Metal Removal Capabilities of Passive Bioreactor Systems: Effects of Organic Matter and Microbial Population Dynamics
Principal Investigator:Linda Figueroa, Associate Professor of Environmental Science & Engineering1
Project TeamDianne Ahmann1 Miranda Logan1
George Aiken4 Marie-Helene Robustelli1David Blowes3 Jason Seyler1
Kenneth Carlson2 �Paolo Hemsi2Nancy DuTeau2 Sriram Ananthanarayan2
Donald Macalady1
Kenneth Reardon2
Charles Shackleford2
Thomas Wildeman1
Sandra Woods2
1Colorado School of Mines2Colorado State University3University of Waterloo4USGS, Boulder
Rocky Mountain Regional Hazardous Substance Research CenterProject 3
Motivation & Need
! All states in Region 8 have environmental problems associated with historic and current mining operations
Issues associated with environmental impacts include:
! Cost effective technologies to clean up mine waste sites
! Less costly and more rational clean–up strategies
WaybrantWaybrant, , BlowesBlowes and and Ptacek Ptacek 19981998
Passive barrierPassive barrier
Research goals & objectives
Goal
The overall goal of this project is to evaluate the effect of organic matter characteristic on microbial population distributions and metal concentration in passive reactive zones and develop modeling tools for design and analysis.
Objectives
1. To evaluate the physical, chemical and biological compositionof the components used to create the permeable reactive zones.2. To determine the effect of the organic substrate characteristics on effluent metal concentration.3. To determine the effect of the substrate and time on variations in microbial population.4. To evaluate the use of mathematical models to relate metal removal and transport to various system parameters.
Approach
Approach
Limitations of previous research have included:
Focus on initial metal removal rates rather than on performance longevity
Limited examination of only sulfate-reducing bacteria
Lack of systematic design protocol (e.g., substrate selection and residence time)
No modeling tool to facilitate design and analysis
Tasks
Tasks that we have initiated are:
Characterization of chemical, physical and biological characteristics of substrate material.
Correlation of substrate characteristics to sustainable activity of sulfate reduction coupled with metals removal in mini-column experiments.
Investigation of the microbial community structure using activity and molecular methods. Target organism are sulfate reducers and non-sulfate-reducing microbial groups, such as cellulolytic bacteria,fermenters, syntrophs, and methanogens
Development of a specific mathematical model that captures the most important aspects of the processes and a numerical implementation of this mathematical model. This implemented model is expected to be added as a new “package” to an existing transport model. Finally, this numerical model will be tested for different conditions, including porous media
Substrate characterization
91.00.180.5826.0Column mixture
1000.0020.0246.0Wood shavings
----Sand
----Limestone
33.30.540.5519.3Inoculum
64.40.3 – 0.6*1.1523.7Dairy manure
99.00.5 – 0.8* 3.1743.0Alfalfa
VS / TS
(%)
Total P
(%)
Total N
(%)
Total org. C
(%)
Component
Sorption experiments
Experimental plan:The study focused on zinc and its sorption by five substrates : one inorganic (limestone) and four organic (wood shavings, brew waste, walnut hulls, and manure).
For each substrate, samples, run in duplicate, are prepared with a known quantity of material and a known ZnSO4 concentration. The volume of ZnSO4 added in each sample is 100 ml. A blank with 100 ml of DI water and a known quantity of substrate is also prepared. All samples are agitated for 24 hours at room temperature. They are then centrifuged, filtered, and acidified before ICP analysis.
Sorption experiments
Theory:The data were fit to a linear
approximation of the Langmuirmodel defined by :
q = K0* ce
Where :q = sorbed quantity
(mg of metal per g of substrate)
K1 = Langmuir adsorption constant
Q = maximum number of sorption site
K0 = K1 * Q (L/g)
Sorption experiment results
Manure sorption
y = 0.9054xR2 = 0.687
0123456789
10
0 2 4 6 8 10 12
Ce (mg/L)
q (m
g/g)
Sorption experiments
Comparative zinc sorption capacities of:Wood shavings, Walnut hulls & Brew waste
y = 0.025x
R2 = 0.9788
y = 0.0111x
R2 = 0.8531
y = 0.1661x
R2 = 0.8206
0
0.1
0.2
0.3
0.4
0.5
0.6
0 5 10 15 20 25 30
Ce (mg/L)
q (m
g/g)
wood shavings walnut hulls brew waste
Column team
Pilot column experiments
Pilot Column Material:
(based on dry weight) 20 % dairy manure 15 % walnut wood shavings10 % alfalfa
5 % wetland inoculum 45 % silica sand (#8 mesh) 5 % limestone
Pilot Column Specifications:Volume = 589 cm3
Mixed material per columns = 520 gramsDry weight per column = 185 gramsFlow rate = 220 ml/dayEstimated hydraulic residence time = 3 days
Pilot Column Influent:
Dinero Tunnel mine drainage, Leadville, CO
Contains 36.6 mg/L Mn, 9.8 mg/L Zn,and 125 mg/L SO4, pH 5.96
Iron
0
10
20
30
0 10 20 30Days
mg/
L ABC
Zinc
0
5
10
0 10 20 30Days
mg/
L ABC
Influent
Manganese
0
15
30
45
0 10 20 30Days
mg/
L
ABC
Sulfur
0
50
100
150
0 10 20 30Days
mg/
L ABC
Pilot Column ResultsPilot Column Results
Microbial Framework
CH4
NOM(cellulose, lignin,
etc.)
