Landscape features and urban heat island

episodic analyses during the dry season in a city of the Pre-Amazon region of Mato Grosso (Brazil)

Resumo

Research Purpose: This study aims to analyze the urban heat island in the city of Sinop (State of Mato Grosso, Brazil), based
on three characteristic episodes of the dry season. The analysis focuses on the spatialization of temperature and the intensity of
the phenomenon, as well as its association with landscape features.
Methodology: The analysis of the urban heat island was grounded in the Urban Climate System and the technicalmethodological
framework for investigating the phenomenon in medium and small-sized cities. Urban and rural landscape
aspects were examined based on the adaptation of Local Climate Zones and the measurement of nighttime air temperature
through mobile transects and fixed points. The data were processed and analyzed using statistical association techniques and
multicriteria linear regression modeling, in order to predict the spatial distribution of air temperature and the intensity of the heat
island.
Findings: The analyses highlight the influence of landscape features, physical elements, and urban spatial organization on
temperature distribution and the formation of the heat island. They reinforce the mitigating role of vegetation and the greater
heating in densely built areas, and also point to the influence of seasonality and regional atmospheric conditions on local thermal
variation.
Originality/Value: The results highlight the effects of changes in local climatic aspects resulting from the reoccupation of the
Pre-Amazon region of Mato Grosso through urban development and agricultural activities, in the generation and spatial
distribution of phenomena such as the urban heat island. Furthermore, the study demonstrates the applicability of multicriteria
modeling in understanding the spatial pattern of the phenomenon and its influencing factors, and emphasizes the importance of
comprehensive analyses within the same seasonal period for a more accurate interpretation and the effective indication of
mitigation and adaptation strategies.