Source Apportionment of Pollution from Non-Coal Mines Report on Work-Package 2 June 2013 Work-package 2: REVIEW OF SOURCE APPORTIONMENT MODELLING TOOLS FOR

POLLUTION FROM NON-COAL MINES

1. Introduction Modelling tools for the assessment of water quality in surface water systems are widely used both by researchers and by environmental regulatory authorities, not only for routine purposes such as consent setting but also as an investigative tool when planning improvements to water quality across river catchments. Such tools range from simple mass-balance approaches to water quality modelling to the more complex physically-based numerical models which simulate the key chemical processes taking place. Applications of modelling tools to metal mining-impacted river catchments vary in their complexity from the use of simple algorithms which enable prediction of the relative contributions of point and diffuse sources to the overall pollutant burden, through to complex models which require substantial volumes of input data. Such data is not currently available for the majority of metal-contaminated river catchments in the UK and thus considerable assumptions have to be made when applying these models. Additionally, the complex physically-based models require specialist understanding to fully appreciate the implications of the model outputs. The following review describes the different modelling tools available, with particular emphasis on data input requirements and ease of use since any potential model must be practically useful to the problem of source apportionment in abandoned non-coal mining districts within the UK.

2. Mass-balance approaches to source apportionment Newcastle University has had considerable success in applying a simple mass-balance approach to source apportionment at the abandoned Force Crag mine in Cumbria. As with many other catchments impacted by abandoned metal mine pollution, both point and diffuse sources contribute to the overall metal burden of the main receiving watercourse, in this case the Coledale Beck. It is well documented from past research at other sites that patterns of metal flux change with hydrological conditions, in particular diffuse sources have been shown to become more important under high flow conditions (Gozzard et al. 2011; Mayes et al. 2008). The mass-balance approach has therefore been applied over the full range of hydrological conditions in order to assess the comparative importance of point and diffuse sources of pollution (Jarvis et al. 2012).

The main advantage to this method of source apportionment is the relatively small data requirements, although synchronous measurement of both river flow and metal concentrations is necessary over the full range of hydrological conditions in order to calculate metal fluxes and how they vary with flow. Although this approach does not identify specific individual sources of diffuse pollution, a simple mass-balance of all the known point sources of pollution within a catchment, together with locations upstream and downstream of the point sources on the main river channel, can indicate discrepancies in metal flux which may be attributed to diffuse pollution. Further investigations of river reaches identified as receiving diffuse pollution may help to identify the specific source, for example surface runoff over waste spoil, groundwater inputs or remobilisation of contaminated sediments held in transient storage within river channels and floodplains (and these issues will be explored in detail as part of the research project of which this review forms a part). The Environment Agency’s SIMCAT (SIMulation of CATchments) model is a stochastic 1D, steady state, mathematical water quality model, based on Monte Carlo simulations, which also uses a mass balance approach. It combines the distribution of water quality and flow within the main river channel with distributions of water quality and flow from river inputs, including mine water. The term stochastic is used to describe models which run many times using different input variables for each run (Cox 2003). The input variables are selected at random from a statistical distribution of possible values and the process is repeated until enough values of the computed variable have been calculated to define its distribution. In SIMCAT around 500 shots (values) are typically used and the variables usually follow a log-normal distribution, although other statistical distributions (e.g. constant value, normal distribution, non-parametric distribution from e.g. a flow duration curve, seasonal (monthly) data) may be assumed. For predictive purposes, such a model is an advancement of the simple mass balance approach described above, which is only applicable to an instantaneous period of time, but it is also heavily dependent on the quality and amount of data used to run the model. As with all statistical exercises, this is a major limitation since the greater the spatial gap between sampling data, the greater the uncertainty of water quality behaviour in that reach of the river. This type of model is therefore more suitable for investigating changes in water quality on a catchment scale, rather than for quantifying small-scale spatial changes. On a small catchment, typical of those impacted by discharges from non-coal mines in the UK (average area approximately 34 km2), a simple mass balance approach using synchronous flow and water quality data covering the full range of hydrological conditions will enable a more accurate representation of pollutant sources to be obtained. The input parameters to SIMCAT for river flows and water quality are thus provided in the form of summary statistics, i.e. mean and standard deviation, which describe the chosen statistical distribution. Similarly, the model results are also output as summary statistics (e.g. mean, standard deviation, percentiles) at selected locations. A disadvantage of using summary statistics for the input data is that the data is typically pre-analysed to eliminate outliers. It is these outliers, however, which likely represent high flow conditions during which it has been shown that diffuse sources of pollutants become more important (Gozzard et al. 2011; Mayes et al. 2008).

