Integrated modelling of Priority Pollutants in stormwater systems

Physics and Chemistry of the Earth 42–44 (2012) 42–51

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Integrated modelling of Priority Pollutants in stormwater systems Luca Vezzaro ⇑, Anna Ledin 1, Peter Steen Mikkelsen Department of Environmental Engineering (DTU Environment), Technical University of Denmark, Building 113, Miljoevej, 2800 Kgs. Lyngby, Denmark

a r t i c l e

i n f o

Article history: Available online 27 July 2011 Keywords: Dynamic stormwater quality model Generalized Likelihood Uncertainty Estimation Uncertainty Stormwater micropollutants Pollution control strategies Integrated model

a b s t r a c t The increasing focus on urban diffuse sources of Priority Pollutants (PPs) has highlighted stormwater as an important contributor to contamination of natural water bodies. This study presents an example of an integrated model developed to be able to quantify PP loads discharged by stormwater systems. The integrated model includes three submodels that simulate (a) stormwater pollutant sources in the catchments, (b) runoff quality and quantity and (c) stormwater treatment. These submodels employ all the generic available information that can be retrieved without extensive on-site data collection campaigns. Given the general lack of data regarding stormwater PPs and the inherent uncertainty of stormwater quality models, the Generalized Likelihood Uncertainty Estimation (GLUE) technique was applied to estimate the results’ uncertainty. The integrated model was used to estimate the total suspended solids (TSS) and copper (Cu) loads discharged from an industrial/residential catchment in Albertslund (Denmark). The results of the runoff estimation were affected by a high level of uncertainty, which was consequently transferred to the other submodels. The estimation of the model uncertainty and its inclusion in the results enables a wider application of this model and provides a tool for assessing PPs pollution reduction strategies. Ó 2011 Elsevier Ltd. All rights reserved.

1. Introduction The rising focus on pollution caused by stormwater discharge and the identification of the environmental risk posed by stormwater Priority Pollutants (PP) (e.g. Kayhanian et al., 2008) increase the need for modelling tools capable of estimating stormwater PP loads discharged to the environment. These tools can be used to perform Source-Flux-Analysis (SFA) for urban catchments and to assess PP control strategies, aiming at improving the environmental status of receiving water bodies as requested by recent regulations such as the EU Water Framework Directive (European Commission, 2000) and the EU Environmental Quality Standard Directive (European Commission, 2008). Several models are available in literature for simulating the various elements of stormwater systems (Huber et al., 2006;Obropta and Kardos, 2007). These models are mainly focusing on the removal of ‘‘traditional’’ pollutants such as overall organic matter pollution, nutrients and suspended solids, and the potential for simulation of PP fluxes is limited. The use of stormwater quality models in practice is also limited by the high uncertainty affecting the results. Despite years of stud-

⇑ Corresponding author. Tel.: +45 45251579; fax: +45 45932850. E-mail address: [email protected] (L. Vezzaro). Present address: The Swedish Research Council for Environment, Agricultural Sciences and Spatial Planning, Kungsbron 21, P.O. Box 1206, SE-111 82 Stockholm, Sweden. 1

1474-7065/$ - see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.pce.2011.07.002

ies and model developments, stormwater quality modelling is in fact a very uncertain field (see the discussion in BertrandKrawjewski, 2007). This uncertainty is magnified when dealing with PPs, as there is a general lack of information regarding these substances (e.g. sources, measurements, removal processes, etc.). This study presents the integration and application of different models for estimating PP fluxes in separate stormwater systems. The integrated model was applied to estimate copper loads from an existing catchment and compare different pollution control strategies. The model uses the limited available knowledge of the system which is combined with uncertainty analysis in order to estimate the uncertainty of model results. This enhances the users’ confidence in the model results and in its potential application for estimation of PP fluxes within urban areas. 2. Material and methods 2.1. Elements of the integrated model The integrated model proposed in this study subdivides the stormwater system into three different parts: (a) the catchment, which includes pollutant sources, (b) the drainage system (estimating runoff quantity and quality) and (c) a treatment before discharge into the receiving waters. This representation can be related to the four-point scheme proposed by Choi and Ball (2002) to classify commonly available stormwater quality models (Fig. 1).

L. Vezzaro et al. / Physics and Chemistry of the Earth 42–44 (2012) 42–51

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Fig. 1. Schematization of the integrated stormwater quality model presented in the study (based on the classification presented by Choi and Ball (2002)), major sources of input data and main equations implemented in the submodels. Main model parameters, system attributes, inputs and equations are listed in Tables 1–3.

