From 97d0465a296ef1f5ae4502f9374ae339c0a8148e Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Sun, 12 Nov 2023 21:10:41 +0100 Subject: Update static docs --- docs/reference/PEC_soil.html | 477 ++++++++++++++++------------------- docs/reference/PEC_sw_focus.html | 522 +++++++++++++++++---------------------- docs/reference/TOXSWA_cwa.html | 412 +++++++++++++++--------------- docs/reference/index.html | 365 ++++++--------------------- docs/reference/reexports.html | 83 +++++++ 5 files changed, 793 insertions(+), 1066 deletions(-) create mode 100644 docs/reference/reexports.html (limited to 'docs/reference') diff --git a/docs/reference/PEC_soil.html b/docs/reference/PEC_soil.html index 3e405a5..e531fab 100644 --- a/docs/reference/PEC_soil.html +++ b/docs/reference/PEC_soil.html @@ -1,68 +1,16 @@ - - - - - - - -Calculate predicted environmental concentrations in soil — PEC_soil • pfm - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Calculate predicted environmental concentrations in soil — PEC_soil • pfm - - - - - - - - - - - - - - + + -

- -

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 + + `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 +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 +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) +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 +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 +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 +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 -'chents' is specified, the DegT50 with destination 'PECgw' will be used), +'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 +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).

+p. 13).

Value

- -

The predicted concentration in soil

Details

Value

+ +

The predicted concentration in soil

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.

Note

- +

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 @@ -289,8 +226,9 @@ from 2017 (p. 92).

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'.

References

- +

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 @@ -306,75 +244,80 @@ from 2017 (p. 92).

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)
#>      scenario
-#> t_avg default
-#>     0     0.1

-# 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")
#>      scenario
-#> t_avg      CTN     CTC      CTS
-#>    0  19.76834 13.8619 10.53795
-#>    21 19.59345 13.7169 10.39882
PEC_soil(1000, interval = 365, DT50 = 250, t_av = c(0, 21),
-               Kom = 1000, scenarios = "EFSA_2017", porewater = TRUE)
#>      scenario
-#> t_avg       CLN       CLC       CLS
-#>    0  0.5541984 0.6779249 0.9816693
-#>    21 0.5484576 0.6693125 0.9609119

-# 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")
#>      scenario
-#> t_avg      CTN      CTC      CTS
-#>    0  21.96827 11.53750 9.145259
-#>    21 21.78517 11.40701 9.017370
PEC_soil(1000, interval = 365, DT50 = 250, t_av = c(0, 21),
-               Kom = 1000, scenarios = "EFSA_2015", porewater = TRUE)
#>      scenario
-#> t_avg       CLN       CLC       CLS
-#>    0  0.7589401 0.6674322 0.9147861
-#>    21 0.7506036 0.6590345 0.8987279

-# 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)

- +

Author

Johannes Ranke

+ +

Examples

PEC_soil(100, interception = 0.25)
+#>      scenario
+#> t_avg default
+#>     0     0.1
+
+# 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")
+#>      scenario
+#> t_avg      CTN     CTC      CTS
+#>    0  19.76834 13.8619 10.53795
+#>    21 19.59345 13.7169 10.39882
+PEC_soil(1000, interval = 365, DT50 = 250, t_av = c(0, 21),
+               Kom = 1000, scenarios = "EFSA_2017", porewater = TRUE)
+#>      scenario
+#> t_avg       CLN       CLC       CLS
+#>    0  0.5541984 0.6779249 0.9816693
+#>    21 0.5484576 0.6693125 0.9609119
+
+# 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")
+#>      scenario
+#> t_avg      CTN      CTC      CTS
+#>    0  21.96827 11.53750 9.145259
+#>    21 21.78517 11.40701 9.017370
+PEC_soil(1000, interval = 365, DT50 = 250, t_av = c(0, 21),
+               Kom = 1000, scenarios = "EFSA_2015", porewater = TRUE)
+#>      scenario
+#> t_avg       CLN       CLC       CLS
+#>    0  0.7589401 0.6674322 0.9147861
+#>    21 0.7506036 0.6590345 0.8987279
+
+# 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)
+

- - + + diff --git a/docs/reference/PEC_sw_focus.html b/docs/reference/PEC_sw_focus.html index 8242f6a..55a7400 100644 --- a/docs/reference/PEC_sw_focus.html +++ b/docs/reference/PEC_sw_focus.html @@ -1,70 +1,18 @@ - - - - - - - -Calculate PEC surface water at FOCUS Step 1 — PEC_sw_focus • pfm - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Calculate PEC surface water at FOCUS Step 1 — PEC_sw_focus • pfm - - - - - - - - - - - - - - + + -

- -

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 = TRUE
-)

- -

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 + + `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 = TRUE +)` + + + + 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 +[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 -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 +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 +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?

- -

Note

+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?

+ +

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 @@ -237,142 +170,141 @@ should be written

multiplying the application rate with the molar weight correction and the formation fraction in water/sediment systems.

Step 2 is not implemented.

References

- +

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

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, overwrite = TRUE, append = FALSE)
#> $f_drift
-#> [1] 0
-#> 
-#> $eq_rate_drift_s
-#> [1] 3000
-#> 
-#> $eq_rate_rd_s
-#> [1] 3000
-#> 
-#> $eq_rate_rd_parent_s
-#> [1] NA
-#> 
-#> $input_drift_s
-#> [1] 0
-#> 
-#> $input_rd_s
-#> [1] 300
-#> 
-#> $f_rd_sw
-#> [1] 0.6850566
-#> 
-#> $f_rd_sed
-#> [1] 0.3149434
-#> 
-#> $PEC
-#>      type
-#> Time         PECsw   TWAECsw       PECsed  TWAECsed
-#>   0   6.850566e+02        NA 2.362075e+03        NA
-#>   1   6.103161e+02 647.68635 2.104370e+03 2233.2225
-#>   2   5.437298e+02 612.03420 1.874780e+03 2110.2939
-#>   4   4.315586e+02 548.76030 1.488014e+03 1892.1255
-#>   7   3.051580e+02 469.88375 1.052185e+03 1620.1592
-#>   14  1.359325e+02 339.57370 4.686951e+02 1170.8501
-#>   21  6.055102e+01 257.45458 2.087799e+02  887.7034
-#>   28  2.697241e+01 203.47173 9.300089e+01  701.5705
-#>   42  5.352005e+00 140.10377 1.845371e+01  483.0778
-#>   50  2.123945e+00 118.24602 7.323361e+00  407.7123
-#>   100 6.585062e-03  59.30629 2.270529e-02  204.4881
-#> 
-#> $PEC_sw_max
-#> [1] 685.0566
-#> 
-#> $PEC_sed_max
-#> [1] 2362.075
-#> 

-# 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)
#> $f_drift
-#> [1] 0.02759
-#> 
-#> $eq_rate_drift_s
-#> [1] 0
-#> 
-#> $eq_rate_rd_s
-#> [1] 200
-#> 
-#> $eq_rate_rd_parent_s
-#> [1] 0
-#> 
-#> $input_drift_s
-#> [1] 0
-#> 
-#> $input_rd_s
-#> [1] 20
-#> 
-#> $f_rd_sw
-#> [1] 0.9375
-#> 
-#> $f_rd_sed
-#> [1] 0.0625
-#> 
-#> $PEC
-#>      type
-#> Time     PECsw  TWAECsw   PECsed TWAECsed
-#>   0   62.50000       NA 31.25000       NA
-#>   1   62.06828 62.28414 31.03414 31.14207
-#>   2   61.63954 62.06890 30.81977 31.03445
-#>   4   60.79093 61.64158 30.39547 30.82079
-#>   7   59.53987 61.00800 29.76994 30.50400
-#>   14  56.71995 59.56326 28.35997 29.78163
-#>   21  54.03358 58.16414 27.01679 29.08207
-#>   28  51.47444 56.80902 25.73722 28.40451
-#>   42  46.71404 54.22460 23.35702 27.11230
-#>   50  44.19417 52.81945 22.09709 26.40973
-#>   100 31.25000 45.08422 15.62500 22.54211
-#> 
-#> $PEC_sw_max
-#> [1] 62.5
-#> 
-#> $PEC_sed_max
-#> [1] 31.25
-#>

- +

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, overwrite = TRUE, append = FALSE)
+#> $f_drift
+#> [1] 0
+#> 
+#> $eq_rate_drift_s
+#> [1] 3000
+#> 
+#> $eq_rate_rd_s
+#> [1] 3000
+#> 
+#> $eq_rate_rd_parent_s
+#> [1] NA
+#> 
+#> $input_drift_s
+#> [1] 0
+#> 
+#> $input_rd_s
+#> [1] 300
+#> 
+#> $f_rd_sw
+#> [1] 0.6850566
+#> 
+#> $f_rd_sed
+#> [1] 0.3149434
+#> 
+#> $PEC
+#>      type
+#> Time         PECsw   TWAECsw       PECsed  TWAECsed
+#>   0   6.850566e+02        NA 2.362075e+03        NA
+#>   1   6.103161e+02 647.68635 2.104370e+03 2233.2225
+#>   2   5.437298e+02 612.03420 1.874780e+03 2110.2939
+#>   4   4.315586e+02 548.76030 1.488014e+03 1892.1255
+#>   7   3.051580e+02 469.88375 1.052185e+03 1620.1592
+#>   14  1.359325e+02 339.57370 4.686951e+02 1170.8501
+#>   21  6.055102e+01 257.45458 2.087799e+02  887.7034
+#>   28  2.697241e+01 203.47173 9.300089e+01  701.5705
+#>   42  5.352005e+00 140.10377 1.845371e+01  483.0778
+#>   50  2.123945e+00 118.24602 7.323361e+00  407.7123
+#>   100 6.585062e-03  59.30629 2.270529e-02  204.4881
+#> 
+#> $PEC_sw_max
+#> [1] 685.0566
+#> 
+#> $PEC_sed_max
+#> [1] 2362.075
+#> 
+
+# 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)
+#> $f_drift
+#> [1] 0.02759
+#> 
+#> $eq_rate_drift_s
+#> [1] 0
+#> 
+#> $eq_rate_rd_s
+#> [1] 200
+#> 
+#> $eq_rate_rd_parent_s
+#> [1] 0
+#> 
+#> $input_drift_s
+#> [1] 0
+#> 
+#> $input_rd_s
+#> [1] 20
+#> 
+#> $f_rd_sw
+#> [1] 0.9375
+#> 
+#> $f_rd_sed
+#> [1] 0.0625
+#> 
+#> $PEC
+#>      type
+#> Time     PECsw  TWAECsw   PECsed TWAECsed
+#>   0   62.50000       NA 31.25000       NA
+#>   1   62.06828 62.28414 31.03414 31.14207
+#>   2   61.63954 62.06890 30.81977 31.03445
+#>   4   60.79093 61.64158 30.39547 30.82079
+#>   7   59.53987 61.00800 29.76994 30.50400
+#>   14  56.71995 59.56326 28.35997 29.78163
+#>   21  54.03358 58.16414 27.01679 29.08207
+#>   28  51.47444 56.80902 25.73722 28.40451
+#>   42  46.71404 54.22460 23.35702 27.11230
+#>   50  44.19417 52.81945 22.09709 26.40973
+#>   100 31.25000 45.08422 15.62500 22.54211
+#> 
+#> $PEC_sw_max
+#> [1] 62.5
+#> 
+#> $PEC_sed_max
+#> [1] 31.25
+#> 
+

- - + + diff --git a/docs/reference/TOXSWA_cwa.html b/docs/reference/TOXSWA_cwa.html index 16dc5db..c8622df 100644 --- a/docs/reference/TOXSWA_cwa.html +++ b/docs/reference/TOXSWA_cwa.html @@ -1,71 +1,16 @@ - - - - - - - -R6 class for holding TOXSWA water concentration data and associated statistics — TOXSWA_cwa • pfm - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -R6 class for holding TOXSWA water concentration data and associated statistics — TOXSWA_cwa • pfm - - - - - - - - - - - - - + + -

- -

R6 class for holding TOXSWA water concentration data and associated statistics

@@ -117,179 +48,236 @@ by read.TOXSWA_cwa." /> 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.

+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.
filename: Length one character vector holding the filename.
zipfile: If not null, giving the path to the zip file from which the file was read.
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.
segment: Length one integer, specifying for which segment the cwa data were read.
substance: The TOXSWA name of the substance.
substance: The TOXSWA name of the substance.
cwas: Dataframe holding the concentrations.
events: List of dataframes holding the event statistics for each threshold.
cwas: Dataframe holding the concentrations.
windows: Matrix of maximum time weighted average concentrations (TWAC_max) + +
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

- -

TOXSWA_cwa$new()
TOXSWA_cwa$moving_windows()
TOXSWA_cwa$get_events()
TOXSWA_cwa$print()
TOXSWA_cwa$clone()

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 +

Public methods

+ +

TOXSWA_cwa$new()
TOXSWA_cwa$moving_windows()
TOXSWA_cwa$get_events()
TOXSWA_cwa$print()
TOXSWA_cwa$clone()

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()`

+ +

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)

+field of the object.

Usage

TOXSWA_cwa$get_events(thresholds, total = FALSE)

+ +

Arguments

thresholds: Threshold values in µg/L.
thresholds: Threshold values in µg/L.
total: If TRUE, the total concentration including the amount adsorbed to +
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)
#> <TOXSWA_cwa> data from file 00003s_pa.cwa segment 20 
-#>                datetime     t t_firstjan t_rel_to_max cwa_mug_per_L
-#> 20  1978-10-01 00:00:00 0.000   273.0000      -55.333             0
-#> 40  1978-10-01 01:00:00 0.042   273.0417      -55.291             0
-#> 60  1978-10-01 02:00:00 0.083   273.0833      -55.250             0
-#> 80  1978-10-01 03:00:00 0.125   273.1250      -55.208             0
-#> 100 1978-10-01 04:00:00 0.167   273.1667      -55.166             0
-#> 120 1978-10-01 05:00:00 0.208   273.2083      -55.125             0
-#>     cwa_tot_mug_per_L
-#> 20                  0
-#> 40                  0
-#> 60                  0
-#> 80                  0
-#> 100                 0
-#> 120                 0
-#> Moving window analysis
-#>    window  max_TWAC max_AUC_h max_AUC_d
-#> 1  7 days 2.3926551  401.9660  16.74859
-#> 2 21 days 0.8369248  421.8101  17.57542
-#> Event statistics for threshold 2 
-#>   t_start   cwa_max duration pre_interval     AUC_h     AUC_d
-#> 1  44.375  4.167238    0.208       44.375  17.77202  0.740501
-#> 2  55.042 40.584010    0.583       10.459 398.21189 16.592162
-#> Event statistics for threshold 10 
-#>   t_start  cwa_max duration pre_interval   AUC_h    AUC_d
-#> 1  55.083 40.58401    0.459       55.083 379.433 15.80971

+ +

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)
+#> <TOXSWA_cwa> data from file 00003s_pa.cwa segment 20 
+#>                datetime     t t_firstjan t_rel_to_max cwa_mug_per_L
+#> 20  1978-10-01 00:00:00 0.000   273.0000      -55.333             0
+#> 40  1978-10-01 01:00:00 0.042   273.0417      -55.291             0
+#> 60  1978-10-01 02:00:00 0.083   273.0833      -55.250             0
+#> 80  1978-10-01 03:00:00 0.125   273.1250      -55.208             0
+#> 100 1978-10-01 04:00:00 0.167   273.1667      -55.166             0
+#> 120 1978-10-01 05:00:00 0.208   273.2083      -55.125             0
+#>     cwa_tot_mug_per_L
+#> 20                  0
+#> 40                  0
+#> 60                  0
+#> 80                  0
+#> 100                 0
+#> 120                 0
+#> Moving window analysis
+#>    window  max_TWAC max_AUC_h max_AUC_d
+#> 1  7 days 2.3926551  401.9660  16.74859
+#> 2 21 days 0.8369248  421.8101  17.57542
+#> Event statistics for threshold 2 
+#>   t_start   cwa_max duration pre_interval     AUC_h     AUC_d
+#> 1  44.375  4.167238    0.208       44.375  17.77202  0.740501
+#> 2  55.042 40.584010    0.583       10.459 398.21189 16.592162
+#> Event statistics for threshold 10 
+#>   t_start  cwa_max duration pre_interval   AUC_h    AUC_d
+#> 1  55.083 40.58401    0.459       55.083 379.433 15.80971
+

- - - + + diff --git a/docs/reference/index.html b/docs/reference/index.html index fed30cc..e5af0b7 100644 --- a/docs/reference/index.html +++ b/docs/reference/index.html @@ -1,66 +1,12 @@ - - - - - - - -Function reference • pfm - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Function reference • pfm - - - - + + -

- -

- - - - - - - - - - -

General utility functions

Functions that are independent of specific fate modelling areas

+ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

+ General utility functions + Functions that are independent of specific fate modelling areas
+
`geomean()`	Calculate the geometric mean
+
`one_box()`	Create a time series of decline data
+
`plot(<one_box>)`	Plot time series of decline data
+
`sawtooth()`	Create decline time series for multiple applications
+
`twa()`	Calculate a time weighted average concentration
+
`max_twa()`	The maximum time weighted average concentration for a moving window
+
`pfm_degradation()`	Calculate a time course of relative concentrations based on an mkinmod model
+
`SFO_actual_twa()`	Actual and maximum moving window time average concentrations for SFO kinetics
+
`FOMC_actual_twa()`	Actual and maximum moving window time average concentrations for FOMC kinetics
- `set_nd_nq()` `set_nd_nq_focus()` -	Set non-detects and unquantified values in residue series without replicates
+
+ `reexports` `set_nd_nq` `set_nd_nq_focus` +	Objects exported from other packages
`TSCF()`	Estimation of the transpiration stream concentration factor
- Predicted environmental concentrations in soil +
+ Predicted environmental concentrations in soil
+
`PEC_soil()`	Calculate predicted environmental concentrations in soil
+
`PEC_soil_mets()`	Calculate initial and accumulation PEC soil for a set of metabolites
+
`soil_scenario_data_EFSA_2015`	Properties of the predefined scenarios from the EFSA guidance from 2015
+
`soil_scenario_data_EFSA_2017`	Properties of the predefined scenarios from the EFSA guidance from 2017
+
`PEC_FOMC_accu_rel()`	Get the relative accumulation of an FOMC model over multiples of an interval
+
`EFSA_washoff_2017`	Subset of EFSA crop washoff default values
- Predicted environmental concentrations in groundwater +
+ Predicted environmental concentrations in groundwater
+
`FOCUS_GW_scenarios_2012`	A very small subset of the FOCUS Groundwater scenario definitions
+
`EFSA_GW_interception_2014`	Subset of EFSA crop interception default values for groundwater modelling
- Predicted environmental concentrations in surface water +
+ Predicted environmental concentrations in surface water
+
`PEC_sw_drift()`	Calculate predicted environmental concentrations in surface water due to drift
+
`drift_data_JKI`	Deposition from spray drift expressed as percent of the applied dose as published by the JKI
+
`PEC_sw_drainage_UK()`	Calculate initial predicted environmental concentrations in surface water due to drainage using the UK method
+
`PEC_sw_sed()`	Calculate predicted environmental concentrations in sediment from surface water concentrations
+
`PEC_sw_focus()`	Calculate PEC surface water at FOCUS Step 1
+
`chent_focus_sw()`	Create a chemical compound object for FOCUS Step 1 calculations
+
`FOCUS_Step_12_scenarios`	Step 1/2 scenario data as distributed with the FOCUS Step 1/2 calculator
+
`PEC_sw_exposit_drainage()`	Calculate PEC surface water due to drainage as in Exposit 3
+
`PEC_sw_exposit_runoff()`	Calculate PEC surface water due to runoff and erosion as in Exposit 3
+
`perc_runoff_exposit`	Runoff loss percentages as used in Exposit 3
+
`perc_runoff_reduction_exposit`	Runoff reduction percentages as used in Exposit
+
`TOXSWA_cwa`	R6 class for holding TOXSWA water concentration data and associated statistics
+
`read.TOXSWA_cwa()`	Read TOXSWA surface water concentrations
+
`plot(<TOXSWA_cwa>)`	Plot TOXSWA surface water concentrations
- Classifications and indicators - Evaluating environmental fate properties +
+ Classifications and indicators + Evaluating environmental fate properties
+
`SSLRC_mobility_classification()`	Determine the SSLRC mobility classification for a chemical substance from its Koc
+
`GUS()` `print(<GUS_result>)`	Groundwater ubiquity score based on Gustafson (1989)
- Work with chent objects containing relevant information +
+ Work with chent objects containing relevant information
+
`endpoint()` `soil_DT50()` `soil_Kfoc()` `soil_N()` `soil_sorption()`	Retrieve endpoint information from the chyaml field of a chent object
- Utilities +
+ Utilities
+
`get_vertex()`	Fit a parabola through three points