In terms of water quality, SIMCAT can simulate conservative substances, such as chloride, which are assumed not to undergo any chemical transformations, and non-conservative substances which are assumed to decay exponentially, as described by a first-order reaction (i.e. the rate of loss of pollutant is proportional to the concentration of the pollutant). Rate constants can be specified by the user either globally over the entire model domain or for individual river reaches. Unlike the physically-based models described in a later section, SIMCAT does not use an advection-dispersion transport model but instead assumes a mixing zone immediately downstream of a discharge, or input, then assumes complete mixing downstream of the mixing zone with contaminants transported at the same velocity as the water. It therefore neglects processes such as sediment deposition under low flow conditions and subsequent remobilisation (along with potential adsorbed metals) when flow increases, which can be an important diffuse source of pollution in rivers draining non-coal mine areas. SIMCAT is a steady state model, which means that it does not take account of changes in flow and water quality over time. This limits the model but also allows it to be applied quickly and with relatively little data compared to dynamic, or transient, models which include time-varying input and output data (Cox 2003). Conceptually, within SIMCAT, river catchments are divided into user-defined “reaches” which represent sections of river between tributaries, or other notable inputs of flow. “Features” may also be defined on reaches, which represent points on a river which affect, or are influenced by, river flow or river water quality. Such points include flow gauging stations, water quality monitoring points and point discharges. Where insufficient flow data are available for a reach, the Environment Agency employ Low Flows Enterprise software (from Wallingford HydroSolutions) to provide flow estimates (mean and 95 percentile) which can be input to the model. Again, this represents a major limitation of the model for use as a source apportionment tool for metal mine-impacted catchments since diffuse sources have been shown to be more important at high flows. It is unlikely therefore that the model will be able to reproduce observed metal concentrations or loads during high flow conditions. However, the project team will be exploring this directly in due course, by comparing modelled outputs from SIMCAT for the Coledale Beck with actual measured data (to be completed). SIMCAT has been applied to several metal-impacted rivers in Wales in order to apportion sources of metals (Williams 2012a,b) but these were large catchments (e.g. Afon Teifi, Afon Tywi) and insufficient data on flows meant that flow estimates were required on the majority of river reaches. Although the model predictions, in general, are comparable with observed metal concentrations, they are only applicable to low flow conditions and do not take account of varying hydrological conditions, which is an essential consideration when assessing the contribution of diffuse sources. Given that the SIMCAT model was specifically developed for use by the Environment Agency as a source apportionment tool, and because there have been efforts to actually apply it in Wales (see above), preliminary trials of SIMCAT were undertaken in May 2013 as part of the review exercise in this project. The trials were undertaken using actual water quality and flow data collected from the Coledale Beck, Cumbria, which is impacted by discharges from the abandoned Force Crag mine. These data were collected by the Newcastle University team, and represent one of the most

