Dust and soiling issues and impacts relating to solar energy systems: Literature review update for 2012–2015
Not Available
Costa, Suellen C. S.↗
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Solar energy dust and soiling R&D progress: Literature review update for 2016
The objective of this literature review and survey is to provide a compilation and assessment of recent published reports for solar-electric device soiling R&D, to extend and update the compendium covering 2012-2015 we published last year. This review provides a comprehensive listing of the publications with references for 2016 - with some preliminary 2017 publications that have appeared at the time of this writing. Photovoltaics (PV) and concentrating solar (thermal) power (CSP) technologies are covered. To guide the reader, tabulated information on the investigative focus of the studies, the location, the duration (if pertinent), the solar-device type, key findings and other useful information within the report is presented.
14 SOLAR ENERGY↗
Costs of mitigating the environmental impacts of hydropower projects in the United States
Developers are required to mitigate the environmental impacts of hydropower projects in the United States (U.S.) but there is very little accessible information on the associated costs. This work compiles and analyses a dataset of environmental impacts mitigation costs for 182 hydropower projects based on documents obtained from the U.S. Federal Energy Regulatory Commission (FERC). The analysis shows that environmental costs vary wildly across hydropower project classes and mitigation measures, even when normalized by plant capacity. Capital costs per kilowatt are generally higher for relicensed conventional hydropower plants, and lower for new and relicensed pumped storage hydropower plants, relative to other project classes. Smaller plants tend to spend a higher share of total project costs on mitigating environmental impacts than larger plants, and measures related to Aquatic Species, Project Operations, and Recreation have the highest capital cost per kilowatt. These findings mean that environmental costs could become a key decision variable for new hydropower developments in the U.S. due to the small scale of most of the remaining potential and the increasing stringency of environmental requirements. Therefore, technologies to reduce the environmental impacts and costs of hydropower would be important for future project developments.
54 ENVIRONMENTAL SCIENCES↗
Current status of anaerobic digestion of food waste in the United States
Utilization of food waste (FW) and food processing waste streams in waste-to-energy technologies has increased over the years in United States (US). This review paper compiled information from published literature and open data sources to obtain a holistic view of the current state of FW availability and its management using anaerobic digestion (AD). The review examined the usage of FW as a feedstock for AD by considering the feedstock composition and availability of FW in the US as well as challenges and opportunities with implementing FW in mono- and co-digestion. Also presented are global trends in FW digestion and the current status of FW codigestion in the US. FW in the US was characterized with regard to volume and composition to show a need for waste management and an opportunity for valuable resource recovery. Challenges and opportunities of processing via AcoD were presented to review best practices for successful operations. Environmental standards, economic incentives, and governmental policies in the US and around the world regarding waste management and utilization were discussed to identify barriers and sources of opportunity for large-scale implementation of AcoD. Additionally, a range of facilities throughout the US was examined for information regarding digestion type, feedstock (s), biogas production and utilization, and energy self-sufficiency to better understand the current status of FW digestion. Several US facilities were outlined as case studies that co-digest FW and produce and utilize biogas for various applications. Future strategies, perspectives, and roadmaps were discussed to elucidate the shaping of future FW management practices.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Ensemble methods for quantification of potassium oxide in ChemCam Mars and laboratory spectra
