A day in the life of an environmental researcher
Curious about what life is like for an environmental researcher?
I've created this page to help non-researchers gain a better understanding of what the scientific work looks like day-to-day.
Lab Work
Field Research
Modeling
At its core, the goal of any research is to ask questions to help us better understand the world around us. As an environmental researcher, my work focuses on answering questions that will help us live more sustainably. As an engineer, I prioritize work with immediate and real-world applications. I particularly care about protecting public health and the environment through drinking and wastewater treatment, and developing strategies to help us reduce greenhouse gas emissions that contribute to climate change.
Through many years of research, I've used different approaches to tackle the problems I care about. As a result, the work I have done may involve growing bacteria in a lab, collecting wind data in the Arizona desert, or coding on my laptop while working from home. Here, I provide an overview of three main different types of research through the lens of my experiences: lab work, field research, and modeling.
Lab work
I started off my research journey doing lab work as an undergrad summer intern, and continued to work in various laboratories throughout grad school. In laboratory-based research, we typically aim to create a highly controlled environment that allows us to study what happens when we change one single part (or variable) in a given system. We work incredibly hard to isolate the causes of any difference we might observe in the tests we run, so as a result laboratory studies are often quite simplified compared to real-world applications.
Methane-consuming bacteria grown in laboratory bottles
You can see the picture of the bacteria that I grew in laboratory bottles - these bacteria eat methane and are called methanotrophs. I grew several sets of this bacteria under the exact same conditions, where the only difference between groups was the mix of gas that I injected into the bottle for them to use to grow. That way, if I saw difference between these groups, I could be fairly confident that the gas mixture is the root cause.
However, if I were to grow these bacteria in an industrial engineering facility, they would be exposed to so many other gases and contaminants. I can't completely replicate these conditions in the lab, and thus don't know exactly how they might impact bacterial growth. While I can use my studies to make reasonable predictions, this shows the limits of laboratory experiments.
Sometimes in biology-based laboratory research, we can't use the exact species we are interested in for our experiment. Maybe we can't grow that particular bacteria or animal in the lab, or perhaps it would be unethical to sacrifice an animal's life in the context of the type of question we are trying to answer. In this case, we might instead study what is called a model organism.
I was interested in seeing how feeding methane-consuming bacteria to fish and shrimp might impact their growth, providing benefits for aquaculture. However, it would have been very hard to grow a high number of the type of shrimp we eat in my lab space. And we need a large number to have a meaningful experiment. So instead, I used brine shrimp - a very small type of shrimp that is actually used to feed fish. Brine shrimp grow to be only a few millimeters long, and you can see the image of one that I took under the microscope. With this model organism, I could hatch thousands at a time, and use hundreds in each experiment.
Microscopic image of a brine shrimp
Laboratory bioreactor (4 liters) growing methane-consuming bacteria
Day-to-day life in the lab follows certain patterns. You need to plan your experiment first: this involves learning how to use any new equipment you want to try out, checking that you have all the supplies you might need, and finalizing your methods and data collection plan. Running the experiment itself can take anywhere from a few hours to weeks or even months. When I grew bacteria in laboratory bottles (above), my experiment might be 3-5 days long, while when I grew bacteria in a 4-liter bioreactor, it was 3 months! Because we are on a specific timeline for data collection, sometimes laboratory researchers have to stop by lab at odd times or on weekends, but I'll try to plan experiments so that I can work reasonable hours.
Lab work is exciting because you get to learn so many hands-on skills, and gain a strong sense of what tools we can use if we want to learn specific things. Sometimes a research group will specialize in one particular kind of analysis (for example, analyzing samples for certain molecules), and apply it to many different areas. On the other hand, some lab groups will focus on an area, like water treatment, and try to use many different tools to answer their questions.
Field Research
While I was a post-doc at Stanford, I led a large field project where we spent two months testing many different technologies that are used for detecting methane leaks. Methane is an incredibly potent greenhouse gas, and our ability to find methane emissions and know how large leaks are is important for preventing these leaks from happening in the first place. In our field research project, my team tested airplanes, satellites, a drone, ground sensors and cameras. By independently testing these sensors, we both evaluated their accuracy while also giving them the opportunity to develop and improve.
Setting up the gas release tower (left) and wind tower (right).
Scientists use field research when we want to answer a question with a test that closely mimics the real world. As a result, there is a huge variety of field research! It can involve collecting samples or measurements from many different contexts, or testing equipment in the environments that we would actually use them.