comprehensive datasets available in terms of catchment monitoring across a range of hydrological conditions in a former mining catchment. Newcastle University staff were assisted in this task by staff at Natural Resources Wales (formerly Environment Agency Wales) in Swansea, who were responsible for the application of SIMCAT to the Welsh catchments referred to above. This exercise was undertaken specifically to evaluate the practical application of SIMCAT in light of the discussion of its theory, discussed above. It is important to note that this was not an exhaustive exercise in which all aspects of SIMCAT were investigated, or every function of the model utilised. Rather, the Coledale Beck data were input, and the model run, in the same manner in which other mining catchment data have been interpreted using SIMCAT by Natural Resources Wales staff. The Coledale Beck data used in the simulation comprised data from 9 rounds of monitoring, with the hydrological condition of the catchment being different on each occasion (as determined by measured flows on both the Coledale Beck and the Newlands Beck). The range of flows, as measured on the Coledale Beck just above Braithwaite, was 111 L/s to 1487 L/s. On each of the 9 monitoring rounds synchronous flow measurements and water quality measurements were made at 11 points, including all point sources of pollution, locations on the Coledale Beck upstream and downstream of the point sources, and locations on the Coledale Beck and Newlands Beck downstream of the Force Crag mine site. In previous applications of SIMCAT (to Welsh mining catchments) flows have been ‘balanced’ first, by assuming that diffuse flow is the difference between the downstream location flow and the sum of the upstream flows. This is reasonable, and is also the approach taken by Newcastle University to calculate diffuse flows. For the trial of SIMCAT using the Coledale Beck data assumptions were made about diffuse flow-rates. Two simulations were run. In both cases the Coledale Beck was divided into two reaches (Reach 1 (upstream) and Reach 2 (downstream)), as indicated on Figure 1. In the first simulation the diffuse flow contribution was assumed to be 5 L/s/km (in which km refers to reach length), implying rather low diffuse flows. In the second simulation the diffuse flow was assumed to be 100 L/s/km for Reach 1 and 120 L/s/km for Reach 2. In all cases the zinc concentration was the metal modelled. The outputs of the model are presented as two graphics (screenshots) for each simulation, one representing outputs for Reach 1 and one for Reach 2. For the simulation at low diffuse flows (5 L/s/km) the results are presented in Figures 2 (upstream reach) and 3 (downstream reach). The results of the second simulation, using high diffuse flows, are shown in Figures 4 (Reach 1) and 5 (Reach 2). For each of the 4 figures actual observed mean values, and modelled mean values, are shown for flow, zinc concentration and zinc load. Figure 1 shows the results for the upper reaches of the Coledale Beck under a scenario of low diffuse flow inputs. It is clear from this that the mean predicted flow for the lower end of the reach is lower than the actual observed mean flow, indicating that the assumption of 5 L/s/km diffuse flow is an underestimate of the actual diffuse flow in this reach. As expected, the predicted concentration of zinc increases

sharply immediately below the Level 1 discharge, and again where spoil drianeg enters the Beck (Surface runoff #7 on Figure 2). The predicted mean concentration of zinc at the end of the reach is slightly greater than the observed mean concentration, again due to the underestimation of diffuse flow.

Figure 1. Map of the Coledale Beck showing Reach 1 and Reach 2, used in SIMCAT simulations under two different diffuse flow component scenarios

(numbered points are: #14 – Level 1 discharge; #4 – end of Reach 1; #2 – downstream monitoring point on Coledale Beck; #1 – Environment Agency flow

gauging station on Newlands Beck)

In Reach 2 of the low diffuse flow simulation (Figure 3), the discrepancies between observed and predicted mean flow and zinc concentration are accentuated compared to the upper reach; the predicted mean flow is much less than the observed mean flow towards the lower end of Reach 2, and the predicted mean zinc concentration is much higher than actual. Again, this is ascribed to the underestimation of diffuse flows, which become more important in the lower reaches of the stream i.e. the error in the estimate of the diffuse flow contribution propagates downstream.

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Figure 2. Output (screenshot) of SIMCAT simulation for upper reach of Coledale Beck, showing predicted (blue line) and observed (crosses) values

for flow, zinc concentration and zinc load moving downstream under a scenario in which diffuse flow input is set at 5 L/s/km

Figure 3. Output (screenshot) of SIMCAT simulation for lower reach of

Coledale Beck, showing predicted (blue line) and observed (crosses) values for flow, zinc concentration and zinc load moving downstream under a

scenario in which diffuse flow input is set at 5 L/s/km

In applications of SIMCAT to former mining catchments these discrepancies in flow are corrected by trial and error i.e. increasing or decreasing the diffuse flow component until the actual mean flow and predicted mean flows are similar. This has been done here in the second scenario, in which diffuse flow inputs are 100 L/s/km and 120 L/s/km for Reach 1 (upper) and Reach 2 (lower) respectively. The results of the simulation are shown in Figures 4 (upper reach) and Figure 5 (lower reach).

