The validation of stormwater biofilters for micropollutant removal using in situ challenge tests

Ecological Engineering 67 (2014) 1–10

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The validation of stormwater biofilters for micropollutant removal using in situ challenge tests Kefeng Zhang a,b,∗ , Anja Randelovic c , Declan Page d , David T. McCarthy a,b , Ana Deletic a,b a

Monash Water for Liveability, Department of Civil Engineering, Monash University, Wellington Road, Clayton, VIC 3800, Australia CRC for Water Sensitive Cities, Melbourne, VIC 3800, Australia c Faculty of Civil Engineering, University of Belgrade, Belgrade 11000, Serbia d CSIRO Land and Water, Waite Laboratories, Waite Road, Urrbrae, SA 5064, Australia b

a r t i c l e

i n f o

Article history: Received 3 September 2013 Received in revised form 8 January 2014 Accepted 24 March 2014 Available online 22 April 2014 Keywords: Validation framework Stormwater biofilters (bioretentions) Challenge condition Stormwater harvesting Drinking water

a b s t r a c t Stormwater harvesting is becoming a popular alternative water resource in water stressed regions. Stormwater biofilters have been recognized as being among the most promising pre-treatment technologies. In this study, a series of challenge tests were conducted as part of a validation framework of stormwater biofilters for selected micropollutants. Two biofilter configurations were studied: a configuration with loamy sand and no submerged zone (LS-noSZ) and another configuration that uses sand and a submerged zone (S-SZ). Biofilter challenge conditions were: (i) treatment volume set at 95th percentile of all treated events and (ii) the maximum and minimum durations of dry period between two events, both based on hydrology simulations using 30 years rainfall data for Melbourne. The hydraulic performance of S-SZ was stable and not affected by either prolonged wet or dry periods, while the outflow rate of LS-noSZ was largely reduced during prolonged wet periods. Biofilters had a removal efficiency of >80% for total petroleum hydrocarbons (TPHs), glyphosate, dibutyl phthalate (DBP), bis-(2-ethylhexyl) phthalate (DEHP), pyrene and naphthalene loads by both configurations under the most challenge conditions; the removal of pentachlorophenol (PCP) and phenol loads was >80% in LS-noSZ and 50–80% in S-SZ, while chloroform had load removal rates between 20% and 50%. Biofilters were less effective in removing atrazine and simazine with load removal 20–50% in LS-noSZ and <20% in S-SZ. Prolonged dry periods benefited the removal of micropollutants while very short dry periods adversely affected micropollutants removal. The study contributes to the development of the overall framework for validation of stormwater biofilters, which is required if these systems are to be applied in stormwater treatment systems for higher end water uses such as drinking water. © 2014 Elsevier B.V. All rights reserved.

1. Introduction Stormwater has increasingly been recognized as a valuable alternative water resource in urban areas and has been harvested for mainly non-potable uses. For example in the state of Victoria, Australia there are 108 stormwater schemes in operation (Hatt et al., 2006; DHV, 2013a). There are only limited examples of stormwater harvesting for potable end-uses; e.g. stormwater recycling via drinking water reservoirs have been in operation in Singapore since 1960s (Philp et al., 2008). However, the

∗ Corresponding author at: Monash Water for Liveability, Department of Civil Engineering, Monash University, Wellington Road, Clayton, VIC 3800, Australia. Tel.: +61 449 860225. E-mail addresses: [email protected], [email protected] (K.F. Zhang). http://dx.doi.org/10.1016/j.ecoleng.2014.03.004 0925-8574/© 2014 Elsevier B.V. All rights reserved.

potential for stormwater harvesting is significant, particularly in water stressed regions. For example, stormwater volumes generated in a number of Australian coastal cities are larger than the volume of potable water consumed in these cities (PMSEIC, 2007), thus rendering the topic of this work expansive to a considerable degree. However, stormwater harvesting for potable uses has seen limited adoption, due to high variability of hazard concentrations in stormwater and hydraulic characteristics (Grant et al., 2012). For example, stormwater can contain between 1 and 36,200 mg/L of sediment (Makepeace et al., 1995), 5.8–112.2 ␮g/L of Pb (Francey et al., 2010), 0.10–6.94 mg/L of TP (Liu et al., 2013), and MPN of 507–16,000 Escherichia coli per 100 mL (McCarthy et al., 2012). Although far more data is needed on levels of organic chemical hazards in stormwater, the levels of herbicides (e.g. glyphosate 0.03–132 ␮g/L) and phthalates (e.g. DEHP 3–58 ␮g/L) are also

