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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Impact of Wildfires on Solar Resource Availability in California in a Changing Climate

Wildfires can emit large amounts of atmospheric particulate matters and influence not only air quality but also availability of photovoltaic (PV) generation due to scattering and absorption of solar radiation. Under anthropogenic changing climate, wildfire activity is projected to increase over western North America due to drier and warmer climate, implying increasing impact on solar resource and larger uncertainty in solar generation especially in regions with faster PV penetration. This study focuses on quantifying the impact of wildfires on aerosol optical depth (AOD) and thus solar resource over California using National Solar Radiation Database (NSRDB), developed by the National Renewable Energy Laboratory (NREL). This assessment includes historical analysis and estimation of solar resource under wildfire scenarios (2020 wildfire and an enhanced wildfire scenario based on 2020 wildfires). Historical analysis for the period of July-October 2019/2020 (low/high fire activity period) shows that the averaged global horizontal irradiance (GHI) and direct normal irradiance (DNI) are reduced by about 30 and 90 W m-2 (5.6 and 15.3%), respectively, during high fire activity period. To create AOD dataset for enhanced fire scenario, 165% increase in burn area (in around 2050) is selected based on comprehensive literature review, which is further applied to the Fire INventory from NCAR (FINN) fire emission. WRF-Chem model with the enhanced FINN emissions is used to simulate and represent a preserving spatial distribution of burned areas and wildfire-emitted aerosols. Our initial analysis suggests that the"enhanced 2020 wildfire" AOD can increase by a factor of 1.3 - 2, which can significantly reduce solar irradiance and increase uncertainty in generation and reliability of power system in extreme wildfire events under a high solar penetration scenario. Overall, this study provides an estimate of the impacts of wildfires on solar resource to make informed decisions on reserve planning, generation scheduling, and reliability investments.

aerosol optical depth↗
DOE OSTI2023

Low-temperature solar thermal-power systems for residential electricity supply under various seasonal and climate conditions

In this work, the performance of low-temperature (<100 degrees C) solar thermal-power systems to satisfy residential electric loads was analyzed. The solar-driven system was designed to provide a fraction of the total electricity demand in a complementary operation with the electric grid. The analysis was conducted for an coperation during seven days each season, considering real solar and climate variables and residential loads at different climate zones in the United States. The efficiency of the system strongly depends on the solar radiation profile and the ambient temperature. Maximum efficiencies of around 9.5% were obtained in the cold and marine climate zones due to the high solar energy input and low heat dissipation temperatures. In these two zones, the system could supply more than 98% of the electricity demand all seasons. At mixed-humid and hot-humid regions, the system supplied around 50% of the electric load in three of the four seasons, but it only supplies about 27% of the electricity needs in the mixed-humid zone during summer and hot-humid zone during winter. The effect of the solar collector field area and the tank volume was also analyzed. In general, larger solar fields positively impact the efficiency. However, the impact of the tank volume varies depending on the solar radiation profile and the load requirements. Average efficiencies for the seven-day operation can be larger than 6% with a proper selection of the solar collector area and tank volume for an Organic Rankine Cycle with a capacity of about 2.6 kW. Finally, an economic analysis of the system was conducted, and the results were compared with a solar PV + battery system of similar capacity. It is expected that the cost for the analyzed solar-thermal system decreases in the coming years with the increased interest on low temperature applications.

14 SOLAR ENERGY↗
DOE OSTI2023

Fast All-sky Radiation Model for Solar applications (FARMS) 2023 [SWR-16-18]

The Fast All-sky Radiation Model for Solar applications (FARMS) is used to compute cloudy irradiance. Radiative transfer (RT) models simulating broadband solar radiation have been widely used by atmospheric scientists to model solar resources for various energy applications such as operational forecasting. Due to the complexity of solving the RT equation, the computation under cloudy conditions can be extremely time consuming though many approximations (e.g. two-stream approach and delta-M truncation scheme) have been utilized. Thus, a more efficient RT model is crucial for model developers as a new option for approximating solar radiation at the land surface with minimal loss of accuracy. We have developed a fast all-sky radiation model for solar applications (FARMS) using the simplified clear-sky RT model, REST2, and simulated cloud transmittances and reflectances from the Rapid Radiation Transfer Model (RRTM) with a sixteen-stream Discrete Ordinates Radiative Transfer (DISORT). Simulated lookup tables (LUTs) of cloud transmittances and reflectances were created by varying cloud optical thicknesses, cloud particle sizes, and solar zenith angles. Equations with optimized parameters were fitted to the cloud transmittances and reflectances to develop the model. Using this model the all-sky solar irradiance at the land surface can be computed rapidly by combining REST2 with the cloud transmittances and reflectances. This new RT model is more than 1000 times faster than those currently utilized in solar resource assessment and forecasting since it does not explicitly solve the RT equation for each individual cloud condition. Our results indicate the accuracy of the fast radiative transfer model is comparable to or better than two-stream approximation in term of computing cloud transmittance and solar radiation.

