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

Subset of EFSA crop interception default values for groundwater modelling

+ Source: R/EFSA_GW_interception_2014.R +
EFSA_GW_interception_2014.Rd
-
+

Subset of EFSA crop interception default values for groundwater modelling

- -

Format

- +
+

Format

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

-

Source

- +
+
+

Source

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

+
-

Examples

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

Examples

+ 
if (FALSE) {
+  # This is the code that was used to define the data
+  bbch <- paste0(0:9, "x")
+  crops <- c(
+    "Beans (field + vegetable)",
+    "Peas",
+    "Summer oilseed rape", "Winter oilseed rape",
+    "Tomatoes",
+    "Spring cereals", "Winter cereals")
+  EFSA_GW_interception_2014 <- matrix(NA, length(crops), length(bbch),
+    dimnames = list(Crop = crops, BBCH = bbch))
+  EFSA_GW_interception_2014["Beans (field + vegetable)", ] <-
+    c(0, 0.25, rep(0.4, 2), rep(0.7, 5), 0.8)
+  EFSA_GW_interception_2014["Peas", ] <-
+    c(0, 0.35, rep(0.55, 2), rep(0.85, 5), 0.85)
+  EFSA_GW_interception_2014["Summer oilseed rape", ] <-
+    c(0, 0.4, rep(0.8, 2), rep(0.8, 5), 0.9)
+  EFSA_GW_interception_2014["Winter oilseed rape", ] <-
+    c(0, 0.4, rep(0.8, 2), rep(0.8, 5), 0.9)
+  EFSA_GW_interception_2014["Tomatoes", ] <-
+    c(0, 0.5, rep(0.7, 2), rep(0.8, 5), 0.5)
+  EFSA_GW_interception_2014["Spring cereals", ] <-
+    c(0, 0, 0.2, 0.8, rep(0.9, 3), rep(0.8, 2), 0.8)
+  EFSA_GW_interception_2014["Winter cereals", ] <-
+    c(0, 0, 0.2, 0.8, rep(0.9, 3), rep(0.8, 2), 0.8)
+  save(EFSA_GW_interception_2014,
+    file = "../data/EFSA_GW_interception_2014.RData")
+}
+EFSA_GW_interception_2014
+#>                            BBCH
+#> Crop                        0x   1x   2x   3x   4x   5x   6x   7x   8x   9x
+#>   Beans (field + vegetable)  0 0.25 0.40 0.40 0.70 0.70 0.70 0.70 0.70 0.80
+#>   Peas                       0 0.35 0.55 0.55 0.85 0.85 0.85 0.85 0.85 0.85
+#>   Summer oilseed rape        0 0.40 0.80 0.80 0.80 0.80 0.80 0.80 0.80 0.90
+#>   Winter oilseed rape        0 0.40 0.80 0.80 0.80 0.80 0.80 0.80 0.80 0.90
+#>   Tomatoes                   0 0.50 0.70 0.70 0.80 0.80 0.80 0.80 0.80 0.50
+#>   Spring cereals             0 0.00 0.20 0.80 0.90 0.90 0.90 0.80 0.80 0.80
+#>   Winter cereals             0 0.00 0.20 0.80 0.90 0.90 0.90 0.80 0.80 0.80
+
+
+
- + - - - + diff --git a/docs/reference/EFSA_washoff_2017.html b/docs/reference/EFSA_washoff_2017.html index abe0b24..1e292af 100644 --- a/docs/reference/EFSA_washoff_2017.html +++ b/docs/reference/EFSA_washoff_2017.html @@ -1,193 +1,124 @@ - - - - - - - -Subset of EFSA crop washoff default values — EFSA_washoff_2017 • pfm - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +Subset of EFSA crop washoff default values — EFSA_washoff_2017 • pfm + Skip to contents + +
+
+
+

Subset of EFSA crop washoff default values

+ Source: R/EFSA_washoff_2017.R +
EFSA_washoff_2017.Rd
-
+

Subset of EFSA crop washoff default values

- -

Format

- +
+

Format

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

-

Source

- +
+
+

Source

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

+
-

Examples

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

Examples

+ 
if (FALSE) {
+  # This is the code that was used to define the data
+  bbch <- paste0(0:9, "x")
+  crops <- c(
+    "Beans (field + vegetable)",
+    "Peas",
+    "Summer oilseed rape", "Winter oilseed rape",
+    "Tomatoes",
+    "Spring cereals", "Winter cereals")
+  EFSA_washoff_2017 <- matrix(NA, length(crops), length(bbch),
+    dimnames = list(Crop = crops, BBCH = bbch))
+  EFSA_washoff_2017["Beans (field + vegetable)", ] <-
+    c(NA, 0.6, rep(0.75, 2), rep(0.8, 5), 0.35)
+  EFSA_washoff_2017["Peas", ] <-
+    c(NA, 0.4, rep(0.6, 2), rep(0.65, 5), 0.35)
+  EFSA_washoff_2017["Summer oilseed rape", ] <-
+    c(NA, 0.4, rep(0.5, 2), rep(0.6, 5), 0.5)
+  EFSA_washoff_2017["Winter oilseed rape", ] <-
+    c(NA, 0.1, rep(0.4, 2), rep(0.55, 5), 0.3)
+  EFSA_washoff_2017["Tomatoes", ] <-
+    c(NA, 0.55, rep(0.75, 2), rep(0.7, 5), 0.35)
+  EFSA_washoff_2017["Spring cereals", ] <-
+    c(NA, 0.4, 0.5, 0.5, rep(0.65, 3), rep(0.65, 2), 0.55)
+  EFSA_washoff_2017["Winter cereals", ] <-
+    c(NA, 0.1, 0.4, 0.6, rep(0.55, 3), rep(0.6, 2), 0.4)
+  save(EFSA_washoff_2017,
+    file = "../data/EFSA_washoff_2017.RData")
+}
+EFSA_washoff_2017
+#>                            BBCH
+#> Crop                        0x   1x   2x   3x   4x   5x   6x   7x   8x   9x
+#>   Beans (field + vegetable) NA 0.60 0.75 0.75 0.80 0.80 0.80 0.80 0.80 0.35
+#>   Peas                      NA 0.40 0.60 0.60 0.65 0.65 0.65 0.65 0.65 0.35
+#>   Summer oilseed rape       NA 0.40 0.50 0.50 0.60 0.60 0.60 0.60 0.60 0.50
+#>   Winter oilseed rape       NA 0.10 0.40 0.40 0.55 0.55 0.55 0.55 0.55 0.30
+#>   Tomatoes                  NA 0.55 0.75 0.75 0.70 0.70 0.70 0.70 0.70 0.35
+#>   Spring cereals            NA 0.40 0.50 0.50 0.65 0.65 0.65 0.65 0.65 0.55
+#>   Winter cereals            NA 0.10 0.40 0.60 0.55 0.55 0.55 0.60 0.60 0.40
+
+
+
- + - - - + diff --git a/docs/reference/FOCUS_GW_scenarios_2012.html b/docs/reference/FOCUS_GW_scenarios_2012.html index 8b55d14..84c2fac 100644 --- a/docs/reference/FOCUS_GW_scenarios_2012.html +++ b/docs/reference/FOCUS_GW_scenarios_2012.html @@ -1,217 +1,152 @@ - - - - - - - -A very small subset of the FOCUS Groundwater scenario definitions — FOCUS_GW_scenarios_2012 • pfm - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +A very small subset of the FOCUS Groundwater scenario definitions — FOCUS_GW_scenarios_2012 • pfm + Skip to contents + +
+
+
+

A very small subset of the FOCUS Groundwater scenario definitions

+ Source: R/FOCUS_GW_scenarios_2012.R +
FOCUS_GW_scenarios_2012.Rd
-
+

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

- 
FOCUS_GW_scenarios_2012
- - -

Format

+
+

Usage

+ 
FOCUS_GW_scenarios_2012
+
+
+

Format

An object of class list of length 2.

-

References

- +
+
+

References

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

+
-

Examples

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

Examples

+ 
FOCUS_GW_scenarios_2012
+#> $names
+#>            Cha            Ham            Jok            Kre            Oke 
+#>   "Châteaudun"      "Hamburg"    "Jokioinen" "Kremsmünster"   "Okehampton" 
+#>            Pia            Por            Sev            Thi 
+#>     "Piacenza"        "Porto"      "Sevilla"        "Thiva" 
+#> 
+#> $soils
+#>    location horizon number pH_H2O perc_clay perc_oc rel_deg
+#> 1       Cha      Ap      1    8.0      30.0    1.39     1.0
+#> 2       Cha      B1      2    8.1      31.0    0.93     0.5
+#> 3       Cha      B2      3    8.2      25.0    0.70     0.5
+#> 4       Cha   II C1      4    8.5      26.0    0.30     0.3
+#> 5       Cha   II C1      5    8.5      26.0    0.30     0.0
+#> 6       Cha   II C2      6    8.5      24.0    0.27     0.0
+#> 7       Cha       M      7    8.3      31.0    0.21     0.0
+#> 8       Ham      Ap      1    6.4       7.2    1.50     1.0
+#> 9       Ham     BvI      2    5.6       6.7    1.00     0.5
+#> 10      Ham    BvII      3    5.6       0.9    0.20     0.3
+#> 11      Ham   Bv/Cv      4    5.7       0.0    0.00     0.3
+#> 12      Ham      Cv      5    5.5       0.0    0.00     0.3
+#> 13      Ham      Cv      6    5.5       0.0    0.00     0.0
+#> 14      Jok      Ap      1    6.2       3.6    4.06     1.0
+#> 15      Jok      Bs      2    5.6       1.8    0.84     0.5
+#> 16      Jok     BC1      3    5.4       1.2    0.36     0.3
+#> 17      Jok     BC2      4    5.4       1.7    0.29     0.3
+#> 18      Jok     BC2      5    5.4       1.7    0.29     0.0
+#> 19      Jok      Cg      6    5.3       1.9    0.21     0.0
+#> 20      Kre    <NA>      1    7.7      14.0    3.60     1.0
+#> 21      Kre    <NA>      2    7.0      25.0    1.00     0.5
+#> 22      Kre    <NA>      3    7.1      27.0    0.50     0.5
+#> 23      Kre    <NA>      4    7.1      27.0    0.50     0.3
+#> 24      Kre    <NA>      5    7.1      27.0    0.50     0.0
+#> 25      Oke       A      1    5.8      18.0    2.20     1.0
+#> 26      Oke     Bw1      2    6.3      17.0    0.70     0.5
+#> 27      Oke      BC      3    6.5      14.0    0.40     0.3
+#> 28      Oke       C      4    6.6       9.0    0.10     0.3
+#> 29      Oke       C      5    6.6       9.0    0.10     0.0
+#> 30      Pia      Ap      1    7.0      15.0    1.26     1.0
+#> 31      Pia      Ap      2    7.0      15.0    1.26     0.5
+#> 32      Pia      Bw      3    6.3       7.0    0.47     0.5
+#> 33      Pia      Bw      4    6.3       7.0    0.47     0.3
+#> 34      Pia      2C      5    6.4       0.0    0.00     0.3
+#> 35      Pia      2C      6    6.4       0.0    0.00     0.0
+#> 36      Por    <NA>      1    4.9      10.0    1.42     1.0
+#> 37      Por    <NA>      2    4.8       8.0    0.78     0.5
+#> 38      Por    <NA>      3    4.8       8.0    0.78     0.3
+#> 39      Por    <NA>      4    4.8       8.0    0.78     0.0
+#> 40      Sev    <NA>      1    7.3      14.0    0.93     1.0
+#> 41      Sev    <NA>      2    7.3      13.0    0.93     1.0
+#> 42      Sev    <NA>      3    7.8      15.0    0.70     0.5
+#> 43      Sev    <NA>      4    8.1      16.0    0.58     0.3
+#> 44      Sev    <NA>      5    8.1      16.0    0.58     0.0
+#> 45      Sev    <NA>      6    8.2      22.0    0.49     0.0
+#> 46      Thi     Ap1      1    7.7      25.3    0.74     1.0
+#> 47      Thi     Ap2      2    7.7      25.3    0.74     0.5
+#> 48      Thi      Bw      3    7.8      29.6    0.57     0.5
+#> 49      Thi      Bw      4    7.8      31.9    0.31     0.3
+#> 50      Thi     Ck1      5    7.8      32.9    0.18     0.3
+#> 51      Thi     Ck1      6    7.8      32.9    0.18     0.0
+#> 
+
+
+
- + - - - + diff --git a/docs/reference/FOCUS_Step_12_scenarios.html b/docs/reference/FOCUS_Step_12_scenarios.html index bbfb592..dc1e4c3 100644 --- a/docs/reference/FOCUS_Step_12_scenarios.html +++ b/docs/reference/FOCUS_Step_12_scenarios.html @@ -1,324 +1,255 @@ - - - - - - - -Step 1/2 scenario data as distributed with the FOCUS Step 1/2 calculator — FOCUS_Step_12_scenarios • pfm - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Step 1/2 scenario data as distributed with the FOCUS Step 1/2 calculator — FOCUS_Step_12_scenarios • pfm + + Skip to contents + +
+
+
+