Monomers(glucose, etc.)
Fatty Acids
(lactate)
H2 Methanogens
FermentersSyntrophs
Sulfate-reducers
SO42-
H2SMe2+
MeS(s)
AcetateCO2
Oligomers(cellobiose,
etc.)
Hydrolysis
Hydrolysis
Pilot column experiments
Total Sulfur - Pilot Column 110 mM Acetate Pulse
0
50
100
150
200
250
300
8 9 10 11 12 13 14 15 16 17 18
Days
mg/
L
Total Sulfur - Pilot Column 25 mM Acetate Pulse
0
50
100
150
200
250
300
8 9 10 11 12 13 14 15 16 17 18
Days
mg/
L
Total Sulfur - Pilot Column 31 mM Acetate Pulse
0
50
100
150
200
250
300
8 9 10 11 12 13 14 15 16 17 18
Days
mg/
L
One day acetate pulse into One day acetate pulse into pilot columnspilot columns
Mini column experimentscommunity interactions
Mini-Column Material:(based on dry weight) 30 % walnut wood shavings 10 % brewery waste 5 % dairy manure5 % wetland inoculum 5 % limestone (#10 mesh)45 % silica sand (#8 mesh)
Mini-Column Specifications:Volume = 40 cm3
Mixed material per column = 19 gramsDry weight per column = 17 gramsFlow rate = 24 ml/dayEstimated hydraulic residence time = 1 day
Mini-Column Influent:Simulated mine drainage mixture comprised of 50mg/L Fe, Mn, and Zn, and 1400 mg/L SO4, pH 6.0Influent sparged with nitrogen to minimize oxidation of Fe
Mini column experimentscommunity interactions
Effluent S5 mM Lactate Pulse
0
100
200
300
400
500
600
700
0 5 10 15 20 25 30 35 40 45Days
Con
cent
ratio
n (m
g/L)
78
pulsestart mine water
influent = 475 mg/L
Mini column experimentssubstrate comparisons
MethodsSubstrates used: Walnut Hulls, Corn Cobs, Brewery Waste, Walnut Wood ShavingsColumns contain individual substrates and are run in duplicate. Simulated mine water comprised of: 50mg/l of Fe, Mn, Zn, and 1400 mg/l SO4
The flow rate through the columns was 24ml/day
Hydraulic residence time approximately 1.0 daysMetals and Total Sulfur were measured on an ICP To prevent volatilization of sulfide gas the effluent was maintained at a pH of 9.0
Influent was sparged with nitrogen to minimize oxidation/precipitation of Iron
Column Description5 grams of each substrate (dry wt basis)
5 % of an inoculum/manure combination mixed in with substrate 5 grams of limestone #10 mesh# 8 mesh sand filling the remaining volume
Mini column experimentssubstrate comparisons
Total Mn
0
10
20
30
40
50
60
70
80
90
0 5 10 15 20 25 30
H1H2C1C2B1B2W1W2
Mini column experimentssubstrate comparisons
Total Zn
0
1
2
3
4
5
6
7
8
9
0 5 10 15 20 25 30
H1H2C1C2B1B2W1W2
Microbial modeling
complex organicsXCÕ
soluble organics, SS1
acetic acid, SS2
carbon dioxide and methane, Sm
Hydrolysis
Acidogenesis
Methanogenesis
Sulfidogenesis
Level 1 schematicLevel 1 schematic
Microbial modeling
Level 1 model
1. Solubilization of cellulose to glucose via extracellular enzymes produced by fermenter
2. Fermentation of glucose to acetate
3. Growth of SRB on acetate
4. Growth of Methanogens on acetate
Microbial modeling
Hydrolysis
(n )(C6H10O5) + H2O = (n-1)(C6H10O5) + C6H12O6
Fermentation
C6H12O6 + 0.336 NH4+
=> 0.336 C5H7O2N + 1.007 H2 +2.158 C2H3O2– + 2.494 H+
Sulfate reduction
C2H3O2– +0.032NH4
+ +1.888 H+ + 0.92 SO42–
=> 0.032 C5H7O2N +1.36 H2O + 1.84 CO2 + 0.92 HS–
Methanogenesis
C2H3O2– +0.02 NH4
+ + 0.8896 H2O + 0.9 H+
=> 0.02 C5H7O2N + 0.9504 CH4 + 1.0096 CO2
Level 1 Level 1 stoichiometriesstoichiometries
Microbial modeling
fermentative growth µfSS1
Ksf +SS1
XB, f
fermenter decay bf XB, f
hydrolysis of cellulose kh
XC'XB, f
Kx,C + XC'XB, f
XB, f
SRB growth µsrSS2
Ksr +SS2
XB, sr
SRB decay bsrXB,sr
Methanogen growth µmSS2
Km +SS2
XB,m
Methanogen decay bmXm
Level 1 process modelsLevel 1 process models
Microbial modeling
ComponentsSS1 = glucoseXC' = cellulose available = function of d2, XC,XL)XL = lignind = nominal particle sizeXC = cellulose totalX B,f = fermenter biomassX B,sr = SRB biomassX B,m = methanogen biomassSS2 = acetateSm = methaneSalk = alkalinitySSO = sulfate
