Package 'pfm'

Title: Utilities for Pesticide Fate Modelling
Description: Utilities for simple calculations of predicted environmental concentrations ('PEC' values) and for dealing with data from some FOCUS pesticide fate modelling software packages.
Authors: Johannes Ranke [aut, cre]
Maintainer: Johannes Ranke <[email protected]>
License: GPL
Version: 0.6.3
Built: 2024-09-26 04:28:59 UTC
Source: https://github.com/jranke/pfm

Help Index


Create a chemical compound object for FOCUS Step 1 calculations

Description

Create a chemical compound object for FOCUS Step 1 calculations

Usage

chent_focus_sw(
  name,
  Koc,
  DT50_ws = NA,
  DT50_soil = NA,
  DT50_water = NA,
  DT50_sediment = NA,
  cwsat = 1000,
  mw = NA,
  max_soil = 1,
  max_ws = 1
)

Arguments

name

Length one character vector containing the name

Koc

Partition coefficient between organic carbon and water in L/kg.

DT50_ws

Half-life in water/sediment systems in days

DT50_soil

Half-life in soil in days

DT50_water

Half-life in water in days (Step 2)

DT50_sediment

Half-life in sediment in days (Step 2)

cwsat

Water solubility in mg/L

mw

Molar weight in g/mol.

max_soil

Maximum observed fraction (dimensionless) in soil

max_ws

Maximum observed fraction (dimensionless) in water/sediment systems

Value

A list with the substance specific properties


Deposition from spray drift expressed as percent of the applied dose as published by the JKI

Description

Deposition from spray drift expressed as percent of the applied dose as published by the German Julius-Kühn Institute (JKI).

Usage

drift_data_JKI

Format

A list currently containing matrices with spray drift percentage data for field crops (Ackerbau), and Pome/stone fruit, early and late (Obstbau frueh, spaet).

Details

The data were extracted from the spreadsheet cited below using the R code given in the file data_generation/drift_data_JKI.R installed with this package. The file itself is not included in the package, as its licence is not clear.

Additional spray drift values were taken from the publication by Rautmann et al. (2001). Specifically, these are the values for early vines, and the values for a 3 m buffer which are incomplete in the spreadsheet.

Note that for vegetables, ornamentals and small fruit, the values for field crops are used for crops < 50 cm, and the vales for late vines are used for crops > 50 cm. In the JKI spreadsheet, it is indicated that these values are used for spray applications with handheld/knapsack equipment (tragbare Spritz- und Sprühgerate).

Values for non-professional use listed in the JKI spreadsheet were not included.

Source

JKI (2010) Spreadsheet 'Tabelle der Abdrifteckwerte.xls', retrieved from http://www.jki.bund.de/no_cache/de/startseite/institute/anwendungstechnik/abdrift-eckwerte.html on 2015-06-11, not present any more 2024-01-31

Rautmann, D., Streloke, M and Winkler, R (2001) New basic drift values in the authorization procedure for plant protection products Mitt. Biol. Bundesanst. Land- Forstwirtsch. 383, 133-141

Examples

drift_data_JKI

Regression parameters for the Rautmann drift data

Description

The parameters were extracted from Appendix B to the FOCUS surface water guidance using the R code given in the file data_generation/drift_parameters_focus.R installed with this package. The appendix itself is not included in the package, as its licence is not clear.

Usage

drift_parameters_focus

Format

A tibble::tibble.

Details

For the hinge distance, Inf was substituted for the cases where no hinge distance is given in the data, in this way parameters C and D are never used for any distance if A and B are used for the case that the distance is smaller than the hinge distance.

References

FOCUS (2014) Generic guidance for Surface Water Scenarios (version 1.4). FOrum for the Co-ordination of pesticde fate models and their USe. http://esdac.jrc.ec.europa.eu/public_path/projects_data/focus/sw/docs/Generic%20FOCUS_SWS_vc1.4.pdf

FOCUS (2001) FOCUS Surface Water Scenarios in the EU Evaluation Process under 91/414/EEC. Report of the FOCUS Working Group on Surface Water Scenarios, EC Document Reference SANCO/4802/2001-rev.2. 245, Appendix B. https://esdac.jrc.ec.europa.eu/public_path/projects_data/focus/sw/docs/FOCUS_SWS_APPENDIX_B.doc

Rautmann, D., Streloke, M and Winkler, R (2001) New basic drift values in the authorization procedure for plant protection products Mitt. Biol. Bundesanst. Land- Forstwirtsch. 383, 133-141

See Also

drift_percentages_rautmann, PEC_sw_drift

Examples

drift_parameters_focus
unique(drift_parameters_focus$crop_group)

Calculate drift percentages based on Rautmann data

Description

Calculate drift percentages based on Rautmann data

Usage

drift_percentages_rautmann(
  distances,
  applications = 1,
  crop_group_RF = c("arable", "hops", "vines, late", "vines, early", "fruit, late",
    "fruit, early", "aerial"),
  formula = c("Rautmann", "FOCUS"),
  widths = 1
)

Arguments

distances

The distances in m for which to get PEC values

applications

Number of applications for selection of drift percentile

crop_group_RF

One of the crop groups as used in drift_parameters_focus

formula

By default, the original Rautmann formula is used. If you specify "FOCUS", mean drift input over the width of the water body is calculated as described in Chapter 5.4.5 of the FOCUS surface water guidance

widths

The widths of the water bodies (only used in the FOCUS formula)

References

FOCUS (2014) Generic guidance for Surface Water Scenarios (version 1.4). FOrum for the Co-ordination of pesticde fate models and their USe. http://esdac.jrc.ec.europa.eu/public_path/projects_data/focus/sw/docs/Generic%20FOCUS_SWS_vc1.4.pdf

See Also

drift_parameters_focus, PEC_sw_drift

Examples

# Compare JKI data with Rautmann and FOCUS formulas for arable crops (default)
# One application on field crops, for 1 m, 3 m and 5 m distance
drift_data_JKI[[1]][as.character(c(1, 3, 5)), "Ackerbau"]
drift_percentages_rautmann(c(1, 3, 5))
drift_percentages_rautmann(c(1, 3, 5), formula = "FOCUS")

# One application to early or late fruit crops
drift_data_JKI[[1]][as.character(c(3, 5, 20, 50)), "Obstbau frueh"]
drift_percentages_rautmann(c(3, 5, 20, 50), crop_group_RF = "fruit, early")
drift_percentages_rautmann(c(3, 5, 20, 50), crop_group_RF = "fruit, early",
  formula = "FOCUS")
drift_data_JKI[[1]][as.character(c(3, 5, 20, 50)), "Obstbau spaet"]
drift_percentages_rautmann(c(3, 5, 20, 50), crop_group_RF = "fruit, late")
drift_percentages_rautmann(c(3, 5, 20, 50), crop_group_RF = "fruit, late",
  formula = "FOCUS")

# We get a continuum if the waterbody covers the hinge distance
# (11.4 m for 1 early app to fruit)
x <- seq(3, 30, by = 0.1)
d <- drift_percentages_rautmann(x, crop_group_RF = "fruit, early", formula = "FOCUS")
plot(x, d, type = "l",
  xlab = "Distance of near edge [m]",
  ylab = "Mean drift percentage over waterbody width",
  main = "One application to fruit, early")
abline(v = 11.4, lty = 2)

Subset of EFSA crop interception default values for groundwater modelling

Description

Subset of EFSA crop interception default values for groundwater modelling

Usage

EFSA_GW_interception_2014

Format

A matrix containing interception values, currently only for some selected crops

Source

European Food Safety Authority (2014) EFSA Guidance Document for evaluating laboratory and field dissipation studies to obtain DegT50 values of active substances of plant protection products and transformation products of these active substances in soil. EFSA Journal 12(5):3662, 37 pp., doi:10.2903/j.efsa.2014.3662

Examples

EFSA_GW_interception_2014

Subset of EFSA crop washoff default values

Description

Subset of EFSA crop washoff default values

Usage

EFSA_washoff_2017

Format

A matrix containing wash-off factors, currently only for some selected crops

Source

European Food Safety Authority (2017) EFSA guidance document for predicting environmental concentrations of active substances of plant protection products and transformation products of these active substances in soil. EFSA Journal 15(10) 4982 doi:10.2903/j.efsa.2017.4982

Examples

EFSA_washoff_2017

Retrieve endpoint information from the chyaml field of a chent object

Description

R6 class objects of class chent represent chemical entities and can hold a list of information loaded from a chemical yaml file in their chyaml field. Such information is extracted and optionally aggregated by this function.

