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The rise of low-cost sensing for managing air pollution in cities

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TLDR
In this article, the authors illustrate the drivers behind current rises in the use of low-cost sensors for air pollution management in cities, whilst addressing the major challenges for their effective implementation.
Abstract
Ever growing populations in cities are associated with a major increase in road vehicles and air pollution. The overall high levels of urban air pollution have been shown to be of a significant risk to city dwellers. However, the impacts of very high but temporally and spatially restricted pollution, and thus exposure, are still poorly understood. Conventional approaches to air quality monitoring are based on networks of static and sparse measurement stations. However, these are prohibitively expensive to capture tempo-spatial heterogeneity and identify pollution hotspots, which is required for the development of robust real-time strategies for exposure control. Current progress in developing low-cost micro-scale sensing technology is radically changing the conventional approach to allow real-time information in a capillary form. But the question remains whether there is value in the less accurate data they generate. This article illustrates the drivers behind current rises in the use of low-cost sensors for air pollution management in cities, whilst addressing the major challenges for their effective implementation.

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Citations
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Applications of low-cost sensing technologies for air quality monitoring and exposure assessment: How far have they gone?

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References
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The Potential of Commercial Sensors for Spatially Dense Short-term Air Quality Monitoring Based on Multiple Short-term Evaluations of 30 Sensor Nodes in Urban Areas in Korea

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Low-Cost Energy-Efficient Air Quality Monitoring System Using Wireless Sensor Network

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Journal ArticleDOI

Efficacy of Paired Electrochemical Sensors for Measuring Ozone Concentrations

TL;DR: The results suggest that paired NO2-B43F and OX-B431 sensors permit quantification of NO2 and O3 in mixture, but that O3 concentration estimates are less accurate and precise than those for NO2.
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Validation and application of a novel solution for environmental monitoring: A three months study at "Minerva Medica" archaeological site in Rome

TL;DR: In this paper, the authors proposed a measuring unit based on an ATmega328P microcontroller, gathering signals from: a 9-axis MIMU; a sensor for temperature and relative humidity; and three gas detection miniature boards (NO, NO2 and SO2).
Proceedings ArticleDOI

Guiding the Data Learning Process with Physical Model in Air Pollution Inference

TL;DR: A hybrid algorithm for air pollution inference by guiding the data learning process with physical model is presented, and the quantitative combination of knowledge from observed dataset and a discretized convective-diffusion model is performed within a multi-task learning scheme.
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