Emission Sources Research Articles

Characterization and mitigation measures for carbon dioxide, methane, and ammonia emissions in dairy barns

The agricultural sector is a major contributor to global greenhouse gas emissions, with dairy production being a significant source. In this context, the study aims to characterize CO₂, CH₄, and NH₃ emissions—key greenhouse gases in dairy barns—and to evaluate strategies for mitigating these emissions. Inside dairy barns, the primary sources of CO₂, CH₄, and NH₃ emissions are linked to the enteric processes of the animals and the waste deposited within the dairy barns. CO₂ mainly originates from animal respiration and the decomposition of organic matter. CH₄ is generated through enteric fermentation in the rumen and the anaerobic decomposition of manure. Additionally, NH₃ is released from the enzymatic breakdown of urea in urine. Mitigation efforts have shown promise within dairy barns through various approaches. Optimizing animal diets by incorporating supplements and controlling protein intake helps reduce methane production from enteric fermentation. Enhanced manure management practices, including separating feces and urine, adjusting manure pH, and increasing cleaning frequency, are effective in minimizing ammonia and methane emissions within dairy barns. Nevertheless, achieving significant emission reductions also requires effective waste management beyond the facilities. This study contributes to the ongoing dialogue on sustainable livestock production by addressing both emission sources and potential solutions in dairy farming.

Evaluating the effects of meteorology and emission changes on ozone in different regions over China based on machine learning

Systematically understanding the impact of meteorological conditions on regional ozone pollution helps to retrieve ozone dataset and evaluate emission changes on ozone variation. Here, more air-quality observation sites were collected, and Random Forest algorithm was applied to retrieve daily maximum 8 h average (MDA8) O3 concentrations at 1 km resolution in 2019-2020 in China. The region-season model and whole-retrieved model were established to compare the contributions of meteorological variables to ozone variations on spatiotemporal scale. The former model outperformed the latter for retrieval capability, but the predictive ability of the latter was slight stronger. This may be associated with the spatiotemporal heterogeneity of meteorological influence. Daily ozone variability in China was mainly influenced by meteorology, especially in North China Plain in autumn. The key factors were temperature, ultraviolet radiation and relative humidity over the whole country, but varied significantly in different regions and seasons. The effects of meteorology and emission sources on ozone were separated by weather normalization technique. Meteorological conditions were particularly favorable for increasing ozone concentrations in spring, but particularly unfavorable in winter. During the COVID-19 lockdown, the increases in ozone in spring and winter of 2020 were the contribution of the combination of meteorology and emissions; while the decreases in ozone in summer and autumn of 2020 were mainly due to the changes of emission sources, although meteorological conditions were unfavorable to ozone mitigation in heavily polluted areas. Our findings provide scientific basis for the prevention and control of ozone pollution for the regional scale in China.

Incorporating meander to account for the impact of low winds in area source modeling; AERMOD as a case study

ABSTRACT A variety of sources of pollutant emissions can be represented as area sources. These include manure lagoons, landfills, wastewater treatment ponds, and highways. A group of point sources can also be treated as an area source. The impact of an area source is usually computed by representing the area source as a set of line sources perpendicular to the wind direction. As for point sources, the Gaussian horizontal concentration distribution used to compute the contributions of the line sources is likely to overestimate ground-level concentrations when the wind speed is comparable to the standard deviation of the horizontal velocity fluctuations. A variety of methods are used to mitigate this overestimation under these conditions, referred to as meander. As an example of one these approaches, we examine that of AERMOD, EPA’s regulatory model. AERMOD includes meander in modeling the impact of point and volume sources, but has not yet incorporated it into AERMOD’s area source algorithm. This paper describes an approach to include meander in AERMOD’s area source algorithm and demonstrates its impact on concentrations associated with area sources. Implications: Inclusion of wind direction meander in modeling dispersion when the wind speed is low is important in ensuring that AERMOD does not overestimate concentrations under these conditions. In view of the importance of area sources of pollution, the results presented in this paper represent a potential enhancement of AERMOD’s ability to estimate the upper end of the concentration distribution, which forms the basis of the regulatory acceptance of the model.

