To  reduce  the  risks  imposed  by  faecal  microorganisms  contained  in  stormwater,  biofilters  have  been developed  for  the  microbial  treatment.  To  better  understand  the  mechanisms  of  faecal  microbial removal  in  biofilters,  and  to  optimize  the  operation  of  biofilters  based  on  the  predictions  of  long-term removal  effects,  modelling  is  an  effective  tool.  In  this  study,  a  comprehensive  model,  which  could represent  the  microbial  transport  and  fate  throughout  the  biofilters  and  includes  the  major  processes and  operational  factors,  is  developed.  The  model  was  calibrated   with  the  measured  data  of Escherichia coli (E.coli) removal that obtained in 8-month laboratory experiments. Generally high Nash Sutcliffe  Efficiencies  (>0.60)  indicate  that  the  calibrated  model  is  in  good  agreement  with  measured data.  Also,  the  optimized  parameter  values  are  comparable  with  values  reported  in  the  literature  and obtained from lab work

Chandrasena, Gayani

Deletic, Ana

McCarthy, David

Ranđelović, Anja

Shen, Pengfei

Urich, Christian

GraFar - Repository of the Faculty of Civil Engineering

NOVATECH 2016 1 A preliminary modelling approach for Escherichia coli removal in stormwater biofilters Approche de modélisation préliminaire pour l’élimination de microbes fécaux des biofiltres d'eaux pluviales  P.F. Shen*, C. Urich**, G.I. Chandrasena, A. Randelovic, A. Deletic, D.T. McCarthy  * Faculty of Civil Engineering, Monash University Clayton Campus, VIC 3800, Melbourne, Australia； pengfei.shen@monash.edu ** Faculty of Civil Engineering, Monash University Clayton Campus, VIC 3800, Melbourne, Australia; christian.urich@monash.edu  RÉSUMÉ Pour réduire les risques causés par les micro-organismes fécaux contenus dans les eaux pluviales, les biofiltres ont été mis au point pour le traitement de microbes. La modélisation est un outil efficace pour mieux comprendre les mécanismes d'élimination de microbes fécaux dans les biofiltres et pour optimiser le fonctionnement des biofiltres sur la base de prévisions de leur élimination à long terme. Dans cette étude, un modèle complet est développé représentant le transport microbien et le sort des microbes à travers les biofiltres, et comprenant les principaux processus et facteurs opérationnels. Le modèle a été calibré avec les données mesurées d'élimination d'Escherichia coli (E. coli) obtenues lors d'expériences de laboratoire d’une durée de 8 mois. Les hauts taux d’efficacités Nash Sutcliffe (> 0,60) indiquent que le modèle calibré représente bien les données mesurées. En outre, les valeurs des paramètres optimisés sont comparables aux valeurs rapportées dans la littérature et obtenus à partir de travaux de laboratoire.  ABSTRACT To reduce the risks imposed by faecal microorganisms contained in stormwater, biofilters have been developed for the microbial treatment. To better understand the mechanisms of faecal microbial removal in biofilters, and to optimize the operation of biofilters based on the predictions of long-term removal effects, modelling is an effective tool. In this study, a comprehensive model, which could represent the microbial transport and fate throughout the biofilters and includes the major processes and operational factors, is developed. The model was calibrated with the measured data of Escherichia coli (E.coli) removal that obtained in 8-month laboratory experiments. Generally high Nash Sutcliffe Efficiencies (>0.60) indicate that the calibrated model is in good agreement with measured data. Also, the optimized parameter values are comparable with values reported in the literature and obtained from lab work.  KEYWORDS Escherichia coli; faecal microorganism; modelling, Stormwater biofilter SESSION 2 INTRODUCTION  Microorganisms/microbes contained in stormwater runoff have been identified as one of the major pollutants (Ferguson et al., 2003, Ortega et al., 2009), and they are also the main pollutant that impedes stormwater harvesting (Fletcher et al., 2008). In particular, faecal microorganisms, which are sourced from the faeces of animals and humans, are the cause of waterborne diseases and present a higher degree of risk compared to non-faecal microorganisms (NHMRC, 2004, Makepeace et al., 1995).   To reduce to harm caused by faecal microorganisms and other pollutants, stormwater biofilters have been widely applied for stormwater treatment (FAWB, 2009) and  proved to be promising for faecal microorganisms removal (Chandrasena et al., 2014a, Chandrasena et al., 2014b). Previous studies indicate that the major processes for faecal microbial removal in biofilters include adsorption, desorption and die-off, and these processes are governed by different operational conditions like temperature.   