case study #2 midwestern lake answer key

Teaching an Online Introductory Biology Lab Using Evolution and Ecology Resources

This playlist can be used in an online, undergraduate (majors-level) introductory biology lab to incorporate core topics in evolution, diversity of life, and ecology. Using case studies, multimedia, and interactive resources, it engages students in data analysis and critical thinking. The topics covered include phylogeny, natural selection, speciation, viruses, bacteria, population ecology, community ecology, ecosystem ecology, and climate change.

This playlist can be used to teach six 3-hour (180-minute) labs in a lab course for a total of 1080 minutes of instruction over a semester.

Lab 1: Evolution

By completing the resources in this lab (resources 1–2 in this playlist), students will be able to:

• Explain how molecular sequences, such as DNA, can be used to study evolutionary relationships.
• Summarize the process and goals of DNA sequence alignment. 
• Interpret a phylogenetic tree.
• Collect and analyze data to quantify phenotypic diversity among populations.
• Explain how similarities or differences in traits can evolve as adaptations to similar or different environmental conditions.
• Perform statistical calculations, including calculations for the mean, standard deviation, standard error of the mean (SEM), and the 95% confidence interval.

Lab 2: Viruses

By completing the resources in this lab (resources 3–5 in this playlist), students will be able to:

• List the ways in which viruses can differ from each other.
• Identify different components and characteristics of viruses and their role in infection.
• Calculate the size of a virus relative to a human cell.
• Use information collected in case studies to distill complex, real-world data, and perform basic calculations to make decisions on the spread of an infectious disease.
• Analyze and interpret data from a scientific figure. 
• Explain the term zoonotic disease and discuss some of the global patterns in mammals that carry these diseases.
• Use appropriate scientific terms, including “reservoir” and “spill over,” in describing a disease outbreak.

Lab 3: Microbes

By completing the resources in this lab (resources 6–8 in this playlist), students will be able to:

• Make observations on an ongoing experiment.
• Make inferences based on observations.
• Describe variation in microbes and their role in the environment. 

Lab 4: Population Ecology

By completing the resources in this lab (resources 9–11 in this playlist), students will be able to:

• Develop scientific explanations and justify claims using evidence.
• Calculate elephant density with sample aerial survey data using the strip transect sampling method.
• Identify the advantages and disadvantages of different survey methods.
• Analyze and interpret gel electrophoresis results to determine relationships between individuals and populations.
• Use allele frequencies to calculate the probability of two individuals sharing the same genetic profile.
• Explain how the geographic and genetic distances between two populations are related.
• Explain how genetic data helps law enforcement officers and conservationists decide where to target their efforts.

Lab 5: Community and Ecosystem Ecology

By completing the resources in this lab (resources 12–14 in this playlist), students will be able to:

• Make connections between climate, vegetation, and biodiversity to describe biome characteristics.
• Identify the types of data needed to test different niche partitioning mechanisms.
• Make claims and offer explanations based on authentic field data.
• Identify limitations of traditional forms of data collection.
• Use DNA sequence data to identify animal and plant species.
• Analyze data using statistical methods.
• Predict whether different organisms in an ecosystem have positive or negative effects on other organisms in that ecosystem.
• Predict how an ecosystem may change when a particular organism is introduced or removed, based on the evidence provided.

Lab 6: Human Impacts

By completing the resources in this lab (resources 15–20 in this playlist), students will be able to:

• Describe recent patterns and trends in atmospheric carbon dioxide concentrations.
• Explain the role of atmospheric CO 2 in ocean acidification.
• Explain the role of atmospheric CO 2 in global warming.
• Use scientific observations to explain how a population changes over time due to human impacts.
• Explain how the selective pressures on a population may impact the frequencies of phenotypes in a population.
• Analyze quantitative data in order to make predictions based on evidence.

Lab 5: Biotechnology

By completing the resources in this lab (resources 11–14 in this playlist), students will be able to:

• Describe how recombinant DNA technology is used to produce transgenic organisms.
• Explain how transgenic organisms can be used to explore biological processes.
• Explain how light production through a reporter gene is used as an external marker of internal molecular events.
• Collect and analyze data, including graphs.
• Apply knowledge of DNA structure, function, and base pairing to describe how CRISPR-Cas9 can be used to inactivate and edit genes.
• Compare two different models of a biological process.
• Explore different types of research projects that apply CRISPR-Cas9 technology.

Creating Phylogenetic Trees from DNA Sequences

This Click & Learn is the first resource in Lab 1: Evolution. It has students analyze DNA sequences to infer evolutionary relationships among organisms and represent those relationships as phylogenetic trees.

To use this resource as part of Lab 1, have students complete the Click & Learn and its associated worksheet.

Lizard Evolution Virtual Lab

This virtual lab is the second resource in Lab 1: Evolution. It has students apply the knowledge they gained from the previous resource to exploring the evolution of the anole lizards in the Caribbean.

To use this resource as part of Lab 1:

• Ask students to complete Modules 1–3, where they use morphology and DNA analysis to investigate questions of natural selection and evolution among populations of Caribbean anoles. It is highly recommended that students complete the measurement tutorials before taking measurements in the modules.
• Have students complete the sections of the accompanying worksheet for Modules 1–3.

Virus Explorer

This Click & Learn is the first resource in Lab 2: Viruses. It has students explore the diversity of viruses based on structure, genome type, host range, transmission mechanism, and vaccine availability.

To use this resource as part of Lab 2, have students explore the Click & Learn and complete the associated worksheet. The extension activity at the end of the worksheet is optional; you may want to assign it if students don’t have a strong understanding of relative size.

Patterns of Zoonotic Disease

This Data Point activity is the second resource in Lab 2: Viruses. It has students analyze a published scientific figure from a study on the global distribution of zoonotic pathogens and their host species.

To use this resource as part of Lab 2, use the questions in the “Educator Materials” to guide a class discussion. The full scientific paper is also available from this resource’s webpage; it can be used to give the students an opportunity to practice reading primary literature.

Epidemiology of Nipah Virus

This activity is the third resource in Lab 2: Viruses. It has students analyze evidence, perform calculations and make predictions based on real-world data about a viral outbreak. Part of the activity involves watching the related video Virus Hunter: Monitoring Nipah Virus in Bat Populations .

To use this resource as part of Lab 2, have students complete the “Student Handout.”

You can supplement this lab by having students research recent viral outbreaks. They can write a mini-case study (using Part 1 of this activity as a template) to demonstrate the knowledge they’ve gained from this lab.

Winogradsky Columns: Microbial Ecology in the Classroom

This activity is the first resource in Lab 3: Microbes. It has students build their own Winogradsky columns, which provide visual examples of the diverse modes of metabolism in the microbial world.

To use this resource as part of Lab 3:

• Have students build Winogradsky columns following the instructions in the “Student Activity” document. If students are not able to build their own columns, you could ask them to analyze Figure 1 from this paper as an alternative.
• Have students set up their columns at the start of the semester so they can collect data throughout the semester.
• Instruct students to take a picture of their column every week and record changes in the column as they occur.

Winogradsky Column: Microbial Ecology in a Bottle

This Click & Learn is the second resource in Lab 3: Microbes. It has students explore a virtual Winogradsky column, which can be used to learn about the diversity of microbes and microbial metabolic strategies.

