Plume Mapping Research Articles

Quantifying gas emissions through Vertical Radial Plume Mapping based on historical information

Vertical Radial Plume Mapping (VRPM) is a method for measuring fluxes of fugitive surface source gases, lacking sufficient temporal sequence information as it inversely calculates fluxes only based on the current cycle’s measurements. We propose an improved VRPM flux inversion model, incorporating Weighted Iterative Average (WIA) corrected Path-Integrated Concentration (PIC) and Adaptive Weighted Sum of Squared Errors (AWSSE). Compared to the original VRPM, our model introduces historical measurement PICs and reconstructed PICs. Using the Bayesian Optimization Gaussian Processes (BO-GP), we derive optimal weight factor for the hyperparameter of the WIA component in the model. Validation on simulated data highlights the superior flux inversion capability of the WIA-AWSSE-VRPM model across diverse surface sources, efficiently leveraging historical data. The improved VRPM method achieves a significant 9.65% reduction in root mean square error (RMSE) within consistent source sets and decreases RMSE from 1.6979 to 0.1513 across varied sources, underscoring its effectiveness in accurately measuring emissions from fugitive sources. Finally, we apply the model to characterize gas emissions in field experiments.

Antarctic iceberg melt rate variability and sensitivity to ocean thermal forcing

AbstractChanges in iceberg calving fluxes and oceanographic conditions around Antarctica have likely influenced the spatial and temporal distribution of iceberg fresh water fluxes to the surrounding ocean basins. However, Antarctic iceberg melt rate estimates have been limited to very large icebergs in the open ocean. Here we use a remote-sensing approach to estimate iceberg melt rates from 2011 to 2022 for 15 study sites around Antarctica. Melt rates generally increase with iceberg draft and follow large-scale variations in ocean temperature: maximum melt rates for the western peninsula, western ice sheet, eastern ice sheet and eastern peninsula are ~50, ~40, ~5 and ~5 m a−1, respectively. Iceberg melt sensitivity to thermal forcing varies widely, with a best-estimate increase in melting of ~24 m a−1°C−1and range from near-zero to ~100 m a−1°C−1. Variations in water shear likely contribute to the apparent spread in thermal forcing sensitivity across sites. Although the sensitivity of iceberg melt rates to water shear prevents the use of melt rates as a proxy to infer coastal water mass temperature variability, additional coastal iceberg melt observations will likely improve models of Southern Ocean fresh water fluxes and have potential for subglacial discharge plume mapping.

Quantifying gas emissions through vertical radial plume mapping with embedded radial basis function interpolation

The Vertical Radial Plume Mapping (VRPM) method proposed by the U.S. EPA in Other Test Method 10 (OTM-10) is widely used to measure nonpoint source emissions. However, the accuracy of the VRPM is affected by the large number of parameters relative to the measurement data and the presence of measurement noise. To improve the accuracy of flux estimation, we propose to apply evenly spaced radial basis function (RBF) interpolation to the measurement data before using the VRPM. The experiment was tested and validated under various measurement configurations. The results indicate that RBF interpolation techniques, especially Gaussian RBF (GSRBF) interpolation, reduce the mean absolute error and root mean square error of flux estimation, leading to improved flux estimation accuracy. The mean emission rate calculated after RBF interpolation is closer to the true value, indicating that this interpolation technique helps to reflect more accurately the average emission level over time. Even in the most challenging validation set, the percentage error of mean flux decreased by 3.8% after interpolation. The proposed method can be a simple and measurement-cost-effective way to improve the accuracy of VRPM measurements.