Influence of local meteorology and gaseous pollutant emissions on atmospheric nanoparticle concentrations in a pedestrian way in urban region

The roadside environment is one of the major sources of nanoparticle emission in the urban regions. The complex mixture of different pollutants makes it hard to understand the dynamics and behavior of nanoparticles. The study aims to analyze the dynamics of atmospheric nanoparticles ranging from 10 to 1000 nm along with local meteorological conditions and gaseous pollutants in a pedestrian way at a busy street in Delhi, a megalopolis in India, during all five major seasons of the study area. Nucleation mode particles were higher during the spring season, whereas the contribution of Aitken mode particles dominated the total particle number concentrations in the rest of the seasons. Due to the influence of vehicular sources, rush hour concentrations were higher than during non-rush hours. Thus, the diurnal pattern in particle concentration strongly coincided with emissions associated with the vehicular flow. During the winter season, the average total particle number concentration was observed to be maximum (4.1 × 104 cm-3) with a higher surface area of particles of 3.5 × 10-3 mm2m-3. Compared to the monsoon season, the concentration of NOX was 5 times higher in winter. The boundary layer height in the study region ranged from 600 to 2400 m during different seasons, and the maximum ventilation coefficient was observed to be >3000 m2s-1 during summer. Precipitation reduced the concentration of particles by half, from 2.2 x 104 to 1.1 x 104 cm-3, due to wet scavenging. The study revealed that the concentrations of particles depend not only on primary emissions but also are influenced by local meteorology and other co-emitted pollutants. Understanding the dynamics of atmospheric nanoparticles in urban roadside environments as outlined in this study is crucial to devise necessary mitigation measures for people residing near the road in order to reduce health impact and improve air quality.

Development and validation of a new asphalt mixture to reduce tyre-road contact noise in interurban areas

Road transport is one of the most important sources of noise emissions having a serious impact on human health. Thus, the objective of this study is to develop an innovative asphalt mixture that decreases noise from tyre and pavement interaction on highways with average speeds of about 80 km/h. To achieve this, a series of mechanical and functional tests were conducted both in the laboratory and at the Gustave Eiffel University Accelerated Pavement Testing (APT) facility. The new experimental mixture achieves a balance between high void content and low macro-texture (wavelengths of 5–50 mm), which improves noise reduction while maintaining good mechanical performance. This is achieved thanks to its two-layer structure, with a top layer of reduced maximum aggregate size (4 mm) and a thickness suitable for optimum sound absorption. Various mechanical tests, including the particle loss test and the water sensitivity test, along with functional evaluations focusing on texture, flow resistivity and sound absorption properties, were performed. Measurements on the APT were taken at three different stages of the pavement service lifetime during the testing phase. The outcome was the production of an asphalt mixture that reduced noise up to 6.8 dB in comparison to an asphalt concrete road.

Environmental Assessment of Organic Dairy Farms in the US: Mideast, Northeast, Southeast, and Mountain Regions

Greenhouse gases (GHGs), ammonia (NH3) emissions, eutrophication potential (EP), and use of fossil energy, direct land, and blue water of organic dairy farms are evaluated in the Mideast, Northeast, Southeast, and Mountain regions of the US. Eighteen archetypical organic dairy farms are modeled with GHGs per kg fat and protein corrected milk (FPCM) ranging between 0.83-1.45 kg CO2-eq with and 0.93-1.59 kg CO2-eq without carbon sequestration. Enteric methane (CH4) is the major contributor (42-58%) of farm GHGs, followed by CH4 from manure (15-27%), energy use (8-18%), and material inputs (3-24%). Enteric CH4 is affected by feed efficiency and milk production, manure CH4 by the type of manure handled and stored, and GHGs from energy by the content of fossil-fuel sources in the electricity grid mix. Manure is the major source of NH3 emissions ranging from 8.2-24.3 g/kg FPCM. Alternative GHG mitigation strategies show potential reductions of up to 23 and 51% for individual and combined strategies, respectively. While enteric CH4 is the greatest GHG contributor, manure management has greater GHG mitigation potential. The choice of CH4 predictive equations, N2O emission factors, allocation, and functional units could increase GHGs up to 43% in the evaluated organic dairy farms.

X-ray communication system with high-repetition-rate pulsed X-ray source and LYSO(Ce) and YAP(Ce) scintillators

X-ray communication offers significant advantages over traditional microwave methods due to its shorter wavelength and higher theoretical bandwidth, enabling efficient space communication and penetration through complex electromagnetic environments. However, current systems face limitations in X-ray emission modulation and high-precision timing detection. To meet the high-frequency transmission demands of space missions, we developed a pulsed X-ray emission source capable of high-frequency modulation. Additionally, we identified specific scintillators with distinct advantages for different transmission frequency ranges, allowing for performance optimization. Experimental results demonstrated a successful transmission rate of 10 MHz, validating the feasibility of MHz-frequency X-ray communication.

