EmissionNet: Air Quality Pollution Forecasting for Agriculture

Abstract

Air pollution from agricultural emissions is a significant yet often overlooked contributor to environmental and public health challenges. Traditional air quality forecasting models rely on physics-based approaches, which struggle to capture complex, nonlinear pollutant interactions. In this work, we explore forecasting N${}_2$O agricultural emissions through evaluating popular architectures, and proposing two novel deep learning architectures, EmissionNet (ENV) and EmissionNet-Transformer (ENT). These models leverage convolutional and transformer-based architectures to extract spatial-temporal dependencies from high-resolution emissions data

The appendix figures are mixed up - several emission plots (e.g. CO2, CH4, GWP) are mislabeled and appear in the wrong order, leading to confusion in interpreting the results