In this paper we test new approaches for predicting the amount of element oxides in rock samples from the ChemCam instrument suite onboard the NASA Curiosity rover by focusing on K 2 O. Using the expanded dataset compiled by Gasda et al. (2021) with and without the Earth to Mars (E2M and NoE2M) transformation discussed in Clegg et al. (2017) we trained blended submodels using the “double blending” technique and compared these to ensemble methods (Random Forest, ExtraTrees, and Gradient Boosting Regression). We found that ensemble methods performed similar to blended submodels when looking at RMSE-P on the laboratory spectra and provided significant advantages when looking at spectra coming from Mars. For the full model, blended submodels achieved an RMSE-P of 0.62 and 0.60 (E2M and NoE2M respectively) while Gradient Boosting Regression resulted in a slightly improved RMSE-P of 0.59 and 0.60. More importantly, by employing a local RMSE-P estimation technique where model performance is evaluated based on nearby test samples we found that using ensemble methods can lower the quantification limit for K 2 O from the current value of ≈0.6 wt% to ≈0.08 wt% using Extra Trees and Random Forest. This would allow for a much larger range of K 2 O values to be quantified on Mars with greater certainty given that most targets seen on Mars tend to have <1 wt% K2O. Finally, we used both Mean Decrease in Impurity (MDI) and permutation importance techniques to investigate the wavelengths used by the ensemble methods and found that they correspond to known potassium emission lines. This suggests that ensemble methods can provide an easier to train and improved alternative to blended submodels for predicting potassium compositions from Laser Induced Breakdown Spectroscopy (LIBS) data.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Spatiotemporal patterns of livestock manure nutrient production in the conterminous United States from 1930 to 2012
Manure nitrogen (N) and phosphorus (P) from livestock husbandry are important components of terrestrial biogeochemical cycling. Assessment of the impacts of livestock manure on terrestrial biogeochemistry requires a compilation and analysis of spatial and temporal patterns of manure nutrients. In this study, we reconstructed county-level manure nutrient data of the conterminous United States (U.S.) in 4- to 5-year increments from 1930 to 2012. Manure N and P were 5.89 +/- 0.64 Tg N yr.(-1) (Mean +/- Standard Deviation) and 1.73 +/- 0.29 Tg P yr.(-1) (1 Tg=10(12) g), and increased by 46% and 92% from 1930 to 2012, respectively. Prior to 1970, manure provided more N to the U.S. lands than chemical fertilizer use. Since 1970, however, increasing chemical N fertilizer use has exceeded manure N production. Manure was the primary P source in the U.S. during 1930-1969 and 1987-2012, but was lower than P fertilizer use in 1974, 1978, and 1982. High-nutrient-production regions shifted towards eastern and western areas of the U.S. Decreasing small farms and increasing Concentrated Animal Feeding Operations (CAFOs) induced concentrated spatial patterns in manure nutrient loads. Counties with cattle or poultry as the primary manure nutrient contributors expanded significantly from 1930 to 2012, whereas regions with sheep and hog as the primary contributors decreased. We identified regions facing environmental threats associated with livestock farming. Effective management of manure should consider the impacts of CAFOs inmanure production, and changes in livestock population structure. The long-term county-level manure nutrient dataset provides improved spatial and temporal information on manure nutrients in the U.S. This dataset is expected to help advance research on nutrient cycling, ammonia volatilization, greenhouse gas (GHG) emissions from livestock husbandry, recovery and reuse of manure nutrients, and impacts of livestock feeding on human health in the context of global change.
Livestock↗
Soil organic carbon accumulation rates on Mediterranean abandoned agricultural lands
Secondary succession on abandoned agricultural lands can produce climate change mitigation co-benefits, such as soil carbon sequestration. However, the accumulation of soil organic carbon (SOC) in Mediterranean regions has been difficult to predict and is subject to multiple environmental and land management factors. Gains, losses, and no significant changes have all been reported. Here we compile chronosequence data (n = 113) from published studies and new field sites to assess the response of SOC to agricultural land abandonment in peninsular Spain. In this work we found an overall SOC accumulation rate of +2.3% yr -1 post-abandonment. SOC dynamics are highly variable and context-dependent. Minimal change occurs on abandoned cereal croplands compared to abandoned woody croplands (+4% yr -1 ). Accumulation is most prevalent within a Goldilocks climatic window of ~13–17 °C and ~450–900 mm precipitation, promoting >100% gains after three decades. Our secondary forest field sites accrued 40.8 Mg C ha -1 (+172%) following abandonment and displayed greater SOC and N depth heterogeneity than natural forests demonstrating the long-lasting impact of agriculture. Although changes in regional climate and crop types abandoned will impact future carbon sequestration, abandonment remains a low-cost, long-term natural climate solution best incorporated in tandem with other multipurpose sustainable land management strategies.
