Plume Concentrations Research Articles

Spatiotemporal Patterns of Chlorophyll-a Concentration in a Hypersaline Lake Using High Temporal Resolution Remotely Sensed Imagery

The Great Salt Lake (GSL) is the largest saline lake in the Western Hemisphere. It supports billion-dollar industries and recreational activities, and is a vital stopping point for migratory birds. However, little is known about the spatiotemporal variation of phytoplankton biomass in the lake that supports these resources. Spectral reflectance provided by three remote sensing products was compared relative to their relationship with field measurements of chlorophyll a (Chl a). The MODIS product MCD43A4 with a 500 m spatial resolution provided the best overall ability to map the daily distribution of Chl a. The imagery indicated significant spatial variation in Chl a, with low concentrations in littoral areas and high concentrations in a nutrient-rich plume coming out of polluted embayment. Seasonal differences in Chl a showed higher concentrations in winter but lower in summer due to heavy brine shrimp (Artemia franciscana) grazing pressure. Twenty years of imagery revealed a 68% increase in Chl a, coinciding with a period of declining lake levels and increasing local human populations, with potentially major implications for the food web and biogeochemical cycling dynamics in the lake. The MCD43A4 daily cloud-free images produced by 16-day temporal composites of MODIS imagery provide a cost-effective and temporally dense means to monitor phytoplankton in the southern (47% surface area) portion of the GSL, but its remaining bays could not be effectively monitored due to shallow depths, and/or plankton with different pigments given extreme hypersaline conditions.

High fidelity simulations of contaminant dispersion in an urban environment with comparison to magnetic resonance imaging measurements

The dispersion of a contaminant in an urban environment has the potential to impact a large population of people. In this work, a complex urban canopy flow based on the Oklahoma City downtown business district circa 2003 is studied using Magnetic Resonance Imaging (MRI) and high-fidelity Large Eddy Simulations (LES). MRI is a novel experimental technique that can provide high-resolution measurements in four dimensions (three spatial and temporal) for lab scale models. The experiments and simulations use the same geometry and boundary conditions providing a one-to-one comparison of the two methods. Results are presented on the time-averaged velocity and concentration fields, the temporal dynamics of the concentration plumes for a transient release, and a novel Cloud Identification Algorithm that can separate plumes produced by periodic contaminant releases used for ensemble averaging over many releases. The MRI and LES datasets both include millions of measurement voxels and the comparisons highlight the complex 3D nature of the flow including strong vertical velocities in spanwise street canyons and flow acceleration in streamwise street canyons. The concentration fields are qualitatively similar albeit the LES shows larger dispersion. A quantitative analysis with performance measures compares the datasets pointwise and demonstrates that the two 3D datasets are similar with respect to many measures including a fractional bias of 0.02 (ideal=0.0), correlation coefficient of 0.87 (ideal = 1.0), and the fraction points within a factor of 2 is 0.98 (ideal = 1.0). Plume analysis compares the arrival and residence time of contaminant and is found to vary significantly with location within the urban environment with arrival times between 0 and 1.25 and differences within the contaminant cloud less than 10% at most locations.

Modeling the Influence of Coastal Site Characteristics on PFAS in Situ Remediation.

The potential performance of a hypothetical colloidal-activated carbon (CAC) in situ remedy for perfluorooctanoic acid (PFOA) and perfluorooctane sulfonic acid (PFOS) in groundwater in coastal zones was evaluated using estimated hydrogeologic and geochemical parameters for a coastal site in the United States. With these parameters, a reactive transport model (ISR-MT3DMS) was used to assess the effects of tidal fluctuations and near-shore geochemistry on CAC performance. The average near-shore ionic strength of 84 mM at the site was conservatively estimated to result in an increase in the adsorption of PFOA to CAC by about 50% relative to non-coastal sites with ionic strength <10 mM. The modeling also confirmed the hypothesis that tidally induced groundwater flow reversals near the shore would result in the accumulation of PFOA at the downgradient edge of the CAC zone. Slow desorption of PFOA from this downgradient CAC boundary may sustain downgradient plume concentrations above a strict cleanup criterion (e.g., USEPA MCL of 0.004 μg/L), for decades; however, there was still a large PFOA mass flux reduction (>99.9%) achieved after several decades at the shore. CAC longevity was substantially greater for PFOS with a similar source concentration; however, the higher PFOS distribution coefficient (Kd) in soil downgradient from the CAC zone resulted in substantially longer flushing times. It is recommended that short-term remedial action objectives for CAC remedies at coastal sites be based on mass flux reduction targets over a period of several decades, given the demonstrated challenges in trying to achieve very low cleanup criteria downgradient of a CAC zone in the short term.