The various elements of the system were modelled acknowledging the limited available information regarding PPs. The developed models in fact use information that can be retrieved from literature and existing databases (e.g. PP inherent properties – see an example in Lützhøft et al., 2008), and they can benefit from any additional information (e.g. measurement of ‘‘traditional’’ pollutants, flow data) that can be used to calibrate the not PP-specific parameters of the model (e.g. hydrological parameters, settling rates, etc.). 2.1.1. Stormwater pollutant sources Catchment and pollutant sources characterization is commonly performed by utilization of GIS software, which enables the identification of different land use categories in the area of interest. The stormwater pollutant loads are calculated by coupling hydrological models with emission data (measured or retrieved from databases). The level of detail of the characterization depends on various factors, such as the investigated substances, the size of the catchment, the purpose of the model and the related output. For example, these models can be used to perform a SFA (e.g. Björklund et al., 2011), thus requiring a detailed description of the PP sources, or to evaluate compliance with discharge limits and/or emission limits before discharge (as in the study presented by Park et al., 2009). For the latter, a lumped description of the catchment may be sufficient to obtain the desired results. This study utilized GIS-based information that is commonly available at the municipal and water authority level, such as building maps, roofing material, economical activities, and drainage network. This information represents a compromise between a lumped representation of the system and a highly detailed characterization of the system (e.g. based on aerial photo analysis, on-site visits, etc.). Based on the available data, potential PP sources were identified by subdividing the catchment into three categories

(roads, roofs, and parking and other impervious areas) and combined with emission factors that were retrieved from the database presented by Lützhøft et al. (2009), which provides an overview of the PP source emission data that are available in literature. The PP sources that were considered for each land usage are listed in Table 6, and this information is used as input to the stormwater quality model (see next section). The uncertainty on PP emission is magnified by the general lack of data. The usage of different datasets can affect the model results (Park et al., 2009) and, potentially, affect the final assessment of PP reduction strategies. Thus, a safety factor (esource) was applied to the identified PP sources (PPrel,ctc), similarly to the approach applied in environmental risk assessment. 2.1.2. Stormwater quantity and quality Several models, characterized by different approaches and level of complexity, are available to simulate stormwater generation and routing across the catchment and through the drainage network (Obropta and Kardos, 2007). Dynamic stormwater quality models can be subdivided in two parts (Choi and Ball, 2002): a hydrological submodel, dealing with generation and routing of runoff, and a quality submodel, simulating the release and transport of stormwater in the system. While various levels of complexity can be used in the former (ranging from finite differential to simple hydrological models), the latter commonly shows a low complexity, due to the general lack of knowledge regarding stormwater pollutants’ processes and measurements (Bertrand-Krawjewski, 2007). This study used an expansion of the conceptual model SEWSYS, developed by Ahlman (2006) for the estimation of stormwater pollutant loads in separate sewer systems (a brief description of SEWSYS is provided in Lindblom et al. (2011)). This conceptual model considers several pollutant sources present in the urban environment (e.g. dry and wet deposition, traffic, building material

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Table 1 List of equations included in the integrated model. Equation

Parameters and state variables

Water fluxes (1) Qeff = ActcKrunR if hcum P hloss Qeff = 0 if hcum < hloss

(2)

Q trm;in ¼ Actc K ctc hctc

(3)

Q trm;out ¼ K trm htrm

5=3

a

Pollutant fluxes P (4) PPrel;ctc ¼ PPsource;i  Asource;i

¼ esource PPrel;ctc  h1 PP ctc  h2 Rn PP ctc

(5)

dPPctc dt

(6)

dSPP dt

¼ F SPP ;in  Q trm;out  C SPP þ

(7)

dX PP dt

¼ F X PP ;in  Q trm;out  C X PP þ

P

SPP  r SPP;i

P

X PP  r X PP;i

Qeff (m3 s1) Actc (m2) Krun (–) R (lm s1) hcum (mm) hloss (mm) Qtrm,in (m3 s1) Kctc (m2/3 s1) hctc (m) Qtrm,out (m3 s1) Ktrm (m3a s1) htrm (m) a (–)

Effective runoff generated in the catchment Catchment impervious surface Runoff coefficient Rainfall intensity Cumulated rainfall height Initial loss Catchment outflow/inflow to treatment unit Routing parameter lumping catchment characteristics Water level in the non-linear reservoir used to represent the catchment Outflow from treatment unit Coefficient for discharge from full pipe Water level above the outlet threshold in the STUMP water compartment Power coefficient for discharge from full pipe

PPrel,ctc (lg s1) PPsource,i (lg m2 s1) Asource,i (m2) esource (–) PPctc (lg) h1 (s1) h2 (lmn s1n) n (–) SPP (g) F SPP ;in (g s1) C SPP (g m3) XPP (g) F X PP ;in (g s1) C X PP (g m3) r SPP;i ; rX PP;i (s1)

Total PP release in the catchment PP release rate from i-th source Surface of i-th pollutant source (see Table 6) Error on estimation of PP release Total PP load available in the catchment Dry weather PP removal rate Rain PP removal rate Coefficient for relationship between rainfall intensity and PP removal Mass of dissolved PP in the unit Flux of dissolved PP entering the treatment unit Concentration of dissolved PP Mass of particulate PP in the unit Flux of particulate PP entering the treatment unit Concentration of particulate PP Process rate for i-th fate process (see Table 2)

corrosion). The model is implemented in MATLAB/SIMULINK™ and the simulations are run with variable time steps, which are automatically defined by the SIMULINK™ solver (in this study the Dormand–Prince method was selected). The hydrological submodel is a slight modification of the model presented in Lindblom et al. (2007, 2011), with a continuous initial loss and runoff reduction factor. Runoff routing across the catchment and in the drainage system is modelled with a lumped approach based on the non-linear reservoir hypothesis. The input to this submodel is rainfall intensity. The quality submodel uses an accumulation-washoff equation (Eq. (5)), which assumes an asymptotic behaviour for the PP mass accumulated on the catchment surface (confirmed by experimental studies, e.g. Vaze and Chiew, 2002). The inputs to this submodel are the pollutant fluxes generated by the different sources within the catchment. 2.1.3. Stormwater treatment There is a limited choice of models for simulation of PPs in stormwater systems, as the majority of the available models (e.g. Table 2 Stoichiometric matrix of the STUMP water quality submodel (adapted from Vezzaro et al. (2010) for the pollutants included in the study). Process

