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ISPRS Int. J. Geo-Inf. 2018, 7(12), 468; https://doi.org/10.3390/ijgi7120468

Optimising Citizen-Driven Air Quality Monitoring Networks for Cities

1
Westfälische Wilhelms-Universität, 48149 Münster, Germany
2
NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, 1099-085 Lisbon, Portugal
*
Author to whom correspondence should be addressed.
Received: 31 August 2018 / Revised: 23 November 2018 / Accepted: 27 November 2018 / Published: 30 November 2018
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Abstract

Air quality has had a significant impact on public health, the environment and eventually on the economy of countries for decades. Effectively mitigating air pollution in urban areas necessitates accurate air quality exposure information. Recent advancements in sensor technology and the increasing popularity of volunteered geographic information (VGI) open up new possibilities for air quality exposure assessment in cities. However, citizens and their sensors are put in areas deemed to be subjectively of interest (e.g., where citizens live, school of their kids or working spaces), and this leads to missed opportunities when it comes to optimal air quality exposure assessment. In addition, while the current literature on VGI has extensively discussed data quality and citizen engagement issues, few works, if any, offer techniques to fine-tune VGI contributions for an optimal air quality exposure assessment. This article presents and tests an approach to minimise land use regression prediction errors on citizen-contributed data. The approach was evaluated using a dataset (N = 116 sensors) from the city of Stuttgart, Germany. The comparison between the existing network design and the combination of locations selected by the optimisation method has shown a drop in spatial mean prediction error by 52%. The ideas presented in this article are useful for the systematic deployment of VGI air quality sensors, and can aid in the creation of higher resolution, more realistic maps for air quality monitoring in cities. View Full-Text
Keywords: air quality monitoring; sensor location optimisation; crowdsourcing; citizen engagement; volunteered geographic information; land use regression; spatial simulated annealing air quality monitoring; sensor location optimisation; crowdsourcing; citizen engagement; volunteered geographic information; land use regression; spatial simulated annealing
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Gupta, S.; Pebesma, E.; Degbelo, A.; Costa, A.C. Optimising Citizen-Driven Air Quality Monitoring Networks for Cities. ISPRS Int. J. Geo-Inf. 2018, 7, 468.

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