Fine Particle Capture by Synthetic Vegetation in a Laboratory Flume

Home , Flume

KRISTEN FAURIA B.S. (University of Oregon) 2010 THESIS

Submitted in partial satisfaction of the requirements for the degree of

MASTER OF SCIENCE

Civil and Environmental Engineering

in the

OFFICE OF GRADUATE STUDIES

of the

UNIVERSITY OF CALIFORNIA

DAVIS

Approved:

______

S. Geoff Schladow, Chair

______

Fabian Bombardelli

______

John Reuter

Committee in Charge

2013

Abstract

[1] Vegetated floodplains and wetlands trap particulates, a process which is important for water quality and wetland form and function. The rate of particle removal by vegetation remains poorly characterized, especially for fine particles with a range of particle sizes. Results from a series of laboratory flume experiments with synthetic grasses that evaluated particle removal rates for a range of particle sizes and experimental treatments are presented. Suspended road dust (1.25 – 109 µm diameter) concentration was monitored in a laboratory flume as flow velocity, initial particle concentration, plant stem density, and the presence of biofilm were varied. Particle concentration was monitored and first-order decay models were fit to the particle concentration data. The presence of vegetation was found to increase the rate of particle capture/decay compared to a bare flume. Additionally, we observed trapping of small (<30µm diameter) particles by vegetation. Particle capture by plants increased with particle size, the presence of biofilm, and stem density. Yet, the rate of particle capture was found to decrease with flow velocity. ANOVA mixed models were used to test the significance of these results; the presence of biofilm, stem density, flow velocity, and initial particle concentration were found to significantly contribute to the rate of particle capture. Additionally, we observed an increase in 3-7µm particles over time.

1. Introduction

[2] The transport and fate of particles in vegetated floodplains and wetlands is fundamental to wetland form and function, pollutant and nutrient transport, primary productivity, land surface elevation, and downstream water quality [e.g. Larsen and Harvey, 2010; Hokosawa and Horie, 1992; Stubblefield et al.

2006; Davies – Colley et al., 2001; Mudd et al., 2010; Johnston, 1991]. Vegetation is important to these processes by stabilizing sediment and capturing suspended particles by both altering hydrodynamic conditions and providing surfaces for particle adhesion. This study focuses on the trapping of particles by vegetation, specifically this study uses laboratory flume experiments to measure the rate of particle capture by emergent vegetation for a range of particle sizes and flow conditions. By reporting capture rates and discussing the effects of particle size, the existence of biofilm, flow velocity, stem density, and initial particle concentration on particulate trapping, the results demonstrate which variables are most important to particulate capture and help inform modelers and floodplain restoration efforts.

[3] The importance of vegetation to the fate and transport of particulates in terrestrial systems with flowing water is well documented. Stumpf [1983] observed the loss of 80 percent of particulates within

12m of a tidal creek; 50 percent of this loss was accounted for by trapping on the stems of vegetation.

Leonard et al. [1995] demonstrated that 4-10 percent of particles captured in a tidal marsh were caught by the stems of vegetation. Huang et al. [2008] and Saiers et al. [2003] measured trapping of very fine (1µm and 0.3µm, respectively) particles on the stems of vegetation in the Florida everglades. For the purpose of particle retention by treatment wetlands, it is known that vegetation increases particle retention by altering the hydrodynamics of flow and by particle adhesion on plant surfaces [Kadlec and Wallace, 2008, pg.

210]. Despite observations of trapping on plant stems, gravitational settling dominates particle trapping in vegetated zones under typical flow conditions [eg. Leonard et al., 1995; Mudd et al., 2010]. It is worth noting, however, that vegetation can enhance settling by altering turbulence intensity and by providing vertically oriented surfaces for settling [eg. Nepf, 1999; Kadlec and Wallace, 2008; Elliot, 2000].

[4] Many particles are too small to settle on the timescale of floodplain inundation even with vegetation induced changes to turbulence intensity. As a result trapping by plant stems, or direct interception, may dominate the trapping of these fine particles [eg. Rubenstein and Koehl, 1977; Huang et al., 2008]. There is value in examining fine particles specifically because fine particles have profound water quality implications; 97% of particles from highway runoff are smaller than 30µm [Li et al., 2005], and metals sorb to small particles [eg. Sansalone and Buchberger, 1997]. Here we define small particles as particles with a diameter less than 30µm based on the findings by Li et al. [2005].

[5] The role of vegetation in open channel flow is often incorporated within a roughness term. However, particle capture on the surfaces of vegetation is more analogous to flow across a cylinder [eg. Rubenstein and Koehl, 1977; Spielman, 1977; Shimeta and Jumars, 1991]. Particle capture by a single cylinder has been investigated experimentally studies [eg. Palmer et al., 2004; Wu et al., 2011]. Palmer et al. [2004] examined the capture of 194 µm spheres by a grease-coated cylinder in a laboratory flume and found a relationship between the collector Reynolds number (Rec), the ratio of particle diameter to collector diameter, and the capture efficiency in turbulent flows (Rec =1-1000). Wu et al. [2011] developed a similar empirical relationship for colloid capture by a single collector in laminar flows (Rec = 0.4 – 40).

These studies did not consider the wake-generating effects of multiple collectors, surface properties (zeta potential and the existence of biofilms) of particles and collectors, and particles < 177µm in turbulent and intermediate turbulent flow. Particulate trapping has been shown to be influenced by biofilms, composites of micro-organisms and organic material that coat submerged surfaces. Jin et al. [2011] observed an increase in particle retention with the existence of biofilm on laboratory plates. Wolyniak et al. [2012] found that the retention of oocysts on biofilm coated natural vegetation depended on roughness.

Finally, Palmer et al. [2004] showed that adding roughness elements to a cylindrical collector increased capture efficiency. These studies suggest that biofilms are important for particle retention.

[6] The goal of this work is to identify the factors that contribute to particulate trapping by vegetation with emphasis on <30µm particles. We present results from a series of laboratory flume experiments with

bladed synthetic vegetation that test the influence of flow velocity, stem density, particle size, particle concentration, and the presence of biofilms on particulate capture. We model the change in particle concentration through time using a first order decay model to characterize each treatment with a single decay constant. This work is partly motivated by ongoing management efforts at Lake Tahoe, CA-NV, where inputs of fine, inorganic particles (<20µm) have been related to lake clarity decline [Jassby et al.,

1999; Swift et al., 2006; Sahoo et al., 2010; Lahontan and NDEP, 2010]. The results will help inform floodplain restoration and efforts in the Tahoe basin to meet fine sediment load reduction targets established by the Lake Tahoe TMDL [Sahoo et al., 2010; Lahontan and NDEP, 2010].