Xie, Yu↗
DOE OSTI2022

Evaluating WRF-Solar EPS cloud mask forecast using the NSRDB

Improving the accuracy of day-ahead solar forecasts using numerical weather prediction models requires improving the forecasting of cloud occurrence and properties. Validating cloud forecasts is challenging because this requires evaluating different types of clouds over a wide variety of regions. This study analyzes the cloud occurrence (or cloud mask) over the contiguous United States (CONUS), predicted by the Weather Research and Forecasting-Solar Ensemble Prediction System (WRF-Solar EPS), to identify the strengths and limitations of the model in reproducing cloud fields. To enable the in-depth analysis of cloud mask forecasts covering CONUS, we use satellite observations from the National Solar Radiation Database (NSRDB). Two evaluation methods are implemented to consider all clouds and partially removed clouds from the 2-km NSRDB in evaluating the 9-km WRF-Solar EPS cloud mask. Cloud detection metrics as well as the frequency of cloud occurrence are used to quantify the monthly performance of WRF-Solar EPS. Mismatched cloud frequency (MCF) is used to assess the model's capability to predict different types of clouds, which are classified using three levels of cloud optical depth (COD) and cloud top height (CTH). The day-ahead forecasts covering the full year of 2018 demonstrate that WRF-Solar EPS produces MCFs ranging from 27%-46%, 13%-34%, and 8%-19% for thin, mid-thickness, and thick clouds, respectively. For three CTH levels, the model shows MCFs ranging from 19%-46%, 16%-33%, and 8%-27% for low-level, middle-level, and high-level clouds, respectively. This comprehensive characterization of model performance helps identify model weakness and will eventually lead to improvements in cloud and solar radiation forecasting.

14 SOLAR ENERGY↗
DOE OSTI2021

Albedo Measurement of the Terra Pave White Albedo Product: Cooperative Research and Development Final Report

The Terra Pave team is creating an eco-friendly, cost-effective road coating that controls dust and dirt on high-traffic roads near solar field installations to reduce the dirt cover on panels, which lowers energy output. The Terra Pave White Albedo product also has the capability to increase the amount of solar radiation reflected from the ground to the backside of bifacial PV modules, thereby increasing the amount of electricity generated. This work will quantify the amount of solar radiation reflected from the Terra Pave White albedo product by measuring its albedo, which is the ratio of the reflected radiation to the incoming radiation. Albedo is measured with two horizontal pyranometers with one ground-facing and the other sky-facing.

14 SOLAR ENERGY↗
DOE OSTI2023

Direct measurement of small particle growth and aging at SGP

Atmospheric aerosols influence climate directly by scattering and absorbing solar radiation and indirectly through their influence on the reflectivity and lifetime of clouds. But that influence is limited to only those particles large enough to efficiently interact with solar radiation (diameter, D p ≳ 100 nm) and/or to form cloud droplets at typical atmospheric supersaturations (D p ≳ 50 nm). Many cloud condensation nuclei (CCN) originate as ~1 nm nucleated particles that must grow to those threshold sizes before being lost to coagulation with larger particles. Though there is consensus that new particle formation and growth contributes significantly to total CCN concentrations, different groups using different models and assumptions have arrived at diverse estimates.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Is turning down the sun a good proxy for stratospheric sulfate geoengineering?