Usage

endpoint(
  chent,
  medium = "soil",
  type = c("degradation", "sorption"),
  lab_field = c(NA, "laboratory", "field"),
  redox = c(NA, "aerobic", "anaerobic"),
  value = c("DT50ref", "Kfoc", "N"),
  aggregator = geomean,
  raw = FALSE,
  signif = 3
)

soil_DT50(
  chent,
  aggregator = geomean,
  signif = 3,
  lab_field = "laboratory",
  value = "DT50ref",
  redox = "aerobic",
  raw = FALSE
)

soil_Kfoc(chent, aggregator = geomean, signif = 3, value = "Kfoc", raw = FALSE)

soil_N(chent, aggregator = mean, signif = 3, raw = FALSE)

soil_sorption(
  chent,
  values = c("Kfoc", "N"),
  aggregators = c(Kfoc = geomean, Koc = geomean, N = mean),
  signif = c(Kfoc = 3, N = 3),
  raw = FALSE
)

Arguments

chent

The chent object to get the information from

medium

The medium for which information is sought

type

The information type

lab_field

If not NA, do we want laboratory or field endpoints

redox

If not NA, are we looking for aerobic or anaerobic data

value

The name of the value we want. The list given in the usage section is not exclusive

aggregator

The aggregator function. Can be mean, geomean, or identity, for example.

raw

Should the number(s) be returned as stored in the chent object (could be a character value) to retain original information about precision?

signif

How many significant digits do we want

values

The values to be returned

aggregators

A named vector of aggregator functions to be used

Details

The functions soil_* are functions to extract soil specific endpoints. For the Freundlich exponent, the capital letter N is used in order to facilitate dealing with such data in R. In pesticide fate modelling, this exponent is often called 1/n.

Value

The result from applying the aggregator function to the values converted to a numeric vector, rounded to the given number of significant digits, or, if raw = TRUE, the values as a character value, retaining any implicit information on precision that may be present.


A very small subset of the FOCUS Groundwater scenario definitions

Description

Currently, only scenario names with acronyms and a small subset of the soil definitions are provided. The soil definitions are from page 46ff. from FOCUS (2012).

Usage

FOCUS_GW_scenarios_2012

Format

An object of class list of length 2.

References

FOCUS (2012) Generic guidance for Tier 1 FOCUS ground water assessments. Version 2.1. FOrum for the Co-ordination of pesticde fate models and their USe. http://focus.jrc.ec.europa.eu/gw/docs/Generic_guidance_FOCV2_1.pdf

Examples

FOCUS_GW_scenarios_2012

Step 1/2 scenario data as distributed with the FOCUS Step 1/2 calculator

Description

The data were extracted from the scenario.txt file using the R code shown below. The text file is not included in the package as its licence is not clear.

Format

A list containing the scenario names in a character vector called 'names', the drift percentiles in a matrix called 'drift', interception percentages in a matrix called 'interception' and the runoff/drainage percentages for Step 2 calculations in a matrix called 'rd'.

Examples

## Not run: 
  # This is the code that was used to extract the data
  scenario_path <- "inst/extdata/FOCUS_Step_12_scenarios.txt"
  scenarios <- readLines(scenario_path)[9:38]
  FOCUS_Step_12_scenarios <- list()
  sce <- read.table(text = scenarios, sep = "\t", header = TRUE, check.names = FALSE,
    stringsAsFactors = FALSE)
  FOCUS_Step_12_scenarios$names = sce$Crop
  rownames(sce) <- sce$Crop
  FOCUS_Step_12_scenarios$drift = sce[, 3:11]
  FOCUS_Step_12_scenarios$interception = sce[, 12:15]
  sce_2 <- readLines(scenario_path)[41:46]
  rd <- read.table(text = sce_2, sep = "\t")[1:2]
  rd_mat <- matrix(rd$V2, nrow = 3, byrow = FALSE)
  dimnames(rd_mat) = list(Time = c("Oct-Feb", "Mar-May", "Jun-Sep"),
                          Region = c("North", "South"))
  FOCUS_Step_12_scenarios$rd = rd_mat
  save(FOCUS_Step_12_scenarios, file = "data/FOCUS_Step_12_scenarios.RData")

## End(Not run)

# And this is the resulting data
FOCUS_Step_12_scenarios

Actual and maximum moving window time average concentrations for FOMC kinetics

Description

Actual and maximum moving window time average concentrations for FOMC kinetics

Usage

FOMC_actual_twa(
  alpha = 1.0001,
  beta = 10,
  times = c(0, 1, 2, 4, 7, 14, 21, 28, 42, 50, 100)
)

Arguments

alpha

Parameter of the FOMC model

beta

Parameter of the FOMC model

times

The output times, and window sizes for time weighted average concentrations

Author(s)

Johannes Ranke

Source

FOCUS (2014) Generic Guidance for Estimating Persistence and Degradation Kinetics from Environmental Fate Studies on Pesticides in EU Registration, Version 1.1, 18 December 2014, p. 251

Examples

FOMC_actual_twa(alpha = 1.0001, beta = 10)

Calculate the geometric mean

Description

Based on some posts in a thread on Stackoverflow http://stackoverflow.com/questions/2602583/geometric-mean-is-there-a-built-in This function returns NA if NA values are present and na.rm = FALSE (default). If negative values are present, it gives an error message. If at least one element of the vector is 0, it returns 0.

Usage

geomean(x, na.rm = FALSE)

Arguments

x

Vector of numbers

na.rm

Should NA values be omitted?

Value

The geometric mean

Author(s)

Johannes Ranke

Examples

geomean(c(1, 3, 9))
geomean(c(1, 3, NA, 9))
## Not run: geomean(c(1, -3, 9)) # returns an error

Fit a parabola through three points

Description

This was inspired by an answer on stackoverflow https://stackoverflow.com/a/717791

Usage

get_vertex(x, y)

Arguments

x

Three x coordinates

y

Three y coordinates


Groundwater ubiquity score based on Gustafson (1989)

Description

The groundwater ubiquity score GUS is calculated according to the following equation

GUS=log10DT50soil(4log10Koc)GUS = \log_{10} DT50_{soil} (4 - \log_{10} K_{oc})

Usage

GUS(...)