2.0 Methods

2.1 Theoretical considerations

[7] Particles can be removed from across-vegetation flow by several mechanisms: gravitational settling, direct interception, inertial impaction, and diffusional deposition [eg. Rubenstein and Koehl, 1977;

Spielman, 1977]. Gravitational settling is the sedimentation of particles or complex aggregated particles due to gravity; Stokes’ law describes particle settling in the absence of turbulence. However, floodplains are transitionally turbulent or have spatially heterogeneous turbulent characteristics. Direct interception is a mechanism through which plant stems trap particles; it is trapping due to travel along stream lines and subsequent collision and adhesion to collectors (plant surfaces). Inertial impaction occurs when particles have sufficient momentum to deviate from their stream lines and impact and stick to a collector. Inertial impaction is negligible for the experiments undertaken because the particles were sufficiently small.

Diffusional deposition, the deposition of particles on a collector due to random processes like Brownian motion or turbulence, is another mechanism for trapping. Interpretation of the results is confounded by the potential for particle flocculation. While giving the impression of particle removal in smaller size classes and reducing the number of particles, flocculation transfers particle mass to larger size classes.

[8] Palmer et al. [2004] examined the trapping of 177- 210µm spherical plastic beads by direct interception on a single grease-coated cylinder in a laboratory flume for intermediate Reynolds number

(and transitional turbulent) conditions. Palmer et al. [2004] found an empirical relationship between the capture efficiency (η), collector Reynolds number (Rec) and ratio of particle diameter to collector diameter (R),

0.718 2.08  0.224(Rec ) (R ) (1).

Capture efficiency is defined by Palmer et al. [2004] and aerosol filtration theory as the ratio of the upstream span of particles, dp, that are captured by a collector of diameter dc,

 ddpc/ (2).

From a geometric perspective on flow through emergent vegetation, the rate of particle removal by direct interception can be written as,

' kDirecInterception ud c l c , (3)

' where and lc is the collector length per unit volume and u is flow velocity. Palmer et al. [2004] use

Stokes’ Law for particle settling in a still fluid to describe particle settling in an emergent wetland where the rate of particle removal by settling is,

ksettling v s / h , (4)

where vs is the Stokes’ settling velocity and h is water depth. From this point forward the empirical relationship (eq. 1) presented by Palmer et al. [2004] will be described as PNPA theory.

2.2 Experimental Design

[9] Laboratory flume experiments to investigate the capture of suspended road dust by synthetic vegetation were conducted. Suspended road dust was recirculated through synthetic vegetation in a laboratory flume and the suspended particle concentration (by size class) was measured. Particle

concentration was expected to change through time according to the two dimensional diffusion – convection equation, dC dC d22 C d C u  D  D  C , (5) dt dxxz dx22 dz

where C is particle concentration, u is flow velocity, Dx and Dz are longitudinal and vertical dispersion coefficients, and  is the rate of particle removal. The flume was assumed to be uniformly mixed by turbulence such that is the only source for changing particle concentration. Thus, concentration decay with time is modeled as,

kt PP o exp , (6)

where P is particle concentration, Po is initial particle concentration, k is the rate of change in suspended particle concentration, and t is time. The rate of change in suspended particle concentration, , integrates all the previously described mechanisms for particle removal: settling, direct interception, inertial impaction, diffusional deposition, and flocculation.

2.2 Experimental Apparatus

[10] A reservoir of 200 gallons of water was recirculated through a 7.90 m long X 0.46 m wide X 0.50 m deep flume at flow rates of 1.2 x 10-3, 3.2 x 10-3, and 4.6 x 10-3 m3s-1 using two pumps (0.5hp and 1.5hp) in parallel (Fig. 1). Each experiment ran for 65 minutes such that the reservoir volume passed through the flume 6.2, 16.5, and 23.7 times for each respective flow rate. A list of the experimental treatments is shown in table two. Suspended particle (road dust) volume concentration was measured with an in situ particle size analyzer (Sequoia LISST-100X; type B, size range 1.25 – 250 µm). The LISST measures particle size distribution and concentration through laser diffraction techniques. We processed the raw scattering intensities measured by the LISST using MATLAB© routines [Andrews et al., 2010]. The

LISST was positioned on its side to prevent lens fouling and measurements were made in the real-time burst operating mode and reported at 1-9 second (average of six seconds) intervals.

[11] Road dust (described below) was suspended in tap water at two different starting particle concentrations (SPCs): 16.6 SD 6.5 µL L-1 and 28.7 SD 6.1 µL L-1. Example particle size distributions of these two starting concentrations are shown in figure 2. To set the desired SPC value, particles were mixed in the flume until the recorded LISST transmission reached 75 or 55 percent, respectively. To mix to these target SPCs, road dust was first stirred into the 200 gallon reservoir. The pumps and LISST were then turned on for ten to twenty minutes to ensure that the reservoir was thoroughly mixed and to check the transmission values. During this time, additional particles were added to the reservoir to reach the desired SPC. After the target SPC was achieved, the pumps and LISST were turned off and the flume water drained into the reservoir for five to ten minutes.

[12] The pumps were initiated to begin the experimental runs after the desired SPC was reached; the

LISST was turned on ninety seconds after the pumps. While an experiment was running, the water in the reservoir was kept agitated by the pumps and the return water from the flume. Despite this, it is possible that particle settling occurred in the reservoir. However, particle settling in the reservoir would occur equally during the control runs (no vegetation in flume) compared to runs with vegetation. Thus, we used the control runs to separate reservoir settling from trapping by vegetation. To avoid initialization effects, time zero for data analysis was set as five minutes after the LISST was turned on. Between experiments the water in the flume drained into the reservoir. The water and particles in the reservoir were replaced after several experiments. At these times the flume bottom was also scrubbed and rinsed free of particles.

Water was not reused between runs with and without dry biofilm.

[13] The road dust was collected in the Lake Tahoe basin by a Tymco DST-6 regenerative air sweeper in the Gardner Mountain, South Lake Tahoe, California subdivision and is characterized by volcanic cinders, eroding sediment, and road material [Russell Wigart, pers. comm., 2011]. The road dust in Lake

Tahoe may contain residues of traction sand that is applied during winter, and road dust characteristics likely vary with the collection location. Figure 3 shows a magnified image of the suspended road dust particles that demonstrates the angularity of the particles. The PSDs in figure 2 show that the initial volume particle concentrations peaked around 11µm. Additionally, the high SPC particle size distribution shows a consistent secondary peak at 4µm and a trough at 7µm (Fig. 2).

