Preprints
https://doi.org/10.5194/essd-2024-314
https://doi.org/10.5194/essd-2024-314
07 Aug 2024
 | 07 Aug 2024
Status: this discussion paper is a preprint. It has been under review for the journal Earth System Science Data (ESSD). The manuscript was not accepted for further review after discussion.

High-resolution global ultrafine particle concentrations through a machine learning model and Earth observations

Pantelis Georgiades, Matthias Kohl, Mihalis A. Nicolaou, Theodoros Christoudias, Andrea Pozzer, Constantine Dovrolis, and Jos Lelieveld

Abstract. Atmospheric pollution is a major concern due to its well-documented and detrimental impacts on human health, with millions of excess deaths attributed to it annually. Particulate matter (PM), comprising airborne pollutants in the form of solid and liquid particles suspended in the air, has been particularly concerning. Historically, research has focused on PM with an aerodynamic diameter less than 10 μm (PM10) and 2.5 μm (PM2.5), referred to as coarse and fine particulate matter, respectively. The long term exposure to both classes of PM have been shown to impact human health, being linked to a range of respiratory and cardiovascular complications. Recently, attention has been drawn to the lower end of the size distribution, specifically ultrafine particles (UFPs), with an aerodynamic diameter less than 100 nm (PM0.1). UFPs can deeply penetrate the respiratory system, reach the bloodstream, and have been increasingly associated with chronic health conditions, including cardiovascular disease. Accurate mapping of UFP concentrations at high spatial resolution is crucial considering strong gradients near the sources. However, due to the relatively recent focus on this class of PM, there is a scarcity of long-term measurements, particularly on the global scale. In this study, we employed a machine learning methodology to produce the first global maps of UFP concentrations at high spatial resolution (1 km) by leveraging limited ground station measurements worldwide. We trained an XGBoost model to predict annual UFP concentrations for a decade (2010–2019) and utilized the conformal prediction framework to provide reliable prediction intervals. This approach makes local-to-global UFP data available to support assessments of the health implications associated with long-term exposure.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Pantelis Georgiades, Matthias Kohl, Mihalis A. Nicolaou, Theodoros Christoudias, Andrea Pozzer, Constantine Dovrolis, and Jos Lelieveld

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2024-314', Anonymous Referee #1, 04 Sep 2024
  • RC2: 'Comment on essd-2024-314', Anonymous Referee #2, 05 Sep 2024
  • RC3: 'Comment on essd-2024-314', Anonymous Referee #3, 06 Sep 2024

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2024-314', Anonymous Referee #1, 04 Sep 2024
  • RC2: 'Comment on essd-2024-314', Anonymous Referee #2, 05 Sep 2024
  • RC3: 'Comment on essd-2024-314', Anonymous Referee #3, 06 Sep 2024
Pantelis Georgiades, Matthias Kohl, Mihalis A. Nicolaou, Theodoros Christoudias, Andrea Pozzer, Constantine Dovrolis, and Jos Lelieveld

Data sets

High-resolution global ultrafine particle concentrations through a machine learning model and Earth observations Pantelis Georgiades and Andrea Pozzer https://doi.org/10.17617/3.YK9I4B

Model code and software

Mapping Atmospheric Ultrafine Particles from the Global to the Local Scale Pantelis Georgiades https://github.com/pantelisgeor/Ultrafine-Particles

Pantelis Georgiades, Matthias Kohl, Mihalis A. Nicolaou, Theodoros Christoudias, Andrea Pozzer, Constantine Dovrolis, and Jos Lelieveld

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Short summary
This study maps global ultrafine particle (UFP) concentrations, pollutants known to affect health, using machine learning. By combining environmental and urban data, we predicted UFP levels at a fine 1 km resolution, highlighting areas of high exposure. Our results provide data for public health policies aimed at reducing air pollution impacts. This research bridges data gaps, offering a valuable tool for understanding and mitigating the health effects of UFP exposure.
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