Articles | Volume 18, issue 20
https://doi.org/10.5194/amt-18-5687-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-18-5687-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
22 Oct 2025
Research article |
| 22 Oct 2025
| 22 Oct 2025
Evaluating the feasibility of using downwind methods to quantify point source oil and gas emissions using continuously monitoring fence-line sensors
The Energy Institute, Colorado State University, CO, 80524, Fort Collins, USA
Stuart N. Riddick
The Energy Institute, Colorado State University, CO, 80524, Fort Collins, USA
Department of Science, Engineering and Aviation, University of the Highlands and Islands Perth, Crieff Road, Perth, PH1 2NX, UK
Elijah Kiplimo
The Energy Institute, Colorado State University, CO, 80524, Fort Collins, USA
Kira B. Shonkwiler
The Energy Institute, Colorado State University, CO, 80524, Fort Collins, USA
Anna Hodshire
The Energy Institute, Colorado State University, CO, 80524, Fort Collins, USA
Daniel Zimmerle
The Energy Institute, Colorado State University, CO, 80524, Fort Collins, USA
Related authors
Stuart N. Riddick, Riley Ancona, Mercy Mbua, Clay S. Bell, Aidan Duggan, Timothy L. Vaughn, Kristine Bennett, and Daniel J. Zimmerle
Atmos. Meas. Tech., 15, 6285–6296, https://doi.org/10.5194/amt-15-6285-2022, https://doi.org/10.5194/amt-15-6285-2022, 2022
Short summary
Short summary
This describes controlled release experiments at the METEC facility in Fort Collins, USA, that investigates the accuracy and precision of five methods commonly used to measure methane emissions. Methods include static/dynamic chambers, hi flow sampling, a backward Lagrangian stochastic method, and a Gaussian plume method. This is the first time that methods for measuring CH4 emissions from point sources less than 200 g CH4 h−1 have been quantitively assessed against references and each other.
Leah D. Gibson, Ezra J. T. Levin, Ethan Emerson, Nick Good, Anna Hodshire, Gavin McMeeking, Kate Patterson, Bryan Rainwater, Tom Ramin, and Ben Swanson
Atmos. Chem. Phys., 25, 2745–2762, https://doi.org/10.5194/acp-25-2745-2025, https://doi.org/10.5194/acp-25-2745-2025, 2025
Short summary
Short summary
From fall 2021 to summer 2023, SAIL-Net, a network of six aerosol measurement nodes, was deployed in the East River watershed (Colorado, USA) to study aerosol variability across space and time in mountainous terrain. We found that aerosol variability is influenced by elevation differences, with the most representative site in the region changing seasonally, suggesting aerosol spatial variability also varies seasonally. This work offers a blueprint for future studies in other mountainous regions.
Stuart N. Riddick, Riley Ancona, Mercy Mbua, Clay S. Bell, Aidan Duggan, Timothy L. Vaughn, Kristine Bennett, and Daniel J. Zimmerle
Atmos. Meas. Tech., 15, 6285–6296, https://doi.org/10.5194/amt-15-6285-2022, https://doi.org/10.5194/amt-15-6285-2022, 2022
Short summary
Short summary
This describes controlled release experiments at the METEC facility in Fort Collins, USA, that investigates the accuracy and precision of five methods commonly used to measure methane emissions. Methods include static/dynamic chambers, hi flow sampling, a backward Lagrangian stochastic method, and a Gaussian plume method. This is the first time that methods for measuring CH4 emissions from point sources less than 200 g CH4 h−1 have been quantitively assessed against references and each other.
Nicole A. June, Anna L. Hodshire, Elizabeth B. Wiggins, Edward L. Winstead, Claire E. Robinson, K. Lee Thornhill, Kevin J. Sanchez, Richard H. Moore, Demetrios Pagonis, Hongyu Guo, Pedro Campuzano-Jost, Jose L. Jimenez, Matthew M. Coggon, Jonathan M. Dean-Day, T. Paul Bui, Jeff Peischl, Robert J. Yokelson, Matthew J. Alvarado, Sonia M. Kreidenweis, Shantanu H. Jathar, and Jeffrey R. Pierce
Atmos. Chem. Phys., 22, 12803–12825, https://doi.org/10.5194/acp-22-12803-2022, https://doi.org/10.5194/acp-22-12803-2022, 2022
Short summary
Short summary
The evolution of organic aerosol composition and size is uncertain due to variability within and between smoke plumes. We examine the impact of plume concentration on smoke evolution from smoke plumes sampled by the NASA DC-8 during FIREX-AQ. We find that observed organic aerosol and size distribution changes are correlated to plume aerosol mass concentrations. Additionally, coagulation explains the majority of the observed growth.