Component SPP

Settlinga Resuspension Sorptionc Desorptionc a

Process rate (ri) XPP 1

b

+1 1 +1

+1 1



   C  1  C TSS TSS n Ab 1 M S

v sed  1  sb s hw



sb E0 scrit;set ksorCTSS

crit;set

ksor kd

Only in the water phase. Only in the sediment compartment. c For numerical reasons, the dynamic description of sorption/desorption processes is neglected in the sediment compartment, and equilibrium is assumed by applying definition of the solids-water partition coefficient (XPP/Ms = kdSPP/(Abhw)). b

Huber et al., 2006) focus on traditional pollutants and do not consider the complex processes affecting PPs (e.g. sorption/desorption to solids, biodegradation, volatilization, etc.). The Stormwater Treatment Unit model for Micro Pollutants (STUMP – Vezzaro et al., 2010) was used in this study. STUMP extends the model proposed by Wong et al. (2006) with PP fate processes, which are modelled by using the substance’s inherent properties (i.e. information that can be easily retrieved from databases). The model is based on serial Continuously Stirred Tank Reactors (CSTR), subdivided into water and sediment compartments. PP are modelled as dissolved (SPP) and particulate (XPP) species, and PP fate processes are modelled according to the substance’s inherent properties (commonly half lives), enabling the simulation of the environmental fate of a wide range of substances through various processes (see Vezzaro et al. (2010) for a detailed description of all the processes modelled by STUMP and Table 2 for the processes modelled in this study). The original STUMP hydraulic submodel (assuming a gravity driven outflow) was modified to achieve a better representation of the modelled system. The outflow of the pond was calculated by using the full-pipe flow equation. The STUMP model can represent a great range of treatment units when the real system and the model have similar hydraulic efficiencies (the ratio between the actual and theoretical hydraulic retention time), as in the original model proposed by Wong et al. (2006). The Basin K was modelled by applying the STUMP model with a configuration of two serial tanks. 2.2. Uncertainty assessment Among the various available methods for uncertainty analysis (e.g. Matott et al., 2009), the Generalized Likelihood Uncertainty Estimation technique (Beven and Binley, 1992) was applied in this study to estimate the model’s prediction bounds. This pseudoBayesian method has already been applied to stormwater quality models (Lindblom et al., 2007, 2011) and integrated urban drainage models (Mannina et al., 2006) and it was applied in this study

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due to the lower number a priori assumptions required in the analysis. There is a lively debate about the application of Bayesian and pseudo-Bayesian methods (e.g. Freni et al., 2009), but this is not investigated in this study. In this study the model performances were evaluated by using the Nash–Sutcliffe based informal likelihood measure:

L½Mðhi jY T ; Z T Þ ¼

1

r2i r2ZT

!N ð8Þ

where L is the likelihood measure, Mðhi jY T ; Z T Þ is the realization of the model M, based on the parameter set hi and conditioned on input data YT and on observed values ZT. The likelihood measure L is calculated by using the ratio of the residuals variance r2i and the variance of the observations r2ZT , while N is a shape factor used to sharpen the likelihood response surface (in this study it was set to 1). Only observed flows above 5 l/s were used to calculate the likelihood measure. The acceptance criterion for behavioural parameter sets (i.e. parameter sets providing the predictions used to estimate the model prediction bounds) was expressed according to the method proposed by Blasone et al. (2008), i.e. as number of observations contained within the prediction bounds. Parameter sets are ranked according to their likelihood measure and the threshold on the likelihood c is lowered until the desired number of observations is covered by the prediction bounds. Compared to the traditional GLUE approach, where c is subjectively defined on the likelihood measure (a value that is difficult to link to physical quantities), this approach has the advantage of expressing the cut-off threshold in a more ‘‘concrete’’ manner while not eliminating the subjectivity of GLUE. In fact, the number of observations contained within the prediction bounds is still subjectively defined by the modeller based on the quality of available data and on the structural limits of the model. If, for example, all the analyzed parameter sets cover 56% of the observations (e.g. due to the model’s structure, error in measurements, etc.), a threshold of 60% can be regarded as excessively stringent for the analyzed model, leading to a re-formulation of the model or to a reduction in the fraction of covered observations. In this study the acceptance criterion was set to include at least 40% of the observed flow (above 5 l/s) within the prediction bounds. The width of the prediction bounds can be evaluated by using the Average Relative Interval Length (ARIL), which was defined by Jin et al. (2010) as:

ARIL ¼

Nobs Limitupper;j  Limit lower;j 1 X Nobs j¼1 Z ðtj Þ

ð9Þ

Table 3 Description of the state variables and parameters listed in Table 2 (adapted from Vezzaro et al. (2010) for the pollutants included in the study). Name 2

