ETC/ATNI Report 1/2021: European air quality maps for 2019. PM10, PM2.5, Ozone, NO2 and NOx Spatial estimates and their uncertainties.

This report presents European air quality maps for 2019, as well as the relevant exposure estimates for health related and vegetation related indicators. It also summarizes exposure estimates in the period 2005–2019. The report provides and documents background materials (maps, exposure estimates) for the European Environment Agency’s Air Quality in Europe 2021 online report. Map downloads:

22 Feb 2022

Jan Horálek, Leona Vlasáková, Markéta Schreiberová, Jana Marková, Philipp Schneider, Pavel Kurfürst, Frédéric Tognet, Jana Schovánková, Ondřej Vlček, Alberto González Ortiz, Joana Soares

Prepared by:

Jan Horálek, Markéta Schreiberová, Leona Vlasáková, Jana Marková, Pavel Kurfürst, Jana Schovánková, Ondřej Vlček: Czech Hydrometeorological Institute (CHMI, CZ)
Philipp Schneider: Norwegian Institute for Air Research (NILU, NO)
Frédéric Tognet: National Institute for Industrial Environment and Risk (INERIS, FR)

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.