From a76221d87485029444c8e684022ca606a0c7e68d Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Wed, 18 Jan 2017 22:41:01 +0100 Subject: Update static docs using pkgdown - Add _pkgdown.yml for a structured function/data reference - Make seealso links active - Make mkinfit calls quiet - Use pkgdown branch from pull request hadley/pkgdown#229 to have topics ordered --- docs/reference/FOCUS_GW_scenarios_2012.html | 171 +++++++++++++++ docs/reference/GUS.html | 181 +++++++++++++++ docs/reference/PEC_soil.html | 229 +++++++++++++++++++ docs/reference/PEC_sw_drainage_UK.html | 137 ++++++++++++ docs/reference/PEC_sw_drift.html | 144 ++++++++++++ docs/reference/PEC_sw_sed.html | 142 ++++++++++++ docs/reference/SFO_actual_twa.html | 138 ++++++++++++ docs/reference/SSLRC_mobility_classification.html | 129 +++++++++++ docs/reference/TOXSWA_cwa.html | 178 +++++++++++++++ docs/reference/drift_data_JKI.html | 255 ++++++++++++++++++++++ docs/reference/endpoint.html | 170 +++++++++++++++ docs/reference/geomean.html | 127 +++++++++++ docs/reference/index.html | 195 +++++++++++++++++ docs/reference/max_twa.html | 116 ++++++++++ docs/reference/one_box-12.png | Bin 0 -> 9901 bytes docs/reference/one_box-2.png | Bin 0 -> 5783 bytes docs/reference/one_box-4.png | Bin 0 -> 5743 bytes docs/reference/one_box-6.png | Bin 0 -> 5743 bytes docs/reference/one_box-8.png | Bin 0 -> 9901 bytes docs/reference/one_box.html | 137 ++++++++++++ docs/reference/pfm_degradation.html | 136 ++++++++++++ docs/reference/plot.TOXSWA_cwa-2.png | Bin 0 -> 7427 bytes docs/reference/plot.TOXSWA_cwa.html | 138 ++++++++++++ docs/reference/plot.one_box-2.png | Bin 0 -> 10136 bytes docs/reference/plot.one_box.html | 131 +++++++++++ docs/reference/read.TOXSWA_cwa.html | 154 +++++++++++++ docs/reference/sawtooth-2.png | Bin 0 -> 6175 bytes docs/reference/sawtooth-6.png | Bin 0 -> 13603 bytes docs/reference/sawtooth-8.png | Bin 0 -> 13603 bytes docs/reference/sawtooth.html | 142 ++++++++++++ docs/reference/soil_scenario_data_EFSA_2015.html | 157 +++++++++++++ docs/reference/twa.html | 138 ++++++++++++ 32 files changed, 3445 insertions(+) create mode 100644 docs/reference/FOCUS_GW_scenarios_2012.html create mode 100644 docs/reference/GUS.html create mode 100644 docs/reference/PEC_soil.html create mode 100644 docs/reference/PEC_sw_drainage_UK.html create mode 100644 docs/reference/PEC_sw_drift.html create mode 100644 docs/reference/PEC_sw_sed.html create mode 100644 docs/reference/SFO_actual_twa.html create mode 100644 docs/reference/SSLRC_mobility_classification.html create mode 100644 docs/reference/TOXSWA_cwa.html create mode 100644 docs/reference/drift_data_JKI.html create mode 100644 docs/reference/endpoint.html create mode 100644 docs/reference/geomean.html create mode 100644 docs/reference/index.html create mode 100644 docs/reference/max_twa.html create mode 100644 docs/reference/one_box-12.png create mode 100644 docs/reference/one_box-2.png create mode 100644 docs/reference/one_box-4.png create mode 100644 docs/reference/one_box-6.png create mode 100644 docs/reference/one_box-8.png create mode 100644 docs/reference/one_box.html create mode 100644 docs/reference/pfm_degradation.html create mode 100644 docs/reference/plot.TOXSWA_cwa-2.png create mode 100644 docs/reference/plot.TOXSWA_cwa.html create mode 100644 docs/reference/plot.one_box-2.png create mode 100644 docs/reference/plot.one_box.html create mode 100644 docs/reference/read.TOXSWA_cwa.html create mode 100644 docs/reference/sawtooth-2.png create mode 100644 docs/reference/sawtooth-6.png create mode 100644 docs/reference/sawtooth-8.png create mode 100644 docs/reference/sawtooth.html create mode 100644 docs/reference/soil_scenario_data_EFSA_2015.html create mode 100644 docs/reference/twa.html (limited to 'docs/reference') diff --git a/docs/reference/FOCUS_GW_scenarios_2012.html b/docs/reference/FOCUS_GW_scenarios_2012.html new file mode 100644 index 0000000..64b42e9 --- /dev/null +++ b/docs/reference/FOCUS_GW_scenarios_2012.html @@ -0,0 +1,171 @@ + + + + + + + + +A very small subset of the FOCUS Groundwater scenario defitions — FOCUS_GW_scenarios_2012 • pfm + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + + +
+ +
+
+
+

