You're using an outdated browser. Please upgrade to a modern browser for the best experience.
Submitted Successfully!
Thank you for your contribution! You can also upload a video entry or images related to this topic. For video creation, please contact our Academic Video Service.
Version Summary Created by Modification Content Size Created at Operation
1 Stuart N. Riddick -- 1398 2025-05-20 04:23:05 |
2 formatted Vicky Zhou Meta information modification 1398 2025-05-20 04:26:29 |

Video Upload Options

We provide professional Academic Video Service to translate complex research into visually appealing presentations. Would you like to try it?
No, upload directly Yes

Confirm

Are you sure to Delete?
Yes No
Cite
If you have any further questions, please contact Encyclopedia Editorial Office.
Riddick, S.N. Quantifying Methane Emission Rates Using Downwind Measurements. Encyclopedia. Available online: https://encyclopedia.pub/entry/58335 (accessed on 05 December 2025).
Riddick SN. Quantifying Methane Emission Rates Using Downwind Measurements. Encyclopedia. Available at: https://encyclopedia.pub/entry/58335. Accessed December 05, 2025.
Riddick, Stuart N.. "Quantifying Methane Emission Rates Using Downwind Measurements" Encyclopedia, https://encyclopedia.pub/entry/58335 (accessed December 05, 2025).
Riddick, S.N. (2025, May 20). Quantifying Methane Emission Rates Using Downwind Measurements. In Encyclopedia. https://encyclopedia.pub/entry/58335
Riddick, Stuart N.. "Quantifying Methane Emission Rates Using Downwind Measurements." Encyclopedia. Web. 20 May, 2025.
Peer Reviewed
Quantifying Methane Emission Rates Using Downwind Measurements
This is a peer-reviewed entry published in Encyclopedia Journal, full entry can be found at 10.3390/encyclopedia5020057

This entry describes the methods used to quantify methane emissions from either point or area sources using downwind methods. The methods described could be used as a practical guide to quantify emissions of any trace gas type from either a point or area emission source. Methane is a relatively strong greenhouse gas, its GWP is 25 times larger than CO2 over a 100-year period, and an increase in methane anthropogenic emissions has been correlated to a changing global climate. Emission estimates that are calculated and used for national inventories are usually derived from bottom-up approaches, however there is now an increasing pressure for these to be validated by direct measurement. Calculating emission rates from downwind measurements has proven to be a versatile and relatively simple approach for direct measurement. Downwind measurement method descriptions are presented here as a practicable guide to quantifying point and area source emissions. Emission quantification is a two-stage process where methane concentration and meteorological data must be measured downwind of a source and then converted to emissions using an atmospheric dispersion model. Only four technology types currently measure in the range typical of downwind methane concentrations: metal oxide sensors, non-dispersive infrared sensors, tunable diode laser absorption spectrometers and optical cavity instruments. The choice of methane measurement is typically determined by the size of the emission source, location and the budget of the project. Meteorological data are essential to quantifying emissions, especially regarding wind speed and direction. In most cases, simple atmospheric dispersion approaches can be used to quantify both area and point emissions using these downwind measurements. Emissions can be generated using limited data (only methane concentration, wind speed, wind direction, and locations are necessary), but quantification uncertainty can be reduced using more input data. Site selection and location of instrument deployment are essential because quantification approaches assume a flat fetch (no aerodynamic obstructions) and constant wind fields. When modeling assumptions are violated, quantification uncertainty can range between +250% and −100% of the actual emission rate. At present there, is no happy medium between modeling complexity and computational time, and this remains the biggest challenge for downwind emission quantification.

methane emission rate quantification downwind meteorology micrometeorology
This entry describes the methods used to quantify methane emissions from either point or area emission sources using downwind measurements. The description of methods could be used as a practical guide to quantify emissions of any trace gas type (e.g., carbon dioxide, hydrogen, nitrous oxide) as gases tend to become very dilute after emission (from ~0.1% to ppm-levels after distances ~1 m) and different gases have negligible buoyancy differences when diluted [1]. Here, methane is used as the example trace gas as it has practical importance to emissions from the energy, waste, agriculture and natural sectors.
