Land use planning based on human-bio meteorological potentials of some selected cities of Iran


Assistant Professor, Department of Geography, Golestan University, Gorgan, Iran


Land use planning based on capabilities, abilities and suitabilities of each region with regard to uniformity and coordination of the effects of their national operation results at the national level, assigns specific role and responsibility to each area. However, one of the integral components in land use planning is considering the potentials and meteorological and climatological limitations of different regions. In the way that many social and economic activities, such as the impact of the climate in agriculture, locating factories, industry and airports, and its role in identifying areas with potential for solar and wind energy is dependent on long-term behavior and pattern of this important indicator. Apart from the role of climate in above-mentioned applications, a lot of activities and industries such as tourism and even supply and demand level of the cooling and heating energy of human settlements are dependent on the behavior and patterns of every region climate. This is in line with a particular branch of meteorology called biometeorology and tourism-climate. On the other hand, everybody knows the importance of this issue that the assessment of ecological potential in any area for land use is based on tourism-climate potential and on the estimation of supply and demand level of the heating and cooling energy which unfortunately, have rarely been considered by managers and authorities. Despite the fact that there have been some studies in the field of bio-climate for different zones of Iran, the example of major weaknesses of these activities is relying on monthly data and short-term time series.
In order to analyze the thermal comfort conditions, the daily and long-term data of temperature, relative humidity, wind speed and cloud cover from 1960 to 2010 were used. Since access to the 50-year long-term data is only available for a limited number of Iran stations. These assessments have been done based on 40 selected stations having the most complete statistical period (Figure 1). It should be noted that the reconstruction of missing data was performed by linear regression, and the results were confirmed after validating the reconstructed data. In addition, the randomness of the observed data and their homogeneity were investigated using Run Test and drawing histogram. In this study, in order to monitor the conditions of human biometeorology, the method of Predicted Mean Vote was used as one of the most important indices of Physiology-temperature. PMV is a 7-point thermal sensation division ranging from less than -3.5 (too cold) to higher than +3.5 (hot) changes (Table 1). To compute this index easier and faster, some software have been designed within which RayMan is one of them. It should be noted that for calculating PMV index, four sets of data and variables are used:
1- Situational variables include latitude and altitude, position and height of the city.
2- Meteorological variables include dry air temperature in Celsius degree, vapor pressure or relative humidity, wind speed and the amount of cloud.
3- The third set of variables includes Individual variables as effective Physiological characteristics in the model. In this regard, the individual characteristics such as height, weight, age and gender should be considered.
4- The fourth set of variables includes the type of clothing and activity. Clothing and activity are determined respectively based on Clo and Watts. It should be noted that the third and fourth sets are considered as default models.
The result of this study showed that in different seasons, several inhibiting factors act on thermal comfort. In hot seasons of the year, the very warm and hot conditions and in cold seasons of the year, the cold stress events have been introduced as inhibiting factors. The results based on long-term monthly averages showed that the percentage maximum of stations having bioclimatic conditions from very warm to hot belongs to July regarding %90 of the stations and maximum of cold to very cold conditions belongs to January with a frequency of 62.5 percent of stations. On the other hand, in October, Maximum stations in Iran with 40 percent of the frequency have experienced a thermal comfort. However, the daily long-term statistics during 1960 - 2010 reflects the fact that Chabahar with % 18, Ahvaz % 28and Hamadan %30.5 in most of the times have recorded respectively as maximum comfort, hot and very cold categories compared to other stations of Iran. Furthermore, the results of this research with the introduction of capacity and thermal comfort inhibiting factors for different parts of the country over the years can play an important role in providing capability and land use planning.


Main Subjects

اسماعیلی، ر.، صابرحقیقت، ا. و ملبوسی، ش.ف 1389، ارزیابی شرایط اقلیم آسایشی بندرچابهار در جهت توسعۀ گردشگری، مجموعه مقالات چهارمین کنگره بین المللی جغرافیدانان جهان اسلام. ایران-زاهدان
آسایش، ح.، 1375، اصول و روش های برنامه‌ریزی ناحیه‌ای، انتشارت دانشگاه پیام نور، تهران
باعقیده، م.، عسگری، ا.، شجاع، ف. و جمال آبادی، ج.، 1393، بررسی و مقایسۀ عملکرد پارامترهای مدل ریمن در تعیین تقویم مناسب گردشگری مطالعه موردی: شهر اصفهان، جغرافیا و توسعه، 36، 135-144.
زیاری، ک.، 1383، مکتب ها، نظریه ها، مدل های برنامه و برنامه ریزی منطقه ای، انتشارات دانشگاه یزد.
