Abstract

The use of low-cost sensors for air quality measurements is expanding rapidly, with an associated rise in the number of citizens measuring air quality themselves. This has major implications for traditional air quality monitoring as performed by Environmental Protection Agencies. Here we reflect on the experiences of the Dutch Institute for Public Health and the Environment (RIVM) with the use of low-cost sensors, particularly NO2 and PM10/PM2.5-sensors, and related citizen science, over the last few years. Specifically, we discuss the Dutch Innovation Program for Environmental Monitoring, which comprises the development of a knowledge portal and sensor data portal, new calibration approaches for sensors, and modelling and assimilation techniques for incorporating these uncertain sensor data into air pollution models. Finally, we highlight some of the challenges that come with the use of low-cost sensors for air quality monitoring, and give some specific use-case examples. Our results show that low-cost sensors can be a valuable addition to traditional air quality monitoring, but so far, their use in official monitoring has been limited. More research is needed to establish robust calibration methods while ongoing work is also aimed at a better understanding of the public’s needs for air quality information to optimize the use of low-cost sensors.

Highlights

  • Air pollution is regarded as an on-going threat to public health and is linked to an estimated400,000 premature deaths in the EU each year [1]

  • Platform for sensor datadata opens up opportunities to important wish of the air quality community, as it allows a more efficient collection and visualization of integrate data from national or global sources, which may lead to the development of data products sensor data, and allows forhelp more powerful data analytics

  • A knowledge portal and a centralized data platform are important tools to connect with citizens and their respective needs, and to facilitate the sharing of air quality data obtained by low-cost sensors

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Summary

Introduction

Air pollution is regarded as an on-going threat to public health and is linked to an estimated400,000 premature deaths in the EU each year [1]. Regulatory monitoring networks are generally strictly regulated and based on a limited number of advanced, quality-assured and costly measurement stations. Due to their low spatial resolution, it is often difficult for regulatory measurement networks to meet all demands from local populations asking for detailed information about pollutants, for instance at their residence [4]. Instead, such information is usually derived from models that come with considerable uncertainties

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