[14] We examined particle removal when the flume was empty (control runs) and by plastic grass mats

(0.46 m wide x 4.0 m long) with stem densities of 2704 and 7209 stems m-2 (Table 1; Fig. 1b, 1c). The mats consisted of bladed stems that were 6 mm wide at their base and that tapered to a point at their tips with an average width of 3mm. The stems were bladed, naturally bent in random orientations, and 13.0 to

20.3 cm long with average lengths of 14.5cm (7209stems m-2) and 16.6cm (2704 stems m-2) (Fig. 1b, 1c).

The mats filled the entire width of the flume and, due to their buoyancy, were held to the base of the flume with aluminum rods.

[15] Biofilms were grown on the plastic mats to make the artificial grass more analogous to natural conditions (Fig. 1c). The plastic mats were submerged in Lake Spafford, a hyper-eutrophic body of water located on the UC Davis campus. After one week, the mats were extracted and gently rinsed to remove loosely attached organic material. The mats were then dried outside for several days to mimic the state of intermittent floodplains that are dry before flooding. Biofilm mass from a total of fifty six stems was measured as 1.19 x 10-2g biofilm per stem or 8.26 x 10-4 g cm-2.

[16] Longitudinal flow velocities were measured 1) with two 3D Acoustic Doppler Velocimeters (ADVs,

Son-Tek) measuring at 5 Hz in front and behind of the grass mats and 2) by measuring flow depth at the

ADV locations and calculating cross-sectional mean velocity. Flow rates were calibrated prior to the experiments using a sluice gate. From this calibration we found that the three flow rate settings correlated with cross-sectional mean velocities with ranges of 1.6 – 2.1, 4.0 - 5.0, and 5.8 – 6.8 cm s-1, respectively; the mean velocities were: 1.8, 4.5, and 6.1 cm s-1. Four aluminium screens (hole diameter 0.6 cm; 17,500

holes/m2) were placed inside the flume upstream of the experimental vegetation to straighten the flow

(Fig. 1a). Additionally, the ADVs did not collect data at a sufficient rate to measure turbulence intensity.

However, we do verify that horizontal velocity varied through time (Fig. 4). The velocity fluctuations shown in figure 4 indicate that the flow in the flume was non-laminar.

2.3 Statistical Analysis

[17] The influence of biofilm, initial particle concentration, flow velocity, and stem density on capture rates (k) was tested with several mixed ANOVA models in a manner similar to Harvey and Bourget

[1995]. Subsets of the data from individual experiments were used to test for the influence of different factors. We could not test all the factors at once because we did not have data in all the factor levels

(replicate data). For example, this means that there was no data for control runs with a high stem density.

Mixed models were compared with a type = 3 ANOVA to determine the best fit ANOVAs. Fixed factors included: biofilm, initial particle concentration, flow velocity, and stem density. Particle diameter and R2 values (the fit of K to the raw data) were set as random blocking factors unless otherwise noted. Due to the presence of two levels within most factors, posterior comparisons were not needed.

3. Results

[18] Suspended particle concentration decreased during all experimental runs for many particle size classes. Particle capture (decay) rates were fitted to particle concentration in each particle bin size. The decay rates, k , are plotted against particle diameter for each experiment type (Figs. 5 and 6). Figure 6 is a rescaled replicate of Figure 5. Higher values for runs with plants compared to control runs show that greater particle removal when plants were present. The control runs also demonstrate a decline in suspended particles, albeit it small, during nearly all runs (i.e. a positive value of k). With one exception

(6.1 cm s-1; SPC 16.6 µL L-1; Fig. 5C), runs with biofilm demonstrated the highest rates of particle removal. Plants without biofilm and with high stem densities exhibited the second highest removal rates

followed by low stem density plants and control experiments without plants (Figs. 5 and 6). Perhaps the strongest response seen in Figures 5 and 6 is the increase in k with particle diameter for most runs.

[19] Figures 5 and 6 show that the high SPC runs exhibited a different removal trend with particle size compared to low SPC runs. For high SPC runs, decreases for 1.25 to 4µm particles, increases for 4 to

~15µm particles and then plateaus for particles greater than ~30µm. Additionally, high SPC runs exhibit an increase in of 3 - 7µm particles. Low SPC runs show that increased roughly linearly with particle size. The low SPC control runs exhibit lower removal rates compared to the high SPC control runs.

Additionally, low and medium flow rate low SPC runs with high density plants and no biofilm exhibited similarly low values. We speculate that this is due to a change in particle source between runs 5c and

6a. While the particles used throughout the experiments were collected from the same location using the same method, the second batch of particles was collected on a different date. This change in particle source/collection date may be a source of variation in our results and indicates that particle type could be important to trapping.

[20] Table 3 lists the percent of particles removed for several size classes and experimental treatments over one hour. These percent removal values were calculated using eq. 6 and the k values. Table 3 demonstrates that treatments with plants removed a higher percentage of particles compared to control treatments. The percentage of removed particles peaked above 90 percent for the 53.2 to 62.8µm size class while particles between 23.2 - 27.4µm peaked at over 80 percent removal. There is a negative removal percentage for 4.44 - 5.24 µm particles from the high SPC experiments, indicating that the 4.44 -

5.24 µm size class increased during the experiment.

[21] ANOVA mixed models reveal statistical differences between treatments/experimental conditions.

Treatment effects were tested in multiple models due to the absence of replicate data. The models presented here were chosen out of a variety of models that best fit the data. Mixed model 10 tests the effects of the presence of plants compared to control, flow velocity, and initial particle concentration on

raw k. For mixed model 10, control vs. plants, flow velocity, and initial particle concentration were set as fixed factors while particle size and k goodness of fit (R2) were set as blocking factors; results from all experimental runs except those with biofilm were included in mixed model 10. Mixed model 10 found that the presence of plants, initial particle concentration, flow rate, and most of their interactions affect raw k in a statistically significant way (table 4). Mixed model 11compared high stem density and low stem density treatments. For mixed model 11, our experimental results were separated into subsets such that only high stem density and low stem density treatments were compared with fixed factors set as stem density and initial particle concentration and blocking factors set as particle size and R2. Table 5 shows that both stem density and initial particle concentration statistically significant to raw k. ANOVA mixed model 2 tested the influence of biofilm on raw k by comparing high density plant experiments with and without biofilm (no control runs or low plant density experiments were included in this test). Biofilm, flow velocity, and initial particle concentration were fixed factors while particle size and R2 were taken into account as blocking factors. Table 6 shows that biofilm, flow velocity, initial particle concentration, and most of their interactions are statistically significant to raw k. In summary, all the tested factors: biofilm, flow velocity, initial particle concentration, the presence of plants, and plant density are statistically significant to particle capture.

