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Assessing Flow Rate and Nominal Pore Diameter as Parameters for
Predicting the Removal of Microorganisms by Ceramic Water Filters
Hem Pokharel, Zachary Shepard, and Vinka Oyanedel-Craver*
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sı Supporting Information
*
ABSTRACT: Ceramic water filters (CWFs) are manufactured worldwide
using local materials and infrastructure. In this study, we assessed flow rate
(FR) and nominal pore diameter (NPD) values as parameters to predict the
microbial removal of CWFs. Two empirical models (flow rate model, FRM,
and nominal pore diameter model, NPDM) were developed based on the log
removal values (LRVs) for total coliform obtained from the operation of
CWFs manufactured under controlled conditions in the United States. The
proposed empirical models were validated using CWFs manufactured in
Nepal and India. The models and principal component analysis (PCA) were
used to identify materials and processes in CWF manufacturing with the
highest impact on LRV. Our results showed that both the FRM and NPDM
have a good predictive capability with high coefficients of determination, R2,
and statistical significance (p) values less than 0.05. PCA showed that the burnout material was the most important variable in the
manufacturing process. Even though both models are appropriate to predict LRV from a CWF, the FRM and NPDM could have
slightly different applications at CWF factories. The FRM could be used to predict LRVs of CWFs manufactured in an already
operational factory as it fits with existing quality control procedures, such as measurement of flow rate. We propose that the NPDM
could be applied in the research and performance enhancement of CWFs as additional filter characteristics can be tested for the
potential to improve LRV. The use of either model could help factories improve the removal of bacteria by their CWFs.
KEYWORDS: Ceramic water filter, nominal pore diameter, point of use water treatment, quality control
1. INTRODUCTION
Point-of-use (POU) water treatment technologies are simple,
socially acceptable, and low-cost technologies that reduce the
prevalence of waterborne pathogens in underserved communities.1−4 These devices are typically made with locally available
materials (clay and burnout material) and infrastructure (mills,
hydraulic presses, kilns).5,6 Mechanical filtration is the primary
mechanism in CWFs, which means that microorganisms are
physically prevented from reaching the filter effluent by small,
tortuous pores.7−10 The microbial removal efficiency of a CWF
varies depending on the properties of the local materials
used.11−13 Most factories utilize flow rate measurements as a
fast and low-cost method of quality controls, but this has no
definitive relationship to microbiological removal performance.14−16
CWFs are ceramic microfiltration devices (depth filter) with
a highly heterogeneous pore size, porosity, discharge area,
tortuosity, raw material composition, and thickness.17 Pore size
is considered one of the most critical parameters for
determining turbidity and pathogen removal in porous water
filters.18 The pores in the CWFs are formed during the firing
stage when sawdust (or other combustible material) is
incinerated producing small and interconnected void spaces
creating a tortuous path.8,19,20 The pore size distribution of a
© XXXX American Chemical Society
CWF is affected by a number of variables, including burnout
material size, quantity, type, clay composition, and application
of the heat and pressure during manufacturing process.21,22
Characterization of the pore size distribution can be obtained
using techniques such as scanning electron microscopy or
mercury intrusion porosimetry.17 These tests cannot be
performed at the CWF factory level due to their high cost
and required equipment. The nominal pore diameter (NPD)
in microfiltration membranes is usually defined in terms of the
largest particle able to penetrate the membrane; it is 5−10
times smaller than the apparent pore diameter measured using
direct microscopic examination of the membrane.23 The
technique for calculating NPD could be applied at CWF
factories as it is low cost and can be calculated using variables
that can be easily measured with the infrastructure of the CWF
factory.
Received: November 12, 2020
Revised: February 14, 2021
Accepted: February 17, 2021
A
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Table 1. Range of Values for Manufacturing Parameters for CWFs Manufactured in the USA
Parameters
Range
Sawdust range
Sawdust sieve opening size
Clay range
Water range
Pressure range
Heat range
160−440 g
<0.42 to >1.77 mm
560−840 g
270−550 g
20 psi (1000 lbs/50 in2) to 300 psi (15,000 lbs/50 in2)
590−1180 °C (cone 022 to cone 4)
which temperature was increased 50 °C per hour until 120 °C.
Then, the temperature increased at a rate of 120 °C per hour
until reaching the desired maximum firing temperature. After
reaching the maximum firing temperature, the CWFs were
cooled to room temperature. Six parameters of CWF
manufacturing were analyzed for their effect on LRV during
this study: amounts of sawdust, clay, and water; sawdust sieve
size opening, molding pressure, and maximum firing temperature. Each parameter was studied using a fit developed from
the LRVs of at least four CWFs.