Anna L. Hodshire, Ezra J. T. Levin, A. Gannet Hallar, Christopher N. Rapp, Dan R. Gilchrist, Ian McCubbin, and Gavin R. McMeeking
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2022-216, https://doi.org/10.5194/amt-2022-216, 2022
Publication in AMT not foreseen
Short summary
Short summary
The new Continuous Flow Diffusion Chamber-Ice Activation Spectrometer collected 4 months of ice nucleating particle (INP) measurements at a 5-minute resolution at the mountainside Storm Peak Laboratory. Most long-term INP measurements are at a time resolution of a day or longer: our instrument is a promising advance towards high-resolution long-term INP measurements. We observe higher peak INP concentrations than previous mountain studies, possibly due to the higher time resolution of our data.
Anna L. Hodshire, Ezra J. T. Levin, A. Gannet Hallar, Christopher N. Rapp, Dan R. Gilchrist, Ian McCubbin, and Gavin R. McMeeking
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2022-29, https://doi.org/10.5194/acp-2022-29, 2022
Preprint withdrawn
Short summary
Short summary
The new Continuous Flow Diffusion Chamber-Ice Activation Spectrometer collected 4 months of ice nucleating particle (INP) measurements at a 5-minute resolution at the mountainside Storm Peak Laboratory. Most long-term INP measurements are at a time resolution of a day or longer: our instrument is a promising advance towards high-resolution long-term INP measurements. We observe higher peak INP concentrations than previous mountain studies, possibly due to the higher time resolution of our data.
Anna L. Hodshire, Emily Ramnarine, Ali Akherati, Matthew L. Alvarado, Delphine K. Farmer, Shantanu H. Jathar, Sonia M. Kreidenweis, Chantelle R. Lonsdale, Timothy B. Onasch, Stephen R. Springston, Jian Wang, Yang Wang, Lawrence I. Kleinman, Arthur J. Sedlacek III, and Jeffrey R. Pierce
Atmos. Chem. Phys., 21, 6839–6855, https://doi.org/10.5194/acp-21-6839-2021, https://doi.org/10.5194/acp-21-6839-2021, 2021
Short summary
Short summary
Biomass burning emits particles and vapors that can impact both health and climate. Here, we investigate the role of dilution in the evolution of aerosol size and composition in observed US wildfire smoke plumes. Centers of plumes dilute more slowly than edges. We see differences in concentrations and composition between the centers and edges both in the first measurement and in subsequent measurements. Our findings support the hypothesis that plume dilution influences smoke aging.
Agnieszka Kupc, Christina J. Williamson, Anna L. Hodshire, Jan Kazil, Eric Ray, T. Paul Bui, Maximilian Dollner, Karl D. Froyd, Kathryn McKain, Andrew Rollins, Gregory P. Schill, Alexander Thames, Bernadett B. Weinzierl, Jeffrey R. Pierce, and Charles A. Brock
Atmos. Chem. Phys., 20, 15037–15060, https://doi.org/10.5194/acp-20-15037-2020, https://doi.org/10.5194/acp-20-15037-2020, 2020
Short summary
Short summary
Tropical upper troposphere over the Atlantic and Pacific oceans is a major source region of new particles. These particles are associated with the outflow from deep convection. We investigate the processes that govern the formation of these particles and their initial growth and show that none of the formation schemes commonly used in global models are consistent with observations. Using newer schemes indicates that organic compounds are likely important as nucleating and initial growth agents.
Lawrence I. Kleinman, Arthur J. Sedlacek III, Kouji Adachi, Peter R. Buseck, Sonya Collier, Manvendra K. Dubey, Anna L. Hodshire, Ernie Lewis, Timothy B. Onasch, Jeffery R. Pierce, John Shilling, Stephen R. Springston, Jian Wang, Qi Zhang, Shan Zhou, and Robert J. Yokelson
Atmos. Chem. Phys., 20, 13319–13341, https://doi.org/10.5194/acp-20-13319-2020, https://doi.org/10.5194/acp-20-13319-2020, 2020
Short summary
Short summary
Aerosols from wildfires affect the Earth's temperature by absorbing light or reflecting it back into space. This study investigates time-dependent chemical, microphysical, and optical properties of aerosols generated by wildfires in the Pacific Northwest, USA. Wildfire smoke plumes were traversed by an instrumented aircraft at locations near the fire and up to 3.5 h travel time downwind. Although there was no net aerosol production, aerosol particles grew and became more efficient scatters.
