Nitrogen dioxide (NO ₂ ) is one among several constituents of air pollution. To restrict its surface-level concentration to within the limits prescribed by regulatory authorities, dedicated monitoring of its spatiotemporal spread is needed. Satellite-based remote sensing of the tropospheric composition can be used to estimate NO ₂ concentration. However, this is not a direct measurement of the surface-level NO ₂ concentration, though several studies have shown that the tropospheric vertical column density (VCD) estimated by the satellite sensor is correlated with surface-level concentration. This review article covers various aspects related to the estimation of surface-level NO ₂ using remotely sensed data. It provides detailed literature, tracing the evolution of the various methods developed for the estimation, from scaling methods to the initial linear regression (LR) models onward to the more recent deep learning (DL) architectures. The performance of these models is critically reviewed.

中文翻译：

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利用卫星遥感和机器学习估算地表 NO2：综述


二氧化氮 (NO ₂ ) 是空气污染的多种成分之一。为了将其表面浓度限制在监管机构规定的范围内，需要对其时空扩散进行专门监测。基于卫星的对流层成分遥感可用于估算 NO ₂浓度。然而，这并不是对地表NO ₂浓度的直接测量，尽管一些研究表明卫星传感器估计的对流层垂直柱密度（VCD）与地表浓度相关。这篇综述文章涵盖了与使用遥感数据估算地表 NO ₂相关的各个方面。它提供了详细的文献，追踪了为估计而开发的各种方法的演变，从缩放方法到初始线性回归（LR）模型再到最近的深度学习（DL）架构。这些模型的性能受到严格审查。