Enhancing Uncrewed Aerial Vehicle Techniques for Monitoring Greenhouse Gas Plumes at Point Sources

The urgency of the global climate crisis necessitates advanced monitoring of greenhouse gases, with an emphasis on capturing their spatial and temporal variability. This study explores techniques to enhance plume detection and concentration measurements using uncrewed aerial vehicles (UAVs) for monitoring at point sources, specifically at an incineration stack. Through preliminary site investigations, our approach employed strategically designed flight paths and an autopilot system to optimize flight operations within the constraints of limited flight time due to battery capacity. We combined a UAV-mounted anemometer with a plume rise model to localize the plume center at a distance of 30 m from the stack center and evaluated its performance by comparing the model-based estimations at different altitudes and angular directions with the observation results. The comparison demonstrated that the results obtained by localizing the plume center using a plume rise model and a UAV-mounted anemometer aligned well with observations based on CO2 concentration analysis. The comparative analysis showed a RMSE of 8.44 m and a MAE of 7.26 m for altitude, and a RMSE of 32.31∘ and a MAE of 25.78∘ for angular direction. Furthermore, we assessed the effectiveness of hovering UAV flights, in which a UAV remains stationary at a fixed point in the air, compared to non-hovering flights in capturing pollutant concentration. While both methods performed similarly in detecting the plume center, non-hovering flights underestimated the CO2 concentration due to insufficient time for measurement despite a sensor response time of less than three seconds. Overall, our proposed hybrid monitoring strategy integrates non-hovering and hovering flights, enhancing both plume detection efficiency and concentration measurement accuracy at point sources.

The origin and implications of primordial helium depletion in the Afar mantle plume

Mantle plumes are responsible for the Earth’s largest volcanic provinces. In the prevailing paradigm, the deep mantle is less degassed than convecting shallow mantle, implying that plume-derived lavas have higher concentrations of primordial volatiles such as helium (He). Demonstrating this has led to explanations that question the established Earth model. Here, we show that the 3He/4He of basalts from the Red Sea display coherent relationships with trace elements, allowing the helium concentration of the Afar plume to be calculated. Contrary to the prevailing model it appears the helium concentration of the Afar plume is 10-25% of the upper mantle. This contradiction is resolved if the plume material itself is a mixture of helium-rich high-3He/4He deep mantle with helium-depleted low-3He/4He recently subducted oceanic crust. This implies that helium-depleted domains may exist in convecting mantle and that moderately high 3He/4He plumes likely do not contain a notable contribution of the deep mantle.

Coupled hydrogeophysical inversion through ensemble smoother with multiple data assimilation and convolutional neural network for contaminant plume reconstruction

