Harnessing naturaland mechanical airflows for surface-based atmospheric pollutant removal
Samuel D. Tomlinson, Aliki M. Tsopelakou, Tzia Ming Onn, Steven R. H. Barrett, Adam M. Boiesand Shaun D. Fitzgerald
1 Department of Engineering, University of Cambridge, Cambridge, UK 2 Centre for Climate Repair, Cambridge, UK 3 Department of Mechanical Engineering, Stanford University, Stanford, USA
May 23, 2025
Removal strategies for atmospheric pollutants are increasingly being considered to mitigate global warming and improve public health. However, surface-based removal techniques, such as sorption, catalysisand filtration, are often limited by pollutant transport and removal rate constraints. We evaluate the atmospheric pollutant transport to surfacesand assess the potential of surface-based removal technologies for applications in airflow through cities, HVAC systemsand over vehicles. If these removal technologies are applied to their surfaces, cities, solar farms, HVAC systemsand filters can achieve atmospheric pollutant removal rates that exceed 1 GtCO 2 e annually. Cities have the highest atmospheric pollutant removal potential, with estimates averaging 30 GtCO 2, 0.06 GtCH 4, 0.0001 GtPM 2.5, 0.007 GtNO x annually. HVAC filters can achieve atmospheric pollutant removal costs as low as $300 per tCO 2 e removed when sorption or catalyst technologies are incorporated into their fibre sheets, outperforming the $2000 per tCO 2 e removal cost when these technologies are applied to city surfaces. This estimate is based on the literature values for these technologies’ costs per square meter. However, our calculations indicate that optimising catalyst propertiesand surface coverage could lower the cost estimates to below $100 per tCO 2 e across these applications. These findings demonstrate that integrating surface-based pollutant removal technologies into infrastructure may offer a scalable pathway to advance climate and health objectives. Greenhouse gases (GHGs) such as carbon dioxide, methane and nitrous oxide continue to rise at significant rates, presenting sustainability and health challenges by accelerating climate change. In 2023, CO 2 levels surpassed 420 ppm, driven by fossil fuel use and deforestation, contributing to biodiversity lossand environmental degradation. The situation is compounded by CH 4 and N 2 O, which, although emitted in smaller quantities, have global warming potentials (GWPs) 84 and 273 times that of CO 2 over a 20-year timescale. These potent GHGs are emitted by anthropogenic sources such as agriculture and fossil fuel processing. However, unlike CO 2, CH 4 and N 2 O have biogenic sources that are difficult to control without disrupting agriculture and ecosystems. In addition to GHGs, atmospheric pollutants such as particulate matter (PM), sulphur dioxide and nitrogen oxides (NO x ) pose environmentaland medical risks. PM, particularly particles smaller than 2.5 µm, is a leading cause of respiratory and cardiovascular diseases, causing millions of premature deaths annually. PM is emitted from anthropogenic sources, such as fossil fuel processing, and natural sources, like wildfires; SO 2 and NO x have similar sources, but contribute to acid rain and ground-level ozone. Given the diffuse nature of anthropogenic, biogenic and natural sources, coupled with the insufficient progress in emissions reductions, there is growing interest in atmospheric pollutant removal. Atmospheric pollutant removal can generally be divided into two categories: surface-based approaches, which rely on the interaction between pollutantsand surfaces, and airborne methods, which use radicals or surface chemistry to break down pollutants in the air. While airborne methods show promise, this study focuses on surface-based removal methods, given that these approaches are more widely explored and developed. Despite the interest in surface-based atmospheric pollutant removal, the scale of potential removal remains unexplored. This study aims to assess the potential of various surface-based strategies by quantifying the scales at whichpollutants can be removed from the atmosphere. Nonetheless, the methods developed here apply to other pollutants, offering a framework for quantifying the removal potential across
contexts. Direct air capture (DAC) technologies, often sorbent (e.g., amines) or solvent-based (e.g., potassium hydroxide), can capture CO 2 through chemical adsorption and absorption processes. For DAC, the fraction of pollutant molecules removed upon contact with the surface, known as the removal efficiency, ranges from 10 [−]
to 10. However, this process requires substantial energy and involves high operational costs. Membrane-based systems, which separate CO 2 through selectively permeable barriers, offer greater energy efficiency than DAC, but face challenges related to initial costsand scalability.