• Have students explore the Click & Learn with the goal of labeling their pictures of the columns they built in the previous activity.
• Once the layers start to emerge in the columns they built, have students measure the thickness of the layers as another way to strengthen their observation skills.
• Have students use the Click & Learn to label regions of their column. They should include the sulfide and oxygen gradients, as well as the energy and carbon source for each layer.

Origins of Antibiotic Resistance

This Data Point activity is the third resource in Lab 3: Microbes. It has students analyze a published scientific figure from a study that looked at antibiotic resistance among bacteria that have never been exposed to antibiotics.

To use this resource as part of Lab 3, use the questions in the “Educator Materials” to guide a class discussion about the evolution of antibiotic resistance. Make sure to point out that not all bacteria cause disease; many bacteria, like the ones studied in the Winogradsky columns, are harmless or even beneficial.

You can supplement the lab by having students research ways in which microbes are helpful to humans or other animals. They can use the I Contain Multitudes video series as a starting point for their research.

The Great Elephant Census

This video is the first resource in Lab 4: Population Ecology. It shows some of the methods that scientists are using to census African elephant populations.

To use this resource as part of Lab 4, have students watch the video as context for the Survey Methods Click & Learn (resource 10 in this playlist).

Survey Methods

This Click & Learn is the second resource in Lab 4: Population Ecology. It has students explore methods used to survey large animal populations, and what they have revealed about the current state of the African elephant population.

To use this resource as part of Lab 4, have students complete the Click & Learn and its accompanying worksheet.

As a supplemental idea, if students have access to a small plot of land (as small as 1 m2), you can direct them to survey a small local plant or insect using the total count and sample count methods described in the Click & Learn. Students can then discuss (or analyze) the two different methods with respect to accuracy and time needed.

CSI Wildlife

This Click & Learn is the third resource in Lab 4: Population Ecology. It has students track information about elephant poaching using DNA profiling.

To use this resource as part of Lab 4:

• Review the three student handouts and select the one that is most appropriate for your course outcomes.
• Have students complete the Click & Learn and the accompanying handout you chose.

Exploring Biomes in Gorongosa National Park

This activity is the first resource in Lab 5: Community and Ecosystem Ecology. It has students explore the concept of biomes, using Gorongosa National Park as a case study. Part of the activity involves exploring the Gorongosa National Park Interactive Map .

To use this resource as part of Lab 5, have students complete the student document for the activity. For the graph in question 6, students can use a graphing software such as Google Sheets or Microsoft Excel in lieu of drawing in the document.

Niche Partitioning and DNA Metabarcoding

This Click & Learn is the second resource in Lab 5: Community and Ecosystem Ecology. It has students explore niche partitioning, a mechanism that enables similar species of animals to coexist.

To use this resource as part of Lab 5, have students complete the Click & Learn and its associated worksheet.

Exploring Trophic Cascades

This Click & Learn is the third resource in Lab 5: Community and Ecosystem Ecology. It has students explore examples of how changes in the population numbers of one species can affect species at other trophic levels and ultimately the entire ecosystem.

To use this resource as part of Lab 5, assign each of the four case studies in the Click & Learn to small groups of students. Each group should work together to present the information from their case study and address discussion questions of your choice. Examples of discussion questions are shown below.

Define the term “trophic cascade.”

Arctic Tundra Case Study

• What is the effect of foxes on seabirds? Why?
• What effect do seabirds have on soil nutrients? Why?
• What effect do soil nutrients have on grass? Why?
• Describe the picture at the end of the case study that illustrates the result of adding foxes to an arctic tundra ecosystem, and explain this result.

African Savanna Case Study

• What is the effect of the rinderpest virus on the wildebeest population? Why?
• What is the effect of vegetation on the frequency and intensity of fires? Why?
• Describe the graph at the end of the case study that illustrates the effect of eradicating rinderpest through a vaccination program. Explain why this would be the result of eliminating the virus.

Midwestern Lake Case Study

• What is the effect of bass on minnows? Why?
• What is the effect of phytoplankton (photosynthetic microorganisms) on carbon influx — that is, removing carbon dioxide (CO2) from the atmosphere? Why?
• What effect does the bass have on the amount of carbon stored in the lake? Why?

Venezuelan Jungle Case Study

• What effect do predators, such as jaguars, have on vegetation? Why?
• What effect would the disappearance of predators have on the structure of the forest?
• What effect would the decline of herbivores have on the ecosystem?

Trends in Atmospheric Carbon Dioxide

This Data Point activity is the first resource in Lab 6: Human Impacts. It has students analyze a published scientific figure from a classic study that measured atmospheric concentrations of carbon dioxide.

To use this resource as part of Lab 6, use the questions in the “Educator Materials” to guide a class discussion.

Ocean Acidification

This activity is the second resource in Lab 6: Human Impacts. It has students simulate the effects of decreasing pH caused by rising levels of atmospheric carbon dioxide.

To use this resource as part of Lab 6:

• Have students reflect on one of the impacts of carbon dioxide increases, ocean acidification, by looking at the accompanying poster.
• Ask students to answer the following question: How does increasing CO2 levels affect the pH of ocean water, and how does that impact calcium-carbonate-shell-building organisms living in the ocean?
• Alternatively, have students read the introduction of the “Student Handout” and answer questions 3, 4, 5, 7, and 8.

Steve Palumbi & Megan Morikawa Study Coral Reef Damage in American Samoa

This video is the third resource in Lab 6: Human Impacts. The video describes the work of biologists researching the mechanisms that allow some corals to tolerate a greater amount of heat stress than other corals.

To use this resource as part of Lab 6, have students watch the video and complete the associated worksheet.

Resistance to Coral Bleaching

This Data Point activity is the fourth resource in Lab 6: Human Impacts. It has students analyze a figure from a scientific paper that was published by Steve Palumbi, one of the scientists featured in the previous resource.

To use this resource as part of Lab 6, use the first three discussion questions in the “Educator Materials” to guide a class discussion.

Analyzing Data on Tuskless Elephants

This activity is the fifth resource in Lab 6: Human Impacts. It has students analyze data to make evidence-based claims about the occurrence of tusklessness in elephant populations.

To use this resource as part of Lab 6, have students work through the activity and view the associated film Selection for Tuskless Elephants (resource 20 in this playlist). This allows students to reflect on human impacts other than global warming that affect our planet.

Selection for Tuskless Elephants

This video is the sixth resource in Lab 6: Human Impacts. The video follows scientists working in Gorongosa National Park, who made the striking observation that many female elephants in the park lack tusks.

To use this resource as part of Lab 6, tell students that the elephants in the video are adapting to a human impact, whereas the corals shown previously (resources 17 and 18 in this playlist) were acclimating to a human impact. Ask students to write a brief summary comparing and contrasting the terms “adaptation” and “acclimation.”

You can supplement this lab by pointing out that humans have an impact on many organisms, not just elephants and corals. Have students observe areas around their home and identify ways that humans may be negatively impacting their local flora and fauna. You may want to provide them with an example to help them get started — for example, roadkill along the highway or planting of invasive plants.

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Original research article, the performance of solvent-based direct air capture across geospatial and temporal climate regimes.