Deliberately blocking out a small portion of the incoming solar radiation would cool the climate. One such approach would be injecting SO2 into the stratosphere, which would produce sulfate aerosols that would remain in the atmosphere for 1–3 years, reflecting part of the incoming shortwave radiation. The cooling produced by the aerosols can offset the warming produced by increased greenhouse gas (GHG) concentrations, but it would also affect the climate differently, leading to residual differences compared to a climate not affected by either. Many climate model simulations of geoengineering have used a uniform reduction of the incoming solar radiation as a proxy for stratospheric aerosols, both because many models are not designed to adequately capture relevant stratospheric aerosol processes, and because a solar reduction has often been assumed to capture the most important differences between how stratospheric aerosols and GHG would affect the climate. Here we show that dimming the sun does not produce the same surface climate effects as simulating aerosols in the stratosphere. By more closely matching the spatial pattern of solar reduction to that of the aerosols, some improvements in this idealized representation are possible, with further improvements if the stratospheric heating produced by the aerosols is included. This is relevant both for our understanding of the physical mechanisms driving the changes observed in stratospheric-sulfate geoengineering simulations, and in terms of the relevance of impact assessments that use a uniform solar dimming.

Visioni, Daniele↗
DOE OSTI2021

Techno-Economic Analysis of a Concentrating Solar Power Plant Using Redox-Active Metal Oxides as Heat Transfer Fluid and Storage Media

We present results for a one-dimensional quasi-steady-state thermodynamic model developed for a 111.7 MW e concentrating solar power (CSP) system using a redox-active metal oxide as the heat storage media and heat transfer agent integrated with a combined cycle air Brayton power block. In the energy charging and discharging processes, the metal oxide CaAl 0.2 Mn 0.8 O 2.9-δ (CAM28) undergoes a reversible, high temperature redox cycle including an endothermic oxygen-releasing reaction and exothermic oxygen-incorporation reaction. Concentrated solar radiation heats the redox-active oxide particles under partial vacuum to drive the reduction extent deeper for increased energy density at a fixed temperature, thereby increasing storage capacity while limiting the required on sun temperature. Direct counter-current contact of the reduced particles with compressed air from the Brayton compressor releases stored chemical and sensible energy, heating the air to 1,200°C at the turbine inlet while cooling and reoxidizing the particles. The cool oxidized particles recirculate through the solar receiver subsystem for another cycle of heating and reduction (oxygen release). We applied the techno-economic model to 1) size components, 2) examine intraday operation with varying solar insolation, 3) estimate annual performance characteristics over a simulated year, 4) estimate the levelized cost of electricity (LCOE), and 5) perform sensitivity analyses to evaluate factors that affect performance and cost. Simulations use hourly solar radiation data from Barstow, California to assess the performance of a 111.7 MW e system with solar multiples (SMs) varying from 1.2 to 2.4 and storage capacities of 6–14 h. The baseline system with 6 h storage and SM of 1.8 has a capacity factor of 54.2%, an increase from 32.3% capacity factor with no storage, and an average annual energy efficiency of 20.6%. Calculations show a system with an output of 710 GWh e net electricity per year, 12 h storage, and SM of 2.4 to have an installed cost of $329 million, and an LCOE of 5.98 ¢/kWh e . This value meets the U.S. Department of Energy’s SunShot 2020 target of 6.0 ¢/kWh e ( U. S Department of Energy, 2012 ), but falls just shy of the 5.0 ¢/kWh e 2030 CSP target for dispatchable electricity ( U. S Department of Energy, 2017 ). The cost and performance results are minimally sensitive to most design parameters. However, a one-point change in the weighted annual cost of capital from 8 to 7% (better understood as a 12.5% change) translates directly to an 11% decrease (0.66 ¢/kWhe) in the LCOE.

Gorman, Brandon T.↗
DOE OSTI2024

TROPOS pyranometer network (PyrNet) observational data set collected during the S2VSR field campaign

During the Small-Scale Variability of Solar Radiation field campaign (S2VSR), a unique sensor network consisting of 60 autonomous pyranometer stations was deployed for a 12-week period at the ARM Southern Great Plains observatory. Stations have been distributed across a 4x4 to 6x6 km2 domain surrounding the Central Facility. Each station has recorded global radiation together with air temperature and relative humidity with a sampling frequency of 1-Hz. The network has provided a data set with an unprecedented level of detail of the small-scale variability of global solar radiation at the surface, resolving fluctuations down to the second- and decameter-scale.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Deliverable 6.7-Final Technical Report: Development Summary and Evaluation of the Solar Uncertainty Integrator (SUNI) Software

The Data Quality and Uncertainty Integration Project was a three-year effort to address stakeholder needs for assessing solar radiation resource data quality based on existing tools for estimating radiometer measurement uncertainties and assessing post-measurement data quality. The annual research objectives for the project addressed a logical progression of effort needed to achieve the ultimate project goal of developing the Solar Uncertainty Integrator (SUNI) software. This final technical report summarizes the development process for achieving these key research objectives and addresses the outreach and code development efforts in the final year of the project to develop a new solar irradiance data uncertainty integration software package.

