Air pollution is a widespread problem due to its impact on both humans and the environment. Providing decision makers with artificial intelligence based solutions requires to monitor the ambient air quality accurately and in a timely manner, as AI models highly depend on the underlying data used to justify the predictions. Unfortunately, in urban contexts, the hyper-locality of air quality, varying from street to street, makes it difficult to monitor using high-end sensors, as the cost of the amount of sensors needed for such local measurements is too high. In addition, development of pollution dispersion models is challenging. The deployment of a low-cost sensor network allows a more dense cover of a region but at the cost of noisier sensing. This paper describes the development and deployment of a low-cost sensor network, discussing its challenges and applications, and is highly motivated by talks with the local municipality and the exploration of new technologies to improve air quality related services. However, before using data from these sources, calibration procedures are needed to ensure that the quality of the data is at a good level. We describe our steps towards developing calibration models and how they benefit the applications identified as important in the talks with the municipality.

中文翻译：

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迈向数据校准和服务开发的步骤

空气污染由于其对人类和环境的影响而成为一个普遍存在的问题。为决策者提供基于人工智能的解决方案需要准确，及时地监控周围的空气质量，因为AI模型高度依赖于用于证明预测合理性的基础数据。不幸的是，在城市环境中，空气质量的超局域性在街道上各不相同，这使得使用高端传感器进行监控变得困难，因为进行此类本地测量所需的传感器数量成本太高。另外，污染扩散模型的开发具有挑战性。低成本传感器网络的部署可以使区域覆盖更密集，但会带来噪声较大的传感成本。本文介绍了低成本传感器网络的开发和部署，与当地市政当局进行的会谈以及探索改善空气质量相关服务的新技术的积极性极大地激发了人们的兴趣。但是，在使用来自这些来源的数据之前，需要进行校准程序以确保数据质量处于良好水平。我们描述了开发校准模型的步骤，以及它们如何使与市政当局的对话中确定的重要应用受益。