54 ENVIRONMENTAL SCIENCES↗
Analysis of biokinetic parameters reveals patterns in mercury accumulation across aquatic species
Mercury (Hg) is a potent neurotoxicant and poses a risk to human health through the ingestion of Hg-contaminated fish. Mercury, especially in its organic form methylmercury (MeHg), biomagnifies up food chains such that even small aqueous concentrations of Hg can result in significant concentrations of total Hg in fish. Understanding the ecological and human health risks associated with Hg and MeHg exposure requires an understanding of the factors that affect its bioaccumulation in aquatic species. We compiled estimates of three biokinetic parameters: uptake rate (k u ), assimilation efficiency (AE), and efflux rate (k e ). These parameters describe contaminant uptake from aqueous (k u ) and dietary (AE) exposure and the rate of excretion (k e ). We found parameter values for 38 and 34 different species of fish and aquatic invertebrates, respectively, and collected 502 parameter values in total. Here, we used a machine learning technique to establish the relationships between experimental and physiological variables and these parameter values. We found differences in which variables were associated with biokinetic parameter values for fish and aquatic invertebrates. The form of Hg was the most impactful variable, influencing values of all parameters except k u for invertebrates, for which aqueous exposure time was the only significant predicator variable. The parameter k e were the only values significantly influenced by more than one variable, with water type (freshwater, brackish, or marine), organism weight, and form of Hg significantly impacting parameter values for fish and/or invertebrates. To our knowledge, this study represents the most extensive review of biokinetic parameters of Hg and MeHg accumulation in aquatic organisms. Environmental parameters found to significantly impact Hg and MeHg bioaccumulation in past studies were not identified as important in our analyses across aquatic ecosystems and species. Our dataset and analysis reveal novel patterns that may help us better understand and manage Hg bioaccumulation.
54 ENVIRONMENTAL SCIENCES↗
Highly efficient removal and sequestration of Cr(VI) in confined MoS 2 interlayer Nanochannels: Performance and mechanism
Environmental contamination by Cr(VI) is of particular concern because of its severe toxicity and high mobility. In this study, we employed two-dimensional MoS 2 nanosheets in the removal of Cr(VI), with an emphasis on revealing the removal mechanisms, and how the compositional and structural uniqueness of 2D MoS 2 nanomaterials intrinsically impact the Cr removal efficiency. Through batch experiments with dispersed nanosheets, we found that MoS 2 nanosheets exhibited a high Cr(VI) removal capacity at ~1100 mg/g via a phase-dependent mechanism. Particularly, the 1T polymorph in the MoS 2 nanosheets removed Cr(VI) through a redox-reaction mechanism, which was different from the adsorptive removal of Cr(VI) by MoS 2 reported previously, highlighting the compositional effects on the removal mechanism and performance. More importantly, the reduced product Cr(III) was concurrently removed via precipitation and adsorption onto the MoS 2 nanosheets, which could avoid the additional pH-elevation step that is typically needed in the conventional treatment. The unique 2D flake-like structure of MoS 2 nanosheets enabled the formation of aligned and ion-accessible nanochannels, where Cr(VI) species were accommodated, reduced and sequestered. The irreversible shrinking of the nanochannels under drying modified the interior of the layer-stacked structure into confined compartments preventing the release and re-oxidation of the immobilized Cr(III). In conclusion, the compiled results highlight the effects of MoS 2 composition and structure on the Cr removal efficiency and mechanism, which has substantial implications on future studies tailoring these unique features of 2D nanomaterials for various remediation scenarios.
42 ENGINEERING↗
Automated construction of clear-sky dictionary from all-sky imager data
All-sky imagers (ASIs) have significant promise as scalable sensors for short-term solar irradiance forecasting. Many of the current computational techniques that use ASIs for this purpose rely on collections of clear-sky images indexed by time of day, solar angle, or both, called clear-sky dictionaries (CSDs). These CSDs act as baselines against which images can be compared to locate and classify clouds within the image frame. CSDs are often compiled by hand, where individuals visually inspect collections of images one at a time to find clear-sky images. This process is not scalable, and it is prone to error. This paper proposes an automated, nonparametric alternative that uses the principles of digital image processing to find clear-sky images within a set of images taken over several days. We use ground-truth measurements of the clearness index to assess the performance of our method, and we show that the images it selects accurately correspond to clear-sky images. We also compare our proposal, which is nonparametric, with a state-of-the-art parametric method. The numerical results indicate that the performance of the method proposed here is superior.
