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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Noise sensitivity: Symptoms, health status, illness behavior and co-occurring environmental sensitivities

Epidemiological evidence on the symptomatic profile, health status and illness behavior of people with subjective sensitivity to noise is still scarce. Also, it is unknown to what extent noise sensitivity co-occurs with other environmental sensitivities such as multi-chemical sensitivity and sensitivity to electromagnetic fields (EMF). A cross-sectional study performed in the Netherlands, combining self-administered questionnaires and electronic medical records of non-specific symptoms (NSS) registered by general practitioners (GP) allowed us to explore this further. The study sample consisted of 5806 participants, drawn from 21 general practices. Among participants, 722 (12.5%) responded “absolutely agree” to the statement “I am sensitive to noise”, comprising the high noise-sensitive (HNS) group. Compared to the rest of the sample, people in the HNS group reported significantly higher scores on number and duration of self-reported NSS, increased psychological distress, decreased sleep quality and general health, more negative symptom perceptions and higher prevalence of healthcare contacts, GP-registered NSS and prescriptions for antidepressants and benzodiazepines. These results remained robust after adjustment for demographic, residential and lifestyle characteristics, objectively measured nocturnal noise exposure from road-traffic and GP-registered morbidity. Co-occurrence rates with other environmental sensitivities varied between 9% and 50%. Individuals with self-declared sensitivity to noise are characterized by high prevalence of multiple NSS, poorer health status and increased illness behavior independently of noise exposure levels. Findings support the notion that different types of environmental sensitivities partly overlap. - Highlights: • People with self-reported noise sensitivity experience multiple non-specific symptoms. • They also report comparatively poorer health and increased illness behavior. • Co-occurrence with other environmental sensitivities is moderate to high. • Road-traffic noise and GP-registered morbidity did not account for these results.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

β-Functionalized push–pull opp-dibenzoporphyrins as sensitizers for dye-sensitized solar cells: the role of the phenylethynyl bridge

A novel class of β-functionalized push–pull zinc opp-dibenzoporphyrins was designed and synthesized as sensitizers for dye-sensitized solar cells. Dibenzoporphyrin YH7 displayed strong push–pull characteristics, which included clearly segregated HOMO/LUMO and fast charge injection from the singlet excited state of zinc porphyrin to the conduction band of TiO 2 . The incorporation of both the phenylethynyl bridge and amino group was critical to achieve the enhanced push–pull effect. The highest PCE value in this series obtained with the YH6 cell was 6.7%, which is comparable to that of the best ruthenium dye, N719 (η = 7.4%), under similar conditions. The high PCE of the YH6 cell is ascribed to its high light harvesting ability and favorable cell kinetics, which is supported by electrochemical impedance spectroscopy studies and femtosecond transient absorption studies. An unusual illumination-induced photodegradation was observed for YH7, which was attributed as the major reason for the unexpected lower PCE of the YH7 cell (η = 5.7%). Furthermore, this work demonstrates the applicability of π-extended porphyrins as a new generation of sensitizers for high performance dye-sensitized solar cells.

Hu, Yi↗
DOE OSTI2019

A2 and A2B2 Benzoporphyrins as sensitizers for dye-sensitized solar cells

The first example of A2B2 tetrabenzoporphyrin (KW-4) was synthesized, characterized and evaluated as a sensitizer for dye-sensitized solar cells. UV-vis and fluorescence spectroscopy revealed red-shifted and broadened absorption spectra of A2B2 tetrabenzoporphyrin as compared with its A2 dibenzo- and A2B2 dibenzoporphyrin analogues, which is a desired feature of dyes for dye-sensitized solar cells. DFT calculations also indicate favorable electron density distribution on the HOMO and LUMO of KW-4. However, the power conversion efficiency of the solar cell based on tetrabenzoporphyrin KW-4 displayed inferior performance than that of the solar cell based on A2 dibenzoporphyrin KW-2. The lower performance of the KW-4 cell was ascribed to two factors: the low lying LUMO energy level leading to less efficient electron injection and the “flat geometry” of the dye on TiO 2 surface facilitating charge recombination and decreasing dye loading. The investigation of anchoring group effect suggests that the acrylic acid group is a better anchoring group than pentadienyl carboxylic acid.