ذوالفقاری، ح.، 1386، تعیین تقویم زمانی مناسب برای گردش در تبریز با استفاده ازشاخص‌های دمای معادل فیزیولوژی(PET)  و متوسط نظرسنجی پیش‌بینی شده (PMV)، پژوهش‌های جغرافیایی، شماره 62 ، -141
ساربانقلی، ح.، اصل بناب، ه.، 1392، معماری همساز با اقلیم دهستان منطقه آذربایجان شرقی با تعیین مناسب‌‌ترین شاخص RayMan، فصلنامه جغرافیای سرزمین، 38، 53-62
فرج زاده، م. و احمدآبادی، ع.، 1388، ارزیابی و پهنه بندی اقلیم گردشگری ایران با استفاده از شاخص اقلیم گردشگری TCI، پژوهشهای جغرافیای طبیعی، شماره 42، 33-78.
مخدوم،. م.، شالوده آمایش سرزمین، 1381، چاپ پنجم، انتشارات دانشگاه تهران.
Abegg, B., Konig, U., Buerki, R. and Elsasser, H., 1998, Climate impact assessment in tourism, Appl Geogr Dev, 51,81–93.
Amiranashvili, A., Matzarakis, A. and Kartvelishvili, L., 2008, Tourism Climate Index in Tbilisi, Transactions of the Georgian Institute of Hydrometeorology, 115, 1-4.
Auliciems, A. and De Dear, R., 1997, Thermal adaptation and variable indoor climate control. In: Auliciems A (ed) Advances in bioclimatology – 5. Human Bioclimatology. Springer, pp 61–86.
Basarin, B., Lukić, T. and Matzarakis, A., 2015, Quantification and assessment of heat and cold waves in Novi Sad,Northern Serbia, Int J Biometeorol, DOI 10.1007/s00484-015-1012-z.
Cengiz, T., Akbulak, C., Caliskan, V. and Kelkit, A., 2008, Climate Comfortable for Tourism: A Case Study of Canakkale, Balwois 2008 – Ohrid, Republic of Macedonia – 27.
Cheng, V., Ng, E., Chan, C. and Givoni, B., 2012, Outdoor thermal comfort study in a sub-tropical climate: a longitudinal study based in Hong Kong, International Journal of Biometeorology,56, 43-56.
Delavar, M., Moradifar, A. and Nikouseresht, R., 2012, Classification of Tourism Region in North Area of Iran by Using of TCI index (Case of Study: Guilan province), Australian Journal of Basic and Applied Sciences, 6(7), 384-396.
Dalman, M. and Salleh, E., 2011, Microclimate and Thermal Comfort of Urban Forms and Canyons in Traditional and Modern Residential Fabrics in Bandar Abbas, Iran, Modern Applied Science, 5, 12-34.
Esmaili1, R. and Fallah Ghalhari, 2014a, Seasonal bioclimatic mapping of Iran for tourism, European Journal of Experimental Biology, 4(3),342-351.
Esmaili1, R. and Fallah Ghalhari, G., 2014b, An Assessment of Bioclimatic Conditions for Tourists- A Case Study of Mashhad, Iran, Atmospheric and Climate Sciences, 4, 137-146.
Fanger, P. O., 1972, Thermal comfort. New York: McGraw-Hill.
Fanger, P. O., 1970, Thermal Comfort. Copenhagen: Danish Technical Press.
Gagge, A. P., Fobelets, A. P. and Berglulg, L. G., 1986, A standart predictive index of human response to the thermal environment, ASHRAE Trans, 92(13), 709–731.
Farajzadeh, H. and Matzarakis, A., 2012, Evaluation of thermal comfort conditions in Ourmieh Lake, Iran, Theor Appl Climatol, 107, 451–459.
Farajzadeha, H. and Matzarakis, A., 2009, Quantification of climate for tourism in the northwest of Iran, Meteorological Applications Meteorol, 16, 545–555.
Höppe, P. R., 1993, Heat balance modeling, Experientia, 49,741–746.
Kim, J. H., Min, Y. K. and Kim, B., 2013, Is the PMV Index an Indicator of Human Thermal Comfort Sensation, International Journal of Smart Home, 7(1), 27-34.
Landsberg, H. E., 1972, The assessment of human bioclimate, a limited review of physical parameters.World Meteorological Organization, Technical Note No. 123, WMO-No. 331, Geneva.
Lin, T. P., Andrade, H., Hwang, R. L., Oliveira, S. and Matzarakis, A., 2008, The Comparison of Thermal Sensation and Acceptable Range for Outdoor Occupants Between Mediterranean and Subtropical Climates, Proceedings 18th International Congress on Biometeorology, Tokio, 22-26 September 2008, 1-4.
Matzarakis, A. and Endler, C., 2010, Climate change and thermal bioclimate in cities: impacts and options for adaptation in Freiburg, Germany, Int J Biometeorol, 54, 479–483.
Mcgregor, G., Markou, M., Bartzokas, A. and Katsoulis, B., 2002, An evaluation of the nature and timing of summer human thermal discomfort in Athens, Greece, climate Research, 21,83 94.
