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Air matters: perspectives, knowledge, and insights on emissions monitoring
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Closing gaps in climate data: advancing N₂O measurement with DFM
Working on AWAIRE as part of my Master’s thesis made this project especially meaningful to me. Led by Explicit, the project focused on developing and field-testing the DFM method for measuring nitrous oxide (N₂O) emissions from wastewater treatment plants. Over a period of two years, we took the DFM-N₂O method from a conceptual idea to an ISO-accredited solution – through controlled release campaigns, technical upgrades, and extensive fieldwork at BIOFOS Avedøre in Denmark. A smarter way to measure In comparative trials, the DFM-N₂O method consistently matched results from traditional approaches with the additional advantage of reducing uncertainty in parameters which are traditionally hard to measure, such as aeration airflow. Applying the DFM-N₂O method on various measurement campaigns around the world, it became clear that the advantage of this remote sensor technology is especially valuable for wastewater treatment sites designed with tickling filters or surface aerators. Why it matters N₂O is a potent greenhouse gas, and interest in monitoring and mitigating it is growing globally. This reinforces the importance of developing accurate measurement tools in such a pioneering field. What’s missing often is reliable, actionable data. Contributing to closing that gap and seeing the method applied across different sites globally has
Why you should care about destruction removal efficiency (DRE)
At Explicit, we work with operators around the world to help them better understand and optimise their flare operations through accurate, data-driven measurement of destruction removal efficiency (DRE). DRE is the ability of a flare system to convert or destroy hydrocarbon emissions through combustion. It is a key indicator of how effectively climate-damaging gases are being transformed into safer by-products, such as water vapour and carbon dioxide. Understanding and improving your DRE is not just about regulatory compliance – it’s about environmental responsibility and operational excellence. Why is DRE Important? Here are the three main reasons why DRE is critical to monitor and optimise: Environmental impact: high DRE means a higher percentage of harmful emissions are destroyed, reducing the environmental impact of flaring. Low DRE can lead to considerable amounts of unburned hydrocarbons released into the atmosphere, contributing to both air pollution and climate change. Safety and regulatory compliance: various jurisdictions have regulations in place on flare performance and emissions. High DRE values can help operators demonstrate compliance. Initiatives such as OGMP 2.0 require Level 5 reporting for oil and gas assets, meaning reconciling DRE from airborne measurements with process data becomes essential. Operational efficiency: optimising and understanding of DRE
Why probability of detection doesn’t belong in methane emissions quantification
In the world of fugitive leak detection, probability of detection (POD) is a popular metric. It tells us how likely an individual sensor or network of sensors is to detect a gas signal in a given scenario and is very helpful for leak detection and repair (LDAR) programmes. But when it comes to quantifying methane emissions using the Drone Flux Measurement (DFM) Method, probability of detection is the wrong metric to focus on. Why probability of detection falls short in quantification Quantifying an emission rate is a far more complex measurement challenge than simply detecting a gas signal. Gas sensors have detection limits, but for aerial site-level surveys, most sensors today are already sensitive enough. The real issue isn’t the signal response: it’s signal noise. The key question is: how low can we detect a signal, before background noise overwhelms it? This is best expressed through measurement uncertainty. Even if a method has an exceptional detection capability, that alone tells us nothing about its ability to quantify emissions. What really matters for quantification? Beyond detection, accurate quantification depends on: Wind flow: you need precise atmospheric data to determine how methane moves through an area. Measurement uncertainty: the lower the uncertainty,
An overlooked challenge in methane emissions monitoring: background concentration isn’t just background
Methane emissions monitoring is evolving rapidly, with drone-based surveys providing more accurate and reliable solutions than ever before. However, one critical challenge remains: How do we correctly account for background methane concentrations in the atmosphere? And how do we subtract them correctly in the reported emission rate? At Explicit, we’ve spent over a decade conducting aerial emission surveys, and one of our key insights is that background methane levels are far more dynamic than traditional survey methods assume. They cannot accurately be expressed – or deducted – as an averaged value over a large survey area like a vertical measurement plane. At least not in a spot observation. The misconception of a fixed background level Most site-level survey techniques treat methane background as a singular, consolidated value, typically between 1.7 and 2.0 ppm, depending on the source. The assumption is that this single number can be subtracted from measured concentrations to determine the actual emissions from a site. But what if that assumption is flawed? The reality is that background concentrations of methane fluctuate significantly at the micro-atmospheric level. These variations can’t be accurately captured by applying a single background value across an entire survey area. If background is treated
Rethinking methane emissions monitoring: It’s not about the leaks
When we talk about methane emissions, the conversation almost always starts with leaks. How to find them, measure them, and mitigate them. At a methane mitigation conference last year, this talk was all the same: leaks, leaks, leaks. As if insufficient maintenance protocols were the root cause of the world’s climate crisis. We beg to differ. If we are serious about reducing methane emissions, we need to shift the focus from leaks to process emissions. This is where the real reduction potential lies: understanding baseline emissions, operational scenarios, and optimizing processes to reduce them. Why process emissions matter more Our field surveys consistently show that the major contributors to methane emissions are not leaks. Instead, vents, flares, turbines, and other sources make up the bulk of emissions. While leaks are certainly important, maintenance protocols alone won’t deliver the emission reductions the industry needs. In fact, maintenance itself can sometimes introduce significant release risks, such as during well workovers. Yet, the industry’s vocabulary remains outdated. Methane emissions monitoring has its roots in leak detection: LDAR (Leak Detection and Repair) surveys came before site-level drone surveys. Bottom-up approaches came before top-down ones. As a result, we continue to use the term “leaks”,
Measurement validity: a frequently overlooked key to accurate methane emissions monitoring
A common question in emissions monitoring is: “What’s your measurement uncertainty?” But a far more important—and often overlooked—question to answer is: “How do you know if your measurements are valid?” Without verifying the validity of your sample, you’re essentially sampling blind. You might as well be guessing. And yet, even experienced professionals who work with advanced detection technologies and survey methods often overlook this fundamental principle. The thing is: you cannot express a meaningful measurement uncertainty without first ensuring that your sample is valid. The role of objective quality parameters So, how do you determine if your measurements are valid? You need objective quality parameters – clear criteria to guide your survey and confirm that your data is trustworthy. These parameters help answer critical questions like: Have you captured the full emission source, or just parts of it? Is the emission you detected actually coming from the target source, or could it be from another adjacent source? Is your sample robust enough to produce reliable results, or should it be discarded? If you’re into the technical details, sample validity is embedded in international standards like ISO 17025, more specifically in clause 7.7. How we ensures validity in methane emissions monitoring
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