A very small subset of the FOCUS Groundwater scenario defitions

+
+ + +

Currently, only a small subset of the soil definitions are provided.

+ + + +

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
+#>
+
+ +
+ +
+
+

Developed by Johannes Ranke.

+
+ +
+

Site built with pkgdown.

+
+ +
+
+ + + diff --git a/docs/reference/GUS.html b/docs/reference/GUS.html new file mode 100644 index 0000000..f65c6fd --- /dev/null +++ b/docs/reference/GUS.html @@ -0,0 +1,181 @@ + + + + + + + + +Groundwater ubiquity score based on Gustafson (1989) — GUS • pfm + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + + +
+ +
+
+
+

Groundwater ubiquity score based on Gustafson (1989)

+
+ + +

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 +half-life according to Gustafson (1989). However, leaching to the sub-soil +can not completely be excluded in field dissipation experiments and Gustafson +did not refer to any normalisation procedure, but says the field study should +be conducted under use conditions.
+
Koc
+
The sorption constant normalised to organic carbon. Gustafson +does not mention the nonlinearity of the sorption constant commonly +found and usually described by Freundlich sorption, therefore it is +unclear at which reference concentration the Koc should be observed +(and if the reference concentration would be in soil or in porewater).
+
chent
+
If a chent is given with appropriate information present in its +chyaml field, this information is used, with defaults specified below.
+
degradation_value
+
Which of the available degradation values should +be used?
+
lab_field
+
Should laboratory or field half-lives be used? This +defaults to lab in this implementation, in order to avoid +double-accounting for mobility. If comparability with the original GUS +values given by Gustafson (1989) is desired, non-normalised first-order +field half-lives obtained under actual use conditions should be used.
+
redox
+
Aerobic or anaerobic degradation data
+
sorption_value
+
Which of the available sorption values should be used? +Defaults to Kfoc as this is what is generally available from the European +pesticide peer review process. These values generally use a reference +concentration of 1 mg/L in porewater, that means they would be expected to +be Koc values at a concentration of 1 mg/L in the water phase.
+
degradation_aggregator
+
Function for aggregating half-lives
+
sorption_aggregator
+
Function for aggregation Koc values
+
x
+
An object of class GUS_result to be printed
+
digits
+
The number of digits used in the print method
+
+ +

Value

+ +

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

+ +

References

+ +

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

+ + +
+ +
+ +
+
+

Developed by Johannes Ranke.

+
+ +
+

Site built with pkgdown.

+
+ +
+
+ + + diff --git a/docs/reference/PEC_soil.html b/docs/reference/PEC_soil.html new file mode 100644 index 0000000..0449f8e --- /dev/null +++ b/docs/reference/PEC_soil.html @@ -0,0 +1,229 @@ + + + + + + + + +Calculate predicted environmental concentrations in soil — PEC_soil • pfm + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + + +
+ +
+
+
+

Calculate predicted environmental concentrations in soil

+
+ + +

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

+ + + 
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, chent = NA, DT50 = NA,
+  Koc = NA, Kom = Koc/1.724, t_avg = 0, scenarios = c("default",
+  "EFSA_2015"), 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, defaults to 365, which is a year if +rate 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
+
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
+
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
+
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), +and corrected using an Arrhenius activation energy of 65.4 kJ/mol. Also +model and scenario adjustment factors from the EFSA guidance 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).
+
+ +

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 (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 (2015, p. 13) can easily be calculated.