Because we can't control conditions in the field the way we would in the lab, we often try to document many field conditions while we're running the experiment. We can then use measurements we collect to try and understand the variability we might see in the results.
In order to test methane sensors, we picked a location in the desert in Arizona to provide a clean background without other methane sources nearby. We then released a known amount of gas, timed with when an airplane or satellite was passing overhead and conducting its measurement. I spent a lot of time working on designing and troubleshooting a system to precisely measure the gas we were releasing. This was our metering system that you can see in the photos of me and our team setting up equipment onsite.
We can't test methane sensing satellites or airplanes in a lab because they are used for measuring incredibly large methane leaks. Also, I don't know a lab large enough to fit an airplane! However, our field experiments let us more closely predict how accurate their measurements are, even though we sometimes couldn't control everything in that happened on the field site - like the weather. And because our team at Stanford tested some of the same sensors multiple years in a row, we were able to see how they improved dramatically each time we tested them - underscoring how important field research is for advancing science and technology development!
Also, when airplanes and satellites first observed methane leaks from oil and gas, the leaks were so much larger than any that had been seen before. In fact, many people didn't believe the results, and thought the sensors weren't accurate. The testing our team conducted was essential for demonstrating these technologies work, and that we need to take their findings seriously.
Setting up the methane metering system in the field in Arizona to precisely measure our gas release rate
(Photo Credit: Richard Chen)
Shelter from a sandstorm in our truck after a hard day setting up the field site!
Field work can be incredibly difficult, but it is also very rewarding. Often, you spend months planning the experiment before going to the field and actually carrying it out. Because we are doing research out in the real world, we have to deal with all kinds of unexpected challenges. In my field experiment, we had to go from Plan A, to Plan B, then C, then D... Field research requires thinking on your feet and making use of what limited resources are available at your field site, all while maintaining the scientific integrity of your experiment. We had to deal with delays in supply chain, sandstorms (see the photo), unexpected equipment failure, logistical miscommunication, and so much more.
However, there are many aspects of field research that make up for all these difficulties. I love how field research lets me learn about so many new kinds of equipment, work closely with a large team of people from all walks of life towards a common goal, and spend time outside, thinking on my feet in response to whatever challenges arise!
Modeling
While scientists are always analyzing data from lab and field research using modeling tools, when I work on a modeling research project it means that my focus is on developing new analysis tools, as opposed to collecting the data itself. I might use existing data from sources that are published, but I also can use calculations or fundamental scientific and engineering principles to make an estimate where no data is available. I started my first modeling project right when the COVID-19 pandemic started, which was convenient because it allowed me to pause my lab work while campus was closed, and work from home on my computer. And I've modeling ever since!
Map of methane emissions and flaring in the United States (El Abbadi et al, 2022)
In most of the modeling work I do, I rely on incredibly large datasets to try to understand patterns on a large geographic scale, and make predictions about how these patterns will change. The map of the United States shows the many different source of methane, where the gas is not currently being used for generating electricity. The data was originally collected either by a satellite that can observe methane flares, or by surveys and reporting to the U.S. Environmental Protection Agency. I can then use these many thousand data points as inputs to my model.
Modeling is exciting because it lets us imagine a world that might not yet exist. We can create different future scenarios and use them to make predictions. However, our model is only as good as the underlying data and assumptions! So it's important that we communicate the limitations of our models. How certain are we in the scenarios we created? How would our results change if we change our key assumptions?
Diagram showing the process model I built to estimate cost for industrial production of protein from methanotrophic bacteria (El Abbadi et al, 2022)
Example coding in Python
Because the results of our models might change so much based on what assumptions or data inputs we use, it is really important to me that I make my work available for other researchers to review and test. As a result, I've moved towards using open access or freely available software. Like many engineers in the US, I was trained in undergrad to use Matlab (which is quite expensive if you are not a student!), but in grad school I used R, and now I exclusively code in Python. R and Python are both available for free. They're a lot of fun to code in, and there are so many resources available to help you learn!
When I'm modeling, I spend most of my time working with data: finding, organizing, and cleaning it, then incorporating it into my model. I also spend time writing the code for my model itself, and learning about new ways to write code that is more efficient and easier for others to read and interpret.
If you've made it to the bottom of this page, congratulations! I hope this was helpful, and gave you an idea of what different types of science might look like, whether you are considering your own future career path or just want to have a better sense of what researchers do every day!