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20-pathogens_microbial_indicators.Rmd
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20-pathogens_microbial_indicators.Rmd
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# Pathogens & Microbial Indicator Organisms
## Contributors
Matthew R. Hipsey
## Overview
Understanding the fate and transport of pathogenic and indicator microbes within drinking water reservoirs, rivers and coastal waters is critical for environmental managers to effectively reduce public health risks. Over the past decade several advances have been made for the simulating organism dynamics using coupled hydrodynamic-organism fate models [@hipsey2008]. These have generally been used for simulating coliform bacteria (Madani et al. 2022), with applications also reported for other organisms such as Cryptosporidium [@hipsey2004] and viruses [@sokolova2012]. In general, these models simulate organism concentrations within water bodies by accounting for external loading (e.g. from stormwater or wastewater inputs), advection and mixing process that occur within the interior of the water body, organism inactivation and sedimentation. This chapter describes the basis for the AED pathogen model based on relevant information from empirical and prior modelling studies, and then summarises the setup requirements of the pathogen module within the AED model framework.
## Model Description
#### General Approach {-}
The general balance equation for organism transport and fate is summarized in @hipsey2008 as:
```{=tex}
\begin{eqnarray}
\frac{D}{Dt}C = \color{darkgray}{ \mathbb{M} + \mathcal{S} } \quad
&+& \overbrace{f_{growth}^{C} -f_{mor}^{C} - f_{set}^{C} +\hat{f}_{res}^{C}}^\text{aed_pathogen} \\ (\#eq:path1)
\end{eqnarray}
```
where $C$ is the organic concentration (orgs m^-3^). Within the AED model framework, the transport terms are solved via hydrodynamic model transport routines and the remaining terms are simulated by the `aed_pathogens` module.
The main term controlling pathogen fate is the mortality or inactivation term, $f_{mor}^{C}$:
<center>
\begin{equation}
f_{mor}^{C} = [k_{g}(T,S,DOC_{L})-k_{d}(T,S,pH)-k_{l}(I_{0},S,DO,pH)-k_{s}(T,S,SS)-k_{p}(T)]C
(\#eq:path2)
\end{equation}
</center>
which relates organism inactivation to the environmental conditions experienced that influence their viability (including temperature, salinity, light intensity, dissolved organic carbon, oxygen and pH). This comprehensive description is usually simplified for specific applications, based on justification of the dominant processes present in any particular waterbody and available data for model setup and validation.
The terms for simulation of organism resuspension and inactivation are described in next.
### Process Descriptions
#### Natural Mortality {-}
Natural mortality, or the ‘dark-death rate’, $k_d$, is an important process determining the net rate of die off of protozoan, viral and bacterial organisms and has been reported to vary for specific organisms due to changes in temperature, salinity and pH. The reported die off rates in the literature however are widely variable, with a synthesis of numerous studies from a range of water bodies presented in @hipsey2008. For freshwater reservoirs, changes in salinity and pH are unlikely to be a significant driver of organism viability relative to the range presented @hipsey2008 and therefore a simple constant die-off rate that depends on temperature is appropriate:
<center>
\begin{equation}
k_{d}(T) = k_{d_{20}}\vartheta _{M}^{T-20}
(\#eq:pathogen2)
\end{equation}
</center>
where $k_{d_{20}}$ is the rate of mortality in the dark at 20C and in freshwater. Since the AED2 implementation of the model to be applied with NPD does not include protozoan grazing as a separate term (as in Eq 1), the grazing effect needs to be embodied within the $k_{d_{20}}$ term. This effectively assumes a constant grazing pressure over time, and if chosen to be a low value this will essentially ensure conservative estimate of the die-off due to grazing. Empirical data from Wivenhoe dam shows the presence of native micro-organisms can increase the background die-off rate by 1.1-3.0x (eg Table 5 in @sidhu2012).
