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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

PNNL Richland Campus Radionuclide Air Emissions Report for Calendar Year 2025

This report documents radionuclide air emissions that result in the 2025 highest effective dose equivalent (EDE) to an offsite member of the public, referred to as the maximally exposed individual (MEI). The report has been prepared in compliance with the Code of Federal Regulations, Title 40, Protection of the Environment, Part 61, National Emission Standards for Hazardous Air Pollutants, Subpart H, “National Emission Standards for Emissions of Radionuclides Other than Radon from Department of Energy Facilities” and Washington Administrative Code Chapter 246-247, “Radiation Protection–Air Emissions.” The total dose to the MEI from all PNNL Richland Campus radionuclide emissions in 2025, including fugitive emissions and radon, is 1.8E-05 mrem (1.8E-07 mSv) EDE, or more than 100,000 times less than the federal and state standard of 10 mrem/yr, with which the PNNL Richland Campus is in compliance. The dose attributable to radon emissions is 1.5E-13 mrem (1.5E-15 mSv) EDE.

40 CFR 61 Subpart H↗
NASA NTRSDate not supplied

Sunglint-aided Methane Retrieval: Using Sentinel-2 to Quantify Offshore Oil and Gas Emissions

The extraction, production, and transportation of oil and gas via activities such as intentional venting and fugitive emissions are leading contributors to anthropogenic methane emissions. Offshore operations comprise a significant percentage of all oil and gas operations, yet emission monitoring over the ocean is insufficient. Due to low surface reflectance over the ocean, remote sensing measurements offshore are limited. Therefore offshore contributions to the overall global methane budget are unknown. Regulators such as the Bureau of Ocean and Energy Management (BOEM) and the Bureau of Safety and Environmental Enforcement (BSEE) are unable to validate operator-reported methane emission estimates. The NASA DEVELOP Program partnered with BOEM, BSEE, and SkyTruth to identify potential offshore methane sources in the Gulf of Mexico. Drawing upon existing retrieval methods to detect and quantify onshore methane emissions (Varon et al., 2021), we selected sunglint scenes to detect methane plumes over the ocean in Sentinel-2 imagery. We detected two methane plumes at the Constitution complex in the Gulf of Mexico and off the coast of Pointe-Noire, Congo. If expanded, these methods could serve a vital role in validating operator reporting and quantifying climate impacts of offshore oil and gas operations - complementing those of next generation satellites.

Katherine Howell↗
DOE OSTI2023

Cleaner Chips: Decarbonization in Semiconductor Manufacturing

The growth of the information and communication technology sector has vastly accelerated in recent decades because of advancements in digitalization and Artificial Intelligence (AI). Scope 1, 2, and 3 greenhouse gas emissions data of the top six semiconductor manufacturing companies (Samsung Electronics, Taiwan Semiconductor Manufacturing Corporation, Micron, SK Hynix, Kioxia, and Intel) were gathered from the publicly accessible Carbon Disclosure Project’s (CDP) website for 2020. Scope 3 emissions had the largest share in total annual emissions with an average share of 52%, followed by Scope 2 (32%) and Scope 1 (16%). Because of the absence of a standardized methodology for Scope 3 emissions estimation, each company used different methodologies that resulted in differences in emissions values. An analysis of the CDP reporting data did not reveal information on strategies implemented by companies to reduce Scope 3 emissions. The use of renewable energy certificates had the largest effect on decarbonization centered on reducing Scope 2 emissions, followed by the deployment of perfluorocarbon reduction technologies to help reduce Scope 1 fugitive emissions. Technology-specific marginal abatement costs of CO2 were also estimated and varied between −416 and 12,215 USD/t CO2 eq., which primarily varied depending on the technology deployed.

42 ENGINEERING↗
DOE OSTI2020

Global methane emissions from coal mining to continue growing even with declining coal production

This paper presents projections of global methane emissions from coal mining under different coal extraction scenarios and with increasing mining depth through 2100. The paper proposes an updated methodology for calculating fugitive emissions from coal mining, which accounts for coal extraction method, coal rank, and mining depth and uses evidence-based emissions factors. A detailed assessment shows that coal mining-related methane emissions in 2010 were higher than previous studies show. This study also uses a novel methodology for calculating methane emissions from abandoned coal mines and represents the first estimate of future global methane emissions from those mines. The results show that emissions from abandoned mines increase faster than those from active ones. Using coal production data from six integrated assessment models, this study shows that by 2100 methane emissions from active underground mines increase by a factor of 4, while emissions from abandoned mines increase by a factor of 8. Abandoned mine methane emissions continue through the century even with aggressive mitigation actions.