Figure 4. Output (screenshot) of SIMCAT simulation for upper reach of Coledale Beck, showing predicted (blue line) and observed (crosses) values

for flow, zinc concentration and zinc load moving downstream under a scenario in which diffuse flow input is set at 100 L/s/km

In this case the actual and predicted mean flows and zinc concentrations are in fact similar for both reaches. This suggests that the estimates of diffuse flow inputs are representative of the actual conditions (on average). The similarity of actual and predicted flow and concentration in the second simulation might appear to imply that SIMCAT is a useful predictor of conditions in a receiving watercourse below an abandoned mine with metal-rich discharges. However, the outputs of the model are in fact a simplification of what is in reality a far more complex, and dynamic situation. The over-simplification arises primarily because to run SIMCAT it is necessary to take summary statistical values (specifically mean and

standard deviation) for modelling purposes. In reality, over a variety of hydrological conditions, flows in the Coledale Beck vary over at least an order of magnitude (111 L/s to 1487 L/s for the 9 events used here), and the influence of these hydrological conditions on metal flux (load) is not a simple dilution relationship. Because mean values are used, in the second simulation it is possible to get predicted flow and concentration to align with observed flow and concentration simply by ‘forcing’ the diffuse flow component to fit the data. This would not be possible of course without having the actual data to ‘validate’ the estimates of diffuse flows.

Figure 5. Output (screenshot) of SIMCAT simulation for lower reach of Coledale Beck, showing predicted (blue line) and observed (crosses) values

for flow, zinc concentration and zinc load moving downstream under a scenario in which diffuse flow input is set at 120 L/s/km

The most clear indication that the SIMCAT predictions do not represent the actual conditions on the Coledale Beck can be seen when looking at the zinc load (flux) data (the third column in Figure 2 – 5). In each case the prediction is that zinc load will remain rather constant moving downstream, other than when there is a point source discharge of zinc-rich water e.g. Level 1, spoil drainage #7. The reality of the situation is very different, and Figure 6 illustrates this point. Figure 6 shows how the flow and zinc flux at two locations on the Coledale Beck (#2 and #4; see Figure 1) vary under different hydrological conditions. The different hydrological events are represented along the x-axis by numbers from 1 – 9. These represent actual flow at

the downstream end of the Coledale Beck (location #2 on Figure 1), from number 1, the lowest flow (111 L/s), to number 9, the highest flow (1487 L/s). The columns on Figure 1 show the measured zinc flux at each of these two locations during each of the 9 monitoring events. It is clear that the zinc flux varies substantially, from 3.00 – 13.88 kg Zn/d at #4 and 2.40 – 14.13 kg Zn/d at #2. This is in sharp contrast to the results of the SIMCAT simulations (Figures 2 – 5), which imply that the zinc load remains relatively constant down the Coledale Beck, only increasing when there is a point source input of zinc-rich, irrespective of diffuse flows. Therefore the SIMCAT simulation does not represent actual conditions. This is of course because of the necessity to use mean values, rather than input different values representing different hydrological conditions. This is an inherent limitation of SIMCAT, which is explicitly a steady-state model i.e. for modelling under conditions of constant flow.

Figure 6. Measured flow and zinc flux data for two locations on the Coledale Beck (see Figure 1), showing the variability of zinc flux under different hydrological conditions (see text for further details) (Unpublished data,

Newcastle University, UK)

For the purposes of source apportionment this is a very significant limitation of the SIMCAT model. This is made very clear in Figure 7, which shows the same type and layout of data as Figure 6, but in this instance compares the zinc flux at location #4 on the Coledale Beck (also shown on Figure 6) to the zinc flux from the main point sources of pollution in the catchment, the Level 1 discharge (#14). It is immediately apparent that the flux of zinc from Level 1 is the dominant contribution to zinc flux at #4 under lower flow conditions (events 1 – 4 on the x-axis of Figure 7), but that under higher flow conditions other sources of zinc must be contributing to the zinc flux in the Coledale Beck. In fact, under low flow conditions the Level 1 discharge contributes more than 100% of the zinc flux at #4, indicating in fact that there is some attenuation of zinc between the two locations. At high flow, however,