• 1 Carbon Solutions, Saint Paul, MN, United States
• 2 Department of Chemical and Biological Engineering, Princeton University, Princeton, NJ, United States
• 3 National Renewable Energy Laboratory, Golden, CO, United States
• 4 Department of Chemical and Petroleum Engineering, University of Calgary, Calgary, AB, Canada

Introduction: Liquid-solvent direct air capture (DAC) is a prominent approach for carbon dioxide removal but knowing where to site these systems is challenging because it requires considering a multitude of interrelated geospatial factors. Two of the most pressing factors are: (1) how should DAC be powered to provide the greatest net removal of CO 2 and (2) how does weather impact its performance?.

Methods: To investigate these questions, this study develops a process-level model of a liquid-solvent DAC system and couples it to a 20-year dataset of temperature and humidity conditions at a ~9km resolution across the contiguous US.

Results and discussion: We find that the amount of CO 2 sequestered could be 30% to 50% greater than the amount of CO 2 removed from the atmosphere if natural gas is burned on site to power DAC, but that the optimal way to power DAC is independent of capture rate (i.e., weather), depending solely on the upstream GHG intensity of electricity and natural gas. Regardless of how it is powered, air temperature and humidity conditions can change the performance of DAC by up to ~3x and can also vary substantially across weather years. Across the continuous US, we find that southern states (e.g., Gulf Coast) are preferrable locations for a variety of reasons, including higher and less variable air temperature and relative humidity. Lastly, we also find the performance of liquid-solvent DAC calculated with monthly means is within 2% of the estimated performance calculated with hourly data for more than a third of the country, including in the states with weather most favorable for liquid-solvent DAC.

1 Introduction

There are two primary reasons why carbon dioxide removal (CDR) technologies may be essential components of future economies. First, unless global CO 2 emissions peak by 2025 or earlier, achieving a 1.5°C or 2°C climate warming goal will require removing CO 2 that was previously emitted from the atmosphere ( IPCC, 2022 ). For reference, the growth of global energy-related CO 2 emissions was positive (i.e., 0.9%), reaching a new high of over 36.8 GtCO 2 in 2022 ( IEA, 2023 ). As such, and supported by recent carbon budget estimates, the window for limiting global warming to 1.5°C without CDR is rapidly closing ( Friedlingstein et al., 2022 ). Second, prior work has also demonstrated it may be less expensive to transition to net-zero carbon economies instead of absolute zero carbon economies due to some technologies, processes, or sectors that are “hard to decarbonize” ( Davis et al., 2018 ). In this role, CDR would be necessary to offset these residual “hard to decarbonize” emissions ( Larson et al., 2020 ; EPRI and GTI Energy, 2022 ).

There are multiple technologies and approaches for CDR and across them, direct air capture (DAC) is one of the most prominent. DAC is a system built for the sole purpose of CDR and has been referred to as a “backstop technology” because as much DAC as needed could, in theory, be deployed ( National Academies of Sciences, 2019 ; Fuhrman et al., 2023 ). When coupled with geologic CO 2 storage, DAC provides CDR through multiple steps: atmospheric CO 2 is absorbed or adsorbed into a solvent or solid material, electricity and heat are used to detach the captured CO 2 and compress it, and then that CO 2 is sequestered to permanently isolate it from the atmosphere. Depending on the cost and availability of other CDR approaches, DAC deployment sufficient to provide gigatonne-levels of CDR may be required by 2050 ( Fuhrman et al., 2023 ).

There are many challenges to scaling DAC to the gigatonne level and, among them, is knowing where to best deploy the technology. Siting DAC is difficult because it requires simultaneously answering a multitude of interrelated geospatial questions relating to a variety of factors including CO 2 storage, energy, weather, environmental impacts (e.g., water consumption), cost, environmental justice, and community acceptance. This study is focused on two of these questions: (1) what energy source(s) provide the greatest net removal of CO 2 ?; and (2) in what locations (in the contiguous United States) are weather conditions most conducive to favorable DAC performance?

DAC is very energy intensive. For example, given the deployment levels of DAC required to achieve climate targets and current energy efficiencies, prior work has found DAC alone in 2100 may require as much as half the total global energy consumed in 2016 ( Realmonte et al., 2019 ). But identifying where this energy should come from in a decarbonized future is difficult. For one, different energy sources have different GHG intensities, and this can impact the overall performance of DAC ( Hanna et al., 2021 ; Terlouw et al., 2021 ). Further, when integrated into the energy system, prior work has shown that, given its high cost, it is optimal for DAC to provide as much CDR as possible, thus be paired with energy sources that are continuously available ( Breyer et al., 2020 ; Bistline and Blanford, 2021 ; Hanna et al., 2021 ). These two difficulties drive the energy challenges related to siting DAC because energy sources that (a) have low carbon footprints and (b) are available continuously are not geospatially ubiquitous. When the other factors, such as cost or land-use availability of these resources are also considered, the challenge grows even more difficult.

There are a wide variety approaches for DAC that are being developed and in this study, we build on our prior work that focuses on liquid-solvent DAC ( An et al., 2022 ). Liquid-solvent DAC is sometimes referred to as “high-temperature DAC” because the process requires 900°C heat to liberate CO 2 , that was captured from the atmosphere, from the liquid solvent ( Keith et al., 2018 ). Given the high temperature of this thermal load, natural gas is typically considered for the energy source but recent work has also considered liquid-solvent DAC heated with electric resistance heating ( Pett-Ridge et al., 2023 ). The electrical requirements of liquid-solvent DAC could be met from the electricity grid (or off-grid renewables) or with an on-site natural gas turbine with CO 2 capture. Electricity is used by nearly all the units that comprise the DAC system and the primary electric loads are for the CO 2 compressor, the air separation unit, and the fans moving air across the air contactor.

In addition to energy, the performance of liquid-solvent DAC is also influenced by ambient air temperature and relatively humidity, as demonstrated in our prior work that found liquid-solvent DAC performance increased with increasing humidity and temperature ( An et al., 2022 ). As such, the optimal locations for deploying DAC will also be influenced by the weather, which substantially increases the complexity of siting DAC given the geospatial complexities of weather. Despite this fact, we are aware of only two studies investigating the geospatial ramifications of weather to DAC performance. The first, Sendi et al. (2022) uses hourly temperature and humidity data for 5 years (2016–2020) and focuses on solid-sorbent (a.k.a., “low-temperature” DAC). The second, Pett-Ridge et al. (2023) studies liquid-solvent DAC systems and uses daily temperature and humidity averages, forecasted by a variety of models in 2050, to inform a county-level resolution analysis.

In this paper, we estimate the performance of liquid-solvent DAC across the contiguous United States at a fine-resolution both geospatially and temporally. This is novel for multiple reasons. First, we use hourly temperature and relative humidity dataset across twenty years (2000–2019) at a ~ 9 km resolution, which totals approximately 12 billion datapoints. To our knowledge, this is the highest resolution dataset yet used to study DAC performance. Further, as this large dataset requires both computational resources and expertise to use, we also investigate, for the first time, when and if using monthly averages of the weather data is an appropriate approximation.