All fired CWFs were visually inspected (for cracking or
warping). CWFs that did not pass the visual inspection were
not included in the tests. For those CWFs that passed,
diameter (cm) was measured at two locations on the disks, and
thickness (cm) was measured at four locations on the disks.
These measurements were averaged to generate the measured
diameters and thicknesses of the disks used in subsequent
calculations. The disks were soaked in water to determine
porosity, which was calculated by dividing the volume of water
(mL) absorbed in half an hour of soaking (volume of void) by
the volume of the disk (mL). Density was calculated by
dividing the fired disk weight (g) by the disk volume (mL).
After the weight and shape measurements, CWFs were placed
at the bottom of a filter holder using wax O rings. The
discharge diameter (cm) was measured at two locations on the
disks to determine the discharge area. The flow rate (liters per
hour, LPH) was measured twice with a constant head at 19 cm
(maintained with a hand pump). Constant head was
maintained by hand; water was added manually to keep a
constant head volume. Variations in head height were low
(about 14%) and were considered negligible for this experiment. Velocity was measured by dividing the flow rate by the
discharge area of the CWFs. These measurements will be used
later for developing the empirical models.
The clay used to manufacture CWFs in Nepal and India was
locally sourced. Both CWF factories in Nepal and India sieved
clay and sawdust using 0.595 mm screens. The CWFs
manufactured in Nepal and India used sawdust collected
from nearby sawmills. The wood source of these mills is
unknown. The clay, sawdust, and water mixtures were
prepared using the same method used for the USA CWFs.
However, instead of a hydraulic press, a screw press without a
pressure gauge was used to press CWFs in Nepal and India. In
both locations, pressed CWFs were dried using a method like
the one used for the USA CWFs. The factory in Nepal used
kerosene and sawdust for firing, while pit firing was used in
India. For the Nepal and India operations, molding pressure
and maximum firing temperature data were unavailable. The
same visual inspections and measurements were performed in
the Nepal and India factories as those described for the USA
CWFs. CWFs were then shipped to the USA for testing.
Filter Testing. Sudbury River water (about 19 L) was
collected on the same day the test was conducted. After
In this study, we present two empirical models based on the
flow rate (FR) through the CWF and nominal pore diameter
(NPD) of the ceramic. The purpose of these models is to
predict the microbial removal of CWFs as a function of
manufacturing variables or filter properties. The flow rate
model (FRM) and nominal pore diameter model (NPDM)
and principal component analysis were used to determine the
most important manufacturing variables in the production of a
CWF. The FRM and NPDM were validated using CWFs
produced at factories in India and Nepal. The models will be
able to assist CWF factories with quality control and
development of new filter designs. While the models developed
here will only be able to accurately predict the LRV of the
filters used in this study, we provide the necessary information
for CWF manufacturers to develop models applicable to their
filters.
2. MATERIALS AND METHODS
A total of 92 CWFs were used to develop the flow rate and
nominal pore diameter empirical models. Of the 92 filters used
in the testing, 76 were from the USA, 10 were from India, and
6 were from Nepal (the CWFs manufactured in India and
Nepal were used for validation). During the production of the
filters, the composition and firing conditions were varied to
understand the effect of each manufacturing variable. These
variables were categorized as raw materials (clay, sawdust,
water content, and sawdust diameter), manufacturing
(pressure during molding and temperature during firing),
and geography (Sudbury, USA; Varanasi, India; and Thimi,
Nepal).
CWF Manufacturing. CWFs were manufactured under the
ranges of conditions listed in Table 1. Table S1 contains the
specific manufacturing location, materials, molding pressure,
and firing temperature for each disk used in the testing.