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Figure 7. Measured flow and zinc flux data for the Level 1 discharge (#14) and the Coledale Beck downstream (#4) (see Figure 1), showing the importance of varying hydrological conditions to the contribution of the point source to the

overall zinc flux of the Beck (Unpublished data, Newcastle University, UK)

In addition to this key issue of the limitations of steady state models such as SIMCAT, which rely on summary statistics data to run, there is an additional issue that would need to be resolved in any future model given the data shown in Figure 6. Specifically, SIMCAT has a function to simulate attenuation of pollutants within a watercourse – a decay constant – either for individual reaches or for entire systems. It is certainly the case that such functionality is useful, and the low flow events (1 – 5) in Figure 6 indicate that attenuation does indeed occur in the Coledale Beck i.e. zinc flux is less at site #2 than site #4. However, Figuire 6 also shows that at higher flows there is actually release of zinc down the watercourse, but SIMCAT does not have a function to simulate such a phenomenon. The variability of metal flux under changing hydrological conditions, and the limitations of SIMCAT to represent such changes, has huge implications for evaluating the importance of different sources of mining pollution to the overall quality of a receiving watercourse, since the example above illustrates that the impact of remediation may vary hugely depending on the hydrological conditions in a catchment. In this case, remediation of Level 1 would result in a major improvement in downstream water quality during low flow conditions, but would be of minor importance during higher flow conditions.

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This is a major limitation of steady-state models such as SIMCAT which, explicitly, cannot make accurate predictions of metal flux (and therefore concentration) under differing hydrological conditions, and yet it is exactly these variations in flow that critically control metal dynamics in abandoned mine catchments. Whilst SIMCAT may have some application to assessing the importance of individual pollutant sources under low flow (i.e. steady state) conditions, it does not appear to be appropriate for source apportionment across a range of hydrological conditions. A further development of the SIMCAT model is SAGIS (Source Apportionment Geographical Information System tool). The project team has not had the opportunity to trial the SAGIS tool in the same way that it has the SIMCAT model. However, it is understood that while SAGIS has some additional functionality, such as improved options for apportionment of pollutants to diffuse sources, it essentially advances SIMCAT primarily through the provision of a GIS function, which enables to user to visualise outputs more clearly. Thus, the underlying principles of SAGIS are the same as SIMCAT, and therefore the limitations of such a steady state model apply equally to it as they do to SIMCAT. A similar model to SIMCAT, QUASAR (QUAlity Simulation Along Rivers), is available commercially from the Centre for Ecology and Hydrology. Unlike SIMCAT, however, this model is capable of simulating the time-varying (i.e. dynamic) transport of solutes within rivers although it is not a full hydrodynamic model like some of the models described below. It can be run in two modes: stochastic mode to allow river regulators to set effluent consent levels, and dynamic (prediction) mode to assess changes in water quality over the length of the river over time. The main limitation of QUASAR is that it does not simulate non-conservative contaminants and, as such, does not take account of decay processes which can act to attenuate the transport of contaminants. It also (like SIMCAT) neglects the processes of dispersion and diffusion during transport and solutes are thus assumed to travel at the river water velocity. This has the advantage of reducing input parameters and leads to a simpler and quicker solver. Whitehead and Jeffrey (1995) described the application of QUASAR to assess the treatment requirements of mine discharges in the River Pelenna system in Wales but they focussed only on iron concentrations. The model was able to identify the dominant discharges together with the level of treatment required to meet river quality objectives. As with SIMCAT, mass balance considerations were used to estimate diffuse inputs into a river reach. Despite its ability to run in dynamic mode, the simplicity of the QUASAR hydraulic model, and the considerable data requirements for running the model in dynamic mode, means that any advantages it has over SIMCAT are somewhat limited. The model INCA-Metals (an updated version of the INtegrated CAtchment model (INCA)) has been specifically developed to simulate the impact of mine discharges on river systems (Whitehead et al. 2009). A total of eight metals can be simulated by INCA-Metals (cadmium, lead, zinc, mercury, arsenic, copper, chromium and manganese), together with cyanide and ammonia. As well as accounting for dilution and mixing, as in the simpler models such as SIMCAT, INCA-metals also takes account of key kinetic chemical processes taking place, including decay and metal adsorption to sediment. Metal remobilisation from sediments is not included, however, as it is assumed that the metals which precipitate are locked up in the bed sediments. This is a major limitation in terms of source apportionment since the

