Overabundance of dissolved carbon dioxide poses a threat to marine life. A new system detects elevated levels of the greenhouse gases and mitigates them on the spot.
A defunct drydock basin arched by a rusting 19th century steel bridge seems an incongruous place to conduct state-of-the-art climate science. But this placid and protected sliver of water connecting Brooklyn’s Navy Yard to the East River was just right for Garrett Boudinot to float a small dock topped with water carbon-sensing gear. And while his system right now looks like a trio of plastic boxes wired up together, it aims to mediate the growing ocean acidification problem, caused by overabundance of dissolved carbon dioxide.
Boudinot, a biogeochemist and founder of a carbon-management startup called Vycarb, is honing his method for measuring CO2 levels in water, as well as (at least temporarily) correcting their negative effects. It’s a challenge that’s been occupying numerous climate scientists as the ocean heats up, and as states like New York recognize that reducing emissions won’t be enough to reach their climate goals; they’ll have to figure out how to remove carbon, too.
To date, though, methods for measuring CO2 in water at scale have been either intensely expensive, requiring fancy sensors that pump CO2 through membranes; or prohibitively complicated, involving a series of lab-based analyses. And that’s led to a bottleneck in efforts to remove carbon as well.
But recently, Boudinot cracked part of the code for measurement and mitigation, at least on a small scale. While the rest of the industry sorts out larger intricacies like getting ocean carbon markets up and running and driving carbon removal at billion-ton scale in centralized infrastructure, his decentralized method could have important, more immediate implications.
Specifically, for shellfish hatcheries, which grow seafood for human consumption and for coastal restoration projects. Some of these incubators for oysters and clams and scallops are already feeling the negative effects of excess carbon in water, and Vycarb’s tech could improve outcomes for the larval- and juvenile-stage mollusks they’re raising. “We’re learning from these folks about what their needs are, so that we’re developing our system as a solution that’s relevant,” Boudinot says.
Ocean acidification can wreak havoc on developing shellfish, inhibiting their shells from growing and leading to mass die-offs.
Ocean waters naturally absorb CO2 gas from the atmosphere. When CO2 accumulates faster than nature can dissipate it, it reacts with H2O molecules, forming carbonic acid, H2CO3, which makes the water column more acidic. On the West Coast, acidification occurs when deep, carbon dioxide-rich waters upwell onto the coast. This can wreak havoc on developing shellfish, inhibiting their shells from growing and leading to mass die-offs; this happened, disastrously, at Pacific Northwest oyster hatcheries in 2007.
This type of acidification will eventually come for the East Coast, too, says Ryan Wallace, assistant professor and graduate director of environmental studies and sciences at Long Island’s Adelphi University, who studies acidification. But at the moment, East Coast acidification has other sources: agricultural runoff, usually in the form of nitrogen, and human and animal waste entering coastal areas. These excess nutrient loads cause algae to grow, which isn’t a problem in and of itself, Wallace says; but when algae die, they’re consumed by bacteria, whose respiration in turn bumps up CO2 levels in water.
“Unfortunately, this is occurring at the bottom [of the water column], where shellfish organisms live and grow,” Wallace says. Acidification on the East Coast is minutely localized, occurring closest to where nutrients are being released, as well as seasonally; at least one local shellfish farm, on Fishers Island in the Long Island Sound, has contended with its effects.
The second Vycarb pilot, ready to be installed at the East Hampton shellfish hatchery.
Courtesy of Vycarb
Besides CO2, ocean water contains two other forms of dissolved carbon — carbonate (CO3-) and bicarbonate (HCO3) — at all times, at differing levels. At low pH (acidic), CO2 prevails; at medium pH, HCO3 is the dominant form; at higher pH, CO3 dominates. Boudinot’s invention is the first real-time measurement for all three, he says. From the dock at the Navy Yard, his pilot system uses carefully calibrated but low-cost sensors to gauge the water’s pH and its corresponding levels of CO2. When it detects elevated levels of the greenhouse gas, the system mitigates it on the spot. It does this by adding a bicarbonate powder that’s a byproduct of agricultural limestone mining in nearby Pennsylvania. Because the bicarbonate powder is alkaline, it increases the water pH and reduces the acidity. “We drive a chemical reaction to increase the pH to convert greenhouse gas- and acid-causing CO2 into bicarbonate, which is HCO3,” Boudinot says. “And HCO3 is what shellfish and fish and lots of marine life prefers over CO2.”
This de-acidifying “buffering” is something shellfish operations already do to water, usually by adding soda ash (NaHCO3), which is also alkaline. Some hatcheries add soda ash constantly, just in case; some wait till acidification causes significant problems. Generally, for an overly busy shellfish farmer to detect acidification takes time and effort. “We’re out there daily, taking a look at the pH and figuring out how much we need to dose it,” explains John “Barley” Dunne, director of the East Hampton Shellfish Hatchery on Long Island. “If this is an automatic system…that would be much less labor intensive — one less thing to monitor when we have so many other things we need to monitor.”