Cited articles
Allen, D. T.: Methane emissions from natural gas production and use: reconciling bottom-up and top-down measurements, Curr. Opin. Chem. Eng., 5, 78–83, https://doi.org/10.1016/j.coche.2014.05.004, 2014.
Bell, C., Ilonze, C., Duggan, A., and Zimmerle, D.: Performance of Continuous Emission Monitoring Solutions under a Single-Blind Controlled Testing Protocol, Environ. Sci. Technol., 57, 5794–5805, https://doi.org/10.1021/acs.est.2c09235, 2023.
Brown, J. A., Harrison, M. R., Rufael, T., Roman-White, S. A., Ross, G. B., George, F. C., and Zimmerle, D.: Informing Methane Emissions Inventories Using Facility Aerial Measurements at Midstream Natural Gas Facilities, Environ. Sci. Technol., 57, 14539–14547, https://doi.org/10.1021/acs.est.3c01321, 2023.
Burba, G.: Eddy Covariance Method for Scientific, Industrial, Agricultural, and Regulatory Applications: A Field Book on Measuring Ecosystem Gas Exchange and Areal Emission Rates, Li-COR Biogeosciences, https://doi.org/10.13140/RG.2.1.4247.8561, 2013.
Carbon Mapper: Carbon Mapper – Science & Technology, Carbon Mapper, https://carbonmapper.org/work/science, last access: 19 February 2025.
Casal, J.: Chapter 6 Atmospheric dispersion of toxic or flammable clouds, in: Industrial Safety Series, vol. 8, Elsevier, 195–248, https://doi.org/10.1016/S0921-9110(08)80008-0, 2008.
Chan, E., Vogel, F., Smyth, S., Barrigar, O., Ishizawa, M., Kim, J., Neish, M., Chan, D., and Worthy, D. E. J.: Hybrid bottom-up and top-down framework resolves discrepancies in Canada's oil and gas methane inventories, Commun. Earth Environ., 5, 566, https://doi.org/10.1038/s43247-024-01728-6, 2024.
Colorado State University: Methane Emissions Technology Evaluation Center (METEC), Colorado State University, https://metec.colostate.edu/, (last access: 18 February 2025), 2025a.
Colorado State University: Advancing Development of Emissions Detection (ADED): Final Report, Colorado State University, https://metec.colostate.edu/wp-content/uploads/sites/37/2025/03/ADED-Final-Report_DE-FE0031873_rev-0131025.pdf (last access: 14 October 2025), 2025b.
Conrad, B. M., Tyner, D. R., and Johnson, M. R.: Robust probabilities of detection and quantification uncertainty for aerial methane detection: Examples for three airborne technologies, Remote Sens. Environ., 288, 113499, https://doi.org/10.1016/j.rse.2023.113499, 2023.
Crenna, B.: An introduction to WindTrax, Thunder Beach Scientific, http://www.thunderbeachscientific.com/downloads/introduction.pdf (last access: 19 February 2025), 2006.
Day, R. E., Emerson, E., Bell, C., and Zimmerle, D.: Point Sensor Networks Struggle to Detect and Quantify Short Controlled Releases at Oil and Gas Sites, Sensors, 24, 2419, https://doi.org/10.3390/s24082419, 2024.
Dumortier, P., Aubinet, M., Lebeau, F., Naiken, A., and Heinesch, B.: Point source emission estimation using eddy covariance: Validation using an artificial source experiment, Agric. For. Meteorol., 266–267, 148–156, https://doi.org/10.1016/j.agrformet.2018.12.012, 2019.
LI-COR Biosciences: EddyPro 7 | Software Downloads, https://www.licor.com/env/support/EddyPro/software.html (last access: 30 January 2025), 2021.
Foken, T. and Wichura, B.: Tools for quality assessment of surface-based flux measurements, Agric. For. Meteorol., 78, 83–105, https://doi.org/10.1016/0168-1923(95)02248-1, 1996.
Foster-Wittig, T. A., Thoma, E. D., and Albertson, J. D.: Estimation of point source fugitive emission rates from a single sensor time series: A conditionally-sampled Gaussian plume reconstruction, Atmos. Environ., 115, 101–109, https://doi.org/10.1016/j.atmosenv.2015.05.042, 2015.