```{r echo=FALSE, message=FALSE, warning=FALSE}
library(ggplot2)
library(plotly)
#### functions####
a_plot <- function(x) {
0.341*1.107^(x-20)
}
b_plot <- function(x) {
0.709*1.056^(x-20)
}
c_plot <- function(x) {
0.482*1.111^(x-20)
}
d_plot <- function(x) {
0.454*1.044^(x-20)
}
e_plot <- function(x) {
0.342*1.102^(x-20)
}
f_plot <- function(x) {
0.164*1.010^(x-20)
}
#### plot ####
plot <- ggplot(data.frame(x = c(0, 40)), aes(x = x)) +
geom_path(aes(colour="Total Coliforms\n(0.341, 1.107)"), stat="function", fun=a_plot) +
geom_path(aes(colour="Faecal Coliforms\n(0.709, 1.056)"), stat="function", fun=b_plot) +
geom_path(aes(colour="<i>E.coli</i>\n(0.482, 1.111)"), stat="function", fun=c_plot) +
geom_path(aes(colour="Enterococci\n(0.454, 1.044)"), stat="function", fun=d_plot) +
geom_path(aes(colour="F+RNA Phages\n(0.342, 1.102)"), stat="function", fun=e_plot) +
geom_path(aes(colour="Somatic Coliphages\n(0.164,1.010)"), stat="function", fun=f_plot) +
xlab("Temperature (˚C)") + ylab('k<sub>d</sub>(T,0,7)(day<sup>-1</sup>)')
plot <- plot + scale_color_discrete(name="Organism Group",
labels=c("Total Coliforms (0.341, 1.107)",
"Faecal Coliforms (0.709, 1.056)",
"E.coli (0.482,1.111)",
"Enterococci (0.454, 1.044)",
"F+RNA Phages (0.342, 1.102)",
"Somatic Coliphages (0.164, 1.010)"))+
theme(legend.title = element_text(color = "black", size = 8),
legend.text = element_text(color = "black", size = 8),
legend.position="right") +
theme_bw()
interactive <- ggplotly(plot) %>%
layout(legend = list(x = 1.0, y =1.0))
interactive
# breaks=c("a_plot", "b_plot", "c_plot", "d_plot", "e_plot", "f_plot"),
# labels=c("Total Coliforms (0.341, 1.107)", "Faecal Coliforms (0.709, 1.056)",
# "E.coli (0.482,1.111)", "Enterococci (0.454, 1.044)",
# "F+RNA Phages (0.342, 1.102)", "Somatic Coliphages (0.164, 1.010)")
#+ ylab(bquote(k[d]~'(T,0,7)'~'('~day^-1~')'))
```
#### Sunlight Inactivation {-}
Depending on the clarity of the water, the light climate of the lake can be a dominant factor influencing organism viability and this has been observed empirically in Wivenhoe Dam by @sidhu2012. Different organism types experience different sensitivity to different light bandwidths, with most organisms sensitive to UV-B and UV-A and some sensitive to light in the visible spectrum [@sinton2007]. @hipsey2008 formulated a multiple band-width model for organisms that included direct and indirect mechanisms for sunlight mediated inactivation by accounting for the effect of salinity, oxygen and pH on free radical formation. Here we use a simplified form that accounts only for direct sunlight denaturation as the indirect mechanism is more specific to MS2 phage relative to rotavirus for example [@verbyla2015]. The implemented expression is therefore:
<center>
\begin{equation}
k_{l} = \sum_{b=1}^{N_{b}}[\varphi k_{b} I_{b}]
(\#eq:pathogen3)
\end{equation}
</center>
where $N_B$ is the number of discrete solar bandwidths to be modeled, $b$ is the bandwidth class {1, 2, … , $N_B$}, $k_b$ is the freshwater inactivation rate coefficient for exposure to the $b^{th}$ class (m^2^ MJ^-1^), $I_b$ is the intensity of the $b^{th}$ bandwidth class (Wm^-2^), $\varphi$ is a constant to convert units from seconds to days and J to MJ (= 8.64*10^-2^).
In AED2, the light intensity is computed for 3 distinct bandwidths, including UV-B, UV-A and PAR, and the attenuation of each through the reservoir water column is based on bandwidth specific light extinction coefficients, that account for the effect of turbidity and chromophoric dissolved organic matter (CDOM) on attenutation.
#### Sedimentation {-}
Sedimentation of organisms occurs at a rate depending on the degree to which the population is attached to suspended particles. Within AED2, this is captured by simulating free and attached organisms and multiple groups of particles may be accounted for. If we assume a single dominant particle size and ignore the effect of salinity on the settling velocity, then the expression in @hipsey2008 for the effective sedimentation velocity reduces to:
<center>
\begin{equation}
k_{s}=(1-f_{a})\frac{V_{c}}{\Delta z}+f_{a}\frac{V_{s}}{\Delta z}
(\#eq:pathogen4)
\end{equation}
</center>
where $f_a$ is the attached fraction, and $V$ is the vertical velocity of organisms or sediment particles.