Additionally, our study is also novel in that we improve the process-level modeling from our prior work ( An et al., 2022 ) in multiple ways to further investigate how liquid-solvent DAC could be powered. In our prior work, we did not explicitly model the scenario where electricity demand was met by the grid and instead used the results from the on-site natural gas turbine scenario to approximate the electric loads and amount of natural gas required for the calciner. As the CO 2 flow through the system decreases when the electricity demand is powered by the grid, this reduces the quantity of natural gas required in the calciner and the quantity of electricity consumed by CO 2 compression. In this study, we relax this simplification from our prior work by explicitly modeling the scenario in which electricity demand is met with the grid.

2 Methods and materials

At a high-level, our methodology consists of two tasks:

1. We built a model of a generic 1 MtCO 2 /yr. solvent-based DAC system using the Institute for the Design of Advanced Energy Systems (IDAES) framework. Using this model, we simulated the performance of DAC under three different modes of operation across a wide range of CO 2 capture rates. Section 2.1 describes this portion of our methods work in more detail.

2. Using a response model derived from our prior work ( An et al., 2022 ) and the IDAES results, we estimated the geospatial performance of the generic 1 MtCO 2 /yr. system using temperature and relative humidity data. Section 2.2 describes this portion of our methods in more detail.

2.1 Process-level model of liquid solvent DAC

Our IDAES model extends the simulations completed in our prior work ( An et al., 2022 ) and is grounded in the work done by Keith et al. (2018) . As shown in Figure 1 , the system consists of four primary units: the air contactor, the pellet reactor, the calciner, and the steam slaker. In the air contactor, the CO 2 in the air contacts a solution of potassium hydroxide, where it reacts to form potassium carbonate. In the pellet reactor, the potassium carbonate reacts with calcium hydroxide to form calcium carbonate, which precipitates out of solution. The calcium carbonate is washed and goes to the calciner, where high heat drives off the CO 2 , producing calcium oxide. The calcium oxide then enters the steam slaker, where calcium hydroxide is regenerated. Section 1 of the Supplementary material provides a more detailed discussion of the assumptions for the IDAES model.

Figure 1 . High-level process diagram of the liquid-solvent based DAC system.

2.1.1 Modes of operation

The energy requirements of a liquid-solvent direct air capture system take two forms ( Keith et al., 2018 ): the heat required to achieve 900°C in the calciner, and the electricity required to power all process units. These energy requirements can be met by three different combinations of natural gas and electricity, as outlined in Figure 2 and listed below:

1. Natural gas is burned on site to provide both the heat in the calciner and to power a combustion turbine that provides electricity. In this study, we refer to this mode as “all natural gas.”

2. Natural gas is burned on site to provide the heat in the calciner but the grid is used for all electricity requirements. In this study, we refer to this mode as “mixed.”

3. An electric calciner is used and all electricity demand is met with the grid. In this study, we refer to this mode as “all grid.” To our knowledge, the technical feasibility of an electric calciner for a 1 MtCO 2 /yr. DAC system has not yet been assessed in the peer-reviewed literature, but we include it in this study because it has been considered in prior life cycle and techno-economic assessments ( Liu et al., 2020 ; McQueen et al., 2021 ).

Figure 2 . Summary of different modes for energy sources. More detailed process diagrams for each of these modes are available in Section 1.2 of the Supplementary material .

Except for the “all grid” mode, the total amount of CO 2 that must be sequestered by DAC will be greater than the total amount of CO 2 captured from the atmosphere for two primary reasons. First, additional CO 2 is generated and captured on site from the natural gas combustion in the calciner and/or turbine. In the “all natural gas” mode, for example, 90% of the CO 2 generated from natural gas in the combustion turbine is captured in a CO 2 absorber, which is mixed with the CO 2 captured from the atmosphere. The remaining 10% of the CO 2 enters the air contactor along with the CO 2 from the atmosphere. The CO 2 generated from the natural gas in the calciner is inherently mixed with the CO 2 driven off from CaCO 3 , and thus all CO 2 generated in the calciner is captured. Second, this increase in CO 2 flow through the system also increases the electricity demand of some components of the DAC process, which in turn requires more natural gas combustion. The largest example of this is the CO 2 compressor, which requires substantially more power as more CO 2 is captured, thus requiring more natural gas combustion on site to provide that power. Our IDEAS simulations fully captures these feedback loops.

2.1.2 CO 2 Removal ratio as a function of GHG intensity of energy

We use the IDAES model to find the preferred energy mode of operation that provides the greatest net removal of CO 2 over the use phase of the DAC system. This portion of our study allows us to answer, for example, when “all grid” mode is preferable to the “all natural gas” mode. As the net removal of CO 2 depends on the upstream greenhouse gas (GHG) intensity of natural gas and electricity, we calculate and use a “CO 2 removal ratio” to identify preferred modes of operation across different CO 2 capture rates. The CO 2 removal ratio metric, R, is calculated with Eq. 1 :

Where Q is the quantity of GHGs emitted by the energy sources (i.e., electricity, natural gas) upstream of the DAC system, G is the quantity of CO 2 that is generated and released (i.e., the small portion that is not captured) on site from natural gas combustion, and A is the quantity of CO 2 captured from the atmosphere by the DAC system. Q is a function of the GHG intensity of electricity and natural gas, and in this study, we estimate the upstream CO 2 e emissions using a range of assumed GHG intensities: 0 to 70 gCO 2 e/MJ for natural gas and 0 to 1,000 gCO 2 e/kWh for electricity. We vary the GHG intensities over ranges (instead of just a case study example of a single set of GHG intensities, for example) because the GHG intensities both vary geospatially across the United States. Further, it is likely that they will also change in the future as more clean energy is deployed on the grid and upstream leakage of natural gas is reduced. As such, these ranges of upstream GHG intensity of natural gas and electricity were chosen to encompass the full range of values observed for each energy source in the United States, down to zero. These values include the full life cycle emissions of generating natural gas and electricity as calculated by GREET ( ANL, 2023 ). Further, the quantity of CO 2 captured, A, includes only the CO 2 captured from the atmosphere and not the additional CO 2 captured from on-site natural gas combustion. We also estimate the “net sequestration” which is the quantity of CO 2 captured from the atmosphere (not including additional CO 2 captured from on-site natural gas combustion), less the sum of the quantity of CO 2 e emitted by the energy sources upstream of the DAC facility and the quantity of CO 2 that is generated and released on site from natural gas combustion.

2.2 Weather data

In this study, we used hourly temperature and relative humidity data for the years 2000 through 2019 from the ERA5-Land dataset ( Munoz Sabater, 2019 ), post-processed by the European Centre for Medium-Range Weather Forecasts. ERA5-Land provides a consistent view of the variability in meteorological variables over several decades at ~9 km horizontal resolution. We selected a reanalysis dataset because it combines model data with observations into a complete and consistent framework representing the physics of the climate system, including climate decades into the past. Across the temporal and spatial boundaries of this study, this dataset included about 12 billion datapoints. Section 2.2.1 describes how this dataset was processed and Section 2.2.2 describes how we applied it to estimate DAC performance. Additional analyses that contextualize and explore the weather data are presented through a time series, exploratory factor analysis, and a sensitivity analysis in Sections 3.1 to 3.3 of the Supplementary material .

