path: root/docs/search.json

[{"path":"https://pkgdown.jrwb.de/pfm/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Johannes Ranke. Author, maintainer.","code":""},{"path":"https://pkgdown.jrwb.de/pfm/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Johannes Ranke (2023). pfm: Utilities Pesticide Fate Modelling. R package version 0.6.0, https://github.com/jranke/pfm, https://pkgdown.jrwb.de/pfm.","code":"@Manual{,   title = {pfm: Utilities for Pesticide Fate Modelling},   author = {{Johannes Ranke}},   year = {2023},   note = {R package version 0.6.0, https://github.com/jranke/pfm},   url = {https://pkgdown.jrwb.de/pfm}, }"},{"path":"https://pkgdown.jrwb.de/pfm/index.html","id":"pfm","dir":"","previous_headings":"","what":"Utilities for Pesticide Fate Modelling","title":"Utilities for Pesticide Fate Modelling","text":"R package pfm provides utilities fate modelling, including dealing FOCUS pesticide fate modelling tools, (currently TOXSWA cwa files), made available GNU public license.","code":""},{"path":"https://pkgdown.jrwb.de/pfm/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Utilities for Pesticide Fate Modelling","text":"easiest way install package probably use drat: Alternatively can install package using devtools package. Using quick = TRUE skips docs, multiple-architecture builds, demos, vignettes.","code":"install.packages(\"drat\") drat::addRepo(\"jranke\") install.packages(\"pfm\") library(devtools) install_github(\"jranke/pfm\", quick = TRUE)"},{"path":"https://pkgdown.jrwb.de/pfm/index.html","id":"use","dir":"","previous_headings":"","what":"Use","title":"Utilities for Pesticide Fate Modelling","text":"Please refer reference.","code":""},{"path":"https://pkgdown.jrwb.de/pfm/index.html","id":"examples","dir":"","previous_headings":"","what":"Examples","title":"Utilities for Pesticide Fate Modelling","text":"One recent nice example usage package visualisation time weighted average sawtooth curve obtained several overlays mkinfit predictions shown .","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/EFSA_GW_interception_2014.html","id":null,"dir":"Reference","previous_headings":"","what":"Subset of EFSA crop interception default values for groundwater modelling — EFSA_GW_interception_2014","title":"Subset of EFSA crop interception default values for groundwater modelling — EFSA_GW_interception_2014","text":"Subset EFSA crop interception default values groundwater modelling","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/EFSA_GW_interception_2014.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Subset of EFSA crop interception default values for groundwater modelling — EFSA_GW_interception_2014","text":"matrix containing interception values, currently selected crops","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/EFSA_GW_interception_2014.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Subset of EFSA crop interception default values for groundwater modelling — EFSA_GW_interception_2014","text":"European Food Safety Authority (2014) EFSA Guidance Document evaluating laboratory field dissipation studies obtain DegT50 values active substances plant protection products transformation products active substances soil. EFSA Journal 12(5):3662, 37 pp., doi:10.2903/j.efsa.2014.3662","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/EFSA_GW_interception_2014.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Subset of EFSA crop interception default values for groundwater modelling — EFSA_GW_interception_2014","text":"","code":"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"},{"path":"https://pkgdown.jrwb.de/pfm/reference/EFSA_washoff_2017.html","id":null,"dir":"Reference","previous_headings":"","what":"Subset of EFSA crop washoff default values — EFSA_washoff_2017","title":"Subset of EFSA crop washoff default values — EFSA_washoff_2017","text":"Subset EFSA crop washoff default values","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/EFSA_washoff_2017.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Subset of EFSA crop washoff default values — EFSA_washoff_2017","text":"matrix containing wash-factors, currently selected crops","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/EFSA_washoff_2017.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Subset of EFSA crop washoff default values — EFSA_washoff_2017","text":"European Food Safety Authority (2017) EFSA guidance document predicting environmental concentrations active substances plant protection products transformation products active substances soil. EFSA Journal 15(10) 4982 doi:10.2903/j.efsa.2017.4982","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/EFSA_washoff_2017.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Subset of EFSA crop washoff default values — EFSA_washoff_2017","text":"","code":"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"},{"path":"https://pkgdown.jrwb.de/pfm/reference/FOCUS_GW_scenarios_2012.html","id":null,"dir":"Reference","previous_headings":"","what":"A very small subset of the FOCUS Groundwater scenario definitions — FOCUS_GW_scenarios_2012","title":"A very small subset of the FOCUS Groundwater scenario definitions — FOCUS_GW_scenarios_2012","text":"Currently, scenario names acronyms small subset soil definitions provided. soil definitions page 46ff. FOCUS (2012).","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/FOCUS_GW_scenarios_2012.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A very small subset of the FOCUS Groundwater scenario definitions — FOCUS_GW_scenarios_2012","text":"","code":"FOCUS_GW_scenarios_2012"},{"path":"https://pkgdown.jrwb.de/pfm/reference/FOCUS_GW_scenarios_2012.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"A very small subset of the FOCUS Groundwater scenario definitions — FOCUS_GW_scenarios_2012","text":"object class list length 2.","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/FOCUS_GW_scenarios_2012.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"A very small subset of the FOCUS Groundwater scenario definitions — FOCUS_GW_scenarios_2012","text":"FOCUS (2012) Generic guidance Tier 1 FOCUS ground water assessments. Version 2.1. FOrum Co-ordination pesticde fate models USe. http://focus.jrc.ec.europa.eu/gw/docs/Generic_guidance_FOCV2_1.pdf","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/FOCUS_GW_scenarios_2012.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A very small subset of the FOCUS Groundwater scenario definitions — FOCUS_GW_scenarios_2012","text":"","code":"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 #>"},{"path":"https://pkgdown.jrwb.de/pfm/reference/FOCUS_Step_12_scenarios.html","id":null,"dir":"Reference","previous_headings":"","what":"Step 1/2 scenario data as distributed with the FOCUS Step 1/2 calculator — FOCUS_Step_12_scenarios","title":"Step 1/2 scenario data as distributed with the FOCUS Step 1/2 calculator — FOCUS_Step_12_scenarios","text":"data extracted scenario.txt file using R code shown . text file included package licence clear.","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/FOCUS_Step_12_scenarios.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Step 1/2 scenario data as distributed with the FOCUS Step 1/2 calculator — FOCUS_Step_12_scenarios","text":"list containing scenario names character vector called 'names', drift percentiles matrix called 'drift', interception percentages matrix called 'interception' runoff/drainage percentages Step 2 calculations matrix called 'rd'.","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/FOCUS_Step_12_scenarios.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Step 1/2 scenario data as distributed with the FOCUS Step 1/2 calculator — FOCUS_Step_12_scenarios","text":"","code":"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 #>"},{"path":"https://pkgdown.jrwb.de/pfm/reference/FOMC_actual_twa.html","id":null,"dir":"Reference","previous_headings":"","what":"Actual and maximum moving window time average concentrations for FOMC kinetics — FOMC_actual_twa","title":"Actual and maximum moving window time average concentrations for FOMC kinetics — FOMC_actual_twa","text":"Actual maximum moving window time average concentrations FOMC kinetics","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/FOMC_actual_twa.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Actual and maximum moving window time average concentrations for FOMC kinetics — FOMC_actual_twa","text":"","code":"FOMC_actual_twa(   alpha = 1.0001,   beta = 10,   times = c(0, 1, 2, 4, 7, 14, 21, 28, 42, 50, 100) )"},{"path":"https://pkgdown.jrwb.de/pfm/reference/FOMC_actual_twa.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Actual and maximum moving window time average concentrations for FOMC kinetics — FOMC_actual_twa","text":"FOCUS (2014) Generic Guidance Estimating Persistence Degradation Kinetics Environmental Fate Studies Pesticides EU Registration, Version 1.1, 18 December 2014, p. 251","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/FOMC_actual_twa.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Actual and maximum moving window time average concentrations for FOMC kinetics — FOMC_actual_twa","text":"alpha Parameter FOMC model beta Parameter FOMC model times output times, window sizes time weighted average concentrations","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/FOMC_actual_twa.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Actual and maximum moving window time average concentrations for FOMC kinetics — FOMC_actual_twa","text":"Johannes Ranke","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/FOMC_actual_twa.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Actual and maximum moving window time average concentrations for FOMC kinetics — FOMC_actual_twa","text":"","code":"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"},{"path":"https://pkgdown.jrwb.de/pfm/reference/GUS.html","id":null,"dir":"Reference","previous_headings":"","what":"Groundwater ubiquity score based on Gustafson (1989) — GUS","title":"Groundwater ubiquity score based on Gustafson (1989) — GUS","text":"groundwater ubiquity score GUS calculated according following equation $$GUS = \\log_{10} DT50_{soil} (4 - \\log_{10} K_{oc})$$","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/GUS.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Groundwater ubiquity score based on Gustafson (1989) — GUS","text":"","code":"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)"},{"path":"https://pkgdown.jrwb.de/pfm/reference/GUS.