coal mine methane, abandoned coal mine methane, em↗
DOE OSTI2024

Addressing Low-Cost Methane Sensor Calibration Shortcomings with Machine Learning

Quantifying methane emissions is essential for meeting near-term climate goals and is typically carried out using methane concentrations measured downwind of the source. One major source of methane that is important to observe and promptly remediate is fugitive emissions from oil and gas production sites but installing methane sensors at the thousands of sites within a production basin is expensive. In recent years, relatively inexpensive metal oxide sensors have been used to measure methane concentrations at production sites. Current methods used to calibrate metal oxide sensors have been shown to have significant shortcomings, resulting in limited confidence in methane concentrations generated by these sensors. To address this, we investigate using machine learning (ML) to generate a model that converts metal oxide sensor output to methane mixing ratios. To generate test data, two metal oxide sensors, TGS2600 and TGS2611, were collocated with a trace methane analyzer downwind of controlled methane releases. Over the duration of the measurements, the trace gas analyzer’s average methane mixing ratio was 2.40 ppm with a maximum of 147.6 ppm. The average calculated methane mixing ratios for the TGS2600 and TGS2611 using the ML algorithm were 2.42 ppm and 2.40 ppm, with maximum values of 117.5 ppm and 106.3 ppm, respectively. A comparison of histograms generated using the analyzer and metal oxide sensors mixing ratios shows overlap coefficients of 0.95 and 0.94 for the TGS2600 and TGS2611, respectively. Overall, our results showed there was a good agreement between the ML-derived metal oxide sensors’ mixing ratios and those generated using the more accurate trace gas analyzer. This suggests that the response of lower-cost sensors calibrated using ML could be used to generate mixing ratios with precision and accuracy comparable to higher priced trace methane analyzers. This would improve confidence in low-cost sensors’ response, reduce the cost of sensor deployment, and allow for timely and accurate tracking of methane emissions.

03 NATURAL GAS↗
DOE OSTI2024

Estimating Total Methane Emissions from the Denver-Julesburg Basin Using Bottom-Up Approaches

Methane is a powerful greenhouse gas with a 25 times higher 100-year warming potential than carbon dioxide and is a target for mitigation to achieve climate goals. To control and curb methane emissions, estimates are required from the sources and sectors which are typically generated using bottom-up methods. However, recent studies have shown that national and international bottom-up approaches can significantly underestimate emissions. In this study, we present three bottom-up approaches used to estimate methane emissions from all emission sectors in the Denver-Julesburg basin, CO, USA. Our data show emissions generated from all three methods are lower than historic measurements. A Tier 1/2 approach using IPCC emission factors estimated 2022 methane emissions of 358 Gg (0.8% of produced methane lost by the energy sector), while a Tier 3 EPA-based approach estimated emissions of 269 Gg (0.2%). Using emission factors informed by contemporary and region-specific measurement studies, emissions of 212 Gg (0.2%) were calculated. The largest difference in emissions estimates were a result of using the Mechanistic Air Emissions Simulator (MAES) for the production and transport of oil and gas in the DJ basin. The MAES accounts for changes to regulatory practice in the DJ basin, which include comprehensive requirements for compressors, pneumatics, equipment leaks, and fugitive emissions, which were implemented to reduce emissions starting in 2014. The measurement revealed that normalized gas loss is predicted to have been reduced by a factor of 20 when compared to 10-year-old normalization loss measurements and a factor of 10 less than a nearby oil and production area (Delaware basin, TX); however, we suggest that more measurements should be made to ensure that the long-tail emission distribution has been captured by the modeling. This study suggests that regulations implemented by the Colorado Department of Public Health and Environment could have reduced emissions by a factor of 20, but contemporary regional measurements should be made to ensure these bottom-up calculations are realistic.