2.2.1 Data processing

The ERA5-Land climate reanalysis dataset provided hourly temperature and dewpoint temperature data for the Contiguous United States (CONUS) covering the 20-year period, 2000–2019. ERA5-Land data were processed in Google Earth Engine ( Gorelick et al., 2017 ), involving filtering and masking to extract the relevant temperature and dewpoint temperature values. As relative humidity was not directly available in the ERA5 dataset, we calculate it using Tetens formula ( Tetens, 1930 ), which is derived from the Clausius-Clapeyron equation. This approach ensured consistency in the calculation of relative humidity across the entire study area. Section 2.3 of the Supplementary material provides more information on the application of geospatial data in Google Earth Engine.

To facilitate an evaluation of the impact of time sampling on estimated DAC sequestration, monthly means of temperature and relative humidity were calculated from the hourly values. This resulted in the creation of companion monthly time series of temperature and relative humidity.

2.2.2 Applying weather data to estimate DAC performance

Both hourly and monthly temperature and relative humidity values were filtered to exclude all hours (or months) when the temperature fell outside the range of 0°C to 40°C. This temperature range is based on our prior work ( An et al., 2022 ), and represents the operation range of the reaction–diffusion process within the DAC system. If the temperature is too low (i.e., about −2°C), the liquid-solvent will freeze, thus halting the operation of the air contactors within the DAC facility. While there is nothing preventing the DAC system from running above 40°C, published data on the kinetics of CO 2 absorption into the solvent is only available up to this temperature ( An et al., 2022 ).

To estimate the capture rate variability across the 20-year period and for each location within CONUS, we employed a multi-step approach. First, we developed regression equations using results from our previous work ( An et al., 2022 ) and the IDAES simulations. These equations enabled us to calculate the CO 2 capture rate for each grid point and time step. These equations relate capture rate to temperature and relative humidity parameters and are provided in Section 2.1 of the Supplementary material .

Next, we utilized the capture rate values alongside the formulas presented in Section 2 of the Supplementary material to determine the CO 2 mass flow rate in metric tons per hour (or month). As seen in Section 2.2 of the Supplementary material , these formulas are regressed from the IDAES simulations, and account for how the mass and energy flows and operating parameters change based on the capture rate and energy modes. Finally, to gain a broader perspective on annual performance variations, we aggregated the hourly (monthly) mass flow rates to annual scales for each location. This multi-step approach, encompassing regressed equations from prior work and IDAES simulations that include detailed system-specific calculations, enabled us to capture the intricate interplay between temperature and humidity conditions, technology characteristics, and resulting CO 2 capture performance across the diverse geographical landscape of the Contiguous US.

2.2.3 DAC performance metric: quantity of CO 2 sequestered

For this portion of our analysis, we present the geospatial performance of the 1 MtCO 2 /yr DAC system using one metric: the quantity of CO 2 sequestered. We use the term “sequestered” to differentiate from the term “captured” because this metric (i.e., CO 2 sequestered) is defined as the sum of the quantity of CO 2 captured from the atmosphere and the amount of CO 2 captured from any on-site natural gas combustion. Unlike our process-level results, the CO 2 sequestered does not account for the upstream GHG intensity of the energy because the upstream GHG intensities of natural gas and electricity change geospatially. Further, the total CO 2 sequestered is not affected by the small quantity of CO 2 that is generated and released (i.e., not captured) on site from natural gas combustion. As a result of using CO 2 sequestration as the performance metric (and not the net sequestration), we ensure that all geospatial and temporal differences in DAC performance are driven entirely by differences in weather and not a combination of weather and the upstream GHG intensity of energy. In other words, the total CO 2 sequestration is the quantity of CO 2 that must be permanently isolated from the atmosphere downstream of the DAC facility. As such, for this study, more favorable weather conditions result in higher CO 2 sequestration, thus higher DAC performance.

We first provide the process-level results of our IDAES simulations (Section 3.1) before discussing the performance of DAC as a function of weather (Section 3.2) and the sensitivity of DAC performance to the time-sampling of the weather data (Section 3.3). Different metrics are used across these results sections. As described in Section 2.1.2, we use the process-level modeling results (Section 3.1) to gain insight into how liquid-solvent DAC should be powered to provide the greatest net-removal of CO 2 . This portion of our study thus incorporates the upstream GHG intensity of the energy sources. But as described in Section 2.2.3, the weather results (Sections 3.2 and 3.3) do not include the GHG intensity of the energy sources to ensure that all geospatial and temporal differences in DAC performance are driven entirely by differences in weather. Before presenting our results, we provide a summary reference of the definitions of these metrics:

• CO 2 capture rate is the percent of CO 2 captured from the atmosphere at the air contactor, which changes because of changes in air temperature and relative humidity.

• The CO 2 removal ratio is defined with Eq. 1 .

• The net sequestration is the quantity of CO 2 captured from the atmosphere (not including additional CO 2 captured from on-site natural gas combustion), less the sum of the quantity of CO 2 e emitted by the energy sources upstream of the DAC facility and the quantity of CO 2 that is generated and released on site from natural gas combustion.

• CO 2 sequestered is the quantity of CO 2 that must be permanently isolated from the atmosphere, downstream of the DAC facility. For example, if the DAC facility is using geologic CO 2 storage for sequestration, this would be the quantity of CO 2 injected into the subsurface. This includes both the CO 2 captured from the atmosphere and from on-site natural gas combustion.

3.1 DAC process-level modeling results

Table 1 shows the amount of CO 2 sequestered for every tonne of CO 2 captured from the atmosphere for the three different energy modes modeled in IDAES. The total amount of CO 2 sequestered is greater than the amount captured from the atmosphere when natural gas is burned on site for two reasons, described in Section 2.1.1. As shown in Table 1 , when both these reasons are accounted for in our IDAES model, the additional amount of CO 2 can account for upwards of ~50% of the total amount of CO 2 captured if natural gas is burned on-site to provide all process heat and power requirements.

Table 1 . Ratio of CO 2 sequestered to CO 2 captured assuming a 75% capture rate.

Figure 3 shows the maximum CO 2 removal ratio and corresponding net sequestration rate as a function of the upstream GHG intensity of both natural gas (y-axis) and electricity (x-axis) for two different air contactor capture rates: 50% in A) and 90% in B). Please see Section 2.1.2 for a definition of the CO 2 removal ratio and net sequestration.

Figure 3 . Maximum CO 2 removal ratio as a function of the upstream GHG intensity of natural gas and electricity for (A) 50% capture rate; (B) 90% capture rate. The solid black lines indicate the boundary of where each energy mode results in the lowest emissions based on the upstream GHG intensity of electricity and natural gas. The two points in (A) are added to give a reference of current upstream GHG intensity combinations: the black dot shows the Permian basin and Western Electricity Coordinating Council (WECC) average (350 gCO 2 e/kWh and 64 gCO 2 e/MJ) as an approximation for New Mexico ( Chen et al., 2022 ); and the yellow star shows the average in the USA (387 gCO 2 e/kWh and 13 gCO 2 e/MJ), based on data from the Greenhouse gases, Regulated Emissions, and Energy use in Technologies (GREET) Model ( ANL, 2023 ).