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Groundwater ubiquity score based on Gustafson (1989) — GUS","text":"... Included generic allow arguments later. Therefore also added specific methods. DT50 Half-life chemical soil. field half-life according Gustafson (1989). However, leaching sub-soil can completely excluded field dissipation experiments Gustafson refer normalisation procedure, says field study conducted use conditions. Koc sorption constant normalised organic carbon. Gustafson mention nonlinearity sorption constant commonly found usually described Freundlich sorption, therefore unclear reference concentration Koc observed (reference concentration soil porewater). chent chent given appropriate information present chyaml field, information used, defaults specified . degradation_value available degradation values used? lab_field laboratory field half-lives used? defaults lab implementation, order avoid double-accounting mobility. comparability original GUS values given Gustafson (1989) desired, non-normalised first-order field half-lives obtained actual use conditions used. redox Aerobic anaerobic degradation data sorption_value available sorption values used? Defaults Kfoc generally available European pesticide peer review process. values generally use reference concentration 1 mg/L porewater, means expected Koc values concentration 1 mg/L water phase. degradation_aggregator Function aggregating half-lives sorption_aggregator Function aggregation Koc values x object class GUS_result printed digits number digits used print method","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/GUS.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Groundwater ubiquity score based on Gustafson (1989) — GUS","text":"list DT50 Koc used well resulting score class GUS_result","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/GUS.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Groundwater ubiquity score based on Gustafson (1989) — GUS","text":"Gustafson, David . (1989) Groundwater ubiquity score: simple method assessing pesticide leachability. Environmental toxicology chemistry 8(4) 339–57.","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/GUS.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Groundwater ubiquity score based on Gustafson (1989) — GUS","text":"Johannes Ranke","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/PEC_FOMC_accu_rel.html","id":null,"dir":"Reference","previous_headings":"","what":"Get the relative accumulation of an FOMC model over multiples of an interval — PEC_FOMC_accu_rel","title":"Get the relative accumulation of an FOMC model over multiples of an interval — PEC_FOMC_accu_rel","text":"Get relative accumulation FOMC model multiples interval","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/PEC_FOMC_accu_rel.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get the relative accumulation of an FOMC model over multiples of an interval — PEC_FOMC_accu_rel","text":"","code":"PEC_FOMC_accu_rel(n, interval, FOMC)"},{"path":"https://pkgdown.jrwb.de/pfm/reference/PEC_FOMC_accu_rel.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get the relative accumulation of an FOMC model over multiples of an interval — PEC_FOMC_accu_rel","text":"n number applications interval Time applications FOMC Named numeric vector containing FOMC parameters alpha beta","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/PEC_FOMC_accu_rel.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get the relative accumulation of an FOMC model over multiples of an interval — PEC_FOMC_accu_rel","text":"numeric vector containing n accumulation factors n applications","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/PEC_soil.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate predicted environmental concentrations in soil — PEC_soil","title":"Calculate predicted environmental concentrations in soil — PEC_soil","text":"basic calculation contaminant concentration bulk soil based complete, instantaneous mixing. interval given, attempt made calculating long term maximum concentration using concepts layed PPR panel opinion (EFSA PPR panel 2012 EFSA guidance PEC soil calculations (EFSA, 2015, 2017).","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/PEC_soil.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate predicted environmental concentrations in soil — PEC_soil","text":"","code":"PEC_soil(   rate,   rate_units = \"g/ha\",   interception = 0,   mixing_depth = 5,   PEC_units = \"mg/kg\",   PEC_pw_units = \"mg/L\",   interval = NA,   n_periods = Inf,   tillage_depth = 20,   leaching_depth = tillage_depth,   crop = \"annual\",   cultivation = FALSE,   chent = NA,   DT50 = NA,   FOMC = NA,   Koc = NA,   Kom = Koc/1.724,   t_avg = 0,   t_act = NULL,   scenarios = c(\"default\", \"EFSA_2017\", \"EFSA_2015\"),   leaching = scenarios == \"EFSA_2017\",   porewater = FALSE )"},{"path":"https://pkgdown.jrwb.de/pfm/reference/PEC_soil.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate predicted environmental concentrations in soil — PEC_soil","text":"rate Application rate units specified rate_units Defaults g/ha interception fraction application rate reach soil mixing_depth Mixing depth cm PEC_units Requested units calculated PEC. mg/kg currently supported PEC_pw_units mg/L currently supported interval Period deeper mixing. default NA, .e. deeper mixing.  annual deeper mixing, set 365 degradation units days n_periods Number periods considered long term PEC calculations tillage_depth Periodic (see interval) deeper mixing cm leaching_depth EFSA (2017) uses mixing depth (ecotoxicological evaluation depth) calculate leaching annual crops tillage takes place. default, losses layer tillage depth taken account implementation. crop Ignored scenarios EFSA_2017. annual crops supported scenarios used. crops single cropping cycle per year currently supported. cultivation mechanical cultivation sense EFSA (2017) take place, .e. twice year depth 5 cm? Ignored scenarios EFSA_2017 chent optional chent object holding substance specific information. Can also name substance character string DT50 specified, overrides soil DT50 endpoints chent object DT50 specified available chent object, zero degradation assumed FOMC specified, named numeric vector containing FOMC parameters alpha beta. overrides degradation endpoints, degradation interval maximum PEC calculated using parameters without temperature correction Koc specified, overrides Koc endpoints chent object Kom Calculated Koc default, can explicitly specified Kom t_avg Averaging times time weighted average concentrations t_act Time series actual concentrations scenarios 'default', DT50 used without correction soil properties specified REACH guidance (R.16, Table R.16-9) used porewater PEC calculations.  \"EFSA_2015\", DT50 taken modelling half-life 20°C pF2 ('chent' specified, DegT50 destination 'PECgw' used), corrected using Arrhenius activation energy 65.4 kJ/mol. Also model scenario adjustment factors EFSA guidance used. leaching leaching taken account? default FALSE, except EFSA_2017 scenarios used. porewater equilibrium porewater concentrations estimated based Kom organic carbon fraction soil instead total soil concentrations?  Based equation (7) given PPR panel opinion (EFSA 2012, p. 24) scenarios specified EFSA guidance (2015, p. 13).","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/PEC_soil.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate predicted environmental concentrations in soil — PEC_soil","text":"predicted concentration soil","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/PEC_soil.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate predicted environmental concentrations in soil — PEC_soil","text":"assumes complete load soil time specified 'interval' (typically 365 days) dosed . PPR panel opinion cited (EFSA PPR panel 2012), temperature correction using Arrhenius equation performed. Total soil porewater PEC values scenarios defined EFSA guidance (2017, p. 14/15) can easily calculated.","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/PEC_soil.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Calculate predicted environmental concentrations in soil — PEC_soil","text":"time weighted average (TWA) concentrations given examples EFSA guidance 2015 (p. 80) reproduced, true TWA concentrations given example EFSA guidance 2017 (p. 92). According EFSA guidance (EFSA, 2017, p. 43), leaching taken account EFSA 2017 scenarios, using evaluation depth (mixing depth) depth layer leaching takes place.  However, amount leaching evaluation depth (often 5 cm) partly mixed back tillage, default function use tillage depth calculation leaching rate. temperature information available selected scenarios, e.g. EFSA scenarios, DT50 groundwater modelling (destination 'PECgw') taken chent object, otherwise DT50 destination 'PECsoil'.","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/PEC_soil.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Calculate predicted environmental concentrations in soil — PEC_soil","text":"EFSA Panel Plant Protection Products Residues (2012) Scientific Opinion science behind guidance scenario selection scenario parameterisation predicting environmental concentrations plant protection products soil. EFSA Journal 10(2) 2562, doi:10.2903/j.efsa.2012.2562 EFSA (European Food Safety Authority) 2017) EFSA guidance document predicting environmental concentrations active substances plant protection products transformation products active substances soil. EFSA Journal 15(10) 4982 doi:10.2903/j.efsa.2017.4982 EFSA (European Food Safety Authority) (2015) EFSA guidance document predicting environmental concentrations active substances plant protection products transformation products active substances soil. EFSA Journal 13(4) 4093 doi:10.2903/j.efsa.2015.4093","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/PEC_soil.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Calculate predicted environmental concentrations in soil — PEC_soil","text":"Johannes Ranke","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/PEC_soil.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate predicted environmental concentrations in soil — PEC_soil","text":"","code":"PEC_soil(100, interception = 0.25) #>      scenario #> t_avg default #>     0     0.1  # This is example 1 starting at p. 92 of the EFSA guidance (2017) # Note that TWA concentrations differ from the ones given in the guidance # for an unknown reason (the values from EFSA (2015) can be reproduced). PEC_soil(1000, interval = 365, DT50 = 250, t_avg = c(0, 21),                Kom = 1000, scenarios = \"EFSA_2017\") #>      scenario #> t_avg      CTN     CTC      CTS #>    0  19.76834 13.8619 10.53795 #>    21 19.59345 13.7169 10.39882 PEC_soil(1000, interval = 365, DT50 = 250, t_av = c(0, 21),                Kom = 1000, scenarios = \"EFSA_2017\", porewater = TRUE) #>      scenario #> t_avg       CLN       CLC       CLS #>    0  0.5541984 0.6779249 0.9816693 #>    21 0.5484576 0.6693125 0.9609119  # This is example 1 starting at p. 79 of the EFSA guidance (2015) PEC_soil(1000, interval = 365, DT50 = 250, t_avg = c(0, 21),                scenarios = \"EFSA_2015\") #>      scenario #> t_avg      CTN      CTC      CTS #>    0  21.96827 11.53750 9.145259 #>    21 21.78517 11.40701 9.017370 PEC_soil(1000, interval = 365, DT50 = 250, t_av = c(0, 21),                Kom = 1000, scenarios = \"EFSA_2015\", porewater = TRUE) #>      scenario #> t_avg       CLN       CLC       CLS #>    0  0.7589401 0.6674322 0.9147861 #>    21 0.7506036 0.6590345 0.8987279  # The following is from example 4 starting at p. 85 of the EFSA guidance (2015) # Metabolite M2 # Calculate total and porewater soil concentrations for tier 1 scenarios # Relative molar mass is 100/300, formation fraction is 0.7 * 1 results_pfm <- PEC_soil(100/300 * 0.7 * 1 * 1000, interval = 365, DT50 = 250, t_avg = c(0, 21),                         scenarios = \"EFSA_2015\") results_pfm_pw <- PEC_soil(100/300 * 0.7 * 1000, interval = 365, DT50 = 250, t_av = c(0, 21),                            Kom = 100, scenarios = \"EFSA_2015\", porewater = TRUE)"},{"path":"https://pkgdown.jrwb.de/pfm/reference/PEC_soil_mets.