- +

- - + + diff --git a/docs/reference/reexports.html b/docs/reference/reexports.html new file mode 100644 index 0000000..2f410e4 --- /dev/null +++ b/docs/reference/reexports.html @@ -0,0 +1,83 @@ + +Objects exported from other packages — reexports • pfm + + +

+ + + +

+ + +

These objects are imported from other packages. Follow the links +below to see their documentation.

mkin: set_nd_nq, set_nd_nq_focus

+ + + +

+ +

+ + +

+ + + + + + + + -- cgit v1.2.1 From d66bd4aa0bf9c4d9b8793a4e308c9e80691b440f Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Sun, 12 Nov 2023 21:40:01 +0100 Subject: Enable links to source, upgrade to bootstrap 5 --- docs/reference/EFSA_GW_interception_2014.html | 247 +++----- docs/reference/EFSA_washoff_2017.html | 247 +++----- docs/reference/FOCUS_GW_scenarios_2012.html | 301 ++++------ docs/reference/FOCUS_Step_12_scenarios.html | 507 +++++++---------- docs/reference/FOMC_actual_twa.html | 240 +++----- docs/reference/GUS.html | 341 +++++------ docs/reference/PEC_FOMC_accu_rel.html | 199 +++---- docs/reference/PEC_soil.html | 114 ++-- docs/reference/PEC_soil_mets.html | 207 +++---- docs/reference/PEC_sw_drainage_UK.html | 263 ++++----- docs/reference/PEC_sw_drift.html | 309 +++++----- docs/reference/PEC_sw_exposit_drainage.html | 345 +++++------ docs/reference/PEC_sw_exposit_runoff.html | 382 ++++++------- docs/reference/PEC_sw_focus.html | 104 ++-- docs/reference/PEC_sw_sed.html | 263 ++++----- docs/reference/Rplot002.png | Bin 0 -> 16671 bytes docs/reference/Rplot003.png | Bin 0 -> 16671 bytes docs/reference/Rplot004.png | Bin 0 -> 12613 bytes docs/reference/Rplot005.png | Bin 0 -> 7342 bytes docs/reference/SFO_actual_twa.html | 224 +++----- docs/reference/SSLRC_mobility_classification.html | 238 +++----- docs/reference/TOXSWA_cwa.html | 125 ++-- docs/reference/TSCF-1.png | Bin 24506 -> 27659 bytes docs/reference/TSCF.html | 213 +++---- docs/reference/chent_focus_sw.html | 265 ++++----- docs/reference/drift_data_JKI.html | 659 ++++++++++------------ docs/reference/endpoint.html | 342 +++++------ docs/reference/geomean.html | 213 +++---- docs/reference/get_vertex.html | 186 ++---- docs/reference/index.html | 517 ++++++++++------- docs/reference/max_twa.html | 267 ++++----- docs/reference/one_box-1.png | Bin 15801 -> 25088 bytes docs/reference/one_box-2.png | Bin 15228 -> 22307 bytes docs/reference/one_box-3.png | Bin 30063 -> 34421 bytes docs/reference/one_box.html | 282 ++++----- docs/reference/perc_runoff_exposit.html | 218 +++---- docs/reference/perc_runoff_reduction_exposit.html | 242 +++----- docs/reference/pfm_degradation.html | 240 +++----- docs/reference/plot.TOXSWA_cwa-1.png | Bin 21821 -> 34847 bytes docs/reference/plot.TOXSWA_cwa-2.png | Bin 21005 -> 33612 bytes docs/reference/plot.TOXSWA_cwa-3.png | Bin 22278 -> 35634 bytes docs/reference/plot.TOXSWA_cwa-4.png | Bin 22682 -> 36081 bytes docs/reference/plot.TOXSWA_cwa-5.png | Bin 16209 -> 23459 bytes docs/reference/plot.TOXSWA_cwa.html | 299 ++++------ docs/reference/plot.one_box-1.png | Bin 16135 -> 24674 bytes docs/reference/plot.one_box-2.png | Bin 32593 -> 30451 bytes docs/reference/plot.one_box-3.png | Bin 37285 -> 42397 bytes docs/reference/plot.one_box.html | 274 ++++----- docs/reference/read.TOXSWA_cwa.html | 302 ++++------ docs/reference/reexports.html | 85 +-- docs/reference/sawtooth-1.png | Bin 16202 -> 24812 bytes docs/reference/sawtooth-2.png | Bin 42340 -> 42397 bytes docs/reference/sawtooth.html | 271 ++++----- docs/reference/soil_scenario_data_EFSA_2015.html | 245 +++----- docs/reference/soil_scenario_data_EFSA_2017.html | 205 +++---- docs/reference/twa.html | 235 +++----- 56 files changed, 4062 insertions(+), 6154 deletions(-) create mode 100644 docs/reference/Rplot002.png create mode 100644 docs/reference/Rplot003.png create mode 100644 docs/reference/Rplot004.png create mode 100644 docs/reference/Rplot005.png (limited to 'docs/reference') diff --git a/docs/reference/EFSA_GW_interception_2014.html b/docs/reference/EFSA_GW_interception_2014.html index d7ec108..63e1730 100644 --- a/docs/reference/EFSA_GW_interception_2014.html +++ b/docs/reference/EFSA_GW_interception_2014.html @@ -1,193 +1,124 @@ - - - - - - - -Subset of EFSA crop interception default values for groundwater modelling — EFSA_GW_interception_2014 • pfm - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +Subset of EFSA crop interception default values for groundwater modelling — EFSA_GW_interception_2014 • pfm + + +

+ + + 0.6.0 - - - + + - - - - - - - -

- -

- +

Subset of EFSA crop interception default values for groundwater modelling

- -

Format

- +

Format

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

Source

- +

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

if (FALSE) {
-  # This is the code that was used to define the data
-  bbch <- paste0(0:9, "x")
-  crops <- c(
-    "Beans (field + vegetable)",
-    "Peas",
-    "Summer oilseed rape", "Winter oilseed rape",
-    "Tomatoes",
-    "Spring cereals", "Winter cereals")
-  EFSA_GW_interception_2014 <- matrix(NA, length(crops), length(bbch),
-    dimnames = list(Crop = crops, BBCH = bbch))
-  EFSA_GW_interception_2014["Beans (field + vegetable)", ] <-
-    c(0, 0.25, rep(0.4, 2), rep(0.7, 5), 0.8)
-  EFSA_GW_interception_2014["Peas", ] <-
-    c(0, 0.35, rep(0.55, 2), rep(0.85, 5), 0.85)
-  EFSA_GW_interception_2014["Summer oilseed rape", ] <-
-    c(0, 0.4, rep(0.8, 2), rep(0.8, 5), 0.9)
-  EFSA_GW_interception_2014["Winter oilseed rape", ] <-
-    c(0, 0.4, rep(0.8, 2), rep(0.8, 5), 0.9)
-  EFSA_GW_interception_2014["Tomatoes", ] <-
-    c(0, 0.5, rep(0.7, 2), rep(0.8, 5), 0.5)
-  EFSA_GW_interception_2014["Spring cereals", ] <-
-    c(0, 0, 0.2, 0.8, rep(0.9, 3), rep(0.8, 2), 0.8)
-  EFSA_GW_interception_2014["Winter cereals", ] <-
-    c(0, 0, 0.2, 0.8, rep(0.9, 3), rep(0.8, 2), 0.8)
-  save(EFSA_GW_interception_2014,
-    file = "../data/EFSA_GW_interception_2014.RData")
-}
-EFSA_GW_interception_2014
#>                            BBCH
-#> Crop                        0x   1x   2x   3x   4x   5x   6x   7x   8x   9x
-#>   Beans (field + vegetable)  0 0.25 0.40 0.40 0.70 0.70 0.70 0.70 0.70 0.80
-#>   Peas                       0 0.35 0.55 0.55 0.85 0.85 0.85 0.85 0.85 0.85
-#>   Summer oilseed rape        0 0.40 0.80 0.80 0.80 0.80 0.80 0.80 0.80 0.90
-#>   Winter oilseed rape        0 0.40 0.80 0.80 0.80 0.80 0.80 0.80 0.80 0.90
-#>   Tomatoes                   0 0.50 0.70 0.70 0.80 0.80 0.80 0.80 0.80 0.50
-#>   Spring cereals             0 0.00 0.20 0.80 0.90 0.90 0.90 0.80 0.80 0.80
-#>   Winter cereals             0 0.00 0.20 0.80 0.90 0.90 0.90 0.80 0.80 0.80

- -

Examples

if (FALSE) {
+  # This is the code that was used to define the data
+  bbch <- paste0(0:9, "x")
+  crops <- c(
+    "Beans (field + vegetable)",
+    "Peas",
+    "Summer oilseed rape", "Winter oilseed rape",
+    "Tomatoes",
+    "Spring cereals", "Winter cereals")
+  EFSA_GW_interception_2014 <- matrix(NA, length(crops), length(bbch),
+    dimnames = list(Crop = crops, BBCH = bbch))
+  EFSA_GW_interception_2014["Beans (field + vegetable)", ] <-
+    c(0, 0.25, rep(0.4, 2), rep(0.7, 5), 0.8)
+  EFSA_GW_interception_2014["Peas", ] <-
+    c(0, 0.35, rep(0.55, 2), rep(0.85, 5), 0.85)
+  EFSA_GW_interception_2014["Summer oilseed rape", ] <-
+    c(0, 0.4, rep(0.8, 2), rep(0.8, 5), 0.9)
+  EFSA_GW_interception_2014["Winter oilseed rape", ] <-
+    c(0, 0.4, rep(0.8, 2), rep(0.8, 5), 0.9)
+  EFSA_GW_interception_2014["Tomatoes", ] <-
+    c(0, 0.5, rep(0.7, 2), rep(0.8, 5), 0.5)
+  EFSA_GW_interception_2014["Spring cereals", ] <-
+    c(0, 0, 0.2, 0.8, rep(0.9, 3), rep(0.8, 2), 0.8)
+  EFSA_GW_interception_2014["Winter cereals", ] <-
+    c(0, 0, 0.2, 0.8, rep(0.9, 3), rep(0.8, 2), 0.8)
+  save(EFSA_GW_interception_2014,
+    file = "../data/EFSA_GW_interception_2014.RData")
+}
+EFSA_GW_interception_2014
+#>                            BBCH
+#> Crop                        0x   1x   2x   3x   4x   5x   6x   7x   8x   9x
+#>   Beans (field + vegetable)  0 0.25 0.40 0.40 0.70 0.70 0.70 0.70 0.70 0.80
+#>   Peas                       0 0.35 0.55 0.55 0.85 0.85 0.85 0.85 0.85 0.85
+#>   Summer oilseed rape        0 0.40 0.80 0.80 0.80 0.80 0.80 0.80 0.80 0.90
+#>   Winter oilseed rape        0 0.40 0.80 0.80 0.80 0.80 0.80 0.80 0.80 0.90
+#>   Tomatoes                   0 0.50 0.70 0.70 0.80 0.80 0.80 0.80 0.80 0.50
+#>   Spring cereals             0 0.00 0.20 0.80 0.90 0.90 0.90 0.80 0.80 0.80
+#>   Winter cereals             0 0.00 0.20 0.80 0.90 0.90 0.90 0.80 0.80 0.80
+

- + - - - + diff --git a/docs/reference/EFSA_washoff_2017.html b/docs/reference/EFSA_washoff_2017.html index abe0b24..1e292af 100644 --- a/docs/reference/EFSA_washoff_2017.html +++ b/docs/reference/EFSA_washoff_2017.html @@ -1,193 +1,124 @@ - - - - - - - -Subset of EFSA crop washoff default values — EFSA_washoff_2017 • pfm - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +Subset of EFSA crop washoff default values — EFSA_washoff_2017 • pfm + + +

+ + + 0.6.0 - - - + + - - - - - - - -

- -

- +

Subset of EFSA crop washoff default values

- -

Format

- +

Format

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

Source

- +

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

if (FALSE) {
-  # This is the code that was used to define the data
-  bbch <- paste0(0:9, "x")
-  crops <- c(
-    "Beans (field + vegetable)",
-    "Peas",
-    "Summer oilseed rape", "Winter oilseed rape",
-    "Tomatoes",
-    "Spring cereals", "Winter cereals")
-  EFSA_washoff_2017 <- matrix(NA, length(crops), length(bbch),
-    dimnames = list(Crop = crops, BBCH = bbch))
-  EFSA_washoff_2017["Beans (field + vegetable)", ] <-
-    c(NA, 0.6, rep(0.75, 2), rep(0.8, 5), 0.35)
-  EFSA_washoff_2017["Peas", ] <-
-    c(NA, 0.4, rep(0.6, 2), rep(0.65, 5), 0.35)
-  EFSA_washoff_2017["Summer oilseed rape", ] <-
-    c(NA, 0.4, rep(0.5, 2), rep(0.6, 5), 0.5)
-  EFSA_washoff_2017["Winter oilseed rape", ] <-
-    c(NA, 0.1, rep(0.4, 2), rep(0.55, 5), 0.3)
-  EFSA_washoff_2017["Tomatoes", ] <-
-    c(NA, 0.55, rep(0.75, 2), rep(0.7, 5), 0.35)
-  EFSA_washoff_2017["Spring cereals", ] <-
-    c(NA, 0.4, 0.5, 0.5, rep(0.65, 3), rep(0.65, 2), 0.55)
-  EFSA_washoff_2017["Winter cereals", ] <-
-    c(NA, 0.1, 0.4, 0.6, rep(0.55, 3), rep(0.6, 2), 0.4)
-  save(EFSA_washoff_2017,
-    file = "../data/EFSA_washoff_2017.RData")
-}
-EFSA_washoff_2017
#>                            BBCH
-#> Crop                        0x   1x   2x   3x   4x   5x   6x   7x   8x   9x
-#>   Beans (field + vegetable) NA 0.60 0.75 0.75 0.80 0.80 0.80 0.80 0.80 0.35
-#>   Peas                      NA 0.40 0.60 0.60 0.65 0.65 0.65 0.65 0.65 0.35
-#>   Summer oilseed rape       NA 0.40 0.50 0.50 0.60 0.60 0.60 0.60 0.60 0.50
-#>   Winter oilseed rape       NA 0.10 0.40 0.40 0.55 0.55 0.55 0.55 0.55 0.30
-#>   Tomatoes                  NA 0.55 0.75 0.75 0.70 0.70 0.70 0.70 0.70 0.35
-#>   Spring cereals            NA 0.40 0.50 0.50 0.65 0.65 0.65 0.65 0.65 0.55
-#>   Winter cereals            NA 0.10 0.40 0.60 0.55 0.55 0.55 0.60 0.60 0.40

- -

Examples

if (FALSE) {
+  # This is the code that was used to define the data
+  bbch <- paste0(0:9, "x")
+  crops <- c(
+    "Beans (field + vegetable)",
+    "Peas",
+    "Summer oilseed rape", "Winter oilseed rape",
+    "Tomatoes",
+    "Spring cereals", "Winter cereals")
+  EFSA_washoff_2017 <- matrix(NA, length(crops), length(bbch),
+    dimnames = list(Crop = crops, BBCH = bbch))
+  EFSA_washoff_2017["Beans (field + vegetable)", ] <-
+    c(NA, 0.6, rep(0.75, 2), rep(0.8, 5), 0.35)
+  EFSA_washoff_2017["Peas", ] <-
+    c(NA, 0.4, rep(0.6, 2), rep(0.65, 5), 0.35)
+  EFSA_washoff_2017["Summer oilseed rape", ] <-
+    c(NA, 0.4, rep(0.5, 2), rep(0.6, 5), 0.5)
+  EFSA_washoff_2017["Winter oilseed rape", ] <-
+    c(NA, 0.1, rep(0.4, 2), rep(0.55, 5), 0.3)
+  EFSA_washoff_2017["Tomatoes", ] <-
+    c(NA, 0.55, rep(0.75, 2), rep(0.7, 5), 0.35)
+  EFSA_washoff_2017["Spring cereals", ] <-
+    c(NA, 0.4, 0.5, 0.5, rep(0.65, 3), rep(0.65, 2), 0.55)
+  EFSA_washoff_2017["Winter cereals", ] <-
+    c(NA, 0.1, 0.4, 0.6, rep(0.55, 3), rep(0.6, 2), 0.4)
+  save(EFSA_washoff_2017,
+    file = "../data/EFSA_washoff_2017.RData")
+}
+EFSA_washoff_2017
+#>                            BBCH
+#> Crop                        0x   1x   2x   3x   4x   5x   6x   7x   8x   9x
+#>   Beans (field + vegetable) NA 0.60 0.75 0.75 0.80 0.80 0.80 0.80 0.80 0.35
+#>   Peas                      NA 0.40 0.60 0.60 0.65 0.65 0.65 0.65 0.65 0.35
+#>   Summer oilseed rape       NA 0.40 0.50 0.50 0.60 0.60 0.60 0.60 0.60 0.50
+#>   Winter oilseed rape       NA 0.10 0.40 0.40 0.55 0.55 0.55 0.55 0.55 0.30
+#>   Tomatoes                  NA 0.55 0.75 0.75 0.70 0.70 0.70 0.70 0.70 0.35
+#>   Spring cereals            NA 0.40 0.50 0.50 0.65 0.65 0.65 0.65 0.65 0.55
+#>   Winter cereals            NA 0.10 0.40 0.60 0.55 0.55 0.55 0.60 0.60 0.40
+

- + - - - + diff --git a/docs/reference/FOCUS_GW_scenarios_2012.html b/docs/reference/FOCUS_GW_scenarios_2012.html index 8b55d14..84c2fac 100644 --- a/docs/reference/FOCUS_GW_scenarios_2012.html +++ b/docs/reference/FOCUS_GW_scenarios_2012.html @@ -1,217 +1,152 @@ - - - - - - - -A very small subset of the FOCUS Groundwater scenario definitions — FOCUS_GW_scenarios_2012 • pfm - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +A very small subset of the FOCUS Groundwater scenario definitions — FOCUS_GW_scenarios_2012 • pfm + + +

+ + + 0.6.0 - - - + + - - - - - - - -

- +

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).

FOCUS_GW_scenarios_2012

- - -

Format

Usage

FOCUS_GW_scenarios_2012

Format

An object of class list of length 2.

