cendrowska, K. 2013. Assessment of biocli- matic conditions in cities for tourism and recrea- tional purposes (a Warsaw case study). Geographia Polonica 86. 55–66. Matzarakis, A. and Mayer, H. 1996. Another kind of environmental stress: thermal stress. WHO Newslett er 18. 7–10. Matzarakis, A., Mayer, H. and Iziomon, M.G. 1999. Application of a universal thermal index: physio- logical equivalent temperature. International Journal of Biometeorology 43. 76–84. Matzarakis, A., Rutz, F. and Mayer, H. 2010. Modelling radiation fl uxes in simple and com- plex environments: basics of the RayMan model. International Journal of Biometeorology 54. 131–139. Mayer, H., Holst, J., Dostal, P., Imbery, F. and Schindler, D. 2008. Human thermal comfort in summer within an urban street canyon in Central Europe. Meteorologische Zeitschrift 17. 241–250. Müller, N., Kuttler, W. and Barlag, A.B. 2014. Counteracting urban climate change: adaptation measures and their effect on thermal comfort. Theoretical and Applied Climatology 115. 243–257. Nikolopoulou, M. and Lykoudis, S. 2006. Thermal comfort in outdoor urban spaces: Analysis across diff erent European countries. Building and Environment 41. 1455–1470. Pearlmutter, D., Jiao, D. and Garb, Y. 2014. The re- lationship between bioclimatic thermal stress and subjective thermal sensation in pedestrian spaces. International Journal of Biometeorology 58. 2111–2127. Pongrácz, R., Bartholy, J. and Bartha, E.B. 2013. Analysis of projected changes in the occurrence of heat waves in Hungary. Advances in Geosciences 35. 115–122. Rupp, R.F., Vásquez, N.G. and Lamberts, R. 2015. A review of human thermal comfort in the built envi- ronment. Energy and Buildings 105. 178–205. Spagnolo, J. and de Dear, R. 2003. A fi eld study of thermal comfort in outdoor and semi-outdoor environments in subtropical Sydney Australia. Building and Environment 38. 721–738. 153Kántor, N. Hungarian Geographical Bulletin 65 (2016) (2) 139–153. Tung, C.-H., Chen, C.-P., Tsai, K.-T., Kántor, N., Hwang, R.-L., Matzarakis, A. and Lin, T.-P. 2014. Outdoor thermal comfort characteristics in the hot and humid region from a gender perspec- tive. International Journal of Biometeorology 58. 1927–1939. UNFPA 2011. The state of world population 2011. Report of the United Nations Population Fund. New York, UNFPA. Unger, J. 1996. Heat island intensity with diff erent meteorological conditions in a medium-sized town: Szeged, Hungary. Theoretical and Applied Climatology 54. 147–151. Unger, J., Lelovics, E. and Gál, T. 2014. Local Climate Zone mapping using GIS methods in Szeged. Hungarian Geographical Bulletin 63. 29–41. Yahia, M.W. and Johansson, E. 2013. Evaluating the behaviour of diff erent thermal indices by investi- gating various outdoor urban environments in the hot dry city of Damascus, Syria. International Journal of Biometeorology 57. 615–630. Yang, W., Wong, N.H. and Jusuf, S.K. 2013a. Thermal comfort in outdoor urban spaces in Singapore. Building and Environment 5. 426–435. Yang, W., Wong, N.H. and Zhang, G. 2013b. A com- parative analysis of human thermal conditions in outdoor urban spaces in the summer season in Singapore and Changsha, China. International Journal of Biometeorology 57. 895–907. Yin, J.F., Zheng, Y.F., Wu, R.J., Tan, J.G., Ye, D.X. and Wang, W. 2012. An analysis of infl uential factors on outdoor thermal comfort in summer. International Journal of Biometeorology 56. 941–948. Zeng, Y. and Dong, L. 2015. Thermal human biom- eteorological conditions and subjective thermal sensation in pedestrian streets in Chengdu, China. International Journal of Biometeorology 59. 