[22] The goodness of fit (R2) of the particle removal rates (k) were evaluated as a function of starting particle concentration, flow velocity, and particle diameter and are plotted in Figure 15. Figure 15 demonstrates that the R2 values suggest that our first order decay model is best for particles greater than

~11µm. Many particles below ~11µm result in low R2 values. Values for R2 that are close to 1.0 suggest a very good fit for the selected first order decay approach (Figure 15).

4. Discussion

[23] For the purpose of comparison to PNPA theory, we apply corrections to the k values presented in

Figures 5 and 6. We refer to the k values reported in Figures 5 and 6 as raw k and introduce three new categories of k: adjusted k, modeled k, and PNPA k. Both adjusted k and modeled k are calculated from

raw k according to the methods described below. PNPA k is calculated from PNPA theory using Eqs. 1 and 3. It is worth recalling that PNPA theory formulates an empirical relationship for particle capture efficiency and scales capture efficiency to model first order decay, kPNPA, according to Eq. 3. For clarity, in the section below we calculate adjusted k and modeled k from raw k and compare modeled k to PNPA k.

[24] To examine our results within the context of PNPA theory we first subtract the effects of settling and flocculation from raw k to create adjusted k and then scale adjusted k from the flume to a theoretical floodplain. To do this first, control runs are used to estimate background settling and flocculation in the flume and the reservoir. We subtract the influence of settling and flocculation from raw k to create adjusted k ( a k value that represents particle removal without the influence of settling or flocculation)

i i i i i according to kp k k ave , where i is the particle bin size, k p is adjusted k, k is raw k from runs with

i plants, and kave is k (control runs) averaged by bin size within each experimental treatment .

[25] Secondly, to compare adjusted k to PNPA k we scale from the flume to a theoretical floodplain. To do this we consider the time rate of travel in the flume and on a theoretical floodplain. In the flume travel

V time of the particles in suspension through the flume system is proportional to T , where V is the total Q T volume of water in circulation in the flume and Q is the flow rate. On a theoretical floodplain travel time

l is proportional to , where l is the length of the floodplain and u is the velocity of the flow. Thus, to u scale the flume to a theoretical floodplain we divide the travel time of particles in the flume by the travel

Vu time of particles in a theoretical floodplain such that the scaling factor, sf  T . By considering our Ql flume system where l is the length of plants in the flume and Q uwh , where w is the width of the

VVTT flume and h is the depth of water, sf , where Vp is the volume of water in the synthetic lwh Vp

vegetation. Now we apply sf to adjusted k to create modeled k (which can be compared to PNPA k).

Modeled k is,

ii' kp k p V T/ V p . (6)

Raw k, adjusted k, and modeled k are compared in Figure 7 (for a representative run). Raw k values are larger than adjusted k for each particle size because the effects of settling and flocculation have been subtracted from raw k to create adjusted k. Modeled k values are mostly larger than adjusted k and show the influence of scaling factor sf.

[26] Now that the differences between raw k, adjusted k, modeled k, and PNPA k have been described we compare modeled k to PNPA k in figures 8 and 9. Figures 8 and 9 show the modeled rates of particle

i ' decline, k p , for each experimental treatment. Figure 9 is a rescaled replicate of figure 8. The high SPC runs show variation in modeled k with particle size. For example, figure 9a-c shows that modeled k increases with particle size for <20µm particles. Figure 9d-e exhibits an increase in modeled k with particle size for particles 5 - 11 µm and a decrease in modeled k with particle size for particles 12- 20 µm.

Figures 8 and 9 also show that modeled k declines as flow velocity increases.

[27] We plot the modeled k results with PNPA k in figures 8 and 9. PNPA k was calculated using equations 1 and 3 and stem densities of 2724 – 7209 stems m-2 and cylinder diameters of 0.003 m

(straight lines, Figs. 8 and 9). Figures 8 and 9 show that modeled k is approximately equal to PNPA k for particles less than approximately 50µm. We observe a decrease in modeled k values with flow velocity; this observation contrasts with the trends of PNPA k. The trend of decreasing modeled k with flow velocity is especially apparent in figure 9a-c.

[28] We report the percent change in particle concentration for specific bin sizes based on modeled k values in table 7. By calculating the percent change in particle concentration (from the modeled k values) for flow through vegetation for one hour, we determined that there can be up to a ~99 percent reduction in

particles for 53.2 - 62.8µm particles. 23.2 - 27.4µm and 10.2 - 12.0µm particles show a decline in particles up to 95 and 85 percent, respectively. Like the previously reported percent changes in particle concentration based on raw k, high SPC treatments show an increase of up to 28 percent over one hour for

4.44 - 5.24µm particles. 1.94-2.29µm particles also demonstrate a decline of up to 13% of particles over one hour based on modeled k values (table 7).

[29] For floodplain restoration efforts it is useful to consider the length of wetland over which a certain fraction of particles are removed from suspension; we choose the length where fifty percent of particles are removed from suspension to maintain consistency with Palmer et al. [2004]. This is length is called

L50 and is calculated from flow velocity, u , and particle decay rate, k, such that,

L50  0.693 u / k . (8)

[30] This model equation matches our lab results that particle concentration decayed

exponentially with time (fig. 17). Moreover, L50 can evaluate different trapping mechanisms separately or together. For example, we evaluate for the effects of particle capture by plants

alone ( L50(p ) ), settling/flocculation ( L50(s,f) ), and the combined effects of settling/flocculation and

particle trapping by plants ( L50(s , f , p ) ). The effects of settling/flocculation and trapping by plants

can combined such that, L50(s , f , p )0.693 u / ( k s , f k p ). (9)

where ksf, is the empirically derived value for the settling/flocculation rate from our control experiments

and k p is the empirically derived value for trapping by plants. We examined four particle sizes: 9.9, 16.3,

43.9, and 85.2 µm. However, it is worth noting that the L50 values reported here should be applied to natural floodplains with caution; these L50 values were derived from our experimental flume system and may yield different results compared to a natural floodplain because of a differing starting PSD, particle type, vegetation type/geometry, temporal properties of the natural floodplain/biofilm.

[31] Our experimental results show that L50(p ) increases with flow velocity and decreases with particle size (Figs. 10a - 13a). This means that the effectiveness of trapping by plants increases as flow velocity decreases and particle size increases. The observation that particle trapping by a collector decreases with increasing flow velocity is consistent with findings by Wu et al. [2011]. Wu et al. [2011] demonstrated that capture efficiency of 1.05µm colloids decreased as flow increases from 0 to 0.2 cm s-1. This negative correlation between capture and colloid velocity has been demonstrated for spherical collectors in porous media [Camesano et al., 1998; Compere et al., 2001; Kretzschmar et al., 1997].