Stationary and drone-assisted methane plume localization with dispersion spectroscopy

This work presents stationary and mobile retroreflector-based remote sensing techniques for methane leak localization and quantification using chirped laser dispersion spectroscopy equipped with a custom laser transceiver capable of continuous tracking of a flying drone and coupled with inverse atmospheric gas dispersion modeling. The techniques demonstrate the ability to localize leaks as low as 0.13 g CH4·s−1, which are up to 25 times smaller than those typically observed at natural gas facilities, as well as actively track a moving retroreflector mounted on a lightweight (∼250 g) drone to enable spatial plume reconstruction. This system exhibited a 2.3 ppm-m sensitivity over pathlengths of 40–150 m. Source localization to within ±7 m is demonstrated using a modified horizontal radial plume mapping technique with a stationary retroreflector grid. Meanwhile, the mobile system utilizing a drone-mounted retroreflector is able to localize a controlled release within ±1 m of its source location and estimate leak rates using inversion techniques assuming type B Gaussian plume stability class within ±30% error with respect to the actual low flow rate releases.

Quantifying Emissions from Fugitive Area Sources Using a Hybrid Method of Multi-Path Optical Remote Sensing and Tomographic Inverse-Dispersion Techniques

Reducing methane (CH4) emissions from anthropogenic activities is critical to climate change mitigation efforts. However, there is still considerable uncertainty over the amount of fugitive CH4 emissions due to large-scale area sources and heterogeneous emission distributions. To reduce the uncertainty and improve the spatial and temporal resolutions, a new hybrid method was developed combining optical remote sensing (ORS), computed tomography (CT), and inverse-dispersion modeling techniques on the basis of which a multi-path scanning system was developed. It uses a horizontal radial plume mapping path configuration and adapts a Lagrangian stochastic dispersion mode into CT reconstruction. The emission map is finally calculated by using a minimal curvature tomographic reconstruction algorithm, which introduces smooth constraints at each pixel. Two controlled-release experiments of CH4 were conducted with different configurations, showing relative errors of only 2% and 3%. Compared with results from the single-path inverse-dispersion method (5–175%), the new method can not only derive the emission distribution but also obtain a more accurate emission rate. The outcome of this research would bring broad application of the ORS-CT and inverse-dispersion techniques to other gases and sources.

Detailed LNAPL plume mapping using electrical resistivity tomography inside an industrial building

Detailed LNAPL plume mapping using electrical resistivity tomography inside an industrial building

Monitoring of Anthropogenic Sediment Plumes in the Clarion-Clipperton Zone, NE Equatorial Pacific Ocean

The abyssal seafloor in the Clarion-Clipperton Zone (CCZ) in the NE Pacific hosts the largest abundance of polymetallic nodules in the deep sea and is being targeted as an area for potential deep-sea mining. During nodule mining, seafloor sediment will be brought into suspension by mining equipment, resulting in the formation of sediment plumes, which will affect benthic and pelagic life not naturally adapted to any major sediment transport and deposition events. To improve our understanding of sediment plume dispersion and to support the development of plume dispersion models in this specific deep-sea area, we conducted a small-scale, 12-hour disturbance experiment in the German exploration contract area in the CCZ using a chain dredge. Sediment plume dispersion and deposition was monitored using an array of optical and acoustic turbidity sensors and current meters placed on platforms on the seafloor, and by visual inspection of the seafloor before and after dredge deployment. We found that seafloor imagery could be used to qualitatively visualise the redeposited sediment up to a distance of 100 m from the source, and that sensors recording optical and acoustic backscatter are sensitive and adequate tools to monitor the horizontal and vertical dispersion of the generated sediment plume. Optical backscatter signals could be converted into absolute mass concentration of suspended sediment to provide quantitative data on sediment dispersion. Vertical profiles of acoustic backscatter recorded by current profilers provided qualitative insight into the vertical extent of the sediment plume. Our monitoring setup proved to be very useful for the monitoring of this small-scale experiment and can be seen as an exemplary strategy for monitoring studies of future, upscaled mining trials. We recommend that such larger trials include the use of AUVs for repeated seafloor imaging and water column plume mapping (optical and acoustical), as well as the use of in-situ particle size sensors and/or particle cameras to better constrain the effect of suspended particle aggregation on optical and acoustic backscatter signals.

Evaluation of a Gaussian dispersion transformation technique for tomographic mapping of the concentration field of atmospheric chemicals using multi-path optical remote sensing.