99–108. Kántor, N. Hungarian Geographical Bulletin 65 (2016) (2) 139–153.154 Since the disintegration of the USSR, the Western world has shown an ever-growing interest in Ukraine, its people and its economy. As the second-largest country in Europe, Ukraine has a strategic geographical position at the crossroads between Europe and Asia. It is a key country for the transit of energy resources from Russia and Central Asia to the European Union, which is one reason why Ukraine has become a priority partner in the neighbourhood policy of the EU. Ukraine has pursued a path towards the democratic consolidation of statehood, which encompasses vigorous economic changes, the development of institutions and integration into European and global political and economic structures. In a complex and controversial world, Ukraine is building collaboration with other countries upon the principles of mutual understanding and trust, and is establishing initiatives aimed at the creation of a system that bestows international security. This recognition has prompted the Institute of Geography of the National Academy of Sciences of Ukraine (Kyiv) and the Geographical Research Institute of the Hungarian Academy of Sciences (Budapest) to initiate cooperation, and the volume entitled “Ukraine in Maps” is the outcome of their joint eff ort. The intention of this publication is to make available the re- sults of research conducted by Ukrainian and Hungarian geographers, to the English-speaking public. This atlas follows in the footsteps of previ- ous publications from the Geographical Research Institute of the Hungarian Academy of Sciences. Similar to the work entitled South Eastern Europe in Maps (2005, 2007), it includes 64 maps, dozens of fi gures and tables accompanied by an explana- tory text, writt en in a popular, scientifi c manner. The book is an att empt to outline the geographical sett ing and geopolitical context of Ukraine, as well as its history, natural environment, population, sett lements and economy. The authors greatly hope that this joint venture will bring Ukraine closer to the reader and make this neighbouring country to the European Union more familiar, and consequently, more appealing. Ukraine in Maps Edited by: Kocsis, K., Rudenko, L. and Schweitzer, F. Institute of Geography National Academy of Sciences of Ukraine Geographical Research Institute Hungarian Academy of Sciences. Kyiv–Budapest, 2008, 148 p. ------------------------------------------ Price: EUR 35.00 Order: Geographical Institute RCAES MTA Library H-1112 Budapest, Budaörsi út 45. E-mail: magyar.arpad@csfk .mta.hu << /ASCII85EncodePages false /AllowTransparency false /AutoPositionEPSFiles true /AutoRotatePages /None /Binding /Left /CalGrayProfile (Dot Gain 20%) /CalRGBProfile (sRGB IEC61966-2.1) /CalCMYKProfile (U.S. Web Coated \050SWOP\051 v2) /sRGBProfile (sRGB IEC61966-2.1) /CannotEmbedFontPolicy /Error /CompatibilityLevel 1.3 /CompressObjects /Tags /CompressPages true /ConvertImagesToIndexed true /PassThroughJPEGImages true /CreateJobTicket false /DefaultRenderingIntent /Default /DetectBlends true /DetectCurves 0.0000 /ColorConversionStrategy /LeaveColorUnchanged /DoThumbnails false /EmbedAllFonts true /EmbedOpenType false /ParseICCProfilesInComments true /EmbedJobOptions true /DSCReportingLevel 0 /EmitDSCWarnings false /EndPage -1 /ImageMemory 1048576 /LockDistillerParams false /MaxSubsetPct 100 /Optimize false /OPM 1 /ParseDSCComments true /ParseDSCCommentsForDocInfo true /PreserveCopyPage true /PreserveDICMYKValues true /PreserveEPSInfo true /PreserveFlatness true /PreserveHalftoneInfo false /PreserveOPIComments true /PreserveOverprintSettings true /StartPage 1 /SubsetFonts true /TransferFunctionInfo /Apply /UCRandBGInfo /Preserve /UsePrologue false /ColorSettingsFile () /AlwaysEmbed [ true ] /NeverEmbed [ true ] /AntiAliasColorImages false /CropColorImages true /ColorImageMinResolution 300 /ColorImageMinResolutionPolicy /OK /DownsampleColorImages true /ColorImageDownsampleType /Bicubic /ColorImageResolution 300 /ColorImageDepth -1 /ColorImageMinDownsampleDepth 1 /ColorImageDownsampleThreshold 1.50000 /EncodeColorImages true /ColorImageFilter /DCTEncode /AutoFilterColorImages true /ColorImageAutoFilterStrategy /JPEG /ColorACSImageDict << /QFactor 0.15 /HSamples [1 1 1 1] /VSamples [1 1 1 1] >> /ColorImageDict << /QFactor 0.15 /HSamples [1 1 1 1] /VSamples [1 1 1 1] >> /JPEG2000ColorACSImageDict << /TileWidth 256 /TileHeight 256 /Quality 30 >> /JPEG2000ColorImageDict << /TileWidth 256 /TileHeight 256 /Quality 30 >> /AntiAliasGrayImages false /CropGrayImages true /GrayImageMinResolution 300 /GrayImageMinResolutionPolicy /OK /DownsampleGrayImages true /GrayImageDownsampleType /Bicubic /GrayImageResolution 300 /GrayImageDepth -1 /GrayImageMinDownsampleDepth 2 /GrayImageDownsampleThreshold 1.50000 /EncodeGrayImages true /GrayImageFilter /DCTEncode /AutoFilterGrayImages true /GrayImageAutoFilterStrategy /JPEG /GrayACSImageDict << /QFactor 0.15 /HSamples [1 1 1 1] /VSamples [1 1 1 1] >> /GrayImageDict << /QFactor 0.15 /HSamples [1 1 1 1] /VSamples [1 1 1 1] >> /JPEG2000GrayACSImageDict << /TileWidth 256 /TileHeight 256 /Quality 30 >> /JPEG2000GrayImageDict << /TileWidth 256 /TileHeight 256 /Quality 30 >> /AntiAliasMonoImages false /CropMonoImages true /MonoImageMinResolution 1200 /MonoImageMinResolutionPolicy /OK /DownsampleMonoImages true /MonoImageDownsampleType /Bicubic /MonoImageResolution 1200 /MonoImageDepth -1 /MonoImageDownsampleThreshold 1.50000 /EncodeMonoImages true /MonoImageFilter /CCITTFaxEncode /MonoImageDict << /K -1 >> /AllowPSXObjects false /CheckCompliance [ /None ] /PDFX1aCheck false /PDFX3Check false /PDFXCompliantPDFOnly false /PDFXNoTrimBoxError true /PDFXTrimBoxToMediaBoxOffset [ 0.00000 0.00000 0.00000 0.00000 ] /PDFXSetBleedBoxToMediaBox true /PDFXBleedBoxToTrimBoxOffset [ 0.00000 0.00000 0.00000 0.00000 ] /PDFXOutputIntentProfile (None) /PDFXOutputConditionIdentifier () /PDFXOutputCondition () /PDFXRegistryName () /PDFXTrapped /False /CreateJDFFile false /Description << /ARA /BGR /CHS /CHT /CZE /DAN /DEU /ESP /ETI /FRA /GRE /HEB /HRV (Za stvaranje Adobe PDF dokumenata najpogodnijih za visokokvalitetni ispis prije tiskanja koristite ove postavke. Stvoreni PDF dokumenti mogu se otvoriti Acrobat i Adobe Reader 5.0 i kasnijim verzijama.) /ITA /JPN /KOR /LTH /LVI /NLD (Gebruik deze instellingen om Adobe PDF-documenten te maken die zijn geoptimaliseerd voor prepress-afdrukken van hoge kwaliteit. De gemaakte PDF-documenten kunnen worden geopend met Acrobat en Adobe Reader 5.0 en hoger.) /NOR /POL /PTB /RUM /RUS /SKY /SLV /SUO /SVE /TUR /UKR /ENU (Use these settings to create Adobe PDF documents best suited for high-quality prepress printing. Created PDF documents can be opened with Acrobat and Adobe Reader 5.0 and later.) /HUN >> /Namespace [ (Adobe) (Common) (1.0) ] /OtherNamespaces [ << /AsReaderSpreads false /CropImagesToFrames true /ErrorControl /WarnAndContinue /FlattenerIgnoreSpreadOverrides false /IncludeGuidesGrids false /IncludeNonPrinting false /IncludeSlug false /Namespace [ (Adobe) (InDesign) (4.0) ] /OmitPlacedBitmaps false /OmitPlacedEPS false /OmitPlacedPDF false /SimulateOverprint /Legacy >> << /AddBleedMarks false /AddColorBars false /AddCropMarks false /AddPageInfo false /AddRegMarks false /ConvertColors /ConvertToCMYK /DestinationProfileName () /DestinationProfileSelector /DocumentCMYK /Downsample16BitImages true /FlattenerPreset << /PresetSelector /MediumResolution >> /FormElements false /GenerateStructure false /IncludeBookmarks false /IncludeHyperlinks false /IncludeInteractive false /IncludeLayers false /IncludeProfiles false /MultimediaHandling /UseObjectSettings /Namespace [ (Adobe) (CreativeSuite) (2.0) ] /PDFXOutputIntentProfileSelector /DocumentCMYK /PreserveEditing true /UntaggedCMYKHandling /LeaveUntagged /UntaggedRGBHandling /UseDocumentProfile /UseDocumentBleed false >> ] >> setdistillerparams << /HWResolution [2400 2400] /PageSize [612.000 792.000] >> setpagedevice