[32] L50(s,f) also decreased with increasing particle size and increased with flow velocity, i.e it required a longer distance for fifty percent of the particles to be removed from the water. This tendancy for particles to be more effectively removed by settling as flow velocity decreases and particle size increases is well known. However, it is worth noting that for 9.9µm particles is less than . This demonstrates that trapping by plants may be more effective than settling and flocculation combined for 9.9µm particles

(fig. 10a). The combined effects of settling, flocculation and plant capture result in the smallest L50 distances (Figs. 10a-13a).

[33] Figures 10b-13b show predictions by PNPA theory (stem diameter= 0.003m, particle density =

2300 kg m-3, eq. 1, 3, 4, and 6). Figures 10-13 show that compared to our calculations using the

experimental data, PNPA theory estimates a larger L50(p ) for 9.9µm particles, similar for 16.32µm

particles, and smaller L50(p ) values for 43.9 and 85.2µm particles. Unlike our calculated L50 values, the modeled PNPA L50 values show a decline in with flow velocity (Figures 10-13). The PNPA model predicts that the trapping rate particles by vegetation increases with flow velocity. This is in contrast to our findings and those by Wu et al. [2011] which show that the time rate of trapping by direct interception decreases with flow velocity. The difference may be partly due to differences in particle size used in experiments by Palmer et al. [2004] compared to the sizes examined by Wu et al. [2011] and this

study; 194µm compared to less than 109µm particles for this study and particles <10.5µm for Wu et al.

[2011]. However, when settling is incorporated into the PNPA model, the PNPA model trends in the same direction (increasing L50 with flow velocity) as ours and reports similar or slightly lower L50 values. To

reiterate, our results demonstrate an increase in L50(p ) (decrease in trapping) with increasing flow velocity while PNPA theory predicts a decrease in (increase in trapping).

[34] The turbulence intensity inside fields of vegetation may also be important to the rate of direct interception of particles. Nepf [1999] demonstrated that turbulence intensity initially increases as stems are introduced to a bare channel. However as stem density increases, Nepf [1999] showed that the turbulence intensity decreases due to a reduction in mean flow velocity. Our experimental set-up differs from Palmer et al. [2004]’s and Wu et al. [2011]’s due to the presence of a vegetative array in place of a single cylinder. We measured a decrease in the mean flow velocities with the presence of vegetation. In addition to trapping, this change in mean flow velocity may have enhanced deposition and resulted in higher trapping rates in a vegetated flume – thereby differentiating our results from those by Palmer et al.

[2004]. However, it is worth noting that the results by Wu et al. [2011] show the same trend of trapping rate with flow velocity although Wu et al. [2011] examined flow across a single cylinder by colloidal particles in laminar flow. PNPA theory predicts shorter L50(p) distances for larger particles compared to our results (figs. 10 - 13). This is counterintuitive to our prediction that a vegetated array would enhance settling of larger particles more than a single cylinder. Modelers for individual projects may wish to validate PNPA theory before application to particles less than 109µm due to our results that suggest that other mechanisms may be at play.

[35] Particle flocculation, both in the flume and the reservoir, complicates our measurements of particle removal. Through flocculation particles can transfer between size bins without actually being removed from suspension. However, particle flocculation occurs naturally in emergent floodplains and wetlands.

We observed what appeared to be flocculation during the flume experiments, with particle concentration

repeatedly increasing in several size bins. Fig. 14 shows the change in particle concentration from the beginning to the end of experimental run 23b. The concentration of 3 - 7µm particles increased during the experimental run while the concentration of smaller and larger particles decreased (Fig. 14). This trend was observed in most other treatments with a high SPC. There were no other particle sources during our experimental runs and thus the particles must have been coming from within the flume system as particles changed size.

[36] Zeta potential analysis showed that flocculation was possible for our experimental conditions

(Supplementary material, table A). However, based on the measured zeta potential values other particle sizes would be conducive to floc. We observed a larger increase in 3-7µm particles in experiments with plants compared to without (table 3). This indicates that the presence of plants may increase particle flocculation presumable by altering the turbulence intensity to allow for flocculation.

[37] Zeta potential measurements from Tahoe area streams indicate that the zeta potential for our experiments was similar to that of natural water bodies (Supplementary material, table A). Because zeta potential is an indicator for the stability of particles in a solution, it is reasonable to assume that flocculation in some natural water bodies will occur to a similar degree as did in this study.

[38] Additional uncertainty in our experimentally measured particle trapping rates may have originated from particle settling on the vertically oriented plant surfaces. This is because we cannot isolate the effect of settling on the vegetation stems from direct interception. It is also worth noting that synthetic vegetation may not have similar surface properties to real vegetation. While the presence of biofilm may make the synthetic vegetation more analogous to real world vegetation, comparing synthetic vegetation to synthetic vegetation with biofilm may not be the same as a comparison between real vegetation with and without biofilm. Biofilm and real vegetation will likely have widely varied surface properties based on their geometries, species, and life stage. Particle types in the suspended load in natural environments will

also be widely varied. However, road runoff in suspended load of particular concern because to the presence of sorbed contaminants on those particles.

5. Conclusions

[39] This study examines the process of particle removal from a continuous distribution of particle sizes in a laboratory flume using submerged, synthetic vegetation. The particle size distribution embraced the size range 1.25 – 109µm, and was provided from road dust removed from roadways in the Lake Tahoe basin. Particle concentration in re-circulating water was found to decay with time in the absence and, to a greater extent, in the presence of synthetic vegetation; a first order decay model for particle concentration matches this trend well for particles larger than ~11µm. The rate of decay increased with particle size for most treatments. We found that synthetic vegetation captures particles at higher rates compared to a bare flume. Flow velocity, initial particle concentration, stem density, and presence of biofilm were found to have statistically significant effects on the rate of particle capture. The rate of particle trapping increased with stem density and the presence of biofilm and decline with increasing flow velocity. In vegetated flows a higher flow velocity resulted in more passes through vegetation; yet, the overall rate of capture was less than in slower flows even after the effects of settling in a bare channel were removed. This decline in direct particle capture with increasing flow velocity contrasts with results by Palmer et al.