Fratini, G., Ibrom, A., Arriga, N., Burba, G., and Papale, D.: Relative humidity effects on water vapour fluxes measured with closed-path eddy-covariance systems with short sampling lines, Agric. For. Meteorol., 165, 53–63, https://doi.org/10.1016/j.agrformet.2012.05.018, 2012.
Horst, T. W. and Lenschow, D. H.: Attenuation of Scalar Fluxes Measured with Spatially-displaced Sensors, Bound.-Layer Meteorol., 130, 275–300, https://doi.org/10.1007/s10546-008-9348-0, 2009.
Hsieh, C.-I., Katul, G., and Chi, T.: An approximate analytical model for footprint estimation of scalar fluxes in thermally stratified atmospheric flows, Adv. Water Resour., 23, 765–772, https://doi.org/10.1016/S0309-1708(99)00042-1, 2000.
Hutchinson, M., Oh, H., and Chen, W.-H.: A review of source term estimation methods for atmospheric dispersion events using static or mobile sensors, Inf. Fusion, 36, 130–148, https://doi.org/10.1016/j.inffus.2016.11.010, 2017.
Ilonze, C., Emerson, E., Duggan, A., and Zimmerle, D.: Assessing the progress of the performance of continuous monitoring solutions under single-blind controlled testing protocol, Environ. Sci. Technol., 58, 10941–10955, https://doi.org/10.1021/acs.est.3c08511, 2024.
Johnson, M. R., Tyner, D. R., and Szekeres, A. J.: Blinded evaluation of airborne methane source detection using Bridger Photonics LiDAR, Remote Sens. Environ., 259, 112418, https://doi.org/10.1016/j.rse.2021.112418, 2021.
Kljun, N., Calanca, P., Rotach, M. W., and Schmid, H. P.: A simple two-dimensional parameterisation for Flux Footprint Prediction (FFP), Geosci. Model Dev., 8, 3695–3713, https://doi.org/10.5194/gmd-8-3695-2015, 2015.
Kormann, R. and Meixner, F. X.: An Analytical Footprint Model For Non-Neutral Stratification, Bound.-Layer Meteorol., 99, 207–224, https://doi.org/10.1023/A:1018991015119, 2001.
Mauder, M. and Foken, T.: Documentation and Instruction Manual of the Eddy Covariance Software Package TK2, Universität Bayreuth, Abteilung Mikrometeorologie, Arbeitsergebnisse Nr. 26, https://epub.uni-bayreuth.de/id/eprint/884/1/ARBERG026.pdf (last access: 14 October 2025), 2004.
Mbua, M., Riddick, S. N., Kiplimo, E., Shonkwiler, K. B., Hodshire, A., and Zimmerle, D.: Evaluating the feasibility of using downwind methods to quantify point source oil and gas emissions using continuous monitoring fence-line sensors, Dryad [data set], https://doi.org/10.5061/dryad.hhmgqnkss, 2025.
Mollel, W., Zimmerle, D., Santos, A., and Hodshire, A.: Using Prototypical Oil and Gas Sites to Model Methane Emissions in Colorado's Denver-Julesburg Basin Using a Mechanistic Emission Estimation Tool, ACS EST Air, 2, 723–735, https://doi.org/10.1021/acsestair.4c00168, 2025.
Moncrieff, J., Clement, R., Finnigan, J., and Meyers, T.: Averaging, Detrending, and Filtering of Eddy Covariance Time Series, in: Handbook of Micrometeorology, vol. 29, edited by: Lee, X., Massman, W., and Law, B., Kluwer Academic Publishers, Dordrecht, 7–31, https://doi.org/10.1007/1-4020-2265-4_2, 2005.
Morin, T. H.: Advances in the Eddy Covariance Approach to CH4 Monitoring Over Two and a Half Decades, J. Geophys. Res. Biogeosciences, 124, 453–460, https://doi.org/10.1029/2018JG004796, 2019.
Polonik, P., Chan, W. S., Billesbach, D. P., Burba, G., Li, J., Nottrott, A., Bogoev, I., Conrad, B., and Biraud, S. C.: Comparison of gas analyzers for eddy covariance: Effects of analyzer type and spectral corrections on fluxes, Agric. For. Meteorol., 272–273, 128–142, https://doi.org/10.1016/j.agrformet.2019.02.010, 2019.
