Research

During my PhD, I was jointly advised by Drs. Claudia Czimczik and Jim Randerson in the University of California, Irvine Department of Earth System Science.

Field Work

In my field work in 2021, I drove the University of California, Riverside (UCR) LIME/AVOCADO, aka the Mobile Lab, a van retrofitted with different air quality sensors, to different wildfires in California. You can read my paper on this work published in Environmental Research Letters here. I was assisted by my faculty advisors and other research staff at UCR. I’m interested in particles from smoke (PM2.5, fine particulate matter), but we can also measure gases using the suite of instruments on the Mobile Lab.

We chased fires using publicly available data from the Purple Air network, AirNow, New York Times and others. These data allow us to see where fires are and how they’re spreading and see smoke forecasts and real-time measurements of air quality. All of these data help us understand where we might actually get some samples from smoke and also where to go to stay safe from the fire.

In the field, I used the MetOne ES-642 Remote Dust Monitor and Purple Air PA-II sensor to measure PM2.5 concentrations in smoke. The Remote Dust Monitor and PA-II measure PM2.5 concentrations by taking in small samples of smoke, shooting lasers at it and measuring how much light bounces back to calculate the concentration of particles, in this case PM2.5. They release this smoke and take new measurements every second (Remote Dust Monitor) to two minutes (PA-II).

I also used a portable sampler (AirMetrics Tactical Air Sampler, aka MiniVol) to collect PM2.5 for analysis in the lab. The MiniVol works like a vacuum sucking in smoke. Parts inside the MiniVol sort the smoke particles and allow us to collect only the fine ones – PM2.5 – on a quartz-fiber filter. We collect PM2.5 over periods from 30 minutes up to 12 hours per filter to collect large enough samples to analyze in the lab.

Other instruments onboard the Mobile Lab include Picarro G2401 and G2313 for measuring different greenhouse and trace gases (carbon dioxide, carbon monoxide, methane, water vapor and reactive nitrogen).

In 2022, I participated in a prescribed fire as part of the SPARx campaign, aimed at helping develop the best practices for prescribed fire in California. Prescribed fire is one form of fuel treatment, used by Indigenous people centuries ago, before Euro-American settlement in California. Since Native American depopulation, California’s fires have gotten more intense. This is largely because fuels that would have otherwise been burned in a prescribed fire are instead building up over decades on the forest floor. When ignited, now extra dry and flammable due to climate change, they burn even hotter than the fuels that burned historically. Additionally, practices like logging and timber harvest (cutting down trees for wood) change the forest structure in ways that allow ladder fuels like small trees and shrubs to spread. Ladder fuels allow fire to spread higher into the forest canopy, like ladders for flames to climb up, killing more precious, protected trees like the giant sequoias in central California. This creates greater danger for humans – communities and firefighters – as well as forests.

In the SPARx prescribed burn at the Blodgett Forest Research Station, I collected PM2.5 to understand which fuels burned. We were able to take inventory of the fuels before and after the fire to compare to our smoke measurements. Can the smoke really tell us which fuels burned? If we know what to expect in smoke from certain fuels, we can measure the smoke during future prescribed fires to assess whether targeted fuels are being effectively targeted. The methods I use to understand fuel consumption (which fuels are burned) is more precise than what we might get from satellites, which capture what’s happening on the land from space at a coarse resolution (like a zoomed-out photo). It’s also easier than getting on the ground and observing every fuel type before and after the fire (like taking a lot of really zoomed-in photos), which isn’t practical in most of California’s wildfires – they often start suddenly, burn for months, and burn over hundreds of thousands of acres.

Lab Work

My work relied on radiocarbon (¹⁴C) as a tool for estimating fuel age. From there, we can identify fuel type.

Radiocarbon

Radiocarbon (¹⁴C) is a radioactive isotope of carbon, meaning that it is more massive (heavier) than a standard ¹²C atom and spontaneously decays to a different element over time. It is produced naturally in the upper atmosphere and oxidized to CO₂. It can also be man-made by nuclear weapons testing, like what occurred in the U.S. in the late 1950s and early 1960s. Plants (aka fuels) take up CO₂ and incorporate the ¹⁴C content of the atmosphere during photosynthesis. Animals eat plants and also have some amount of ¹⁴C. ¹⁴C in organisms (plants and animals) decays over time at a predictable rate and this is the principle for carbon dating! If we find a fossil with a measureable amount of ¹⁴C, we can estimate its age using carbon dating.

Fossils are part of a “closed system,” meaning they don’t exchange carbon with any other part of the Earth system after they die. Therefore, the amount of ¹⁴C at the time the fossil is measured tells us how long ago the organism died (the fossil’s age). Plants and plant products (like PM2.5 from fire) are part of an “open system” that is constantly exchanging carbon with other parts of the Earth system, both losing and gaining ¹⁴C over time via respiration, decomposition, and fire. Because of this, the calculation for fuel age is not as straight forward as carbon-dating a fossil. Instead, we need to compare the amount of ¹⁴C (∆¹⁴C) in the sample to the ∆¹⁴C of the atmosphere, which changes with time, to get the age of the fuel.

Sealed-tube combustion with cupric oxide

The first step to measuring total carbon and ¹⁴C content of PM2.5 samples is reducing the solid carbon to CO₂ gas via combustion. I do this by placing some of the PM2.5 sample in a glass tube with a reagent that, when combusted with the PM2.5 at 900ºC, turns the solid carbon to carbon dioxide.

Vacuum line gas extraction & cryogenic trapping

The next step is extracting the CO₂ from its glass tube and trapping it in a new glass tube. This step is necessary because when combusted, the sample produces other gases, not just CO₂. First, the other gases are frozen using a water/alcohol mixture, which is cold enough to freeze water and other gases while letting the CO₂ remain in gas form. Then, the pressure generated by the CO₂ is measured and converted to a mass of carbon. Lastly, the CO₂ is frozen into its final tube using liquid nitrogen. This tube contains iron and zinc, which are used to turn the gas into solid graphite.

Sealed-tube graphitization

In its new Pyrex tube, accompanied by iron and zinc reagents, the CO₂ is heated at 515ºC to reduce it to graphite. The resulting solid graphite can then be analyzed for its ¹⁴C content in an accelerator mass spectrometer.

Radiocarbon measurement via accelerator mass spectrometry 

The ¹⁴C content of a given PM2.5 sample is measured alongside several standards of both “modern” and “dead” carbon in the Keck Carbon Cycle Accelerator Mass Spectrometer.

Elemental and isotopic analysis

Samples are prepared for elemental (total carbon, total nitrogen) and isotopic (stable carbon, stable nitrogen) content by placing pieces of the aerosol sample in small tins that are analyzed by an elemental analyzer coupled to an isotope ratio mass spectrometer.

Using Models

In science, a model takes information (input) and simulates results to give us an output.

This is an example of the model output when I input several possible fuel ages, from 5 to 75 years. Here, we’re assuming a wide range of possible fuel ages. For each age, the model simulates how much ¹⁴C we would see in the PM2.5. In this case, I actually measured in the lab the amount of ¹⁴C represented along the y-axis in the red shaded portion of the graph. Therefore, I can assume my actual fuel age is also represented in the red shaded portion along the x-axis of the graph.