Over the past decade, it has become accepted that the changing global climate is a response to the increase in atmospheric gas species’ concentration [2][3][4]. This has largely been attributed to an increase in gas emissions from anthropogenic activities, which have resulted from changes to industrial manufacturing, agricultural processes and energy demand [2]. Due to their absorption of infrared radiation, several gases have been identified as greenhouse gases (GHGs), e.g., carbon dioxide, methane, nitrous oxide and sulfur hexafluoride. Each gas type has the capacity to absorb a different amount of infrared radiation, typically standardized against carbon dioxide, which has a global warming potential (GWP) of one, and more heat is trapped in atmosphere with a larger abundance of atmospheric GHGs.
Methane is a relatively strong greenhouse gas as its GWP is 25 times larger than CO2 over a 100-year period [2][4]. The 100-year GWP (GWP100) is universally accepted as the standard GWP metric for comparison between gases [5]. Until recently, most GHG emissions at site, region and national levels have been calculated using bottom-up methods, where emissions are calculated by multiplying the number of emission activities within the area of study by an emission factor [6]. Emission factors are a quantitative assessment of how emissive a source is; units are typically “grams of gas per unit time”, and usually derived from a measurement study. More recently, it has become apparent that there are significant differences between bottom-up emission estimates and the amount of gas that has been emitted [7][8][9][10][11][12], with the discrepancy largely attributed to incorrect or inaccurate emission factors [10][13][14][15].
With climate goal deadlines looming, there has been an increased demand for the validation of emission estimates [16][17][18]. Typically, this has been adopted as “Measurement, Monitoring, Reporting and Verification Frameworks”, which require greenhouse gas emissions to be verified through direct measurement. Direct measurement can be made in a number of ways: enclosing the source in a chamber; summing individual sources within an area; inferring emissions from downwind measurements; using tracer flux; making eddy covariance measurements; applying a mass balance approach; or remotely sensing the plume using aircraft or satellite [7][19][20][21][22][23][24][25][26][27][28][29]. The advantages and disadvantages of these methods are described in a number of papers [23][30] but are often limited by either high quantification thresholds—10+ kg CH4 h−1 for aircraft [22] and 100+ kg CH4 h−1 for satellites [7][11][31]—or the need for direct access to the emission source.
Inferring emissions from downwind measurements does not suffer from these shortcomings and has proved to be a versatile and relatively robust approach for quantifying emissions. Sources can either be point or area sources and emissions can be calculated using relatively little data, e.g., methane concentration and wind speed, at a minimum. Uncertainty in quantification decreases with an increase in input data quality. Downwind methods have been used to quantify methane emissions from onshore oil and gas facilities [27][32], offshore oil and gas facilities [33][34], landfill [29][35][36], agriculture [37][38][39][40], and natural sources [37], with emission ranging from 10 g CH4 h−1 to 100 kg CH4 h−1 [33].
Methane concentration data are essential for calculating the emission. The size of the concentration observed is a function of distance downwind, the size of the emission, the wind speed and atmospheric stability. Typically, expected downwind methane concentrations are less than 20 parts per million by volume (ppm) [27][37][38][41]. Background methane concentrations are also required; these are typically measured upwind of the source and usually observed to be ~1.8 ppm [12][37][42][43]. Coupled with concentration data, meteorological data are also required with wind speed being essential. This can be measured using a standard rotating-cup anemometer, but better quantification accuracy can be achieved using a 3D sonic anemometer. Other meteorological data are desirable but not essential, including wind direction, temperature, relative humidity atmospheric pressure, and solar irradiance. Similarly, micrometeorological data are desirable but emissions can be quantified without quantitative data. These data are usually measured by a 3D sonic anemometer but can also be derived from lower-tech instruments. Details are provided in full below.
Point sources are defined as a single identifiable source of emission, e.g., a crack in a pipe, while an area source is a group of sources emitted over a finite region. There is a degree of fluidity between how a source may be defined, point or area, and this is usually a function of observation distance. For example, a landfill can be considered an assemblage of individual point sources when measuring on-site, an area source at a middle distance, and a single-point source when observed from even farther away. The distance at which the source transitions between types is dependent on the size and distribution of the sources and needs to be determined for each individual emission scenario. Emissions are typically presented as the emission rate in units of “grams of gas per unit time” for point sources or as a flux in units of “grams of gas per unit area per unit time” for area sources.