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate initial and accumulation PEC soil for a set of metabolites — PEC_soil_mets","title":"Calculate initial and accumulation PEC soil for a set of metabolites — PEC_soil_mets","text":"Calculate initial accumulation PEC soil set metabolites","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/PEC_soil_mets.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate initial and accumulation PEC soil for a set of metabolites — PEC_soil_mets","text":"","code":"PEC_soil_mets(rate, mw_parent, mets, interval = 365, ...)"},{"path":"https://pkgdown.jrwb.de/pfm/reference/PEC_soil_mets.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate initial and accumulation PEC soil for a set of metabolites — PEC_soil_mets","text":"rate Application rate units specified mw_parent molecular weight parent compound mets dataframe metabolite identifiers rownames columns \"mw\", \"occ\" \"DT50\" holding molecular weight, maximum occurrence soil soil DT50 interval interval accumulation calculations ... arguments passed PEC_soil","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/PEC_sw_drainage_UK.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate initial predicted environmental concentrations in surface water due to drainage using the UK method — PEC_sw_drainage_UK","title":"Calculate initial predicted environmental concentrations in surface water due to drainage using the UK method — PEC_sw_drainage_UK","text":"implements method specified UK data requirements handbook checked spreadsheet published CRC website","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/PEC_sw_drainage_UK.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate initial predicted environmental concentrations in surface water due to drainage using the UK method — PEC_sw_drainage_UK","text":"","code":"PEC_sw_drainage_UK(   rate,   interception = 0,   Koc,   latest_application = NULL,   soil_DT50 = NULL,   model = NULL,   model_parms = NULL )"},{"path":"https://pkgdown.jrwb.de/pfm/reference/PEC_sw_drainage_UK.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate initial predicted environmental concentrations in surface water due to drainage using the UK method — PEC_sw_drainage_UK","text":"rate Application rate g/ha interception fraction application rate reach soil Koc sorption coefficient normalised organic carbon L/kg latest_application Latest application date, formatted e.g. \"01 July\" soil_DT50 Soil degradation half-life, SFO kinetics used model soil degradation model used. Either one \"FOMC\", \"DFOP\", \"HS\", \"IORE\", mkinmod object model_parms named numeric vector containing model parameters","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/PEC_sw_drainage_UK.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate initial predicted environmental concentrations in surface water due to drainage using the UK method — PEC_sw_drainage_UK","text":"predicted concentration surface water µg/L","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/PEC_sw_drainage_UK.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Calculate initial predicted environmental concentrations in surface water due to drainage using the UK method — PEC_sw_drainage_UK","text":"HSE's Chemicals Regulation Division (CRD) Active substance PECsw calculations (UK specific authorisation requests) https://www.hse.gov.uk/pesticides/topics/pesticide-approvals/pesticides-registration/data-requirements-handbook/fate/active-substance-uk.htm accessed 2019-09-27 Drainage PECs Version 1.0 (2015) Spreadsheet published 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","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/PEC_sw_drainage_UK.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Calculate initial predicted environmental concentrations in surface water due to drainage using the UK method — PEC_sw_drainage_UK","text":"Johannes Ranke","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/PEC_sw_drainage_UK.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate initial predicted environmental concentrations in surface water due to drainage using the UK method — PEC_sw_drainage_UK","text":"","code":"PEC_sw_drainage_UK(150, Koc = 100) #> [1] 8.076923"},{"path":"https://pkgdown.jrwb.de/pfm/reference/PEC_sw_drift.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate predicted environmental concentrations in surface water due to drift — PEC_sw_drift","title":"Calculate predicted environmental concentrations in surface water due to drift — PEC_sw_drift","text":"basic, vectorised form simple calculation contaminant concentration surface water based complete, instantaneous mixing input via spray drift.","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/PEC_sw_drift.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate predicted environmental concentrations in surface water due to drift — PEC_sw_drift","text":"","code":"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\" )"},{"path":"https://pkgdown.jrwb.de/pfm/reference/PEC_sw_drift.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate predicted environmental concentrations in surface water due to drift — PEC_sw_drift","text":"rate Application rate units specified applications Number applications selection drift percentile water_depth Depth water body cm drift_percentages Percentage drift values calculate PECsw. 'drift_data' 'distances' NULL. drift_data Source drift percentage data. 'JKI', drift_data_JKI included package used. 'RF', Rautmann formula used, implemented crop type number applications crop Crop name (use German names JKI data), defaults \"Ackerbau\" distances distances m get PEC values rate_units Defaults g/ha PEC_units Requested units calculated PEC. µg/L currently supported","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/PEC_sw_drift.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate predicted environmental concentrations in surface water due to drift — PEC_sw_drift","text":"predicted concentration surface water","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/PEC_sw_drift.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Calculate predicted environmental concentrations in surface water due to drift — PEC_sw_drift","text":"Johannes Ranke","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/PEC_sw_drift.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate predicted environmental concentrations in surface water due to drift — PEC_sw_drift","text":"","code":"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"},{"path":"https://pkgdown.jrwb.de/pfm/reference/PEC_sw_exposit_drainage.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate PEC surface water due to drainage as in Exposit 3 — PEC_sw_exposit_drainage","title":"Calculate PEC surface water due to drainage as in Exposit 3 — PEC_sw_exposit_drainage","text":"reimplementation calculation described Exposit 3.02 spreadsheet file, worksheet \"Konzept Drainage\". Although four groups compounds (\"Gefährdungsgruppen\"), one distinction made calculations, compounds low mobility (group 1) compounds modest high mobility (groups 2, 3 4). implementation, group derived Koc, given explicitly. details, see discussion function arguments .","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/PEC_sw_exposit_drainage.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate PEC surface water due to drainage as in Exposit 3 — PEC_sw_exposit_drainage","text":"","code":"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 )"},{"path":"https://pkgdown.jrwb.de/pfm/reference/PEC_sw_exposit_drainage.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Calculate PEC surface water due to drainage as in Exposit 3 — PEC_sw_exposit_drainage","text":"Excel 3.02 spreadsheet available https://www.bvl.bund.de/DE/04_Pflanzenschutzmittel/03_Antragsteller/04_Zulassungsverfahren/07_Naturhaushalt/psm_naturhaush_node.html#doc1400590bodyText3","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/PEC_sw_exposit_drainage.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate PEC surface water due to drainage as in Exposit 3 — PEC_sw_exposit_drainage","text":"rate application rate g/ha interception fraction intercepted crop Koc sorption coefficient soil organic carbon used determine mobility. trigger value 550 L/kg used order decide Koc >> 500. mobility Overrides determined Koc. DT50 soil half-life days t_drainage time application drainage event, degradation occurs, days V_ditch volume ditch assumed 1 m * 100 m * 30 cm = 30 m3 V_drainage drainage volume, equivalent 1 mm precipitation 1 ha spring/summer 10 mm autumn/winter/early spring. dilution dilution factor","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/PEC_sw_exposit_drainage.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate PEC surface water due to drainage as in Exposit 3 — PEC_sw_exposit_drainage","text":"list containing following components perc_runoff runoff percentages dissolved bound substance runoff matrix containing dissolved bound input different distances PEC_sw_runoff matrix containing PEC values dissolved bound substance different distances. rate given g/ha, PECsw microg/L.","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/pfm/reference/PEC_sw_exposit_drainage.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate PEC surface water due to drainage as in Exposit 3 — PEC_sw_exposit_drainage","text":"","code":"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  #>"},{"path":"https://pkgdown.jrwb.de/pfm/reference/PEC_sw_exposit_runoff.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate PEC surface water due to runoff and erosion as in Exposit 3 — PEC_sw_exposit_runoff","title":"Calculate PEC surface water due to runoff and erosion as in Exposit 3 — PEC_sw_exposit_runoff","text":"reimplementation calculation described Exposit 3.02 spreadsheet file, worksheet \"Konzept Runoff\".","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/PEC_sw_exposit_runoff.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate PEC surface water due to runoff and erosion as in Exposit 3 — PEC_sw_exposit_runoff","text":"","code":"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 )"},{"path":"https://pkgdown.jrwb.de/pfm/reference/PEC_sw_exposit_runoff.