#### Resupension {-}
Resuspension of organisms accumulated within the sediment has been show to be a relatively important terms in environments where high currents or waves exist. In reservoirs this can occur in the lake margins or deeper in the lake where internal waves or river underflows increase the shear stress at the sediment. The amount of organisms that resuspend depends not only on the shear stress but also n the concentration of organisms in the surficial layers of the sediment. This may be modelled by accounting for the deposited organisms, decay within the sediment and resuspension rate, however, this is notoriously difficult given potentially complex dynamics of organisms in the sediment. Instead we assume that a standard background concentration exists within different sediment regions (based on depth and local geomorphology) and simulate resuspension rate as:
<center>
\begin{equation}
C_{r}(\tau)=\alpha_{c}\frac{(\tau - \tau_{c_{s}})}{\tau _{ref}}\frac{1}{\Delta z_{bot}}
(\#eq:pathogen5)
\end{equation}
</center>
where, $\alpha_C$ is the rate of organism suspension (orgs m^-2^s^-1^), which occurs when the critical shear stress is exceeded in the relative computational cell.
+C_{r}(\tau,SS_{SED},C_{SED})+
### Variable Summary
#### State Variables {-}
```{r 20-statevariables, echo=FALSE, message=FALSE, warning=FALSE}
library(knitr)
library(kableExtra)
state_variables <- read.csv("tables/20-pathogens_microbial_indicators/state_variables.csv", check.names=FALSE)
kable(state_variables,"html", escape = F, align = "c", caption = "State variables") %>%
kable_styling(full_width = F, position = "center",
font_size = 12) %>%
column_spec(1, width_min = "13em") %>%
column_spec(2, width_min = "12em") %>%
column_spec(3, width_min = "12em") %>%
column_spec(4, width_min = "10em") %>%
column_spec(5, width_min = "13em") %>%
row_spec(1:1, background = 'white') %>%
scroll_box(width = "770px",
fixed_thead = FALSE)
```
#### Diagnostics {-}
<center>
```{r 20-diagnostics, echo=FALSE, message=FALSE, warning=FALSE}
#library(knitr)
#library(kableExtra)
#diagnostics <- read.csv("tables/20-pathogens_microbial_indicators/diagnostics.csv", check.names=FALSE)
#kable(diagnostics,"html", escape = F, align = "c", caption = "Diagnostics") %>%
# kable_styling(diagnostics, bootstrap_options = "basic",
# full_width = F, position = "center") %>%
# column_spec(1, width_min = "12em") %>%
# column_spec(2, width_min = "15em") %>%
# column_spec(3, width_min = "12em") %>%
# column_spec(4, width_min = "13em") %>%
# column_spec(5, width_min = "15em") %>%
# row_spec(1:1, background = 'white') %>%
# scroll_box(width = "725px", height = "500px",
# fixed_thead = FALSE)
```
</center>
### Parameter Summary
```{r 20-parametersummmary, echo=FALSE, message=FALSE, warning=FALSE}
library(knitr)
library(kableExtra)
options(kableExtra.html.bsTable = F)
parameter_summary <- read.csv("tables/20-pathogens_microbial_indicators/parameter_summary.csv", check.names=FALSE)
kable(parameter_summary,"html", escape = F, align = "c", caption = "Diagnostics",
bootstrap_options = "hover") %>%
kable_styling(parameter_summary, bootstrap_options = "hover", "striped",
full_width = F, position = "center",
font_size = 12) %>%
column_spec(1, width_min = "6em") %>%
column_spec(2, width_min = "4em") %>%
column_spec(3, width_min = "4em") %>%
column_spec(4, width_min = "4em") %>%
column_spec(5, width_min = "4em") %>%
column_spec(6, width_min = "7em") %>%
column_spec(7, width_min = "4em") %>%
row_spec(1:40, background = 'white') %>%
scroll_box(width = "770px", height = "500px",
fixed_thead = FALSE)
```
### Optional Module Links
### Feedbacks to the Host Model
## Setup & Configuration
### Setup Example
```{r 20-parameterconfig, echo=FALSE, message=FALSE, warning=FALSE}