1a and detailed in Supplementary Sec. 3, these environments consist of substantial airflow volumes that drive atmospheric pollutant fluxes to surfaces. Average streamwise flow rates are approximately 2 × 10 m /s through natural environments, 4 × 10 m /s in internal systemsand 8 × 10 m /s around external systems. Theoretical predictions, empirical data, global measurementsand industry standards provide a range of estimates in Fig. 1a, spanning from 3 × 10 to 4 × 10 m /s for cities, 1 × 10 to 3 × 10 m /s for HVAC systemsand 1 × 10 to 7 × 10 m /s for automobiles. Key parameters, including length, surface area, velocity and the number of environments are summarised in Fig. 1b–g, Supplementary Tabs. 2–4 and Secs.
2.1–2.3. The parameter distributions not only capture uncertainty but reveal that surface area and airflow velocity are the dominant factors influencing pollutant removal efficiency across applications. Surface fluxes of atmospheric pollutants
Fig. 2a shows normalised atmospheric pollutant fluxes to the surfaces of natural environments in citiesand solar farms. GHG fluxes exceed other pollutant fluxes due to higher diffusivitiesand atmospheric concentrations. Atmospheric pollutant fluxes to surfaces are also influenced by length scales, with cities exhibiting average surface fluxes of 2 × 10 [−] mol/m s for CO 2, while solar farms achieve average surface fluxes of 2 × 10 [−] mol/m s for CO 2 . Smaller length scales reduce the boundary layer thickness, which increases the turbulence intensity and improves the atmospheric pollutant transfer to surfaces. As a result, in these natural environments, atmospheric pollutant fluxes to surfaces are averaged across scales.
Using empirical formulae detailed in the Methods, average atmospheric pollutant fluxes at the repeating unit scale are larger than those at the combined unit scale due to steeper concentration gradients. Figs. 2a–b highlight the enhanced atmospheric pollu
2 Figure 1: Flow rates through global environments. (a) Streamwise flow rates (Q) for natural airflows (e.g., cities, solar farms), internal airflows (e.g., HVAC, combustion, DAC systems) and external airflows (e.g., aeroplanes, trains, automobiles), with symbols denoting the methods used to estimate flow rates. Schematics of the velocity field (u), lengthscales (l, δ, wand h) and flow rate for a (b) city, (c) HVAC system and (d) aeroplane. (e) Global wind speed distribution, with city data highlighted in black. (f ) Streamwise velocity profile through an HVAC system. (g) Boundary layer development across an aerofoil surface.
tant transport to the surfaces of internal airflows in HVAC, combustion, DAC systemsand HVAC filters. Comparing HVAC, combustion and DAC systems, which show average surface fluxes of 7×10 [−], 2×10 [−] and 6×10 [−] mol/m s for CO 2 in Fig. 2c, demonstrates that higher airflow velocities enhance atmospheric pollutant transport. The estimated value for DAC is comparable to 4 × 10 [−] mol/m s estimated in McQueen et al.. At the duct wall, the rate at whichpollutants move perpendicular to the surface (normal flux) depends on diffusion and is proportional to Dc/h, where Dis the diffusivity of the pollutant, cis its concentration and his the height of the channel. In contrast, for HVAC filters, the pollutant flux through the porous fibre sheet is proportional to u n c, where u n is the airflow velocity through the fibre sheet. Since D and care typically small relative to u n and h, u n cis generally larger than Dc/h, highlighting the benefits of utilising advective over diffusive transport to improve atmospheric pollutant fluxes to surfaces. Lastly, we evaluate the normalised atmospheric pollutant fluxes to the surfaces of external airflows over aeroplanes, trainsand automobiles. Automobiles demonstrate higher pollutant fluxes to surfaces than trainsand aeroplanes, due to smaller characteristic lengths. However, aeroplanes generate more turbulent transport due to their higher velocities, resulting in a pollutant flux that is approximately 25% of the automobile flux across the entire surface. Surface flow rates of atmospheric pollutants
Fig.
