1 URBAN STORMWATER PARTICLE AND DISINFECTION MODELING By JOSHUA A. DICKENSON A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2011
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1
URBAN STORMWATER PARTICLE AND DISINFECTION MODELING
By
JOSHUA A. DICKENSON
A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY
Literature Review .................................................................................................... 18
2 DISCRETE PHASE MODEL REPRESENTATION OF PARTICULATE MATTER FOR SIMULATING PARTICULATE MATTER SEPARATION BY HYDRODYNAMIC UNIT OPERATIONS ................................................................. 23 Overview ................................................................................................................. 23
Supernatant sampling protocol .................................................................. 31 Mass recovery methodology and protocol ................................................. 32 Laboratory analyses ................................................................................... 32 SSC methodology and protocol ................................................................. 33 PSD methodology and protocol ................................................................. 33
Head loss by manual measurement ........................................................... 34 Turbidity ..................................................................................................... 34 QA/QC ....................................................................................................... 35 Efficiency calculation .................................................................................. 35
CFD Model Dataset Creation ........................................................................... 36
Model Validation ............................................................................................... 38 Results and Discussion........................................................................................... 38
Power Law Model (PLM): ................................................................................. 43 Computational Time ......................................................................................... 44
3 OVERALL RATE KINETICS MODEL OF SODIUM HYPOCHLORITE DEMAND BY THE DISSOLVED AND PARTICULATE MATTER FRACTIONS IN URBAN RAINFALL-RUNOFF............................................................................................... 58 Methodology ........................................................................................................... 60
Analytical Methods ........................................................................................... 64 Parallel Second Order Demand Model for Dissolved Phase ............................ 65 Second Order Potential Driving Model for the PM Fractions ............................ 68 Model Evaluation .............................................................................................. 70
Results and Discussion........................................................................................... 70 Control Reactors .............................................................................................. 70
Kinetics Model for Dissolved Phase ................................................................. 70 PM Kinetic Model ............................................................................................. 73
4 SODIUM HYPOCHLORITE DISINFECTION OF INDICATOR ORGANISMS ASSOCIATED WITH URBAN STORMWATER PARTICLES ................................. 84 Methodology ........................................................................................................... 86
5 ADVANCED COMPUTATIONAL MODELING OF FREE CHLORINE DEMAND AND DISINFECTION IN UNIT OPERATIONS AND PRECESSES LOADED BY URBAN STORMWATER ...................................................................................... 107 Objectives ............................................................................................................. 108 Methodology ......................................................................................................... 109
Table page 2-1 Table of cumulative gamma distribution modeled gradations ............................. 45
2-2 Morsi and Alexander drag equation and coefficients for a sphere (1972) ........... 45
2-3 Hydrodynamic separator experimental run information and operational parameters ......................................................................................................... 46
2-5 PM gradations with mean power law model parameters .................................... 47
2-6 Discrete phase model computational time for the baffled hydrodynamic separator ............................................................................................................ 48
2-7 Discrete phase model computational time for the screened hydrodynamic separator ............................................................................................................ 48
3-1 Summary of hydrologic and PM event mean concentration indices for captured events. ................................................................................................. 76
3-2 Global parallel 2nd order demand model parameters for the dissolved phase. ... 76
3-3 Hypochlorite event-based ultimate demand of urban stormwater fractions for the monitored storms. ......................................................................................... 77
4-1 Batch reactor experimental matrix of PM fractions, HOCl dose, and event date. ................................................................................................................... 99
4-3 Event mobilization of indicator organisms and percentage of transported organisms associated with each PM fraction. ................................................... 101
5-1 Model parameters for the dissolved parallel second order and PM potential driving force equations. .................................................................................... 120
9
LIST OF FIGURES
Figure page 2-1 Experimental validation of full scale units ........................................................... 49
2-2 Cumulative PSDs utilized in the study. ............................................................... 50
2-3 Results from the full-scale experimental testing on the baffled HS ..................... 51
2-4 Results comparing influent loading concentrations on the baffled HS ................ 52
2-5 Computational results for the screened HS ........................................................ 53
2-6 Computational results for the baffled HS ............................................................ 54
2-7 CFD per-particle size efficiency surfaces for both the screened HS (A) and the baffled HS (B) ............................................................................................... 55
2-8 CFD per-particle size efficiency differential surface for the screened HS and the baffled HS ..................................................................................................... 56
2-9 Predictive results of the power law model for RPD with increasing DN .............. 57
3-1 PSD of quintessential fractions from the batch reactors ..................................... 78
3-2 Physical representation of the parameters of the parallel 2nd order demand model ................................................................................................................ 79
3-3 Predictive fit of the dissolved fraction parallel 2nd order demand model ............. 80
3-4 Transient loading of CODd on the small urban catchment in north central Florida ................................................................................................................ 81
3-5 Maximum particle free chlorine demand ............................................................. 82
3-6 The modeling results of the second order PM chlorine demand model .............. 83
4-1 Event mean most probable number per 100 mL box-plot for twenty-five wet weather events on a small urban watershed in north central Florida ................ 102
4-2 Hypochlorite inactivation kinetics of particle associated coliform organisms on suspended, settleable, and sediment PM ......................................................... 103
4-3 Log removal of particle associated coliforms for the 04-Nov-2010 (Panel A, B) event with an initial hypochlorite dose of 45 mg/L ........................................ 104
4-4 Log removal of particle associated coliforms on sediment PM across the inoculation doses of 15, 30, and 45 mg/L ......................................................... 105
10
4-5 Partitioning of particle associated organisms to suspended, settleable, and sediment PM fractions ...................................................................................... 106
5-1 Physical batch reactor showing stirplate, aluminum foil jacket, and water quality electrodes .............................................................................................. 121
5-2 Illustration of the fluid zones within the batch reactor ....................................... 122
5-3 Histogram analysis of the computational mesh CFD free chlorine concentration .................................................................................................... 123
5-4 Comparison of the second order CFD dissolved demand model with experimental results ......................................................................................... 124
5-5 Comparison of the second order potential driving PM CFD model with experimental results. ........................................................................................ 125
5-6 Comparison of the composite dissolved and PM CFD model with batch reactor data. ..................................................................................................... 126
A-2 Schematic of monitored urban sub-catchment in Gainesville, FL showing contributing impervious surface. ....................................................................... 133
A-3 Control CSBRs showing hypochlorite kinetics in Nanopure DI for 8 h at 15 mg/L and 24 h at 45 mg/L ................................................................................. 134
A-4 Control CSBRs comparing autoclave sterilized and non-autoclave sterilized stormwater Matrix ............................................................................................. 135
11
LIST OF ABBREVIATIONS
ADB Azide dextrose broth
BGB Brilliant green bile broth
BHI 6.5% NaCl brain heart infusion broth
CFD Computational fluid dynamics
COD Chemical oxygen demand
CODd Dissolved chemical oxygen demand
CSBR Continuously stirred batch reactor
DN Discritization number
DOC Dissolved organic carbon
DPD N,N-diethyl-p-phenylenediamine
DPM Discrete phase model
EMC Event mean concentration
EPA The United States Environmental Protection Agency
FC Fecal coliform
FS Fecal streptococcus
HS Hydrodynamic separator
LTB Lauryl triptose broth
LTB-MUG Lauryl triptose broth amended with 4-methylumbelliferyl-β-D-glucuronide
MBE Mass balance error
MPN Most probable number
MS4 Multiple separate storm sewer system
NJCAT New Jersey Corporation for Advanced Technology
NJDEP New Jersey Department of Environmental Protection
NRMSE Normalized root mean square error
12
PCR Polymerase chain reaction
PLM Power law model
PND Particle number density
PSD Particle size distribution
PM Particulate matter
RANS Reynolds averaged Navier-Stokes
RPD Relative percent difference
RPDave The average relative percent difference
RTD Residence time distribution
SE Standard error
SSC Suspended sediment concentration
TMDL Total maximum daily load
UDF User defined function
VF Volume fraction
WWTP Waste water treatment plant
ΔEMC The change in the event mean concentration
13
Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy
URBAN STORMWATER PARTICLE AND DISINFECTION MODELING
By
Joshua A. Dickenson
May 2011
Chair: John Sansalone Major: Environmental Engineering Sciences
Urban stormwater is a component of the complex hydrologic water cycle whose
genesis is the result of anthropogenic modification of environmental hydrologic
pathways. This hydrologic modification results in volumetric transport of water and the
mobilization of potential particulate, chemical, biological, and nutrient contaminants.
Moving forward with sustainable development and re-development will require
identification of new technologies and novel reuse resources and an integrated design
and management approach that mitigates deleterious environmental impacts of urban
stormwater runoff and establishes potential reuse applications to alleviate over-
exploitation of current environmental freshwater sources. However, due to the inherent
differences in the nature and constituents of urban stormwater as compared to
well-studied wastewater and environmental water, research needs to characterize and
identify the necessary methods to facilitate safe and sustainable potentials for reuse.
This document proffers experimental research and modeling that explores the role of
particle separation with implications for the potential of utilizing chlorine disinfection for
urban stormwater for reuse. From the experimental perspective, findings include
significant loadings of planktonic and particle associated bacteriological indicator
14
organisms (PAOs) observed in stormwater runoff from a small urban catchment at the
University of Florida as well as high levels of chlorine demand due to dissolved and
particulate constituents of the runoff. Free chlorine disinfection applied to reactors with
differing fractions of event mobilized particulate matter (PM) found that sediment PM
(ϕ > 0.75 µm) exerts a shielding effect that protects PAOs from disinfection at the
applied doses while PAOs in the suspended PM (0.45 µm < ϕ < 25 µm) and settleable
d5m (µm) 29.2 58.8 87.6 8.1 16.3 24.2 0.5 0.9 1.4 a Uniformly distributed; b Gradations of medium distribution; c Hetero-disperse. The characteristics of selected gradations including gamma distribution parameters (k, λ), and the sorting coefficient (σI) that are utilized in this study. Gradation #8 is the gamma curve fit of the NJDEP gradation (R2 = 0.99). The other gradations were selected to systematically explore the effect of uniformity and d50m on discretization error and were chosen so that the uniform gradations are very well sorted (σI < 0.23), the medium gradations transect the boundary between moderately sorted and poorly sorted gradations (σI = 1.0), and the other hetero-disperse gradations (#7, #9) have similar sorting to the NJDEP distribution (#8). The distribution indices (percentiles) necessary for the calculation of the sorting coefficient are also included.
Table 2-2. Morsi and Alexander drag equation and coefficients for a sphere (1972) Reynolds Number K1 K2 K3
<0.1 24.0 0 0 0.1 < Re < 1.0 22.73 0.0903 3.69 1.0 < Re < 10.0 29.1667 -3.8889 1.222 10.0 < Re < 100.0 46.5 -116.67 0.6167 100.0 < Re < 1000.0 98.33 -2778 0.3644 1000.0 < Re < 5000.0 148.62 -4.75 X 104 0.357 5000.0 < Re < 10,000.0 -490.546 57.87 X 104 0.46 10,000.0 < Re < 50,000.0 -1662.5 5.4167 X 106 0.5191
46
Table 2-3. Hydrodynamic separator experimental run information and operational parameters
a Effluent event mean concentration; b Total mass captured is the sum of suspended PM in supernatant and settled PM recovered as wet slurry from the unit; c The actual value is slightly less than 0, since mean turbidity reduction is within the range of instrument resolution it is considered essentially as zero.
Table 2-5. PM gradations with mean power law model parameters
Model parameter Un
ifo
rm
me
an
Me
diu
m
me
an
He
tero
-
dis
pe
rse
me
an
Un
ifo
rm
up
pe
r
95
%
Me
diu
m
up
pe
r
95
%
He
tero
-
dis
pe
rse
up
pe
r
95
%
Mean RPD: c -0.012 1.24 1.46 0.55 1.64 1.75
Mean RPD: m -1.07 -1.51 -1.66 -1.07 -1.51 -1.66
Maximum RPD: c 0.46 1.54 1.69 0.92 2.02 2.00
Maximum RPD: m -1.04 -1.43 -1.56 -1.04 -1.43 -1.56
The mean model parameters represent the power law parameters that are the result of the least square fit of the residuals. The upper 95% model parameters represent the upper bound of the 95% confidence interval for the parameter values.