As mentioned above, quantification uncertainty depends on the quality of the input data. For very far-field measurements, up to 10 km away, the uncertainty has been estimated at plus/minus a factor of two (+100%, −50%) [33]. For nearer measurements (less than 1 km), downwind methods’ quantification uncertainty has been measured at ±60% [37][44] using controlled releases. At even closer distances (<5 m), downwind methods’ quantification uncertainty has been measured at less than ±15% [20][30]. The main cause of uncertainty at small distances is parameterizing the lateral dispersion of the plume, and this can be overcome by measuring directly downwind of the emission source.
In recent years, many publications have described methods that use downwind measurements to quantify methane emissions. In this entry, method descriptions are presented as a practicable guide to quantifying point and area source emissions with the inclusion of suggestions that could be used to work around missing data or overcome instrumentation shortcomings.

References

  1. Schefer, R.; Houf, W.; Williams, T. Investigation of Small-Scale Unintended Releases of Hydrogen: Momentum-Dominated Regime. Int. J. Hydrogen Energy 2008, 33, 6373–6384.
  2. IPCC. Climate Change 2013—The Physical Science Basis: Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change; Cambridge University Press: Cambridge, UK, 2014; ISBN 978-1-107-41532-4.
  3. IPCC. Summary for Policymakers. In Global Warming of 1.5 °C. An IPCC Special Report on the Impacts of Global Warming of 1.5 °C Above Pre-Industrial Levels and Related Global Greenhouse Gas Emission Pathways, in the Context of Strengthening the Global Response to the Threat of Climate Change, Sustainable Development, and Efforts to Eradicate Poverty; Cambridge University Press: Cambridge, UK; New York, NY, USA, 2022; pp. 3–24.
  4. IPCC. Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Sixth Assess-Ment Report of the Intergovernmental Panel on Climate Change; Pörtner, H.-O., Roberts, D.C., Tignor, M., Poloczanska, E.S., Mintenbeck, K., Alegría, A., Craig, M., Langsdorf, S., Löschke, S., Möller, V., et al., Eds.; Cambridge University Press: Cambridge, UK, 2022; ISBN 978-1-009-32584-4.
  5. UNFCCC Paris Agreement. United Nations Framework Convention on Climate Change. FCCC/CP/2015/L.9/Rev.1. Available online: https://unfccc.int/documents/9064 (accessed on 16 June 2023).
  6. Nisbet, E.; Weiss, R. Top-Down Versus Bottom-Up. Science 2010, 328, 1241–1243.
  7. Cooper, J.; Dubey, L.; Hawkes, A. Methane detection and quantification in the upstream oil and gas sector: The role of satellites in emissions detection, reconciling and reporting. Environ. Sci. Atmos. 2022, 2, 9–23.
  8. Zavala-Araiza, D.; Lyon, D.R.; Alvarez, R.A.; Davis, K.J.; Harriss, R.; Herndon, S.C.; Karion, A.; Kort, E.A.; Lamb, B.K.; Lan, X.; et al. Reconciling divergent estimates of oil and gas methane emissions. Proc. Natl. Acad. Sci. USA 2015, 112, 15597–15602.
  9. van der Gon, H.D.; Beevers, S.; D’Allura, A.; Finardi, S.; Honoré, C.; Kuenen, J.; Perrussel, O.; Radice, P.; Theloke, J.; Uzbasich, M.; et al. Discrepancies Between Top-Down and Bottom-Up Emission Inventories of Megacities: The Causes and Relevance for Modeling Concentrations and Exposure. In Air Pollution Modeling and Its Application XXI.; Steyn, D.G., Trini Castelli, S., Eds.; Springer: Berlin/Heidelberg, Germany, 2012; pp. 199–204.
