Fluoride and Arsenic levels in Maine

Fluoride and arsenic are two major water pollutants that are found in household wells in Maine. One of the issues with having your own well instead of town water is controlling the arsenic and fluoride levels and making sure that they are not above the recommended level. Having an over exposure to arsenic in water can lead to skin, lung and bladder cancer (Green Facts). Fluoride on the other hand is good for preventing tooth decay but too much of it can leave your joints stiff and actually weaken your bones overall (Department of Municipal Affairs). This report will cover the fluoride and arsenic levels in Maine wells focusing on the percent of wells above the recommended level, and which towns are the worst to build house wells in. For reference, towns with less than 10 wells tested are excluded.

f_and_a_by_location <- fluoride %>% inner_join(arsenic, by = "location")
na_omit_f_a_location<- na.omit(f_and_a_by_location)

output1 <-head(na_omit_f_a_location %>%select( "Town in Maine" =location, "Percent of wells above fluoride guideline" =percent_wells_above_guideline.x,"Percent of wells above arsenic guideline" =percent_wells_above_guideline.y)%>% arrange(desc(`Percent of wells above fluoride guideline` + `Percent of wells above arsenic guideline`)),10)

output1 %>% kable() %>% kable_styling(position = "float_right")
Town in Maine Percent of wells above fluoride guideline Percent of wells above arsenic guideline
Otis 30.0 39.6
Manchester 3.3 58.9
Surry 18.3 40.3
Monmouth 3.1 49.5
Blue Hill 9.6 42.7
Mercer 15.6 36.4
Columbia 1.9 50.0
Gorham 0.0 50.1
Orland 8.6 40.7
Eliot 0.0 49.3

This data frame is showcasing the towns with the highest total percentage of wells above the recommended level. It is basically adding the percentage of wells above fluoride and arsenic and finding the towns with the highest combined value. On the surface this seems to make sense and provides us with the worst towns overall but, one of the issues with this data is that it may be double counting some wells. For example if a well is above both arsenic and fluoride levels it will boost the percentage of both fluoride and arsenic.

Since this data-frame might not be the best tool to decide the worst towns to build a well let’s look at the top 10 highest well above guidelines in fluoride and arsenic individually instead of combined.

output2<-head(na_omit_f_a_location %>% select(Town = location, "Percent of wells above fluoride guideline" =percent_wells_above_guideline.x,"Percent of wells above arsenic guideline" =percent_wells_above_guideline.y)%>% arrange(desc(`Percent of wells above fluoride guideline`)), 10)

output2 %>% kable() %>% kable_styling(position = "float_right")
Town Percent of wells above fluoride guideline Percent of wells above arsenic guideline
Otis 30.0 39.6
Dedham 22.5 17.5
Denmark 19.6 0.0
Surry 18.3 40.3
Prospect 17.5 4.0
Eastbrook 16.1 10.7
Mercer 15.6 36.4
Fryeburg 15.4 0.0
Brownfield 15.2 4.2
Stockton Springs 14.3 15.9 
output3<-head(na_omit_f_a_location %>% select(Town = location, "Percent of wells above fluoride guideline" =percent_wells_above_guideline.x,"Percent of wells above arsenic guideline" =percent_wells_above_guideline.y)%>% arrange(desc(`Percent of wells above arsenic guideline`)), 10)

output3%>% kable() %>% kable_styling(position = "float_right")
Town Percent of wells above fluoride guideline Percent of wells above arsenic guideline
Manchester 3.3 58.9
Gorham 0.0 50.1
Columbia 1.9 50.0
Monmouth 3.1 49.5
Eliot 0.0 49.3
Columbia Falls 0.0 48.0
Winthrop 3.1 44.8
Hallowell 0.0 44.6
Buxton 1.0 43.4
Blue Hill 9.6 42.7

One piece of information that stands out is that having a well above arsenic exposure seems to be more common than fluoride. The highest town with a fluoride exposure is Otis with 30% of the wells over the recommended level compared to the Blue Hill which has an arsenic over exposure level of 42.7% and is only the tenth highest. This points to the possibility that arsenic levels seems to be more of a problem than fluoride. Below are the number of towns with at least 15% of their wells over the recommended level of fluride and arsenic respectivly.

f_over_20<- na_omit_f_a_location %>% filter(percent_wells_above_guideline.x >=15)

a_over_20<- na_omit_f_a_location %>% filter(percent_wells_above_guideline.y >=15)

f_over_2_c<-nrow(f_over_20)
C_over_2_c<-nrow(a_over_20)

f_c_c <-data.frame(f_over_2_c,C_over_2_c)
output4<- f_c_c %>% select("Fluoride"= f_over_2_c, "Arsenic"= C_over_2_c)

output4%>% kable() %>% kable_styling(position = "float_right")
Fluoride Arsenic
9 112

This data frame confirms that arsenic contamination is a much bigger problem in Maine than fluoride contamination.

Furthermore, when looking at these two data-frames it becomes apparent that there are no towns that are on both lists. What could be interesting to see is whether or not these towns are close to each other or not. It would make sense if some of these towns clustered together as fluoride and arsenic are minerals and if a one town has a high level of over the limit wells the towns next to it will likewise have a high percentage as well.

Map of the ten worst towns with the highest percentage of wells over Arsenic and Flouride levels

This map of Maine shows the 10 worst towns with Fluoride and Arsenic over level based on percentages. It is interesting to note how there does some to be some groupage with these towns along with some lone wolfs. Furthermore, this map provides a very basic overview of where you might want to reconsider building house wells if town water is available. Or at the very least buy a higher quality filter. Two areas in Maine that stand out as having a high percentage of wells over the limit of fluoride and arsenic respectively are,

  • The towns to the left of Augusta.

  • Fryeburg and its surrounding towns.

Biliography

Department of Municipal Affairs. (2006, February 26). Fluoride in Well Water | Water Resources Management. Retrieved September 28, 2018, from https://www.mae.gov.nl.ca/waterres/cycle/groundwater/well/fluoride.html

Green Facts. (2018, August 28). Arsenic. Retrieved September 28, 2018, from https://www.greenfacts.org/en/arsenic/l-2/arsenic-7.htm

Issues preparing the data

Overall, I did not have too much trouble with preparing the data. One of the issues that I had was going back and looking for errors in my code however, I feel this is part of the process in writing an R report. The biggest issue that I ran into was trying to create a bar plot to just show the number of towns above a certain % level. I was able to get the numbers but I was not able to figure out a way to plot it on a bar chart. Other than that I was able to make what I wanted to make in R.