Project Final Report
for
Water Resources Research Institute Program
under
Section 104, Water Resources Research Act of 1984
To the Alabama Water Resources Research Institute
In support of the
Research Project
“TREATMENT OF HEAVY METAL-CONTAMINATED RUNOFF USING STRAW
COATED WITH SULFIDE”
By
Robert W. Peters, Ph.D., P.E. and
Associate Professor of Environmental Engineering
Dept. of Civil and Environmental Engineering
University of Alabama at Birmingham
Birmingham, AL 35294-4440
phone: (205)-934-8434
FAX: (205)-934-9855
e-mail: rpeters@eng.uab.edu
Joseph J. Gauthier, Ph.D.
Associate Professor of Biology
Department of Biology
University of Alabama/Birmingham
Birmingham, AL 35294
phone: (205)-934-5659
FAX: (205)-975-6097
e-mail: gauthier@uab.edu
Sandra A. Nunez
and
Dept. of Civil and Environmental Engineering
University of Alabama at Birmingham
Birmingham, AL 35294-4440
phone: (205)-934-8434
FAX: (205)-934-9855
e-mail: sandra20@uab.edu
Lisa Ann Blankinship
Department of Biology
University of Alabama/Birmingham
Birmingham, AL 35294
phone: (205)-934-7553
FAX: (205)-975-6097
e-mail: Suriana@aol.com
November 14, 2006
i
Abstract
Runoff from industrial sites may contain a variety of heavy metals that can adversely affect the
ecosystem. One approach to this problem involves capturing the heavy metals on an adsorbent, such as
straw. The uptake of heavy metals [iron (Fe), cadmium (Cd), lead (Pb), and zinc (Zn)] was studied as
various solution pH values. Batch isotherm tests were performed using straw and straw coated with
sulfide for solution pH in the range of ~2 to ~6. The adsorption of heavy metals on straw was modeled
using the Langmuir and Freundlich isotherm models:
Langmuir Model:
Freundlich Model:
qe =
qe =
(Qo b C)/[1 + bC]
k C1/n
The effect of solution pH on the adsorption parameters (Qo and b in the Langmuir model, and k and
1/n in the Freundlich model) was determined. Results are presented for the heavy metals (Fe, Cd, Pb, and
Zn) adsorption onto straw.
A limitation of straw and similar substrates used as biofilters is their biodegradability. As the straw
decomposes, binding sites may be lost, adsorbed materials released and permeability of the biofilter
reduced. The stability of both untreated straw and sulfide-treated straw as a biofilter in this application
was investigated. Portions of straw, unamended and amended with sulfides and heavy metals, were
removed from columns and analyzed for total dry solids and various organic components of straw. The
use of straw as an adsorbent for adsorption of heavy metals offers an effective, low-cost treatment
technology for capturing heavy metals contained in industrial runoff.
Straw that had been soaked in iron-laden water and dried, was subjected to continuous flow
throughput conditions. Beds packed with straw were subjected to water flowing through the beds.
Desorption of iron from the straw was monitored as a function of time (throughput). Simultaneously, the
stability of the straw was addressed in terms of the biodegradability of the straw. Analyses were
performed on straw samples for acid detergent fiber, acid detergent lignin, and neutral detergent
digestion. Results indicated there was a significant loss of the readily biodegradable fraction of the straw
after one week. The lignocellulose fraction remained stable for the duration of the 16 weeks treatment
period. About 41% of the straw is readily biodegradable, 57% is slowly degradable, and 2% ends up as
ash. About 10-15% of the straw was lost due to mechanical aspects of washing.
Heavy metals were allowed to pass through beds packed with straw for adsorption and uptake by
straw. The breakthrough characteristics of passing the solutions through the straw were determined for
single heavy metals systems involving iron and lead. These breakthrough curves were compared to those
obtained for multimetal systems (involving iron and lead simultaneously). The results showed that at high
concentrations, both iron and lead were removed to nearly the same efficiency, while lower
concentrations allowed the breakthrough curve to be shifted to the right achieving greater time before
breakthrough. In addition, when iron and lead were combined, iron was adsorbed and retained more
effectively by the straw than lead, resulting in a preferential adsorption of the iron.
From the Bed Depth Service Time (BDST) analysis, the straw exhaustion rate was calculated. When
iron and lead were combined, the straw exhaustion rate was higher compared to that for iron alone and
lead alone.
i
Acknowledgments
The authors want to acknowledge the financial support of the Alabama Water Resources Research
Institute which funded this project. Additionally, the authors acknowledge the support of the Department
of Civil and Environmental Engineering and the Department of Biology at the University of Alabama at
Birmingham for use of specialized laboratory and research equipment, and the Department of Biomedical
Engineering for use of the atomic absorption spectrophotometer system for analysis of the heavy metal
concentrations in solution.
ii
Table of Contents
Abstract ............................................................................................................................................ i
Acknowledgments........................................................................................................................... ii
List of tables................................................................................................................................... iv
List of figures.................................................................................................................................. v
1
Introduction............................................................................................................................. 1
2
Nature, Scope and Objective................................................................................................... 2
3
Background ............................................................................................................................. 2
4
Methodology ........................................................................................................................... 5
4.1 Task 1. Perform bench-scale batch adsorption/desorption tests of various concentrations
of heavy metals in contact with straw to determine the uptake of heavy metals........................ 5
4.2 Task 2. Model the adsorption uptake using Langmuir and Freundlich isotherm models... 6
4.3 Task 3. Performing continuous flow through packed bed reactors containing straw which
have been untreated (S1) and treated to sulfide (S2), iron solution (S3) and sulfide follow by
iron solution (S4), and analizing the biodegradability of straw.................................................. 6
4.4 Task 4. Perform continuous flow processing of heavy metal-laden solutions (single and
multiple heavy metals) through packed bed reactors containing straw. ..................................... 7
4.5 Task 5. Model the bed depth-service time behavior of the continuous heavy metal
solution throughput in the packed bed system............................................................................ 8
4.6 Task 6. Perform preliminary scale-up design for pilot-scale activities. ............................. 8
4.7 Theoretical .......................................................................................................................... 8
5
Results and Discussion ......................................................................................................... 10
5.1 Results for Task 1 and 2.................................................................................................... 10
5.2 Results for Task 3 ............................................................................................................. 26
5.2.1
Mass Balance ........................................................................................................ 26
5.2.2
Straw Biodegradation Results............................................................................... 30
5.3 Results for Task 4 ............................................................................................................. 35
5.4 Results for Task 5 and 6.................................................................................................... 39
6
Summary and Conclusions ................................................................................................... 43
7
Synopsis ................................................................................................................................ 44
8
Publications........................................................................................................................... 48
9
References Cited ................................................................................................................... 50
iii
List of tables
Page
Table 1. Summary of Langmuir and Freundlich Isotherm Constants........................................... 25
Table 2. Summary of the Results for Bed Depth Service Time Relationship. ............................. 43
iv
List of figures
Page
Figure 1. Typical Calibration Curve for Heavy Metal Standard Solution...................................... 6
Figure 2. Langmuir and Freundlich Isotherms Using DI-Water for Selected Heavy Metals
(Linearized Form). ................................................................................................................ 12
Figure 3. Langmuir and Freundlich Isotherms for Selected Heavy Metals Using DI-Water. ...... 13
Figure 4. Langmuir and Freundlich Isotherms for Selected Heavy Metals Using Buffer Solution
(Linearized Form). ................................................................................................................ 14
Figure 5. Langmuir and Freundlich Isotherms for Selected Heavy Metals Using Buffer Solution.
............................................................................................................................................... 15
Figure 6. Competitive Adsorption Langmuir and Freundlich Isotherm for Iron and Lead Using
Deionized Water (Linearized Form)..................................................................................... 16
Figure 7. Competitive Adsorption Langmuir and Freundlich Isotherm for Iron and Lead Using
Buffer Solution (Linearized Form). ...................................................................................... 17
Figure 8. Competitive Adsorption Langmuir and Freundlich Isotherm for Iron and Lead Using
Deionized water. ................................................................................................................... 18
Figure 9. Competitive adsorption Langmuir and Freundlich Isotherms for Iron and Lead Using
Buffer Solution...................................................................................................................... 18
Figure 10. Repeatability of the Isotherms Results for Iron (deionized water) Using Linearized
Form...................................................................................................................................... 19
Figure 11. Repeatability of the Isotherms Results for Iron (deionized water) Using Isotherm
Equation. ............................................................................................................................... 20
Figure 12. Repeatability of the Isotherms Results for Lead (deionized water) Using Linearized
Form...................................................................................................................................... 21
Figure 13. Repeatability of the Isotherms Results for Lead (deionized water) Using Isotherm
Equation. ............................................................................................................................... 22
Figure 14. Repeatability of the Isotherms Results for the Combined Iron and Lead Systems
(Linearized Form). ................................................................................................................ 23
Figure 15. Repeatability of the Isotherms Results for the Combined Iron and Lead Systems. ... 24
Figure 16. Desorption of Sodium Sulfide by Continuous Flow Conditions (S2)......................... 27
Figure 17. Desorption of Iron by Continuos Flow Conditions (S3). ............................................ 27
Figure 18. Desorption of Iron when Combined with Sodium Sulfide by Continuous Flow
Conditions (S4). ................................................................................................................... 28
Figure 19. Desorption of Sodium Sulfide when Combined with Iron by Continuous Flow
Conditions (S4). .................................................................................................................... 28
Figure 20. Comparing S2-S3 and S4 Experiments at 16 and 8 Weeks Treatment. ...................... 29
Figure 21. Total Loss of Straw Treated with Basal Medium or Deionized Water under Aerobic
or Anaerobic Conditions. ..................................................................................................... 31
Figure 22. % Biodegradability of Straw Treated with Basal Medium or Deionized Water under
Aerobic or Anaerobic Conditions. ........................................................................................ 31
Figure 23. Total Average Loss of Straw Treated with Basal Medium or Deionized Water under
Anaerobic or Aerobic Conditions. ........................................................................................ 32
v
Figure 24. Total Loss of Straw Treated with Sulfide, Iron, and Iron+ Sulfide from Columns. ... 32
Figure 25. Total Loss of Straw (Average) from Columns. ........................................................... 33
Figure 26. Biostability of Straw Treated with Sulfide, Iron, and Sulfide + Iron from Columns.. 33
Figure 27. Average Biostability of Straw Treated with Sulfide, Iron, and Sulfide + Iron from
Column.................................................................................................................................. 34
Figure 28. Iron Concentration in Leachate from Packed Columns. ............................................. 34
Figure 29. Straw Biodegradation Results. .................................................................................... 35
Figure 30. Breakthrough Curve Showing Up-flow System Set Up.............................................. 36
Figure 31. Breakthrough Curve for Iron alone and Lead alone and the Combined (iron and lead)
System at Different Flow Rates. ........................................................................................... 37
Figure 32. Comparison of Iron Alone and Lead Alone with the Combination of the Iron + Lead
System................................................................................................................................... 38
Figure 33. Bed Depth Service Time Relationship for Iron Using Estimated Data....................... 40
Figure 34 Bed Depth Service Time Relationship for Lead Using Estimated Data ...................... 40
Figure 35. Bed Depth Service Time Relationship for Iron Combined with Lead Using Estimated
Data. ...................................................................................................................................... 41
Figure 36. Bed Depth Service Time Relationship for Lead Combined with Iron Using Estimated
Data. ...................................................................................................................................... 41
Figure 37. Bed Depth Service Time Comparing Iron Alone with Iron Combined with Lead
Using Estimated Data. .......................................................................................................... 42
Figure 38. Bed Depth Service Time Comparing Lead Alone with Lead Combined with Iron
Using Estimated Data. .......................................................................................................... 42
vi
1
Introduction
Runoff from construction sites, roofs, and roadways is known to contain heavy metals as trace
contaminants, and can affect the bioecosystems near these runoff sites. Urban stormwater runoff has been
recognized as a substantial source of pollutants to receiving waters [Davis et al., 2001]. Urban settings are
a focal point for environmental contamination due to emissions from industrial and municipal activities
and the widespread use of motor vehicles [Callender and Rice, 2000]. During storm events, a considerable
increase in the concentrations of particle number, suspended solids mass, organic carbon, iron, and zinc
have been observed in runoff streams [Characklis and Wiesner, 1997]; the concentration of zinc in runoff
was highly correlated with organic carbon and iron exists primarily in the macrocolloidal fraction. Hares
and Ward [1999] studied the concentration of motorway-derived contaminants including V, Cr, Mn, Co,
Ni, Cu, Zn, Mo, Cd, Sb, and Pb, that were measured in unfiltered storm water collected during the initial
stages of storm events. A higher level of motor-derived heavy metal contamination exists in stormwater
runoff from road sections with a higher average daily traffic density. The transport of anthropogenic
constituents by runoff from urban roadways can adversely impact the quality of adjacent receiving waters
and soils [Sansalone et al., 1996]. Heavy metal elements are the most persistent constituents found in
pavement runoff [Sansalone et al., 1996]. Legret and Pagotto [1999] conducted a study investigating the
quality of pavement runoff water from a 275-m motorway section over a one-year time frame, during
which 50 rain events were sampled. Two different types of pollution were revealed. The first type was
identified as chronic pollution and included suspended solids, chemical oxygen demand, total
hydrocarbons, lead, and zinc. The second type of pollution was seasonal and includes chlorides, sulfates,
suspended solids, and heavy metals due to the use of deicing salt in the wintertime. Runoff from roads
has negative effects on biotic integrity in both terrestrial and aquatic ecosystems [Trombulak and Frissell,
2000]. Roads affect soil density, temperature, soil water content, light levels, dust, surface waters,
patterns of runoff, and sedimentation, as well as adding heavy metals (especially lead), salts, organic
molecules, ozone, and nutrients to roadside environments [Trombulak and Frissell, 2000]. The runoff
chemistry from uncontrolled discharges of highway runoff can significantly impact receiving water
quality and may require remediation by appropriate stormwater best management practices [Marsalek et
al., 1997].
