Dynamic performance of biosand filters

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Dynamic performance of biosand filters THOMAS J. LYNN,1 PAULINE WANJUGI,2 VALERIE J. HARWOOD,2 AND SARINA J. ERGAS1 1Department 2Department

of Civil and Environmental Engineering, University of South Florida, Tampa, Fla. of Integrative Biology, University of South Florida, Tampa, Fla.

Biosand filters (BSFs) are point-of-use water treatment systems that can provide safe and affordable potable water to households in developing countries. However, little information is available on the dynamic performance of BSFs or for selecting local materials to use as filter media. In this study, water quality dynamics, biofilm characteristics, and hydraulic performance were investigated in two full-scale BSFs. During four months of operation with sewage-contaminated surface water, average log 10 removals of 1.7, 1.2, and 0.6 were observed for

Escherichia coli, total coliforms, and total heterotrophs, respectively. Average removal efficiencies for turbidity, ultraviolet absorbance at 254 nm, and total organic carbon were 86, 36, and 27%, respectively. Dynamic analysis showed that significant removal of indicator organisms and total organic carbon occurred during the pause period between charging the BSFs. Biofilm characterization revealed that more particle-associated biomass exists within the BSF column compared with the schmutzdecke layer.

Keywords: biofilm quantification, biosand filter, hydraulic conductivity, tracer studies Approximately 4 billion cases with 1.8 million deaths occur annually from diarrhea, much of which is attributed to consumption of unsafe water (WHO, 2007). Several different technologies can be used to improve drinking water at the household scale, including biosand filters (BSFs), coagulation, clay pot filters, point-of-use solar disinfection (SODIS), distillation, bleach chlorination, and boiling (Manser, 2012; Sobsey et al, 2008; Mintz et al, 2001). BSFs are a modification of the slow sand filter; however, BSFs are operated under batch-loading rather than continuousloading conditions (Manz et al, 1993). Advantages of BSFs include a short waiting period to produce treated water compared with point-of-use technologies such as clay pot filters, distillation, and SODIS; absence of residual tastes or odors compared with bleach chlorination; and no increase in temperature from the treated product water compared with SODIS, distillation, and boiling (Manser, 2012; Sobsey et al, 2008). BSFs are generally used to treat water that is collected from a surface water source on a daily basis. Each charge (volume) of raw water—approximately 20 L—is poured into the reservoir and is conveyed through a filter comprising three layers of media (sand, separation, and underdrain; Elliott et al, 2008). The separation layer prevents sand particles from entering and clogging the inlet of the discharge pipe located in the underdrain layer. Water flow is driven through the media by force of gravity (CAWST, 2009). The system is designed to maintain a constant water level above the filter column during the pause period (the time between BSF charges). BSF removal performance typically improves over several weeks of use as a biologically active layer of biofilm, or schmutzdecke, forms and matures on top of the filter column (CAWST, 2009; Elliott et al, 2008). As the schmutz-

decke forms, the flow through the BSF will slow to a point deemed unsatisfactory by the user (< 0.5 L/min), at which point the filter must be cleaned. Cleaning is generally accomplished by agitating the top surface of the sand column and scooping out the biofilm suspended in the water (Kubare & Haarhoff, 2010). Communications with development workers (Keller, 2011; Rowse, 2011) determined that many BSFs in operation do not produce an adequate flow rate and that sand media is often purchased and shipped overseas from developed countries. The conventional way to determine adequate BSF flow rates is to measure the maximum flow rate (MFR; the maximum volume of product water collected in 1 min). However, the results can be misleading because a measurement of the MFR does not give necessary information to determine the total waiting time to collect a desired amount (e.g., 10 or 20 L) of treated water. In addition, recommendations by different agencies for MFRs for BSFs (0.5–1.1 L/min) are quite variable (Hydraid, 2010; CAWST, 2009; Elliott et al, 2008; Stauber et al, 2006). A common parameter used to evaluate hydraulic performance in BSFs and other filtration technologies is the effective diameter of the media, or 10% of the media mass able to pass through a specified sieve size (D10). This parameter is useful in estimating the size of the smaller particles in the filter media because smaller particles tend to occupy pore spaces between the larger-sized particles that restrict flow through the filter (Barrett et al, 1991). Recommended D10 values (0.15–0.30 mm) for BSFs are based on slow sand filter studies (Manz et al, 1993; Barrett et al, 1991); however, there are no previously published studies confirming these recommended D10 ranges for BSFs. Jenkins et al (2011) studied BSFs with D10 = 0.15 mm and D10 = 0.52 mm (outside