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Calculate PEC surface water due to runoff and erosion as in Exposit 3 — PEC_sw_exposit_runoff","text":"Excel 3.02 spreadsheet available https://www.bvl.bund.de/DE/04_Pflanzenschutzmittel/03_Antragsteller/04_Zulassungsverfahren/07_Naturhaushalt/psm_naturhaush_node.html#doc1400590bodyText3","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/PEC_sw_exposit_runoff.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate PEC surface water due to runoff and erosion as in Exposit 3 — PEC_sw_exposit_runoff","text":"rate application rate g/ha interception fraction intercepted crop Koc sorption coefficient soil organic carbon DT50 soil half-life days t_runoff time application runoff event, degradation occurs, days exposit_reduction_version version reduction factors used. \"3.02\" current version used Germany, \"3.01a\" version additional percentages 3 m 6 m buffer zones used Switzerland. \"3.01a2\" version introduced consistency previous calculations performed 3 m buffer zone Switzerland, reduction applied dissolved bound fraction. V_ditch volume ditch assumed 1 m * 100 m * 30 cm = 30 m3 V_event unreduced runoff volume, equivalent 10 mm precipitation 1 ha dilution dilution factor","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/PEC_sw_exposit_runoff.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate PEC surface water due to runoff and erosion as in Exposit 3 — PEC_sw_exposit_runoff","text":"list containing following components perc_runoff runoff percentages dissolved bound substance runoff matrix containing dissolved bound input different distances PEC_sw_runoff matrix containing PEC values dissolved bound substance different distances. rate given g/ha, PECsw microg/L.","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/pfm/reference/PEC_sw_exposit_runoff.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate PEC surface water due to runoff and erosion as in Exposit 3 — PEC_sw_exposit_runoff","text":"","code":"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 #>  #> $PEC_sw_runoff #>           dissolved     bound     total #> No buffer 0.8447893 3.6303107 4.4751000 #> 3 m       0.7844472 3.1462693 3.9307165 #> 5 m       0.7321507 3.1462693 3.8784200 #> 6 m       0.7106169 2.4985080 3.2091248 #> 10 m      0.6275578 1.0113008 1.6388586 #> 20 m      0.4392904 0.4719404 0.9112308 #>"},{"path":"https://pkgdown.jrwb.de/pfm/reference/PEC_sw_focus.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate PEC surface water at FOCUS Step 1 — PEC_sw_focus","title":"Calculate PEC surface water at FOCUS Step 1 — PEC_sw_focus","text":"reimplementation FOCUS Step 1 2 calculator version 3.2, authored Michael Klein, R. Note results multiple applications compared corresponding results single application. current, done automatically implementation. Step 1 PECs calculated. However, input files generated suitable input also Step 2 used FOCUS calculator.","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/PEC_sw_focus.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate PEC surface water at FOCUS Step 1 — PEC_sw_focus","text":"","code":"PEC_sw_focus(   parent,   rate,   n = 1,   i = NA,   comment = \"\",   met = NULL,   f_drift = NA,   f_rd = 0.1,   scenario = FOCUS_Step_12_scenarios$names,   region = c(\"n\", \"s\"),   season = c(NA, \"of\", \"mm\", \"js\"),   interception = c(\"no interception\", \"minimal crop cover\", \"average crop cover\",     \"full canopy\"),   met_form_water = TRUE,   txt_file = \"pesticide.txt\",   overwrite = FALSE,   append = TRUE )"},{"path":"https://pkgdown.jrwb.de/pfm/reference/PEC_sw_focus.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate PEC surface water at FOCUS Step 1 — PEC_sw_focus","text":"parent list containing substance specific parameters, e.g. conveniently generated chent_focus_sw. rate application rate g/ha. Overriden applications given explicitly n number applications application interval comment comment input file met list containing metabolite specific parameters.  e.g. conveniently generated chent_focus_sw.  NULL, PEC calculated compound, parent. f_drift fraction application rate reaching waterbody via drift. NA, derived scenario name number applications via drift data defined FOCUS_Step_12_scenarios f_rd fraction amount applied reaching waterbody via runoff/drainage. Step 1, assumed 10%, parent metabolite scenario name scenario. Must one scenario names given FOCUS_Step_12_scenarios region 'n' Northern Europe 's' Southern Europe. NA, Step 1 PECsw calculated season '' October February, 'mm' March May, 'js' June September. NA, step 1 PECsw calculated interception One 'interception' (default), 'minimal crop cover', 'average crop cover' 'full canopy' met_form_water metabolite formation water taken account? can switched check influence compare previous versions Steps 12 calculator txt_file name, potentially full path Steps.12 input text file specification run(s) written overwrite existing file location specified txt_file overwritten? takes effect append FALSE. append input text file appended?","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/PEC_sw_focus.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Calculate PEC surface water at FOCUS Step 1 — PEC_sw_focus","text":"formulas input waterbody via runoff/drainage parent subsequent formation metabolite water documented model description coming calculator. one expect, appears (get results) calculated multiplying application rate molar weight correction formation fraction water/sediment systems. Step 2 implemented.","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/PEC_sw_focus.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Calculate PEC surface water at FOCUS Step 1 — PEC_sw_focus","text":"FOCUS (2014) Generic guidance Surface Water Scenarios (version 1.4). FOrum Co-ordination pesticde fate models USe. http://esdac.jrc.ec.europa.eu/public_path/projects_data/focus/sw/docs/Generic%20FOCUS_SWS_vc1.4.pdf Website Steps 1 2 calculator Joint Research Center European Union: http://esdac.jrc.ec.europa.eu/projects/stepsonetwo","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/PEC_sw_focus.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate PEC surface water at FOCUS Step 1 — PEC_sw_focus","text":"","code":"# Parent only dummy_1 <- chent_focus_sw(\"Dummy 1\", cwsat = 6000, DT50_ws = 6, Koc = 344.8) PEC_sw_focus(dummy_1, 3000, f_drift = 0, overwrite = TRUE, append = FALSE) #> $f_drift #> [1] 0 #>  #> $eq_rate_drift_s #> [1] 3000 #>  #> $eq_rate_rd_s #> [1] 3000 #>  #> $eq_rate_rd_parent_s #> [1] NA #>  #> $input_drift_s #> [1] 0 #>  #> $input_rd_s #> [1] 300 #>  #> $f_rd_sw #> [1] 0.6850566 #>  #> $f_rd_sed #> [1] 0.3149434 #>  #> $PEC #>      type #> Time         PECsw   TWAECsw       PECsed  TWAECsed #>   0   6.850566e+02        NA 2.362075e+03        NA #>   1   6.103161e+02 647.68635 2.104370e+03 2233.2225 #>   2   5.437298e+02 612.03420 1.874780e+03 2110.2939 #>   4   4.315586e+02 548.76030 1.488014e+03 1892.1255 #>   7   3.051580e+02 469.88375 1.052185e+03 1620.1592 #>   14  1.359325e+02 339.57370 4.686951e+02 1170.8501 #>   21  6.055102e+01 257.45458 2.087799e+02  887.7034 #>   28  2.697241e+01 203.47173 9.300089e+01  701.5705 #>   42  5.352005e+00 140.10377 1.845371e+01  483.0778 #>   50  2.123945e+00 118.24602 7.323361e+00  407.7123 #>   100 6.585062e-03  59.30629 2.270529e-02  204.4881 #>  #> $PEC_sw_max #> [1] 685.0566 #>  #> $PEC_sed_max #> [1] 2362.075 #>   # Metabolite new_dummy <- chent_focus_sw(\"New Dummy\", mw = 250, Koc = 100) M1 <- chent_focus_sw(\"M1\", mw = 100, cwsat = 100, DT50_ws = 100, Koc = 50,   max_ws = 0, max_soil = 0.5) PEC_sw_focus(new_dummy, 1000, scenario = \"cereals, winter\", met = M1) #> $f_drift #> [1] 0.02759 #>  #> $eq_rate_drift_s #> [1] 0 #>  #> $eq_rate_rd_s #> [1] 200 #>  #> $eq_rate_rd_parent_s #> [1] 0 #>  #> $input_drift_s #> [1] 0 #>  #> $input_rd_s #> [1] 20 #>  #> $f_rd_sw #> [1] 0.9375 #>  #> $f_rd_sed #> [1] 0.0625 #>  #> $PEC #>      type #> Time     PECsw  TWAECsw   PECsed TWAECsed #>   0   62.50000       NA 31.25000       NA #>   1   62.06828 62.28414 31.03414 31.14207 #>   2   61.63954 62.06890 30.81977 31.03445 #>   4   60.79093 61.64158 30.39547 30.82079 #>   7   59.53987 61.00800 29.76994 30.50400 #>   14  56.71995 59.56326 28.35997 29.78163 #>   21  54.03358 58.16414 27.01679 29.08207 #>   28  51.47444 56.80902 25.73722 28.40451 #>   42  46.71404 54.22460 23.35702 27.11230 #>   50  44.19417 52.81945 22.09709 26.40973 #>   100 31.25000 45.08422 15.62500 22.54211 #>  #> $PEC_sw_max #> [1] 62.5 #>  #> $PEC_sed_max #> [1] 31.25 #>"},{"path":"https://pkgdown.jrwb.de/pfm/reference/PEC_sw_sed.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate predicted environmental concentrations in sediment from surface\nwater concentrations — PEC_sw_sed","title":"Calculate predicted environmental concentrations in sediment from surface\nwater concentrations — PEC_sw_sed","text":"method 'percentage' equivalent used CRD spreadsheet PEC calculator","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/PEC_sw_sed.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate predicted environmental concentrations in sediment from surface\nwater concentrations — PEC_sw_sed","text":"","code":"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\") )"},{"path":"https://pkgdown.jrwb.de/pfm/reference/PEC_sw_sed.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate predicted environmental concentrations in sediment from surface\nwater concentrations — PEC_sw_sed","text":"PEC_sw Numeric vector matrix surface water concentrations µg/L corresponding sediment concentration estimated percentage percentage sediment, used percentage method method method used calculation sediment_depth Depth sediment layer water_depth Depth water body cm sediment_density density sediment L/kg (equivalent g/cm3) PEC_sed_units units estimated sediment PEC value","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/PEC_sw_sed.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate predicted environmental concentrations in sediment from surface\nwater concentrations — PEC_sw_sed","text":"predicted concentration sediment","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/PEC_sw_sed.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Calculate predicted environmental concentrations in sediment from surface\nwater concentrations — PEC_sw_sed","text":"Johannes Ranke","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/PEC_sw_sed.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate predicted environmental concentrations in sediment from surface\nwater concentrations — PEC_sw_sed","text":"","code":"PEC_sw_sed(PEC_sw_drift(100, distances = 1), percentage = 50) #>      1 m  #> 2.130769"},{"path":"https://pkgdown.jrwb.de/pfm/reference/SFO_actual_twa.html","id":null,"dir":"Reference","previous_headings":"","what":"Actual and maximum moving window time average concentrations for SFO kinetics — SFO_actual_twa","title":"Actual and maximum moving window time average concentrations for SFO kinetics — SFO_actual_twa","text":"Actual maximum moving window time average concentrations SFO kinetics","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/SFO_actual_twa.