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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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Proceedings ArticleDOI

SmartAQnet: remote and in-situ sensing of urban air quality

TL;DR: The project “SmartAQnet”, funded by the German Federal Ministry of Transport and Digital Infrastructure (BMVI), is based on a pragmatic, data driven approach, which for the first time combines existing data sets with a networked mobile measurement strategy in the urban space.
Journal ArticleDOI

Highly sensitive and on-site NO2 SERS sensors operated under ambient conditions

Sunho Kim, +2 more
- 25 Jun 2018 - 
TL;DR: A high-performance, on-site, rapid NO2 gas sensor that could be operated under ambient conditions by combining a highly sensitive 3D porous SERS substrate and a handheld Raman spectrometer is developed.
Journal ArticleDOI

Spatial modelling of particulate matter air pollution sensor measurements collected by community scientists while cycling, land use regression with spatial cross-validation, and applications of machine learning for data correction

TL;DR: In this article, a machine learning model was developed to adjust the sensor observations, which demonstrated their highest errors during periods of high humidity, and the mean bias was reduced to −0.5μg/m3.
Journal ArticleDOI

Calibration Model of a Low-Cost Air Quality Sensor Using an Adaptive Neuro-Fuzzy Inference System.

TL;DR: DiracSense, a custom-made LAQS that monitors the gas pollutants ozone (O3), nitrogen dioxide (NO2), and carbon monoxide (CO) is presented and its performance is investigated based on laboratory calibration and field experiments to suggest that the ANFIS model is promising as a calibration tool since it has the capability to improve the accuracy and performance of the low-cost electrochemical sensor.
Journal ArticleDOI

Citizen-Based Air Quality Monitoring: The Impact on Individual Citizen Scientists and How to Leverage the Benefits to Affect Whole Regions

TL;DR: In this paper, the authors present the results from 53 interviews with involved residents and show that the active involvement of individuals in a complex process such as measuring tropospheric ozone can have important impacts on their knowledge and attitudes.
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