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variable and catchment specific (Zgheib et al., 2012). Moreover, stormwater treatment systems often experience both wetting (during rainfall/runoff events) and drying periods (where the systems are idle). During these wet periods, both flow rates and hazard concentrations change significantly, during and between subsequent events (Hatt et al., 2009). Water Sensitive Urban Design (WSUD) technologies are effective stormwater treatment systems; stormwater biofilters, wetlands, and sand filters can dampen the high variability in stormwater quality to predictable and manageable levels (Wong et al., 2012). They are passive systems that employ natural treatment processes such as biodegradation. However, WSUD technologies do not receive any credit for their treatment performance within stormwater harvesting schemes, due to the dearth of developed methods for their validation, as is the case for recognized treatment processes commonly used in water recycling, such as membrane filtration (USEPA, 2005). Validation is the documented act of demonstrating that (i) a treatment system can produce water of the required quality under a defined range of operating conditions and (ii) it can be monitored in real time to provide assurance that the water quality objectives are being continuously met (DHV, 2013b). There are validation frameworks developed for highly engineered water treatment systems for pathogen removal, such as membrane filtration (USEPA, 2005), ultraviolet (UV) disinfection (USEPA, 2006), activated sludge process and media filtration (DHV, 2013b). Validation consists of three main steps, namely: (1) pre-validation preparation, (2) validation monitoring and (3) operational monitoring. Pre-validation preparation is required to obtain information prior to full scale challenge test, including the identification of target pollutants, determination of challenge conditions, and specification of the system removal requirements. Validation monitoring confirms the removal capacity of a system using defined environmental performance criteria (e.g. temperature, residence time, pH, hydraulics loading rate). Challenge tests are the most widely accepted method to confirm the removal credit of a treatment system. Finally, operational monitoring ensures the long term performance of the system by monitoring surrogate operational parameters (DHV, 2013b). This validation framework focuses on drinking water treatment and water recycling, but it needs additional development before application to WSUD stormwater systems. This is primarily because stormwater has very different characteristics than wastewater or typical drinking water sources leading to considerably different operational conditions for stormwater harvesting schemes. WSUD technologies use natural processes, which are difficult to characterize, complex and have never previously been validated (Halliwell, 2011). Validation of stormwater biofilters for treatment of micropollutants is the focus of this study. Stormwater biofilter, also known as bioretention and rain garden, is a commonly used low energy treatment method consisting of a planted soil-based filter media, which usually has a high sand content (e.g. loamy sand) underlaid by a sand transition layer, and is regarded as the most advanced WSUD system (FAWB, 2009). Such methods are very effective for the removal of sediments, metals, nutrients (Henderson et al., 2007; Blecken et al., 2009; Feng et al., 2012) and even faecal indicator bacteria (Hathaway et al., 2011; Chandrasena et al., 2012), thus are becoming popular for stormwater harvesting (Wong et al., 2012). However, there is little reported information on micropollutant removal efficiency (DiBlasi et al., 2008), despite this being an important consideration when treated stormwater is harvested for potable use. What is known is that the main removal processes of micropollutants in these systems are adsorption and biodegradation. These processes are largely influenced by the physico-chemical properties of the micropollutants, biofilter media

characteristics and environmental conditions (e.g. temperature and redox status). Similar processes operate in biofilters to those that occur in slow sand filters, engineered and natural wetlands and aquifers, where the fate of micropollutants is more widely reported (Alvord and Kadlec, 1995; Pavelic et al., 2006; Imfeld et al., 2009). An existing validation framework for recycling of wastewater for non-potable uses with a focuses on pathogens (DHV, 2013b) has been applied here for use with biofilters and micropollutants. This framework was applied using the removal of selected micropollutants: total petroleum hydrocarbons (TPHs), polycyclic aromatic hydrocarbons (PAHs), glyphosate, triazines, phthalates, trihalomethanes and phenols. This paper is the first step in the development of a validation framework for stormwater biofilters when used for stormwater harvesting. The specific objectives of the study were to: • assess the hydraulic performance of biofilters under challenge conditions; • assess removal efficiencies for selected micropollutants under challenge conditions (including investigation of intra-event variability); • determine the challenge conditions for the biofilters in a hypothetical case of the systems being used within a drinking water treatment scheme. The result of this study will contribute to the development of the first validation framework for WSUD stormwater treatment systems. 2. Methods 2.1. Experimental site The biofilter system treats stormwater runoff from a multistorey car park, which is then stored in an open pond for subsequent irrigation of a sports oval, located at Monash University, Melbourne, Australia. This system is a modification (one of the cells has been replaced with the current best Australian practice design (FAWB, 2009)) of the Monash Car park biofiltration system that has been previously studied for sediment, metal and nutrient removal (Hatt et al., 2009). The biofilter has three different cells, two of which were used in this study with detailed characteristics described in Table S1 of the Supplementary Material. Fig. 1 shows the site and configurations of the two cells used here. LS-noSZ has no submerged zone and uses loamy sand (sand 84.2%, silt 3.0%, clay 12.8%) with relatively high nutrient content (1600 mg/kg total nitrogen and 320 mg/kg total phosphors) and total Soil Organic Matter (SOM 4.6%, well above prescribed Australian best practice for biofiltration design (FAWB, 2009)) and is planted with Carex appressa. S-SZ contains a submerged zone and uses sand (sand 96.0%, silt 0.8%, clay 3.2%; SOM 0.4%), with characteristics that follow Australian best practice. It should be noted that the SOM content in both cells did not change over the 14 months of field measurements. S-SZ is predominantly planted with Melaleuca ericifolia and some C. appressa. Both types of vegetation have previously been reported to aid in the removal of nutrients from stormwater (Read et al., 2008). 2.2. Pre-validation preparation The validation process requires that suitable challenge conditions be determined. These include the target concentrations of micropollutants in the inflows, as well as the hydraulic conditions

K.F. Zhang et al. / Ecological Engineering 67 (2014) 1–10

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Fig. 1. Monash Car Park stormwater harvesting system and configuration of biofiltration cells selected for this study.