In the field of groundwater, accurate delineation of contaminant plumes is critical for designing effective remediation strategies. Typically, this identification poses a challenge as it involves solving an inverse problem with limited concentration data available. To improve the understanding of contaminant behavior within aquifers, hydrogeophysics emerges as a powerful tool by enabling the combination of non-invasive geophysical techniques (i.e., electrical resistivity tomography—ERT) and hydrological variables. This paper investigates the potential of the Ensemble Smoother with Multiple Data Assimilation method to address the inverse problem at hand by simultaneously assimilating observed ERT data and scattered concentration values from monitoring wells. A novelty aspect is the integration of a Convolutional Neural Network (CNN) to replace and expedite the expensive geophysical forward model. The proposed approach is applied to a synthetic case study, simulating a tracer test in an unconfined aquifer. Five scenarios are compared, allowing to explore the effects of combining multiple data sources and their abundance. The outcomes highlight the efficacy of the proposed approach in estimating the spatial distribution of a concentration plume. Notably, the scenario integrating apparent resistivity with concentration values emerges as the most promising, as long as there are enough concentration data. This underlines the importance of adopting a comprehensive approach to tracer plume mapping by leveraging different types of information. Additionally, a comparison was conducted between the inverse procedure solved using the full geophysical forward model and the CNN model, showcasing comparable performance in terms of results, but with a significant acceleration in computational time.

Measurement of engine exhaust plume temperature and concentration distributions with tomographic absorption spectroscopy and learning-based absorbance recovery

Measuring the spatial distribution of temperature and species concentration is important to the thermodynamic analysis and validation of modern combustion systems. In this work, we developed a measurement method based on tomographic absorption spectroscopy (TAS) for the quantitative measurement of two-dimensional distributions of gas temperature and concentration. A learning-based absorbance recovery method was proposed and incorporated into TAS to address the baseline error induced distortion of absorbance and improve the accuracy of single path measurement. The developed TAS system, consisting of 12 laser beams, was demonstrated on a diesel fueled small turbojet engine. Temperature and H2O concentration distributions of the exhaust plume were measured with laser wavelength scan rates of 20 kHz. Temperature variations in microsecond-scale were captured and the time-averaged center temperature agreed well with thermocouple measurements. The developed TAS method demonstrates the feasibility of real-time and accurate tomographic imaging for practical combustion flows, which could be effectively applied for widespread combustion diagnostics.

Ship Emission Measurements Using Multirotor Unmanned Aerial Vehicles: Review

This review investigates the ship emission measurements using multirotor unmanned aerial vehicles (UAVs). The monitoring of emissions from shipping is a priority globally, because of the necessity to reduce air pollution and greenhouse gas emissions. Moreover, there is widespread global effort to extensively measure vessel fuel sulfur content (FSC). The majority of studies indicate that more commonly used methods for measuring ship emission with UAVs is the sniffing method. Most of the research is concerned with determining the fuel sulfur content. Fuel sulfur content can be determined by the ratio of CO2 and SO2 concentration in the exhaust gas plume. For CO2, the non-dispersive infrared (NDIR) method is used, the most common measuring range reaches 0–2000 ppm, the overall measuring range 0–10,000 ppm, and detection accuracy is ±5–300 ppm. For SO2, the electrochemical (EC) method is used, the measuring range reaches 0–100 ppm, and the detection accuracy is ±5 ppm. Common UAV characteristics, used in measurement with ships, involve the following: 8–10 m/s of wind resistance, 5–6 kg maximum payload, and a flight distance ranging from 5 to 10 km. This can change in the near future, since a variety of emission measuring devices that can be mounted on UAVs are available on the market. The range of available elements differs from device to device, but available ranges are allowed and the accuracy provides good possibilities for wider research into ship emissions.

Real time hydrogen plume spatiotemporal evolution forecasting by using deep probabilistic spatial-temporal neural network