14 SOLAR ENERGY↗
Feature review of photovoltaic modeling software utilizing blind performance assessment
While confidence in photovoltaic (PV) modeling software has always been essential, the rapid pace of new PV plant developments makes accuracy and credibility more critical than ever. Independent assessments, particularly through blind modeling comparisons, are therefore necessary to ensure unbiased benchmarking across PV modeling software. Previous studies have been limited by a narrow range of models compared, anonymized results, or system size. This study presents results from the first-ever onymous blind modeling comparison, evaluated using both lab- and utility-scale fixed-tilt, monofacial, south-facing systems at sub-hourly time intervals. Seven commercially used PV software tools were compared: 3E SynaptiQ, PlantPredict, PVsyst, RatedPower, SAM, SolarFarmer, and Solargis Evaluate. Predictions were submitted directly by software representatives, providing unique insights into each software’s implementation and resulting prediction behavior. Notable features, including plane-of-array (POA) transposition model, module temperature model, shading model, and performance model were analyzed and compared. Four summary tables compile these features of the software, serving as a resource to help users understand the methodological differences and select the most suitable software for their applications. The software tools show deviations from mean error in annual yield up to 2.5 % in the lab-scale system, increasing to 6.0 % for the utility-scale system. These differences arise from a combination of user decisions and the inherent behavior of the software, indicating the need for continuous and rigorous validation of modeling methods using these software tools against complex, real-world systems.
14 SOLAR ENERGY↗
A calculator to quantify cover crop effects on soil health and productivity
Not Available
Jian, Jinshi↗
Inhibition of human anthracycline reductases by emodin — A possible remedy for anthracycline resistance
The clinical application of anthracyclines, like daunorubicin and doxorubicin, is limited by two factors: dose-related cardiotoxicity and drug resistance. Both have been linked to reductive metabolism of the parent drug to their metabolites daunorubicinol and doxorubicinol, respectively. These metabolites show significantly less anti-neoplastic properties as their parent drugs and accumulate in cardiac tissue leading to chronic cardiotoxicity. Therefore, we aimed to identify novel and potent natural inhibitors for anthracycline reductases, which enhance the anticancer effect of anthracyclines by preventing the development of anthracycline resistance. Human enzymes responsible for the reductive metabolism of daunorubicin were tested for their sensitivity towards anthrachinones, in particular emodin and anthraflavic acid. Intense inhibition kinetic data for the most effective daunorubicin reductases, including IC{sub 50}- and K{sub i}-values, the mode of inhibition, as well as molecular docking, were compiled. Subsequently, a cytotoxicity profile and the ability of emodin to reverse daunorubicin resistance were determined using multiresistant A549 lung cancer and HepG2 liver cancer cells. Emodin potently inhibited the four main human daunorubicin reductases in vitro. Further, we could demonstrate that emodin is able to synergistically sensitize human cancer cells towards daunorubicin at clinically relevant concentrations. Therefore, emodin may yield the potential to enhance the therapeutic effectiveness of anthracyclines by preventing anthracycline resistance via inhibition of the anthracycline reductases. In symphony with its known pharmacological properties, emodin might be a compound of particular interest in the management of anthracycline chemotherapy efficacy and their adverse effects. - Highlights: • Natural and synthetic compounds were identified as inhibitors for human daunorubicin reductases. • Emodin is a potent inhibitor for human daunorubicin reductases. • Emodin synergistically sensitizes multiresistant human cancer cells towards daunorubicin.
60 APPLIED LIFE SCIENCES↗
Chemical-damage MINE: A database of curated and predicted spontaneous metabolic reactions
Spontaneous reactions between metabolites are often neglected in favor of emphasizing enzyme-catalyzed chemistry because spontaneous reaction rates are assumed to be insignificant under physiological conditions. However, synthetic biology and engineering efforts can raise natural metabolites' levels or introduce unnatural ones, so that previously innocuous or nonexistent spontaneous reactions become an issue. Problems arise when spontaneous reaction rates exceed the capacity of a platform organism to dispose of toxic or chemically active reaction products. While various reliable sources list competing or toxic enzymatic pathways' side-reactions, no corresponding compilation of spontaneous side-reactions exists, nor is it possible to predict their occurrence. We addressed this deficiency by creating the Chemical Damage (CD)-MINE resource. First, we used literature data to construct a comprehensive database of metabolite reactions that occur spontaneously in physiological conditions. We then leveraged this data to construct 148 reaction rules describing the known spontaneous chemistry in a substrate-generic way. We applied these rules to all compounds in the ModelSEED database, predicting 180,891 spontaneous reactions. The resulting (CD)-MINE is available at https://minedatabase.mcs.anl. gov/cdmine/#/home and through developer tools. We also demonstrate how damage-prone intermediates and end products are widely distributed among metabolic pathways, and how predicting spontaneous chemical damage helps rationalize toxicity and carbon loss using examples from published pathways to commercial products. We explain how analyzing damage-prone areas in metabolism helps design effective engineering strategies. Finally, we use the CD-MINE toolset to predict the formation of the novel damage product N-carbamoyl proline, and present mass spectrometric evidence for its presence in Escherichia coli.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Improved scaling laws for the shock-induced dispersal of a dense particle curtain