03 NATURAL GAS↗
DOE OSTI2024

PNNL-Sequim Campus Radionuclide Air Emissions Report for Calendar Year 2023

The U.S. Department of Energy Office of Science (DOE-SC) Pacific Northwest Site Office has oversight and stewardship duties associated with the Pacific Northwest National Laboratory Sequim campus. Some research projects have the potential to emit low levels of radioactive materials. This report is prepared to document compliance with the Code of Federal Regulation, Title 40, Protection of the Environment, Part 61, National Emission Standards for Hazardous Air Pollutants, Subpart H, “National Emission Standards for Emissions of Radionuclides Other than Radon from Department of Energy Facilities” and Washington Administrative Code Chapter 246-247, "Radiation Protection–Air Emissions." Compliance is indicated by comparing the estimated effective dose equivalent (EDE) to the maximally exposed individual (MEI) member of the public with the 10 millirem per year (mrem/yr) U.S. Environmental Protection Agency (EPA) standard. The PNNL-Sequim campus has only fugitive emissions sources.

40 CFR 61 Subpart H↗
NASA NTRS2015

Crosscutting Airborne Remote Sensing Technologies for Oil and Gas and Earth Science Applications

Airborne imaging spectroscopy has evolved dramatically since the 1980s as a robust remote sensing technique used to generate 2-dimensional maps of surface properties over large spatial areas. Traditional applications for passive airborne imaging spectroscopy include interrogation of surface composition, such as mapping of vegetation diversity and surface geological composition. Two recent applications are particularly relevant to the needs of both the oil and gas as well as government sectors: quantification of surficial hydrocarbon thickness in aquatic environments and mapping atmospheric greenhouse gas components. These techniques provide valuable capabilities for petroleum seepage in addition to detection and quantification of fugitive emissions. New empirical data that provides insight into the source strength of anthropogenic methane will be reviewed, with particular emphasis on the evolving constraints enabled by new methane remote sensing techniques. Contemporary studies attribute high-strength point sources as significantly contributing to the national methane inventory and underscore the need for high performance remote sensing technologies that provide quantitative leak detection. Imaging sensors that map spatial distributions of methane anomalies provide effective techniques to detect, localize, and quantify fugitive leaks. Airborne remote sensing instruments provide the unique combination of high spatial resolution (<1 m) and large coverage required to directly attribute methane emissions to individual emission sources. This capability cannot currently be achieved using spaceborne sensors. In this study, results from recent NASA remote sensing field experiments focused on point-source leak detection, will be highlighted. This includes existing quantitative capabilities for oil and methane using state-of-the-art airborne remote sensing instruments. While these capabilities are of interest to NASA for assessment of environmental impact and global climate change, industry similarly seeks to detect and localize leaks of both oil and methane across operating fields. In some cases, higher sensitivities desired for upstream and downstream applications can only be provided by new airborne remote sensing instruments tailored specifically for a given application. There exists a unique opportunity for alignment of efforts between commercial and government sectors to advance the next generation of instruments to provide more sensitive leak detection capabilities, including those for quantitative source strength determination.

hyperspectral↗
NASA NTRS2022

Gulf of Mexico Health & Air Quality Ii: Mapping Methane Emission Plumes Using Sunglint-Configured Imagery for Monitoring Offshore Oil and Gas Activity

Offshore oil and gas production in the United States is a major source of anthropogenic greenhouse gas emissions and accounts for nearly 30% of global oil and gas production. Methane venting and flaring are primary contributors to offshore emissions, and monitoring these activities is crucial for mitigating greenhouse gas emissions. Limited ground truthing and intermittent offshore satellite revisits make monitoring venting and flaring challenging. The Bureau of Ocean Energy Management (BOEM) and the Bureau of Safety and Environmental Enforcement (BSEE) oversee offshore oil and gas activity but rely primarily on operator-reported data. The non-profit organization SkyTruth monitors natural resources like methane and identifies sources of fugitive emissions. By combining BOEM and BSEE’s operational data along with observations from Sentinel-2 Multispectral Instrument (MSI), Landsat 8 Operational Land Imager (OLI) and Landsat 9 OLI-2, and PRecursore IperSpettrale della Missione Applicativa (PRISMA), the team further identified ultra-emitter point sources in the Gulf of Mexico using sunglint-configured imagery. We quantified these plume emission rates using the methodology from Varon et al. (2020). The team found three plumes in the Gulf of Mexico occurring between 2020 and 2022 using Sentinel-2 MSI and Landsat 9 OLI-2 imagery, in addition to the single plume identified by the Gulf of Mexico Health & Air Quality I team, and successfully quantified three plumes. Our statistical retrieval of three PRISMA images tasked over areas of interest yielded no methane plumes, despite a successful test of a known plume in Assam, India. These analyses serve as a proof of concept for the utility of remote sensing for methane emission monitoring offshore, which can complement regulator emission inventories and validate self-reported operator records.