Horizontal radial plume mapping is a cost-effective optical remote sensing method for sensitive mapping concentration distribution of atmospheric chemicals in real time. However, its sparse sampling poses challenges for reconstruction algorithms. Neither non-smooth nor smooth algorithms can recover the realistic plume shape. A new approach called Gaussian dispersion transformation (GDT) has been proposed. It first reconstructs the emission rates from unknown sources. Then concentrations are calculated through a transformation matrix defined by a Gaussian dispersion model. Smoothness regularization is also applied during the reconstruction. The method was evaluated by using randomly generated maps. It shows significant improvement over a reconstructed plume shape. The nearness shows 72%-117% better than the non-negative least-square (NNLS) algorithm and 15%-26% better than the low third derivative (LTD) algorithm. A controlled-release field experiment of methane was also conducted. The realistic concentration distribution was calculated by using a Lagrangian stochastic dispersion model. The GDT algorithm successfully recovered the realistic plume shape. The nearness shows approximately 16% better than the NNLS and the LTD algorithms. Finally, a sensitivity analysis shows that the wind direction and atmospheric stability are the main parameters that affect the performance of the GDT algorithm.

Can Forel–Ule Index Act as a Proxy of Water Quality in Temperate Waters? Application of Plume Mapping in Liverpool Bay, UK

The use of ocean colour classification algorithms, linked to water quality gradients, can be a useful tool for mapping river plumes in both tropical and temperate systems. This approach has been applied in operational water quality programs in the Great Barrier Reef to map river plumes and assess trends in marine water composition and ecosystem health during flood periods. In this study, we used the Forel–Ule colour classification algorithm for Sentinel-3 OLCI imagery in an automated process to map monthly, annual and long-term plume movement in the temperate coastal system of Liverpool Bay (UK). We compared monthly river plume extent to the river flow and in situ water quality data between 2017–2020. The results showed a strong positive correlation (Spearman’s rho = 0.68) between the river plume extent and the river flow and a strong link between the FUI defined waterbodies and nutrients, SPM, turbidity and salinity, hence the potential of the Forel–Ule index to act as a proxy for water quality in the temperate Liverpool Bay water. The paper discusses how the Forel–Ule index could be used in operational water quality programs to better understand river plumes and the land-based inputs to the coastal zones in UK waters, drawing parallels with methods that have been developed in the GBR and Citclops project. Overall, this paper provides the first insight into the systematic long-term river plume mapping in UK coastal waters using a fast, cost-effective, and reproducible workflow. The study created a novel water assessment typology based on the common physical, chemical and biological ocean colour properties captured in the Forel–Ule index, which could replace the more traditional eutrophication assessment regions centred around strict geographic and political boundaries. Additionally, the Forel–Ule assessment typology is particularly important since it identifies areas of the greatest impact from the land-based loads into the marine environment, and thus potential risks to vulnerable ecosystems.

Bubble vent localization for marine hydrocarbon seep surveys

Commercial success of marine seep hunting exploration campaigns involves acquisition of high-quality bathymetry and backscatter along with targeted coring of shallow geochemical sampling of seep sediments. The sharp lateral chemical gradient encompassing seafloor seeps requires accurate identification of seep sites from high-resolution acoustic data. Active seafloor seeps featuring plumes of gas bubbles and oil droplets rising into the water column can be imaged with modern multibeam echosounders providing an effective approach to remotely characterizing seafloor seeps. Interpreting the seafloor position of gas plume emissions in multibeam data using existing mapping methodology is hindered by slow processing due to large files sizes, a manual “by eye” qualitative assessment of each sonar ping searching for plume anomalies, skill and fatigue of the geoscientist, and environmental or acquisition artifacts that can mask the precise location of gas emission on the seafloor. These limitations of midwater backscatter mapping create a qualitative data set with varying inherent positional errors that can lead to missed or incorrect observations about seep-related seafloor features and processes. By vertically integrating midwater multibeam amplitude samples, a 2D midwater backscatter raster can be generated and draped over seafloor morphology, providing a quantitative synoptic overview of the spatial distribution of gas plume emission sites for more refined seafloor interpretation. We reprocess multibeam midwater data set from NOAA Cruise EX1402L2 in the northwestern Gulf of Mexico using a vertical amplitude stacking technique. Constructed midwater backscatter surfaces are compared with digitized plume positions collected during the survey for a comparison into assessing uncertainty in mapping approaches. Our results show that the accuracy of manually digitizing gas emission sites varies considerably when compared with the midwater backscatter amplitude maps. This quantitative plume mapping technique offers multiple advantages over traditional geopicking from cost effectiveness, offshore efficiency, repeatability, and higher accuracy, ultimately improving the detectability and sampling of active seafloor seeps through precisely located cores.

