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Publikationstyp
Wissenschaftlicher Artikel
Erscheinungsjahr
2022
Burden of disease due to ambient particulate matter in Germany - explaining the differences in the available estimates
Burden of disease due to ambient particulate matter in Germany - explaining the differences in the available estimates
Herausgeber
Quelle
International Journal of Environmental Research and Public Health
19 (2022), Heft 20
19 (2022), Heft 20
Schlagwörter
Luftverunreinigung, Schwebstaub, Umweltbedingte Krankheitslast [EBD], Bundesrepublik Deutschland
Zitation
Kienzler, Sarah, Dietrich Plaß, Christian Schuster, Myriam Tobollik and Dirk Wintermeyer, 2022. Burden of disease due to ambient particulate matter in Germany - explaining the differences in the available estimates. International Journal of Environmental Research and Public Health [online]. 2022. vol. 19 (2022), Heft 20. DOI 10.60810/openumwelt-564. Verfügbar unter: https://openumwelt.de/handle/123456789/2554
Zusammenfassung englisch
Ambient particulate matter (PM2.5) pollution is an important threat to human health. The aim of this study is to estimate the environmental burden of disease (EBD) for the German population associated with PM2.5 exposure in Germany for the years 2010 until 2018. The EBD method was used to quantify relevant indicators, e.g., disability-adjusted life years (DALYs), and the life table approach was used to estimate the reduction in life expectancy caused by long-term PM2.5 exposure. The impact of varying assumptions and input data was assessed. From 2010 to 2018 in Germany, the annual population-weighted PM2.5 concentration declined from 13.7 to 10.8 (micro)g/m3. The estimates of annual PM2.5-attributable DALYs for all disease outcomes showed a downward trend. In 2018, the highest EBD was estimated for ischemic heart disease (101.776; 95% uncertainty interval (UI) 62,713-145,644), followed by lung cancer (60,843; 95% UI 43,380-79,379). The estimates for Germany differ from those provided by other institutions. This is mainly related to considerable differences in the input data, the use of a specific German national life expectancy and the selected relative risks. A transparent description of input data, computational steps, and assumptions is essential to explain differing results of EBD studies to improve methodological credibility and trust in the results. Furthermore, the different calculated indicators should be explained and interpreted with caution. 2022 by the authors