The accidental leakage of gaseous hydrogen from hydrogen refueling station facilities has the potential to result in a large fire and explosion catastrophe. The ability to forecast the spatiotemporal evolution of hydrogen plumes in real-time is crucial for monitoring the distribution of plume concentrations and mitigating the risk of fire and explosion. The utilization of deep learning has been extensively employed in a diverse range of spatiotemporal forecasting tasks. However, these approaches encounter limitations in accurately quantifying uncertainty when predicting spatiotemporal concentrations and plume boundaries. This research paper introduces a novel model called DPSTNN_H2Evolution, which is a deep probabilistic spatial-temporal neural network designed for forecasting the spatiotemporal evolution of hydrogen plumes. The benchmark dataset is constructed by the implementation of numerical simulations pertaining to the inadvertent leak of hydrogen within a hydrogen refueling station. The findings of the study indicate that incorporating quantified uncertainty information might enhance the precision of plume boundaries and the resilience of plume concentrations when predicting the spatiotemporal development of hydrogen. By employing Monte Carlo sampling with a sample size of m=100 and a dropout rate of p=0.1, the model demonstrates the ability to provide real-time inference for 10 instances of plumes, while maintaining a high level of accuracy with R2 = 0.9829. In comparison to the current state-of-the-art model, the proposed model demonstrates superior accuracy and robustness in forecasting the spatiotemporal evolution of hydrogen plumes. In general, our proposed model has the potential to serve as a dependable option for the development of a digital twin for emergency management of hydrogen refueling stations.

Estimating emission flux of H2S from fumarolic fields using vertical sensor array system

The emission flux of volatiles from each fumarolic field in volcanic and geothermal areas can be used to evaluate the current state of magmatic activity and predict its future trends. The emission flux of SO2 has been quantified in many fumarolic fields using remote sensing techniques, such as differential optical absorption spectroscopy (DOAS). However, most of these remote sensing techniques are inapplicable to fumarolic fields emitting volatiles depleted in SO2 to which most of the geothermal fields are classified. In this study, we developed a vertical sensor array system to quantify the emission flux of H2S from each fumarolic field by integrating the cross-sectional distributions of H2S concentrations in the volcanic plume using the vertical sensor array system. In Iwo-yama of the Kirishima volcanic complex, the cross-sectional distribution of H2S concentrations was determined using the walking traverse method by moving the vertical sensor array system in the plume perpendicular to the direction of plume transport. The emission flux of SO2 (2.2 ± 0.4 ton SO2/day) was estimated from that of H2S using the walking traverse method (2.6 ± 0.5 ton H2S/day) and the molar ratio of the plume (SO2/H2S=0.45) corresponds well with that estimated optically by JMA. We concluded that the emission flux quantified using the vertical sensor array system was reliable. In the Oyunuma pond in the Kuttara volcano, the emission flux of H2S was quantified as 2.0 ton H2S/day through the fixed point method, wherein the vertical sensor array system was fixed in one point, whereas the cross sectional distribution of H2S in the plume was estimated using the natural variation in wind direction. The topography is often irregular and wind direction is variable in most fumarolic fields; thus, in general, the fixed point method should be more suitable to determine the emission flux of H2S from fumarolic fields, wherein H2S occupies a major portion of the total sulfur emission.

Sustained increase in suspended sediments near global river deltas over the past two decades

River sediments play a critical role in sustaining deltaic wetlands. Therefore, concerns are raised about wetlands’ fate due to the decline of river sediment supply to many deltas. However, the dynamics and drivers of suspended sediment near deltaic coasts are not comprehensively assessed, and its response to river sediment supply changes remains unclear. Here we examine patterns of coastal suspended sediment concentration (SSC) and river sediment plume area (RPA) for 349 deltas worldwide using satellite images from 2000 to 2020. We find a global increase in SSC and RPA, averaging +0.46% and +0.48% yr−1, respectively, with over 59.0% of deltas exhibiting an increase in both SSC and RPA. SSC and RPA increases are prevalent across all continents, except for Asia. The relationship between river sediment supply and coastal SSCs varies between deltas, with as much as 45.2% of the deltas showing opposing trends between river sediments and coastal SSCs. This is likely because of the impacts of tides, waves, salinity, and delta morphology. Our observed increase in SSCs near river delta paints a rare promising picture for wetland resilience against sea-level rise, yet whether this increase will persist remains uncertain.