for the system. These conditions set the boundaries for which the validation will be accepted. Total petroleum hydrocarbons (TPHs), polycyclic aromatic hydrocarbons (PAHs), glyphosate, triazines (simazine, atrazine and prometryn), phthalates (dibutyl phthalate, di-(2-ethylhexyl) phthalate), trihalomethanes (THMs) and phenols (phenol, pentachlorophenol) were selected as they are reported to be often present in stormwater (Cole et al., 1984; Makepeace et al., 1995; Duncan, 1999; Göbel et al., 2007; Zgheib et al., 2012). They are important for assuring drinking water safety. They are also representatives of different micropollutant groups, making them a good subject for our study. Table 1 summaries the physicochemical properties of the selected micropollutants, together with their Australian Drinking Water Guideline values (NHMRC-NRMMC, 2011). The reported concentrations found in the literature were compiled, from which 95th percentile concentrations were determined as the challenge concentrations (Table 1). Event mean concentration (EMC) from each publication was considered where possible, however measured values of single samples were not considered. In

this way at least 15 EMC values were gathered for each micropollutant and the 95th percentile concentrations were calculated based on distribution test. The 95th percentile was adopted as the challenge concentration for consistency with the validation of pathogen removal in wastewater recycling schemes (DHV, 2013b). As the 95th percentile concentrations were far below Australian Drinking Water Guideline (ADWG) values for some micropollutants, we targeted to test for inflows with arbitrary concentrations of approximately two times ADWG targets – this was an attempt to simulate possible spills or any other challenge operational conditions that we may have in practice because the stormwater data sets on the majority of the tested pollutants are very small and therefore may not include extreme conditions. Due to knowledge gaps in understanding how stormwater biofilters remove micropollutants, in order to determine challenge operational conditions for the studied systems we had to rely on prior knowledge of micropollutant behaviour in similar soil/plant based systems (e.g. constructed wetland; Imfeld et al., 2009) and overall field operational conditions of stormwater biofilters (Hatt

Table 1 Summary of the micropollutants’ physico-chemical properties, 95th percentile stormwater concentrations and Australian drinking water guideline (ADWG) values. Physico-chemical propertiesa

Pollutants

Solubility [mg/L] TPHs PAHs

Herbicides

Phthalates THMs

Phenols a b c d

Pyrene Naphthalene

KOC

–c

–

0.1 28

4.8 3.2

Glyphosate Atrazine Simazine Prometryn DBP DEHP Chloroform

12,425 38 6 41 10 15 8452

3.1 2.1 2.3 2.7 2.9 5.1 1.8

PCP Phenol

19 83,119

3.2 1.7

KHenry [Pa m3 /mol]

pKa –

–

1.3 54.9

– –

346 36

0.1 0.9

0.8 1.7 1.7 4.1 – – –

4.9 10.0

Target concentrationb [␮g/L]

ADWG [␮g/L]

Volatilization Adsorption Adsorption Adsorption Biodegradation

147 mL Diesel in 5KL 100 140

NAd 150 70

2000 60 60 60 70 50 400

1000 20 20 20 35 10 200

60 200

10 NAd

Half-lives in soil [d]

–

1.4 × 10−5 3.9 × 10−4 1.8 × 10−4 9.5 × 10−4 0.2 0.8 330.2

Expected removal processes

32 85 77 83 16 65 74

48 4.9

Median values compiled from Mackay et al. (2006). Equates to 95th percentile concentration (DEHP, PCP and phenol) or doubled ADWG values. Physico-chemical properties vary dramatically with different petroleum chemicals therefore not presented. No ADWG value.

Adsorption Biodegradation Adsorption Biodegradation Adsorption Biodegradation Volatilization Adsorption Adsorption

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et al., 2009). As Table 1 shows, adsorption and biodegradation are in all likelihood the two most important treatment processes for the systems. Adsorption takes place in stormwater biofilter media during wet periods (as it does in aquifer materials; e.g. Ying et al., 2003), and therefore could be significantly affected by the hydraulic conditions during rainfall events that happen either immediately after one big event, or following a long dry period. Biodegradation occurs mostly during dry periods over a longer duration (as it does in stormwater wetlands; e.g. Imfeld et al., 2013), hence the length of dry period is important. Temperature is always believed to affect activity of microbes that are responsible for micropollutant removal (e.g. Onysko et al., 2000). Moreover, larger volumes of inflow carry heavier loads of pollutant into systems, which would result in the competition of adsorption given limited adsorption sites within a given system, and thereafter impact biodegradation due to a greater quantity of micropollutants within the system (as it does in aquifer systems, e.g. Broholm et al., 2001). The following parameters were therefore considered to be the key operational variables, which need to be considered for validation of stormwater harvesting biofiltration systems:

1. Duration of dry periods between successive storm events – Hatt et al. (2008) found that long drying periods are detrimental to nitrogen removal, while very short dry periods are not desirable for pathogen removal (Chandrasena et al., 2012), and – as discussed above – should exert an impact upon biodegradation of the selected micropollutants (Table 1). 2. Volume of water to be treated per event – it is evident that the impact of residual water in the submerged zone and in soil voids affects the treatment performance of pathogens and other pollutants (Li et al., 2012), so it is prudent to assume that it will have an impact on both adsorption and biodegradation of micropollutants. 3. Temperature, which is important for biodegradation impacts on both micropollutants (Onysko et al., 2000) and a vast number of other pollutants (Blecken et al., 2010). 4. Infiltration rate (i.e. velocity through biofilter), Chandrasena et al. (2012) and Li et al. (2012) found that it was important for microbe removal, and definitely will be important for adsorption of micropollutants.