As shown on the right side of both subplots of Figure 3 , when the GHG intensity of electricity is high enough, the “all natural gas” mode is preferred because the upstream GHG intensity from electricity is greater than the upstream GHG intensity of natural gas. As the upstream GHG intensity of electricity emissions decreases (i.e., moving to the left in the subplots), the “mixed mode” becomes preferred, and then at very low upstream GHG intensity of electricity, the “all grid” mode becomes preferred. As seen with the two reference points in Figure 3 , there are locations in the United States in which the “mixed mode” and “all natural gas mode” would result in the maximum CO 2 removal ratio. For example, on average in the USA (yellow star), the “all natural gas mode” results in the greatest CO 2 removal ratio, and is thus would be the preferred energy mode. In New Mexico for comparison (black dot), the “mixed mode” is preferred because the GHG intensity of electricity decreases and the GHG intensity of natural gas increases relative to the USA average. And, if GHG intensity of electricity decreases enough in New Mexico (i.e., below ~300 gCO 2 e/kWh), all else equal (i.e., moving the black dot to the left), the “all grid mode” would become preferred.

Figure 3 also shows that the net CO 2 sequestration increases with increasing capture rate. For example, the dark blue color indicates a net CO 2 sequestration greater than 0.6 MtCO 2 /yr. in Figure 3A and a net CO 2 sequestration greater than 1.1 MtCO 2 /yr. in Figure 3B . This difference between these subplots occurs because less CO 2 is captured from the atmosphere when the annual CO 2 capture rate is 50%, as in A), compared to 90%, as in B). Despite this difference, the boundary line where different operational modes are preferable changes very minimally as a function of CO 2 capture rate. As such, Figure 3 demonstrates that the optimal mode of operation is independent of the CO 2 capture rate (i.e., weather conditions) and is thus only a function of the upstream GHG intensities of both natural gas and electricity. This finding is consistent with our prior work and can be seen even more clearly in Section 1.4 of the Supplementary material .

3.2 DAC performance as a function of weather

Figure 4 shows the average total CO 2 sequestered across all 20 years of weather data, demonstrating the rich complexity of differences in performance of high-temperature DAC across the contiguous United States. In other words, Figure 4 shows how much CO 2 would need to be sequestered if the same 1 MtCO 2 /yr. liquid-solvent DAC facility was constructed across the United States. As stated in Section 2.2.2, this does not account for the upstream GHG intensity of natural gas or electricity so that all geospatial differences observed can be entirely contributed to changes in weather.

Figure 4 . Total CO 2 sequestration averaged across all 20 years of weather data. The color bar shows the differences between the three energy mode scenarios (grid, mixed, natural gas). As previously discussed in Table 1 , the three energy mode options yield different quantities of total CO 2 sequestered due to on-site natural gas combustion.

Figure 4 shows the clear impact of latitude and, as expected, the potential for CO 2 sequestration decreases with increasing latitude. The map also shows the dwindling effect that elevation (cold and dry) has on liquid-solvent DAC performance, exemplified by the complex terrain of the US Mountain West and Great Basin. Interestingly, it also demonstrates that the West Coast has relatively high DAC potential within about 200 km of the ocean and in the far south of the Southwestern US. Overall, these geospatial trends are driven predominantly by the sensitivity of the liquid solvent DAC process to temperature and humidity. As further discussed in Section 3.2 of the Supplementary material , about 76% of the variability is driven by temperature and the remaining 24% is driven by relative humidity. Additionally, the results of the sensitivity analysis in Section 3.3 of the Supplementary material show that achieving a 10% change in CO 2 capture rate efficiency requires a 62% change in relative humidity, but only an 11°C change in air temperature. These findings are consistent with our prior work that found the capture rate was more sensitive to temperature than relative humidity ( An et al., 2022 ).

Figure 5 further demonstrates two additional advantages of southern latitudes compared to northern latitudes. The first advantage of southern latitudes compared to northern latitudes can also be seen by the broadening of the 1-sigma range in the mean (the grey band and area) when moving from south to north. That is, the interannual variability in CO 2 sequestration is smaller in the south than the north. For example, across the average year of weather, the CO 2 sequestration at Miami’s latitude would only fall by as little as 0.01 Mt. year over year, from its typical 1.05 to 1.04 MtCO 2 /yr 1 (in “all grid” mode). Conversely at a higher latitude locations like Minneapolis sequestration can decrease by about 0.1 Mt., from 0.6 MtCO 2 /yr. to 0.5 MtCO 2 /yr. (in “all grid” mode) based solely on interannual weather variability.

Figure 5 . Variation in CO 2 sequestration by latitude averaged across all 20 years of weather data. This figure shows the mean trend in CO 2 sequestration across the contiguous United States and its temporal variability. The min and max values for each latitudinal band are also shown. Reference cities are listed on the horizontal axis: Miami (MIA), San Antonio (SAN), New Orleans (NOL), San Diego (SDO), Phoenix (PHX), Atlanta (ATL), Saint Louis (STL), Washington DC (DC), Salt Lake City (SLC), Boston (BOS), Minneapolis (MSP), and Seattle (SEA).

The second advantage of southern latitudes shown in Figure 5 is that the asymmetry of the minimum and maximum lines relative to the mean grows with increasing latitudes. The asymmetry is that the line of minimum CO 2 sequestration deviates on average about 30% below the central lines, whereas the line of maximum sequestration deviates from the mean by only 20% on average. While the broadening of this asymmetrical range at latitudes below New Orleans are clearly driven by latitudinal differences in land surface area, north of there, the broadening asymmetry reveals a latitude-based bias with stronger minimum value outliers at higher latitudes. In addition to underscoring the favorability of lower latitudes for higher CO 2 sequestration potential, it also highlights important considerations for regional DAC assessments.

Figure 6 illustrates the distribution of annual DAC sequestration data, encompassing a 20-year period from 2000 through 2019. This plot consists of 20 lines, each representing the distribution of all hourly DAC sequestration values throughout CONUS for that year.

Figure 6 . Probability density of annual CO 2 sequestration for different weather years. For ease of presentation, the CO 2 sequestration values shown on the x-axis assume “all grid” mode operation. As seen in the color bar of Figure 4 or the y-axis of Figure 5 , the values shown on the x-axis would simply be proportionally larger if either of the other two modes were plotted instead.

As shown in Figure 6 , the distribution of DAC sequestration across the contiguous United States follows a bimodal pattern in most years with peaks at 0.6 MtCO 2 /yr. and 0.9 MtCO 2 /yr. While the latitudinal data shown previously in Figure 5 suggests some difference among years, when viewed through Figure 6 , those differences between years become more apparent. The shape of individual years is emphasized, and it is apparent that a notable amount of variability exists in potential sequestration from year to year. For example, no 2 years exhibit the same sequestration curve, which suggests the performance in 1 year will be different than in another, depending on the geospatial location.

Figure 6 suggests that while the performance of DAC will change each year, the consistency of the performance is higher in locations with the highest sequestration potential (i.e., > 0.8 MtCO 2 /yr). For example, the spread among years is more pronounced on the left compared to the right. In other words, Figure 6 demonstrates a fourth advantage of lower latitudes: there is less variability in performance across weather years.

