Comportamento temporal e espacial do quantitativo da coleta de resíduos sólidos em Uberlândia-MG

• Ano de defesa: 2023
• Autor(a) principal: Dornelas, Juliana Messias
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso embargado
• Idioma: por
• Instituição de defesa: Universidade Federal de Uberlândia; Brasil; Programa de Pós-graduação em Qualidade Ambiental
• Palavras-chave em Português: lixo; garbage; coleta seletiva; selective collect; gestão de resíduos sólidos urbanos; urban solid waste management; CNPQ::ENGENHARIAS::ENGENHARIA SANITARIA::SANEAMENTO BASICO; Desenvolvimento sustentável; Eliminação de resíduos - Uberlândia (MG); Coleta seletiva de lixo - Uberlândia (MG); Lixo - Eliminação - Uberlândia (MG)
• Link de acesso: https://repositorio.ufu.br/handle/123456789/36818; http://doi.org/10.14393/ufu.di.2023.17

The volume of urban solid waste (RSU) production and its destination is a concern for the public authorities of the municipalities. Statistical analysis of the behavior of this quantity of MSW produced is a tool with an important contribution to public policies in the sector. This work aimed to apply time series analysis and cluster analysis techniques to describe the temporal and spatial behavior of MSW production in the city of Uberlândia-MG. Data from the volume of MSW collected in the city were used, both from conventional regular collection and from selective collection, which are managed and made available by the Municipal Department of Water and Sewage (DMAE). The time series methodology was applied to: verify the occurrence of seasonality and trend components in the data, adjustment of statistical models to describe the behavior of this quantity over time and use of the adjusted ones to make predictions of future volumes. In the cluster analysis, the collection sectors were grouped according to the volumes collected monthly and, later, for visualization, the collection sectors were designated and presented on the map of the urban region of the municipality. The results indicated the occurrence of seasonality and trend components in the analyzed variables and that time series models can be used to describe the behavior of the produced volume of MSW. The cluster analysis allowed the separation of the collection sectors into three groups and, in this way, it was possible to visualize, through maps, the spatial arrangement of these sectors by collected volume. It is concluded that statistical data analysis tools can significantly contribute to a better understanding of the behavior of the collected volume of MSW in the city and, consequently, contribute to the urban planning of this service provided to the population.