Background and aims: lack of thermal comfort in outdoor environments, can cause disorders and diseases, negative impact on productivity and performance, and even can cause death. Therefore, it is important to evaluate it. Various indices and models have been used to assess the level of comfort of humans. In most evaluations has been tried to estimate the comfort level using meteorological data such as temperature, humidity, Wind speed, solar radiation or a combinations. These indices provide climate data in a way that reflects individual’s response to climate conditions and, in a numerical classification, range from very appropriate to very inappropriate. These indices facilitate interpreting the complex effects of atmospheric elements on human comfort and allow comparison of different places from climatic comfort point of view. Today, bioclimatic studies, from human comfort point of view, are the basis of many management plans, especially health and treatment. Therefore, in this study, Becker and neurotic pressure bio climate index were used to evaluate discomfort in three different climates of Iran.
Methods: In this study, thermal comfort was assessed in three different climates of Iran based on the Köppen climate classification including the semi-arid and cold climate (Arak), the humid subtropical climate (Sari) and the hot desert climates (Bandar Abbas) were done using of Becker and neurotic pressure bio climate index.

Becker index known as Cooling Power index (CP) originated in the inclusion of only two climate parameters in the bioclimatic analysis by air temperature and wind speed. Cooling Power index has the advantage of being computed very easily; they are easily understood not only by specialists, but also by the wide public and have a good temporal applicability and availability for the studied territory. The cooling power of the environment depends on the
difference between human body temperature and air temperature and also on the wind speed.

The Neurotic pressure index determines the physiological pressure to provide comfort between absorption and heat release. The purpose of this index is to describe the comfort level using temperature, humidity and wind speed. This index offers separate equations for temperatures below and greater than 20 ° C.

Meteorological data between 2014-2000 was used from the Meteorological Organization, including temperature, relative humidity and wind speed, to calculate the indices. Then the mean of the mentioned parameters was calculated daily for 15 years. Excel software and SPSS 22, were used for data analysis, descriptive statistics and linear regression were used.
Results: The highest and lowest air temperature monthly mean values in Sari were recorded for August (27.55 ± 0.57°C)and January (7.9 ± 0.68°C), respectively.The highest and lowest monthly mean relative humidity values were recorded forDecember (81 ± 2.6 %) and August (73.7 ± 1.94%), respectively. In Arak and during the surveyed years, the highest average temperature (28.1 ± 0.65) was observed in July, and the lowest average temperature (-1.14 ± 1.94) was observed in January. Also, the highest humidity rate (70.67 ± 3.87) was observed in January and the lowest humidity rate (22.96 ± 1.25) was observed in July. Also in Bandar Abbas, the highest average temperature (34.27 ± 0.26) was observed in July and the lowest average temperature (17.44 ± 0.62) was observed in January. The highest humidity (68.39 ± 2.41)was recorded in August and the lowest humidity rate (57 ± 4) was in December. The mean of Becker index in Arak,Sari and Bandar Abbas was respectively 15.76 ± 6.3, 13.73 ± 5.2 and 8.93 ± 5.5µcal/cm²/s. According to the results of this index, the three climates covered cool to hot, warm and sultry. The mean of neurotic pressure index in months with temperatures below 20 ° C in Arak was 672 ± 148, in the city of Sari, 605.115.6 and in Bandar Abbas, 567.66 ± 46.76 kcal/hr/m² and in months with a temperature above 20 ° C, respectively, was 3.37 ± 2. 5.42 ± 4.9 and 11.64 ± 7 kcal/hr/m². Interpretation of this index showed that the studied areas have cold conditions (Arak) to fully uncomfortable condition (Bandar Abbas). Also, there was a high correlation between the two indexes in three different climates (R²> 0.9).
Conclusion: The results of this study showed that the comfort indices reveal the comfort and discomfort periods in three different climates of the country, and despite some differences, show relatively similar manifestations of the comfort climate of under study cities. Results have shown that the studied areas are characterized by biochemical variation from hot to very cold conditions throughout the year. Regarding the advantageous of using these indices, it should be noted that the data measured by meteorological organizations can be used to forecast and timely announcement of heat and cold stresses as well as thermal comfort in different regions. It should be noted that thermal comfort varies with respect to the race, age, type of activity, clothing, metabolism rate, accommodation, etc. Evaluating the trend of thermal comfort indices can identify high-risk areas, which in this context presence of comprehensive and innovative policy making and planning, according to the climate changes and the ability to forecast and control the risks resulting from these changes, seems necessary for community members.