14 SOLAR ENERGY↗
DOE OSTI2023

Summary of atmospheric characteristics of days with inland penetrating sea breezes from 2015 to 2021

Abstract Sea breezes penetrate inland more than 100 km. Using 7 years of meteorological observations, we have identified 470 cases of deep inland (>100 km) penetrating sea breezes at the Savannah River Site between March and October (27% of days) of 2015–2021. We compared measurements of temperature, dewpoint temperature, incoming solar radiation, cloud fraction, and lightning on days of sea breeze initiation, the day after the sea breeze passage, and all other nonsea breeze (NSB) days for these 8 months over the 7 years. Days of sea breeze initiation were found to have lower cloud fraction, higher temperature, and greater incoming solar radiation compared with NSB days. Variations occurred by time of year as days after the sea breeze passage were found to have higher dewpoint temperature than NSB days in the spring. Lightning density measurements indicated that residual sea breeze conditions could drive earlier initiation of deep convection on days following the sea breeze than normal non sea breeze days. This data set provides a 7‐year record of sea breezes which can be leveraged for future studies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Global ocean dimethyl sulfide climatology estimated from observations and an artificial neural network

Marine dimethyl sulfide (DMS) is important to climate due to the ability of DMS to alter Earth's radiation budget. Knowledge of the global-scale distribution, seasonal variability, and sea-to-air flux of DMS is needed in order to improve understanding of atmospheric sulfur, aerosol/cloud dynamics, and albedo. Here we examine the use of an artificial neural network (ANN) to extrapolate available DMS measurements to the global ocean and produce a global climatology with monthly temporal resolution. A global database of 82 996 ship-based DMS measurements in surface waters was used along with a suite of environmental parameters consisting of latitude–longitude coordinates, time of day, time of year, solar radiation, mixed layer depth, sea surface temperature, salinity, nitrate, phosphate, and silicate. Linear regressions of DMS against the environmental parameters show that on a global-scale mixed layer depth and solar radiation are the strongest predictors of DMS. These parameters capture ~9 % and ~7 % of the raw DMS data variance, respectively. Multilinear regression can capture more of the raw data variance (~39 %) but strongly underestimates DMS in high-concentration regions. In contrast, the artificial neural network captures ~66 % of the raw data variance in our database. Like prior climatologies our results show a strong seasonal cycle in surface ocean DMS with the highest concentrations and sea-to-air fluxes in the high-latitude summertime oceans. We estimate a lower global sea-to-air DMS flux (20.12±0.43 Tg S yr –1 ) than the prior estimate based on a map interpolation method when the same gas transfer velocity parameterization is used. Our sensitivity test results show that DMS concentration does not change unidirectionally with each of the environmental parameters, which emphasizes the interactions among these parameters. The ANN model suggests that the flux of DMS from the ocean to the atmosphere will increase with global warming. Given that larger DMS fluxes induce greater cloud albedo, this corresponds to a negative climate feedback.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

ClimGen: Learning the Forcing-Response Relationship in Climate System

Solar Radiation Management (SRM) is emerging as a potential geoengineering strategy to address the anthropogenic impact on climate, but its effective implementation requires an iterative and large ensemble of highly accurate and efficient climate projections. Traditional climate projections rely on executing computationally demanding and time-consuming numerical climate models. Recent advances in machine learning (ML) aim to enhance these approaches by emulating traditional methods. In this work, we propose a novel framework for directly learning the relationship between solar radiation flux at the top of the atmosphere and the corresponding surface temperature response. To evaluate the feasibility of this direct ML-based projection, we developed a dataset using an intermediate complexity model, incorporating a comprehensive suite of different forcing patterns and evaluation metrics to rigorously assess the ML model’s performance. We introduce a Conditional Denoising Diffusion Probabilistic Model (cDDPM) for this task, which demonstrates encouraging skill in representing climate statistics under previously unseen forcing patterns. This approach provides a promising pathway for direct climate projections by accurately learning the forcing-response relationship, with a wide range of applications in impact mitigation, emissions policy design, and SRM strategies.

