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利用机器学习和统计方法对希腊长期细颗粒物浓度和人群暴露进行时空建模。

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

作者信息

Kakouri Anastasia, Kontos Themistoklis, Grivas Georgios, Filippis Georgios, Korras-Carraca Marios-Bruno, Matsoukas Christos, Gkikas Antonis, Athanasopoulou Eleni, Speyer Orestis, Chatzidiakos Charalampos, Gerasopoulos Evangelos

机构信息

Department of Environment, University of the Aegean, Greece; Institute for Environmental Research & Sustainable Development, National Observatory of Athens, 11810 Athens, Greece.

Department of Environment, University of the Aegean, Greece.

出版信息

Sci Total Environ. 2025 Jan 1;958:178113. doi: 10.1016/j.scitotenv.2024.178113. Epub 2024 Dec 18.

Abstract

The lack of high-resolution, long-term PM 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 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) gridded dataset of PM 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 = 0.73, MAE = 2.2 μg m), validated against ground-based measurements. Winter months consistently showed the highest PM levels, averaging 16.8 μg m, over the domain, due to residential biomass burning (BB) and limited atmospheric dispersion. Summer months had the lowest concentrations, averaging 10.3 μg m, 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 concentrations exceeding the WHO air quality guideline (AQG) of 5 μg m. Moreover, the dataset revealed elevated PM levels across several regions of mainland Greece. Notably, 70 % to 90 % of the population experience levels exceeding 10 μg m 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 assessment in Greece that can support air quality management and health studies.

摘要

希腊和东地中海地区缺乏高分辨率、长期的颗粒物(PM)观测数据,这阻碍了空间模型的开发,而这些模型对于为健康研究提供具有代表性的暴露估计至关重要。这项工作提出了一种空间建模方法来填补这一空白,并首次在希腊全国范围内通过整合现场观测、气象、排放和卫星气溶胶光学厚度(AOD)数据等,评估PM的空间变异性。开发了一个2015年至2022年希腊全国范围内PM浓度的高分辨率(1公里)网格化数据集,并在不同预测场景下评估了七种统计、机器学习和混合模型。随机森林(RF)模型表现出卓越性能(R = 0.73,平均绝对误差(MAE)= 2.2微克/立方米),经地面测量验证。由于居民生物质燃烧(BB)以及大气扩散受限,冬季月份该区域的PM水平始终最高,平均为16.8微克/立方米。夏季月份浓度最低,平均为10.3微克/立方米,而在2020年新冠疫情封锁期间全国范围内出现了大幅下降。人口暴露分析表明,全体希腊人口长期暴露于超过世界卫生组织空气质量指南(AQG)5微克/立方米的PM浓度之下。此外,该数据集显示希腊大陆多个地区的PM水平有所升高。值得注意的是,在希腊大陆中部和北部地区,如色萨利、中马其顿和约阿尼纳,70%至90%的人口经历的PM水平超过10微克/立方米。受居民BB严重影响的约阿尼纳地区记录的污染水平高达世界卫生组织AQG的五倍,凸显了针对性干预的迫切需求。与哥白尼大气监测服务(CAMS)数据集相比,高分辨率RF模型在月平均浓度方面表现卓越,使其成为希腊长期PM评估的可靠工具,能够支持空气质量管理和健康研究。

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