calculate_exposure: Calculate exposure to active ingredients present in applied...

In leighseverson/purexposure: Pull and Calculate Exposure to CA Pesticide Use Registry Records

Description

For a particular location, buffer radius, date range, and active ingredient or class of active ingredients, calculate_exposure calculates an estimate of exposure in kg of active ingredient per m^2.

Usage

calculate_exposure(
  clean_pur_df,
  location,
  radius,
  time_period = NULL,
  start_date = NULL,
  end_date = NULL,
  chemicals = "all",
  aerial_ground = FALSE,
  verbose = TRUE,
  ...
)

Arguments

clean_pur_df	A data frame returned by pull_clean_pur that includes data for the county of your location (before running pull_clean_pur, you can use the find_location_county function to figure this out), the time period, and the active ingredients or chemical classes for which you want to calculate exposure.
location	A length-one character string. Either a California address including street name, city, state, and 5-digit zip code, or a pair of coordinates in the form "longitude, latitude".
radius	A numeric value greater than zero that gives the radius in meters defining the buffer around your location in which you would like to calculate exposure. For reference, the length and width of a PLS section is about 1,609 meters (1 mile). That of a township could range from about 9,656 to 11,265 meters (6-7 miles).
time_period	Optional. A character string giving a time period over which you would like to calculate exposure in days, weeks, months, or years. For example, if you enter "6 months" for time_period, calculate_exposure will calculate exposure for every six month period starting January 1 of the earliest year present in the clean_pur_df data frame. Start and end dates can be optionally specified with the start_date and end_date arguments. Alternatively, to calculate exposure over only one specified time period, you can leave this argument NULL and specify start and end dates.
start_date	Optional. "yyyy-mm-dd" specifying the start date for exposure estimation. This date should be present in the clean_pur_df data frame.
end_date	Optional. "yyyy-mm-dd" specifying the end date for exposure estimation. This date should be present in the clean_pur_df data frame.
chemicals	Either "all" or "chemical_class". The default is "all", which will calculate exposure to the summed active ingredients present in the clean_pur_df data frame. Enter "chemical_class" to calculate exposure to each of the chemical classes present in the chemical_class column of your clean_pur_df data frame.
aerial_ground	TRUE / FALSE for whether you would like to incorporate aerial/ground application into exposure calculations. If aerial_ground = TRUE, there should be an aerial_ground column in the input clean_pur_df data frame. There will be a value of exposure calculated for each chemical ("all" or by chemical class) and for each method of application: aerial or ground. The default is FALSE.
verbose	TRUE / FALSE for whether you would like a message to print out while the function is running. The default is TRUE.
...	Used internally.

Value

A list with five elements:

exposure

A data frame with 9 columns: exposure, the estimate of exposure in kg/m^2, chemicals, (either "all", indicating that all active ingredients present in the clean_pur_df were summed or the chemical class(es) specified in the clean_pur_df data frame), start_date, end_date, aerial_ground, which can take values of "A" = aerial, "G" = ground, and "O" = others, (if the aerial_ground argument is FALSE, aerial_ground will be NA in the exposure data frame), location, radius, the radius in meters for the buffer extending from the location, and the longitude and latitude of the location.

meta_data

A data frame with 12 columns and at least one row for every section or township intersected by the specified buffer extending from the given location. Columns include pls, giving either the Public Land Survey (PLS) section (9 characters long) or township (7 characters long), chemicals, percent, the percent that the PLS unit is overlapped by the buffer, kg, the total amount of kg applied for the specified chemicals and date range in that section or township, kg_intersection, the amount of kilograms applied multiplied by the percent of overlap, start_date and end_date, aerial_ground, which can take values of "A" (aerial), "G" (ground), or "O" (other), and will be NA if exposure calculations did not take aerial/ground application into account, none_recorded, logical for whether any pesticide application was recorded for the specified section or township, date range, and chemicals, location, and radius.

buffer_plot_df

A data frame with 24 columns. Contains spatial plotting data for the buffer and overlapping sections or townships. You can use the df_plot function to quickly plot and get a rough idea of the area for which exposure was calculated, before moving on to other plot_* functions.

county_plot

A ggplot2 plot showing the location of your specified buffer in the context of the county. Depending on if your clean_pur_df data frame was summed by section or township, the county will be shown with the relevant PLS units.

clean_pur_df

The data frame supplied to the clean_pur_df argument, filtered to the county and date range for which exposure was calculated.

Note

• If the time_period, start_date, and end_date arguments are all left as NULL (their defaults), then exposure will be estimated across the entire date range of the clean_pur_df data frame.

• If you pulled PUR data from pull_clean_pur specifying sum_application = TRUE and unit = "township", then exposure will be calculated based on townships. Using the df_plot function to plot the returned buffer_plot list element could be helpful to see the difference between calculating exposure based on sections or townships for a certain buffer radius.

• This function takes advantage of the Google Maps Geocoding API, and is limited by the standard usage limit of 2,500 free requests per day and 50 requests per second. https://developers.google.com/maps/documentation/geocoding/usage-limits

Examples

library(magrittr)

clean_pur <- readRDS(system.file("extdata", "fresno_clean.rds",
                                 package = "purexposure"))
fresno_spdf <- readRDS(system.file("extdata", "fresno_spdf.rds",
                                   package = "purexposure"))
exposure_list <- calculate_exposure(clean_pur, location = "-120.098794, 36.532866",
                                    radius = 3000, spdf = fresno_spdf)

# specify time intervals
exp_list2 <- calculate_exposure(clean_pur,
                                location = "13883 Lassen Ave, Helm, CA 93627",
                                radius = 3000,
                                time_period = "4 months")
exp_list2$exposure

# calculate exposure by township
clean_pur2 <- pull_clean_pur(1995, counties = "san bernardino",
                             sum_application = TRUE, unit = "township")
exp_list3 <- calculate_exposure(clean_pur2,
                                location = "-116.45, 34.96",
                                radius = 5000)
df_plot(exp_list3$buffer_plot)
exp_list3$county_plot

# calculate exposure by specified chemical classes
# this is an example of `none_recorded = TRUE`
chemical_class_df <- rbind(find_chemical_codes(2000, "methylene"),
                           find_chemical_codes(2000, "aldehyde")) 
   dplyr::rename(chemical_class = chemical)
exp_list4 <- pull_clean_pur(1995, "fresno",
                            chemicals = chemical_class_df$chemname,
                            sum_application = TRUE,
                            sum = "chemical_class",
                            chemical_class = chemical_class_df) 
   calculate_exposure(location = "13883 Lassen Ave, Helm, CA 93627",
                      radius = 1500,
                      chemicals = "chemical_class")
exp_list4$meta_data

# incorporate aerial/ground application information
exp_list5 <- pull_clean_pur(2000, "yolo") 
   calculate_exposure(location = "-121.9018, 38.7646",
   radius = 2500,
   aerial_ground = TRUE)
exp_list5$exposure

leighseverson/purexposure documentation built on Aug. 13, 2021, 6:34 p.m.