Characterizing Tracer Flux Ratio Methods for Methane Emission Quantification Using Small Unmanned Aerial Systems

Ezekiel Alaba1, Bryan Rainwater2, Ryan Brouwer2, Daniel Zimmerle2

1Mechanical Engineering, Colorado State University, 2CSU Energy Institute

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Background​

  • Leak detection and quantification of oil and gas emissions are essential for assessing impacts on air quality, mitigating losses from operations, and improving safety.
  • Recent research on methane detection and quantification using drone platforms has significantly advanced, showcasing various methodologies and technologies that enhance the accuracy and efficiency of emissions monitoring.
  • The tracer flux ratio (TFR) method is a technique to quantify emissions of greenhouse gases and pollutants. It uses a tracer gas with a known flow rate to estimate emissions and is particularly useful in complex environments like natural gas plants, where direct measurements are difficult. Importantly, TFR does not require meteorological or dispersion modeling.
Tracer Drone flying at METEC
Tracer Drone at the METEC facility

Methodology​

Box one: ∆[𝑪𝑯𝟒]=𝜶FCH4 Box Two: ∆[𝑪𝑯𝟒]/∆ [T]=𝜶FCH4/𝜶FT=FCH4/FT Box Three: FCH4=FT*∆[𝑪𝑯𝟒]/∆[T]

∆ [𝑪𝑯𝟒 ] Downwind Methane Concentration, 𝑭CH𝟒 Emission Rate (CH4)
∆ [𝑻] Downwind Tracer Concentration, 𝑭T Tracer Flowrate

Showing the N2O Plume, CH4 Plume, and Co-dispersion
  • Estimates facility-level emissions without meteorological data.
  • Simplified estimation process by eliminating modeling complexities
  • Tracer drone allows optimized flight paths for better downwind concentration measurement.
  • Provides more accurate and comprehensive emission data.
  • Can easily adapt to variations in wind direction.

Results​

Curtain flight patterns with projected longitude, projected latitude, Altitude, and CH4 concentration (PPM)
Figure 1a: Curtain flight pattern.
Arc flight patterns with projected longitude, projected latitude, Altitude, and CH4 concentration (PPM)
Figure 1b: Arc flight pattern.
Time series plot of CH4 and tracer concentrations with Methane and Nitrous Oxide PPM
Figure 2a: Time series plot of CH4 and tracer concentrations.
Time series plot of CH4 and tracer concentrations with Methane and Nitrous Oxide PPM
Figure 2b: Time series plot of CH4 and tracer concentrations
Linear regression of CH4 vs. Tracer concentrations with Methane ppm and Nitrous Oxide ppm. Slope: 10.44, R2: 0.54
Figure 3a: Linear regression of CH4 vs. Tracer concentrations
Linear regression of CH4 vs. Tracer concentrations with Methane ppm and Nitrous Oxide ppm. Slope: 12.83, R2: 0.20
Figure 3b: Linear regression of CH4 vs. Tracer concentrations
Relative error & uncertainty in CH4 rate estimates with slope, cumulative sum, integrated area, and peak value.
Figure 4 Relative error & uncertainty in CH4 rate estimates.
Spatially interpolated CH4 concentration heat map with altitude, CH4 Concentration (ppm), and East-West Distance (m)
Figure 5 Spatially interpolated CH4 concentration heat map

Conc. Ratio

WA-ER (%)CA-ER (%)
ErrorUncertaintyErrorUncertainty
CurtainArcCurtainArcCurtainArcCurtainArc
Slope

-38.22

-1.59

4.85

25.94-31.7-8.5012.5013.50
Peak Value-39.93

0.38

40.5012.16

-14.5

-17.8

19.0111.10
Cum. Sum-59.17-35.0524.67

2.30

-57.6-35.4

7.30

3.70
Int. Area-58.72-34.1524.972.66-57.6-35.47.303.70

Conclusions and Next Steps


Key Outcomes

  1. Development of a robust Tracer Flux Ratio model that minimizes uncertainties in methane emission quantification and provides a framework for consistent comparisons across equipment.
  2. Innovations in drone-based emission measurements that overcome biases and blind spots inherent in ground-based measurements, enabling improved coverage of elevated and distributed sources.
  3. Spatial insights into facility-level methane emission profiles, offering a clearer picture of emission variability and helping identify localized high-emission areas.
  4. Best practices for drone flight path optimization, demonstrating that tailored flight patterns (curtain or arc) improve capture of plume structures and reduce sampling gaps.

Next Steps

  1. Design of Experiments for uncertainty analysis: A structured approach will be used to evaluate six experimental factors, each with three levels. This factorial design will allow systematic testing of interactions, quantification of uncertainty, and identification of the most influential parameters in drone-based emission estimates.
  2. Pipeline applications: Expanding drone deployment for underground pipeline methane emission detection and quantification.
  3. Rotor effect evaluation: Comparing measured plume transects with simulated UAV results to characterize rotor-induced mixing and its impact on observed plume structure.

Acknowlegments and Contact Information​

Funding

This material is based upon work supported by the Department of Energy under Award Number(s) DE-FE0032276.

Project PI:
Bryan Rainwater
Research Scientist
CSU Energy Institute, Colorado State University
[email protected]

Poster Author:
Ezekiel Alaba
Doctoral Student
Mechanical Engineering, Colorado State University
[email protected]


References​

Alaba, Ezekiel, et al. “Characterizing Tracer Flux Ratio Methods for Methane Emission Quantification Using Small Unmanned Aerial System.” Methane 4.3 (2025): 18.

Equipment at the METEC Site