Water is vital to life. The crisis in Flint, MI has caused many people to question the quality of their drinking water, as well as the government oversight of such public utilities.
The government does conduct studies to monitor drinking water quality, but nobody would disagree that we'd be better off if average citizens could double-check those results.
Just a map.
In short, the end-all solution to this problem is quite simply a map. Crowdsourced, powered by the people, and any other groups that want to contribute their data. Get advanced water testing devices in the hands of millions of communities by reducing costs and ease-of-use, and let those communities contribute to the map.
The data produced by the amateur test kits will certainly not be of the highest quality, but as the number of data points increases, the more reliable the data becomes -- this correlation is described by the Law of Large Numbers theorem.
Gamify Public Health
An effective way to get people to contribute to this study is to offer them rewards. No, you don't have to have a ton of money to hand out to them; give them psychological rewards -- the reward of feeling like they're helping their community. Similar to how Google Maps gamifies maps contributions, this app could show the user graphs of their contributions over time, stats about how many contaminants they've detected, making them feel like the local hero for discovering hazards, and helping make the community a safer place.
Not only is the map the final resting place for the test data, but it can be used to help communities see which areas have out-of-date data, or no data at all, so they can work to fill in the gaps on the map.
It's important that this data is actionable; something you can take to your town hall or administrative body to say "here's where the problem is, please fix it."
Simple data visualization can help amateurs identify the likely location of a contaminant. These visualizations can be created by connecting the dots and filling in the gaps, a mathematical process called interpolation.
To even further improve accuracy, the data (1b) can be "wrapped" to fit publicly available schematics (1a) of water utility lines, drainage sewers, and hydrological flood studies. This would help place the data points within a logical context, answering questions like "what's upstream?" and "what's downstream?"
One method of achieving this visualization would be "Surface fitting" (3a) which is a type of interpolation for 3D curves.
Let's build it.
If you've got the skills and you'd like to take a stab at this, feel free to send your github project link in the comments at the bottom of this page. (click here if you don't see the comment box) With your permission, I'll promote your project in the body of the article.