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Thermal discomfort analysis using UTCI and MEMI (PET and PMV) in outdoor environments: case study of two climates in Iran (Arak & Bandar Abbas)

2019; Wiley; Volume: 74; Issue: S1 Linguagem: Inglês

10.1002/wea.3612

1477-8696

Mehdi Asghari, Gholamheidar Teimori, Marzieh Abbasinia, Fahimeh Shakeri, Reza Tajik, Mohammad Javad Ghannadzadeh, Gholamabbas Fallah Ghalhari,

Climate Change and Health Impacts

Abstract Objectives The aim of this study was to analyse the thermal discomfort of outdoor environments in two climates of Iran using physiological equivalent temperature (PET), predicted mean vote (PMV) indices and Universal Thermal Climate Index (UTCI). Methods In this study, the meteorological data recorded by the meteorological organisation in a 15‐year period, including temperature, air velocity, cloudiness, relative humidity and water vapour pressure, in two different climates of Iran were surveyed. According to classification of the Köppen, Arak as a representative of a semi‐arid and cold climate and Bandar Abbas as a representative of a hot and dry climate were used to calculate thermal indices. The PMV index was based on the ASHRAE 7‐point thermal sensation scale, and the values of PMV and PET indices were extracted using R ayman 1.2 software. The values of the UTCI were calculated using B io K lima 2.6 software. Results The results showed that the maximum and minimum temperatures in two climates were related to July and January, respectively. In Arak, the means of the UTCI, PET and PMV indices were 14.41 ± 11.5, 15.81 ± 1.23 and −0.77 ± 2.15, respectively. In Bandar Abbas as well, the mean of the UTCI, PET and PMV indices were 22.56 ± 9, 22.73 ± 7.7 and 0.73 ± 7.7, respectively. In Arak, there was moderate heat stress to moderate cold stress in this region, whereas in Bandar Abbas, the days had a strong heat stress to low cold stress. There are very strong correlations between the indices used for two different climates. Conclusion The bioclimatic comfort indices used were able to demonstrate the comfort and discomfort of people during different months of the year in two surveyed climates, and despite trivial differences, they provided relatively uniform representations of climate comfort for surveyed cities. Using thermal comfort indices, high‐risk areas can be identified, and appropriate measures can be adopted to reduce the effects of the changes.