The Climate Conversation: Part Two

Michael Gonsior is a Professor at the University of Maryland Center for Environmental Science, USA. His research focuses on “the discovery of structures of deep ocean dissolved organic matter (DOM) molecules, so that we can use them as reactivity tracers to better constrain marine organic carbon turnover.” He was voted as one of the Top 20 Planet Protectors on The Analytical Scientist 2024 Power List. 

In your opinion, what is the most pressing climate change issue right now, and how can analytical science help address it?
 

The simplest solution in theory – and the hardest to achieve in practice – is to prevent carbon dioxide from entering the atmosphere in the first place. While we’re making progress, including in alternative energy technologies and developing electric vehicles, the scale of removing CO₂ from the atmosphere is vastly different from preventing it from being emitted in the first place. That’s where the real impact lies.

From an analytical science perspective, the biggest challenge is understanding the complexities of the carbon cycle. We’re looking at tracking highly complex mixtures and pinpointing specific, indicative tracers. This helps us understand the activity and residence time of carbon in different pools, like the atmosphere and the ocean. Knowing how long a particular carbon molecule stays in a specific part of the carbon cycle helps us predict its feedback effects on CO₂ levels.

To put this in perspective, let’s consider the ocean – a massive carbon reservoir. If just 1 percent more of the ocean’s dissolved organic carbon were to mineralize annually, it would offset the entire annual anthropogenic CO₂ production. Understanding these large carbon pools and how changes within them contribute to positive or negative feedback loops is essential for predicting climate outcomes and making informed decisions about climate strategies. Analytical science plays a critical role here in uncovering these mechanisms and helping us see where we’re heading.

Are there any specific tools, methods, or research developments currently helping to tackle these issues? 
 

One major area of advancement is in mass spectrometry for analyzing complex mixtures and unknown contaminants. In my lab, we’re using an extremely sensitive triple quadrupole mass spectrometer, which is traditionally a targeted tool. But I’m actually using it in an unconventional way by systematically scanning all potential transitions across the entire mass range of organic matter samples. In this case, we’re focusing on deep ocean samples, and this approach allows us to pinpoint specific molecular transitions. It’s a slow, meticulous process, but it’s opening doors to new tracers and even potentially the first structural identifications of deep ocean molecules, especially in refractory organic matter.

In the broader field, the trend has been toward high-resolution instruments, like the Orbitrap and FT-ICR-MS systems. However, a drawback with these high-resolution instruments is that they’re typically less sensitive than triple quadrupole systems, which is why I turned to the latter. Sensitivity and speed are still major hurdles for high-resolution instruments.

Looking ahead, I’m excited about the development of hybrid mass spectrometers – systems that combine the strengths of different instrument types. For example, some researchers are pairing triple quadrupole instruments with time-of-flight mass spectrometers to get high resolution and high sensitivity together. These hybrid systems aren’t widely commercialized yet, but I think they’ll be game-changers, especially for complex environmental samples where precise molecular structures are essential for understanding the system. That’s the direction I see making a real impact in Earth System Science.

Do you anticipate AI making a significant impact in your field?
 

Big data and AI are increasingly important, especially in how we handle and analyze massive datasets. I’ve been thinking about this for a while, inspired by how communication technology has used big data for decades, especially in real-time analytics. In our field, though, we’re still playing catch-up. Imagine having AI that can dig into every peer-reviewed paper and answer specific questions – saving time and guiding researchers quickly to relevant studies. This could be particularly helpful for students starting in a new field, giving them a faster grasp of existing knowledge with links directly to source material.

AI is also revolutionizing data analysis. In our lab, we recently implemented a machine learning algorithm to interpret complex datasets from different analytical tools, like optical and high-resolution mass spec data. This trend of integrating machine learning with analytical chemistry is exciting because it allows us to correlate and interpret diverse datasets effectively.

Looking ahead, I’m interested in the concept of “digital twins” for ecosystems. This idea is still in its infancy, but it could be powerful. Essentially, we’d have a digital replica of an ecosystem, allowing us to test scenarios – like increasing temperature or nutrient levels – to predict future impacts like toxic algae blooms. This would give us a much stronger predictive capability than traditional models. And yes, models have their limitations – there’s a saying that “all models are wrong, but some are useful.” The more reliable data we feed into them, the more accurate and valuable these digital models will become.

What are you most optimistic about when it comes to global efforts to address climate change?
 

I could take the pessimistic route, but I’d rather share a different perspective that might seem a bit pessimistic but is actually hopeful in its own way. Often, we speak as though we’re separate from the environment – as if we’re somehow removed from nature. In reality, that’s an illusion. We’re probably the only species that sees itself as separate from nature, but that viewpoint doesn’t make sense. Nature itself isn’t under threat; it’s we who are shaping our world, defining how we want it to look and feel. Nature will carry on; the Earth will still be here in a million years, no matter what we do. But what we’re really impacting is our place within it, our quality of life, and the balance of ecosystems we depend on.

What gives me hope is that we’re slowly – painfully slowly – starting to realize we’re part of the environment, not separate from it. We’re moving, though gradually, towards understanding our interconnectedness with nature. People are traveling more, and while you could argue that’s counterproductive for the environment, it does broaden our perspectives. It lets us experience different environments, ecosystems, and communities firsthand, and that shifts mindsets in powerful ways. The fact that we have access to information more rapidly than ever is helping, too. Despite its challenges, the flow of knowledge allows us to see more of the world and our impact on it.

In the end, I think the biggest shift – and the one we’re slowly seeing – is realizing that climate change is about us. We’re not really “threatening nature”; we’re shaping the conditions for our own survival and the future we want. That’s a powerful motivator, and if we can fully internalize that, I believe it’ll drive real change.

From an analytical perspective, is there any good data or predictions that offer some hope for climate mitigation, even if they’re small?
 

There are glimpses of hope, small ways that natural processes might help slow down climate impacts. For example, recent research shows potential feedback effects in the marine environment that could dampen climate impact, even if only slightly. Some findings suggest that changes in the stoichiometry of ocean organisms could help mitigate carbon release, or that natural shifts in water temperature could lead to small amounts of additional carbon sequestration. But these are minor mitigations – they won’t stop climate change; they may just slow it a bit.

Unfortunately, there’s not much optimism for big, immediate reversals. Climate science today may affect change hundreds of years down the line, but the next few generations will still experience severe impacts. That said, it doesn’t mean we shouldn’t pursue these measures. Science has to think long-term because, realistically, politicians often don’t focus on changes beyond the next few decades. But scientists know that if we want any chance of reversing these impacts over centuries, we have to start pushing for policy change now. Once a system on a planetary scale gets thrown off balance, it takes an immense amount of time and effort to correct.

And it’s crucial to understand: we won’t engineer our way out of this entirely. While technology and engineering can support solutions, they won’t solve the problem alone – especially not in the near term. It’s a complex, planetary-scale issue that requires sustained efforts across science, policy, and society.