The significance of air pollution and the problems associated with it are fueling deployments of air quality monitoring stations worldwide. The most common approach for air quality monitoring is to rely on environmental monitoring stations, which unfortunately are very expensive both to acquire and to maintain. Hence, environmental monitoring stations are typically sparsely deployed, resulting in limited spatial resolution for measurements. Recently, low-cost air quality sensors have emerged as an alternative that can improve the granularity of monitoring. The use of low-cost air quality sensors, however, presents several challenges: They suffer from cross-sensitivities between different ambient pollutants; they can be affected by external factors, such as traffic, weather changes, and human behavior; and their accuracy degrades over time. Periodic re-calibration can improve the accuracy of low-cost sensors, particularly with machine-learning-based calibration, which has shown great promise due to its capability to calibrate sensors in-field. In this article, we survey the rapidly growing research landscape of low-cost sensor technologies for air quality monitoring and their calibration using machine learning techniques. We also identify open research challenges and present directions for future research.

中文翻译：

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低成本室外空气质量监测和传感器校准

空气污染的重要性及其相关问题正在推动全球空气质量监测站的部署。空气质量监测最常见的方法是依靠环境监测站，不幸的是，这些监测站的获取和维护都非常昂贵。因此，环境监测站通常部署稀疏，导致测量的空间分辨率有限。最近，低成本的空气质量传感器已经成为一种可以提高监测粒度的替代品。然而，低成本空气质量传感器的使用带来了几个挑战：它们受到不同环境污染物之间的交叉敏感性的影响；它们可能会受到外部因素的影响，例如交通、天气变化和人类行为；并且它们的准确性会随着时间的推移而降低。重新校准可以提高低成本传感器的精度，特别是基于机器学习的校准，由于它能够在现场校准传感器，因此显示出巨大的前景。在本文中，我们调查了用于空气质量监测的低成本传感器技术及其使用机器学习技术进行校准的快速发展的研究前景。我们还确定了开放的研究挑战并提出了未来研究的方向。