To find challenging conditions for the above operational parameters, a model (MUSIC V5.1, for simulation of urban hydrology and WSUD systems) (eWater, 2012) was set-up for the Monash Car park system. This model was then used to determine the challenge hydraulic conditions – i.e. duration of dry weather periods, wet weather event volume (the volume that is treated in one single event) and frequency of wet weather events based on the past 30-years of Melbourne rainfall data. The duration of dry weather periods was calculated by analyzing the inflows into the biofilters (from the MUSIC model output), and the 95th percentile dry period was selected as the challenge dry weather period, which was found to be 21 dry days. Two challenge scenarios were proposed for the wet weather events: (1) the challenge volume of a single wet weather event and (2) the challenge volumes of two consecutive events, within 12 h of each other. The latter is important for determination of extreme wet weather conditions, where a large storm event is followed by very short dry period (i.e. the system has no resting or recovery period prior to another event). These two scenarios were determined using the outputs from the MUSIC model. For the first case, the 95th percentile cumulative volume for a single event was 4 pore volumes (PV); a PV roughly equals to 3.5 m3 for each biofilter. For the second scenario, the 95th percentile of

two consecutive events that occur less than 12 h apart was 3 PVs for each event. 2.3. Validation monitoring/challenge tests Two series of in situ experiments were conducted, each consisting of three separate challenge tests (i.e. six challenge tests in total). These challenge tests have covered different operational conditions, ranging from the above selected challenge scenario conditions to more typical operational conditions (Table 2). The 1st series of challenge tests (CT1.1, CT1.2, and CT1.3) was conducted in winter of 2011, whereas the 2nd series (CT2.1, CT2.2, CT2.3) was performed in summer 2012. Between CT1.2 and CT1.3 and after CT2.3, the biofilters received two natural stormwater events (Table 2). Semi-synthetic stormwater (water quality is shown in Table S2 of the Supplementary Material) was prepared in the distribution tank (4.2 m3 ) using water from an adjacent stormwater pond. Stormwater sediment (from a local wetland inlet basin), raw sewage (from the Pakenham treatment plant), commercial diesel fuel (from local fuel station) and selected micropollutants (from Sigma–Aldrich) were added and then well mixed manually to attain the target concentration (Table 1). All the micropollutants were prepared before the experiment in glass vials in order to be added directly into the distribution tank. During each test, in order to simulate challenge infiltration rates and make the biofilters work under full capacity, attempts were made to control the ponding depth of each biofilter to a stable level of 470 ± 10 mm from the surface of the biofilter (which is close to the overflow weirs). In the outlet, outflow rates were recorded by using v-notch weirs equipped with ultrasonic depth sensors (Siemens Miltronics), which were calibrated using manual flow measurements before and during the tests. 2.4. Sampling and analysis In the 1st series of challenge tests, a flow-weighted composite sample of the inflow water was collected, while during the 2nd series, three composite inflow samples that consisted of three discrete samples were collected during the course of each event. In addition, 10 discrete outflow samples were taken over the course of the test from each cell in both series. During the natural events of the 2nd Series (after CT2.3), natural stormwater grab samples were taken from the distribution tank; outflow samples were collected using autosamplers (Sigma 900). This sampling was completed after two rainfall events, after which time the micropollutant concentrations returned to below reporting limits in both the inflow and outflow samples. Once collected, samples were stored on ice, after which they were delivered to a NATA accredited laboratory for analysis. All the samples were analyzed for THMs, phenols, phthalates, PAHs and triazines using GCMS, for glyphosate using HPLC and for TPHs using GC FID, the analytical method is according to USEPA SW 846 Rev 2007. The LOR (limit of report) of THMs, phenols, PAHs and phthalates is 1 ␮g/L; the LOR of glyphosate is 30 ␮g/L; the LOR of triazines is 2 ␮g/L; The LOR of TPHs is 100 ␮g/L. In the 1st series of challenge tests, PAHs and phenols were not analyzed in outflow discrete samples. Electric conductivity (EC) was measured for all samples using HACH sensION 378. Total dissolved solid (TDS) was then calculated based on a correlation between EC and TDS determined by laboratory experiments. To obtain an estimate of the ‘overall’ effluent quality of an entire event, the pollutant concentrations from 10 discrete samples were used alongside flow measurements to calculate the Event Mean outflow Concentration (EMC). It should be noted that in cases

K.F. Zhang et al. / Ecological Engineering 67 (2014) 1–10

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Table 2 Detailed information of challenge tests.

1st series

Date

Inflow volume [m3 ]/percentilea

Preceding dry periods [h]/percentilea

Challenge Test 1 (CT1.1) Challenge Test 2 (CT1.2) Natural events (NEs) Challenge Test 3 (CT1.3)

16-08-2011 31-08-2011 –b 22-09-2011

8.4 (2.4PVs)/85th 8.4 (2.4PVs)/85th 11.6 (3.3 PVs) 8.4 (2.4PVs)/85th

84/40th 352/90th – 240/80th

10.9–19.2 8.2–15.2 – 11.5–22.9

Challenge Test 1 (CT2.1) Challenge Test 2 (CT2.2)

19-11-2012 20-11-2012

66/30th 10/ < 1stc

6.8–23.6 8.6–27.4

Challenge Test 3 (CT2.3) Natural Event 1 (NE1)d Natural Event 2 (NE2)d

11-12-2012 15-12-2012 19-12-2012

10.5 (3PVs)/95th 6.3 (1.8 PVs) for LS-noSZ/80th 10.5 (3PVs) for S-SZ/95th 14 (4PVs)/95th 2.1 (0.60PV) 2.2 (0.63PV)

