Initial situation VS New scenario

Treatments and field barriers live in one table

ambre does not consider “treatment” and “practices” as different kinds of thing. Both are just log-reductions – a number of log10 units of pathogen removed – stored side by side in one table, config_ambre$treatment$processes. Its entries are coded by barrier types : Q. for water quality (treatment processes), E. for on-field equipment, P. for cultivation and irrigation practices.

tn <- sort(unique(config_ambre$treatment$processes$TreatmentName))
head(tn, 15)
#>  [1] "E.1 - Automatic irrigation"           
#>  [2] "E.1.1 - Micro-sprinkler"              
#>  [3] "E.1.2 - Surface drip irrigation"      
#>  [4] "E.1.3 - Subsurface drip irrigation"   
#>  [5] "E.2 - Mechanised crop maintenance"    
#>  [6] "E.3 - Mechanised harvesting"          
#>  [7] "E.4 - Signage"                        
#>  [8] "E.5 - Fences"                         
#>  [9] "E.7 - Sheet mulching"                 
#> [10] "E.8 - Personnal Protective Equipement"
#> [11] "P.1 - Non-edilble crop"               
#> [12] "P.10 - Rinsing with drinking water"   
#> [13] "P.11 - Washing with disinfectant"     
#> [14] "P.2 - Distance of 70m"                
#> [15] "P.3 - Night-time irrigation"

Each process is characterised by a distribution of pathogen log-reductions and identified by its TreatmentID. Depending on the scenario, one or more processes can be added to the existing treatment train to represent an improvement of the reuse scheme.

How the Excel prepares two trains

The Excel input file describes the initial reuse scheme InitialProcessName. Additional processes can then be introduced to build a modified scenario while keeping every other parameter identical SupplementaryProcessName.

This approach makes it possible to quantify the benefit of a proposed intervention under exactly the same exposure assumptions.

Your input file carries four columns describing the barrier chain:

readxl::read_excel(
  system.file("input_1culture_2pop.xlsx", package = "ambre")
)[, c("STEPtreatmentName", "CollectiveTreatmentName",
      "InitialProcessName", "SupplementaryProcessName")]
#> # A tibble: 2 × 4
#>   STEPtreatmentName      CollectiveTreatmentName InitialProcessName
#>   <chr>                  <chr>                   <chr>             
#> 1 Q.1 - Activated Sludge Q.2 - Maturation Pond   Q.6 - Chlorination
#> 2 Q.1 - Activated Sludge Q.2 - Maturation Pond   Q.6 - Chlorination
#> # ℹ 1 more variable: SupplementaryProcessName <chr>

From these, create_scenario() assembles two candidate trains:

• the initial train – wastewater treatment plant + collective treatment + initial process;
• the supplementary train – wastewater treatment plant + collective treatment + initial process + * supplementary process.

The switch is a single line

run_qmra_initial_situation() and run_qmra_supplementary_processs() are the same pipeline. The only difference is one argument passed deep inside them, update_treatment_scheme(initial_situation = ...), which decides which train is injected into the config before the log-reductions are simulated:

• run_qmra_treatment() uses initial_situation = TRUE -> the initial train;
• run_qmra_barrier() uses initial_situation = FALSE -> the supplementary train.

Everything else – inflow, exposure volume, dose, dose-response, DALYs – is computed identically. In particular, only the concentration log-reduction differs between the two runs; the exposure side is unchanged.

Run and overlay the two strategies

plot_comparison_qmra_initial_vs_supplementary_processes() runs both pipelines on the same input scenario file and pathogen and returns three side-by-side comparisons – log-reduction, and DALYs – each a cowplot panel with the initial result on the left and the supplementary result on the right.

library(dplyr)
scenario_example <- create_scenario(filepath = system.file("input_1culture_2pop.xlsx", package = "ambre"))
regulation_reduction <- config_ambre$regulation$regulation_value |> filter(Country == "France") |>
                          select(-c(Concentration, Country, RegulationID))
regulation_concentration <- config_ambre$regulation$regulation_value |> filter(Country == "France") |>
                        select(-c(Country, RegulationID, Reduction))

comparison <- plot_comparison_qmra_initial_vs_supplementary_processes(
  scenario = scenario_example,
  pathogen = c("Campylobacter jejuni"),
  regulationLog = regulation_reduction,
  regulationConcentration = regulation_concentration
)

names(comparison)
#> [1] "log_reduction" "dalys"

Log-reduction – how much each train removes. This is where the two strategies visibly diverge, because it is the only step that differs.

comparison$log_reduction
#> $Bacteria

DALYs – the health burden per person per year, both panels sharing the red WHO 1e-6 reference line. Reading the two boxplots against that line tells you whether either strategy – or which one – brings the scheme under the tolerable target.

comparison$dalys
#> $`Campylobacter jejuni`

For how to read these ranges and conclude, see vignette("d-interpreting-risk", package = "ambre").

Scope and current limits

The multi-barrier idea is powerful, but be honest about what the engine credits today:

• Barrier credits add in log space. Within a train, the log-reductions of the successive barriers are summed. Combining a modest treatment with a good field practice can reach the target without ever-heavier treatment – exactly the argument behind the WHO multi-barrier guidance and EU Regulation 2020/741 (which lets a scheme meet a reclaimed-water class through additional barriers rather than more treatment).
• Some data relating to barriers vary depending on the route of exposure, the crop and agricultural practices config_ambre$treatment contains the tables barriere_path, barriere_specific and barriere_decay. These are used to update the source database config_ambre$processes according to the simulated scenario; with function retrieve_specific_barrier

To see the underlying database for yourself, read vignette("h-config-ambre", package = "ambre").