Urban climate and health: analysis of the correlation between heat island, PM10 concentration and spatial distribution of respiratory diseases in Sinop (MT)

• Grant number: 21/08670-1
• Support Opportunities: Scholarships in Brazil - Doctorate
• Start date: June 01, 2022
• End date: January 31, 2026
• Field of knowledge: Humanities - Geography
• Principal Investigator: Margarete Cristiane de Costa Trindade Amorim
• Grantee: Luis Flávio de Araújo
• Host Institution: Faculdade de Ciências e Tecnologia (FCT). Universidade Estadual Paulista (UNESP). Campus de Presidente Prudente. Presidente Prudente , SP, Brazil

Abstract

Inequality in human activities, especially during the formation of urban areas, can alter the mechanisms and properties of the local climate, which influences these areas and social groups that inhabit them. In turn, these factors also influence local climate through various means. The climate-society-environment relationship may result in diverse social and environmental impacts, such as the formation of heat island, thermal discomfort, and concentration of particulate matter (PM10). The intensity of each component of the relationship may impact human health, especially the respiratory system. Thus, this project aims to identify and analyze the correlations between the formation of heat island, PM10 concentration, cases of respiratory diseases, and weather systems in Sinop (MT). For this reason, we will investigate the socioeconomic and environmental aspects that underlie the configuration of the urban area and the heat island, the spatial distribution and seasonal dynamics of PM10, the cases of consultations and hospitalizations due to respiratory diseases, and the possible correlations among them. To develop the project, we will create heat island maps through field campaigns and spatial modeling and formulate graphs and tables that describe the climate variables and weather systems. In addition, we will characterize and spatialize PM10 and respiratory diseases, and summarize their correlation with the climate variables to verify the most vulnerable areas and population from the perspective of social vulnerability.