Estimativa das emissões de monóxido de carbono por veículos automotivos na cidade de Campinas/SP utilizando o modelo CAL3QHCR

• Autor(a) principal: Zambonini, Fernanda [UNESP]
• Data de Publicação: 2012
• Tipo de documento: Trabalho de conclusão de curso
• Idioma: por
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://hdl.handle.net/11449/121800
• topic: Ar - Poluição; Modelos matematicos; Monoxido de carbono; Automoveis; Campinas (SP)

Air pollution is an environmental issue worldwide and frequently cause negative effects on population health and ecosystems on cities. The relationship between climate and atmospheric pollution can be used as a surrogate to the intensity of air pollution. The present and quantity of some gases can be used as indicators to air quality: particulate matter (PM), sulfur dioxide (SO2), carbon monoxide (CO), ozone (O3), and nitrogen dioxide (NO2). Among those gases, CO has its major source within the cities, where automobiles are the main emitter. But measure pollutant concentration are challenging, sometimes because the lack of good equipments due to high costs and of the large variability of models that varies in precision, way of measure and distribution of sellers. Modeling are useful when there are an intend to evaluate air pollution, its sources and evaluate scenarios. This work aims to use CAL3QHCR model developed by the U.S Environmental Protection Agency (EPA) to generate predictive surfaces of CO concentration distribution on a site within Campinas city, located in São Paulo state, Brazil. CAL3QHCR model use data urban automobile circulation to generate spatial results for CO distribution. We observed that the pollution concentration was lower on our modeling than the concentrations measured by Companhia Ambiental do Estado de São Paulo (CETESB), the main environmental company on the São Paulo state. Also the correlation between average estimates of our model and the measure by CETESB was weak, indicating that the model used on this study need to be or better parameterized, or the scale we measured of CO emissions need to be rescaled. Although the model failed to correlate to CETESB data, maybe one that explore the estimated emissions distributed within the sites to understand spatial distributions of CO on the regions. Also, the generated information can also be used to other studies, and come to be useful to explain heat island