Chen, Tse-Chun [BATTELLE (PACIFIC NW LAB)] (ORCID:↗
DOE OSTI2023

Improving snow albedo modeling in the E3SM land model (version 2.0) and assessing its impacts on snow and surface fluxes over the Tibetan Plateau

With the highest albedo of the land surface, snow plays a vital role in Earth's surface energy budget and water cycle. Snow albedo is primarily controlled by snow grain properties (e.g., size and shape) and light-absorbing particles (LAPs) such as black carbon (BC) and dust. The mixing state of LAPs in snow also has impacts on LAP-induced snow albedo reduction and surface radiative forcing (RF). However, most land surface models assume that snow grain shape is spherical and LAPs are externally mixed with the snow grains. This study improves the snow radiative transfer model in the Energy Exascale Earth System Model version 2.0 (E3SM v2.0) Land Model (ELM v2.0) by considering non-spherical snow grain shapes (i.e., spheroid, hexagonal plate, and Koch snowflake) and internal mixing of dust–snow, and it systematically evaluates the impacts on the surface energy budget and water cycle over the Tibetan Plateau (TP). A series of ELM simulations with different treatments of snow grain shape, mixing state of BC–snow and dust–snow, and sub-grid topographic effects (TOP) on solar radiation are performed. Compared with two remote sensing snow products derived from the Moderate Resolution Imaging Spectroradiometer, the control ELM simulation (ELM_Control) with the default configurations of spherical snow grain shape, internal mixing of BC–snow, external mixing of dust–snow, and without TOP as well as the ELM simulation with new model features (ELM_New) can both capture the overall snow distribution reasonably. Additionally, ELM_New overall shows smaller biases in snow cover fraction than ELM_Control in spring when snowmelt is important for water management. The estimated LAP-induced RF in ELM_New ranges from 0 to 19.3 W m -2 with the area-weighted average value of 1.5 W m -2 that is comparable to the reported values in existing studies. The Koch snowflake shape, among other non-spherical shapes, shows the largest difference from the spherical shape in spring when snow processes related to the surface energy budget and water cycle have high importance. The impacts of the mixing state of LAP in snow are smaller than the shape effects and depend on snow grain shape. Compared to external mixing, internal mixing of LAP–snow can lead to larger snow albedo reduction and snowmelt, which further affect the surface energy budget and water cycle. The individual contributions of non-spherical snow shape, mixing state of LAP–snow, and local topography impacts on the snow and surface fluxes have different signs and magnitudes, and their combined effects may be negative or positive due to complex and nonlinear interactions among the factors. Overall, the changes in net solar radiation in spring due to individual and combined effects range from -28.6 to 16.9 W m -2 and -29.7 to 12.2 W m -2 , respectively. This study advances understanding of the role of snow grain shape and mixing state of LAP–snow in land surface processes and offers guidance for improving snow simulations and RF estimates in Earth system models under climate change.

58 GEOSCIENCES↗
DOE OSTI2024

National Climate Database (NCDB)

The National Climate Database (NCDB) is a high resolution, bias-corrected climate dataset consisting of the three most widely used variables of solar radiation- global horizontal (GHI), direct normal (DNI), and diffuse horizontal irradiance (DHI)- as well as other meteorological data. The goal of the NCDB is to provide unbiased high temporal and spatial resolution climate data needed for renewable energy modeling. The NCDB is modeled using a statistical downscaling approach with Regional Climate Model (RCM)-based climate projections obtained from the North American Coordinated Regional Climate Downscaling Experiment (NA-CORDEX; linked below). Daily climate projections simulated by the Canadian Regional Climate Model 4 (CanRCM4) forced by the second-generation Canadian Earth System Model (CanESM2) for two Representative Concentration Pathways (RCP4.5 or moderate emissions scenario and RCP8.5 or highest baseline emission scenario) are selected as inputs to the statistical downscaling models. The National Solar Radiation Database (NSRDB) is used to build and calibrate statistical models.