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

+ Source: R/FOCUS_Step_12_scenarios.R +
FOCUS_Step_12_scenarios.Rd
-
+

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

- -

Format

- +
+

Format

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

+
-

Examples

- 

-if (FALSE) {
-  # This is the code that was used to extract the data
-  scenario_path <- "inst/extdata/FOCUS_Step_12_scenarios.txt"
-  scenarios <- readLines(scenario_path)[9:38]
-  FOCUS_Step_12_scenarios <- list()
-  sce <- read.table(text = scenarios, sep = "\t", header = TRUE, check.names = FALSE,
-    stringsAsFactors = FALSE)
-  FOCUS_Step_12_scenarios$names = sce$Crop
-  rownames(sce) <- sce$Crop
-  FOCUS_Step_12_scenarios$drift = sce[, 3:11]
-  FOCUS_Step_12_scenarios$interception = sce[, 12:15]
-  sce_2 <- readLines(scenario_path)[41:46]
-  rd <- read.table(text = sce_2, sep = "\t")[1:2]
-  rd_mat <- matrix(rd$V2, nrow = 3, byrow = FALSE)
-  dimnames(rd_mat) = list(Time = c("Oct-Feb", "Mar-May", "Jun-Sep"),
-                          Region = c("North", "South"))
-  FOCUS_Step_12_scenarios$rd = rd_mat
-  save(FOCUS_Step_12_scenarios, file = "data/FOCUS_Step_12_scenarios.RData")
-}
-
-# And this is the resulting data
-FOCUS_Step_12_scenarios
#> $names
-#>  [1] "cereals, spring"                  "cereals, winter"                 
-#>  [3] "citrus"                           "cotton"                          
-#>  [5] "field beans"                      "grass / alfalfa"                 
-#>  [7] "hops"                             "legumes"                         
-#>  [9] "maize"                            "oil seed rape, spring"           
-#> [11] "oil seed rape, winter"            "olives"                          
-#> [13] "pome / stone fruit, early applns" "pome / stone fruit, late applns" 
-#> [15] "potatoes"                         "soybeans"                        
-#> [17] "sugar beets"                      "sunflowers"                      
-#> [19] "tobacco"                          "vegetables, bulb"                
-#> [21] "vegetables, fruiting"             "vegetables, leafy"               
-#> [23] "vegetables, root"                 "vines, early applns"             
-#> [25] "vines, late applns"               "appln, aerial"                   
-#> [27] "appln, hand (crop < 50 cm)"       "appln, hand (crop > 50 cm)"      
-#> [29] "no drift (incorp or seed trtmt)" 
-#> 
-#> $drift
-#>                                       1      2      3      4      5      6
-#> cereals, spring                   2.759  2.438  2.024  1.862  1.794  1.631
-#> cereals, winter                   2.759  2.438  2.024  1.862  1.794  1.631
-#> citrus                           15.725 12.129 11.011 10.124  9.743  9.204
-#> cotton                            2.759  2.438  2.024  1.862  1.794  1.631
-#> field beans                       2.759  2.438  2.024  1.862  1.794  1.631
-#> grass / alfalfa                   2.759  2.438  2.024  1.862  1.794  1.631
-#> hops                             19.326 17.723 15.928 15.378 15.114 14.902
-#> legumes                           2.759  2.438  2.024  1.862  1.794  1.631
-#> maize                             2.759  2.438  2.024  1.862  1.794  1.631
-#> oil seed rape, spring             2.759  2.438  2.024  1.862  1.794  1.631
-#> oil seed rape, winter             2.759  2.438  2.024  1.862  1.794  1.631
-#> olives                           15.725 12.129 11.011 10.124  9.743  9.204
-#> pome / stone fruit, early applns 29.197 25.531 23.960 23.603 23.116 22.760
-#> pome / stone fruit, late applns  15.725 12.129 11.011 10.124  9.743  9.204
-#> potatoes                          2.759  2.438  2.024  1.862  1.794  1.631
-#> soybeans                          2.759  2.438  2.024  1.862  1.794  1.631
-#> sugar beets                       2.759  2.438  2.024  1.862  1.794  1.631
-#> sunflowers                        2.759  2.438  2.024  1.862  1.794  1.631
-#> tobacco                           2.759  2.438  2.024  1.862  1.794  1.631
-#> vegetables, bulb                  2.759  2.438  2.024  1.862  1.794  1.631
-#> vegetables, fruiting              2.759  2.438  2.024  1.862  1.794  1.631
-#> vegetables, leafy                 2.759  2.438  2.024  1.862  1.794  1.631
-#> vegetables, root                  2.759  2.438  2.024  1.862  1.794  1.631
-#> vines, early applns               2.699  2.496  2.546  2.499  2.398  2.336
-#> vines, late applns                8.028  7.119  6.898  6.631  6.636  6.431
-#> appln, aerial                    33.200 33.200 33.200 33.200 33.200 33.200
-#> appln, hand (crop < 50 cm)        2.759  2.438  2.024  1.862  1.794  1.631
-#> appln, hand (crop > 50 cm)        8.028  7.119  6.898  6.631  6.636  6.431
-#> no drift (incorp or seed trtmt)   0.000  0.000  0.000  0.000  0.000  0.000
-#>                                       7      8     >8
-#> cereals, spring                   1.578  1.512  1.512
-#> cereals, winter                   1.578  1.512  1.512
-#> citrus                            9.102  8.656  8.656
-#> cotton                            1.578  1.512  1.512
-#> field beans                       1.578  1.512  1.512
-#> grass / alfalfa                   1.578  1.512  1.512
-#> hops                             14.628 13.520 13.520
-#> legumes                           1.578  1.512  1.512
-#> maize                             1.578  1.512  1.512
-#> oil seed rape, spring             1.578  1.512  1.512
-#> oil seed rape, winter             1.578  1.512  1.512
-#> olives                            9.102  8.656  8.656
-#> pome / stone fruit, early applns 22.690 22.241 22.241
-#> pome / stone fruit, late applns   9.102  8.656  8.656
-#> potatoes                          1.578  1.512  1.512
-#> soybeans                          1.578  1.512  1.512
-#> sugar beets                       1.578  1.512  1.512
-#> sunflowers                        1.578  1.512  1.512
-#> tobacco                           1.578  1.512  1.512
-#> vegetables, bulb                  1.578  1.512  1.512
-#> vegetables, fruiting              1.578  1.512  1.512
-#> vegetables, leafy                 1.578  1.512  1.512
-#> vegetables, root                  1.578  1.512  1.512
-#> vines, early applns               2.283  2.265  2.265
-#> vines, late applns                6.227  6.173  6.173
-#> appln, aerial                    33.200 33.200 33.200
-#> appln, hand (crop < 50 cm)        1.578  1.512  1.512
-#> appln, hand (crop > 50 cm)        6.227  6.173  6.173
-#> no drift (incorp or seed trtmt)   0.000  0.000  0.000
-#> 
-#> $interception
-#>                                  no interception minimal crop cover
-#> cereals, spring                                0               0.00
-#> cereals, winter                                0               0.00
-#> citrus                                         0               0.80
-#> cotton                                         0               0.30
-#> field beans                                    0               0.25
-#> grass / alfalfa                                0               0.40
-#> hops                                           0               0.20
-#> legumes                                        0               0.25
-#> maize                                          0               0.25
-#> oil seed rape, spring                          0               0.40
-#> oil seed rape, winter                          0               0.40
-#> olives                                         0               0.70
-#> pome / stone fruit, early applns               0               0.20
-#> pome / stone fruit, late applns                0               0.20
-#> potatoes                                       0               0.15
-#> soybeans                                       0               0.20
-#> sugar beets                                    0               0.20
-#> sunflowers                                     0               0.20
-#> tobacco                                        0               0.20
-#> vegetables, bulb                               0               0.10
-#> vegetables, fruiting                           0               0.25
-#> vegetables, leafy                              0               0.25
-#> vegetables, root                               0               0.25
-#> vines, early applns                            0               0.40
-#> vines, late applns                             0               0.40
-#> appln, aerial                                  0               0.20
-#> appln, hand (crop < 50 cm)                     0               0.20
-#> appln, hand (crop > 50 cm)                     0               0.20
-#> no drift (incorp or seed trtmt)                0               0.00
-#>                                  average crop cover full canopy
-#> cereals, spring                                0.20        0.70
-#> cereals, winter                                0.20        0.70
-#> citrus                                         0.80        0.80
-#> cotton                                         0.60        0.75
-#> field beans                                    0.40        0.70
-#> grass / alfalfa                                0.60        0.75
-#> hops                                           0.50        0.70
-#> legumes                                        0.50        0.70
-#> maize                                          0.50        0.75
-#> oil seed rape, spring                          0.70        0.75
-#> oil seed rape, winter                          0.70        0.75
-#> olives                                         0.70        0.70
-#> pome / stone fruit, early applns               0.40        0.65
-#> pome / stone fruit, late applns                0.40        0.65
-#> potatoes                                       0.50        0.70
-#> soybeans                                       0.50        0.75
-#> sugar beets                                    0.70        0.75
-#> sunflowers                                     0.50        0.75
-#> tobacco                                        0.70        0.75
-#> vegetables, bulb                               0.25        0.40
-#> vegetables, fruiting                           0.50        0.70
-#> vegetables, leafy                              0.40        0.70
-#> vegetables, root                               0.50        0.70
-#> vines, early applns                            0.50        0.60
-#> vines, late applns                             0.50        0.60
-#> appln, aerial                                  0.50        0.70
-#> appln, hand (crop < 50 cm)                     0.50        0.70
-#> appln, hand (crop > 50 cm)                     0.50        0.70
-#> no drift (incorp or seed trtmt)                0.00        0.00
-#> 
-#> $rd
-#>          Region
-#> Time      North South
-#>   Oct-Feb     5     4
-#>   Mar-May     2     4
-#>   Jun-Sep     2     3
-#>
-
- -
+
+

