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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/amt-2020-67
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-2020-67
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 03 Mar 2020

Submitted as: research article | 03 Mar 2020

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This preprint is currently under review for the journal AMT.

Application of Low-Cost Fine Particulate Mass Monitors to Convert Satellite Aerosol Optical Depth Measurements to Surface Concentrations in North America and Africa

Carl Malings1,2, Daniel Westervelt3, Aliaksei Hauryliuk4, Albert A. Presto4, Andrew Grieshop5, Ashley Bittner5, Matthias Beekmann1,2, and R. Subramanian1,2 Carl Malings et al.
  • 1OSU-EFLUVE -Observatoire Sciences de l’Univers-Enveloppes Fluides de la Ville à l’Exobiologie, Université Paris-Est-Créteil, CNRS UMS 3563, Ecole Nationale des Ponts et Chaussés, Université de Paris, France
  • 2Laboratoire Interuniversitaire des Systèmes Atmosphériques (LISA), UMR 7583, CNRS, Université Paris-Est-Créteil, Université de Paris, Institut Pierre Simon Laplace, Créteil, France
  • 3Lamont-Doherty Earth Observatory, Columbia University, New York, NY, USA
  • 4Center for Atmospheric Particle Studies, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA
  • 5Department of Civil, Construction and Environmental Engineering, North Carolina State University, Raleigh, NC, USA

Abstract. Low-cost particulate mass sensors provide opportunities to assess air quality at unprecedented spatial and temporal resolutions. Established traditional monitoring networks have limited spatial resolution and are frequently absent in less-developed countries (e.g. in sub-Saharan Africa). Satellites provide snapshots of regional air pollution, but require ground-truthing. Low-cost monitors can supplement and extend data coverage from these sources worldwide, providing a better overall air quality picture. We demonstrate such a multi-source data integration using two case studies. First, in Pittsburgh, Pennsylvania, both traditional monitoring and dense low-cost sensor networks are present, and are compared with satellite aerosol optical depth (AOD) data from NASA's MODIS system. We assess the performance of linear conversion factors for AOD to surface PM2.5 using both networks, and identify relative benefits provided by the denser low-cost sensor network. In particular, with 10 or more ground monitors in the city, there is a two-fold reduction in worst-case surface PM2.5 estimation mean absolute error compared to using only a single ground monitor. Second, in Rwanda, Malawi, and the Democratic Republic of the Congo, traditional ground-based monitoring is lacking and must be substituted with low-cost sensor data. Here, we assess the ability of regional-scale satellite retrievals and local-scale low-cost sensor measurements to complement each other. In Rwanda, we find that combining local ground monitoring information with satellite data provides a 40 % improvement (in terms of surface PM2.5 estimation accuracy) with respect to using ground monitoring data alone. Overall, we find that combining ground-based low-cost sensor and satellite data can improve and expand spatio-temporal air quality data coverage in both well-monitored and data-sparse regions.

Carl Malings et al.

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Codes and Dataset for "Application of Low-Cost Fine Particulate Mass Monitors to Convert Satellite Aerosol Optical Depth Measurements to Surface Concentrations in North America and Africa" C. Malings, D. Westervelt, A. Hauryliuk, A. A. Presto, A. Grieshop, A. Bittner, M. Beekmann, and R. Subramanian https://doi.org/10.5281/zenodo.3691833

Carl Malings et al.

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Short summary
Most air quality information comes from accurate but expensive instruments. These can be supplemented by lower-cost sensors to increase the density of ground data and expand monitoring into less well instrumented areas, like Sub-Saharan Africa. In this paper, we look at how low-cost sensor data can be combined with satellite information on air quality (which require ground data to properly calibrate their measures) and assess the benefits these low-cost sensors provide in this context.
Most air quality information comes from accurate but expensive instruments. These can be...
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