3 quantifies the flow rates of atmospheric CO 2, CH 4, PM 2.5 and NO x to all global surfaces. These flow rates are evaluated by multiplying the fluxes from Fig. 2 by the relevant surface area for each application and the number of each application. Naturaland internal airflows with extensive surface areas yield the highest atmospheric pollutant flow rates to their surfaces, e.g., an average of 30 GtCO 2 /y for cities, 10 GtCO 2 /y for HVAC systemsand 4 GtCO 2 /y for HVAC filters. Conversely, external airflowsand small-surface
3 Figure 2: Atmospheric pollutant fluxes to the surfaces of global environments. (a) Atmospheric pollutant fluxes (j), normalised by diffusivity (D) and concentration (c), to the surfaces of natural airflows (e.g., cities, solar farms), internal airflows (e.g., HVAC, combustion, DAC systems) and external airflows (e.g., aeroplanes, trains, automobiles), with symbols representing the methods used to estimate fluxes. (b) Atmospheric pollutant fluxes, normalised by concentration, to the surfaces of HVAC filters. (c) Atmospheric pollutant fluxes to surfaces, averaged across the different methods, for CO 2, CH 4, N 2 O, PM 2.5, SO 2 and NO x . Example concentration boundary layer, velocities (u and u n ), lengths (l, δ, w, h, w p, hp ) and normal pollutant flux to a (d) city surface, (e) building surface, (f) duct walland (g) HVAC filter sheet. area internal systems show lower atmospheric pollutant flow rates to their surface, e.g., an average of 0.1 GtCO 2 /y for aeroplanes, 0.7 GtCO 2 /y for trains, 7 × 10 [−] GtCO 2 /y for combustion systemsand 3 × 10 [−] GtCO 2 /y for DAC systems, despite external airflows exhibiting higher atmospheric pollutant fluxes to their surfaces in Fig. 2a–c. Integrating surface-based pollutant removal technologies into global infrastructure is likely more readily achieved by incorporating technologies into newlyproduced surfaces. Comparing atmospheric pollutant flow rates to total existing surfaces with annually produced surfaces, a decrease is observed across applications, with the reduction varying between applications. For example, solar farmsand cities have average flow rates of 30 GtCO 2 /y and 0.08 GtCO 2 /y to their total surfaces. However, when accounting for annually produced surfaces, the average flow rate to new surfaces in cities is more significantly re
duced than for solar farms. This is largely driven by the global growth of solar farms, expanding at approximately 20% annually, compared with the 4% annual growth of cities. Removal rates of atmospheric pollutants
Having established the atmospheric pollutant transport rates to the surfaces of various naturaland mechanical environments, we assume ideal removal efficienciesand compare the potential atmospheric pollutant removal rates with the 1 GtCO 2 e/y target. This target appears feasible for cities, solar farms, HVAC systems, filters, aeroplanes, trainsand automobiles, based on the potential atmospheric pollutant flow rates to existing surfaces. When focusing only on potential atmospheric pollutant flow rates to annually produced surfaces, feasible applications narrow to cities, solar farms, HVAC systemsand filters. 4 Current ecosystems
1 Gt/y
Current oxidation
1 GtCO 2 e/y
Current DAC systems
Figure 3: Atmospheric pollutant flow rates to the surfaces of global environments. Flow rates (q) of atmospheric (a) CO 2, (b) CH 4, (c) PM 2.5 and (d) NO x to the total existing surfaces in natural (e.g., cities, solar farms), internal (e.g., HVAC systems, HVAC filters, combustion systems, DAC systems) and external (e.g., aeroplanes, trains, automobiles) environments, with symbols representing the methods used to estimate flow rates. Horizontal lines mark removal rates for atmospheric CO 2, CH 4, PM 2.5 and NO x due to ecosystems, oxidation, deposition and reduction. However, the removal efficiency is often suboptimal, falling short of the transport flux to surfaces. For example, DAC systems exhibit removal efficiencies around 10 [−], such that the average pollutant removal potential is 1 GtCO 2 /y for HVAC systems. With 10 DAC systems removing, on average, around 10 tCO 2 /y each, atmospheric removal rates are currently limited to approx