496/95th 89 84

9.0–27.3 18.6–23.1 16.2–30.8

2nd series

a b c d

Daily air temperature

Corresponding percentile value of 30-year rainfall statistic using MUSIC. 3 rainfall events observed on 09-09-2011 (10.6 mm), 10-09-2011 (3.11 mm) and 11-09-2011 (4.2 mm) but no samples were taken during this period. <1st percentile of dry periods, extreme wet condition. 3.2 mm rainfall observed on 15-12-2012 and 4.8 mm on 19-12-2012.

where concentrations were lower than the detectable limits, half of the lowest detectable limit was taken as the concentration for determination of EMC and mass balances, as is the practice in literature (Dombeck et al., 1998). A simple mass balance calculation was also undertaken using the lowest detection limit, which confirmed that the results were not sensitive to this assumption. 3. Results and discussion 3.1. Hydraulic performance A water balance, including measured inflow and outflow volumes, estimated storage change and evaporation and vegetationuptake was produced for each biofilter over each series of challenge tests (see Table S3 of the Supplementary Material). The estimated errors of the water balance were between 2.3% and 5.9% of the total inflows. The inflow and outflow rates, as well as cumulative treated volumes during the 2nd Series challenge tests are presented in Fig. 2. According to the total volume and recorded outflow rates, the average hydraulic retention time (HRT) of the two biofilters was estimated to be about 3–5 h. This represents a typical HRT of a well-designed stormwater biofilter (FAWB, 2009). The outflow hydrographs from S-SZ were similar between all of the challenge tests – similar results were also seen for the 1st series of tests (data is not shown here). However, the outflow hydrographs from

LS-noSZ were significantly reduced during CT2.2, causing LS-noSZ to overflow during this event. A lower hydraulic conductivity of the filter media of LS-noSZ is linked to the reduction in infiltration capacity after prolonged wet periods (there was only 10 h between CT2.1 and CT2.2). This is known as soil swelling (Dif and Bluemel, 1991). In the 1st Series of challenge tests, this behaviour was not exhibited because the conditions were less challenging as the minimum dry period was 84 h, meaning that the system had time to recover before the subsequent wet weather period. This indeed suggests that extreme wet conditions could be of high importance for hydraulic performance, but only in systems in excess of a certain clay content, whereas it seems that it should not be a problem for biofilters designed for best-practice (i.e. as per FAWB recommendations (2009)). 3.2. Treatment performance The results of the measured inflow concentrations, outflow Event Mean Concentrations (EMCs) and calculated mass balances of the tested micropollutants for two series of challenge tests are shown in Table 3. The uncertainties in the micropollutant mass balance were estimated using the water balance errors for the 1st series of challenge tests. For the 2nd series, a conservative tracer, total dissolved solids (TDS), was also used to account for dilution and other effects and then used to estimate the uncertainties of the micropollutant mass balance.

Fig. 2. Outflow rates and cumulate treated volumes of the 2nd series challenge tests. LS-noSZ, biofilter with loamy sand media.

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Table 3 Measured inflow concentration, outflow event mean concentration (EMC) and mass balance. Measured concentrations

Calculated mass balances

Inflow ± STD (␮g/L)

LS-noSZ

Outflow EMC (␮g/L) LS-noSZ

2nd series tests TDS [ppm] TPHs Pyrene Naphthalene Glyphosate Atrazine Simazine Prometryn DBP DEHP Chloroform PCP Phenol a b c

S-SZ

12,700 ± 707 1950 ± 353 55 ± 13 47 ± 6 53 ± 4 33 ± 5 24 ± 10 43 ± 15

CT1.1 <100 NA 14 3 4 <1 <1 9

CT1.2 <100 54 34c 11 9 <1 <1 24

CT1.3 <100 100 17 6 2 <1 <1 19

CT1.1 <100 NA 32 7 13 <1 <1 15

CT1.2 <100 41 65 25 26 <1 <1 49

CT1.3 <100 105 23 7 5 <1 <1 28

In 324.7 32.8 1.76 0.94 1.02 0.45 0.60 1.09

Out 1.2 1.3 0.52 0.16 0.12 0.01 0.01 0.42

Reductiona 323.5 ± 12.9 (99.6%) 31.5 ± 1.3 (96.0%) 1.24 ± 0.05 (70.5%) 0.78 ± 0.03 (80.3%) 0.90 ± 0.04 (88.2%) 0.44 ± 0.02 (97.8%) 0.59 ± 0.02 (98.3) 0.67 ± 0.03 (61.5%)

Max Ad.b – 1168.7 3.3 4.5 12.6 12.5 1438.4 1.3

In 324.7 32.8 1.76 0.94 1.02 0.45 0.60 1.09

Out 1.2 1.2 0.95 0.31 0.35 0.01 0.01 0.74

Reductiona 323.5 ± 19.1 (99.6%) 31.5 ± 1.9 (96.0%) 1.24 ± 0.05 (70.5%) 0.78 ± 0.04 (83.0%) 0.90 ± 0.04 (88.2%) 0.44 ± 0.03 (97.2%) 0.59 ± 0.03 (98.3%) 0.67 ± 0.02 (61.5%)

Max Ad.b – 144.1 0.4 0.6 1.6 1.5 177.3 0.2

214 4300 ± 220 10 ± 2.6 17 ± 6.6 1600 ± 100 48 ± 6 42 ± 3 50 ± 4 42 ± 4 17 ± 8 59 ± 7 27 ± 6 203 ± 15