  10. Vaughn, T.L.; Bell, C.S.; Pickering, C.K.; Schwietzke, S.; Heath, G.A.; Pétron, G.; Zimmerle, D.J.; Schnell, R.C.; Nummedal, D. Temporal variability largely explains top-down/bottom-up difference in methane emission estimates from a natural gas production region. Proc. Natl. Acad. Sci. USA 2018, 115, 11712–11717.
  11. Varon, D.J.; Jacob, D.J.; Hmiel, B.; Gautam, R.; Lyon, D.R.; Omara, M.; Sulprizio, M.; Shen, L.; Pendergrass, D.; Nesser, H.; et al. Continuous weekly monitoring of methane emissions from the Permian Basin by inversion of TROPOMI satellite observations. Atmos. Chem. Phys. 2023, 23, 7503–7520.
  12. Barkley, Z.; Davis, K.; Miles, N.; Richardson, S.; Deng, A.; Hmiel, B.; Lyon, D.; Lauvaux, T. Quantification of oil and gas methane emissions in the Delaware and Marcellus basins using a network of continuous tower-based measurements. Atmos. Chem. Phys. 2023, 23, 6127–6144.
  13. Riddick, S.N.; Mauzerall, D.L. Likely substantial underestimation of reported methane emissions from United Kingdom upstream oil and gas activities. Energy Environ. Sci. 2023, 16, 295–304.
  14. Riddick, S.N.; Mbua, M.; Santos, A.; Hartzell, W.; Zimmerle, D.J. Potential Underestimate in Reported Bottom-up Methane Emissions from Oil and Gas Operations in the Delaware Basin. Atmosphere 2024, 15, 202.
  15. Riddick, S.N.; Mbua, M.; Anand, A.; Kiplimo, E.; Santos, A.; Upreti, A.; Zimmerle, D.J. Estimating Total Methane Emissions from the Denver-Julesburg Basin Using Bottom-Up Approaches. Gases 2024, 4, 236–252.
  16. EU European Commission. Energy, Climate Change, Environment. EU Emissions Trading System (EU ETS). Monitoring, Reporting and Verification. Available online: https://climate.ec.europa.eu/eu-action/eu-emissions-trading-system-eu-ets/monitoring-reporting-and-verification_en (accessed on 5 February 2025).
  17. UK BEIS United Kingdom, Department for Energy Security and Net Zero and Department for Business, Energy & Industrial Strategy. Monitoring, Reporting and Verification of Greenhouse Gas Removals (GGRs): Task and Finish Group Report. Available online: https://www.gov.uk/government/publications/monitoring-reporting-and-verification-of-ggrs-task-and-finish-group-report (accessed on 5 February 2025).
  18. US DoE United State of America. Department of Energy. Fossil Energy and Carbon Management. Greenhouse Gas Supply Chain Emissions Measurement, Monitoring, Reporting and Verification Framework. Available online: https://www.energy.gov/sites/default/files/2024-03/Greenhouse%20Gas%20Supply%20MMRV%20Fact%20Sheet%20Mar2024.pdf (accessed on 5 February 2025).
  19. Riddick, S.N.; Ancona, R.; Bell, C.S.; Duggan, A.; Vaughn, T.L.; Bennett, K.; Zimmerle, D.J. Quantitative comparison of methods used to estimate methane emissions from small point sources. Atmos. Meas. Tech. Discuss. 2022. Available online: https://amt.copernicus.org/preprints/amt-2022-9/amt-2022-9-ATC2.pdf (accessed on 5 February 2025).
  20. Riddick, S.N.; Mbua, M.; Riddick, J.C.; Houlihan, C.; Hodshire, A.L.; Zimmerle, D.J. Uncertainty Quantification of Methods Used to Measure Methane Emissions of 1 g CH4 h−1. Sensors 2023, 23, 9246.
  21. Kunkel, W.M.; Carre-Burritt, A.E.; Aivazian, G.S.; Snow, N.C.; Harris, J.T.; Mueller, T.S.; Roos, P.A.; Thorpe, M.J. Extension of Methane Emission Rate Distribution for Permian Basin Oil and Gas Production Infrastructure by Aerial LiDAR. Environ. Sci. Technol. 2023, 57, 12234–12241.
