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How opportunistic mobile monitoring can enhance air quality assessment?

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Abstract

The deteriorating air quality in urban areas, particularly in developing countries, has led to increased attention being paid to the issue. Daily reports of air pollution are essential to effectively manage public health risks. Pollution estimation has become crucial to expanding spatial and temporal coverage and estimating pollution levels at different locations. The emergence of low-cost sensors has enabled high-resolution data collection, either in fixed or mobile settings, and various approaches have been proposed to estimate air pollution using this technology. The objective of this study is to enhance the data from fixed stations by incorporating opportunistic mobile monitoring (OMM) data. The main research question we are dealing with is: How can we augment fixed station data through OMM? In order to address the challenge of limited OMM data availability, we leverage existing data collected during periods when the pollution maps align with those observed by the fixed stations. By combining the fixed and mobile data, we apply interpolation techniques to produce more accurate pollution maps. The efficacy of our approach is validated through experiments conducted on a real-life dataset.

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Data Availability

Two types of data were used in our work. Public datasets can be retrieved from the portal https://openaq.org and https://www.habitatmap.org/aircasting (Chicago Experiment). However, due to the GDPR restrictions, personal data from Versailles campaign are provided under request in an aggregated form. Please contact the corresponding author.

Notes

  1. This methodology is motivated by the context of GoGreen Routes https://gogreenroutes.eu/

  2. Please note that OMM (opportunistic mobile monitoring) and MCS (opportunistic mobile crowd sensing) are the same. For the rest of this paper, we will use OMM to refer to opportunistic mobile monitoring.

  3. https://www.elichens.com/

  4. https://www.habitatmap.org/aircasting

  5. https://openaq.org/

  6. https://docs.openaq.org/docs/about-api

  7. https://github.com/HabitatMap/AirCasting

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Acknowledgements

This work has supported by the H2020 EU GO GREEN ROUTES funded under the research and innovation programme H2020- EU.3.5.2 grant agreement No 869764. We would like to express our sincere gratitude to all the participants who contributed in the data collection in Versailles. We are thankful to all volunteers as their invaluable contributions were instrumental in enriching the quality and depth of our research.

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Authors and Affiliations

  1. DAVID Lab, UVSQ-Université Paris-Saclay, Versailles, France

    Mohammad Abboud, Yehia Taher & Karine Zeitouni

  2. LASTIG, Univ Gustave Eiffel, IGN-ENSG, Saint-Mandé, France

    Ana-Maria Olteanu-Raimond

Authors
  1. Mohammad Abboud
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  2. Yehia Taher
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  3. Karine Zeitouni
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  4. Ana-Maria Olteanu-Raimond
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Correspondence to Mohammad Abboud.

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Abboud, M., Taher, Y., Zeitouni, K. et al. How opportunistic mobile monitoring can enhance air quality assessment?. Geoinformatica (2024). https://doi.org/10.1007/s10707-024-00516-w

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