Techniques are needed to treat heavy metal-contaminated runoff. Several new techniques have been
developed as means to treat stormwater runoff; for example, more than 130 sites in the U.S. treat
stormwater runoff from impervious surfaces with humic filter media that feature a specially processed
leaf compost [Richman, 1997; Anon., 1997]. That approach has been tested at shopping centers,
restaurants, highways, hospitals, and single retail outlets. That treatment system uses a pelletized
compost medium to trap particulates, absorb organic compounds, and is reportedly capable of removing
up to 90% of the solids, 85% of the oil and greases, and 82-98% of the heavy metals. The use of
biosurfactants to remediate sediments contaminated by heavy metals has been studied [Mulligan et al.,
2001a], but such approaches treat the soil contaminated by runoff after the fact, rather than trying to treat
the runoff at the source. Mulligan et al. [2001b] have examined various remediation technologies for
their ability to treat metal contaminated soils and groundwater; among the technologies studied are:
isolation, solidification/stabilization, immobilization, electrokinetics, toxicity reduction, bioleaching,
physical separation and extraction (soil washing and in-situ flushing). The design of vegetative
constructed wetlands has also been explored for treatment of highway runoff [Shutes et al., 1999]. Peanut
hull pellets have been shown to adequately uptake Cu, Cd, Pb, and Zn under both steady state and
transient rate conditions [Brown et al., 2000]. This project seeks to develop an efficient and low-cost
technology to capture heavy metals from contaminated runoff, namely using unamended straw or straw
that has been coated with sulfide compounds, to bind the heavy metals to the straw.
1
2 Nature, Scope and Objective
The scope of the research was three-fold: 1. Performing bench-scale batch isotherm characterization
of selected heavy metals onto straw, 2. Performing continuous flow of heavy metal solutions through a
packed-bed column containing straw, and 3. Modeling the bed depth-service time behavior of the heavy
metal solutions through the columns to determine column breakthrough.
The objectives for this research project are listed below:
• To determine whether sulfide adsorbed on the surface of straw/hay will serve as an effective
binding agent/precipitation agent for removal of heavy metals from solution (e.g., run-off from
sites);
• To identify preliminary conditions (e.g., pH, sulfide dosage/unit weight of straw, etc.) whereby
heavy metals are effectively removed from solution;
• To determine adsorptive capacities of the heavy metals on the straw; and
• To determine the break-through characteristics of the heavy metals through the packed-bed straw
reactors.
Six tasks were performed in this project: (1) Performing bench-scale batch adsorption/desorption tests
of various concentration of heavy metals in contact with straw to determine the uptake of heavy metals;
(2) Modeling the adsorption uptake using Langmuir and Freundlich isotherm models; (3) Performing
continuous flow through packed bed reactors containing straw which have been untreated (S1) and
treated to sulfide (S2), iron solution (S3) and sulfide followed by iron (S4); (4) Performing continuous
flow processing of heavy metal-laden solutions (selected heavy metals) through packed bed reactors
containing straw; (5) modeling the bed depth-service time behavior of the continuous heavy metal
solution throughput in the packed bed system; and (6) Performing preliminary scale-up design for pilotscale activities. This research was performed in the laboratory facilities in the Department of Civil and
Environmental Engineering at the University of Alabama at Birmingham (UAB).
3 Background
Areinzo et al. [2001] conducted a study to determine the contaminant level of the Sarno River basin
in southwestern Italy associated with the impact of land use and urban runoff. Six sampling site locations
were selected to compare to two earlier studies conducted in 1975 and 1985. At each location, twelve
samples were collected over time from October 1997 through September 1998. The river quality was
monitored for various water characteristics, including dissolved oxygen concentration, biological oxygen
demand (BOD5), chemical oxygen demand (COD), settleable solids, coliform bacteria densities, and
heavy metal concentrations. The heavy metal dissolved concentrations were low at all sampling sites.
Most of the analyzed contaminants increased from their values obtained in 1985, with a decline in
chromium concentrations observed which was attributed to recent effective treatment of wastewater from
the tanning plants.
2
As part of the National Water-Quality Assessment Program of the U.S. Geological Survey, Callender
and Rice [2000] investigated streambed-sediment and dated reservoir-sediment samples collected from
the Chattahoochee River Basin (flowing from northern Georgia through Atlanta to the Gulf of Mexico)
for total lead (Pb) and zinc (Zn) concentrations. Correlations among population density, traffic density,
and total and anthropogenic Pb and Zn concentrations indicated that population density was strongly
related to traffic density, and the total and anthropogenic Pb and Zn concentrations in the environment
were strongly related to traffic density and was a predictor of Pb and Zn concentrations in the
environment derived from anthropogenic activities. Increased vehicular usage kept Zn concentrations
elevated in runoff from population centers which were reflected in the continued enrichment of Zn in
aquatic sediments. Sediments from rural areas also contained elevated concentrations of Zn, possibly due
to substantial power plant emissions for this region of the country, as well as vehicular traffic.
Highway runoff from a mountain road in northeast Portugal was monitored for runoff water entering
an infiltration pond. Concentrations were determined for the following heavy metals: cadmium (Cd),
chromium (Cr), copper (Cu), lead (Pb), and zinc (Zn). These heavy metals were of interest due to their
potential toxicity to the environment. Concentrations of cadmium and chromium were usually lower than
the detection limit of 1 μg/L. Copper, lead, and zinc concentration levels were found between 1 and 54
μg/L, 1 and 200 μg/L, and 50 to 1460 μg/L. A first flush effect was observed, with the first 50% of the
runoff volume for each event transporting 61% – 69% of the total suspended solids, Cu, Pb, and Zn loads.
The heavy metals sorbed to the soil. The researchers conducted sorption/desorption experiments of Cu,
Pb, and Zn involving soils at the highway infiltration pond and with several other Portuguese soils. Soil
pH had a significant effect in controlling the Cu, Pb, and Zn sorption processes. Lowering the pH resulted
in higher desorption rates. Sorption strength of the soils also was observed to have a significant effect of
the ability of the soil to protect groundwaters from highway runoff.
Samples of stormwater runoff from the Skyway Bridge in Burlington, Ontario, were analyzed for five
heavy metals (Cd, Cu, Ni, Pb, and Zn) and 14 polycyclic aromatic hydrocarbons (PAHs) in dissolved and
particulate-bound phases [Marsalek et al., 1997]. The highest mean event-mean concentrations in wholewater samples were found for Zn, Cu, and Pb with 0.337, 0.136, and 0.072 mg/L, respectively. Cu, Ni,
and Zn concentrations in the dissolved phase accounted for 35% to 45% of the concentrations of the
whole-water samples. Mean concentrations of Zn, Cu, and Pb were 997, 314, and 402 mg/g, respectively,
in runoff sediment. These concentrations indicated that the sediment was “grossly polluted” according to
the Ontario Ministry of Environment and Energy guidelines for sediment quality. Metal concentrations in
the <45 mm size fraction were greater than in the whole-sediment samples; however, with respect to
metal loads, this enrichment was insignificant since this size fraction represented less than 1% of the total
mass of solids.
Lee et al. [1997] used a sequential extraction procedure to determine the distribution of Pb, Zn, Cd,
Mn, and Fe in core sediments of a retention pond along a French motorway. The goal of their study was
to determine how the movement of each metal was associated with the other metals and with other solid
phases. The concentrations of the metals were highest in the upper layer of the sediment, due to a
significant increase in the non-detrital fraction of the metals at the sediment surface. Surface enrichment
by Pb and Zn may be due to an upward migration of heavy metals, after being released by early
diagenetic processes. Cd enrichment at the surface was attributed to dissolution of Cd from polluted
roadside soil during rainstorms and redeposition in the retention pond.
Sedlak et al. [1997] investigated the complexation behavior of Cu and Ni in wastewater effluents and
surface runoff discharging into San Francisco Bay. Most of the dissolved Cu and Ni in wastewater
effluents and surface runoff was complexed. Moderately strong metal-complexing ligands associated with
activated sludge biopolymers and humic substances, were responsible for the about 20% of the Ni and 5%
- 50% of the Cu. The remaining Cu and Ni was complexed by ligands with apparent stability constants
3
comparable to those of synthetic chelating agents. Strongly complexed Cu was present at concentrations
below 40 nM and accounted for more than 75% of the Cu discharged by wastewater treatment plants and
~ 25% of the Ni in surface runoff. Strong Ni complexes are extremely stable in seawater. The
investigators concluded that the existence of strong metal-complexing ligands in wastewater effluent and
surface runoff must be accounted for when evaluating metal treatability and biogeochemistry.
Igloria et al. [1996] investigated the performance of three Washington State soils for their ability to
attenuate trace heavy metals (Cd, Cu, Pb, and Zn) in contaminated water from highway runoff. They
employed large-scale soil columns (0.3-m diameter and 1.0-m deep) under loading conditions similar to
actual field infiltration basins. The soil columns were periodically to produce “quasi-field” conditions,
with the corresponding metal transport being observed as a function of depth. Their data indicated that
metals speciation and background metals present in the soil are important factors because they affect
expected metals removals rates of the soils. The hydraulics of the system played a secondary role to the
geochemistry. Their results suggest that infiltration can be a viable alternative in disposing of runoff at
low metals concentrations.
Davis et al. [2001] investigated the loadings of lead, copper, cadmium, and zinc from various sources
in an urban environment. Specific sources studied included building siding and roofs; automobile brakes,
tires, and oil leakage; and wet and dry atmospheric deposition. Important sources identified were building
siding for all four metals, vehicle brake emissions for copper, and tire wear for zinc. Atmospheric
deposition was an important source for Cd, Cu, and Pb. Loadings and source distributions depended on
building and automobile density assumptions, and the type of materials present in the area examined.
Sansalone et al. [1996] instrumented a section of urban highway pavement in Cincinnati, Ohio having
an average daily traffic count of ~150,000 vehicles in order to sample lateral sheet flow from the
pavement. A trench was dug along the highway; the trench was lined with an impermeable polyethylene,
filled with sand, and capped with porous pavement [Anonymous, 1998]. Field samples from two diverse
rainfall events were analyzed to determine metal element partitioning between dissolved and particulatebound fractions. Their results [Sansalone et al., 1996; Sansalone and Buchberger, 1997] indicated that
dissolved metal element washo-ff response was a function of the degree to which a heavy metal is in the
dissolved form. The particulate-bound metal element washoff response was primarily a function of the
rainfall intensity. Results indicated that for both events Cu, Cd, Ni, and Zn were mainly in the dissolved
form, while Fe, Pb, and Al were mainly in the particulate-bound form. Cr and Pb partitioning was
intermediate to those two cases. The overall heavy metals removed by the trench system were 82% to
97% [Anonymous, 1998]. Dissolved fractions of Zn, Cd, and Cu showed a weak first flush for all events.
Event mean concentrations of Zn, Cd, and Cu were greater than surface water quality discharge standards
[Sansalone and Buchberger, 1997].
Singh et al. [2000] investigated heavy metal fluxes from runoff and percolation under simulated
rainfall at a slope of 19% and a rainfall intensity of ~ 40 mm/hr (corresponding to about half the mean
annual erosivity of rain under Belgian weather conditions. Surface runoff and percolating water samples
were analyzed for suspended solids, total dissolved carbon, and heavy metals (Cd, Cr, Cu, Ni, Pb, and
Zn). Runoff rate and sediment yields were highest for a silt loam sediment, characterized by low clay and
organic matter content. Metal concentrations in the runoff and percolating water varied widely between
the sediments studies and were related to the total metal content in the sediment. In runoff and percolating
water from contaminated sediments, metal concentrations exceeded the Netherlands A reference values
for groundwater quality. Very high metal fluxes were observed for recently oxidized dredged sediment.
Metal transport per unit surface are through percolating water was two to twenty times greater than in
surface runoff.
4
Zobrist et al. [2000] investigated the concentrations of total carbon, nitrogen, phosphorus, heavy
metals (Cd, Cu, Cr, Fe, Mn, Pb, and Zn), and pesticides (triazines, acetamides, phenoxy acids) in roof
runoff from an inclined tile roof, an inclined polyester roof, and a flat gravel roof. Runoff from the first
two roofs showed initially high concentrations declining rapidly to lower constant levels. The first-flush
effect was modeled using a first-order rate law (wash-off function). For most constituents, concentrations
in the runoff were in the range of the wet deposition after the first few mm runoff depth and total loads in
the runoff corresponded approximately to the total (dry and wet) atmospheric deposition load. The flat
gravel roof depicted a different behavior; rainwater was first retained before it overflowed. Gravel is
weathered, with most pollutants being partially retained in the gravel layer. However, corrosion of Cu in
drains produced slightly elevated Cu concentrations, causing the direct disposal of runoff to be in question
[Zobrist et al., 2000].
