Effects of microclimate and human parameters on outdoor thermal sensation in the high-density tropical context of Dhaka.
Int J Biometeorol
Springer Science and Business Media LLC
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Sharmin, T., & Steemers, K. (2020). Effects of microclimate and human parameters on outdoor thermal sensation in the high-density tropical context of Dhaka.. Int J Biometeorol, 64 (2), 187-203. https://doi.org/10.1007/s00484-018-1607-2
A thermal comfort questionnaire survey was carried out in the high-density, tropical city Dhaka. Comfort responses from over 1300 subjects were collected at six different sites, alongside meteorological parameters. The effect of personal and psychological parameters was examined in order to develop predictive models. Personal parameters included gender, age, activity, profession-type (indoor or outdoor-based), exposure to air-conditioned space and sweat-levels. Psychological parameters, such as 'the reason for visiting the place' and 'next destination is air-conditioned', had statistically significant effects on thermal sensation. Other parameters, such as 'body type', 'body exposure to sun', 'time living in Dhaka', 'travelling in last_30 min', and 'hot food' did not have any significant impact. Respondents' humidity, wind speed and solar radiation sensation had profound impacts and people were found willing to adjust to the thermal situations with adaptive behaviour. Based on actual sensation votes from the survey, empirical models are developed to predict outdoor thermal sensation in the case study areas. Ordinal linear regression techniques are applied for predicting thermal sensation by considering meteorological and personal conditions of the field survey. The inclusion of personal and weather opinion factors produced an improvement in models based on meteorological factors. The models were compared with the actual thermal sensation using the cross-tabulation technique. The predictivity of the three models (meteorological, thermos-physiological and combined parameter) as expressed by the gamma coefficient were 0.575, 0.636 and 0.727, respectively. In all three models, better predictability was observed in the 'Slightly Warm' (71% in meteorological model) and 'Hot' (64.9% in combined parameter model) categories-the most important ones in a hot-humid climate.
Humans, Cities, Humidity, Microclimate, Bangladesh, Thermosensing
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External DOI: https://doi.org/10.1007/s00484-018-1607-2
This record's URL: https://www.repository.cam.ac.uk/handle/1810/279942
Attribution 4.0 International
Licence URL: https://creativecommons.org/licenses/by/4.0/