Table 2 presents a view of the average change in DAC performance due to interannual fluctuations in weather conditions across space and time. Across weather years, the sequestration of the average location in the Contiguous US fluctuates between 0.72 MtCO 2 /yr. in the lowest performing years to 0.80 MtCO 2 /yr. in the highest performing years, resulting in an interannual variability range of approximately 11.5%. Additionally, the location exhibiting the lowest average sequestration rate (over 20 years) stands at 0.28 MtCO 2 /yr., and the highest is 1.07 MtCO 2 /yr., emphasizing the significant geographical disparities in DAC performance across geography.

Table 2 . Summary statistics indicating the range over which DAC performance varies in response to year-to-year fluctuations in weather.

3.3 Sensitivity of DAC performance to the temporal granularity of weather data

Figure 7 illustrates the potential for CO 2 sequestration by DAC, based on average annual totals across all 20 years of weather data. Figure 7A is the same as Figure 4 except that it categorizes the removal potential into different classes, from high to low for improved visual comparison. The color bar of each subplot shows the sequestration potential across all three modes of operation.

Figure 7 . Differences in potential CO 2 sequestration due to temporal granularity of weather data: (A) CO 2 sequestration calculated with hourly weather data; (B) CO 2 sequestration calculated with monthly mean weather data; (C) Residual (hourly – monthly) in CO 2 sequestration, i.e., the difference between hourly and monthly mean weather input; and (D) Locations for which the hourly and monthly CO 2 sequestration is less than 2% different.

Figure 7A illustrates that the highest sequestration potential is concentrated along the southern Gulf-Atlantic zone but that this high sequestration zone does not extend further west beyond Southeastern Texas. Even though high temperatures are common to the climate normal in the US Southwest, high humidity is not. In other words, while the temperature has a larger impact on the performance of DAC compared to humidity (as previously discussed and further presented in Section 3.2 of the Supplementary material ), temperature alone is an inadequate predictor of CO 2 sequestration potential.

Figure 7A also shows that much of the contiguous United States falls into the moderate sequestration potential category of 0.7 to 0.9 MtCO 2 /yr. (“all grid” mode values). Representing nearly all of the Western, Midwestern, and Northeastern US, this zone covers a wide range of landscapes, land-use types and climate conditions. Note however, as mentioned in Figures 5 , 6 , that the regularity in sequestration decreases with increasing latitude, which has implications for year-to-year predictability in sequestration totals in this zone. Despite some of the highest and most predictable temperatures, only restricted parts of the Southwestern US including parts of Southern California fall into the moderately high yield category (0.9–0.95 MtCO 2 /yr). This effect is due to low humidity conditions, which limit CO 2 removal due to excessive evaporation that limit the flux of CO 2 into solution at the air contactor.

Collectively, the subplots in Figure 7 show that CO 2 sequestration tends to be slightly overestimated throughout locations in warm to mild climates if it is computed from monthly means, as exemplified by the broader high sequestration (yellow) zone in Figure 7B . This overestimation by monthly weather data is attributable to the data filtering process. Because liquid-solvent DAC cannot operate at sub-zero temperatures, those data points are excluded from both data series. No month in the Southern US averaged less than 0°C, therefore all monthly data are used. However, in that same region there are locations where the hourly temperature does fall below 0°C and are thus excluded from the total, which lowers the sequestration amount relative to the method computed from the monthly average, thus giving rise to the bias in DAC performance when estimated from monthly weather data.

The bias in CO 2 sequestration when computed from monthly values is visualized in Figure 7C , which maps where monthly data tend to either over- or underestimate CO 2 sequestration potential. Overall, sequestration computed from monthly weather data is more likely to be overestimated than underestimated. The mismatch (hourly – monthly) ranges from −12.2% (monthly overestimates) to +8.3% (monthly underestimates) and is on average − 1.1%. The mismatch is generally small but can lead to substantial over/underestimation of CO 2 sequestration over time (e.g., if the DAC facility is operating for a decade or more). The spatial variability of mismatch also suggests that the choice of location will also dictate the temporal granularity of the weather data that is used to project sequestration potential.

Regionally, the most pronounced mismatch is in the mid latitudes, visualized in dark purple in Figure 7C . This is somewhat an artefact of the choice of month (as opposed to year) as a sampling unit for comparison. This high mismatch band occurs at these mid-latitudes because it is far enough north that some winter months begin to be filtered due to low temperatures, which eliminates entire months in the monthly data, whereas in the hourly data, only majority fractions of the month are filtered, and the remainder still adds to the total sequestration, thus giving rise to mismatch error.

Figure 7D is the same as Figure 7B except that it is limited to displaying locations where the disparity between monthly and hourly data is 2% or less. Thus, this map highlights areas where using monthly weather data with lower temporal resolution is a reasonably accurate approach. Interestingly, though not unexpected, the locations where monthly weather data are the best approximation of hourly are those in the high sequestration zone. This can be contributed to the overall consistency of warm weather in the high sequestration zone, as previously discussed.

Table 3 classifies the contiguous US by the temporal mismatch between hourly and monthly values and shows that DAC sequestration based on monthly values are accurate to within 2% error for 39% of the land area in the Contiguous US.

Table 3 . The Proportion of land area that each category of mismatch amount occupies.

4 Discussion of results

Overall, our results demonstrate the critical importance of considering weather when siting DAC. For example, we find that the total range in temperature and humidity conditions throughout the Contiguous US can result in a ~ 3x difference in DAC performance ( Table 2 ; Figure 4 ). As the purpose of DAC, and thus the underlying business model for DAC companies, is to provide CDR, our study demonstrates that the importance of weather cannot be understated. While the decisions focusing on where to locate DAC are driven by a variety of interrelated geospatial factors, our results suggest that future DAC site developers should consider weather early in the siting process. Or at a minimum, exclude areas from considering in which the weather is likely to be unfavorable.

Our results are also different compared to Pett-Ridge et al. (2023) , who found western Texas and northern Alaksa to be more favorable for liquid-solvent DAC compared to the Gulf Coast. There are two primary reasons for this discrepancy. First, Pett-Ridge et al. (2023) considered the quantity of technically recoverable natural gas within their analysis, which resulted in concluding that regions with large natural gas reserves were most favorable to liquid solvent DAC (i.e., West Texas, northern Alaska). Second, Pett-Ridge et al. (2023) used daily averages of forecasted data for 2050, averaged across variety of datasets from different climate models. As a result of this averaging of modeled weather, the temporal variability in their data is more muted compared to the data used in this study. Further, the forecasted weather data used in Pett-Ridge et al. (2023) is also generally warmer than that used in this study, and thus generally more favorable to liquid-solvent DAC performance compared to this study.

In addition to demonstrating the importance of considering weather, our study also had multiple implications for energy decisions related to liquid-solvent DAC siting. First, we find that the preferred mode of operation is very sensitive to the upstream GHG intensities of electricity and natural gas. While our results demonstrate it is possible to make energy decisions based on these two factors alone, it is much harder to accurately quantify the upstream GHG intensity of natural gas compared to electricity. As such, DAC decision makers may benefit from performing rigorous uncertainty analyses if initial results suggest the “mixed” or “all natural gas” modes are preferred. Additionally, if either modes that burn natural gas on site are preferred, decision makers should also be aware that this design decision may require constructing upwards of ~30% to ~50% more CO 2 compression, transportation, and storage infrastructure compared to the “all grid” mode. Even in the Gulf Coast, which is one of the most favorable locations for geologic CO 2 storage in the country ( Ogland-Hand et al., 2023 ), needing 30 to 50% more CO 2 transportation and storage infrastructure will likely have substantial cost, feasibility, and timeline ramifications to deploying and operating the DAC facility.