Mason et al. [1999] investigated the behavior of heavy metals (Cd, Cu, Cr, Pb, and Zn), nutrients
(organic carbon, phosphorus, and nitrogen parameters), and major ions during percolation of roof runoff
water through an artificial infiltration site. The concentrations of these components were determined in
rainwater, roof runoff, and infiltrating water at various depths in the soil. Concentrations of most
parameters in roof runoff were highest during the “first flush” at the beginning of rain events. Despite
rapid infiltration caused by strong preferential flow, differences were observed in the infiltration behavior
between individual components. Cl-, NO3-, SO4=, orthophosphate, and the major portion of dissolved
organic carbon behaved conservatively during infiltration, whereas NH4+ concentration decreased
probably as a result of nitrification. The concentrations of Ca, Mg, Na, K, and alkalinity were regulated
by dissolution of soil material. The change in Cu, Cd, and Cr concentration during infiltration was due
primarily to concentration dynamics of roof runoff inflow with only limited retention by the soil,
indicating that these metals have high mobility in the upper soil layers. In the short term, Pb and Zn
exhibited the opposite behavior with strong retention in the upper soil layers. However, in the longer
term, zinc and lead were also transported through the deeper soil layers of the unsaturated zone. The high
mobility of Cd and Cu was attributed to complexation by ligands in solution, and of Cr due to the
presence of Cr+6 species.
4 Methodology
Heavy Metals concentrations used in this project were 1-12 mg/L; this range may be several orders of
magnitude higher than the typical adsorption range in industrial runoff. However, to minimize analytical
complexity dealing with concentrations and digestion, use of low ppm concentrations was selected.
Wheat straw was collected from a nearby farm and cut in small pieces (~ ½ inch in length).
Approximately 50 g of straw was soaked with 1 liter of deionized ≥ 18 MΏ resistivity water and left
soaking for 3 hours. The straw was them filtered and dried overnight.
Procedure for six different tasks
4.1
Task 1. Perform bench-scale batch adsorption/desorption tests of various concentrations
of heavy metals in contact with straw to determine the uptake of heavy metals.
Batch laboratory experiments was conducted for various heavy metal solutions [e.g., iron (Fe),
cadmium (Cd), chromium (Cr), lead (Pb)] using deionized water and acetic acid buffer solution (0.1 Mol
sodium acetate with 0.1 Mol acetic acid) to obtain different pH values, involving various loadings (heavy
5
metal concentration per unit mass of straw treated with deionized ≥ 18 MΏ resistivity water). The heavy
metal solutions were made for various concentrations ranging from ~1 to ~12 mg/L. These solutions
were stirred for one hour using a Corning Stirrer PC 353, and 10 mL portions were collected to determine
the initial concentration. The heavy metal solutions were placed in contact with the straw, and samples
were collected to determine the final concentrations. Past experience with soils indicated that a contact
time of ~ 3 hrs was sufficient to obtain pseudo-equilibrium [Peters and Shem, 1992]. The contact time
requirement were determined by conducting several experiments in which samples were grabbed
periodically. The initial and final heavy metal concentration was determined using a Perkin Elmer
AAnalyst™ 800 atomic absorption spectroscopy (AAS) system and using standard methods (Clesceri,
L.S et al., 1998). This system required making a standard solution for each metal. Figure 1 shows a
typical calibration plot of concentration versus absorbance using the lead standard solution.
Lead Standard Solution
0.12
Absorbance
0.1
0.08
0.06
Slope = 0.15566
Correlative Coefficient=1.000
Intercept = 0
0.04
0.02
0
0
2
4
6
8
10
12
Concentration (mg/l)
Figure 1. Typical Calibration Curve for Heavy Metal Standard Solution.
4.2
Task 2. Model the adsorption uptake using Langmuir and Freundlich isotherm models.
The heavy metal uptake by the straw was modeled using the Langmuir and Freundlich isotherm
models. A description of these models was provided in the theoretical section. The data collected from
Task 1 was used to model the isotherms. The model parameters were determined using the above data for
each of the isotherm models (Qo and b for the Langmuir model, and KN and 1/n for the Freundlich
model).
4.3
Task 3. Performing continuous flow through packed bed reactors containing straw which
have been untreated (S1) and treated to sulfide (S2), iron solution (S3) and
sulfide follow by iron solution (S4), and analizing the biodegradability of straw.
The straw was comminuted (cut up into small pieces). For treatment with sulfide solutions, the straw
was soaked overnight in a 0.1 M sulfide solution, and then separated from the sulfide solution, and
6
allowed to air dry. Columns were packed with approximately 50g of wheat straw soaked in a 0.1 mol
sulfide solution (S2), and 10 mg/L iron solution (S3), or a sulfide solution followed by an iron solution
(S4). Columns packed with untreated straw (S1) were set up as controls. All treatments (S1, S2, S3, and
S4) were preformed in triplicate for designated times (initial, 1-wk, 2-wk, 4-wk, 8-wk, and 16-wk).
Columns were watered daily and eluate collected every other day for two weeks then once a week for the
remaining time. All wheat straw was removed from columns at the end of 1-wk, 2-wk, 4-wk, 8-wk, or 16wk periods and allowed to dry. Wheat straw was ground to a fine powder for neutral detergent fiber
(NDF) analysis. One gram of sample was mixed with 0.5-g sodium sulfate, 100-mL of neutral detergent
solution, and 2-mL of decahydronapthalene. Each sample was boiled for one hour and then filtered to
remove excess neutral detergent solution. Samples were washed in hot water and acetone to eliminate
remaining neutral detergent solution. NDF digestions are useful for removal of the readily biodegradable
fraction of wheat straw such as small molecules, proteins, lipids and carbohydrates. Samples were ashed
for 4-hr at 500ºC to determine the mineral content.
Anaerobic samples were prepared by placing wheat straw (50-g) in a glass jar, filling completely with
either basal medium or DI water, covering with parafilm, and loosely sealing. After a few days in both
treatments, gas production was observed. Aerobic samples were prepared by placing wheat straw (20-g)
into a 2-L flask, adding enough basal medium or DI water for wheat straw to move freely within the flask
(approximately 500-mL), covering with aluminum foil, and placing on an agitator for each time period.
Aerobic samples were taken from a common flask for each treatment condition. Both anaerobic and
aerobic samples were maintained at room temperature.
Samples were prepared for neutral detergent fiber (NDF) digestions by grinding air-dried wheat straw
(5-g) from each treatment and time to a fine powder. One gram of sample was mixed with 0.5-g sodium
sulfate, 100-mL of neutral detergent solution, and 2-mL of decahydronaphthalene. Each sample was
boiled for one hour and then filtered to remove excess neutral detergent solution. Samples were washed
in hot water and acetone to eliminate remaining neutral detergent solution. NDF digestions are useful for
determining the readily biodegradable fraction of wheat straw, and microbial biomass consisting of
molecules, proteins, lipids and carbohydrates. Samples were also ashed for 4-hr at 500ºC to determine the
mineral content.
4.4
Task 4. Perform continuous flow processing of heavy metal-laden solutions (single and
multiple heavy metals) through packed bed reactors containing straw.
The purpose of this task was to provide preliminary identification of whether the presence of single
or multiple heavy metal contaminants (Fe, Pb, and Fe + Pb) results in heavy metal uptake inhibition of
various heavy metal species, or whether it can enhance uptake through coadsorption/coprecipitation. The
columns were packed with 25 g straw and 10 mg/L of heavy metal concentrations was passed through the
packed bed columns using a silicone master flex tubing 96400-13 and a Cole-Palmer Instrument pump
model 7013-20. More than 100 bed volumes throughput were sent through the columns containing the
straw. Samples of the initial and effluent from the packed bed column were collected periodically, in
order to generate the data needed to model the bed depth-service time relationship. hese samples were
acid-prepped for dissolved heavy metal analysis on the atomic absorption spectroscopy (AAS) system.
Sufficient heavy metal solution was passed through the column resulting in exhaustion of the sorbent
material. The initial heavy metal concentration and concentrations of the effluent samples collected over
time were determined using AAS techniques.
7
4.5
Task 5. Model the bed depth-service time behavior of the continuous heavy metal
solution throughput in the packed bed system.
The data collected from task 4 were used to model the bed depth – service time (BDST) behavior for
continuous treatment applications. A brief description of the BDST system and model is provided in
theoretical section. The model parameters “a” and “b” were determined for use in Task 6.
4.6
Task 6. Perform preliminary scale-up design for pilot-scale activities.
The estimated data from Task 5 above were used to suggest a procedure design for follow-on pilotscale activities (using larger scale columnar flow systems).
4.7
Theoretical
1. Performing bench-scale batch isotherm characterization of the selected heavy metals onto straw.
Three adsorption models are widely used in the study of adsorption processes: Langmuir, Freundlich,
and the Bennett-Emmett Teller (BET) models. The Langmuir model is based upon monolayer adsorption
of the adsorbate on a homogeneous surface. This model has been widely used and is based on the
hypothesis of monolayer adsorption, but it usually can only be applied at low adsorbate concentrations.
The Freundlich model is basically a power-law model, and usually gives good curve fitting over a wider
adsorbate concentration range. The values of the adsorption constants vary markedly with adsorbate
concentration. This model is largely an empirical equation, although the model is based on the sound
assumption that surface sites are heterogeneous. However, the Freundlich model adsorption constants
provide few physical insights into the adsorption mechanism. The BET isotherm is based on a multilayer
adsorption mechanism and can describe the adsorption behavior of chemical species from gas or solution
phase over a wide concentration range [Wang et al., 1998]. However, this adsorption model does not
determine the adsorption energy from its adsorption constants. Use of multilayer adsorption models is
fairly complex and usually contains too many constants making them difficult to use. Due to this fact, the
analyses of heavy metals adsorption onto straw were restricted to using the Langmuir and Freundlich
models.
Equations describing the Langmuir and Freundlich isotherm models are listed below:
Langmuir:
qe =
Qobc .
1 + bC
{1}
Freundlich:
qe =
KN C1/n
{2}
where qe is the amount of solute (heavy metal) adsorbed per unit weight of adsorbent, C is the measured
solute concentration in solution at equilibrium, Qo is the amount of solute adsorbed per unit weight of
adsorbent in forming a complete monolayer coverage on the adsorbent (straw) surface, b is a constant
related to the net enthalpy of adsorption, and KN and 1/n are empirical constants.
These equations are linearized using the equations listed below:
8
Langmuir:
1 =
qe
1 + 1
1
Qo bQo C
{3}
Freundlich:
ln qe = ln KN + 1 ln C
n
or
{4}
log qe = log KN + 1 log C
n
{5}
Thus, a plot of 1/qe versus 1/C should yield a straight line if the Langmuir model provides an
adequate description of the adsorption process. The intercept will be 1/Qo and the slope will be 1/bQo.
Similarly, a plot of log qe versus log C should yield a straight line if the Freundlich model provides an
adequate description of the adsorption process. The slope is 1/n and the intercept is log KN. A value of 1/n
< 1 indicates a strong affinity for adsorption. Peters and Shem [1992] used these models to describe the
adsorption/desorption characteristics of lead onto various types of soil, both in the absence and presence
of a chelating agent, ethylenediaminetetraacetic acid (EDTA). Over the range of EDTA concentrations
employed in the study (0.01 to 0.10M), no significant difference in the adsorption isotherm parameters
was observed as a result of the applied EDTA concentration. The presence of EDTA significantly altered
the adsorption/desorption behavior of lead on the soil, resulting in less metal being adsorbed. Soil with
higher silt/clay content had a greater amount of lead adsorbed on it (as compared with sandy soil).
2. Performing continuous flow of heavy metal solutions through a packed-bed column containing straw,
and modeling the bed depth-service time behavior of the heavy metal solutions through the columns
to determine column breakthrough.
The most common method for evaluating pilot column data is the graphical analysis of the
breakthrough curve. Bohart and Adams [1920] developed a procedure for analyzing data obtained from
granular activated carbon adsorption pilot systems. Their procedure, known as the bed depth – service
time (BDST) analysis, was streamlined by Hutchins [1973], and has since been used in various drinking
water treatment [Sanks, 1978; American Water Works Association, 1990] and industrial and hazardous
waste treatment systems [Eckenfelder, 1989; Low et al., 1994; LaGrega et al., 1994; and Watts, 1997].
The first step in the procedure involves drawing horizontal lines through each of the breakthrough curves
at defined points, such as C/Co = 0.10 and C/Co = 0.90. The C/Co = 0.10 value represents breakthrough,
and C/Co is often defined as exhaustion. The horizontal difference between the exhaustion line
(C/Co = 90%) and the breakthrough line (C/Co = 10%) is defined as the height of the adsorption zone (D).
The C/Co = 0.90 and C/Co = 0.10 (i.e., service) times are each plotted as a function of bed depth, and
are then fit to a straight line by linear regression techniques:
t = ax+b
{6}
From the fit of the data, the constants a and b may be further analyzed to develop parameters that can
be used for full-scale design. The a (slope) parameter is:
a = slope (h/m) =
where:
103 x N
Co x ν
{7}
9
N =
Co =
ν =
sorptive capacity of the adsorbent =
mass of contaminant removed, (kg)
Volume of adsorbent, (m3)
influent contaminant concentration, (mg/L)
superficial velocity through the column [m3/(m2-h) = (m/h)]
The b (intercept) parameter is:
b =
intercept (h)
where:
K =
C =
= - ⎛ 103 ⎞ x
⎝ K Co ⎠
⎡ ⎛ Co ⎞ - 1 ⎤
⎣⎝ C⎠
⎦
{8}
the adsorption rate constant [m3/(kg-h)]
contaminant concentration at breakthrough, (mg/L)
After the results of regression analysis has been completed and a and b have been determined, the
procedure may be carried further. The column at time t = 0 (the x-intercept for the C/Co = 0.10 effluent
concentration represents the critical bed depth, which is the minimum column depth to obtain an
acceptable effluent concentration at time t = 0). The velocity of the adsorption zone as it moves down the
column and the time for a column to become exhausted is:
adsorption velocity (m/h)
exhaustion time (h) =
=
1 .
a (h/m)
{9}
d (m)
.
adsorption velocity (m/h)
{10}
where d is the height of the pilot columns (m).