imately 10 [−] GtCO 2 /y. CH 4 and N 2 O present additional challenges due to their low atmospheric concentrations but can also achieve removal efficiencies around 10 [−]. However, most efficiency values are reported at elevated temperatures, with room temperature conversion rates still being an area of active research. When multiplied by the removal efficiency, cities, solar farmsand
5 Table 1: Cost of surface-based atmospheric pollutant removal in global environments. The potential removal rate of atmospheric CO 2 and CH 4 per year using a removal efficiency of 10 [−], surface area produced per year, technology cost per year based on cost per square meter, cost per tonne of atmospheric CO 2 and CH 4 removed and cost per tonne of CO 2 e removed for different naturaland internal flows. Data for these naturaland internal flows are divided into cities, solar farms, HVAC systemsand filters that have potential removal rates of atmospheric CO 2 and CH 4 that exceed the 1 GtCO 2 e/y target in Fig. 3.
Carbon dioxide
Environment Rem. rate (t/y) Surf. area Tech. cost ($/y) Rem. cost ($/t) Rem. cost
Cities, 3 × 10 ], 1 × 10 ], 7 × 10 ], 9 × 10 ], 9 × 10 ]
Solar farms, 2 × 10 ], 3 × 10 ], 2 × 10 ], 2 × 10 ], 2 × 10 ]
HVAC systems, 1 × 10 ], 6 × 10 ], 4 × 10 ], 5 × 10 ], 5 × 10 ]
HVAC filters, 2 × 10 ], 8 × 10 ], 6 × 10 ], 3 × 10 ], 3 × 10 ]
Methane
Environment Rem. rate (t/y) Surf.
The cost per catalyst surface area is determined by multiplying the cost per kg by the catalyst massand dividing by the catalyst surface area, yielding $0.02 to $120.2 per m of catalyst. Fluxes are computed for the total surface area using Tab. 1 and adjusted for catalyst coverage. Therefore, we estimate the cost per ton of CO 2 e removed to range from $1.4 × 10 to $2 × 10 for citiesand $3.2 to $5.7 × 10 for HVAC filters, the latter meeting the $100 per tCO 2 e target. Further reductions are possible by increasing catalyst coverage or reducing particle size, potentially bringing city-scale applications within target
costs. Discussion
The potential for removing low concentrations of atmospheric pollutants as a mitigation strategy for climate change and public health crises has yet to be fully ex
6 plored. Surface-based removal technologies have been proposed as a scalable approach to achieving these reductions. For example, it has been shown that DAC may remove CO 2 from ambient air at scale, catalytic coatings in urban areas can reduce the concentration of volatile organic compounds (VOCs) and NO x, and HEPA filters can lower PM levels. To evaluate whether wider deployment is warranted, we have developed a framework to assess the potential scale of surface-based atmospheric pollutant removal, leveraging pre-existing airflows in cities, HVAC systemsand transportation. Surface-based pollutant removal technologies in cities, solar farms, HVAC systemsand filters could achieve potential atmospheric pollutant removal rates exceeding 1 GtCO 2 e per year, with optimisation for higher surface fluxes or lower materials costs potentially reducing the minimum cost estimate to under $100 per tCO 2 e. Building upon the existing capabilities of HVAC systems to remove PM and VOCs, by integrating other surface-based pollutant removal technologies, could enhance their cost-effectivenessand scalability. A similar integration of technologies was suggested during the COVID-19 pandemic, when HVAC systems were adapted to mitigate airborne viruses, using technologies such as UV-C light and antimicrobial coatings. This demonstrated the adaptability of HVAC systemsand their potential for multifunctional air quality management to address broader environmental challenges. Furthermore, HVAC filters are regularly replaced, potentially making the maintenance of any surface-based removal technologies straightforward and allowing their performance to be sustained over time. Refining pollutant removal models can not only enhance the accuracy of predictions but also provide insights into optimisation. Adsorption/desorption models, such as