From aed160d7f0eaf5865e2bd9bf6c4b1c9d7b13d911 Mon Sep 17 00:00:00 2001 From: Ranke Johannes Date: Wed, 31 Jan 2024 13:16:17 +0100 Subject: Reorganise data generation - Use inst/data_generation for R code generating data as in some of my other packages - data/*.RData files were checked using https://github.com/jranke/dotfiles/blob/main/bin/rda_diff contents were not changed - Remove ChangeLog, the history is in the git logs - Update docs and some links contained therein - use \doi{} markup - Move logs to log directory --- docs/reference/EFSA_GW_interception_2014.html | 171 ++----- docs/reference/EFSA_washoff_2017.html | 171 ++----- docs/reference/FOCUS_GW_scenarios_2012.html | 248 ++++------ docs/reference/FOCUS_Step_12_scenarios.html | 327 +++---------- docs/reference/FOMC_actual_twa.html | 186 +++---- docs/reference/GUS.html | 287 +++++------ docs/reference/PEC_FOMC_accu_rel.html | 149 ++---- docs/reference/PEC_soil.html | 477 ++++++++---------- docs/reference/PEC_soil_mets.html | 159 ++---- docs/reference/PEC_sw_drainage_UK.html | 226 +++------ docs/reference/PEC_sw_drift.html | 271 ++++------- docs/reference/PEC_sw_exposit_drainage.html | 289 +++++------ docs/reference/PEC_sw_exposit_runoff.html | 341 ++++++------- docs/reference/PEC_sw_focus.html | 528 +++++++++----------- docs/reference/PEC_sw_sed.html | 224 +++------ docs/reference/Rplot002.png | Bin 0 -> 16733 bytes docs/reference/Rplot003.png | Bin 0 -> 16733 bytes docs/reference/Rplot004.png | Bin 0 -> 12802 bytes docs/reference/Rplot005.png | Bin 0 -> 7538 bytes docs/reference/SFO_actual_twa.html | 170 ++----- docs/reference/SSLRC_mobility_classification.html | 185 +++---- docs/reference/TOXSWA_cwa.html | 412 ++++++++-------- docs/reference/TSCF-1.png | Bin 24506 -> 39919 bytes docs/reference/TSCF.html | 154 ++---- docs/reference/chent_focus_sw.html | 233 ++++----- docs/reference/drift_data_JKI.html | 566 +++++++++------------- docs/reference/endpoint.html | 295 +++++------ docs/reference/geomean.html | 156 ++---- docs/reference/get_vertex.html | 135 ++---- docs/reference/index.html | 365 +++----------- docs/reference/max_twa.html | 213 +++----- docs/reference/one_box-1.png | Bin 15801 -> 24719 bytes docs/reference/one_box-2.png | Bin 15228 -> 21981 bytes docs/reference/one_box-3.png | Bin 30063 -> 34076 bytes docs/reference/one_box.html | 236 ++++----- docs/reference/perc_runoff_exposit.html | 170 +++---- docs/reference/perc_runoff_reduction_exposit.html | 190 +++----- docs/reference/pesticide.txt | 3 - docs/reference/pfm_degradation.html | 210 +++----- docs/reference/plot.TOXSWA_cwa-1.png | Bin 21821 -> 34458 bytes docs/reference/plot.TOXSWA_cwa-2.png | Bin 21005 -> 33311 bytes docs/reference/plot.TOXSWA_cwa-3.png | Bin 22278 -> 35208 bytes docs/reference/plot.TOXSWA_cwa-4.png | Bin 22682 -> 35650 bytes docs/reference/plot.TOXSWA_cwa-5.png | Bin 16209 -> 23109 bytes docs/reference/plot.TOXSWA_cwa.html | 258 ++++------ docs/reference/plot.one_box-1.png | Bin 16135 -> 24259 bytes docs/reference/plot.one_box-2.png | Bin 32593 -> 29965 bytes docs/reference/plot.one_box-3.png | Bin 37285 -> 41819 bytes docs/reference/plot.one_box.html | 242 ++++----- docs/reference/read.TOXSWA_cwa.html | 243 ++++------ docs/reference/reexports.html | 83 ++++ docs/reference/sawtooth-1.png | Bin 16202 -> 24497 bytes docs/reference/sawtooth-2.png | Bin 42340 -> 41819 bytes docs/reference/sawtooth.html | 218 +++------ docs/reference/soil_scenario_data_EFSA_2015.html | 179 ++----- docs/reference/soil_scenario_data_EFSA_2017.html | 192 +++----- docs/reference/twa.html | 183 +++---- 57 files changed, 3328 insertions(+), 6017 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 delete mode 100644 docs/reference/pesticide.txt create mode 100644 docs/reference/reexports.html (limited to 'docs/reference') diff --git a/docs/reference/EFSA_GW_interception_2014.html b/docs/reference/EFSA_GW_interception_2014.html index d7ec108..8bc0b51 100644 --- a/docs/reference/EFSA_GW_interception_2014.html +++ b/docs/reference/EFSA_GW_interception_2014.html @@ -1,67 +1,12 @@ - - - - - - - -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 - - - - + + -
-
- -
- -
+

Subset of EFSA crop interception default values for groundwater modelling

@@ -112,82 +43,58 @@

Subset of EFSA crop interception default values for groundwater modelling

+
+ 
EFSA_GW_interception_2014
+
- -

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

+ 
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
+
+
+
-
-
-

Developed by Johannes Ranke.

+
+

Developed by Johannes Ranke.

-

Site built with pkgdown 1.5.1.

+

Site built with pkgdown 2.0.7.

