Statistical modeling of diffusive CO2 emissions before the creation of the SINOP hydroelectric reservoir, Brazil

Abstract

Abstract Several discussions have arisen about energy from hydroelectric plants being considered clean energy and its reservoirs have been investigated due to the large emission of greenhouse gases (GHG), such as carbon dioxide, methane, and nitrous oxide. The present work shows a statistical study of the diffusive CO2 emissions before the formation of the reservoir of the hydroelectric power plant (HPP) of SINOP, Brazil. The association between emissions collected at the surface (water-air) and at the bottom of the reservoir (sediment-water) was investigated during four data collection campaigns, carried out from November 2017 to September 2018. This study aims to compare the effect of reservoir depth on the diffusive flow of CO2 at 34 collection points. The variable depth analyzed was defined from points collected on the surface and bottom of the reservoir. The objective is to detect whether different periods of time and whether the depth of the reservoir have a direct impact on the behavior of diffusive CO2 emissions. As the measurements of the observational unit are repeatedly observed, there is a multilevel structure, individuals are independent of each other, but there is an intra-individual correlation. Considering this data configuration, an estimation of generalized equations (GEE) was performed, which is a technique that estimates the intra-individual correlation matrix and thus produces estimates for the parameters of the generalized regression models (Generalized Regression Models – GLM) that are not biased. The study showed that the average diffusive CO2 emissions are higher on the reservoir surface. The study also found that, on average, there are more emissions during the rainy season in the region than during the dry season.

Keywords

• repeated measures
• carbon dioxide
• generalized estimation equations
• greenhouse gases