Global Centre for Clean Air Research (GCARE), Department of Civil and Environmental Engineering, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford GU2 7XH, United Kingdom.
Global Centre for Clean Air Research (GCARE), Department of Civil and Environmental Engineering, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford GU2 7XH, United Kingdom; Environmental Flow (EnFlo) Research Centre, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford GU2 7XH, United Kingdom.
Sci Total Environ. 2017 Dec 31;607-608:691-705. doi: 10.1016/j.scitotenv.2017.06.266. Epub 2017 Jul 27.
Low-cost sensor technology can potentially revolutionise the area of air pollution monitoring by providing high-density spatiotemporal pollution data. Such data can be utilised for supplementing traditional pollution monitoring, improving exposure estimates, and raising community awareness about air pollution. However, data quality remains a major concern that hinders the widespread adoption of low-cost sensor technology. Unreliable data may mislead unsuspecting users and potentially lead to alarming consequences such as reporting acceptable air pollutant levels when they are above the limits deemed safe for human health. This article provides scientific guidance to the end-users for effectively deploying low-cost sensors for monitoring air pollution and people's exposure, while ensuring reasonable data quality. We review the performance characteristics of several low-cost particle and gas monitoring sensors and provide recommendations to end-users for making proper sensor selection by summarizing the capabilities and limitations of such sensors. The challenges, best practices, and future outlook for effectively deploying low-cost sensors, and maintaining data quality are also discussed. For data quality assurance, a two-stage sensor calibration process is recommended, which includes laboratory calibration under controlled conditions by the manufacturer supplemented with routine calibration checks performed by the end-user under final deployment conditions. For large sensor networks where routine calibration checks are impractical, statistical techniques for data quality assurance should be utilised. Further advancements and adoption of sophisticated mathematical and statistical techniques for sensor calibration, fault detection, and data quality assurance can indeed help to realise the promised benefits of a low-cost air pollution sensor network.
低成本传感器技术通过提供高密度的时空污染数据，有可能彻底改变空气污染监测领域。这些数据可用于补充传统的污染监测，改善暴露评估，并提高社区对空气污染的认识。然而，数据质量仍然是一个主要问题，阻碍了低成本传感器技术的广泛应用。不可靠的数据可能会误导毫无戒心的用户，并可能导致令人震惊的后果，例如报告可接受的空气污染物水平，而实际上这些水平已经超过了人类健康安全的限制。本文为最终用户提供了科学指导，以有效地部署用于监测空气污染和人们暴露的低成本传感器，同时确保合理的数据质量。我们回顾了几种低成本颗粒和气体监测传感器的性能特征，并通过总结这些传感器的能力和局限性，为最终用户提供了正确选择传感器的建议。还讨论了有效部署低成本传感器和维护数据质量的挑战、最佳实践和未来展望。为了保证数据质量，建议采用两阶段传感器校准过程，包括制造商在受控条件下进行实验室校准，并由最终用户在最终部署条件下进行例行校准检查进行补充。对于例行校准检查不切实际的大型传感器网络，应使用数据质量保证的统计技术。进一步推进和采用复杂的数学和统计技术进行传感器校准、故障检测和数据质量保证，确实有助于实现低成本空气污染传感器网络的预期效益。