-
-
+
- - + + diff --git a/docs/reference/EFSA_washoff_2017.html b/docs/reference/EFSA_washoff_2017.html index abe0b24..8c95681 100644 --- a/docs/reference/EFSA_washoff_2017.html +++ b/docs/reference/EFSA_washoff_2017.html @@ -1,67 +1,12 @@ - - - - - - - -Subset of EFSA crop washoff default values — EFSA_washoff_2017 • pfm - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Subset of EFSA crop washoff default values — EFSA_washoff_2017 • pfm - - - - + + -
-
- -
- -
+

Subset of EFSA crop washoff default values

@@ -112,82 +43,58 @@

Subset of EFSA crop washoff default values

+
+ 
EFSA_washoff_2017
+
- -

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

+ 
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
+
+
+
-
-
-

Developed by Johannes Ranke.

+
+

Developed by Johannes Ranke.

-

Site built with pkgdown 1.5.1.

+

Site built with pkgdown 2.0.7.

-
-
+
- - + + diff --git a/docs/reference/FOCUS_GW_scenarios_2012.html b/docs/reference/FOCUS_GW_scenarios_2012.html index 8b55d14..75c3ff4 100644 --- a/docs/reference/FOCUS_GW_scenarios_2012.html +++ b/docs/reference/FOCUS_GW_scenarios_2012.html @@ -1,68 +1,13 @@ - - - - - - - -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 - - - - + + -
-
- -
- -
+

A very small subset of the FOCUS Groundwater scenario definitions

@@ -114,104 +45,107 @@ soil definitions are from page 46ff. from FOCUS (2012)." /> soil definitions are from page 46ff. from FOCUS (2012).

- 
FOCUS_GW_scenarios_2012
- - -

Format

+
+ 
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
+#> 
+
+
+
-
-
-

Developed by Johannes Ranke.

+
+

Developed by Johannes Ranke.

-

Site built with pkgdown 1.5.1.

+

Site built with pkgdown 2.0.7.

-
-
+
- - + + diff --git a/docs/reference/FOCUS_Step_12_scenarios.html b/docs/reference/FOCUS_Step_12_scenarios.html index bbfb592..0216e4e 100644 --- a/docs/reference/FOCUS_Step_12_scenarios.html +++ b/docs/reference/FOCUS_Step_12_scenarios.html @@ -1,68 +1,13 @@ - - - - - - - -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 - - - - + + -
-
- -
- -
+

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

@@ -115,210 +46,78 @@ 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

+ 

+# \dontrun{
+  # 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]
+#> Warning: cannot open file 'inst/extdata/FOCUS_Step_12_scenarios.txt': No such file or directory
+#> Error in file(con, "r"): cannot open the connection
+  FOCUS_Step_12_scenarios <- list()
+  sce <- read.table(text = scenarios, sep = "\t", header = TRUE, check.names = FALSE,
+    stringsAsFactors = FALSE)
+#> Error in eval(expr, envir, enclos): object 'scenarios' not found
+  FOCUS_Step_12_scenarios$names = sce$Crop
+#> Error in eval(expr, envir, enclos): object 'sce' not found
+  rownames(sce) <- sce$Crop
+#> Error in eval(expr, envir, enclos): object 'sce' not found
+  FOCUS_Step_12_scenarios$drift = sce[, 3:11]
+#> Error in eval(expr, envir, enclos): object 'sce' not found
+  FOCUS_Step_12_scenarios$interception = sce[, 12:15]
+#> Error in eval(expr, envir, enclos): object 'sce' not found
+  sce_2 <- readLines(scenario_path)[41:46]
+#> Warning: cannot open file 'inst/extdata/FOCUS_Step_12_scenarios.txt': No such file or directory
+#> Error in file(con, "r"): cannot open the connection
+  rd <- read.table(text = sce_2, sep = "\t")[1:2]
+#> Error in eval(expr, envir, enclos): object 'sce_2' not found
+  rd_mat <- matrix(rd$V2, nrow = 3, byrow = FALSE)
+#> Error in eval(expr, envir, enclos): object 'rd' not found
+  dimnames(rd_mat) = list(Time = c("Oct-Feb", "Mar-May", "Jun-Sep"),
+                          Region = c("North", "South"))
+#> Error: object 'rd_mat' not found
+  FOCUS_Step_12_scenarios$rd = rd_mat
+#> Error in eval(expr, envir, enclos): object 'rd_mat' not found
+  save(FOCUS_Step_12_scenarios, file = "data/FOCUS_Step_12_scenarios.RData")
+#> Warning: cannot open compressed file 'data/FOCUS_Step_12_scenarios.RData', probable reason 'No such file or directory'
+#> Error in gzfile(file, "wb"): cannot open the connection
+# }
+
+# And this is the resulting data
+FOCUS_Step_12_scenarios
+#> list()
+
+
+
-
-
-

Developed by Johannes Ranke.

+
+

Developed by Johannes Ranke.

-

Site built with pkgdown 1.5.1.

+

Site built with pkgdown 2.0.7.

-
-
+
- - + + diff --git a/docs/reference/FOMC_actual_twa.html b/docs/reference/FOMC_actual_twa.html index a365556..1fa9b97 100644 --- a/docs/reference/FOMC_actual_twa.html +++ b/docs/reference/FOMC_actual_twa.html @@ -1,64 +1,12 @@ - - - - - - - -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 - - - - + + -
-
- -
- -
+

Actual and maximum moving window time average concentrations for FOMC kinetics

@@ -109,79 +43,77 @@

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

+
+ 
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

-

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
-
- +

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
+
+
+
-
-
-

Developed by Johannes Ranke.

+
+

Developed by Johannes Ranke.

-

Site built with pkgdown 1.4.1.

+

Site built with pkgdown 2.0.7.

-
-
+
- - + + diff --git a/docs/reference/GUS.html b/docs/reference/GUS.html index e371c1a..dfe3b80 100644 --- a/docs/reference/GUS.html +++ b/docs/reference/GUS.html @@ -1,66 +1,14 @@ - - - - - - - -Groundwater ubiquity score based on Gustafson (1989) — GUS • pfm - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Groundwater ubiquity score based on Gustafson (1989) — GUS • pfm - - - - - - - - - - - - - - + + -
-
- -
- -
+

Groundwater ubiquity score based on Gustafson (1989)

@@ -113,139 +47,136 @@ 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 +

+ 
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

+ +
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.