## S3 method for class 'numeric'
GUS(DT50, Koc, ...)

## S3 method for class 'chent'
GUS(
  chent,
  degradation_value = "DT50ref",
  lab_field = "laboratory",
  redox = "aerobic",
  sorption_value = "Kfoc",
  degradation_aggregator = geomean,
  sorption_aggregator = geomean,
  ...
)

## S3 method for class 'GUS_result'
print(x, ..., digits = 1)

Arguments

...

Included in the generic to allow for further arguments later. Therefore this also had to be added to the specific methods.

DT50

Half-life of the chemical in soil. Should be a field half-life according to Gustafson (1989). However, leaching to the sub-soil can not completely be excluded in field dissipation experiments and Gustafson did not refer to any normalisation procedure, but says the field study should be conducted under use conditions.

Koc

The sorption constant normalised to organic carbon. Gustafson does not mention the nonlinearity of the sorption constant commonly found and usually described by Freundlich sorption, therefore it is unclear at which reference concentration the Koc should be observed (and if the reference concentration would be in soil or in porewater).

chent

If a chent is given with appropriate information present in its chyaml field, this information is used, with defaults specified below.

degradation_value

Which of the available degradation values should be used?

lab_field

Should laboratory or field half-lives be used? This defaults to lab in this implementation, in order to avoid double-accounting for mobility. If comparability with the original GUS values given by Gustafson (1989) is desired, non-normalised first-order field half-lives obtained under actual use conditions should be used.

redox

Aerobic or anaerobic degradation data

sorption_value

Which of the available sorption values should be used? Defaults to Kfoc as this is what is generally available from the European pesticide peer review process. These values generally use a reference concentration of 1 mg/L in porewater, that means they would be expected to be Koc values at a concentration of 1 mg/L in the water phase.

degradation_aggregator

Function for aggregating half-lives

sorption_aggregator

Function for aggregation Koc values

x

An object of class GUS_result to be printed

digits

The number of digits used in the print method

Value

A list with the DT50 and Koc used as well as the resulting score of class GUS_result

Author(s)

Johannes Ranke

References

Gustafson, David I. (1989) Groundwater ubiquity score: a simple method for assessing pesticide leachability. Environmental toxicology and chemistry 8(4) 339–57.


The maximum time weighted average concentration for a moving window

Description

If you generate your time series using sawtooth, you need to make sure that the length of the time series allows for finding the maximum. It is therefore recommended to check this using plot.one_box using the window size for the argument max_twa.

Usage

max_twa(x, window = 21)

Arguments

x

An object of type one_box

window

The size of the moving window

Details

The method working directly on fitted mkinfit objects uses the equations given in the PEC soil section of the FOCUS guidance and is restricted SFO, FOMC and DFOP models and to the parent compound

References

FOCUS (2006) “Guidance Document on Estimating Persistence and Degradation Kinetics from Environmental Fate Studies on Pesticides in EU Registration” Report of the FOCUS Work Group on Degradation Kinetics, EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics

See Also

twa

Examples

pred <- sawtooth(one_box(10),
  applications = data.frame(time = c(0, 7), amount = c(1, 1)))
max_twa(pred)
pred_FOMC <- mkinfit("FOMC", FOCUS_2006_C, quiet = TRUE)
max_twa(pred_FOMC)

Create a time series of decline data

Description

Create a time series of decline data

Usage

one_box(x, ini, ..., t_end = 100, res = 0.01)

## S3 method for class 'numeric'
one_box(x, ini = 1, ..., t_end = 100, res = 0.01)

## S3 method for class 'character'
one_box(x, ini = 1, parms, ..., t_end = 100, res = 0.01)

## S3 method for class 'mkinfit'
one_box(x, ini = "model", ..., t_end = 100, res = 0.01)

Arguments

x

When numeric, this is the half-life to be used for an exponential decline. When a character string specifying a parent decline model is given e.g. FOMC, parms must contain the corresponding parameters. If x is an mkinfit object, the decline is calculated from this object.

ini

The initial amount. If x is an mkinfit object, and ini is 'model', the fitted initial concentrations are used. Otherwise, ini must be numeric. If it has length one, it is used for the parent and initial values of metabolites are zero, otherwise, it must give values for all observed variables.

...

Further arguments passed to methods

t_end

End of the time series

res

Resolution of the time series

parms

A named numeric vector containing the model parameters

Value

An object of class one_box, inheriting from ts.

Examples

# Only use a half-life
pred_0 <- one_box(10)
plot(pred_0)

# Use a fitted mkinfit model
require(mkin)
fit <- mkinfit("FOMC", FOCUS_2006_C, quiet = TRUE)
pred_1 <- one_box(fit)
plot(pred_1)

# Use a model with more than one observed variable
m_2 <- mkinmod(parent = mkinsub("SFO", "m1"), m1 = mkinsub("SFO"))
fit_2 <- mkinfit(m_2, FOCUS_2006_D, quiet = TRUE)
pred_2 <- one_box(fit_2, ini = "model")
plot(pred_2)

Get the relative accumulation of an FOMC model over multiples of an interval

Description

Get the relative accumulation of an FOMC model over multiples of an interval

Usage

PEC_FOMC_accu_rel(n, interval, FOMC)

Arguments

n

number of applications

interval

Time between applications

FOMC

Named numeric vector containing the FOMC parameters alpha and beta

Value

A numeric vector containing all n accumulation factors for the n applications


Calculate predicted environmental concentrations in soil

Description

This is a basic calculation of a contaminant concentration in bulk soil based on complete, instantaneous mixing. If an interval is given, an attempt is made at calculating a long term maximum concentration using the concepts layed out in the PPR panel opinion (EFSA PPR panel 2012 and in the EFSA guidance on PEC soil calculations (EFSA, 2015, 2017).

Usage

PEC_soil(
  rate,
  rate_units = "g/ha",
  interception = 0,
  mixing_depth = 5,
  PEC_units = "mg/kg",
  PEC_pw_units = "mg/L",
  interval = NA,
  n_periods = Inf,
  tillage_depth = 20,
  leaching_depth = tillage_depth,
  crop = "annual",
  cultivation = FALSE,
  chent = NA,
  DT50 = NA,
  FOMC = NA,
  Koc = NA,
  Kom = Koc/1.724,
  t_avg = 0,
  t_act = NULL,
  scenarios = c("default", "EFSA_2017", "EFSA_2015"),
  leaching = scenarios == "EFSA_2017",
  porewater = FALSE
)

Arguments

rate

Application rate in units specified below

rate_units

Defaults to g/ha

interception

The fraction of the application rate that does not reach the soil

mixing_depth

Mixing depth in cm

PEC_units

Requested units for the calculated PEC. Only mg/kg currently supported

PEC_pw_units

Only mg/L currently supported

interval

Period of the deeper mixing. The default is NA, i.e. no deeper mixing. For annual deeper mixing, set this to 365 when degradation units are in days

n_periods

Number of periods to be considered for long term PEC calculations

tillage_depth

Periodic (see interval) deeper mixing in cm

leaching_depth

EFSA (2017) uses the mixing depth (ecotoxicological evaluation depth) to calculate leaching for annual crops where tillage takes place. By default, losses from the layer down to the tillage depth are taken into account in this implementation.

crop

Ignored for scenarios other than EFSA_2017. Only annual crops are supported when these scenarios are used. Only crops with a single cropping cycle per year are currently supported.

cultivation

Does mechanical cultivation in the sense of EFSA (2017) take place, i.e. twice a year to a depth of 5 cm? Ignored for scenarios other than EFSA_2017

chent

An optional chent object holding substance specific information. Can also be a name for the substance as a character string