Across the Sound at the hatchery he runs, Dunne annually produces 30 million hard clams, 6 million oysters, and “if we’re lucky, some years we get a million bay scallops,” he says. These mollusks are destined for restoration projects around the town of East Hampton, where they’ll create habitat, filter water, and protect the coastline from sea level rise and storm surge. So far, Dunne’s hatchery has largely escaped the ill effects of acidification, although his bay scallops are having a finicky year and he’s checking to see if acidification might be part of the problem. But “I think it's important to have these solutions ready-at-hand for when the time comes,” he says. That’s why he’s hosting a second, 70-liter Vycarb pilot starting this summer on a dock adjacent to his East Hampton operation; it will amp up to a 50,000 liter-system in a few months.
If it can buffer water over a large area, absolutely this will benefit natural spawns. -- John “Barley” Dunne.
Boudinot hopes this new pilot will act as a proof of concept for hatcheries up and down the East Coast. The area from Maine to Nova Scotia is experiencing the worst of Atlantic acidification, due in part to increased Arctic meltwater combining with Gulf of St. Lawrence freshwater; that decreases saturation of calcium carbonate, making the water more acidic. Boudinot says his system should work to adjust low pH regardless of the cause or locale. The East Hampton system will eventually test and buffer-as-necessary the water that Dunne pumps from the Sound into 100-gallon land-based tanks where larvae grow for two weeks before being transferred to an in-Sound nursery to plump up.
Dunne says this could have positive effects — not only on his hatchery but on wild shellfish populations, too, reducing at least one stressor their larvae experience (others include increasing water temperatures and decreased oxygen levels). “If it can buffer water over a large area, absolutely this will [benefit] natural spawns,” he says.
No one believes the Vycarb model — even if it proves capable of functioning at much greater scale — is the sole solution to acidification in the ocean. Wallace says new water treatment plants in New York City, which reduce nitrogen released into coastal waters, are an important part of the equation. And “certainly, some green infrastructure would help,” says Boudinot, like restoring coastal and tidal wetlands to help filter nutrient runoff.
In the meantime, Boudinot continues to collect data in advance of amping up his own operations. Still unknown is the effect of releasing huge amounts of alkalinity into the ocean. Boudinot says a pH of 9 or higher can be too harsh for marine life, plus it can also trigger a release of CO2 from the water back into the atmosphere. For a third pilot, on Governor’s Island in New York Harbor, Vycarb will install yet another system from which Boudinot’s team will frequently sample to analyze some of those and other impacts. “Let's really make sure that we know what the results are,” he says. “Let's have data to show, because in this carbon world, things behave very differently out in the real world versus on paper.”
A mosquito under the microscope.
The association between climate and infectious disease is poorly understood, says Irina Tezaur, a computational scientist at Sandia National Laboratories. “Correlations have been observed but it’s not known if these correlations translate to causal relationships.”
To make accurate longer-term predictions, scientists need more empirical data, multiple datasets specific to locations and diseases, and the ability to calculate risks that depend on unpredictable nature and human behavior. Another obstacle is that climate scientists and epidemiologists are not collaborating effectively, so some researchers are calling for a multidisciplinary approach, a new field called Outbreak Science.
Climate scientists are far ahead of epidemiologists in gathering essential data.
Earth System Models—combining the interactions of atmosphere, ocean, land, ice and biosphere—have been in place for two decades to monitor the effects of global climate change. These models must be combined with epidemiological and human model research, areas that are easily skewed by unpredictable elements, from extreme weather events to public environmental policy shifts.
“There is never just one driver in tracking the impact of climate on infectious disease,” says Joacim Rocklöv, a professor at the Heidelberg Institute of Global Health & Heidelberg Interdisciplinary Centre for Scientific Computing in Germany. Rocklöv has studied how climate affects vector-borne diseases—those transmitted to humans by mosquitoes, ticks or fleas. “You need to disentangle the variables to find out how much difference climate makes to the outcome and how much is other factors.” Determinants from deforestation to population density to lack of healthcare access influence the spread of disease.
Even though climate change is not the primary driver of infectious disease today, it poses a major threat to public health in the future, says Rocklöv.
The promise of predictive modeling
“Models are simplifications of a system we’re trying to understand,” says Jeremy Hess, who directs the Center for Health and the Global Environment at University of Washington in Seattle. “They’re tools for learning that improve over time with new observations.”
Accurate predictions depend on high-quality, long-term observational data but models must start with assumptions. “It’s not possible to apply an evidence-based approach for the next 40 years,” says Rocklöv. “Using models to experiment and learn is the only way to figure out what climate means for infectious disease. We collect data and analyze what already happened. What we do today will not make a difference for several decades.”
To improve accuracy, scientists develop and draw on thousands of models to cover as many scenarios as possible. One model may capture the dynamics of disease transmission while another focuses on immunity data or ocean influences or seasonal components of a virus. Further, each model needs to be disease-specific and often location-specific to be useful.
“All models have biases so it’s important to use a suite of models,” Tezaur stresses.
The modeling scientist chooses the drivers of change and parameters based on the question explored. The drivers could be increased precipitation, poverty or mosquito prevalence, for instance. Later, the scientist may need to isolate the effect of one driver so that will require another model.
