Modeling Reactive Transport Processes to Optimize Use of the Subsurface Environment

Transcription

1 Modeling Reactive Transport Processes to Optimize Use of the Subsurface Environment dr. Boris van Breukelen ritical Zone Hydrology Group, the Netherlands

2 Use of the Subsurface ATES MNA S

3 Reactive Transport Modeling (RTM) oupling of flow/transport model & hydro/bio/geochemical reaction model. Groundwater / vadoze zone / surface water 1 to 3-D Quantitative explanation of water chemical composition; identify main processes/factors; prediction/management tool Provides a broader view of integrated system behavior (Steefel et al. EPSL, 2005).

4 Main hydrogeochemical processes Advection-Reaction-Dispersion (ARD) equation: t = v + D x L 2 q 2 x t Element concentrations are influenced by multiple interplaying reactions

5 Outline Reactive Transport Modeling (RTM) as research tool to quantify the processes controlling water quality evolution for 1. MNA of landfill leachate: process insight 2. Artificial recharge (ASR/SAR/SIR): predictive capability 3. Isotope-based MNA of pollution: integrating isotope hydrology/geochemistry with RTM onclusions

6 Topic 1: Process Insight Groundwater flow direction (~4m/yr) ell: Inlet (solution 0) Outlet

7 Reactive transport modelling plume pristine Leachate

8 Reactive transport modelling plume + No processes Only iron-reduction All biogeochemical reactions 2+ H2 O+ 4FeOOH+ 7H 4Fe + HO3 + 6H2O a,fe + HO ao3,feo3+ H +

9 Simulation of δ 13 -DI See further: Van Breukelen et al., J. ontam. Hydrol. 70: DI decreases from ~80 to ~50 mm in flow direction Oxidation δ 13 -DO (-27 ) => δ 13 -DI Oxidation δ 13 -H 4 (-53 ) => δ 13 -DI Precipitation a,feo 3 ( pos) => δ 13 -DI

10 Topic 2: Predictive capability E (meq/l) X X

11 RTM studies done

12 Post-audit: predicting 2007 with 1981 model

13 Flow path conceptualization & automatic model calibration (PEST) of (1) Es, (2) exchange coefficients, and (3) initial dune water composition

14 Results of improved model alibration dataset Validation dataset Predictive performance is good although parameter correlation is high

15 Model prediction statistics Former model (1 FP) Improved model (6 FPs) Root Mean Square Weighted Error alibration Validation Root Mean Square Weighted Error alibration Validation

16 Aquifer Storage and Recovery al Validation (c 2-14) Source: Andreas Antoniou et al. in review

17 Subsurface Arsenic/Iron Removal (SAR/SIR) standard volume abstracted / volume injected Source: Moshiur Rahman et al. in preparation [with TU Delft; NWO WOTRO]

18 Topic 3: RTM of Isotope Fractionation (98.9%) Dilution Lower concentration, but equal isotope ratio Organic LMW contaminant (like BTEX, PE, MTBE) Biodegradation ontaminant: higher ratio, becomes enriched in Reaction product: lower ratio, becomes depleted in 13

19 The Rayleigh equation Degradation 12 -contaminant: Degradation 13 -contaminant: 12 d dt 13 d dt = k = k [ 12 L [ 13 H ] ] Integration leads to the well known Rayleigh equation: Isotope ratio: = Fraction remaining: (Kinetic) isotopic fractionation factor: 13 R 12 f 0 R t = R α = 0 k k H L f ( α 1) 1

20 alculating the extent of (bio)degradation in the field Δ R t ln f = R δ 0 f ( α 1) Downgradient ε lab δ δ sample Source R sample = 1 Rreference = ε lab ( 1 ) 100(%) Biodegrada tion = f ε = ( α 1 ) 1000 Source: Van Breukelen (2007) ES&T: 41, ; Mancini et al (2002) EST

21 Isotope fractionation in groundwater TIME R A, t=t SPAE R A, t=0 Sorption Hydrodynamic dispersion A Diffusion A B D

22 Isotope Fractionation Reactive Transport Modeling (IF-RTM) [PE] δ PE_ll PE_lh PE_hh & have slightly different: - degradation rate constants - diffusion coefficients - solid-water partitioning coefficients Isotope ratios are calculated from isotopologue concentrations after the model run: δ 13 = ( 2 PE_hh + PE_lh) /( 2 PE_ll + PE_lh) R reference 1

23 PE plume Angus Site Source: Hunkeler et al. JH 2004

24 PE plume Angus Site Source: Hunkeler et al. JH 2004

25 Transverse Dispersion: New Insights D = D + D transverse D = τ aq t + pore a v t D mechanical D t = D τ aq + v vd / D +123 Source: hiogna et al. ES&T 2010 d aq

26 RTM of Angus field site: DIF effects assical parameterization New parameterization A B α V = 1.5 mm α V = 0.2 mm α V = 0.2 mm D Daq Daq d = atv Dt = + v τ τ vd / D t + aq Source: Van Breukelen & Rolle ES&T 2012

27 Fringe-degradation & Diffusion-induced isotope fractionation Source: Van Breukelen & Rolle ES&T 2012

28 atchment Scale Solute Transport Source: Stefanie Lutz et al. in preparation HydroGeoSphere Model (University of Waterloo)

29 onclusions RTMs provide quantitive explanation for changes in chemical water composition RTMs may show good predictive performance even when parameter correlation is high Simulation of isotope data with RTMs provides additional model constraints and aids in (in)validating model conceptualisation RTMs are very useful to understand the performance of subsurface water technologies

30 Acknowledgements Reinert Karlsen (Frank Smits, Theo Olsthoorn); Waternet/VU MSc internship: MAR Andreas Anthoniou, Koen Zuurbier; KWR/VU: ASR Matthijs Bonte; KWR/VU: ATES Tony Appelo, Jasper Griffioen, Pieter Stuyfzand Moshiur Rahman (Mark Bakker); TU Delft: SAR Massimo Rolle; Uni Stanford/Tuebingen: MNA Tomasz Kuder; Uni Oklahoma, US ESTP project: MNA Stefanie Lutz, Philip Stack, Héloïse Thouement: EU SI: ENVIRONMENT ITN: MNA