Concentration reduction matrix calculation
regulation_matrix_concentration.RdCalculate the difference of achieve concentration in effluent according to regulation
Examples
# \donttest{
library(dplyr)
library(purrr)
scenario_example <- create_scenario(system.file("input_1culture_2pop.xlsx",
package = "ambre"))
scenario_exposure <- scenario_example |> mutate(
volume = map(
.x = config,
.f = ~ simulate_exposure(config = .x)))
#> Simulated exposure: volume per event
#> Create 1000 random distribution(s): triangle (n: 365, min: 0.500000, max: 3.000000, mode = 1.750000)
#> Simulated exposure: volume per event
#> Create 1000 random distribution(s): triangle (n: 365, min: 0.500000, max: 3.000000, mode = 1.750000)
scenario_patho <- update_pathogen(scenario = scenario_exposure,
pathoName = c("Rotavirus", "Campylobacter jejuni", "Norovirus"))
concentration = map(.x = scenario_patho$config,
.f= ~ simulate_inflow(.x))
#> Simulated pathogen: Campylobacter jejuni
#> Create 1000 random distribution(s): uniform (n: 365, min: 100.000000, max: 5000.000000)
#> Simulated pathogen: Norovirus
#> Create 1000 random distribution(s): uniform (n: 365, min: 0.000000, max: 1000000.000000)
#> Simulated pathogen: Rotavirus
#> Create 1000 random distribution(s): uniform (n: 365, min: 100.000000, max: 5000.000000)
#> Providing inflow events ... ok. (0.09 secs)
#> Providing inflow paras ... ok. (0.00 secs)
#> Simulated pathogen: Campylobacter jejuni
#> Create 1000 random distribution(s): uniform (n: 365, min: 100.000000, max: 5000.000000)
#> Simulated pathogen: Norovirus
#> Create 1000 random distribution(s): uniform (n: 365, min: 0.000000, max: 1000000.000000)
#> Simulated pathogen: Rotavirus
#> Create 1000 random distribution(s): uniform (n: 365, min: 100.000000, max: 5000.000000)
#> Providing inflow events ... ok. (0.09 secs)
#> Providing inflow paras ... ok. (0.00 secs)
scenario_concentration <- scenario_patho |> mutate(inflow_concentration = concentration)
scenario_ini_dose <- initial_dose_calculation(scenario = scenario_concentration)
scenario_barrier <- update_treatment_scheme(scenario = scenario_ini_dose, initial_situation = TRUE)
scenario_barrier$config <- purrr::pmap(
list(
config = scenario_barrier$config,
barrierID = scenario_barrier$SupplementaryProcessID,
cropHeight = scenario_barrier$CropHeight,
matrixID = scenario_barrier$MatrixID,
popID = scenario_barrier$PopulationID,
decay = scenario_barrier$nb_day_decay
),
function(config, barrierID, cropHeight, matrixID, popID, decay) {
if (barrierID %in% c(24, 26, 27, 28) &&
matrixID %in% c(3, 5)) {
update_logreduction_specific_barrier(
config = config,
cropHeight = cropHeight,
barrierID = barrierID
)
} else if (barrierID %in% c(12)) {
update_logreduction_decay(
config = config,
cropHeight = cropHeight,
barrierID = barrierID,
popID = popID,
nb_day_decay = decay
)
} else if (barrierID %in% c(9,13,14,15,16,17,18,24,26,27,28)) {
update_logreduction_path(
config = config,
matrixID = matrixID,
barrierID = barrierID,
popID = popID
)
} else {
config
}
}
)
scenario_logreduction <- scenario_barrier |>
mutate(log_reduction = map(config, simulate_treatment))
#> Create 1000 random distribution(s): uniform (n: 365, min: 1.000000, max: 2.000000)
#> Create 1000 random distribution(s): uniform (n: 365, min: 1.000000, max: 2.000000)
#> Create 1000 random distribution(s): uniform (n: 365, min: 1.000000, max: 3.000000)
#> Create 1000 random distribution(s): uniform (n: 365, min: 1.000000, max: 2.000000)
#> Create 1000 random distribution(s): uniform (n: 365, min: 5.000000, max: 7.000000)
#> Create 1000 random distribution(s): uniform (n: 365, min: 2.000000, max: 4.000000)
#> Simulated treatment: Q.1 - Activated Sludge for Bacteria
#> Simulated treatment: Q.1 - Activated Sludge for Viruses
#> Simulated treatment: Q.2 - Maturation Pond for Bacteria
#> Simulated treatment: Q.2 - Maturation Pond for Viruses
#> Simulated treatment: Q.6 - Chlorination for Bacteria
#> Simulated treatment: Q.6 - Chlorination for Viruses
#> Simulated treatment: Q.1 - Activated Sludge for Bacteria
#> Simulated treatment: Q.1 - Activated Sludge for Viruses
#> Simulated treatment: Q.2 - Maturation Pond for Bacteria
#> Simulated treatment: Q.2 - Maturation Pond for Viruses
#> Simulated treatment: Q.6 - Chlorination for Bacteria
#> Simulated treatment: Q.6 - Chlorination for Viruses
#> Joining with `by = join_by(TreatmentID)`
#> Joining with `by = join_by(TreatmentID)`
#> Create 1000 random distribution(s): uniform (n: 365, min: 1.000000, max: 2.000000)
#> Create 1000 random distribution(s): uniform (n: 365, min: 1.000000, max: 2.000000)
#> Create 1000 random distribution(s): uniform (n: 365, min: 1.000000, max: 3.000000)
#> Create 1000 random distribution(s): uniform (n: 365, min: 1.000000, max: 2.000000)
#> Create 1000 random distribution(s): uniform (n: 365, min: 5.000000, max: 7.000000)
#> Create 1000 random distribution(s): uniform (n: 365, min: 2.000000, max: 4.000000)
#> Simulated treatment: Q.1 - Activated Sludge for Bacteria
#> Simulated treatment: Q.1 - Activated Sludge for Viruses
#> Simulated treatment: Q.2 - Maturation Pond for Bacteria
#> Simulated treatment: Q.2 - Maturation Pond for Viruses
#> Simulated treatment: Q.6 - Chlorination for Bacteria
#> Simulated treatment: Q.6 - Chlorination for Viruses
#> Simulated treatment: Q.1 - Activated Sludge for Bacteria
#> Simulated treatment: Q.1 - Activated Sludge for Viruses
#> Simulated treatment: Q.2 - Maturation Pond for Bacteria
#> Simulated treatment: Q.2 - Maturation Pond for Viruses
#> Simulated treatment: Q.6 - Chlorination for Bacteria
#> Simulated treatment: Q.6 - Chlorination for Viruses
#> Joining with `by = join_by(TreatmentID)`
#> Joining with `by = join_by(TreatmentID)`
regulation_concentration <- config_ambre$regulation$regulation_value |>
dplyr::filter(Country == "France") |>
dplyr::select(-c(Country, RegulationID, Reduction))
regulation_matrix_concentration(scenario = scenario_logreduction,
regulation = regulation_concentration)
# }