- -
- +

Author

Johannes Ranke

+
+
+
-
-
-

Developed by Johannes Ranke.

+
+

Developed by Johannes Ranke.

-

Site built with pkgdown 1.4.1.

+

Site built with pkgdown 2.0.7.

-
-
+
- - + + diff --git a/docs/reference/PEC_FOMC_accu_rel.html b/docs/reference/PEC_FOMC_accu_rel.html index e4d9bf4..ebe531f 100644 --- a/docs/reference/PEC_FOMC_accu_rel.html +++ b/docs/reference/PEC_FOMC_accu_rel.html @@ -1,61 +1,12 @@ - - - - - - - -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 - - - - + + -
-
- -
- -
+

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

@@ -104,64 +40,55 @@
-

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

+
+ 
PEC_FOMC_accu_rel(n, interval, FOMC)
+
-

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

+
+

Arguments

+
n
+

number of applications

+ + +
interval
+

Time between applications

+ + +
FOMC
+

Named numeric vector containing the FOMC parameters alpha and beta

+ +
+
+

Value

-
-
+
-
-
-

Developed by Johannes Ranke.

+
+

Developed by Johannes Ranke.

-

Site built with pkgdown 1.4.1.

+

Site built with pkgdown 2.0.7.

-
-
+
- - + + diff --git a/docs/reference/PEC_soil.html b/docs/reference/PEC_soil.html index 3e405a5..f7e1da5 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 - - - - - - - - - - - - - - + + -
-
- -
- -
+

Calculate predicted environmental concentrations in soil

@@ -117,164 +51,167 @@ 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).

- 
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)
+
+
+
-
-
-

Developed by Johannes Ranke.

+
+

Developed by Johannes Ranke.

-

Site built with pkgdown 1.4.1.

+

Site built with pkgdown 2.0.7.

-
-
+
- - + + diff --git a/docs/reference/PEC_soil_mets.html b/docs/reference/PEC_soil_mets.html index c3b09f4..0864211 100644 --- a/docs/reference/PEC_soil_mets.html +++ b/docs/reference/PEC_soil_mets.html @@ -1,61 +1,12 @@ - - - - - - - -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

@@ -104,68 +40,59 @@
-

Calculate initial and accumulation PEC soil for a set of metabolites

-
- 
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 +

+ 
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

- +maximum occurrence in soil and their soil DT50

-
-
+
-
-
-

Developed by Johannes Ranke.

+
+

Developed by Johannes Ranke.

-

Site built with pkgdown 1.4.1.

+

Site built with pkgdown 2.0.7.

-
-
+
- - + + diff --git a/docs/reference/PEC_sw_drainage_UK.html b/docs/reference/PEC_sw_drainage_UK.html index bdcf5af..b0be7f3 100644 --- a/docs/reference/PEC_sw_drainage_UK.html +++ b/docs/reference/PEC_sw_drainage_UK.html @@ -1,65 +1,13 @@ - - - - - - - -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 - - - - + + -
-
- -
- -
+

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

@@ -111,97 +45,97 @@ published on the CRC website" /> 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

-

References

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

Arguments

+
rate
+

Application rate in g/ha

+ + +
interception
+

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

+ + +
Koc
+

The sorption coefficient normalised to organic carbon in L/kg

+ + +
latest_application
+

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

+ + +
soil_DT50
+

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

+ + +
model
+

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

+ +
model_parms
+

A named numeric vector containing the model parameters

+ +
+
+

Value

+ + +

The predicted concentration in surface water in µg/L

+
+ - +

Author

Johannes Ranke

+
+ +
+

Examples

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

Developed by Johannes Ranke.

+
+

Developed by Johannes Ranke.

-

Site built with pkgdown 1.4.1.

+

Site built with pkgdown 2.0.7.

-
-
+
- - + + diff --git a/docs/reference/PEC_sw_drift.html b/docs/reference/PEC_sw_drift.html index e2c325b..c017454 100644 --- a/docs/reference/PEC_sw_drift.html +++ b/docs/reference/PEC_sw_drift.html @@ -1,69 +1,14 @@ - - - - - - - -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 - - - - - - - - - - - - - + + -
-
- -
- -
+

Calculate predicted environmental concentrations in surface water due to drift

@@ -116,106 +47,118 @@ 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 = 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] +

+ 
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
+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

+
+
+

Author

+

Johannes Ranke

+
+ +
+

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 
+
+
+
-
-
-

Developed by Johannes Ranke.

+
+

Developed by Johannes Ranke.

-

Site built with pkgdown 1.5.1.

+

Site built with pkgdown 2.0.7.

-
-
+
- - + + diff --git a/docs/reference/PEC_sw_exposit_drainage.html b/docs/reference/PEC_sw_exposit_drainage.html index 5bd1d80..becfa0c 100644 --- a/docs/reference/PEC_sw_exposit_drainage.html +++ b/docs/reference/PEC_sw_exposit_drainage.html @@ -1,70 +1,18 @@ - - - - - - - -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 - - - - + + -
-
- -
- -
+

Calculate PEC surface water due to drainage as in Exposit 3

@@ -121,123 +55,126 @@ 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

+
+ 
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

-

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 + +

V_drainage
+

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

+ + +
dilution
+

The dilution factor

+ +
+
+

Value

+ + +

A list containing the following components

+

+
perc_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.

+ -
- -

See also

- -

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

- -

Examples

- 
  PEC_sw_exposit_drainage(500, Koc = 150)
#> $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 
-#>
-
- +
+

See also

+

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

+
+
+

Examples

+ 
  PEC_sw_exposit_drainage(500, Koc = 150)
+#> $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 
+#> 
+
+
+
-
-
-

Developed by Johannes Ranke.