sunglint↗
DOE OSTI2021

Smart Methane Emission Detection System Development Final Report

Working with the Department of Energy's National Energy Technology Laboratory, Southwest Research Institute® (SwRI®) developed a system to identify methane leaks reliably, accurately, and autonomously at critical midstream sections of the natural gas distribution network in real- time for the purpose of mitigating methane emissions using Optical Gas Imaging (OGI) cameras. SwRI's Smart Leak Detection – Methane (SLED/M) adds a high degree of automation to the process of methane leak detection to minimize sources of human error, minimize response time to a leak event, and maximize midstream visibility. Furthermore, SwRI has been working towards integrating Quantitative OGI (QOGI) capabilities into this existing technology. By leveraging Deep Learning, SwRI now has the capability to estimate fugitive emission leak rates quickly and reliably, which allows operators to detect emissions, quantify leak rate, prioritize repairs, and validate the repairs in a single instrument. The next generation QOGI technology leverages the same cameras used in Leak Detection and Repair (LDAR) programs, with improvements in safety and speed for traditional quantification-based repairs, ultimately leading to less overhead cost for the operators.

03 NATURAL GAS↗
DOE OSTI2021

Smart Methane Emission Detection System Development (Final Report)

Working with the Department of Energy's National Energy Technology Laboratory, Southwest Research Institute® (SwRI®) developed a system to identify methane leaks reliably, accurately, and autonomously at critical midstream sections of the natural gas distribution network in real-time for the purpose of mitigating methane emissions using Optical Gas Imaging (OGI) cameras. SwRI's Smart Leak Detection – Methane (SLED/M) adds a high degree of automation to the process of methane leak detection to minimize sources of human error, minimize response time to a leak event, and maximize midstream visibility. Furthermore, SwRI has been working towards integrating Quantitative OGI (QOGI) capabilities into this existing technology. By leveraging Deep Learning, SwRI now has the capability to estimate fugitive emission leak rates quickly and reliably, which allows operators to detect emissions, quantify leak rate, prioritize repairs, and validate the repairs in a single instrument. The next generation QOGI technology leverages the same cameras used in Leak Detection and Repair (LDAR) programs, with improvements in safety and speed for traditional quantification-based repairs, ultimately leading to less overhead cost for the operators. The goals for this research were to develop two types of models with the following goals: Run in real-time on the edge (≥ 12 Hz), Classification: Achieve less than 5% false positive detection, Classification: Achieve ≥ 95% methane plume detection rate, Regression: achieve ≤ 10 standard cubic feet per hour (scfh) prediction > 70% of the time. In order to achieve these results, multiple infrared (IR) and other sensors were investigated in tandem with the midwave IR (MWIR) OGI to provide additional information to train the underlying models. Information on atmospheric conditions including humidity, temperature, pressure, and solar radiation was provided by a weather station. Several machine learning and deep learning architectures and methods, including looking at quantized classification networks and regressions networks, were explored. As further data was collected, curated, and labeled, it allowed for more refined regressive networks to be adequately trained, leading to better insight into the true flow rates being observed. An important valuable deliverable of this research effort was the development of an advanced network which underwent multiple iterations capable of giving a continuous output. The current network has a predicted mean average percentage error (MAPE) of 12.3% just outside our target goal of 10.00%, but an accuracy of 97.78% at ±50 scfh, well within the overall goal for the Department of Energy (DOE) program. Upon closer inspection, it was observed that more than 10% of datapoints contributing to the MAPE predictions were the result of low flow rate predictions and are beyond the sensitivity of instrument measurement as a result of normal operational variation and noise.

03 NATURAL GAS↗
DOE OSTI2021

Smart Methane Emission Detection System Development (Final Report)