Equation {9} can be expanded to determine the rate at which the adsorbent becomes exhausted on a
mass basis:
Adsorbent
Exhaustion rate
=
1
x
a (h/m)
(cross-sectional area) (m2) x ⎛bulk density of ⎞ (kg/m3)
⎝ adsorbent
⎠
These relationships can be used to scale-up the adsorption system and predict response to changes in
operating criteria such as different velocities and breakthrough concentrations [Watts, 1997; Cooney,
1999; McKay, 1996].
5 Results and Discussion
5.1
Results for Task 1 and 2
(1) Performing bench-scale batch adsorption/desorption tests of various concentration of heavy
metals in contact with straw to determine the uptake of heavy metals, and (2) Modeling the
adsorption uptake using Langmuir and Freundlich isotherm models.
10
Batch isotherm experiments were performed in which the straw was loaded at a dosage of 10 g/L, and
various heavy metal solutions ( iron, lead, cadmium, zinc) with concentrations ranging from ~1 to ~12
mg/L. After contact with the straw, the residual concentration of each heavy metal remaining in solution
was determined using atomic absorption spectrometer techniques. Calibration curves for each heavy
metal using deionized water typically had correlation coefficients exceeding 0.8 for Langmuir isotherm
and 0.7 for Freundlich isotherm as shown in Figure 2 and 3, showing highly residual concentrations could
be achieved. Likewise calibrations curves for heavy metals in buffer solution have correlative coefficients
above 0.9 for both isotherm models, as shown in Figures 4 and 5. The results of adsorption of iron,
cadmium, lead, and chromium on unamended straw are shown in Figures 2 to 5, respectively. The results
are presented in the linearized form of the Langmuir and Freundlich isotherm models. From the results
shown in these figures, the parameters for the two isotherm models (Langmuir and Freundlich) were
obtained for each of these heavy metal systems. The results are summarized in Table 1. Figures 2 and 3
for the Langmuir isotherms, lead is best adsorbed followed by iron, cadmium and chromium. In the
Freundlich isotherm, lead is best adsorbed followed by iron, cadmium and chromium. The correlation
coefficients for the adsorption models typically exceeded 0.7, and usually exceeded 0.85, showing the
adsorption models provided a fairly good description for the adsorption of heavy metals by the straw. For
any particular heavy metal, the correlation coefficients were usually higher for the Langmuir than for the
Freundlich model, indicating that the Langmuir model provided a slightly better description for the
adsorption of the heavy metals by the straw than the Freundlich model. Also there is not significant
change in solution pH after contact with straw.
11
a)
120
80
Fe, y = 7.3609x - 0.1721
R2 = 0.8284
1/qe , (g/mg)
100
Cr, y = 229.08x - 12.717
R2 = 0.9522
60
Lead
Iron
Pb, y = 1.9752x + 0.3395
R2 = 0.8365
Cadmium
40
Cd, y = 0.589x + 4.5997
R2 = 0.8315
Chromium
20
0
0
1
2
3
4
5
6
7
8
9
10 11
1/Cf, (L/mg)
b)
1
log qe (mg/g)
0.1
1
Pb
10
100
Fe
0.1
Pb, y = 0.4179x 0.543
R2 = 0.7272
Fe, y = 0.0897x 1.143
R2 = 0.9117
Cd
0.01
Cd, y = 0.1597x 0.0887
R2 = 0.3796
Cr
Lead
Iron
Cadmium
Chromium
Cr, y = 0.0024x 1.7679
R2 = 0.9926
0.001
log Cf (mg/L)
Figure 2. Langmuir and Freundlich Isotherms Using DI-Water for Selected Heavy Metals
(Linearized Form).
a) Plot of Langmuir Isotherm for Selected Heavy Metals Showing 1/qe versus 1/Cf.
b) Plot of Freundlich Isotherm for Selected Heavy Metals Showing log qe versus log Cf.
12
a)
1.4
Pb
1.2
qe , (mg/g)
1
Fe
0.8
Lead
0.6
Iron
Cadmium
0.4
Cd
Chromium
0.2
Cr
0
0
3
6
9
12
15
Cf , (mg/L)
b)
1.2
qe , (mg/g)
1
Pb
Fe
0.8
Lead
0.6
Iron
0.4
Cadmium
Cd
Chromium
0.2
Cr
0
0
2
4
6
8
10
12
Cf, (mg/L)
Figure 3.
Langmuir and Freundlich Isotherms for Selected Heavy Metals Using DI-Water.
a) Plot of Langmuir Isotherm for Selected Heavy Metals Showing qe versus Cf.
b) Plot of Freundlich Isotherm for Selected Heavy Metals Showing qe versus Cf.
13
a)
140
Cr, y = 121.79x + 0.3808
R2 = 0.9493
120
Cr
Cd, y = 25.42x + 1.1592
R2 = 0.9983
1/q e , (g/mg)
100
Chromium
80
Fe, y = 6.7681x + 0.1907
R2 = 0.9989
60
Pb, y = 2.1293x + 0.7835
R2 = 0.9943
Cd
40
Cadmium
Iron
Lead
20
Fe
Pb
0
0
0.5
1
1.5
2
2.5
1/Cf , (L/mg)
b)
1
0.1
1
Pb
10
Pb, y = 0.3265x0.6003
R 2 = 0.9521
0.1
log qe (mg/g)
100
Fe
Cd
Fe, y = 0.1545x1.0805
R 2 = 0.9929
0.01
Cd, y = 0.0351x0.8624
Cr
Lead
Iron
Cadmium
Chromium
R 2 = 0.9948
Cr, y = 0.0099x0.805
R 2 = 0.941
0.001
log Cf (m g/L)
Figure 4.
Langmuir and Freundlich Isotherms for Selected Heavy Metals Using Buffer Solution
(Linearized Form).
a) Plot of Langmuir Isotherm for Selected Heavy Metals Showing 1/qe versus 1/Cf.
b) Plot of Freundlich Isotherm for Selected Heavy Metals Showing log qe versus log Cf.
14
a)
0.9
0.8
Pb
0.7
qe, (mg/g)
0.6
Fe
Lead
0.5
Iron
Cd
0.4
Cadmium
0.3
0.2
Chromium
Cr
0.1
0
0
2
4
6
8
10
12
Cf, (mg/L)
b)
0.9
Pb
0.8
0.7
qe, (mg/g)
0.6
Lead
0.5
Fe
0.4
Iron
Cd
Cadmium
0.3
Chromium
0.2
Cr
0.1
0
0
2
4
6
8
10
12
Cf, ( mg/L)
Figure 5.
Langmuir and Freundlich Isotherms for Selected Heavy Metals Using Buffer Solution.
a) Plot of Langmuir Isotherm for Selected Heavy Metals Showing qe versus Cf.
b) Plot of Freundlich Isotherm for Selected Heavy Metals Showing qe versus Cf.
15
Iron and lead were chosen to show the competitive adsorption. When iron and lead were combined
using either deionized water or buffer solution, iron shows higher adsorptive capacity at higher
equilibrium concentrations; however, lead has higher adsorptive capacities at lower equilibrium
concentrations, for both of the isotherms (Langmuir and Freundlich) as shown in Figures 6 to 9. Also the
intercept of the lines in Figures 6 and 7, showed similar adsorptive capacities for iron and lead at
concentrations of approximately 2mg/L in both isotherms Langmuir and Freundlich. There is not a
significant change in solution pH after contact with straw.
a)
12
10
1/qe , (g/mg)
Fe
Pb
8
Iron
6
Fe, y = 3.1163x + 1.3343
R2 = 0.9938
4
Lead
Pb, y = 1.7618x + 2.0017
R2 = 0.9474
2
0
0
1
2
3
4
5
6
1/Cf , (L/mg)
b)
1
0.1
1
Fe
10
log qe (mg/g)
Pb
0.1
Iron
Fe, y = 0.2206x 0.5147
R2 = 0.9308
Pb, y = 0.2096x0.3049
lead
R 2 = 0.7877
0.01
log C (mg/L)
Figure 6.
Competitive Adsorption Langmuir and Freundlich Isotherm for Iron and Lead Using
Deionized Water (Linearized Form)
a) Plot of Langmuir Isotherm ( at pH 2.64) for Iron and Lead Showing 1/qe versus 1/Cf.
b) Plot of Freundlich Isotherm ( at pH 2.64) for Iron and Lead Showing log qe versus
log Cf.
16
a)
9
7
Fe, y = 8.0351x - 1.481
R 2 = 0.9767
6
Fe
1/qe, (g/mg)
8
5
Iron
4
Pb
3
Lead
2
Pb, y = 4.2691x + 0.4101
R 2 = 0.9855
1
0
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1/Cf , (L/mg)
b)
1
0.1
Pb, y = 0.2249x0.7548
1
10
log qe (mg/g)
R 2 = 0.9393
Pb
0.1
lead
Fe
Fe, y = 0.1328x1.5768
R 2 = 0.9929
Iron
0.01
log Cf (mg/L)
Figure 7.
Competitive Adsorption Langmuir and Freundlich Isotherm for Iron and Lead Using
Buffer Solution (Linearized Form).
a) Plot of Langmuir Isotherm (at pH 4.30) for Iron and Lead Showing 1/qe versus 1/Cf.
b) Plot of Freundlich Isotherm (at pH 4.30) for Iron and Lead Showing log qe versus
log Cf.
17
b)
a)
0.7
0.6
Fe
0.6
0.5
Pb
0.3
Iron
0.2
q e , ( m g /g )
0.5
0.4
q e , (m g / g )
Fe
0.4
0.1
0.1
0
0
2
4
6
8
10
Pb
0.2
Lead
0
Iron
0.3
0
12
2
4
Cf , (mg/L)
Figure 8.
6
8
Lead
10
Cf, ( mg/L)
Competitive Adsorption Langmuir and Freundlich Isotherm for Iron and Lead Using
Deionized water.
a) Plot of Langmuir Isotherm (at pH 4.30) for Iron and Lead Showing qe versus Cf.
b) Plot of Freundlich Isotherm (at pH 4.30) for Iron and Lead Showing qe versus Cf.
b)
a)
1.2
1.4
1
Fe
1.2
q e , (m g /g )
Iron
0.8
Pb
0.6
Lead
0.4
q e , (m g/g)
Fe
1
0.8
Iron
0.6
Pb
0.4
Lead
0.2
0.2
0
0
0
0
2
4
6
Cf, (mg/L)
Figure 9.
8
10
2
4
6
12
Cf, ( mg/L)
Competitive adsorption Langmuir and Freundlich Isotherms for Iron and Lead Using
Buffer Solution
a) Plot of Langmuir Isotherm (at pH 4.30) for Iron and Lead Showing qe versus Cf.
b) Plot of Freundlich Isotherm (at pH 4.30) for Iron and Lead Showing qe versus Cf.
18
To establish the adsorption isotherm and evaluate the adsorption capacity of straw, two heavy metals
(iron and lead) were run in triplicate. These experiments were done for iron alone, lead alone and the
combination of iron with lead, using deionized water; the results are shown in Figures 10 to 15.
The Figures 10 and 11 showed that iron has the same trend in the three tests performed for both of
isotherms models. As it can be seen in Figure 10 and 11 that the three lines (Fe 1, Fe 2, Fe 3) overlap one
upon the other showing the consistency of the results. Figures 12 to 15 illustrate the results for lead alone
and the combination of iron and lead, it seems to be that the straight lines follow the same behavior but
they do not overlap one on top of the other as it could be seen in iron alone. These plots indicate a
generalized trend that the higher the residual lead concentrations the higher the amount of lead adsorbed
onto the straw consistent with both Langmuir and Freundlich models. The fact that the isotherm plots do
not overlap one on top each other may be due to analytical errors and differences in the structure of straw
used in each experiment, the same explanation apply for the combined iron and lead system.
The correlation coefficients were higher for iron than for lead in both cases of iron alone and when
iron was combined with lead. However, iron and lead and their combination have good correlation
coefficients indicating a high degree of linearity and confirm that the adsorption of these heavy metals on
straw can be adequately describing the two isotherm models theories.
a)
b)
1
20
Fe (2) y = 7.6544x + 0.0326
R2 = 0.9929
15
Fe (1) y = 8.2418x + 0.0328
R2 = 0.9863
0.1
Fe (1)
Initial
10
Fe (2)
Replica 1
5
Fe (3) y = 7.0278x + 0.3484
R2 = 0.9773
Fe (3)
Replica 2
lo g q e ( m g /g )
1 / q e , (g / m g )
25
Fe(1) y = 0.1359x 0.883 1
R2 = 0.9087
10
Fe (1)
Initial
0.1
= 0.1326x 0.8853
Fe (2) y
R2 = 0.9157
Fe (3) y = 0.1236x 0.9452
R2 = 0.9446
0
0
0.5
1
1.5
1/Cf , (L/mg)
2
2.5
3
Fe (2)
Replica 1
Fe (3)
Replica 2
0.01
log C (mg/L)
Figure 10. Repeatability of the Isotherms Results for Iron (deionized water) Using Linearized
Form.
a) Plot of Langmuir Isotherm (at pH 3.5) for Iron Showing 1/qe versus 1/Cf.
b) Plot of Freundlich Isotherm (at pH 3.5) for Iron Showing log qe versus log Cf.
