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Institute of

WO Forest Ecosystems


Mercury deposition in forest ecosystems

Lead Institute WO Institute of Forest Ecosystems

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© Nils König, NW-FVA

Atmospheric input of mercury (Hg) causes long-term pressure on ecosystems. While the paths of mercury in wetlands have been extensively studied, they are poorly understood for forest ecosystems (Xun Wang et al., 2017). Nevertheless, the accumulation rate in soil-productive forests is about 2.5% higher than in the soils of low-vegetation areas; this can easily be explained by the combing effect of (evergreen) trees and is also known for other pollutants.


Background and Objective

Mercury (Hg) is one of the priority substances in the Geneva Air Pollution Control Convention (CLRTAP). In the Heavy Metals Protocol (Aarhus 1998, updated 2012) regulations for reducing the emission of cadmium (Cd), lead (Pb) and Hg into the atmosphere were agreed. The risk assessment with the methods developed within the CLRTAP for calculating and checking critical loads for Hg and their exceedances shows a widespread exceedance of the ecological load limits for terrestrial ecosystems, in particular forests, for Germany (and Europe). In the National Strategy on Biological Diversity, Germany has set itself the target of achieving by 2020 the Critical Loads u. a. for heavy metals. This goal is unlikely to be achieved.


The project aims to bridge existing knowledge gaps regarding atmospheric inputs of mercury and their distribution in forest ecosystems. It is intended to support the development of a methodology to measure wet mercury deposition under the canopy of forests, which nevertheless provides reliable, reproducible results when using the simplest possible technical means. This creates the prerequisites for recording the actual entry in forest soils, which up to now can only be quantified with great uncertainty with the help of existing models (e.g. land use-dependent Hg deposition calculation with the EMEP model). The measurement results should be evaluated together with measured Hg concentrations in leaves, needles and soils (from the intensive forest environmental monitoring) in order to obtain information on Hg flows in, and possibly also for reemission from forest ecosystems.



Measurement of mercury in the wet deposition under the forest canopy on one exemplary Level II-plot for method development.

Preliminary Results

Schad T, Sanders TGM, Werner W, Eghdami H (2018) Erarbeitung von Vorschlägen für ein repräsentatives Messnetz zur Überwachung der Wirkungen bodennahen Ozons in Umsetzung der Richtlinie (EU) 2016/2284, Artikel 9 und Anhang V : Abschlussbericht. Dessau: Umweltbundesamt, 141 p, Texte UBA 114

Involved external Thünen-Partners

Funding Body

  • Umweltbundesamt (UBA)
    (national, öffentlich)


6.2018 - 7.2022

More Information

Projekt type:
Project status: ongoing


  1. 0

    Krüger I, Schmitz A, Sanders TGM (2021) Climate condition affects foliar nutrition in main European tree species. Ecol Indic 130:108052, DOI:10.1016/j.ecolind.2021.108052

  2. 1

    Wang S, Zhang Y, Ju W, Chen JM, Cescatti A, Sardans J, Janssens IA, Wu M, Berry JA, Campbell E, Fernandez-Martinez M, Alkama R, Sitch S, Smith WK, Yuan W, He W, Lombardozzi D, Kautz M, Sanders TGM, Krüger I, et al (2021) Response to Comments on "Recent global decline of CO2 fertilization effects on vegetation photosynthesis". Science 373(6562):1-8, DOI:10.1126/science.abg7484

  3. 2

    Krüger I, Sanders TGM, Holzhausen M, Schad T, Schmitz A, Strich S (2020) Am Puls des Waldes : Umweltwandel und seine Folgen - ausgewählte Ergebnisse des intensiven forstlichen Umweltmonitorings. Berlin: BMEL, 51 p

  4. 3

    Sanders TGM, Krüger I, Holzhausen M (2020) Das intensive forstliche Monitoring - Level II. Eberswalde: Thünen-Institut für Waldökosysteme, 2 p, Project Brief Thünen Inst 2020/25, DOI:10.3220/PB1608106763000