References

- +

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
#> $names
-#>            Cha            Ham            Jok            Kre            Oke 
-#>   "Châteaudun"      "Hamburg"    "Jokioinen" "Kremsmünster"   "Okehampton" 
-#>            Pia            Por            Sev            Thi 
-#>     "Piacenza"        "Porto"      "Sevilla"        "Thiva" 
-#> 
-#> $soils
-#>    location horizon number pH_H2O perc_clay perc_oc rel_deg
-#> 1       Cha      Ap      1    8.0      30.0    1.39     1.0
-#> 2       Cha      B1      2    8.1      31.0    0.93     0.5
-#> 3       Cha      B2      3    8.2      25.0    0.70     0.5
-#> 4       Cha   II C1      4    8.5      26.0    0.30     0.3
-#> 5       Cha   II C1      5    8.5      26.0    0.30     0.0
-#> 6       Cha   II C2      6    8.5      24.0    0.27     0.0
-#> 7       Cha       M      7    8.3      31.0    0.21     0.0
-#> 8       Ham      Ap      1    6.4       7.2    1.50     1.0
-#> 9       Ham     BvI      2    5.6       6.7    1.00     0.5
-#> 10      Ham    BvII      3    5.6       0.9    0.20     0.3
-#> 11      Ham   Bv/Cv      4    5.7       0.0    0.00     0.3
-#> 12      Ham      Cv      5    5.5       0.0    0.00     0.3
-#> 13      Ham      Cv      6    5.5       0.0    0.00     0.0
-#> 14      Jok      Ap      1    6.2       3.6    4.06     1.0
-#> 15      Jok      Bs      2    5.6       1.8    0.84     0.5
-#> 16      Jok     BC1      3    5.4       1.2    0.36     0.3
-#> 17      Jok     BC2      4    5.4       1.7    0.29     0.3
-#> 18      Jok     BC2      5    5.4       1.7    0.29     0.0
-#> 19      Jok      Cg      6    5.3       1.9    0.21     0.0
-#> 20      Kre    <NA>      1    7.7      14.0    3.60     1.0
-#> 21      Kre    <NA>      2    7.0      25.0    1.00     0.5
-#> 22      Kre    <NA>      3    7.1      27.0    0.50     0.5
-#> 23      Kre    <NA>      4    7.1      27.0    0.50     0.3
-#> 24      Kre    <NA>      5    7.1      27.0    0.50     0.0
-#> 25      Oke       A      1    5.8      18.0    2.20     1.0
-#> 26      Oke     Bw1      2    6.3      17.0    0.70     0.5
-#> 27      Oke      BC      3    6.5      14.0    0.40     0.3
-#> 28      Oke       C      4    6.6       9.0    0.10     0.3
-#> 29      Oke       C      5    6.6       9.0    0.10     0.0
-#> 30      Pia      Ap      1    7.0      15.0    1.26     1.0
-#> 31      Pia      Ap      2    7.0      15.0    1.26     0.5
-#> 32      Pia      Bw      3    6.3       7.0    0.47     0.5
-#> 33      Pia      Bw      4    6.3       7.0    0.47     0.3
-#> 34      Pia      2C      5    6.4       0.0    0.00     0.3
-#> 35      Pia      2C      6    6.4       0.0    0.00     0.0
-#> 36      Por    <NA>      1    4.9      10.0    1.42     1.0
-#> 37      Por    <NA>      2    4.8       8.0    0.78     0.5
-#> 38      Por    <NA>      3    4.8       8.0    0.78     0.3
-#> 39      Por    <NA>      4    4.8       8.0    0.78     0.0
-#> 40      Sev    <NA>      1    7.3      14.0    0.93     1.0
-#> 41      Sev    <NA>      2    7.3      13.0    0.93     1.0
-#> 42      Sev    <NA>      3    7.8      15.0    0.70     0.5
-#> 43      Sev    <NA>      4    8.1      16.0    0.58     0.3
-#> 44      Sev    <NA>      5    8.1      16.0    0.58     0.0
-#> 45      Sev    <NA>      6    8.2      22.0    0.49     0.0
-#> 46      Thi     Ap1      1    7.7      25.3    0.74     1.0
-#> 47      Thi     Ap2      2    7.7      25.3    0.74     0.5
-#> 48      Thi      Bw      3    7.8      29.6    0.57     0.5
-#> 49      Thi      Bw      4    7.8      31.9    0.31     0.3
-#> 50      Thi     Ck1      5    7.8      32.9    0.18     0.3
-#> 51      Thi     Ck1      6    7.8      32.9    0.18     0.0
-#>

- -

Examples

FOCUS_GW_scenarios_2012
+#> $names
+#>            Cha            Ham            Jok            Kre            Oke 
+#>   "Châteaudun"      "Hamburg"    "Jokioinen" "Kremsmünster"   "Okehampton" 
+#>            Pia            Por            Sev            Thi 
+#>     "Piacenza"        "Porto"      "Sevilla"        "Thiva" 
+#> 
+#> $soils
+#>    location horizon number pH_H2O perc_clay perc_oc rel_deg
+#> 1       Cha      Ap      1    8.0      30.0    1.39     1.0
+#> 2       Cha      B1      2    8.1      31.0    0.93     0.5
+#> 3       Cha      B2      3    8.2      25.0    0.70     0.5
+#> 4       Cha   II C1      4    8.5      26.0    0.30     0.3
+#> 5       Cha   II C1      5    8.5      26.0    0.30     0.0
+#> 6       Cha   II C2      6    8.5      24.0    0.27     0.0
+#> 7       Cha       M      7    8.3      31.0    0.21     0.0
+#> 8       Ham      Ap      1    6.4       7.2    1.50     1.0
+#> 9       Ham     BvI      2    5.6       6.7    1.00     0.5
+#> 10      Ham    BvII      3    5.6       0.9    0.20     0.3
+#> 11      Ham   Bv/Cv      4    5.7       0.0    0.00     0.3
+#> 12      Ham      Cv      5    5.5       0.0    0.00     0.3
+#> 13      Ham      Cv      6    5.5       0.0    0.00     0.0
+#> 14      Jok      Ap      1    6.2       3.6    4.06     1.0
+#> 15      Jok      Bs      2    5.6       1.8    0.84     0.5
+#> 16      Jok     BC1      3    5.4       1.2    0.36     0.3
+#> 17      Jok     BC2      4    5.4       1.7    0.29     0.3
+#> 18      Jok     BC2      5    5.4       1.7    0.29     0.0
+#> 19      Jok      Cg      6    5.3       1.9    0.21     0.0
+#> 20      Kre    <NA>      1    7.7      14.0    3.60     1.0
+#> 21      Kre    <NA>      2    7.0      25.0    1.00     0.5
+#> 22      Kre    <NA>      3    7.1      27.0    0.50     0.5
+#> 23      Kre    <NA>      4    7.1      27.0    0.50     0.3
+#> 24      Kre    <NA>      5    7.1      27.0    0.50     0.0
+#> 25      Oke       A      1    5.8      18.0    2.20     1.0
+#> 26      Oke     Bw1      2    6.3      17.0    0.70     0.5
+#> 27      Oke      BC      3    6.5      14.0    0.40     0.3
+#> 28      Oke       C      4    6.6       9.0    0.10     0.3
+#> 29      Oke       C      5    6.6       9.0    0.10     0.0
+#> 30      Pia      Ap      1    7.0      15.0    1.26     1.0
+#> 31      Pia      Ap      2    7.0      15.0    1.26     0.5
+#> 32      Pia      Bw      3    6.3       7.0    0.47     0.5
+#> 33      Pia      Bw      4    6.3       7.0    0.47     0.3
+#> 34      Pia      2C      5    6.4       0.0    0.00     0.3
+#> 35      Pia      2C      6    6.4       0.0    0.00     0.0
+#> 36      Por    <NA>      1    4.9      10.0    1.42     1.0
+#> 37      Por    <NA>      2    4.8       8.0    0.78     0.5
+#> 38      Por    <NA>      3    4.8       8.0    0.78     0.3
+#> 39      Por    <NA>      4    4.8       8.0    0.78     0.0
+#> 40      Sev    <NA>      1    7.3      14.0    0.93     1.0
+#> 41      Sev    <NA>      2    7.3      13.0    0.93     1.0
+#> 42      Sev    <NA>      3    7.8      15.0    0.70     0.5
+#> 43      Sev    <NA>      4    8.1      16.0    0.58     0.3
+#> 44      Sev    <NA>      5    8.1      16.0    0.58     0.0
+#> 45      Sev    <NA>      6    8.2      22.0    0.49     0.0
+#> 46      Thi     Ap1      1    7.7      25.3    0.74     1.0
+#> 47      Thi     Ap2      2    7.7      25.3    0.74     0.5
+#> 48      Thi      Bw      3    7.8      29.6    0.57     0.5
+#> 49      Thi      Bw      4    7.8      31.9    0.31     0.3
+#> 50      Thi     Ck1      5    7.8      32.9    0.18     0.3
+#> 51      Thi     Ck1      6    7.8      32.9    0.18     0.0
+#> 
+

- + - - - + diff --git a/docs/reference/FOCUS_Step_12_scenarios.html b/docs/reference/FOCUS_Step_12_scenarios.html index bbfb592..dc1e4c3 100644 --- a/docs/reference/FOCUS_Step_12_scenarios.html +++ b/docs/reference/FOCUS_Step_12_scenarios.html @@ -1,324 +1,255 @@ - - - - - - - -Step 1/2 scenario data as distributed with the FOCUS Step 1/2 calculator — FOCUS_Step_12_scenarios • pfm - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Step 1/2 scenario data as distributed with the FOCUS Step 1/2 calculator — FOCUS_Step_12_scenarios • pfm + + + +

+ + + 0.6.0 - + + - -

- - - -

- -

- +

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

- +

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


-if (FALSE) {
-  # 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")
-}
-
-# And this is the resulting data
-FOCUS_Step_12_scenarios
#> $names
-#>  [1] "cereals, spring"                  "cereals, winter"                 
-#>  [3] "citrus"                           "cotton"                          
-#>  [5] "field beans"                      "grass / alfalfa"                 
-#>  [7] "hops"                             "legumes"                         
-#>  [9] "maize"                            "oil seed rape, spring"           
-#> [11] "oil seed rape, winter"            "olives"                          
-#> [13] "pome / stone fruit, early applns" "pome / stone fruit, late applns" 
-#> [15] "potatoes"                         "soybeans"                        
-#> [17] "sugar beets"                      "sunflowers"                      
-#> [19] "tobacco"                          "vegetables, bulb"                
-#> [21] "vegetables, fruiting"             "vegetables, leafy"               
-#> [23] "vegetables, root"                 "vines, early applns"             
-#> [25] "vines, late applns"               "appln, aerial"                   
-#> [27] "appln, hand (crop < 50 cm)"       "appln, hand (crop > 50 cm)"      
-#> [29] "no drift (incorp or seed trtmt)" 
-#> 
-#> $drift
-#>                                       1      2      3      4      5      6
-#> cereals, spring                   2.759  2.438  2.024  1.862  1.794  1.631
-#> cereals, winter                   2.759  2.438  2.024  1.862  1.794  1.631
-#> citrus                           15.725 12.129 11.011 10.124  9.743  9.204
-#> cotton                            2.759  2.438  2.024  1.862  1.794  1.631
-#> field beans                       2.759  2.438  2.024  1.862  1.794  1.631
-#> grass / alfalfa                   2.759  2.438  2.024  1.862  1.794  1.631
-#> hops                             19.326 17.723 15.928 15.378 15.114 14.902
-#> legumes                           2.759  2.438  2.024  1.862  1.794  1.631
-#> maize                             2.759  2.438  2.024  1.862  1.794  1.631
-#> oil seed rape, spring             2.759  2.438  2.024  1.862  1.794  1.631
-#> oil seed rape, winter             2.759  2.438  2.024  1.862  1.794  1.631
-#> olives                           15.725 12.129 11.011 10.124  9.743  9.204
-#> pome / stone fruit, early applns 29.197 25.531 23.960 23.603 23.116 22.760
-#> pome / stone fruit, late applns  15.725 12.129 11.011 10.124  9.743  9.204
-#> potatoes                          2.759  2.438  2.024  1.862  1.794  1.631
-#> soybeans                          2.759  2.438  2.024  1.862  1.794  1.631
-#> sugar beets                       2.759  2.438  2.024  1.862  1.794  1.631
-#> sunflowers                        2.759  2.438  2.024  1.862  1.794  1.631
-#> tobacco                           2.759  2.438  2.024  1.862  1.794  1.631
-#> vegetables, bulb                  2.759  2.438  2.024  1.862  1.794  1.631
-#> vegetables, fruiting              2.759  2.438  2.024  1.862  1.794  1.631
-#> vegetables, leafy                 2.759  2.438  2.024  1.862  1.794  1.631
-#> vegetables, root                  2.759  2.438  2.024  1.862  1.794  1.631
-#> vines, early applns               2.699  2.496  2.546  2.499  2.398  2.336
-#> vines, late applns                8.028  7.119  6.898  6.631  6.636  6.431
-#> appln, aerial                    33.200 33.200 33.200 33.200 33.200 33.200
-#> appln, hand (crop < 50 cm)        2.759  2.438  2.024  1.862  1.794  1.631
-#> appln, hand (crop > 50 cm)        8.028  7.119  6.898  6.631  6.636  6.431
-#> no drift (incorp or seed trtmt)   0.000  0.000  0.000  0.000  0.000  0.000
-#>                                       7      8     >8
-#> cereals, spring                   1.578  1.512  1.512
-#> cereals, winter                   1.578  1.512  1.512
-#> citrus                            9.102  8.656  8.656
-#> cotton                            1.578  1.512  1.512
-#> field beans                       1.578  1.512  1.512
-#> grass / alfalfa                   1.578  1.512  1.512
-#> hops                             14.628 13.520 13.520
-#> legumes                           1.578  1.512  1.512
-#> maize                             1.578  1.512  1.512
-#> oil seed rape, spring             1.578  1.512  1.512
-#> oil seed rape, winter             1.578  1.512  1.512
-#> olives                            9.102  8.656  8.656
-#> pome / stone fruit, early applns 22.690 22.241 22.241
-#> pome / stone fruit, late applns   9.102  8.656  8.656
-#> potatoes                          1.578  1.512  1.512
-#> soybeans                          1.578  1.512  1.512
-#> sugar beets                       1.578  1.512  1.512
-#> sunflowers                        1.578  1.512  1.512
-#> tobacco                           1.578  1.512  1.512
-#> vegetables, bulb                  1.578  1.512  1.512
-#> vegetables, fruiting              1.578  1.512  1.512
-#> vegetables, leafy                 1.578  1.512  1.512
-#> vegetables, root                  1.578  1.512  1.512
-#> vines, early applns               2.283  2.265  2.265
-#> vines, late applns                6.227  6.173  6.173
-#> appln, aerial                    33.200 33.200 33.200
-#> appln, hand (crop < 50 cm)        1.578  1.512  1.512
-#> appln, hand (crop > 50 cm)        6.227  6.173  6.173
-#> no drift (incorp or seed trtmt)   0.000  0.000  0.000
-#> 
-#> $interception
-#>                                  no interception minimal crop cover
-#> cereals, spring                                0               0.00
-#> cereals, winter                                0               0.00
-#> citrus                                         0               0.80
-#> cotton                                         0               0.30
-#> field beans                                    0               0.25
-#> grass / alfalfa                                0               0.40
-#> hops                                           0               0.20
-#> legumes                                        0               0.25
-#> maize                                          0               0.25
-#> oil seed rape, spring                          0               0.40
-#> oil seed rape, winter                          0               0.40
-#> olives                                         0               0.70
-#> pome / stone fruit, early applns               0               0.20
-#> pome / stone fruit, late applns                0               0.20
-#> potatoes                                       0               0.15
-#> soybeans                                       0               0.20
-#> sugar beets                                    0               0.20
-#> sunflowers                                     0               0.20
-#> tobacco                                        0               0.20
-#> vegetables, bulb                               0               0.10
-#> vegetables, fruiting                           0               0.25
-#> vegetables, leafy                              0               0.25
-#> vegetables, root                               0               0.25
-#> vines, early applns                            0               0.40
-#> vines, late applns                             0               0.40
-#> appln, aerial                                  0               0.20
-#> appln, hand (crop < 50 cm)                     0               0.20
-#> appln, hand (crop > 50 cm)                     0               0.20
-#> no drift (incorp or seed trtmt)                0               0.00
-#>                                  average crop cover full canopy
-#> cereals, spring                                0.20        0.70
-#> cereals, winter                                0.20        0.70
-#> citrus                                         0.80        0.80
-#> cotton                                         0.60        0.75
-#> field beans                                    0.40        0.70
-#> grass / alfalfa                                0.60        0.75
-#> hops                                           0.50        0.70
-#> legumes                                        0.50        0.70
-#> maize                                          0.50        0.75
-#> oil seed rape, spring                          0.70        0.75
-#> oil seed rape, winter                          0.70        0.75
-#> olives                                         0.70        0.70
-#> pome / stone fruit, early applns               0.40        0.65
-#> pome / stone fruit, late applns                0.40        0.65
-#> potatoes                                       0.50        0.70
-#> soybeans                                       0.50        0.75
-#> sugar beets                                    0.70        0.75
-#> sunflowers                                     0.50        0.75
-#> tobacco                                        0.70        0.75
-#> vegetables, bulb                               0.25        0.40
-#> vegetables, fruiting                           0.50        0.70
-#> vegetables, leafy                              0.40        0.70
-#> vegetables, root                               0.50        0.70
-#> vines, early applns                            0.50        0.60
-#> vines, late applns                             0.50        0.60
-#> appln, aerial                                  0.50        0.70
-#> appln, hand (crop < 50 cm)                     0.50        0.70
-#> appln, hand (crop > 50 cm)                     0.50        0.70
-#> no drift (incorp or seed trtmt)                0.00        0.00
-#> 
-#> $rd
-#>          Region
-#> Time      North South
-#>   Oct-Feb     5     4
-#>   Mar-May     2     4
-#>   Jun-Sep     2     3
-#>