library(knitr)
library(kableExtra)
parameter_config <- read.csv("tables/20-pathogens_microbial_indicators/parameter_config.csv", check.names=FALSE)
kable(parameter_config,"html", escape = F, align = "c", caption = "Parameters and configuration",
bootstrap_options = "hover") %>%
kable_styling(parameter_config, bootstrap_options = "hover",
full_width = F, position = "center",
font_size = 12) %>%
column_spec(1, width_min = "14em") %>%
column_spec(2, width_min = "14em") %>%
column_spec(3, width_min = "6em") %>%
column_spec(4, width_min = "6em") %>%
column_spec(5, width_min = "6em") %>%
column_spec(6, width_min = "6em") %>%
row_spec(1:1, background = 'white') %>%
scroll_box(width = "770px",
fixed_thead = FALSE)
```
<br>
An example `nml` block for the phytoplankton module is shown below:
```{style="max-height: 500px;"}
&aed2_pathogens
num_pathogens = 2
the_pathogens = 1,3
dbase = './the_path_to/aed2_pathogen_pars.nml'
! OPTIONAL VARS HERE
resuspension
num_ss
ss_set
ss_tau
ss_ke
sim_sedorgs
oxy_variable
epsilon
tau_0
tau_0_min
Ktau_0
extra_diag
att_ts
/
```
<br>
**2018 : aed2_pathogen_pars.nml** parameter formatting style:
```{style="max-height: 500px;"}
!-------------------------------------------------------------------------------
! aed2_pathogen_pars.nml : PATHOGEN PARAMETER DATABASE
!-------------------------------------------------------------------------------
! p_name : [ string] - pathogen name
! coef_grwth_uMAX : [ real] - Max growth rate at 20C
! coef_grwth_Tmin : [ real] - Tmin and Tmax, f(T)
! coef_grwth_Tmax : [ real] - Tmin and Tmax, f(T)
! coef_grwth_T1 : [ real] - coef_grwth_T1 and coef_grwth_T2
! coef_grwth_T2 : [ real] - coef_grwth_T1 and coef_grwth_T2
! coef_grwth_Kdoc : [ real] - Half-saturation for growth, coef_grwth_Kdoc
! coef_grwth_ic : [ real] - coef_grwth_ic
! coef_mort_kd20 : [ real] - Mortality rate (Dark death rate) @ 20C and 0 psu
! coef_mort_theta : [ real] - Temperature multiplier for mortality: coef_mort_theta
! coef_mort_c_SM : [ real] - Salinity effect on mortality
! coef_mort_alpha : [ real] - Salinity effect on mortality
! coef_mort_beta : [ real] - Salinity effect on mortality
! coef_mort_c_PHM : [ real] - pH effect on mortality
! coef_mort_K_PHM : [ real] - pH effect on mortality
! coef_mort_delta_M : [ real] - pH effect on mortality
! coef_mort_fdoc : [ real] - Fraction of mortality back to doc
! coef_light_kb_vis : [ real] - Light inactivation
! coef_light_kb_uva : [ real] - Light inactivation
! coef_light_kb_uvb : [ real] - Light inactivation
! coef_light_cSb_vis : [ real] - Salinity effect on light inactivation
! coef_light_cSb_uva : [ real] - Salinity effect on light inactivation
! coef_light_cSb_uvb : [ real] - Salinity effect on light inactivation
! coef_light_kDOb_vis : [ real] - DO effect on light
! coef_light_kDOb_uva : [ real] - DO effect on light
! coef_light_kDOb_uvb : [ real] - DO effect on light
! coef_light_cpHb_vis : [ real] - pH effect on light inactivation
! coef_light_cpHb_uva : [ real] - pH effect on light inactivation
! coef_light_cpHb_uvb : [ real] - pH effect on light inactivation
! coef_light_KpHb_vis : [ real] - pH effect on light inactivation
! coef_light_KpHb_uva : [ real] - pH effect on light inactivation
! coef_light_KpHb_uvb : [ real] - pH effect on light inactivation
! coef_light_delb_vis : [ real] - exponent for pH effect on light inactivation
! coef_light_delb_uva : [ real] - exponent for pH effect on light inactivation
! coef_light_delb_uvb : [ real] - exponent for pH effect on light inactivation
! coef_pred_kp20 : [ real] - Loss rate due to predation and temp multiplier
! coef_pred_theta_P : [ real] - Loss rate due to predation and temp multiplier