Long‐term evaluation of hydrocarbon degradation rates within the source zone under natural and enhanced attenuation for site closure purposes

AbstractDespite addressing the cleanup of petroleum hydrocarbon contamination plumes in groundwater, challenges with site closure persist due to the ongoing long‐term contaminant release from source zones. Thus, source zone monitoring even in the presence of residual light nonaqueous‐phase liquid (LNAPL) is mandated by many countries' regulatory agencies before site closure. In this study, four independent controlled‐release sites, each containing a particular fraction of ethanol in gasoline, that is, 10%, 24%, 25%, and 85% (v/v), were subjected to monitored natural attenuation (MNA) alone (E24 and E85), and with nitrate (E25) and sulfate (E10) biostimulation at the vicinity of the source zone. To the best of the authors' knowledge, this is the longest controlled gasohol release monitoring study performed to date at the source zone for site closure (nearly 18 years of monitoring). Both natural source zone depletion and biostimulation were found to reduce benzene concentrations to acceptable remediation target levels according to environmental regulatory councils. Benzene biodegradation rates (λs) were one order of magnitude higher than those reported in the literature for diluted concentrations in groundwater plumes. The decay rate of benzene varied between 0.45 and 1.75/year and was strongly influenced by the mass of ethanol. Compared to biostimulation with nitrate (half‐life of 1.52 years), MNA alone resulted in faster benzene removal rates (half‐life of 0.96 years). Anaerobic biostimulation with sulfate had negligible effects on benzene, toluene, ethylbenzene, and xylene (o‐, m‐, and p‐xylene [BTEX]) biodegradation compared to natural attenuation alone. The highest ethanol concentration (E85) led to faster benzene removal (with a half‐life of 0.40 years), which was even higher than MNA under lower ethanol concentrations (E24). Benzene half‐life was 2.2‐fold slower in the site with the lowest ethanol (E10) compared to E85, indicating that the negative effects of ethanol on BTEX biodegradation appeared to be short‐lived and may need to be re‐evaluated over the long term. Benzene decay rates were approximately six times slower in the source zone than the rates obtained for groundwater plumes, emphasizing the importance of targeting the LNAPL after plume retreat. The study's findings can assist practitioners in better predicting the lines of evidence for BTEX bioremediation and remediation cleanup times at a lower cost in the presence of ethanol, as well as providing guidance for determining whether biostimulation is necessary to expedite source zone remediation for site closure.

Assessment of smoke plume height products derived from multisource satellite observations using lidar-derived height metrics for wildfires in the western US

Abstract. As wildfires intensify and fire seasons lengthen across the western US, the development of models that can predict smoke plume concentrations and track wildfire-induced air pollution exposures has become critical. Wildfire smoke plume height is a key indicator of the vertical placement of plume mass emitted from wildfire-related aerosol sources in climate and air quality models. With advancements in Earth observation (EO) satellites, spaceborne products for aerosol layer height or plume injection height have recently emerged with increased global-scale spatiotemporal resolution. However, to evaluate column radiative effects and refine satellite algorithms, vertical profiles of regionally representative aerosol properties from wildfires need to be measured directly. In this study, we conducted the first comprehensive evaluation of four passive satellite remote-sensing techniques specifically designed for retrieving plume height. We compared these satellite products with the airborne Wyoming Cloud Lidar (WCL) measurements during the 2018 Biomass Burning Flux Measurements of Trace Gases and Aerosols (BB-FLUX) field campaign in the western US. Two definitions, namely, “plume top” and “extinction-weighted mean plume height”, were used to derive the representative heights of wildfire smoke plumes, based on the WCL-derived vertical aerosol extinction coefficient profiles. Using these two definitions, we performed a comparative analysis of multisource satellite-derived plume height products for wildfire smoke. We provide a discussion related to which satellite product is most appropriate for determining plume height characteristics near a fire event or estimating downwind plume rise equivalent height, under multiple aerosol loadings. Our findings highlight the importance of understanding the sensitivity of different passive remote-sensing techniques on space-based wildfire smoke plume height observations, in order to resolve ambiguity surrounding the concept of “effective smoke plume height”. As additional aerosol-observing satellites are planned in the coming years, our results will inform future remote-sensing missions and EO satellite algorithm development. This bridges the gap between satellite observations and plume rise modeling to further investigate the vertical distribution of wildfire smoke aerosols.

