| Autor: |
Di Curzio D; Department of Water Management, Delft University of Technology, Stevinweg 1, 2628 CN Delft, Netherlands., Laureni M; Department of Water Management, Delft University of Technology, Stevinweg 1, 2628 CN Delft, Netherlands., Broholm MM; Department of Environmental and Resource Engineering, Technical University of Denmark, Bygningstorvet 115, 2800 Kongens Lyngby, Denmark., Weissbrodt DG; Department of Biotechnology and Food Science, Norwegian University of Science and Technology, Sem Sælandsvei 8, 7034 Trondheim, Norway., van Breukelen BM; Department of Water Management, Delft University of Technology, Stevinweg 1, 2628 CN Delft, Netherlands. |
| Abstrakt: |
Biomarkers such as functional gene mRNA (transcripts) and proteins (enzymes) provide direct proof of metabolic regulation during the reductive dechlorination (RD) of chlorinated ethenes (CEs). Yet, current models to simulate their spatiotemporal variability are not flexible enough to mimic the homologous behavior of RDase functional genes. To this end, we developed new enzyme-based kinetics to model the concentrations of CEs together with the transcript and enzyme levels during RD. First, the model was calibrated to existing microcosm data on RD of cis-DCE. The model mirrored the tceA and vcrA gene expression and the production of their enzymes in Dehalococcoides spp. Considering tceA and vcrA as homologous instead of nonhomologous improved fitting of the mRNA time series. Second, CEs and biomarker patterns were explored as a proof of concept under groundwater flow conditions, considering degraders occurring in immobile and mobile states. Under both microcosm and flow conditions, biomarker-rate relationships were nonlinear hysteretic because tceA and vcrA acted as homologous genes. The mobile biomarkers additionally undergo advective-dispersive transport, which increases the nonlinearity and makes the observed patterns even more challenging to interpret. The model offers a thorough mechanistic description of RD while also allowing simulation of spatiotemporal dynamic patterns of various key biomarkers in aquifers. |