+ +

Note

+ +

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

+ +

References

+ +

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

+

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

+ + +

Examples

+ 
PEC_soil(100, interception = 0.25)
#>      scenario
+#> t_avg default
+#>     0     0.1

+# 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)
+
+ +
+ +
+
+

Developed by Johannes Ranke.

+
+ +
+

Site built with pkgdown.

+
+ +
+
+ + + diff --git a/docs/reference/PEC_sw_drainage_UK.html b/docs/reference/PEC_sw_drainage_UK.html new file mode 100644 index 0000000..db0f85f --- /dev/null +++ b/docs/reference/PEC_sw_drainage_UK.html @@ -0,0 +1,137 @@ + + + + + + + + +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

+
+ + +

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

+ + + 
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

+ + +

Examples

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

Developed by Johannes Ranke.

+
+ +
+

Site built with pkgdown.

+
+ +
+
+ + + diff --git a/docs/reference/PEC_sw_drift.html b/docs/reference/PEC_sw_drift.html new file mode 100644 index 0000000..a168de3 --- /dev/null +++ b/docs/reference/PEC_sw_drift.html @@ -0,0 +1,144 @@ + + + + + + + + +Calculate predicted environmental concentrations in surface water due to drift — PEC_sw_drift • pfm + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + + +
+ +
+
+
+

Calculate predicted environmental concentrations in surface water due to drift

+
+ + +

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.

+ + + 
PEC_sw_drift(rate, applications = 1, water_depth = 30,
+  drift_percentages = NULL, drift_data = "JKI", 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
+
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
+
+ +
+ +
+
+

Developed by Johannes Ranke.

+
+ +
+

Site built with pkgdown.

+
+ +
+
+ + + diff --git a/docs/reference/PEC_sw_sed.html b/docs/reference/PEC_sw_sed.html new file mode 100644 index 0000000..6ede4c7 --- /dev/null +++ b/docs/reference/PEC_sw_sed.html @@ -0,0 +1,142 @@ + + + + + + + + +Calculate predicted environmental concentrations in sediment from surface + — PEC_sw_sed • pfm + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + + +
+ +
+
+
+

Calculate predicted environmental concentrations in sediment from surface +

+
+ + +

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

+ + + 
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
+
+ +
+ +
+
+

Developed by Johannes Ranke.

+
+ +
+

Site built with pkgdown.

+
+ +
+
+ + + diff --git a/docs/reference/SFO_actual_twa.html b/docs/reference/SFO_actual_twa.html new file mode 100644 index 0000000..4285191 --- /dev/null +++ b/docs/reference/SFO_actual_twa.html @@ -0,0 +1,138 @@ + + + + + + + + +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

+
+ + +

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

+ +

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

+ + +

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
+
+ +
+ +
+
+

Developed by Johannes Ranke.

+
+ +
+

Site built with pkgdown.

+
+ +
+
+ + + diff --git a/docs/reference/SSLRC_mobility_classification.html b/docs/reference/SSLRC_mobility_classification.html new file mode 100644 index 0000000..f207a7a --- /dev/null +++ b/docs/reference/SSLRC_mobility_classification.html @@ -0,0 +1,129 @@ + + + + + + + + +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

+
+ + +

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

+ +

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

+ + +

Examples

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

Developed by Johannes Ranke.

+
+ +
+

Site built with pkgdown.

+
+ +
+
+ + + diff --git a/docs/reference/TOXSWA_cwa.html b/docs/reference/TOXSWA_cwa.html new file mode 100644 index 0000000..3f9310a --- /dev/null +++ b/docs/reference/TOXSWA_cwa.html @@ -0,0 +1,178 @@ + + + + + + + + +R6 class for holding TOXSWA cwa concentration data and associated statistics — TOXSWA_cwa • pfm + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + + +
+ +
+
+
+

R6 class for holding TOXSWA cwa concentration data and associated statistics

+
+ + +

An R6 class for holding TOXSWA cwa concentration data and some associated statistics. +Usually, an instance of this class will be generated by read.TOXSWA_cwa.