48
Table 2-6. Discrete phase model computational time for the baffled hydrodynamic separator
Times are in seconds. The results show that doubling the DN generally doubles the computational time. This result combined with the diminishing convergence rate of the RPD for higher DNs demonstrates the exponentially increasing computational time to achieve the same improvement in the models RPD error due to discritization. Table 2-7. Discrete phase model computational time for the screened hydrodynamic
Times are in seconds. The results show that doubling the DN generally doubles the computational time. This result combined with the diminishing convergence rate of the RPD for higher DNs demonstrates the exponentially increasing computational time to achieve the same improvement in the models RPD error due to discritization.
49
Figure 2-1. Experimental validation of full scale units. Experimental validation of the change in event mean concentration for the screened HS (A) and baffled HS (B). CFD models from experiments conducted on full-scale 1.8m (6-ft) diameter units under laboratory conditions, loaded with the hetero-disperse NJDEP regulatory gradation (gradation #8). As required, the CFD model results were linearly interpolated to provide data points at concurrent flow rates for RPD calculation. Range bars show experimental mass balance recovery (%). Influent flow is demarcated by Q in each subfigure. Reported volumes are calculated from the water level under static conditions.
50
% f
ine
r by m
ass
0
20
40
60
80
100
Uniform - #1
( = 162.94, = 0.20)
Medium - #4
( = 2.28, = 17.01)
Hetero-disperse - #7
( = 0.57, = 116.32)
% f
ine
r by m
ass
0
20
40
60
80
100Uniform - #2
( = 177.19, = 0.38)
Medium - #5
( = 2.30, = 33.82)
Hetero-disperse - #8
( = 0.56, = 232.64)
Particle Diameter (mm)
1101001000
% f
ine
r by m
ass
0
20
40
60
80
100Uniform - #3
( = 163.07,= 0.61)
Medium - #6
( = 2.30, = 50.33)
Hetero-disperse - #9
( = 0.56, = 353.16)
% f
ine
r by m
ass
0
20
40
60
80
100Indianapolis PSD
New Jersey PSD
Figure 2-2. Cumulative PSDs utilized in the study. The cumulative PSDs selected to explore the effect of gradation dispersity (σI) and d50m on modeling error due to PSD discretization. The vertical axis represents the percentage by mass of particles that are finer than the diameter on the corresponding horizontal axis. There are three sets of three gradations. Each set of gradations is centered on a common focus (33.3µm, 66.7µm, or 100µm). Each gradation set contains gradations with σI = 0.11, 1.03, and 2.64. From an experimental perspective, the hetero-disperse gradation centered on 66.7µm (#8) is the gamma curve fit of the New Jersey (NJDEP) gradation (R2 = 0.99) and the uniform gradation centered on 100µm (#3: σI = 0.11, d50m = 100) is analogous to an OK-110 gradation (σI = 0.21, d50m = 110).
51
Actual Flow Rate (gpm)
0 71 142 213 284 355
E
MC
and
Mass (
%)
0
20
40
60
80
100
CInf
= 300 mg/L
MB
E (
%)
CInf
= 100 mg/L
Percentage of Design Flow Rate (0.64cfs)(%)
0 25 50 75 100 125
-10
-5
0
5
100 25 50 75 100 125
-10
-5
0
5
10
0 71 142 213 284 355
E
MC
and
Mass (
%)
0
20
40
60
80
100
Mass
EMC
CInf
= 100 mg/L
-10/100 -10/100
Mass
EMC
MB
E (
%)
Figure 2-3. Results from the full-scale experimental testing on the baffled HS. Baffled
HS treatment performance based on EMC and Mass for influent mass loading concentrations of 300 mg/L and 100 mg/L and matched to a corresponding mass balance error (MBE).
52
0 25 50 75 100 125
EM
C (
%)
0
20
40
60
80
100
Percentage of design flow rate (%)
0 71 142 213 284 355
CInf
= 300 mg/L
CInf
= 100 mg/L
Actual flow rate (gpm)
Percentage of Design Flow Rate (%)
0 71 142 213 284 355
0
20
40
60
80
1000 25 50 75 100 125
CInf
= 300 mg/L
CInf
= 100 mg/L
M
ass (
%)
Figure 2-4. Results comparing influent loading concentrations on the baffled HS. This figure demonstrates the baffled HS treatment performance by comparing
EMC efficiency and Mass efficiency across the two influent mass loading concentrations. The results show that the unit performs similarly for both influent mass loadings and that at the tested concentrations that particle-particle interaction is negligible validating the use of the discrete phase model without accounting for particle collisions.
53
Discretization Number (DN)
2 4 6 8 10 12 14 16
I = 0.11
(k = 163.07, = 0.61)
I = 1.03
(k = 2.30,= 50.33)
I = 2.65
(k = 0.56, = 353.16)
Discretization Number (DN)
2 4 6 8 10 12 14 16
I = 0.11
(k = 177.19, = 0.38)
I = 1.03
(k = 2.30, = 33.82)
I = 2.64
(k = 0.56, = 232.64)
Discretization Number (DN)
0 2 4 6 8 10 12 14 16
Ma
xim
um
RP
D
0
10
20
30
40
50
60
I = 0.11
(k = 162.94, = 0.20)
I = 1.03
(k = 2.28, = 17.01)
I = 2.63
(k = 0.57, = 116.32)
Me
an
RP
D
0
10
20
30
40
50
I = 0.11
(k = 162.94, = 0.20)
I = 1.03
(k = 2.28, = 17.01)
I = 2.63
(k = 0.57, = 116.32)
I = 0.11
(k = 163.07, = 0.61)
I = 1.03
(k = 2.30, = 50.33)
I = 2.65
(k = 0.56, = 353.16)
I = 0.11
(k = 177.19, = 0.38)
I = 1.03
(k = 2.30, = 33.82)
I = 2.64
(k = 0.56, = 232.64)
d50m
= 33.3 m
A
d50m
= 33.3 m
B
d50m
= 66.7 m
d50m
= 66.7 m
d50m
= 100 m
d50m
= 100 m
C
ED F
Figure 2-5. Computational results for the screened HS. Mean and maximum RPDs based on DN as calculated against DN = 128. In this manner, the RPD is a characterization of error due solely to PSD discretization. Results show rapidly decreasing error for higher DNs as well as the effect of σI at low DNs for the mono-disperse (σI = 0.11), moderately dispersed (σI = 1.03), and hetero-dispersed (σI = 2.64) gradations. Results for DNs higher than 16 are not displayed due to high convergence. The d50m is only a reasonable characterization and model input for mono-disperse gradations.
54
Discretization Number (DN)
2 4 6 8 10 12 14 16
I = 0.11
(k = 163.07, = 0.61)
I = 1.03
(k = 2.30, = 50.33)
I = 2.65
(k = 0.56, = 353.16)
Discretization Number (DN)
2 4 6 8 10 12 14 16
I = 0.11
(k = 117.19, = 17.01)
I = 1.03
(k = 2.30, = 33.82)
I = 2.64
(k = 0.56, = 232.64)
Discretization Number (DN)
0 2 4 6 8 10 12 14 16
Maxim
um
RP
D
0
10
20
30
40
50
60
I = 0.11
(k = 162.94, = 0.20)
I = 1.03
(k = 2.28, = 17.01)
I = 2.63
(k = 0.57, = 116.32)
Mean R
PD
0
10
20
30
40
50
= 0.11
(k = 162.94, = 0.20)
= 1.03
(k = 2.28, = 17.01)
I = 2.63
(k = 0.57, = 116.32)
I = 0.11
(k = 163.07, = 0.61)
I = 1.03
(k = 2.30, = 50.33)
I = 2.65
(k = 0.56, = 353.16)
I = 0.11
(k = 117.19, = 17.01)
I = 1.03
(k = 2.30, = 33.82)
I = 2.64
(k = 0.56, = 232.64)
d50m
= 33.3 m
A
d50m
= 33.3 m
B
d50m
= 66.7 m
d50m
= 66.7 m
d50m
= 100 m
d50m
= 100 m
C
ED F
Figure 2-6. Computational results for the baffled HS. Mean and maximum RPDs based on DN as calculated against DN = 128. In this manner, the RPD is a characterization of error due solely to PSD discretization. Results show rapidly decreasing error for higher DNs as well as the effect of σI at low DNs for the mono-disperse (σI = 0.11), moderately dispersed (σI = 1.03), and hetero-dispersed (σI = 2.64) gradations. Results for DNs higher than 16 are not displayed due to high convergence. The d50m is only a reasonable characterization and model input for mono-disperse gradations.
55
Figure 2-7. CFD per-particle size efficiency surfaces for both the screened HS (A) and the baffled HS (B). These surfaces represent a “fingerprint” of the performance of the solids separator at steady flow rates across the spectrum of designed flows. The top plane in each surface represents particles that are completely captured and the bottom plane in each surface represents particle sizes that are negligibly captured by the solids separators.
56
Figure 2-8. CFD per-particle size efficiency differential surface for the screened HS and the baffled HS. This represents the performance differential per particle size for the two units – positive indicating better performance from the baffled HS.
57
Discretization Number (DN)
0.1 1 10 100
Discretization Number (DN)
0.1 1 10
Discretization Number (DN)
0.1 1 10
Maxim
um
RP
D
0.001
0.01
0.1
1
10
Mean
RP
D
0.001
0.01
0.1
1
10
100
Baffled HS
Screened HS
Convergence Model (CM)
Upper 95% CM
Uniformity: Uniform
A
Uniformity: Uniform
B
Uniformity: Medium
Uniformity: Medium
Uniformity: Hetero-disperse
Uniformity: Hetero-disperse
C
ED F
R2
= 0.84 R2
= 0.95 R2
= 0.98
R2
= 0.88 R2
= 0.93 R2
= 0.97
Figure 2-9. Predictive results of the power law model for RPD with increasing DN. Cumulative baffled HS and screened HS convergence data modeled by the power law. Panels (a) through (c) are model mean RPDs in increasing order of hetero-dispersivity and panels (d) through (f) are model maximum RPDs in increasing order of hetero-dispersivity. The convergence model is based on a least squares linear regression of the log – log plot of the data. An upper bound model (Upper 95% CM) constructed from the standard deviation of the regression residuals is given for more conservative estimation of RPD.
58
CHAPTER 3 OVERALL RATE KINETICS MODEL OF SODIUM HYPOCHLORITE DEMAND BY THE
DISSOLVED AND PARTICULATE MATTER FRACTIONS IN URBAN RAINFALL-RUNOFF
Urban runoff is a significant source of hetero-disperse PM, chemical and
microbial loadings to receiving waters and combined sewer overflows. Effective
treatment and reuse options are needed given urban water demands and regulations
such as total maximum daily loads (Code of Federal Regulations 2001), and numeric
nutrient criteria in Florida (USEPA 2010). Urban runoff microbial loads can impair
receiving waters (Jin et al. 2004, USEPA 1984) and in reuse applications pose a
potential public health risk if untreated (DEC 2006). Chlorination is the most commonly
utilized disinfectant worldwide (Hrudey and Hrudey 2004) and is utilized to provide a
residual in reclaimed wastewater utilized for irrigation in the built environs. However,
the efficacy of chlorine disinfection is highly dependent on the available residual over
time (Chick 1908, Fair et al. 1948).
Many kinetic studies of free and total chlorine demand in source waters and
wastewater have been undertaken. Taras (1950) model chlorine demand of inorganic
and organic substances with a power law function. Hass and Karra (1984) evaluate
chlorine demand in wastewater utilizing first order, power law and parallel first-order
models. Clark describes chlorine demand in drinking waters utilizing a second order
model (1998) and, in conjunction with Sivaganesan, describes chlorine demand with a
parallel second-order model for raw and finished waters (2002). Huang and McBean
extend Clark‟s work on finished waters employing Bayesian statistics to determine
model parameters (2007). Warton et al. utilize piecewise first order functions and
dissolved organic carbon (DOC) to normalize models to provide dose independence
59
(2006). However, there are fewer chlorine demand kinetic models for urban runoff or
wet-weather combined sewer overflows (CSOs). While the potential for DBPs exists for
waters with natural organic matter (Rook 1977), an EPA study concludes that
chlorination/dechlorination is preferred in decentralized CSO treatment. The study cited
high costs of decentralized ozone generation for intermittent wet-weather events, PM
shielding in ultraviolet inactivation schemes, and the public health hazard associated
with Cl2 gas storage required for chlorine dioxide generation (USEPA 2003).