Here, experiments were performed within Sandia National Labs’ Multiphase Shock Tube to measure and quantify the shock-induced dispersal of a shock/dense particle curtain interaction. Following interaction with a planar travelling shock wave, schlieren imaging at 75 kHz was used to track the upstream and downstream edges of the curtain. Data were obtained for two particle diameter ranges ( $d_{p}=106{-}125$ , $300{-}355~\unicode[STIX]{x03BC}\text{m}$ ) across Mach numbers ranging from 1.24 to 2.02. Using these data, along with data compiled from the literature, the dispersion of a dense curtain was studied for multiple Mach numbers (1.2–2.6), particle sizes ( $100{-}1000~\unicode[STIX]{x03BC}\text{m}$ ) and volume fractions (9–32 %). Data were non-dimensionalized according to two different scaling methods found within the literature, with time scales defined based on either particle propagation time or pressure ratio across a reflected shock. The data refelct that spreading of the particle curtain is a function of the volume fraction, with the effectiveness of each time scale based on the proximity of a given curtain’s volume fraction to the dilute mixture regime. It is observed that volume fraction corrections applied to a traditional particle propagation time scale result in the best collapse of the data between the two time scales tested here. In addition, a constant-thickness regime has been identified, which has not been noted within previous literature.
42 ENGINEERING↗
Measuring photometric redshifts for high-redshift radio source surveys
With the advent of deep, all-sky radio surveys, the need for ancillary data to make the most of the new, high-quality radio data from surveys like the Evolutionary Map of the Universe (EMU), GaLactic and Extragalactic All-sky Murchison Widefield Array survey eXtended, Very Large Array Sky Survey, and LOFAR Two-metre Sky Survey is growing rapidly. Radio surveys produce significant numbers of Active Galactic Nuclei (AGNs) and have a significantly higher average redshift when compared with optical and infrared all-sky surveys. Thus, traditional methods of estimating redshift are challenged, with spectroscopic surveys not reaching the redshift depth of radio surveys, and AGNs making it difficult for template fitting methods to accurately model the source. Machine Learning (ML) methods have been used, but efforts have typically been directed towards optically selected samples, or samples at significantly lower redshift than expected from upcoming radio surveys. This work compiles and homogenises a radio-selected dataset from both the northern hemisphere (making use of Sloan Digital Sky Survey optical photometry) and southern hemisphere (making use of Dark Energy Survey optical photometry). We then test commonly used ML algorithms such as k-Nearest Neighbours (kNN), Random Forest, ANNz, and GPz on this monolithic radio-selected sample. We show that kNN has the lowest percentage of catastrophic outliers, providing the best match for the majority of science cases in the EMU survey. We note that the wider redshift range of the combined dataset used allows for estimation of sources up to z = 3 before random scatter begins to dominate. When binning the data into redshift bins and treating the problem as a classification problem, we are able to correctly identify ≈ 76% of the highest redshift sources—sources at redshift z > 2.51 —as being in either the highest bin (z > 2.51) or second highest (z = 2.25).
79 ASTRONOMY AND ASTROPHYSICS↗
The DESC stellarator code suite Part 3: Quasi-symmetry optimization
The DESC stellarator optimization code takes advantage of advanced numerical methods to search the full parameter space much faster than conventional tools. Only a single equilibrium solution is needed at each optimization step thanks to automatic differentiation, which efficiently provides exact derivative information. A Gauss–Newton trust-region optimization method uses second-order derivative information to take large steps in parameter space and converges rapidly. With just-in-time compilation and GPU portability, high-dimensional stellarator optimization runs take orders of magnitude less computation time with DESC compared to other approaches. This paper presents the theory of the DESC fixed-boundary local optimization algorithm along with demonstrations of how to easily implement it in the code. Example quasi-symmetry optimizations are shown and compared to results from conventional tools. Three different forms of quasi-symmetry objectives are available in DESC, and their relative advantages are discussed in detail. In the examples presented, the triple product formulation yields the best optimization results in terms of minimized computation time and particle transport. This paper concludes with an explanation of how the modular code suite can be extended to accommodate other types of optimization problems.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