CT2.1 210 <100 <1 2 99 25 22 11 <1 <1 32 1 2

CT2.2 210 <100 <1 2 116 28 32 14 <1 <1 38 6 1

CT2.3 212 <100 <1 2 187 27 24 15 <1 <1 40 4 18

CT2.1 210 <100 <1 3 29 35 33 20 <1 <1 40 2 1

CT2.2 210 <100 <1 1 106 42 49 29 <1 <1 47 19 3

CT2.3 214 <100 <1 3 70 49 43 32 <1 <1 49 11 106

In 7441.9 148.3 0.30 0.56 47.5 1.45 1.30 1.39 1.28 0.58 1.85 0.80 6.10

Out 6744.4 1.6 0.02 0.06 4.0 0.77 0.72 0.40 0.02 0.02 1.1 0.10 0.65

Reductiona 697.5 (9.4%) 146.7 ± 13.8 (98.9%) 0.28 ± 0.03 (93.3%) 0.50 ± 0.05 (89.3%) 43.4 ± 4.0 (91.6%) 0.68 ± 0.06 (46.9%) 0.58 ± 0.05 (44.6%) 0.99 ± 0.09 (71.2%) 1.26 ± 0.12 (98.4%) 0.56 ± 0.05 (96.6%) 0.75 ± 0.07 (40.5%) 0.70 ± 0.07 (87.5%) 5.45 ± 0.51 (89.3%)

Max Ad.b – – 300.4 12.8 958.9 2.9 4.0 11.9 15.9 1018.8 1.8 20.4 4.8

In 8336.0 160.9 0.33 0.62 54.2 1.67 1.49 1.60 1.45 0.63 2.08 0.94 7.02

Out 7630.3 1.8 0.02 0.08 2.2 1.44 1.40 0.94 0.02 0.02 1.52 0.36 1.53

Reductiona 705.7 (8.5%) 159.1 ± 13.5 (98.9%) 0.31 ± 0.03 (93.9%) 0.54 ± 0.05 (87.1%) 52.0 ± 4.4 (95.9%) 0.23 ± 0.02 (13.8%) 0.09 ± 0.01 (6.0%) 0.66 ± 0.06 (41.3%) 1.43 ± 0.12 (98.6%) 0.61 ± 0.05 (96.8%) 0.56 ± 0.05 (26.9%) 0.58 ± 0.05 (61.7%) 5.86 ± 0.50 (78.2%)

Max Ad.b – – 37.0 1.6 118.2 0.4 0.5 1.5 2.0 125.6 0.2 2.5 0.6

Uncertainties in mass balance = mass reduction × water balance error (1st series) and = mass reduction × TDS balance error (2nd series), percentage removal in parentheses. Max adsorption: theoretical maximum mass of micropollutants that can be adsorbed onto the organic carbon of biofilter soils before breakthrough (equals to KOC × fOC × Cinflow × mass of soil). Bold indicates mean value exceeded ADWG value.

K.F. Zhang et al. / Ecological Engineering 67 (2014) 1–10

1st series tests TPHs Glyphosate Atrazine Simazine Prometryn DBP DEHP Chloroform

S-SZ

K.F. Zhang et al. / Ecological Engineering 67 (2014) 1–10

The micropollutants were generally classified according to the removal efficiencies: good removal (>80%, e.g. TPHs, glyphosate, DBP, DEHP, pyrene and naphthalene); intermediate removal (50% < X < 80%, e.g. phenol and PCP in S-SZ); intermediate removal (20% < X < 50%, e.g. Chloroform) and poor removal (<20%, e.g. atrazine and simazine in S-SZ). Generally, the removal performance of biofilters in the 1st series tests was better than that in the 2nd series, especially for triazines (that can be grouped into intermediate category in the 1st series), a fact mainly due to the more challenging conditions conducted in the 2nd series (Table 2). This, to some degree, confirms that the selected operational variables for challenging the systems are of importance for the tested micropollutants. TPHs, pyrene and phthalates (DEHP and DBP) were 4.0, Table 1). Micropollutants can be absorbed into soil organic matter (SOM), because most of these molecules are dominated by apolar groups; i.e. aliphatic and/or aromatic carbon (René and Schwarzenbach, 1993; Oliveira et al., 2001). As biofilters are soil-based systems, this is an important process, as is the case for wetlands (Alvord and Kadlec, 1995) and aquifers (Schwab et al., 2006). The KOC values (Table 1) and the filter media properties (SOM percentage and mass of soil), was used to determine the theoretical maximum mass of micropollutants that can be adsorbed prior to breakthrough (Table 3). The mass reduction of pyrene, DEHP and DBP was lower than the maximum adsorption mass, indicating that the biofilters may still have capacity to absorb more of these micropollutants. For the adsorbed micropollutants, other removal processes (e.g. biodegradation) may also be occurring during the dry periods, allowing regeneration of adsorption sites. Zhao et al. (2004) reported that adsorption contributed to the removal of DBP in a vertical flow constructed wetland and that the adsorbed DBP was inclined to be efficiently degraded by microorganisms. Naphthalene was also well removed. Naphthalene has a moderate adsorption tendency (Log Koc = 2.74, Table 1) and may undergo biodegradation in soils (t1/2 = 36 d, Table 1). Glyphosate showed good removal (>80% in all tests) by biofilters. Glyphosate attaches to soil readily (Log KOC = 3.1) and the mass reduction was lower than predicted by KOC values (Table 3). Indeed, the adsorption of glyphosate could also occur through the phosphonic acid moiety in its phosphonate anion form and is influenced by the amount of vacant phosphate sorption sites (Sheals et al., 2002; Gimsing et al., 2004). Forlani et al. (1999) and Liu et al. (1991) also found that glyphosate could be degraded by soil microorganisms with half-lives varying from 7 d to 60 d. It should be noted that for those micropollutants which were well removed (>80%), outflow EMC values met ADWG values in all challenge tests. LS-noSZ biofilter performed better in removing PCP than S-SZ due to the higher SOM in LS-noSZ (4.6%) than that in S-SZ (0.4%). Similar results were found for all other micropollutants, except for those which had good removal in both biofilters (Table 3). PCP adsorption has previously been found to be reversible and highly sensitive to pH and organic content of soil (Lorenzen et al., 1986). PCP is a weak organic acid (pKa = 4.9, Table 1) and it adsorbed less onto soils as pH increased. Outflow PCP EMC values of S-SZ in CT2.2 and CT2.3 were much higher than that in CT2.1. This could be because the adsorption sites were limited in this sandy media and these were mostly occupied during CT2.1, leaving fewer sites for adsorption to occur during CT2.2 and CT2.3. Similar to PCPs, the LS-noSZ biofilter showed better removal (>80%) of phenols as compared with the S-SZ biofilter (50–80%).

