Comparison of the Gaussian plume and puff atmospheric dispersion models for methane modeling on oil and gas sites

Jia, Meng; Daniels, William; Hammerling, Dorit

doi:10.26434/chemrxiv-2023-hc95q-v2

Cited by 3 publications

(3 citation statements)

References 25 publications

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“…For emissions >1 kg/h, the framework has an average quantification error of 4.3%, and 90% of rate estimates fall within a factor of [−2.0, 1.8] or a percent difference of [−49%, 79%] from the true emission rate. At a high level, the framework simulates methane concentrations from all potential sources on a given site using the Gaussian puff atmospheric dispersion model . The simulation predictions are then pattern matched against the actual CMS concentration observations to determine the most likely emission source and rate.…”

Section: Methodsmentioning

confidence: 99%

“…At a high level, the framework simulates methane concentrations from all potential sources on a given site using the Gaussian puff atmospheric dispersion model. 44 The simulation predictions are then pattern matched against the actual CMS concentration observations to determine the most likely emission source and rate. Quantification uncertainty is provided by resampling the available data many times, pattern matching on each sample, and then taking the 5 th and 95 th percentiles of the resulting sample-specific rate estimates.…”

Section: Methodsmentioning

confidence: 99%

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Toward Multiscale Measurement-Informed Methane Inventories: Reconciling Bottom-Up Site-Level Inventories with Top-Down Measurements Using Continuous Monitoring Systems

Daniels

Wang

Ravikumar

et al. 2023

Environ. Sci. Technol.

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Government policies and corporate strategies aimed at reducing methane emissions from the oil and gas sector increasingly rely on measurement-informed, site-level emission inventories, as conventional bottom-up inventories poorly capture temporal variability and the heavy-tailed nature of methane emissions. This work is based on an 11-month methane measurement campaign at oil and gas production sites. We find that operator-level top-down methane measurements are lower during the end-of-project phase than during the baseline phase. However, gaps persist between end-of-project topdown measurements and bottom-up site-level inventories, which we reconcile with high-frequency data from continuous monitoring systems (CMS). Specifically, we use CMS to (i) validate specific snapshot measurements and determine how they relate to the temporal emission profile of a given site and (ii) create a measurement-informed, site-level inventory that can be validated with top-down measurements to update conventional bottom-up inventories. This work presents a real-world demonstration of how to reconcile CMS rate estimates and top-down snapshot measurements jointly with bottom-up inventories at the site level. More broadly, it demonstrates the importance of multiscale measurements when creating measurement-informed, site-level emission inventories, which is a critical aspect of recent regulatory requirements in the Inflation Reduction Act, voluntary methane initiatives such as the Oil and Gas Methane Partnership 2.0, and corporate strategies.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Toward Multiscale Measurement-Informed Methane Inventories: Reconciling Bottom-Up Site-Level Inventories with Top-Down Measurements Using Continuous Monitoring Systems

Daniels

Wang

Ravikumar

et al. 2023

Environ. Sci. Technol.

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“…The modeling of a dynamic system can be categorized broadly into physics-based modeling and data-driven modeling. Physics-based atmospheric dispersion models typically fall into the categories of box models, Gaussian models, Lagrangian models or Eulerian models [20][21][22][23][24]. Box models are overly simplified since they assume that emissions are evenly distributed throughout a fixed volume.…”

Section: Introductionmentioning

confidence: 99%

A Control-Theoretic Spatio-Temporal Model for Wildfire Smoke Propagation Using UAV-Based Air Pollutant Measurements

Ragbir,

Kaduwela,

Lan

et al. 2024

Drones

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Wildfires have the potential to cause severe damage to vegetation, property and most importantly, human life. In order to minimize these negative impacts, it is crucial that wildfires are detected at the earliest possible stages. A potential solution for early wildfire detection is to utilize unmanned aerial vehicles (UAVs) that are capable of tracking the chemical concentration gradient of smoke emitted by wildfires. A spatiotemporal model of wildfire smoke plume dynamics can allow for efficient tracking of the chemicals by utilizing both real-time information from sensors as well as future information from the model predictions. This study investigates a spatiotemporal modeling approach based on subspace identification (SID) to develop a data-driven smoke plume dynamics model for the purposes of early wildfire detection. The model was learned using CO2 concentration data which were collected using an air quality sensor package onboard a UAV during two prescribed burn experiments. Our model was evaluated by comparing the predicted values to the measured values at random locations and showed mean errors of 6.782 ppm and 30.01 ppm from the two experiments. Additionally, our model was shown to outperform the commonly used Gaussian puff model (GPM) which showed mean errors of 25.799 ppm and 104.492 ppm, respectively.

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Estimating Methane Emission Durations Using Continuous Monitoring Systems

Daniels,

Jia,

Hammerling

2024

Environ. Sci. Technol. Lett.

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We propose a method for estimating methane emission durations on oil and gas sites, referred to as the Probabilistic Duration Model (PDM), that uses concentration data from continuous monitoring systems (CMS). The PDM probabilistically addresses a key limitation of CMS: nondetect times, or the times when wind blows emitted methane away from the CMS sensors (resulting in no detections). Output from the PDM can be used to bound the duration of emissions detected by survey-based technologies, such as plane or satellites, that have limited ability to characterize durations due to the typically low temporal frequency (e.g., quarterly) at which they observe a given source. Linear regression indicates that the PDM has a bias of −4.9% (R 2 = 0.80) when evaluated on blinded controlled releases at the Methane Emissions Technology Evaluation Center (METEC), with 86.8% of estimates within a factor of 2× error from the true duration. We apply the PDM to a typical production site in the Appalachian Basin and use it to bound the duration of survey-based measurements. We find that failing to account for CMS nondetect times results in underestimated emission durations of up to a factor of 65× (6400%) on this site.

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Comparison of the Gaussian plume and puff atmospheric dispersion models for methane modeling on oil and gas sites

Cited by 3 publications

References 25 publications

Toward Multiscale Measurement-Informed Methane Inventories: Reconciling Bottom-Up Site-Level Inventories with Top-Down Measurements Using Continuous Monitoring Systems

Toward Multiscale Measurement-Informed Methane Inventories: Reconciling Bottom-Up Site-Level Inventories with Top-Down Measurements Using Continuous Monitoring Systems

A Control-Theoretic Spatio-Temporal Model for Wildfire Smoke Propagation Using UAV-Based Air Pollutant Measurements

Estimating Methane Emission Durations Using Continuous Monitoring Systems

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