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the recommended range) and observed statistically significant increases in fecal coliform log10 removal (0.16–0.30) using BSFs with D10 = 0.15 mm and a raw water fecal coliform concentration of 1.48 × 105 cfu/100 mL. Only a few published experimental studies have evaluated the hydraulic characteristics of BSFs. Tracer studies performed by Elliott et al (2008) showed that the initial hydraulic behavior of BSFs was similar to that of plug flow reactors (Morrill Dispersion Index [MDI] = 1.3). However, the study was performed under continuous flow conditions before BSF operation rather than after a schmutzdecke layer was established. Bradley et al (2011) observed similar results (MDI = 1.4); however, little detail was provided on the methodology used in this study. The standard method for calculating the MDI was developed to quantify the hydraulic characteristics of large sedimentation basins (Tchobanoglous et al, 2003). Because of the size of these basins, the volume of the tracer solution is neglected. To calculate the MDI for small-scale systems, such as BSFs, the volume of the feedwater spiked with the tracer solution should not be neglected to accurately quantify hydraulic characteristics. Few studies of BSFs have reported dynamic water quality performance; however, research on this topic can give insight into appropriate BSF charge volumes, media layer depths, and porosities. Baumgartner et al (2007) observed decreased total coliform concentrations in product water that was detained in the BSF from a previous charge. Elliott et al (2008) observed similar results with Escherichia coli. However, these studies only investigated dynamic performance of indicator bacteria removal and did not link indicator bacteria removal to other water quality parameters such as turbidity, ultraviolet absorbance at 254 nm (UV254), total organic carbon (TOC), or total nitrogen (TN) removal. The overall goal of this research is to improve water treatment at the household scale through improving understanding of the dynamic performance of BSFs. (A manuscript providing more detail on the water quality performance of the BSFs used in this study is in preparation.) The specific objectives of this study were to (1) investigate the dynamic water quality performance of BSFs, (2) investigate the biomass characteristics along the length of the BSF filter column, (3) provide guidance for proper BSF sand media selection, and (4) investigate the hydraulic performance of BSFs over time.

MATERIALS AND METHODS BSFs. The 107-day study began July 25, 2011, and ended Nov. 8, 2011. Two full-scale BSFs were used.1 Construction, operation, and maintenance procedures were performed in accordance with the BSF manufacturer’s handbook (Hydraid, 2010). A general schematic of the BSF type used in the study is shown in Figure 1. The depths of the gravel, sand, and standing water layer were 13.3, 40.6, and 3.8 cm, respectively. Media characterization. Sand and gravel media were prepared in accordance with the BSF manufacturer’s handbook (Hydraid, 2010). Two types of sand media—sand media 1 (native sand)2 and sand media 23—were tested to determine a suitable sand media type for the BSF experiment. A grain size analysis was performed on each sand media type (ASTM, 2007). BSF units

were then constructed with each sand media type and MFRs (maximum volume of effluent collected in 1 min) were determined. Although the D10 for sand type 1 was 0.15 mm, which is within the recommended D10 range of 0.15–0.30 mm, the MFR of 250 mL/min was well below the suggested minimum flow rate of 500 mL/min. Troubleshooting procedures, provided by the BSF manufacturer to increase the flow rate, were implemented; these included stirring the top 25 cm of submerged sand media and scooping out the suspended particles and removing the sand media layer and washing the sand to remove fine particles. However, the MFR remained at 250 mL/min. Unlike sand 1, sand 2 exhibited a D10 (0.24 mm) and an MFR (950 mL/min) within recommended ranges. On the basis of these observations, sand 2 was chosen for the filters used in the study. BSF operation. Raw water was pumped from Lake Behnke (University of South Florida, Tampa, Fla.) into a 200-L reservoir and mixed for 30 s before being discharged into 20-L buckets. Water in the buckets was spiked with 1% (v/v) primary effluent from the Howard F. Curren Wastewater Treatment Facility (Tampa, Fla.). The mean, standard deviation, and maximum and minimum raw water total coliform concentrations were 4.13 × 104, 1.57 × 104, 7.15 × 105, and 1.20 × 104 cfu/100 mL, respectively. The daily charge volume for each filter was 20 L divided into two 10-L sets. The second 10 L was charged to the filter after the water level returned to the elevation of the diffuser plate. The pause period (or time between daily 20-L charges) varied between 20 and 24 h. BSF cleaning procedures involved charging 4 L of raw water into the reservoir, gently agitating the top of the filter column, swirling the water in the reservoir, and discarding the water in the reservoir (Hydraid, 2010). BSF cleaning was normally performed each time the MFR fell below 500 mL/min (Hydraid, 2010). However, at the end of the study, the MFR from one of the filters remained below 500 mL/min after cleaning. In this instance, cleaning was not repeated. In addition, cleaning was performed on days 16, 41, and 76. Analytical methods. Standard Methods (2012) were used to measure turbidity (method 2130 B), TOC (method 5310 B), UV254 (method 5910 B), conductivity (method 2510 B), TN (method 4500-N), total coliforms (method 9222 C), total heterotrophs (method 9215 C), and E. coli (Method 9260 F). A scanning spectrophotometer was used to measure UV254 absorbance,4 a portable turbidimeter was used to measure turbidity,5 a TOC analyzer was used to measure TOC and TN,6 and a multiparameter meter was used to measure pH and conductivity.7 Method detection limits for TOC, TN, and turbidity were 0.11 mg/L, 0.03 mg/L, and 0.1 ntu, respectively. Falling head tests. Falling head tests were performed to evaluate the drawdown time and the relative hydraulic conductivity in the BSFs. The drawdown time was defined as the length of time for the water level to decrease from the highest stage (top of the BSF) to the elevation of the diffuser plate base (11.4 cm). The drawdown measurement was undertaken during the second 10-L charge because the first 10-L charge did not completely fill the reservoir. The relative hydraulic conductivity is equal to the actual hydraulic conductivity only for the clean bed sand media. As the

