Distributed wind projects, which are connected at the distribution level of an electricity system or in off-grid applications to serve specific or local energy needs, often rely solely on wind resource models to establish wind speed and energy generation expectations. Historically, anemometer loan programs have provided an affordable avenue for more accurate onsite wind resource assessment, and the lowering cost of lidar systems has shown similar advantages for more recent assessments. While a full 12 months of onsite wind measurement is the standard for correcting model-based long-term wind speed estimates for utility-scale wind farms, the time and capital investment involved in gathering onsite measurements must be reconciled with the energy needs and funding opportunities that drive expedient deployment of distributed wind projects. Much literature exists to quantify the performance of correcting long-term wind speed estimates with 1 or more years of observational data, but few studies explore the impacts of correcting with months-long observational periods. This study aims to answer the question of how short you can go in terms of the observational time period needed to make impactful improvements to model-based long-term wind speed estimates. Three algorithms, multivariable linear regression, adaptive regression splines, and regression trees, are evaluated for their skill at correcting long-term wind resource estimates from the European Centre for Medium-Range Weather Forecasts Reanalysis version 5 (ERA5) using months-long periods of observational data from 66 locations across the US. On average, correction with even 1 month of observations provides significant improvement over the baseline ERA5 wind speed estimates and produces median bias magnitudes and relative errors within 0.22 m s−1 and 4 percentage points of the median bias magnitudes and relative errors achieved using the standard 12 months of data for correction. However, in cases when the shortest observational periods (1 to 2 months) used for correction are not well correlated with the overlapping ERA5 reference, the resultant long-term wind speed errors are worse than those produced using ERA5 without correction. Summer months, which are characterized by weaker relative wind speeds and standard deviations for most of the evaluation sites, tend to produce the worst results for long-term correction using months-long observations. The three tested algorithms perform similarly for long-term wind speed bias; however, regression trees perform notably worse than multivariable linear regression and adaptive regression splines in terms of correlation when using 6 months or less of observational data for correction. Translating the analysis to wind energy, median relative errors in the capacity factor are on average within 10 % using 1 month of training. If the observation period used for correction is not well correlated with the reference data, however, misrepresentation of the observed capacity factor can be substantial. The risk associated with poor correlation between the observed and reference datasets decreases with increasing training period length. In the worst-correlation scenarios, the median capacity factor relative errors from using 1, 3, and 6 months are within 47 %, 26 %, and 16 %, respectively.

The atmospheric dynamics that occur near the intersection of land and water offer exciting and challenging opportunities for wind energy deployment in coastal locations. New models and tools are continually being developed in support of wind resource assessment, and three recent products are explored in this work for their performance in representing characteristics of the wind resource at coastal locations: the Global Wind Atlas 3 (GWA3), the 2023 National Offshore Wind dataset (NOW-23), and the wind climate simulations that are a component of the Wind Integration National Dataset (WIND) Toolkit Long-Term Ensemble Dataset (WTK-LED Climate). These relatively new products are freely available and user-friendly so that anyone – from a utility-scale developer to a resident or business owner – can evaluate the potential for wind energy generation at their location of interest. The validations in this work provide guidance on the accuracy of wind resource assessments for coastal customers interested in installing small or midsize wind turbines (≤ 1 MW in capacity) to support energy needs at the residential, business, or community scale, such as the island and remotely located participants of the U.S. Department of Energy's Energy Transitions Initiative Partnership Project. At 23 coastal locations across the United States, dataset performance varies according to different evaluation metrics. All three recent datasets tend to overestimate the observed coastal wind resource. GWA3 produces the smallest annual average wind speed relative errors, whereas WTK-LED Climate is in best agreement in terms of representing diurnal wind speed cycles. NOW-23 is the highest performing of the datasets for representing seasonal and interannual trends in the coastal wind resource. While GWA3 and WTK-LED Climate are relatively insensitive to the dataset output heights selected for wind resource assessment at small and midsize wind turbine hub heights (20–60 m), significant variation in the NOW-23 representation of wind shear across the wind profile in the lowest 100 m of the atmosphere leads to notable differences in wind speed estimates according to the dataset output heights selected for evaluation. GWA3 exhibits challenges in the representation of observed wind speed diurnal cycles at small and midsize turbine hub heights, likely due to the dataset's consistent treatment of hourly wind speed trends regardless of altitude.

