Water Quality Data Analysis by State
The skills I demoed here can be learned through taking Data Science with Machine Learning bootcamp with NYC Data Science Academy.
It’s undeniable that drinking water is essential to all humankind. Our quality of life is contingent on the proper quality or quantity of it. In fact, Andrew Wheeler, the head of the Environmental Protection Agency, states how drinking water is currently a bigger problem than climate change by the sheer amount of people dying from contaminated water. Data shows the dangers of contaminated water is not limited to third world countries but also exists within the USA. When I heard about the water crisis in Flint, Michigan, I decided to look more into the details of what really affects the quality of water.
As a person who lived in New York for a long time, and has an older brother living in New Jersey, the fact that I could drink tap water from New York but not from New Jersey was shocking. Which defining features from each state could possibly make such a difference to such close-by places? First, I decided to focus on drinking water violation points from each state.
Drinking water violation points, a weighted point system that tracks the water violations by factors such as contaminants and treatment, was revealed to be highly decisive on the water quality ranking of different states - as the drinking water violation points got higher, the water quality ranking tended to be lower.
Focusing on the fact that drinking water violation points track contaminants, I hypothesized that pollution would also correlate to the ranking of water quality for each state. This led me to compare the correlation between water quality ranking and amount of industrial toxins (in pounds per square miles) as well as between water quality ranking and pollution rankings for each state.
However, surprisingly, there were no strong correlations between water quality ranking and pollution ranking or industrial toxins. Even without the outliers of the amount of industrial toxins, the correlation tends to have less toxins as the ranking gets lower when I had hypothesized the opposite. With confusing results, I led myself to expand the scope to look into the ranking of pollution.
Correlation Between Pollution and Industrial Toxins
While pollution ranking and the industrial toxin levels of each state don’t seem to highly affect the ranking of drinking water quality, they seem to have a high correlation between them. To narrow down why there is such a high positive correlation with industrial toxins but not with quality of water, I generated a few more comparisons regarding the amount of industrial toxins per state.
While trying to map the industrial toxins of each state for different features, I was able to find that industrial toxins seem to have a negative exponential relationship with GDP. GDP tended to be lower in the states with high industrial toxins, and as GDP tended to be higher, the possibility of having high amounts of toxins significantly decreased, which led me to conclude that industrial toxins could be improved with more generated GDP. But that begged the question, how could we increase GDP?
I found that GDP tended to be higher when there was a larger population and a higher percentage of college educated people - indicating that when there were more people, especially educated people, in the states, the state would get richer and tend to solve toxin problems.
While I was able to learn more about industrial toxins and pollution levels, it was harder for me to find out more about the features that affect the water qualities for each state, which led me to do some more research on it.
Though industrial toxins and overall pollution is a result of human action or inaction, water quality is largely dependent on many geographical and geological factors beyond human control. Some factors include sedimentation, erosion, dissolved oxygen, pH, runoff, and decayed organic materials. Because of the wide range of contributing complex factors, it is difficult to graph a direct correlation between water quality and any one factor.
Being unable to figure out about the true features that affect the water quality leads me to hope for more data about the geographical and geological features for each state, which could be used to conduct more research about the water and how to improve it. I also hope to accurately identify outliers in my data analysis and configure the results and correlations accordingly. Through this, I want to provide data that might help improve the quality of water in the states by careful analysis of contributing and correlating factors.