Preprints
https://doi.org/10.5194/essd-2023-104
https://doi.org/10.5194/essd-2023-104
27 Mar 2023
 | 27 Mar 2023
Status: this preprint is currently under review for the journal ESSD.

A Pan-European, High-Resolution, Daily Total, Fine-Mode and Coarse-Mode Aerosol Optical Depth dataset based on Quantile Machine Learning

Zhao-Yue Chen, Raul Méndez, Hervé Petetin, Aleksander Lacima, Carlos Pérez García-Pando, and Joan Ballester

Abstract. Ambient particulate matter (PM) is a widespread air pollutant, consisting of a mixture of different particle species suspended in the air that negatively affects human health. Given the generally sparse distribution of in-situ PM measurement networks, spatially-resolved PM estimates are typically derived from Aerosol Optical Depth (AOD) obtained from satellites. However, satellite AOD data over land is affected by several limitations (e.g., data gaps; coarser resolution; higher uncertainty; unavailable or unreliable size fraction information), which weakens the relationship between AOD and PM. We have developed a 0.1 degree resolution daily AOD data set over Europe over the period 2003–2020, based on new Quantile Machine Learning (QML) models. The dataset provides reliable full-coverage AOD along with Fine-mode AOD (fAOD) and Coarse-mode AOD (cAOD), based on AERONET (AErosol RObotic NETwork) site observations and climate and air quality reanalyses. Our results show that the three QML AOD products guarantee better quality with an out-of-sample R2 equal to 0.68 for AOD, 0.66 for fAOD and 0.65 for cAOD, which is 23–92 %, 11–13 % and 115–132 % higher than the corresponding satellite or reanalysis products, respectively. Over 88.8 %, 80.5 % and 88.6 % of QML AOD, fAOD and cAOD predictions fall within ± 20 % Expected Error (EE) envelopes, respectively. Previous studies reported that Europe is one of the regions with the poorest satellite AOD-PM correlation (Pearson correlation coefficient (PCC) around 0.1). Our results show that the three QML products are more correlated with ground-level PMs, especially when they are paired with their corresponding PMs in terms of size: AOD with PM10, fAOD with PM2.5 and cAOD with PM coarse (R = 0.41, 0.45 and 0.26, respectively). Our results show that different PM size fractions may be better predicted using different AOD size fractions, instead of total AOD. QML long-term aerosol dataset (and associated models) not only fix some problems of existing AOD data, but also provide better tools to monitor and analyse fine-mode and coarse-mode aerosols in spatial and temporal dimensions, and to further investigate their impacts on human health, climate, visibility, and biogeochemical cycling. The QML datasets can be downloaded from https://doi.org/10.5281/zenodo.7756570 (Chen et al., 2023).

Zhao-Yue Chen et al.

Status: open (until 18 Jun 2023)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2023-104', Anonymous Referee #1, 25 Apr 2023 reply
    • AC1: 'Reply on RC1', Zhaoyue Chen, 23 May 2023 reply
  • RC2: 'Comment on essd-2023-104', Anonymous Referee #2, 25 Apr 2023 reply
    • AC2: 'Reply on RC2', Zhaoyue Chen, 23 May 2023 reply

Zhao-Yue Chen et al.

Data sets

A Pan-European, Quantile Machine learning (QML) based, Total, Fine-Mode and Coarse-Mode Aerosol Optical Depth dataset (QML AOD)) Zhao-yue Chen, Raul Méndez, Hervé Petetin, Aleksander Lacima, Carlos Pérez García-Pando, and Joan Ballester https://doi.org/10.5281/zenodo.7756570

Zhao-Yue Chen et al.

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Short summary
Given in the limitations of existing AOD and its size fraction information, a new 18-year daily Aerosol Optical Depth (AOD) dataset over Europe has been developed based on quantile machine learning (QML) models. This dataset improves the ability to monitor and analyse fine-mode and coarse-mode aerosols. They provide better tools to investigate negatively affect human health and have impacts on climate, visibility, and biogeochemical cycling.