+ + + 
TOXSWA_cwa
+ +

Format

+ +

An R6Class generator object.

+ +

Fields

+ +

+

+
filename
Length one character vector.

+

basedir
Length one character vector.

+

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

+

cwas
Dataframe holding the concentrations.

+

events
List of dataframes holding the event statistics for each threshold.

+

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

+ +

Methods

+ +

+

+
get_events(threshold, total = FALSE)
+ Populate a datataframe with event information for the specified threshold value + in µg/L. If total = TRUE, the total concentration including the amount + adsorbed to suspended matter will be used. The resulting dataframe is stored in the + events field of the object. +
+
moving_windows(windows, total = FALSE)
+ Add to the windows field described above. + Again, if total = TRUE, the total concentration including the amount + adsorbed to 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",
+                                             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
+
+ +
+ +
+
+

Developed by Johannes Ranke.

+
+ +
+

Site built with pkgdown.

+
+ +
+
+ + + diff --git a/docs/reference/drift_data_JKI.html b/docs/reference/drift_data_JKI.html new file mode 100644 index 0000000..527c1cb --- /dev/null +++ b/docs/reference/drift_data_JKI.html @@ -0,0 +1,255 @@ + + + + + + + + +Deposition from spray drift expressed as percent of the applied dose as + — 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

+ +

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

+ +

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

+ +

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.

+ + +

Examples

+ 

+## Not run: ------------------------------------
+#   # 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")
+#   names(JKI_crops) <- c("Field crops", "Pome/stone fruit, early", "Pome/stone fruit, late")
+#   drift_data_JKI <- list()
+# 
+#   for (n in 1:8) {
+#     drift_data_raw <- read_excel(abdrift_path, sheet = n + 1, skip = 2)
+#     drift_data <- as.matrix(drift_data_raw[1:9, 2:4]) 
+#     dimnames(drift_data) <- list(distance = as.integer(drift_data_raw[1:9, 1]),
+#                                             crop = JKI_crops)
+#     drift_data_JKI[[n]] <- drift_data
+#   }
+#   save(drift_data_JKI, file = "data/drift_data_JKI.RData")
+## ---------------------------------------------
+
+# And this is the resulting data
+drift_data_JKI
#> [[1]]
+#>         crop
+#> distance Ackerbau Obstbau frueh Obstbau spaet
+#>       1      2.77            NA            NA
+#>       3        NA         29.20         15.73
+#>       5      0.57         19.89          8.41
+#>       10     0.29         11.81          3.60
+#>       15     0.20          5.55          1.81
+#>       20     0.15          2.77          1.09
+#>       30     0.10          1.04          0.54
+#>       40     0.07          0.52          0.32
+#>       50     0.06          0.30          0.22
+#> 
+#> [[2]]
+#>         crop
+#> distance Ackerbau Obstbau frueh Obstbau spaet
+#>       1      2.38            NA            NA
+#>       3        NA         25.53         12.13
+#>       5      0.47         16.87          6.81
+#>       10     0.24          9.61          3.11
+#>       15     0.16          5.61          1.58
+#>       20     0.12          2.59          0.90
+#>       30     0.08          0.87          0.40
+#>       40     0.06          0.40          0.23
+#>       50     0.05          0.22          0.15
+#> 
+#> [[3]]
+#>         crop
+#> distance Ackerbau Obstbau frueh Obstbau spaet
+#>       1      2.01            NA            NA
+#>       3        NA         23.96         11.01
+#>       5      0.41         15.79          6.04
+#>       10     0.20          8.96          2.67
+#>       15     0.14          4.24          1.39
+#>       20     0.10          2.01          0.80
+#>       30     0.07          0.70          0.36
+#>       40     0.05          0.33          0.21
+#>       50     0.04          0.19          0.13
+#> 
+#> [[4]]
+#>         crop
+#> distance Ackerbau Obstbau frueh Obstbau spaet
+#>       1      1.85            NA            NA
+#>       3        NA         23.61         10.12
+#>       5      0.38         15.42          5.60
+#>       10     0.19          8.66          2.50
+#>       15     0.13          4.01          1.28
+#>       20     0.10          1.89          0.75
+#>       30     0.06          0.66          0.35
+#>       40     0.05          0.31          0.20
+#>       50     0.04          0.17          0.13
+#> 
+#> [[5]]
+#>         crop
+#> distance Ackerbau Obstbau frueh Obstbau spaet
+#>       1      1.75            NA            NA
+#>       3        NA         23.12          9.74
+#>       5      0.36         15.06          5.41
+#>       10     0.18          8.42          2.43
+#>       15     0.12          3.83          1.24
+#>       20     0.09          1.81          0.72
+#>       30     0.06          0.63          0.34
+#>       40     0.05          0.30          0.20
+#>       50     0.04          0.17          0.13
+#> 
+#> [[6]]
+#>         crop
+#> distance Ackerbau Obstbau frueh Obstbau spaet
+#>       1      1.64            NA            NA
+#>       3        NA         22.76          9.21
+#>       5      0.34         14.64          5.18
+#>       10     0.17          8.04          2.38
+#>       15     0.11          3.71          1.20
+#>       20     0.09          1.75          0.68
+#>       30     0.06          0.61          0.31
+#>       40     0.04          0.29          0.17
+#>       50     0.03          0.16          0.11
+#> 
+#> [[7]]
+#>         crop
+#> distance Ackerbau Obstbau frueh Obstbau spaet
+#>       1      1.61            NA            NA
+#>       3        NA         22.69          9.10
+#>       5      0.33         14.45          5.11
+#>       10     0.17          7.83          2.33
+#>       15     0.11          3.62          1.20
+#>       20     0.08          1.71          0.67
+#>       30     0.06          0.60          0.30
+#>       40     0.04          0.28          0.17
+#>       50     0.03          0.16          0.11
+#> 
+#> [[8]]
+#>         crop
+#> distance Ackerbau Obstbau frueh Obstbau spaet
+#>       1      1.52            NA            NA
+#>       3        NA         22.24          8.66
+#>       5      0.31         14.09          4.92
+#>       10     0.16          7.58          2.29
+#>       15     0.11          3.48          1.14
+#>       20     0.08          1.65          0.65
+#>       30     0.05          0.57          0.29
+#>       40     0.04          0.27          0.16
+#>       50     0.03          0.15          0.11
+#>
+
+ +
+ +
+
+