Examining an Oil Spill Plume Mapping Method based on Satellite NIR Data

Reliable information on the spreading of oil plume on water caused by massive oil spills is essential for making proper clean-up measures. Satellite remote sensing technology has advantages over other methods in terms of larger coverage and without ex- pensive operating costs to detect oil spills. In this study, an oil plume delineation method based on the Near-Infrared (NIR) satellite data is used to examine oil spill plume area and size for the BP Deepwater Horizon Oil Spill in the offshore water of Gulf of Mexico and for the recent Norilsk oil spill in a Northern inland water region. To get accurate results noise signals such as land from the data are masked out using SNAP based DEM data and Normalized Difference Water Index method, whereas cloud signals are removed using MODIS cloud masking. Cox-Munk model is used to compute the sun glint radiance. Results of DP oil spill case depicts a 4838.84 km2 thicker oil plume along with the 20635.53 km2 thinner portion of the oil slicks using MODIS NIR data at a 500-meter resolution. It is subsequently applied to the recent Norilsk Oil Spill using higher resolution Sentinel-2 NIR data to test the method for detecting spill plume in an inland river water system. Reasonable high-resolution results at 10 meter have been obtained for the smaller scale oil spill onto river water compared to larger offshore area, considering that the river site has complex conditions including shallow water and river reddish soil close to oil color. The developed method is suitable for detecting thick oil plume in ocean or deep inland water bodies.

Examining an Oil Spill Plume Mapping Method based on Satellite NIR Data

Reliable information on the spreading of oil plume on water caused by massive oil spills is essential for making proper clean-up measures. Satellite remote sensing technology has advantages over other methods in terms of larger coverage and without ex- pensive operating costs to detect oil spills. In this study, an oil plume delineation method based on the Near-Infrared (NIR) satellite data is used to examine oil spill plume area and size for the BP Deepwater Horizon Oil Spill in the offshore water of Gulf of Mexico and for the recent Norilsk oil spill in a Northern inland water region. To get accurate results noise signals such as land from the data are masked out using SNAP based DEM data and Normalized Difference Water Index method, whereas cloud signals are removed using MODIS cloud masking. Cox-Munk model is used to compute the sun glint radiance. Results of DP oil spill case depicts a 4838.84 km2 thicker oil plume along with the 20635.53 km2 thinner portion of the oil slicks using MODIS NIR data at a 500-meter resolution. It is subsequently applied to the recent Norilsk Oil Spill using higher resolution Sentinel-2 NIR data to test the method for detecting spill plume in an inland river water system. Reasonable high-resolution results at 10 meter have been obtained for the smaller scale oil spill onto river water compared to larger offshore area, considering that the river site has complex conditions including shallow water and river reddish soil close to oil color. The developed method is suitable for detecting thick oil plume in ocean or deep inland water bodies.

Differences in the Estimation of Wildfire-Associated Air Pollution by Satellite Mapping of Smoke Plumes and Ground-Level Monitoring.