+
+

Developed by Johannes Ranke.

-

Site built with pkgdown 1.4.1.

+

Site built with pkgdown 2.0.7.

-
-
+
- - + + diff --git a/docs/reference/PEC_sw_exposit_runoff.html b/docs/reference/PEC_sw_exposit_runoff.html index 81549e0..101274c 100644 --- a/docs/reference/PEC_sw_exposit_runoff.html +++ b/docs/reference/PEC_sw_exposit_runoff.html @@ -1,65 +1,13 @@ - - - - - - - -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 - - - - + + -
-
- -
- -
+

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

@@ -111,152 +45,157 @@ in the worksheet "Konzept Runoff"." /> in the worksheet "Konzept Runoff".

- 
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
-)
- -

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 +

+ 
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
+)
+
+ + +
+

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

- -

Source

+and the bound fraction.

-

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

-

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 +

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

+ +
+
+

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.

+ -
- -

See also

- -

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

- -

Examples

- 
  PEC_sw_exposit_runoff(500, Koc = 150)
#> $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
-#>
-
- +
+

See also

+

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

+
+
+

Examples

+ 
  PEC_sw_exposit_runoff(500, Koc = 150)
+#> $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
+#> 
+
+
+
-
-
-

Developed by Johannes Ranke.

+
+

Developed by Johannes Ranke.

-

Site built with pkgdown 1.4.1.

+

Site built with pkgdown 2.0.7.

-
-
+
- - + + diff --git a/docs/reference/PEC_sw_focus.html b/docs/reference/PEC_sw_focus.html index 8242f6a..605d2cc 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 - - - - - - - - - - - - - - + + -
-
- -
- -
+

Calculate PEC surface water at FOCUS Step 1

@@ -117,119 +51,118 @@ authored by Michael Klein, in R. Note that results for multiple applications should be compared to the corresponding results for a single application. At current, this is not done automatically in this implementation. Only Step 1 PECs are calculated. However, -input files are generated that are suitable as input also for Step 2 -to be used with the FOCUS calculator.

+input files can be generated that are suitable as input for +the FOCUS calculator.

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

+

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, if it exists?

+ +
+
+

Note

The formulas for input to the waterbody via runoff/drainage of the parent and subsequent formation of the metabolite in water is not documented in the model description coming with the calculator. As one would @@ -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
-#>
-
- +
-
-
-

Developed by Johannes Ranke.

+
+

Developed by Johannes Ranke.

-

Site built with pkgdown 1.4.1.

+

Site built with pkgdown 2.0.7.

-
-
+
- - + + diff --git a/docs/reference/PEC_sw_sed.html b/docs/reference/PEC_sw_sed.html index 458eeb7..247c8d7 100644 --- a/docs/reference/PEC_sw_sed.html +++ b/docs/reference/PEC_sw_sed.html @@ -1,67 +1,15 @@ - - - - - - - -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 - - - - + + -
-
- -
- -
+

Calculate predicted environmental concentrations in sediment from surface @@ -114,89 +48,89 @@ water concentrations

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.

+
+

Developed by Johannes Ranke.

-

Site built with pkgdown 1.4.1.

+

Site built with pkgdown 2.0.7.

-
-
+
- - + + diff --git a/docs/reference/Rplot002.png b/docs/reference/Rplot002.png new file mode 100644 index 0000000..8269e66 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..8269e66 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..9ec61d5 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..02941cf 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..855ca3c 100644 --- a/docs/reference/SFO_actual_twa.html +++ b/docs/reference/SFO_actual_twa.html @@ -1,64 +1,12 @@ - - - - - - - -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 - - - - + + -
-
- -
- -
+

Actual and maximum moving window time average concentrations for SFO kinetics

@@ -109,71 +43,69 @@

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

+
+ 
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.

-

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
-
- +

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
+
+
+
-
-
-

Developed by Johannes Ranke.

+
+

Developed by Johannes Ranke.

-

Site built with pkgdown 1.4.1.

+

Site built with pkgdown 2.0.7.

-
-
+
- - + + diff --git a/docs/reference/SSLRC_mobility_classification.html b/docs/reference/SSLRC_mobility_classification.html index b0ae939..a50d906 100644 --- a/docs/reference/SSLRC_mobility_classification.html +++ b/docs/reference/SSLRC_mobility_classification.html @@ -1,62 +1,13 @@ - - - - - - - -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 - - - - + + -
-
- -
- -
+

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

@@ -105,86 +41,81 @@ checked against the spreadsheet published on the CRC website" />
-

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

+
+ 
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

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

- 
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
+#> 
+
+
+
-
-
-

Developed by Johannes Ranke.

+
+

Developed by Johannes Ranke.

-

Site built with pkgdown 1.4.1.

+

Site built with pkgdown 2.0.7.

-
-
+
- - + + diff --git a/docs/reference/TOXSWA_cwa.html b/docs/reference/TOXSWA_cwa.html index 16dc5db..a64ad40 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.

-

Format

-

An R6Class generator object.

-

Public fields

+
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

+

+
+
+

Methods

-

Public methods

- - -

-

Method new()

-

Create a TOXSWA_cwa object from a file

Usage

-

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

- -

Arguments

-

-
filename

The filename

- -
basedir

The directory to look in

- -
zipfile

Optional path to a zipfile holding the file

- -
segment

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

- -
substance

The TOXSWA substance name (for TOXSWA 4 or higher)

- -
total

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

- -

-

-

Method moving_windows()

-

Add to the `windows` field described above.