Sawdust was obtained from a sawmill in Hopkinton, MA. The
exact type of sawdust is unknown, but the facility handles
mostly pine and oak lumber. Red art clay was purchased from
Braintree Pottery Facilities (Braintree, MA), and all USA
CWFs were manufactured at Village Forward (previously
Solutions Benefiting Life), a nonprofit organization in the
Town of Sudbury, MA. Both clay and sawdust were sieved
using a sieve with openings listed in Table S1 and then
manually mixed for 10 min. After clay and sawdust were mixed,
water was slowly added and mixed manually for 30 min. The
amounts of sawdust, clay, and water are listed in Table S1. One
kilogram of the mixture was then pressed into a disk-shaped
mold (20 cm diameter and 8 cm height) using a hydraulic
press that applied the pressures listed in Table S1. The pressed
CWFs were dried indoors for a week protected from sunlight
to ensure uniform drying. After drying for 1 week, they were
placed in the sun from mornings to evenings and kept inside
the facility at night for a second week. Dried CWFs were fired
in a kiln. The firing program consisted of a 2 h drying period in
B
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Figure 1. Data sets for establishing FRM and NPDM from USA manufactured disks. (a) LRV as a function of flow rate. (b) LRV as a function of
NPD. Samples were grouped in 5 LPH or 1 μm intervals and averaged to generate the data points in (a) and (b), respectively. Error bars represent
the standard deviation of the samples within the 5 LPH or 1 μm intervals. The data points here are used to establish the FRM and NPDM
displayed in eqs 3 and 4
The calculated NPD determines the size of the particle (in
this case the microorganism) permitted through the tortuous
path of the CWF. This makes it an important factor in the
determination of the log removal value (LRV) of the filter.
The flow rate (FR) and nominal pore diameter (NPD)
empirical models were built using the LRV of total coliform for
the CWFs manufactured in the USA using an industrial grade
red art clay and modifying the ratios of raw materials and the
manufacturing parameters. This stage was also used to assess
the impact of the manufacturing variables on the microbial
removal performance as LRV. The variables studied here
included raw materials (sawdust diameter, sawdust:water ratio,
sawdust:clay ratio, and clay:water ratio) and manufacturing
parameters (pressure during molding and firing temperature).
The LRV of each scenario was plotted as a function of either
NPD or FR (calculated based on the above equations), and the
fit of the appropriate empirical model to the data points was
determined. Principal component analysis (PCA), a modeling
technique that can be utilized to find patterns in complex data
sets, was used to determine the most important variable in the
filter manufacturing process.28
The derived empirical models were then applied to LRV
data collected from CWFs produced in Nepal and India.
Regression analysis was used to determine the robustness of
the fit and has been used widely in the literature for making
predictions about drinking water quality.29−32 Three aspects of
the fit were assessed: selection criteria (outcome measures,
such as flow rate and NPD, on LRV predictability), selecting
the predictor (efficiency, R2, residual), and assessing the
accuracy of the prediction (standard error of the mean, SEM,
and standard error estimate, SEE).33 The selection was later
confirmed using PCA in all variables (included in the
Supporting Information).
collection, it was left to settle and reach room temperature for
three hours. The average total coliform count at the Sudbury
River during the research period was 1525 total coliform CFU
with a range from 480 to 3480 total coliform CFU (only 4 days
during the experimental period) as shown in the Table S2 of
the Supporting Information. CWF holders were filled with the
Sudbury River water to a level of 19 cm. Two 100 mL samples
for total coliform counts were collected in clean, sterilized
beakers from each CWF after two hours of operation. MColiblue broth (Millipore, MA) was used to determine the
colony forming units (CFU) using an established methodology.24 Samples were incubated for 24 h at 25 °C. Unlike
most locally produced CWFs, the CWFs here were not
impregnated with colloidal silver since many previous reports
have addressed its importance.25−27 Additionally, it has been
shown that properties of the ceramic matrix are the main
contributors to microbial removal for CWFs.7−10
Determination of FR and NPD. Equations 1 and 2 show
the FR and NPD equations (respectively) used in this study.
These equations were derived based on the information in
Derivations 1 and 2 and Scheme 1, which can be found in the
Supporting Information.
FR = Q = −
NPD =
kA(ρgh)
μL
vμ
ρgτ
(1)
(2)
In eqs 1 and 2, FR and Q are variables for flow rate (cm3/s), k
is the intrinsic permeability of the clay disk (cm2), A is the disk
area (cm2), ρ is the water density (g/cm3), g is acceleration
due to gravity (cm/s2), h is the height of the water over the
disk cm), μ is the water viscosity (g/cm s), L is the disk width
(cm), NPD is the nominal pore diameter (cm), v is the
hydraulic conductivity of the water through the CWF (cm/s),
and τ is the tortuosity (unitless).