Array↗
DOE OSTI2020

Bayesian Structural Time Series for Behind-the-Meter Photovoltaic Disaggregation: Preprint

Distributed photovoltaic (PV) generation often occurs ``behind the meter": a grid operator can only observe the net load, which is the sum of the gross load and distributed PV generation. This lack of observability poses a challenge to system operation at both bulk level and distribution level. The lack of real-time or near-future disaggregated estimates of gross load and PV generation will lead to over scheduling of energy production and regulation reserves, reliability constraints violations, wear and tear of controller devices, and potentially cascading failures of a system. In this paper we propose the use of a Bayesian Structural Time Series (BSTS) model with local solar irradiance measurements to disaggregate the summed PV generation and gross load signals at a downstream measurement site. BSTSs are a highly expressive model class that blends classic time series models with the powerful Bayesian state space estimation framework. Disaggregation is done probabilistically, which automatically quantifies the uncertainties of the estimated PV generation and gross load consumption. Depending on the data availability in real-time, it can be used to disaggragate PV and gross load at customer site, or can be used at the feeder level. In this paper, we focus on solving the problem at feeder level. We compare the performance of a BSTS model as well as a handful of state-of-the-art methods on a Pecan Street AMI dataset, using the National Solar Radiation Database (NSRDB) to estimate local irradiance.

Bayesian structural time series↗
DOE OSTI2020

Where Are White Roofs More Effective in Cooling the Surface?

Solar reflective roofs (also called white or cool roofs) are a common heat mitigation strategy. By reflecting more sunlight than regular roofs, cool roofs experience lower surface temperatures and produce less heating of the surrounding air. The effectiveness of cool roofs, quantified by the surface temperature difference between cool roofs and regular roofs ??????, is known to vary spatially. A common perception is that ?????? is controlled by solar radiation that reaches the roof surface. Here we use an Earth System Model and a surface energy balance model to show that the spatial variability of ??????, when normalized by the albedo difference between cool roofs and regular roofs (????), is in fact more controlled by an energy distribution factor that encodes the efficiencies of surface energy balance components in dissipating heat. Our results suggest that painting the roof white is more effective in reducing the roof surface temperature when the roof has less water holding capacity and smaller thermal admittance and is located in places with more solar radiation, less precipitation, and lower wind speed.

