Air pollution exposure monitoring using portable low-cost air quality sensors

Pranvera Kortoçi,Naser Hossein Motlagh, Martha Arbayani Zaidan,Pak Lun Fung,Samu Varjonen, Andrew Rebeiro-Hargrave, Jarkko V. Niemi, Petteri Nurmi, Tareq Hussein, Tuukka Petäjä, Markku Kulmala, Sasu Tarkoma

Smart Health(2022)

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摘要
Urban environments with a high degree of industrialization are infested with hazardous chemicals and airborne pollutants. These pollutants can have devastating effects on human health, causing both acute and chronic diseases such as respiratory infections, lung cancer, and heart disease. Air pollution monitoring is vital not only to citizens, warning them on the health risks of air pollutants, but also to policy-makers, assisting them on drafting regulations and laws that aim at minimizing those health risks. Currently, air pollution monitoring predominantly relies on expensive high-end static sensor stations. These stations produce only aggregated information about air pollutants, and are unable to capture variations in individual's air pollution exposure. As an alternative, this article develops a citizen-based air pollution monitoring system that captures individual exposure levels to air pollutants during daily indoor and outdoor activities. We present a low-cost portable sensor and carry out a measurement campaign using the sensors to demonstrate the validity and benefits of citizen-based pollution measurements. Specifically, we (i) successfully classify the data into indoor and outdoor, and (ii) validate the consistency and accuracy of our outdoor-classified data to the measurements of a high-end reference monitoring station. Our experimental results further prove the effectiveness of our campaign by (i) providing fine-grained air pollution insights over a wide geographical area, (ii) identifying probable causes of air pollution dependent on the area, and (iii) providing citizens with personalized insights about air pollutants in their daily commute.
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关键词
Air quality,Air pollution,Internet of things,Low-cost sensor,Data classification,Wood smoke
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