Week 8: More Results and a Slight Bump in the Road

Last week, I got back my second full set of sampling results. The data, which is attached to this post, shows a pattern that is actually not that consistent with what I saw in my first round of testing, as it contains a few details that make me want to look into more factors that are influencing the results.

For this set, the total PFAS concentrations were 33.7 ppt (middle), 29.8 ppt (bottom), and 39.2 ppt (surface). The surface sample is the highest, the bottom is the lowest, and the middle falls somewhere in between, whereas my first sample set had the bottom as the highest and the middle as the lowest. This shows that the general structure may not be as set in stone as I thought, which does not fully align with what I originally expected.

Though I explored the density hypothesis in my last post, since this new discovery, I’ve been looking at other possible reasons. This pattern could be explained by a combination of stratification, mixing, and sediment interaction. Surface water may have higher concentrations due to atmospheric deposition or runoff, and the middle layer might act more like a mixing zone, which could explain why the results varied so much. As for the bottom, I will keep looking into why the results jumped so much.

This week also came with an unexpected challenge. Part of my Van Dorn sampler actually broke off during use, which was definitely not ideal. I ended up having to go to Home Depot and buy some PVC cement to repair it. Before doing that, I also made sure to check the chemical composition to confirm that it wouldn’t introduce any additional PFAS contamination into my samples. Fortunately, everything is now fixed and functioning properly, and I’ve continued sampling without further issues. Aditionally, it finally rained this week, meaning I was able to get a sample influenced by precipitation, which was something I was worried about not being able to do!

On the analysis side, I’ve started applying computational methods to make sense of all this data. I’ve been organizing everything into structured datasets and using graphs to visualize trends, especially how PFAS concentrations change with depth. I’m also beginning to explore regression models and simulations to understand how environmental factors might influence these values. Even with a relatively small dataset, these methods are already helping me see patterns that aren’t immediately obvious from the raw numbers.

Hopefully, as the dataset keeps growing, the patterns become clearer, and I’ll start to move from just collecting data to actually interpreting it.