The velocity uniformity and flow angle results from the 3430 stack verification tests, performed in April 2023, demonstrated that the CFD model results may be used to support the qualification of the stack sampling location. The measured velocity uniformity verification test result was 2.1 %COV. This value is well within the uniformity criterion, which is that the velocity uniformity be =20 %COV. Additionally, this value is well within the criterion that the actual stack measurement must be within 5% of the surrogate stack (i.e., CFD modeled stack); in this case the CFD modeled average result of 2.85 %COV for the nominal operating range of 22,800 cfm to 62,400 cfm. Additionally, the measured average flow angle at the 3430 stack monitor location was 5.6 degrees. The result is =20 degrees, so the criterion is met.

The Hanford Tank Waste Treatment and Immobilization Plant low activity waste (LAW) facility stack monitor locations were qualified using scale model stacks to mitigate the risk of identifying that sampling locations do not meet the qualification criteria on the full-scale stack. As required by the American National Standards Institute/Health Physics Society (ANSI/HPS) N13.1-1999 standard, the scale model and its sampling location were geometrically similar to the actual stack and the Reynolds numbers for both the actual and model stacks were >10,000. An additional criterion is that the product of the hydraulic diameter and mean velocity (DV) of the full-scale stack must be between 1/6 DV and 6 DV of the scale model stack tests. Verification tests of the LAW stacks were performed at normal operating conditions. The minimum 1/6 DV value, along with the maximum 6 DV value from the scale model testing determines the range of conditions for which the full-scale stack may be operated and remain in compliance with the stack verification criterion. Based on these DV values, the corresponding stack flow rates for each of the LAW stacks are 815–55,758 scfm for LV-S1, 980–112,078 scfm for LV-S2, 264–22,901 scfm for LV-S3, and 981–79,832 scfm for LV-C2. The remaining criteria for the stack verification to be considered valid involve the flow angle and velocity uniformity results. First, the flow angle at the full-scale stack must be =20°. Second, the velocity uniformity at the full-scale stack must be =20% coefficient of variance (COV). Finally, the velocity uniformity results for the actual and scale model stack tests must agree within 5% COV. These criteria were met through the full-scale stack tests at the LAW facility. Flow angle results were primarily less than 10°, except for one LV-S2 Fan A result, which was 13.2°; all flow angle results were within the =20° criterion. The velocity uniformity results for each test condition ranged between 1.5 COV and 9.2% COV, which were all within the range of the target % COV values from the scale model tests. Based on these stack verification test results, the four LAW filtered exhaust stack sampling locations meet the qualification criteria provided in the ANSI/HPS N13.1-1999 standard for all fan operating configurations. This includes single-fan operating conditions for LV-S1 and LV-S2, dual-fan operations for LV-S3 at both the continuous air monitor and record sampler locations, and both the single-fan as well as the dual-fan operations for LV-C2. Further changes to the system configuration or operating conditions that are outside the bounds described in this report may require additional tests or analyses to determine compliance with the standard.

Here we evaluate the transport of respiratory droplets that carry SARS-CoV-2 through central air handling systems in multiroom buildings. Respiratory droplet size modes arise from the bronchioles representing the lungs and lower respiratory tract, the larynx representing the upper respiratory tract including vocal cords, or the oral cavity. The size distribution of each mode remains largely conserved, although the magnitude of each droplet mode changes as infected individuals breathe, speak, sing, laugh, cough, and sneeze. Here we evaluate how each type of respiratory droplet transits through central ventilation systems and the implications thereof for infectivity of COVID-19. We find that while larger oral droplets can transmit through the air handling systems, their size and concentration are greatly reduced with but few oral droplets leaving the source room. In contrast, the smaller droplets that originate from the bronchioles and larynx are much more effective in transiting through the air handling system into connected rooms. This suggests that the ratio of lower respiratory or deep lung infections may increase relative to upper respiratory infections in rooms connected by central air handling systems. Also, increasing the temperature and humidity in the range considered after the droplets have achieved an "equilib-rium" size reduces the probability of infection.

