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Harnessing natural and mechanical airflows for surface-based atmospheric pollutant removal

Samuel D. Tomlinson, Aliki M. Tsopelakou, Tzia M. Onn, Steven R. H. Barrett, Adam M. Boies, Shaun D. Fitzgerald

Abstract

Removal strategies for atmospheric pollutants are increasingly being considered to mitigate global warming and improve public health. However, the global potential of surface-based removal techniques has not yet been quantified based on limits of pollutant transport and removal rates. We evaluate the atmospheric pollutant transport to surfaces and assess the potential of surface-based removal technologies for global-scale deployment across a variety of configurations, including air interaction with the built environment, mechanical ventilation and convection systems, and over the global transportation fleet Cities provide the highest removal potential, with median annual estimates of 30 GtCO$_2$, 0.06 GtCH$_4$, 0.007 GtNO$_\text{x}$ and 0.0001 GtPM$_{2.5}$ transported to their total surface area. Cities, solar farms, HVAC systems and filters have the potential to exceed 1 GtCO$_2$/yr (1 GtCO$_2$e/yr for CH$_4$, 20-year GWP) of removal when literature-based CO$_2$-sorbent (CH$_4$-catalyst) efficiencies are applied across their total surface area. These values represent theoretical upper bounds and are intended for comparison across applications rather than application-specific deployment. HVAC filters have the potential to achieve materials costs as low as \$600 per tCO$_2$ removed (\$2000 per tCO$_2$e) when CO$_2$-sorption (CH$_4$-catalyst) technologies are incorporated into their fibre sheets and maintained through routine filter replacement, compared with \$3000 per tCO$_2$ (\$10000 per tCO$_2$e) for city surfaces, based on the literature values for these technologies' materials costs. These findings demonstrate that integrating surface-based pollutant removal technologies into infrastructure may offer a pathway to advance climate and health objectives, though further studies are needed to assess their feasibility in application, and application-implementation rates and cost.

Harnessing natural and mechanical airflows for surface-based atmospheric pollutant removal

Abstract

Removal strategies for atmospheric pollutants are increasingly being considered to mitigate global warming and improve public health. However, the global potential of surface-based removal techniques has not yet been quantified based on limits of pollutant transport and removal rates. We evaluate the atmospheric pollutant transport to surfaces and assess the potential of surface-based removal technologies for global-scale deployment across a variety of configurations, including air interaction with the built environment, mechanical ventilation and convection systems, and over the global transportation fleet Cities provide the highest removal potential, with median annual estimates of 30 GtCO, 0.06 GtCH, 0.007 GtNO and 0.0001 GtPM transported to their total surface area. Cities, solar farms, HVAC systems and filters have the potential to exceed 1 GtCO/yr (1 GtCOe/yr for CH, 20-year GWP) of removal when literature-based CO-sorbent (CH-catalyst) efficiencies are applied across their total surface area. These values represent theoretical upper bounds and are intended for comparison across applications rather than application-specific deployment. HVAC filters have the potential to achieve materials costs as low as \_22000 per tCOe) when CO-sorption (CH-catalyst) technologies are incorporated into their fibre sheets and maintained through routine filter replacement, compared with \_210000 per tCOe) for city surfaces, based on the literature values for these technologies' materials costs. These findings demonstrate that integrating surface-based pollutant removal technologies into infrastructure may offer a pathway to advance climate and health objectives, though further studies are needed to assess their feasibility in application, and application-implementation rates and cost.

Paper Structure

This paper contains 8 sections, 14 equations, 3 figures, 1 table.

Table of Contents

  1. Introduction
  2. Methods
  3. Scaling theory
  4. Empirical relationships
  5. Energy, power and drag measurements
  6. Industry standards
  7. Fully-developed velocity and pollutant profiles
  8. Parameter estimation and uncertainty

Figures (3)

  • 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. Box-and-whisker plots show the distribution of flow rates, where boxes represent the interquartile range (25th–75th percentiles), central lines denote medians, whiskers show the full range of values, and symbols indicate mean flow rates derived from different estimation methods.Scaling \ref{['eq:scaling_Q']}--\ref{['eq:scaling_eq']}, energy \ref{['eq:nat']}--\ref{['eq:int']}, empirical \ref{['eq:empirical']}--\ref{['eq:aliki']}, industry \ref{['eq:cfm_pp']}--\ref{['eq:ach']} and drag \ref{['eq:dragg']} approaches (using Tables S1--S3) are used to estimate $Q$. Schematics of the velocity field ($u$), lengthscales ($l$, $\delta$, $w$ and $h$) and flow rate for a (B) city, (C) HVAC system and (D) aeroplane. (E) Global wind speed distribution (m/s), with city data highlighted in black. (F) Streamwise velocity profile (m/s) through an HVAC system. (G) Boundary layer development (m/s) across an aerofoil surface.
  • Figure 2: Atmospheric pollutant fluxes to the surfaces of global environments. (A) Atmospheric pollutant fluxes ($\boldsymbol{j}$), 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). (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$_\text{x}$. Fluxes are estimated using scaling \ref{['eq:scaling_Q']}--\ref{['eq:scaling_eq']} and empirical \ref{['eq:empirical']}--\ref{['eq:aliki']} approaches, with parameter distributions from Tables S1--S3. Example concentration boundary layer, velocities ($u$ and $u_{\boldsymbol{n}}$), lengths ($l$, $\delta$, $w$, $h$, $w_p$, $h_p$) and normal pollutant flux to a (D) city surface (mol/m$^3$), (E) building surface (mol/m$^3$), (F) duct wall (mol/m$^3$) and (G) HVAC filter sheet (mol/m$^3$).
  • Figure 3: Atmospheric pollutant flow rates to the surfaces of global environments. Flow rates ($\boldsymbol{q}$) of atmospheric (A) CO$_2$, (B) CH$_4$, (C) PM$_{2.5}$ and (D) NO$_\text{x}$ 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$_\text{x}$ due to ecosystems, oxidation, deposition and reduction friedlingstein2022globaldlugokencky2011globaltian2023globalwu2018comparisonDore2007Xu2009zhao2022. Flow rates are derived using scaling \ref{['eq:scaling_Q']}--\ref{['eq:scaling_eq']}, energy \ref{['eq:nat']}--\ref{['eq:int']}, empirical \ref{['eq:empirical']}--\ref{['eq:aliki']}, industry \ref{['eq:cfm_pp']}--\ref{['eq:ach']} and drag \ref{['eq:dragg']} approaches and Tables S1--S3.