19
a)
0.9
0.8
Fe (2)
0.7
q e , (m g/g)
0.6
0.5
0.4
Fe (1)
Initial
Fe (1)
Fe (3)
Fe (2)
Replica 1
0.3
0.2
Fe (3)
Replica 2
0.1
0
0
2
4
Cf , (mg/L)
6
8
b)
0.8
0.7
Fe (2)
q e , (m g/g)
0.6
Fe (1)
Initial
0.5
Fe (1)
0.4
0.3
Fe (2)
Replica 1
Fe (3)
0.2
Fe (3)
Replica 2
0.1
0
0
Figure 11.
2
4
Cf , ( mg/L)
6
8
Repeatability of the Isotherms Results for Iron (deionized water) Using Isotherm
Equation.
a) Plot of Langmuir Isotherm (at pH 3.5) for Iron Showing qe versus Cf.
b) Plot of Freundlich Isotherm (at pH 3.5) for Iron Showing qe versus Cf.
20
a)
3
Pb (3) y = 0.5409x + 1.7462
R2 = 0.8858
1/q e , (g/mg)
2.5
2
Pb (1)
Initial
1.5
Pb (2) y = 0.332x + 1.5011
R2 = 0.9465
1
Pb (2)
Replica 1
Pb (3)
Replica 2
0.5 Pb (1) y = 0.7032x + 1.0859
R2 = 0.9547
0
0
0.5
1
1/Cf , (L/mg)
1.5
2
b)
1
0.1
1
Pb (1)
y = 0.5471x0.264
10
log q e (mg/g)
R 2 = 0.849
Pb (1)
Initial
Pb (2)
y = 0.527x0.1239
Pb (3)
R 2 = 0.8594
y = 0.4445x0.1153
R 2 = 0.6936
Pb (2)
Replica 1
Pb (3)
Replica 2
0.1
log C (mg/L)
Figure 12.
Repeatability of the Isotherms Results for Lead (deionized water) Using Linearized
Form.
a) Plot of Langmuir Isotherm (at pH 3.93) for Lead Showing 1/qe versus 1/Cf.
b) Plot of Freundlich Isotherm (at pH 3.93) for Lead Showing log qe versus log Cf.
21
a)
0.9
Pb (1)
0.8
0.7
Pb (2)
q e , (mg/g)
0.6
Pb (1)
Initial
0.5
0.4
Pb (2)
Replica 1
Pb (3)
0.3
0.2
Pb (3)
Replica 2
0.1
0
0
1
2
3
4
5
Cf, (mg/L)
6
7
8
b)
0.9
Pb (3)
0.8
q e , (mg/g)
0.7
Pb (2)
0.6
Pb (1)
Initial
0.5
0.4
0.3
Pb (2)
Replica 1
Pb (3)
0.2
Pb (3)
Replica 2
0.1
0
0
Figure 13.
2
4
Cf, ( mg/L)
6
8
Repeatability of the Isotherms Results for Lead (deionized water) Using Isotherm
Equation.
a) Plot of Langmuir Isotherm (at pH 3.93) for Lead Showing qe versus Cf.
b) Plot of Freundlich Isotherm (at pH 3.93) for Lead Showing qe versus Cf.
22
a)
16
Fe (3)
y = 4.9321x + 2.8056
R2 = 0.989
14
Fe (2)
y = 5.026x + 2.593
R2 = 0.9961
1/qe , (g/mg)
12
Pb (2)
y = 3.6882x + 3.4672
Pb (3)
R2 = 0.941
y = 2.3003x + 3.9462
R2 = 0.936
Fe (1) Initial
Fe (2) Replica 1
10
Fe (3) Replica 2
8
Pb (1)
y = 1.7618x + 2.0017
R2 = 0.9474
6
Pb (1) Initial
4
Pb (2) Replica 1
Fe (1)
y = 3.1163x + 1.3343
R2 = 0.9938
2
Pb (3) Replica 2
0
0
1
2
3
4
5
6
1/Cf , (L/mg)
b)
1
Pb (1)
y = 0.2096x 0.3049
R2 = 0.7877
log qe (mg/g)
0.1
1
10
100
Fe (2)
y = 0.1364x 0.4114
R2 = 0.9316
0.1
Fe (1) Initial
Fe (2) Replica 1
Fe (3) Replica 2
Pb (2)
y = 0.13x 0.3159
R2 = 0.6077
Pb (3)
y = 0.137x 0.2653
R2 = 0.5718
Fe (1)
y = 0.2206x 0.5147
R2 = 0.9308
Fe (3)
y = 0.1054x 0.602
R2 = 0.951
Pb (1) Initial
Pb (2) Replica1
Pb (3) Replica 2
0.01
log C (mg/L)
Figure 14.
Repeatability of the Isotherms Results for the Combined Iron and Lead Systems
(Linearized Form).
a) Plot Langmuir Isotherm (at pH 3.0) for Iron and Lead Showing 1/qe versus 1/Cf.
b) Plot Freundlich Isotherm (at pH 3.0) for Iron and Lead Showing log qe versus log Cf.
23
a)
0.6
0.5
Fe (1)
Initial
Fe (2)
Replica1
Fe (3)
Replica 2
Pb (1)
Initial
qe , (mg/g)
0.4
0.3
0.2
Pb (2)
Replica 1
Pb (3)
Replica 2
0.1
0
0
2
4
6
8
10
12
Cf, (mg/L)
b)
0.7
0.6
Fe (1)
Initial
Fe (2)
Replica 1
Fe (3)
Replica 2
Pb (1)
Initial
Pb (2)
Replica 2
Pb (3)
Replica 2
qe , (mg/g)
0.5
0.4
0.3
0.2
0.1
0
0
2
4
6
8
10
12
Cf , ( mg/L)
Figure 15.
Repeatability of the Isotherms Results for the Combined Iron and Lead Systems.
a) Plot Langmuir Isotherm (at pH 3.0) for Iron and Lead Showing qe versus Cf.
b) Plot Freundlich Isotherm (at pH 3.0) for Iron and Lead Showing qe versus Cf.
24
From the results shown in these figures, the parameters for the Langmuir and Freundlich isotherm
models were determined for each of the heavy metals systems and the combined of iron and lead system.
These parameters are Qo and b for the Langmuir isotherm, and KN and 1/n for the Freundlich isotherm.
The highest adsorption capacity (Qo) using deionized water was: iron followed by lead, cadmium and
chromium, and the highest adsorption capacity using buffer solutions was: iron followed by chromium,
lead and cadmium. Because the value of 1/n was less than 1.0 for most of the heavy metals, the
adsorption of these heavy metals onto straw appears to be very favorable. The results are
summarized in Table 1.
Table 1. Summary of Langmuir and Freundlich Isotherm Constants.
Deionized
Water
and
Buffer
Solution
Deionized
Water
Buffer
Solution.
(Acetic Acid
and Sodium
Acetate)
Langmuir Isotherm
Model
Freundlich Isotherm
Model
Heavy Metals and pH
Qo
b
r2
KN
1/n
r2
Iron at pH 3.56
[5.811]
[0.023]
0.8284
0.090
1.143
0.9117
Lead pH= 3.72
2.946
0.172
0.8365
0.418
0.543
0.7272
Cadmium at pH= 3.79
0.217
7.809
0.8315
0.160
0.089
0.3796
[0.079]
[0.056]
0.9522
0.002
1.768
0.9926
0.749
0.428
0.9938
0.221
0.515
0.9308
0.500
1.136
0.9474
0.210
0.305
0.7877
5.244
0.028
0.9989
0.155
1.081
0.9929
Lead pH= 4.36
1.276
0.368
0.9943
0.327
0.600
0.9521
Cadmium at pH= 4.33
0.863
0.046
0.9983
0.035
0.862
0.9948
Chromium at pH= 4.39
Iron at
Iron and Lead
pH= 4.30
combine
Lead at
pH= 4.30
2.626
0.003
0.9493
0.010
0.805
0.9941
[0.675]
[0.184]
0.9767
0.133
1.577
0.9929
2.438
0.096
0.9855
0.225
0.755
0.9393
Chromium at pH= 4.19
Iron at
Iron and Lead
ph= 2.64
combine
Lead at
pH= 2.64
Iron at pH= 4.10
25
5.2
Results for Task 3
(3) Performing continuous flow through packed bed reactors containing straw which was untreated or
treated with sodium sulfide, and analyzing the biodegradability of the straw.
Four sets of experiments were performed:
S1: Straw alone subjected to water;
S2: Straw coated with sodium sulfide subjected to water;
S3: Straw with iron adsorption subjected to water; and
S4: Straw coated with sodium sulfide with iron adsorption subjected to water.
Treatment Times: 1,2,4,8, and 16 weeks.
5.2.1
Mass Balance
Columns that are 2.0-inches in diameter and 1.0-ft long were packed with straw (~50-gm). To these
columns, water was applied on a daily basis to completely fill the straw-packed columns; this water was
then allowed to drain and was collected for subsequent analysis. This procedure simulates rainfall events
in the Birmingham region. These straw degradation studies employ four different conditions: S1 – straw
alone subjected to water; S2 – straw coated with sulfide subjected to water; S3 – straw with iron
adsorption subjected to water; and S4 – straw coated with sulfide with iron adsorption subjected to water.
The experiments with these columns were performed in triplicate. Treatment times employed in the study
were 1, 2, 4, 8, and 16 weeks. From Figures 16 to 20, it appears that release of sodium sulfide, iron, and
sodium sulfide with iron, was completed after 20 days, with the maximum desorption occurring within
the first two weeks for all conditions. The overall modeling shows the trend of conditions (S2), (S3), and
(S4).
Comparing the condition S3 versus S4 in a period of time of 16 weeks with S3 versus S4 in a period
of time of 8 weeks in Figure 20 (a) (b), it shows that in 16 weeks a slightly release of iron (S3) in
contrasted with the release of iron when it is combined with sodium sulfide where it happens easily. From
Figure 20 (c), it can be seen that after 10 days the desorption of iron is completed when iron was
combined with sodium sulfide, more time is required to completely desord the iron than when iron is
alone.
26
b)
a)
200
160
Column
16 w eeks
120
Column
8 w eeks
Column
4 w eeks
80
Column
2 w eeks
40
Column
1 w eek
M ass S o d iu m S u lfid e, (m g )
M a s s S o d iu m S u lfid e , (m g )
200
Column
16 w eeks
160
50
100
Overall: y = 84.021x
R 2 = 0.3533
120
Column
2 w eeks
40
Column
1 w eek
0
Overall
modeling
0
150
20
40
60
80
Time, (days)
Time, (days)
Figure 16.
Column
4 w eeks
80
0
0
Column
8 w eeks
-1.0572
Desorption of Sodium Sulfide by Continuous Flow Conditions (S2).
a) Mass Balance Sodium Sulfide.
b) Mass Balance Sodium Sulfide Including Overall Modeling.
b)
a)
0.35
0.3
Column
16 w eeks
0.25
Column
8 w eeks
M a s s Iro n , (m g )
0.3
Column
16 w eeks
0.25
0.2
Column
8 w eeks
0.15
Column
4 w eeks
0.1
Column
2 w eeks
0.05
Column
1 w eek
M a s s Iro n , (m g )
0.35
Overall: y = 0.3874x-0.7997
R2 = 0.728
0.2
Column
4 w eeks
0.15
Column
2 w eeks
0.1
Column
1 w eek
0.05
overall
modeling
0
0
0
20
40
60
80
100
120
0
20
Time, (days)
Figure 17.
40
60
80
100
120
Time, (days)
Desorption of Iron by Continuos Flow Conditions (S3).
a) Mass Balance Iron.
b) Mass Balance Iron Including Overall Modeling.
27
b)
0.45
0.45
0.4
0.4
Column
16 w eeks
0.35
0.3
Column
8 w eeks
0.25
Column
4 w eeks
0.2
0.15
Column
2 w eeks
0.1
M ass Iro n , (mg )
M ass Iro n , (mg )
a)
0.35
Column
8 w eeks
0.3
Column
4 w eeks
0.25
-0.9135
Overall: y = 0.2167x
0.2
Column
2 w eeks
R2 = 0.724
0.15
Column
1 w eek
0.1
Column
1 w eek
0.05
Column
16 w eeks
overall
modeling
0.05
0
0
0
20
40
0
60
20
30
40
50
60
Time, (days)
Time, (days)
Figure 18.
10
Desorption of Iron when Combined with Sodium Sulfide by Continuous Flow
Conditions (S4).
a) Mass Balance Iron when Combined with Sodium Sulfide.
b) Mass Balance Iron when Combined with Sodium Sulfide Including Overall Modeling.
b)
a)
50
Column
16 w eeks
40
Column
8 w eeks
30
Column
4 w eeks
20
Column
2 w eeks
10
Column
1 w eek
0
M ass S o d iu m S ulfide, (m g )
M ass S odium S ulfide, (m g)
50
40
Column
16 w eeks
Overall: y = 158.22x -1.9144
R2 = 0.9723
Column
8 w eeks
30
Column
4 w eeks
Column
2 w eeks
20
Column
1 w eek
10
Overall
modeling
0
0
Figure 19.
20
40
Time, (days)
60
0
20
40
Time, (days)
60
Desorption of Sodium Sulfide when Combined with Iron by Continuous Flow
Conditions (S4).
a) Mass Balance Sodium Sulfide when Combined with Iron.
b) Mass Balance Sodium Sulfide when Combined with Iron Including Overall
Modeling.