Examples

+ 

+if (FALSE) {
+  # This is the code that was used to extract the data
+  scenario_path <- "inst/extdata/FOCUS_Step_12_scenarios.txt"
+  scenarios <- readLines(scenario_path)[9:38]
+  FOCUS_Step_12_scenarios <- list()
+  sce <- read.table(text = scenarios, sep = "\t", header = TRUE, check.names = FALSE,
+    stringsAsFactors = FALSE)
+  FOCUS_Step_12_scenarios$names = sce$Crop
+  rownames(sce) <- sce$Crop
+  FOCUS_Step_12_scenarios$drift = sce[, 3:11]
+  FOCUS_Step_12_scenarios$interception = sce[, 12:15]
+  sce_2 <- readLines(scenario_path)[41:46]
+  rd <- read.table(text = sce_2, sep = "\t")[1:2]
+  rd_mat <- matrix(rd$V2, nrow = 3, byrow = FALSE)
+  dimnames(rd_mat) = list(Time = c("Oct-Feb", "Mar-May", "Jun-Sep"),
+                          Region = c("North", "South"))
+  FOCUS_Step_12_scenarios$rd = rd_mat
+  save(FOCUS_Step_12_scenarios, file = "data/FOCUS_Step_12_scenarios.RData")
+}
+
+# And this is the resulting data
+FOCUS_Step_12_scenarios
+#> $names
+#>  [1] "cereals, spring"                  "cereals, winter"                 
+#>  [3] "citrus"                           "cotton"                          
+#>  [5] "field beans"                      "grass / alfalfa"                 
+#>  [7] "hops"                             "legumes"                         
+#>  [9] "maize"                            "oil seed rape, spring"           
+#> [11] "oil seed rape, winter"            "olives"                          
+#> [13] "pome / stone fruit, early applns" "pome / stone fruit, late applns" 
+#> [15] "potatoes"                         "soybeans"                        
+#> [17] "sugar beets"                      "sunflowers"                      
+#> [19] "tobacco"                          "vegetables, bulb"                
+#> [21] "vegetables, fruiting"             "vegetables, leafy"               
+#> [23] "vegetables, root"                 "vines, early applns"             
+#> [25] "vines, late applns"               "appln, aerial"                   
+#> [27] "appln, hand (crop < 50 cm)"       "appln, hand (crop > 50 cm)"      
+#> [29] "no drift (incorp or seed trtmt)" 
+#> 
+#> $drift
+#>                                       1      2      3      4      5      6
+#> cereals, spring                   2.759  2.438  2.024  1.862  1.794  1.631
+#> cereals, winter                   2.759  2.438  2.024  1.862  1.794  1.631
+#> citrus                           15.725 12.129 11.011 10.124  9.743  9.204
+#> cotton                            2.759  2.438  2.024  1.862  1.794  1.631
+#> field beans                       2.759  2.438  2.024  1.862  1.794  1.631
+#> grass / alfalfa                   2.759  2.438  2.024  1.862  1.794  1.631
+#> hops                             19.326 17.723 15.928 15.378 15.114 14.902
+#> legumes                           2.759  2.438  2.024  1.862  1.794  1.631
+#> maize                             2.759  2.438  2.024  1.862  1.794  1.631
+#> oil seed rape, spring             2.759  2.438  2.024  1.862  1.794  1.631
+#> oil seed rape, winter             2.759  2.438  2.024  1.862  1.794  1.631
+#> olives                           15.725 12.129 11.011 10.124  9.743  9.204
+#> pome / stone fruit, early applns 29.197 25.531 23.960 23.603 23.116 22.760
+#> pome / stone fruit, late applns  15.725 12.129 11.011 10.124  9.743  9.204
+#> potatoes                          2.759  2.438  2.024  1.862  1.794  1.631
+#> soybeans                          2.759  2.438  2.024  1.862  1.794  1.631
+#> sugar beets                       2.759  2.438  2.024  1.862  1.794  1.631
+#> sunflowers                        2.759  2.438  2.024  1.862  1.794  1.631
+#> tobacco                           2.759  2.438  2.024  1.862  1.794  1.631
+#> vegetables, bulb                  2.759  2.438  2.024  1.862  1.794  1.631
+#> vegetables, fruiting              2.759  2.438  2.024  1.862  1.794  1.631
+#> vegetables, leafy                 2.759  2.438  2.024  1.862  1.794  1.631
+#> vegetables, root                  2.759  2.438  2.024  1.862  1.794  1.631
+#> vines, early applns               2.699  2.496  2.546  2.499  2.398  2.336
+#> vines, late applns                8.028  7.119  6.898  6.631  6.636  6.431
+#> appln, aerial                    33.200 33.200 33.200 33.200 33.200 33.200
+#> appln, hand (crop < 50 cm)        2.759  2.438  2.024  1.862  1.794  1.631
+#> appln, hand (crop > 50 cm)        8.028  7.119  6.898  6.631  6.636  6.431
+#> no drift (incorp or seed trtmt)   0.000  0.000  0.000  0.000  0.000  0.000
+#>                                       7      8     >8
+#> cereals, spring                   1.578  1.512  1.512
+#> cereals, winter                   1.578  1.512  1.512
+#> citrus                            9.102  8.656  8.656
+#> cotton                            1.578  1.512  1.512
+#> field beans                       1.578  1.512  1.512
+#> grass / alfalfa                   1.578  1.512  1.512
+#> hops                             14.628 13.520 13.520
+#> legumes                           1.578  1.512  1.512
+#> maize                             1.578  1.512  1.512
+#> oil seed rape, spring             1.578  1.512  1.512
+#> oil seed rape, winter             1.578  1.512  1.512
+#> olives                            9.102  8.656  8.656
+#> pome / stone fruit, early applns 22.690 22.241 22.241
+#> pome / stone fruit, late applns   9.102  8.656  8.656
+#> potatoes                          1.578  1.512  1.512
+#> soybeans                          1.578  1.512  1.512
+#> sugar beets                       1.578  1.512  1.512
+#> sunflowers                        1.578  1.512  1.512
+#> tobacco                           1.578  1.512  1.512
+#> vegetables, bulb                  1.578  1.512  1.512
+#> vegetables, fruiting              1.578  1.512  1.512
+#> vegetables, leafy                 1.578  1.512  1.512
+#> vegetables, root                  1.578  1.512  1.512
+#> vines, early applns               2.283  2.265  2.265
+#> vines, late applns                6.227  6.173  6.173
+#> appln, aerial                    33.200 33.200 33.200
+#> appln, hand (crop < 50 cm)        1.578  1.512  1.512
+#> appln, hand (crop > 50 cm)        6.227  6.173  6.173
+#> no drift (incorp or seed trtmt)   0.000  0.000  0.000
+#> 
+#> $interception
+#>                                  no interception minimal crop cover
+#> cereals, spring                                0               0.00
+#> cereals, winter                                0               0.00
+#> citrus                                         0               0.80
+#> cotton                                         0               0.30
+#> field beans                                    0               0.25
+#> grass / alfalfa                                0               0.40
+#> hops                                           0               0.20
+#> legumes                                        0               0.25
+#> maize                                          0               0.25
+#> oil seed rape, spring                          0               0.40
+#> oil seed rape, winter                          0               0.40
+#> olives                                         0               0.70
+#> pome / stone fruit, early applns               0               0.20
+#> pome / stone fruit, late applns                0               0.20
+#> potatoes                                       0               0.15
+#> soybeans                                       0               0.20
+#> sugar beets                                    0               0.20
+#> sunflowers                                     0               0.20
+#> tobacco                                        0               0.20
+#> vegetables, bulb                               0               0.10
+#> vegetables, fruiting                           0               0.25
+#> vegetables, leafy                              0               0.25
+#> vegetables, root                               0               0.25
+#> vines, early applns                            0               0.40
+#> vines, late applns                             0               0.40
+#> appln, aerial                                  0               0.20
+#> appln, hand (crop < 50 cm)                     0               0.20
+#> appln, hand (crop > 50 cm)                     0               0.20
+#> no drift (incorp or seed trtmt)                0               0.00
+#>                                  average crop cover full canopy
+#> cereals, spring                                0.20        0.70
+#> cereals, winter                                0.20        0.70
+#> citrus                                         0.80        0.80
+#> cotton                                         0.60        0.75
+#> field beans                                    0.40        0.70
+#> grass / alfalfa                                0.60        0.75
+#> hops                                           0.50        0.70
+#> legumes                                        0.50        0.70
+#> maize                                          0.50        0.75
+#> oil seed rape, spring                          0.70        0.75
+#> oil seed rape, winter                          0.70        0.75
+#> olives                                         0.70        0.70
+#> pome / stone fruit, early applns               0.40        0.65
+#> pome / stone fruit, late applns                0.40        0.65
+#> potatoes                                       0.50        0.70
+#> soybeans                                       0.50        0.75
+#> sugar beets                                    0.70        0.75
+#> sunflowers                                     0.50        0.75
+#> tobacco                                        0.70        0.75
+#> vegetables, bulb                               0.25        0.40
+#> vegetables, fruiting                           0.50        0.70
+#> vegetables, leafy                              0.40        0.70
+#> vegetables, root                               0.50        0.70
+#> vines, early applns                            0.50        0.60
+#> vines, late applns                             0.50        0.60
+#> appln, aerial                                  0.50        0.70
+#> appln, hand (crop < 50 cm)                     0.50        0.70
+#> appln, hand (crop > 50 cm)                     0.50        0.70
+#> no drift (incorp or seed trtmt)                0.00        0.00
+#> 
+#> $rd
+#>          Region
+#> Time      North South
+#>   Oct-Feb     5     4
+#>   Mar-May     2     4
+#>   Jun-Sep     2     3
+#> 
+
+
+
- + - - - + diff --git a/docs/reference/FOMC_actual_twa.html b/docs/reference/FOMC_actual_twa.html index a365556..f7b0140 100644 --- a/docs/reference/FOMC_actual_twa.html +++ b/docs/reference/FOMC_actual_twa.html @@ -1,187 +1,119 @@ - - - - - - - -Actual and maximum moving window time average concentrations for FOMC kinetics — FOMC_actual_twa • pfm - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +Actual and maximum moving window time average concentrations for FOMC kinetics — FOMC_actual_twa • pfm + Skip to contents + +
+
+
+