Developed by Johannes Ranke.

+
+ +
+

Site built with pkgdown.

+
+ +
+
+ + + diff --git a/docs/reference/endpoint.html b/docs/reference/endpoint.html new file mode 100644 index 0000000..57a7652 --- /dev/null +++ b/docs/reference/endpoint.html @@ -0,0 +1,170 @@ + + + + + + + + +Retrieve endpoint information from the chyaml field of a chent object — endpoint • pfm + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + + +
+ +
+
+
+

Retrieve endpoint information from the chyaml field of a chent object

+
+ + +

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.

+ + + 
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
+
+ +

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

+ +

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.

+ + +
+ +
+ +
+
+

Developed by Johannes Ranke.

+
+ +
+

Site built with pkgdown.

+
+ +
+
+ + + diff --git a/docs/reference/geomean.html b/docs/reference/geomean.html new file mode 100644 index 0000000..4634f99 --- /dev/null +++ b/docs/reference/geomean.html @@ -0,0 +1,127 @@ + + + + + + + + +Calculate the geometric mean — geomean • pfm + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + + +
+ +
+
+
+

Calculate the geometric mean

+
+ + +

Based on some posts in a thread on Stackoverflow +http://stackoverflow.com/questions/2602583/geometric-mean-is-there-a-built-in +This function checks for negative values, removes NA values per default and +returns 0 if at least one element of the vector is 0.

+ + + 
geomean(x, na.rm = TRUE)
+ +

Arguments

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

Value

+ +

The geometric mean

+ + +

Examples

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

Developed by Johannes Ranke.

+
+ +
+

Site built with pkgdown.