There have been some related successes, such as the latest models for mosquito-borne diseases like Dengue, Zika and malaria as well as those for flu and tick-borne diseases, says Hess.
Rocklöv was part of a research team that used test data from 2018 and 2019 to identify regions at risk for West Nile virus outbreaks. Using AI, scientists were able to forecast outbreaks of the virus for the entire transmission season in Europe. “In the end, we want data-driven models; that’s what AI can accomplish,” says Rocklöv. Other researchers are making an important headway in creating a framework to predict novel host–parasite interactions.
Modeling studies can run months, years or decades. “The scientist is working with layers of data. The challenge is how to transform and couple different models together on a planetary scale,” says Jeanne Fair, a scientist at Los Alamos National Laboratory, Biosecurity and Public Health, in New Mexico.
Disease forecasting will require a significant investment into the infrastructure needed to collect data about the environment, vectors, and hosts a tall spatial and temporal resolutions.
And it’s a constantly changing picture. A modeling study in an April 2022 issue of Nature predicted that thousands of animals will migrate to cooler locales as temperatures rise. This means that various species will come into closer contact with people and other mammals for the first time. This is likely to increase the risk of emerging infectious disease transmitted from animals to humans, especially in Africa and Asia.
Other things can happen too. Global warming could precipitate viral mutations or new infectious diseases that don’t respond to antimicrobial treatments. Insecticide-resistant mosquitoes could evolve. Weather-related food insecurity could increase malnutrition and weaken people’s immune systems. And the impact of an epidemic will be worse if it co-occurs during a heatwave, flood, or drought, says Hess.
The devil is in the climate variables
Solid predictions about the future of climate and disease are not possible with so many uncertainties. Difficult-to-measure drivers must be added to the empirical model mix, such as land and water use, ecosystem changes or the public’s willingness to accept a vaccine or practice social distancing. Nor is there any precedent for calculating the effect of climate changes that are accelerating at a faster speed than ever before.
The most critical climate variables thought to influence disease spread are temperature, precipitation, humidity, sunshine and wind, according to Tezaur’s research. And then there are variables within variables. Influenza scientists, for example, found that warm winters were predictors of the most severe flu seasons in the following year.
The human factor may be the most challenging determinant. To what degree will people curtail greenhouse gas emissions, if at all? The swift development of effective COVID-19 vaccines was a game-changer, but will scientists be able to repeat it during the next pandemic? Plus, no model could predict the amount of internet-fueled COVID-19 misinformation, Fair noted. To tackle this issue, infectious disease teams are looking to include more sociologists and political scientists in their modeling.
Addressing the gaps
Currently, researchers are focusing on the near future, predicting for next year, says Fair. “When it comes to long-term, that’s where we have the most work to do.” While scientists cannot foresee how political influences and misinformation spread will affect models, they are positioned to make headway in collecting and assessing new data streams that have never been merged.
Disease forecasting will require a significant investment into the infrastructure needed to collect data about the environment, vectors, and hosts at all spatial and temporal resolutions, Fair and her co-authors stated in their recent study. For example real-time data on mosquito prevalence and diversity in various settings and times is limited or non-existent. Fair also would like to see standards set in mosquito data collection in every country. “Standardizing across the US would be a huge accomplishment,” she says.
Understanding how climate change contributes to the spread of disease is critical for thwarting future calamities.
Jeanne Fair
Hess points to a dearth of data in local and regional datasets about how extreme weather events play out in different geographic locations. His research indicates that Africa and the Middle East experienced substantial climate shifts, for example, but are unrepresented in the evidentiary database, which limits conclusions. “A model for dengue may be good in Singapore but not necessarily in Port-au-Prince,” Hess explains. And, he adds, scientists need a way of evaluating models for how effective they are.
The hope, Rocklöv says, is that in the future we will have data-driven models rather than theoretical ones. In turn, sharper statistical analyses can inform resource allocation and intervention strategies to prevent outbreaks.
Most of all, experts emphasize that epidemiologists and climate scientists must stop working in silos. If scientists can successfully merge epidemiological data with climatic, biological, environmental, ecological and demographic data, they will make better predictions about complex disease patterns. Modeling “cross talk” and among disciplines and, in some cases, refusal to release data between countries is hindering discovery and advances.
It’s time for bold transdisciplinary action, says Hess. He points to initiatives that need funding in disease surveillance and control; developing and testing interventions; community education and social mobilization; decision-support analytics to predict when and where infections will emerge; advanced methodologies to improve modeling; training scientists in data management and integrated surveillance.
Establishing a new field of Outbreak Science to coordinate collaboration would accelerate progress. Investment in decision-support modeling tools for public health teams, policy makers, and other long-term planning stakeholders is imperative, too. We need to invest in programs that encourage people from climate modeling and epidemiology to work together in a cohesive fashion, says Tezaur. Joining forces is the only way to solve the formidable challenges ahead.
This article originally appeared in One Health/One Planet, a single-issue magazine that explores how climate change and other environmental shifts are increasing vulnerabilities to infectious diseases by land and by sea. The magazine probes how scientists are making progress with leaders in other fields toward solutions that embrace diverse perspectives and the interconnectedness of all lifeforms and the planet.