Wang, Linying↗
DOE OSTI2020

NNSS Soils Monitoring: Plutonium Valley (CAU 366) FY2019

Desert Research Institute (DRI) conducted a field assessment of the potential for radionuclide-contaminated soil to be transported from the Plutonium Valley Dispersion Sites Contamination Area (CA) because of both wind and storm water runoff. This activity supported U.S. Department of Energy (DOE) Environmental Management Nevada Program (EM NV) efforts to establish post-closure monitoring plans for the Plutonium Valley Dispersion Sites Corrective Action Unit (CAU) 366. The DRI task was intended to identify the likely mechanism(s) of transport and determine the meteorological conditions that might cause the movement of radionuclide-contaminated soils. The emphasis of the work was on collecting sediment transported by channelized storm runoff and measuring airborne dust concentrations and associated wind conditions. These data will facilitate an appropriate post-closure monitoring program. In 2011, DRI installed meteorological monitoring stations north and south of the Plutonium Valley CA—known as Pu Valley North and Pu Valley South, respectively—as well as a fluvial sediment sampling station within the CA. Since installation, temperature, wind speed, wind direction, relative humidity, precipitation, solar radiation, barometric pressure, soil temperature, volumetric soil moisture content, and airborne particulate concentrations have been collected at both meteorological stations. The maximum, minimum, and average or total (as appropriate) for each of these parameters were recorded for each 10-minute interval. The sediment sampling station included an automatically activated sampling pump with collection bottles for suspended sediment, which was activated when sufficient flow was present in the channel, and passive traps for bedload material that was transported down the channel during runoff events. This report presents data collected from these stations during fiscal year (FY) 2019. During the FY2019 (October 1, 2018, through September 30, 2019) reporting period, the warmest month was July and the coldest month was February. Monthly total solar radiation was highest in June or July (depending on the station) and lowest in December. Monthly mean wind speeds were highest during February. At the Pu Valley North station, winds were commonly northerly, northwesterly, and southerly. At the Pu Valley South station, the wind direction was more variable and winds frequently came from most directions, excluding westerly and northwesterly winds. Winds above 15.0 miles per hour (mph) (24.1 kilometers per hour [km/hr]) were frequently southerly at both stations, and less-frequent strong winds were northerly (at the Pu Valley South station) or northwesterly (at the Pu Valley North station). Monthly average relative humidity ranged from 18 percent in August to 62 percent in January and February. Monthly total precipitation ranged from zero at both stations in September to 2.23 inches (in) (56.6 millimeters [mm]) during February at the Pu Valley South station and 2.09 in (53.1 mm) during March at the Pu Valley North station. From October 1, 2018, through September 30, 2019, the total precipitation was 8.23 in (209 mm) and 8.15 in (207 mm) at the Pu Valley South and Pu Valley North stations, respectively. The largest daily precipitation totals of 1.46 in (37.1 mm) and 1.14 in (29.0 mm) occurred at the Pu Valley South and Pu Valley North stations, respectively, on March 6, 2019. However, the ISCO autosampler was not activated during any storm event in FY2019 because the channel water depth measurements were insufficient for suspended sediment sample collection. Additionally, because no significant flow through the channel was indicated, no bedload samples were collected. Light breezes of 0.0 mph to 5.0 mph (0.0 km/hr to 8.0 km/hr) occurred most frequently (approximately 55 percent to 57 percent of the time). The frequency of occurrence diminished exponentially as the wind speed increased such that winds in excess of 25.0 mph (40.2 km/hr) occurred less than 0.01 percent of the time. Winds in excess of 15.0 mph (24.1 km/hr) were most commonly southerly, which is likely controlled by the topography of the valley because topographic highs define the east and west sides of the valley and converge toward the north.The concentrations of PM2.5 (particulate matter with an aerodynamic diameter ≤2.5 micrometers [μm]) and PM10 (particulate matter with an aerodynamic diameter ≤10 μm) in the air generally increase as wind speed increases. However, the particle profiler at the Pu Valley South station regularly malfunctioned, which resulted in poor data quality and a limited period of record within FY2019. Therefore, discussion of the annual dust observations is mostly limited to the Pu Valley North station. At the Pu Valley North station, significant increases in windblown dust concentrations were observed when wind speeds exceeded 15.0 mph (24.1 km/hr), especially during southerly winds. When all wind speeds were considered, high dust concentrations at the Pu Valley North station were frequently associated with northerly, northwesterly, or southerly winds. The majority of PM10 transport at the Pu Valley North station occurred when winds were blowing northerly and northwesterly because of the high frequency of winds up to 15.0 mph (24.1 km/hr). The ratio of PM10 to PM2.5 is a qualitative indicator of the proximity of dust sources to the observation point and the values of the PM10 to PM2.5 ratio tend to be higher nearer to the source. The PM10 to PM2.5 ratio increased from 1.2 to 2.8 with higher wind speeds at the Pu Valley North station. This increase suggests that some locally sourced PM10 was present in the air when local winds exceeded 15.0 mph (24.1 km/hr). Local dust suspension is interpreted during major dust events when peaks in the PM10 to PM2.5 ratio occur simultaneously with the maximum wind speed and the peak PM10 concentration. Analysis of the five major dust events in FY2019 revealed that northerly winds were dominant in three events and southerly winds were dominant in two events. Local suspension of dust within Plutonium Valley is suspected to have occurred during two of the five major events (April 9, 2019, and September 28 to 29, 2019), and a nonlocal source of dust is presumed for the three other major dust events (October 14, 2018; May 16, 2019; and September 2, 2019). The three suspected regional dust transport events exhibited winds that typically did not exceed the 15.0 mph (24.1 km/hr) wind speed threshold for local dust transport and featured PM10 to PM2.5 ratios that indicate large-scale regional dust transport. Similar event characteristics (e.g., wind speed and PM10 concentration) were observed at air monitoring stations approximately 70 mi (113 km) to the northwest within the Tonopah Test Range (TTR). Therefore, these three regional events may not reflect transport from the Plutonium Valley CA. Routine monitoring of meteorological and hydrologic parameters at Plutonium Valley has been discontinued at the request of EM NV, effective at the conclusion of FY2019. Environmental data acquisition has been completed, and both Pu Valley North and Pu Valley South meteorological stations within Plutonium Valley as well as the ISCO autosampler station within the CA were removed on October 14, 2019.

54 ENVIRONMENTAL SCIENCES↗