  5. 4

    Sanders TGM, Spathelf P, Bolte A (2019) The response of forest trees to abiotic stress. Burleigh Dodds Ser Agric Sci 71:99-116, DOI:10.19103/AS.2019.0057.05

  6. 5

    Krause S, Strer M, Mund J-P, Sanders TGM (2019) UAV remote sensing data handling: A transition from testing to long-term data acquisition for intensive forest monitoring. J Photogramm Remote Sensing Geoinf Sci 28(39):167-174

  7. 6

    Prescher A-K, Schmitz A, Sanders TGM, Nussbaumer A, Karlsson GP, Neirynck J, Gottardini E, Hansen K, Johnson J, Nieminen TM, Schaub M, Ukonmaanaho L, Vanguelova EI, Verstraeten A, Waldner P (2018) Change in sulphur pools in forest ecosystems following the reduction of atmospheric sulphur dioxide. Geophys Res Abstr 20:9027

  8. 7

    Seidling W, Travaglini D, Meyer P, Waldner P, Fischer R, Granke O, Chirici G, Corona P (2014) Dead wood and stand structure - relationships for forest plots across Europe. iForest 7: 269-281, DOI:10.3832/ifor1057-007

  9. 8

    Giordani P, Calatayud V, Stofer S, Seidling W, Granke O, Fischer R (2014) Detecting the nitrogen critical loads on European forests by means of epiphytic lichens : a signal-to-noise evaluation. Forest Ecol Manag 311(1):29-40, DOI:10.1016/j.foreco.2013.05.048

  10. 9

    Seidling W, Kanold A, Kompa T, Lambertz B, Scheibe O, Schiller M, Schmiedinger A, Wenzel A, Werner W, Zoldan JW (2014) Vegetationserhebungen: Bearbeiterunterschiede bei Artenzahlen von Gefäßpflanzen. Tuexenia 34:329-346

  11. 10

    Sanders TGM, Seidling W (2013) Damaging agents in different forest types for adapted risk management. In: Building bridges in ecology : linking systems, scales and disciplines ; GfÖ 43rd Annual Meeting of the Ecological Society of Germany, Austria and Switzerland ; September 9 to 13, 2013, Potsdam, Germany ; book of abstracts. Göttingen: Gesellschaft für Ökologie, pp 128-129

  12. 11

    Seidling W, Ziche D, Beck W (2012) Climate responses and interrelations of stem increment and crown transparency in Norway spruce, Scots pine, and common beech. Forest Ecol Manag 284:196-204, DOI:10.1016/j.foreco.2012.07.015

  13. 12

    Ziche D, Seidling W (2010) Homogenisation of climate time series from ICP forests level II monitoring sites in Germany based on interpolated climate data. Ann Forest Sci 67(8):804/1-804/6, DOI:10.1051/forest/2010051

  14. 13

    Ziche D, Seidling W (2010) Homogenisierte Klimadaten aus dem Waldmonitoring . AFZ Der Wald 65(24):11-13

  15. 14

    Cox F, Barsoum N, Lilleskov EA, Bidartondo MI, Seidling W (2010) Mykorrhizierung von Kiefernwurzeln : Stickstoffverfügbarkeit als Einflussfaktor. AFZ Der Wald 65(24):8-10

  16. 15

    Ziche D, Seidling W (2009) Benefits of meteorological measurements at forest monitoring sites compared with interpolated climatic data. In: Kaennel Dobbertin M (ed) Long-term ecosystem research: Understanding the present to shape the future : International Conference, Zurich, Switzerland, September 7-10, 2009 ; Abstracts. p 28

  17. 16

    Seidling W, Beck W, Ziche D (2009) Crown condition and radial stem wood increment: documentation of complex relationships. In: Kaennel Dobbertin M (ed) Long-term ecosystem research: Understanding the present to shape the future : International Conference, Zurich, Switzerland, September 7-10, 2009 ; Abstracts. p 95

  18. 17

    Seidling W, Lux W, Strich S, Bolte A (2007) Forstliches Umweltmonitoring in Deutschland unter Forest-Focus. AFZ Der Wald 62(11):577-579

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