Neural network analysis on the effect of heat fluxes on greenhouse gas emissions from anaerobic swine waste treatment lagoon

Abstract

Abstract. In this study, we examined the various meteorological factors (i.e., air temperatures, solar radiation, and heat fluxes) that potentially affect greenhouse gas (GHG) emissions from swine waste lagoon. GHG concentrations (methane and carbon dioxide) were monitored using a photoacoustic gas analyzer. The GHG emissions from the lagoon were monitored continuously for a twenty-four hour cycle, twice a week during a winter month at a height of fifty centimeters above the lagoon surface. Meteorological data were also monitored simultaneously. Heat fluxes were tabulated and correlated to the averaged GHG concentrations. Multi-layer perceptron (MLP) neural network predictive model was built based on the most important meteorological parameters. The results from MLP neural networks analysis show that GHG emissions from the swine waste lagoon were affected by heat fluxes such as net solar radiation, sensible heat, and latent heat of vaporization. Thus it is important to consider environmental conditions (i.e., meteorological parameters such as solar radiation, latent heat and etc.) in formulating management or abatement strategies for reducing GHG emissions from swine waste lagoons or any other air pollutant emissions from livestock waste receptacles.