Wildfires, which are becoming more frequent and intense in many countries, pose serious threats to human health. To determine health impacts and provide public health messaging, satellite-based smoke plume data are sometimes used as a proxy for directly measured particulate matter levels. We collected data on particulate matter <2.5 μm in diameter (PM2.5) concentration from 16 ground-level monitoring stations in the San Francisco Bay Area and smoke plume density from satellite imagery for the 2017–2018 California wildfire seasons. We tested for trends and calculated bootstrapped differences in the median PM2.5 concentrations by plume density category on a 0–3 scale. The median PM2.5 concentrations for categories 0, 1, 2, and 3 were 16, 22, 25, and 63 μg/m3, respectively, and there was much variability in PM2.5 concentrations within each category. A case study of the Camp Fire illustrates that in San Francisco, PM2.5 concentrations reached their maximum many days after the peak for plume density scores. We found that air pollution characterization by satellite imagery did not precisely align with ground-level PM2.5 concentrations. Public health practitioners should recognize the need to combine multiple sources of data regarding smoke patterns when developing public guidance to limit the health effects of wildfire smoke.

An evaluation of airborne SWIR imaging spectrometers for CH4 mapping: Implications of band positioning, spectral sampling and noise

The development of instruments and methods to assist in methane (CH4) emissions mapping is essential to the fossil fuel industry. Early identification of local CH4 sources can benefit both petroleum hydrocarbon prospecting and environmental monitoring. Generic airborne imaging spectrometers operating in the shortwave infrared (SWIR) wavelength range (1000−2400 nm) have shown their suitability for this task. However, to date, there is no airborne scanner specifically designed for the detection of CH4 plumes. To overcome current handicaps and achieve better results at local scale, further investigation in sensor design is needed to evaluate which components can be adjusted to improve CH4 mapping with airborne sensors. Here, we focus on the evaluation of currently operational airborne imaging spectrometers for CH4 mapping in the SWIR range. Data acquired over areas with known CH4 emissions by scientific and industry-grade airborne hyperspectral sensors were examined. The research was conducted in three steps: analysis of sensor design, image processing and noise simulation. In the first step, differences in the spectral sampling between the sensors were analyzed. A reference CH4 signature from HITRAN spectral database was resampled to the spectral sampling of each airborne sensor. The center wavelength of diagnostic CH4 absorption features (identified in the convolved signatures) in relation to the position of band centers from each equipment was examined. For the image processing stage, a new CH4 index and a classic matched filtering were used to map CH4 plumes. To assess the impact of the signal-to-noise ratio (SNR) of the airborne sensors on CH4 plume mapping, white noise was added to the data to simulate images with varying SNR levels. Results demonstrated that the wavelength position of band centers is a key for CH4 mapping. The CH4 plumes could be mapped only with scientific-grade sensors, in which the band centers were closer to the center of CH4 features. Simulations with the addition of random noise demonstrated that a noisier signal is probably the reason why the industry-grade sensor tested here failed to map CH4 plumes, given that all instruments have a comparable spectral sampling. Furthermore, the simulations also demonstrated that the density of the plume has also a weight on the mapping of CH4 sources, once the image that captured the densest plume requested a higher addition of noise to be lost. The overall investigation indicates that a hyperspectral airborne sensor with bands properly positioned and scientific-grade SNR would better resolve the narrow CH4 features in the SWIR range.

Oil Plume Mapping: Adaptive Tracking and Adaptive Sampling From an Autonomous Underwater Vehicle

We have developed an adaptive sampling algorithm for an Explorer autonomous underwater vehicle (AUV) to conduct in-situ analysis of acoustic measurements to perform autonomous oil plume detection and tracking. The methodology of the tracking phase involves ongoing analysis of the detected plume, assessing target validity and proximity for AUV decision-making for plume mapping. We previously introduced the bumblebee flight path, a new biomimetic search pattern designed to maximize the spatial coverage in the oil plume detection phase. This paper focuses on a new tracking strategy as the key adaptive stage in our plume delineation. For initial development we used a 360-degree scanning sonar sensor model. Simulations were done with different plume models to assess the performance of the developed adaptive sampling algorithm. A convergence study demonstrated that the algorithm could successfully track the boundary of a non-regular shaped/patchy oil plume at up to a 0.05Hz sampling frequency. A sensitivity study identified the correlations between plume feature complexity and the anticipated range of acoustic measurement update delays. The decision-making architecture consists of three separate components which implements either proximity or boundary following control and contributes to the final decision on the next desired heading of the vehicle. A weight ratio, that determined the relative allocation of each component, was varied to study its impact on the tracking performance of the AUV. The novelty of our approach is in addressing the discontinuous and patchy nature of realistic oil plumes. Our sampling algorithm and its performance in simulations is a significant step beyond the practical limitations of existing gradient-following methods because it accounts for the oil patches and droplets which gradient-following algorithms do not.