Actual and maximum moving window time average concentrations for FOMC kinetics

+ Source: R/FOMC_actual_twa.R +
FOMC_actual_twa.Rd
-
+

Actual and maximum moving window time average concentrations for FOMC kinetics

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

Arguments

- - - - - - - - - - - - - - -
alpha

Parameter of the FOMC model

beta

Parameter of the FOMC model

times

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

- -

Source

+
+

Usage

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

Source

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

+
+
+

Arguments

+
alpha
+

Parameter of the FOMC model

-

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

Groundwater ubiquity score based on Gustafson (1989)

+ Source: R/GUS.R +
GUS.Rd
-
+

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

- 
GUS(...)
-
-# S3 method for numeric
-GUS(DT50, Koc, ...)
-
-# S3 method for chent
-GUS(
-  chent,
-  degradation_value = "DT50ref",
-  lab_field = "laboratory",
-  redox = "aerobic",
-  sorption_value = "Kfoc",
-  degradation_aggregator = geomean,
-  sorption_aggregator = geomean,
-  ...
-)
-
-# S3 method for GUS_result
-print(x, ..., digits = 1)
- -

Arguments

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
...

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

DT50

Half-life of the chemical in soil. Should be a field +

+

Usage

+ 
GUS(...)
+
+# S3 method for numeric
+GUS(DT50, Koc, ...)
+
+# S3 method for chent
+GUS(
+  chent,
+  degradation_value = "DT50ref",
+  lab_field = "laboratory",
+  redox = "aerobic",
+  sorption_value = "Kfoc",
+  degradation_aggregator = geomean,
+  sorption_aggregator = geomean,
+  ...
+)
+
+# S3 method for GUS_result
+print(x, ..., digits = 1)
+
+ +
+

Arguments

+
...
+

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

+ + +
DT50
+

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

Koc

The sorption constant normalised to organic carbon. Gustafson +be conducted under use conditions.

+ + +
Koc
+

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

chent

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

degradation_value

Which of the available degradation values should -be used?

lab_field

Should laboratory or field half-lives be used? This +(and if the reference concentration would be in soil or in porewater).

+ + +
chent
+

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

+ + +
degradation_value
+

Which of the available degradation values should +be used?

+ + +
lab_field
+

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

redox

Aerobic or anaerobic degradation data

sorption_value

Which of the available sorption values should be used? +field half-lives obtained under actual use conditions should be used.

+ + +
redox
+

Aerobic or anaerobic degradation data

+ + +
sorption_value
+

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

degradation_aggregator

Function for aggregating half-lives

sorption_aggregator

Function for aggregation Koc values

x

An object of class GUS_result to be printed

digits

The number of digits used in the print method

- -

Value

- -

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

-

References

+be Koc values at a concentration of 1 mg/L in the water phase.

+ + +
degradation_aggregator
+

Function for aggregating half-lives

+ + +
sorption_aggregator
+

Function for aggregation Koc values

+ +
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

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

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

+ Source: R/PEC_soil.R +
PEC_FOMC_accu_rel.Rd
-
- +

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

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

Arguments

- - - - - - - - - - - - - - -
n

number of applications

interval

Time between applications

FOMC

Named numeric vector containing the FOMC parameters alpha and beta

- -

Value

+
+

Usage

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

Arguments

+
n
+

number of applications

-

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

+ +
interval
+

Time between applications

+ + +
FOMC
+

Named numeric vector containing the FOMC parameters alpha and beta

+ +
+
+

Value

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

Calculate predicted environmental concentrations in soil

- +
+
+
+
+

Calculate predicted environmental concentrations in soil

+ Source: R/PEC_soil.R +
PEC_soil.Rd
-
+

This is a basic calculation of a contaminant concentration in bulk soil based on complete, instantaneous mixing. If an interval is given, an attempt is made at calculating a long term maximum concentration using @@ -51,7 +58,8 @@ the concepts layed out in the PPR panel opinion (EFSA PPR panel 2012 and in the EFSA guidance on PEC soil calculations (EFSA, 2015, 2017).

-
+
+

Usage

PEC_soil(
   rate,
   rate_units = "g/ha",
@@ -78,8 +86,8 @@ and in the EFSA guidance on PEC soil calculations (EFSA, 2015, 2017).

 )
-
-

Arguments

+
+

Arguments

rate

Application rate in units specified below

@@ -195,14 +203,14 @@ soil concentrations? Based on equation (7) given in the PPR panel opinion p. 13).

-
-

Value

+
+

Value

The predicted concentration in soil

-
-

Details

+
+

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 @@ -210,8 +218,8 @@ 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 @@ -227,8 +235,8 @@ from 2017 (p. 92).

(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 @@ -245,13 +253,13 @@ from 2017 (p. 92).

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

-
-

Author

+
+

Author

Johannes Ranke

-
-

Examples

+
+

Examples

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

                            Kom = 100, scenarios = "EFSA_2015", porewater = TRUE)
-
- -
+
-
+

Developed by Johannes Ranke.

-
+

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

Calculate initial and accumulation PEC soil for a set of metabolites

+ Source: R/PEC_soil.R +
PEC_soil_mets.Rd
+
- +
+

Calculate initial and accumulation PEC soil for a set of metabolites

+
- - +
+

Usage

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

Arguments

+
rate
+

Application rate in units specified below

- - - +
mw_parent
+

The molecular weight of the parent compound

- - - - - - - -
-
- +
mets
+

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

- -
+
interval
+

The interval for accumulation calculations

-
-
-
-

Calculate initial and accumulation PEC soil for a set of metabolites

- - -
-
- -

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 -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

- +
...
+

Further arguments are passed to PEC_soil

-
-
-
-
+
-
-
-

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

+ Source: R/PEC_sw_drainage_UK.R +
PEC_sw_drainage_UK.Rd
+
- - +
+

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

+
- - - +
+

Usage

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

Arguments

+
rate
+

Application rate in g/ha

+
interception
+

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

- - - +
Koc
+

The sorption coefficient normalised to organic carbon in L/kg

+
latest_application
+

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

- - - +
soil_DT50
+

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

- - - - - - - -
-
- +
model
+

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

- -
+
model_parms
+

A named numeric vector containing the model parameters

-
-
-
-

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

+
+
+

Value

- -
-
-

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

+

The predicted concentration in surface water in µg/L

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

Arguments

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
rate

Application rate in g/ha

interception

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

Koc

The sorption coefficient normalised to organic carbon in L/kg

latest_application

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

soil_DT50

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

model

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

model_parms

A named numeric vector containing the model parameters

- -

Value

- -

The predicted concentration in surface water in µg/L

-

References

- +
+

References

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

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

- -

Examples

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

Author

Johannes Ranke

-
-
+
+ +
+

Examples

+ 
PEC_sw_drainage_UK(150, Koc = 100)
+#> [1] 8.076923
+
+
+
- + - - - + diff --git a/docs/reference/PEC_sw_drift.html b/docs/reference/PEC_sw_drift.html index e2c325b..3e1d4c8 100644 --- a/docs/reference/PEC_sw_drift.html +++ b/docs/reference/PEC_sw_drift.html @@ -1,221 +1,166 @@ - - - - - - +Calculate predicted environmental concentrations in surface water due to drift — PEC_sw_drift • pfm + Skip to contents + -Calculate predicted environmental concentrations in surface water due to drift — PEC_sw_drift • pfm +
+
+
+

Calculate predicted environmental concentrations in surface water due to drift

+ Source: R/PEC_sw_drift.R +
PEC_sw_drift.Rd
+
- - - +
+

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

+
- - +
+

Usage

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

Arguments

+
rate
+

Application rate in units specified below

- - - +
applications
+

Number of applications for selection of drift percentile

+
water_depth
+

Depth of the water body in cm

- - +
drift_percentages
+

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

+
drift_data
+

Source of drift percentage data. If 'JKI', the [drift_data_JKI] +included in the package is used. If 'RF', the Rautmann formula is used, if +implemented for the crop type and number of applications

- - - - - - - - - - -
-
- - +
distances
+

The distances in m for which to get PEC values

-
-
-
-
-

Calculate predicted environmental concentrations in surface water due to drift

+
rate_units
+

Defaults to g/ha

+ + +
PEC_units
+

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

+ +
+
+

Value

- -
-
-

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

+

The predicted concentration in surface water

+
+
+

Author

+

Johannes Ranke

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

Arguments

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
rate

Application rate in units specified below

applications

Number of applications for selection of drift percentile

water_depth

Depth of the water body in cm

drift_percentages

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

drift_data

Source of drift percentage data. If 'JKI', the [drift_data_JKI] -included in the package is used. If 'RF', the Rautmann formula is used, if -implemented for the crop type and number of applications

crop

Crop name (use German names for JKI data), defaults to "Ackerbau"

distances

The distances in m for which to get PEC values

rate_units

Defaults to g/ha

PEC_units

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

- -

Value

- -

The predicted concentration in surface water

- -

Examples

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

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

-# Using custom drift percentages is also supported
-PEC_sw_drift(100, drift_percentages = c(2.77, 0.95, 0.57, 0.48, 0.29, 0.15, 0.06, 0.03))
#>     2.77 %     0.95 %     0.57 %     0.48 %     0.29 %     0.15 %     0.06 % 
-#> 0.92333333 0.31666667 0.19000000 0.16000000 0.09666667 0.05000000 0.02000000 
-#>     0.03 % 
-#> 0.01000000
-
- -
+
+