Usage

-

TOXSWA_cwa$moving_windows(windows, total = FALSE)

- -

Arguments

-

-
windows

Window sizes in days

- -
total

If TRUE, the total concentration including the amount adsorbed to +

+

Public methods

+ +

+

Method new()

+

Create a TOXSWA_cwa object from a file

+

Usage

+

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

+
+ +
+

Arguments

+

filename
+

The filename

+ + +
basedir
+

The directory to look in

+ + +
zipfile
+

Optional path to a zipfile holding the file

+ + +
segment
+

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

+ + +
substance
+

The TOXSWA substance name (for TOXSWA 4 or higher)

+ + +
total
+

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

+ + +

+
+ +

+

Method moving_windows()

+

Add to the `windows` field described above.

+

Usage

+

TOXSWA_cwa$moving_windows(windows, total = FALSE)

+
+ +
+

Arguments

+

windows
+

Window sizes in days

+ + +
total
+

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

-

-

-

Method get_events()

+ +

+
+ +

+

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.

-

Arguments

-

-
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
+
+
+
-
-
-

Developed by Johannes Ranke.

+
+

Developed by Johannes Ranke.

-

Site built with pkgdown 1.5.1.

+

Site built with pkgdown 2.0.7.

-
-
+
- - + + diff --git a/docs/reference/TSCF-1.png b/docs/reference/TSCF-1.png index efa9e0a..82c0ea8 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..62ad5a0 100644 --- a/docs/reference/TSCF.html +++ b/docs/reference/TSCF.html @@ -1,72 +1,17 @@ - - - - - - - -Estimation of the transpiration stream concentration factor — TSCF • pfm - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Estimation of the transpiration stream concentration factor — TSCF • pfm - - - - - - - - - - - - - + + -
-
- -
- -
+

Estimation of the transpiration stream concentration factor

@@ -122,60 +53,63 @@ the compound should not be ionogenic (weak acid/base) or ionic.

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

-

Details

-

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

-

References

+
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)
+legend("topright", lty = 1:2, bty = "n",
+  legend = c("Briggs et al. (1982)", "Dettenmaier et al. (2009)"))
+
+
+
+
-
-
-

Developed by Johannes Ranke.

+
+

Developed by Johannes Ranke.

-

Site built with pkgdown 1.5.1.

+

Site built with pkgdown 2.0.7.

-
-
+
- - + + diff --git a/docs/reference/chent_focus_sw.html b/docs/reference/chent_focus_sw.html index 70fd16d..13129b8 100644 --- a/docs/reference/chent_focus_sw.html +++ b/docs/reference/chent_focus_sw.html @@ -1,64 +1,12 @@ - - - - - - - -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

@@ -109,98 +43,93 @@

Create a chemical compound object for FOCUS Step 1 calculations

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

Arguments

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
name

Length one character vector containing the name

Koc

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

DT50_ws

Half-life in water/sediment systems in days

DT50_soil

Half-life in soil in days

DT50_water

Half-life in water in days (Step 2)

DT50_sediment

Half-life in sediment in days (Step 2)

cwsat

Water solubility in mg/L

mw

Molar weight in g/mol.

max_soil

Maximum observed fraction (dimensionless) in soil

max_ws

Maximum observed fraction (dimensionless) in water/sediment -systems

- -

Value

- -

A list with the substance specific properties

+
+ 
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)

-
- +
+

Value

+ + +

A list with the substance specific properties

+
+
-
-
-

Developed by Johannes Ranke.

+
+

Developed by Johannes Ranke.

-

Site built with pkgdown 1.4.1.

+

Site built with pkgdown 2.0.7.

-
-
+
- - + + diff --git a/docs/reference/drift_data_JKI.html b/docs/reference/drift_data_JKI.html index 36472d1..b8a0907 100644 --- a/docs/reference/drift_data_JKI.html +++ b/docs/reference/drift_data_JKI.html @@ -1,70 +1,15 @@ - - - - - - - -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 @@ -118,23 +49,24 @@ 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 -on 2015-06-11

+on 2015-06-11, not present any more 2024-01-31

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

-

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 +80,226 @@ 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

+ 
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
+#> 
+
+
+
-
-
-

Developed by Johannes Ranke.

+
+

Developed by Johannes Ranke.

-

Site built with pkgdown 1.5.1.

+

Site built with pkgdown 2.0.7.

-
-
+
- - + + diff --git a/docs/reference/endpoint.html b/docs/reference/endpoint.html index 39071ee..8ca3467 100644 --- a/docs/reference/endpoint.html +++ b/docs/reference/endpoint.html @@ -1,67 +1,15 @@ - - - - - - - -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 - - - - - - - - - - - - - - + + -
-
- -
- -
+

Retrieve endpoint information from the chyaml field of a chent object

@@ -115,136 +49,131 @@ 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 +

+ 
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 +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.

+
- +
-
-
-

Developed by Johannes Ranke.

+
+

Developed by Johannes Ranke.

-

Site built with pkgdown 1.4.1.

+

Site built with pkgdown 2.0.7.

-
-
+
- - + + diff --git a/docs/reference/geomean.html b/docs/reference/geomean.html index 34befc3..540b33b 100644 --- a/docs/reference/geomean.html +++ b/docs/reference/geomean.html @@ -1,68 +1,16 @@ - - - - - - - -Calculate the geometric mean — geomean • pfm - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Calculate the geometric mean — geomean • pfm - - - - - - - - - - - - - - + + -
-
- -
- -
+

Calculate the geometric mean

@@ -111,64 +45,68 @@ If at least one element of the vector is 0, it returns 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)
+
+ 
geomean(x, na.rm = FALSE)
+
-

Arguments

- - - - - - - - - - -
x

Vector of numbers

na.rm

Should NA values be omitted?

+
+

Arguments

+
x
+

Vector of numbers

-

Value

-

The geometric mean

+
na.rm
+

Should NA values be omitted?

-

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
-
- +
+

Value

+ +

The geometric mean

+
+

Author

Johannes Ranke

+
+ +
+

Examples

+ 
geomean(c(1, 3, 9))
+#> [1] 3
+geomean(c(1, 3, NA, 9))
+#> [1] NA
+geomean(c(1, -3, 9)) # returns an error
+#> Error in geomean(c(1, -3, 9)): Only defined for positive numbers
+
+
+
-
-
-

Developed by Johannes Ranke.