Participatory science for coastal water quality: freshwater plume mapping and volunteer retention in a randomized informational intervention

Among the biggest threats to coastal water quality are freshwater discharges. It is difficult to predict the spatial extent of freshwater plumes at marine beaches because processes governing mass transport in the surf zone are complex. Participatory science approaches could facilitate collecting shoreline data, although volunteer sampling campaigns can be challenged by data quality and volunteer retention. The goals of this study were to (1) work with volunteers to estimate safe swimming distances at beaches that receive polluted discharges, and (2) test whether informational feedback to volunteers increased retention. Forty-six volunteers participated over 12 weeks in 2019 by collecting 1452 salinity measurements at beaches near the mouths of two Central California freshwater discharges and completing participation surveys. These measurements resulted in 145 distinct estimates of safe swimming distances (D90), spanning a range of environmental conditions during rainy and dry periods. Median D90s were 150 and 100 m at San Pedro Creek south and north, and 490 and 330 m at San Lorenzo River west and east, respectively. D90 was significantly associated with adjacent freshwater discharge rate at both discharges and tide level at one discharge. On average, the odds of volunteers conducting sampling decreased by 4% (95% CI: 1%, 7%) with each successive week. A randomized intervention providing repeated data feedback via email to volunteers did not affect their retention in the study.

Towards accurate methane point-source quantification from high-resolution 2-D plume imagery

Abstract. Methane is the second most important anthropogenic greenhouse gas in the Earth climate system but emission quantification of localized point sources has been proven challenging, resulting in ambiguous regional budgets and source category distributions. Although recent advancements in airborne remote sensing instruments enable retrievals of methane enhancements at an unprecedented resolution of 1–5 m at regional scales, emission quantification of individual sources can be limited by the lack of knowledge of local wind speed. Here, we developed an algorithm that can estimate flux rates solely from mapped methane plumes, avoiding the need for ancillary information on wind speed. The algorithm was trained on synthetic measurements using large eddy simulations under a range of background wind speeds of 1–10 m s−1 and source emission rates ranging from 10 to 1000 kg h−1. The surrogate measurements mimic plume mapping performed by the next-generation Airborne Visible/Infrared Imaging Spectrometer (AVIRIS-NG) and provide an ensemble of 2-D snapshots of column methane enhancements at 5 m spatial resolution. We make use of the integrated total methane enhancement in each plume, denoted as integrated methane enhancement (IME), and investigate how this IME relates to the actual methane flux rate. Our analysis shows that the IME corresponds to the flux rate nonlinearly and is strongly dependent on the background wind speed over the plume. We demonstrate that the plume width, defined based on the plume angular distribution around its main axis, provides information on the associated background wind speed. This allows us to invert source flux rate based solely on the IME and the plume shape itself. On average, the error estimate based on randomly generated plumes is approximately 30 % for an individual estimate and less than 10 % for an aggregation of 30 plumes. A validation against a natural gas controlled-release experiment agrees to within 32 %, supporting the basis for the applicability of this technique to quantifying point sources over large geographical areas in airborne field campaigns and future space-based observations.