- -

Examples


+if (FALSE) {
+  # 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")
+}
+
+# And this is the resulting data
+FOCUS_Step_12_scenarios
+#> $names
+#>  [1] "cereals, spring"                  "cereals, winter"                 
+#>  [3] "citrus"                           "cotton"                          
+#>  [5] "field beans"                      "grass / alfalfa"                 
+#>  [7] "hops"                             "legumes"                         
+#>  [9] "maize"                            "oil seed rape, spring"           
+#> [11] "oil seed rape, winter"            "olives"                          
+#> [13] "pome / stone fruit, early applns" "pome / stone fruit, late applns" 
+#> [15] "potatoes"                         "soybeans"                        
+#> [17] "sugar beets"                      "sunflowers"                      
+#> [19] "tobacco"                          "vegetables, bulb"                
+#> [21] "vegetables, fruiting"             "vegetables, leafy"               
+#> [23] "vegetables, root"                 "vines, early applns"             
+#> [25] "vines, late applns"               "appln, aerial"                   
+#> [27] "appln, hand (crop < 50 cm)"       "appln, hand (crop > 50 cm)"      
+#> [29] "no drift (incorp or seed trtmt)" 
+#> 
+#> $drift
+#>                                       1      2      3      4      5      6
+#> cereals, spring                   2.759  2.438  2.024  1.862  1.794  1.631
+#> cereals, winter                   2.759  2.438  2.024  1.862  1.794  1.631
+#> citrus                           15.725 12.129 11.011 10.124  9.743  9.204
+#> cotton                            2.759  2.438  2.024  1.862  1.794  1.631
+#> field beans                       2.759  2.438  2.024  1.862  1.794  1.631
+#> grass / alfalfa                   2.759  2.438  2.024  1.862  1.794  1.631
+#> hops                             19.326 17.723 15.928 15.378 15.114 14.902
+#> legumes                           2.759  2.438  2.024  1.862  1.794  1.631
+#> maize                             2.759  2.438  2.024  1.862  1.794  1.631
+#> oil seed rape, spring             2.759  2.438  2.024  1.862  1.794  1.631
+#> oil seed rape, winter             2.759  2.438  2.024  1.862  1.794  1.631
+#> olives                           15.725 12.129 11.011 10.124  9.743  9.204
+#> pome / stone fruit, early applns 29.197 25.531 23.960 23.603 23.116 22.760
+#> pome / stone fruit, late applns  15.725 12.129 11.011 10.124  9.743  9.204
+#> potatoes                          2.759  2.438  2.024  1.862  1.794  1.631
+#> soybeans                          2.759  2.438  2.024  1.862  1.794  1.631
+#> sugar beets                       2.759  2.438  2.024  1.862  1.794  1.631
+#> sunflowers                        2.759  2.438  2.024  1.862  1.794  1.631
+#> tobacco                           2.759  2.438  2.024  1.862  1.794  1.631
+#> vegetables, bulb                  2.759  2.438  2.024  1.862  1.794  1.631
+#> vegetables, fruiting              2.759  2.438  2.024  1.862  1.794  1.631
+#> vegetables, leafy                 2.759  2.438  2.024  1.862  1.794  1.631
+#> vegetables, root                  2.759  2.438  2.024  1.862  1.794  1.631
+#> vines, early applns               2.699  2.496  2.546  2.499  2.398  2.336
+#> vines, late applns                8.028  7.119  6.898  6.631  6.636  6.431
+#> appln, aerial                    33.200 33.200 33.200 33.200 33.200 33.200
+#> appln, hand (crop < 50 cm)        2.759  2.438  2.024  1.862  1.794  1.631
+#> appln, hand (crop > 50 cm)        8.028  7.119  6.898  6.631  6.636  6.431
+#> no drift (incorp or seed trtmt)   0.000  0.000  0.000  0.000  0.000  0.000
+#>                                       7      8     >8
+#> cereals, spring                   1.578  1.512  1.512
+#> cereals, winter                   1.578  1.512  1.512
+#> citrus                            9.102  8.656  8.656
+#> cotton                            1.578  1.512  1.512
+#> field beans                       1.578  1.512  1.512
+#> grass / alfalfa                   1.578  1.512  1.512
+#> hops                             14.628 13.520 13.520
+#> legumes                           1.578  1.512  1.512
+#> maize                             1.578  1.512  1.512
+#> oil seed rape, spring             1.578  1.512  1.512
+#> oil seed rape, winter             1.578  1.512  1.512
+#> olives                            9.102  8.656  8.656
+#> pome / stone fruit, early applns 22.690 22.241 22.241
+#> pome / stone fruit, late applns   9.102  8.656  8.656
+#> potatoes                          1.578  1.512  1.512
+#> soybeans                          1.578  1.512  1.512
+#> sugar beets                       1.578  1.512  1.512
+#> sunflowers                        1.578  1.512  1.512
+#> tobacco                           1.578  1.512  1.512
+#> vegetables, bulb                  1.578  1.512  1.512
+#> vegetables, fruiting              1.578  1.512  1.512
+#> vegetables, leafy                 1.578  1.512  1.512
+#> vegetables, root                  1.578  1.512  1.512
+#> vines, early applns               2.283  2.265  2.265
+#> vines, late applns                6.227  6.173  6.173
+#> appln, aerial                    33.200 33.200 33.200
+#> appln, hand (crop < 50 cm)        1.578  1.512  1.512
+#> appln, hand (crop > 50 cm)        6.227  6.173  6.173
+#> no drift (incorp or seed trtmt)   0.000  0.000  0.000
+#> 
+#> $interception
+#>                                  no interception minimal crop cover
+#> cereals, spring                                0               0.00
+#> cereals, winter                                0               0.00
+#> citrus                                         0               0.80
+#> cotton                                         0               0.30
+#> field beans                                    0               0.25
+#> grass / alfalfa                                0               0.40
+#> hops                                           0               0.20
+#> legumes                                        0               0.25
+#> maize                                          0               0.25
+#> oil seed rape, spring                          0               0.40
+#> oil seed rape, winter                          0               0.40
+#> olives                                         0               0.70
+#> pome / stone fruit, early applns               0               0.20
+#> pome / stone fruit, late applns                0               0.20
+#> potatoes                                       0               0.15
+#> soybeans                                       0               0.20
+#> sugar beets                                    0               0.20
+#> sunflowers                                     0               0.20
+#> tobacco                                        0               0.20
+#> vegetables, bulb                               0               0.10
+#> vegetables, fruiting                           0               0.25
+#> vegetables, leafy                              0               0.25
+#> vegetables, root                               0               0.25
+#> vines, early applns                            0               0.40
+#> vines, late applns                             0               0.40
+#> appln, aerial                                  0               0.20
+#> appln, hand (crop < 50 cm)                     0               0.20
+#> appln, hand (crop > 50 cm)                     0               0.20
+#> no drift (incorp or seed trtmt)                0               0.00
+#>                                  average crop cover full canopy
+#> cereals, spring                                0.20        0.70
+#> cereals, winter                                0.20        0.70
+#> citrus                                         0.80        0.80
+#> cotton                                         0.60        0.75
+#> field beans                                    0.40        0.70
+#> grass / alfalfa                                0.60        0.75
+#> hops                                           0.50        0.70
+#> legumes                                        0.50        0.70
+#> maize                                          0.50        0.75
+#> oil seed rape, spring                          0.70        0.75
+#> oil seed rape, winter                          0.70        0.75
+#> olives                                         0.70        0.70
+#> pome / stone fruit, early applns               0.40        0.65
+#> pome / stone fruit, late applns                0.40        0.65
+#> potatoes                                       0.50        0.70
+#> soybeans                                       0.50        0.75
+#> sugar beets                                    0.70        0.75
+#> sunflowers                                     0.50        0.75
+#> tobacco                                        0.70        0.75
+#> vegetables, bulb                               0.25        0.40
+#> vegetables, fruiting                           0.50        0.70
+#> vegetables, leafy                              0.40        0.70
+#> vegetables, root                               0.50        0.70
+#> vines, early applns                            0.50        0.60
+#> vines, late applns                             0.50        0.60
+#> appln, aerial                                  0.50        0.70
+#> appln, hand (crop < 50 cm)                     0.50        0.70
+#> appln, hand (crop > 50 cm)                     0.50        0.70
+#> no drift (incorp or seed trtmt)                0.00        0.00
+#> 
+#> $rd
+#>          Region
+#> Time      North South
+#>   Oct-Feb     5     4
+#>   Mar-May     2     4
+#>   Jun-Sep     2     3
+#> 
+

- + - - - + diff --git a/docs/reference/FOMC_actual_twa.html b/docs/reference/FOMC_actual_twa.html index a365556..f7b0140 100644 --- a/docs/reference/FOMC_actual_twa.html +++ b/docs/reference/FOMC_actual_twa.html @@ -1,187 +1,119 @@ - - - - - - - -Actual and maximum moving window time average concentrations for FOMC kinetics — FOMC_actual_twa • pfm - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +Actual and maximum moving window time average concentrations for FOMC kinetics — FOMC_actual_twa • pfm + + +

+ + + 0.6.0 - - - + + - - - - - - - -

- -

- +

Actual and maximum moving window time average concentrations for FOMC kinetics

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

- -

Source

Usage

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

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

Arguments

alpha: Parameter of the FOMC model

- +

Author

Johannes Ranke

+ +

Examples

FOMC_actual_twa(alpha = 1.0001, beta = 10)
+#>         actual       twa
+#> 0   1.00000000       NaN
+#> 1   0.90908224 0.9530973
+#> 2   0.83331814 0.9115995
+#> 4   0.71426168 0.8411664
+#> 7   0.58820408 0.7580202
+#> 14  0.41663019 0.6253074
+#> 21  0.32254415 0.5387324
+#> 28  0.26312277 0.4767543
+#> 42  0.19227599 0.3925054
+#> 50  0.16663681 0.3583198
+#> 100 0.09088729 0.2397608
+

- + - - - + diff --git a/docs/reference/GUS.html b/docs/reference/GUS.html index e371c1a..d2aa03b 100644 --- a/docs/reference/GUS.html +++ b/docs/reference/GUS.html @@ -1,251 +1,184 @@ - - - - - - - -Groundwater ubiquity score based on Gustafson (1989) — GUS • pfm - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +$$GUS = \log_{10} DT50_{soil} (4 - \log_{10} K_{oc})$$">Groundwater ubiquity score based on Gustafson (1989) — GUS • pfm + + +

+ + + 0.6.0 - - - + + - - - - - - - -

- -

- +

The groundwater ubiquity score GUS is calculated according to the following equation $$GUS = \log_{10} DT50_{soil} (4 - \log_{10} K_{oc})$$

GUS(...)
-
-# S3 method for numeric
-GUS(DT50, Koc, ...)
-
-# S3 method for chent
-GUS(
-  chent,
-  degradation_value = "DT50ref",
-  lab_field = "laboratory",
-  redox = "aerobic",
-  sorption_value = "Kfoc",
-  degradation_aggregator = geomean,
-  sorption_aggregator = geomean,
-  ...
-)
-
-# S3 method for 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 + + Usage + `GUS(...) + +# S3 method for numeric +GUS(DT50, Koc, ...) + +# S3 method for chent +GUS( + chent, + degradation_value = "DT50ref", + lab_field = "laboratory", + redox = "aerobic", + sorption_value = "Kfoc", + degradation_aggregator = geomean, + sorption_aggregator = geomean, + ... +) + +# S3 method for 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 +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 +(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? +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

References

+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

+ +

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

References

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

- -

- +

Author

Johannes Ranke

+ + - + - - - + diff --git a/docs/reference/PEC_FOMC_accu_rel.html b/docs/reference/PEC_FOMC_accu_rel.html index e4d9bf4..00553cd 100644 --- a/docs/reference/PEC_FOMC_accu_rel.html +++ b/docs/reference/PEC_FOMC_accu_rel.html @@ -1,167 +1,94 @@ - - - - - - - -Get the relative accumulation of an FOMC model over multiples of an interval — PEC_FOMC_accu_rel • pfm - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +Get the relative accumulation of an FOMC model over multiples of an interval — PEC_FOMC_accu_rel • pfm + + +

+ + + 0.6.0 - - - + + - - - - - - - -

- - - -

- -

- +

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

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

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

- -

- + - - - + diff --git a/docs/reference/PEC_soil.html b/docs/reference/PEC_soil.html index e531fab..abe8e5a 100644 --- a/docs/reference/PEC_soil.html +++ b/docs/reference/PEC_soil.html @@ -1,49 +1,56 @@ -Calculate predicted environmental concentrations in soil — PEC_soil • pfmCalculate predicted environmental concentrations in soil — PEC_soil • pfm + + -

- +

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 @@ -51,7 +58,8 @@ 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",
@@ -78,8 +86,8 @@ and in the EFSA guidance on PEC soil calculations (EFSA, 2015, 2017).
 )

Arguments

rate: Application rate in units specified below

Value

The predicted concentration in soil

Details

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

Note

(destination 'PECgw') is taken from the chent object, otherwise the DT50 with destination 'PECsoil'.

References

in soil. EFSA Journal 13(4) 4093 doi:10.2903/j.efsa.2015.4093

Author

Johannes Ranke

Examples

PEC_soil(100, interception = 0.25)
 #>      scenario
 #> t_avg default
@@ -297,26 +305,22 @@ from 2017 (p. 92).
                            Kom = 100, scenarios = "EFSA_2015", porewater = TRUE)

- -

- diff --git a/docs/reference/PEC_soil_mets.html b/docs/reference/PEC_soil_mets.html index c3b09f4..ddaa9d1 100644 --- a/docs/reference/PEC_soil_mets.html +++ b/docs/reference/PEC_soil_mets.html @@ -1,171 +1,98 @@ - - - - - - - -Calculate initial and accumulation PEC soil for a set of metabolites — PEC_soil_mets • pfm - - - - - - - +Calculate initial and accumulation PEC soil for a set of metabolites — PEC_soil_mets • pfm + + - - - +

- +

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

+ - - - + diff --git a/docs/reference/PEC_sw_drainage_UK.html b/docs/reference/PEC_sw_drainage_UK.html index bdcf5af..8e649b9 100644 --- a/docs/reference/PEC_sw_drainage_UK.html +++ b/docs/reference/PEC_sw_drainage_UK.html @@ -1,207 +1,142 @@ - - - - - - +Calculate initial predicted environmental concentrations in surface water due to drainage using the UK method — PEC_sw_drainage_UK • pfm + + -Calculate initial predicted environmental concentrations in surface water due to drainage using the UK method — PEC_sw_drainage_UK • pfm +

- - +

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

PEC_sw_drainage_UK.Rd

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

The predicted concentration in surface water in µg/L

- -

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

References

- +

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 + 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 + 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)
#> [1] 8.076923

- +

Author

Johannes Ranke

+ +

Examples

PEC_sw_drainage_UK(150, Koc = 100)
+#> [1] 8.076923
+

- + - - - + diff --git a/docs/reference/PEC_sw_drift.html b/docs/reference/PEC_sw_drift.html index e2c325b..3e1d4c8 100644 --- a/docs/reference/PEC_sw_drift.html +++ b/docs/reference/PEC_sw_drift.html @@ -1,221 +1,166 @@ - - - - - - +Calculate predicted environmental concentrations in surface water due to drift — PEC_sw_drift • pfm + + -Calculate predicted environmental concentrations in surface water due to drift — PEC_sw_drift • pfm +

- - - +

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 = 30,
+  drift_percentages = NULL,
+  drift_data = c("JKI", "RF"),
+  crop = "Ackerbau",
+  distances = c(1, 5, 10, 20),
+  rate_units = "g/ha",
+  PEC_units = "µg/L"
+)

- - - +

Arguments

rate: Application rate in units specified below
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. +'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 formula is used, if +implemented for the crop type and number of applications
crop: Crop name (use German names for JKI data), defaults to "Ackerbau"
distances: The distances in m for which to get PEC values
rate_units: Defaults to g/ha
PEC_units: Requested units for the calculated PEC. Only µg/L currently supported

Value

PEC_sw_drift.Rd

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.

The predicted concentration in surface water

Author

Johannes Ranke

PEC_sw_drift(
-  rate,
-  applications = 1,
-  water_depth = 30,
-  drift_percentages = NULL,
-  drift_data = c("JKI", "RF"),
-  crop = "Ackerbau",
-  distances = c(1, 5, 10, 20),
-  rate_units = "g/ha",
-  PEC_units = "µg/L"
-)

- -

Arguments

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

rate	Application rate in units specified below
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. -'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 formula is used, if -implemented for the crop type and number of applications
crop	Crop name (use German names for JKI data), defaults to "Ackerbau"
distances	The distances in m for which to get PEC values
rate_units	Defaults to g/ha
PEC_units	Requested units for the calculated PEC. Only µg/L currently supported

- -

Value

- -

The predicted concentration in surface water

- -

Examples

PEC_sw_drift(100)
#>        1 m        5 m       10 m       20 m 
-#> 0.92333333 0.19000000 0.09666667 0.05000000 
# Alternatively, we can use the formula for a single application to "Ackerbau" from the paper
-PEC_sw_drift(100, drift_data = "RF")
#>        1 m        5 m       10 m       20 m 
-#> 0.92350000 0.19114149 0.09699222 0.04921742 

-# This makes it possible to also use different substances
-PEC_sw_drift(100, distances = c(1, 3, 5, 6, 10, 20, 50, 100), drift_data = "RF")
#>        1 m        3 m        5 m        6 m       10 m       20 m       50 m 
-#> 0.92350000 0.31512171 0.19114149 0.15990435 0.09699222 0.04921742 0.02007497 
-#>      100 m 
-#> 0.01018678 

-# 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))
#>     2.77 %     0.95 %     0.57 %     0.48 %     0.29 %     0.15 %     0.06 % 
-#> 0.92333333 0.31666667 0.19000000 0.16000000 0.09666667 0.05000000 0.02000000 
-#>     0.03 % 
-#> 0.01000000

- -

Examples

PEC_sw_drift(100)
+#>        1 m        5 m       10 m       20 m 
+#> 0.92333333 0.19000000 0.09666667 0.05000000 
+# Alternatively, we can use the formula for a single application to "Ackerbau" from the paper
+PEC_sw_drift(100, drift_data = "RF")
+#>        1 m        5 m       10 m       20 m 
+#> 0.92350000 0.19114149 0.09699222 0.04921742 
+
+# This makes it possible to also use different substances
+PEC_sw_drift(100, distances = c(1, 3, 5, 6, 10, 20, 50, 100), drift_data = "RF")
+#>        1 m        3 m        5 m        6 m       10 m       20 m       50 m 
+#> 0.92350000 0.31512171 0.19114149 0.15990435 0.09699222 0.04921742 0.02007497 
+#>      100 m 
+#> 0.01018678 
+
+# 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))
+#>     2.77 %     0.95 %     0.57 %     0.48 %     0.29 %     0.15 %     0.06 % 
+#> 0.92333333 0.31666667 0.19000000 0.16000000 0.09666667 0.05000000 0.02000000 
+#>     0.03 % 
+#> 0.01000000 
+

+ - + - - - + diff --git a/docs/reference/PEC_sw_exposit_drainage.html b/docs/reference/PEC_sw_exposit_drainage.html index 5bd1d80..74f1b60 100644 --- a/docs/reference/PEC_sw_exposit_drainage.html +++ b/docs/reference/PEC_sw_exposit_drainage.html @@ -1,117 +1,60 @@ - - - - - - - -Calculate PEC surface water due to drainage as in Exposit 3 — PEC_sw_exposit_drainage • pfm - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Calculate PEC surface water due to drainage as in Exposit 3 — PEC_sw_exposit_drainage • pfm + + +

+ + + 0.6.0 - - - + + - - - - - - - -

- +

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 @@ -121,123 +64,123 @@ the group is derived only from the Koc, if not given explicitly. For details, see the discussion of the function arguments below.

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

- -

Source

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
+)

Source

Excel 3.02 spreadsheet available from - https://www.bvl.bund.de/DE/04_Pflanzenschutzmittel/03_Antragsteller/04_Zulassungsverfahren/07_Naturhaushalt/psm_naturhaush_node.html#doc1400590bodyText3

Value

+ https://www.bvl.bund.de/DE/04_Pflanzenschutzmittel/03_Antragsteller/04_Zulassungsverfahren/07_Naturhaushalt/psm_naturhaush_node.html#doc1400590bodyText3

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

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.

- -

Examples

  PEC_sw_exposit_drainage(500, Koc = 150)
#> $perc_drainage_total
-#> spring autumn 
-#>    0.2    1.0 
-#> 
-#> $perc_peak
-#> spring autumn 
-#>   12.5   25.0 
-#> 
-#> $PEC_sw_drainage
-#>   spring   autumn 
-#> 1.562500 4.807692 
-#>

- +

Examples

  PEC_sw_exposit_drainage(500, Koc = 150)
+#> $perc_drainage_total
+#> spring autumn 
+#>    0.2    1.0 
+#> 
+#> $perc_peak
+#> spring autumn 
+#>   12.5   25.0 
+#> 
+#> $PEC_sw_drainage
+#>   spring   autumn 
+#> 1.562500 4.807692 
+#> 
+

- + - - - + diff --git a/docs/reference/PEC_sw_exposit_runoff.html b/docs/reference/PEC_sw_exposit_runoff.html index 81549e0..a3845ee 100644 --- a/docs/reference/PEC_sw_exposit_runoff.html +++ b/docs/reference/PEC_sw_exposit_runoff.html @@ -1,262 +1,202 @@ - - - - - - +Calculate PEC surface water due to runoff and erosion as in Exposit 3 — PEC_sw_exposit_runoff • pfm + + -Calculate PEC surface water due to runoff and erosion as in Exposit 3 — PEC_sw_exposit_runoff • pfm +

- - +

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 = Inf,
+  t_runoff = 3,
+  exposit_reduction_version = c("3.02", "3.01a", "3.01a2", "2.0"),
+  V_ditch = 30,
+  V_event = 100,
+  dilution = 2
+)

- - - +

Source

Excel 3.02 spreadsheet available from + https://www.bvl.bund.de/DE/04_Pflanzenschutzmittel/03_Antragsteller/04_Zulassungsverfahren/07_Naturhaushalt/psm_naturhaush_node.html#doc1400590bodyText3

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

Examples

  PEC_sw_exposit_runoff(500, Koc = 150)
+#> $perc_runoff
+#> dissolved     bound 
+#>     0.248     0.001 
+#> 
+#> $runoff
+#>           dissolved   bound   total
+#> No buffer     1.240 0.00500 1.24500
+#> 5 m           0.744 0.00300 0.74700
+#> 10 m          0.496 0.00075 0.49675
+#> 20 m          0.248 0.00025 0.24825
+#> 
+#> $PEC_sw_runoff
+#>           dissolved       bound    total
+#> No buffer  4.769231 0.019230769 4.788462
+#> 5 m        4.133333 0.016666667 4.150000
+#> 10 m       3.542857 0.005357143 3.548214
+#> 20 m       2.480000 0.002500000 2.482500
+#> 
+  PEC_sw_exposit_runoff(600, Koc = 10000, DT50 = 195, exposit = "3.01a")
+#> $perc_runoff
+#> dissolved     bound 
+#>     0.037     0.159 
+#> 
+#> $runoff
+#>            dissolved      bound      total
+#> No buffer 0.21964521 0.94388078 1.16352600
+#> 3 m       0.16473391 0.66071655 0.82545046
+#> 5 m       0.13178713 0.56632847 0.69811560
+#> 6 m       0.12080487 0.42474635 0.54555122
+#> 10 m      0.08785809 0.14158212 0.22944020
+#> 20 m      0.04392904 0.04719404 0.09112308
+#> 
+#> $PEC_sw_runoff
+#>           dissolved     bound     total
+#> No buffer 0.8447893 3.6303107 4.4751000
+#> 3 m       0.7844472 3.1462693 3.9307165
+#> 5 m       0.7321507 3.1462693 3.8784200
+#> 6 m       0.7106169 2.4985080 3.2091248
+#> 10 m      0.6275578 1.0113008 1.6388586
+#> 20 m      0.4392904 0.4719404 0.9112308
+#> 
+

+ - + - - - + diff --git a/docs/reference/PEC_sw_focus.html b/docs/reference/PEC_sw_focus.html index 55a7400..78f6453 100644 --- a/docs/reference/PEC_sw_focus.html +++ b/docs/reference/PEC_sw_focus.html @@ -1,5 +1,11 @@ -Calculate PEC surface water at FOCUS Step 1 — PEC_sw_focus • pfmCalculate PEC surface water at FOCUS Step 1 — PEC_sw_focus • pfm + + -

- +

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 @@ -55,7 +64,8 @@ input files are generated that are suitable as input also for Step 2 to be used with the FOCUS calculator.