+
+

Developed by Johannes Ranke.

-

Site built with pkgdown 1.4.1.

+

Site built with pkgdown 2.0.7.

-
-
+
- - + + diff --git a/docs/reference/get_vertex.html b/docs/reference/get_vertex.html index 3d0bc2d..c6dc00c 100644 --- a/docs/reference/get_vertex.html +++ b/docs/reference/get_vertex.html @@ -1,62 +1,13 @@ - - - - - - - -Fit a parabola through three points — get_vertex • pfm - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Fit a parabola through three points — get_vertex • pfm - - - - + + -
-
- -
- -
+

Fit a parabola through three points

@@ -105,55 +41,46 @@ https://stackoverflow.com/a/717791" />
-

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

- +
+ 
get_vertex(x, y)
+
-
- +
-
-
-

Developed by Johannes Ranke.

+
+

Developed by Johannes Ranke.

-

Site built with pkgdown 1.4.1.

+

Site built with pkgdown 2.0.7.

-
-
+
- - + + diff --git a/docs/reference/index.html b/docs/reference/index.html index fed30cc..3d660d3 100644 --- a/docs/reference/index.html +++ b/docs/reference/index.html @@ -1,66 +1,12 @@ - - - - - - - -Function reference • pfm - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Function reference • pfm - - - - + + -
-
- -
- -
+

Reference

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

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

- +
+
-
-
-

Developed by Johannes Ranke.

+
+

Developed by Johannes Ranke.

-

Site built with pkgdown 1.6.1.

+

Site built with pkgdown 2.0.7.

-
-
+
- - + + diff --git a/docs/reference/max_twa.html b/docs/reference/max_twa.html index 1d24c2a..c0387d7 100644 --- a/docs/reference/max_twa.html +++ b/docs/reference/max_twa.html @@ -1,65 +1,16 @@ - - - - - - - -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 - - - - - - - - - - - - - + + -
-
- -
- -
+

The maximum time weighted average concentration for a moving window

@@ -108,101 +44,90 @@ max_twa." />
- -

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

+
+ 
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

- -

See also

- -

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 
-#> 
pred_FOMC <- mkinfit("FOMC", FOCUS_2006_C, quiet = TRUE)
-max_twa(pred_FOMC)
#>       21 
-#> 18.22124
-
- +
+

See also

+

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 
+#> 
+pred_FOMC <- mkinfit("FOMC", FOCUS_2006_C, quiet = TRUE)
+max_twa(pred_FOMC)
+#>       21 
+#> 18.22124 
+
+
+
-
-
-

Developed by Johannes Ranke.

+
+

Developed by Johannes Ranke.

-

Site built with pkgdown 1.4.1.

+

Site built with pkgdown 2.0.7.

-
-
+
- - + + diff --git a/docs/reference/one_box-1.png b/docs/reference/one_box-1.png index 10bc9f7..eb4d2ee 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..1b34c2d 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..e4e41ea 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..62875b3 100644 --- a/docs/reference/one_box.html +++ b/docs/reference/one_box.html @@ -1,64 +1,12 @@ - - - - - - - -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

@@ -109,99 +43,105 @@

Create a time series of decline data

- 
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)
+
+ 
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 +

+

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 +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)
-
- +
+

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"))
+#> 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)
+
+
+
+
-
-
-

Developed by Johannes Ranke.

+
+

Developed by Johannes Ranke.

-

Site built with pkgdown 1.4.1.

+

Site built with pkgdown 2.0.7.

-
-
+
- - + + diff --git a/docs/reference/perc_runoff_exposit.html b/docs/reference/perc_runoff_exposit.html index 38c1f70..ca7b25d 100644 --- a/docs/reference/perc_runoff_exposit.html +++ b/docs/reference/perc_runoff_exposit.html @@ -1,67 +1,12 @@ - - - - - - - -Runoff loss percentages as used in Exposit 3 — perc_runoff_exposit • pfm - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Runoff loss percentages as used in Exposit 3 — perc_runoff_exposit • pfm - - - - + + -
-
- -
- -
+

Runoff loss percentages as used in Exposit 3

@@ -112,64 +43,71 @@

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

+
+ 
perc_runoff_exposit
+
- -

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/SharedDocs/Downloads/04_Pflanzenschutzmittel/zul_umwelt_exposit.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
+
+
+
-
-
-

Developed by Johannes Ranke.

+
+

Developed by Johannes Ranke.

-

Site built with pkgdown 1.5.1.

+

Site built with pkgdown 2.0.7.

-
-
+
- - + + diff --git a/docs/reference/perc_runoff_reduction_exposit.html b/docs/reference/perc_runoff_reduction_exposit.html index dd37f4b..6075ed0 100644 --- a/docs/reference/perc_runoff_reduction_exposit.html +++ b/docs/reference/perc_runoff_reduction_exposit.html @@ -1,67 +1,12 @@ - - - - - - - -Runoff reduction percentages as used in Exposit — perc_runoff_reduction_exposit • pfm - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Runoff reduction percentages as used in Exposit — perc_runoff_reduction_exposit • pfm - - - - + + -
-
- -
- -
+

Runoff reduction percentages as used in Exposit

@@ -112,80 +43,83 @@

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

- 
perc_runoff_reduction_exposit
- - -

Format

+
+ 
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

-

Source

+
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/SharedDocs/Downloads/04_Pflanzenschutzmittel/zul_umwelt_exposit.html

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

+
-

Examples

- 
print(perc_runoff_reduction_exposit)
-
#> $`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
+#> 
+
+
+
-
-
-

Developed by Johannes Ranke.

+
+

Developed by Johannes Ranke.

-

Site built with pkgdown 1.6.1.