A2B2↗
DOE OSTI2016

Novel chemoresistive CH{sub 4} sensor with 10 ppm sensitivity based on multiwalled carbon nanotubes functionalized with SnO{sub 2} nanocrystals

Chemoresistive sensors based on multiwalled carbon nanotubes (MWCNTs) functionalized with SnO{sub 2} nanocrystals (NCs) have great potential for detecting trace gases at low concentrations (single ppm levels) at room temperature, because the SnO{sub 2} nanocrystals act as active sites for the chemisorption of gas molecules, and carbon nanotubes (CNTs) act as an excellent current carrying platform, allowing the adsorption of gas on SnO{sub 2} to modulate the resistance of the CNTs. However, uniform conjugation of SnO{sub 2} NCs with MWCNTs is challenging. An effective atomic layer deposition based approach to functionalize the surface of MWCNTs with SnO{sub 2} NCs, resulting in a novel CH{sub 4} sensor with 10 ppm sensitivity, is presented in this paper. Scanning electron microscopy, transmission electron microscopy (TEM), energy dispersive x-ray spectroscopy, and Raman spectroscopy were implemented to study the morphology, elemental composition, and the crystal quality of SnO{sub 2} functionalized MWCNTs. High resolution TEM images showed that the crystal quality of the functionalizing SnO{sub 2} NCs was of high quality with clear lattice fringes and the dimension almost three times smaller than shown thus far in literature. A lift-off based photolithography technique comprising bilayer photoresists was optimized to fabricate SnO{sub 2} functionalized MWCNTs-based chemoresistor sensor, which at room temperature can reliably sense below 10 ppm of CH{sub 4} in air. Such low level gas sensitivity, with significant reversible relative resistance change, is believed to be the direct result of the successful functionalization of the MWCNT surface by SnO{sub 2} NCs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Photogeneration of Hydrogen from Water by a Robust Dye-Sensitized Photocathode

We report here on a novel photocathode with a “donor-dye-catalyst” assembly structure for water reduction. The photoelectrocatalytic performance of the photocathode under mild conditions, with a photocurrent of -56 µA/cm2 and a Faradaic yield of 53%, is superior relative to other reported photocathodes with surface attached molecular catalysts. Detailed electron transfer analyses, based on transient absorption measurements, show that the successful application of this photocathode originates mainly from the slow back electron transfer following light excitation. The results also demonstrate that addition of the long-chain assembly to the macro-mesoporous electrode surface plays a fundamental role in providing sufficient catalyst for water reduction.

catalysis, hydrogen↗
DOE OSTI2019

Using Bayesian Networks for Sensitivity Analysis of Complex Biogeochemical Models

Sensitivity analysis is a vital tool in numerical modeling to identify important parameters and processes that contribute to the overall uncertainty in model outputs. We developed a new sensitivity analysis method to quantify the relative importance of uncertain model processes that contain multiple uncertain parameters. The method is based on the concepts of Bayesian networks (BNs) to account for complex hierarchical uncertainty structure of a model system. We derived a new set of sensitivity indices using the methodology of variance-based global sensitivity analysis with the Bayesian inference. The framework is capable of representing the detailed uncertainty information of a complex model system using BNs and affords flexible grouping of different uncertain inputs given their characteristics and dependency structures. We have implemented the method on a real-world biogeochemical model at the groundwater-surface water interface within the Hanford Site’s 300 Area. The uncertainty sources of the model were first grouped into forcing scenario and three different processes based on our understanding of the complex system. The sensitivity analysis results indicate that both the reactive transport and groundwater flow processes are important sources of uncertainty for carbon-consumption predictions. Within the groundwater flow process, the structure of geological formations is more important than the permeability heterogeneity within a given geological formation. Our new sensitivity analysis framework based on BNs offers substantial flexibility for investigating the importance of combinations of interacting uncertainty sources in a hierarchical order, and it is expected to be applicable to a wide range of multi-physics models for complex systems.

Dai, Heng↗
DOE OSTI2015

Second-order adjoint sensitivity analysis methodology (2ND-ASAM) for computing exactly and efficiently first- and second-order sensitivities in large-scale systems - 14356

This work presents the second-order adjoint sensitivity analysis methodology (2.-ASAM) for computing exactly and efficiently the second-order functional derivatives ('sensitivities') of system responses to the system's model parameters. For a physical system comprising N{sub α} parameters and N{sub r} responses, forward methods require a total of at least (N{sup 2}{sub α}/2+3N{sub α}/2) large-scale computations for obtaining all of the first- and second-order sensitivities, for all N{sub r} system responses. On the other hand, for one functional-type system response, the 2.-ASAM requires one large-scale computation using the first-level adjoint sensitivity system (1.-LASS) for obtaining all of the first-order sensitivities, followed by at most (2N{sub α}+1) large-scale computations using the second-level adjoint sensitivity systems (2.-LASS) for obtaining all of the second-order sensitivities. The second-order sensitivities contribute decisively to causing asymmetries in the response distribution, since they are the leading contributors to the third-order response correlations (skewness). They also cause the 'expected value of the response' to differ from the 'computed nominal value of the response'. The implementation of the 2.-ASAM requires very little additional effort beyond the construction of the adjoint sensitivity system needed for computing the first-order sensitivities. Only the sources on the right-sides of the diffusion (differential) operator needed to be modified; the left-side of the differential equations (and hence the 'solver' in large-scale practical applications) remain unchanged. The application of the 2.-ASAM is illustrated on a benchmark heat conduction/convection problem, which makes transparent the underlying mathematical derivations. For this illustrative problem, 4 'large-scale' adjoint computations suffice for computing exactly all of the 6 first- and 21 distinct second-order derivatives. The 2.-ASAM presented in this work should enable the hitherto very difficult, if not intractable, exact computation of all of the second-order response sensitivities for large-systems involving many parameters, which is expected to affect significantly other fields that need efficiently computed second-order response sensitivities, such as optimization, data assimilation/adjustment, model calibration, and predictive modeling. (author)