Rey-Sanchez, C., Arias-Ortiz, A., Kasak, K., Chu, H., Szutu, D., Verfaillie, J., and Baldocchi, D.: Detecting Hot Spots of Methane Flux Using Footprint-Weighted Flux Maps, J. Geophys. Res. Biogeosciences, 127, e2022JG006977, https://doi.org/10.1029/2022JG006977, 2022.
Riddick, S. N. and Mauzerall, D. L.: Likely substantial underestimation of reported methane emissions from United Kingdom upstream oil and gas activities, Energy Environ. Sci., 16, 295–304, https://doi.org/10.1039/D2EE03072A, 2023.
Riddick, S. N., Ancona, R., Cheptonui, F., Bell, C. S., Duggan, A., Bennett, K. E., and Zimmerle, D. J.: A cautionary report of calculating methane emissions using low-cost fence-line sensors, Elem. Sci. Anthr., 10, 00021, https://doi.org/10.1525/elementa.2022.00021, 2022a.
Riddick, S. N., Cheptonui, F., Yuan, K., Mbua, M., Day, R., Vaughn, T. L., Duggan, A., Bennett, K. E., and 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, 22, 7410, https://doi.org/10.3390/s22197410, 2022b.
Riddick, S. N., Mbua, M., Anand, A., Kiplimo, E., Santos, A., Upreti, A., and Zimmerle, D. J.: Estimating Total Methane Emissions from the Denver-Julesburg Basin Using Bottom-Up Approaches, Gases, 4, 236–252, https://doi.org/10.3390/gases4030014, 2024a.
Riddick, S. N., Mbua, M., Santos, A., Hartzell, W., and Zimmerle, D. J.: Potential Underestimate in Reported Bottom-up Methane Emissions from Oil and Gas Operations in the Delaware Basin, Atmosphere, 15, 202, https://doi.org/10.3390/atmos15020202, 2024b.
Tagliaferri, F., Invernizzi, M., Capra, F., and Sironi, S.: Validation study of WindTrax reverse dispersion model coupled with a sensitivity analysis of model-specific settings, Environ. Res., 222, 115401, https://doi.org/10.1016/j.envres.2023.115401, 2023.
UNEP: Homepage | The Oil & Gas Methane Partnership 2.0, United Nations Environment Programme, https://www.ogmpartnership.org/ (last access: 14 October 2025), 2024.
US EPA: Standard Operating Procedure for Analysis of US EPA Geospatial Measurement of Air Pollution Remote Emission Quantification by Direct Assessment (GMAP-REQDA) Method Data for Methane Emission Rate Quantification using the Point Source Gaussian Method, U.S. Environmental Protection Agency, Office of Research and Development, National Risk Management Research Laboratory, https://www.epa.gov/sites/production/files/2020-08/documents/otm_33a_appendix_f1_psg_analysis_sop.pdf (last access: 14 October 2025), 2013.
US EPA: Natural Gas and Petroleum Systems in the GHG Inventory: Additional Information on the 1990–2021 GHG Inventory, U.S. Environmental Protection Agency, https://www.epa.gov/ghgemissions/ (last access: 14 October 2025), 2023.
Vickers, D. and Mahrt, L.: Quality Control and Flux Sampling Problems for Tower and Aircraft Data, J. Atmospheric Ocean. Technol., 14, 512–526, https://doi.org/10.1175/1520-0426(1997)014<0512:QCAFSP>2.0.CO;2, 1997.
Webb, E. K., Pearman, G. I., and Leuning, R.: Correction of flux measurements for density effects due to heat and water vapour transfer, Q. J. R. Meteorol. Soc., 106, 85–100, https://doi.org/10.1002/qj.49710644707, 1980.
WindTrax 2.0: Thunder beach scientific, http://thunderbeachscientific.com/windtrax.html (last access: 14 October 2025), 2020.
Zimmerle, D., Dileep, S., and Quinn, C.: Unaddressed Uncertainties When Scaling Regional Aircraft Emission Surveys to Basin Emission Estimates, Environ. Sci. Technol., 58, 6575–6585, https://doi.org/10.1021/acs.est.3c08972, 2024.
Short summary
Accurate methane quantification from oil and gas sites is critical for reliable reporting and climate assessments. This study tested downwind models using point sensors. A non-standard eddy covariance (EC) failed due to instrumentation issues, while the Gaussian plume inverse model (GPIM) and backward Lagrangian stochastic (bLs) model gave more reliable results. The bLs was the most accurate for single sources, with the best performance at 15 min averaging and 5° wind sectors.
Accurate methane quantification from oil and gas sites is critical for reliable reporting and...