  22. Duren, R.M.; Thorpe, A.K.; Foster, K.T.; Rafiq, T.; Hopkins, F.M.; Yadav, V.; Bue, B.D.; Thompson, D.R.; Conley, S.; Colombi, N.K.; et al. California’s methane super-emitters. Nature 2019, 575, 180–184.
  23. Denmead, O.T. Approaches to measuring fluxes of methane and nitrous oxide between landscapes and the atmosphere. Plant Soil 2008, 309, 5–24.
  24. Ravikumar, A.P.; Wang, J.; Brandt, A.R. Are Optical Gas Imaging Technologies Effective For Methane Leak Detection? Environ. Sci. Technol. 2017, 51, 718–724.
  25. Livingston, G.P.; Hutchinson, G.L. Enclosure-Based Measurement of Trace Gas Exchange: Applications and Sources of Error. In Biogenic Trace Gases: Measuring Emissions from Soil and Water; Matson, P.A., Harris, R.C., Eds.; Blackwell Science Ltd.: Oxford, UK, 1995; pp. 14–51.
  26. Pekney, N.J.; Diehl, J.R.; Ruehl, D.; Sams, J.; Veloski, G.; Patel, A.; Schmidt, C.; Card, T. Measurement of methane emissions from abandoned oil and gas wells in Hillman State Park, Pennsylvania. Carbon Manag. 2018, 9, 165–175.
  27. Caulton, D.R.; Li, Q.; Bou-Zeid, E.; Fitts, J.P.; Golston, L.M.; Pan, D.; Lu, J.; Lane, H.M.; Buchholz, B.; Guo, X.; et al. Quantifying uncertainties from mobile-laboratory-derived emissions of well pads using inverse Gaussian methods. Atmos. Chem. Phys. 2018, 18, 15145–15168.
  28. Lamb, B.K.; McManus, J.B.; Shorter, J.H.; Kolb, C.E.; Mosher, B.; Harriss, R.C.; Allwine, E.; Blaha, D.; Howard, T.; Guenther, A.; et al. Development of Atmospheric Tracer Methods To Measure Methane Emissions from Natural Gas Facilities and Urban Areas. Environ. Sci. Technol. 1995, 29, 1468–1479.
  29. Sonderfeld, H.; Bösch, H.; Jeanjean, A.P.R.; Riddick, S.N.; Allen, G.; Ars, S.; Davies, S.; Harris, N.; Humpage, N.; Leigh, R.; et al. CH4 emission estimates from an active landfill site inferred from a combined approach of CFD modelling and in situ FTIR measurements. Atmos. Meas. Tech. 2017, 10, 3931–3946.
  30. Riddick, S.N.; Ancona, R.; Mbua, M.; Bell, C.S.; Duggan, A.; Vaughn, T.L.; Bennett, K.; Zimmerle, D.J. A quantitative comparison of methods used to measure smaller methane emissions typically observed from superannuated oil and gas infrastructure. Atmos. Meas. Tech. 2022, 15, 6285–6296.
  31. MacLean, J.-P.W.; Girard, M.; Jervis, D.; Marshall, D.; McKeever, J.; Ramier, A.; Strupler, M.; Tarrant, E.; Young, D. Offshore methane detection and quantification from space using sun glint measurements with the GHGSat constellation. Atmos. Meas. Tech. 2024, 17, 863–874.
  32. Riddick, S.N.; Mbua, M.; Santos, A.; Emerson, E.W.; Cheptonui, F.; Houlihan, C.; Hodshire, A.L.; Anand, A.; Hartzell, W.; Zimmerle, D.J. Methane emissions from abandoned oil and gas wells in Colorado. Sci. Total Environ. 2024, 922, 170990.
  33. Yacovitch, T.I.; Daube, C.; Herndon, S.C. Methane Emissions from Offshore Oil and Gas Platforms in the Gulf of Mexico. Environ. Sci. Technol. 2020, 54, 3530–3538.
  34. Riddick, S.N.; Mauzerall, D.L.; Celia, M.; Harris, N.R.P.; Allen, G.; Pitt, J.; Staunton-Sykes, J.; Forster, G.L.; Kang, M.; Lowry, D.; et al. Methane emissions from oil and gas platforms in the North Sea. Atmos. Chem. Phys. 2019, 19, 9787–9796.