The constituents of urban runoff differ from potable water sources and wastewater.
Kim and Sansalone (2010) compared PM from untreated runoff to wastewater treatment
plant (WWTP) influent. The study demonstrated that influent PM was relatively fine
Parallel Second Order Demand Model for Dissolved Phase
Urban stormwater runoff contains a complex matrix of dissolved inorganic and
organic species (Dean et al. 2005). Clark and Savaganesan‟s (2002) parallel second
order model demonstrated applicability to the complex chemistry of both raw and
finished waters. This model is chosen to represent the kinetics of free chlorine demand
in urban runoff. The model assumes that the chlorine demand kinetics is the result of
two parallel reactions.
66
(3-1)
In these reactions, , and pn are stoichiometric coefficients, Pn are products,
and are the concentration of free chlorine participating in each reaction and
and
are the concentration of reactants in the solution that react with the free chlorine. An
analytical solution of Equation 3-1 is Equation 3-2
( ) ( )
( )
( )( )
( )( )
(3-2)
(Note that in Equation 18 in Clark and Savaganesan (2002), and
( ) as presented in Equation 3-2). In this expression, Cl(t) = chlorine
concentration at time, t; Cl0 is the initial concentration of free chlorine ( +
); X =
( ); k1 and k2 are rate constants; and Rn is defined in Equation 3-3.
(3-3)
Substituting Equation 3-3 into Equation 3-2 gives Equation 3-4.
( )
(
)
(
)
( )(
)
( )(
)
(3-4)
To aid in the development and physical significance of the model a hypothetical ultimate
chlorine demand term is introduced, CB0 which is the sum of the initial concentrations of
and
and reacts on a one to one stoichiometric basis with free chlorine (
). Note that the 1:1 stoichiometric relationship gives . Utilizing these
relationships gives Equation 3-5.
67
( ) (
)
(
)
( )( ( ) ( )
)
( ) ( )
( )(
( ) ( )
) (3-5)
In addition, CB0 is assumed to be a fractional component represented by the dissolved
chemical oxygen demand (CODd). CODd is a reasonable model parameter given the
ease of measurement and that the basis of the measurement is an oxidation reaction.
Substituting CB0 = fCODd into the model of Clark and Savaganesan returns a solution of
the form in Equation 3-6.
( )
( )
( )( ) ( ) (3-6)
In this expression the physical parameters are the initial chlorine dose as
Cl0 [mg/L], CODd [mg/L], and time, t. The four model parameters are f, the fractional
component of the CODd that represents the ultimate chlorine demand (unitless), X
(unitless), the proportion of the chlorine demand that reacts quickly at rate k1, and k1
and k2 the quick and slow, respectively, reaction rate constants with the units of
[L1mg-1min-1]. Figure 3-2 illustrates the physical significance of the parameters of the
second order demand model for the dissolved phase.
To determine the model parameters, the non-linear curve fitting using the
Levenberg-Marquardt algorithm (Marquardt 1963) to estimate the parameters of the
non-linear functions as well as the parameter standard error. In addition, the model
parameters are combined for sixteen datasets to obtain globally best-fitting parameters
based on the catchment runoff data. The remaining datasets are utilized as non-
influencing verification datasets of the model.
68
Second Order Potential Driving Model for the PM Fractions
As a source water, urban rainfall-runoff contains a heterodisperse PM phase that
from a treatment perspective contains three significant PM fractions, suspended,
settleable, and sediment based on size and mechanistic delimiters. The chlorine
demand for each PM fraction is determined by subtracting the chlorine demand of the
dissolved runoff matrix from the overall chlorine demand of the batch reactor containing
a PM fraction. A model of the dissolved phase demand is determined from two replicate
reactors from the same dissolved runoff matrix source and dosed with the same initial
hypochlorite concentration.
Studies have demonstrated the applicability of a second order potential driving
model for kinetics of adsorption in runoff for dissolved metals (Liu et al. 2005) and
phosphorus (Wu and Sansalone 2011) for simulating overall mass transfer. The second
order potential driving model has the following form (Liu et al 2005).
( )( ) (3-3)
In this expression Ct is the concentration of free chlorine at time, t; Ce is the
concentration of free chlorine at equilibrium; St is the number of active reaction sites on
the PM at time, t; Se is the number of active reaction sites on the PM at equilibrium; and
kPM is the mass transfer rate constant. Equation 3-3 models the instantaneous free
chlorine demand from PM as a function of the available PM reaction sites and the
concentration of the available free chlorine. Substituting:
(3-4)
(3-5)
69
Substituting Equations 3-4 and 3-5 into Equation 3-3 and linearizing gives Equation 3-6
(Liu et al. 2005):
( )
(3-6)
In this expression is the sorbent(PM)/solute(HOCl) ratio. From Equation 3-6, the final
mass transfer form of the model (Equation 3-10) is obtained by substituting Equations 3-
7 through 3-9 in Equation 3-6 (Wu and Sansalone 2011).
( )
(3-7)
( )
(3-8)
(3-9)
Where W/V is the mass of PM [g] over the volume of the reactor [L], which is the SSC
[g/L]; qt [mg/g] is the ratio of the mass of free chlorine transferred to the mass of PM in
the reactor at time, t [min]; and qe [mg/g] is the ratio of the mass of free chlorine
transferred to the mass of PM in the reactor at equilibrium.
(3-10)
Equation 3-10 is the linearized second order potential driving model for the overall mass
transfer of free chlorine from the solute phase into surface reactions on constituent PM.
Initial values for kPM [g1mg-1min-1] and qe are determined experimentally by plotting t/qt
versus t. Resulting parameter values favor the experimental endpoint due to the
linearization, and are further refined by minimizing the normalized root mean square
error (NRMSE).
70
Model Evaluation
NRMSE is utilized to evaluate model performance.
√∑ ( )
(3-11)
In this expression Oi is the observed value at measurement i; Ei is the modeled value at
measurement i; n is the total number of measurements; and Clo is the initial chlorine
dose.
Results and Discussion
Control Reactors
The results from the control reactors demonstrate that no detectable
environmental losses of free chlorine due to UV light or volatization occur during the
duration of the batch reactor experiments (Figure A-3). In addition, autoclaving the
runoff matrix resulted in no detectable change to the model parameters (Figure A-4).
Kinetics Model for Dissolved Phase
Figure 3-3, panel A, and Table 3-2 summarize the kinetic model parameters for
the inter-event CSBR dataset of the dissolved phase (n = 16). The fractional
component of CODd reacting on a 1:1 basis with free-chlorine as chlorine demand, f, is
0.36 ± 0.025 (95% C.L). X, which represents the portion of fCODd that reacts rapidly
are 0.39 ± 0.035. k1 is 0.07 ± 0.033 (L1mg-1min-1) and k2 is (2.93 ±0.79) X 104 (L1mg-
1min-1). Figure 3-3 also demonstrates the predictive capability for the second order
demand model for the dissolved phase using four non-influencing datasets. The data,
second order model, and a 95% confidence bands for model parameters are shown.
From the figure, the multi-modal decay rates are visibly apparent from the datasets
confirming the selection of a parallel model for the analysis (c.f. Panel D). One of the
71
strengths of the parallel second order dissolved model is the characterization of the
initial phase of chlorine demand, thus, allowing the entire curve to be modeled as
opposed to modeling decay after an “instantaneous” demand. The 95% confidence
band illustrates the sensitivity of the model to the four model parameters with the
parameter sensitivity being f > X > k2 > k1.
The model parameters are developed on an inter-event basis. Thus, the given
model parameters and ranges represent loading characteristics which are consistent for
the catchment and event independent. The rate constants, k1 and k2, demonstrate that
there are parallel reactions with separate rate constants and that these rate constants
differ by two orders of magnitude. This difference provides additional validation for the
use of a parallel model in identification of two separate reaction rates of disparate
values. The parameter, X, indicates that approximately 39% of the chlorine demand is
exerted by dissolved components that react rapidly with free chlorine. This
proportionality is consistent (±3.5%) for the catchment across multiple events and
results from the dissolved inorganic and organic loads. Further research is needed to
elucidate the value of X for similar and dissimilar loadings on differing watersheds and,
in particular, to illuminate if X is related to the ratio of DOC to COD. The fraction, f, of
the COD that exerts a chlorine demand is consistent (±2.5%) across events for the
catchment. Similar to X, this fraction may be a result of the type and proportion of the
biogenic and anthropogenic loadings for a catchment.
The NRMSE of the dissolved model on the non-influencing datasets is < 6% in
each case. The use of non-influencing datasets is an important reliability indicator of
the second order chlorine demand model. As with any model, it is necessary to predict
72
the behavior of a system given initial conditions and system reaction rates. Predicting
the chlorine residual concentration over time in urban runoff can be determined by this
model. From a system design standpoint, this is a powerful tool in the development of
disinfection unit process in a runoff treatment train.
Figure 4-4 illustrates the intra-event mass transport of CODd which is a primary
parameter of the second order, parallel chlorine demand model summarized in Equation
2. The 21 August 2010 event is a low intensity, low volume, short duration event that is
flow limited with respect to CODd. In contrast the 27 September 2010 event is a high
intensity, high volume, long duration event of low previous dry hours (PDH). The long
duration of the low intensity falling limb of intermittent runoff results in dissolution of
particulate bound COD into the dissolved phase. This increases the mass loading of
the dissolved phase at the end of the event and accounts for the inverted cumulative
distribution of CODd during the tail end of the event. The 04 November 2010 event is of
moderate intensity, moderate duration, moderate volume, and 910 PDH. The extended
dry time increases buildup of CODd on the catchment and this event transports the
highest cumulative mass of CODd. The 16 November 2010 event is low volume, low
intensity and low duration. The cumulative distribution indicates that this event is
weakly mass limited. The inter-event variation of CODd load demonstrates the influence
of hydrologic parameters on transport and illustrates the intra- and inter-event temporal
variability of chlorine demand. As a result, a treatment water quality volume (WQV)
cannot be defined for CODd a priori and corroborates a previous study for CODd on a
disparate watershed (Sansalone et al. 2005) of differing land use.
73
PM Kinetic Model
The ultimate free chlorine demand of the reactor constituents is potentially limited
by the chlorine dose if the theoretical demand exceeds the dose value. For the second
order potential driving model for free chlorine demand this ultimate free chlorine
demand is represented by qe. Figure 3-5 presents the results of plotting the modeled qe
versus the maximum qe that that is available to the PM fraction, qe_max, given the initial
hypochlorite dose, the SSC of the reactor, and the dissolved kinetic chlorine demand.
As can be seen from the figure, for under-chlorinated reactors qe = qe_max as the
maximum chlorine demand per PM mass cannot exceed the available free chlorine in
the reactor. As the chlorine available in the reactor increases, the relationship between
qe and qe_max enters a transitional region, where increasing the qe_max continues to
increase the value of qe, but the values are not equal. This transitional region
asymptotically converges on a maximum value for qe which is the maximum mass
transfer of the chlorine out of the dissolved phase to reactions with PM. Suspended PM
achieves a maximum modeled value of approximately 350 Cl2/g PM, settleable material
achieves a maximum modeled value of approximately 320 mg/g, and sediment PM
reaches an asymptotic maximum of 720 mg/g. The proximity of the maximum qe value
of the suspended and settleable PM fractions is attributed to the similar siliceous, low
organic crystalline structure of these fractions. For the sampled events, PM in these
fractions have a low volatile fraction with a range of 24-58%. However, the sediment
PM fraction is much more organic in nature, by the surrogate measurement of the
volatile fraction of the SSC, and has volatile fractions from 59-75%.