Due to financial and temporal limitations, the small wind community relies upon simplified wind speed models and energy production simulation tools to assess site suitability and produce energy generation expectations. While efficient and user-friendly, these models and tools are subject to errors that have been insufficiently quantified at small wind turbine heights. This study leverages observations from meteorological towers and sodars across the United States to validate wind speed estimates from the Wind Integration National Dataset (WIND) Toolkit, the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5), and the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), revealing average biases within ±0.5 m s−1 at small wind hub heights. Observations from small wind turbines across the United States provide references for validating energy production estimates from the System Advisor Model (SAM), Wind Report, MyWindTurbine.com, and Global Wind Atlas 3 (GWA3), which are seen to overestimate actual annual capacity factors by 2.5, 4.2, 11.5, and 7.3 percentage points, respectively. In addition to quantifying the error metrics, this paper identifies sources of model and tool discrepancies, noting that interannual fluctuation in the wind resource, wind speed class, and loss assumptions produces more variability in estimates than different horizontal and vertical interpolation techniques. The results of this study provide small wind installers and owners with information about these challenges to consider when making performance estimates and thus possible adjustments accordingly. Looking to the future, recognizing these error metrics and sources of discrepancies provides model and tool researchers and developers with opportunities for product improvement that could positively impact small wind customer confidence and the ability to finance small wind projects.

This article presents results from an experimental study to ascertain the transmissibility of the SARS-CoV-2 virus between rooms in a building that are connected by a central ventilation system. Respiratory droplet surrogates made of mucus and virus mimics were released in one room in a test building, and measurements of concentration levels were made in other rooms connected via the ventilation system. The paper presents experimental results for different ventilation system configurations, including ventilation rate, filtration level (up to MERV-13), and fractional outdoor air intake. The most important finding is that respiratory droplets can and do transit through central ventilation systems, suggesting a mechanism for viral transmission (and COVID-19 specifically) within the built environment in reasonable agreement with well-mixed models. We also find the deposition of small droplets (0.5-4 μm) on room walls to be negligibly small.

Here we evaluate the influence of mucus layers on the evaporation time and transport of enveloped viruses, including SARS-CoV-2. Enveloped viruses must remain moist to be fully infective. Yet, the Wells model based on water droplets divides respiratory droplets into either quickly evaporated aerosolized particles termed droplet nuclei (<10 s) or liquid droplets that fall to the nearest surface, leaving no physical mechanism for airborne transmission of fully infective enveloped viruses over large distances (greater than a few meters). Yet, the role of mucus layers on evaporation times has not been considered even though the formation of mucus shells around liquid cores of respiratory droplets has been shown experimentally. Here we show that mucus shells increase the drying time by orders of magnitude so that enveloped virions may remain well hydrated and, thus, fully infective at substantial distances. This provides a mechanism by which infective enveloped virus particles can transmit as aerosols within buildings and between buildings over extended distances. This analysis is important because public health agencies typically follow the Wells model to establish health policies including social/physical distancing guidelines.

The COVID-19 pandemic has raised concern of viral spread within buildings. Although near-field transmission and infectious spread within individual rooms are well studied, the impact of aerosolized spread of SARS-CoV-2 via air handling systems within multiroom buildings remains unexplored. This study evaluates the concentrations and probabilities of infection for both building interior and exterior exposure sources using a well-mixed model in a multiroom building served by a central air handling system (without packaged terminal air conditioning). In particular, we compare the influence of filtration, air change rates, and the fraction of outdoor air. When the air supplied to the rooms comprises both outdoor air and recirculated air, we find filtration lowers the concentration and probability of infection the most in connected rooms. We find that increasing the air change rate removes virus from the source room faster but also increases the rate of exposure in connected rooms. Therefore, slower air change rates reduce infectivity in connected rooms at shorter durations. We further find that increasing the fraction of virus-free outdoor air is helpful, unless outdoor air is infective in which case pathogen exposure inside persists for hours after a short-term release. Increasing the outdoor air to 33% or the filter to MERV-13 decreases the infectivity in the connected rooms by 19% or 93% respectively, relative to a MERV-8 filter with 9% outdoor air based on 100 quanta/h of 5 μm droplets, a breathing rate of 0.48 m3/h, and the building dimensions and air handling system considered.