A potential bioaerosol source from kitchen chimneys in restaurants

Bioaerosols are ubiquitous and have a substantial impact on the atmosphere and human health. Despite the identification of numerous bioaerosol emission sources, the contribution of anthropogenic sources remains inadequately understood. In kitchens, oil stains accumulated at the vent may discharge microorganisms into the environment with airflow, potentially discharging bioaerosol pollution. This putative anthropogenic source of bioaerosols has been long ignored. To investigated whether kitchen chimneys can be a potential source for bioaerosols, air samples, oil stains from in/out chimneys, and surface sand samples were collected near several commercial restaurants. PCoA showed that sampling sites significantly impacted microbiomes, whereas SourceTracker analysis led to the finding that waste grease significantly contributed to bioaerosol composition. Both findings agree with the kitchen chimney as a source of microbes in bioaerosols in the surrounding environment. Furthermore, despite the low biodiversity, a high proportion of stress-tolerant and potential pathogenic bacteria and fungi were found in residual culinary grease, which may escape into the air causing potential risks to human beings. These results led to the proposal that airborne microbiota can originate from cooking waste grease. Immediate actions should be taken into account to enhance disinfection and sterilization aimed at fume vents.

The source of volatile organic compounds pollution and its effect on ozone in high-altitude areas

Volatile organic compounds (VOCs) are crucial precursors in the formation of ozone (O3). The sources of pollution are complex and significantly impact O3 generation. Long-term exposure to high-concentration O3 environments causes serious damage to organisms. High-altitude areas experience continuous high temperatures and strong solar radiation, which can easily produce O3 through photochemical reactions, making these areas prone to frequent air pollution. This study utilizes the National Positioning Station of the Yinchuan Urban Ecosystem in Ningxia to conduct field synchronous observations, gathering data on VOCs, meteorological variables, and O3. By employing machine learning algorithms, we examined the seasonal distribution characteristics of VOCs, their pollution sources, and their impact on O3. The results show that the seasonal distribution of VOCs areas is low in spring and autumn and high in summer and winter. The components with higher volume fraction contribution are m-tolualdehyde in spring and summer, ethane in autumn, and acetylene in winter. The main emission sources of VOCs pollution are hydrocarbon volatile emission in spring and winter, solvent volatilization emission in summer, and industrial sources in autumn. VOCs have a negative effect on O3, with a greater impact in winter and spring, evidenced by standardized effect values of −0.26 and −0.24. The key components affecting O3 in VOCs vary by season. Aromatic hydrocarbons are the key components in spring and winter, with contribution rates of 22 % and 21.3 %. Alkenes are the key components in summer and autumn (24.5 % and 26.8 %). Among meteorological variables, temperature is the key factor affecting O3, while wind speed is the key factor affecting O3 only in winter. This study aims to clarify the sources of VOCs pollution in different seasons and their impact on O3, providing a theoretical basis and technical support for the prevention and control of VOCs and O3 pollution in high-altitude areas.

The effect of Atmospheric Boundary Layer Meteorology in climate-related gases and VOCs concentrations over 3 European cities using an aerial platform

In the frame of the EU Horizon2020 ICARUS project, a N.A.S.A Awarded Light Manned Aircraft equipped with high-tech scientific instrumentation was used to perform an aerial mapping over Athens, Thessaloniki and Ljubljana greater areas. This study aimed to evaluate the effect of Atmospheric Boundary Layer (ABL) on Green House Gases (GHGs) and Volatile Organic Compounds (VOCs) concentrations over urban and rural areas of the above cities. Simultaneous ground-based measurements were performed in the respective regions. Air samples were pressurized with a stainless-steel bellows compressor into electropolished stainless steel canisters and analyzed by the use of a novel gas chromatographic system. The estimation of the mixing height (ΜΗ) of the ABL was based on the synoptic scale atmospheric circulation and the prevailing background wind. It was found that the MH variation, following the prevailing meteorological conditions, results in different concentration profiles in the lower troposphere over the examined regions. The pollutants concentrations were generally decreasing with altitude in the ABL. Under certain meteorological conditions, vertical mixing plus horizontal transport can cause a high pollution level at the top of ABL. The pollutants concentrations were low over less industrialized and upwind regions, suggesting that local emission sources play significant role on the GHGs and VOCs levels over the regions. However, due to the large scale of sampling area that the aircraft covers the above gradients in concentrations are relative low. AQ modelling activities for simulating the cases studied in the current or any relative future work could reduce operating costs and allow projections of potential impacts.