+

Site built with pkgdown 2.0.7.

-
-
+
- - + + diff --git a/docs/reference/pesticide.txt b/docs/reference/pesticide.txt deleted file mode 100644 index 4618e8b..0000000 --- a/docs/reference/pesticide.txt +++ /dev/null @@ -1,3 +0,0 @@ -Active Substance Compound Comment Mol mass a.i. Mol mass met. Water solubility KOC assessed compound KOC parent compound DT50 Max. in Water Max. in Soil asessed compound App. Rate Number of App. Time between app. App. Type DT50 soil parent compound DT50 soil DT50 water DT50 sediment Region / Season Interception class -Dummy 1 cereals, spring n NA Dummy 1 cereals, spring n NA -99.00 -99.00 6000.00 344.80 0.00E+00 6.00 0.00E+00 0.00E+00 3000.00 1.00 0.00E+00 0.00E+00 0.00E+00 -99.00 -99.00 -99.00 0.00E+00 1.00 -M1 cereals, winter n NA M1 cereals, winter n NA 250.00 100.00 100.00 50.00 100.00 100.00 0.00E+00 50.00 1000.00 1.00 0.00E+00 1.00 -99.00 -99.00 -99.00 -99.00 0.00E+00 1.00 diff --git a/docs/reference/pfm_degradation.html b/docs/reference/pfm_degradation.html index 8d06107..724a1aa 100644 --- a/docs/reference/pfm_degradation.html +++ b/docs/reference/pfm_degradation.html @@ -1,67 +1,12 @@ - - - - - - - -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

@@ -112,80 +43,83 @@

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

- 
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
+
+ 
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

+
+ +
+

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
+
+
+
-
-
-

Developed by Johannes Ranke.

+
+

Developed by Johannes Ranke.

-

Site built with pkgdown 1.5.1.

+

Site built with pkgdown 2.0.7.

-
-
+
- - + + diff --git a/docs/reference/plot.TOXSWA_cwa-1.png b/docs/reference/plot.TOXSWA_cwa-1.png index c6a278a..11a34cc 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..327a21b 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..65524b5 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..1d82cfc 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..d28c87c 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..857b154 100644 --- a/docs/reference/plot.TOXSWA_cwa.html +++ b/docs/reference/plot.TOXSWA_cwa.html @@ -1,65 +1,13 @@ - - - - - - - -Plot TOXSWA surface water concentrations — plot.TOXSWA_cwa • pfm - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Plot TOXSWA surface water concentrations — plot.TOXSWA_cwa • pfm - - - - + + -
-
- -
- -
+

Plot TOXSWA surface water concentrations

@@ -111,106 +45,112 @@ segment of a TOXSWA surface water body." /> 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,
-  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 +

+ 
# 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

- - -

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")
-
- +

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")
+
+
+
+
-
-
-

Developed by Johannes Ranke.

+
+

Developed by Johannes Ranke.

-

Site built with pkgdown 1.4.1.

+

Site built with pkgdown 2.0.7.

-
-
+
- - + + diff --git a/docs/reference/plot.one_box-1.png b/docs/reference/plot.one_box-1.png index cf3f132..066f466 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..307dc3c 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..cea8b0b 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..2fc946e 100644 --- a/docs/reference/plot.one_box.html +++ b/docs/reference/plot.one_box.html @@ -1,64 +1,12 @@ - - - - - - - -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

@@ -109,97 +43,101 @@

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 = "Residue",
-  max_twa = NULL,
-  max_twa_var = dimnames(x)[[2]][1],
-  ...
-)
- -

Arguments

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
x

The object of type one_box to be plotted

xlim

Limits for the x axis

ylim

Limits for the y axis

xlab

Label for the x axis

ylab

Label for the y axis

max_twa

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

max_twa_var

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

...

Further arguments passed to methods

- -

See also

- -

sawtooth

- -

Examples

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

-# Use a fitted mkinfit model
-m_2 <- mkinmod(parent = mkinsub("SFO", "m1"), m1 = mkinsub("SFO"))
#> 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 = 1)
-pred_2_saw <- sawtooth(pred_2, 2, 7)
-plot(pred_2_saw, max_twa = 21, max_twa_var = "m1")
-
- +
-
-
-

Developed by Johannes Ranke.

+
+

Developed by Johannes Ranke.

-

Site built with pkgdown 1.4.1.

+

Site built with pkgdown 2.0.7.

-
-
+
- - + + diff --git a/docs/reference/read.TOXSWA_cwa.html b/docs/reference/read.TOXSWA_cwa.html index 9c5c2a0..2685c21 100644 --- a/docs/reference/read.TOXSWA_cwa.html +++ b/docs/reference/read.TOXSWA_cwa.html @@ -1,69 +1,17 @@ - - - - - - - -Read TOXSWA surface water concentrations — read.TOXSWA_cwa • pfm - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Read TOXSWA surface water concentrations — read.TOXSWA_cwa • pfm - - - - - - - - - - - - - - + + -
-
- -
- -
+

Read TOXSWA surface water concentrations

@@ -119,103 +53,102 @@ 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 +

+ 
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"))
+
+
+
-
-
-

Developed by Johannes Ranke.

+
+

Developed by Johannes Ranke.

-

Site built with pkgdown 1.4.1.

+

Site built with pkgdown 2.0.7.

-
-
+
- - + + diff --git a/docs/reference/reexports.html b/docs/reference/reexports.html new file mode 100644 index 0000000..9b8cb48 --- /dev/null +++ b/docs/reference/reexports.html @@ -0,0 +1,83 @@ + +Objects exported from other packages — reexports • pfm + + +
+
+ + + +
+
+
+

Objects exported from other packages

+ + +
+ +
+

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

+
mkin
+

set_nd_nq, set_nd_nq_focus

+ + +
+ + + +
+ +
+ + +
+

Developed by Johannes Ranke.