DT50

If specified, overrides soil DT50 endpoints from a chent object If DT50 is not specified here and not available from the chent object, zero degradation is assumed

FOMC

If specified, it should be a named numeric vector containing the FOMC parameters alpha and beta. This overrides any other degradation endpoints, and the degradation during the interval and after the maximum PEC is calculated using these parameters without temperature correction

Koc

If specified, overrides Koc endpoints from a chent object

Kom

Calculated from Koc by default, but can explicitly be specified as Kom here

t_avg

Averaging times for time weighted average concentrations

t_act

Time series for actual concentrations

scenarios

If this is 'default', the DT50 will be used without correction and soil properties as specified in the REACH guidance (R.16, Table R.16-9) are used for porewater PEC calculations. If this is "EFSA_2015", the DT50 is taken to be a modelling half-life at 20°C and pF2 (for when 'chent' is specified, the DegT50 with destination 'PECgw' will be used), and corrected using an Arrhenius activation energy of 65.4 kJ/mol. Also model and scenario adjustment factors from the EFSA guidance are used.

leaching

Should leaching be taken into account? The default is FALSE, except when the EFSA_2017 scenarios are used.

porewater

Should equilibrium porewater concentrations be estimated based on Kom and the organic carbon fraction of the soil instead of total soil concentrations? Based on equation (7) given in the PPR panel opinion (EFSA 2012, p. 24) and the scenarios specified in the EFSA guidance (2015, p. 13).

Details

This assumes that the complete load to soil during the time specified by 'interval' (typically 365 days) is dosed at once. As in the PPR panel opinion cited below (EFSA PPR panel 2012), only temperature correction using the Arrhenius equation is performed.

Total soil and porewater PEC values for the scenarios as defined in the EFSA guidance (2017, p. 14/15) can easily be calculated.

Value

The predicted concentration in soil

Note

While time weighted average (TWA) concentrations given in the examples from the EFSA guidance from 2015 (p. 80) are be reproduced, this is not true for the TWA concentrations given for the same example in the EFSA guidance from 2017 (p. 92).

According to the EFSA guidance (EFSA, 2017, p. 43), leaching should be taken into account for the EFSA 2017 scenarios, using the evaluation depth (here mixing depth) as the depth of the layer from which leaching takes place. However, as the amount leaching below the evaluation depth (often 5 cm) will partly be mixed back during tillage, the default in this function is to use the tillage depth for the calculation of the leaching rate.

If temperature information is available in the selected scenarios, as e.g. in the EFSA scenarios, the DT50 for groundwater modelling (destination 'PECgw') is taken from the chent object, otherwise the DT50 with destination 'PECsoil'.

Author(s)

Johannes Ranke

References

EFSA Panel on Plant Protection Products and their Residues (2012) Scientific Opinion on the science behind the guidance for scenario selection and scenario parameterisation for predicting environmental concentrations of plant protection products in soil. EFSA Journal 10(2) 2562, doi:10.2903/j.efsa.2012.2562

EFSA (European Food Safety Authority) 2017) EFSA guidance document for predicting environmental concentrations of active substances of plant protection products and transformation products of these active substances in soil. EFSA Journal 15(10) 4982 doi:10.2903/j.efsa.2017.4982

EFSA (European Food Safety Authority) (2015) EFSA guidance document for predicting environmental concentrations of active substances of plant protection products and transformation products of these active substances in soil. EFSA Journal 13(4) 4093 doi:10.2903/j.efsa.2015.4093

Examples

PEC_soil(100, interception = 0.25)

# This is example 1 starting at p. 92 of the EFSA guidance (2017)
# Note that TWA concentrations differ from the ones given in the guidance
# for an unknown reason (the values from EFSA (2015) can be reproduced).
PEC_soil(1000, interval = 365, DT50 = 250, t_avg = c(0, 21),
               Kom = 1000, scenarios = "EFSA_2017")
PEC_soil(1000, interval = 365, DT50 = 250, t_av = c(0, 21),
               Kom = 1000, scenarios = "EFSA_2017", porewater = TRUE)

# This is example 1 starting at p. 79 of the EFSA guidance (2015)
PEC_soil(1000, interval = 365, DT50 = 250, t_avg = c(0, 21),
               scenarios = "EFSA_2015")
PEC_soil(1000, interval = 365, DT50 = 250, t_av = c(0, 21),
               Kom = 1000, scenarios = "EFSA_2015", porewater = TRUE)

# The following is from example 4 starting at p. 85 of the EFSA guidance (2015)
# Metabolite M2
# Calculate total and porewater soil concentrations for tier 1 scenarios
# Relative molar mass is 100/300, formation fraction is 0.7 * 1
results_pfm <- PEC_soil(100/300 * 0.7 * 1 * 1000, interval = 365, DT50 = 250, t_avg = c(0, 21),
                        scenarios = "EFSA_2015")
results_pfm_pw <- PEC_soil(100/300 * 0.7 * 1000, interval = 365, DT50 = 250, t_av = c(0, 21),
                           Kom = 100, scenarios = "EFSA_2015", porewater = TRUE)

Calculate initial and accumulation PEC soil for a set of metabolites

Description

Calculate initial and accumulation PEC soil for a set of metabolites

Usage

PEC_soil_mets(rate, mw_parent, mets, interval = 365, ...)

Arguments

rate

Application rate in units specified below

mw_parent

The molecular weight of the parent compound

mets

A dataframe with metabolite identifiers as rownames and columns "mw", "occ" and "DT50" holding their molecular weight, maximum occurrence in soil and their soil DT50

interval

The interval for accumulation calculations

...

Further arguments are passed to PEC_soil


Calculate initial predicted environmental concentrations in surface water due to drainage using the UK method

Description

This implements the method specified in the UK data requirements handbook and was checked against the spreadsheet published on the CRC website

Usage

PEC_sw_drainage_UK(
  rate,
  interception = 0,
  Koc,
  latest_application = NULL,
  soil_DT50 = NULL,
  model = NULL,
  model_parms = NULL
)

Arguments

rate

Application rate in g/ha

interception

The fraction of the application rate that does not reach the soil

Koc

The sorption coefficient normalised to organic carbon in L/kg

latest_application

Latest application date, formatted as e.g. "01 July"

soil_DT50

Soil degradation half-life, if SFO kinetics are to be used

model

The soil degradation model to be used. Either one of "FOMC", "DFOP", "HS", or "IORE", or an mkinmod object

model_parms

A named numeric vector containing the model parameters

Value

The predicted concentration in surface water in µg/L

Author(s)

Johannes Ranke

References

HSE's Chemicals Regulation Division (CRD) Active substance PECsw calculations (for UK specific authorisation requests) https://www.hse.gov.uk/pesticides/topics/pesticide-approvals/pesticides-registration/data-requirements-handbook/fate/active-substance-uk.htm accessed 2019-09-27

Drainage PECs Version 1.0 (2015) Spreadsheet published at https://www.hse.gov.uk/pesticides/topics/pesticide-approvals/pesticides-registration/data-requirements-handbook/fate/pec-tools-2015/PEC%20sw-sed%20(drainage).xlsx accessed 2019-09-27

Examples

PEC_sw_drainage_UK(150, Koc = 100)

Calculate predicted environmental concentrations in surface water due to drift

Description

This is a basic, vectorised form of a simple calculation of a contaminant concentration in surface water based on complete, instantaneous mixing with input via spray drift.