Usage

PEC_sw_focus(
   parent,
   rate,
@@ -77,8 +87,8 @@ to be used with the FOCUS calculator.
 )

Arguments

parent: A list containing substance specific parameters, e.g. conveniently generated by [chent_focus_sw].; Should the input text file be appended?

Note

correction and the formation fraction in water/sediment systems.

Step 2 is not implemented.

References

@@ -181,8 +191,8 @@ should be written

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, overwrite = TRUE, append = FALSE)
@@ -284,26 +294,22 @@ should be written
 #>

- -

- diff --git a/docs/reference/PEC_sw_sed.html b/docs/reference/PEC_sw_sed.html index 458eeb7..3432a75 100644 --- a/docs/reference/PEC_sw_sed.html +++ b/docs/reference/PEC_sw_sed.html @@ -1,202 +1,137 @@ - - - - - - +Calculate predicted environmental concentrations in sediment from surface +water concentrations — PEC_sw_sed • pfm + + -Calculate predicted environmental concentrations in sediment from surface -water concentrations — PEC_sw_sed • pfm +

- - +

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 = 5,
+  water_depth = 30,
+  sediment_density = 1.3,
+  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 L/kg (equivalent to +g/cm3)
PEC_sed_units: The units of the estimated sediment PEC value

Value

PEC_sw_sed.Rd

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

The predicted concentration in sediment

- -

PEC_sw_sed(
-  PEC_sw,
-  percentage = 100,
-  method = "percentage",
-  sediment_depth = 5,
-  water_depth = 30,
-  sediment_density = 1.3,
-  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 L/kg (equivalent to -g/cm3)
PEC_sed_units	The units of the estimated sediment PEC value

- -

Value

- -

The predicted concentration in sediment

- -

Examples

PEC_sw_sed(PEC_sw_drift(100, distances = 1), percentage = 50)
#>      1 m 
-#> 2.130769

- +

+ +

Examples

PEC_sw_sed(PEC_sw_drift(100, distances = 1), percentage = 50)
+#>      1 m 
+#> 2.130769 
+

+ - - - + diff --git a/docs/reference/Rplot002.png b/docs/reference/Rplot002.png new file mode 100644 index 0000000..a32c4ab Binary files /dev/null and b/docs/reference/Rplot002.png differ diff --git a/docs/reference/Rplot003.png b/docs/reference/Rplot003.png new file mode 100644 index 0000000..a32c4ab Binary files /dev/null and b/docs/reference/Rplot003.png differ diff --git a/docs/reference/Rplot004.png b/docs/reference/Rplot004.png new file mode 100644 index 0000000..5973602 Binary files /dev/null and b/docs/reference/Rplot004.png differ diff --git a/docs/reference/Rplot005.png b/docs/reference/Rplot005.png new file mode 100644 index 0000000..2db42a3 Binary files /dev/null and b/docs/reference/Rplot005.png differ diff --git a/docs/reference/SFO_actual_twa.html b/docs/reference/SFO_actual_twa.html index b5fa5da..e07138c 100644 --- a/docs/reference/SFO_actual_twa.html +++ b/docs/reference/SFO_actual_twa.html @@ -1,179 +1,111 @@ - - - - - - - -Actual and maximum moving window time average concentrations for SFO kinetics — SFO_actual_twa • pfm - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +Actual and maximum moving window time average concentrations for SFO kinetics — SFO_actual_twa • pfm + + +

+ + + 0.6.0 - - - + + - - - - - - - -

- -

- +

Actual and maximum moving window time average concentrations for SFO kinetics

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

- -

Source

Usage

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

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

Arguments

DT50: The half-life.

- +

Author

Johannes Ranke

+ +

Examples

SFO_actual_twa(10)
+#>           actual       twa
+#> 0   1.0000000000       NaN
+#> 1   0.9330329915 0.9661297
+#> 2   0.8705505633 0.9337803
+#> 4   0.7578582833 0.8733416
+#> 7   0.6155722067 0.7923030
+#> 14  0.3789291416 0.6400113
+#> 21  0.2332582479 0.5267498
+#> 28  0.1435872944 0.4412651
+#> 42  0.0544094102 0.3248093
+#> 50  0.0312500000 0.2795222
+#> 100 0.0009765625 0.1441286
+

+ - - - + diff --git a/docs/reference/SSLRC_mobility_classification.html b/docs/reference/SSLRC_mobility_classification.html index b0ae939..398734d 100644 --- a/docs/reference/SSLRC_mobility_classification.html +++ b/docs/reference/SSLRC_mobility_classification.html @@ -1,190 +1,122 @@ - - - - - - - -Determine the SSLRC mobility classification for a chemical substance from its Koc — SSLRC_mobility_classification • pfm - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +Determine the SSLRC mobility classification for a chemical substance from its Koc — SSLRC_mobility_classification • pfm + + +

+ + + 0.6.0 - - - + + - - - - - - - -

- +

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

SSLRC_mobility_classification(Koc)

- -

Arguments

- - - - - - -

Koc	The sorption coefficient normalised to organic carbon in L/kg

- -

Value

Usage

SSLRC_mobility_classification(Koc)

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

Arguments

Koc: The sorption coefficient normalised to organic carbon in L/kg

Value

References

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

References

- - -

Examples

SSLRC_mobility_classification(100)
#> $`Mobility classification`
-#> [1] "Moderately mobile"
-#> 
-#> $`Percentage drained per mm of drain water`
-#> [1] 0.7
-#> 
SSLRC_mobility_classification(10000)
#> $`Mobility classification`
-#> [1] "Non mobile"
-#> 
-#> $`Percentage drained per mm of drain water`
-#> [1] 0.008
-#>

- +

Author

Johannes Ranke

+ +

Examples

SSLRC_mobility_classification(100)
+#> $`Mobility classification`
+#> [1] "Moderately mobile"
+#> 
+#> $`Percentage drained per mm of drain water`
+#> [1] 0.7
+#> 
+SSLRC_mobility_classification(10000)
+#> $`Mobility classification`
+#> [1] "Non mobile"
+#> 
+#> $`Percentage drained per mm of drain water`
+#> [1] 0.008
+#> 
+

- + - - - + diff --git a/docs/reference/TOXSWA_cwa.html b/docs/reference/TOXSWA_cwa.html index c8622df..a5a9a49 100644 --- a/docs/reference/TOXSWA_cwa.html +++ b/docs/reference/TOXSWA_cwa.html @@ -1,49 +1,56 @@ -R6 class for holding TOXSWA water concentration data and associated statistics — TOXSWA_cwa • pfmR6 class for holding TOXSWA water concentration data and associated statistics — TOXSWA_cwa • pfm + + -

- +

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 @@ -52,12 +59,12 @@ by read.TOXSWA_cwa.

Format

An R6Class generator object.

Public fields

filename: Length one character vector holding the filename.

Methods

Public methods

@@ -220,8 +227,8 @@ suspended matter will be used.

Examples

H_sw_R1_stream  <- read.TOXSWA_cwa("00003s_pa.cwa",
                                  basedir = "SwashProjects/project_H_sw/TOXSWA",
                                  zipfile = system.file("testdata/SwashProjects.zip",
@@ -230,53 +237,25 @@ suspended matter will be used.
 H_sw_R1_stream$moving_windows(c(7, 21))
 print(H_sw_R1_stream)
 #> <TOXSWA_cwa> data from file 00003s_pa.cwa segment 20 
-#>                datetime     t t_firstjan t_rel_to_max cwa_mug_per_L
-#> 20  1978-10-01 00:00:00 0.000   273.0000      -55.333             0
-#> 40  1978-10-01 01:00:00 0.042   273.0417      -55.291             0
-#> 60  1978-10-01 02:00:00 0.083   273.0833      -55.250             0
-#> 80  1978-10-01 03:00:00 0.125   273.1250      -55.208             0
-#> 100 1978-10-01 04:00:00 0.167   273.1667      -55.166             0
-#> 120 1978-10-01 05:00:00 0.208   273.2083      -55.125             0
-#>     cwa_tot_mug_per_L
-#> 20                  0
-#> 40                  0
-#> 60                  0
-#> 80                  0
-#> 100                 0
-#> 120                 0
-#> Moving window analysis
-#>    window  max_TWAC max_AUC_h max_AUC_d
-#> 1  7 days 2.3926551  401.9660  16.74859
-#> 2 21 days 0.8369248  421.8101  17.57542
-#> Event statistics for threshold 2 
-#>   t_start   cwa_max duration pre_interval     AUC_h     AUC_d
-#> 1  44.375  4.167238    0.208       44.375  17.77202  0.740501
-#> 2  55.042 40.584010    0.583       10.459 398.21189 16.592162
-#> Event statistics for threshold 10 
-#>   t_start  cwa_max duration pre_interval   AUC_h    AUC_d
-#> 1  55.083 40.58401    0.459       55.083 379.433 15.80971
+#> Error in head(self$cwas): could not find function "head"

- -

- diff --git a/docs/reference/TSCF-1.png b/docs/reference/TSCF-1.png index efa9e0a..048befb 100644 Binary files a/docs/reference/TSCF-1.png and b/docs/reference/TSCF-1.png differ diff --git a/docs/reference/TSCF.html b/docs/reference/TSCF.html index b635ba2..eb1398b 100644 --- a/docs/reference/TSCF.html +++ b/docs/reference/TSCF.html @@ -1,119 +1,58 @@ - - - - - - - -Estimation of the transpiration stream concentration factor — TSCF • pfm - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +or ionic.">Estimation of the transpiration stream concentration factor — TSCF • pfm + + +

+ + + 0.6.0 - - - + + - - - - - - - -

- - - -

- -

- +

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 @@ -122,60 +61,62 @@ the compound should not be ionogenic (weak acid/base) or ionic.

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

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.

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)"))

- -

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)
+#> Error in curve(TSCF_2, -1, 5, add = TRUE, lty = 2): could not find function "curve"
+legend("topright", lty = 1:2, bty = "n",
+  legend = c("Briggs et al. (1982)", "Dettenmaier et al. (2009)"))
+#> Error in legend("topright", lty = 1:2, bty = "n", legend = c("Briggs et al. (1982)",     "Dettenmaier et al. (2009)")): could not find function "legend"
+

+ - - - + diff --git a/docs/reference/chent_focus_sw.html b/docs/reference/chent_focus_sw.html index 70fd16d..5ffa6b4 100644 --- a/docs/reference/chent_focus_sw.html +++ b/docs/reference/chent_focus_sw.html @@ -1,206 +1,135 @@ - - - - - - +Create a chemical compound object for FOCUS Step 1 calculations — chent_focus_sw • pfm + + -Create a chemical compound object for FOCUS Step 1 calculations — chent_focus_sw • pfm +

- - +

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

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

+ + + -

+ - - - + diff --git a/docs/reference/drift_data_JKI.html b/docs/reference/drift_data_JKI.html index 36472d1..f97856a 100644 --- a/docs/reference/drift_data_JKI.html +++ b/docs/reference/drift_data_JKI.html @@ -1,131 +1,66 @@ - - - - - - - -Deposition from spray drift expressed as percent of the applied dose as -published by the JKI — drift_data_JKI • pfm - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Deposition from spray drift expressed as percent of the applied dose as +published by the JKI — drift_data_JKI • pfm + + + +

Deposition from spray drift expressed as percent of the applied dose as +

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

- -

Format

- +

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).

Source

- +

Source

JKI (2010) Spreadsheet 'Tabelle der Abdrifteckwerte.xls', retrieved from http://www.jki.bund.de/no_cache/de/startseite/institute/anwendungstechnik/abdrift-eckwerte.html @@ -133,8 +68,9 @@ on 2015-06-11

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

Details

- +

Details

The data were extracted from the spreadsheet cited below using the R code given in the example section. The spreadsheet is not included in the package as its licence is not clear.

@@ -148,272 +84,269 @@ 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.

Examples


-if (FALSE) {
-  # This is the code that was used to extract the data
-  library(readxl)
-  abdrift_path <- "inst/extdata/Tabelle der Abdrifteckwerte.xls"
-  JKI_crops <- c("Ackerbau", "Obstbau frueh", "Obstbau spaet", "Weinbau frueh", "Weinbau spaet",
-    "Hopfenbau", "Flaechenkulturen > 900 l/ha", "Gleisanlagen")
-  names(JKI_crops) <- c("Field crops", "Pome/stone fruit, early", "Pome/stone fruit, late",
-    "Vines early", "Vines late", "Hops", "Areic cultures > 900 L/ha", "Railroad tracks")
-  drift_data_JKI <- list()
-
-  for (n in 1:8) {
-    drift_data_raw <- read_excel(abdrift_path, sheet = n + 1, skip = 2)
-    drift_data <- matrix(NA, nrow = 9, ncol = length(JKI_crops))
-    dimnames(drift_data) <- list(distance = drift_data_raw[[1]][1:9],
-                                            crop = JKI_crops)
-    if (n == 1) { # Values for railroad tracks only present for one application
-      drift_data[, c(1:3, 5:8)] <- as.matrix(drift_data_raw[c(2:7, 11)][1:9, ])
-    } else {
-      drift_data[, c(1:3, 5:7)] <- as.matrix(drift_data_raw[c(2:7)][1:9, ])
-    }
-    drift_data_JKI[[n]] <- drift_data
-  }
-
-  # Manual data entry from the Rautmann paper
-  drift_data_JKI[[1]]["3", "Ackerbau"] <- 0.95
-  drift_data_JKI[[1]][, "Weinbau frueh"] <- c(NA, 2.7, 1.18, 0.39, 0.2, 0.13, 0.07, 0.04, 0.03)
-  drift_data_JKI[[2]]["3", "Ackerbau"] <- 0.79
-  drift_data_JKI[[2]][, "Weinbau frueh"] <- c(NA, 2.53, 1.09, 0.35, 0.18, 0.11, 0.06, 0.03, 0.02)
-  drift_data_JKI[[3]]["3", "Ackerbau"] <- 0.68
-  drift_data_JKI[[3]][, "Weinbau frueh"] <- c(NA, 2.49, 1.04, 0.32, 0.16, 0.10, 0.05, 0.03, 0.02)
-  drift_data_JKI[[4]]["3", "Ackerbau"] <- 0.62
-  drift_data_JKI[[4]][, "Weinbau frueh"] <- c(NA, 2.44, 1.02, 0.31, 0.16, 0.10, 0.05, 0.03, 0.02)
-  drift_data_JKI[[5]]["3", "Ackerbau"] <- 0.59
-  drift_data_JKI[[5]][, "Weinbau frueh"] <- c(NA, 2.37, 1.00, 0.31, 0.15, 0.09, 0.05, 0.03, 0.02)
-  drift_data_JKI[[6]]["3", "Ackerbau"] <- 0.56
-  drift_data_JKI[[6]][, "Weinbau frueh"] <- c(NA, 2.29, 0.97, 0.30, 0.15, 0.09, 0.05, 0.03, 0.02)
-  drift_data_JKI[[7]]["3", "Ackerbau"] <- 0.55
-  drift_data_JKI[[7]][, "Weinbau frueh"] <- c(NA, 2.24, 0.94, 0.29, 0.15, 0.09, 0.05, 0.03, 0.02)
-  drift_data_JKI[[8]]["3", "Ackerbau"] <- 0.52
-  drift_data_JKI[[8]][, "Weinbau frueh"] <- c(NA, 2.16, 0.91, 0.28, 0.14, 0.09, 0.04, 0.03, 0.02)
-
-  # Save the data
-  save(drift_data_JKI, file = "data/drift_data_JKI.RData")
-}
-
-# And these are the resulting data
-drift_data_JKI
#> [[1]]
-#>         crop
-#> distance Ackerbau Obstbau frueh Obstbau spaet Weinbau frueh Weinbau spaet
-#>       1      2.77            NA            NA            NA            NA
-#>       3      0.95         29.20         15.73          2.70          8.02
-#>       5      0.57         19.89          8.41          1.18          3.62
-#>       10     0.29         11.81          3.60          0.39          1.23
-#>       15     0.20          5.55          1.81          0.20          0.65
-#>       20     0.15          2.77          1.09          0.13          0.42
-#>       30     0.10          1.04          0.54          0.07          0.22
-#>       40     0.07          0.52          0.32          0.04          0.14
-#>       50     0.06          0.30          0.22          0.03          0.10
-#>         crop
-#> distance Hopfenbau Flaechenkulturen > 900 l/ha Gleisanlagen
-#>       1         NA                       4.440           NA
-#>       3      19.33                          NA  0.018721696
-#>       5      11.57                       0.180  0.014363896
-#>       10      5.77                       0.050  0.010026007
-#>       15      3.84                       0.020  0.008124366
-#>       20      1.79                       0.012  0.006998158
-#>       30      0.56                       0.005  0.005670811
-#>       40      0.25                       0.003           NA
-#>       50      0.13                       0.002  0.004350831
-#> 
-#> [[2]]
-#>         crop
-#> distance Ackerbau Obstbau frueh Obstbau spaet Weinbau frueh Weinbau spaet
-#>       1      2.38            NA            NA            NA            NA
-#>       3      0.79         25.53         12.13          2.53          7.23
-#>       5      0.47         16.87          6.81          1.09          3.22
-#>       10     0.24          9.61          3.11          0.35          1.07
-#>       15     0.16          5.61          1.58          0.18          0.56
-#>       20     0.12          2.59          0.90          0.11          0.36
-#>       30     0.08          0.87          0.40          0.06          0.19
-#>       40     0.06          0.40          0.23          0.03          0.12
-#>       50     0.05          0.22          0.15          0.02          0.08
-#>         crop
-#> distance Hopfenbau Flaechenkulturen > 900 l/ha Gleisanlagen
-#>       1         NA                       3.780           NA
-#>       3      17.73                          NA           NA
-#>       5       9.60                       0.160           NA
-#>       10      4.18                       0.040           NA
-#>       15      2.57                       0.020           NA
-#>       20      1.21                       0.011           NA
-#>       30      0.38                       0.005           NA
-#>       40      0.17                       0.003           NA
-#>       50      0.09                       0.002           NA
-#> 
-#> [[3]]
-#>         crop
-#> distance Ackerbau Obstbau frueh Obstbau spaet Weinbau frueh Weinbau spaet
-#>       1      2.01            NA            NA            NA            NA
-#>       3      0.68         23.96         11.01          2.49          6.90
-#>       5      0.41         15.79          6.04          1.04          3.07
-#>       10     0.20          8.96          2.67          0.32          1.02
-#>       15     0.14          4.24          1.39          0.16          0.54
-#>       20     0.10          2.01          0.80          0.10          0.34
-#>       30     0.07          0.70          0.36          0.05          0.18
-#>       40     0.05          0.33          0.21          0.03          0.11
-#>       50     0.04          0.19          0.13          0.02          0.08
-#>         crop
-#> distance Hopfenbau Flaechenkulturen > 900 l/ha Gleisanlagen
-#>       1         NA                       3.420           NA
-#>       3      15.93                          NA           NA
-#>       5       8.57                       0.150           NA
-#>       10      3.70                       0.040           NA
-#>       15      2.26                       0.020           NA
-#>       20      1.05                       0.010           NA
-#>       30      0.34                       0.004           NA
-#>       40      0.15                       0.003           NA
-#>       50      0.08                       0.002           NA
-#> 
-#> [[4]]
-#>         crop
-#> distance Ackerbau Obstbau frueh Obstbau spaet Weinbau frueh Weinbau spaet
-#>       1      1.85            NA            NA            NA            NA
-#>       3      0.62         23.61         10.12          2.44          6.71
-#>       5      0.38         15.42          5.60          1.02          2.99
-#>       10     0.19          8.66          2.50          0.31          0.99
-#>       15     0.13          4.01          1.28          0.16          0.52
-#>       20     0.10          1.89          0.75          0.10          0.33
-#>       30     0.06          0.66          0.35          0.05          0.17
-#>       40     0.05          0.31          0.20          0.03          0.11
-#>       50     0.04          0.17          0.13          0.02          0.08
-#>         crop
-#> distance Hopfenbau Flaechenkulturen > 900 l/ha Gleisanlagen
-#>       1         NA                       2.290           NA
-#>       3      15.38                          NA           NA
-#>       5       8.26                       0.120           NA
-#>       10      3.55                       0.030           NA
-#>       15      2.17                       0.020           NA
-#>       20      0.93                       0.009           NA
-#>       30      0.31                       0.004           NA
-#>       40      0.14                       0.002           NA
-#>       50      0.08                       0.002           NA
-#> 
-#> [[5]]
-#>         crop
-#> distance Ackerbau Obstbau frueh Obstbau spaet Weinbau frueh Weinbau spaet
-#>       1      1.75            NA            NA            NA            NA
-#>       3      0.59         23.12          9.74          2.37          6.59
-#>       5      0.36         15.06          5.41          1.00          2.93
-#>       10     0.18          8.42          2.43          0.31          0.98
-#>       15     0.12          3.83          1.24          0.15          0.51
-#>       20     0.09          1.81          0.72          0.09          0.33
-#>       30     0.06          0.63          0.34          0.05          0.17
-#>       40     0.05          0.30          0.20          0.03          0.11
-#>       50     0.04          0.17          0.13          0.02          0.08
-#>         crop
-#> distance Hopfenbau Flaechenkulturen > 900 l/ha Gleisanlagen
-#>       1         NA                       2.120           NA
-#>       3      15.12                          NA           NA
-#>       5       7.99                       0.110           NA
-#>       10      3.36                       0.030           NA
-#>       15      2.03                       0.010           NA
-#>       20      0.88                       0.008           NA
-#>       30      0.29                       0.004           NA
-#>       40      0.14                       0.002           NA
-#>       50      0.07                       0.002           NA
-#> 
-#> [[6]]
-#>         crop
-#> distance Ackerbau Obstbau frueh Obstbau spaet Weinbau frueh Weinbau spaet
-#>       1      1.64            NA            NA            NA            NA
-#>       3      0.56         22.76          9.21          2.29          6.41
-#>       5      0.34         14.64          5.18          0.97          2.85
-#>       10     0.17          8.04          2.38          0.30          0.95
-#>       15     0.11          3.71          1.20          0.15          0.50
-#>       20     0.09          1.75          0.68          0.09          0.32
-#>       30     0.06          0.61          0.31          0.05          0.17
-#>       40     0.04          0.29          0.17          0.03          0.11
-#>       50     0.03          0.16          0.11          0.02          0.07
-#>         crop
-#> distance Hopfenbau Flaechenkulturen > 900 l/ha Gleisanlagen
-#>       1         NA                       1.980           NA
-#>       3      14.90                          NA           NA
-#>       5       7.79                       0.100           NA
-#>       10      3.23                       0.030           NA
-#>       15      1.93                       0.010           NA
-#>       20      0.83                       0.008           NA
-#>       30      0.28                       0.004           NA
-#>       40      0.13                       0.002           NA
-#>       50      0.07                       0.001           NA
-#> 
-#> [[7]]
-#>         crop
-#> distance Ackerbau Obstbau frueh Obstbau spaet Weinbau frueh Weinbau spaet
-#>       1      1.61            NA            NA            NA            NA
-#>       3      0.55         22.69          9.10          2.24          6.33
-#>       5      0.33         14.45          5.11          0.94          2.81
-#>       10     0.17          7.83          2.33          0.29          0.94
-#>       15     0.11          3.62          1.20          0.15          0.49
-#>       20     0.08          1.71          0.67          0.09          0.31
-#>       30     0.06          0.60          0.30          0.05          0.16
-#>       40     0.04          0.28          0.17          0.03          0.10
-#>       50     0.03          0.16          0.11          0.02          0.07
-#>         crop
-#> distance Hopfenbau Flaechenkulturen > 900 l/ha Gleisanlagen
-#>       1         NA                       1.930           NA
-#>       3      14.63                          NA           NA
-#>       5       7.60                       0.100           NA
-#>       10      3.13                       0.030           NA
-#>       15      1.86                       0.010           NA
-#>       20      0.81                       0.008           NA
-#>       30      0.26                       0.004           NA
-#>       40      0.12                       0.002           NA
-#>       50      0.06                       0.001           NA
-#> 
-#> [[8]]
-#>         crop
-#> distance Ackerbau Obstbau frueh Obstbau spaet Weinbau frueh Weinbau spaet
-#>       1      1.52            NA            NA            NA            NA
-#>       3      0.52         22.24          8.66          2.16          6.26
-#>       5      0.31         14.09          4.92          0.91          2.78
-#>       10     0.16          7.58          2.29          0.28          0.93
-#>       15     0.11          3.48          1.14          0.14          0.49
-#>       20     0.08          1.65          0.65          0.09          0.31
-#>       30     0.05          0.57          0.29          0.04          0.16
-#>       40     0.04          0.27          0.16          0.03          0.10
-#>       50     0.03          0.15          0.11          0.02          0.07
-#>         crop
-#> distance Hopfenbau Flaechenkulturen > 900 l/ha Gleisanlagen
-#>       1         NA                       1.640           NA
-#>       3      13.53                          NA           NA
-#>       5       7.15                       0.090           NA
-#>       10      3.01                       0.020           NA
-#>       15      1.82                       0.010           NA
-#>       20      0.78                       0.007           NA
-#>       30      0.25                       0.003           NA
-#>       40      0.12                       0.002           NA
-#>       50      0.06                       0.001           NA
-#>