Ab (m ) C TSS (gTSS l1) CTSS (gTSS l1) E0 (g m2 d1) hw (m) kd (m3 gTSS1) ksor (m3 gTSS1 d1) Ms (g) n (–) sb (Pa) scrit,res (Pa) scrit,sed (Pa) vsed (m d1)

Description Bottom area of the CSTR Background TSS concentration in the CSTR TSS concentration in the CSTR Erodability constant Water level MP solid-water partition coefficient MP sorption rate Mass of settled solids in the bottom compartment Power of erosion term Bottom shear stress Critical shear stress for resuspension Critical shear stress for settling Average settling velocity for particles

Table 4 Parameter intervals included in the uncertainty analysis of the catchment and pond hydrological submodels. Parameter

Initial interval [min; max]

Interval after SCEM-UA

Krun (–) hloss (mm) Kctc (m2/3 s1) Ktrm (m3a s1) a (–)

[0; 1] [0.01; 0.90] [1  103; 2  102] [0.10; 0.25] [0.3; 0.7]

[0.400; 0.687] [0.026; 0.900] [1.19  103; 1.89  102] [0.100; 0.250] [0.301; 0.700]

where Nobs is the number of available observations; Limitupper,j and Limitlower,j are the upper and lower prediction bounds at the time tj, and ZT(tj) is the observed value at tj. The information expressed by ARIL is useful to assess the relative width of prediction bounds when looking at time series: given a defined fraction of observations contained within the bounds, lower ARIL values imply narrower bounds and thus a lower uncertainty. An initial 2000-set sample of the most sensitive model parameters (identified by applying global sensitivity analysis techniques to each submodel separately – see Vezzaro (2011) for the stormwater quantity and quality submodel and Vezzaro et al. (2011) for the treatment submodel) was generated by sampling from uniform distributions defined by the intervals listed in Table 4. The regions of the parameter space that ensured better likelihood values were identified by applying the Shuffled Complex Evolution Metropolis (SCEM-UA) algorithm described in Vrugt et al. (2003), which provided a new sample of 2000 parameter sets. The latter was then used in the GLUE analysis. The uncertainty of the different elements of the integrated stormwater system was analyzed separately. The behavioural parameter sets were estimated for each single submodel independently: the behavioural parameters of the pond submodel, for example, were identified by using measured input data (disregarding the output from the catchment submodel). The behavioural parameters were subsequently used to estimate the prediction bounds of the integrated model. Potential interactions between submodels were not considered in this preliminary study. 2.3. The Albertslund case study The model was applied to an industrial–residential catchment in Albertslund (Denmark). Runoff is collected by separate drainage pipes and open channels and sent to a retention pond (Basin K) before discharge to a stream (Fig. 2). Industrial activities were identified to be major sources of stormwater pollutants and sediment contamination. Table 6 shows the most relevant characteristics of the system, which were measured or extracted from information provided by the municipality. This information was used to set the model for the estimation of TSS and copper (Cu) loads discharged from the catchment and assess different scenarios for reduction of these quantities. Rainfall intensity was obtained from a station in the Danish Water Pollution Committee network, operated by the Danish Meteorological Institute (Jørgensen et al., 1998), located approximately 3 km south-west from the catchment. As part of the efforts of Albertslund municipality to improve the quality of receiving waters (Høg et al., 2005), various studies have been monitoring the pollution in the retention pond. Flow measurements were taken at the pond inlet and outlet (Fig. 2) by using an ultrasonic probe for a period of approximately 13 months (September 2009–October 2010). Flow data collected during wintertime (December–March) were not used in the uncertainty analysis as the model does not consider snow melting. The collected measurements were used for the uncertainty analysis of the flow calculated by the catchment and the treatment submodels. Grab samples were taken during two separate rain

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L. Vezzaro et al. / Physics and Chemistry of the Earth 42–44 (2012) 42–51

Fig. 2. Map of the study area.

Table 5 Default parameter intervals for quality submodels. Parameter

Interval [min; max]

esource (–) h1 (s1) h2 (lm1) C TSS (gTSS l1) E0 (g m2 d1) kd (l kg1) ksor (m3 gTSS1 d1) n (–) scrit,res (Pa)

[102; 102] [1.5  105; 1.5  104] [0; 56] [3; 20] [294; 648] [5  103; 105] [0.05; 1.5] [1.45; 3.64] [0.03; 0.62]

scrit,sed (Pa)

[0.02; 0.10]

vsed (m d

1

)

[17; 2600]

Source Lindblom et al. (2007) Lindblom et al. (2007) From measured values Schaaff et al. (2006) Shafer et al. (2004) Lindblom et al. (2009) Lumborg (2005) Lower bound defined by the observing the range of simulated shear stress in the pond (30–40 mPa), higher bound defined from Parchure and Mehta (1985) Lower bound defined by the observing the minimum simulated shear stress in the pond (20 mPa), higher bound defined from Van Der Ham and Winterwerp (2001) Bentzen and Larsen (2009)

Table 6 Relevant data of the simulated system, classification of PP source, and characteristic of data used for uncertainty analysis. Main data

Data source

Total area (ha) (Actc) Average traffic load (vehicle/d) Pond surface (m2) Permanent water volume (m3)

