Análise estatística e espacial da distribuição e viabilidade do gênero Canoparmelia spp. como bioindicador de poluição atmosférica

• Autor(a) principal: Almeida, Felipe Trevisan de
• Data de Publicação: 2023
• Tipo de documento: Trabalho de conclusão de curso
• Idioma: por
• Título da fonte: Repositório Institucional da UFU
• Texto Completo: https://repositorio.ufu.br/handle/123456789/39055
• topic: Poluição do ar; Air pollution; Biomonitoramento; Biomonitoring; Bioindicadores; Bioindicators; Líquens; Lichens; CNPQ::CIENCIAS DA SAUDE::SAUDE COLETIVA

The vehicle fleet stands out as one of the main diffuse sources of atmospheric pollution in cities, being responsible for the emission of gases and pollutants into the air from the combustion of fossil fuels, which cause harm to the environment and the health of exposed populations. In this context, considering the importance of measuring the polluting activity and monitoring how its effects impact the environment, a biointegration study was conducted to assess the viability of the genus Canoparmelia spp. as a bioindicator of atmospheric pollution. To this end, a survey of lichen distribution and coverage on trees of the species Mangifera indica was carried out in a municipal park located alongside a federal highway. For the execution of statistical and geospatial analyses, the field survey was conducted in a way that considered three different zones of the park: one closer to the edges of BR - 050; another in the center of the park; and finally, the third zone was delimited within a playground, being the point farthest from the highway. The results of the comparison of areas and their respective lichen coverages through ANOVA, Tukey showed the sensitivity of the genus to bioindicate environments impacted by atmospheric pollution. The results of the geospatial analysis also confirm that the greater distribution of lichens corresponds to the more preserved areas. Based on the results, it was possible to confirm the hypothesis that lichen coverage decreased in the zones closer to the sources of atmospheric pollutants, both through the statistical model and the geospatial analysis.