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schmutzdecke layer increases head loss through the system, the overall filter bed becomes vertically stratified. Because the hydraulic conductivity evaluates flow-through media without a schmutzdecke layer, only the relative hydraulic conductivity can be calculated after filter ripening. Tracer studies. Tracer tests were periodically performed on the BSFs to evaluate actual hydraulic performance under intermittent flow conditions. Each tracer test was conducted for five days (Monday–Friday). A conservative tracer (200 mg/L potassium chloride) was added to the raw water on Mondays and Tuesdays. Grab samples were collected at 2-L intervals up to 80 L for each tracer test. Samples were allowed to come to room temperature in the laboratory, and conductivity was measured as described previously. The conductivity value was adjusted to account for the background conductivity of the raw water and was converted to a potassium chloride concentration based on a calibration curve. Grab-sample tests. Grab samples were collected on the final day of the study from the two filters. Samples were taken at 2-L increments up to 18 L. Note that only one BSF was sampled for TOC, TN, and total coliforms, whereas both BSFs were sampled for turbidity and UV254 absorbance. With the assumption that the BSF behaves as an ideal plug flow reactor, the volume discharged could be related to the depth at which each sample volume was located during the pause period. The calculated gravel, filter (sand plus gravel), and total BSF water volumes were approximately 3, 13, and 16 L, respectively. The remaining 4 L were therefore charged and discharged on the same day. Biofilm tests. At the completion of the experiment, one filter was “sacrificed” following draining of the filter. Media samples were collected from the top of the schmutzdecke layer in each filter (0–1.3 cm) and at varying depths within the sand column (5.1, 12.7, and 38.1 cm) to quantify microbial biomass within the BSF. On the basis of visual observations, the schmutzdecke layer was assumed to be 1.3 cm in depth. The sample taken at 5.1 cm from the top of the column was assumed to represent a layer from below the schmutzdecke layer (1.3 cm) to half the distance (9 cm) of the next sample point (12.7 cm). The sample taken at 12.7 cm was assumed to represent a layer half the distance (9 cm) from the previous sample point (5.1 cm) to half the distance (25 cm) of the final sample point. The sample taken at 38.1 cm was assumed to represent a layer half the distance (25 cm) from the sample taken at 12.7 cm to the bottom of the sand column (41 cm). Triplicate samples were collected at each depth to ensure that representative samples were obtained. Media samples were stored at −80°C until extraction and analysis for total protein concentration, as described in the following section. Total protein concentration was used as a surrogate measurement for biomass. To extract the protein, frozen media samples were allowed to thaw overnight at 4°C. A 0.3-g aliquot of the media sample was added into a preloaded bead tube8 with 500 mL buffer (10 mM Tris-Cl, 0.5 mM ethylenediaminetetraacetic acid) and 60 µL of a lysis solution.9 The bead tubes were vortexed for 30 seconds and then placed in a bead-beater for 40 seconds. After bead-beating, samples were allowed to stand for 30 min at 4°C, and then the supernatant was collected into a

sterile 1.5-mL microcentrifuge tube. Analysis of the extracted schmutzdecke or sand samples for protein was performed according to manufacturer’s instructions using an assay kit with bicinchoninic acid.10

DATA ANALYSIS Falling head tests. The relative hydraulic conductivity, K (cm/s), was calculated from the drawdown time using the following (Bedient & Huber, 2002): r 2L h1 ln       K   r 2ct h2 

(1)

in which r and rc are the average radius of the storage reservoir above the standing water column and the sand media column (cm), respectively; L is the length of the sand media column (cm); t is the drawdown time (s); and h1 and h2 are the difference in elevation from the standing water level to the top of the BSF and diffuser plate (cm), respectively. The relative locations of r, rc, L, h1, and h2 in the BSF are shown in Figure 1. The D10 (mm) was calculated from the K (cm/s; Hazen, 1930): K  CD2 (2) 10

in which C is an uniformity distribution constant, varying from 1.0 to 1.5 (Hazen, 1930). In Eqs 1 and 2, the headloss in the separation and gravel layers was assumed to be negligible. Modified MDI calculation. The MDI can be calculated from the tracer data (Morrill, 1932): t(M90%)    mMDI =  t(M10%)

(3) 

in which t(M90%) and t(M10%) are the times when 90 and 10% of the total mass of the tracer has passed through the reactor (s), respectively. Data from the tracer study can be used to calculate the MDI, which can be used to quantitatively measure the hydraulic performance of the BSF compared with ideal plug flow (PFR) and ideal completely mixed flow (CMFR) reactors. Ideal PFRs and CMFRs have MDI values of 1 and approximately 22, respectively (Tchobanoglous et al, 2003). In sedimentation basin applications, the volume of the applied tracer solution is neglected because the volume of the tracer solution is small compared with the volume of the basin. To analyze tracer data from a BSF, a modified MDI (mMDI) must be used because the volume of the tracer solution is large enough to significantly change the calculated MDI value. For example, t(M90%) equals t(M10%) for an ideal PFR (MDI = 1) only because the tracer solution volume is assumed to be infinitesimally small. However, t(M90%) is greater than t(M10%) for an ideal PFR (MDI > 1) if the volume of the tracer solution is considered. Therefore, the MDI was modified for this study to calculate the volumetric efficiency of the two control filters by including the tracer solution volume. The mMDI (Eq 4) is calculated based on the volume of water discharged (instead of time)