The reaction rate constant, kPM, also varies with qe_max. This parameter governs
the rate of the reaction and lowers as qe_max increases. This is a result of the model
74
governing an overall mass transfer rate. For lower values of qe_max the chlorine reacts
with the easiest to access sites on the PM. For higher values of qe_max in addition to
reacting with the most accessible sites, the free chlorine also reacts through the macro-
pore structure of the PM involving a diffusion process that slows the overall reaction rate
as measured experimentally. Thus, the second order diffusion model does not account
for this variability. The range of kPM values for this experimental construct is 3.95 X 10-6
g1mg-1min-1 (super-chlorinated reactors) to 9.2 X 10-3 g1mg-1min-1. However, the
variability is the strongest for under-chlorinated reactors, thus appropriate design of the
chlorination process given the criteria of SSC allows for a predictable kPM.
Figure 3-6 demonstrates the model fit of the second order PM kinetic model. The
results indicate the model fit the experimental data with R2 values ranging from 0.89 to
0.97 for the different fractions. This indicates the applicability of the second order
potential driving model to the overall mass transfer of hypochlorite from the dissolved
phase to the particulate phase.
Table 3-3 presents a comparison of the modeled demand of the dissolved and
particulate fractions on an event mean basis for the monitored storms. The sediment
PM fraction accounts for greater than 55% of all potential hypochlorite demand for each
event and up to 93.6% of the potential hypochlorite demand for the 27 September 2010
event. In addition, the demand from all PM fractions for the monitored events represent
over 77% of the potential chlorine demand illustrating the potential benefits of treatment
by sedimentation or filtration prior to chlorination from a chlorine demand perspective.
The dissolved phase demand, represented by fCODd, exerts a range of hypochlorite
demand from 7.6 mg/L to 88.7 mg/L. Previous authors (Clark 1998) have linked
75
chlorine demand to disinfectant by product formation, thus, providing motivation for
demand removal prior to treatment with hypochlorite. Effective dissolved phase
demand reduction can be accomplished through the use of membrane treatment.
Overall the two models developed in this study provide a methodology for
estimating the kinetic chlorine demand of urban rainfall-runoff PM fractions and
dissolved phase. Results indicate a significant chlorine demand by each PM fraction,
although the predominance of the demand is from the sediment PM fraction of higher
organic content as compared to suspended or settleable PM fractions. This study
models the chlorine demand kinetics of PM fractions and the dissolved phase of runoff
by combining rainfall-runoff event monitoring utilizing CODd, the gravimetric-based PSD
of PM fractions, and a batch reactor framework for the generation of chlorine demand
parameters. Results enable the evaluation of hypochlorite disinfection as a unit process
for the conditioning of urban rainfall-runoff for reuse. Implementation of these results
allows the balancing of primary and secondary unit operation requirements for source
area runoff reuse with hypochlorite demand by each PM fraction and the dissolved
runoff phase.
76
Table 3-1. Summary of hydrologic and PM event mean concentration indices for captured events.
Event (2010)
PDH (h)
Rainfall duration
(min)
Total runoff
volume (L)
Qmed
(L/s) Qp
(L/s)
PM fractions (mg/L)
Suspended Settleable Sediment
7-Auga 24 48 2623 1.01 4.3 13.1 (3-50)
32.2 (8-99)
222.5 (6-21414)
21-Augb 83 31 299 0.03 1.5 2.2 (0.5-4)
36.8 (6-192)
301.1 (18-3295)
27-Sep 10 388 3842 0.01 10.9 44.5 (16-190)
50.0 (1-289)
874.1 (2-6035)
4-Nov 910 43 996 0.13 3.5 93.6 (15-319)
51.5 (4-225)
486.6 (5-18145)
16-Nov 286 34 307 0.01 1.8 123.2 (30-247)
137.8 (4-340)
332.2 (24-3208)
a The event on 7 August 2010 was utilized for stormwater matrix collection. b Baseline event. PDH is previous dry hours; Qmed is the median runoff flow rate; and Qp is the peak runoff flow rate. Table 3-2. Global parallel 2nd order demand model parameters for the dissolved phase.
Parameter Units Mean SEa
k1 L1mg-1min-1 0.070 0.015
k2 L1mg-1min-1 2.93 X 10-4 3.66 X 10-5
X - 0.39 0.016
f - 0.36 0.012 The global adjusted R2 = 0.988. The results indicate that the rate constant k1 is over two orders of magnitude greater than k2. In addition, X indicates that approximately 40% of the chlorine demand is exerted by dissolved components that react quickly with free chlorine. f indicates that the estimated ultimate chlorine demand is approximately 36%
of the CODd. aStandard Error = √ ⁄ , where s is the standard deviation of the sample
mean and n = 16.
77
Table 3-3. Hypochlorite event-based ultimate demand of urban stormwater fractions for the monitored storms.
Calculations utilize f = 0.36, suspended qe = 350 mg/g, settleable qe = 320 mg/g, and sediment qe = 720 mg/g. Results indicate that the constituents of the sediment PM fraction exert the largest portion of HOCl demand on an event mean basis.
78
0.1 1 10 100 1000 10000
0
2
4
6
8
10
Settleable25 m < < 75 m
R2 Suspended PM
R4 Settleable PM
R6 Sediment PM
% f
ine
r b
y m
ass f
or
ea
ch
PM
fra
ctio
n
Particle diameter (m)
Suspended0.45 < < 25 m
Sediment75 m < < 4750 m
Figure 3-1. PSD of quintessential fractions from the batch reactors. The figure
demonstrates validation by laser diffraction analysis of the wet sieve fractionation procedure. Note that the upper bound of the laser diffraction analysis is 2mm.
79
0 100 200 300 400 5000
5
10
15
20
25
30
35
40
45
50
Batch Reactor (R20) data
Parallel 2nd Order Demand Model
HO
Cl/O
Cl- (
mg
/L)
Reaction Time (min)
Clo={HOCl/OCl
-}
o
CB0
=f(CODo)
X = C
1
B
CB0
k2
k1
C*
Figure 3-2. Physical representation of the parameters of the parallel 2nd order demand
model. Clo is the initial concentration of free chlorine; CB0 is the ultimate free chlorine demand of the reactor (note a/b = 1) which is estimated by a fractional component, f, of the reactor initial dissolved COD; X is the ratio of the ultimate chlorine demand that reacts quickly with the second order rate constant k1; (1-X) is the ratio of the ultimate chlorine demand that reacts slowly with the second order rate constant k2; and C* represents the remaining free chlorine concentration at equilibrium. Note that as a parallel model, the effects of the slower reaction, k2, are also exerted during the initial portion, but as the time-scale of this reaction is several orders of magnitude slower than the quick initial reaction, the initial interval is well illustrated by the parameters k1 and X.
80
Figure 3-3. Predictive fit of the dissolved fraction parallel 2nd order demand model. The
model parameters are derived from the global best fit of the experimental dataset and the demand model shown utilizes these parameters and the initial conditions of the reactor. Models are shown within a 95% C.L. The experimental data are four datasets excluded from the parameter estimation analysis to independently verify the derived model for the watershed. NRMSE is reported for each panel and show that for all models NRMSE is < 6%.
0
5
10
15
20
25
30
35
40
45
500 100 200 300 400
Time (min)
Storm Date: 21 August 2010
Co = 30.4 mg/L | COD = 91.18 mg/L
Parallel 2nd Order Demand Model
k1 = 0.0701 L
1mg
-1min
-1
k2 = 2.93 X 10
-4 L
1mg
-1min
-1
X = 0.39
f = 0.36
Model Parameter 95% C.L.
HO
Cl/O
Cl- (
mg
/L)
NRMSE: 4.7%
A
0 100 200 300 4000
5
10
15
20
25
30
35
40
45
NRMSE: 5.7%
7 August 2010
Co = 29.0 mg/L | COD = 33.5 mg/L
HO
Cl/O
Cl- (
mg
/L)
Time (min)
D
0 100 200 300 400
0
5
10
15
20
25
30
35
40
45
50
NRMSE: 3.7%
Storm Date: 4 November 2010
Co = 45.6 mg/L | COD = 148.2 mg/L
Time (min)
HO
Cl/O
Cl- (
mg
/L)
C
0 100 200 300 4000
5
10
15
20
25
30
35
40
45
NRMSE: 3.7%
Storm Date: 21 August 2010
Co = 43.4 mg/L | COD = 91.18 mg/L
HO
Cl/O
Cl- (
mg
/L)
Time (min)
C
81
Figure 3-4. Transient loading of CODd on the small urban catchment in north central
Florida. With respect to CODd the 21-Aug-2010 event illustrates an event that is flow limited and the extended event on 27-Sep-2010 illustrates potential dissolution of PM COD during the low flow period at the end of the event. The November storms illustrate mass limited events. This panoply of CODd loadings is indicatory of the nature of antecedent conditions and hydrologic parameters on CODd transport, thus, a treatment WQV cannot be defined for this parameter a priori.
0.0
0.2
0.4
0.6
0.8
1.0
1.2
0.0 0.2 0.4 0.6 0.8 1.0
Runoff
Normalized Cumulative Volume
16 November 2010 | Qp=1.8 L/s | COD
d max= 28.3 g
CODd cum
= 69.5 g | Vrunoff
= 307 L
No
rma
lize
d C
OD
d
0.0 0.2 0.4 0.6 0.8 1.00.0
0.2
0.4
0.6
0.8
1.0
1.24 November 2010 | Q
p=3.5 L/s | COD
d max= 52.9 g
CODd cum
= 166 g | Vrunoff
= 996 L
Normalized Cumulative Volume
Q/Q
p CODd
Cum. CODd
0.0
0.2
0.4
0.6
0.8
1.0
1.2
0.0 0.2 0.4 0.6 0.8 1.0
Normalized Cumulative Volume
27 September 2010 | Qp= 10.9 L/s | COD
d max= 35.1 g
CODd cum
= 137 g | Vrunoff
= 3842 L
No
rma
lize
d C
OD
d
0.0
0.2
0.4
0.6
0.8
1.0
1.2
0.0 0.2 0.4 0.6 0.8 1.0
Normalized Cumulative VolumeQ
/Qp
21 August 2010 | Qp= 1.5 L/s | COD
d max= 4.9 g
CODd cum
= 22.2 g | Vrunoff
= 299 L
82
0 1000 2000 3000 4000
0
200
400
600
800
Sediment PM
75 m < < 4750 m
Settleable PM
25 m < < 75 m
Suspended PM
0.45 m < < 25 m
qe (
mg
/g)
qe_max
(mg/g)
q e =
qe_m
ax
Figure 3-5. Maximum particle free chlorine demand. For under-chlorinated reactors, the
modeled chlorine demand at equilibrium, qe, is limited by the maximum chlorine available in the reactor, qe_max. For the over-chlorinated reactors, qe for the suspended PM reaches an asymptotic maximum of 350 mg/g, qe for the settleable PM reaches an asymptotic maximum of 320 mg/g, and qe for the sediment PM reaches an asymptotic maximum of 720 mg/g. In the reactors chlorinated near the plateau point there is a transitional region where qe increases up to the asymptotic maximum.
83
Figure 3-6. The modeling results of the second order PM chlorine demand model. The
results indicate an excellent model fit for all PM fractions (R2 > 0.89).
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.2
0.4
0.6
0.8
1.0
Mo
de
led
qt/q
e
R2 = 0.94
Composite PM
Measured qt/q
e
D
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.2
0.4
0.6
0.8
1.0
Sediment
75 m < < 4650 m
Mo
de
led
qt/q
e
Experimental qt/q
e
R2 = 0.97
C
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.2
0.4
0.6
0.8
1.0
Mo
de
led
qt/q
e
Measured qt/q
e
R2 = 0.89
Settleable
25 m < < 75 m
B
0.0
0.2
0.4
0.6
0.8
1.0
0.0 0.2 0.4 0.6 0.8 1.0
Measured qt/q
e
R2 = 0.93
Suspended
0.45 m < < 25 m
Mo
de
led
qt/q
e
A
84
CHAPTER 4 SODIUM HYPOCHLORITE DISINFECTION OF INDICATOR ORGANISMS
ASSOCIATED WITH URBAN STORMWATER PARTICLES
Water resources and reuse thereof are increasingly central themes of sustainable
development for the developed and developing world. Future water needs will be
addressed by integrating systems that reduce water consumption, reusing water
discharges and simultaneously moving towards hydrologic restoration. In urban areas
rainfall-runoff relationships have a significant impact on the hydrologic cycle and in such
areas have been subject to significant anthropogenic modification due to urban activities
such as traffic and the high imperviousness of such watersheds. Rainfall-runoff
transports particulate matter (PM), microbial, chemical and nutrient loadings and can
impair receiving waters (Heaney and Huber 1984, House et al. 1993). With respect to
hydrology urban modification of the rainfall-runoff relationship diverts a significant
volume of water annually from pre-developed pathways (Marselek et al. 1993) with the
commensurate increases in peak flow, volume and transported load. Such scenarios in
urban areas makes runoff a critical hydrologic component for an integrated
management approach involving low impact development and reuse. Hatt et al. (2006)
reports that the current research base is inadequate to support the present
implementation of urban runoff reuse. With respect to urban water reuse there is the
need for identification of PM-associated microbial distribution and the appropriate unit
operations and processes for PM separation and corresponding disinfection in order to
minimize public health risks.