28
a)
b)
0.35
0.3
0.3
0.25
Mass Iron, (mg)
Mass Iron (m g)
0.25
0.2
0.15
0.1
0.2
0.15
0.1
0.05
0.05
0
0
0
20
40
60
80
100
120
0
10
Time (Days)
20
30
40
50
60
Time, (days)
Column 16 weeks (S3) Iron only
Column 8 weeks (S3) Iron only
Column 16 weeks (S4) Combination of Iron and Sodium Sulfide
Column 8 weeks (S4) Combination of Iron and Sodium Sulfide
c)
Mass Sodium Sulfide, (mg)
200
180
160
140
120
100
80
60
40
20
0
0
10
20
30
40
50
60
Time, (days)
Colum n 8 weeks (S2) Sodium Sufide only
Colum n 8 weeks (S4) Com bination of Iron and Sodium Sulfide
Figure 20.
Comparing S2-S3 and S4 Experiments at 16 and 8 Weeks Treatment.
a) Mass Balance S3 versus S4 16 Weeks.
b) Mass Balance S3 versus S4 8 Weeks.
c) Mass Balance S2 versus S4 8 Weeks.
29
5.2.2
Straw Biodegradation Results
Figures 21 to 29 show the results for biodegradation of straw.
Comparison of straw maintained in anaerobic deinozed water, anaerobic basal, and aerobic basal
media showed that the greatest total weight loss occurred by the 8th week of treatment. The aerobic basal
sample produced a 3.50% greater total weight loss than the aerobic deionized water. The anaerobic
deionized water sample produced a 2.10% greater total weight loss than the anaerobic basal sample. The
anaerobic deionized water sample showed a 2.06g greater weight loss than the anaerobic basal sample.
The aerobic basal sample showed a 3.74g greater weight loss than the aerobic deinozed water sample.
This is logical in that the basal medium contains nutrients that would support microbial growth and the
aerobic environment allows for more efficient catabolism of organic components of the wheat straw. The
samples suspended in basal medium show the same general curve for both aerobic and anaerobic
environments. Overall, percent weight loss reaches its peak at week 8 and decreases to week 16. This
could reflect the readily biodegradable fraction being removed by the first half of the treatment period.
The initial sample, T0, contains approximately 42% readily biodegradable material as determined by
the NDF analysis. Percent biostability decreases over time for most samples. Anaerobic basal and
deionized water and aerobic deionized water samples show a decrease in readily biodegradable material
from the initial sample to week 4. The amount of readily biodegradable material decreases from
approximately 42% in the initial samples (T0) to approximately 20% by the fourth week of treatment. For
the anaerobic basal and aerobic deionized water sample, there is a slight increase in the amount of readily
biodegradable material (approximately 25%) by the sixteenth week of treatment. The anaerobic
deionized water sample shows a further slight decrease (approximately 15%) by week 8 before recovering
in week 16 to a small increase of 4% (approximately 19%).
The aerobic basal sample shows a slight decrease for the initial amount of readily biodegradable
material by week 4 (approximately 38%). By the sixteenth week of treatment, this sample has increased
its readily biodegradable fraction to approximately 50%.
NDF does not distinguish the source of the readily biodegradable material. This material may come
from the growth of microbes degrading the wheat straw. The increase observed in the aerobic samples
(basal and DI water) and in the anaerobic basal sample can be explained as an increase in microbial
biomass as treatment time increases. It was noted that both anaerobic samples retained a golden-brown
color and the straw remained whole. The aerobic DI water sample straw showed a brown coloration,
more turbid flask supernatant, and some visible degradation of wheat straw. The most visible degradation
was observed in the aerobic basal flask where wheat straw was a dull, dark brown color, flask supernatant
showed high turbidity, wheat straw appeared to be shredded, and minute pieces of straw floated in the
flask supernatant. It was noted that the aerobic basal and the DI water supernatant contained particles that
would settle out, and more precipitant or settle able solids was observed in the aerobic basal sample than
the aerobic DI water sample.
30
Total Loss of Straw Treated with Basal Medium or DI Water
under Aerobic or Anaerobic Conditions
40
% Loss
30
Anaerobic Basal Medium
Anaerobic DDI water
20
Aerobic Basal Medium
Aerobic DDI water
10
0
0
5
10
15
20
Time (weeks)
Figure 21.
Total Loss of Straw Treated with Basal Medium or Deionized Water under Aerobic
or Anaerobic Conditions.
% Degradation
% Biodegradability of Straw Treated with Basal Medium or DI Water
under Aerobic or Anaerobic Conditions
80
70
60
50
40
30
20
10
0
Anaerobic Treated
Anaerobic DDI
Aerobic Treated
Aerobic DDI
0
5
10
15
20
Time (weeks)
Figure 22.
% Biodegradability of Straw Treated with Basal Medium or Deionized Water under
Aerobic or Anaerobic Conditions.
31
Total Average Loss of Straw Treated with Basal Medium or
DI Water under Anaerobic or Aerobic Conditions
R2 = 1
40
% Loss
30
20
10
0
0
2
4
6
8
10
12
14
16
18
Time (weeks)
Figure 23.
Total Average Loss of Straw Treated with Basal Medium or Deionized Water under
Anaerobic or Aerobic Conditions.
Total Loss of Straw Treated with Sulfide, Iron, and Iron + Sulfide from Columns
80
Total % Loss
70
60
Untreated
50
Sulfide Teated
40
Iron Treated
30
Sulfide+Iron Treated
20
10
0
0
2
4
6
8
10
12
14
16
18
Time, (weeks)
Figure 24.
Total Loss of Straw Treated with Sulfide, Iron, and Iron+ Sulfide from Columns.
32
Total Loss of Straw (Average) from Columns
80
y = 0.0069x3 - 0.2584x2 + 3.889x + 2.1249
2
R = 0.9368
70
% Loss
60
50
40
30
20
10
0
0
2
4
6
8
10
12
14
16
18
Time, (weeks)
Figure 25.
Total Loss of Straw (Average) from Columns.
Biostability of Straw Treated with Sulfide, Iron, and Sulfide+Iron
from Columns
80
% Biodegradable
70
60
Untreated
50
Sulfide Treated
40
Iron Treated
30
Sulfide+Iron Treated
20
10
0
0
5
10
15
20
Time, (weeks)
Figure 26.
Biostability of Straw Treated with Sulfide, Iron, and Sulfide + Iron from Columns.
33
Average Biostability of Straw Treated with Sulfide, Iron, and
Sulfide+Iron from Columns
80.00
% Biodegradable
70.00
60.00
50.00
40.00
30.00
20.00
10.00
0.00
0
2
4
6
8
10
12
14
16
18
Time, (weeks)
Average Biostability of Straw Treated with Sulfide, Iron, and Sulfide + Iron from
Column.
1.1
1.0
0.9
Iron Concentration, (mg/L)
Figure 27.
0.8
0.7
0.6
0.5
Column 12
Column 16
0.4
Column 19
0.3
0.2
0.1
0.0
0
20
40
60
80
100
120
Treatment Time, (days)
Figure 28. Iron Concentration in Leachate from Packed Columns.
34
Fraction Biodegradable Material as a
Function of Straw Treatment
0.50
Fraction Biodegradable Material
0.45
0.40
0.35
0.30
x - sigma
0.25
Mean
x + sigma
0.20
0.15
0.10
0.05
S
416
S
41
S
316
S
31
S
216
S
21
S
116
S
11
O
rig
in
al
0.00
Straw Treatment
Figure 29. Straw Biodegradation Results.
5.3
Results for Task 4
(4) Performing continuous flow processing of heavy metal-laden solutions (selected heavy metals)
through packed bed reactors containing straw.
Heavy metals-laden water was allowed to pass through beds packed with straw for adsorption and
uptake by the straw. The breakthrough characteristics of passing the solutions through the straw were
determined for single heavy metals systems involving iron and lead. These breakthrough characteristics
were determined using an up-flow column system, as show in the Figure 30. These breakthrough curves
were compared to those obtained for the multimetal systems involving iron and lead simultaneously, as
shown in Figures 31 to 32.
The low adsorptive capacities of lead and iron resulted in fairly rapid breakthrough characteristics
(less than 5-10 bed volumes for lead and iron onto straw). However, at high concentrations, both lead and
iron were removed to nearly the same efficiency, while lower heavy metals concentrations allowed the
breakthrough curve to be shifted to the right, achieving greater time before breakthrough as shown in the
Figure 31. In addition, when iron and lead were combined, iron was adsorbed and retained more
effectively by the straw than lead, resulting in a preferential adsorption of the iron; thus lead breaks
through the column before iron, indicating a competition for the site, as shown in Figure 31 (c).
35
Comparing iron alone and lead alone with the combination of iron + lead, from Figure 32 (a) (b) it
was observed that iron alone and lead alone required more time to reach the breakthrough point than
when iron and lead were combined, due to the competition of the sites when both heavy metals were
together.
Heavy Metal
Solution Influent
Effluent
Figure 30.
Breakthrough Curve Showing Up-flow System Set Up.
36
b)
a)
1.2
1.2
1
1
Iron 10 mg/L
(10 mL/min)
0.8
Iron 5 mg/L
(10 mL/min)
0.6
0.4
Iron 10 mg/L
(5 mL/Min)
0.2
Iron 5 mg/L
(5 mL/min)
C /C o
C /C o
0.8
0
0
20
40
60
80
100
120
140
160
Lead 10 mg/L
(10 ml/min)
0.6
0.4
Lead 5 mg/L
(10 ml/Min)
0.2
Lead 5 mg/L
(5 ml/min)
0
180
0
20
Number of Bed Volumes
40
60
80
100
120
140
Number of Bed Volumes
c)
1.2
C /C o
1
0.8
Iron 5mg/L
(10 ml/min)
0.6
Lead 5 mg/L
(10ml/min)
0.4
Iron 5 mg/L
(5 ml/min)
Lead 5 mg/L
(5 ml/min)
0.2
0
0
20
40
60
80
100
120
140
Number of Bed Volumes
Figure 31.
Breakthrough Curve for Iron alone and Lead alone and the Combined (iron and lead)
System at Different Flow Rates.
a) Breakthrough Curve for Iron Showing Concentrations versus Through Put.
b) Breakthrough Curve for Lead Showing Concentrations versus Through Put.
c) Breakthrough Curve for Iron + Lead Showing Concentrations versus Through Put.
37
a)
1.2
"Iron 5 mg/L
(10 mL/min)"
w hen combined
w ith Lead
1
Iron 5 mg/L
(10 mL/min)
alone
C/C o
0.8
0.6
Iron 5 mg/L
(5 mL/min)
w hen combined
w ith Lead
0.4
Iron 5 mg/L
(5 mL/min)
alone
0.2
0
0
50
100
150
200
Number of Bed Volumes
b)
1.2
Lead 5 mg/L
(10 mL/min)
w hen combined
w ith Iron
1
Lead 5 mg/L
(10 mL/min)
alone
C/Co
0.8
0.6
Lead 5 mg/L
(5 mL/min)
w hen
Combined w ith
Iron
Lead 5 mg/L
(5 mL/min)
alone
0.4
0.2
0
0
20
40
60
80
100
120
140
Number of Bed Volumes
Figure 32.
Comparison of Iron Alone and Lead Alone with the Combination of the Iron + Lead
System.
a) Breakthrough Curve for Iron Alone Compared with the Combination of Iron +Lead Showing
Concentration versus Through Put.
b) Breakthrough Curve for Lead Alone Compared with the Combination of Iron +Lead Showing
Concentration versus Through Put.
38
5.4
Results for Task 5 and 6
(5) Modeling the bed depth-service time behavior of the continuous heavy metal solution throughput in
the packed bed system; and (6) Performing preliminary scale-up design for pilot-scale activities.
The data collected from Task 4 was used to model the bed depth – service time (BDST) behavior for
continuous treatment applications. A brief description of the BDST system and model is provided in
theoretical section. The model parameters “a” and “b” were determined to suggest a procedure design.
Estimated Data were used to propose a procedure design for follow-on pilot-scale activities (using larger
scale columnar flow systems).
The estimated data for the breakthrough curves of iron and lead and iron with lead was selected and
using for analysis of the Bed Depth Service Time system. Plots of these data are shown in Figures 33a,
34a, 35a, and 36a, where the values of C/Co=0.90 and C/Co= 0.10 were found for solutions of iron, lead,
iron combined with lead, and lead combined with iron, respectively. These values gave the times to which
the breakthrough point and exhaustion point were reached respectively. Plots were performed using this
times and the bed depths, as described in Figures 33b, 34b, 35b, and 36b, and showing a straight line
behavior and correlative coefficients close to one, indicating that the flow rate and composition of the
effluent were reasonably constant throughout the test. From these plots, the slope “a” which is the amount
of time required for the adsorption wave front to move through one ft of straw, the intercept “b” which is
the time required for the adsorption wave front to pass through the critical bed depth, “D” which is the
critical bed depth, and “Q” (straw exhaustion rate) were determined and are summarized in Table 2.
From Table 2, it can be seen that the slope “a” was higher for the heavy metals alone than for the
combined sample, and the same results were obtained for the intercept “b”. Similar situations were found
for the parameter “D” which is the minimum column depth to obtain an acceptable effluent concentration
at time zero; “D” was higher for iron alone and lower for lead + iron. Additionally, it was found that the
minimum number of columns in series required for the system were two for iron, lead, and (iron + lead)
systems, but it is suggested to use three columns for better results.
The straw exhaustion rate (Q) was calculated using the slope “a”, the cross section of the column “A”,
and the density of the straw (ρ). It was found that when iron and lead were combined, the straw
exhaustion rate was higher compare to the straw exhaustion rate for iron alone and lead alone. Iron and
lead showed similar straw exhaustion rate values.