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    V(Mx%) = Vw + fVT(5) FIGURE 1

Biosand filter schematic showing the locations of variables used in Eq 1

Reservoir

Diffuser plate

h1

r

V(M90%) Vw  0.9Vt   mMDIPFR =      V(M10%) Vw  0.1Vt 

h2

h1/2

in which [V(Mx%)] is the volume of water discharged when Mx% of tracer has been discharged, Vw is the volume of water initially in the reactor, f is the fraction of the total mass of tracer discharged (Mx%/MT), and VT is the total tracer solution volume. Note that the total mass fraction (Mx%/MT) and total volume fraction discharged (Vx%/VT) are equal. Thus, the mMDI of an ideal PFR (mMDIPFR) is calculated as:

Standing water

For an ideal CMFR, Eqs 7 and 8 are the mass balance equations derived for the cases in which the tracer solution is applied and the water without the tracer solution is applied, respectively.

Sand layer Outlet

L/2

L

rC

(6)

Gravel layer

L—sand, r—average radius of the storage reservoir above the standing water column, rc—average radius of the storage reservoir above the sand media column, h1 and h2—difference in elevation from the standing water level to the top of the biosand filter and diffuser plate, respectively Values for variables: L = 41.4 cm; r = 16.8 cm; rc = 14.5 cm; h1 = 16.5 cm; h2 = 5.1 cm.

after 90 and 10% of the total mass of the tracer has passed through the reactor: V(M90%) t(M90%) Q(t) ×     mMDI(4)      t(M10%) V(M10%)   Q(t)

in which Q(t) is a function representing the flow rate through the BSF with respect to time (L/s); and V(M90%) and V(M10%) are the volumes discharged when 90 and 10% of the total mass of the tracer has passed through the reactor, respectively. If the percent of the total mass of tracer passing through an ideal PFR is known, then the total volume of water passing through the reactor can be calculated from

∂M     MT – M + Vw  ∂V 

(7)

∂M      0 – M = Vw  ∂V 

(8)

in which M is the mass of the tracer discharged (g). For an ideal CMFR, Eqs 7 and 8 must be solved simultaneously to find V(Mx%). Note that an ideal mMDICMFR is approximately 22, which is equal to an ideal MDICMFR. Statistical analysis. Measurements from two BSFs were used to obtain data for all parameters, with the exception of protein concentration and grab samples of TOC, TN, and total coliforms, in which measurements from only one BSF were used. Arithmetic means were calculated for raw water total coliform counts, total protein concentration, MFR, drawdown, drawdown at a specified MFR, initial relative hydraulic conductivity, and final relative hydraulic conductivity. Arithmetic mean and standard deviations were calculated for mMDI, weekly relative hydraulic conductivity and turbidity (raw and product water) for grab samples, and UV254 absorbance (raw and product water) for grab samples. Linear regression correlation coefficients were calculated based on the relationship between TOC/UV254 absorbance, TOC/TN, and TOC/total coliforms. A linear regression model and correlation coefficient was calculated based on the relationship between relative hydraulic conductivity and MFR.

RESULTS AND DISCUSSION Water quality and quantity performance. A summary of the water quality performance over the four months of this study is shown in Table 1. The average MFR of 0.57 L/min was within the recommended range of 0.5–1.1 L/min (Hydraid, 2010; CAWST, 2009; Elliott et al, 2008; Stauber et al, 2006). The average drawdown time for the BSF filters (D10 = 0.24 mm) was 29.0 min. Drawdown time data can be useful in determining an appropriate sand media size range in relation to the maximum waiting period for users to obtain 10 L of product water. The estimated waiting period for BSFs (D10 = 0.24 mm) to produce 20 L of drinking water, which can support a family of

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TABLE 1

Overall water quality and quantity performance of the four-month biosand filter study Mean Raw Water†

Parameter*

Mean Product Water†

Maximum flow rate (214) Drawdown (188) Escherichia coli (58)

Removal

569 (89) mL/min 29.0 (6.1) min 1.11 × 104 (9.42 × 103) cfu/100 mL 104

(1.57 ×

104)

cfu/100 mL

Total coliforms (58)

4.13 ×

Total heterotrophs (58)

3.79 × 106 (2.49 × 106) cfu/100 mL

Turbidity (214)

2.21 × 102 (2.21 × 102) cfu/100 mL 2.57 ×

103

(4.10 ×

103)

log10 1.7

cfu/100 mL

log10 1.2

8.51 × 105 (1.01 × 106) cfu/100 mL

log10 0.6

7.5 (0.4) ntu

1.1 (0.5) ntu

86%

UV254 absorbance (214)

0.15 (0.02) ABS

0.10 (0.02) ABS

36%

Total organic carbon (58)

5.3 (0.8) mg/L

3.9 (1.3) mg/L

27%

Total nitrogen (58)

1.0 (0.4) mg/L

0.9 (0.3) mg/L

9%

ABS—absorbance units, UV254—ultraviolet absorbance at 254 nm *Value in parentheses is the number of samples taken. †Value in parentheses is the standard deviation.