[2004] but is in alignment with those by Wu et al. [2011]. From our first order decay model we calculated the percentage of particles removed by various flow treatments over one hour and found that vegetation can remove up to 99 percent of suspended particles. We also observed an increase in the concentration of

3-7µm particles during high SPC runs. Our findings suggest that the presence of vegetation enhances particle capture across all particle classes that were tested. We were unable to differentiate between enhanced removal due to settling vs. direct interception. However, our findings show that lower flow rates, higher stem densities, and the presence of biofilm all enhance trapping. The initial particle concentration was found to have a complex, but significant effect on the rate of particle capture. We hypothesize that this is due to flocculation and transfer of particle between size bins.

Acknowledgements

This work was supported by a SNPLMA grant. Kristen Fauria is supported in part by the National

Science Foundation Graduate Student Fellowship Program (NSF GRFP). Deret Kehlet and Bill Sluis are gratefully acknowledged for their technical assistance building the flume experimental set-up and Aileen

Luo is much appreciated for her assistance running the experiments. We thank William Fleenor for his thoughtful input to the flume design and for providing the ADVs and thank Russell Wigart for supplying the road dust particles and for thoughtful discussion. We acknowledge Emily Parry and Thomas Young for their assistance with zeta potential analysis and thank Debbie Hunter for imaging the road dust particles. We are in debt to Rachel Kerwin for her guidance through the statistical analysis of the data presented here. Thoughtful reviews by John Reuter improved this manuscript.

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7. Supplementary Material

Water samples were taken during most experimental runs and analyzed for zeta potential. Zeta potential is the charge of the electrokinetic double layer that characterizes the repulsive or adhesive force between particles and tendency for particles to flocculate [eg. Yao et al., 1971]. Colloidal systems with highly positive or negative zeta potentials tend to have colloids that stay in suspensions while systems with zeta potentials close to zero contain colloids that coagulate and stick to surfaces. Zeta potential values during the experimental runs ranged from -7.10mV to -18.14mV with an average value of -12.51mV and standard deviation of 2.18 mV. To compare these values to zeta potential values in natural water bodies, water samples were taken at several streams in the Tahoe basin and analyzed for zeta potential. The average zeta potential found in Lake Tahoe stream water was -11.74 with a standard deviation of 2.22

(Table A, supplementary material). The zeta potential of the flume water is comparable to natural streams.

Both zeta potential values are in the range that indicates intermediate particle stability/flocculation.

Table A: Zeta Potential of streams in the Tahoe Basin and in Lake Spafford UC Davis, CA (ARB).

Sample Location Sample Date Zeta Potential (mV) Conductance PH

Incline Creek (ICC) 4/25/2013 -9.08 140 6.83 Third Creek (TCC) 4/30/2013 -9.23 68 6.39 Third Creek (TCC) 4/25/2013 -11.54 102 6.16 Trout Creek (TRS) 4/25/2013 -11.38 96 7.22 Trout Creek (TRS) 4/30/2013 -11.68 77 6.27 Upper Truckee (UTS) 4/25n/2013 -16.10 57 6.28

Third Creek (TCC) 5/3/2013 -13.18 70 6.37

8. Tables

Table 1: Comparison of experimental conditions to natural floodplains. Values for natural floodplains are from: flow velocity: e.g. Valiela et al. [1978]; flow depth: Kadlec [1990]; Vegetation Diameter: Andrews Tahoe measurements; Collector Reynolds number: Kadlec [1990]; Suspended Particle Concentration: LTIMP values; Vegetation density: Valiela et al. [1978].

Particle Trapping Variable Experiments Natural Floodplain Channel average flow velocity (cm s-1) 1.8 ; 4.5; 6.1 0-25

Flow depth (cm) 14 - 17 1 - 50

Stem diameter (cm) 0.1- 0.4 0.1 - 3.0

Stem (collector) Reynolds number 18 - 558 5 - 1000

-1 Suspended particle concentration (µL L ) 9.61 - 49.64

Vegetation density (stems m-2) 0; 2724;7209 0 – 2500

Table 2: Summary of experimental treatments showing flow rate, velocity, and initial particle concentrations (1.25-250µm diameter). ND indicates no recorded flow data. Flow along width and length of flume was assumed to be homogenous. Uncertainties reflect one standard deviation.

Run ID Date Treatment Flow Rate Velocity Starting particle (m3/s) (m/s) concentration (µL L-1) 1a 9/6/2012 No plants 0.0012 0.018 29.07 0.35 1b 9/7/2012 No plants 0.0012 0.019 34.62 1.15 1c 9/7/2012 No plants 0.0012 0.019 29.52 0.46 2a 9/7/2012 No plants 0.0032 0.047 27.54 0.45 2d 9/10/2012 No plants 0.0032 0.045 15.02 0.32 2e 9/10/2012 No plants 0.0032 0.045 12.50 0.25 3a 9/10/2012 No plants 0.0046 0.063 15.34 0.59 3b 9/11/2012 No plants 0.0046 0.061 15.53 0.63 3c 9/11/2012 No plants 0.0046 0.061 14.25 0.19 4a 9/13/2012 High density plants no biofilm 0.0012 0.018 4b 9/13/2012 High density plants no biofilm 0.0012 0.018 4c 9/13/2012 High density plants no biofilm 0.0012 0.018 5a 9/14/2012 High density plants no biofilm 0.0032 0.045 18.47 0.51 5b 9/14/2012 High density plants no biofilm 0.0032 0.045 13.08 0.51

