Estimation of pan-European, daily total, fine-mode and coarse-mode Aerosol Optical Depth at 0.1° resolution to facilitate air quality assessments
Cita com:
hdl:2117/405673
Document typeArticle
Defense date2024
PublisherElsevier
Rights accessOpen Access
Except where otherwise noted, content on this work
is licensed under a Creative Commons license
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Attribution 4.0 International
ProjectFRAGMENT - FRontiers in dust minerAloGical coMposition and its Effects upoN climaTe (EC-H2020-773051)
ACTRIS IMP - Aerosol, Clouds and Trace Gases Research Infrastructure Implementation Project (EC-H2020-871115)
MONITOREO Y DIAGNOSTICO DE LA FORMACION DE OZONO DESDE EL ESPACIO (AEI-PID2020-113840RA-I00)
ACTRIS IMP - Aerosol, Clouds and Trace Gases Research Infrastructure Implementation Project (EC-H2020-871115)
MONITOREO Y DIAGNOSTICO DE LA FORMACION DE OZONO DESDE EL ESPACIO (AEI-PID2020-113840RA-I00)
Abstract
Aerosol Optical Depth (AOD) data derived from satellites is crucial for estimating spatially-resolved PM concentrations, but existing AOD data over land remain affected by several limitations (e.g., data gaps, coarser resolution, higher uncertainty or lack of size fraction data), which weakens the AOD-PM relationship. We developed a 0.1° resolution daily AOD data set over Europe over the period 2003–2020, based on two-stage Quantile Machine Learning (QML) frameworks. Our approach first fills gaps in satellite AOD data and then constructs three components' models to obtain reliable full-coverage AOD along with Fine-mode AOD (fAOD) and Coarse-mode AOD (cAOD). These models are based on AERONET (AErosol RObotic NETwork) observations, Gap-filled satellite AOD, climate and atmospheric composition reanalyses. Our QML AOD products exhibit 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 91.6 %, 81.6 %, and 88.9 % of QML AOD, fAOD and cAOD predictions fall within ±20 % Expected Error (EE) envelopes, respectively. Previous studies reported that a weak satellite AOD-PM correlation across Europe (Pearson correlation coefficient (PCC) around 0.1). Our QML products exhibit higher correlations with ground-level PMs, particularly when broadly matched by size: AOD with PM10, fAOD with PM2.5, cAOD with PM coarse (R = 0.41, 0.45 and 0.26, respectively). Different AOD fractions more effectively distinct PM size fractions, than total AOD. Our QML aerosol dataset and models pioneer full-coverage, daily high-resolution monitoring of fine-mode and coarse-mode aerosols, effectively addressing existing AOD challenges for further PMs exposures' estimations. This dataset opens avenues for more in-depth exploration of the impacts of aerosols on human health, climate, visibility, and biogeochemical processes, offering valuable insights for air quality management and environmental health risk assessment.
CitationChen, Z.-Y. [et al.]. Estimation of pan-European, daily total, fine-mode and coarse-mode Aerosol Optical Depth at 0.1° resolution to facilitate air quality assessments. "Science of The Total Environment", 2024, vol. 918, 170593.
ISSN0048-9697
Publisher versionhttps://www.sciencedirect.com/science/article/pii/S0048969724007319
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