To better understand the mechanisms of faecal microbial removal in biofilters and to optimize the operation of biofilters based on the predictions of long-term removal, modelling approach is an important tool. Only a few models have been developed for microbial removal in stormwater biofilters, and they all have some inadequacies: Chandrasena et al. (2013) developed a model to simulate the outflow concentration continuously, but this model is incapable of revealing the transport of microorganisms throughout the biofilters; in addition, the impact of operational factors are not included. Zhang et al. (2010) utilized one-dimensional advection-dispersion equation to model of transport of E. coli in biofilter media during 6 hours of continuously simulated run-off conditions, however, this model only considered the processes of adsorption and die-off, and does not reflect any operational factors. Moreover, the simulations were only limited to single wet event. Zhang et al. (2012) used the first order kinetic to model the die-off process in stormwater biofilter media during the dry period, and different temperatures were tested as an operational factor. However, this model did not incorporate any wet weather event.  On the other hand, if it is not confined into biofilters, some models developed for other infiltration applications (e.g. aquifer recharge). The widely used approach for microbial transport in porous media modelling is the adsorption-dispersion equation (Foppen et al., 2007, Tufenkji, 2007). This equation models the movement of microorganisms through advection, dispersion and retention-related processes like adsorption and desorption (Bradford et al., 2006, Gargiulo et al., 2008).    To sum up, there is no available model so far for microbial removal in biofilters that could meet the following requirements: (1) represent the transport and fate of faecal microorganisms throughout biofilters; (2) include the governing processes of microbial removal; (3) reflect the influences of the key factors for microbial removal; (4) represent both wet weather events and dry periods. These requirements are significant for the long-term prediction of faecal microbial removal and the optimization of biofilters. Therefore, the main objective of this study was to develop and test a predictive model that meets all these requirements for the removal of the faecal indicator microorganism, Escherichia coli (E. coli), in the stormwater biofilters.  1 MATERIALS AND METHODS   1.1 Model description  The schematic of a typical stormwater biofilter for modelling is shown in Figure 1. This biofilter was divided into three zones: ponding zone (PZ), unsaturated zone (USZ), and saturated zone/submerged zone (SZ). hp, hover and Ap represent the water depth in PZ, the threshold of water level when overflow occurs, and surface area of PZ respectively; S, nusz, husz represent the saturation, porosity and depth of USZ respectively; nsz and hsz represent the porosity and depth of SZ respectively; A is the surface area of the biofilter. NOVATECH 2016 3  Figure 1 Schematic of a typical stormwater biofilter for modelling  The model simulates advection and dispersion of faecal microorganisms, as well as the three governing processes of faecal microbial removal in biofilters: adsorption, desorption and die-off. Especially, the die-off process is governed by temperature. These processes are modelled using a so-called ‘three tank’ approach (also known as a bucket approach). The three zones (i.e. PZ, USZ, SZ) by represented three tanks respectively. The major modelling equations are shown in Table 1. Table 1 Major modelling equations Major modelling equations Eq. No. The die-off rate:  µ = µ0θT−20°C (1) Unsaturated zone:   (2) Adsorption, desorption and die-off of adsorbed microbes in the soil phase:    ∂M∂t =nuszSρ kattcusz − kdet M − µM (3) Saturated zone:  Microbial mass balance in the water phase in SZ:   (4) Adsorption, desorption and die-off of adsorbed microbes in the soil phase:    ∂M ′∂t=nszρ kattcsz − kdet M′− µM ′ (5)  Where S is saturation; nusz and nsz are porosities of USZ and SZ respectively (-); cusz and csz are E. coli concentrations in liquid phase of USZ and SZ respectively (MPN/100ml); katt and kdet are adsorption and desorption rate respectively (s-1); M1 and M’1 are E. coli concentrations in the solid phase due to adsorption/desorption in USZ and SZ respectively (MPN/100ml); D1 and D2 are the dispersion coefficients in USZ and SZ respectively (m2/s); q1 and q2 are the specific flows/fluxes in USZ and SZ respectively (m/s); µ is the die-off rate (s-1); µ0 is the first order die-off rate coefficient at given reference conditions of the system of interest (e.g. standard temperature) (s-1), θ is the correction coefficient for temperature in die-off (-). SESSION 4 1.2 Laboratory measurements used for model testing  The model was tested with the data obtained from 8-month laboratory experiments. The biofilters (5 replicates) used washed sand as filter media, and no plant was cultivated on the top of the biofilters. The depth of PZ, USZ and SZ are 280 mm, 400 mm and 440 mm separately.  