Matzarakis, A., 2006, Weather- and climate- related information for tourism, Tourism Hosp. Plan. Dev., 3(2), 99-115.
Mathai, a., Rabadi, N. and Grosland, N., 2004, Digital human modeling and virtual reality for FCS. Technical report no. VSR-04-02, University of Iota. USA.
Matzarakis, A., 2001, Climate and Bioclimatic Information for the Tourism in Greece. Proceedings of the 1st International workshop on climate, tourism and recreation, International society of biometeorology, commission on climate, tourism and recreation.
Matzarakis, A., Rutz, F. and Mayer, H., 2007, Modelling Radiation fluxesin simple and complex environments – Application of the RayMan model, International Journal of Biometeorology, 51, 323-334.
Matzarakis, A., Rutz, F. and Mayer, H., 2010, Modelling Radiation fluxesin simple and complex environments – Basic softhe RayMan model, International Journal of Biometeorology 54, 131-139.
Mokhtari, M. and Anvari, M., 2015, A Comparative study of tourism comforting climate in Iran (case study in the Markazi province and southern Kharasan province of Iran) with TCI model in GIS environment, Journal of Novel Applied Sciences, 4-2, 151-156.
Mazon, J., 2014, The influence of thermal discomfort on the attention index of teenagers: an experimental evaluation, International Journal of Biometeorology, 58, 717-724.
Mieczkowski, Z., 1985, The tourism climatic index: a method of evaluating world climates for tourism, Canadian Geographer, 29(3),220-233.
Olgyay, V.,1953, Application of Climatic Data to house Desingn, U.S.Hosing and home Finance Agenc.Washington, D, C. 2 vol.
Olu Ola, O., Bogda, M. and Prucnal, O., 2003, Choice of thermal index for architectural design with climate in Nigeria; Habitat international, 44, 23-44.
Parsons, K. C., 1993, Human thermal environments. London:Taylor & Francis.
Parsons, K. C., 2003, Human thermal environments: the effects of hot, moderate, and col environments on human health, comfort and performance. Taylor & Francis, London
Perch-Nielsen, S. L., Amelung, B. and Knutti, R., 2010, Time is of the essence: adaptation of tourism demand to climate change in Europe, climate change, 103(3), 363-381.
Park, S. and Tuller, S., 2014, Advanced view factor analysis method for radiation exchange, International Journal of Biometeorology,58, 161-178.
Rudel, E., Matzarakis, A. and Koch, E., 2007, Summer Tourism in Austria and Climate Change, In: Oxley, L. and Kulasiri, D. (eds) MODSIM 2007 International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand, December 2007, pp. 1934-1939. ISBN: 978-0-9758400-4-7.
Roshan, Gh., Ranjbar, F. and Orosa, J., 2010, Simulation of global warming effect on outdoor thermal comfort conditions, International Journal of Environmental Science & Technology, 7(3), 571-580.
Roshan, GH. R. Orosa, J. and Nasrabadi, T., 2012, Simulation of climate change impact on energy consumption in buildings, case study of Iran, Energy Policy 49,731–739.
Roshan, GH., Yousefi, R. and Fitchett, J. M., 2015, Long-term trends in tourism climate index scores for 40 stations across Iran: the role of climate change and influence on tourism sustainability, Int J Biometeorol, doi:10.1007/s00484-015-1003-0.
Ramazanipour, M. and Behzadmoghaddam, E., 2013, Analysis of Tourism Climate Index of Chaloos City. Int J of Humanities and Management Sciences, 1, 290–292.
Safaeipoor, M., Shabankari, M. and Taghavi, T., 2013, The Effective Bioclimatic Indices on Evaluating Human Comfort (ACase Study: Shiraz City), Geography and Environmental Planning Journal, 50(2), 34-55.
Steadman, R. G., 1979, The assessment of sultriness. Part I: A temperature humidity index based on human physiology and clothing science, J Applied Meteorol, 18, 861–873.
Taffé, P., 1997, A qualitative response model of thermal comfort. Build Environ, 32,115–121.
Terjung, W. H., 1968, World patterns of the Monthly Comfort index, Int. J. Biometeor. 12(2), 119-141.
Thorsson, S., Lindqvist, M. and Lindqvist, S., 2004,Thermal bioclimatic conditions and patterns of behaviour in an urban park in Göteborg, Sweden, International Journal of Biometeorology, 48, 149-156.
Thomson, Madeleine, C., Ricardo, J. and Martin, B., 2008, Seasonal Forecasts, Climatic Change and Human Health: Health and Climate, Springer Science + Business Media B.V,232 pages.
Yee Yan, Y., 2005, Human Thermal climates in china, physical Geography , 26, 163-176.
Zaninovic, K., 2001, Biometeorological potial of Croatian Adriatic coast, Meteorological and hydrological service of Croatia, International society of Biometeorology, 2,257-262.