- -

Examples


+if (FALSE) {
+  # This is the code that was used to extract the data
+  library(readxl)
+  abdrift_path <- "inst/extdata/Tabelle der Abdrifteckwerte.xls"
+  JKI_crops <- c("Ackerbau", "Obstbau frueh", "Obstbau spaet", "Weinbau frueh", "Weinbau spaet",
+    "Hopfenbau", "Flaechenkulturen > 900 l/ha", "Gleisanlagen")
+  names(JKI_crops) <- c("Field crops", "Pome/stone fruit, early", "Pome/stone fruit, late",
+    "Vines early", "Vines late", "Hops", "Areic cultures > 900 L/ha", "Railroad tracks")
+  drift_data_JKI <- list()
+
+  for (n in 1:8) {
+    drift_data_raw <- read_excel(abdrift_path, sheet = n + 1, skip = 2)
+    drift_data <- matrix(NA, nrow = 9, ncol = length(JKI_crops))
+    dimnames(drift_data) <- list(distance = drift_data_raw[[1]][1:9],
+                                            crop = JKI_crops)
+    if (n == 1) { # Values for railroad tracks only present for one application
+      drift_data[, c(1:3, 5:8)] <- as.matrix(drift_data_raw[c(2:7, 11)][1:9, ])
+    } else {
+      drift_data[, c(1:3, 5:7)] <- as.matrix(drift_data_raw[c(2:7)][1:9, ])
+    }
+    drift_data_JKI[[n]] <- drift_data
+  }
+
+  # Manual data entry from the Rautmann paper
+  drift_data_JKI[[1]]["3", "Ackerbau"] <- 0.95
+  drift_data_JKI[[1]][, "Weinbau frueh"] <- c(NA, 2.7, 1.18, 0.39, 0.2, 0.13, 0.07, 0.04, 0.03)
+  drift_data_JKI[[2]]["3", "Ackerbau"] <- 0.79
+  drift_data_JKI[[2]][, "Weinbau frueh"] <- c(NA, 2.53, 1.09, 0.35, 0.18, 0.11, 0.06, 0.03, 0.02)
+  drift_data_JKI[[3]]["3", "Ackerbau"] <- 0.68
+  drift_data_JKI[[3]][, "Weinbau frueh"] <- c(NA, 2.49, 1.04, 0.32, 0.16, 0.10, 0.05, 0.03, 0.02)
+  drift_data_JKI[[4]]["3", "Ackerbau"] <- 0.62
+  drift_data_JKI[[4]][, "Weinbau frueh"] <- c(NA, 2.44, 1.02, 0.31, 0.16, 0.10, 0.05, 0.03, 0.02)
+  drift_data_JKI[[5]]["3", "Ackerbau"] <- 0.59
+  drift_data_JKI[[5]][, "Weinbau frueh"] <- c(NA, 2.37, 1.00, 0.31, 0.15, 0.09, 0.05, 0.03, 0.02)
+  drift_data_JKI[[6]]["3", "Ackerbau"] <- 0.56
+  drift_data_JKI[[6]][, "Weinbau frueh"] <- c(NA, 2.29, 0.97, 0.30, 0.15, 0.09, 0.05, 0.03, 0.02)
+  drift_data_JKI[[7]]["3", "Ackerbau"] <- 0.55
+  drift_data_JKI[[7]][, "Weinbau frueh"] <- c(NA, 2.24, 0.94, 0.29, 0.15, 0.09, 0.05, 0.03, 0.02)
+  drift_data_JKI[[8]]["3", "Ackerbau"] <- 0.52
+  drift_data_JKI[[8]][, "Weinbau frueh"] <- c(NA, 2.16, 0.91, 0.28, 0.14, 0.09, 0.04, 0.03, 0.02)
+
+  # Save the data
+  save(drift_data_JKI, file = "data/drift_data_JKI.RData")
+}
+
+# And these are the resulting data
+drift_data_JKI
+#> [[1]]
+#>         crop
+#> distance Ackerbau Obstbau frueh Obstbau spaet Weinbau frueh Weinbau spaet
+#>       1      2.77            NA            NA            NA            NA
+#>       3      0.95         29.20         15.73          2.70          8.02
+#>       5      0.57         19.89          8.41          1.18          3.62
+#>       10     0.29         11.81          3.60          0.39          1.23
+#>       15     0.20          5.55          1.81          0.20          0.65
+#>       20     0.15          2.77          1.09          0.13          0.42
+#>       30     0.10          1.04          0.54          0.07          0.22
+#>       40     0.07          0.52          0.32          0.04          0.14
+#>       50     0.06          0.30          0.22          0.03          0.10
+#>         crop
+#> distance Hopfenbau Flaechenkulturen > 900 l/ha Gleisanlagen
+#>       1         NA                       4.440           NA
+#>       3      19.33                          NA  0.018721696
+#>       5      11.57                       0.180  0.014363896
+#>       10      5.77                       0.050  0.010026007
+#>       15      3.84                       0.020  0.008124366
+#>       20      1.79                       0.012  0.006998158
+#>       30      0.56                       0.005  0.005670811
+#>       40      0.25                       0.003           NA
+#>       50      0.13                       0.002  0.004350831
+#> 
+#> [[2]]
+#>         crop
+#> distance Ackerbau Obstbau frueh Obstbau spaet Weinbau frueh Weinbau spaet
+#>       1      2.38            NA            NA            NA            NA
+#>       3      0.79         25.53         12.13          2.53          7.23
+#>       5      0.47         16.87          6.81          1.09          3.22
+#>       10     0.24          9.61          3.11          0.35          1.07
+#>       15     0.16          5.61          1.58          0.18          0.56
+#>       20     0.12          2.59          0.90          0.11          0.36
+#>       30     0.08          0.87          0.40          0.06          0.19
+#>       40     0.06          0.40          0.23          0.03          0.12
+#>       50     0.05          0.22          0.15          0.02          0.08
+#>         crop
+#> distance Hopfenbau Flaechenkulturen > 900 l/ha Gleisanlagen
+#>       1         NA                       3.780           NA
+#>       3      17.73                          NA           NA
+#>       5       9.60                       0.160           NA
+#>       10      4.18                       0.040           NA
+#>       15      2.57                       0.020           NA
+#>       20      1.21                       0.011           NA
+#>       30      0.38                       0.005           NA
+#>       40      0.17                       0.003           NA
+#>       50      0.09                       0.002           NA
+#> 
+#> [[3]]
+#>         crop
+#> distance Ackerbau Obstbau frueh Obstbau spaet Weinbau frueh Weinbau spaet
+#>       1      2.01            NA            NA            NA            NA
+#>       3      0.68         23.96         11.01          2.49          6.90
+#>       5      0.41         15.79          6.04          1.04          3.07
+#>       10     0.20          8.96          2.67          0.32          1.02
+#>       15     0.14          4.24          1.39          0.16          0.54
+#>       20     0.10          2.01          0.80          0.10          0.34
+#>       30     0.07          0.70          0.36          0.05          0.18
+#>       40     0.05          0.33          0.21          0.03          0.11
+#>       50     0.04          0.19          0.13          0.02          0.08
+#>         crop
+#> distance Hopfenbau Flaechenkulturen > 900 l/ha Gleisanlagen
+#>       1         NA                       3.420           NA
+#>       3      15.93                          NA           NA
+#>       5       8.57                       0.150           NA
+#>       10      3.70                       0.040           NA
+#>       15      2.26                       0.020           NA
+#>       20      1.05                       0.010           NA
+#>       30      0.34                       0.004           NA
+#>       40      0.15                       0.003           NA
+#>       50      0.08                       0.002           NA
+#> 
+#> [[4]]
+#>         crop
+#> distance Ackerbau Obstbau frueh Obstbau spaet Weinbau frueh Weinbau spaet
+#>       1      1.85            NA            NA            NA            NA
+#>       3      0.62         23.61         10.12          2.44          6.71
+#>       5      0.38         15.42          5.60          1.02          2.99
+#>       10     0.19          8.66          2.50          0.31          0.99
+#>       15     0.13          4.01          1.28          0.16          0.52
+#>       20     0.10          1.89          0.75          0.10          0.33
+#>       30     0.06          0.66          0.35          0.05          0.17
+#>       40     0.05          0.31          0.20          0.03          0.11
+#>       50     0.04          0.17          0.13          0.02          0.08
+#>         crop
+#> distance Hopfenbau Flaechenkulturen > 900 l/ha Gleisanlagen
+#>       1         NA                       2.290           NA
+#>       3      15.38                          NA           NA
+#>       5       8.26                       0.120           NA
+#>       10      3.55                       0.030           NA
+#>       15      2.17                       0.020           NA
+#>       20      0.93                       0.009           NA
+#>       30      0.31                       0.004           NA
+#>       40      0.14                       0.002           NA
+#>       50      0.08                       0.002           NA
+#> 
+#> [[5]]
+#>         crop
+#> distance Ackerbau Obstbau frueh Obstbau spaet Weinbau frueh Weinbau spaet
+#>       1      1.75            NA            NA            NA            NA
+#>       3      0.59         23.12          9.74          2.37          6.59
+#>       5      0.36         15.06          5.41          1.00          2.93
+#>       10     0.18          8.42          2.43          0.31          0.98
+#>       15     0.12          3.83          1.24          0.15          0.51
+#>       20     0.09          1.81          0.72          0.09          0.33
+#>       30     0.06          0.63          0.34          0.05          0.17
+#>       40     0.05          0.30          0.20          0.03          0.11
+#>       50     0.04          0.17          0.13          0.02          0.08
+#>         crop
+#> distance Hopfenbau Flaechenkulturen > 900 l/ha Gleisanlagen
+#>       1         NA                       2.120           NA
+#>       3      15.12                          NA           NA
+#>       5       7.99                       0.110           NA
+#>       10      3.36                       0.030           NA
+#>       15      2.03                       0.010           NA
+#>       20      0.88                       0.008           NA
+#>       30      0.29                       0.004           NA
+#>       40      0.14                       0.002           NA
+#>       50      0.07                       0.002           NA
+#> 
+#> [[6]]
+#>         crop
+#> distance Ackerbau Obstbau frueh Obstbau spaet Weinbau frueh Weinbau spaet
+#>       1      1.64            NA            NA            NA            NA
+#>       3      0.56         22.76          9.21          2.29          6.41
+#>       5      0.34         14.64          5.18          0.97          2.85
+#>       10     0.17          8.04          2.38          0.30          0.95
+#>       15     0.11          3.71          1.20          0.15          0.50
+#>       20     0.09          1.75          0.68          0.09          0.32
+#>       30     0.06          0.61          0.31          0.05          0.17
+#>       40     0.04          0.29          0.17          0.03          0.11
+#>       50     0.03          0.16          0.11          0.02          0.07
+#>         crop
+#> distance Hopfenbau Flaechenkulturen > 900 l/ha Gleisanlagen
+#>       1         NA                       1.980           NA
+#>       3      14.90                          NA           NA
+#>       5       7.79                       0.100           NA
+#>       10      3.23                       0.030           NA
+#>       15      1.93                       0.010           NA
+#>       20      0.83                       0.008           NA
+#>       30      0.28                       0.004           NA
+#>       40      0.13                       0.002           NA
+#>       50      0.07                       0.001           NA
+#> 
+#> [[7]]
+#>         crop
+#> distance Ackerbau Obstbau frueh Obstbau spaet Weinbau frueh Weinbau spaet
+#>       1      1.61            NA            NA            NA            NA
+#>       3      0.55         22.69          9.10          2.24          6.33
+#>       5      0.33         14.45          5.11          0.94          2.81
+#>       10     0.17          7.83          2.33          0.29          0.94
+#>       15     0.11          3.62          1.20          0.15          0.49
+#>       20     0.08          1.71          0.67          0.09          0.31
+#>       30     0.06          0.60          0.30          0.05          0.16
+#>       40     0.04          0.28          0.17          0.03          0.10
+#>       50     0.03          0.16          0.11          0.02          0.07
+#>         crop
+#> distance Hopfenbau Flaechenkulturen > 900 l/ha Gleisanlagen
+#>       1         NA                       1.930           NA
+#>       3      14.63                          NA           NA
+#>       5       7.60                       0.100           NA
+#>       10      3.13                       0.030           NA
+#>       15      1.86                       0.010           NA
+#>       20      0.81                       0.008           NA
+#>       30      0.26                       0.004           NA
+#>       40      0.12                       0.002           NA
+#>       50      0.06                       0.001           NA
+#> 
+#> [[8]]
+#>         crop
+#> distance Ackerbau Obstbau frueh Obstbau spaet Weinbau frueh Weinbau spaet
+#>       1      1.52            NA            NA            NA            NA
+#>       3      0.52         22.24          8.66          2.16          6.26
+#>       5      0.31         14.09          4.92          0.91          2.78
+#>       10     0.16          7.58          2.29          0.28          0.93
+#>       15     0.11          3.48          1.14          0.14          0.49
+#>       20     0.08          1.65          0.65          0.09          0.31
+#>       30     0.05          0.57          0.29          0.04          0.16
+#>       40     0.04          0.27          0.16          0.03          0.10
+#>       50     0.03          0.15          0.11          0.02          0.07
+#>         crop
+#> distance Hopfenbau Flaechenkulturen > 900 l/ha Gleisanlagen
+#>       1         NA                       1.640           NA
+#>       3      13.53                          NA           NA
+#>       5       7.15                       0.090           NA
+#>       10      3.01                       0.020           NA
+#>       15      1.82                       0.010           NA
+#>       20      0.78                       0.007           NA
+#>       30      0.25                       0.003           NA
+#>       40      0.12                       0.002           NA
+#>       50      0.06                       0.001           NA
+#> 
+

+ - - - + diff --git a/docs/reference/endpoint.html b/docs/reference/endpoint.html index 39071ee..5e038b1 100644 --- a/docs/reference/endpoint.html +++ b/docs/reference/endpoint.html @@ -1,250 +1,182 @@ - - - - - - +Retrieve endpoint information from the chyaml field of a chent object — endpoint • pfm + + -Retrieve endpoint information from the chyaml field of a chent object — endpoint • pfm +

- - +

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

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 + 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

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.