Deep learning applied to CO2 power plant emissions quantification using simulated satellite images

Abstract. The quantification of emissions of greenhouse gases and air pollutants through the inversion of plumes in satellite images remains a complex problem that current methods can only assess with significant uncertainties. The anticipated launch of the CO2M (Copernicus Anthropogenic Carbon Dioxide Monitoring) satellite constellation in 2026 is expected to provide high-resolution images of CO2 (carbon dioxide) column-averaged mole fractions (XCO2), opening up new possibilities. However, the inversion of future CO2 plumes from CO2M will encounter various obstacles. A challenge is the low CO2 plume signal-to-noise ratio due to the variability in the background and instrumental errors in satellite measurements. Moreover, uncertainties in the transport and dispersion processes further complicate the inversion task. To address these challenges, deep learning techniques, such as neural networks, offer promising solutions for retrieving emissions from plumes in XCO2 images. Deep learning models can be trained to identify emissions from plume dynamics simulated using a transport model. It then becomes possible to extract relevant information from new plumes and predict their emissions. In this paper, we develop a strategy employing convolutional neural networks (CNNs) to estimate the emission fluxes from a plume in a pseudo-XCO2 image. Our dataset used to train and test such methods includes pseudo-images based on simulations of hourly XCO2, NO2 (nitrogen dioxide), and wind fields near various power plants in eastern Germany, tracing plumes from anthropogenic and biogenic sources. CNN models are trained to predict emissions from three power plants that exhibit diverse characteristics. The power plants used to assess the deep learning model's performance are not used to train the model. We find that the CNN model outperforms state-of-the-art plume inversion approaches, achieving highly accurate results with an absolute error about half of that of the cross-sectional flux method and an absolute relative error of ∼ 20 % when only the XCO2 and wind fields are used as inputs. Furthermore, we show that our estimations are only slightly affected by the absence of NO2 fields or a detection mechanism as additional information. Finally, interpretability techniques applied to our models confirm that the CNN automatically learns to identify the XCO2 plume and to assess emissions from the plume concentrations. These promising results suggest a high potential of CNNs in estimating local CO2 emissions from satellite images.

CFD Simulation Models and Diffusion Models for Predicting Carbon Dioxide Plumes following Tank and Pipeline Ruptures—Laboratory Test and a Real-World Case Study

Ruptures of pipelines can result in dangerous fluids spreading toward populated areas. It is critical for designers to have tools that can accurately predict whether populated areas might be within a plume rupture zone. Numerical simulations using computational fluid dynamics (CFD) are compared here with experimental and real-world carbon dioxide ruptures. The experimental data were used to validate the computer model; subsequently, the algorithm was used for a real-world rupture from 2020 that occurred in the USA. From experiments, CFD predictions were superior to diffusion model results based on measurements made downstream of the release (within 1% concentration). Results from the real-world simulation confirm that a nearby town was in a plume pathway. Citizens in the town sought medical attention consistent with the calculated plume concentrations. CFD predictions of the airborne concentration of carbon dioxide in the town approximately 1 mile (1.5 km) downstream of the rupture reveal time-averaged concentrations of ~5%. One person was unconscious for ~45 min at a distance of 0.6 miles from the rupture site; other unconscious persons were in the center of the town (~1 mile from the rupture site) and ~1.2 miles from the rupture. These reports are in excellent agreement with the calculated plume concentrations in the region.