5c 9/14/2012 High density plants no biofilm 0.0032 0.045 13.32 0.43 6a 9/17/2012 High density plants no biofilm 0.0046 0.059 11.57 0.26 6b 9/17/2012 High density plants no biofilm 0.0046 ND 23.12 0.78 6c 9/17/2012 High density plants no biofilm 0.0046 0.060 14.69 0.41 7a 9/18/2012 High density plants dry biofilm 0.0012 0.018 13.50 0.78 7b 9/18/2012 High density plants dry biofilm 0.0012 0.018 10.42 0.31 7c 9/18/2012 High density plants dry biofilm 0.0012 0.018 11.09 0.23 8a 9/19/2012 High density plants dry biofilm 0.0032 0.044 15.59 0.69 8b 9/19/2012 High density plants dry biofilm 0.0032 0.044 9.61 0.14 8c 9/19/2012 High density plants dry biofilm 0.0032 0.044 11.93 0.47 9a 9/20/2012 High density plants dry biofilm 0.0046 0.059 16.49 0.67 9b 9/20/2012 High density plants dry biofilm 0.0046 0.059 10.20 0.41 9c 9/20/2012 High density plants dry biofilm 0.0046 0.059 9.57 0.27 10a 9/21/2012 No plants 0.0012 0.019 32.79 0.92 10b 9/21/2012 No plants 0.0012 0.019 25.76 0.54 11a 9/22/2012 No plants 0.0032 0.047 28.63 0.83 11b 9/22/2012 No plants 0.0032 0.047 25.68 0.97 12a 9/22/2012 No plants 0.0046 0.062 26.26 0.95 12b 9/22/2012 No plants 0.0046 0.062 23.13 0.56 13a 9/23/2012 High density plants no biofilm 0.0012 0.018 28.80 0.89 13b 9/23/2012 High density plants no biofilm 0.0012 0.018 24.24 0.53 14a 9/23/2012 High density plants no biofilm 0.0032 0.046 25.74 0.75 14b 9/23/2012 High density plants no biofilm 0.0032 0.046 26.68 0.86 15a 9/23/2012 High density plants no biofilm 0.0046 0.060 24.45 0.42 15b 9/23/2012 High density plants no biofilm 0.0046 0.060 49.64 2.17 16a 9/24/2012 High density plants dry biofilm 0.0012 0.019 33.94 1.57 16b 9/24/2012 High density plants dry biofilm 0.0012 0.018 38.83 1.71 17a 9/24/2012 High density plants dry biofilm 0.0032 0.047 29.35 1.38 17b 9/24/2012 High density plants dry biofilm 0.0032 0.047 31.28 0.90 18a 9/24/2012 High density plants dry biofilm 0.0046 0.060 26.49 0.93 18b 9/24/2012 High density plants dry biofilm 0.0046 0.060 25.81 0.66 19a 9/25/2012 Low density plants no biofilm 0.0012 0.017 18.45 0.52 19b 9/25/2012 Low density plants no biofilm 0.0012 ND 16.66 0.57 20a 9/25/2012 Low density plants no biofilm 0.0032 0.046 18.69 0.50 20b 9/25/2012 Low density plants no biofilm 0.0032 0.046 11.50 0.69 21a 9/25/2012 Low density plants no biofilm 0.0046 0.061 13.15 0.56 21b 9/26/2012 Low density plants no biofilm 0.0046 ND 24.60 1.07 22a 9/26/2012 Low density plants no biofilm 0.0012 0.019 32.88 1.31 22b 9/26/2012 Low density plants no biofilm 0.0012 0.019 25.00 0.56 23a 9/26/2012 Low density plants no biofilm 0.0032 0.048 25.31 0.77 23b 9/26/2012 Low density plants no biofilm 0.0032 0.048 22.33 0.86

Particle size (µm) 1.94-2.29 4.44 - 5.24 10.2 - 12.0 23.2 - 27.4 53.2 - 62.8 Particle Bin Number 5 10 15 20 25 Experimental treatment & run number Control; low SPC (2a) 2.94% 5.49% 12.94% 11.48% 4.96% Low density plants; low SPC (20a) 4.14% 15.73% 54.33% 73.73% 84.54% High density plants; low SPC (5a) 7.88% 14.59% 49.01% 52.11% 36.36% High density plants with biofilm; low SPC (8a) 7.19% 13.40% 55.79% 73.60% 87.42% Control; High SPC (11a) 2.91% -12.11% 42.04% 72.01% 77.27% Low density plants; high SPC (23a) 7.27% -11.43% 63.83% 75.94% 85.25% High density plants; high SPC (14a) 7.41% -26.52% 73.06% 84.44% 90.06% High density plants with biofilm; high SPC (17a) 5.06% -19.55% 76.29% 87.30% 91.79%

Table 3: Percent removal percentages for five particle sizes and eight experimental treatments. Total time of treatment is equal to one hour. These percentage values were determined from the raw k values for removal. The percentage of particles removed increases with most plant treatments compared to a bare flume.

Mixed Significance Model 10 Source of variation F -value dF dF residual P-value level Control 9.16 1 1081.00 2.54E-03 ** Flow velocity 10.29 2 1081.00 3.78E-05 *** Initial particle concentration 67.61 1 1069.63 5.73E-16 *** Control x Flow velocity 0.87 2 1080.55 4.20E-01 Control x initial particle concentration 17.52 1 1076.20 3.07E-05 *** Flow velocity x initial particle concentration 7.02 2 1079.11 9.39E-04 *** Control x flow velocity x initial particle concentration 5.27 2 1080.63 5.30E-03 ** Table 4: Mixed model 10. The data was subset such that only treatments without biofilm were considered. Control vs. plants, flow velocity, and initial particle concentration were set as fixed factors. Bin size and R2 values were random blocking factors. The model was fit to raw k values. All fixed factors are found to be significant as well as most of their interactions. . Significance codes (highest to lower significance): 0 level: ‘***’; 0.001 level: ‘**’; 0.01: ‘*’; 0.05 level: ‘.’.

Mixed dF Significance Model 11 Source of variation F -value dF residual P-value level Stem Density 36.07 1 666.09 3.13E-09 *** Initial particle concentration 22.65 2 666.56 3.04E-10 *** Stem Density x initial particle concentration 22.64 2 666.24 3.07E-10 *** Table 5: Mixed model 11. The data was subset such that only treatments with plants and without biofilm were considered. Flow velocity and stem density were set as fixed factors. Bin size and R2 values were random blocking factors. The model was fit to raw k values. All fixed factors are found to be significant as well as most of their interactions. . Significance codes (highest to lower significance): 0 level: ‘***’; 0.001 level: ‘**’; 0.01: ‘*’; 0.05 level: ‘.’.

Mixed dF Significance Model 2 Source of variation F -value dF residual P-value level Biofilm 3.08 1 801.00 7.95E-02 . Flow velocity 1.31 2 796.40 7.15E-04 *** Initial particle concentration 63.69 1 801.00 5.04E-15 *** Biofilm x flow velocity 0.01 2 799.86 9.91E-01 Biofilm x initial particle concentration 20.80 1 801.00 5.90E-06 *** Flow velocity x initial particle concentration 34.55 2 200.51 4.05E-15 *** Biofilm x flow velocity x initial particle concentration 12.05 2 200.20 7.10E-06 *** Table 6: Mixed model 2. The data was subset such that only treatments with plants and without biofilm were considered. Flow velocity and stem density were set as fixed factors. Bin size and R2 values were random blocking factors. The model was fit to raw k values. All fixed factors are found to be significant as well as most of their interactions. Significance codes (highest to lower significance): 0 level: ‘***’; 0.001 level: ‘**’; 0.01: ‘*’; 0.05 level: ‘.’.