+
+ +
+
+ + + diff --git a/docs/reference/index.html b/docs/reference/index.html new file mode 100644 index 0000000..e458048 --- /dev/null +++ b/docs/reference/index.html @@ -0,0 +1,195 @@ + + + + + + + + +Function reference • pfm + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + + +
+ +
+
+
+

Function reference

+

version 0.4-1

+
+ +
+

General utility functions

+

Functions that are independent of specific fate modelling areas

+ + +

Calculate the geometric mean

+ + +

Create a time series of decline data

+ + +

Plot time series of decline data

+ + +

Create decline time series for multiple applications

+ + +

Calculate a time weighted average concentration

+ + +

The maximum time weighted average concentration for a moving window

+ +

Predicted environmental concentrations in soil

+

+ + +

Calculate predicted environmental concentrations in soil

+ + +

Properties of the predefined scenarios from the EFSA guidance from 2015

+ +

Predicted environmental concentrations in surface water

+

+ + +

Calculate predicted environmental concentrations in surface water due to drift

+ + +

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

+ + +

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

+ + +

R6 class for holding TOXSWA cwa concentration data and associated statistics

+ + +

Read TOXSWA surface water concentrations

+ + +

Plot TOXSWA surface water concentrations

+ +

Classifications and indicators

+

Evaluating environmental fate properties

+ + +

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

+ + +

Groundwater ubiquity score based on Gustafson (1989)

+ + +
+
+ + +
+ +
+
+

Developed by Johannes Ranke.

+
+ +
+

Site built with pkgdown.

+
+ +
+
+ + + diff --git a/docs/reference/max_twa.html b/docs/reference/max_twa.html new file mode 100644 index 0000000..4bb1be9 --- /dev/null +++ b/docs/reference/max_twa.html @@ -0,0 +1,116 @@ + + + + + + + + +The maximum time weighted average concentration for a moving window — max_twa • pfm + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + + +
+ +
+
+
+

The maximum time weighted average concentration for a moving window

+
+ + +

The maximum time weighted average concentration for a moving window

+ + + 
max_twa(x, window = 21)
+ +

Arguments

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

See also

+ +

twa

+ + +
+ +
+ +
+
+

Developed by Johannes Ranke.

+
+ +
+

Site built with pkgdown.

+
+ +
+
+ + + diff --git a/docs/reference/one_box-12.png b/docs/reference/one_box-12.png new file mode 100644 index 0000000..31e2f4b Binary files /dev/null and b/docs/reference/one_box-12.png differ diff --git a/docs/reference/one_box-2.png b/docs/reference/one_box-2.png new file mode 100644 index 0000000..84094b9 Binary files /dev/null and b/docs/reference/one_box-2.png differ diff --git a/docs/reference/one_box-4.png b/docs/reference/one_box-4.png new file mode 100644 index 0000000..10ebb35 Binary files /dev/null and b/docs/reference/one_box-4.png differ diff --git a/docs/reference/one_box-6.png b/docs/reference/one_box-6.png new file mode 100644 index 0000000..10ebb35 Binary files /dev/null and b/docs/reference/one_box-6.png differ diff --git a/docs/reference/one_box-8.png b/docs/reference/one_box-8.png new file mode 100644 index 0000000..31e2f4b Binary files /dev/null and b/docs/reference/one_box-8.png differ diff --git a/docs/reference/one_box.html b/docs/reference/one_box.html new file mode 100644 index 0000000..791528a --- /dev/null +++ b/docs/reference/one_box.html @@ -0,0 +1,137 @@ + + + + + + + + +Create a time series of decline data — one_box • pfm + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + + +
+ +
+
+
+

Create a time series of decline data

+
+ + +

The time series starts with the amount specified for the first application. +This does not create objects of type ts.

+ + + 
one_box(x, t_end = 100, res = 0.01, ...)
+
+# S3 method for numeric
+one_box(x, t_end = 100, res = 0.01, ...)
+
+# S3 method for mkinfit
+one_box(x, t_end = 100, res = 0.01, ...)
+ +

Arguments

+
+
x
+
When numeric, this is the half-life to be used for an exponential +decline. If x is an mkinfit object, the decline is calculated from this object
+
t_end
+
End of the time series
+
res
+
Resolution of the time series
+
...
+
Further arguments passed to methods
+
+ + +

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)
+pred_2 <- one_box(fit_2)
+plot(pred_2)
+
+ +
+ +
+
+

Developed by Johannes Ranke.

