A model for the data extrapolation of greenhouse gas emissions in the Brazilian hydroelectric system

Rosa, Luiz Pinguelli; Santos, Marco Aurélio dos; Gesteira, Claudio; Xavier, Adilson Elias

doi:10.1088/1748-9326/11/6/064012

Cited by 9 publications

(4 citation statements)

References 17 publications

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“…Other models that can identify more complex non-linear responses, such as Random Forests [6] and Monte Carlo simulation [21], were also used to elucidate the relationship between CO 2 emissions and the factors of environment or dams, and also to predict CO 2 emissions from other under-sampled reservoirs in the nearby geographic region. Unlike these models with the inputs of environmental factors, a mathematical model with the theory of Self-Organized Criticality (SOC) was employed to extrapolate values from one reservoir to another directly without any other features [22]. These pioneering models showed a low degree of precision and regional limitations; therefore, developing and optimizing models of reservoir CO 2 emissions is still one of the future research directions [6,12].…”

Section: Introductionmentioning

confidence: 99%

Estimating Carbon Dioxide (CO2) Emissions from Reservoirs Using Artificial Neural Networks

Chen

Huang

2018

Water

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Freshwater reservoirs are considered as the source of atmospheric greenhouse gas (GHG), but more than 96% of global reservoirs have never been monitored. Compared to the difficulty and high cost of field measurements, statistical models are a better choice to simulate the carbon emissions from reservoirs. In this study, two types of Artificial Neural Networks (ANNs), Back Propagation Neural Network (BPNN) and Generalized Regression Neural Network (GRNN), were used to predict carbon dioxide (CO 2 ) flux emissions from reservoirs based on the published data. Input variables, which were latitude, age, the potential net primary productivity, and mean depth, were selected by Spearman correlation analysis, and then the rationality of these inputs was proved by sensitivity analysis. Besides this, a Multiple Non-Linear Regression (MNLR) and a Multiple Linear Regression (MLR) were used for comparison with ANNs. The performance of models was assessed by statistical metrics both in training and testing phases. The results indicated that ANNs gave more accurate results than regression models and GRNN provided the best performance. With the help of this GRNN, the total CO 2 emitted by global reservoirs was estimated and possible CO 2 flux emissions from a planned reservoir was assessed, which illustrated the potential application of GRNN.

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Section: Introductionmentioning

confidence: 99%

Estimating Carbon Dioxide (CO2) Emissions from Reservoirs Using Artificial Neural Networks

Chen

Huang

2018

Water

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“…However, recently there is increased awareness that these reservoirs could be contributing to the increase of GHGs in the atmosphere (Deemer et al, 2016). Concerns have been raised around the world (IHA, 2010), also creating awareness in the Amazon region, where several studies and monitoring campaigns have taken place (Devol et al, 1990;Kemenes et al, 2007;Lima, 2005;Richey et al, 2002;Rosa et al, 2016;Rosa et al, 2004;Santos et al, 2008;Santos et al, 2006).…”

Section: H I G H L I G H T Smentioning

confidence: 99%

Conjunctive use of in situ gas sampling and chromatography with geospatial analysis to estimate greenhouse gas emissions of a large Amazonian hydroelectric reservoir

Brandao

Mannaerts

Brandão

et al. 2019

Science of The Total Environment

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“…Gas emission analysis was conducted using two methodologies: diffusive flux analysis and bubbling flux analysis. Diffusive flux used a flotation chamber with a volume of 0.001 m 3 and area of 0.045 m 2 , and the samples were run at times zero, 2, 4 and 8 min 22°58'36" 43°12'56" 3.5 (Marcelino et al, 2015;Rosa et al, 2016;Santos et al, 2016;De Mello et al, 2017). This time is related to the flotation chamber volume (1,000 mL) in order to obtain measurements before saturation.…”

Section: Samplesmentioning

confidence: 99%

Greenhouse gas emission from a eutrophic coastal lagoon in Rio de Janeiro, Brazil

Fonseca-Viana

Santos²,

Bernardes³

et al. 2019

LAJAR

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Greenhouse gases increased in concentrations over pre-industrial values by 257% for methane and 145% for carbon dioxide in 2016. Such increased levels are the main climate change drivers and may affect aquatic systems that accumulate and carry carbon to the ocean and the atmosphere. Additionally, these systems are sensitive to environmental changes since their physical, chemical and biological properties respond rapidly to changes. Therefore, this study focus on the greenhouse gases dynamic over an urban eutrophic tropical lagoon. Samplings were performed in the Rodrigo de Freitas Lagoon by covering four periods in 2016 (April, June, October and December). Mean diffusive flux was -1,466.8 mg m -2 d -1 of carbon dioxide and 113.7 mg m -2 d -1 of methane. Regarding the bubbling, fluxes were 58.28 mg m -2 d -1 for methane and negligible for carbon dioxide (mean value of 5.01 mg m -2 d -1 ). Environmental parameters such as depth, water temperature and sediment particle size were strongly related to the fluxes. In conclusion, the region is a sink of carbon dioxide and a source of methane to the atmosphere. Additionally, the rivers discharge impacts the lagoon by generating a methane hotspot emission region.

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A model for the data extrapolation of greenhouse gas emissions in the Brazilian hydroelectric system

Cited by 9 publications

References 17 publications

Estimating Carbon Dioxide (CO2) Emissions from Reservoirs Using Artificial Neural Networks

Estimating Carbon Dioxide (CO2) Emissions from Reservoirs Using Artificial Neural Networks

Conjunctive use of in situ gas sampling and chromatography with geospatial analysis to estimate greenhouse gas emissions of a large Amazonian hydroelectric reservoir

Greenhouse gas emission from a eutrophic coastal lagoon in Rio de Janeiro, Brazil

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