Is soil contamination a missing driver of soil heterotrophic respiration in land surface models? A study case with copper –
Year: 2024
Type: article
Abstract: Land Surface Models (LSMs) are crucial elements of Earth System Models used to estimate the effects of anthropogenic greenhouse gas (GHG) emissions on Earth's climate. Nevertheless, as well as land use change and direct GHG emissions, anthropogenic activities are also associated with contaminant emissions and depositions. Although contamination has a recognized impact on soil processes such as GHG emissions, soil contamination is currently not considered as an important process to consider into LSMs. In this study we hypothesized that soil contamination has significant impact on soil CO 2 emissions such as heterotrophic respiration (Rh). To this end, we analyzed how soil contamination may account for residuals of modeled Rh from 11 LSMs as compared to Rh products derived from observations over Europe. We used generalized least squared mixed models to evaluate the primary factors driving of the model residuals. Among contaminants, we focused on copper (Cu), which is widely used in industry or agriculture, causing significant diffuse contamination. Additionally, research demonstrated a strong correlation between soil Rh and Cu availability to soil fauna and various soil pedological and climatic parameters. Hence, we completed our analysis by including pedo-environmental parameters and by analyzing Rh against a proxy of bioavailable Cu. Our findings indicate that Cu is a non-negligible variable in explaining the Rh models inaccuracy considering either total or free Cu forms. Therefore, it can be concluded that Cu should not be disregarded as a key factor in predicting Rh. • Soil contamination affects microbial processes and is widely spread. • Biases of land surface model's heterotrophic respiration are not fully understood. • We hypothesized that soil contamination might be a missing driver. • 11 land surfaces models residuals were analyzed against soil Cu contamination. • Total and free Cu in soils explain partially some of the models biases.
Authors: Laura Sereni, Isabelle Lamy, Bertrand Guenet
Institutions: AgroParisTech, Université Paris-Saclay, Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement, Écologie Fonctionnelle et Écotoxicologie des Agroécosystèmes, Institut des Géosciences de l'Environnement, Université Grenoble Alpes, Centre National de la Recherche Scientifique, Université Paris Sciences et Lettres, École Normale Supérieure - PSL, Laboratoire de Géologie de l’École Normale Supérieure
Language: English
Subfield: Soil Science
Domain: Life Sciences
SDGs: Life on land, Climate action
Open Access status: hybrid
Estimated APC paid: $3,990
Funder: Agence Nationale de la Recherche
OpenAlex ID: W4404552167