+
+ +
+

Site built with pkgdown.

+
+ +
+
+ + + diff --git a/docs/reference/pfm_degradation.html b/docs/reference/pfm_degradation.html new file mode 100644 index 0000000..d98778b --- /dev/null +++ b/docs/reference/pfm_degradation.html @@ -0,0 +1,136 @@ + + + + + + + + +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

+
+ + +

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

+ + + 
pfm_degradation(model = "SFO", DT50 = 1000, parms = c(k_parent_sink =
+  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
+#> 1    0 1.0000000
+#> 2    1 0.9330330
+#> 3    2 0.8705506
+#> 4    3 0.8122524
+#> 5    4 0.7578583
+#> 6    5 0.7071068
+
+ +
+ +
+
+

Developed by Johannes Ranke.

+
+ +
+

Site built with pkgdown.

+
+ +
+
+ + + diff --git a/docs/reference/plot.TOXSWA_cwa-2.png b/docs/reference/plot.TOXSWA_cwa-2.png new file mode 100644 index 0000000..6f9de30 Binary files /dev/null and b/docs/reference/plot.TOXSWA_cwa-2.png differ diff --git a/docs/reference/plot.TOXSWA_cwa.html b/docs/reference/plot.TOXSWA_cwa.html new file mode 100644 index 0000000..7cbe238 --- /dev/null +++ b/docs/reference/plot.TOXSWA_cwa.html @@ -0,0 +1,138 @@ + + + + + + + + +Plot TOXSWA surface water concentrations — plot.TOXSWA_cwa • pfm + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + + +
+ +
+
+
+

Plot TOXSWA surface water concentrations

+
+ + +

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

+ + + 
# S3 method for TOXSWA_cwa
+plot(x, time_column = c("datetime", "t", "t_firstjan",
+  "t_rel_to_max"), xlab = "default", ylab = "default", add = FALSE,
+  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?
+
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
+
+ + +

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)
+
+ +
+ +
+
+

Developed by Johannes Ranke.

+
+ +
+

Site built with pkgdown.

+
+ +
+
+ + + diff --git a/docs/reference/plot.one_box-2.png b/docs/reference/plot.one_box-2.png new file mode 100644 index 0000000..4007876 Binary files /dev/null and b/docs/reference/plot.one_box-2.png differ diff --git a/docs/reference/plot.one_box.html b/docs/reference/plot.one_box.html new file mode 100644 index 0000000..1870a55 --- /dev/null +++ b/docs/reference/plot.one_box.html @@ -0,0 +1,131 @@ + + + + + + + + +Plot time series of decline data — plot.one_box • pfm + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + + +
+ +
+
+
+

Plot time series of decline data

+
+ + +

Plot time series of decline data

+ + + 
# S3 method for one_box
+plot(x, xlim = range(time(x)), ylim = c(0, max(x)),
+  xlab = "Time", ylab = "Fraction of initial", 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
+
+ + +

Examples

+ 
plot(sawtooth(one_box(10), 3, 7), max_twa = 21)
+
+ +
+ +
+
+

Developed by Johannes Ranke.

+
+ +
+

Site built with pkgdown.

+
+ +
+
+ + + diff --git a/docs/reference/read.TOXSWA_cwa.html b/docs/reference/read.TOXSWA_cwa.html new file mode 100644 index 0000000..3afec9c --- /dev/null +++ b/docs/reference/read.TOXSWA_cwa.html @@ -0,0 +1,154 @@ + + + + + + + + +Read TOXSWA surface water concentrations — read.TOXSWA_cwa • pfm + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + + +
+ +
+
+
+

Read TOXSWA surface water concentrations

+
+ + +

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

+ + + 
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 (FOCUS TOXSWA 4, i.e. TOXSWA 4.4.2 or similar).
+
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 TOXSWA 4 .out files, the default value "parent" leads +to reading concentrations of the parent compound. Alternatively, the substance +of interested can be selected by its code name.
+
total
+
Set this to TRUE in order to read total concentrations as well. This is +only necessary for .out files as generated by TOXSWA 4.4.2 or similar, not for .cwa +files. For .cwa files, the total concentration is always read as well.
+
windows
+
Numeric vector of width of moving windows in days, for calculating +maximum time weighted average concentrations and areas under the curve.
+
thresholds
+
Numeric vector of threshold concentrations in µg/L for +generating event statistics.
+
+ +

Value

+ +

An instance of an R6 object of class +TOXSWA_cwa.