Langmuir–Hinshelwood or Eley–Rideal, and multicomponent diffusion models, such as Maxwell–Stefan, can help identify conditions that maximise reaction rates. Similarly, dispersion models, including Gaussian plume, Eulerian and Lagrangian approaches, can enable placement of removal surfaces to enhance the removal efficiency. Lastly, our analyses excluded energy consumption to focus on existing naturaland mechanical airflows, assuming thin and smooth coatings with consistent flow resistance. However, rough coatings with increased surface area could enhance turbulence, flow rates to the surface and therefore removal efficiency. Scaling surface-based atmospheric pollutant removal technologies presents challenges that must be addressed, including material degradation, surface contamination and infrastructure integration. While sorption systems generally maintain consistent pollutant removal efficiencies, the catalytic removal of CH 4 is variable and may decrease over time or under varying conditions (e.g., relative humidity). Advances in materialsand reaction mechanisms can improve these efficienciesand expand the applicability of catalysis. However, largescale deployment of catalysts may introduce risks, such as the conversion of other species (e.g., VOCs) to more toxic forms. These findings highlight the potential of integrating surface-based atmospheric pollutant removal technologies into urban and industrial systems to support climate ac
tion and protect public health. Methods
Scaling theory
To characterise atmospheric pollutant transport dynamics across various systems, we define velocity (u, m/s), length (l, m) and surface area scales specific to each flow application (e.g., cities, HVAC systems, transport).
We also estimate the number of occurrences (n) for each flow type to ensure realistic scaling for global estimations. The limitations of this scaling approach and additional context are provided in Supplementary Sec. 3.1. The thickness of the turbulent boundary layer (δ, m) is evaluated using δ = 0.4l/Re[/], where the Reynolds number Re = ul/ν and ν is the kinematic viscosity. The normal pollutant flux j y at the surface is determined by the diffusive flux, −Dc y, as the normal velocity at the surface is zero. The normal pollutant flux is simplified here using Fick’s law, which assumes diffusion driven solely by concentration gradients without accounting for molecular interactions or multicomponent effects. This normal pollutant flux depends on the diffusivity D, concentration c and the boundary layer thickness δ c = δ/Sc[/]
(m), where the Schmidt number Sc = ν/D. An effective diffusivity D e is included to account for enhanced mixing, which alters pollutant transport rates. Using these scales, the streamwise flow rate of pollutants Q through the boundary layer of each application is given by Q ≈ nuδl. The normal flow rate of pollutants q y (mol/s) to the surface of each application is given by q y ≈ [ns][][c] .
The formulas in– provide a means to estimate atmospheric pollutant transport through, and to the surface of, global environments. As shown in Supplementary Fig. 1, we calculate the global streamwise flow rate through the boundary layers of the world’s cities (solar farms) using. Empirical relationships
Empirical models can be used to link flow characteristics with the potential for pollutant removal. These relationships have been applied in CH 4 and N 2 O removal studies via photocatalysis. These relationships are evaluated using l, sand u, supplemented with measured values such as wall shear stresses, mass transfer coefficients (m c in m/s) and heat transfer coefficients. A detailed discussion of these empirical relationshipsand their applications can be found in Supplementary Sec. 3.2. Analogies exist between key dimensionless numbers: the Nusselt number (Nu = h c l/k), the Sherwood number (Sh = m c l/D) and the drag coefficient, where k is the thermal conductivity (W/mK) and ρ is the fluid density. These analogies allow for
7 the formulation of the normal pollutant flow rate to the surface of each application as follows q y ≈[.][Re][/] [Sc] l[/] [nsDc] .