range of recommended MFRs is high (0.5–1.1 L/min), the range of collection periods will increase to a greater extent. This is consistent with Eq 1. For example, assume the MFR is linearly correlated with the relative hydraulic conductivity (correlation coefficient, R2 = 0.80 from linear regression analysis). A 25% reduction in the MFR will increase the drawdown time by 33%; however, a 50% reduction in the MFR will increase the drawdown time by 100%. The drawdown method used in this study allows users or development workers to use only a timer, instead of a timer and

FIGURE 2

Maximum flow rate versus mean drawdown time in the biosand filters

60

Drawdown Time—min

five, was less than 1 h (WHO/SEARO, 2005). In addition, BSFs are capable of producing enough product water for a family of five for drinking, food preparation, and personal hygiene in less than 4 h of operation (WHO/SEARO, 2005). If BSFs are used for multiple activities, the initial product water discharged from the BSF should be used for drinking water. This will be further explained in the discussion of the grab sample results. Flow rate results. The relationship between MFRs and drawdown time (minimum time to collect 10 L of effluent) is shown in Figure 2. The relative hydraulic conductivity decreased from 6.8 × 10−2 cm/s on day 1 to 3.0 × 10−2 cm/s on day 107 (Figure 3). To obtain an appropriate range of drawdown times and D10 values, a low (0.5-L/min) and high (1.0-L/min) MFR was manipulated by using Figure 2 data (to find drawdown time) and Eqs 1 and 2 (to find D10). Drawdown times of 33.3 and 14.3 min were estimated for MFRs of 0.5 and 1.0 L/min, respectively. Relative hydraulic conductivities of 2.9 × 10−2 and 6.8 × 10−2 cm/s were calculated by using Eq 1 when the drawdown time was 33 and 14 min, respectively. With the use of the previously described procedure with Eq 2, D10 values of 0.14–0.17 mm and 0.21–0.26 mm were calculated for MFRs of 0.5 and 1.0 L/min, respectively. The range of values accounts for the variability in the coefficient C in Eq 2. The hydraulic conductivity of the sand media can give researchers and practitioners more insight into how to produce adequate flow rates through the BSF. By correlating the MFR and the relative hydraulic conductivity using Eq 2, the D10 of the sand media can be calculated based on the desired initial MFR. The minimum calculated D10 (0.14–0.17 mm) that corresponded to the minimum flow rate (0.5 L/min) is comparable to the recommended D10 of 0.15 mm. The minimum and maximum D10 values can be related to standard sieve sizes of #100 and #60 for minimum and MFRs, respectively. These sieve sizes can then be used to ensure adequate initial hydraulic performance while still providing a size in the recommended range to meet water quality objectives. Thus if an appropriate sand source is available, the problem of the user having to use nonlocal sand media can be solved, thereby decreasing the cost of BSF installation. The conventional method of measuring MFRs can be useful in determining product water collection periods. However, when the

40

20

0 300

500

700

900

Maximum Flow Rate—mL/min Error bars show the highest and lowest drawdown times corresponding to the measured maximum flow rate

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FIGURE 3

Average mMDI and relative hydraulic conductivity over time in the BSFs

mMDI mMDICMFR mMDIPFR 8.E-02

18

6.E-02

12

4.E-02

6

2.E-02

0

K—cm/s

mMDI

K 24

0.E+00 0

4

8 Week

12

16

BSF—biosand filter, K—relative hydraulic conductivity, KCl—potassium chloride, MDI—Morrill Dispersion Index, mMDI—modified MDI, mMDICMFR—modified MDI of ideal completely mixed flow, mMDIPFRmodified MDI of ideal plug flow

This indicates that the hydraulic performance of the BSFs did not change significantly with time. Therefore, additional strategies to improve the hydraulic performance of BSFs in comparison to ideal PFRs are not needed. In addition, the decrease in the relative hydraulic conductivity observed in the course of the study did not appear to change the mMDI in the BSF, as shown in Figure 3. This provides evidence that the hydraulic characteristics of BSFs will be similar to ideal PFRs even when sand types with varying hydraulic conductivities are used in the filter column. The mMDI method used in this study to determine hydraulic performance relative to PFRs and CMFRs offers a practical approach to the analysis of tracer study data from small-scale, intermittent flow and/or falling head reactors. The method also eliminates the requirement of keeping time during tracer studies. Small-scale reactor tracer studies should evaluate the potential impact tracer solution volumes can have on values determined for the mMDI or conventional MDI method. Grab-sample tests. Grab sample test results are shown in Figure 5. Turbidity, TOC, and total coliform counts were found to be predominantly lower for samples that were within the BSF pore volume during the pause period, as discussed in the following section. The lowest product water turbidities—0.45 and 0.51 ntu—were observed for the eighth litre discharged, which was from water that was located near the middle of the sand layer during the pause period. Afterward, product water turbidities increased with each sample taken.

The dashed and solid lines represent the ideal mMDIPFR (2.60) and mMDICMFR (21.85), respectively. Error bars show the standard deviation.