Exceptionally low mercury concentrations and fluxes from the 2021 and 2022 eruptions of Fagradalsfjall volcano, Iceland

Mercury (Hg) is naturally released by volcanoes and geothermal systems, but the global flux from these natural sources is highly uncertain due to a lack of direct measurements and uncertainties with upscaling Hg/SO2 mass ratios to estimate Hg fluxes. The 2021 and 2022 eruptions of Fagradalsfjall volcano, southwest Iceland, provided an opportunity to measure Hg concentrations and fluxes from a hotspot/rift system using modern analytical techniques. We measured gaseous Hg and SO2 concentrations in the volcanic plume by near-source drone-based sampling and simultaneous downwind ground-based sampling. Mean Hg/SO2 was an order of magnitude higher at the downwind locations relative to near-source data. This was attributed to the elevated local background Hg at ground level (4.0 ng m−3) likely due to emissions from outgassing lava fields. The background-corrected plume Hg/SO2 mass ratio (5.6 × 10−8) therefore appeared conservative from the near-source to several hundred meters distant, which has important implications for the upscaling of volcanic Hg fluxes based on SO2 measurements. Using this ratio and the total SO2 flux from both eruptions, we estimate the total mass of gaseous Hg released from the 2021 and 2022 Fagradalsfjall eruptions was 46 ± 33 kg, equivalent to a flux of 0.23 ± 0.17 kg d−1. This is the lowest Hg flux estimate in the literature for active open-conduit volcanoes, which range from 0.6 to 12 kg d−1 for other hotspot/rift volcanoes, and 0.5–110 kg d−1 for arc volcanoes. Our results suggest that Icelandic volcanic systems are fed from an especially Hg-poor mantle. Furthermore, we demonstrate that the aerial near-source plume Hg measurement is feasible with a drone-based active sampling configuration that captures all gaseous and particulate Hg species, and recommend this as the preferred method for quantifying volcanic Hg emissions going forward.

A History Matching Study for the FluidFlower Benchmark Project

In this study, we conduct a comprehensive history matching study for the FluidFlower benchmark model. This benchmark was prepared and organized by the University of Bergen, the University of Stuttgart, and Massachusetts Institute of Technology, for promoting understanding of the complex physics of geological carbon storage (GCS) through in-house experiments and numerical simulations. This paper synthesizes the experiences of history matching the benchmark data encountered by the Delft-DARTS and CSIRO participants. History matching is first performed based on a low-dimensional-zonated structured model using a simple Poisson-like solver. The permeability of six facies and two faults is inferred in this stage to match the digitized concentration data. The history matching is then further enhanced to richer spatial and physical models to capture the spatial variation of permeability and buoyancy effects, using an unstructured grid. Efficient adjoint methods are used to evaluate the gradient used in the optimization of data misfits or equivalent Bayesian log-likelihoods. With efficient optimization methods available for both maximum a posteriori model inference and Randomized Maximum Likelihood methods for model uncertainty, we perform history matching using both binary and continuous concentration observations. The results show that the tracer plumes in the enriched model match the experimental plumes more accurately compared with the results from the parsimonious-zonated model. The history matching results based on the concentration observations provide more similar plume shapes compared with the case based on the binary observations. The permeability difference between the model before and after history matching reveals that the tracer plume zone and the high permeable zone are the regions of high sensitivity in terms of data misfit between the model response and observations. Surprisingly, CO2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$_2$$\\end{document} concentration plume forecasts based on these history-matched models were not especially sensitive to the improvements observed in the enhanced model.

Light transmittance measurement method of solid rocket motor plume based on laser modulation spectrum analysis

AbstractThe light transmittance measurement method, which is used to characterize smoke characteristic signal of solid rocket motor plume, can provide important reference for development and design of low smoke characteristic signal solid propellants and motors. The light transmittance measurement method of solid rocket motor plume based on laser modulation spectrum analysis is proposed in this paper. By using a 405 nm laser as the light source and a narrow‐band filter detection method, the influence of plume radiation on the measurement of light transmittance of solid rocket motor plume smoke can be minimized. Additionally, combined with the laser modulation spectrum analysis technology, the signal amplitude is selected by the laser modulation frequency to improve the signal‐to‐noise ratio of the signal. Compared with the laser constant current measuring mode, laser high‐frequency modulation measuring mode with spectrum analysis effectively improves the optical transmittance measurement accuracy. Based on this, a solid rocket motor plume smoke transmittance test system is developed to measure the plume smoke of standard test motors with different propellants and combustion chamber pressures. The results demonstrate that the system can effectively measure the light transmittance of the plume smoke. The light transmittance of solid rocket motor plume can be increased by reducing the aluminum content of solid propellant or increasing the pressure of the combustion chamber. Thus, this research provides an effective measuring tool for evaluating smoke concentration of solid rocket motor plume, which is beneficial to develop low smoke characteristic signal solid propellants and motors.

