用深度学习预测农业氮氧化物排放，提升空气质量预报精度
EmissionNet: Air Quality Pollution Forecasting for Agriculture
- 提出EmissionNet与EmissionNet-Transformer两种新模型
- 在高分辨率数据上实现更精准的时空污染预测
- 适合环境监测与农业政策制定者使用
农业排放导致的空气污染是环境与公共健康挑战的重要来源，但常被忽视。传统空气质量预测依赖物理模型，难以捕捉复杂的非线性污染物相互作用。本文评估了多种主流架构，并提出两种新型深度学习模型：EmissionNet（ENV）和EmissionNet-Transformer（ENT）。这两个模型结合卷积与Transformer结构，从高分辨率排放数据中提取时空依赖关系，显著提升对农田氮氧化物（N₂O）排放的预测能力。
原文摘要 · Abstract (English)
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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