Semi-natural storm water was used to dose the columns. The stormwater runoff volume and the lengths of dry periods were respectively simulated by the dosing scheme of the dosing quantity and dosing frequency.   There were 16 sampling events. For each event, the outflow was separated into two segments (1) “old water” - the water remains in the SZ after previous dry days, which could be generally clean due to the sufficient time for die-off; (2) “new water” - the treated newly-dosed water. Both old and new water samples were analyzed for the concentrations of an indicator of faecal microbes, E. coli. 1.3 Model calibration In this model, four parameters need to be calibrated: katt, kdet, θ, and µ0. A modified Monte-Carlo based method (Vezzaro et al., 2013) was used for calibrated. Nash Sutcliffe Efficiency criterion (E) is utilized for parameter selection/model evaluation (Nash and Sutcliffe, 1970):  E = 1−(U0t −Umt )2t=1T∑(U0t −U0 )2t=1T∑                                          Where Ū0 is the mean of observed value; Um is modeled value; Ut0 is observed value at time t.  The four were calibrated altogether, with the data obtained from old and new water samples collected in the 16 sampling events (174 data in total). 2 RESULTS AND CONCLUSIONS  All the modelled and measured data (i.e. E. coli concentrations) were logged for the calibration process. That is because, the E. coli concentrations vary significantly in different events (e.g. 60 ~ 18000); with the logged concentrations, the error term/residuals could be normalized and so that the peaks would not be overemphasized for the calculation of Nash-Sutcliffe Coefficient (E).  For the best-fit parameter set, E = 0.60 was achieved. Figure 2 shows the model performance using the best-fit parameter set. It is evident that the model performed well as the majority of the measured concentrations are scattered around the 1:1 line.  Figure 2 Model performance using the best-fit parameter set (Dashed lines indicate the 1:1 line between modelled and measured E. coli concentrations, while dotted lines indicate error bars (+/- one order of magnitude))  The calibrated parameter values were also compared with those measured in the laboratory experiments and reported in the literature (Table 2). The results indicate that, for the best-fit parameter set, the modelled data and measured data fit very well. In addition, the ranges of parameter values are generally located in the ranges obtained from literatures and experiments. NOVATECH 2016 5 Table 2 Calibrated values of key parameters  katt (h-1) kdet (h-1) θ (-) µ0 (day-1) Best-fit (modelled) 2.15 0.094 1.226 0.54 Calibrated range (modelled) 1.33 ~ 3.95 0.00007 ~ 0.960 1.041 ~ 1.791 -0.03 ~ 2 Literature/Measured 0.20 ~ 5.862 0.00006 ~ 2.028 1.105 ~ 1.133 0.22 ~ 1.23 CONCLUSIONS AND FUTURE WORK The model developed in this study of E. coli removal performance by stormwater biofilters showed good agreement with the measured performance. Hence, it can be concluded that faecal microbial removal in the stormwater biofilters can be adequately simulated using the selected processes (adsorption, desorption and die-off) and operational factor (temperature). However, this model still needs to be calibrated and tested against other types of biofilters to represent the influence of filter media types and plants. LIST OF REFERENCES Bradford, S. A., Simunek, J. & Walker, S. L. 2006. Transport and straining of E. coli O157:H7 in saturated porous media. Water Resources Research, 42, W12S12. Chandrasena, G. I., Deletic, A. & Mccarthy, D. T. 2013. Evaluating Escherichia coli removal performance in stormwater biofilters: a preliminary modelling approach. Water Science Technology, 67, 2467-75. Chandrasena, G. I., Deletic, A. & Mccarthy, D. T. 2014b. Survival of Escherichia coli in stormwater biofilters. Environmental Science and Pollution Research, 21, 5391-401. Chandrasena, G. I., Pham, T., Payne, E. G., Deletic, A. & Mccarthy, D. T. 2014a. E. coli removal in laboratory scale stormwater biofilters: Influence of vegetation and submerged zone. Journal of Hydrology, 519, 814-822. Fawb 2009. Adoption Guidelines for Stormwater Biofiltration Systems. 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A preliminary modelling approach for Escherichia coli removal in stormwater biofilters

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A preliminary modelling approach for Escherichia coli removal in stormwater biofilters

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