FIGURE 4

Week 1 tracer study results comparing the KCl tracer concentration and f with the volume of product water discharged from the BSF KCl f 1.0

200

0.9 0.8

160

0.6

120

0.5 0.4

80

f (MX%/ MT)

0.7 KCl—mg/L

calibrated container, to determine whether the filter has an initial adequate flow rate. The recommended method for determining adequate flow through the BSF is to measure the maximum volume discharged in 1 min (Hydraid, 2010; CAWST, 2009). However, the method can improperly identify the hydraulic behavior through the BSF because of the short time period measurements taken. Kubare and Haarhoff (2010) also acknowledged this problem but offered solutions that are impractical for users to implement, such as assuming a number of specific media properties for the sand column. Tracer studies. The results from a typical tracer test are shown in Figure 4. The volume of water discharged when M10% and M90% have been discharged from the BSF was calculated to be 19.0 and 55.4 L, respectively. These values were then used in Eq 4 to obtain an mMDI of 2.92. The mMDIPFR was calculated to be 2.60. This indicates BSFs are hydraulically similar to ideal PFRs. Incorrect comparisons could have resulted if the conventional MDIPFR (= 1) was compared with the result (mMDI = 2.92) obtained from the tracer study. However, BSF tracer studies using the conventional MDI method determined MDI values of 1.3 (Elliott et al, 2008) and 1.4 (Bradley et al, 2011). These values may have been obtained by neglecting the tracer solution volume in the calculations. The mMDI results from the filters varied between 2.86 and 3.01 during the duration of the study, as shown in Figure 3.

0.3 0.2

40

0.1 0.0

0 0

20

40

60

80

Volume Discharged—L BSF—biosand filter, f—total mass fraction of the tracer solution volume, KCl—potassium chloride, MX%/MT—total mass fraction discharged

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FIGURE 5

Turbidity (A), total organic carbon (B), total nitrogen (C), and total coliforms (D) measured at specific product water volumes on the last day of the study

B

A 1.4

5

Raw water = 6.18 ntu

4.5

2.5 2 1.5 1 0.5

0

0 0

2

4

6

8

10

12

14

16

18

0

20

2

4

6

Volume Filtered—L

8

10

12

14

16

18

20

Volume Filtered—L

D

C Raw water = 1.99 mg/L

1.8

3.5E+03

1.6

Raw water = 2.05E+04 cfu/100 mL

1.0E+03

Water

1.5E+03

5.0E+02

0.2 0

Water Level

2.0E+03

Sand

0.4

Sand

0.6

Sand Water

0.8

Water Level

1

2.5E+03

Sand

1.2

Gravel

Total Coliforms—cfu/100 mL

3.0E+03

1.4

Gravel

Total Nitrogen—mg/L

Water Level

Water Level

Water

0.2

Sand

Gravel

0.4

Sand

0.6

3

Sand Water

0.8

4 3.5

Sand

1

Gravel

Total Organic Carbon—mg/L

1.2

Turbidity—ntu

Raw water = 6.0 mg/L

0.0E+00 0

2

4

6

8

10

12

14

16

18

20

0

2

4

6

8

10

12

14

16

18

20

Volume Filtered—L

Volume Filtered—L

Error bars in part A represent standard deviations. Vertical lines represent layer boundaries during the pause period.

The lowest product water TOC concentrations of about 3 mg/L were observed for the first 10 L discharged. TOC concentrations increased to 4 mg/L between the tenth and twelfth litres discharged and to 4.5 mg/L between the sixteenth and eighteenth litres discharged. TOC is an important water quality characteristic to monitor in BSFs, especially if chlorination of the product water is applied for posttreatment. The mixing of chlorine and water containing organic matter can produce potential human carcinogens or disinfection by-products (Tchobanoglous et al, 2003). Product water total coliform counts followed a similar pattern as product water TOC concentrations (correlation coefficient = 0.92). However, a rapid increase in total coliform counts was observed between the eighth (4.50 × 102 cfu/100 mL) and tenth

(1.85 × 103 cfu/100 mL) litres discharged. Results of this study were similar to Baumgartner et al (2007), who collected composite samples at different time points and observed decreasing total coliform removals of 95.9, 81.0, and 79.1% at the 5-, 10-, and 20-L product water collection points, respectively. Product water TN concentration patterns were more complex. The lowest product water TN concentration (0.8 mg/L) was measured at the tenth litre discharged. Mean product water TN concentrations between zero and the sixth litre discharged and between the fourteenth and eighteenth litre discharged were 1.5 and 1.4 mg/L, respectively. No correlation was observed between product water TN and TOC patterns. Previous studies by Murphy et al (2010) observed dynamic nitrogen cycling in a BSF and sug-

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FIGURE 6

Mean total protein concentrations and calculated percent of total protein with depth within the biosand filter sand layer

Biofilm Total protein 40

30 800

20

Total Protein—%

Biofilm—mg biofilm/g media

1,200

400 10

0

0 0–1.3

1.3–9

9–25

25–41

Depth—cm Error bars represent maximum and minimum values.

