Trace metal transport modeling using OpenMI: a case study of Zenne river, Brussels Belgium Chrismar Punzal a,b,* , Narayan Kumar Shrestha a , Olkeba Tolessa Leta a , Bruno De Fraine c , Ann van Griensven a,d , Marc Elskens e , Willy Bauwens a . a Department of Hydrology and Hydraulic Engineering, Vrije Universiteit Brussel, Brussels, Belgium. b Katholieke Universiteit Leuven, 3000 Leuven, Belgium. c Department of Computer Science, Vrije Universiteit Brussel, Brussels, Belgium. d UNESCO-IHE Institute for Water Education, Core of Hydrology and Water Resources, The Netherlands. e Laboratory of Analytical and Environmental Chemistry, Vrije Universiteit Brussel, Brussels, Belgium. *Corresponding author. Tel.: +32 (0)2629 3036; fax: +32(0)2629 3022. E-mail address: [email protected]Introduction: In 2000, the European Water Framework Directive was developed, requiring a good ecological status for all surface waters in all member states by 2015 (EC, 2000). In order to achieve this, Environmental Quality Standards (EQS) for several substances and pollutants have been laid down (EC, 2008). Since then, large investments were made for the management of the wastewaters of Brussels. This improved the water quality flowing into the river Zenne (Garnier et al., 2012). Despite these investments, the river still receives high loads of pollutants, especially considering the low discharge of the river and the water quality downstream from Brussels does not comply with the requirements set by the EU-WFD. It is in this context that an interuniversity, multidisciplinary research project ‘Good Ecological Status of the river Zenne (GESZ)’ was launched to evaluate the effects of the wastewater management plans in the river basin on the ecological functioning of the river. With this project, different water quantity and quality processes need to be considered: the hydrology in the river basin, the hydraulics in the river, in the canal and in the sewers, erosion and sediment transport, the carbon-nitrogen- phosphorus (C-N-P) cycle, the transport of trace metals and the transport and decay of faecal indicator bacteria. In such a framework, dynamics of trace metals needs to be considered. In the 2011 report of the Flemish Environment Agency (VMM), several metals, such as zinc, arsenic and cadmium had high concentrations in water surfaces under their jurisdiction. Nevertheless, average concentrations of metals except for arsenic have decreased by more or less 50% in the last 10 years (Steertegem, 2011). Since many of the processes interact with each other, an integrated model considering all the processes is needed. The Open Modelling Interface or OpenMI (Gregersen et al., 2007; Moore and Tindall, 2005) was used in integrating the different models simulating these processes. This paper presents an OpenMI-based integrated trace metal transport model consisting of five models. Materials and methods: Simulators: Five models were used to form an integrated trace metal dynamics model linked dynamically through OpenMI. 2nd OpenWater symposium and workshops(held at VUB, Brussels, September 16-17, 2013)
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Trace metal transport modeling using OpenMI: a case study of Zenne river,
Brussels Belgium
Chrismar Punzala,b,*
, Narayan Kumar Shresthaa, Olkeba Tolessa Leta
a, Bruno De Fraine
c,
Ann van Griensvena,d
, Marc Elskense, Willy Bauwens
a.
aDepartment of Hydrology and Hydraulic Engineering, Vrije Universiteit Brussel, Brussels,
c: streamwater temperature g: water quality parameters for rural
d: TSS catchments
2nd OpenWater symposium and workshops(held at VUB, Brussels, September 16-17, 2013)
Figure 2 shows how the parametric partitioning coefficient values fair when compared with
the observed log equation. The equations are able to mimic the observed values. There are
some overestimations with the values of log of cadmium. All other log values are well
within the prediction interval of 1 standard deviation, which is actually 68% confidence
interval as log values are normally distributed.
Figure 2. Observed vs calculated log values. The calculated log values are based on a
parametric log model developed using MLR. Broken lines indicate 1-standard deviation
prediction interval. Solid line corresponds to bisector line.
Total, dissolved and particulate metal concentrations of cadmium, copper, lead and zinc
along the Zenne river for years 2007 to 2008 were simulated. In general, the model was able
to simulate the metal concentrations. The values of the metals were well within the range of
the observed values from GESZ measurement campaigns. However, overestimations were
observed for total and dissolved zinc for all stations. Total copper was also underestimated
for Lot and Vilvoorde. Particulate concentrations for all metals were overestimated for station
Eppegem. Figure 3 shows a sample simulation results for total and dissolved zinc at station
Vilvoorde for the span of years 2007 to 2008.
2nd OpenWater symposium and workshops(held at VUB, Brussels, September 16-17, 2013)
Figure 3. Simulated and observed total and dissolved zinc concentrations for station
Vilvoorde for period January 1, 2007 to December 31, 2008. Solid lines indicate simulation
values, circular markers are observed values. Broken lines show the range of GESZ measured
values at station Haren Buda.
Conclusions:
This paper was able to show the possibility of creating an integrated trace metal transport
model in OpenMI for Zenne river. It was shown that the model was able to calculate for total
metal concentrations with modest errors graphically. Also, dissolved and particulate metal
concentration could be determined. The availability of more observed values would make the
discrepancies in the simulations more quantifiable.
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2nd OpenWater symposium and workshops(held at VUB, Brussels, September 16-17, 2013)