Working with the Department of Energy’s National Energy Technology Laboratory, Southwest Research Institute® (SwRI®) developed a system to identify methane leaks reliably, accurately, and autonomously at critical midstream sections of the natural gas distribution network in real- time for the purpose of mitigating methane emissions using Optical Gas Imaging (OGI) cameras. SwRI’s Smart Leak Detection – Methane (SLED/M) adds a high degree of automation to the process of methane leak detection to minimize sources of human error, minimize response time to a leak event, and maximize midstream visibility. Furthermore, SwRI has been working towards integrating Quantitative OGI (QOGI) capabilities into this existing technology. By leveraging Deep Learning, SwRI now has the capability to estimate fugitive emission leak rates quickly and reliably, which allows operators to detect emissions, quantify leak rate, prioritize repairs, and validate the repairs in a single instrument. The next generation QOGI technology leverages the same cameras used in Leak Detection and Repair (LDAR) programs, with improvements in safety and speed for traditional quantification-based repairs, ultimately leading to less overhead cost for the operators. The goals for this research were to develop two types of models with the following goals: 1. Run in real-time on the edge (≥ 12 Hz) 2. Classification: Achieve less than 5% false positive detection 3. Classification: Achieve ≥ 95% methane plume detection rate 4. Regression: achieve ≤ 10 standard cubic feet per hour (scfh) prediction > 70% of the time In order to achieve these results, multiple infrared (IR) and other sensors were investigated in tandem with the midwave IR (MWIR) OGI to provide additional information to train the underlying models. Information on atmospheric conditions including humidity, temperature, pressure, and solar radiation was provided by a weather station. Several machine learning and deep learning architectures and methods, including looking at quantized classification networks and regressions networks, were explored. As further data was collected, curated, and labeled, it allowed for more refined regressive networks to be adequately trained, leading to better insight into the true flow rates being observed. An important valuable deliverable of this research effort was the development of an advanced network which underwent multiple iterations capable of giving a continuous output. The current network has a predicted mean average percentage error (MAPE) of 12.3% just outside our target goal of 10.00%, but an accuracy of 97.78% at ±50 scfh, well within the overall goal for the Department of Energy (DOE) program. Upon closer inspection, it was observed that more than 10% of datapoints contributing to the MAPE predictions were the result of low flow rate predictions and are beyond the sensitivity of instrument measurement as a result of normal operational variation and noise.

03 NATURAL GAS↗
DOE OSTI2025

Review of the potential and challenges of MOF-based adsorption heat pumps for sustainable cooling and heating in the buildings

Heat pumps are regarded as the most suitable means to meet the future demand of heating and cooling in the building, as they require small input of electrical energy and output high thermal energy. Vapor compression heat pump (VCHP) and transcritical-CO 2 heat pump are the two major heat pump technologies currently dominating the market. However, VCHP system suffers from its fugitive emission of hydrofluorocarbon (HFC) refrigerants, and transcritical-CO 2 system requires high operating pressure (typically 9–13 MPa). For low-to-medium temperature heating and cooling applications thermally driven adsorption heat pump (AHP) provides a better alternative to VCHP and transcritical-CO 2 system as AHP uses natural refrigerants and operates at a much lower pressure. But currently available AHP technology has the limitation of low coefficient of performance (COP). Advances in synthesizing metal-organic frameworks (MOF) with high gas adsorption capacity have created an opportunity to address the problem of low COP of current AHP technology. Here, this article reviews the progress made in the past decade in utilizing MOF and conventional adsorbents with different working fluids in AHP technology and compares their performance. The article also summarizes the key challenges for the MOF-based AHP technology and provides the authors’ perspective to address these challenges.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2025

Process and environmental safety of thermochemical conversion of biomass

Biomass presents a promising opportunity for converting waste into valuable energy, fuels, and chemicals through various thermochemical processes, including hydrothermal carbonization, hydrothermal liquefaction, pyrolysis, gasification, and combustion. However, these processes operate under extreme conditions, introducing significant safety hazards that necessitate meticulous management to prevent accidents. This review discusses potential hazards, risk mitigation strategies, and safety management practices, emphasizing the importance of integrated safety measures into the design and operation of these processes. It also highlights the critical need for robust safety engineering and environmental management strategies tailored to each thermo-chemical process. As these processes transition from laboratory to industrial scale, there is an imperative to develop a clear and concise pathway for emerging industries to achieve regulatory compliance, achieve safety certification, and enable effective stewardship of potential fugitive emissions. By addressing safety and environmental concerns, stakeholders can optimize economic benefits, rural development, and achieve sustained domestic energy generation benefits offered by biomass conversion technologies. Further research on plant design optimization, operational safety, environmental evaluation standardization, and innovative waste management solutions will support the safe and effective scaling of these technologies, ultimately contributing to sustainable management and resource recovery.

