Spatiotemporal modeling of long-term PM 2.5 concentrations and population exposure in Greece, using machine learning and statistical methods.

Autor: Kakouri A; Department of Environment, University of the Aegean, Greece; Institute for Environmental Research & Sustainable Development, National Observatory of Athens, 11810 Athens, Greece. Electronic address: nkakouri@noa.gr., Kontos T; Department of Environment, University of the Aegean, Greece., Grivas G; Institute for Environmental Research & Sustainable Development, National Observatory of Athens, 11810 Athens, Greece., Filippis G; Department of Environment, University of the Aegean, Greece., Korras-Carraca MB; Laboratory of Meteorology & Climatology, Department of Physics, University of Ioannina, 45110 Ioannina, Greece; Center for the Study of Air Quality and Climate Change, Institute for Chemical Engineering Sciences, Foundation for Research and Technology Hellas, Patras, Greece., Matsoukas C; Department of Environment, University of the Aegean, Greece., Gkikas A; Research Centre for Atmospheric Physics and Climatology, Academy of Athens, Athens, Greece., Athanasopoulou E; Institute for Environmental Research & Sustainable Development, National Observatory of Athens, 11810 Athens, Greece., Speyer O; Institute for Environmental Research & Sustainable Development, National Observatory of Athens, 11810 Athens, Greece., Chatzidiakos C; Institute for Environmental Research & Sustainable Development, National Observatory of Athens, 11810 Athens, Greece., Gerasopoulos E; Institute for Environmental Research & Sustainable Development, National Observatory of Athens, 11810 Athens, Greece. Electronic address: egera@noa.gr.
Jazyk: angličtina
Zdroj: The Science of the total environment [Sci Total Environ] 2024 Dec 18; Vol. 958, pp. 178113. Date of Electronic Publication: 2024 Dec 18.
DOI: 10.1016/j.scitotenv.2024.178113
Abstrakt: The lack of high-resolution, long-term PM 2.5 observations in Greece and the Eastern Mediterranean hampers the development of spatial models that are crucial for providing representative exposure estimates to health studies. This work presents a spatial modeling approach to address this gap and assess PM 2.5 spatial variability for the first time on a national level in Greece, by integrating in situ observations, meteorology, emissions and satellite AOD data among others. A high-resolution (1 km 2 ) gridded dataset of PM 2.5 concentrations across Greece from 2015 to 2022 was developed, and seven statistical, machine learning, and hybrid models were evaluated under different prediction scenarios. Random Forest (RF) models demonstrated superior performance, (R 2  = 0.73, MAE = 2.2 μg m -3 ), validated against ground-based measurements. Winter months consistently showed the highest PM 2.5 levels, averaging 16.8 μg m -3 , over the domain, due to residential biomass burning (BB) and limited atmospheric dispersion. Summer months had the lowest concentrations, averaging 10.3 μg m -3 , while substantial decreases nationwide were observed during the 2020 COVID-19 lockdown. Population exposure analysis indicated that the entire Greek population was exposed to long-term PM 2.5 concentrations exceeding the WHO air quality guideline (AQG) of 5 μg m -3 . Moreover, the dataset revealed elevated PM 2.5 levels across several regions of mainland Greece. Notably, 70 % to 90 % of the population experience levels exceeding 10 μg m -3 in Central and Northern regions of continental Greece like Thessaly, Central Macedonia, and Ioannina. The Ioannina region, which is severely impacted by residential BB, recorded pollution levels up to five times the WHO AQG highlighting the urgent need for targeted interventions. The high-resolution RF model's superior performance for monthly average concentrations, compared to the Copernicus Atmosphere Monitoring Service (CAMS) dataset, renders it a reliable tool for long-term PM 2.5 assessment in Greece that can support air quality management and health studies.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024 Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
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