remobilisation of sediment metals is thought to be one of the main diffuse sources of metals to rivers. The mass balance model is dynamic and produces daily estimates of river flows and water quality (concentrations and fluxes) at discrete locations along the length of the river. The estimates are made by calculating the metal contributions from various inputs and considering transformations, such as metal adsorption to sediment, that take place. Conceptually, the model assumes two storage zones: a soil zone and a groundwater zone, with a mass of metals delivered to the river channel from both. The mass of metals is then routed downstream taking account of inputs from point sources and precipitation and sedimentation of metals. It is thus far more complex than SIMCAT and QUASAR but still based around a mass balance framework. The added complexity, however, leads to substantially greater input requirements. The user must supply input data describing properties such as soil drainage volumes, soil water flow rates, soil moisture deficit, maximum groundwater effective depth, effective rainfall, slope of river channel and hydraulic radius. Additionally, rates of metal adsorption to the soil sediment and aquifer matrix are required. It is unlikely that such parameters will be available for the typical metal-contaminated rivers in the UK draining non-coal mine areas and it would be a costly and time-consuming exercise to generate them. Alternatively, input parameters may be assumed and varied during the calibration process in an attempt to reproduce observed metal concentrations but this has obvious implications on model reliability. The INCA-Metals model has been successfully applied to a large scale river catchment in Romania (Whitehead et al. 2009) for which a substantial dataset was available, including 15 minute flow data from river flow gauges throughout the catchment. Such a large scale river is in stark contrast to the small river catchments in the UK impacted by drainage from non-coal mines (the average area of impacted streams is 34 km2). The model was also calibrated on five years of data which would rarely be available for upland mine catchments in the UK. It suggests that models such as INCA-Metals are better suited to large catchments, particularly given that the mass balances of water and metals are based on a 1 km2 cell. Such catchments are also more likely to have substantial historical datasets available from which data can be obtained for calibration purposes, if not for use as model input parameters.

3. Process-based models The United States Environmental Protection Agency (USEPA) has developed several water quality models which are widely used around the world, including QUAL2K and WASP7. QUAL2K is a modernised version of the QUAL2E model, developed in the 1980’s. It is a 1D, steady-state model which can simulate up to 15 water quality determinants within a river system. As with SIMCAT, a river is conceptualised within QUAL2K as a series of reaches but the complexity is far greater (Cox 2003). Solute transport is modelled by means of both advection and dispersion and the model additionally takes account of physical, chemical and biological processes that occur within the river. The model appears more applicable, however, to the simulation of DO, BOD, nitrate and phosphorus and it has not been possible to discover any applications of QUAL2K to metal-contaminated catchments. In contrast, the USEPA model WASP5 (a previous version of the currently available WASP7) has been used to model the transport and sediment/water interactions of metals in a mining area in Montana (Caruso 2004), albeit under low flow, steady state conditions. It has the advantage over the other models described above in that