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Actual and maximum moving window time average concentrations for SFO kinetics — SFO_actual_twa","text":"","code":"SFO_actual_twa(DT50 = 1000, times = c(0, 1, 2, 4, 7, 14, 21, 28, 42, 50, 100))"},{"path":"https://pkgdown.jrwb.de/pfm/reference/SFO_actual_twa.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Actual and maximum moving window time average concentrations for SFO kinetics — SFO_actual_twa","text":"FOCUS (2014) Generic Guidance Estimating Persistence Degradation Kinetics Environmental Fate Studies Pesticides EU Registration, Version 1.1, 18 December 2014, p. 251","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/SFO_actual_twa.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Actual and maximum moving window time average concentrations for SFO kinetics — SFO_actual_twa","text":"DT50 half-life. times output times, window sizes time weighted average concentrations","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/SFO_actual_twa.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Actual and maximum moving window time average concentrations for SFO kinetics — SFO_actual_twa","text":"Johannes Ranke","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/SFO_actual_twa.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Actual and maximum moving window time average concentrations for SFO kinetics — SFO_actual_twa","text":"","code":"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"},{"path":"https://pkgdown.jrwb.de/pfm/reference/SSLRC_mobility_classification.html","id":null,"dir":"Reference","previous_headings":"","what":"Determine the SSLRC mobility classification for a chemical substance from its Koc — SSLRC_mobility_classification","title":"Determine the SSLRC mobility classification for a chemical substance from its Koc — SSLRC_mobility_classification","text":"implements method specified UK data requirements handbook checked spreadsheet published CRC website","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/SSLRC_mobility_classification.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Determine the SSLRC mobility classification for a chemical substance from its Koc — SSLRC_mobility_classification","text":"","code":"SSLRC_mobility_classification(Koc)"},{"path":"https://pkgdown.jrwb.de/pfm/reference/SSLRC_mobility_classification.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Determine the SSLRC mobility classification for a chemical substance from its Koc — SSLRC_mobility_classification","text":"Koc sorption coefficient normalised organic carbon L/kg","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/SSLRC_mobility_classification.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Determine the SSLRC mobility classification for a chemical substance from its Koc — SSLRC_mobility_classification","text":"list containing classification percentage compound transported per 10 mm drain water","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/SSLRC_mobility_classification.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Determine the SSLRC mobility classification for a chemical substance from its Koc — SSLRC_mobility_classification","text":"HSE's Chemicals Regulation Division (CRD) Active substance PECsw calculations (UK specific authorisation requests) https://www.hse.gov.uk/pesticides/topics/pesticide-approvals/pesticides-registration/data-requirements-handbook/fate/active-substance-uk.htm accessed 2019-09-27 Drainage PECs Version 1.0 (2015) Spreadsheet published 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","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/SSLRC_mobility_classification.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Determine the SSLRC mobility classification for a chemical substance from its Koc — SSLRC_mobility_classification","text":"Johannes Ranke","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/SSLRC_mobility_classification.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Determine the SSLRC mobility classification for a chemical substance from its Koc — SSLRC_mobility_classification","text":"","code":"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 #>"},{"path":"https://pkgdown.jrwb.de/pfm/reference/TOXSWA_cwa.html","id":null,"dir":"Reference","previous_headings":"","what":"R6 class for holding TOXSWA water concentration data and associated statistics — TOXSWA_cwa","title":"R6 class for holding TOXSWA water concentration data and associated statistics — TOXSWA_cwa","text":"R6 class holding TOXSWA water concentration (cwa) data associated statistics.  like maximum moving window average concentrations, dataframes holding events exceeding specified thresholds.  Usually, instance class generated read.TOXSWA_cwa.","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/TOXSWA_cwa.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"R6 class for holding TOXSWA water concentration data and associated statistics — TOXSWA_cwa","text":"R6Class generator object.","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/TOXSWA_cwa.html","id":"public-fields","dir":"Reference","previous_headings":"","what":"Public fields","title":"R6 class for holding TOXSWA water concentration data and associated statistics — TOXSWA_cwa","text":"filename Length one character vector holding filename. basedir Length one character vector holding directory file came . zipfile null, giving path zip file file read. segment Length one integer, specifying segment cwa data read. substance TOXSWA name substance. cwas Dataframe holding concentrations. events List dataframes holding event statistics threshold. windows Matrix maximum time weighted average concentrations (TWAC_max) areas curve µg/day * h (AUC_max_h) µg/day * d (AUC_max_d) requested moving window sizes days.","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/pfm/reference/TOXSWA_cwa.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"R6 class for holding TOXSWA water concentration data and associated statistics — TOXSWA_cwa","text":"TOXSWA_cwa$new() TOXSWA_cwa$moving_windows() TOXSWA_cwa$get_events() TOXSWA_cwa$print() TOXSWA_cwa$clone()","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/TOXSWA_cwa.html","id":"method-new-","dir":"Reference","previous_headings":"","what":"Method new()","title":"R6 class for holding TOXSWA water concentration data and associated statistics — TOXSWA_cwa","text":"Create TOXSWA_cwa object file","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/TOXSWA_cwa.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"R6 class for holding TOXSWA water concentration data and associated statistics — TOXSWA_cwa","text":"","code":"TOXSWA_cwa$new(   filename,   basedir,   zipfile = NULL,   segment = \"last\",   substance = \"parent\",   total = FALSE )"},{"path":"https://pkgdown.jrwb.de/pfm/reference/TOXSWA_cwa.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"R6 class for holding TOXSWA water concentration data and associated statistics — TOXSWA_cwa","text":"filename filename basedir directory look zipfile Optional path zipfile holding file segment Either \"last\" number segment read data substance TOXSWA substance name (TOXSWA 4 higher) total total concentrations read ? FALSE, free concentrations read","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/TOXSWA_cwa.html","id":"method-moving-windows-","dir":"Reference","previous_headings":"","what":"Method moving_windows()","title":"R6 class for holding TOXSWA water concentration data and associated statistics — TOXSWA_cwa","text":"Add windows field described .","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/TOXSWA_cwa.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"R6 class for holding TOXSWA water concentration data and associated statistics — TOXSWA_cwa","text":"","code":"TOXSWA_cwa$moving_windows(windows, total = FALSE)"},{"path":"https://pkgdown.jrwb.de/pfm/reference/TOXSWA_cwa.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"R6 class for holding TOXSWA water concentration data and associated statistics — TOXSWA_cwa","text":"windows Window sizes days total TRUE, total concentration including amount adsorbed suspended matter used.","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/TOXSWA_cwa.html","id":"method-get-events-","dir":"Reference","previous_headings":"","what":"Method get_events()","title":"R6 class for holding TOXSWA water concentration data and associated statistics — TOXSWA_cwa","text":"Populate datataframe event information specified threshold value.  resulting dataframe stored events field object.","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/TOXSWA_cwa.html","id":"usage-2","dir":"Reference","previous_headings":"","what":"Usage","title":"R6 class for holding TOXSWA water concentration data and associated statistics — TOXSWA_cwa","text":"","code":"TOXSWA_cwa$get_events(thresholds, total = FALSE)"},{"path":"https://pkgdown.jrwb.de/pfm/reference/TOXSWA_cwa.html","id":"arguments-2","dir":"Reference","previous_headings":"","what":"Arguments","title":"R6 class for holding TOXSWA water concentration data and associated statistics — TOXSWA_cwa","text":"thresholds Threshold values µg/L. total TRUE, total concentration including amount adsorbed suspended matter used.","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/TOXSWA_cwa.html","id":"method-print-","dir":"Reference","previous_headings":"","what":"Method print()","title":"R6 class for holding TOXSWA water concentration data and associated statistics — TOXSWA_cwa","text":"Print TOXSWA_cwa object","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/TOXSWA_cwa.html","id":"usage-3","dir":"Reference","previous_headings":"","what":"Usage","title":"R6 class for holding TOXSWA water concentration data and associated statistics — TOXSWA_cwa","text":"","code":"TOXSWA_cwa$print()"},{"path":"https://pkgdown.jrwb.de/pfm/reference/TOXSWA_cwa.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"R6 class for holding TOXSWA water concentration data and associated statistics — TOXSWA_cwa","text":"objects class cloneable method.","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/TOXSWA_cwa.html","id":"usage-4","dir":"Reference","previous_headings":"","what":"Usage","title":"R6 class for holding TOXSWA water concentration data and associated statistics — TOXSWA_cwa","text":"","code":"TOXSWA_cwa$clone(deep = FALSE)"},{"path":"https://pkgdown.jrwb.de/pfm/reference/TOXSWA_cwa.html","id":"arguments-3","dir":"Reference","previous_headings":"","what":"Arguments","title":"R6 class for holding TOXSWA water concentration data and associated statistics — TOXSWA_cwa","text":"deep Whether make deep clone.","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/TOXSWA_cwa.