Usage

PEC_sw_drift(
  rate,
  applications = 1,
  water_depth = as_units("30 cm"),
  drift_percentages = NULL,
  drift_data = c("JKI", "RF"),
  crop_group_JKI = c("Ackerbau", "Obstbau frueh", "Obstbau spaet", "Weinbau frueh",
    "Weinbau spaet", "Hopfenbau", "Flaechenkulturen > 900 l/ha", "Gleisanlagen"),
  crop_group_RF = c("arable", "hops", "vines, late", "vines, early", "fruit, late",
    "fruit, early", "aerial"),
  distances = c(1, 5, 10, 20),
  formula = c("Rautmann", "FOCUS"),
  water_width = as_units("100 cm"),
  side_angle = 90,
  rate_units = "g/ha",
  PEC_units = "µg/L"
)

Arguments

rate

Application rate in units specified below, or with units defined via the units package.

applications

Number of applications for selection of drift percentile

water_depth

Depth of the water body in cm

drift_percentages

Percentage drift values for which to calculate PECsw. Overrides 'drift_data' and 'distances' if not NULL.

drift_data

Source of drift percentage data. If 'JKI', the drift_data_JKI included in the package is used. If 'RF', the Rautmann drift data are calculated either in the original form or integrated over the width of the water body, depending on the 'formula' argument.

crop_group_JKI

When using the 'JKI' drift data, one of the German names as used in drift_parameters_focus. Will only be used if drift_data is 'JKI'.

crop_group_RF

One of the crop groups as used in drift_parameters_focus

distances

The distances in m for which to get PEC values

formula

By default, the original Rautmann formula is used. If you specify "FOCUS", mean drift input over the width of the water body is calculated as described in Chapter 5.4.5 of the FOCUS surface water guidance

water_width

Width of the water body in cm

side_angle

The angle of the side of the water relative to the bottom which is assumed to be horizontal, in degrees. The SYNOPS model assumes 45 degrees here.

rate_units

Defaults to g/ha. For backwards compatibility, only used if the specified rate does not have units::units].

PEC_units

Requested units for the calculated PEC. Only µg/L currently supported

Details

It is recommened to specify the arguments rate, water_depth and water_width using units::units from the units package.

Value

The predicted concentration in surface water

Author(s)

Johannes Ranke

See Also

drift_parameters_focus, drift_percentages_rautmann

Examples

PEC_sw_drift(100)
# Alternatively, we can use the formula for a single application to
# "Ackerbau" from the paper
PEC_sw_drift(100, drift_data = "RF")

# This makes it possible to also use different distances
PEC_sw_drift(100, distances = c(1, 3, 5, 6, 10, 20, 50, 100), drift_data = "RF")

# or consider aerial application
PEC_sw_drift(100, distances = c(1, 3, 5, 6, 10, 20, 50, 100), drift_data = "RF",
  crop_group_RF = "aerial")

# Using custom drift percentages is also supported
PEC_sw_drift(100, drift_percentages = c(2.77, 0.95, 0.57, 0.48, 0.29, 0.15, 0.06, 0.03))

# The influence of assuming a 45° angle of the sides of the waterbody and the width of the
# waterbody can be illustrated
PEC_sw_drift(100)
PEC_sw_drift(100, drift_data = "RF")
PEC_sw_drift(100, drift_data = "RF", formula = "FOCUS")
PEC_sw_drift(100, drift_data = "RF", formula = "FOCUS", side_angle = 45)
PEC_sw_drift(100, drift_data = "RF", formula = "FOCUS", side_angle = 45, water_width = 200)

Calculate PEC surface water due to drainage as in Exposit 3

Description

This is a reimplementation of the calculation described in the Exposit 3.02 spreadsheet file, in the worksheet "Konzept Drainage". Although there are four groups of compounds ("Gefährdungsgruppen"), only one distinction is made in the calculations, between compounds with low mobility (group 1) and compounds with modest to high mobility (groups 2, 3 and 4). In this implementation, the group is derived only from the Koc, if not given explicitly. For details, see the discussion of the function arguments below.

Usage

PEC_sw_exposit_drainage(
  rate,
  interception = 0,
  Koc = NA,
  mobility = c(NA, "low", "high"),
  DT50 = Inf,
  t_drainage = 3,
  V_ditch = 30,
  V_drainage = c(spring = 10, autumn = 100),
  dilution = 2
)

Arguments

rate

The application rate in g/ha

interception

The fraction intercepted by the crop

Koc

The sorption coefficient to soil organic carbon used to determine the mobility. A trigger value of 550 L/kg is used in order to decide if Koc >> 500.

mobility

Overrides what is determined from the Koc.

DT50

The soil half-life in days

t_drainage

The time between application and the drainage event, where degradation occurs, in days

V_ditch

The volume of the ditch is assumed to be 1 m * 100 m * 30 cm = 30 m3

V_drainage

The drainage volume, equivalent to 1 mm precipitation on 1 ha for spring/summer or 10 mm for autumn/winter/early spring.

dilution

The dilution factor

Value

A list containing the following components

perc_drainage_total

Gesamtaustrag (total fraction of the residue drained)

perc_peak

Stoßbelastung (fraction drained at event)

PEC_sw_drainage

A matrix containing PEC values for the spring and autumn scenarios. If the rate was given in g/ha, the PECsw are in microg/L.

Source

Excel 3.02 spreadsheet available from https://www.bvl.bund.de/SharedDocs/Downloads/04_Pflanzenschutzmittel/zul_umwelt_exposit.html

See Also

perc_runoff_exposit for runoff loss percentages and perc_runoff_reduction_exposit for runoff reduction percentages used

Examples

PEC_sw_exposit_drainage(500, Koc = 150)

Calculate PEC surface water due to runoff and erosion as in Exposit 3

Description

This is a reimplementation of the calculation described in the Exposit 3.02 spreadsheet file, in the worksheet "Konzept Runoff".

Usage

PEC_sw_exposit_runoff(
  rate,
  interception = 0,
  Koc,
  DT50 = set_units(Inf, "d"),
  t_runoff = set_units(3, "days"),
  exposit_reduction_version = c("3.02", "3.01a", "3.01a2", "2.0"),
  V_ditch = set_units(30, "m3"),
  V_event = set_units(100, "m3"),
  dilution = 2
)

Arguments

rate

The application rate in g/ha

interception

The fraction intercepted by the crop

Koc

The sorption coefficient to soil organic carbon

DT50

The soil half-life in days

t_runoff

The time between application and the runoff event, where degradation occurs, in days

exposit_reduction_version

The version of the reduction factors to be used. "3.02" is the current version used in Germany, "3.01a" is the version with additional percentages for 3 m and 6 m buffer zones used in Switzerland. "3.01a2" is a version introduced for consistency with previous calculations performed for a 3 m buffer zone in Switzerland, with the same reduction being applied to the dissolved and the bound fraction.

V_ditch

The volume of the ditch is assumed to be 1 m * 100 m * 30 cm = 30 m3

V_event

The unreduced runoff volume, equivalent to 10 mm precipitation on 1 ha

dilution

The dilution factor

Details

It is recommened to specify the arguments rate, Koc, DT50, t_runoff, V_ditch and V_event using units::units from the units package.

Value

A list containing the following components

perc_runoff

The runoff percentages for dissolved and bound substance

runoff

A matrix containing dissolved and bound input for the different distances

PEC_sw_runoff

A matrix containing PEC values for dissolved and bound substance for the different distances. If the rate was given in g/ha, the PECsw are in microg/L.