Details

- +

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.

- -

+ - - - + diff --git a/docs/reference/geomean.html b/docs/reference/geomean.html index 34befc3..20ee17d 100644 --- a/docs/reference/geomean.html +++ b/docs/reference/geomean.html @@ -1,174 +1,115 @@ - - - - - - - -Calculate the geometric mean — geomean • pfm - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +If at least one element of the vector is 0, it returns 0.">Calculate the geometric mean — geomean • pfm + + +

+ + + 0.6.0 - - - + + - - - - - - - -

- -

- +

Based on some posts in a thread on Stackoverflow -http://stackoverflow.com/questions/2602583/geometric-mean-is-there-a-built-in +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.

geomean(x, na.rm = FALSE)

Usage

geomean(x, na.rm = FALSE)

+ +

Arguments

x: Vector of numbers
na.rm: Should NA values be omitted?

x	Vector of numbers
na.rm	Should NA values be omitted?

Value

+ -

Examples

geomean(c(1, 3, 9))
#> [1] 3
geomean(c(1, 3, NA, 9))
#> [1] NA
if (FALSE) geomean(c(1, -3, 9)) # returns an error

- +

Author

Johannes Ranke

+ +

Examples

geomean(c(1, 3, 9))
+#> [1] 3
+geomean(c(1, 3, NA, 9))
+#> [1] NA
+if (FALSE) geomean(c(1, -3, 9)) # returns an error
+

- + - - - + diff --git a/docs/reference/get_vertex.html b/docs/reference/get_vertex.html index 3d0bc2d..aee1a70 100644 --- a/docs/reference/get_vertex.html +++ b/docs/reference/get_vertex.html @@ -1,159 +1,87 @@ - - - - - - - -Fit a parabola through three points — get_vertex • pfm - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +Fit a parabola through three points — get_vertex • pfm + + +

+ + + 0.6.0 - - - + + - - - - - - - -

- +

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

get_vertex(x, y)

- -

Arguments

- - - - - - - - - - -

x	Three x coordinates
y	Three y coordinates

- +

Usage

get_vertex(x, y)

- +

Three y coordinates

+ +

+ + - - - + diff --git a/docs/reference/index.html b/docs/reference/index.html index e5af0b7..f41d356 100644 --- a/docs/reference/index.html +++ b/docs/reference/index.html @@ -1,241 +1,326 @@ -Function reference • pfmFunction reference • pfm + + -

- +

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

- General utility functions - Functions that are independent of specific fate modelling areas -
- `geomean()` -	Calculate the geometric mean
- `one_box()` -	Create a time series of decline data
- `plot(<one_box>)` -	Plot time series of decline data
- `sawtooth()` -	Create decline time series for multiple applications
- `twa()` -	Calculate a time weighted average concentration
- `max_twa()` -	The maximum time weighted average concentration for a moving window
- `pfm_degradation()` -	Calculate a time course of relative concentrations based on an mkinmod model
- `SFO_actual_twa()` -	Actual and maximum moving window time average concentrations for SFO kinetics
- `FOMC_actual_twa()` -	Actual and maximum moving window time average concentrations for FOMC kinetics
- `reexports` `set_nd_nq` `set_nd_nq_focus` -	Objects exported from other packages
- `TSCF()` -	Estimation of the transpiration stream concentration factor
- Predicted environmental concentrations in soil - -
- `PEC_soil()` -	Calculate predicted environmental concentrations in soil
- `PEC_soil_mets()` -	Calculate initial and accumulation PEC soil for a set of metabolites
- `soil_scenario_data_EFSA_2015` -	Properties of the predefined scenarios from the EFSA guidance from 2015
- `soil_scenario_data_EFSA_2017` -	Properties of the predefined scenarios from the EFSA guidance from 2017
- `PEC_FOMC_accu_rel()` -	Get the relative accumulation of an FOMC model over multiples of an interval
- `EFSA_washoff_2017` -	Subset of EFSA crop washoff default values
- Predicted environmental concentrations in groundwater - -
- `FOCUS_GW_scenarios_2012` -	A very small subset of the FOCUS Groundwater scenario definitions
- `EFSA_GW_interception_2014` -	Subset of EFSA crop interception default values for groundwater modelling
- Predicted environmental concentrations in surface water - -
- `PEC_sw_drift()` -	Calculate predicted environmental concentrations in surface water due to drift
- `drift_data_JKI` -	Deposition from spray drift expressed as percent of the applied dose as -published by the JKI
- `PEC_sw_drainage_UK()` -	Calculate initial predicted environmental concentrations in surface water due to drainage using the UK method
- `PEC_sw_sed()` -	Calculate predicted environmental concentrations in sediment from surface -water concentrations
- `PEC_sw_focus()` -	Calculate PEC surface water at FOCUS Step 1
- `chent_focus_sw()` -	Create a chemical compound object for FOCUS Step 1 calculations
- `FOCUS_Step_12_scenarios` -	Step 1/2 scenario data as distributed with the FOCUS Step 1/2 calculator
- `PEC_sw_exposit_drainage()` -	Calculate PEC surface water due to drainage as in Exposit 3
- `PEC_sw_exposit_runoff()` -	Calculate PEC surface water due to runoff and erosion as in Exposit 3
- `perc_runoff_exposit` -	Runoff loss percentages as used in Exposit 3
- `perc_runoff_reduction_exposit` -	Runoff reduction percentages as used in Exposit
- `TOXSWA_cwa` -	R6 class for holding TOXSWA water concentration data and associated statistics
- `read.TOXSWA_cwa()` -	Read TOXSWA surface water concentrations
- `plot(<TOXSWA_cwa>)` -	Plot TOXSWA surface water concentrations
- Classifications and indicators - Evaluating environmental fate properties -
- `SSLRC_mobility_classification()` -	Determine the SSLRC mobility classification for a chemical substance from its Koc
- `GUS()` `print(<GUS_result>)` -	Groundwater ubiquity score based on Gustafson (1989)
- Work with chent objects containing relevant information - -
- `endpoint()` `soil_DT50()` `soil_Kfoc()` `soil_N()` `soil_sorption()` -	Retrieve endpoint information from the chyaml field of a chent object
- Utilities - -
- `get_vertex()` -	Fit a parabola through three points

- - -

General utility functions

+ +

Functions that are independent of specific fate modelling areas

+ + +

+ + + + +

+ + geomean() +: Calculate the geometric mean

+ + one_box() +: Create a time series of decline data

+ + plot(<one_box>) +: Plot time series of decline data

+ + sawtooth() +: Create decline time series for multiple applications

+ + twa() +: Calculate a time weighted average concentration

+ + max_twa() +: The maximum time weighted average concentration for a moving window

+ + pfm_degradation() +: Calculate a time course of relative concentrations based on an mkinmod model

+ + SFO_actual_twa() +: Actual and maximum moving window time average concentrations for SFO kinetics

+ + FOMC_actual_twa() +: Actual and maximum moving window time average concentrations for FOMC kinetics

+ + reexports set_nd_nq set_nd_nq_focus +: Objects exported from other packages

+ + TSCF() +: Estimation of the transpiration stream concentration factor

Predicted environmental concentrations in soil

+ + + + +

+ + PEC_soil() +: Calculate predicted environmental concentrations in soil

+ + PEC_soil_mets() +: Calculate initial and accumulation PEC soil for a set of metabolites

+ + soil_scenario_data_EFSA_2015 +: Properties of the predefined scenarios from the EFSA guidance from 2015

+ + soil_scenario_data_EFSA_2017 +: Properties of the predefined scenarios from the EFSA guidance from 2017

+ + PEC_FOMC_accu_rel() +: Get the relative accumulation of an FOMC model over multiples of an interval

+ + EFSA_washoff_2017 +: Subset of EFSA crop washoff default values

Predicted environmental concentrations in groundwater

+ + + + +

+ + FOCUS_GW_scenarios_2012 +: A very small subset of the FOCUS Groundwater scenario definitions

+ + EFSA_GW_interception_2014 +: Subset of EFSA crop interception default values for groundwater modelling

Predicted environmental concentrations in surface water

+ + + + +

+ + PEC_sw_drift() +: Calculate predicted environmental concentrations in surface water due to drift

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

+ + PEC_sw_drainage_UK() +: Calculate initial predicted environmental concentrations in surface water due to drainage using the UK method

+ + PEC_sw_sed() +: Calculate predicted environmental concentrations in sediment from surface +water concentrations

+ + PEC_sw_focus() +: Calculate PEC surface water at FOCUS Step 1

+ + chent_focus_sw() +: Create a chemical compound object for FOCUS Step 1 calculations

+ + FOCUS_Step_12_scenarios +: Step 1/2 scenario data as distributed with the FOCUS Step 1/2 calculator

+ + PEC_sw_exposit_drainage() +: Calculate PEC surface water due to drainage as in Exposit 3

+ + PEC_sw_exposit_runoff() +: Calculate PEC surface water due to runoff and erosion as in Exposit 3

+ + perc_runoff_exposit +: Runoff loss percentages as used in Exposit 3

+ + perc_runoff_reduction_exposit +: Runoff reduction percentages as used in Exposit

+ + TOXSWA_cwa +: R6 class for holding TOXSWA water concentration data and associated statistics

+ + read.TOXSWA_cwa() +: Read TOXSWA surface water concentrations

+ + plot(<TOXSWA_cwa>) +: Plot TOXSWA surface water concentrations

Classifications and indicators

+ +

Evaluating environmental fate properties

+ + +

+ + + + +

+ + SSLRC_mobility_classification() +: Determine the SSLRC mobility classification for a chemical substance from its Koc

+ + GUS() print(<GUS_result>) +: Groundwater ubiquity score based on Gustafson (1989)

Work with chent objects containing relevant information

+ + + + +

+ + endpoint() soil_DT50() soil_Kfoc() soil_N() soil_sorption() +: Retrieve endpoint information from the chyaml field of a chent object

Utilities

+ + + + +

+ + + +

+ + get_vertex() +: Fit a parabola through three points

- diff --git a/docs/reference/max_twa.html b/docs/reference/max_twa.html index 1d24c2a..98088fa 100644 --- a/docs/reference/max_twa.html +++ b/docs/reference/max_twa.html @@ -1,208 +1,137 @@ - - - - - - - -The maximum time weighted average concentration for a moving window — max_twa • pfm - - - - - - - - - - - - - - - - - - - - - - - - - - - -The maximum time weighted average concentration for a moving window — max_twa • pfm - +max_twa."> + + +

+ + + 0.6.0 - - - + + - - - - - - - -

- - - -

- -

- +

- -

If you generate your time series using sawtooth, +

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 +plot.one_box using the window size for the argument max_twa.

max_twa(x, window = 21)

- -

Arguments

- - - - - - - - - - -

x	An object of type `one_box`
window	The size of the moving window

- -

Details

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 +

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, + 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

- -

Examples

pred <- sawtooth(one_box(10),
-  applications = data.frame(time = c(0, 7), amount = c(1, 1)))
-max_twa(pred)
#> $max
-#>    parent 
-#> 0.9537545 
-#> 
-#> $window_start
-#> parent 
-#>      0 
-#> 
-#> $window_end
-#> parent 
-#>     21 
-#> 
pred_FOMC <- mkinfit("FOMC", FOCUS_2006_C, quiet = TRUE)
-max_twa(pred_FOMC)
#>       21 
-#> 18.22124

- +

Examples

pred <- sawtooth(one_box(10),
+  applications = data.frame(time = c(0, 7), amount = c(1, 1)))
+max_twa(pred)
+#> $max
+#>    parent 
+#> 0.9537545 
+#> 
+#> $window_start
+#> parent 
+#>      0 
+#> 
+#> $window_end
+#> parent 
+#>     21 
+#> 
+pred_FOMC <- mkinfit("FOMC", FOCUS_2006_C, quiet = TRUE)
+max_twa(pred_FOMC)
+#>       21 
+#> 18.22124 
+

+ - - - + diff --git a/docs/reference/one_box-1.png b/docs/reference/one_box-1.png index 10bc9f7..b280532 100644 Binary files a/docs/reference/one_box-1.png and b/docs/reference/one_box-1.png differ diff --git a/docs/reference/one_box-2.png b/docs/reference/one_box-2.png index 9cbb045..8c8188c 100644 Binary files a/docs/reference/one_box-2.png and b/docs/reference/one_box-2.png differ diff --git a/docs/reference/one_box-3.png b/docs/reference/one_box-3.png index ec1cc0c..3d77b8e 100644 Binary files a/docs/reference/one_box-3.png and b/docs/reference/one_box-3.png differ diff --git a/docs/reference/one_box.html b/docs/reference/one_box.html index cf58a58..d143079 100644 --- a/docs/reference/one_box.html +++ b/docs/reference/one_box.html @@ -1,207 +1,147 @@ - - - - - - +Create a time series of decline data — one_box • pfm + + -Create a time series of decline data — one_box • pfm +

- - +

Create a time series of decline data

- - - +

Usage

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

one_box.Rd

Create a time series of decline data

An object of class one_box, inheriting from ts.

one_box(x, ini, ..., t_end = 100, res = 0.01)
-
-# S3 method for numeric
-one_box(x, ini = 1, ..., t_end = 100, res = 0.01)
-
-# S3 method for character
-one_box(x, ini = 1, parms, ..., t_end = 100, res = 0.01)
-
-# S3 method for 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"))
#> Successfully compiled differential equation model from auto-generated C code.
fit_2 <- mkinfit(m_2, FOCUS_2006_D, quiet = TRUE)
#> Warning: Observations with value of zero were removed from the data
pred_2 <- one_box(fit_2, ini = "model")
-plot(pred_2)

- -

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"))
+#> Temporary DLL for differentials generated and loaded
+fit_2 <- mkinfit(m_2, FOCUS_2006_D, quiet = TRUE)
+#> Warning: Observations with value of zero were removed from the data
+pred_2 <- one_box(fit_2, ini = "model")
+plot(pred_2)
+
+

+ - + - - - + diff --git a/docs/reference/perc_runoff_exposit.html b/docs/reference/perc_runoff_exposit.html index 38c1f70..ee29f54 100644 --- a/docs/reference/perc_runoff_exposit.html +++ b/docs/reference/perc_runoff_exposit.html @@ -1,175 +1,109 @@ - - - - - - - -Runoff loss percentages as used in Exposit 3 — perc_runoff_exposit • pfm - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +Runoff loss percentages as used in Exposit 3 — perc_runoff_exposit • pfm + + +

+ + + 0.6.0 - - - + + - - - - - - - -

- -

- +

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

- -

Format

- +

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 + 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

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/EN/04_PlantProtectionProducts/03_Applicants/04_AuthorisationProcedure/08_Environment/ppp_environment_node.html

- -

Examples

print(perc_runoff_exposit)
#>              Koc_lower_bound dissolved bound
-#> 0-20                       0     0.110 0.000
-#> >20-50                    20     0.151 0.000
-#> >50-100                   50     0.197 0.000
-#> >100-200                 100     0.248 0.001
-#> >200-500                 200     0.224 0.004
-#> >500-1000                500     0.184 0.020
-#> >1000-2000              1000     0.133 0.042
-#> >2000-5000              2000     0.084 0.091
-#> >5000-10000             5000     0.037 0.159
-#> >10000-20000           10000     0.031 0.192
-#> >20000-50000           20000     0.014 0.291
-#> >50000                 50000     0.001 0.451

- -

+ https://www.bvl.bund.de/EN/04_PlantProtectionProducts/03_Applicants/04_AuthorisationProcedure/08_Environment/ppp_environment_node.html

+ +

Examples

print(perc_runoff_exposit)
+#>              Koc_lower_bound dissolved bound
+#> 0-20                       0     0.110 0.000
+#> >20-50                    20     0.151 0.000
+#> >50-100                   50     0.197 0.000
+#> >100-200                 100     0.248 0.001
+#> >200-500                 200     0.224 0.004
+#> >500-1000                500     0.184 0.020
+#> >1000-2000              1000     0.133 0.042
+#> >2000-5000              2000     0.084 0.091
+#> >5000-10000             5000     0.037 0.159
+#> >10000-20000           10000     0.031 0.192
+#> >20000-50000           20000     0.014 0.291
+#> >50000                 50000     0.001 0.451
+

- + - - - + diff --git a/docs/reference/perc_runoff_reduction_exposit.html b/docs/reference/perc_runoff_reduction_exposit.html index dd37f4b..f398ca1 100644 --- a/docs/reference/perc_runoff_reduction_exposit.html +++ b/docs/reference/perc_runoff_reduction_exposit.html @@ -1,191 +1,125 @@ - - - - - - - -Runoff reduction percentages as used in Exposit — perc_runoff_reduction_exposit • pfm - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +Runoff reduction percentages as used in Exposit — perc_runoff_reduction_exposit • pfm + + +

+ + + 0.6.0 - - - + + - - - - - - - -

- +

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

perc_runoff_reduction_exposit

- - -

Format

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

+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/EN/04_PlantProtectionProducts/03_Applicants/04_AuthorisationProcedure/08_Environment/ppp_environment_node.html

+ https://www.bvl.bund.de/EN/04_PlantProtectionProducts/03_Applicants/04_AuthorisationProcedure/08_Environment/ppp_environment_node.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)
-
#> $`3.02`
-#>           dissolved bound
-#> No buffer         0     0
-#> 5 m              40    40
-#> 10 m             60    85
-#> 20 m             80    95
-#> 
-#> $`3.01a`
-#>           dissolved bound
-#> No buffer         0     0
-#> 3 m              25    30
-#> 5 m              40    40
-#> 6 m              45    55
-#> 10 m             60    85
-#> 20 m             80    95
-#> 
-#> $`3.01a2`
-#>           dissolved bound
-#> No buffer         0     0
-#> 3 m              25    25
-#> 
-#> $`2.0`
-#>           dissolved bound
-#> No buffer       0.0   0.0
-#> 20 m           97.5  97.5
-#>

- -

Examples

print(perc_runoff_reduction_exposit)
+#> $`3.02`
+#>           dissolved bound
+#> No buffer         0     0
+#> 5 m              40    40
+#> 10 m             60    85
+#> 20 m             80    95
+#> 
+#> $`3.01a`
+#>           dissolved bound
+#> No buffer         0     0
+#> 3 m              25    30
+#> 5 m              40    40
+#> 6 m              45    55
+#> 10 m             60    85
+#> 20 m             80    95
+#> 
+#> $`3.01a2`
+#>           dissolved bound
+#> No buffer         0     0
+#> 3 m              25    25
+#> 
+#> $`2.0`
+#>           dissolved bound
+#> No buffer       0.0   0.0
+#> 20 m           97.5  97.5
+#> 
+

- + - - - + diff --git a/docs/reference/pfm_degradation.html b/docs/reference/pfm_degradation.html index 8d06107..0ae35ba 100644 --- a/docs/reference/pfm_degradation.html +++ b/docs/reference/pfm_degradation.html @@ -1,191 +1,119 @@ - - - - - - - -Calculate a time course of relative concentrations based on an mkinmod model — pfm_degradation • pfm - - - - - +Calculate a time course of relative concentrations based on an mkinmod model — pfm_degradation • pfm + + - +

- - - +

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

Johannes Ranke

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

Examples

head(pfm_degradation("SFO", DT50 = 10))
+#> Error in head(pfm_degradation("SFO", DT50 = 10)): could not find function "head"
+

- -

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

- - -

Examples

head(pfm_degradation("SFO", DT50 = 10))
#>   time    parent
-#> 0    0 1.0000000
-#> 1    1 0.9330330
-#> 2    2 0.8705506
-#> 3    3 0.8122524
-#> 4    4 0.7578583
-#> 5    5 0.7071068

