1 Introduction
1.1 Background
Radon (222Rn) is a noble gas that is colorless, odorless, and tasteless, and has a half-life of 3.83 days. Production of radon in the subsurface is driven by the decay of uranium (238U), which decays into radium (226Ra) and subsequently radon (222Rn) before reaching the stable lead isotope 206Pb. As radon gas is produced underground, it travels upwards through rock and soil by means of pore spaces and carrier fluids before being released to the atmosphere. Additionally, the significant atomic mass of a radon atom means that radon gas will generally settle in lower-lying areas with little ventilation, such as mines, caves, and basements. Radioactive decay of radon and its daughter products (218Po, 214Po) releases energy in the form of alpha particles (4He) which can cause damage to lung tissue, and prolonged exposure to radon gas has been identified as the second leading cause of lung cancer in the United States.
Radon concentrations are highest near regions with surficial and underlying geologic material containing elevated uranium content, typically associated with intermediate to felsic igneous rocks. Southeastern Oregon has some of the highest uranium content in the U.S. in Tertiary volcanic rocks (McDermitt caldera, Steens Mountain, White King/Lucky Lass mines near Lakeview), and northern Oregon (particularly the Portland metropolitan area) is subject to high radon concentrations due to uranium-rich Quaternary deposits from the Missoula floods, 15-13 kya. Due to Oregon’s general predisposition for significant radon hazards, continuous radon monitoring occurs both indoors and outdoors throughout the state by various methods.
1.2 Data collection
The primary dataset I chose for this project is a shapefile
containing spatial data regarding Oregon’s potential radon hazard, which
was published by the Oregon Department of Geology and Mineral Industries
(DOGAMI) and downloaded via the data.gov website. The foremost product
of this dataset is the FIN_RN_RNK column, which is a set of
polygons describing that area’s radon hazard based on a rank of 1, 2, or
3 (low, moderate, or high hazard). The final rank is determined based on
five main factors (each with their own hazard rank), which are given as
the following from lowest to highest importance: geologic material’s
uranium content, mean aerial radiometric measurements, mean indoor air
test measurements, occurrences where mean aerial radiometric
measurements are greater than mean indoor air test results, and uranium
mine locations. Additionally, the dataset also includes data for the
primary source that was used to determine a polygon’s final rank, and a
set of Oregon’s main geologic groups and terranes. To improve the visual
cohesion of plotted maps, shapefiles for the state and county outlines
of Oregon were obtained from the National Weather Service and U.S.
Census Bureau.
2 Preparing the data
2.1 Initialize packages and read in shapefiles
Since the main dataset is in the shapefile format, we will need to
utilize the sf package to read in the data and
ggplot2 to plot it. Additionally, the
grDevices package will help generate unique color palettes
for large categorical datasets.
library(sf)
library(ggplot2)
library(grDevices)Now that we have all the necessary packages, we can read in the data
from the shapefiles using the st_read function.
setwd("C:/Users/Zachary/Documents/School/Winter_2024/GEOG490/data/")
shp <- st_read("Radon_data.shp")
oregon <- st_read("Oregon.shp")
oregon_counties <- st_read("OregonCounties.shp")The attribute table of the radon data shapefile has many various columns, but there are only a few that are relevant to the analysis we will perform, so we can assign the data from these columns to a new object.
data <- shp[, c("FIN_RN_RNK", "TERRANE_GR", "RNK_SOURCE", "RNK_SRCE2", "AREA_KM2")]This code simply defines some color palettes that will be used to make later plots.
colors <- sample(hcl(seq(15, 345, length.out = 66), 40, 70), 66)
fin_rn_rnk_colors <- c("azure", "lightblue", "lightblue4")
rnk_source_colors <- c("lightblue", "salmon", "lightgreen", "plum", "gold")3 Plotting the data
3.1 Radon potential rank map
To start out, we can first look at the final radon rank data to get
an idea of where the most significant radon hazards are in the state of
Oregon. To do this, we can use the geom_sf function to plot
the data on a map, and set the fill color to be the
FIN_RN_RNK data column. Additionally, we can use this same
function to plot the oregon and
oregon_counties data to overlay an outline of the state and
county borders.
fin_rn_rnk <- ggplot() +
geom_sf(data = shp, aes(fill = as.factor(FIN_RN_RNK)), color = "NA") +
scale_fill_manual(values = fin_rn_rnk_colors, name = "Rank") +
ggtitle("Oregon Radon Potential Rank") +
geom_sf(data = oregon_counties, fill = "transparent", lwd=0.05, color = "black") +
geom_sf(data = oregon, fill = "transparent", color = "black") +
theme(legend.position = "bottom", plot.title = element_text(hjust = 0.5))
ggsave("fin_rn_rnk.png", fin_rn_rnk)