The Pacific Northwest National Laboratory (PNNL) 3420 Building, which is part of the Physical Sciences Facility (PSF), houses radiological capabilities that results in the requirement that emissions monitoring must be conducted for potential radionuclides in the exhaust air discharge of this building. The air monitoring system is required to conform to Title 40 of the Code of Federal Regulations part 61 (40 CFR 61) Subpart H, which in turns requires a sampling probe in the exhaust stream to conform to the criteria of American National Standards Institute / Health Physics Society (ANSI/HPS) N13.1-2011, Sampling and Monitoring Releases of Airborne Radioactive Substances from the Stack and Ducts of Nuclear Facilities. To support the air emissions permit for the 3420 Building on the PNNL campus, stack testing that used computational fluid dynamics (CFD) modeling as a surrogate stack and verification tests of velocity uniformity and flow angle on the retrofitted facility stack was performed. The ANSI/HPS N13.1-2011 criteria for the air monitoring probe location are that velocity uniformity, gaseous tracer uniformity, and particulate tracer uniformity must be less than or equal to 20%COV. Furthermore, no point in the sampling location may have a gaseous tracer concentration that varies from the mean concentration by more than 30%. Additionally, the flow angle at the sampling location must not be more than 20°. The CFD modeling of the stack, as reported by Recknagle et al. (2018) demonstrated that the stack meets the criteria at the probe location. The velocity uniformity and flow angle results from the 3420 stack verification tests, performed in October 2020, demonstrated that the CFD model results may be used to support the qualification of the stack sampling location. The measured velocity uniformity verification test result was 1.4%COV. This value is well within the uniformity criterion, which is that the velocity uniformity be =20%COV. Additionally, this value is well within the criterion that the actual stack measurement must be within 5% of the surrogate stack result of 2.1%COV when all four fans were operating. Additionally, the measured average flow angle at the 3420 stack monitor location was 15.5 degrees. Although this is higher than expected based on the CFD model, the result is =20 degrees, so the criterion is met. Based on these stack verification test results, the reconfigured 3420 Building filtered exhaust stack meets the qualification criteria given in the ANSI/HPS N13.1-2011 standard. Further changes to the system configuration or operating conditions that are outside the bounds described in this and the CFD report (Recknagle et al., 2018) may require additional tests and additional analysis to determine compliance with the standard.

Sampling and monitoring at the Waste Treatment and Immobilization Plant (WTP) Effluent Management Facility (EMF) exhaust is required to meet the regulatory criteria that governs stacks that may exceed the 0.1-millirem per year threshold limit given in Title 40 of the Code of Federal Regulations, Part 61, National Emissions Standards for Hazardous Air Pollutants, Subpart H, National Emission Standard for Emissions of Radionuclides other than Radon from Department of Energy Facilities. As a result, the flow conditions and probe location must be within certain bounds as prescribed by the American National Standards Institute/Health Physics Society (ANSI/HPS) standard N13.1-1999. Compliance with the standard may be demonstrated by tests on the stack itself, or with a surrogate stack (i.e., a scale model or other geometrically similar stack). Pacific Northwest National Laboratory (PNNL) has performed scale model stack tests for 11 of the 20 emission points that are planned for the WTP. Additionally, PNNL has full scale stack results from Idaho National Laboratory and PNNL facility stacks to use as references. One or more WTP scale model stack results were expected to be appropriate as a surrogate stack to the EMF. This report describes the EMF stack monitoring location as well as an assessment of existing surrogate stacks used to qualify the EMF stack sampling location.

The ANSI/HPS N13.1–2011 standard requires gaseous tracer uniformity testing for sampling associated with stacks used in radioactive air emissions. Sulfur hexafluoride (SF6), a greenhouse gas with a high global warming potential, has long been the gas tracer used in such testing. To reduce the impact of gas tracer tests on the environment, nitrous oxide (N2O) was evaluated as a potential replacement to SF6. The physical evaluation included the development of a test plan to record percent coefficient of variance and the percent maximum deviation between the two gases while considering variables such as fan configuration, injection position, and flow rate. Statistical power was calculated to determine how many sample sets were needed, and computational fluid dynamic modeling was utilized to estimate overall mixing in stacks. Results show there are no significant differences between the behaviors of the two gases, and SF6 modeling corroborated N2O test results. Although, in principle, all tracer gases should behave in an identical manner for measuring mixing within a stack, the series of physical tests guided by statistics was performed to demonstrate the equivalence of N2O testing to SF6 testing in the context of stack qualification tests. The results demonstrate that N2O is a viable choice leading to a four times reduction in global warming impacts for future similar compliance driven testing.

To address concerns about worker exposures on the Physical Science Laboratory (PSL) rooftop, a tracer study was conducted to measure gaseous tracer concentrations downwind of six stacks on the southern half of the PSL building (PSL-1). These concerns were raised, in part, due to the non-standard configuration of the stacks on this building. Five of the six stacks were only about 8 feet tall, with one shorter stack that was essentially level with the roof deck. These stacks were reconfigured in August 2016, and these exhaust points on PSL-1 are now 18 feet tall. This report describes the objectives of the tracer tests performed on PSL-1, provides an overview of how the tests were executed, and presents results of the tests. The tests on the PSL rooftop were a follow-on project from a similar study performed on the LSL-II ventilation exhaust (Flaherty and Antonio, 2016).