3. RESULTS AND DISCUSSION
Modeling LRV Using Flow Rate and NPD. All of the
models shown in this study are based on the data presented in
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Table 2. Error in Validation of the NPDM and FRM Using CWFs from India and Nepal
Statistic
NPDM India
NPDM Nepal
FRM India
FRM Nepal
Average error (%)
SEE
13.8
0.38
10.1
0.11
20.2
0.41
9.3
0.11
Table S3, which contains the porosity, tortuosity, flow rate,
pore size, and LRV values for each disk. Figure 1 shows the
relationships between the NPD (Figure 1a) or FR (Figure 1b)
and LRV. This LRV data were collected from 76 filters on
CWFs manufactured in the USA. The LRV values were
averaged at 1 μm and 5 LPH intervals for the NPD and FR
models, respectively. There were five samples per 1 μm or 5
LPH interval, on average. The standard deviation (σ) of the
LRV value was calculated at each interval. The LRV values
plotted in Figure 1 were used to fit an empirical relationship
between NPD or flow rate and LRV (eqs 3 and 4)
LRVFRM = − 0.588ln(flow rate) + 3.47
(3)
LRVNPDM = − 1.08ln(NPD) + 3.4
(4)
the prediction. The values are reported in Table 2. The average
error and SEE of the NPDM (11.9% and 0.25) were slightly
lower than the FRM (14.8% and 0.26), indicating that the
predictive capability of the NPDM was slightly better than the
FRM. The predictive capacities in both types of models were
better for Nepal CWFs than the CWFs made in India.
Differences in microbial removal between these groups could
be due to the local clays used the construction or differences in
manufacturing practices at the two factories.11−13,35 CWF
performance varies widely depending on where they were
constructed.11−13 Overall, both models showed strong
predictive capabilities with the NPDM being slightly better
based on the average error and average SEE.
In order to develop the FRM, CWFs were manufactured
under a range of conditions that produced filters with flow
rates of 5−25 L/h. These conditions were helpful in producing
the models that were used in the study but are not necessarily
representative of the conditions in the field. The acceptable
range for flow rates in most CWF factories is 1−5 L/h.25
Previous studies analyzing the microbial removal of ceramic
disks have used much lower flow rates (0.03−0.036 L/h)
because the area of those disks (11.3−33.2 cm2) is much
smaller than the disks used in the current study (314.2
cm2).25,26,36 The size of the disk filters used here increases the
volume of throughput, which allows a comparison to the range
of flow rates for ceramic pot filters manufactured in CWF
factories. High flow rate filters, such as those used to create the
FRM, have a lower LRV initially compared to filters with a
standard flow rate.37 While the flow rates measured here may
not be ideal for use in the field, they were essential for the
establishment of the FRM. The use of filters with flow rates
that are higher than what would usually be utilized in the field
allowed us to capture trends in LRV as they relate to higher
flow rates. The NPD range in these samples conform to pore
sizes measured in previous studies, which range between 2 and
5 μm.19,20 However, in previous studies, the pore sizes were
measured using mercury intrusion porosimetry, while here they
are calculated using eq 2. This represents a range of pore sizes
that are microbiologically relevant and can physically prevent
microorganisms from reaching the effluent of the filter.
The presented empirical FRM and NPDM could offer CWF
manufacturers a framework to improve their filters at a low
cost. The FRM and NPDM could have slightly different
applications for CWF manufacturers. The FRM could be
applied for quality control because it fits with procedures that
are already in place. This model could be used to estimate LRV
during the daily quality control tests. The FRM would be able
to provide simple, low-cost, and fast estimation of LRV for
daily factory operations.
The NPDM would be more applicable for CWF factories
that are adjusting their manufacturing practices to make
improvements in filter performance. The application of this
model allows CWF factories to predict how changing their
manufacturing parameters will affect performance. Tortuosity
is the main parameter in the NPDM. In the NPD equation (eq
2), tortuosity is the only variable that is related to the CWF.
Tortuosity (Derivation 2, line 8, Supporting Information) is a
These equations represent the flow rate model (FRM, eq 3)
and the nominal pore diameter model (NPDM, eq 4). Both
models satisfactorily fit the experimental data, with high R2
values and p < 0.05. Logarithmic curves had a better fit for the
data compared to other model types (linear, exponent, and
power) as shown in the Figures S4a and b of the Supporting
Information. The entire data set was used to determine the
most appropriate model type (Figure S1). Bins based on flow
rate and pore size were used for the model shown in Figure 1
because this provides a more accurate prediction of LRV.
Figure S2A and B show that predicting LRV using the bin
method provides a more linear regression (R2 = 0.93−0.94)
compared to using the entire data set (R2 = 0.53−0.55). The
cutoff values for the FRM and NPDM (25 LPH and 5 μm,
respectively) were selected based on the operational
parameters at CWF manufacturing sites. Most CWF factories
want their filters to have a flow rate ranging from 1 to 5 LPH
and a pore size around 2 μm, so including points higher than
25 LPH and 5 μm in our model would not be applicable to
filter manufacturers.