The U.S. EPA has documented the use of indicator organisms as surrogate
indices of pathogenic organisms in receiving waters through epidemiologic correlations
of illness and issued ambient water chemistry criteria (USEPA 1984). Jin et al. (2006)
85
investigated indicator organism loadings by urban rainfall-runoff vectors into Lake
Pontchartrain and subsequent recreational water closings. Charaklis et al. (2005) has
documented that indicator organisms in urban runoff exist as both planktonic and
particle associated organisms, and Krometis et al. (2007) has documented that there is
no significant variation in intra-event partitioning of indicator organisms in runoff, but that
there is a significant variation in intra-event microbial loading rates. Finally, He et al.
(2008) examined runoff for reuse and found that a retention basin could produce water
of acceptable microbial levels during dry weather periods, but that runoff events
mobilized significant microbial loadings in excess of public reuse guidelines in Alberta,
Canada.
Chlorination has been in use for over a century and is the most common form of
disinfection in practice today (Hrudey and Hrudey 2004). However, urban rainfall-runoff
is a complex matrix of dissolved and heterodisperse PM fractions (Sansalone and Kim
2008, Kim and Sansalone 2010). With respect to the impact of PM on disinfection
LeChavallier et al. (1981) documented the hindering effect of PM, using turbidity as a
surrogate, on the disinfection of environmental surface water in Oregon. Berman et al.
(1988) documented similar findings for the organic PM in wastewaters and in addition
found that chlorine permeated smaller PM faster than PM of larger diameter. Dietrich
(2003) extended these findings and modeled the intra-particle transport of free chlorine
with a radial diffusion model. Winward et al. (2008) extrapolated this research to the
use of chlorine as a disinfectant for grey water reuse applications. In addition, studies
have modeled the kinetics of inactivation for bacteriological and protozoan organisms
utilizing the Chick-Watson (Chick 1908), the Hom (Hom 1972), a modified Hom (Finch
86
et al. 1993, Haas and Joffe 1994), and rational models (Gyurek and Finch 1998).
Contributions to this existing body of knowledge are the distribution of indicator
organisms as a function of PM fractions and the efficacy of chlorination as a function of
PM fractions in urban runoff.
Given the heterodispersivity, granulometry, organic content and distribution of
nutrients for urban runoff PM fractions (Dickenson and Sansalone 2009, Berretta and
Sansalone 2011) the present study examines the association of indicator organisms
with PM fractions and the efficacy of disinfection for these PM fractions. Specifically it is
hypothesized that organisms do not distribute equally across the PM gradation.
Furthermore it is hypothesized that disinfection efficacy is not equal for each PM
fraction.
Methodology
The catchment for this study is an urban source area located in Gainesville, FL.
The land use is a 13,000 m2 carpark field site of which a 500 m2 catchment was
delineated, instrumented and monitored for this study (Figure A-2). The surface area of
the carpark is 75% asphalt pavement and 25% raised vegetated islands with mature
trees and delineated by vertical concrete curbs. The catchment has an average daily
traffic loading of 530 vehicles (observed). Physical (PM), chemical (nutrients, metals
and organic compounds) and microbial loading sources are anthropogenic (tire,
vehicular and pavement abrasion and urban litter) and biogenic sources (leaf litter,
grass clippings, insects, and small urban animals and bird feces). The catchment
drains by sheet and gutter flow to a catch basin that was modified to allow all flow to be
diverted for full cross-sectional flow manual sampling during a monitored rainfall-runoff
event. Rainfall is measured with a tipping bucket rain gage and flows are measured
87
real-time with a calibrated Parshall flume, an ultrasonic transducer and data logger.
During a rainfall event runoff is sampled at volumetrically-spaced intervals in replicate
and each set of replicates composited to construct paired event-based replicates for an
event.
PM Fractionation
During the monitoring phase of the study a series of rainfall-runoff events were
sampled. The hydrologic and PM indices are presented in Table 4-1. Approximately
100 L of runoff was sampled for each event and immediately (within 30 minutes)
transferred to the laboratory for analysis. Prior to each of these events additional runoff
from a previous event loading the same catchment was collected and filtered (0.45 µm
membrane). This filtered runoff served as a dissolved matrix for re-suspending each
PM fraction. The dissolved matrix was autoclaved at 121°C for one hour to render the
dissolved matrix biologically inert. The runoff matrix was then stored at 4°C. In this
study “runoff matrix” refers to the filtered and autoclaved dissolved fraction of runoff.
PM was fractionated by wet sieving and microfiltration from the sampled 100 L volume
into, sediment PM (4250 – 75 µm, Kim and Sansalone 2008), settleable PM (75 – 25
µm, Kim and Sansalone 2008), and suspended PM (25 – 0.45 µm, Kim and Sansalone
2008). Sediment and settleable PM fractions were immediately re-suspended in 1 L of
runoff matrix as PM concentrate and filtrate from the 25 µm sieve is set aside as
suspended PM. For each of the events analysis included microbial enumeration for
total coliform, E. coli, fecal streptococcus, and enterococcus organisms partitioned to
each PM fraction in replicate composite samples for each runoff events. Batch reaction
testing elaborated the chlorine inactivation kinetics for total coliform partitioned to each
88
PM fraction (Kim and Sansalone 2008) of the runoff events. In all cases, microbial
samples are analyzed immediately or maintained at 4°C and analyzed within six hours.
Microbiological Enumeration
Microbiological enumeration of the organisms utilizes the multiple tube
fermentation, the most probable number (MPN) method. This method is selected due to
its applicability to turbid waters (Eaton et al. 1999). For the event microbial monitoring,
total coliforms, E. coli, fecal streptococcus and enterococcus organisms are enumerated
according to Standard Methods 9221B, 9223B and 9230B (Eaton et al 1998). For total
coliform organisms and E. coli, samples are inoculated aseptically into a five row by five
dilution tube bank of lauryl triptose broth amended with 4-methylumbelliferyl-β-D-
glucuronide (LTB-MUG) and incubated at 35°C. At 24 and 48 h the cultures were
checked for lactose fermentation (gas bubbles in an inverted vial) and fluorescence
under a 366 nm UV light. Lactose fermentation in LTB incubated at 35°C represents a
presumptive positive for total coliform organisms and fluorescence indicates β-
glucuronidase enzymatic activity (Feng and Hartman 1982) – confirming the presence
of E. coli. The most dilute row with all positive lactose fermenting tubes and all positive
tubes of higher dilution were transferred aseptically to brilliant green bile broth (BGB)
and incubated at 35°C. At 24 and 48 h the cultures were checked for lactose
fermentation and positive tubes represented confirmed positives for total coliform
organisms. Reference microbiological controls were simultaneously processed for
quality assurance including E. coli (ATCC: 25922, positive lactose fermentation, positive
UV fluorescence), E. aerogenes (ATCC: 13048, positive lactose fermentation, negative
UV fluorescence), E. faecalis (ATCC: 29212, negative lactose fermentation, negative
UV fluorescence), and a non-inoculated blank.
89
Fecal streptococcus and enterococcus organisms were enumerated using
samples inoculated aseptically into a five row by four dilution tube bank of azide
dextrose broth (ADB) and incubated at incubated at 35°C. At 24 and 48 hrs the cultures
were checked for turbidity with positive samples aseptically transferred to
Enterococcosel™ agar (BBL) for confirmation. Esculin hydrolysis on Enterococcosel™
agar results in a black halo around the colonies and is characteristic of fecal
streptococci (Isenberg et al 1970). Positive fecal streptococci plates are aseptically
transferred to 6.5% NaCl brain heart infusion broth (BHI) and incubated at 45°C, the
Sherman criteria for enterococcus organisms. At 24 and 48hrs the BHI broth was
checked for turbidity, with positive tubes indicating the presence of enterococcus
organisms. Reference microbiological controls were simultaneously processed for
quality assurance including E. coli (ATCC: 25922, negative ADB turbidity, negative
Sediment 15 232.2 75.9 218.4 769.9 344.0 a Characteristic particle size of 10% finer by volume. b Characteristic particle size of 50% finer by volume. c Characteristic particle size of 90% finer by volume. d De
Brouckere volumetric mean: ∑ , which is analogous to the number mean volume size.
101
Table 4-3. Event mobilization of indicator organisms and percentage of transported organisms associated with each PM fraction.
27 Sept 2010
4 Nov 2010
16 Nov 2010
27 Sept 2010
4 Nov 2010
16 Nov 2010
[%] [%] [%]
Su
spe
nd
ed PM g 170.8 92.3 36.2 4.6 15.0 20.8
T. Coliform MPN•104 549 1303 179 55.4 69.4 77.4
E. Coli MPN•104 32.3 1.7 11.9 95.1 32.6 94.6
F. Strep MPN•104 89.1 417 48.3 51.1 62.1 83.4
Enterococcus MPN•104 20.4 107 10.9 71.1 47.5 75.4
Se
ttle
ab
le PM g 192.0 38.4 40.4 5.2 6.3 23.3
T. Coliform MPN•104 19.1 179 28.6 1.9 9.5 12.4
E. Coli MPN•104 0.5 2.6 0.7 1.4 50.5 5.2
F. Strep MPN•104 4.8 70.0 4.2 2.8 10.4 7.2
Enterococcus MPN•104 1.9 18.8 2.5 6.5 8.4 17.1
Se
dim
ent PM g 3357 483.5 97.5 90.2 78.7 56.0
T. Coliform MPN•104 424 395 23.9 42.7 21.1 10.3
E. Coli MPN•104 1.2 0.9 <0.1 3.5 16.9 0.2
F. Strep MPN•104 81.0 185 5.4 46.2 27.5 9.3
Enterococcus MPN•104 6.4 99.0 1.1 22.4 44.1 7.5
Percentages are weighted by PM loading. Results indicate that organisms are highly mobilized in the suspended fraction relative to mobilization in the settleable and sediment fractions, with the exception of E. coli during the 04 November 2010 event.
102
T. C
oli
form
E. C
oli
F. S
trep
toco
ccus
Ente
roco
ccus10
0
101
102
103
104
105
106
L3
L2
L1
MP
N/1
00
mL
A
B C
Mean
10%1%
25%
50%
90%
Legend
75%
99%
Figure 4-1. Event mean most probable number per 100 mL box-plot for twenty-five wet
weather events on a small urban watershed in north central Florida. Comparative EPA regulatory guidance for freshwater recreational ambient bacteriological density is shown as (A), 126 MPN/100 ml (geometric mean) for E. coli. Comparative regulatory guidance in Florida for unrestricted urban reuse for wastewater effluent is shown as (B), 25 MPN/100 ml (single sample) for fecal coliforms. Comparative regulatory guidance for brackish/saltwater recreational use is shown as (C), 35 MPN/100 ml for Enterococcus organisms. Comparative Australian regulatory guidelines for urban runoff reuse are: (L1), <1 MPN/100 ml for non-potable residential reuse; (L2), <10 MPN/100 ml for reuse in area with un-restricted access; and (L3), <1000 MPN/100 ml for reuse in areas with restricted access.
103
Figure 4-2. Hypochlorite inactivation kinetics of particle associated coliform organisms
on suspended, settleable, and sediment PM. Results indicate shielding of bacteria on sediment PM throughout the duration of the experiment and rapid inactivation of particle associated coliform organisms on the settleable and suspended fractions. Sediment fractions maintained bacterial densities on the order of 103 MPN/100 ml at the end of the 8 h HOCl reactor experiment.