! coef_sett_fa : [ real] - Attached fraction in water column
! coef_sett_w_path : [ real] - Sedimentation velocity (m/d) at 20C (-ve means down) for NON-ATTACHED orgs
! coef_resus_epsilonP : [ real] - Pathogen resuspension rate
! coef_resus_tauP_0 : [ real] - Critical shear stress for organism resuspension
&pathogen_data
pd%p_name = 'crypto', 'ecoli', 'totalcoli'
pd%coef_grwth_uMAX = 0, 3, 2.4
pd%coef_grwth_Tmin = 4, 4, 4
pd%coef_grwth_Tmax = 35, 35, 35
pd%coef_grwth_T1 = 0.008, 0.008, 0.008
pd%coef_grwth_T2 = 0.1, 0.1, 0.1
pd%coef_grwth_Kdoc = 0, 0.3, 0.3
pd%coef_grwth_ic = 1.0E-9, 1.0E-9, 1.0E-9
pd%coef_mort_kd20 = 0.03, 0.48, 0.34
pd%coef_mort_theta = 1.08, 1.08, 1.11
pd%coef_mort_c_SM = 0, 2.0E-10, 2.0E-7
pd%coef_mort_alpha = 0, 6.1, 4.2
pd%coef_mort_beta = 0, 0.25, 0.25
pd%coef_mort_c_PHM = 0, 50, 50
pd%coef_mort_K_PHM = 0, 6, 6
pd%coef_mort_delta_M = 0, 4, 4
pd%coef_mort_fdoc = 0, 0.5, 0.5
pd%coef_light_kb_vis = 0, 0.097, 0.097
pd%coef_light_kb_uva = 0, 1.16, 1.16
pd%coef_light_kb_uvb = 0, 36.4, 36.4
pd%coef_light_cSb_vis = 0.0067, 0.0067, 0.0067
pd%coef_light_cSb_uva = 0.0067, 0.0067, 0.0067
pd%coef_light_cSb_uvb = 0.0067, 0.0067, 0.0067
pd%coef_light_kDOb_vis = 0.5, 0.5, 0.5
pd%coef_light_kDOb_uva = 0.5, 0.5, 0.5
pd%coef_light_kDOb_uvb = 0.5, 0.5, 0.5
pd%coef_light_cpHb_vis = 10, 10, 10
pd%coef_light_cpHb_uva = 10, 10, 10
pd%coef_light_cpHb_uvb = 10, 10, 10
pd%coef_light_KpHb_vis = 5, 5, 5
pd%coef_light_KpHb_uva = 5, 5, 5
pd%coef_light_KpHb_uvb = 5, 5, 5
pd%coef_light_delb_vis = 3, 3, 3
pd%coef_light_delb_uva = 3, 3, 3
pd%coef_light_delb_uvb = 3, 3, 3
pd%coef_pred_kp20 = 0, 0.2, 0.2
pd%coef_pred_theta_P = 1, 1.04, 1.04
pd%coef_sett_fa = 0, 0.94, 0.81
pd%coef_sett_w_path = -2.5E-6, -5.0E-7, -5.0E-7
pd%coef_resus_epsilonP = 0.01, 0.01, 0.01
pd%coef_resus_tauP_0 = 0.01, 0.01, 0.01
/
```
[Go to the aed2_pathogen_pars.nml Parameter Database](https://aquatic.science.uwa.edu.au/research/models/AED/aed2_dbase/db_edit.php)
<br>
**2014: aed2_pathogen_pars.nml** style parameters (note: not compatible with the online parameter database)
```{style="max-height: 500px;"}
!----------------------------------------------------------------!
! coef_grwth_uMAX !-- Max growth rate at 20C
! coef_grwth_Tmin, coef_grwth_Tmax !-- Tmin and Tmax, f(T)
! coef_grwth_T1, coef_grwth_T2 !-- coef_grwth_T1 and coef_grwth_T2
! coef_grwth_Kdoc !-- Half-saturation for growth, coef_grwth_Kdoc
! coef_grwth_ic !-- coef_grwth_ic
! coef_mort_kd20 !-- Mortality rate (Dark death rate) @ 20C and 0 psu
! coef_mort_theta !-- Temperature multiplier for mortality: coef_mort_theta
! coef_mort_c_SM, coef_mort_alpha, coef_mort_beta !-- Salinity effect on mortality
! coef_mort_c_PHM, coef_mort_K_PHM, coef_mort_delta_M !-- pH effect on mortality
! coef_mort_fdoc !-- Fraction of mortality back to doc
! coef_light_kb_vis, coef_light_kb_uva, coef_light_kb_uvb !-- Light inactivation
! coef_light_cSb_vis, coef_light_cSb_uva, coef_light_cSb_uvb !-- Salinity effect on light inactivation
! coef_light_kDOb_vis, coef_light_kDOb_uva, coef_light_kDOb_uvb !-- DO effect on light
! coef_light_cpHb_vis, coef_light_cpHb_uva, coef_light_cpHb_uvb !-- pH effect on light inactivation
! coef_light_KpHb_vis, coef_light_KpHb_uva, coef_light_KpHb_uvb !-- pH effect on light inactivation
! coef_light_delb_vis, coef_light_delb_uva, coef_light_delb_uvb !-- exponent for pH effect on light inactivation
! coef_pred_kp20, coef_pred_theta_P !-- Loss rate due to predation and temp multiplier
! coef_sett_fa !-- Attached fraction in water column
! coef_sett_w_path !-- Sedimentation velocity (m/d) at 20C (-ve means down) for NON-ATTACHED orgs
!----------------------------------------------------------------!
