![Dr. Nicholas Spada](https://factor.niehs.nih.gov/sites/niehs-factor/files/2024/02/community-impact/ai-air-pollution-body1.jpg)
Dr. Nicholas Spada, an aerosol scientist at the University of California, Davis (UC Davis), recently developed artificial intelligence (AI) technology to record the impact of passing coal trains on air quality. His team published findings showing that coal trains have a greater impact on fine particulate matter levels in Richmond, California, a city 20 miles north of San Francisco, than other types of trains. NIEHS supports this project through the UC Davis Environmental Health Sciences Core.
Exposure to fine particulate matter (a mixture of tiny solid and liquid particles in the air we breathe) has been linked to a variety of harmful health effects. These effects include cardiovascular and respiratory disease, adverse birth outcomes, cognitive and developmental impairments, and premature death.
Environmental Factor spoke with Spada to find out who inspired his journey in science, what the future of artificial intelligence looks like, and how environmental justice plays a role in his work.
EF: What was the defining moment in your scientific journey?
Spada: My first visit to the Advanced Light Source at Lawrence Berkeley National Laboratory. There we use high-energy X-ray beams to measure metals in particulate matter samples. When I first visited as an undergraduate, I knew I was on to something very special, and that it was everything I had dreamed science could achieve. Even now, when we work in nuclear facilities that run high-energy proton beams every day, it has not lost its luster. It’s great to be a part of that.
EF: Can you tell us more about the specific mentors who inspired you?
Spada: Dr. Tom Cahill (PhD) has been a huge influence on me. Tom recruited me into his lab as an undergraduate under the guise of nanotechnology, but his lab was actually focused on air quality research. This was a pivotal moment in my career. Tom established the air quality program at UC Davis, as well as the national monitoring system that tracks air quality in U.S. national parks and other wilderness areas. He was a pioneer and a great storyteller. He taught me a lot about environmental justice work and the importance of keeping priorities aligned.
My current mentor at UC Davis, Dr. Nicole Hyslop (PhD), is also an inspiration. My favorite thing about working with her is that when we look at data, she’s always thinking about the underlying nature of what it all means and how it fits into the bigger picture.
![Dr. Nicholas Spada (left) and Dhawal Majithia (right) with the train model they used to develop and test a computer vision system that can track real-life trains.](https://factor.niehs.nih.gov/sites/niehs-factor/files/2024/02/community-impact/ai-air-pollution-body2.jpg)
EF: What prompted you to study coal-related pollution?
Spada: As an undergraduate project, I measured ultrafine particles released from a maintenance rail yard in Roseville, California. It was the first time I’d seen science lead to action – our research led to new rules banning trains from idling for more than 30 minutes.
Many years later, in 2018, I met Dr. Bart Ostro (PhD) at a conference at Caltech and he invited me to collaborate on an air quality project near the Richmond Coal Freight Terminal. The goal is to assess the impact of another proposed rail terminal next door in Auckland. To achieve this goal, we began working as volunteer researchers in the evenings and weekends. Eventually, it turned into a large, multi-year project.
EF: How will artificial intelligence impact future environmental health research?
Spada: Artificial intelligence is a hot word right now. In our case, we trained a computer to understand what a coal train looked like. This is a huge benefit for us because train times at night are unpredictable and it’s not always safe or convenient for human observers to record them. Artificial intelligence can be a great tool, just like handheld calculators help us do math faster. But our goal is not to replace people. We want people to do creative, interesting work and computers to do routine, boring work.
With the new system we are able to record the time of each passing coal train, while also recording the air quality to establish a link between the two. We collected a million images, and then the research team used a cool video game-like software to classify and verify them, which provided a lot of confidence in our data. Because we have done the upfront development work, we can easily retrain it for different future research projects.
![Dhawal Majithia inspects the surveillance system that records the movements of coal trains before they are unloaded at the terminal.](https://factor.niehs.nih.gov/sites/niehs-factor/files/2024/02/community-impact/ai-air-pollution-body3.jpg)
EF: What role does environmental justice play in your work?
Spada: Environmental justice is the most important aspect to me and is the new frontier in air quality research. We’ve had extremely smart, talented people working in this area for over half a century, and if you look at long-term trends across the country, we’ve improved our air quality tremendously.
But the next big question on my mind is how to use research to improve air quality at the community level. How do we build a system that provides people with meaningful information about air quality every day so they can make decisions, such as whether to take their kids to soccer practice that day?
As researchers, we need to ensure communities know how to access tools and resources every day to help them make good decisions for their health.
(Lindsay Key is a contract writer for the NIEHS Office of Communications and Public Liaison.)