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European air quality maps for 2005 including uncertainty analysis. ETC/ACC Technical Paper, 2007/7
2008
The paper provides the annual update of the European air quality concentration maps and population exposure estimates for human health related indicators of pollutants PM10 (annual average, 90.4 percentile of daily means), PM2.5 (annual average), ozone (93.2 percentile of maximum daily 8-hour means, SOMO35, SOMO10) and NO2 (annual average), and vegetation related ozone indicators (AOT40 for vegetation and for forests) for the year 2017. The report contains also NOx annual average concentration map for 2017. The trends in exposure estimates in the period 2005-2017 for PM10 and ozone, resp. in the period 2007-2017 for PM2.5 are summarized. The analysis is based on interpolation of annual statistics of the 2017 observational data reported by EEA Member countries in 2016 and stored in the Air Quality e-reporting database. The mapping method is the Regression – Interpolation – Merging Mapping. It combines monitoring data, chemical transport model results and other supplementary data using linear regression model followed by kriging of its residuals (residual kriging). The paper presents the mapping results and gives an uncertainty analysis of the interpolated maps.
ETC/ATNI
2020
The report provides the annual update of the European air quality concentration maps and population exposure estimates for human health related indicators of pollutants PM10 (annual average, 90.4 percentile of daily means), PM2.5 (annual average), ozone (93.2 percentile of maximum daily 8-hour means, SOMO35, SOMO10) and NO2 (annual average), and vegetation related ozone indicators (AOT40 for vegetation and for forests) for the year 2018. The report contains also Phytotoxic ozone dose (POD) for wheat and potato maps and NOx annual average maps for 2018. The POD maps are presented for the first time in this regular mapping report. The trends in exposure estimates in the period 2005-2018 are summarized. The analysis is based on interpolation of annual statistics of the 2018 observational data reported by the EEA member and cooperating countries and other voluntary reporting countries and stored in the Air Quality e-reporting database. The mapping method is the Regression – Interpolation – Merging Mapping. It combines monitoring data, chemical transport model results and other supplementary data using linear regression model followed by kriging of its residuals (residual kriging). The paper presents the mapping results and gives an uncertainty analysis of the interpolated maps.
ETC/ATNI
2021
The report provides the annual update of the European air quality concentration maps and population exposure estimates for human health related indicators of pollutants PM10 (annual average, 90.4 percentile of daily means), PM2.5 (annual average), ozone (93.2 percentile of maximum daily 8-hour means, SOMO35, SOMO10) and NO2 (annual average), and vegetation related ozone indicators (AOT40 for vegetation and for forests) for the year 2019. The report contains also Phytotoxic ozone dose (POD) for wheat, potato and tomato maps and NOx annual average map for 2019. The POD map for tomato is presented for the first time in this regular mapping report. The trends in exposure estimates in the period 2005–2019 are summarized. The analysis is based on the interpolation of the annual statistics of the 2019 observational data reported by the EEA member and cooperating countries and other voluntary reporting countries and stored in the Air Quality e-reporting database. The mapping method is the Regression – Interpolation – Merging Mapping (RIMM). It combines monitoring data, chemical transport model results and other supplementary data using linear regression model followed by kriging of its residuals (residual kriging). The paper presents the mapping results and gives an uncertainty analysis of the interpolated maps. It also presents concentration change in 2019 in comparison to the five-year average 2014-2018 using the difference maps.
ETC/ATNI
2021
The report provides the annual update of the European air quality concentration maps and population exposure estimates for human health related indicators of pollutants PM10 (annual average, 90.4 percentile of daily means), PM2.5 (annual average), ozone (93.2 percentile of maximum daily 8-hour means, SOMO35, SOMO10), NO2 (annual average) and benzo(a)pyrene (annual average), and vegetation related ozone indicators (AOT40 for vegetation and for forests) for the year 2020. The report contains also Phytotoxic ozone dose (POD) for wheat, potato and tomato maps and NOx annual average map for 2020. The benzo(a)pyrene map is presented for the first time in this regular mapping report. The trends in exposure estimates in the period 2005–2020 are summarized. The analysis for 2020 is based on the interpolation of the annual statistics of the 2020 observational data reported by the EEA member and cooperating countries and other voluntary reporting countries and stored in the Air Quality e-reporting database, complemented, when needed, with measurements from additional sources. The mapping method is the Regression – Interpolation – Merging Mapping (RIMM). It combines monitoring data, chemical transport model results and other supplementary data using linear regression model followed by kriging of its residuals (residual kriging). The paper presents the mapping results and gives an uncertainty analysis of the interpolated maps. It also presents concentration change in 2020 in comparison to the five-year average 2015-2019 using the difference maps.