39
a)
b)
1.1
80
1
70
0.9
S e rv ic e T im e , (h rs )
0.8
C/C o
0.7
0.6
0.5
0.4
0.3
0.2
0.1
C/Co=90%
60
50
y = 0.9142x + 0.3333
R2 = 0.9999
40
D=26.80 cm
30
20
y = 0.9142x - 24.167
R2 = 0.9999
10
0
0
10
20
30
40
50 60
70
Tim e, (hours)
Iron 5 mg/L (10ml/min)
80
90
100
110
0
"Iron 5mg/L (10ml/min)
-10
10
30
50
70
90
Bed Depth, (cm)
Iron 5mg/L (10ml/min)
Figure 33.
C/Co=10%
Bed Depth Service Time Relationship for Iron Using Estimated Data.
a) Breakthrough Curve for Iron Showing C/Co versus Time Using Estimated Data.
b) Bed Depth Service Time for Iron Using Estimated Data.
b)
1.1
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
80
70
0
20
40
60
80
Time, (hr)
Lead 5 mg/L
(10 ml/min)
Lead 5 mg/L (10 ml/min)
Figure 34 .
100
S e r v ic e T im e , (h rs )
C/C o
a)
C/Co=90%
60
50
y = 0.9142x - 0.3667
R2 = 0.9999
40
30
C/Co=10%
D= 26.5 cm
20
y = 0.9142x - 24.617
R2 = 0.9999
10
0
Lead 5 mg/L (10ml/min)
0
20
40
60
80
100
Bed Depth, (cm)
Bed Depth Service Time Relationship for Lead Using Estimated Data
a) Breakthrough Curve for Lead Showing C/Co versus Time Using Estimated Data.
b) Bed Depth Service Time for Lead Using Estimated Data.
40
b)
a)
1.2
50
1
45
Service Time, (hrs)
C /C o
C/Co=90%
40
0.8
0.6
0.4
0.2
35
30
y = 0.541x - 0.3667
R2 = 0.9998
25
20
C/Co= 10%
D= 26.3 cm
15
10
0
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70
Time, (hr)
Iron 5 mg/L
(10 ml/min)
y = 0.5411x - 14.618
R2 = 0.9997
5
0
0
Iron 5 mg/L (10 ml/min)
20
40
Iron 5 mg/L (10 ml/min)
Figure 35.
60
80
100
Bed Depth, (cm)
Bed Depth Service Time Relationship for Iron Combined with Lead Using Estimated
Data.
a) Breakthrough Curve for Iron Combined with Lead Showing C/Co versus Time.
b) Bed Depth Service Time Iron Combined with Lead Using Estimated Data.
a)
b)
1.2
18
1
16
S e rv ic e T im e , (h rs )
C/C o
0.8
0.6
0.4
0.2
0
0
5
10
15
20
25
30
35
Time, (hr)
Lead 5 mg/L (10ml/min)
Lead 5 mg/L (10ml/min)
Figure 36.
Lead 5 mg/L (10ml/min)
40
C/Co=90%
14
12
y = 0.1959x - 0.3333
R2 = 0.9997
10
8
C/Co= 10%
D= 25.3 cm
6
4
y = 0.1959x - 5.2832
R2 = 0.9994
2
0
0
20
40
60
80
100
Bed Depth, (cm)
Bed Depth Service Time Relationship for Lead Combined with Iron Using Estimated
Data.
a) Breakthrough Curve for Lead Combined with Iron Showing C/Co versus Time.
b) Bed Depth Service Time Lead Combined with Iron Using Estimated Data.
41
Bed Depth-Service Time Iron
Combined with Lead Using Estimated Data
Bed Depth-Service Time for Iron Using
Estimated Data
50
80
70
45
C/Co=90%
C/Co=90%
S e r v i c e T i m e , (h r s )
40
50
S ervice T im e, (h rs)
60
y = 0.9142x + 0.3333
R2 = 0.9999
40
D=26.80 cm
30
20
C/Co=10%
y = 0.9142x - 24.167
R2 = 0.9999
10
35
30
y = 0.541x - 0.3667
R2 = 0.9998
25
20
C/Co= 10%
D= 26.3 cm
15
10
y = 0.5411x - 14.618
R2 = 0.9997
5
0
-10
10
30
50
70
0
90
0
20
40
Bed Depth, (cm)
Figure 37.
80
100
Bed Depth, (cm)
Bed Depth Service Time Comparing Iron Alone with Iron Combined with Lead Using
Estimated Data.
Bed Depth-Service Time for Lead Using
Estimated Data
Bed Depth-Service Time for Lead
Combined with Iron Using Estimated Data
80
18
70
C/Co=90%
16
60
50
y = 0.9142x - 0.3667
R2 = 0.9999
40
30
S e rv ic e T im e , (h rs )
S ervice T im e, (h rs)
60
C/Co=10%
D= 26.5 cm
20
y = 0.9142x - 24.617
R2 = 0.9999
10
C/Co=90%
14
12
y = 0.1959x - 0.3333
R2 = 0.9997
10
8
C/Co= 10%
D= 25.3 cm
6
4
y = 0.1959x - 5.2832
R2 = 0.9994
2
0
0
20
40
60
Bed Depth, (cm)
Figure 38.
80
100
0
0
20
40
60
80
100
Bed Depth, (cm)
Bed Depth Service Time Comparing Lead Alone with Lead Combined with Iron
Using Estimated Data.
42
Table 2. Summary of the Results for Bed Depth Service Time Relationship
Solution
Heavy Metal
D
(cm)
d
(cm)
n
(#)
A
a
(cm2) (hr/cm)
b
(hr)
ρ
(mg/cm3)
Height of
Height Number of Column Slope Intercept Density
Adsorption Zone
of
Full Scale
Area
of
Column Columns
Straw
Q
(mg/hr)
Straw
Exhaustion
Rate
Iron alone
26.8
26.8
2.0
7.0686 0.9142
24.167
152.16
1176.502
Lead alone
26.5
26.8
2.0
7.0686 0.9142
24.617
152.16
1176.502
Iron +Lead
26.3
26.8
2.0
7.0686 0.5411
14.618
152.16
1987.725
Lead + Iron
25.3
26.8
2.0
7.0686 0.1959
5.2832
152.16
5490.343
6 Summary and Conclusions
Isotherm studies were performed investigating the uptake of heavy metals using straw. The results
suggest that the adsorption of iron, cadmium, and chromium is better described by the Langmuir isotherm
than by the Freundlich isotherm model. Lead adsorption onto straw can be described equally well using
either the Langmuir or Freundlich isotherms. No significant change in solution pH was observed after
contact with the straw.
Better correlation coefficient and better adsorption capacities were obtained when using a buffer
solution. Adsorption capacities of iron and lead were greater than 1.5 mg/g of straw compared to 0.5 mg/g
for the combined iron and lead system. Additionally, the adsorption capacities of cadmium and chromium
were less than 0.3 mg/L.
Sulfide enhanced wheat Straw may prove effective in trapping heavy metals from urban runoff, but
only if the sulfide can be attached to a more stable component of wheat straw such as lignin, cellulose, or
hemicellulose. Bales of wheat straw treated solely by soaking in sulfide solutions will last only a month
before the sulfide is washed off by rain events. However, bales pre-treated to remove the readily
biodegradable fraction and the treated to attach the sulfide to a more stable component may be able to
retain the sulfide and any trapped heavy metals during rain events.
Through periodic wetting and drying of the straw to simulate rainfall events, a loss of ~20% of the
dry weight of the straw was observed after 8 weeks. About 41% of the straw is readily biodegradable,
57% is slowly degradable, and 2% consists of ash. About 10-15% of the straw was lost due to mechanical
aspects of washing.
43
NDF does not distinguish the source of the readily biodegradable material. This material may come
from the wheat straw itself or from the microbes degrading the wheat straw. The increase observed in the
aerobic samples (basal and DI Water) and in the anaerobic basal samples can be explained as an increase
in microbial biomass as treatment time increases. It was noted that both anaerobic samples retained a
golden-brown color and the straw remained whole. The aerobic DI Water sample straw showed a brown
coloration, more turbid flask supernatant, and some visible degradation of wheat straw. The most visible
degradation was observed in the aerobic basal flask where wheat straw was a dull, dark brown color, flask
supernatant showed high turbidity, the wheat straw appeared to be shredded, and minute pieces of straw
floated in the flask supernatant. It was noted that the aerobic basal and DI Water supernatant contained
particles that would settle out, though more precipitates was observed in the aerobic basal sample than the
aerobic DI water sample.
The low adsorptive capacities of lead and iron result in fairly rapid breakthrough characteristics (less
than 5-10 bed volumes for lead and iron onto straw). However, at high concentrations, both lead and Iron
were removed to nearly the same efficiency, while lower heavy metals concentrations allowed the
breakthrough curve to be shifted to the right achieving greater time before breakthrough. In addition,
when iron and lead were combined, iron was adsorbed and retained more effectively by the straw than
lead, resulting in a preferential adsorption of the iron; thus, lead breaks through the column before iron,
indicating a competition for the site.
From the Bed Depth Service Time analysis, the straw exhaustion rate (Q) was calculated using the
slope (a), the cross section of the column (A), and the density of the straw (ρ). When iron and lead were
combined, the straw exhaustion rate was higher compared to that for iron alone and lead alone. Iron and
lead exhibit similar straw exhaustion rates. From knowledge of the Bed Depth Service Time parameters
the system can be scaled to handled large through put system and thereby be applicable for field
demonstrations.
7 Synopsis
Below is the Synopsis that was submitted to the Alabama Water Resources Research Institute.
“TREATMENT OF HEAVY METAL-CONTAMINATED RUNOFF USING
STRAW COATED WITH SULFIDE”
A statement of the problem and research objectives:
Runoff from construction sites, roofs, and roadways is known to contain heavy metals as trace
contaminants, and can affect the bioecosystems near these runoff sites. Urban stormwater runoff has been
recognized as a substantial source of pollutants to receiving waters [Davis et al., 2001]. Urban settings are
a focal point for environmental contamination due to emissions from industrial and municipal activities
and the widespread use of motor vehicles [Callender and Rice, 2000]. During storm events, a considerable
increase in the concentrations of particle number, suspended solids mass, organic carbon, iron, and zinc
have been observed in runoff streams [Characklis and Wiesner, 1997]; the concentration of zinc in runoff
was highly correlated with organic carbon and iron exists primarily in the macro colloidal fraction. Hares
44
and Ward [1999] studied the concentration of motorway-derived contaminants including V, Cr, Mn, Co,
Ni, Cu, Zn, Mo, Cd, Sb, and Pb, were measured in unfiltered stormwater collected during the initial
stages of storm events. A higher level of motor-derived heavy metal contamination exists in stormwater
runoff from road sections with a higher average daily traffic density. The transport of anthropogenic
constituents by runoff from urban roadways can adversely impact the quality of adjacent receiving waters
and soils [Sansalone et al., 1996]. Heavy metal elements are the most persistent constituents found in
pavement runoff [Sansalone et al., 1996]. Legret and Pagotto [1999] conducted a study investigating the
quality of pavement runoff water from a 275-m motorway section over a one-year time frame, during
which 50 rain events were sampled. Two different types of pollution were revealed; the first type was
identified as chromic pollution and included suspended solids, chemical oxygen demand, total
hydrocarbons, lead, and zinc. The second type of pollution was seasonal and incorporates chlorides,
sulfates, suspended solids, and heavy metals due to the use of deicing salt in the wintertime. Runoff from
roads have negative effects on biotic integrity in both terrestrial and aquatic ecosystems [Trombulak and
Frissell, 2000]. Roads affect soil density, temperature, soil water content, light levels, dust, surface
waters, patterns of runoff, and sedimentation, as well as adding heavy metals (especially lead), salts,
organic molecules, ozone, and nutrients to roadside environments [Trombulak and Frissell, 2000]. The
runoff chemistry from uncontrolled discharges of highway runoff can significantly impact receiving water
quality and may require remediation by appropriate stormwater best management practices [Marsalek et
al., 1997]. This project seeks to develop an efficient and low-cost technology to capture heavy metals
from contaminated runoff, namely using straw that has been coated with sulfide compounds to bind the
heavy metals to the straw.
•
•
•
•
The objectives for this research project are listed below:
To determine whether sulfide adsorbed on the surface of straw/hay will serve as an effective binding
agent/precipitation agent for removal of heavy metals from solution (e.g., run-off from sites);
To identify preliminary conditions (e.g., pH, sulfide dosage/unit weight of straw, etc.) whereby heavy
metals are effectively removed from solution;
To determine adsorptive capacities of the heavy metals on the straw; and
To determine the break-through characteristics of the heavy metals through the pack-bed straw
reactors.
a. A brief explanation of methodology:
The scope of the research is three-fold: 1. Performing bench-scale batch isotherm characterization of
the selected heavy metals onto straw and hay (both untreated and treated with sulfide compounds), 2.