4 November 2010C
o = 45 mg/L
0 100 200 300 400 500
Reactor Time (min)
Event:16-Nov-2010C
o = 15 mg/L
0 100 200 300 400 500
Reactor Time (min)
27 September 2010C
o = 30 mg/L
0 100 200 300 40010
0
101
102
103
104
105
106
MP
N/1
00
mL
Reactor Time (min)
21 August 2010C
o = 45 mg/L
100
101
102
103
104
105
106
MP
N/1
00
mL
21 August 2010C
o = 30 mg/L
100
101
102
103
104
105
106
0 100 200 300 400
Reactor Time (min)
MP
N/1
00
mL
Suspended
Settleable
Sediment
Detection
Limit
21 August 2010C
o = 15 mg/L
104
Figure 4-3. Log removal of particle associated coliforms for the 04-Nov-2010 (Panel A, B) event with an initial hypochlorite dose of 45 mg/L. Results indicate that particle associated coliforms on suspended and settleable PM and rapidly achieve maximum log removal. Particle associated coliforms on sediment material (Panel C) only achieve 20-60% of the maximum potential log removal for the reactor, demonstrating particle shielding of associated coliforms.
0 100 200 300 400 500
Time (min)
15 mg/L (16-Nov-10)
30 mg/L (27-Sep-10)
45 mg/L (4-Nov-10)
15 mg/L (21-Aug-10)
30 mg/L (21-Aug-10)
45 mg/L (21-Aug-10)
Sediment
0 100 200 300 400
0
20
40
60
80
100
% M
ax L
og
Rem
ov
al
Time (min)
Suspended
Settleable
Sediment CB
A-6
-5
-4
-3
-2
-1
0
10 5000 10000 15000 20000
Ct (mgmin/L)
{HOCl/OCl-}
o = 45 mg/L
Suspended
Setteable
Sediment
Rem
ov
al (
log
-un
its)
A
105
0 5000 10000 15000 20000
-3
-2
-1
0 15 mg/L
30 mg/L
45 mg/L
Re
mo
va
l (l
og
-un
its)
Ct (mgmin/L)
{HOCl/OCl-}
o
Figure 4-4. Log removal of particle associated coliforms on sediment PM across the
inoculation doses of 15, 30, and 45 mg/L. Results indicate that increasing chlorine doses penetrate sediment PM with increasing efficaciousness. Oscillations in removal are attributed to the effects of sampling a heterodisperse particle size distribution within the sediment fraction.
106
Susp
ended
Set
tlea
ble
Sed
imen
t
Susp
ended
Set
tlea
ble
Sed
imen
t
Susp
ended
Set
tlea
ble
Sed
imen
t
Susp
ended
Set
tlea
ble
Sed
imen
t100
101
102
103
104
105
106 EnterococcusF. Strep.E. coli
MP
N/m
g o
f P
M
T. Coliforms
Figure 4-5. Partitioning of particle associated organisms to suspended, settleable, and
sediment PM fractions. For each organism, the suspended PM fraction contains the highest bacterial density followed by the settleable and sediment fractions. In particular, the sediment PM fraction, which exhibits the greatest organism shielding potential contains the lowest density of the indicator E. coli ( < 25 MPN/mg PM) and enterococcus ( < 300 MPN/mg PM) organisms.
107
CHAPTER 5 ADVANCED COMPUTATIONAL MODELING OF FREE CHLORINE DEMAND AND DISINFECTION IN UNIT OPERATIONS AND PRECESSES LOADED BY URBAN
STORMWATER
Urban rainfall-runoff is a water which has come under increasing scrutiny for an
integrated management approach (Heaney and Sample 2000). Development in the
United States and elsewhere has resulted in increased volumetric transport of water
with constituent microbiological, particulate, and nutrient loadings to receiving waters
(House et al 1993). In order to reverse this trend, technologies, research, and
integrated management systems need to continue to be developed to reduce, treat, and
reuse urban rainfall-runoff. Urban rainfall-runoff exhibits temporally varying water
volume and quality and transports constituent particulate, microbial, heavy metal, and
nutrient environmental loadings in both dissolved and particulate fractions (Sansalone
and Kim 2008, Sansalone and Buchberger 1997, Christina and Sansalone 2003, Kim
and Sansalone 2010). The implementation of a source for reuse effectuates the need
for the consideration of public health and safety.
Chlorination as a form of microbial inactivation is the oldest chemical oxidizing
reaction utilized for the public health of drinking waters and waste waters and is
currently the most widely used inactivation process world-wide (Hrudey and Hrudey
2004). From the earliest use of this process, researchers documented the importance
of the concentration of disinfectant over time, the contact time (CT), to the level of
microbial inactivation (Chick 1908, Fair et al. 1948). Continued studies developed
generalized batch inactivation models as the Hom model (Hom 1972) and the
incomplete gamma Hom model (Haas and Joffe 1994) as organisms were shown to
exhibit disparate inactivation kinetics as compared to the Chick-Watson model and for
108
utilization on water with disinfectant demand. Extension of batch reactor data to full
scale flow through reactors has been demonstrated (Haas et al. 1998) as well as the
utilization of reactor residence time distributions (RTD) to determine reactor CT values
(Bellamy et al. 1998). The most advanced modeling technique of inactivation kinetics in
potable water applied to date is computational fluid dynamics (CFD) (Greene et al.
2004, Baawain et al. 2006, Goula et al. 2008, Greene et al. 2004) whereby it is utilized
to improve reactor design and model first order chlorine demand. CFD is the numerical
solution of the fundamental equations of fluid motion involving the simulation of transient
flow fields, chemical reactions, and particle fate and transport in spatially complex
geometry.
In urban stormwater, CFD has enhanced the modeling of PM separation for
transient flows (Sansalone and Pathapati 2009) and heterodisperse particle size
distributions (Dickenson and Sansalone 2009) as well as re-entrainment of PM by
scouring mechanisms (Pathapati and Sansalone 2011). In addition, in Chapter 3, urban
stormwater batch reactor experimentation indicated parallel second order chlorine
demand kinetics for the dissolved fraction and a second order potential driving force
model for particulate fractions. As a result, there is the requisite need to formulate
discretized finite-rate chlorine kinetic dissolved and PM equations for modeling transient
and complex flows encountered in urban stormwater runoff.
Objectives
The objective of this study is to discretize the analytical parallel second order
dissolved and potential driving PM finite-rate free chlorine demand models for utilization
in CFD. The computational dissolved, PM, and composite CPD model of sodium
hypochlorite kinetic demand in urban rainfall-runoff is validated by batch reactor data.
109
Methodology
CFD is the numerical solution of the fundamental partial differential equations
that govern fluid flow and particle and chemical transport. CFD can be implemented in
both Lagrangian, fluid particle tracking schemes, or Eulerian, fluid flux through control
volume schemes. CFD is capable of modeling both laminar and turbulent flows, where
the turbulent flow characteristics are numerically simulated through direct numerical
simulation (DNS) or modeled by solving the bulk equations of motion coupled with a
turbulent flow closure model, an example of which is the Reynolds averaged Navier-
Stokes (RANS) equations with a variant of the k-ε two equation turbulent model.
Contradistinguishing between DNS and RANS are computational time and turbulent
scale resolution. DNS is computationally expensive, particularly at high Reynolds
numbers and for many industrial flows (White 2006). RANS simulations are
computationally less expensive than DNS, but the simulations generalize detailed
turbulent structure information. A third available option is a large eddy simulation (LES).
LES employs a scaling filter that delineates turbulent eddy length scales larger than the
scaling filter for direct numerical resolution and models turbulent structures smaller than
the filter length scale. This technique has been shown to be superior to RANS k-ε
models in disinfection flow through reactors (Wols et al. 2010). The LES filtered
continuity (Equation 5-1) and momentum (Equation 5-2) equations are:
(5-1)
*( )
(
)+ (5-2)
110
where ρ is fluid density; xi is the ith direction vector; is the filtered velocity in the
ith direction; is the filtered pressure; is the kinematic fluid viscosity; and is the
turbulent viscosity. In the present implementation, the Smagorinsky model is utilized to
model the turbulent viscosity of the filtered turbulent eddies:
( ) | | (5-3)
where Cs = 0.1 is the Smagorinsky constant; Ls is the sub-grid characteristic filter
length scale of the finite volume mesh; and | | is the local strain rate tensor. The reader
is encouraged to refer to Lesieur and Metais (1996) for a more comprehensive
development of the LES-Smagorinsky turbulent model.
To model the transport of PM, a mixed mode Eulerian-Lagrangian reference frame
is utilized where the fluid velocity and pressure flow fields are modeled in an Eulerian
reference frame and the PM is modeled as discrete particles in a Lagrangian reference
frame. PM transport modeled in the discrete phase is integrated across the fluid
velocity and pressure flow fields modeled in the Eulerian reference frame. This scheme
does not account for particle influence on the velocity and pressure flow fields and, thus,
is restricted to dilute fluid flows of < 10% volume fraction (VF) (Brennen 1996). Even
with this restriction, many flow situations, including the batch reactors in this present
study, are successfully modeled as dilute flows (the concentrations of interest for the
present study are less than 1% as VF).
In the discrete phase, PM transport and fate is simulated by integrating Newton‟s
second law (Equation 5-4) for representative particles across the numerical fluid
domain.
111
( ) ( )
(5-4)
(5-5)
| |
(5-6)
(5-7)
The formulation of equation (5-4) is particle acceleration
equal to the
summation of the forces per unit particle mass. The quantity ( ) is drag force
per unit particle mass; and the quantity ( )
is buoyancy/gravitational force per unit
particle mass. Equation (5-6) is the definition of the relative Reynolds number for flow
around a sphere. In equation (5-5) and (5-6) ρ is the fluid‟s density; ρp is the particle‟s
density; dp is particle diameter; vp_i is particle velocity in the ith direction; vi is the
localized fluid velocity in the ith direction; and µ is the dynamic viscosity. Equation (5-7)
is the drag coefficient for spherical particles with the constants K1, K2, and K3 defined in
Morsi and Alexander (1972). In a transient Eulerian-Lagrangian solution framework the
discrete phase is numerically solved by iteration in interwoven intervals with the
Eulerian solution space.
The computational modeling of the free chlorine concentration, transport, and
decay is a coupled set of Eulearian-Lagrangian equations extending the analytical work
of dissolved and particulate chlorine demand in urban stormwater in Chapter 3 whereby
chlorine reactions are broken into three component reactions: a second order, fast
112
demand reaction with the dissolved fraction (rate constant: k1), a second order, slow
demand reaction with the dissolved fraction (rate constant: k2), and a potential driving
PM model (rate constant: kpm). The kinetic governing equations implemented in
Chapter 3 are the second order parallel dissolved model (Equation 5-8) and the PM
potential driving model (Equation 5-9):
(5-8)
( ) (5-9)
where Ct is the total free chlorine concentration; Cf is the fast acting free chlorine
concentration; Cs is the slow acting free chlorine concentration; DMDf is the
concentration of the chlorine demand reacting with the fast acting free chlorine; DMDs is
the concentration of the chlorine demand reacting with the slow acting free chlorine; qe
is the mass of free chlorine consumed in PM surface reactions per mass PM at
equilibrium; and qt is the mass of free chlorine consumed in PM surface reactions per
mass PM at time t.