Measuring Black Hole Light Echoes with Very Long Baseline Interferometry

Light passing near a black hole can follow multiple paths from an emission source to an observer due to strong gravitational lensing. Photons following different paths take different amounts of time to reach the observer, which produces an echo signature in the image. The characteristic echo delay is determined primarily by the mass of the black hole, but it is also influenced by the black hole spin and inclination to the observer. In the Kerr geometry, echo images are demagnified, rotated, and sheared copies of the direct image and lie within a restricted region of the image. Echo images have exponentially suppressed flux, and temporal correlations within the flow make it challenging to directly detect light echoes from the total light curve. In this Letter, we propose a novel method to search for light echoes by correlating the total light curve with the interferometric signal at high spatial frequencies, which is a proxy for indirect emission. We explore the viability of our method using numerical general relativistic magnetohydrodynamic simulations of a near-face-on accretion system scaled to M87-like parameters. We demonstrate that our method can be used to directly infer the echo delay period in simulated data. An echo detection would be clear evidence that we have captured photons that have circled the black hole, and a high-fidelity echo measurement would provide an independent measure of fundamental black hole parameters. Our results suggest that detecting echoes may be achievable through interferometric observations with a modest space-based very long baseline interferometry mission.

Unveiling industrial emissions in a large European river: Insights from data mining of high-frequency measurements

Despite the tremendous efforts to improve river water quality, chemical contamination remains a significant issue. Besides well-known contaminants, in recent years, pollutants of industrial origin received increasing attention because of the huge knowledge gap regarding their occurrence, fate and environmental risks. Moreover, such pollutants often exhibit high concentration fluctuations over time, which makes them less predictable and measurable with classical short-time campaigns.This study provides insights into the different sources of chemical contamination of the Rhine River based on temporal high-frequency LC-HRMS monitoring data from a single location. A newly developed prioritization strategy selected nearly 3000 substances as potentially major contaminants. A novel classification analysis based on temporal behavior identified 53% of these compounds (accounting for 62% of the time-integrated intensity recorded in the dataset) as originating from irregular emission sources. Irregular emissions can originate from industrial production cycles. After delimiting other potential irregular sources, we have strong evidence indicating that a considerable share of the irregular emissions likely comes from industrial activities. This finding is supported by the structural elucidation of sixteen irregularly emitted substances, for which the industrial origin was successfully confirmed. Those compounds include 3-chloro-5-(trifluoromethyl)pyridine-2-carboxylic acid and 4-(dimethylamino)-2,2-diphenylpentanenitrile. In addition, 40 other compounds exhibited temporal emission patterns similar to the sixteen industrial compounds, which strongly suggests a common contamination source. Finally, 100 top-ranking compounds were selected for further structural elucidation and emission reduction measures. The computational approach outlined within this study can be effectively applied in other large river catchments to identify unknown contaminants stemming from industrial sources.

Reinforcement Learning for EV Fleet Smart Charging with On-Site Renewable Energy Sources

In 2020, the transportation sector was the second largest source of carbon emissions in the UK and in Newcastle upon Tyne, responsible for about 33% of total emissions. To support the UK’s target of reaching net zero emissions by 2050, electric vehicles (EVs) are pivotal in advancing carbon-neutral road transportation. Optimal EV charging requires a better understanding of the unpredictable output from on-site renewable energy sources (ORES). This paper proposes an integrated EV fleet charging schedule with a proximal policy optimization method based on a framework for deep reinforcement learning. For the design of the reinforcement learning environment, mathematical models of wind and solar power generation are created. In addition, the multivariate Gaussian distributions derived from historical weather and EV fleet charging data are utilized to simulate weather and charging demand uncertainty in order to create large datasets for training the model. The optimization problem is expressed as a Markov decision process (MDP) with operational constraints. For training artificial neural networks (ANNs) through successive transition simulations, a proximal policy optimization (PPO) approach is devised. The optimization approach is deployed and evaluated on a real-world scenario comprised of council EV fleet charging data from Leicester, UK. The results show that due to the design of the rewards function and system limitations, the charging action is biased towards the time of day when renewable energy output is maximum (midday). The charging decision by reinforcement learning improves the utilization of renewable energy by 2–4% compared to the random charging policy and the priority charging policy. This study contributes to the reduction in battery charging and discharging, electricity sold to the grid to create benefits and the reduction in carbon emissions.