gested that simultaneous nitrification and denitrification occurs within the medium. BSFs used in this study were full-scale commercially available units that were not modified to include sample ports at intermediate depths. However, system hydraulics were similar to an ideal PFR; as a result, water quality dynamics of the charged water can be considered as three distinct sections: (1) water that was detained in the lower section of the BSF media below the schmutzdecke (zero to tenth litre discharged), (2) water that was detained in the BSF media just below and above the schmutzdecke layer (tenth to sixteenth litre discharged), and (3) water that was charged into and exited the BSF on the same day (sixteenth to twentieth litre discharged). Processes occurring in the lower section may include biodegradation, decay, sloughing, and filtration. During the pause period, organisms consume oxygen and bioavailable TOC, which promotes denitrification if nitrate is present in the water. After the bioavailable TOC and electron acceptors have been depleted, bacteria begin to die off, which would increase TN concentrations from ammonification. The next time the BSF is charged, dead organisms (partially composed of nitrogen) are sloughed off the media and may be discharged with the product water (increasing turbidity). Processes occurring in the schmutzdecke section may include biodegradation and filtration. During the pause period, organisms

consume bioavailable TOC present in the water, schmutzdecke, and supernatant (standing water); however, the excess TOC available in this section and oxygen diffusion from the surface provide an environment for microbial growth rather than die-off. The next time the BSF is charged, excess TOC is filtered/adsorbed by the media, and any sloughing that occurs in this region is filtered in the upper region of the sand column. Processes occurring to water that is charged and discharged on the same day include filtration and biodegradation. When the BSF is charged, TOC and suspended particles are primarily filtered, adsorbed, and/or biodegraded in the schmutzdecke layer. Filtration may be less efficient in this section because particles are suspended in the water rather than adsorbed to the sand media or present as biofilms. In addition, less time is available for biodegradation compared with the other two sections. Future BSF design modifications should consider increasing the BSF pore volume 19 cm below the sand filter column to 20 L. Such modifications may involve increasing the column depth, cross-sectional area and/or porosity of the system. The approximate diameter for a circular BSF with the same depth as the one used in this study and an increased pore volume would be 38 cm. Increasing the depth of the sand filter is not recommended because this will increase headloss, which decreases the flow rate of the BSF. Biofilm tests. Analysis of total protein concentration in the biofilm associated with filter media grains was conducted to understand the location and quantity of organisms in the sand column. Higher protein concentrations (about threefold higher) were observed at the top of the BSF (schmutzdecke layer) compared with deeper in the BSF (5.1, 12.7, and 38.1 cm; Figure 6). Higher protein concentrations in the top layers suggest that the schmutzdecke layer is more likely to promote microbial growth and retain microbes through adsorption and filtration, whereas microbial growth is less likely to occur in the sand column. The protein concentration and weight distribution along the BSF sand filter column is shown in Figure 6. The percentage of total protein in the schmutzdecke layer was calculated to be 22%. Even though the schmutzdecke layer contains the highest protein concentration in the BSF, the biofilm-associated protein content was observed to be greater in the remaining BSF sand filter column because of the greater volume represented by that zone (78%).

CONCLUSIONS Two full-scale BSFs were tested to evaluate hydraulic and water quality dynamic performance. Results from the dynamic water quality tests and biofilm quantification indicate that removal of turbidity, total coliforms, and TOC occurs within the sand layer rather than solely in the schmutzdecke. Reduction in total coliform counts followed a pattern that differed from other water quality parameters in that greater removal occurred in the lower sand layer compared with the upper sand and schmutzdecke layers of the BSFs. Falling head tests revealed that #60–#100 sieve sizes could be used to prepare sand media from local sources. Tracer studies from this investigation used an mMDI method, which can quantify the hydraulic performance of small-scale reactors such as BSFs. Future research involving recommended

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Lynn et al | http://dx.doi.org/10.5942/jawwa.2013.105.0116 Journal - American Water Works Association Peer-Reviewed

BSF sand media sizes should include a field survey investigating maximum waiting periods tolerated by users.

Bradley, I.; Straub, A.; Maraccini, P.; Markazi, S.; & Nguyen, T.H., 2011. Iron Oxide Amended Biosand Filters for Virus Removal. Water Research, 45:15:4501. http://dx.doi.org/10.1016/j.watres.2011.05.045.

ACKNOWLEDGMENT

CAWST (Centre for Affordable Water and Sanitation Technology), 2009. BioSand Filter Manual: Design, Construction, Installation, Operation and Maintenance, Calgary, Alta., Canada.

This material is based on work supported by the University of South Florida College of Engineering Interdisciplinary Scholarship Program and the National Science Foundation under grant DUE-0965743. The authors thank James Mihelcic, Ryan Schweitzer, Matthew Gaston, and Katrina Gordon for their assistance with this research.

ABOUT THE AUTHORS Thomas J. Lynn is a doctoral candidate in the Department of Civil and Environmental Engineering, University of South Florida, Tampa, Fla. He earned his bachelor of science in civil engineering and a master’s degree in environmental engineering from the University of South Florida. He is a registered professional engineer in the state of Florida. Pauline Wanjugi is a doctoral candidate and Valerie J. Harwood is a professor in the Department of Integrative Biology at the University of South Florida. Sarina J. Ergas (to whom correspondence should be addressed) is a professor in the Department of Civil and Environmental Engineering, 4202 E. Fowler Ave., ENB 118, University of South Florida, Tampa, FL 33620; [email protected].