The differential equation set is formulated on the following assumptions:
The dissolved reactions (fast/slow) are parallel reactions without
interaction
The dissolved chlorine reactions involve a 1:1 reaction with a theoretical
chlorine demand (fast/slow)
The initial dissolved chlorine demand is a fraction, f, of the dissolved
chemical oxygen demand, CODd
PM chlorine demand is driven by a chlorine reaction potential and is
limited by the local available free chlorine
113
Potential particulate chlorine demand dissolution into the dissolved
fraction is accounted for in the PM demand
At a uniform temperature, the fast dissolved free chlorine demand reaction is
modeled by a combined Eulerian-Lagrangian mass transport equation:
( )⏟
( )
⏟
( )
⏟
⏟
⏟
(5-10)
where YCl-F is the mass fraction of the free chlorine in the fast reaction; YDMD-F is
the mass fraction of the chlorine demand in the fast reaction; ρ is the species density; ui
is the local velocity vector; D is the diffusion coefficient of the species; µt is the turbulent
viscosity; Sct is the turbulent Schmidt number; k is the dissolved second order rate
constant with units [L3M-1T-1]; cpm is the local concentration of PM as represented by
discrete particles; kpm is the PM potential driving model rate constant with units
[M1M-1T1]; and
( ( )
) (5-11)
where
is limited by the lower value of the PM potential driving model and the
local free chlorine concentration. qe is the mass of free chlorine consumed in PM
surface reactions per mass PM at equilibrium with units [M1M-1]; qt is the mass of free
chlorine consumed in PM surface reactions per mass PM at time t with units [M1M-1];
and is defined as Equation 5-12.
(5-12)
where YCl-T is the sum of the mass fractions of the free chlorine in the fast and
slow (YCl-S) reactions and ensures the proportionate removal of free chlorine from the
114
dissolved fractions. The convection, turbulent diffusion, and dissolved demand terms
are solved in Eulerian space, and the PM demand is solved in Lagrangian space with
the time rate of change solved in both reference frames. Similarly, the mass transport
equation for the slow reaction is defined in Equation 5-13:
( )
( ) (
)
( )
(5-13)
and two additional transport equations for chlorine demand for j = F,S are defined
as Equation 5-14.
( )
( ) (
)
(5-14)
Numerical requirements for the mixed transport and reaction Eulerian-Lagrangian
dissolved and PM species model require that the PM chlorine demand does not exceed
the local species mass within the numerical cell within the Lagrangian timestep
advancement when the reactor is not in an overall limiting condition (when the
volumetric mean ρYCl-T >>
). Qualitatively, this is governed by mesh spacing, overall
chlorine concentration, PM mass loading, the number of representative particles in the
discrete phase, and the timestep of the simulation. In the absence of experimental data
validating data, the solution must demonstrate mesh, particle number, and timestep
independence.
The numerical Eulerian-Lagrangian kinetic chlorine model is computationally
simulated in ANSYS Fluent 13.0 where the demand reaction terms in the above
115
equations are programed in C computer code and compiled as external dynamic linked
libraries.
Batch Reactor Setup and Initialization
The batch reactors are modeled as three-dimensional continuously stirred batch
reactors (CSBRs) of 1700 ml volume with a diameter of 140 mm, height of 130 mm, and
a flat bottom with a 20 mm fillet with the sidewalls. The mesh has 84 thousand
tetrahedral cells resulting in a mean cell volume of 0.02 ml. The CSBRs consist of three
distinct fluid zones – a 50 ml free chlorine injection zone, a rotating zone containing the
stir rod, and a bulk fluid zone. The mixing within the batch reactor is motivated by a 6
mm by 40 mm stir rod set within a hemispherical rotating mesh at the bottom center of
the reactor. The hemispherical moving mesh has a sliding interface with the bulk fluid
zone and rotates at 350 rpm. The free chlorine injection zone is contained within the
1700 ml of the batch reactor, which is illustrated in Figure 5-1. The reactor is initialized
with all species fractions set to zero and the velocity and pressure field are brought to
periodic steady state by solving 60 s of flow time at a 1 s timestep. The initial values of
the chlorine species are then patched to the free chlorine injection zone to mimic the
physical hypochlorite injection of the physical CSBRs with the concentration of the
patched zone scaled over the volume of the reactor to provide a mean concentration of
the initial experimental value which was nominally 15, 30, or 45 mg/L depending on the
modeled batch reactor. Chlorine demand species that react on a 1:1 basis are patched
to the CSBR domain where YDMD-F = X•fCODd/ρ and YDMD-S = (1-X)•fCODd/ρ where
CODd is the dissolved chemical oxygen demand and X and f are model parameters is
the second order dissolved model defined for a small urban catchment in Gainesville,
FL in Table 5-1. For CSBRs with PM interaction, 1400 representative discrete particles
116
are injected at t = 61s along the viewing plane that bisects the CSBR in Figure 5-1.
Timesteps for the CSBR are 1 s for the first minute and 5 s thereafter.
CSBR Validation
CSBR validation is performed on a dataset of 9 experimental batch reactors from
Chapter 3 with runoff from a small paved urban catchment for events that occurred
during the fall of 2010. A control batch reactor with hypochlorite addition to chlorine
demand free water is used as a control for the mixing of the simulation. Four of these
batch reactors elaborate performance on dissolved urban rainfall-runoff and validate the
CFD finite-rate parallel dissolved kinetic model and the remaining batch reactors
validate the PM potential driving model and the composite PM and dissolved model.
The criteria of the model performance for the CFD species model is the normalized root
mean square error (NRMSE):
√∑ ( )
(5-15)
where Oi is the observed value at measurement i; Ei is the modeled value at
measurement i; n is the total number of measurements; and Co is the initial chlorine
dose. For the experimental dataset, n = 8. The NRMSE defined as in equation (5-15)
illuminates the model performance relative to the initial chlorine dose of the reactor. For
both the dissolved and PM laden reactors, the volumetric mean of YCl-T of the reactor is
utilized as the estimate of the chlorine concentration at the sample time.
Results and Discussion
Figure 5-3 presents a histogram mixing analysis of the CSBR of the initial mixing
phase of a simulated hypochlorite injection, of reactor average value Co, with no
117
chlorine demand. Initially 96.5% of the cells within the CSBR contain no chlorine
species with the remainder containing the high chlorine dose of the injection. At 5 s, the
89% of the CSBR volume elements contain chlorine concentration with ±5 mg/L of Co.
At 15s, 100% of the CSBR volume elements contain a chlorine concentration with ±1
mg/L of Co. The experimental batch reactor found the target Co concentration at the
earliest sample time of 1 min and the CFD simulation corroborates this finding.
Figure 5-4 presents the validation of the parallel dissolved model under disparate
initial conditions. The correlation of the dissolved chlorine demand in urban stormwater
to the CODd is apparent comparing panels C and D, which illustrates a high CODd, high
demand sample, and a low CODd, low demand sample, respectively. Overall the data
is illustrative of the second order nature of the dissolved demand wherein the kinetic
rate is dependent on the concentration of both the free chlorine and dissolved demand
and may be limited by either constituent depending on the initial chlorine dose and the
water quality of the sample. In each case in the dissolved CSBRs there is a clear initial
demand followed by a chlorine demand of slower timescale and the CFD kinetic model
correctly predicts the exhaustion of the fast rate demand portion with respect to the
experimental data. The NRMSE of the reactors are 3.3%, 4.0%, 3.8%, and 5.4%, for
panels A through D, respectively. The modeled reactors utilized in the validation of the
CFD implementation of the second order parallel analytical rate expression for the
dissolved phase were non-influential in the derivation of the model constants. Thus,
these reactors are examples of the predictive capability of the CFD model on typical
loadings given the CODd of the runoff and Co for the small urban catchment in
Gainesville, FL with the characteristic model parameters in Table 5-1. These
118
characteristic parameters are a result of the typical event particulate and dissolved
loadings of the catchment and were found to be consistent on an inter-event basis.
Figure 5-5 presents the validation of the potential driving PM kinetic model with
CSBRs laden with PM in a dissolved stormwater matrix. The dissolved demand is
subtracted from the total demand to produce the PM demand in the batch reactor. This
PM demand is modeled by the potential driving model which is governed by the
available reaction sites on the PM mass within the CSBR and the available free chlorine
dose. These characteristics are modeled through qe and kpm. NRMSEs for the CFD PM
kinetic demand model are 7.1%, 7.9% 2.6% and 10.3%, for panels A through D with a
slight modification where Co = qe in the determination of the NRMSE. As can be seen
from the figure, the timescale of the PM potential driving model is on the order of the
slow second order dissolved demand reaction. It is also important to note that the
capacity of PM chlorine demand is high with respect to PM mass and represents a
pronounced potential chlorine sink in urban rainfall-runoff at even low PM loadings.
The results from the composite particulate and dissolved CFD kinetic model are
presented in Figure 5-6. The NRMSEs for the composite model are 2.4%, 4.3%, 2.6%,
and 3.9% for panels A through D, thus the composite model is capable of reproducing
the experimental CSBR kinetic reaction utilizing all three simultaneous reactions.
The extension of the CFD composite kinetic model to a flow through chlorine
contactor for urban rainfall runoff incorporates a few important considerations. PM
transport is essential in modeling urban stormwater disinfection processes as sediment
PM shielding of associated organisms has been established in stormwater runoff in
Chapter 4. Thus, a modeled unit operation and process must ensure that all particles
119
greater than 75 µm are captured by the unit. The dissolved species transport model
should not require significant modifications for flow through reactors as implemented in
this study. To directly model bacteriological, viral, or protozoan transport and
inactivation, a generalized scalar equation can be solved utilizing a kinetic inactivation
sink term such as the Hom model (Greene et al. 2004) overlaying the composite CFD
kinetic model presented in this study. The analytical potential driving model, from which
the CFD model is derived for the PM surface reaction, is derived for initial hypochlorite
doses to find the chlorine demand at equilibrium. Additional research investigating the
maximum qe values for PM fractions with continual exposure to low doses of free
chlorine is warranted to investigate the performance of the PM model under continually
limiting chlorine application. However, even with this limitation, the implementation of
the PM demand model in this study would either equal the reaction under a continual
low-dose chlorination experiment or exceed the value and remain a conservative
estimator of the chlorine concentration in this case.
120
Table 5-1. Model parameters for the dissolved parallel second order and PM potential driving force equations.
Dissolved
PM
k1 0.07 L1mg-1min-1
Reactor V4 R13 V5 V1 k2 2.9 L1mg-1min-1
kpm 0.83 2.19 1.00 1.80 g1mg-1min-1
f 0.39 -
qe 102 105 85 154 mg/g X 0.36 -
121
Figure 5-1. Physical batch reactor showing stirplate, aluminum foil jacket, and water
quality electrodes. Utilized reactor volume is 1700 ml and the stirplate is set to 350 rpm. The batch reactor is sealed tightly with a lid when water quality measurements are not being made.
122
Figure 5-2. Illustration of the fluid zones within the batch reactor. The hemispherical
rotating zone is shown containing the stir rod at a single frame. The hypochlorite injection region is a cylindrical zone bisected by the viewing plane and is represented by the red region. The bulk fluid zone is shown in blue. Reactor volume is 1700 ml and is comprised of approximately 41 thousand tetrahedral cells.
HOCl Injection Region
Bulk Fluid Zone
Stir Rod in Rotating Fluid
Zone
123
Figure 5-3. Histogram analysis of the computational mesh CFD free chlorine
concentration during the initial mixing phase in a batch reactor with an overall initial Co = 45 mg/L. After 5s of mixing, 90% of the computational mesh exhibits a free chlorine concentration within ±5 mg/L. After 15 s of mixing, 100% of the computational mesh exhibits a free chlorine concentration within ±1 mg/L. The uniformity of the chlorine concentration in the reactor enables increasing the timestep of the simulation.
<-5 -5 -4 -3 -2 -1 Co 1 2 3 4 5 >50
20
40
60
80
p
df
(%)
Cell Deviation from Co (mg/L)
Elapsed Time: 15s
<-5 -5 -4 -3 -2 -1 Co 1 2 3 4 5 >50
20
40
60
80
pd
f (%
)
Cell Deviation from Co (mg/L)
Elapsed Time: 10s
<-5 -5 -4 -3 -2 -1 Co 1 2 3 4 5 >5
0
20
40
60
80
100
pd
f (%
)
Cell Deviation from Co (mg/L)
Elapsed Time: 5s
0
20
40
60
80
100<-5 -5 -4 -3 -2 -1 Co 1 2 3 4 5 >5
Cell Deviation from Co (mg/L)
pd
f (%
)
Elapsed Time: 0s
124
Figure 5-4. Comparison of the second order CFD dissolved demand model with
experimental results. NRMSE values are reported and indicate that the CFD model accurately (NRMSE < 6%) accounts for the complex reaction dynamics of the urban stormwater demand reactor containing dissolved matrix of disparate initial water quality conditions and demand.