Examples

+ 
PEC_sw_drift(100)
+#>        1 m        5 m       10 m       20 m 
+#> 0.92333333 0.19000000 0.09666667 0.05000000 
+# Alternatively, we can use the formula for a single application to "Ackerbau" from the paper
+PEC_sw_drift(100, drift_data = "RF")
+#>        1 m        5 m       10 m       20 m 
+#> 0.92350000 0.19114149 0.09699222 0.04921742 
+
+# This makes it possible to also use different substances
+PEC_sw_drift(100, distances = c(1, 3, 5, 6, 10, 20, 50, 100), drift_data = "RF")
+#>        1 m        3 m        5 m        6 m       10 m       20 m       50 m 
+#> 0.92350000 0.31512171 0.19114149 0.15990435 0.09699222 0.04921742 0.02007497 
+#>      100 m 
+#> 0.01018678 
+
+# Using custom drift percentages is also supported
+PEC_sw_drift(100, drift_percentages = c(2.77, 0.95, 0.57, 0.48, 0.29, 0.15, 0.06, 0.03))
+#>     2.77 %     0.95 %     0.57 %     0.48 %     0.29 %     0.15 %     0.06 % 
+#> 0.92333333 0.31666667 0.19000000 0.16000000 0.09666667 0.05000000 0.02000000 
+#>     0.03 % 
+#> 0.01000000 
+
+
+ - + - - - + diff --git a/docs/reference/PEC_sw_exposit_drainage.html b/docs/reference/PEC_sw_exposit_drainage.html index 5bd1d80..74f1b60 100644 --- a/docs/reference/PEC_sw_exposit_drainage.html +++ b/docs/reference/PEC_sw_exposit_drainage.html @@ -1,117 +1,60 @@ - - - - - - - -Calculate PEC surface water due to drainage as in Exposit 3 — PEC_sw_exposit_drainage • pfm - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Calculate PEC surface water due to drainage as in Exposit 3 — PEC_sw_exposit_drainage • pfm + Skip to contents + +
+
+
+

Calculate PEC surface water due to drainage as in Exposit 3

+ Source: R/PEC_sw_exposit_runoff.R +
PEC_sw_exposit_drainage.Rd
-
+

This is a reimplementation of the calculation described in the Exposit 3.02 spreadsheet file, in the worksheet "Konzept Drainage". Although there are four groups of compounds ("Gefährdungsgruppen"), only one distinction is made in the @@ -121,123 +64,123 @@ the group is derived only from the Koc, if not given explicitly. For details, see the discussion of the function arguments below.

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

Arguments

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
rate

The application rate in g/ha

interception

The fraction intercepted by the crop

Koc

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

mobility

Overrides what is determined from the Koc.

DT50

The soil half-life in days

t_drainage

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

V_ditch

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

V_drainage

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

dilution

The dilution factor

- -

Source

+
+

Usage

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

Source

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

-

Value

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

+
+
+

Arguments

+
rate
+

The application rate in g/ha

+ + +
interception
+

The fraction intercepted by the crop

+ + +
Koc
+

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

+ + +
mobility
+

Overrides what is determined from the Koc.

+ + +
DT50
+

The soil half-life in days

+ + +
t_drainage
+

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

+ + +
V_ditch
+

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

-

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

+ Source: R/PEC_sw_exposit_runoff.R +
PEC_sw_exposit_runoff.Rd
+
- - +
+

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

+
- - - +
+

Usage

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

Source

+

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

+
+
+

Arguments

+
rate
+

The application rate in g/ha

+
interception
+

The fraction intercepted by the crop

- - - +
Koc
+

The sorption coefficient to soil organic carbon

+
DT50
+

The soil half-life in days

- - - +
t_runoff
+

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

- - - - - - - -
-
- +
exposit_reduction_version
+

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

- -
+
V_ditch
+

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

-
-
-
-

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

- - -
-
-

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

-
+
V_event
+

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

- 
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

-

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

+
dilution
+

The dilution factor

-

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 +

+
+

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

Calculate PEC surface water at FOCUS Step 1

- +
+
+
+
+

Calculate PEC surface water at FOCUS Step 1

+ Source: R/PEC_sw_focus.R +
PEC_sw_focus.Rd
-
+

This is a reimplementation of the FOCUS Step 1 and 2 calculator version 3.2, authored by Michael Klein, in R. Note that results for multiple applications should be compared to the corresponding results for a @@ -55,7 +64,8 @@ input files are generated that are suitable as input also for Step 2 to be used with the FOCUS calculator.

-
+
+

Usage

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

 )
-
-

Arguments

+
+

Arguments

parent

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

@@ -161,8 +171,8 @@ should be written

Should the input text file be appended?

-
-

Note

+
+

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 @@ -171,8 +181,8 @@ should be written

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

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

http://esdac.jrc.ec.europa.eu/projects/stepsonetwo

-
-

Examples

+
+

Examples

# Parent only
 dummy_1 <- chent_focus_sw("Dummy 1", cwsat = 6000, DT50_ws = 6, Koc = 344.8)
 PEC_sw_focus(dummy_1, 3000, f_drift = 0, overwrite = TRUE, append = FALSE)
@@ -284,26 +294,22 @@ should be written

 #>
-
- -
+
-
+

Developed by Johannes Ranke.

-
+

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..3432a75 100644 --- a/docs/reference/PEC_sw_sed.html +++ b/docs/reference/PEC_sw_sed.html @@ -1,202 +1,137 @@ - - - - - - +Calculate predicted environmental concentrations in sediment from surface +water concentrations — PEC_sw_sed • pfm + Skip to contents + -Calculate predicted environmental concentrations in sediment from surface -water concentrations — PEC_sw_sed • pfm +
+
+
+

Calculate predicted environmental concentrations in sediment from surface +water concentrations

+ Source: R/PEC_sw_sed.R +
PEC_sw_sed.Rd
+
- - +
+

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

+
- - - +
+

Usage

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

Arguments

+
PEC_sw
+

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

+
percentage
+

The percentage in sediment, used for the percentage method

- - - +
method
+

The method used for the calculation

+
sediment_depth
+

Depth of the sediment layer

- - - +
water_depth
+

Depth of the water body in cm

- - - - - - - -
-
- +
sediment_density
+

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

- -
+
PEC_sed_units
+

The units of the estimated sediment PEC value

-
-
-
-

Calculate predicted environmental concentrations in sediment from surface -water concentrations

+
+
+

Value

- -
-
-

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

+

The predicted concentration in sediment

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

Arguments

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
PEC_sw

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

percentage

The percentage in sediment, used for the percentage method

method

The method used for the calculation

sediment_depth

Depth of the sediment layer

water_depth

Depth of the water body in cm

sediment_density

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

PEC_sed_units

The units of the estimated sediment PEC value

- -

Value

- -

The predicted concentration in sediment

- -

Examples

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

Examples

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

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

Actual and maximum moving window time average concentrations for SFO kinetics

+ Source: R/SFO_actual_twa.R +
SFO_actual_twa.Rd
-
+

Actual and maximum moving window time average concentrations for SFO kinetics

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

Arguments

- - - - - - - - - - -
DT50

The half-life.

times

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

- -

Source

+
+

Usage

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

Source

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

+
+
+

Arguments

+
DT50
+

The half-life.

-

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

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

+ Source: R/SSLRC_mobility_classification.R +
SSLRC_mobility_classification.Rd
-
- +

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

-
- 
SSLRC_mobility_classification(Koc)
- -

Arguments

- - - - - - -
Koc

The sorption coefficient normalised to organic carbon in L/kg

- -

Value

+
+

Usage

+ 
SSLRC_mobility_classification(Koc)
+
-

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

+
+

Arguments

+
Koc
+

The sorption coefficient normalised to organic carbon in L/kg

+ +
+
+

Value

-

References

+

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

+
+
+

References

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

R6 class for holding TOXSWA water concentration data and associated statistics

- +
+
+
+
+

R6 class for holding TOXSWA water concentration data and associated statistics

+ Source: R/TOXSWA_cwa.R +
TOXSWA_cwa.Rd
-
+

An R6 class for holding TOXSWA water concentration (cwa) data and some associated statistics. like maximum moving window average concentrations, and dataframes holding the events exceeding specified @@ -52,12 +59,12 @@ by read.TOXSWA_cwa.

-
-

Format

+
+

Format

An R6Class generator object.

-
-

Public fields

+
+

Public fields

filename

Length one character vector holding the filename.

@@ -94,8 +101,8 @@ for the requested moving window sizes in days.

-
-

Methods

+
+

Methods

Public methods

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

-
-

Examples

+
+

Examples

H_sw_R1_stream  <- read.TOXSWA_cwa("00003s_pa.cwa",
                                  basedir = "SwashProjects/project_H_sw/TOXSWA",
                                  zipfile = system.file("testdata/SwashProjects.zip",
@@ -230,53 +237,25 @@ suspended matter will be used.

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

Developed by Johannes Ranke.

-
+

Site built with pkgdown 2.0.7.

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

Estimation of the transpiration stream concentration factor

+ Source: R/TSCF.R +
TSCF.Rd
-
+

The FOCUS groundwater guidance (FOCUS 2014, p. 41) states that a reliable measured log Kow for neutral pH must be available in order to apply the Briggs equation. It is not clarified when it can be regarded reliable, but the @@ -122,60 +61,62 @@ the compound should not be ionogenic (weak acid/base) or ionic.

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

Usage

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

Arguments

- - - - - - - - - - -
log_Kow

The decadic logarithm of the octanol-water partition constant

method

Short name of the estimation method.

+
+

Arguments

+
log_Kow
+

The decadic logarithm of the octanol-water partition constant

-

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

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..5ffa6b4 100644 --- a/docs/reference/chent_focus_sw.html +++ b/docs/reference/chent_focus_sw.html @@ -1,206 +1,135 @@ - - - - - - +Create a chemical compound object for FOCUS Step 1 calculations — chent_focus_sw • pfm + Skip to contents + -Create a chemical compound object for FOCUS Step 1 calculations — chent_focus_sw • pfm +
+
+
+

Create a chemical compound object for FOCUS Step 1 calculations

+ Source: R/PEC_sw_focus.R +
chent_focus_sw.Rd
+
- - +
+

Create a chemical compound object for FOCUS Step 1 calculations

+
- - - +
+

Usage

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

Arguments

+
name
+

Length one character vector containing the name

+
Koc
+

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

- - - +
DT50_ws
+

Half-life in water/sediment systems in days

+
DT50_soil
+

Half-life in soil in days

- - - +
DT50_water
+

Half-life in water in days (Step 2)

- - - - - - - -
-
- +
DT50_sediment
+

Half-life in sediment in days (Step 2)

- -
+
cwsat
+

Water solubility in mg/L

-
-
-
-

Create a chemical compound object for FOCUS Step 1 calculations

- - -
-
-

Create a chemical compound object for FOCUS Step 1 calculations

-
+
mw
+

Molar weight in g/mol.

- 
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

-
- -
+
max_ws
+

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

+ +
+
+

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

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

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

- - + Source: R/drift_data_JKI.R +
drift_data_JKI.Rd
-
+

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

- -

Format

- +
+

Format

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

-

Source

- +
+
+

Source

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

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

-

Details

- +
+
+

Details

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

@@ -148,272 +84,269 @@ these values are used for spray applications with handheld/knapsack equipment (tragbare Spritz- und Sprühgerate).