94.7 800–2000 (max. 6200) 6400 5000

GIS Municipal traffic plan GIS Technical design documents

Catchment characterization

PP sources

Road area (ha) (Asource,road)

6.44

Roof area (ha) (Asource,roof)

38.8

Parking and other impervious area (ha) (Asource,other)

29.6

Dry and wet deposition Corrosion of Zn surfaces Oil discharge Wearing of asphalt Wearing of tyres Wearing of brakes Dry and wet deposition Corrosion of Cu roofs Dry and wet deposition

Data for uncertainty analysis

Pond inflow and outflow a

Start

End

Resolution

No. of events

2009/09/22

2010/11/01a

2 min

216

Due to interruptions in monitoring and snow melting processes, the available measurements covered approximately 141 days.

events and various water quality parameters were analyzed (e.g. total suspended solids TSS, heavy metals). The uncertainty of these quality measurements is however high and they were only used for a qualitative comparison with the simulated bounds the stormwater quality model, which was run by using values sampled by the interval listed in Table 5.

3. Result and discussion 3.1. Catchment submodel Initially all the 2000 parameter sets were rejected as the prediction bounds covered only 30% of the observations. Several sources

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L. Vezzaro et al. / Physics and Chemistry of the Earth 42–44 (2012) 42–51

Fig. 3. Results for catchment hydraulic submodel (left: period from 2009/09/23 20:00 to 2009/09/25 02:00; right: period from 2009/10/02 00:00 to 2009/10/04 20:00). Top: recorded rainfall intensity. Middle: measured flow against the simulated bounds generated by the model without delay (best 785 sets, but no set was accepted as behavioural). Bottom: measured flow against the prediction bounds generated by the model with delay (785 behavioural sets).

of uncertainty can be pinpointed to explain these results: data uncertainty (e.g. rain gauge located too far from the catchment, high noise in the outlet flow measurements) and model uncertainty (e.g. simplified description of the hydraulic routing through the catchment). Fig. 3 (middle) shows a clear example of the influence of input data on the model results: (case i) no rainfall is recorded but runoff is observed, (case ii) there is a delay in the simulation of the event (for example, for the event shown in Fig. 3 (middle) and starting on 2009/10/03 at 03:09, the delay was about 45 min), and (case iii) the recorded rain event did not generate significant observed runoff, but the model reproduced flows. All these phenomena can be explained by the location of the rain gauge used to collect the input data, located about 3 km from the catchment. Events with high intensity commonly affects small areas, meaning that the catchment received rainfall which was not recorded by the gauge (case i), or that the gauge recorded rainfall which did not interest the catchment (case iii). Finally, long events with low rainfall intensity can reach the catchment after they are recorded (case ii). As shown by the different scales for flow in Fig. 3, the magnitude of localized events (case i and iii) was such that the generated runoff was lower than low intensity events with long duration (case ii). To partially compensate the uncertainty due to the placement of the rain gauge, the input rain series was adjusted by adding a delay factor, improving the model performance in terms of observations coverage (Fig. 3 – bottom). The results obtained by the modified model are listed in Table 7. The number of behavioural parameter sets increased to 785 and the model prediction bounds

covered 40% of the observed values with an ARIL of 1.21. The catchment submodel tended to overestimate the total volume discharged from the catchment during the simulation period (right column in Table 7), with a maximum overestimation of 68%. This volume overestimation should be taken into account when looking at the estimated PP loads over long time intervals, as potential over- and underestimations may be caused by the hydrological submodel rather than by the quality submodel. Despite the improvements in the performance of the model, the delay factor failed to account for rainfall spatial variability (case i and iii): this uncertainty can be reduced by on site rainfall measurements or interpolation with data collected in other rain gauges in order to provide a better estimate of precipitation in the catchment. 3.2. Pond submodel The simulated hydraulic efficiency of the two-tank STUMP configuration was 0.30, which is similar to the value suggested by Jansons et al. (2005) for ponds with similar layout. The model flow predictions showed quite high likelihood values (above 0.8). Although this shows a good agreement between simulated and measured values, the estimated prediction bounds covered a limited fraction of the observed data. The behavioural parameter sets were defined to cover at least 40% of the data (Table 7), leading to the identification of 240 behavioural parameter sets. Despite the relatively small fraction of observations within the prediction bounds, the model provided good (ranging from a minimum of

Table 7 Acceptance threshold and number of behavioural parameter sets identified by the GLUE analysis for the catchment and the pond submodels. Minimum and maximum, mean and standard deviation (r) of behavioural parameter sets. Model Output

Covered observations (%)

Threshold on likelihood (c)

ARIL

Error on cumulated volume (%)

Catchment outflow (m3/s) Pond outflow (m3/s)

40.3 40.3

0.517 0.823

1.21 0.57

8/+68 23/+10

Behavioural parameter

Interval [min; max]

Mean

r

Krun (–) hloss (mm) Kctc (m2/3 s1) Ktrm (m3a s1) a (–)

[0.401; 0.543] [0.027; 0.831] [5.60  103; 1.70  102] [0.100; 0.249] [0.334; 0.699]

0.453 0.393 1.15  102 0.175 0.499

0.027 0.145 1.71  103 0.033 0.085

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Fig. 4. Model prediction bounds for four events recorded at the pond outlet.