! p_name coef_grwth_uMAX, coef_grwth_Tmin, coef_grwth_Tmax, coef_grwth_T1, coef_grwth_T2, coef_grwth_Kdoc, coef_grwth_ic, coef_mort_kd20, coef_mort_theta, coef_mort_c_SM, coef_mort_alpha, coef_mort_beta, coef_mort_c_PHM, coef_mort_K_PHM, coef_mort_delta_M, coef_mort_fdoc, coef_light_kb_vis, coef_light_kb_uva, coef_light_kb_uvb, coef_light_cSb_vis, coef_light_cSb_uva, coef_light_cSb_uvb, coef_light_kDOb_vis, coef_light_kDOb_uva, coef_light_kDOb_uvb, coef_light_cpHb_vis, coef_light_cpHb_uva, coef_light_cpHb_uvb, coef_light_KpHb_vis, coef_light_KpHb_uva, coef_light_KpHb_uvb, coef_light_delb_vis, coef_light_delb_uva, coef_light_delb_uvb, coef_pred_kp20, coef_pred_theta_P, coef_sett_fa, coef_sett_w_path
&pathogen_data
pd = 'crypto', 0.0, 4.0, 35.0, 0.008, 0.1, 0.0, 1e-9, 0.03, 1.14, 0.00, 0.0, 0.00, 0.0, 0.0, 0.0, 0.0, 0.000, 2.13, 33.7, 0.0067, 0.0067, 0.0067, 0.5, 0.5, 0.5, 10.0, 10.0, 10.0, 5.0, 5.0, 5.0, 3.0, 3.0, 3.0, 0.0, 1.00, 0.00, -2.\5e-6,
'ecoli', 0.0, 4.0, 35.0, 0.008, 0.1, 0.3, 1e-9, 0.48, 1.08, 2e-10, 6.1, 0.25, 50.0, 6.0, 4.0, 0.5, 0.097, 1.16, 36.4, 0.0067, 0.0067, 0.0067, 0.5, 0.5, 0.5, 10.0, 10.0, 10.0, 5.0, 5.0, 5.0, 3.0, 3.0, 3.0, 0.0, 1.04, 0.94, -0.5e-6,
'fcoli', 0.0, 4.0, 35.0, 0.008, 0.1, 0.3, 1e-9, 0.71, 1.06, 2e-3, 1.8, 0.25, 50.0, 6.0, 4.0, 0.5, 0.097, 1.16, 36.4, 0.0067, 0.0067, 0.0067, 0.5, 0.5, 0.5, 10.0, 10.0, 10.0, 5.0, 5.0, 5.0, 3.0, 3.0, 3.0, 0.0, 1.04, 0.81, -0.5e-6,
'ent', 0.0, 4.0, 35.0, 0.008, 0.1, 0.3, 1e-9, 0.45, 1.04, 0.00, 0.0, 0.25, 50.0, 6.0, 4.0, 0.5, 0.882, 1.16, 17.2, 0.0067, 0.0067, 0.0067, 0.5, 0.5, 0.5, 10.0, 10.0, 10.0, 5.0, 5.0, 5.0, 3.0, 3.0, 3.0, 0.0, 1.04, 0.81, -0.5e-6,
'totalcoli', 0.0, 4.0, 35.0, 0.008, 0.1, 0.3, 1e-9, 0.34, 1.11, 2e-7, 4.2, 0.25, 50.0, 6.0, 4.0, 0.5, 0.097, 1.16, 36.4, 0.0067, 0.0067, 0.0067, 0.5, 0.5, 0.5, 10.0, 10.0, 10.0, 5.0, 5.0, 5.0, 3.0, 3.0, 3.0, 0.0, 1.04, 0.81, -0.5e-6,
/
```
## Case Studies & Examples
### Case Study
### Publications