it can simulate more advanced processes such as solute interactions between bed sediment and water, and exchange between dissolved and particulate phases. In addition, WASP5 can simulate the fate and transport of metals in up to 3D and in both steady-state and transient / dynamic modes. Variations in flow and transport over time can therefore be modelled whereas models such as SIMCAT and QUAL2K are steady-state and thus limited to periods when river flows and chemistry are essentially constant. The disadvantage of the additional capabilities of the model, however, is the data requirements which are substantially greater than in the models described above. For instance, in order to simulate diffusion and dispersion, exchange coefficients and mixing lengths must be specified. Similarly, the distribution of metals between the dissolved and particulate phases (including sorbed and precipitated metals) requires a lumped partition coefficient (Kd) which can be input as spatially variable or a constant value. These parameters must be manipulated during calibration in order to achieve the correct metal concentrations in the water and bed sediment. Clearly, such parameters are unlikely to be available for typical river catchments in the UK which are impacted by discharges from non-coal mine areas. As a result, they will need to be estimated which has an influence on the accuracy of any simulations and the confidence the user can have in the model results. In the application described by Caruso (2004), to assist model calibration laboratory adsorption / desorption batch tests were undertaken in order to estimate Kd values for the partitioning of metals between the water and bed sediment for different solids to water ratios at different locations within the catchment. Although such laboratory work would be possible to improve confidence in certain input variables it would obviously be both time consuming and costly. Zhen-Gang et al. (2002) applied another USEPA model, the state-of-the-art hydrodynamic model EFDC (Environmental Fluid Dynamics Code) to a shallow river in Massachusetts receiving metals from industrial discharges. EFDC simulates 3D flow, transport and biogeochemical processes in surface water systems, including sediment transport and the fate and transport of metals in the water and sediment phases. The application described by Zhen-Gang et al. (2002) involved a time-varying 1D simulation which was able to successfully simulate three storm events. While point sources were found to be important to sediment contamination in the river, diffuse sources from resuspension of bed sediment were also found to contribute significantly to the sediment and metals within the river. Contaminant transport within EFDC is provided by an internal sub-model which is functionally similar to the WASP model described above and, as such, has the same limitations of large data requirements and data that is not readily available for typical mining-impacted rivers, e.g. sediment density, sediment settling velocity, critical deposition shear stress, critical resuspension shear stress. As noted above, although these parameters can be varied during the calibration procedure to reproduce observed metal concentrations in the river water and bed sediment, relying on such a large number of assumptions has obvious effects on the reliability of model outputs. Nevertheless, if such data is available, or it is possible to undertake laboratory testing to obtain more accurate estimates, there is no doubt that complex models, such as EFDC and WASP, are capable of simulating the many diverse processes which take place in rivers during the transport of metals. The U.S. Geological Survey (USGS) has also been instrumental in developing water quality models, including specifically for the simulation of trace metal fate and

transport in rivers. The OTIS (One Dimensional Transport with Inflow and Storage) solute transport model was developed primarily for conservative contaminants but it is also capable of simulating non-conservative contaminants that are subject to sorption processes (Runkel et al. 1999). Transport processes within the model include advection, dispersion, lateral inflow (additional inputs from groundwater flow, springs etc) and transient storage (the temporary storage of contaminants in slow-moving areas such as eddies and stagnant pools, together with flow through the streambed). OTIS is effectively a mass balance model, since it is formed by writing mass balance equations for the main channel and the storage zone, but it includes additional processes to the mass balance approaches described above, in particular chemical reactions for kinetic sorption and first-order decay. The OTEQ (One Dimensional Transport with Equilibrium Chemistry) model is an adaptation of OTIS which combines the solute transport elements of OTIS with a chemical equilibrium submodel, MINTEQ (Runkel et al. 1996a). In addition to the processes described above, simulated by OTIS, OTEQ includes acid / base reactions, complexation, precipitation / dissolution and sorption. This type of model represents an alternative to the modelling of chemical reactions using kinetic rate equations and assumes that chemical reactions are sufficiently fast relative to hydrologic processes. Conceptually, the model considers chemical components as dissolved, mobile precipitate and mobile sorbed, with the latter two contained within the streambed. These three mobile phases are subjected to hydrologic transport while the dissolved phase is supplemented by dissolution of precipitates and desorption. OTEQ has been applied to several metal-contaminated rivers (Runkel et al. 1996b, 1999) with success but, as with many of the other models described above, the data input requirements are beyond those which are typically available for mining-impacted rivers in the UK and, therefore, a large number of assumptions would have to be made. Although the simulation of such a complex chemistry represents a better portrayal of the actual processes taking place in river systems, it is debateable how reliable the model outputs would be given the need for parameter estimation. A modified version of OTIS, OTIS-P, has been developed to address this problem whereby parameter estimates are obtained by linear regression. This has shown improved simulation results but assumptions must still be made and the reliability of model results considered. Additionally, both OTIS and OTEQ are not user-friendly, since they require large numbers of input files to be created and they are not yet available as windows-based models.

