Giovanni Gualtieri , Lorenzo Brilli , Federico Carotenuto , Alice Cavaliere , Beniamino Gioli , Tommaso Giordano , Simone Putzolu , Carolina Vagnoli , Alessandro Zaldei
{"title":"Assessing capability of Copernicus Atmosphere Monitoring Service to forecast PM2.5 and PM10 hourly concentrations in a European air quality hotspot","authors":"Giovanni Gualtieri , Lorenzo Brilli , Federico Carotenuto , Alice Cavaliere , Beniamino Gioli , Tommaso Giordano , Simone Putzolu , Carolina Vagnoli , Alessandro Zaldei","doi":"10.1016/j.apr.2025.102567","DOIUrl":null,"url":null,"abstract":"<div><div>The accuracy of Copernicus Atmosphere Monitoring Service (CAMS) European forecasts of PM<sub>2.5</sub> and PM<sub>10</sub> hourly concentrations was assessed against hourly observations collected from low-cost stations during the 2022–2023 heating season in the Padana Plain (Italy). The intercomparison of all 11 air quality models integrated into the CAMS framework returned root mean square error (RMSE) values ranging 20.3–37.5 (PM<sub>2.5</sub>) and 22.2–37.8 μg/m<sup>3</sup> (PM<sub>10</sub> concentrations), while hourly variation of observations was poorly captured (<em>r</em> = 0.16–0.41 and 0.25–0.47, respectively). Agreeing with prior research, CAMS models exhibited a marked daily variability in forecasting particulate matter (PM) observations, with the largest discrepancies occurring during the early morning and evening hours. PM<sub>2.5</sub> observations were best predicted by the CHIMERE model, while PM<sub>10</sub> observations by the MINNI model. CAMS Ensemble returned the best <em>r</em> values among all models, while, since all (or the majority of) models over-predicted the observations, it failed to best fit their magnitude, returning mean bias of +8.1 for PM<sub>2.5</sub> and +4.0 μg/m<sup>3</sup> for PM<sub>10</sub> concentrations.</div><div>This study demonstrated that further efforts are still needed to improve the performance of CAMS models in estimating PM concentrations. However, rather than acting on model final output, e.g. by implementing bias-correction techniques, a more robust strategy could be to act upstream, i.e. by adjusting the settings of the individual CAMS models. The latter could include a more region-specific characterisation of the emission input data to avoid unrealistic overweighting of anthropogenic emissions, increasing the number of surface stations used for PM concentration assimilation, or adjusting PM chemical composition.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 8","pages":"Article 102567"},"PeriodicalIF":3.9000,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Atmospheric Pollution Research","FirstCategoryId":"93","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1309104225001692","RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
The accuracy of Copernicus Atmosphere Monitoring Service (CAMS) European forecasts of PM2.5 and PM10 hourly concentrations was assessed against hourly observations collected from low-cost stations during the 2022–2023 heating season in the Padana Plain (Italy). The intercomparison of all 11 air quality models integrated into the CAMS framework returned root mean square error (RMSE) values ranging 20.3–37.5 (PM2.5) and 22.2–37.8 μg/m3 (PM10 concentrations), while hourly variation of observations was poorly captured (r = 0.16–0.41 and 0.25–0.47, respectively). Agreeing with prior research, CAMS models exhibited a marked daily variability in forecasting particulate matter (PM) observations, with the largest discrepancies occurring during the early morning and evening hours. PM2.5 observations were best predicted by the CHIMERE model, while PM10 observations by the MINNI model. CAMS Ensemble returned the best r values among all models, while, since all (or the majority of) models over-predicted the observations, it failed to best fit their magnitude, returning mean bias of +8.1 for PM2.5 and +4.0 μg/m3 for PM10 concentrations.
This study demonstrated that further efforts are still needed to improve the performance of CAMS models in estimating PM concentrations. However, rather than acting on model final output, e.g. by implementing bias-correction techniques, a more robust strategy could be to act upstream, i.e. by adjusting the settings of the individual CAMS models. The latter could include a more region-specific characterisation of the emission input data to avoid unrealistic overweighting of anthropogenic emissions, increasing the number of surface stations used for PM concentration assimilation, or adjusting PM chemical composition.
期刊介绍:
Atmospheric Pollution Research (APR) is an international journal designed for the publication of articles on air pollution. Papers should present novel experimental results, theory and modeling of air pollution on local, regional, or global scales. Areas covered are research on inorganic, organic, and persistent organic air pollutants, air quality monitoring, air quality management, atmospheric dispersion and transport, air-surface (soil, water, and vegetation) exchange of pollutants, dry and wet deposition, indoor air quality, exposure assessment, health effects, satellite measurements, natural emissions, atmospheric chemistry, greenhouse gases, and effects on climate change.