Fig. 5. Model prediction bounds for TSS (left) and copper (right) concentrations along with measured values (grab samples – shown as crosses).

23% to a maximum of +10%) estimation of the cumulated volume discharged from the pond. The model tended to overestimate the small and very high flows, while the medium flows tended to be underestimated. An example of the calculated model prediction bounds is shown in

Fig. 4: while the model is able to satisfactorily reproduce the pattern of the measured data, it encounters difficulties in matching the exact observed values. A complete evaluation of the model’s performance should also include measurement uncertainty, as quite significant noise was observed in the inlet data.

Table 8 Estimated pollutant loads discharged from the Basin K catchment during the measurement period. Model output

TSS load (tonTSS) Cu load (kgCu) a b

Inlet to the pond

Simulated removal efficiency (%)b

Outlet from the pond a

Median

Range

23.8 39.4

[2.25; 103] [12.9; 120]

a

r

Median

Range

r

13.0 19.1

12.6 23.4

[1.65; 39.3] [6.56; 79.7]

5.75 11.4

Expressed as minimum and maximum value. Expressed as median value ± standard deviation (r), minimum and maximum values in brackets.

47.6 ± 5.01 [5.72; 63.4] 41.9 ± 8.06 [7.34; 62.3]

L. Vezzaro et al. / Physics and Chemistry of the Earth 42–44 (2012) 42–51 Table 9 Estimated pollutant loads discharged from the Basin K for different scenarios. Model output

Inlet to the pond Median

Range

a

Scenario A (area disconnection) TSS load 19.5 [1.85; 84.6] (tonTSS) [11.0; 104] Cu load (kgCu) 33.7 Scenario B (pond enlargement) TSS load 23.8 [2.25; 103] (tonTSS) [12.9; 120] Cu load (kgCu) 39.4 a

Outlet from the pond

r

Median

Rangea

r

10.7

8.26

[1.20; 26.1]

3.71

16.3

16.8

[4.58; 61.3]

8.60

13.0

9.03

[1.45; 27.8]

3.91

19.1

19.1

[5.00; 70.9]

10.0

Expressed as minimum and maximum value.

3.3. Integrated model The integrated model was run by selecting the hydraulic parameters sets from the behavioural sets identified by GLUE (Table 7) and by sampling the water quality parameters from the intervals listed in Table 5. As limited quality measurements were available and could not be used to estimate the parameters of the quality submodels, a rough evaluation of the model performances was made by plotting the available measured concentrations (obtained by grab samples) against the simulated intervals. The measured values

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were within the simulated bounds (Fig. 5), suggesting no evident under- or overestimation of the water quality parameters. More detailed water quality data would enable a thorough uncertainty analysis, leading to a sensible reduction in the uncertainty of simulated PP loads. The integrated model was applied to estimate the TSS and copper load discharged from the Basin K catchment during the measurement period. Table 8 shows median values for TSS and Cu loads, along with their ranges calculated by the using the behavioural parameter sets. Previous studies have estimated the accumulation of sediments in the pond as 1.1 cm/yr (Hoa, 2006), which is about two times greater than the modelled value (median value 0.68 cm/yr and r 0.42 cm/yr, assuming a density of Ms of 2300 kg/m3 based on the values listed by Zanders (2005)). The simulated Cu removal efficiency in the retention pond was comparable with data from similar structures, but their ranges were quite wide, ranging from values below 10% up to above 60%. This great variation was due by the default intervals listed in Table 5: as described by Vezzaro et al. (2011), the calibration of the parameters linked to particle settling and sediment resuspension would lead to a reduction in the uncertainty of the pond mass balance and thus to lower uncertainty bounds in the estimated removal efficiency. Additional stormwater quality measurements are thus needed to reduce the uncertainty of the treatment submodel. Two scenarios were run to assess potential strategies to reduce TSS and copper discharge:

Fig. 6. Cumulative distributions of estimated pollutant loads (TSS above, Cutot below) discharged from the catchment (pond inlet, left) and from Basin K (right) for different scenarios.

Fig. 7. Distribution of percentage reduction of TSS (left) and Cutot loads (right) at the pond outlet for the two scenarios.