Source

Excel 3.02 spreadsheet available from https://www.bvl.bund.de/SharedDocs/Downloads/04_Pflanzenschutzmittel/zul_umwelt_exposit.html

See Also

perc_runoff_exposit for runoff loss percentages and perc_runoff_reduction_exposit for runoff reduction percentages used

Examples

PEC_sw_exposit_runoff(500, Koc = 150)
  PEC_sw_exposit_runoff(600, Koc = 10000, DT50 = 195, exposit = "3.01a")

Calculate PEC surface water at FOCUS Step 1

Description

This is a reimplementation of the FOCUS Step 1 and 2 calculator version 3.2, authored by Michael Klein, in R. Note that results for multiple applications should be compared to the corresponding results for a single application. At current, this is not done automatically in this implementation. Only Step 1 PECs are calculated. However, input files can be generated that are suitable as input for the FOCUS calculator.

Usage

PEC_sw_focus(
  parent,
  rate,
  n = 1,
  i = NA,
  comment = "",
  met = NULL,
  f_drift = NA,
  f_rd = 0.1,
  scenario = FOCUS_Step_12_scenarios$names,
  region = c("n", "s"),
  season = c(NA, "of", "mm", "js"),
  interception = c("no interception", "minimal crop cover", "average crop cover",
    "full canopy"),
  met_form_water = TRUE,
  txt_file = "pesticide.txt",
  overwrite = FALSE,
  append = FALSE
)

Arguments

parent

A list containing substance specific parameters, e.g. conveniently generated by chent_focus_sw.

rate

The application rate in g/ha. Overriden when applications are given explicitly

n

The number of applications

i

The application interval

comment

A comment for the input file

met

A list containing metabolite specific parameters. e.g. conveniently generated by chent_focus_sw. If not NULL, the PEC is calculated for this compound, not the parent.

f_drift

The fraction of the application rate reaching the waterbody via drift. If NA, this is derived from the scenario name and the number of applications via the drift data defined by the FOCUS_Step_12_scenarios

f_rd

The fraction of the amount applied reaching the waterbody via runoff/drainage. At Step 1, it is assumed to be 10%, be it the parent or a metabolite

scenario

The name of the scenario. Must be one of the scenario names given in FOCUS_Step_12_scenarios

region

'n' for Northern Europe or 's' for Southern Europe. If NA, only Step 1 PECsw are calculated

season

'of' for October to February, 'mm' for March to May, and 'js' for June to September. If NA, only step 1 PECsw are calculated

interception

One of 'no interception' (default), 'minimal crop cover', 'average crop cover' or 'full canopy'

met_form_water

Should the metabolite formation in water be taken into account? This can be switched off to check the influence and to compare with previous versions of the Steps 12 calculator

txt_file

the name, and potentially the full path to the Steps.12 input text file to which the specification of the run(s) should be written

overwrite

Should an existing file a the location specified in txt_file be overwritten? Only takes effect if append is FALSE.

append

Should the input text file be appended, if it exists?

Note

The formulas for input to the waterbody via runoff/drainage of the parent and subsequent formation of the metabolite in water is not documented in the model description coming with the calculator. As one would expect, this appears to be (as we get the same results) calculated by multiplying the application rate with the molar weight correction and the formation fraction in water/sediment systems.

Step 2 is not implemented.

References

FOCUS (2014) Generic guidance for Surface Water Scenarios (version 1.4). FOrum for the Co-ordination of pesticde fate models and their USe. http://esdac.jrc.ec.europa.eu/public_path/projects_data/focus/sw/docs/Generic%20FOCUS_SWS_vc1.4.pdf

Website of the Steps 1 and 2 calculator at the Joint Research Center of the European Union: http://esdac.jrc.ec.europa.eu/projects/stepsonetwo

Examples

# Parent only
dummy_1 <- chent_focus_sw("Dummy 1", cwsat = 6000, DT50_ws = 6, Koc = 344.8)
PEC_sw_focus(dummy_1, 3000, f_drift = 0)

# Metabolite
new_dummy <- chent_focus_sw("New Dummy", mw = 250, Koc = 100)
M1 <- chent_focus_sw("M1", mw = 100, cwsat = 100, DT50_ws = 100, Koc = 50,
  max_ws = 0, max_soil = 0.5)
PEC_sw_focus(new_dummy, 1000, scenario = "cereals, winter", met = M1)

Calculate predicted environmental concentrations in sediment from surface water concentrations

Description

The method 'percentage' is equivalent to what is used in the CRD spreadsheet PEC calculator

Usage

PEC_sw_sed(
  PEC_sw,
  percentage = 100,
  method = "percentage",
  sediment_depth = set_units(5, "cm"),
  water_depth = set_units(30, "cm"),
  sediment_density = set_units(1.3, "kg/L"),
  PEC_sed_units = c("µg/kg", "mg/kg")
)

Arguments

PEC_sw

Numeric vector or matrix of surface water concentrations in µg/L for which the corresponding sediment concentration is to be estimated

percentage

The percentage in sediment, used for the percentage method

method

The method used for the calculation

sediment_depth

Depth of the sediment layer

water_depth

Depth of the water body in cm

sediment_density

The density of the sediment in kg/L (equivalent to g/cm3)

PEC_sed_units

The units of the estimated sediment PEC value

Value

The predicted concentration in sediment

Author(s)

Johannes Ranke

Examples

library(pfm)
library(units)
PEC_sw_sed(PEC_sw_drift(100, distances = 1), percentage = 50)

Runoff loss percentages as used in Exposit 3

Description

A table of the loss percentages used in Exposit 3 for the twelve different Koc classes

Usage

perc_runoff_exposit

Format

A data frame with percentage values for the dissolved fraction and the fraction bound to eroding particles, with Koc classes used as row names

Koc_lower_bound

The lower bound of the Koc class

dissolved

The percentage of the applied substance transferred to an adjacent water body in the dissolved phase

bound

The percentage of the applied substance transferred to an adjacent water body bound to eroding particles

Source

Excel 3.02 spreadsheet available from https://www.bvl.bund.de/SharedDocs/Downloads/04_Pflanzenschutzmittel/zul_umwelt_exposit.html

Examples

print(perc_runoff_exposit)

Runoff reduction percentages as used in Exposit

Description

A table of the runoff reduction percentages used in Exposit 3 for different vegetated buffer widths

Usage

perc_runoff_reduction_exposit

Format

A named list of data frames with reduction percentage values for the dissolved fraction and the fraction bound to eroding particles, with vegetated buffer widths as row names. The names of the list items are the Exposit versions from which the values were taken.

dissolved

The reduction percentage for the dissolved phase

bound

The reduction percentage for the particulate phase

Source

Excel 3.02 spreadsheet available from https://www.bvl.bund.de/SharedDocs/Downloads/04_Pflanzenschutzmittel/zul_umwelt_exposit.html

Agroscope version 3.01a with additional runoff factors for 3 m and 6 m buffer zones received from Muris Korkaric (not published). The variant 3.01a2 was introduced for consistency with previous calculations performed by Agroscope for a 3 m buffer zone.

