| Abstract |
The purpose of this project is to use principles of machine learning to predict and analyze the effects of nitrous oxide in relation to ocean dead zones. This project uses an algorithmic predictive formula to find the trend and the average increase of the nitrous oxide emissions relative to carbon dioxide emissions. Using this information, this project attempts to demonstrate the direct correlation between hypoxic zones and a continuous increase in global nitrous oxide emissions. Nitrous oxide (N2O) is a long-lived potent greenhouse gas that has an average atmospheric lifetime of 114 years. It contributes towards ozone depletion and global warming. Ocean dead zones are a net source for nitrous oxide emissions, accounting for about 25% of global N2O emissions. |