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 Scenario A: disconnection of 50% of the parking areas and 30% of the roofs and roads corresponding to a 40% reduction of the impervious area  Scenario B: doubling of the pond volume. The results of the two scenarios are shown in Table 9, Figs. 6 and 7. Scenario A led to a reduction of TSS and Cu loads discharged from the catchment. Also, the reduction of the contributing area decreased the hydraulic short-circuiting of the pond and thus improved settling conditions. The load reduction at the pond inlet in scenario A was not directly proportional to the reduction in impervious area (40%): the pollutant release rates, in fact, differ according to land usage. For example, roads (about 24% of the impervious areas) were responsible for about 44% of Cu loads, but only 30% was disconnected. These results show how catchment characterization is an essential step in the elaboration of pollution control strategies, which can explicitly target the major PP sources (in the studied catchment, runoff from roads). The enlargement of the pond layout (Scenario B) reduced the pollutant loads discharged from the pond but was generally less effective than Scenario A. The two scenarios show that source control would play a more significant role in reduction of TSS and copper loads in the system. As shown in Fig. 7, Scenario A generally led to a higher reduction of both the TSS and Cutot loads discharged from the pond compared to Scenario B: the median reduction for TSS loads was 33.9% (Scenario A) and 27.8% (Scenario B), and 27.6% (Scenario A) and 17.7% (Scenario B) for total copper loads. Also, the uncertainty affecting the expected load reduction was smaller for Scenario A, as highlighted by the narrower distributions shown in Fig. 7. Additional stormwater treatment should focus on removal of the dissolved fraction (e.g. filtrations, flocculation, etc.), which was not affected by the two scenarios, mainly addressing particle removal through settling. Stormwater PP can be found in dissolved form (e.g. Zgheib et al., 2010) with potential toxic effect on the aquatic environment: for example, dissolved heavy metals are explicitly targeted by water quality legislations. The ability of the proposed model to simulate PP partitioning is therefore an important feature for model based evaluation of PP pollution control strategies. 4. Conclusion The preliminary results of this study illustrate the potential application of integrated stormwater models in the assessment of PP loads discharged in the environment. The model employs the available information and measurements to estimate the PPs’ fate in the study area. The model results were affected by significant uncertainty, which may be reduced by additional measurements (e.g. flow-proportional water quality samples to estimate the water quality parameters, on site rainfall measurements to reduce the significant input uncertainty) and by a re-formulation of the model (e.g. use of more detailed hydraulic models). Despite the intrinsic uncertainty affecting the model results, a comparison between different pollution control options highlighted the more appropriate solution for the simulated system. The model results are thus a useful tool for urban water managers that are facing diffuse pollution in urban areas. Acknowledgments Partial funding was received from the Interreg IVB North Sea Region Programme via the project ‘‘Impact of Climate Change on the Quality of Urban and Coastal Waters (Diffuse Pollution) – DiPOL’’. The authors show their gratitude to Hans-Henrik Høg

(Albertslund municipality), Thomas Aabling and Søren Gabriel (Orbicon A/S) and Andreas Mørch-Madsen and Gudny Kristin Tryggvadottir (BSc students) for providing the catchment data and measurements used in this study. References Ahlman, S., 2006. Modelling of substance flows in urban drainage systems. PhD thesis. Chalmers University of Technology, Göteborg, Sweden. Bentzen, T.R., Larsen, T., 2009. Heavy metal and PAH concentrations in highway runoff deposits fractionated on settling velocities. Journal of Environmental Engineering 135, 1244–1247. Bertrand-Krawjewski, J.L., 2007. Stormwater pollutant loads modelling: epistemological aspects and case studies on the influence of field data sets on calibration and verification. Water Science and Technology 55, 1–17. Beven, K., Binley, A., 1992. Future of distributed models: model calibration and uncertainty prediction. Hydrological Processes 6, 279–298. Björklund, K., Malmqvist, P.A., Strömvall, A.M., 2011. Simulating organic pollutant flows in urban stormwater: development and evaluation of a model for nonylphenols and phthalates. Water Science and Technology 63, 508–515. Blasone, R.S., Vrugt, J.A., Madsen, H., Rosbjerg, D., Robinson, B.A., Zyvoloski, G.A., 2008. Generalized likelihood uncertainty estimation (GLUE) using adaptative Markov Chain Monte Carlo sampling. Advances in Water Resources 31, 630– 648. Choi, K., Ball, J.E., 2002. Parameter estimation for urban runoff modelling. Urban Water 4, 31–41. European Commission. Directive 2000/60/EC of the European Parliament and of the Council of 23 October 2000 establishing a framework for Community action in the field of water policy.2000, 2000/60/EC. European Commission. Directive 2008/105/EC of the European Parliament and of the Council of 16 December 2008 on environmental quality standards in the field of water policy, amending and subsequently repealing Council Directives 82/176/EEC, 83/513/EEC, 84/156/EEC, 84/491/EEC, 86/280/EEC and amending Directive 2000/60/EC of the European Parliament and of the Council.2008, 2008/105/EC. Freni, G., Mannina, G., Viviani, G., 2009. Urban runoff modelling uncertainty: comparison among Bayesian and pseudo-Bayesian methods. Environmental Modelling and Software 24, 1100–1111. Hoa, E., 2006. Heavy metal contamination in stormwater sediments. MSc thesis. Technical University of Denmark, Institute of Environment and Resources, Kgs. Lyngby, Denmark. Høg, H., Gabriel, S., Henze, A., Krayer von Krauss, M., 2005. Sustainable urban drainage and public participation in Albertslund. In: Proceeding of 10th International Conference on Urban Drainage, Copenhagen, Denmark, 21–26 August, 2005. Huber, W.C., Cannon, L., Stouder, M., 2006. BMP modeling concepts and simulation, Report EPA/600/R-06/033, US Environmental Protection Agency, Office of Research and Development, Washington DC, USA. Jansons, K., German, J., Howes, T., 2005. Evaluating hydrodynamic behaviour and pollutant removal in various stormwater treatment pond configurations. In: Proceeding of 10th International Conference on Urban Drainage, Copenhagen, Denmark, 21–26 August, 2005. Jin, X., Xu, C.-Y., Zhang, Q., Singh, V.P., 2010. Parameter and modeling uncertainty simulated by GLUE and a formal Bayesian method for a conceptual hydrological model. Journal of Hydrology 383, 147–155. Jørgensen, H.K., Rosenørn, S., Madsen, H., Mikkelsen, P.S., 1998. Quality control of rain data used for urban runoff systems. Water Science and Technology 37, 113–120. Kayhanian, M., Stransky, C., Bay, S., Lau, S.-L., Stenstrom, M.K., 2008. Toxicity of urban highway runoff with respect to storm duration. Science of the Total Environment 389, 386–406. Lindblom, E., Ahlman, S., Mikkelsen, P.S., 2007. How uncertain is model-based prediction of copper loads in stormwater runoff? Water Science and Technology 56, 65–72. Lindblom, E., Press-Kristensen, K., Vanrolleghem, P.A., Mikkelsen, P.S., Henze, M., 2009. Dynamic experiments with high bisphenol-A concentrations modelled with an ASM model extended to include a separate XOC degrading microorganism. Water Research 43, 3169–3176. Lindblom, E., Ahlman, S., Mikkelsen, P.S., 2011. Uncertainty-based calibration and prediction of a stormwater surface accumulation-washout model using sampled Zn, Cu, Pb and Cd field data. Water Research 45, 3823–3835. Lumborg, U., 2005. Modelling the deposition, erosion, and flux of cohesive sediment through Øresund. Journal of Marine Systems 56, 179–193. Lützhøft, H.-C. H., Eriksson, E., Scholes, L., Donner, E., Wickman, T., Lecloux, A., Ledin, A. 2008. Database Presenting Basic Information about EU WFD Priority Substances. ScorePP deliverable report D3.1, . Lützhøft, H.-C. H., Eriksson, E., Donner, E., Wickmann, T., Banovec, P., Mikkelsen, P. S., Ledin, A., 2009. Quantifying releases of priority pollutants from urban sources. In: Proceedings of WEFTEC 09, 82nd Annual Water Environment Federation Technical Exhibition and Conference, Orlando, Florida, USA, 10–14 October, 2009. Mannina, G., Freni, G., Viviani, G., Saegrov, S., Hafskjold, L.S., 2006. Integrated urban water modelling with uncertainty analysis. Water Science and Technology 54, 379–386.