0
5
10
15
20
25
30
0 100 200 300 400
Reactor Time (min)
Storm Date: 21-Aug-2010
Co = 30.4 mg/L | COD
d = 91.18 mg/L
CFD Model
k1 = 0.0701 L
1mg
-1min
-1
k2 = 2.93 X 10
-4 L
1mg
-1min
-1
X = 0.39
f = 0.36
HO
Cl/O
Cl- (
mg
/L)
NRMSE: 3.3%
A
0 100 200 300 4000
5
10
15
20
25
30
35
40
45
NRMSE: 5.4%
Storm Date: 7-Aug-2010
Co = 29.0 mg/L | COD
d = 33.5 mg/L
CFD Model
HO
Cl/O
Cl- (
mg
/L)
Reactor Time (min)
D
0 100 200 300 400
0
5
10
15
20
25
30
35
40
45
50
NRMSE: 3.8%
Storm Date: 4-Nov-2010
Co = 45.6 mg/L | COD
d = 148.2 mg/L
CFD Model
Reactor Time (min)
HO
Cl/O
Cl- (
mg
/L)
C
0 100 200 300 4000
5
10
15
20
25
30
35
40
45
NRMSE: 4.0%
Storm Date: 21-Aug-2010
Co = 43.4 mg/L | COD
d = 91.18 mg/L
CFD Model
HO
Cl/O
Cl- (
mg
/L)
Reactor Time (min)
B
125
Figure 5-5. Comparison of the second order potential driving PM CFD model with
experimental results.
0
20
40
60
80
1000 100 200 300 400
Reactor Time (min)
Storm Date: 27-Sep-2010
Co = 31.8 mg/L | V4
Settleable | PM = 149.4 mg/L
CFD Model
qt (
mg
/g)
NRMSE: 7.1%
A
0 100 200 300 4000
20
40
60
80
100
120
140
160
NRMSE: 10.3%
Storm Date: 27-Sep-2010
Co = 29.0 mg/L
Suspended | PM = 36.18 mg/L
CFD Model
qt (
mg
/g)
Reactor Time (min)
D
0 100 200 300 400
0
20
40
60
80
100
NRMSE: 2.6%
Storm Date: 4-Nov-2010
Co = 31.5 mg/L | V5
Sediment | PM = 148.2 mg/L
CFD Model
Reactor Time (min)
qt (
mg
/g)
C
0 100 200 300 4000
20
40
60
80
100
120
140
160
NRMSE: 7.9%
Storm Date: 21-Aug-2010
Co = 46.6 mg/L | R13
Settleable | PM = 197.8 mg/L
CFD Model
qt (
mg
/g)
Reactor Time (min)
B
126
Figure 5-6. Comparison of the composite dissolved and PM CFD model with batch
reactor data. NRMSEs and RPDs are reported and are less than 5% in each case. Results validate the finite rate CFD kinetic model developed for free chlorine demand in urban stormwater.
0
5
10
15
20
25
30
35
40
45
500 100 200 300 400
Reactor Time (min)
Storm Date: 27-Sep-2010
Co = 30.4 mg/L | COD = 18.2 mg/L
Settleable | PM = 91.18 mg/L
CFD Model
HO
Cl/O
Cl- (
mg
/L)
NRMSE: 2.4%
A
0 100 200 300 4000
5
10
15
20
25
30
35
40
45
NRMSE: 3.9%
Storm Date: 27-Sept-2010
Co = 32.0 mg/L | COD = 46.7 mg/L
Suspended | PM = 36.1 mg/L
CFD Model
HO
Cl/O
Cl- (
mg
/L)
Reactor Time (min)
D
0 100 200 300 400
0
5
10
15
20
25
30
35
40
45
50
NRMSE: 2.6%
Storm Date: 27-Sep-2010
Co = 31.6 mg/L | COD = 18.2 mg/L
Sediment | PM = 330.9 mg/L
CFD Model
Reactor Time (min)
HO
Cl/O
Cl- (
mg
/L)
C
0 100 200 300 4000
5
10
15
20
25
30
35
40
45
NRMSE: 4.3%
Storm Date: 21-Aug-2010
Co = 46.6 mg/L | COD = 43.3 mg/L
Settleable | PM = 197.8 mg/L
CFD Model
HO
Cl/O
Cl- (
mg
/L)
Reactor Time (min)
B
127
CHAPTER 6 CONCLUSION
Urban stormwater particle transport and disinfection reactions are complex
phenomena with coupled transport and reaction kinetics across both solid and liquid
phases. In addition, stormwater volumetric and particle transport are the result of
stochastic rainfall events that render the volumetric and particulate matter (PM) loading
difficult to determine or estimate a priori.
Free Chlorine Kinetics
Dissolved Phase Reaction Kinetics
The reaction kinetics of the dissolved phase of the urban stormwater runoff exhibit
parallel second order characteristics. The ultimate modeled chlorine demand of the
water in a batch reaction is determined to correlate well with the dissolved chemical
oxygen demand (CODd). CODd is a simple and expedient analytical procedure and it is
possible to process many samples simultaneously. However, certain formulations of
COD reagents generate hazardous waste, thus the suitability and regulatory usability of
non-hazardous waste formulations remains to be determined.
The bimodal proportionality of the parallel reaction of the second order kinetics is
stable on the inter-event reference frame for the small paved urban watershed utilized in
this study for runoff collection. This result is expected to be function of the ratio of the
inorganic to organic loadings on the watershed. This loading ratio can be expressed in
terms of the dissolved organic carbon ratio (DOC) to CODd. This ratio has been
determined to be consistent and a characteristic parameter of the watershed. However,
as the experimental matrix only included a single catchment, the full elaboration of the
relationship between the bimodal proportionality of the reaction is beyond the resolution
128
of the experimental construct. Thus, future research regarding the relationship of this
proportionality factor, X, to DOC/CODd for similar and dissimilarly loaded catchments is
warranted.
Particulate Kinetics
The reaction kinetics of free chlorine with the various particulate matter (PM)
fractions mobilized by urban stormwater runoff are well modeled by a second order
potential driving model. This model relates the kinetic parameters to the available
surface reaction sites and a driving potential of available free chlorine. As free chlorine
is an added component to the kinetic reference frame, there are three general resulting
cases for reaction whereby the batch reactor is under-chlorinated, super-chlorinated, or
transitionally chlorinated. There remains a case where the reactor is severely
under-chlorinated, where the dissolved demand immediately quenches all available
chlorine and this is most suitably modeled by the dissolved model without particulate
consideration. In the under-chlorinated model the parameter of maximal mass transfer,
qe, is limited by the maximum value of chlorine mass available for reaction with the
particulate phase, qe max. qe max is the initial mass of chlorine inoculated into the reactor
less the chlorine that reacts with the dissolved phase normalized of the PM mass
contained within the reactor. In the super-chlorinated reactors, the chlorine mass
transfer is limited by the available reaction sites on the PM. This in effect exhausts the
chlorine demand of the PM fraction of the reactor. This maximum sorbance per PM unit
mass is characteristic of the PM fraction with the suspended and settleable fraction
exerting similar maximal demand due to their similar low volatile fractions and siliceous
composition. The maximum sorbance per PM unit mass for the sediment fractions is
129
double the sorbance for the suspended and settleable fraction and is attributed to the
high volatile fraction and, thereby, the increased organic density of that fraction.
Computational Modeling
PM Fate and Transport
The dispersivity of the granulometry of the PM phase influences the computational
accuracy of a Lagrangian-Eulerian computational fluid dynamics (CFD) model. The
fundamental fluid mechanics driving this phenomenon are non-linear drag forces, with
respect to particle diameter, exerted on mobilized PM entrained in the flow. In the
framework of a Lagrangian-Eularian PM fate and transport model, PM is represented by
discrete particle sizes and the non-linear nature of the mechanics reduces the
computational accuracy of the when the simulated particle sizes inadequately represent
a disperse influent gradation.
The computational structure for the investigation involves a convergence study
that incrementally increases the number of representative particle sizes, the
discritization number (DN), by a factor of 2 until solution difference is negligible –
achieved at a DN of 128. Average relative percent difference (RPD) calculations
ascertain the influence of gradation dispersivity on computational accuracy.
Mono-disperse, or uniformly disperse gradations, are well represented by a median
particle size, the d50m, which correlates to a DN of 1. However, gradations of medium
dispersivity and heterodisperse gradation require a DN of 8 to 16, depending on overall
gradation fineness, for computational modeling. Overall gradation fineness influences
the DN requirement mechanistically as the non-linearity of particle drag forces increases
for very small particle diameters. Thus, a DN of 8 suffices for particle granulometries
130
with d50m = 66.7 µm and d50m = 100 µm, however, a DN of 16 is required for finer
gradations near a d50m of 33.3 µm.
The Lagrangian-Eulerian CFD model also enabled the development of a novel
CFD rubric for the performance evaluation of hydrodynamic and baffled separators. A
composite dataset of the per-particle removal efficiency across a spectrum of flow rates
generates a performance surface for the unit. The performance surface is akin to a unit
“fingerprint” and is unique to the fate and transport behavior of the unit given a particle
density. Mathematical comparison of the performance surfaces of two distinct
separators yields a multi-dimensional, differential performance evaluation that extends
the depth found in typical performance studies utilizing a singular gradation. In addition,
the data from the performance surface can be utilized to model the efficiency of a unit
operation given a specific gradation without the need for additional computational time.
CFD Free Chlorine Reaction Kinetics
Advanced modeling of the fate, transport, and kinetics of free chlorine in a
transient simulation of urban stormwater runoff is possible in CFD. The coupled
dynamics of the chlorine kinetics with the dissolved and PM phases under transient
loading conditions generate a dynamic physical-chemical network with multiple reaction
pathways. CFD models the fundamental equations of motion for PM, chemical species,
and fluid flow. The analytical models developed in Chapter 3 are successfully ported to
discrete equivalents in differential space for computational modeling in CFD and
validated on the dataset generated in the CSBRs for rainfall-runoff events captured in
the fall of 2010 in Gainesville, FL and generated NRMSEs of less than 6% in all cases.
Advanced CFD modeling utilizing validated CFD models allows for the integrated
and iterative design of treatment systems without the need for pilot scale testing of each
131
design variant. This reduces research and development expenses for such testing and
permits exploration of non-traditional design implementations which were previously
Figure A-2. Schematic of monitored urban sub-catchment in Gainesville, FL showing contributing impervious surface.
134
0 500 1000 1500
0
10
20
30
40
50
15 mg/L
45 mg/L
HO
Cl/O
Cl- (
mg
/L)
Reaction Time (min)
Figure A-3. Control CSBRs showing hypochlorite kinetics in Nanopure DI for 8 h at 15 mg/L and 24 h at 45 mg/L. Results indicate no detectable environmental loss of hypochlorite due to UV or volatilization during the experimental timeframe (p < 0.05).
135
0 100 200 300 400 500
0
10
20
30
40
50 30 mg/L
30 mg/L
30 mg/L Autoclaved
30 mg/L Autoclaved
HO
Cl/O
Cl- (
mg
/L)
Reaction Time (min)
Figure A-4. Control CSBRs comparing autoclave sterilized and non-autoclave sterilized stormwater Matrix. Results indicate that there is not a significant difference between autoclaved and non-autoclaved matrix (p < 0.05).
136
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BIOGRAPHICAL SKETCH
Joshua Dickenson was born and raised in Jacksonville, FL and was
homeschooled from 3rd through 12th grades. In 2005, Joshua graduated Cum Laude
from the University of Florida with a Bachelor of Science in Mechanical Engineering.
After his bachelor‟s, Joshua spent 7 months in Bundibugyo, Uganda working with a
Christian non-governmental organization implementing water development projects.
While there he developed a passion for providing clean water to the poorest of the
world. In 2007, Joshua matriculated at the University of Florida in the Environmental
Engineering Sciences Department in a combination master‟s and doctoral program. In
May 2010, Joshua received a Master of Engineering degree from the University of
Florida. In May 2011, Joshua received Doctor of Philosophy Degree in Environmental
Engineering and Science from the University of Florida. Joshua pursues life to the
fullest, loves his family deeply, enjoys deep, intimate relationships, and ultimately owes