Diagnosis of GHG Emissions in an Offshore Oil and Gas Production Facility

This work presents a diagnosis of greenhouse gas (GHG) emissions for floating production storage and offloading (FPSO) platforms for oil and gas production offshore, using calculation methodologies from the American Petroleum Institute (API) and U.S. Environmental Protection Agency (EPA). To carry out this analysis, design data of an FPSO platform is used for the GHG emissions estimation, considering operations under steady conditions and oil and gas processing system simulations in the Aspen HYSYS® software. The main direct emission sources of GHG are identified, including the main combustion processes (gas turbines for electric generation and gas turbine-driven CO2 compressors), flaring and venting, as well as fugitive emissions. The study assesses a high CO2 content in molar composition of the associated gas, an important factor that is considered in estimating fugitive emissions during the processes of primary separation and main gas compression. The resulting information indicates that, on average, 95% of total emissions are produced by combustion sources. In the latest production stages of the oil and gas field, it consumes 2 times more energy and emits 2.3 times CO2 in terms of produced hydrocarbons. This diagnosis provides a baseline and starting point for the implementation of energy efficiency measures and/or carbon capture and storage (CCS) technologies on the FPSO in order to reduce CO2 and CH4 emissions, as well as identify the major sources of emissions in the production process.

Secondary Organic Aerosol Formation from the Photooxidation of Aromatic Ketone Intermediate Volatile Organic Compounds.

Oxygenated aromatics are substantial secondary organic aerosol (SOA) precursors, such as benzyl alcohol and phenolic compounds. Aromatic ketone intermediate volatile organic compounds (IVOCs), as a subclass of oxygenated aromatics, were found in anthropogenic source emissions and may contribute to SOA formation. However, the SOA yields and formation pathways of aromatic ketone IVOCs remain unknown. In this study, the photooxidations of aromatic ketone IVOCs in the absence and presence of NOx were studied in an oxidation flow reactor, and the particle- and gas-phase oxidation products were measured. The maximum SOA yields of benzophenone and 1,2-diphenylethanone are 0.24 and 0.33-0.35, respectively, relatively high among oxygenated aromatics. The SOA yields in the presence of NOx are 2-3 times higher than those in the absence of NOx in the late stage of oxidation. As the photooxidation proceeds, H/C of SOA slightly increases with O/C, and a greater amount of more-oxidized ring-retaining products exists in the particle phase in the presence of NOx. Based on gas-phase products and possible reaction pathways, functionalization of benzoic acid via the phenolic pathway is favored in the presence of NOx. Thus, our study highlights the significant SOA formation from aromatic ketone IVOCs, especially in the presence of NOx during long-time photooxidation.

Addressing companies’ low-carbon transition challenges requires diversified investments in environmental initiatives

The energy, utilities, industrial, and material sectors are crucial suppliers of essential goods and services, but their business operations are among the largest sources of anthropogenic greenhouse gas emissions. Consequently, companies in these sectors play a pivotal role in the low-carbon transition and face substantial stakeholder pressure to manage their transition risks and reduce their environmental impact. Here, we argue that effective responses to transition challenges require diversifying investments in adaptation and mitigation initiatives across a broad range of activities and goals. Analysing financial and nonfinancial data from a global sample of publicly traded companies, we find that those who extensively diversify their investments are better able to reduce their emissions over time. Diversification also reduces carbon pricing risk, thereby lowering exposure to transition risks, under several climate policy scenarios. Our findings provide empirical evidence that business leaders in critical sectors for the low-carbon transition should incorporate well-diversified investments in adaptation and mitigation initiatives into their sustainability strategies to manage interconnected transition challenges.