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

+
-

Examples

- 

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

+
+

Examples

+ 

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

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..5e038b1 100644 --- a/docs/reference/endpoint.html +++ b/docs/reference/endpoint.html @@ -1,250 +1,182 @@ - - - - - - +Retrieve endpoint information from the chyaml field of a chent object — endpoint • pfm + Skip to contents + -Retrieve endpoint information from the chyaml field of a chent object — endpoint • pfm +
+
+
+

Retrieve endpoint information from the chyaml field of a chent object

+ Source: R/endpoint.R +
endpoint.Rd
+
- - +
+

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

+
- - - +
+

Usage

+ 
endpoint(
+  chent,
+  medium = "soil",
+  type = c("degradation", "sorption"),
+  lab_field = c(NA, "laboratory", "field"),
+  redox = c(NA, "aerobic", "anaerobic"),
+  value = c("DT50ref", "Kfoc", "N"),
+  aggregator = geomean,
+  raw = FALSE,
+  signif = 3
+)
+
+soil_DT50(
+  chent,
+  aggregator = geomean,
+  signif = 3,
+  lab_field = "laboratory",
+  value = "DT50ref",
+  redox = "aerobic",
+  raw = FALSE
+)
+
+soil_Kfoc(chent, aggregator = geomean, signif = 3, value = "Kfoc", raw = FALSE)
+
+soil_N(chent, aggregator = mean, signif = 3, raw = FALSE)
+
+soil_sorption(
+  chent,
+  values = c("Kfoc", "N"),
+  aggregators = c(Kfoc = geomean, Koc = geomean, N = mean),
+  signif = c(Kfoc = 3, N = 3),
+  raw = FALSE
+)
+
- - - +
+

Arguments

+
chent
+

The chent object to get the information from

+
medium
+

The medium for which information is sought

- - - +
type
+

The information type

+
lab_field
+

If not NA, do we want laboratory or field endpoints

- - - +
redox
+

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

- - - - - - - -
-
- +
value
+

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

- -
+
aggregator
+

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

-
-
-
-

Retrieve endpoint information from the chyaml field of a chent object

- - -
-
-

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

-
- - 
endpoint(
-  chent,
-  medium = "soil",
-  type = c("degradation", "sorption"),
-  lab_field = c(NA, "laboratory", "field"),
-  redox = c(NA, "aerobic", "anaerobic"),
-  value = c("DT50ref", "Kfoc", "N"),
-  aggregator = geomean,
-  raw = FALSE,
-  signif = 3
-)
-
-soil_DT50(
-  chent,
-  aggregator = geomean,
-  signif = 3,
-  lab_field = "laboratory",
-  value = "DT50ref",
-  redox = "aerobic",
-  raw = FALSE
-)
-
-soil_Kfoc(chent, aggregator = geomean, signif = 3, value = "Kfoc", raw = FALSE)
-
-soil_N(chent, aggregator = mean, signif = 3, raw = FALSE)
-
-soil_sorption(
-  chent,
-  values = c("Kfoc", "N"),
-  aggregators = c(Kfoc = geomean, Koc = geomean, N = mean),
-  signif = c(Kfoc = 3, N = 3),
-  raw = FALSE
-)
- -

Arguments

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
chent

The chent object to get the information from

medium

The medium for which information is sought

type

The information type

lab_field

If not NA, do we want laboratory or field endpoints

redox

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

value

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

aggregator

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

raw

Should the number(s) be returned as stored in the chent +

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

Calculate the geometric mean

+ Source: R/geomean.R +
geomean.Rd
-
+

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

- 
geomean(x, na.rm = FALSE)
+
+

Usage

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

Arguments

+
x
+

Vector of numbers

-

Arguments

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

Vector of numbers

na.rm

Should NA values be omitted?

-

Value

+
na.rm
+

Should NA values be omitted?

-

The geometric mean

+
+
+

Value

+ -

Examples

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

Author

Johannes Ranke

-
-
+
+ +
+

Examples

+ 
geomean(c(1, 3, 9))
+#> [1] 3
+geomean(c(1, 3, NA, 9))
+#> [1] NA
+if (FALSE) geomean(c(1, -3, 9)) # returns an error
+
+
+
- + - - - + diff --git a/docs/reference/get_vertex.html b/docs/reference/get_vertex.html index 3d0bc2d..aee1a70 100644 --- a/docs/reference/get_vertex.html +++ b/docs/reference/get_vertex.html @@ -1,159 +1,87 @@ - - - - - - - -Fit a parabola through three points — get_vertex • pfm - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +Fit a parabola through three points — get_vertex • pfm + Skip to contents + +
+
+
+

Fit a parabola through three points

+ Source: R/PEC_soil.R +
get_vertex.Rd
-
- +

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

-
- 
get_vertex(x, y)
- -

Arguments

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

Three x coordinates

y

Three y coordinates

- +
+

Usage

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

Three y coordinates

+ +
-
-
-

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

Reference

+ +
+
+
+
+

Function reference

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

General utility functions

-

Functions that are independent of specific fate modelling areas

-
-

geomean()

-

Calculate the geometric mean

-

one_box()

-

Create a time series of decline data

-

plot(<one_box>)

-

Plot time series of decline data

-

sawtooth()

-

Create decline time series for multiple applications

-

twa()

-

Calculate a time weighted average concentration

-

max_twa()

-

The maximum time weighted average concentration for a moving window

-

pfm_degradation()

-

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

-

SFO_actual_twa()

-

Actual and maximum moving window time average concentrations for SFO kinetics

-

FOMC_actual_twa()

-

Actual and maximum moving window time average concentrations for FOMC kinetics

-

reexports set_nd_nq set_nd_nq_focus

-

Objects exported from other packages

-

TSCF()

-

Estimation of the transpiration stream concentration factor

-

Predicted environmental concentrations in soil

-

-
-

PEC_soil()

-

Calculate predicted environmental concentrations in soil

-

PEC_soil_mets()

-

Calculate initial and accumulation PEC soil for a set of metabolites

-

soil_scenario_data_EFSA_2015

-

Properties of the predefined scenarios from the EFSA guidance from 2015

-

soil_scenario_data_EFSA_2017

-

Properties of the predefined scenarios from the EFSA guidance from 2017

-

PEC_FOMC_accu_rel()

-

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

-

EFSA_washoff_2017

-

Subset of EFSA crop washoff default values

-

Predicted environmental concentrations in groundwater

-

-
-

FOCUS_GW_scenarios_2012

-

A very small subset of the FOCUS Groundwater scenario definitions

-

EFSA_GW_interception_2014

-

Subset of EFSA crop interception default values for groundwater modelling

-

Predicted environmental concentrations in surface water

-

-
-

PEC_sw_drift()

-

Calculate predicted environmental concentrations in surface water due to drift

-

drift_data_JKI

-

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

-

PEC_sw_drainage_UK()

-

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

-

PEC_sw_sed()

-

Calculate predicted environmental concentrations in sediment from surface -water concentrations

-

PEC_sw_focus()

-

Calculate PEC surface water at FOCUS Step 1

-

chent_focus_sw()

-

Create a chemical compound object for FOCUS Step 1 calculations

-

FOCUS_Step_12_scenarios

-

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

-

PEC_sw_exposit_drainage()

-

Calculate PEC surface water due to drainage as in Exposit 3

-

PEC_sw_exposit_runoff()

-

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

-

perc_runoff_exposit

-

Runoff loss percentages as used in Exposit 3

-

perc_runoff_reduction_exposit

-

Runoff reduction percentages as used in Exposit

-

TOXSWA_cwa

-

R6 class for holding TOXSWA water concentration data and associated statistics

-

read.TOXSWA_cwa()

-

Read TOXSWA surface water concentrations

-

plot(<TOXSWA_cwa>)

-

Plot TOXSWA surface water concentrations

-

Classifications and indicators

-

Evaluating environmental fate properties

-
-

SSLRC_mobility_classification()

-

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

-

GUS() print(<GUS_result>)

-

Groundwater ubiquity score based on Gustafson (1989)

-

Work with chent objects containing relevant information

-

-
-

endpoint() soil_DT50() soil_Kfoc() soil_N() soil_sorption()

-

Retrieve endpoint information from the chyaml field of a chent object

-

Utilities

-

-
-

get_vertex()

-

Fit a parabola through three points

- - -
+
+

General utility functions

+ +

Functions that are independent of specific fate modelling areas

+ + +
+ + + + +
+ + geomean() +
+
Calculate the geometric mean
+
+ + one_box() +
+
Create a time series of decline data
+
+ + plot(<one_box>) +
+
Plot time series of decline data
+
+ + sawtooth() +
+
Create decline time series for multiple applications
+
+ + twa() +
+
Calculate a time weighted average concentration
+
+ + max_twa() +
+
The maximum time weighted average concentration for a moving window
+
+ + pfm_degradation() +
+
Calculate a time course of relative concentrations based on an mkinmod model
+
+ + SFO_actual_twa() +
+
Actual and maximum moving window time average concentrations for SFO kinetics
+
+ + FOMC_actual_twa() +
+
Actual and maximum moving window time average concentrations for FOMC kinetics
+
+ + reexports set_nd_nq set_nd_nq_focus +
+
Objects exported from other packages
+
+ + TSCF() +
+
Estimation of the transpiration stream concentration factor
+
+

Predicted environmental concentrations in soil

+ + + + +
+ + + + +
+ + PEC_soil() +
+
Calculate predicted environmental concentrations in soil
+
+ + PEC_soil_mets() +
+
Calculate initial and accumulation PEC soil for a set of metabolites
+
+ + soil_scenario_data_EFSA_2015 +
+
Properties of the predefined scenarios from the EFSA guidance from 2015
+
+ + soil_scenario_data_EFSA_2017 +
+
Properties of the predefined scenarios from the EFSA guidance from 2017
+
+ + PEC_FOMC_accu_rel() +
+
Get the relative accumulation of an FOMC model over multiples of an interval
+
+ + EFSA_washoff_2017 +
+
Subset of EFSA crop washoff default values
+
+

Predicted environmental concentrations in groundwater

+ + + + +
+ + + + +
+ + FOCUS_GW_scenarios_2012 +
+
A very small subset of the FOCUS Groundwater scenario definitions
+
+ + EFSA_GW_interception_2014 +
+
Subset of EFSA crop interception default values for groundwater modelling
+
+

Predicted environmental concentrations in surface water

+ + + + +
+ + + + +
+ + PEC_sw_drift() +
+
Calculate predicted environmental concentrations in surface water due to drift
+
+ + drift_data_JKI +
+
Deposition from spray drift expressed as percent of the applied dose as +published by the JKI
+
+ + PEC_sw_drainage_UK() +
+
Calculate initial predicted environmental concentrations in surface water due to drainage using the UK method
+
+ + PEC_sw_sed() +
+
Calculate predicted environmental concentrations in sediment from surface +water concentrations
+
+ + PEC_sw_focus() +
+
Calculate PEC surface water at FOCUS Step 1
+
+ + chent_focus_sw() +
+
Create a chemical compound object for FOCUS Step 1 calculations
+
+ + FOCUS_Step_12_scenarios +
+
Step 1/2 scenario data as distributed with the FOCUS Step 1/2 calculator
+
+ + PEC_sw_exposit_drainage() +
+
Calculate PEC surface water due to drainage as in Exposit 3
+
+ + PEC_sw_exposit_runoff() +
+
Calculate PEC surface water due to runoff and erosion as in Exposit 3
+
+ + perc_runoff_exposit +
+
Runoff loss percentages as used in Exposit 3
+
+ + perc_runoff_reduction_exposit +
+
Runoff reduction percentages as used in Exposit
+
+ + TOXSWA_cwa +
+
R6 class for holding TOXSWA water concentration data and associated statistics
+
+ + read.TOXSWA_cwa() +
+
Read TOXSWA surface water concentrations
+
+ + plot(<TOXSWA_cwa>) +
+
Plot TOXSWA surface water concentrations
+
+

Classifications and indicators

+ +

Evaluating environmental fate properties

+ + +
+ + + + +
+ + SSLRC_mobility_classification() +
+
Determine the SSLRC mobility classification for a chemical substance from its Koc
+
+ + GUS() print(<GUS_result>) +
+
Groundwater ubiquity score based on Gustafson (1989)
+
+

Work with chent objects containing relevant information

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

Utilities

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

Developed by Johannes Ranke.