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"R6 class for holding TOXSWA water concentration data and associated statistics — TOXSWA_cwa","text":"","code":"H_sw_R1_stream  <- read.TOXSWA_cwa(\"00003s_pa.cwa\",                                  basedir = \"SwashProjects/project_H_sw/TOXSWA\",                                  zipfile = system.file(\"testdata/SwashProjects.zip\",                                              package = \"pfm\")) H_sw_R1_stream$get_events(c(2, 10)) H_sw_R1_stream$moving_windows(c(7, 21)) print(H_sw_R1_stream) #> <TOXSWA_cwa> data from file 00003s_pa.cwa segment 20  #>                datetime     t t_firstjan t_rel_to_max cwa_mug_per_L #> 20  1978-10-01 00:00:00 0.000   273.0000      -55.333             0 #> 40  1978-10-01 01:00:00 0.042   273.0417      -55.291             0 #> 60  1978-10-01 02:00:00 0.083   273.0833      -55.250             0 #> 80  1978-10-01 03:00:00 0.125   273.1250      -55.208             0 #> 100 1978-10-01 04:00:00 0.167   273.1667      -55.166             0 #> 120 1978-10-01 05:00:00 0.208   273.2083      -55.125             0 #>     cwa_tot_mug_per_L #> 20                  0 #> 40                  0 #> 60                  0 #> 80                  0 #> 100                 0 #> 120                 0 #> Moving window analysis #>    window  max_TWAC max_AUC_h max_AUC_d #> 1  7 days 2.3926551  401.9660  16.74859 #> 2 21 days 0.8369248  421.8101  17.57542 #> Event statistics for threshold 2  #>   t_start   cwa_max duration pre_interval     AUC_h     AUC_d #> 1  44.375  4.167238    0.208       44.375  17.77202  0.740501 #> 2  55.042 40.584010    0.583       10.459 398.21189 16.592162 #> Event statistics for threshold 10  #>   t_start  cwa_max duration pre_interval   AUC_h    AUC_d #> 1  55.083 40.58401    0.459       55.083 379.433 15.80971"},{"path":"https://pkgdown.jrwb.de/pfm/reference/TSCF.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimation of the transpiration stream concentration factor — TSCF","title":"Estimation of the transpiration stream concentration factor — TSCF","text":"FOCUS groundwater guidance (FOCUS 2014, p. 41) states reliable measured log Kow neutral pH must available order apply Briggs equation. clarified can regarded reliable, equation stated produced non-ionic compounds, suggesting compound ionogenic (weak acid/base) ionic.","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/TSCF.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimation of the transpiration stream concentration factor — TSCF","text":"","code":"TSCF(log_Kow, method = c(\"briggs82\", \"dettenmaier09\"))"},{"path":"https://pkgdown.jrwb.de/pfm/reference/TSCF.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimation of the transpiration stream concentration factor — TSCF","text":"log_Kow decadic logarithm octanol-water partition constant method Short name estimation method.","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/TSCF.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimation of the transpiration stream concentration factor — TSCF","text":"Dettenmaier equation given show views subject exist.","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/TSCF.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Estimation of the transpiration stream concentration factor — TSCF","text":"FOCUS (2014) Generic Guidance Tier 1 FOCUS Ground Water Assessments. Version 2.2, May 2014 Dettenmaier EM, Doucette WJ Bugbee B (2009) Chemical hydrophobicity uptake plant roots. Environ. Sci. Technol 43, 324 - 329","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/TSCF.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimation of the transpiration stream concentration factor — TSCF","text":"","code":"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)\"))"},{"path":"https://pkgdown.jrwb.de/pfm/reference/chent_focus_sw.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a chemical compound object for FOCUS Step 1 calculations — chent_focus_sw","title":"Create a chemical compound object for FOCUS Step 1 calculations — chent_focus_sw","text":"Create chemical compound object FOCUS Step 1 calculations","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/chent_focus_sw.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a chemical compound object for FOCUS Step 1 calculations — chent_focus_sw","text":"","code":"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 )"},{"path":"https://pkgdown.jrwb.de/pfm/reference/chent_focus_sw.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a chemical compound object for FOCUS Step 1 calculations — chent_focus_sw","text":"name Length one character vector containing name Koc Partition coefficient organic carbon water L/kg. DT50_ws Half-life water/sediment systems days DT50_soil Half-life soil days DT50_water Half-life water days (Step 2) DT50_sediment Half-life sediment days (Step 2) cwsat Water solubility mg/L mw Molar weight g/mol. max_soil Maximum observed fraction (dimensionless) soil max_ws Maximum observed fraction (dimensionless) water/sediment systems","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/chent_focus_sw.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a chemical compound object for FOCUS Step 1 calculations — chent_focus_sw","text":"list substance specific properties","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/drift_data_JKI.html","id":null,"dir":"Reference","previous_headings":"","what":"Deposition from spray drift expressed as percent of the applied dose as\npublished by the JKI — drift_data_JKI","title":"Deposition from spray drift expressed as percent of the applied dose as\npublished by the JKI — drift_data_JKI","text":"Deposition spray drift expressed percent applied dose published German Julius-Kühn Institute (JKI).","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/drift_data_JKI.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Deposition from spray drift expressed as percent of the applied dose as\npublished by the JKI — drift_data_JKI","text":"list currently containing matrices spray drift percentage data field crops (Ackerbau), Pome/stone fruit, early late (Obstbau frueh, spaet).","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/drift_data_JKI.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Deposition from spray drift expressed as percent of the applied dose as\npublished by the JKI — drift_data_JKI","text":"JKI (2010) Spreadsheet 'Tabelle der Abdrifteckwerte.xls', retrieved http://www.jki.bund.de/no_cache/de/startseite/institute/anwendungstechnik/abdrift-eckwerte.html 2015-06-11 Rautmann, D., Streloke, M Winkler, R (2001) New basic drift values authorization procedure plant protection products Mitt. Biol. Bundesanst. Land- Forstwirtsch. 383, 133-141","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/drift_data_JKI.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Deposition from spray drift expressed as percent of the applied dose as\npublished by the JKI — drift_data_JKI","text":"data extracted spreadsheet cited using R code given example section. spreadsheet included package licence clear. Additional spray drift values taken publication Rautmann et al. (2001). Specifically, values early vines, values 3 m buffer incomplete spreadsheet. Note vegetables, ornamentals small fruit, values field crops used crops < 50 cm, vales late vines used crops > 50 cm. JKI spreadsheet, indicated values used spray applications handheld/knapsack equipment (tragbare Spritz- und Sprühgerate). Values non-professional use listed JKI spreadsheet included.","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/drift_data_JKI.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Deposition from spray drift expressed as percent of the applied dose as\npublished by the JKI — drift_data_JKI","text":"","code":"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 #>"},{"path":"https://pkgdown.jrwb.de/pfm/reference/endpoint.html","id":null,"dir":"Reference","previous_headings":"","what":"Retrieve endpoint information from the chyaml field of a chent object — endpoint","title":"Retrieve endpoint information from the chyaml field of a chent object — endpoint","text":"R6 class objects class chent represent chemical entities can hold list information loaded chemical yaml file chyaml field. information extracted optionally aggregated function.","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/endpoint.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Retrieve endpoint information from the chyaml field of a chent object — endpoint","text":"","code":"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 )"},{"path":"https://pkgdown.jrwb.de/pfm/reference/endpoint.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Retrieve endpoint information from the chyaml field of a chent object — endpoint","text":"chent chent object get information medium medium information sought type information type lab_field NA, want laboratory field endpoints redox NA, looking aerobic anaerobic data value name value want. list given usage section exclusive aggregator aggregator function. Can mean, geomean, identity, example. raw number(s) returned stored chent object (character value) retain original information precision? signif many significant digits want values values returned aggregators named vector aggregator functions used","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/endpoint.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Retrieve endpoint information from the chyaml field of a chent object — endpoint","text":"result applying aggregator function values converted numeric vector, rounded given number significant digits, , raw = TRUE, values character value, retaining implicit information precision may present.","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/endpoint.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Retrieve endpoint information from the chyaml field of a chent object — endpoint","text":"functions soil_* functions extract soil specific endpoints. Freundlich exponent, capital letter N used order facilitate dealing data R. pesticide fate modelling, exponent often called 1/n.","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/geomean.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate the geometric mean — geomean","title":"Calculate the geometric mean — geomean","text":"Based posts thread Stackoverflow http://stackoverflow.com/questions/2602583/geometric-mean----built-function returns NA NA values present na.rm = FALSE (default). negative values present, gives error message. least one element vector 0, returns 0.","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/geomean.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate the geometric mean — geomean","text":"","code":"geomean(x, na.rm = FALSE)"},{"path":"https://pkgdown.jrwb.de/pfm/reference/geomean.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate the geometric mean — geomean","text":"x Vector numbers na.rm NA values omitted?","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/geomean.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate the geometric mean — geomean","text":"geometric mean","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/geomean.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Calculate the geometric mean — geomean","text":"Johannes Ranke","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/geomean.