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Phenol is very mobile in soil systems (KOC = 1.7) and biodegrades quickly (t1/2 = 4.9 d, Table 1). Abira et al. (2005) found that phenol was easily removed due to both biodegradation and adsorption onto soils in a surface constructed wetland. However, in this study outflow phenol concentrations peaked during CT2.3. Due to the high mobility of phenol, it might experience short circuiting through some unavoidable cracks in biofilters after a long period of dry days, therefore causing this peak. Biofilters showed average removal of chloroform by between 26.9% and 61.5%. Chloroform is resistant to biodegradation (t1/2 > 120 d) and weakly adsorbed (KOC = 1.8, Table 1) in soilsbased systems such as aquifers (Pavelic et al., 2006), but quite volatile (KHenry = 330.2 Pa·m3 /mol), which may have contributed to its removal. The triazines herbicides were poorly removed by biofilters (especially in S-SZ) and the outflow EMCs of simazine and atrazine were always above ADWG (Table 3). Triazines are mobile in soil systems (KOC = 2.1–2.7). Although they are biodegradable, as reported in similar soil-based systems such as wetland (Alvord and Kadlec, 1996) and aquifers (Hoyle and Arthur, 2000), the reported rates are quite slow and variable, with half-lives varying from weeks to years (Mackay et al., 2006). Hence, it was expected that they would not appreciably biodegrade within the hydraulic retention time of 3–5 h of the two biofilters. However, they might have an opportunity to biodgrade during dry periods, which is considerably longer. It can be seen from Table 3 that the simazine EMC values of CT2.3 were lower than that of CT2.2. One suggested reason might be that longer dry periods benefited simazine removal, as biodegradation of adsorbed simazine occurred during this period. However, after the dry period between CT1.1 and CT1.2, the EMC values of simazine in CT1.2 were higher than that during CT1.1, in contrast to the 2nd series of challenge tests. Temperature is important for biodegradation (Blecken et al., 2010). Moreover, differences in the ambient temperatures between the 1st and 2nd challenge tests could account for these contrasting results. Indeed, the 1st series of challenge tests were conducted in winter, whereas the 2nd were undertaken in summer. 3.3. Intra-event variability Fig. 3 shows how the concentrations of selected micropollutants vary over the duration of the challenge tests and natural storm events with a coefficient of variation between 48.6% and 114.8% for these two selected micropollutants over the 2nd challenge tests. Micropollutants were well removed at the very beginning of the series. The outflow concentrations increased over the duration of each test, and then dropped towards the end. This drop is probably due to low infiltration rates through the biofilter because after inflows stopped, the hydraulic head decreased, which resulted in longer residence times (2–4 h longer) and therefore better removal due to adsorption. The starting outflow concentrations of CT2.2 were within the range of the finishing concentrations of CT2.1 for the majority of micropollutants, the pollutographs of simazine and chloroform are presented in Fig. 3. This suggests that micropollutants were retained in the biofilters during CT2.1, and no significant degradation occurred during the short dry period of 10 h ( inflow concentrations) recorded during CT2.2 and CT2.3. However, after a prolonged dry period of ∼21 days between CT2.2 and CT2.3, the starting concentrations of micropollutants in CT2.3 were much lower than the final concentrations of the antecedent tests (Fig. 3). This supports the conclusion that a longer dry period is useful in removal of micropollutants, while it also supports the

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Fig. 3. Pollutographs of simazine and chloroform in the 2nd series of challenge tests.

hypothesis that the most challenge conditions are when two consecutive events occur with only a short resting period in between. This is similar to what Chandrasena et al. (2012) has found for microorganisms in biofilters. During the natural storm events of the 2nd series that occurred immediately after CT2.3, natural stormwater flushed micropollutants that were weakly adsorbed and relatively mobile; e.g. simazine and chloroform (Fig. 3) and pentachlorophenol (pollutographs not shown). However, their concentrations decreased rapidly during the two storm events; after a total of 3.5 m3 (1 PV) passed through LS-noSZ and 2.8 m3 (0.8 PV) passed through S-SZ, micropollutants were less than the LOR in the outflow. Although no samples were taken during the natural event between CT1.2 and CT1.3, it was also observed that the starting chloroform concentrations of CT1.3 dropped to lower levels (<1 ␮g/L, pollutographs not shown). 4. Conclusions The S-SZ biofilter (sand, with saturated zone) that is designed according to the best Australian practice showed good hydraulic