Parametersµf = maximum specific growth rate of fermentersbf = decay constant of fermentersKsf = half-saturation constant of fermenterskhc = hydrolysis rate of cellulosekhs = hydrolysis rate of particulate organicsKxf = hydrolysis saturation ratio for celluloseKcf = hydrolysis saturation ratio for particulate organicsµsr = maximum specific growth rate of SRBKsr = half-saturation constant of SRBbsr = decay constant for SRBµm = maximum specific growth rate of methanogensKm = half-saturation constant of methanogensbm = decay constant for methanogens
Level 1 components and parametersLevel 1 components and parameters
Microbial modelingLevel 2 schematicLevel 2 schematic
CH4
NOM(cellulose, lignin,
etc.)
Monomers(glucose, etc.)
Fatty Acids
(lactate)
H2 Methanogens
FermentersSyntrophs
Sulfate-reducers
SO42-
H2S Me2+
MeS(s)
AcetateCO2
Oligomers(cellobiose,
etc.)
Hydrolysis
Hydrolysis
Microbial modeling
Level 2 model1. Solubilization of cellulose to cellobiose via extracellular enzymes produced by fermenters
2. Solubilization of cellobiose to glucose via extracellular enzymes produced by fermenters
3. Fermentation of glucose to lactate
4. Fermentation of glucose to lactate and hydrogen
5. Fermentation of lactate to acetate
6. Fermentation of lactate to acetate and hydrogen
7. Growth of SRB on lactate
8. Growth of SRB on hydrogen
9. Growth of SRB on acetate
10. Growth of Methanogens on acetate
11. Growth of Methanogens on hydrogen
System Model
System Model
System Modeling Approach
! Development of unique numerical model of anaerobic biodegradation of organic substrates and microbially facilitated precipitation of metals
! Numerical simulations will be done using USGS MODFLOW2000 and MT3DMS
Ongoing and Future work
Ongoing and Future Work- Continue pulse experiments with more replicates, negative controls, larger pulse concentrations, more frequent sampling, and increased hydraulic residence times- Analyze samples on IC to determine sulfur speciation- Use results of slow-step characterization to further investigate relevant microbial activities
Modeling
-Continue calibration of existing flow models
- Develop stoichiometries and process equations for Level 2 microbial model
-Develop numerical model for microbial processes
Substrate characterization- Examine correlation between biodegradability and sulfate reducing activity relative to substrate components of soluble sugar and starch, protein, cellulose/hemicellulose, lignin
- Analyze the initial sulfate reduction potential of each substrate.
- Determine the longevity of each substrate.
- Measure the sorption characteristics of each substrate on all three metals of concern.
- Identify the most effective residence time for each substrate.
- Modify experimental set-up for gas collection, which will facilitate mass balance analysis and alleviate pressure buildup.
Ongoing and Future work
Ongoing and Future Work- Continue pulse experiments with more replicates, negative controls, larger pulse concentrations, more frequent sampling, and increased hydraulic residence times- Analyze samples on IC to determine sulfur speciation- Use results of slow-step characterization to further investigate relevant microbial activities
Microbial activity
-Continue pulse experiments with more replicates, negative controls, larger pulse concentrations, more frequent sampling, and increased hydraulicresidence times
-Analyze samples on IC to determine sulfur speciation
- Use results of slow-step characterization to further investigate relevant microbial activities
- Apply molecular tools in conjunction with activity measurements
SorptionFurther work is needed to establish the sorption capacity for
- other substrates,
- for the complete range of expected of metal concentrations
- for other metals