Performing continuous flow of heavy metal solutions through a packed-bed column containing straw, and
3. Modeling the bed depth-service time behavior of the heavy metal solutions through the columns to
determine column breakthrough.
a. Principal findings and significance:
Isotherm experiments were performed in which different dosages of straw were subjected to different
concentrations of heavy metal solutions (containing iron, cadmium, chromium, and lead). Results from
these experiments are summarized in Tables 1 and 2. The adsorption/uptake of heavy metals onto the
straw were modeled using the Langmuir and Freundlich isotherms:
Langmuir: qe = Qo b C
Freundlich:
qe = KN C1/n
1 + bC
45
Table 1. Summary of Isotherm Parameters for Heavy Metal Adsorption onto Straw (Straw Dosage = 1.0
gm/L); pH ~ 2.2
Isotherm Model
Parameter
Heavy Metal
Iron
Cadmium
Chromium
Lead
Langmuir
Qo, (mg/g)
3.979
0.305
-0.281
1.116
b, (L/mg)
0.234
0.587
-0.051
6.114
Freundlich
KN
0.671
0.106
0.011
0.854
1/n
0.567
0.4945
1.402
0.223
Table 2. Summary of Isotherm Parameters for Iron Adsorption Using Different Straw Dosages and Initial
pH Levels.
Isotherm Model
Parameter
Dosage = 1.0 g/L
Dosage = 10.0 g/L
pH ~ 2.6
pH ~ 4.05
pH ~ 4.1
Langmuir
Qo, (mg/g)
3.979
2.569
5.244
b, (L/mg)
0.234
0.172
0.028
Freundlich
KN
0.671
0.374
0.1545
1/n
0.567
0.561
1.081
Straw is an excellent medium for constructing a barrier to urban and industrial runoff. It is readily
available and relatively inexpensive. Straw also has the potential for chemical modification to increase it
ability to remove pollutants such as heavy metals from runoff. Being a plant material, straw is potentially
biodegradable. The rate and extent of biodegradation influences its usefulness as a pollution barrier,
especially if it chemical modified to improve its ability to remove heavy metals and other pollutants.
Plant material such as straw consists primarily of two fractions, the readily biodegradable portion and
the slowly degradable portion. The readily degradable fraction consists of small molecules such as sugars,
amino acids, and metabolic intermediates that are present in the plant cells. It also includes
macromolecules such as proteins, carbohydrates, lipids, and nucleic acids. These components are easily
and rapidly degraded by microorganisms unless they are entrapped in cell-wall bound cells and are not
accessible to microorganisms. The slowly biodegradable fraction consists primarily of plant structural
polymers such as cellulose and lignin. These structures are degraded by microorganisms, but degradation
is a slow process.
In this study, the rate of biodegradation of straw was determined. Four straw preparations were
investigated: untreated straw, straw treated with sulfide, straw treated with iron, and straw treated with
sulfide plus iron. Each of these straw preparations were packed into columns (50 grams of straw) and
treated with water on a daily basis. The column experiments were performed in triplicate. After 1, 2, 4,
8, and 16 weeks, the columns were dissembled and samples (10 grams) prepared for analysis by blending
to a fine powder. Each sample was treated with neutral detergent to extract the readily degradable
fraction. Total loss of straw from the columns was determined by comparing initial and final weight of the
straw at various times.
Total loss of straw. On average, about 10-15% of the total weight of the straw was lost during the first
few weeks. Although the initial rate of loss of rapid, after 8 weeks, the rate of straw loss from the columns
was significantly reduced. By week 16, ~25% of the total weight of the straw was lost. The rate of loss of
straw treated with different chemicals appears generally similar, with the most rapid loss occurring during
the first week. The heterogeneity of straw clearly limits the sensitivity of measuring the neutral detergent
fraction. Consequently, trends beyond those described above cannot be determined with certainty. For
example, it appears that the loss of straw was reversed in S4, the straw treated with sulfide + iron, but this
is unlikely. An increase in the biodegradable fraction would occur if there were microbial growth on an
exogenous substrate, but none was provided in this experiment. The conclusions from this part of the
stuydy are that ~10% of the total straw is rapidly lost from the columns during the first 2 to 4 weeks, and
an additional 15% is slowly lost during the next 8 to 12 weeks.
Biodegradability of the straw. Initially, ~42% of the straw was readily biodegradable, based on neutral
detergent solubility. About half of this material disappeared within the first couple of weeks, leaving
46
~20% of the straw as “readily biodegradable”. These results are consistent with the pattern of total loss of
straw during this early period. The most rapid loss of the readily biodegradable fraction occurred during
the first two weeks, regardless of the type of treatment (untreated, sulfide, iron, or sulfide+iron). The
remaining readily biodegradable material is probably entrapped within cells by cell walls that contain
cellulose, and are not accessible to microbial degradation until the cellulose is degraded. Following the
phase of rapid removal of biodegradable material, there appears to be a slight increase in the
biodegradable fraction when looking at the pattern for the average biodegradability of the straw. This may
be due to the accumulation of microbial biomass that is growing on the slowly degradable fraction
(cellulose and lignin). Visual and microscopic observations revealed the presence of bacteria and fungi
during this phase. The slight increase in biomass due to the increase numbers of microbial cells are too
small to be reflected in the total loss of straw measurements. From examination of patterns of degradation
for different straw treatments (untreated, sulfide, iron, and sulfide+iron), it is concluded that ~50% of the
readily biodegradable portion of the straw is degraded during the first few weeks of exposure to water in
columns. The straw then remains relatively stable for the remainder of the test period. If sulfide is bound
to the readily degradable fraction (e.g., proteins) or retained in cells that become susceptible to
biodegradation during the first few weeks, it would be rapidly lost. However, if the biodegradable fraction
were removed by incubation of the straw in water, reagents might bind to the more slowly degradable
fraction and therefore be more stable and provide longer functionality.
A list of all publications/presentations and manuscripts produced:
During the conduct of this project (when the project synopsis was submitted), a total of six
technical/scientific presentations and one conference proceedings were produced. These
publications/presentations are listed below.
Conference Presentations:
Nunez, S., and R.W. Peters, 2002. “Treatment of Heavy Metal-Laden Runoff Using Straw: Isotherm
Results for Selected Heavy Metal Systems”, Paper presented at the Alabama Water Resources
Conference, Orange Beach, AL, (September 4-6).
Nunez, S., and R.W. Peters, 2002. “Treatment of Heavy Metal-Contaminated Runoff Using Straw”,
Poster Paper presented at the 2002 Annual American Institute of Chemical Engineers (AIChE) Meeting,
Indianapolis, IN, (November 3-8).
Blankinship, L.A., S. Nunez, R.W. Peters, and J.J. Gauthier, 2003. “Biostability of Wheat Straw as a
Sulfide-Enhanced Biofilter”, Paper presented to the Alabama Academy of Sciences, Jacksonville, AL,
(March 19-22).
Peters, R.W., S. Nunez, L.A. Blankinship, and J. Gauthier, 2003. “Stability of Straw Coated wuth Sulfide
and Used for Treatment of Heavy Metal-Contaminated Runoff”, Paper presented at the 2003 Spring
National AIChE Meeting, New Orleans, LA, (March 30-April 3).
Nunez, S., and R.W. Peters, 2003. “Effect of pH on the Adsorption of Heavy Metals onto Straw”, Paper
accepted for presentation at the 2003 Annual AIChE Meeting, San Francisco, CA, (November 16-21).
Nunez, S., R.W. Peters, L.A. Blankinship, and J.J. Gauthier, 2003. “Flow through Straw Packed Beds for
Desorption of Iron and Straw Stability/Degradation”, Paper accepted for presentation at the 2003 Annual
AIChE Meeting, San Francisco, CA, (November 16-21).
47
Conference Proceedings:
Peters, R.W., S. Nunez, L.A. Blankinship, and J. Gauthier, 2003. “Stability of Straw Coated wuth Sulfide
and Used for Treatment of Heavy Metal-Contaminated Runoff”, pp. 139-151 in Conference Proceedings:
Green Chemical Engineering Topical Conference, 2003 Spring National AIChE Meeting, New Orleans,
LA, (March 30-April 3).
A list of all students (and their educational level) supported on the project:
Ms. Sandra Nunez – seeking M.S. degree in environmental engineering
Department of Civil and Environmental Engineering
University of Alabama at Birmingham
Birmingham, AL 35294-4440
Ms. Lisa Ann Blankinship-seeking Ph.D. degree in Biology
Department of Biology
University of Alabama at Birmingham
Birmingham, AL 35294-4440
8 Publications
Publications and conference presentations resulting from this project are listed below.
Publications:
Nunez, S., R.W. Peters, L.A. Blankinship, and J.J. Gauthier, 2005. “Competitive Adsorption of
Heavy Metals onto Straw”, Proc. 2005 American Institute of Chemical Engineers (AIChE)
Meeting, Cincinnati, OH, (October 30 – November 4); (7 pages).
Nunez, S., R.W. Peters, L.A. Blankinship, and J.J. Gauthier, 2004. “Heavy Metal Adsorption
Behavior on Straw”, Paper presented in the session on “Green Chemistry and Reaction
Engineering” at the 2004 Annual American Institute of Chemical Engineers (AIChE) Meeting,
Austin, Texas, November 7–12, 2004.
Nunez, S., 2004. “Treatment of Heavy Metal-Contaminated Runoff Using Straw Coated with
Sulfide”; University of Alabama at Birmingham, Birmingham, AL; (Non-thesis report); Spring
semester 2004.
Peters, R.W.*, and J.J. Gauthier, 2003. “Project Synopsis: “Treatment of Heavy MetalContaminated Runoff Using Straw Coated with Sulfide”, submitted to the Alabama Water
Resources Research Institute, Auburn, AL, (May).
Peters, R.W., S. Nunez, L.A. Blankenship, and J. Gauthier, 2003. “Stability of Straw Coated
with Sulfide and Used for Treatment of Heavy Metal-Contaminated Runoff”, pp. 139-151 in
Proc. Green Chemical Engineering Topical Conference, 2003 Spring National American
Institute of Chemical Engineers (AIChE) Meeting, New Orleans, LA, (March 30-April3).
48
Presentations at National Conferences:
Peters, R.W., and S. Nunez, 2006. “Competitive Adsorption Behavior on the Removal of Iron
and Lead from Solution”, Paper presented at the 2006 Annual American Institute of Chemical
Engineers (AIChE) Meeting, San Francisco, CA, (November 12–17).
Peters, R.W., S. Nunez, L.A. Blankenship, and J. Gauthier, 2006. “Stability of Straw Coated
with Sulfide and Used for Treatment of Heavy Metal-Contaminated Runoff”, Paper submitted to
Journal of Environmental Management, Paper presented in the. Green Chemical Engineering
Topical Conference, 2003 Spring National American Institute of Chemical Engineers (AIChE)
Meeting, New Orleans, LA, (March 30-April 3).
Nunez, S.A., and R.W. Peters, 2005. “Treatment of Heavy Metal Contaminated Runoff Using
Straw”, Paper presented at UAB Minority Research Day, University of Alabama at Birmingham,
Birmingham, AL, (April 18).
Nunez, S., R.W. Peters, L.A. Blankinship, and J.J. Gauthier, 2004. “Heavy Metal Adsorption
Behavior on Straw”, Paper presented in the session on “Green Chemistry and Reaction
Engineering” at the 2004 Annual American Institute of Chemical Engineers (AIChE) Meeting,
Austin, Texas, November 7–12, 2004.
Nunez, S., R.W. Peters, L.A. Blankinship, and J.J. Gauthier, 2003. “Flow through Straw Packed
Beds for Desorption of Iron and Straw Stability/Degradation”, Paper presented at the 2003
American Institute of Chemical Engineers (AIChE) Meeting, San Francisco, CA, (November 1621).
Nunez, S., R.W. Peters, L.A. Blankenship, and J.J. Gauthier, 2003. “Flow through Packed Straw
Beds: Breakthrough Curves, Heavy Metal Desorption, and Straw Stability/ Degradation”, Paper
presented at the Florida Section of the American Air & Waste Management Association Annual
Conference, Orlando, FL, (September 7-9).
Nunez, S., and R.W. Peters, 2003. “Single and Multiple Heavy Metal Adsorption onto Straw in
Batch and Continuous Flow Operation: Competitive Adsorption Effects”, Paper presented at the
17th Annual Alabama Water Resources Conference, Orange Beach, AL, (September 3-5).
Nunez, S., and R.W. Peters, 2002. “Treatment of Heavy Metal-Laden Runoff Using Straw:
Isotherm Results for Selected Heavy Metal Systems”, Paper presented at the 2002 Alabama
Water Resources Conference, Orange Beach, AL, (September 4-6).
Poster Papers Presented at National Conferences:
Nunez, S., and R.W. Peters, 2005. “Effect of pH on the Adsorption of Heavy Metals onto
Straw”, Poster paper presented at the Association for Environmental Health and Sciences
(AEHS) “15th Annual West Coast Conference on Soils, Sediments, and Water”, San Diego,
California, (March 14-17).
49
Blankinship, L.A., J. Davis, S. Nunez, R.W. Peters, and J.J. Gauthier, 2003. “Biodegradation of
Sulfide-Enhanced Wheat Straw Used as a Biofilter for Urban Run-Off”, Poster paper presented
at the American Society of Microbiology Southeastern and South Carolina Branch Meeting,
Athens, GA, (October 30-November 1).
Nunez, S., and R.W. Peters, 2002. “Treatment of Heavy Metal-Contaminated Runoff Using
Straw”, Poster paper presented at the 2002 Annual American Institute of Chemical Engineers
(AIChE), Indianapolis, IN, (November 3-8).
Copies of these papers and presentations are available upon request.
Research Opportunities for Undergraduate University Engineering Students and High
School Students (Engineering Outreach):
Laura Chamlee, undergraduate student (through the REU Program) – worked on the straw
project during summer 2003.
E’Lana Hopkins, high school student (through an NIH internship program) – worked on the
straw project during summer 2003; gave presentation in Washington D.C. based on her research
experience and received 1st place award in Program for sophomore student.
Pannipa Boothnate (through the REU Program) – helped with the straw project during summer
2003.
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