ETC/HE
2023
European air quality maps of ozone and PM10 for 2006 and their uncertainty analysis. ETC/ACC Technical paper, 2008/8
2008
European air quality maps of ozone and PM10 for 2007 and their uncertainty analysis. ETC/ACC Technical paper, 2009/9
2010
European air quality maps of ozone and PM10 for 2008 and their uncertainty analysis. ETC/ACC Technical paper, 2010/10
2010
2003
Biogenic volatile organic compound (BVOC) emissions from European vegetation are a major precursor of tropospheric ozone and remain a key uncertainty in regional air-quality modelling. We present two high-resolution (0.1° × 0.1°) European BVOC emission datasets developed within the EU SEEDS project aimed at supporting scientific development within Copernicus Atmospheric Monitoring Service (CAMS). The datasets include BVOC species consistent with the RACM chemical mechanism and are generated by coupling the SURFEX land surface model with the MEGAN3.0 emission model.Emissions based on two land surface model simulations were analysed: (i) an open-loop SURFEX simulation available for 2018–2022, and (ii) a data-assimilation simulation in which satellite leaf area index (LAI) observations are assimilated, available for 2018–2020. In both cases, SURFEX is configured to allow vegetation phenological responses to meteorological variability, enabling a realistic representation of phenology. Evaluation against independent datasets shows that both simulations capture temporal variability in LAI and root-zone soil moisture, with improved skill in the analysis configuration.Given its importance for atmospheric chemistry, we focus on isoprene emissions. Interannual and seasonal variability in isoprene emissions is shown to be primarily driven by LAI variability, with specific events (e.g. summer 2019) linked to drought-induced vegetation stress simulated by SURFEX. Daily variability in isoprene emissions is evaluated against in-situ online isoprene concentration measurements at eight western European sites, revealing moderate to strong correlations across most site-year combinations. Comparisons with other bottom-up European isoprene inventories show that SURFEX-MEGAN3.0 emissions lie between the lower CAMS-GLOB-BIOv3.1 and higher MEGAN-MACC estimates, with differences in seasonality attributable largely to the underlying LAI datasets.These results highlight the important role of vegetation phenology, particularly LAI variability, in controlling BVOC emissions on monthly to interannual timescales, and demonstrate the added value of an Earth-system approach for BVOC emission modelling in support of air-quality assessments.ReferencesHamer, . D., Markelj, M., Rojas-Munoz, O., Bonan, B., Calvet, J.-C., Marécal, V., Guenther, A., Trimmel, H., Vallejo, I., Eckhardt, S., Sousa Santos, G., Sindelarova, K., Simpson, D., Schmidbauer, N., and Tarrasón, L.: Two Biogenic Volatile Organic Compound Emission Datasets over Europe Based on Land Surface Modelling and Satellite Data Assimilation, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2025-442, in review, 2025.
2026
European cities air quality ranking: a new methodology
The EEA has introduced the European City Air Quality Viewer, a tool to assess and compare air quality in European cities. However, this method provides an incomplete picture of air quality as it relies solely on PM2.5 data from monitoring stations, excluding cities lacking monitoring stations and other relevant pollutants such as NO2 and O3. A promising alternative to the current methodology is proposed to reduce these limitations, offering a comprehensive approach to assessing and comparing health risks linked to exposure to multiple pollutants in urban settings. Leveraging continuous air quality maps and population-weighted concentrations enhances coverage and consistency in risk estimation across cities. Additionally, it allows for ranking based on multiple pollutants, unlike the current method, which focuses solely on PM2.5 levels. This approach integrates mortality risk assessments associated with PM2.5, NO2, and O3 exposure, aligning with the Environmental Burden of Disease assessments published by the ETC HE, together with the EEA.
ETC/HE
2024
2014
2001
2015
2017
European Environmental Outlook 2005: Background document air quality 1990-2030. ETC/ACC Technical paper, 2005/2
2005
2006
2005
European pollen reanalysis, 1980–2022, for alder, birch, and olive
The dataset presents a 43 year-long reanalysis of pollen seasons for three major allergenic genera of trees in Europe: alder (Alnus), birch (Betula), and olive (Olea). Driven by the meteorological reanalysis ERA5, the atmospheric composition model SILAM predicted the flowering period and calculated the Europe-wide dispersion pattern of pollen for the years 1980–2022. The model applied an extended 4-dimensional variational data assimilation of in-situ observations of aerobiological networks in 34 European countries to reproduce the inter-annual variability and trends of pollen production and distribution. The control variable of the assimilation procedure was the total pollen release during each flowering season, implemented as an annual correction factor to the mean pollen production. The dataset was designed as an input to studies on climate-induced and anthropogenically driven changes in the European vegetation, biodiversity monitoring, bioaerosol modelling and assessment, as well as, in combination with intra-seasonal observations, for health-related applications.
2024
European Registry of Materials: global, unique identifiers for (undisclosed) nanomaterials
Management of nanomaterials and nanosafety data needs to operate under the FAIR (findability, accessibility, interoperability, and reusability) principles and this requires a unique, global identifier for each nanomaterial. Existing identifiers may not always be applicable or sufficient to definitively identify the specific nanomaterial used in a particular study, resulting in the use of textual descriptions in research project communications and reporting. To ensure that internal project documentation can later be linked to publicly released data and knowledge for the specific nanomaterials, or even to specific batches and variants of nanomaterials utilised in that project, a new identifier is proposed: the European Registry of Materials Identifier. We here describe the background to this new identifier, including FAIR interoperability as defined by FAIRSharing, identifiers.org, Bioregistry, and the CHEMINF ontology, and show how it complements other identifiers such as CAS numbers and the ongoing efforts to extend the InChI identifier to cover nanomaterials. We provide examples of its use in various H2020-funded nanosafety projects.
2022
2009