FOOTNOTES 1Hydraid

BioSand water filter, Grand Rapids, Mich. sand, University of South Florida Botanical Gardens, Tampa, Fla. 3Sand and gravel, Seffner Rock and Gravel, Tampa, Fla. 4Thermo Scientific 335906P-000 Genesys 10 UV spectrophotometer, catalog no. 335906P, Thermo Fisher Scientific, Waltham, Mass. 5Hach 2100Q portable turbidimeter, Hach Co., Loveland, Colo. 6Shimadzu TOC-V CSH total organic carbon/total nitrogen analyzer, Shimadzu Scientific Instruments, Columbia, Md. 7Orion 5-Star pH/RDO/conductivity portable multiparameter meter, Thermo Scientific Inc., Beverly, Mass. 8Preloaded bead tube S0205-50, GeneRite, North Brunswick, N.J. 9C1 solution in Power Soil DNA isolation kit, MO BIO Laboratories Inc., Carlsbad, Calif. 10Micro BCA assay kit, Thermo Scientific, Rockford, Ill. 2Native

PEER REVIEW Date of submission: 02/26/2013 Date of acceptance: 06/05/2013

Elliott, M.A.; Stauber, C.E.; Koksal, F.; DiGiano, F.A.; & Sobsey, M.D., 2008. Reductions of E. coli, Echovirus Type 12 and Bacteriophages in an Intermittently Operated Household-Scale Slow Sand Filter. Water Research, 42:10–11:2662. http://dx.doi.org/10.1016/j.watres.2008.01.016. Hazen, A., 1930. Water Supply. American Civil Engineers Handbook. John Wiley & Sons, New York, N.Y. HydrAid, 2010. HydrAid BioSand Water Filter Handbook. Triple Quest, LLC, Grand Rapids, Mich. Jenkins, M.W.; Tiwari, S.K.; & Darby, J., 2011. Bacterial, Viral and Turbidity Removal by Intermittent Slow Sand Filtration for Household Use in Developing Countries: Experimental Investigation and Modeling. Water Research, 45:18:6227. http://dx.doi.org/10.1016/j.watres.2011.09.022. Keller, C., 2011. Personal communication. Kubare, M. & Haarhoff, J., 2010. Rational Design of Domestic Biosand Filters. Journal of Water Supply: Research and Technology–Aqua, 59:1:1. Manser, N., 2012. Technical and Economic Assessment of Adobe as the Primary Building Material on the Water Yield of a Single Basin Solar Still. Master’s thesis, Department of Civil & Environmental Engineering, University of South Florida, Tampa, Fla. Manz, D. H.; Buzinus, B. J.; & Morales, C., 1993. Final Report on the Nicaragua Household Water Supply and Testing Project. Division of International Development. University of Calgary, Calgary, Alta., Canada. Mintz, E.; Bartram, J.; Lochery, P.; & Wegelin, M., 2001. Not Just a Drop in the Bucket: Expanding Access to Point-of-Use Water Treatment Systems. American Journal of Public Health, 91:10:1565. Morrill, A.B., 1932. Sedimentation Basin Research and Design. Journal AWWA, 23:1442. Murphy, H.M.; McBean, E.A.; & Farahbakhsh, K., 2010. A Critical Evaluation of Two Point-of-Use Water Treatment Technologies: Can They Provide Water that Meets WHO Drinking Water Guidelines. Journal of Water and Health, 2:4:611. Rowse, L., 2011. Personal communication. Sobsey, M.D.; Stauber, C.E.; Casanova, L.M.; Brown, J.M.; & Elliott, M.A., 2008. Point of Use Household Drinking Water Filtration: A Practical, Effective Solution for Providing Sustained Access to Safe Drinking Water in the Developing World. Environment Science & Technology, 42:12:4261. http://dx.doi.org/10.1021/es702746n. Standard Methods for the Examination of Water and Wastewater, 2005 (22nd ed.). APHA, AWWA, and WEF, Washington.

ASTM (American Society for Testing and Materials), 2007. Standard Test Method for Particle Size Analysis of Soils. West Conshohocken, Pa.

Stauber, C.E.; Elliott, M.A.; Koksal, F.; Ortiz, G.M.; DiGiano, F.A.; & Sobsey, M.D., 2006. Characterisation of the Biosand Filter for E. coli Reductions from Household Drinking Water Under Controlled Laboratory and Field Use Conditions. Water Science & Technology, 54:3:1.

Barrett, J.M.; Bryck, J.; Collins, M.R.; Jononis, B.A.; & Logsdon, G.S., 1991. Manual of Design for Slow Sand Filtration. Awwa Research Foundation and AWWA, Denver.

Tchobanoglous, G.; Burton, F.; & Stensel, H.D., 2003 (4th ed.). Wastewater Engineering: Treatment and Reuse. Metcalf & Eddy Inc.–McGraw Hill, New York, N.Y.

Baumgartner, J.; Murcott, S.; & Ezzati, M., 2007. Reconsidering “Appropriate Technology”: The Effects of Operating Conditions on the Bacterial Removal Performance of Two Household Drinking-Water Filter Systems. Environmental Research Letters, 2:2. http://dx.doi.org/10.1088/1748-9326/2/2/024003.

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REFERENCES

Bedient, P.B. & Huber, W.C., 2002 (6th ed.). Ground Water Hydrology. Hydrology and Floodplain Analysis. Prentice Hall, Upper Saddle River, N.J.

WHO/SEARO (WHO South-East Asia Regional Office), 2005. Minimum Water Quantity Needed for Domestic Uses: Technical Notes for Emergencies. Technical Note 9. New Delhi, India.

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