+ + +

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"))
+
+ +
+ +
+
+

Developed by Johannes Ranke.

+
+ +
+

Site built with pkgdown.

+
+ +
+
+ + + diff --git a/docs/reference/sawtooth-2.png b/docs/reference/sawtooth-2.png new file mode 100644 index 0000000..e6ff97d Binary files /dev/null and b/docs/reference/sawtooth-2.png differ diff --git a/docs/reference/sawtooth-6.png b/docs/reference/sawtooth-6.png new file mode 100644 index 0000000..161ecb4 Binary files /dev/null and b/docs/reference/sawtooth-6.png differ diff --git a/docs/reference/sawtooth-8.png b/docs/reference/sawtooth-8.png new file mode 100644 index 0000000..161ecb4 Binary files /dev/null and b/docs/reference/sawtooth-8.png differ diff --git a/docs/reference/sawtooth.html b/docs/reference/sawtooth.html new file mode 100644 index 0000000..e24186e --- /dev/null +++ b/docs/reference/sawtooth.html @@ -0,0 +1,142 @@ + + + + + + + + +Create decline time series for multiple applications — sawtooth • pfm + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + + +
+ +
+
+
+

Create decline time series for multiple applications

+
+ + +

If the number of application cycles n is greater than 1, the +application pattern specified in applications is repeated n +times, with an interval i.

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

Arguments

+
+
x
+
A one_box object
+
n
+
The number of applications. If applications is specified, n is not used
+
i
+
The interval between applications. If applications is specified, i +is not used
+
applications
+
A data frame holding the application times in the first column and +the corresponding amounts applied in the second column for each application cycle. +If n is one, the application pattern specified here is used only once.
+
+ + +

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)
+pred_2 <- one_box(fit_2)
+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 
+#> 0.6627707 0.8542785 
+#> 
+#> $window_start
+#> parent     m1 
+#>   0.00  32.09 
+#> 
+#> $window_end
+#> parent     m1 
+#>  21.00  53.09 
+#>
+
+ +
+ +
+
+

Developed by Johannes Ranke.

+
+ +
+

Site built with pkgdown.

+
+ +
+
+ + + diff --git a/docs/reference/soil_scenario_data_EFSA_2015.html b/docs/reference/soil_scenario_data_EFSA_2015.html new file mode 100644 index 0000000..f842d08 --- /dev/null +++ b/docs/reference/soil_scenario_data_EFSA_2015.html @@ -0,0 +1,157 @@ + + + + + + + + +Properties of the predefined scenarios from the EFSA guidance from 2015 — soil_scenario_data_EFSA_2015 • pfm + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + + +
+ +
+
+
+

Properties of the predefined scenarios from the EFSA guidance from 2015

+
+ + +

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

+ +

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

+ +

Source

+ +

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

+ + +

Examples

+ 
## Not run: ------------------------------------
+#   # 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
+
+ +
+ +
+
+

Developed by Johannes Ranke.

+
+ +
+

Site built with pkgdown.

+
+ +
+
+ + + diff --git a/docs/reference/twa.html b/docs/reference/twa.html new file mode 100644 index 0000000..414b372 --- /dev/null +++ b/docs/reference/twa.html @@ -0,0 +1,138 @@ + + + + + + + + +Calculate a time weighted average concentration — twa • pfm + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + + +
+ +
+
+
+

Calculate a time weighted average concentration

+
+ + +

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

Arguments

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

See also

+ +

max_twa

+ + +

Examples

+ 
pred <- sawtooth(one_box(10),
+  applications = data.frame(time = c(0, 7), amount = c(1, 1)))
+max_twa(pred)
#> $max
+#>    parent 
+#> 0.9537545 
+#> 
+#> $window_start
+#> parent 
+#>      0 
+#> 
+#> $window_end
+#> parent 
+#>     21 
+#>
+
+ +
+ + +
+ + + -- cgit v1.2.1