-
+

Site built with pkgdown 2.0.7.

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

The maximum time weighted average concentration for a moving window

+ Source: R/twa.R +
max_twa.Rd
-
- -

If you generate your time series using sawtooth, +

+

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

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

Arguments

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

An object of type one_box

window

The size of the moving window

- -

Details

+
+

Usage

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

Arguments

+
x
+

An object of type one_box

+ + +
window
+

The size of the moving window

+ +
+
+

Details

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

- -

References

- -

FOCUS (2006) “Guidance Document on Estimating Persistence and +

+
+

References

+

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

- -

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

Create a time series of decline data

+ Source: R/twa.R +
one_box.Rd
+
- - +
+

Create a time series of decline data

+
- - - +
+

Usage

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

Arguments

+
x
+

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

+
ini
+

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

- - - +
...
+

Further arguments passed to methods

+
t_end
+

End of the time series

- - - +
res
+

Resolution of the time series

- - - - - - - -
-
- +
parms
+

A named numeric vector containing the model parameters

- - -
- -
-
-
-

Create a time series of decline data

+
+
+

Value

- -
-
-

Create a time series of decline data

+

An object of class one_box, inheriting from ts.

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

Arguments

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

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

ini

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

...

Further arguments passed to methods

t_end

End of the time series

res

Resolution of the time series

parms

A named numeric vector containing the model parameters

- -

Value

- -

An object of class one_box, inheriting from ts.

- -

Examples

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

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

-# Use a model with more than one observed variable
-m_2 <- mkinmod(parent = mkinsub("SFO", "m1"), m1 = mkinsub("SFO"))
#> Successfully compiled differential equation model from auto-generated C code.
fit_2 <- mkinfit(m_2, FOCUS_2006_D, quiet = TRUE)
#> Warning: Observations with value of zero were removed from the data
pred_2 <- one_box(fit_2, ini = "model")
-plot(pred_2)
-
- -
+
+

Examples

+ 
# Only use a half-life
+pred_0 <- one_box(10)
+plot(pred_0)
+
+
+# Use a fitted mkinfit model
+require(mkin)
+fit <- mkinfit("FOMC", FOCUS_2006_C, quiet = TRUE)
+pred_1 <- one_box(fit)
+plot(pred_1)
+
+
+# Use a model with more than one observed variable
+m_2 <- mkinmod(parent = mkinsub("SFO", "m1"), m1 = mkinsub("SFO"))
+#> Temporary DLL for differentials generated and loaded
+fit_2 <- mkinfit(m_2, FOCUS_2006_D, quiet = TRUE)
+#> Warning: Observations with value of zero were removed from the data
+pred_2 <- one_box(fit_2, ini = "model")
+plot(pred_2)
+
+
+
+ - + - - - + diff --git a/docs/reference/perc_runoff_exposit.html b/docs/reference/perc_runoff_exposit.html index 38c1f70..ee29f54 100644 --- a/docs/reference/perc_runoff_exposit.html +++ b/docs/reference/perc_runoff_exposit.html @@ -1,175 +1,109 @@ - - - - - - - -Runoff loss percentages as used in Exposit 3 — perc_runoff_exposit • pfm - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +Runoff loss percentages as used in Exposit 3 — perc_runoff_exposit • pfm + Skip to contents + +
+
+
+

Runoff loss percentages as used in Exposit 3

+ Source: R/PEC_sw_exposit_runoff.R +
perc_runoff_exposit.Rd
-
+

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

- -

Format

- +
+

Format

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

-
Koc_lower_bound

The lower bound of the Koc class

-
dissolved

The percentage of the applied substance transferred to an + bound to eroding particles, with Koc classes used as row names

Koc_lower_bound
+

The lower bound of the Koc class

+ +
dissolved
+

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

-
bound

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

- -
-

Source

+
bound
+

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

+ +
+
+

Source

Excel 3.02 spreadsheet available from - https://www.bvl.bund.de/EN/04_PlantProtectionProducts/03_Applicants/04_AuthorisationProcedure/08_Environment/ppp_environment_node.html

- -

Examples

- 
print(perc_runoff_exposit)
#>              Koc_lower_bound dissolved bound
-#> 0-20                       0     0.110 0.000
-#> >20-50                    20     0.151 0.000
-#> >50-100                   50     0.197 0.000
-#> >100-200                 100     0.248 0.001
-#> >200-500                 200     0.224 0.004
-#> >500-1000                500     0.184 0.020
-#> >1000-2000              1000     0.133 0.042
-#> >2000-5000              2000     0.084 0.091
-#> >5000-10000             5000     0.037 0.159
-#> >10000-20000           10000     0.031 0.192
-#> >20000-50000           20000     0.014 0.291
-#> >50000                 50000     0.001 0.451
-
- -
+ https://www.bvl.bund.de/EN/04_PlantProtectionProducts/03_Applicants/04_AuthorisationProcedure/08_Environment/ppp_environment_node.html

+
+ +
+

Examples

+ 
print(perc_runoff_exposit)
+#>              Koc_lower_bound dissolved bound
+#> 0-20                       0     0.110 0.000
+#> >20-50                    20     0.151 0.000
+#> >50-100                   50     0.197 0.000
+#> >100-200                 100     0.248 0.001
+#> >200-500                 200     0.224 0.004
+#> >500-1000                500     0.184 0.020
+#> >1000-2000              1000     0.133 0.042
+#> >2000-5000              2000     0.084 0.091
+#> >5000-10000             5000     0.037 0.159
+#> >10000-20000           10000     0.031 0.192
+#> >20000-50000           20000     0.014 0.291
+#> >50000                 50000     0.001 0.451
+
+
+
- + - - - + diff --git a/docs/reference/perc_runoff_reduction_exposit.html b/docs/reference/perc_runoff_reduction_exposit.html index dd37f4b..f398ca1 100644 --- a/docs/reference/perc_runoff_reduction_exposit.html +++ b/docs/reference/perc_runoff_reduction_exposit.html @@ -1,191 +1,125 @@ - - - - - - - -Runoff reduction percentages as used in Exposit — perc_runoff_reduction_exposit • pfm - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +Runoff reduction percentages as used in Exposit — perc_runoff_reduction_exposit • pfm + Skip to contents + +
+
+
+

Runoff reduction percentages as used in Exposit

+ Source: R/PEC_sw_exposit_runoff.R +
perc_runoff_reduction_exposit.Rd
-
+

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

- 
perc_runoff_reduction_exposit
- - -

Format

+
+

Usage

+ 
perc_runoff_reduction_exposit
+
+
+

Format

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

-
dissolved

The reduction percentage for the dissolved phase

-
bound

The reduction percentage for the particulate phase

- -
+from which the values were taken.

dissolved
+

The reduction percentage for the dissolved phase

-

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

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

+
-

Examples

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

Examples

+ 
print(perc_runoff_reduction_exposit)
+#> $`3.02`
+#>           dissolved bound
+#> No buffer         0     0
+#> 5 m              40    40
+#> 10 m             60    85
+#> 20 m             80    95
+#> 
+#> $`3.01a`
+#>           dissolved bound
+#> No buffer         0     0
+#> 3 m              25    30
+#> 5 m              40    40
+#> 6 m              45    55
+#> 10 m             60    85
+#> 20 m             80    95
+#> 
+#> $`3.01a2`
+#>           dissolved bound
+#> No buffer         0     0
+#> 3 m              25    25
+#> 
+#> $`2.0`
+#>           dissolved bound
+#> No buffer       0.0   0.0
+#> 20 m           97.5  97.5
+#> 
+
+
+
- + - - - + diff --git a/docs/reference/pfm_degradation.html b/docs/reference/pfm_degradation.html index 8d06107..0ae35ba 100644 --- a/docs/reference/pfm_degradation.html +++ b/docs/reference/pfm_degradation.html @@ -1,191 +1,119 @@ - - - - - - - -Calculate a time course of relative concentrations based on an mkinmod model — pfm_degradation • pfm - - - - - +Calculate a time course of relative concentrations based on an mkinmod model — pfm_degradation • pfm + Skip to contents + - +
+
+
+

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

+ Source: R/pfm_degradation.R +
pfm_degradation.Rd
+
- - - +
+

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

+
+
+

Usage

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

Arguments

+
model
+

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

- - +
DT50
+

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

+
parms
+

The parameters used for the degradation model

- - - +
years
+

For how many years should the degradation be predicted?

- - - - - - - -
-
- +
step_days
+

What step size in days should the output have?