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate the geometric mean — geomean","text":"","code":"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"},{"path":"https://pkgdown.jrwb.de/pfm/reference/get_vertex.html","id":null,"dir":"Reference","previous_headings":"","what":"Fit a parabola through three points — get_vertex","title":"Fit a parabola through three points — get_vertex","text":"inspired answer stackoverflow https://stackoverflow.com//717791","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/get_vertex.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fit a parabola through three points — get_vertex","text":"","code":"get_vertex(x, y)"},{"path":"https://pkgdown.jrwb.de/pfm/reference/get_vertex.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fit a parabola through three points — get_vertex","text":"x Three x coordinates y Three y coordinates","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/max_twa.html","id":null,"dir":"Reference","previous_headings":"","what":"The maximum time weighted average concentration for a moving window — max_twa","title":"The maximum time weighted average concentration for a moving window — max_twa","text":"generate time series using sawtooth, need make sure length time series allows finding maximum. therefore recommended check using plot.one_box using window size argument max_twa.","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/max_twa.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"The maximum time weighted average concentration for a moving window — max_twa","text":"","code":"max_twa(x, window = 21)"},{"path":"https://pkgdown.jrwb.de/pfm/reference/max_twa.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"The maximum time weighted average concentration for a moving window — max_twa","text":"x object type one_box window size moving window","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/max_twa.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"The maximum time weighted average concentration for a moving window — max_twa","text":"method working directly fitted mkinfit objects uses equations given PEC soil section FOCUS guidance restricted SFO, FOMC DFOP models parent compound","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/max_twa.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"The maximum time weighted average concentration for a moving window — max_twa","text":"FOCUS (2006) “Guidance Document Estimating Persistence Degradation Kinetics Environmental Fate Studies Pesticides EU Registration” Report FOCUS Work Group Degradation Kinetics, EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/pfm/reference/max_twa.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"The maximum time weighted average concentration for a moving window — max_twa","text":"","code":"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"},{"path":"https://pkgdown.jrwb.de/pfm/reference/one_box.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a time series of decline data — one_box","title":"Create a time series of decline data — one_box","text":"Create time series decline data","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/one_box.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a time series of decline data — one_box","text":"","code":"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)"},{"path":"https://pkgdown.jrwb.de/pfm/reference/one_box.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a time series of decline data — one_box","text":"x numeric, half-life used exponential decline. character string specifying parent decline model given e.g. FOMC, parms must contain corresponding parameters. x mkinfit object, decline calculated object. ini initial amount. x mkinfit object, ini 'model', fitted initial concentrations used. Otherwise, ini must numeric. length one, used parent initial values metabolites zero, otherwise, must give values observed variables. ... arguments passed methods t_end End time series res Resolution time series parms named numeric vector containing model parameters","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/one_box.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a time series of decline data — one_box","text":"object class one_box, inheriting ts.","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/one_box.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a time series of decline data — one_box","text":"","code":"# 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)"},{"path":"https://pkgdown.jrwb.de/pfm/reference/perc_runoff_exposit.html","id":null,"dir":"Reference","previous_headings":"","what":"Runoff loss percentages as used in Exposit 3 — perc_runoff_exposit","title":"Runoff loss percentages as used in Exposit 3 — perc_runoff_exposit","text":"table loss percentages used Exposit 3 twelve different Koc classes","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/perc_runoff_exposit.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Runoff loss percentages as used in Exposit 3 — perc_runoff_exposit","text":"data frame percentage values dissolved fraction fraction bound eroding particles, Koc classes used row names Koc_lower_bound lower bound Koc class dissolved percentage applied substance transferred adjacent water body dissolved phase bound percentage applied substance transferred adjacent water body bound eroding particles","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/perc_runoff_exposit.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Runoff loss percentages as used in Exposit 3 — perc_runoff_exposit","text":"Excel 3.02 spreadsheet available https://www.bvl.bund.de/EN/04_PlantProtectionProducts/03_Applicants/04_AuthorisationProcedure/08_Environment/ppp_environment_node.html","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/perc_runoff_exposit.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Runoff loss percentages as used in Exposit 3 — perc_runoff_exposit","text":"","code":"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"},{"path":"https://pkgdown.jrwb.de/pfm/reference/perc_runoff_reduction_exposit.html","id":null,"dir":"Reference","previous_headings":"","what":"Runoff reduction percentages as used in Exposit — perc_runoff_reduction_exposit","title":"Runoff reduction percentages as used in Exposit — perc_runoff_reduction_exposit","text":"table runoff reduction percentages used Exposit 3 different vegetated buffer widths","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/perc_runoff_reduction_exposit.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Runoff reduction percentages as used in Exposit — perc_runoff_reduction_exposit","text":"","code":"perc_runoff_reduction_exposit"},{"path":"https://pkgdown.jrwb.de/pfm/reference/perc_runoff_reduction_exposit.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Runoff reduction percentages as used in Exposit — perc_runoff_reduction_exposit","text":"named list data frames reduction percentage values dissolved fraction fraction bound eroding particles, vegetated buffer widths row names. names list items Exposit versions values taken. dissolved reduction percentage dissolved phase bound reduction percentage particulate phase","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/perc_runoff_reduction_exposit.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Runoff reduction percentages as used in Exposit — perc_runoff_reduction_exposit","text":"Excel 3.02 spreadsheet available https://www.bvl.bund.de/EN/04_PlantProtectionProducts/03_Applicants/04_AuthorisationProcedure/08_Environment/ppp_environment_node.html Agroscope version 3.01a additional runoff factors 3 m 6 m buffer zones received Muris Korkaric (published). variant 3.01a2 introduced consistency previous calculations performed Agroscope 3 m buffer zone.","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/perc_runoff_reduction_exposit.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Runoff reduction percentages as used in Exposit — perc_runoff_reduction_exposit","text":"","code":"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 #>"},{"path":"https://pkgdown.jrwb.de/pfm/reference/pfm_degradation.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate a time course of relative concentrations based on an mkinmod model — pfm_degradation","title":"Calculate a time course of relative concentrations based on an mkinmod model — pfm_degradation","text":"Calculate time course relative concentrations based mkinmod model","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/pfm_degradation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate a time course of relative concentrations based on an mkinmod model — pfm_degradation","text":"","code":"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) )"},{"path":"https://pkgdown.jrwb.de/pfm/reference/pfm_degradation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate a time course of relative concentrations based on an mkinmod model — pfm_degradation","text":"model degradation model used. Either parent model like 'SFO' 'FOMC', mkinmod object DT50 half-life. used simple exponential decline calculated (SFO model). parms parameters used degradation model years many years degradation predicted? step_days step size days output ? times output times","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/pfm_degradation.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Calculate a time course of relative concentrations based on an mkinmod model — pfm_degradation","text":"Johannes Ranke","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/pfm_degradation.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate a time course of relative concentrations based on an mkinmod model — pfm_degradation","text":"","code":"head(pfm_degradation(\"SFO\", DT50 = 10)) #> Error in head(pfm_degradation(\"SFO\", DT50 = 10)): could not find function \"head\""},{"path":"https://pkgdown.jrwb.de/pfm/reference/plot.TOXSWA_cwa.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot TOXSWA surface water concentrations — plot.TOXSWA_cwa","title":"Plot TOXSWA surface water concentrations — plot.TOXSWA_cwa","text":"Plot TOXSWA hourly concentrations chemical substance specific segment TOXSWA surface water body.","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/plot.TOXSWA_cwa.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot TOXSWA surface water concentrations — plot.TOXSWA_cwa","text":"","code":"# 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\",   ... )"},{"path":"https://pkgdown.jrwb.de/pfm/reference/plot.TOXSWA_cwa.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot TOXSWA surface water concentrations — plot.TOXSWA_cwa","text":"x TOXSWA_cwa object plotted. time_column used time axis. \"t_firstjan\" chosen, time given days relative first January first year. xlab, ylab Labels x y axis. add add existing plot? threshold_factor factor data lower maximum order get thinned plotting (see next argument). thin_low integer greater 1, data close zero (smaller 1/threshold_factor maximum) series thinned factor order decrease amount data included plots total total concentration water plotted, including substance sorbed suspended matter? LC_TIME Specification locale used format dates ... arguments passed plot adding existing plot","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/plot.TOXSWA_cwa.