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 N2O 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
@article{arxiv.2507.05416,
title = {EmissionNet: Air Quality Pollution Forecasting for Agriculture},
author = {Prady Saligram and Tanvir Bhathal},
journal= {arXiv preprint arXiv:2507.05416},
year = {2025}
}
Comments
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