4. Conclusions The fate and transport of metals within river systems is complex and the majority of commercially available modelling tools are not capable of simulating such a diverse range of processes. Those that are capable tend to have such large data requirements that modelling exercises could become excessively costly and time-consuming just to collect the relevant data to be used as input parameters since it is unlikely that the required data would be currently available for UK rivers draining non-coal mine areas. In addition, optimal performance of such models is strongly favoured by large datasets to be used for calibration purposes and, again, it is unlikely that sufficient data would be available for UK rivers. Successful simulations tend to have been applied to large-scale catchments for which sufficient data is

available for calibration and parameter estimation, compared to the relatively small metal-impacted catchments in the UK. In terms of developing an effective source apportionment tool for pollution from non-coal mines, it may be more beneficial therefore to adapt one of the less complex mass-balance approaches for which limited input data is required. It is essential when considering diffuse sources of pollution that the full range of hydrological conditions are simulated since diffuse sources have been shown to become more important under high flow conditions. Many of the models described in this review are incapable of simulating such conditions. Particular attention has been paid in this respect to the Environment Agency’s SIMCAT model, which has been trialled using data collected by Newcastle University. The trial demonstrated the limitations of such steady state models; specifically, they do not have the capability to accurately apportion sources of pollution under varying hydrological conditions, and yet the variability of flow in mining-impacted catchments is a crucial driver of metal flux in such catchments, and therefore of how remedial interventions may or may not result in improved water quality. References Caruso B.S. (2004) Modelling metals transport and sediment/water interactions in a mining impacted mountain stream. Journal of the American Water Resources Association, 40(6), 1603-1615 Cox B.A. (2003) A review of currently available in-stream water quality models and their applicability for simulating dissolved oxygen in lowland rivers. The Science of the Total Environment, 314-316, 335-377 Gozzard E., Mayes W.M., Potter H.A.B. and Jarvis A.P. (2011) Seasonal and spatial variation of diffuse (non-point) source zinc pollution in a historically metal mined river catchment, UK. Environmental Pollution, 159, 3113-3122 Mayes W.M., Potter H.A.B. and Jarvis A.P. (2008) Quantifying the impacts of diffuse mine water pollution in a historically heavily coal mined catchment. Environmental Pollution, 151, 165-175 Jarvis A., Gandy C., Davis J. and Orme P. (2012) Catchment-scale monitoring on the Coledale Beck to establish the comparative importance of point and diffuse sources of mine water pollution under varying hydrological conditions. Newcastle University Report to the Coal Authority Runkel R.L., Bencala K.E. and Broshears R.E. (1996a) Reactive solute transport in streams: 1. Development of an equilibrium-based model. Water Resources Research, 32, 409-418 Runkel R.L., McKnight D.M, and Bencala K.E. (1996b) Reactive solute transport in streams: 2. Simulation of a pH modification experiment. Water Resources Research, 32, 419-430

Runkel R.L., Bencala K.E. and Kimball B.A. (1999) Modelling solute transport and geochemistry in streams and rivers using OTIS and OTEQ. In: Morganwalp D.W. and Buxton H.T. (Eds) U.S. Geological Survey Toxic Substances Hydrology Program – Proceedings of the Technical Meeting, Charleston, South Carolina, March 8-12 1999 Vol 1: Contamination from Hardrock Mining: U.S. Geological Survey Water Resources Investigations Report 99-4018A Whitehead P.G., Butterfield D. and Wade A.J. (2009) Simulating metals and mine discharges in river basins using a new integrated catchment model for metals: pollution impacts and restoration strategies in the Aries-Mures river system in Transylvania, Romania. Hydrology Research, 40, 323-346 Whitehead P.G. and Jeffrey H. (1995) Heavy metals from acid mine drainage – impacts and modelling strategies. IAHS 230, 55-68 Williams T. (2012a) WFD Abandoned Mines Project: Afon Teifi catchment. Technical Report No. A&R_SW_12_02 William T. (2012b) WFD Abandoned Mines Project: Afon Tywi catchment. Technical Report No. A&R_SW_12_03 Zhen-Gang J., Hamrick J.H. and Pagenkopf J. (2002) Sediment and metals modelling in shallow river. Journal of Environmental Engineering, 128(2), 105-119