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot TOXSWA surface water concentrations — plot.TOXSWA_cwa","text":"Johannes Ranke","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/plot.TOXSWA_cwa.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot TOXSWA surface water concentrations — plot.TOXSWA_cwa","text":"","code":"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\")"},{"path":"https://pkgdown.jrwb.de/pfm/reference/plot.one_box.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot time series of decline data — plot.one_box","title":"Plot time series of decline data — plot.one_box","text":"Plot time series decline data","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/plot.one_box.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot time series of decline data — plot.one_box","text":"","code":"# 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],   ... )"},{"path":"https://pkgdown.jrwb.de/pfm/reference/plot.one_box.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot time series of decline data — plot.one_box","text":"x object type one_box plotted xlim Limits x axis ylim Limits y axis xlab Label x axis ylab Label y axis max_twa numeric value given, maximum time weighted average concentration(s) /shown graph. max_twa_var Variable maximum time weighted average shown max_twa NULL. ... arguments passed methods","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/pfm/reference/plot.one_box.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot time series of decline data — plot.one_box","text":"","code":"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\")"},{"path":"https://pkgdown.jrwb.de/pfm/reference/read.TOXSWA_cwa.html","id":null,"dir":"Reference","previous_headings":"","what":"Read TOXSWA surface water concentrations — read.TOXSWA_cwa","title":"Read TOXSWA surface water concentrations — read.TOXSWA_cwa","text":"Read TOXSWA hourly concentrations chemical substance specific segment TOXSWA surface water body. Per default, data last segment imported. TOXSWA 4 reports values end hour (ConLiqWatLayCur) summary file, use value well instead hourly averages (ConLiqWatLay). TOXSWA 5.5.3 variable renamed ConLiqWatLay file.","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/read.TOXSWA_cwa.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Read TOXSWA surface water concentrations — read.TOXSWA_cwa","text":"","code":"read.TOXSWA_cwa(   filename,   basedir = \".\",   zipfile = NULL,   segment = \"last\",   substance = \"parent\",   total = FALSE,   windows = NULL,   thresholds = NULL )"},{"path":"https://pkgdown.jrwb.de/pfm/reference/read.TOXSWA_cwa.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Read TOXSWA surface water concentrations — read.TOXSWA_cwa","text":"filename filename cwa file (TOXSWA 2.x.y  similar) file using FOCUS TOXSWA 4 (.e. TOXSWA 4.4.2) higher. basedir path directory cwa file resides. zipfile Optional path zip file containing cwa file. segment segment data read. Either \"last\", segment number. substance .files, default value \"parent\" leads reading concentrations parent compound. Alternatively, substance interested can selected code name. total Set TRUE order read total concentrations well. necessary .files generated TOXSWA 4.4.2 similar, .cwa files. .cwa files, total concentration always read well. windows Numeric vector width moving windows days, calculating maximum time weighted average concentrations areas curve. thresholds Numeric vector threshold concentrations µg/L generating event statistics.","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/read.TOXSWA_cwa.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Read TOXSWA surface water concentrations — read.TOXSWA_cwa","text":"instance R6 object class TOXSWA_cwa.","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/read.TOXSWA_cwa.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Read TOXSWA surface water concentrations — read.TOXSWA_cwa","text":"Johannes Ranke","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/read.TOXSWA_cwa.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Read TOXSWA surface water concentrations — read.TOXSWA_cwa","text":"","code":"H_sw_D4_pond  <- read.TOXSWA_cwa(\"00001p_pa.cwa\",                                  basedir = \"SwashProjects/project_H_sw/TOXSWA\",                                  zipfile = system.file(\"testdata/SwashProjects.zip\",                                                        package = \"pfm\"))"},{"path":"https://pkgdown.jrwb.de/pfm/reference/reexports.html","id":null,"dir":"Reference","previous_headings":"","what":"Objects exported from other packages — reexports","title":"Objects exported from other packages — reexports","text":"objects imported packages. Follow links see documentation. mkin set_nd_nq, set_nd_nq_focus","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/sawtooth.html","id":null,"dir":"Reference","previous_headings":"","what":"Create decline time series for multiple applications — sawtooth","title":"Create decline time series for multiple applications — sawtooth","text":"application pattern specified applications, n disregarded.","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/sawtooth.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create decline time series for multiple applications — sawtooth","text":"","code":"sawtooth(   x,   n = 1,   i = 365,   applications = data.frame(time = seq(0, (n - 1) * i, length.out = n), amount = 1) )"},{"path":"https://pkgdown.jrwb.de/pfm/reference/sawtooth.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create decline time series for multiple applications — sawtooth","text":"x one_box object n number applications. applications specified, n ignored interval applications. applications specified, ignored applications data frame holding application times first column corresponding amounts applied second column.","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/sawtooth.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create decline time series for multiple applications — sawtooth","text":"","code":"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  #>"},{"path":"https://pkgdown.jrwb.de/pfm/reference/soil_scenario_data_EFSA_2015.html","id":null,"dir":"Reference","previous_headings":"","what":"Properties of the predefined scenarios from the EFSA guidance from 2015 — soil_scenario_data_EFSA_2015","title":"Properties of the predefined scenarios from the EFSA guidance from 2015 — soil_scenario_data_EFSA_2015","text":"Properties predefined scenarios used Tier 1, Tier 2A Tier 3A concentration soil given EFSA guidance (2015, p. 13/14). Also, scenario model adjustment factors p. 15 p. 17 included.","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/soil_scenario_data_EFSA_2015.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Properties of the predefined scenarios from the EFSA guidance from 2015 — soil_scenario_data_EFSA_2015","text":"data frame one row scenario. Row names scenario codes, e.g. CTN Northern scenario total concentration soil. Columns mostly self-explanatory. rho dry bulk density top soil.","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/soil_scenario_data_EFSA_2015.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Properties of the predefined scenarios from the EFSA guidance from 2015 — soil_scenario_data_EFSA_2015","text":"EFSA (European Food Safety Authority) (2015) EFSA guidance document predicting environmental concentrations active substances plant protection products transformation products active substances soil. EFSA Journal 13(4) 4093 doi:10.2903/j.efsa.2015.4093","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/soil_scenario_data_EFSA_2015.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Properties of the predefined scenarios from the EFSA guidance from 2015 — soil_scenario_data_EFSA_2015","text":"","code":"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"},{"path":"https://pkgdown.jrwb.de/pfm/reference/soil_scenario_data_EFSA_2017.html","id":null,"dir":"Reference","previous_headings":"","what":"Properties of the predefined scenarios from the EFSA guidance from 2017 — soil_scenario_data_EFSA_2017","title":"Properties of the predefined scenarios from the EFSA guidance from 2017 — soil_scenario_data_EFSA_2017","text":"Properties predefined scenarios used Tier 1, Tier 2A Tier 3A concentration soil given EFSA guidance (2017, p. 14/15). Also, scenario model adjustment factors p. 16 p. 18 included.","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/soil_scenario_data_EFSA_2017.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Properties of the predefined scenarios from the EFSA guidance from 2017 — soil_scenario_data_EFSA_2017","text":"data frame one row scenario. Row names scenario codes, e.g. CTN Northern scenario total concentration soil. Columns mostly self-explanatory. rho dry bulk density top soil.","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/soil_scenario_data_EFSA_2017.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Properties of the predefined scenarios from the EFSA guidance from 2017 — soil_scenario_data_EFSA_2017","text":"EFSA (European Food Safety Authority) (2017) EFSA guidance document predicting environmental concentrations active substances plant protection products transformation products active substances soil. EFSA Journal 15(10) 4982 doi:10.2903/j.efsa.2017.4982","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/soil_scenario_data_EFSA_2017.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Properties of the predefined scenarios from the EFSA guidance from 2017 — soil_scenario_data_EFSA_2017","text":"","code":"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"},{"path":"https://pkgdown.jrwb.de/pfm/reference/twa.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate a time weighted average concentration — twa","title":"Calculate a time weighted average concentration — twa","text":"moving average built using values past, earliest possible time maximum time series returned one window passed.","code":""},{"path":"https://pkgdown.jrwb.de/pfm/reference/twa.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate a time weighted average concentration — twa","text":"","code":"twa(x, window = 21)  # S3 method for one_box twa(x, window = 21)"},{"path":"https://pkgdown.jrwb.de/pfm/reference/twa.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate a time weighted average concentration — twa","text":"x object type one_box window size moving window","code":""},{"path":[]},{"path":"https://pkgdown.jrwb.de/pfm/reference/twa.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate a time weighted average concentration — twa","text":"","code":"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  #>"}]