The impact of the carbon reduction policy effectiveness on energy companies' ESG performance

As one of the largest sources of carbon emissions, energy companies are motivated to improve their environmental, social, and governance (ESG) performance to attract investment and meet sustainability goals under the constraints of carbon reduction policies. However, it is currently unclear whether and how carbon reduction policies can help energy companies improve their ESG performance. Therefore, this paper comprehensively examines the impact of carbon reduction policies on the ESG performance of energy companies. First, we use the content analysis method to construct a novel index as a proxy of policy influence based on policy text, i.e., the carbon reduction policy effectiveness (CRPE) index. Then, we empirically examine the impact of CRPE on the ESG performance of energy companies using the panel data of Chinese A-share listed energy companies from 2010 to 2022. The empirical study shows that the CRPE can significantly improve the ESG performance of energy companies and remains robust to robustness tests. Mechanism tests show that it is due to the increase in executives with environmental backgrounds and executive compensation. For the three indicators (E, S, and G) of ESG performance, CRPE promotes environmental and social performance but reduces governance performance. Notably, the effect of CRPE on the ESG performance of clean energy companies and state-owned enterprises (SOEs) is more significant. These findings enrich environmental economic theories and provide a reference for energy companies to promote ESG practices and the low-carbon transition, helping to accelerate the process of carbon neutrality.

Legacy and Novel Per- and Polyfluoroalkyl Substances in Surface Soils across China: Source Tracking and Main Drivers for the Spatial Variation.

China aims to actively control the contamination of globally concerning per- and polyfluoroalkyl substances (PFASs). Evaluation of the current situation can provide a critical reference point for tracking the effectiveness of ongoing progress. Herein, we present the first comprehensive assessment of the spatial variations of 20 legacy and 54 novel PFASs in Chinese background soils in 2021. Novel PFASs were extensively detected in 98.4% of the samples, with 21 species being first reported, which greatly facilitated the appointment of diverse emission sources that aligned with local industrial structures. However, legacy PFASs still dominated the ∑74PFAS profile (median 0.51 ng/g, 0.050-8.33 ng/g). The spatial heterogeneity of soil PFASs was positively driven by economic development and atmospheric deposition, enabling the establishment of predictive models to project the national distribution and temporal trends. Elevated PFAS levels were predominantly distributed in the more industrialized eastern and southern regions, as well as other coastal areas with greater precipitation. ∑74PFAS in surface soils was estimated to increase by 12.9 pg/(g year) over 2002-2021, which would continue alongside economic growth, albeit with greater contributions from novel alternatives. Our work provides comprehensive baseline and predictive data to inform policies toward PFAS control in China.

Development of Predictive Models for Energy Demand and CO2 Emission Trends for Households in Makurdi Metropolis

This study aimed at developing predictive models for energy demand and CO2 emission trends for households in Makurdi metropolis. As part of the study, households were randomly selected to represent a reasonable spread. Four major emission sources: electricity; transportation; solid waste and cooking fuels were considered, and data on the sources were obtained using records from relevant departments as well as questionnaires, surveys, and interviews of the occupants. This data was analyzed and used to calculate the CO2 emissions of the households using IPCC standard guidelines and formulae. The CO2 emission and energy demand predictive models were also developed using Design Expert 13. The ANOVA for the CO2 emission and energy demand shows a significant model (Significance F value of ≤ 0.0001) and a high R-squared value of 0.9981 and 0.9802 respectively. This indicates the adequacy of the models, and they can therefore be used to predict the CO2 and energy demand of households in Makurdi metropolis. The limitation remains the imbalance in living standards across the respective areas of the metropolis which definitely impacts on the demand and use of energy. It will be useful for policy makers and implementers in ensuring sustainable energy demand planning and CO2 emissions mitigation.

A numerical study of Lamb wave localization in thin plates using a passive co-linear phased array

Lamb waves have become increasingly popular in the field of aerospace vehicle non-destructive testing and evaluation as well as structural health monitoring. These guided waves possess the ability to travel long distances and exhibit a notable inclination to interact with existing damage. This work has numerically explored for the first time the use of a passive co-linear phased array to localize emission sources over a wide range of variables. Three localization methods are explored, namely, reverse beamforming, wavefront curvature ranging, and hyperbolic lateration in a direction comparison without modeling transducers. It was shown that both reverse beamforming and wavefront curvature ranging could localize an emission with <1% error in both range and bearing, while hyperbolic lateration was significantly worse. A relationship between bearing error and bearing was demonstrated, presenting the ability to develop new methods with correction factors that can localize emissions with even greater accuracy.