There are caveats to our findings. For example, our findings are not intended to universally apply to regions with different weather patterns or geographic characteristics outside the contiguous United States. Additional caveats that were beyond the scope of this study but could be areas of focus for future work are listed below:

• We consider a generic 1 MtCO 2 /yr. liquid-solvent DAC facility and do not consider any feedback in its design to the weather conditions. Our process-level model was developed based on the information presented by Keith et al. (2018) and has not been validated against real-world performance (e.g., measured by Carbon Engineering) or other DAC system frameworks. As such, it is possible that the performance of DAC could be improved above what is shown here by optimizing the process-level design for the weather in a specific location or as future process-level design improvements are made to the liquid-solvent DAC system.

• We do not consider cost trade-offs in this analysis. For example, it is likely that “all grid” or “mixed mode” systems will have a higher cost than an “all natural gas” systems, which could also influence which energy mode is preferred.

• We do not consider the GHG emissions resulting from the construction of the DAC facility, CO 2 transportation infrastructure, or CO 2 storage infrastructure. These emissions could be considered in future work that further examines which energy mode is preferred ( Madhu et al., 2021 ).

• We only consider temperature and relatively humidity, but additional weather parameters can also substantially influence the performance of DAC. For example, surface air pressure changes the partial pressure of CO 2 and can consequently constrain CO 2 capture efficiency. As the variability of air pressure, often associated with elevation changes, is substantial across the United States, this weather parameter in particular will be important to consider in future work. While the study relies on fine-resolution spatial and temporal weather data (the horizontal resolution of ERA5 Land hourly reanalysis data is ~9 km), not all processes and feedbacks in the physical weather system are completely represented by the underlying weather model (e.g., sub-grid-scale variability and local extreme events, vegetation dynamics). This limitation constrains the generalizability of our findings.

• Our study only used weather data from prior years, but weather patterns will change because of climate change. Changes in long-term weather trends might affect the extrapolation of these results into the future. For example, rising temperatures and precipitation would lead to better DAC performance. In contrast, rising temperatures but drier conditions, or colder temperatures but increased humidity may increase, or decrease, performance. As such, the impact of climate change on DAC performance is likely to vary regionally.

• Our prior work found that the water consumption of liquid-solvent DAC also changes as a function of air temperature and relative humidity ( An et al., 2022 ), but we did not incorporate water consumption into this analysis. Based on our prior work and others [e.g., Pett-Ridge et al. (2023) ], removing gigatonnes of CO 2 with liquid-solvent DAC may require billions of tonnes of fresh water. Further, some locations that this analysis suggests are most favorable to liquid-solvent DAC deployment may also face water shortages in the future [e.g., Florida ( NRDC, 2010 )]. As such, it is possible that locations with favorable CO 2 removal performance may be poor candidates for DAC deployment due to unfavorable water consumption.

5 Conclusion

In this study we created a process-level model of a liquid-solvent DAC system in IDAES and coupled it to a fine-resolution spatial and temporal dataset of temperature and humidity conditions to explore the impact of ambient weather on DAC performance. We found that:

1. The amount of CO 2 sequestered can be far greater than the amount of CO 2 removed from the atmosphere if natural gas is used on site to power DAC. For example, when weather conditions yield a 75% capture rate, 1.48 tCO 2 are sequestered for every tonne of CO 2 removed from the atmosphere in the “all natural gas” mode ( Table 1 ).

2. The preferred energy mode of operation ( i.e. , “all grid,” “all natural gas,” “mixed”) for liquid-solvent DAC facilities is independent of air contactor capture rate and depends solely on the upstream GHG intensities of electricity and natural gas. As such, the decision of how to power liquid-solvent DAC could be made without considering weather ( Figure 3 ).

3. Temperature and humidity conditions exert substantial influence over the performance of liquid-solvent DAC. Across the Contiguous US, the ambient weather conditions of a location can translate to a 3x difference in CO 2 sequestration ( Table 2 ; Figure 4 ). For example, in “all grid” mode the average DAC performance varies over the Contiguous US from 0.3 to 1.1 MtCO 2 /yr. While both temperature and relative humidity drive changes in DAC performance, temperature has about 3 times the influence compared to relative humidity ( Supplementary Table S4 ).

4. The performance of liquid-solvent DAC can vary substantially across weather years, depending on location. In most years, the variability performance across weather years follows a bimodal pattern ( Table 2 ; Figure 6 ).

5. Southern tier states in the US are preferrable for liquid-solvent DAC systems for multiple reasons. In southern latitudes: the average CO 2 sequestration potential is higher on average across weather years ( Figure 4 ); the minimum sequestration potential is higher on average ( Figure 5 ); and there is less variability in total sequestration potential across weather years ( Figure 6 ).

6. The estimated performance of liquid-solvent DAC calculated with monthly means is within 2% of the estimated performance calculated with hourly data for more than a third of the country ( Table 3 ). Notably, the regions of the country that are most favorable to liquid-solvent DAC (i.e., the Southeastern US) can be approximated with monthly means because these locations have the most consistently warm temperature and relatively humidity conditions across time ( Figure 7 ). But, depending on the desired accuracy, approximately one-tenth to one-half of the US experiences weather conditions too variable to be reliably represented by coarse time-sampled weather data.

Data availability statement

The original contributions presented in the study are included in the article/ Supplementary material , further inquiries can be directed to the corresponding author.

Author contributions

B-GB: Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing – original draft. CG: Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Writing – original draft. KA: Resources, Writing – review & editing. SM: Resources, Writing – review & editing. RM: Resources, Supervision, Writing – review & editing. JO-H: Conceptualization, Funding acquisition, Methodology, Project administration, Supervision, Visualization, Writing – original draft.

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This material is based upon work supported by the U.S. Department of Energy, Small Business Innovations Research program under Award DE-SC0022486. The content is solely the responsibility of the authors and does not necessarily represent the official views of the Department of Energy. The work of SM was supported in part by funding from the Canada First Research Excellence Fund.

Acknowledgments

The authors would like to thank Jeffrey A. Bennett for assistance with technical vocabulary related to life cycle assessment.

Conflict of interest

B-GB, CG, RM, and JO-H were employed by Carbon Solutions.

The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fclim.2024.1394728/full#supplementary-material

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Keywords: direct air capture, climate, carbon footprint, IDAES, GIS

Citation: Brooks B-G, Geissler CH, An K, McCoy ST, Middleton RS and Ogland-Hand JD (2024) The performance of solvent-based direct air capture across geospatial and temporal climate regimes. Front. Clim . 6:1394728. doi: 10.3389/fclim.2024.1394728

Received: 02 March 2024; Accepted: 17 April 2024; Published: 29 April 2024.

Reviewed by:

Copyright © 2024 Brooks, Geissler, An, McCoy, Middleton and Ogland-Hand. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Jonathan D. Ogland-Hand, [email protected]

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