L. Vezzaro et al. / Physics and Chemistry of the Earth 42–44 (2012) 42–51 Matott, L.S., Babendreier, J.E., Purucker, S.T., 2009. Evaluating uncertainty in integrated environmental models: a review of concepts and tools. Water Resources Research 45, W06421. Obropta, C.C., Kardos, J.S., 2007. Review of urban stormwater quality models: deterministic, stochastic, and hybrid approaches. Journal of the American Water Resources Association 43, 1508–1523. Parchure, T.M., Mehta, A.J., 1985. Erosion of soft cohesive sediment deposits. Journal of Hydraulic Engineering 111, 1308–1326. Park, M.H., Swamikannu, X., Stenstrom, M.K., 2009. Accuracy and precision of the volume-concentration method for urban stormwater modeling. Water Research 43, 2773–2786. Schaaff, E., Grenz, C., Pinazo, C., Lansard, B., 2006. Field and laboratory measurements of sediment erodibility: a comparison. Journal of Sea Research 55, 30–42. Shafer, M.M., Hoffmann, S.R., Overdier, J.T., Armstrong, D.E., 2004. Physical and kinetic speciation of copper and zinc in three geochemically contrasting marine estuaries. Environmental Science and Technology 38, 3810–3819. Van Der Ham, R., Winterwerp, J.C., 2001. Turbulent exchange of fine sediments in a tidal channel in the Ems/Dollard estuary. Part II: Analysis with a 1DV numerical model. Continental Shelf Research 21, 1629–1647. Vaze, J., Chiew, F.H.S., 2002. Experimental study of pollutant accumulation on an urban road surface. Urban Water 4, 379–389.

51

Vezzaro, L., 2011. Source-Flux-Fate Modelling of Priority Pollutants in Stormwater Systems. PhD dissertation, Technical University of Denmark, Department of Environmental Engineering, Kgs. Lyngby, Denmark. (accessed 12.05.11). Vezzaro, L., Eriksson, E., Ledin, A., Mikkelsen, P.S., 2010. Dynamic stormwater treatment unit model for micropollutants (STUMP) based on substance inherent properties. Water Science and Technology 62, 622–629. Vezzaro, L., Eriksson, E., Ledin, A., Mikkelsen, P.S., 2011. Modelling the fate of organic micropollutants in stormwater ponds. Science of the Total Environment 409, 2597–2606. Vrugt, J.A., Gupta, H.V., Bouten, W., Sorooshian, S., 2003. A shuffled complex evolution metropolis algorithm for optimization and uncertainty assessment of hydrologic model parameters. Water Resources Research 39, 1201. Wong, T.H.F., Fletcher, T.D., Duncan, H.P., Jenkins, G.A., 2006. Modelling urban stormwater treatment – a unified approach. Ecological Engineering 27, 58–70. Zanders, J.M., 2005. Road sediment: characterization and implications for the performance of vegetated strips for treating road run-off. Science of the Total Environment 339, 41–47. Zgheib, S., Moilleron, R., Saad, M., Chebbo, G., 2010. Partition of pollution between dissolved and particulate phases: What about emerging substances in urban stormwater catchments? Water Research 45, 913–925.