Empirical Model of Thermal Comfort for Medium-Sized Cities in Subtropical Climate

The present study sought to elaborate an empirical model of thermal comfort for medium-sized cities in subtropical climate, based on a cross-sectional survey in the city of Santa Maria, state of Rio Grande do Sul, Brazil. The research was based on the collection of meteorological, subjective and ind...

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Bibliographic Details
Published in:Atmosphere 2019-10, Vol.10 (10), p.576
Main Authors: Gobo, João Paulo Assis, Faria, Marlon Resende, Galvani, Emerson, Amorim, Margarete Cristiane de Costa Trindade, Celuppi, Maria Cristina, Wollmann, Cássio Arthur
Format: Article
Language:English
Subjects:
Acclimatization
Adequacy
Air temperature
Atmosphere
Atmospheric models
Bioclimatology
Body fat
Body mass index
Body temperature
Climate
Comfort indexes
Covariance
Distribution
Empirical models
Environmental factors
Environmental impact
Formulas (mathematics)
Growth models
Humid climates
HyperText Markup Language
Kurtosis
Linearity
Low temperature
Manganese
Normality
Older people
Open spaces
Physiology
Population
Population studies
Prediction models
Probability theory
Robustness (mathematics)
Semantics
Statistical analysis
Statistical methods
Subtropical climates
Temperature
Thermal comfort
thermal sensation
Tropical climate
Tropical climates
Underweight
urban climate
Urban environments
urban planning
Weight
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Summary:The present study sought to elaborate an empirical model of thermal comfort for medium-sized cities in subtropical climate, based on a cross-sectional survey in the city of Santa Maria, state of Rio Grande do Sul, Brazil. The research was based on the collection of meteorological, subjective and individual data collected simultaneously in August 2015, January and July 2016, which were submitted to multiple linear regression for the elaboration of the Bioclimatic Model for Subtropical Medium-Sized Cities (MBCMS). The proposed model was validated through a normality test, obtained by the measure of obliquity and kurtosis of the distribution, heteroscedasticity and covariance, as well as by the comparison between already traditional models in the literature, such as PET, SET and PMV, which were calibrated to the study area, and the results observed for MBCMS. The results presented high multiple R-squared and adjusted R-squared, 0.928 and 0.925, respectively, for the proposed model, as well as an F-statistic of 447.6. In the validation, the MBCMS presented R equal to 0.83 and an accuracy score 60% more efficient than the PET, SET and PMV indexes.
ISSN:2073-4433
2073-4433
DOI:10.3390/atmos10100576