EmissionNet: Air Quality Pollution Forecasting for Agriculture

Prady Saligram; Tanvir Bhathal

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arXiv · 2507.05416

EmissionNet: Air Quality Pollution Forecasting for Agriculture

Prady Saligram·Tanvir Bhathal

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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

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Prady Saligram, Tanvir Bhathal. 2025-07-07. EmissionNet: Air Quality Pollution Forecasting for Agriculture. https://arxiv.org/abs/2507.05416

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