Following Tsopelakou et al., we can use to estimate the streamwise flow rate through each application as
Q ≈ uA ≈ lm c P,
where Pis the wetted perimeter and A is the crosssectional area of the channel or boundary layer. Fully-developed velocity and pollutant profiles
We employ established formulas to determine fullydeveloped velocity and pollutant profiles, including the Monin–Obukhov (M-O) similarity theory for natural airflowsand approximate solutions of the Navier-Stokes equations for internal airflows, with explanations provided in Supplementary Sec. 3.3. For external airflows over cities or solar farms, we utilise M-O similarity theory, which incorporates the effects of buoyancy and surface roughness under varying thermal conditions. The normal flow rate of pollutants to the surface of the environment is q y ≈ [ns] h¯ [ [)][c] [,]
where h [¯] represents the average disturbance height and y 0 is the roughness length. For y 0 = δ [∗], where δ [∗] is the viscous sublayer thickness, we recover the log-law profile. This log law applies to external airflows over smooth or mildly rough surfacesand can be used to calculate pollutant fluxes near vehicles. For internal airflows within HVAC systems, we approximate solutions of the Navier-Stokes equationsand boundary conditions. The normal flow rate of pollutants to the channel walls is given by q y ≈ [ns][(][D][ +] h [ D] [e] [)][c],
where 2his the channel diameter. For internal airflows through HVAC filters, we adjust the velocity from the duct flow to the pleat flow. We apply the principle of mass conservation to match average velocities, leading to Au ≈ mA p u n . The flow rate of pollutants to the pleat channel walls is q y ≈ [nms][(][D] h [ +] p [ D] [e] [)][c] . The flow rate of pollutants to the fibre sheet is q n ≈ nmA p Eu n c,
where E is the collection efficiency, related to the permeability and porosity of the filter medium. Energy, power and drag measurements
We now outline formulas to determine the flow rate based on energy, power and drag measurements. More details are given in Supplementary Sec. 3.4. For external flows over urban areas or solar farms, we establish a control volume with a length land height δ. In the case of uni-directional, steady-state flow over a surface with a heat flux φ, energy conservation dictates that the energy entering the control volume must balance with the energy exiting it.
This relationship can be expressed as
nsφ Q ≈ ρc p ∆T [,] q y ≈ [Ql][][c],
where c p is the specific heat capacity of air at constant pressure and ∆T (K) is the temperature difference between the surface and the surrounding atmosphere. The flow rates are related using the scaling theory discussed in Supplementary Sec. 3.1, such that Ql(D + D e )c/ ∼ (uδ)l(D + D e )c/ ∼ l(D + D e )c/δ ∼ q y . In the context of internal flow through HVAC systems, the total power consumed by the fans denoted as P (kW) and the specific fan power (SFP), can be utilised to evaluate the streamwise flow rate through the duct as follows Q ≈ [LP]
SFP [,]
where L is the leakage factor. For external airflows acting on transportation systems, the streamwise flow rate through the boundary layer can be estimated using the drag force acting on the aeroplane, train or automobile. This is expressed by the equation
Q ≈[nF] [d],
ρuC d where F d (N) is the drag force. Industry standards
For internal airflows through HVAC ducting, the streamwise flow rate can be evaluated using three estimates. First, Q can be expressed in terms of the cubic feet per minute per person, given by
Q ≈ LCFM pp n pp,
where n pp (–) is the total number of occupants in the building. Second, Q can be calculated based on the CFM per unit area, described by the equation Q ≈ LCFM pm 2 a,
where a is the total floor area of the building. Finally, Q can be assessed using the air changes per hour (ACH, s), given by Q ≈ LACHv,
where v is the volume of air within the building.