Examples

print(perc_runoff_reduction_exposit)

Calculate a time course of relative concentrations based on an mkinmod model

Description

Calculate a time course of relative concentrations based on an mkinmod model

Usage

pfm_degradation(
  model = "SFO",
  DT50 = 1000,
  parms = c(k_parent = log(2)/DT50),
  years = 1,
  step_days = 1,
  times = seq(0, years * 365, by = step_days)
)

Arguments

model

The degradation model to be used. Either a parent only model like 'SFO' or 'FOMC', or an mkinmod object

DT50

The half-life. This is only used when simple exponential decline is calculated (SFO model).

parms

The parameters used for the degradation model

years

For how many years should the degradation be predicted?

step_days

What step size in days should the output have?

times

The output times

Author(s)

Johannes Ranke

Examples

head(pfm_degradation("SFO", DT50 = 10))

Plot time series of decline data

Description

Plot time series of decline data

Usage

## S3 method for class 'one_box'
plot(
  x,
  xlim = range(time(x)),
  ylim = c(0, max(x)),
  xlab = "Time",
  ylab = "Residue",
  max_twa = NULL,
  max_twa_var = dimnames(x)[[2]][1],
  ...
)

Arguments

x

The object of type one_box to be plotted

xlim

Limits for the x axis

ylim

Limits for the y axis

xlab

Label for the x axis

ylab

Label for the y axis

max_twa

If a numeric value is given, the maximum time weighted average concentration(s) is/are shown in the graph.

max_twa_var

Variable for which the maximum time weighted average should be shown if max_twa is not NULL.

...

Further arguments passed to methods

See Also

sawtooth

Examples

dfop_pred <- one_box("DFOP", parms = c(k1 = 0.2, k2 = 0.02, g = 0.7))
plot(dfop_pred)
plot(sawtooth(dfop_pred, 3, 7), max_twa = 21)

# Use a fitted mkinfit model
m_2 <- mkinmod(parent = mkinsub("SFO", "m1"), m1 = mkinsub("SFO"))
fit_2 <- mkinfit(m_2, FOCUS_2006_D, quiet = TRUE)
pred_2 <- one_box(fit_2, ini = 1)
pred_2_saw <- sawtooth(pred_2, 2, 7)
plot(pred_2_saw, max_twa = 21, max_twa_var = "m1")

Plot TOXSWA surface water concentrations

Description

Plot TOXSWA hourly concentrations of a chemical substance in a specific segment of a TOXSWA surface water body.

Usage

## S3 method for class 'TOXSWA_cwa'
plot(
  x,
  time_column = c("datetime", "t", "t_firstjan", "t_rel_to_max"),
  xlab = "default",
  ylab = "default",
  add = FALSE,
  threshold_factor = 1000,
  thin_low = 1,
  total = FALSE,
  LC_TIME = "C",
  ...
)

Arguments

x

The TOXSWA_cwa object to be plotted.

time_column

What should be used for the time axis. If "t_firstjan" is chosen, the time is given in days relative to the first of January in the first year.

xlab, ylab

Labels for x and y axis.

add

Should we add to an existing plot?

threshold_factor

The factor by which the data have to be lower than the maximum in order to get thinned for plotting (see next argument).

thin_low

If an integer greater than 1, the data close to zero (smaller than 1/threshold_factor of the maximum) in the series will be thinned by this factor in order to decrease the amount of data that is included in the plots

total

Should the total concentration in water be plotted, including substance sorbed to suspended matter?

LC_TIME

Specification of the locale used to format dates

...

Further arguments passed to plot if we are not adding to an existing plot

Author(s)

Johannes Ranke

Examples

H_sw_D4_pond  <- read.TOXSWA_cwa("00001p_pa.cwa",
  basedir = "SwashProjects/project_H_sw/TOXSWA",
  zipfile = system.file("testdata/SwashProjects.zip", package = "pfm"))
plot(H_sw_D4_pond)
plot(H_sw_D4_pond, time_column = "t")
plot(H_sw_D4_pond, time_column = "t_firstjan")
plot(H_sw_D4_pond, time_column = "t_rel_to_max")

H_sw_R1_stream  <- read.TOXSWA_cwa("00003s_pa.cwa",
  basedir = "SwashProjects/project_H_sw/TOXSWA",
  zipfile = system.file("testdata/SwashProjects.zip", package = "pfm"))
plot(H_sw_R1_stream, time_column = "t_rel_to_max")

Read TOXSWA surface water concentrations

Description

Read TOXSWA hourly concentrations of a chemical substance in a specific segment of a TOXSWA surface water body. Per default, the data for the last segment are imported. As TOXSWA 4 reports the values at the end of the hour (ConLiqWatLayCur) in its summary file, we use this value as well instead of the hourly averages (ConLiqWatLay). In TOXSWA 5.5.3 this variable was renamed to ConLiqWatLay in the out file.

Usage

read.TOXSWA_cwa(
  filename,
  basedir = ".",
  zipfile = NULL,
  segment = "last",
  substance = "parent",
  total = FALSE,
  windows = NULL,
  thresholds = NULL
)

Arguments

filename

The filename of the cwa file (TOXSWA 2.x.y or similar) or the out file when using FOCUS TOXSWA 4 (i.e. TOXSWA 4.4.2) or higher.

basedir

The path to the directory where the cwa file resides.

zipfile

Optional path to a zip file containing the cwa file.

segment

The segment for which the data should be read. Either "last", or the segment number.

substance

For .out files, the default value "parent" leads to reading concentrations of the parent compound. Alternatively, the substance of interested can be selected by its code name.

total

Set this to TRUE in order to read total concentrations as well. This is only necessary for .out files as generated by TOXSWA 4.4.2 or similar, not for .cwa files. For .cwa files, the total concentration is always read as well.

windows

Numeric vector of width of moving windows in days, for calculating maximum time weighted average concentrations and areas under the curve.

thresholds

Numeric vector of threshold concentrations in µg/L for generating event statistics.

Value

An instance of an R6 object of class TOXSWA_cwa.

Author(s)

Johannes Ranke

Examples

H_sw_D4_pond  <- read.TOXSWA_cwa("00001p_pa.cwa",
                                 basedir = "SwashProjects/project_H_sw/TOXSWA",
                                 zipfile = system.file("testdata/SwashProjects.zip",
                                                       package = "pfm"))

Create decline time series for multiple applications

Description

If the application pattern is specified in applications, n and i are disregarded.

Usage

sawtooth(
  x,
  n = 1,
  i = 365,
  applications = data.frame(time = seq(0, (n - 1) * i, length.out = n), amount = 1)
)

Arguments

x

A one_box object

n

The number of applications. If applications is specified, n is ignored

i

The interval between applications. If applications is specified, i is ignored

applications

A data frame holding the application times in the first column and the corresponding amounts applied in the second column.

Examples

applications = data.frame(time = seq(0, 14, by = 7), amount = c(1, 2, 3))
pred <- one_box(10)
plot(sawtooth(pred, applications = applications))

m_2 <- mkinmod(parent = mkinsub("SFO", "m1"), m1 = mkinsub("SFO"))
fit_2 <- mkinfit(m_2, FOCUS_2006_D, quiet = TRUE)
pred_2 <- one_box(fit_2, ini = 1)
pred_2_saw <- sawtooth(pred_2, 2, 7)
plot(pred_2_saw, max_twa = 21, max_twa_var = "m1")

max_twa(pred_2_saw)

Actual and maximum moving window time average concentrations for SFO kinetics

Description

Actual and maximum moving window time average concentrations for SFO kinetics

Usage

SFO_actual_twa(DT50 = 1000, times = c(0, 1, 2, 4, 7, 14, 21, 28, 42, 50, 100))

Arguments

DT50

The half-life.

times

The output times, and window sizes for time weighted average concentrations

Author(s)

Johannes Ranke

Source

FOCUS (2014) Generic Guidance for Estimating Persistence and Degradation Kinetics from Environmental Fate Studies on Pesticides in EU Registration, Version 1.1, 18 December 2014, p. 251

Examples

SFO_actual_twa(10)

Properties of the predefined scenarios from the EFSA guidance from 2015

Description

Properties of the predefined scenarios used at Tier 1, Tier 2A and Tier 3A for the concentration in soil as given in the EFSA guidance (2015, p. 13/14). Also, the scenario and model adjustment factors from p. 15 and p. 17 are included.