An SF6 tracer release was performed in the LSL-II ventilation stack over the weekend of March 5, 2016. The primary purpose of this study was to experimentally determine the gaseous concentration of material from a fume hood to the fan outlet, as well as at typical worker locations, to gain an understanding of potential worker exposures impacts. Five different fan operating configurations were utilized to ensure that the full spectrum of historical operating configurations was addressed. Some summary points from this study include: •Relatively high concentrations were observed within the stack area. –Between 50 and 100% of the exhaust concentration may be observed within the stack. •Background concentrations were observed outside the stack area. –Workers outside the stack itself, but on the roof, are unlikely to be impacted by the exhaust. •Elevated concentrations on the order of 25% of the exhaust concentrations were observed within the Penthouse. •Transport time from a laboratory fume hood to the exhaust fan is within one to two minutes. •Penthouse concentrations climb from background levels to steady state over 15+ minutes. •Wind speed and wind direction did not play a significant role in the test outcomes. –A slight bias in the concentration distribution may be discernable based on wind speed and direction. •When both fans are operating, material from fume hoods on the east side preferentially flow through the east fan, while material from fume hoods on the west side preferentially flow through the west fan. This effectively doubles the concentration at that fan. This mixing study will inform other study components to develop a more complete picture of the worker potential exposure from LSL-II Rooftop activities. Estimating the mean concentration in the stack from chemical inventories and fume hood emissions for both current and historical laboratory activities is a separate effort. These estimates of mean ventilation concentrations will utilize this mixing study to estimate the potential exposure to workers working in and around the LSL-II stack.

This document reports on a series of tests conducted to assess the proposed air sampling location for the National Research Universal reactor (NRU) complex exhaust stack, located in Chalk River, Ontario, Canada, with respect to the applicable criteria regarding the placement of an air sampling probe. Due to the age of the equipment in the existing monitoring system, and the increasing difficulty in acquiring replacement parts to maintain this equipment, a more up-to-date system is planned to replace the current effluent monitoring system, and a new monitoring location has been proposed. The new sampling probe should be located within the exhaust stack according to the criteria established by the American National Standards Institute/Health Physics Society (ANSI/HPS) N13.1-1999, Sampling and Monitoring Releases of Airborne Radioactive Substances from the Stack and Ducts of Nuclear Facilities. These criteria address the capability of the sampling probe to extract a sample that represents the effluent stream. The internal Pacific Northwest National Laboratory (PNNL) project for this task was 65167, Atomic Energy Canada Ltd. Chalk River Effluent Duct Flow Qualification. The testing described in this document was guided by the Test Plan: Testing of the NRU Stack Air Sampling Position (TP-STMON-032).

The Canister Storage Building (CSB), located in the 200-East Area of the Hanford Site, is a 42,000 square foot facility used to store spent nuclear fuel from past activities at the Hanford Site. Because the facility has the potential to emit radionuclides into the environment, its ventilation exhaust stack has been equipped with an air monitoring system. Subpart H of the National Emissions Standards for Hazardous Air Pollutants requires that a sampling probe be located in the exhaust stack in accordance with criteria established by the American National Standards Institute/Health Physics Society Standard N13.1-1999, Sampling and Monitoring Releases of Airborne Radioactive Substances from the Stack and Ducts of Nuclear Facilities.

The U.S. Department of Energy (DOE), in collaboration with the National Oceanic and Atmospheric Administration (NOAA), has recently completed the installation of three new wind profiling radars on the Washington and Oregon coasts. These systems operate at a frequency of 449 MHz and provide mean wind profiles to a height of roughly 8 km, with the maximum measurement height depending on time-varying atmospheric conditions. This is roughly half the depth of the troposphere at these latitudes. Each system is also equipped with a radio acoustic sounding system (RASS), which provides a measure of the temperature profile to heights of approximately 2 km. Other equipment deployed alongside the radar includes a surface meteorological station and GPS for column water vapor. This project began in fiscal year 2014, starting with equipment procurements and site selection. In addition, environmental reviews, equipment assembly and testing, site access agreements, and infrastructure preparations have been performed. Finally, with equipment deployment with data collection and dissemination, the primary tasks of this project have been completed. The three new wind profiling radars have been deployed at airports near Coos Bay, OR, and Astoria, OR, and at an industrial park near Forks, WA. Data are available through the NOAA Earth Systems Research Laboratory Data Display website, and will soon be made available through the DOE Atmosphere to Electrons data archive and portal as well.