- -
+
times
+

The output times

-
-
-
-

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

- - +
+
+

Author

+

Johannes Ranke

-
-

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

+
+

Examples

+ 
head(pfm_degradation("SFO", DT50 = 10))
+#> Error in head(pfm_degradation("SFO", DT50 = 10)): could not find function "head"
+
- - 
pfm_degradation(
-  model = "SFO",
-  DT50 = 1000,
-  parms = c(k_parent = log(2)/DT50),
-  years = 1,
-  step_days = 1,
-  times = seq(0, years * 365, by = step_days)
-)
- -

Arguments

- - - - - - - - - - - - - - - - - - - - - - - - - - -
model

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

DT50

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

parms

The parameters used for the degradation model

years

For how many years should the degradation be predicted?

step_days

What step size in days should the output have?

times

The output times

- - -

Examples

- 
head(pfm_degradation("SFO", DT50 = 10))
#>   time    parent
-#> 0    0 1.0000000
-#> 1    1 0.9330330
-#> 2    2 0.8705506
-#> 3    3 0.8122524
-#> 4    4 0.7578583
-#> 5    5 0.7071068
-
- -
+
- + - - - + diff --git a/docs/reference/plot.TOXSWA_cwa-1.png b/docs/reference/plot.TOXSWA_cwa-1.png index c6a278a..19dfbe6 100644 Binary files a/docs/reference/plot.TOXSWA_cwa-1.png and b/docs/reference/plot.TOXSWA_cwa-1.png differ diff --git a/docs/reference/plot.TOXSWA_cwa-2.png b/docs/reference/plot.TOXSWA_cwa-2.png index 869c43c..85ffe4b 100644 Binary files a/docs/reference/plot.TOXSWA_cwa-2.png and b/docs/reference/plot.TOXSWA_cwa-2.png differ diff --git a/docs/reference/plot.TOXSWA_cwa-3.png b/docs/reference/plot.TOXSWA_cwa-3.png index 315c741..f2bb9cf 100644 Binary files a/docs/reference/plot.TOXSWA_cwa-3.png and b/docs/reference/plot.TOXSWA_cwa-3.png differ diff --git a/docs/reference/plot.TOXSWA_cwa-4.png b/docs/reference/plot.TOXSWA_cwa-4.png index a0a88f9..6ee8f66 100644 Binary files a/docs/reference/plot.TOXSWA_cwa-4.png and b/docs/reference/plot.TOXSWA_cwa-4.png differ diff --git a/docs/reference/plot.TOXSWA_cwa-5.png b/docs/reference/plot.TOXSWA_cwa-5.png index 3ac506d..989f777 100644 Binary files a/docs/reference/plot.TOXSWA_cwa-5.png and b/docs/reference/plot.TOXSWA_cwa-5.png differ diff --git a/docs/reference/plot.TOXSWA_cwa.html b/docs/reference/plot.TOXSWA_cwa.html index 1262c71..09e9258 100644 --- a/docs/reference/plot.TOXSWA_cwa.html +++ b/docs/reference/plot.TOXSWA_cwa.html @@ -1,216 +1,157 @@ - - - - - - +Plot TOXSWA surface water concentrations — plot.TOXSWA_cwa • pfm + Skip to contents + -Plot TOXSWA surface water concentrations — plot.TOXSWA_cwa • pfm +
+
+
+

Plot TOXSWA surface water concentrations

+ Source: R/TOXSWA_cwa.R +
plot.TOXSWA_cwa.Rd
+
- - +
+

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

+
- - - +
+

Usage

+ 
# S3 method for TOXSWA_cwa
+plot(
+  x,
+  time_column = c("datetime", "t", "t_firstjan", "t_rel_to_max"),
+  xlab = "default",
+  ylab = "default",
+  add = FALSE,
+  threshold_factor = 1000,
+  thin_low = 1,
+  total = FALSE,
+  LC_TIME = "C",
+  ...
+)
+
- - - +
+

Arguments

+
x
+

The TOXSWA_cwa object to be plotted.

+
time_column
+

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

- - - +
xlab, ylab
+

Labels for x and y axis.

+
add
+

Should we add to an existing plot?

- - - +
threshold_factor
+

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

- - - - - - - -
-
- +
thin_low
+

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

- -
+
total
+

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

-
-
-
-

Plot TOXSWA surface water concentrations

- - -
-
-

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

-
+
LC_TIME
+

Specification of the locale used to format dates

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

Plot time series of decline data

+ Source: R/twa.R +
plot.one_box.Rd
+
- - +
+

Plot time series of decline data

+
- - - +
+

Usage

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

Arguments

+
x
+

The object of type one_box to be plotted

+
xlim
+

Limits for the x axis

- - - +
ylim
+

Limits for the y axis

+
xlab
+

Label for the x axis

- - - +
ylab
+

Label for the y axis

- - - - - - - -
-
- +
max_twa
+

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

- -
+
max_twa_var
+

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

-
-
-
-

Plot time series of decline data

- - -
-
-

Plot time series of decline data

-
+
...
+

Further arguments passed to methods

- 
# 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")
-
-
+
+

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"))
+#> Temporary DLL for differentials generated and loaded
+fit_2 <- mkinfit(m_2, FOCUS_2006_D, quiet = TRUE)
+#> Warning: Observations with value of zero were removed from the data
+pred_2 <- one_box(fit_2, ini = 1)
+pred_2_saw <- sawtooth(pred_2, 2, 7)
+plot(pred_2_saw, max_twa = 21, max_twa_var = "m1")
+
+
+
+ - + - - - + diff --git a/docs/reference/read.TOXSWA_cwa.html b/docs/reference/read.TOXSWA_cwa.html index 9c5c2a0..32a9b1d 100644 --- a/docs/reference/read.TOXSWA_cwa.html +++ b/docs/reference/read.TOXSWA_cwa.html @@ -1,116 +1,58 @@ - - - - - - - -Read TOXSWA surface water concentrations — read.TOXSWA_cwa • pfm - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +renamed to ConLiqWatLay in the out file.">Read TOXSWA surface water concentrations — read.TOXSWA_cwa • pfm + Skip to contents + +
+
+
+

Read TOXSWA surface water concentrations

+ Source: R/TOXSWA_cwa.R +
read.TOXSWA_cwa.Rd
-
+

Read TOXSWA hourly concentrations of a chemical substance in a specific segment of a TOXSWA surface water body. Per default, the data for the last segment are imported. As TOXSWA 4 reports the values at the end of the hour @@ -119,103 +61,99 @@ of the hourly averages (ConLiqWatLay). In TOXSWA 5.5.3 this variable was renamed to ConLiqWatLay in the out file.

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

Arguments

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
filename

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

basedir

The path to the directory where the cwa file resides.

zipfile

Optional path to a zip file containing the cwa file.

segment

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

substance

For .out files, the default value "parent" leads +

+

Usage

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

Arguments

+
filename
+

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

+ + +
basedir
+

The path to the directory where the cwa file resides.

+ + +
zipfile
+

Optional path to a zip file containing the cwa file.

+ + +
segment
+

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

+ + +
substance
+

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

total

Set this to TRUE in order to read total concentrations as well. This is +of interested can be selected by its code name.

+ + +
total
+

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

windows

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

thresholds

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

- -

Value

- -

An instance of an R6 object of class -TOXSWA_cwa.

- -

Examples

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

Value

+ + +

An instance of an R6 object of class +TOXSWA_cwa.

+
+
+

Author

Johannes Ranke

-
-
+
+
+

Examples

+ 
H_sw_D4_pond  <- read.TOXSWA_cwa("00001p_pa.cwa",
+                                 basedir = "SwashProjects/project_H_sw/TOXSWA",
+                                 zipfile = system.file("testdata/SwashProjects.zip",
+                                                       package = "pfm"))
+
+
+ - + - - - + diff --git a/docs/reference/reexports.html b/docs/reference/reexports.html index 2f410e4..a98a861 100644 --- a/docs/reference/reexports.html +++ b/docs/reference/reexports.html @@ -1,5 +1,12 @@ -Objects exported from other packages — reexports • pfmObjects exported from other packages — reexports • pfm + + Skip to contents -
-
-
-
-

Objects exported from other packages

- +
+
+
+
+

Objects exported from other packages

+ Source: R/reexports.R +
reexports.Rd
-
+

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

mkin
@@ -57,26 +67,21 @@ below to see their documentation.

-
- -
+
-
+

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

Create decline time series for multiple applications

+ Source: R/twa.R +
sawtooth.Rd
-
+

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

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

Arguments

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

A one_box object

n

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

i

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

applications

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

- - -

Examples

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

-m_2 <- mkinmod(parent = mkinsub("SFO", "m1"), m1 = mkinsub("SFO"))
#> Successfully compiled differential equation model from auto-generated C code.
fit_2 <- mkinfit(m_2, FOCUS_2006_D, quiet = TRUE)
#> Warning: Observations with value of zero were removed from the data
#> Warning: Shapiro-Wilk test for standardized residuals: p =  0.0165
pred_2 <- one_box(fit_2, ini = 1)
-pred_2_saw <- sawtooth(pred_2, 2, 7)
-plot(pred_2_saw, max_twa = 21, max_twa_var = "m1")

-max_twa(pred_2_saw)
#> $max
-#>    parent        m1 
-#> 0.7834480 0.8617048 
-#> 
-#> $window_start
-#> parent     m1 
-#>   0.00  26.85 
-#> 
-#> $window_end
-#> parent     m1 
-#>  21.00  47.85 
-#>
-
- -
+
+

Usage

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

Arguments

+
x
+

A one_box object

+ + +
n
+

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

+ + +
i
+

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

+ + +
applications
+

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

+ +
+ +
+

Examples

+ 
applications = data.frame(time = seq(0, 14, by = 7), amount = c(1, 2, 3))
+pred <- one_box(10)
+plot(sawtooth(pred, applications = applications))
+
+
+m_2 <- mkinmod(parent = mkinsub("SFO", "m1"), m1 = mkinsub("SFO"))
+#> Temporary DLL for differentials generated and loaded
+fit_2 <- mkinfit(m_2, FOCUS_2006_D, quiet = TRUE)
+#> Warning: Observations with value of zero were removed from the data
+pred_2 <- one_box(fit_2, ini = 1)
+pred_2_saw <- sawtooth(pred_2, 2, 7)
+plot(pred_2_saw, max_twa = 21, max_twa_var = "m1")
+
+
+max_twa(pred_2_saw)
+#> $max
+#>    parent        m1 
+#> 0.7834481 0.8617049 
+#> 
+#> $window_start
+#> parent     m1 
+#>   0.00  26.85 
+#> 
+#> $window_end
+#> parent     m1 
+#>  21.00  47.85 
+#> 
+
+
+
-
-
-

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

Properties of the predefined scenarios from the EFSA guidance from 2015

+ Source: R/soil_scenario_data_EFSA_2015.R +
soil_scenario_data_EFSA_2015.Rd
-
+

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

- -

Format

- +
+

Format

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

-

Source

- +
+
+

Source

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

+
-

Examples

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

Examples

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

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

Properties of the predefined scenarios from the EFSA guidance from 2017

+ Source: R/soil_scenario_data_EFSA_2017.R +
soil_scenario_data_EFSA_2017.Rd
-
+

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

- -

Format

- +
+

Format

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
-
- -
+
+

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
+
+
+
-
-
-

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

Calculate a time weighted average concentration

+ Source: R/twa.R +
twa.Rd
-
- +

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

-
- 
twa(x, window = 21)
+    

+    
Usage

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

+    

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

Arguments

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

An object of type one_box

window

The size of the moving window

- -

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