Usage

soil_scenario_data_EFSA_2015

Format

A data frame with one row for each scenario. Row names are the scenario codes, e.g. CTN for the Northern scenario for the total concentration in soil. Columns are mostly self-explanatory. rho is the dry bulk density of the top soil.

Source

EFSA (European Food Safety Authority) (2015) EFSA guidance document for predicting environmental concentrations of active substances of plant protection products and transformation products of these active substances in soil. EFSA Journal 13(4) 4093 doi:10.2903/j.efsa.2015.4093

Examples

soil_scenario_data_EFSA_2015

Properties of the predefined scenarios from the EFSA guidance from 2017

Description

Properties of the predefined scenarios used at Tier 1, Tier 2A and Tier 3A for the concentration in soil as given in the EFSA guidance (2017, p. 14/15). Also, the scenario and model adjustment factors from p. 16 and p. 18 are included.

Usage

soil_scenario_data_EFSA_2017

Format

A data frame with one row for each scenario. Row names are the scenario codes, e.g. CTN for the Northern scenario for the total concentration in soil. Columns are mostly self-explanatory. rho is the dry bulk density of the top soil.

Source

EFSA (European Food Safety Authority) (2017) EFSA guidance document for predicting environmental concentrations of active substances of plant protection products and transformation products of these active substances in soil. EFSA Journal 15(10) 4982 doi:10.2903/j.efsa.2017.4982

Examples

soil_scenario_data_EFSA_2017

waldo::compare(soil_scenario_data_EFSA_2017, soil_scenario_data_EFSA_2015)

Determine the SSLRC mobility classification for a chemical substance from its Koc

Description

This implements the method specified in the UK data requirements handbook and was checked against the spreadsheet published on the CRD website

Usage

SSLRC_mobility_classification(Koc)

Arguments

Koc

The sorption coefficient normalised to organic carbon in L/kg

Value

A list containing the classification and the percentage of the compound transported per 10 mm drain water

Author(s)

Johannes Ranke

References

HSE's Chemicals Regulation Division (CRD) Active substance PECsw calculations (for UK specific authorisation requests) https://www.hse.gov.uk/pesticides/topics/pesticide-approvals/pesticides-registration/data-requirements-handbook/fate/active-substance-uk.htm accessed 2019-09-27

Drainage PECs Version 1.0 (2015) Spreadsheet published at https://www.hse.gov.uk/pesticides/topics/pesticide-approvals/pesticides-registration/data-requirements-handbook/fate/pec-tools-2015/PEC%20sw-sed%20(drainage).xlsx accessed 2019-09-27

Examples

SSLRC_mobility_classification(100)
SSLRC_mobility_classification(10000)

R6 class for holding TOXSWA water concentration data and associated statistics

Description

An R6 class for holding TOXSWA water concentration (cwa) data and some associated statistics. like maximum moving window average concentrations, and dataframes holding the events exceeding specified thresholds. Usually, an instance of this class will be generated by read.TOXSWA_cwa.

Format

An R6Class generator object.

Public fields

filename

Length one character vector holding the filename.

basedir

Length one character vector holding the directory where the file came from.

zipfile

If not null, giving the path to the zip file from which the file was read.

segment

Length one integer, specifying for which segment the cwa data were read.

substance

The TOXSWA name of the substance.

cwas

Dataframe holding the concentrations.

events

List of dataframes holding the event statistics for each threshold.

windows

Matrix of maximum time weighted average concentrations (TWAC_max) and areas under the curve in µg/day * h (AUC_max_h) or µg/day * d (AUC_max_d) for the requested moving window sizes in days.

Methods

Public methods


Method new()

Create a TOXSWA_cwa object from a file

Usage
TOXSWA_cwa$new(
  filename,
  basedir,
  zipfile = NULL,
  segment = "last",
  substance = "parent",
  total = FALSE
)
Arguments
filename

The filename

basedir

The directory to look in

zipfile

Optional path to a zipfile holding the file

segment

Either "last" or the number of the segment for which to read the data

substance

The TOXSWA substance name (for TOXSWA 4 or higher)

total

Should total concentrations be read in? If FALSE, free concentrations are read


Method moving_windows()

Add to the windows field described above.

Usage
TOXSWA_cwa$moving_windows(windows, total = FALSE)
Arguments
windows

Window sizes in days

total

If TRUE, the total concentration including the amount adsorbed to suspended matter will be used.


Method get_events()

Populate a datataframe with event information for the specified threshold value. The resulting dataframe is stored in the events field of the object.

Usage
TOXSWA_cwa$get_events(thresholds, total = FALSE)
Arguments
thresholds

Threshold values in µg/L.

total

If TRUE, the total concentration including the amount adsorbed to suspended matter will be used.


Method print()

Print a TOXSWA_cwa object

Usage
TOXSWA_cwa$print()

Method clone()

The objects of this class are cloneable with this method.

Usage
TOXSWA_cwa$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

Examples

H_sw_R1_stream  <- read.TOXSWA_cwa("00003s_pa.cwa",
                                 basedir = "SwashProjects/project_H_sw/TOXSWA",
                                 zipfile = system.file("testdata/SwashProjects.zip",
                                             package = "pfm"))
H_sw_R1_stream$get_events(c(2, 10))
H_sw_R1_stream$moving_windows(c(7, 21))
print(H_sw_R1_stream)

Estimation of the transpiration stream concentration factor

Description

The FOCUS groundwater guidance (FOCUS 2014, p. 41) states that a reliable measured log Kow for neutral pH must be available in order to apply the Briggs equation. It is not clarified when it can be regarded reliable, but the equation is stated to be produced for non-ionic compounds, suggesting that the compound should not be ionogenic (weak acid/base) or ionic.

Usage

TSCF(log_Kow, method = c("briggs82", "dettenmaier09"))

Arguments

log_Kow

The decadic logarithm of the octanol-water partition constant

method

Short name of the estimation method.

Details

The Dettenmaier equation is given to show that other views on the subject exist.

References

FOCUS (2014) Generic Guidance for Tier 1 FOCUS Ground Water Assessments. Version 2.2, May 2014 Dettenmaier EM, Doucette WJ and Bugbee B (2009) Chemical hydrophobicity and uptake by plant roots. Environ. Sci. Technol 43, 324 - 329

Examples

plot(TSCF, -1, 5, xlab = "log Kow", ylab = "TSCF", ylim = c(0, 1.1))
TSCF_2 <- function(x) TSCF(x, method = "dettenmaier09")
curve(TSCF_2, -1, 5, add = TRUE, lty = 2)
legend("topright", lty = 1:2, bty = "n",
  legend = c("Briggs et al. (1982)", "Dettenmaier et al. (2009)"))

Calculate a time weighted average concentration

Description

The moving average is built only using the values in the past, so the earliest possible time for the maximum in the time series returned is after one window has passed.

Usage

twa(x, window = 21)

## S3 method for class 'one_box'
twa(x, window = 21)

Arguments

x

An object of type one_box

window

The size of the moving window

See Also

max_twa

Examples

pred <- sawtooth(one_box(10),
  applications = data.frame(time = c(0, 7), amount = c(1, 1)))
max_twa(pred)