The accuracy of the models was based on the standard
deviation, SEM for each interval, percentage error, and the
residuals of the sample results. The standard deviation (σ)
describes the variability between individuals in a sample. SEM
describes the uncertainty of how the sample mean represents
the population mean.34 The average LRV standard deviation,
SEM of LRV, average percentage error of LRV, and average
residual for the FRM were 0.7, 0.22, 31%, and 0.18,
respectively. Similarly, average LRV standard deviation, SEM
of LRV, average percentage error of LRV, and average residual
for the NPDM were 0.57, 0.21, 29%, and 0.14, respectively.
Since the NPDM has lower values in each statistic described
above, it is a more statistically valid method of predicting the
LRV of the CWFs compared to the FRM. The plots of the
residuals for both empirical models are shown in the
Supporting Information (Figure S3).
The FR and NPD empirical models were validated using 16
CWFs made in Nepal (n = 6) and India (n = 10). The
validation data is shown in Figure S4. The average percentage
error and SEE between the measured and the predicted LRV
were calculated for both sets of CWFs (Nepal and India) and
both models (NPDM and FRM) to determine the accuracy of
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Figure 2. Effect of manufacturing variables on LRV plotted as a function of flow rate. These results are based on LRV measured for filters made in
the USA with manufacturing parameters varied within the ranges listed in Table 1. Here, each panel represents a set of CWFs made by varying
different parameters (Table 1). The parameters varied here are (a) sawdust diameter, (b) sawdust:water ratio, (c) clay:sawdust ratio, (d) clay:water
ratio, (e) firing temperature, and (f) pressure during molding. Markers represent the LRV of a filter with a known flow rate. These data points are
used to generate the lines of best fit, which are plotted in each panel. The equations and R2 and p values are also listed in each panel.
Figure 3. Effect of manufacturing variables on LRV plotted as a function of nominal pore diameter (NPD). The filters used to generate these
graphs were manufactured in the USA with manufacturing parameters varied within the ranges listed in Table 1. As in Figure 2, each panel
represents a set of filters with varied manufacturing parameters presented in Table S1. The variables in the panels are (a) sawdust diameter, (b)
sawdust:water ratio, (c) clay:sawdust ratio, (d) clay:water ratio, (e) firing temperature, and (f) pressure during molding. NPD was calculated for
each filter and plotted on the axis. The measured LRV for each filter is then plotted in the figure based on its NPD. These data points are used to
generate the lines of best fit, which are plotted in each panel. The equations and R2 and p values are listed in each panel.
Manufacturers could also change the thickness of the filter
by changing the pressing procedure or adding more raw
material to each filter. Changes in any of these manufacturing
parameters would affect the tortuosity of the filters, which
would influence the NPD and LRV. The model would allow
factories to predict how altering the raw materials in their
function of porosity, C2 (a dimensionless constant referring to
the length of the tortuous flow path), and the thickness of the
filter, L. These variables could change based on the
manufacturing parameters. Alterations to the ratio of clay to
sawdust or the grain size of the clay or sawdust could change
the porosity and C2, which can have an effect on tortuosity
(Derivation 2, line 8, Supporting Information). 25,38,39
E
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filters would affect the LRV. This would save time and money
by preventing a “trial and error” approach to improving LRV.
In order to apply any of the models discussed here in the
field, CWF manufacturers would need to generate filters with a
range of flow rate or NPD. The factories would then measure
the microbial removal of the filters and develop their own
versions of eqs 3 and 4. After adaptation with factory-specific
data, the models would be accurate for the filters produced at
the specific CWF factory. Once equations specific to the CWF
factory were developed, they could be used to determine
microbial removal from flow rate or NPD of new filters.
Effects of Manufacturing Parameters. Figures 2 and 3
show the effect of manufacturing variables on LRV. The raw
data for these figures can be found in Table S3. Samples
generated in each set were divided into bins based on their
flow rate (Figure 2) or NPD (Figure 3). The plots generated
by the collected data were then compared to either the NPDM
or FRM results. The best R2 values for both models were
obtained in the variables that include sawdust (sawdust
diameter, sawdust:water ratio, or clay:sawdust ratio). The
sawdust diameter showed the highest fit with respect to LRV:
R2 = 0.99 for the NPDM (Figure 2a) and R2 = 0.97 (Figure
3a) for the FRM. PCA (Figure S5a−f) shows that the sawdust
percentage and diameter had more of an impact determining
the LRV of the CWFs compared to the other manufacturing
variables.
