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Lightweight Assimilation of Open Urban Ambient Air Quality Monitoring Data and Numerical Simulations with Unknown Uncertainty

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Abstract

Accurate monitoring systems and numerical models of urban ambient air quality are essential to reduce the risks to public health. The growing quantity of online open data provide new opportunities for assimilation algorithms to improve ambient air quality monitoring, including estimates of their uncertainty. The assimilation of large-scale numerical simulations with observations from urban ambient air quality monitoring stations requires uncertainty estimates from both data sources to cope with unknown events and changing environmental conditions. However, uncertainty estimates from open access numerical models and monitoring stations are frequently unavailable. To address this gap, we propose a lightweight data-driven framework for data assimilation on low-powered embedded hardware suitable for open data without uncertainty estimates, including Internet of Things (IoT) systems. The algorithms are compared and validated using open data from a reference ambient air quality monitoring station during two time periods, the first in fall (October to November) and the second in winter (January to February). Open numerical model data were obtained during these periods from the System for Integrated modeLing of Atmospheric coMposition (SILAM). The algorithms are also demonstrated on two IoT sensors located 60 m and 700 m from the reference station. This work is significant because it offers a computationally lightweight approach to sequentially assimilate station, sensor, and numerical simulation data that do not have prior uncertainty estimates. The proposed method can be applied to impute missing data, to improve the reporting accuracy of air quality observations, and to provide missing uncertainty estimates.

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Data Availability

The data used in the study and Python scripts of the described algorithms are accessible via GitHub by https://github.com/effie-ms/rls-assimilation and distributed under the MIT license.

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Acknowledgements

We would like to thank Thinnect OÜ (https://thinnect.com/ - the Estonian IoT edge network service provider) for providing the IoT data for experiments.

Funding

Lizaveta Miasayedava’s contribution to this work was supported by the European Union through European Social Fund Project “ICT programme.” The contributions of Jaanus Kaugerand and Jeffrey A. Tuhtan were funded by the project ISC2PT II (Intelligent Smart City and Critical Infrastructure Protection Technologies), funded by the European Regional Development Fund within the framework of the EU Smart Specialisation programme. Jeffrey A. Tuhtan’s contribution was also funded in part by the Estonian Research Council Grant PRG 1243.

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Authors and Affiliations

  1. Research Laboratory for Proactive Technologies, Tallinn University of Technology, Ehitajate tee 5, Tallinn, 12616, Estonia

    Lizaveta Miasayedava & Jaanus Kaugerand

  2. Department of Computer Systems, Tallinn University of Technology, Ehitajate tee 5, Tallinn, 12616, Estonia

    Lizaveta Miasayedava & Jeffrey A. Tuhtan

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  1. Lizaveta Miasayedava
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All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by L.M. The first draft of the manuscript was written by L.M. All authors read and approved the final manuscript.

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Correspondence to Lizaveta Miasayedava.

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Miasayedava, L., Kaugerand, J. & Tuhtan, J.A. Lightweight Assimilation of Open Urban Ambient Air Quality Monitoring Data and Numerical Simulations with Unknown Uncertainty. Environ Model Assess 28, 961–975 (2023). https://doi.org/10.1007/s10666-023-09909-x

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