استادیار، گروه جغرافیا، دانشگاه گلستان، گرگان، ایران
عنوان مقاله [English]
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.