+
+ +
+

Site built with pkgdown 2.0.7.

+
+ +
+ + + + + + + + diff --git a/docs/reference/sawtooth-1.png b/docs/reference/sawtooth-1.png index 2952433..062202c 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..cea8b0b 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..8e29b9c 100644 --- a/docs/reference/sawtooth.html +++ b/docs/reference/sawtooth.html @@ -1,68 +1,13 @@ - - - - - - - -Create decline time series for multiple applications — sawtooth • pfm - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Create decline time series for multiple applications — sawtooth • pfm - - - - + + -
-
- -
- -
+

Create decline time series for multiple applications

@@ -114,81 +45,88 @@ n and i are disregarded." /> 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 
-#>
+
+ 
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 
+#> 
+
+
+
-
-
-

Developed by Johannes Ranke.

+
+

Developed by Johannes Ranke.

-

Site built with pkgdown 1.5.1.

+

Site built with pkgdown 2.0.7.

-
-
+
- - + + diff --git a/docs/reference/soil_scenario_data_EFSA_2015.html b/docs/reference/soil_scenario_data_EFSA_2015.html index cb3cf14..abea480 100644 --- a/docs/reference/soil_scenario_data_EFSA_2015.html +++ b/docs/reference/soil_scenario_data_EFSA_2015.html @@ -1,69 +1,14 @@ - - - - - - - -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 — soil_scenario_data_EFSA_2015 • pfm - - - - - - - - - - - - - + + -
-
- -
- -
+

Properties of the predefined scenarios from the EFSA guidance from 2015

@@ -116,81 +47,65 @@ 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.

+
+ 
soil_scenario_data_EFSA_2015
+
- -

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
+ doi:10.2903/j.efsa.2015.4093

+
+ +
+

Examples

+ 
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.

+
+

Developed by Johannes Ranke.

-

Site built with pkgdown 1.5.1.

+

Site built with pkgdown 2.0.7.

-
-
+
- - + + diff --git a/docs/reference/soil_scenario_data_EFSA_2017.html b/docs/reference/soil_scenario_data_EFSA_2017.html index 7e8044e..96cdd1b 100644 --- a/docs/reference/soil_scenario_data_EFSA_2017.html +++ b/docs/reference/soil_scenario_data_EFSA_2017.html @@ -1,69 +1,14 @@ - - - - - - - -Properties of the predefined scenarios from the EFSA guidance from 2017 — soil_scenario_data_EFSA_2017 • pfm - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Properties of the predefined scenarios from the EFSA guidance from 2017 — soil_scenario_data_EFSA_2017 • pfm - - - - - - - - - - - - - + + -
-
- -
- -
+

Properties of the predefined scenarios from the EFSA guidance from 2017

@@ -116,61 +47,98 @@ 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.

+
+ 
soil_scenario_data_EFSA_2017
+
- -

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) (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
+ 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
+
+waldo::compare(soil_scenario_data_EFSA_2017, soil_scenario_data_EFSA_2015)
+#> `old` is length 12
+#> `new` is length 10
+#> 
+#> `names(old)[8:12]`: "rho" "f_sce" "f_mod" "FOCUS_zone" "prec"
+#> `names(new)[8:10]`: "rho" "f_sce" "f_mod"                    
+#> 
+#> `old$Country`: "Estonia" "Poland"  "France" "Denmark" "Austria"        "Spain"
+#> `new$Country`: "Estonia" "Germany" "France" "Denmark" "Czech Republik" "Spain"
+#> 
+#> `old$T_arit`: 5.7 7.4 10.2 8.0 9.3 15.4
+#> `new$T_arit`: 4.7 8.0 11.0 8.2 9.1 12.8
+#> 
+#> `old$T_arr`: 7.6  9.3 11.7 9.2 11.3 16.7
+#> `new$T_arr`: 7.0 10.1 12.3 9.8 11.2 14.7
+#> 
+#> `old$Texture`: "Coarse" "Coarse" "Medium"      "Medium" "Medium" "Medium"
+#> `new$Texture`: "Coarse" "Coarse" "Medium fine" "Medium" "Medium" "Medium"
+#> 
+#> `old$f_om`: 0.220 0.122 0.070 0.025 0.018 0.010
+#> `new$f_om`: 0.118 0.086 0.048 0.023 0.018 0.011
+#> 
+#> `old$theta_fc`: 0.244 0.244 0.349 0.349 0.349 0.349
+#> `new$theta_fc`: 0.244 0.244 0.385 0.347 0.347 0.347
+#> 
+#> `old$rho`: 0.707 0.934 1.117 1.371 1.432 1.521
+#> `new$rho`: 0.950 1.050 1.220 1.390 1.430 1.510
+#> 
+#> `old$f_sce`: 1.4 1.4 1.4 1.6 1.6 1.6
+#> `new$f_sce`: 3.0 2.0 2.0 2.0 1.5 1.5
+#> 
+#> And 3 more differences ...
+
+
+
-
-
-

Developed by Johannes Ranke.

+
+

Developed by Johannes Ranke.

-

Site built with pkgdown 1.5.1.

+

Site built with pkgdown 2.0.7.

-
-
+
- - + + diff --git a/docs/reference/twa.html b/docs/reference/twa.html index 2c21c67..f23c6f3 100644 --- a/docs/reference/twa.html +++ b/docs/reference/twa.html @@ -1,63 +1,14 @@ - - - - - - - -Calculate a time weighted average concentration — twa • pfm - - - - - - - - - - - - - - - - - - - - - - - - - - - -Calculate a time weighted average concentration — twa • pfm - - - - - - - - - - - - - + + -
-
- -
- -
+

Calculate a time weighted average concentration

@@ -106,82 +42,73 @@ is after one window has passed." />
-

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)
+    

+    
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

- -

See also

+
+

Arguments

+
x
+

An object of type one_box

-

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 
-#>
-
- +
+

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