Evidence for natural attenuation of 1,4-dioxane in a glacial aquifer system

Although 1,4-dioxane is generally thought to be recalcitrant, recent studies suggest it may degrade in the subsurface under ideal conditions. A fuller understanding of natural attenuation processes affecting 1,4-dioxane is therefore needed to assess its potential for in situ bioremediation. This investigation employed multiple lines of evidence to evaluate attenuation of 1,4-dioxane at the Gelman Site beneath the city of Ann Arbor, Michigan, USA. Data from a network of groundwater monitoring wells were used to determine attenuation metrics at individual wells and at the scale of a prominent 1,4-dioxane plume. A series of plume maps and historical remediation data were used to calculate changes in aqueous phase mass storage, mass influx rates, and mass removal rates over a 12-year period (2005–2017). Individual point and plume-scale metrics indicate that attenuation may be occurring at rates too small to meaningfully contribute to remediation results at the site. Conversely, plume-scale mass balance calculations reveal a 1,4-dioxane storage surplus for the first 6 years, followed by a storage deficit during the remaining 6 years that cannot be explained by mass influx or removal estimates, respectively. Mass balance deficits observed in this aquifer system are attributable to biodegradation and/or unrecognized discharge to surface water and storm drain systems at rates similar to remedial pump-and-treat mass removal during 2011–2017.

Soil contamination sampling intensity: determining accuracy and confidence using a Monte Carlo simulation

Soil pollution is an extensive global problem, and effective management depends on accurate characterization and mapping of the extent of soil contamination. The objectives of this study were to determine the accuracy of different sampling intensities and the optimal number of samples required to minimize remediation project costs. To determine the accuracy associated with different sampling intensities, a Monte Carlo simulation was conducted. A simulated contaminant plume was created based on inverse distance weighting, kriging, or multivariate adaptive regression splines. Different sampling intensities, with grid spacing ranging from approximately 10% to 50% of the site extent, were used to generate a plume map using random forest model with a Euclidean distance matrix as predictors. The relative error was then determined as part of a Monte Carlo simulation that ran 10 000 simulations for each grid intensity for a total of 90 000 simulations. The optimal number of samples was determined based on economic factors, and the error functions generated with the Monte Carlo simulations. Average error ranged from 57% for 25 data points to 5% for 2800 data points. The 90th percentile error ranged from 100% to 0.3% for the sample data point range. Based on these results, the optimal number of samples, depending on pricing, ranged from 31 samples for a 10 m3 contaminant plume to 3475 samples for a 10 000 m3 soil contaminant plume.

Evaluating the effects of surface properties on methane retrievals using a synthetic airborne visible/infrared imaging spectrometer next generation (AVIRIS-NG) image

Atmospheric methane has been increasing since the beginning of the industrial era due to anthropogenic emissions. Methane has many sources, both natural and anthropogenic, and there continues to be considerable uncertainty regarding the contribution of each source to the total methane budget. Thus, remote sensing techniques for monitoring and measuring methane emissions are of increasing interest. Recently, the Airborne Visible-Infrared Imaging Spectrometer - Next Generation (AVIRIS-NG) has proven to be a valuable instrument for quantitative mapping of methane plumes. Despite this success, uncertainties remain regarding the sensitivity of the retrieval algorithms, including the influence of albedo and the impact of surfaces that may cause spurious signals. To explore these sensitivities, we applied the Iterative Maximum a Posterior Differential Optical Absorption Spectroscopy (IMAP-DOAS) methane retrieval algorithm to synthetic reflected radiances with variable methane concentrations, albedo, surface cover, and aerosols. This allowed for characterizing retrieval performance, including potential sensitivity to variable surfaces, low albedo surfaces, and surfaces known to cause spurious signals. We found that dark surfaces (below 0.10 μWcm−2nm−1sr−1 at 2139 nm), such as water and green vegetation, and materials with absorption features in the 2200–2400 nm range caused higher errors in retrieval results. We also found that aerosols have little influence on retrievals in the SWIR. Results from the synthetic scene are consistent with those observed in IMAP-DOAS retrievals for real AVIRIS-NG scenes containing methane plumes from a waste dairy lagoon and coal mine ventilation shafts. Understanding the effect of surface properties on methane retrievals is important given the increased use of AVIRIS-NG to map gas plumes from a diversity of sources over variable landscapes.