See how detailed CH₄ and CO₂ plume structure can provide greater context for source attribution, combustion performance, and emissions quantification.
Read time: 5 minutes
When measuring an emissions plume, the final quantified emission rate is important, but it is only part of the information collected during a survey. There is also valuable information contained in the shape of the plume itself.
Where does an enhancement begin and end? Are there several distinct peaks within the plume? Do methane (CH₄) and carbon dioxide (CO₂) rise and fall together as the drone passes through it? And if both gases appear in the same area, are they actually part of the same emissions process or simply two different plumes that happen to overlap?
Being able to answer those questions depends on preserving the fine-scale structure within the measurement.
That substructure gives us another layer of explainability behind the final emissions number. Rather than looking only at whether CH₄ and CO₂ were present in the same broad region, we can examine the smaller peaks, troughs, and concentration changes within each flight transect and see how those patterns relate to one another.
Looking Inside the Plume
Industrial sites can contain several emissions sources operating at the same time. A methane vent may be located near a turbine exhaust, while a flare produces both combustion CO₂ and some residual CH₄. Depending on wind direction and site geometry, those different plumes can occupy the same portion of a measurement plane.
That means spatial overlap alone is not enough to establish that two gases came from the same process.
The internal structure of the plume gives us more information.
Consider a vent releasing CH₄ and a nearby turbine producing CO₂. Their broad plume envelopes might overlap as they move downwind, but the smaller concentration fluctuations within those plumes may behave independently. A flare plume is different: residual CH₄ and combustion-generated CO₂ originate from the same process and initially share the same transport and dilution history.
When the detailed CH₄ and CO₂ traces repeatedly rise and fall together within a plume, that correspondence provides additional evidence that the gases may share an emissions process.
The distinction is subtle but important. Two gases can appear in the same general area without being meaningfully related. Looking at the internal peaks and troughs helps us move beyond broad overlap and ask whether the gases are actually behaving together.
From Overlap to Stronger Evidence
A single matching peak is not enough to make a source attribution. Turbulence, plume mixing, and chance alignment can all create similar-looking features.
The evidence becomes more useful when the same relationship appears repeatedly.
If CH₄ and CO₂ enhancements remain aligned within individual transects, if their internal features repeatedly correspond across neighboring flight paths and elevations, and if the measured wind field and known source geometry support the same interpretation, we have a much stronger basis for associating the gases with a common emissions process.
This does not mean correlation alone proves causality. Separate sources can sometimes develop correlated behavior through shared atmospheric transport. Source attribution still requires the full context of the measurement, including wind, geometry, sensor response, operating conditions, and the persistence of the observed relationship.
What plume substructure provides is another piece of physical evidence.
For an operator, that means the measurement can help explain not only that an enhancement was observed, but also why the data may support one source interpretation over another.
Why This Is Particularly Useful for Combustion Sources
The relationship between CH₄ and CO₂ is especially relevant around combustion.
Carbon dioxide is a primary product of hydrocarbon combustion, while methane measured in the same plume can represent fuel that was not fully combusted. When detailed CH₄ and CO₂ enhancements repeatedly correspond within a flare plume, that pattern can support the interpretation that both gases are associated with the combustion process.
This has practical value when investigating flare performance.
The relationship between the two gases does not, by itself, calculate flare destruction efficiency or prove the source beyond all alternatives. But combined with the measured wind field, source location, gas composition, and other operating information, it provides a richer dataset for understanding combustion behavior and identifying situations that may warrant further investigation.
The Measurement Cadence That Makes This Visible
Seeing this fine-scale structure requires enough temporal resolution to preserve changes occurring as the drone moves through the plume.
SeekOps reports gas concentration at 10 samples per second (10 Hz) using a passive flow-through sensor architecture. At a flight speed of approximately 5 meters per second, successive reported measurements are separated by roughly 50 centimeters along the flight path.
During even a relatively short plume crossing, that provides multiple observations across the plume envelope and allows analysts to compare the timing and shape of CH₄ and CO₂ enhancements within an individual transect.
The important point is not simply that SeekOps collects data at 10 Hz. The value of the 10 Hz record is the plume structure it preserves.
Reporting cadence is also only one component of temporal resolution. Sensor response, air exchange through the cell, internal mixing, processing, clock alignment, noise, and any onboard averaging all influence the smallest feature that can be reliably interpreted.
SeekOps’ passive architecture avoids the transport through tubing associated with pumped sampling systems, which can introduce delays or smoothing depending on system design. For example, Morales et al. 2022 shows broad agreement between a laser-based system and AirCore system for the purpose of mass balance estimation, but only the laser-based system offered the fine temporal resolution required to resolve genuine plume substructure. Together, the passive measurement approach and 10 Hz reporting cadence preserve useful within-plume structure for detailed analysis. The active system in the Morales et al. study was shown to smooth the native signal by 20 seconds, while introducing a lag of 7 seconds. Both of these instrumental effects can confound the association of plumes to individual industrial sources, a form of information loss.
Why Granularity Matters Beyond Multi-Gas Measurements
The same principle applies even when measuring methane alone.
Methane plumes are not smooth, stationary clouds. They stretch, meander, split apart, and change shape in response to wind, turbulence, nearby structures, and changing atmospheric conditions. Two observations taken fractions of a second apart can therefore encounter meaningfully different concentrations.
That variability is not necessarily noise that should be averaged away. It can contain information about plume boundaries, narrow enhancement regions, plume meandering, and whether the measurement plane fully captured the emissions.
By retaining detailed concentration profiles across successive transects, SeekOps can examine how those features evolve throughout the survey. This provides context for the observations that ultimately contribute to the quantified emission rate and can help analysts understand why a particular measurement looks the way it does.
The goal is not simply to collect more data. It is to preserve more of what actually happened in the atmosphere.
Building the Full Picture
Fine-scale correspondence between CH₄ and CO₂ is not interpreted in isolation.
Analysts consider whether the gas records are properly synchronized, whether correspondence exists within the plume rather than only in the broad envelope, whether similar patterns recur across neighboring transects, and whether the wind field and source geometry support the proposed source.
Sensor response, signal-to-noise ratio, background corrections, and operating conditions also matter. There is no single correlation coefficient or threshold that can determine source attribution for every survey.
This broader context is what turns measurement granularity into explainability.
A detailed plume record gives analysts more evidence to work with, but that evidence becomes most useful when combined with the other information SeekOps collects in situ, including wind speed and direction, temperature, pressure, position, and site context.
From Detecting a Plume to Understanding It
Eventually, hundreds or thousands of individual observations have to become something useful: an emission rate, a plume visualization, an uncertainty estimate, or information an operator can act on.
The better we preserve the structure within those observations, the more context we have behind the final result.
SeekOps’ 10 Hz passive measurement approach allows us to retain useful fine-scale plume structure while the drone moves through the measurement plane. When CH₄ and CO₂ are measured together, that structure gives us an opportunity to examine not only whether the gases overlap, but whether their internal behavior provides evidence that they share an emissions process.
That is the real value of data granularity.
It isn’t simply about how many measurements are collected each second. It is about what those measurements allow us to explain.
References
Morales, R., Ravelid, J., Vinkovic, K., Korbeń, P., Tuzson, B., Emmenegger, L., Chen, H., Schmidt, M., Humbel, S., & Brunner, D. (2022). Controlled-release experiment to investigate uncertainties in UAV-based emission quantification for methane point sources. Atmospheric Measurement Techniques, 15, 2177–2198. https://doi.org/10.5194/amt-15-2177-2022