Discharge characteristics and parameter diagnosis of brush-shaped air plasma plumes under auxiliary discharge

Atmospheric pressure plasma jet (APPJ) can produce plasma plumes rich in active species, which has a wide scope of applications. From the perspective of applications, it is one of the hot issues in APPJ research to generate a diffuse plasma plume on a large scale. At present, large-scale plasma plume has been produced by noble working gases, which is more economic and valuable if it is reproduced by air used as the working gas. In this work, an APPJ with an auxiliary discharge is proposed, with which a large-scale air plasma plume with a brush shape is produced. Results indicate that the brush-shaped air plume can exist by changing voltage amplitude (&lt;i&gt;V&lt;/i&gt;&lt;sub&gt;p&lt;/sub&gt;) in a certain range. The length and brightness of the plasma plume increase with &lt;i&gt;V&lt;/i&gt;&lt;sub&gt;p&lt;/sub&gt; increasing. The waveforms of voltage and light emission signalindicate that the discharge can start at most once within half a cycle of applied voltage. The probability of discharge and the intensity of light emission pulse for each half a voltage cycle increase with &lt;i&gt;V&lt;/i&gt;&lt;sub&gt;p&lt;/sub&gt; increasing, but the voltage value at the discharge moment decreases with &lt;i&gt;V&lt;/i&gt;&lt;sub&gt;p&lt;/sub&gt; increasing. High-speed imaging study shows that the generation mechanisms of diffuse brush-shaped air plasma plumes and small-scale air plasma are similar, both originating from the temporal superposition of bifurcated normal flow light. In addition, optical emission spectra from the brush-shaped air plasma plume are utilized to study electron temperature, electron density, molecular vibrational temperature, and gas temperature. With &lt;i&gt;V&lt;/i&gt;&lt;sub&gt;p&lt;/sub&gt; increasing, gas temperature is low and almost unchanged, while electron density, electron temperature, and molecular vibrational temperature increase. In addition, OH concentration of the plasma plume is investigated by laser-induced fluorescence, indicating that OH is uniformly distributed, and its concentration increases with the &lt;i&gt;V&lt;/i&gt;&lt;sub&gt;p&lt;/sub&gt; increasing. All these results mentioned above are qualitatively analyzed.

PROCESSING AND ANALYSIS OF SO2 EMISSIONS DATA IN TEMPERATE AND CIDPOLAR LATITUDES IN 2020-2021 ACCORDING TO OMPS/SUOMI NPP SATELLITE DATA

The study is devoted to the processing and analysis of data on SO2 emissions into the troposphere and lower stratosphere in volcanic areas and technogenic areas. Measurement data from the OMPS spectrophotometer (Suomi NPP spacecraft) in the UV range were used. Primary information processing was performed using the DOAS algorithm. Studied for observation time 2020-2021 SO2 emissions over volcanic areas using the example of Etna stratovolcan and the industrial zone of the Polar Branch of Norilsk Nickel. As a result of processing and analysis of measured OMPS data SO2 concentrations were determined at five atmospheric altitudes. It is shown that the largest intakes of sulfur dioxide into the troposphere and lower stratosphere are observed over the study area in the spring both in 2020 and 2021. The monthly trace of its emission can be traced in the atmospheric layer from 1 to 18 km and the SO2 concentration decreases with height. This dependence was also observed with changes in SO2 concentration in the volcanic plume. This is explained by the fact that at high altitudes sulfur dioxide is converted into sulfuric acid aerosol.