Figure 3 shows that the NPDM is most strongly correlated
to variables involving the burnout material: sawdust diameter,
sawdust:water ratio, and sawdust:clay ratio. The link between
pore size, burnout material, and LRV has been demonstrated
in previous studies, but this is the first mathematical
relationship demonstrated.25,38,39 PCA analysis (Figure S5)
showed that the sawdust diameter and percentage was the
most important variable for the LRV. Changing the size and
quantity of the sawdust will have the largest impact on the
LRV by altering the pore structure of the CWF. CWFs utilizing
less sawdust or sawdust of a smaller grain size have a higher
LRV because they have smaller pores.8,35−37 A smaller average
pore size increases the likelihood of microorganisms adsorbing
to or becoming trapped in the ceramic.7,14,25,36,40
Every CWF factory utilizes a unique set of raw materials and
manufacturing procedures in the construction of their filter.
Some factories use burnout materials that were not used in this
study, such as rice husks, and every factory uses a different
source of clay.7,25,40 The unique set of construction materials
and procedures used at each factory limits the applicability of
the models developed in our study. This study is limited to
demonstrating the changes in LRV caused by sawdust and red
art clay. The principal components in their filters may also be
different if the raw materials are significantly different
compared to those used in our study. For example, the use
of rice husks instead of sawdust could change the results as rice
husks have different properties compared to sawdust. Our
results can be used to understand trends, such as the
importance of the grain size and amount of sawdust over
manufacturing procedures (such as firing temperature and
molding pressure) for microbial removal, but cannot be related
to specific changes in LRV at filter factories without prior
optimization.
Article
NPD- and FR-based models. These models can be applied
once they have been adapted with factory-specific microbial
removal data. The validation of the models using filters
produced in India and Nepal demonstrates that these models
may be widely applicable. While the NPDM is more accurate,
we would recommend the FRM for application for quality
control at CWF factories because it works with the existing
procedures. This model could be useful for CWF factories that
need an easy method to estimate LRV for their filters using
information they are already measuring (flow rate). The
NPDM could be useful for CWF manufacturers who wish to
redesign their filters to improve the LRV. In this application,
the accuracy of the model will help guide improvements in the
filter design. This model also highlights the importance of the
pore diameter in the removal of microorganisms. Control over
the NPD will help CWF factories control their LRV. PCA and
model results show that the raw materials used in the
construction of the filters are more likely to effect LRV
compared to differences in the manufacturing practices. CWF
manufacturers should focus on engineering the burnout
materials to make targeted improvements in LRV. The results
of this study have the potential to improve the quality of CWFs
produced and therefore available water quality and related
health impacts in underserved communities.
■
ASSOCIATED CONTENT
sı Supporting Information
*
The Supporting Information is available free of charge at
https://pubs.acs.org/doi/10.1021/acsestengg.0c00216.
Raw data for manufacturing and bacterial removal,
derivations for equations used here (with helpful
schematics), and plots of residuals, principal component
analysis information, and regressions for predicted vs
actual LRV (PDF)
■
AUTHOR INFORMATION
Corresponding Author
Vinka Oyanedel-Craver − Civil and Environmental
Engineering, University of Rhode Island, Kingston, Rhode
Island 02881, United States; Email: craver@uri.edu
Authors
Hem Pokharel − Civil and Environmental Engineering,
University of Rhode Island, Kingston, Rhode Island 02881,
United States; Drinking Water, Bureau of Water Resources,
Massachusetts Department of Environmental Protection,
Springfield, Massachusetts 01103, United States;
orcid.org/0000-0001-6641-0334
Zachary Shepard − Civil and Environmental Engineering,
University of Rhode Island, Kingston, Rhode Island 02881,
United States; orcid.org/0000-0003-0079-8537
Complete contact information is available at:
https://pubs.acs.org/10.1021/acsestengg.0c00216
Notes
The authors declare no competing financial interest.
■
ACKNOWLEDGMENTS
This work was funded by NSF CBET Award No. 1350789.
This work was partially funded by the Rhode Island Water
Resources Center. Special thanks to Village Forward, formerly
Solutions Benefiting Life, for their assistance and support
4. CONCLUSIONS
In this study, we developed two empirical models that could be
used to evaluate and predict the performance of CWFs: the
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during this project. We would also like to thank Dr. Ali Akanda
for his insights into our models.
■
Article
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