09 - BIOMASS FUELS↗
DOE OSTI2024

Biohydrogen: prospects for industrial utilization and energy resiliency in rural communities

Biohydrogen (bioH 2 ) production in rural regions of the United States leveraged from existing biomass waste streams serves two extant needs: rural energy resiliency and decarbonization of heavy industry, including the production of ammonia and other H 2 -dependent nitrogenous products. We consider bioH 2 production using two different strategies: (1) dark fermentation (DF) and (2) anaerobic digestion followed by steam methane reforming of the biogas (AD-SMR). Production of bioH 2 from biomass waste streams is a potentially ‘greener’ pathway in comparison to natural gas-steam methane reforming (NG-SMR), especially as fugitive emissions from these wastes are avoided. It also provides a decarbonizing potential not found in water-splitting technologies. Based on literature on DF and AD of crop residues, woody biomass residues from forestry wastes, and wastewaters containing fats, oils, and grease (FOG), we outline scenarios for bioH 2 production and displacement of fossil fuel derived methane. Finally, we compare the costs and carbon intensity (CI) of bioH 2 production with those of other H 2 production pathways.

08 HYDROGEN↗
NASA NTRS1993

The Ferrofluids Story

A new Ferrofluidics exclusion seal promises improvement in controlling "fugitive emissions" -vapors that escape into the atmosphere from petroleum refining and chemical processing facilities. These are primarily volatile organic compounds, and their emissions are highly regulated by the EPA. The ferrofluid system consists of a primary mechanical seal working in tandem with a secondary seal. Ferrofluids are magnetic liquids - fluids in which microscopic metal particles have been suspended, allowing the liquid to be controlled by a magnetic force. The concept was developed in the early years of the Space program, but never used. Two Avco scientists, however, saw commercial potential in ferrofluids and formed a company. Among exclusion seal commercial applications are rotary feedthrough seals, hydrodynamic bearings and fluids for home and automotive loudspeakers. Ferrofluidics has subsidiaries throughout the world.

Source record↗
DOE OSTI2020

Remediation of Temporary Storage Sites in Support of the Port Hope Area Initiative - 20295

The Port Hope Area Initiative is a community-based solution for the long-term management of historic low level radioactive waste (LLRW) resulting from 60 years of uranium and radium processing operations in the Town of Port Hope which is located in Ontario, Canada. The Eldorado refinery, on the north shore of Lake Ontario, began refining radium-226 from pitchblende ore, later transitioning to the refining of uranium. Through the history of the operation, LLRW was deposited throughout the town of Port Hope as a result of fugitive emissions from the plant and/or through the re-use of process residues as building material and backfill. Historical clean-up activities conducted in the late 1970's involved the remediation of approximately 400 properties and the relocation of 100,000 cubic metres of contaminated soil to a disposal facility in Chalk River operated by Atomic Energy of Canada Limited (AECL). Owing to space limitations at that disposal facility, any LLRW identified through construction monitoring since that time has been stored in the community at three temporary storage sites located throughout the town. These include: the Centre Pier mound that contained approximately of 19,800 m{sup 3} of LLRW-impacted soil that originated from the construction of a new water treatment plant; two mounds located at a licensed storage facility containing LLRW obtained from residential clean-up activities (11,000 m{sup 3}); and a small pad adjacent to the municipal sewage treatment plant containing 2200 m{sup 3} of LLRW-containing sludge. With the construction of a new long-term waste management facility (LTWMF) that has been designed to house all of the LLRW identified within Port Hope, the three sites were early candidates for remediation. The clean-up of the three temporary storage sites was a significant milestone for the Port Hope Area Initiative. After a decade of planning and consultation, this work represents the first sites in the municipality to be remediated with the waste being safety removed and transferred to the newly constructed LTWMF. This paper discusses the challenges associated with the clean-up activities for these three sites and the strategies employed to address those challenges. These included weather-related challenges, owing to the seasons over which the work was conducted as well as those associated with working within a closely-knit community. Canadian Nuclear Laboratories (CNL), working on behalf of the federal government, has worked diligently to develop a positive and trusting relationship with the community. Consequently, the successful execution of this project needed to be sensitive to, and respectful of the needs of the community. In addition to the usual Health, Safety and Environment training, project staff received community awareness training that spoke to the history of this community-based initiative and the expected behavior when working within the community. Transportation routes were defined based on safety and the need to minimize disruption to local traffic while haul-times where scheduled around school bus hours to enhance public safety. The successful completion of this first of many remediation projects to be completed under the Port Hope Area Initiative reflected years of careful planning. Nevertheless, there were a number of 'lessons learned' that have been applied on other ongoing projects be completed under the Port Hope Area Initiative. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