Wait, I'm Going to Be Working on Physical WHAT?
The past two projects I wrote about were based on my Environmental Engineering coursework. Their primary purpose was to teach me something about this broad field, and as such they were not particularly applicable to anything outside of the classroom or lab environment.
In fact, it was not until the summer after my junior year that I got to work on an actual research project in a discipline related to Env. Eng. Though I had done research at MIT previously, these experiences had not really been in my field of interest (or, rather, had made me uninterested in their field): my freshman/sophomore year I worked in an environmental genomics lab, and my junior year I worked with health systems analysis. The lab environments and skills I gained from these experiences were invaluable, but I could not see myself in either profession long term.
In fact, I was waffling about where I saw myself in the wide spectrum of environmental engineering throughout most of my college career.
Until I was accepted into a Research Experience for Undergraduates (REU) program my junior year to work with a Physical Oceanographer, and my research focus swung a hard right to center on fluid dynamics.
I applied to this REU mainly because it was something to do over the summer away from college. By my junior year I was relatively sick of the campus; I had been there almost nonstop for two and a half years, only leaving about two weeks each year to go home. I wanted to get away, and I wanted a program that would make my trip worthwhile.
I was fortunate enough to be accepted, even though I was a professed engineer and the program was technically geared towards marine scientists. As a result, I was one of two students paired with a physical oceanographer (the other came from a physics background).
At first, of course, I had no idea what physical oceanography was or how my training could have prepared me for that field. I was vaguely confident that something I had learned in the past three years would come in handy, but I had no idea what. It depended heavily on the project I was to work on.
I was also deathly afraid of the prospect of fieldwork in a marine biology/oceanography lab, but that is a notion best left alone for now.
When I arrived at the program, I learned that I would be working on the Amazon River Plume. I also learned that physical oceanographers spend a good deal of time in front of their computers, and what my project would really test was my ability to do math and utilize MATLAB for days on end without driving myself crazy. As the only engineer in a program populated with scientists, it was nice to have something familiar to fall back on.
My supervisor had collected data from the plume the summer previous on an extensive research cruise. I was tasked with taking this data and "making something of it" (a common theme in my research since, it seems). I ended up flailing around with MATLAB for the first week or so until my supervisor and I decided on a more specific direction.
The meat of my project became looking at CTD (salinity, temperature, depth) and ADCP (velocity) data from the cruise to determine a method for finding the age and volume of the plume based on remote sensing data (satellite data). This involved a great deal of correlation analyses and verification of the cruise data against remote sensing data gleaned from various online sources. It was also a project idea to which I feel I had a significant contribution.
I have to say that the measurements from this research cruise were impressive. I learned in my first week the benefits of having an extensive data set--and I also learned that no research cruise can possibly collect as much data as a scientist would like for the analysis stage. Thus sometime in my early days as an REU student I began exploring the uses of remote sensing data.
Satellite data was an attractive option because it provided a significant amount of spacial coverage of any region of the world, really. There are enough research satellites in orbit now to give a good amount of information on, for instance, sea surface temperature. The trick is that they can only supply information about surface properties (and, you know, there are also these things called clouds which throw everything off now and again).
Given the amount of potential spacial resolution in comparison to the relative dearth of in situ data, I was very much attracted to the idea of figuring out how to extract more information from these research satellites. My project, therefore, became an attempt to link the data from the Amazon River Plume research cruise to remote sensing data in hopes of finding a correlation that would remove the need for the cruise data altogether.
The ResultThree months of data crunching, correlation testing, and generally plotting as many pretty pictures as possible resulted in a moderately robust relationship between K490, the light attenuation coefficient at 490 nm, and plume thickness.
You might be thinking "wait, where did that come from? Weren't we talking about plume age and volume ten seconds ago?"
Well, yes. But to get plume volume you need surface area and thickness, right? Satellite imagery can easily supply the former; my job boiled down to figuring out how to get the latter. This happened in several steps.
- Find a relationship between plume thickness (PT) and sea surface salinity (SSS) using the in situ data from the research cruise.
- This was a reasonable correlation to attempt to find. You would expect SSS to be greater the more the plume mixed with surrounding waters, which would ideally occur concurrently with dissipating thickness.
- It turns out that this correlation exists, but not as neatly as I would have liked. PT corresponds to a range of SSS--basically, the best you can get is an approximation of PT given SSS (to within 3 or so meters).
- This step was actually a correlation between SSS and CDOM, again based on in situ data. This correlation was an incredibly nice exponential decay.
- Explains a lot, since we expect a younger plume to have significantly more CDOM than an older plume given the fact that the plume is a nutrient, sediment, and organism-rich body of water infiltrating a relatively nutrient-poor environment.
- At last, I was able to match the in situ data with remote sensing data. The relationship between CDOM and K490 was very strange--something logarithmic provided the best fit, which made little sense to me scientifically. I am still puzzling this one out.
- Again, though, it makes sense that a relationship should exist since the optical properties of the water, represented by K490, are highly depended on what sorts of organisms and particles are in the water (CDOM).
In the end, therefore, I found what amounted to an equation to translate between PT and K490, an equation of debatable robustness. Had I more data from different seasons, and just more in situ data in general, this relationship could have been made more accurate. Still, no research projects is without its flaws, and the fact that a relationship of any robustness whatsoever existed was exciting to discover.
My exploration into plume age was slightly less complex, and generally involved applying ADCP data to satellite images of the plume extent. Once you have distance and velocity, calculating time is a simple multiplication step. The plume age I found, of course, was a broad estimate of how long ago a particular particle of water had left the mouth of the Amazon River.
In short, there are several caveats to my results, but both are definitively results. They can be used by anyone interested in the plume's extent (for purposes of tracking organisms or particles/chemical reactions) to at least allow them an idea of the magnitude of the plumes impact on its surrounding environment.
This was the first real contribution I made to a research project, and I was able to create two posters to send to conferences based on my results from this summer alone. There was a real sense of accomplishment from unearthing these relationships and having a hand in designing the research I was conducting.
I was hooked on the idea of being able to do this again in the future. Even the prospect of sitting in an office for days on end doing data crunching was not enough to deter my interest in future work of this kind. I wanted to do useful work in the future, and this research project was the closest I had gotten to this goal.
However, I felt as if I was missing some of the more fun aspects of oceanography research; namely, the fieldwork and modeling. Thus I decided the summer after my junior year that I would return to research in the ocean, but only if I could fill in the gaps I had missed. I want to experience the system I study firsthand and get a feel for its physics before diving into the data crunching.
I also want to be able to model the system in order to discover more about it than fieldwork alone can reveal. I now have an idea of how to use cruise data and even satellite data to verify a model of any aquatic system.
With these goals in mind, I left the REU program completely satisfied, for the first time ever, with the outcome of a research project.