- -

- + - - - + diff --git a/docs/reference/plot.TOXSWA_cwa-1.png b/docs/reference/plot.TOXSWA_cwa-1.png index c6a278a..19dfbe6 100644 Binary files a/docs/reference/plot.TOXSWA_cwa-1.png and b/docs/reference/plot.TOXSWA_cwa-1.png differ diff --git a/docs/reference/plot.TOXSWA_cwa-2.png b/docs/reference/plot.TOXSWA_cwa-2.png index 869c43c..85ffe4b 100644 Binary files a/docs/reference/plot.TOXSWA_cwa-2.png and b/docs/reference/plot.TOXSWA_cwa-2.png differ diff --git a/docs/reference/plot.TOXSWA_cwa-3.png b/docs/reference/plot.TOXSWA_cwa-3.png index 315c741..f2bb9cf 100644 Binary files a/docs/reference/plot.TOXSWA_cwa-3.png and b/docs/reference/plot.TOXSWA_cwa-3.png differ diff --git a/docs/reference/plot.TOXSWA_cwa-4.png b/docs/reference/plot.TOXSWA_cwa-4.png index a0a88f9..6ee8f66 100644 Binary files a/docs/reference/plot.TOXSWA_cwa-4.png and b/docs/reference/plot.TOXSWA_cwa-4.png differ diff --git a/docs/reference/plot.TOXSWA_cwa-5.png b/docs/reference/plot.TOXSWA_cwa-5.png index 3ac506d..989f777 100644 Binary files a/docs/reference/plot.TOXSWA_cwa-5.png and b/docs/reference/plot.TOXSWA_cwa-5.png differ diff --git a/docs/reference/plot.TOXSWA_cwa.html b/docs/reference/plot.TOXSWA_cwa.html index 1262c71..09e9258 100644 --- a/docs/reference/plot.TOXSWA_cwa.html +++ b/docs/reference/plot.TOXSWA_cwa.html @@ -1,216 +1,157 @@ - - - - - - +Plot TOXSWA surface water concentrations — plot.TOXSWA_cwa • pfm + + -Plot TOXSWA surface water concentrations — plot.TOXSWA_cwa • pfm +

- - +

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

- - - +

Usage

# S3 method for 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

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

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")
+
+

+ - + - - - + diff --git a/docs/reference/plot.one_box-1.png b/docs/reference/plot.one_box-1.png index cf3f132..5149343 100644 Binary files a/docs/reference/plot.one_box-1.png and b/docs/reference/plot.one_box-1.png differ diff --git a/docs/reference/plot.one_box-2.png b/docs/reference/plot.one_box-2.png index 0e152d6..685384e 100644 Binary files a/docs/reference/plot.one_box-2.png and b/docs/reference/plot.one_box-2.png differ diff --git a/docs/reference/plot.one_box-3.png b/docs/reference/plot.one_box-3.png index ad93165..5c9fbcc 100644 Binary files a/docs/reference/plot.one_box-3.png and b/docs/reference/plot.one_box-3.png differ diff --git a/docs/reference/plot.one_box.html b/docs/reference/plot.one_box.html index 75b8fe9..9236503 100644 --- a/docs/reference/plot.one_box.html +++ b/docs/reference/plot.one_box.html @@ -1,205 +1,143 @@ - - - - - - +Plot time series of decline data — plot.one_box • pfm + + -Plot time series of decline data — plot.one_box • pfm +

- - +

Plot time series of decline data

- - - +

Usage

# S3 method for 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

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

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"))
+#> Temporary DLL for differentials generated and loaded
+fit_2 <- mkinfit(m_2, FOCUS_2006_D, quiet = TRUE)
+#> Warning: Observations with value of zero were removed from the data
+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")
+
+

+ - + - - - + diff --git a/docs/reference/read.TOXSWA_cwa.html b/docs/reference/read.TOXSWA_cwa.html index 9c5c2a0..32a9b1d 100644 --- a/docs/reference/read.TOXSWA_cwa.html +++ b/docs/reference/read.TOXSWA_cwa.html @@ -1,116 +1,58 @@ - - - - - - - -Read TOXSWA surface water concentrations — read.TOXSWA_cwa • pfm - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +renamed to ConLiqWatLay in the out file.">Read TOXSWA surface water concentrations — read.TOXSWA_cwa • pfm + + +

+ + + 0.6.0 - - - + + - - - - - - - -

- - - -

- -

- +

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 @@ -119,103 +61,99 @@ of the hourly averages (ConLiqWatLay). In TOXSWA 5.5.3 this variable was renamed to ConLiqWatLay in the out file.

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 + + 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 +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.

- -

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"))

- +

Value

+ + +

An instance of an R6 object of class +TOXSWA_cwa.

Author

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"))
+

+ - + - - - + diff --git a/docs/reference/reexports.html b/docs/reference/reexports.html index 2f410e4..a98a861 100644 --- a/docs/reference/reexports.html +++ b/docs/reference/reexports.html @@ -1,5 +1,12 @@ -Objects exported from other packages — reexports • pfmObjects exported from other packages — reexports • pfm + + -

- +

These objects are imported from other packages. Follow the links below to see their documentation.

mkin

- -

- diff --git a/docs/reference/sawtooth-1.png b/docs/reference/sawtooth-1.png index 2952433..6cf67ed 100644 Binary files a/docs/reference/sawtooth-1.png and b/docs/reference/sawtooth-1.png differ diff --git a/docs/reference/sawtooth-2.png b/docs/reference/sawtooth-2.png index 87da954..5c9fbcc 100644 Binary files a/docs/reference/sawtooth-2.png and b/docs/reference/sawtooth-2.png differ diff --git a/docs/reference/sawtooth.html b/docs/reference/sawtooth.html index fea24d4..d22afa4 100644 --- a/docs/reference/sawtooth.html +++ b/docs/reference/sawtooth.html @@ -1,194 +1,133 @@ - - - - - - - -Create decline time series for multiple applications — sawtooth • pfm - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +Create decline time series for multiple applications — sawtooth • pfm + + +

+ + + 0.6.0 - - - + + - - - - - - - -

- +

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

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"))
#> Successfully compiled differential equation model from auto-generated C code.
fit_2 <- mkinfit(m_2, FOCUS_2006_D, quiet = TRUE)
#> Warning: Observations with value of zero were removed from the data
#> Warning: Shapiro-Wilk test for standardized residuals: p =  0.0165
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)
#> $max
-#>    parent        m1 
-#> 0.7834480 0.8617048 
-#> 
-#> $window_start
-#> parent     m1 
-#>   0.00  26.85 
-#> 
-#> $window_end
-#> parent     m1 
-#>  21.00  47.85 
-#>

- -

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"))
+#> Temporary DLL for differentials generated and loaded
+fit_2 <- mkinfit(m_2, FOCUS_2006_D, quiet = TRUE)
+#> Warning: Observations with value of zero were removed from the data
+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)
+#> $max
+#>    parent        m1 
+#> 0.7834481 0.8617049 
+#> 
+#> $window_start
+#> parent     m1 
+#>   0.00  26.85 
+#> 
+#> $window_end
+#> parent     m1 
+#>  21.00  47.85 
+#> 
+

+ - - - + diff --git a/docs/reference/soil_scenario_data_EFSA_2015.html b/docs/reference/soil_scenario_data_EFSA_2015.html index cb3cf14..239596c 100644 --- a/docs/reference/soil_scenario_data_EFSA_2015.html +++ b/docs/reference/soil_scenario_data_EFSA_2015.html @@ -1,196 +1,129 @@ - - - - - - - -Properties of the predefined scenarios from the EFSA guidance from 2015 — soil_scenario_data_EFSA_2015 • pfm - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +scenario and model adjustment factors from p. 15 and p. 17 are included.">Properties of the predefined scenarios from the EFSA guidance from 2015 — soil_scenario_data_EFSA_2015 • pfm + + +

+ + + 0.6.0 - - - + + - - - - - - - -

- -

- +

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.

- -

Format

- +

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

- +

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

if (FALSE) {
-  # This is the code that was used to define the data
-  soil_scenario_data_EFSA_2015 <- data.frame(
-    Zone = rep(c("North", "Central", "South"), 2),
-    Country = c("Estonia", "Germany", "France", "Denmark", "Czech Republik", "Spain"),
-    T_arit = c(4.7, 8.0, 11.0, 8.2, 9.1, 12.8),
-    T_arr = c(7.0, 10.1, 12.3, 9.8, 11.2, 14.7),
-    Texture = c("Coarse", "Coarse", "Medium fine", "Medium", "Medium", "Medium"),
-    f_om = c(0.118, 0.086, 0.048, 0.023, 0.018, 0.011),
-    theta_fc = c(0.244, 0.244, 0.385, 0.347, 0.347, 0.347),
-    rho = c(0.95, 1.05, 1.22, 1.39, 1.43, 1.51),
-    f_sce = c(3, 2, 2, 2, 1.5, 1.5),
-    f_mod = c(2, 2, 2, 4, 4, 4),
-    stringsAsFactors = FALSE,
-    row.names = c("CTN", "CTC", "CTS", "CLN", "CLC", "CLS")
-  )
-  save(soil_scenario_data_EFSA_2015, file = '../data/soil_scenario_data_EFSA_2015.RData')
-}
-
-# And this is the resulting dataframe
-soil_scenario_data_EFSA_2015
#>        Zone        Country T_arit T_arr     Texture  f_om theta_fc  rho f_sce
-#> CTN   North        Estonia    4.7   7.0      Coarse 0.118    0.244 0.95   3.0
-#> CTC Central        Germany    8.0  10.1      Coarse 0.086    0.244 1.05   2.0
-#> CTS   South         France   11.0  12.3 Medium fine 0.048    0.385 1.22   2.0
-#> CLN   North        Denmark    8.2   9.8      Medium 0.023    0.347 1.39   2.0
-#> CLC Central Czech Republik    9.1  11.2      Medium 0.018    0.347 1.43   1.5
-#> CLS   South          Spain   12.8  14.7      Medium 0.011    0.347 1.51   1.5
-#>     f_mod
-#> CTN     2
-#> CTC     2
-#> CTS     2
-#> CLN     4
-#> CLC     4
-#> CLS     4

- -

Examples

if (FALSE) {
+  # This is the code that was used to define the data
+  soil_scenario_data_EFSA_2015 <- data.frame(
+    Zone = rep(c("North", "Central", "South"), 2),
+    Country = c("Estonia", "Germany", "France", "Denmark", "Czech Republik", "Spain"),
+    T_arit = c(4.7, 8.0, 11.0, 8.2, 9.1, 12.8),
+    T_arr = c(7.0, 10.1, 12.3, 9.8, 11.2, 14.7),
+    Texture = c("Coarse", "Coarse", "Medium fine", "Medium", "Medium", "Medium"),
+    f_om = c(0.118, 0.086, 0.048, 0.023, 0.018, 0.011),
+    theta_fc = c(0.244, 0.244, 0.385, 0.347, 0.347, 0.347),
+    rho = c(0.95, 1.05, 1.22, 1.39, 1.43, 1.51),
+    f_sce = c(3, 2, 2, 2, 1.5, 1.5),
+    f_mod = c(2, 2, 2, 4, 4, 4),
+    stringsAsFactors = FALSE,
+    row.names = c("CTN", "CTC", "CTS", "CLN", "CLC", "CLS")
+  )
+  save(soil_scenario_data_EFSA_2015, file = '../data/soil_scenario_data_EFSA_2015.RData')
+}
+
+# And this is the resulting dataframe
+soil_scenario_data_EFSA_2015
+#>        Zone        Country T_arit T_arr     Texture  f_om theta_fc  rho f_sce
+#> CTN   North        Estonia    4.7   7.0      Coarse 0.118    0.244 0.95   3.0
+#> CTC Central        Germany    8.0  10.1      Coarse 0.086    0.244 1.05   2.0
+#> CTS   South         France   11.0  12.3 Medium fine 0.048    0.385 1.22   2.0
+#> CLN   North        Denmark    8.2   9.8      Medium 0.023    0.347 1.39   2.0
+#> CLC Central Czech Republik    9.1  11.2      Medium 0.018    0.347 1.43   1.5
+#> CLS   South          Spain   12.8  14.7      Medium 0.011    0.347 1.51   1.5
+#>     f_mod
+#> CTN     2
+#> CTC     2
+#> CTS     2
+#> CLN     4
+#> CLC     4
+#> CLS     4
+

+ - - - + diff --git a/docs/reference/soil_scenario_data_EFSA_2017.html b/docs/reference/soil_scenario_data_EFSA_2017.html index 7e8044e..8e5739e 100644 --- a/docs/reference/soil_scenario_data_EFSA_2017.html +++ b/docs/reference/soil_scenario_data_EFSA_2017.html @@ -1,176 +1,109 @@ - - - - - - - -Properties of the predefined scenarios from the EFSA guidance from 2017 — soil_scenario_data_EFSA_2017 • pfm - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +scenario and model adjustment factors from p. 16 and p. 18 are included.">Properties of the predefined scenarios from the EFSA guidance from 2017 — soil_scenario_data_EFSA_2017 • pfm + + +

+ + + 0.6.0 - - - + + - - - - - - - -

- -

- +

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.

- -

Format

- +

Format

Source

- +

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
#>        Zone Country T_arit T_arr Texture  f_om theta_fc   rho f_sce f_mod
-#> CTN   North Estonia    5.7   7.6  Coarse 0.220    0.244 0.707   1.4     3
-#> CTC Central  Poland    7.4   9.3  Coarse 0.122    0.244 0.934   1.4     3
-#> CTS   South  France   10.2  11.7  Medium 0.070    0.349 1.117   1.4     3
-#> CLN   North Denmark    8.0   9.2  Medium 0.025    0.349 1.371   1.6     4
-#> CLC Central Austria    9.3  11.3  Medium 0.018    0.349 1.432   1.6     4
-#> CLS   South   Spain   15.4  16.7  Medium 0.010    0.349 1.521   1.6     4
-#>     FOCUS_zone prec
-#> CTN    Hamburg  639
-#> CTC    Hamburg  617
-#> CTS    Hamburg  667
-#> CLN    Hamburg  602
-#> CLC Châteaudun  589
-#> CLS    Sevilla  526

- -

Examples

soil_scenario_data_EFSA_2017
+#>        Zone Country T_arit T_arr Texture  f_om theta_fc   rho f_sce f_mod
+#> CTN   North Estonia    5.7   7.6  Coarse 0.220    0.244 0.707   1.4     3
+#> CTC Central  Poland    7.4   9.3  Coarse 0.122    0.244 0.934   1.4     3
+#> CTS   South  France   10.2  11.7  Medium 0.070    0.349 1.117   1.4     3
+#> CLN   North Denmark    8.0   9.2  Medium 0.025    0.349 1.371   1.6     4
+#> CLC Central Austria    9.3  11.3  Medium 0.018    0.349 1.432   1.6     4
+#> CLS   South   Spain   15.4  16.7  Medium 0.010    0.349 1.521   1.6     4
+#>     FOCUS_zone prec
+#> CTN    Hamburg  639
+#> CTC    Hamburg  617
+#> CTS    Hamburg  667
+#> CLN    Hamburg  602
+#> CLC Châteaudun  589
+#> CLS    Sevilla  526
+

+ - - - + diff --git a/docs/reference/twa.html b/docs/reference/twa.html index 2c21c67..fbcbe9a 100644 --- a/docs/reference/twa.html +++ b/docs/reference/twa.html @@ -1,187 +1,116 @@ - - - - - - - -Calculate a time weighted average concentration — twa • pfm - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +is after one window has passed.">Calculate a time weighted average concentration — twa • pfm + + +

+ + + 0.6.0 - - - + + - - - - - - - -

- +

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.

twa(x, window = 21)
+    
+    Usage
+    twa(x, window = 21)
+
+# S3 method for one_box
+twa(x, window = 21)
+    
 
-# S3 method for one_box
-twa(x, window = 21)

- -

Arguments

- - - - - - - - - - -

x	An object of type `one_box`
window	The size of the moving window

- -

Arguments

x: An object of type one_box

- -

Examples

pred <- sawtooth(one_box(10),
+  applications = data.frame(time = c(0, 7), amount = c(1, 1)))
+max_twa(pred)
+#> $max
+#>    parent 
+#> 0.9537545 
+#> 
+#> $window_start
+#> parent 
+#>      0 
+#> 
+#> $window_end
+#> parent 
+#>     21 
+#> 
+

- + - - - + -- cgit v1.2.1 From 4079a2c496aa4e8c6dc2a93afedcbedbf7a852b5 Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Sun, 12 Nov 2023 21:59:34 +0100 Subject: Enable markdown and rebuild docs --- docs/reference/EFSA_washoff_2017.html | 8 ++--- docs/reference/FOCUS_GW_scenarios_2012.html | 12 +++---- docs/reference/FOCUS_Step_12_scenarios.html | 6 ++-- docs/reference/FOMC_actual_twa.html | 4 +-- docs/reference/GUS.html | 6 ++-- docs/reference/PEC_soil.html | 40 +++++++++++----------- docs/reference/PEC_sw_drainage_UK.html | 10 +++--- docs/reference/PEC_sw_drift.html | 2 +- docs/reference/PEC_sw_exposit_drainage.html | 14 ++++---- docs/reference/PEC_sw_exposit_runoff.html | 14 ++++---- docs/reference/PEC_sw_focus.html | 28 +++++++-------- docs/reference/SFO_actual_twa.html | 4 +-- docs/reference/SSLRC_mobility_classification.html | 12 +++---- docs/reference/TOXSWA_cwa.html | 32 ++++++++++++++--- docs/reference/TSCF-1.png | Bin 27659 -> 40587 bytes docs/reference/TSCF.html | 6 ++-- docs/reference/endpoint.html | 12 +++---- docs/reference/max_twa.html | 8 ++--- docs/reference/perc_runoff_exposit.html | 14 ++++---- docs/reference/perc_runoff_reduction_exposit.html | 8 ++--- docs/reference/soil_scenario_data_EFSA_2015.html | 26 +++++++------- docs/reference/soil_scenario_data_EFSA_2017.html | 26 +++++++------- 22 files changed, 155 insertions(+), 137 deletions(-) (limited to 'docs/reference') diff --git a/docs/reference/EFSA_washoff_2017.html b/docs/reference/EFSA_washoff_2017.html index 1e292af..b424de9 100644 --- a/docs/reference/EFSA_washoff_2017.html +++ b/docs/reference/EFSA_washoff_2017.html @@ -54,10 +54,10 @@

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

+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

diff --git a/docs/reference/FOCUS_GW_scenarios_2012.html b/docs/reference/FOCUS_GW_scenarios_2012.html index 84c2fac..2e9982b 100644 --- a/docs/reference/FOCUS_GW_scenarios_2012.html +++ b/docs/reference/FOCUS_GW_scenarios_2012.html @@ -1,6 +1,6 @@ -A very small subset of the FOCUS Groundwater scenario definitions — FOCUS_GW_scenarios_2012 • pfmA very small subset of the FOCUS Groundwater scenario definitions — FOCUS_GW_scenarios_2012 • pfmProperties of the predefined scenarios from the EFSA guidance from 2015 — soil_scenario_data_EFSA_2015 • pfmProperties of the predefined scenarios from the EFSA guidance from 2017 — soil_scenario_data_EFSA_2017 • pfmProperties of the predefined scenarios from the EFSA guidance from 2017 — soil_scenario_data_EFSA_2017 • pfm

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 predicted environmental concentrations in soil

Arguments

Arguments

Value

Details

Value

Details

Note

Note

References

References

Examples

Contents

Author

Examples

Calculate PEC surface water at FOCUS Step 1

Arguments

Arguments

Note

Note

References

References

Examples

Contents

Examples

R6 class for holding TOXSWA water concentration data and associated statistics

Format

Public fields

Format

Public fields

Methods

Methods

Public methods

Method new()

Usage

Arguments

Method moving_windows()

Usage

Arguments

Public methods

Method new()

Usage

Arguments

Method moving_windows()

Usage

Arguments

Method get_events()

Method get_events()

Usage

Usage

Arguments

Arguments

Method print()

Usage

Method clone()

Usage

Arguments

Examples

Method print()

Usage

Method clone()

Usage

Arguments

Examples

Reference

General utility functions

General utility functions

Predicted environmental concentrations in soil

Predicted environmental concentrations in soil

Predicted environmental concentrations in groundwater

Predicted environmental concentrations in groundwater

Predicted environmental concentrations in surface water

Predicted environmental concentrations in surface water

Classifications and indicators

Classifications and indicators

Work with chent objects containing relevant information

Work with chent objects containing relevant information

Utilities

Utilities

Objects exported from other packages

Method `new()`

Method `moving_windows()`

Method `new()`

Method `moving_windows()`

Method `get_events()`

Method `get_events()`

Method `print()`

Method `clone()`

Method `print()`

Method `clone()`