  35. Riddick, S.N.; Connors, S.; Robinson, A.D.; Manning, A.J.; Jones, P.S.D.; Lowry, D.; Nisbet, E.; Skelton, R.L.; Allen, G.; Pitt, J.; et al. Estimating the size of a methane emission point source at different scales: From local to landscape. Atmos. Chem. Phys. 2017, 17, 7839–7851.
  36. Riddick, S.N.; Hancock, B.R.; Robinson, A.D.; Connors, S.; Davies, S.; Allen, G.; Pitt, J.; Harris, N.R.P. Development of a low-maintenance measurement approach to continuously estimate methane emissions: A case study. Waste Manag. 2018, 73, 210–219.
  37. Riddick, S.N.; Cheptonui, F.; Yuan, K.; Mbua, M.; Day, R.; Vaughn, T.L.; Duggan, A.; Bennett, K.E.; Zimmerle, D.J. Estimating Regional Methane Emission Factors from Energy and Agricultural Sector Sources Using a Portable Measurement System: Case Study of the Denver–Julesburg Basin. Sensors 2022, 22, 7410.
  38. Golston, L.M.; Pan, D.; Sun, K.; Tao, L.; Zondlo, M.A.; Eilerman, S.J.; Peischl, J.; Neuman, J.A.; Floerchinger, C. Variability of Ammonia and Methane Emissions from Animal Feeding Operations in Northeastern Colorado. Environ. Sci. Technol. 2020, 54, 11015–11024.
  39. Laubach, J.; Kelliher, F.M.; Knight, T.W.; Clark, H.; Molano, G.; Cavanagh, A. Methane emissions from beef cattle—A comparison of paddock- and animal-scale measurements. Aust. J. Exp. Agric. 2008, 48, 132.
  40. Flesch, T.; Wilson, J.; Harper, L.; Crenna, B. Estimating gas emissions from a farm with an inverse-dispersion technique. Atmos. Environ. 2005, 39, 4863–4874.
  41. Allen, G.; Hollingsworth, P.; Kabbabe, K.; Pitt, J.R.; Mead, M.I.; Illingworth, S.; Roberts, G.; Bourn, M.; Shallcross, D.E.; Percival, C.J. The development and trial of an unmanned aerial system for the measurement of methane flux from landfill and greenhouse gas emission hotspots. Waste Manag. 2019, 87, 883–892.
  42. Chen, J.; Dietrich, F.; Maazallahi, H.; Forstmaier, A.; Winkler, D.; Hofmann, M.E.G.; Denier van der Gon, H.; Röckmann, T. Methane emissions from the Munich Oktoberfest. Atmos. Chem. Phys. 2020, 20, 3683–3696.
  43. Shah, A.; Laurent, O.; Broquet, G.; Philippon, C.; Kumar, P.; Allegrini, E.; Ciais, P. Determining methane mole fraction at a landfill site using the Figaro Taguchi gas sensor 2611-C00 and wind direction measurements. Environ. Sci. Atmos. 2024, 4, 362–386.
  44. Edie, R.; Robertson, A.M.; Field, R.A.; Soltis, J.; Snare, D.A.; Zimmerle, D.; Bell, C.S.; Vaughn, T.L.; Murphy, S.M. Constraining the accuracy of flux estimates using OTM 33A. Atmos. Meas. Tech. 2020, 13, 341–353.
More
© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license; additional terms may apply. By using this site, you agree to the Terms and Conditions and Privacy Policy.
Upload a video for this entry
Information
Subjects: Meteorology & Atmospheric Sciences
Contributor MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register : Stuart N. Riddick
View Times: 147
Online Date: 20 May 2025
Table of Contents
    1000/1000
    Hot Most Recent
    Notice
    You are not a member of the advisory board for this topic. If you want to update advisory board member profile, please contact office@encyclopedia.pub.
    OK
    Confirm
    Only members of the Encyclopedia advisory board for this topic are allowed to note entries. Would you like to become an advisory board member of the Encyclopedia?
    Yes
    No
    Academic Video Service
    Feedback