performance under the challenge conditions. There was no observed impact by prolonged wet or dry periods, thus operations occurred within rather stable hydraulic conditions. However, the outflow rate of the biofilter that had been designed using loamy sand (LS-noSZ) with a high content of nutrients and organics that are well above prescribed Australian standards was largely reduced during prolonged wet periods. Therefore, the use of standard biofiltration soil specification was validated for proper hydraulic behaviour of the systems. TPHs, glyphosate, DBP, DEHP, pyrene and naphthalene demonstrated good removal (>80% load reduction). Pentachlorophenol (PCP) and phenol had >80% load removal in LS-noSZ and 50%-80% in S-SZ, while the removal of chloroform exhibited load removal rates of between 20% and 50%. Biofilters were less effective in removal of simazine and atrazine. All showed 20–50% load reduction in LSnoSZ and <20% in S-SZ. Prolonged inflows of micropollutants into biofilters will eventually lead to break through. In this way, we proved that well designed biofilters can produce water of required quality for only some of the micropollutants tested under the range of operational conditions, including challenge conditions.

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The volumes of stormwater to be treated, as well as consequent wet and dry periods, are important operational variables to be included in the validation. Prolonged dry periods may actually benefit the removal of some micropollutants and should not necessarily be regarded as challenge operational conditions. This contrasts with the adverse impact of prolonged dry periods for TN removal (Hatt et al., 2009). Challenge conditions for micropollutants removal are more appropriately related to very short periods between successive storm events, which is a similar result to that found by Chandrasena et al. (2012) for faecal microbe removal by stormwater biofilters. This study is the first step in the development of a validation framework for WSUD stormwater treatment systems. However, in order to fully validate the biofilters studied, long term operational monitoring needs to be put in place to provide assurance that water quality objectives are being continuously met. Acknowledgements This study was funded by CRC for Water Sensitive Cities and Chinese Scholarship Council (CSC) (Grant no. 2011609012). The authors also wish to acknowledge the support of Stefan Filip, Gayani Chandrasena, Christelle Schang, Wenjun Feng, Dusan Jovanovic, Harsha Fowdar, Harpreet Kandra, Frank Winston and Monash Water for Liveability at Monash University. Appendix A. Supplementary data Supplementary material related to this article can be found, in the online version, at http://dx.doi.org/10.1016/j.ecoleng.2014. 03.004. References Abira, A., van Bruggen, J.J., Denny, P., 2005. Potential of a tropical subsurface constructed wetland to remove phenol from pre-treated pulp and papermill wastewater. Water Sci. Technol. 51 (9), 173–176. Alvord, H.H., Kadlec, R.H., 1995. The interaction of atrazine with wetland sorbents. Ecol. Eng. 5 (4), 469–479. Alvord, H.H., Kadlec, R.H., 1996. Atrazine fate and transport in the Des Plaines Wetlands. Ecol. Model. 90 (1), 97–107. ´ A., Fletcher, T.D., Viklander, M., 2009. Impact of a Blecken, G.-T., Zinger, Y., Deletic, submerged zone and a carbon source on heavy metal removal in stormwater biofilters. Ecol. Eng. 35 (5), 769–778. ´ A., Fletcher, T.D., Hedström, A., Viklander, M., 2010. Blecken, G.-T., Zinger, Y., Deletic, Laboratory study on stormwater biofiltration: nutrient and sediment removal in cold temperatures. J. Hydrol. 394 (3–4), 507–514. Broholm, M.M., Rügge, K., Tuxen, N., Højberg, A.L., Mosbæk, H., Bjerg, P.L., 2001. Fate of herbicides in a shallow aerobic aquifer: a continuous field injection experiment (Vejen, Denmark). Water Resour. Res. 37 (12), 3163–3176. Chandrasena, G.I., Deletic, A., Ellerton, J., McCarthy, D.T., 2012. Evaluating Escherichia coli removal performance in stormwater biofilters: a laboratoryscale study. Water Sci. Technol. 66 (5), 1132–1138. Cole, R.H., Frederick, R.E., Healy, R.P., Rolan, R.G., 1984. Preliminary findings of the priority pollutant monitoring project of the nationwide urban runoff program. J. Water Pollut. Control Fed. 56 (7), 898–908. DHV, 2013a. Review of the Public Health Regulatory Framework for Alternative Water Supplies in Victoria. Department of Health, Victoria. DHV, 2013b. Guidelines for Validating Treatment Processes for Pathogen ReductionSupporting Class A Water Recycling Schemes in Victoria. Department of Health, Victoria. DiBlasi, C.J., Li, H., Davis, A.P., Ghosh, U., 2008. Removal and fate of polycyclic aromatic hydrocarbon pollutants in an urban stormwater bioretention facility. Environ. Sci. Technol. 43 (2), 494–502. Dif, A., Bluemel, W., 1991. Expansive soils under cyclic drying and wetting. Geotech. Test. J. 14 (1), -. Dombeck, G.D., Perry, M.W., Phinney, J.T., 1998. Mass balance on water column trace metals in a free-surface-flow-constructed wetlands in Sacramento, California. Ecol. Eng. 10 (4), 313–339. Duncan, H., 1999. Urban Stormwater Quality: A Statistical Overview. CRC for Catchment Hydrology, Melbourne. eWater, 2012. Model for urban stormwater improvement conceptualisation (MUSIC) User Manual. eWater Cooperative Research Centre, Canberra.

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