Particle size (µm) 1.94-2.29 4.44 - 5.24 10.2 - 12.0 23.2 - 27.4 53.2 - 62.8 Particle Bin Number 5 10 15 20 25 Experimental treatment & run number Low density plants; low SPC (20a) 2.45% 25.72% 78.17% 95.12% 99.21% High density plants; low SPC (5a) 12.07% 21.86% 68.43% 72.26% 57.46% High density plants with biofilm; low SPC (8a) 10.32% 18.95% 78.33% 94.23% 99.41% Low density plants; high SPC (23a) 13.15% 8.29% 69.41% 56.27% 79.93% High density plants; high SPC (14a) 12.87% -28.76% 85.06% 85.54% 92.29% High density plants with biofilm; high SPC (17a) 6.91% -10.89% 89.32% 91.53% 95.36%

9. Figures

B C

Figure 1: A) plan and cross sectional view of flume experimental set-up. B) high density plants in the flume during an experimental run C) high density plants with dry biofilm before an experimental run.

Figure 2: Initial PSD of a high SPC run (filled circles; run 22a) and a low SPC run (open circles; run 19b) from LISST measurement. Both high and low SPCs have a peak in the PSD at ~11µm. The high SPC PSDs have a secondary peak at ~4µm and a trough at ~7µm.

Figure 3: Image of road dust (total magnification 200X – Phase imaging). Courtesy of Debbie Hunter – UC Davis Tahoe Environmental Research Center.

Figure 4: Time series longitudinal velocity profile of the flow rate settings in the flume. The measurements were taken 5cm from the flume base (10 cm ADV setting, 5cm blanking distance) at 5Hz. The data shown here are from ADV1 (Fig. 1A) and run numbers: 20B, 21B, and 22A. The variability in the longitudinal flow velocities shows that the flow in the flume was non-laminar.

Figure 5: Particle diameter (µm) vs. (s-1). Each treatment was repeated two or three times and each run is shown. A) Depth averaged flow velocity: 1.8cm s-1; SPC: 16.6 SD 6.5 µL L-1. B) Depth averaged flow velocity: 4.5 cm s-1; SPC: 16.6 SD 6.5 µL L-1. C) Depth averaged flow velocity: 6.1 cm s-1; SPC: 16.6 SD 6.5 µL L-1. D) Depth averaged flow velocity: 1.8cm s-1; SPC: 28.7 SD 6.1 µL L-1. E) Depth averaged flow velocity: 4.5cm s-1; SPC: 28.7 SD 6.1 µL L-1. F) Depth averaged flow velocity: 6.1cm s-1; SPC: 28.7 SD 6.1 µL L-1.

Figure 6: Rescaled replicate of figure 5 such that fine particles are more visible. Each treatment was repeated two or three times and each run is shown. A) Depth averaged flow velocity: 1.8cm s-1; SPC: 16.6 SD 6.5 µL L-1. B) Depth averaged flow velocity: 4.5 cm s-1; SPC: 16.6 SD 6.5 µL L-1. C) Depth averaged flow velocity: 6.1 cm s-1; SPC: 16.6 SD 6.5 µL L-1. D) Depth averaged flow velocity: 1.8cm s-1; SPC: 28.7 SD 6.1 µL L-1. E) Depth averaged flow velocity: 4.5cm s-1; SPC: 28. 7 SD 6.1 µL L-1. F) Depth averaged flow velocity: 6.1cm s-1; SPC: 28.7 SD 6.1 µL L-1.

Fig 7: Comparison of raw k, adjusted k, and modeled k from run number 14a. Adjusted k values are less than raw k values. Modeled k values are adjusted k values that have been amplified by the relative exposure of the water in the flume to vegetation in the flume.

Figure 8: Particle diameter vs. K’ (s-1) is plotted against predicted K’ (grey lines = 2724 stems/m2 plants; black lines = 7209stems/m2 plants) by Palmer et al. [2004]. The first row of plots represents a SPC of 16.6 +/- 6.5 µL L-1, the second row represents 28.7 +/- 6.1 µL L-1. The first, second, and third columns show data with depth-averaged flow velocities of 1.8, 4.5, and 6.1 cm/s respectively.

Figure 9: Rescaled replicate of figure 8. Particle diameter vs. K’ (s-1) is plotted against predicted K’ (grey lines = 2724 stems/m2 plants; black lines = 7209stems/m2 plants) by Palmer et al. [2004]. The first row of plots represents a SPC of 16.6 +/- 6.5 µL L-1, the second row represents 28.7 +/- 6.1 µL L-1. The first, second, and third columns show data with depth-averaged flow velocities of 1.8, 4.5, and 6.1 cm/s respectively.

A B

Figure 10: L50 due to capture by plants, settling, and particle flocculation in a typical wetland for 9.12 – 10.8 µm (mean 9.9 µm) particles. A) The results of our experiments (SPC =28.7+/- 6.1µL L-1). 3 B) L50 predicted by Palmer et al. [2004] (stem diameter = 0.003m, particle density = 2300kg/m ).

A B

Figure 11: L50 due to capture by plants, settling, and particle flocculation in a typical wetland for -1 15 – 17.7 µm (mean 16.3 µm) particles. A) Our experimental results (SPC =28.7 SD 6.1µL L ) . B) L50 predicted by Palmer et al. [2004] (stem diameter = 0.003m, particle density = 2300kg/m3 ).

A B

Figure 12: L50 due to capture by plants, settling, and particle flocculation in a typical wetland for 40.5 – 47.7 µm (mean 43.9 µm) particles. A) The results of our experiments (SPC =28.7+/- -1 6.1µL L ) . B) L50 predicted by Palmer et al. [2004] (stem diameter = 0.003m, particle density = 2300kg/m3 ).

[2004] (stem diameter = 0.003m, particle density = 2300kg/m3 ).

A B

Figure 13: L50 due to capture by plants, settling, and particle flocculation in a typical wetland for 78.4 – 92.6 µm (mean 85.2 µm) particles. A) The results of our experiments (SPC =28.7+/- 6.1µL L-1) 3 . B) L50 predicted by Palmer et al. [2004] (stem diameter = 0.003m, particle density = 2300kg/m ).

Figure 14: Initial (solid line) and ending (dashed line) PSD from experiment 23b. More 3- 7µm particles are present at the end of the experiment compared to the beginning. We attribute this to the mechanism of flocculation. Particles in other bins decline from the start to the end of the experiment.

Figure 15: Heat map of goodness of fit (R2) of the first order decay model plotted against particle diameter and flow velocity. The plots use the same scale and are sorted by experimental treatment. The best fits occur for particles above ~ 8µm. Low SPC experiments also have poor fits for particles above ~50µm.