42 ENGINEERING↗
DOE OSTI2017

A new process sensitivity index to identify important system processes under process model and parametric uncertainty

Hydrological models are always composed of multiple components that represent processes key to intended model applications. When a process can be simulated by multiple conceptual-mathematical models (process models), model uncertainty in representing the process arises. While global sensitivity analysis methods have been widely used for identifying important processes in hydrologic modeling, the existing methods consider only parametric uncertainty but ignore the model uncertainty for process representation. To address this problem, this study develops a new method to probe multimodel process sensitivity by integrating the model averaging methods into the framework of variance-based global sensitivity analysis, given that the model averaging methods quantify both parametric and model uncertainty. A new process sensitivity index is derived as a metric of relative process importance, and the index includes variance in model outputs caused by uncertainty in both process models and model parameters. For demonstration, the new index is used to evaluate the processes of recharge and geology in a synthetic study of groundwater reactive transport modeling. The recharge process is simulated by two models that converting precipitation to recharge, and the geology process is also simulated by two models of different parameterizations of hydraulic conductivity; each process model has its own random parameters. The new process sensitivity index is mathematically general, and can be applied to a wide range of problems in hydrology and beyond.

Dai, Heng↗
DOE OSTI2018

Sensitivity of Regulated Flow Regimes to Climate Change in the Western United States

Water management activities or flow regulations modify water fluxes at the land surface and affect water resources in space and time. We hypothesize that flow regulations change the sensitivity of river flow to climate change with respect to unmanaged water resources. Quantifying these changes in sensitivity could help elucidate the impacts of water management at different spatiotemporal scales and inform climate adaptation decisions. In this study, we compared the emergence of significant changes in natural and regulated river flow regimes across the Western United States from simulations driven by multiple climate models and scenarios. We find that significant climate change-induced alterations in natural flow do not cascade linearly through water management activities. At the annual time scale, 50% of the Hydrologic Unit Code 4 (HUC4) sub-basins over the Western U.S. regions tend to have regulated flow regime more sensitive to the climate change than natural flow regime. Seasonality analyses show that the sensitivity varies remarkably across the seasons. We also find that the sensitivity is related to the level of water management. For 35% of the HUC4 sub-basins with the highest level of water management, the summer and winter flows tend to show a heightened sensitivity to climate change due to the complexity of joint reservoir operations. We further demonstrate that the impacts of considering water management in models are comparable to those that arises from uncertainties across climate models and emission scenarios. This prompts further climate adaptation studies research about nonlinearity effects of climate change through water management activities.

Zhou, Tian↗
DOE OSTI2017

Discrete Adjoint Sensitivity Analysis of Hybrid Dynamical Systems With Switching [Discrete Adjoint Sensitivity Analysis of Hybrid Dynamical Systems]

Sensitivity analysis is an important tool for describing power system dynamic behavior in response to parameter variations. It is a central component in preventive and corrective control applications. The existing approaches for sensitivity calculations, namely, finite-difference and forward sensitivity analysis, require a computational effort that increases linearly with the number of sensitivity parameters. In this paper, we investigate, implement, and test a discrete adjoint sensitivity approach whose computational effort is effectively independent of the number of sensitivity parameters. The proposed approach is highly efficient for calculating sensitivities of larger systems and is consistent, within machine precision, with the function whose sensitivity we are seeking. This is an essential feature for use in optimization applications. Moreover, our approach includes a consistent treatment of systems with switching, such as dc exciters, by deriving and implementing the adjoint jump conditions that arise from state-dependent and time-dependent switchings. The accuracy and the computational efficiency of the proposed approach are demonstrated in comparison with the forward sensitivity analysis approach. In conclusion, this paper focuses primarily on the power system dynamics, but the approach is general and can be applied to hybrid dynamical systems in a broader range of fields.

42 ENGINEERING↗
DOE OSTI2017

Projected sensitivity of the SuperCDMS SNOLAB experiment

SuperCDMS SNOLAB will be a next-generation experiment aimed at directly detecting low-mass particles (with masses ≤ 10 GeV/c^2) that may constitute dark matter by using cryogenic detectors of two types (HV and iZIP) and two target materials (germanium and silicon). The experiment is being designed with an initial sensitivity to nuclear recoil cross sections ~1×10^-43 cm^2 for a dark matter particle mass of 1 GeV/c^2, and with capacity to continue exploration to both smaller masses and better sensitivities. The phonon sensitivity of the HV detectors will be sufficient to detect nuclear recoils from sub-GeV dark matter. A detailed calibration of the detector response to low-energy recoils will be needed to optimize running conditions of the HV detectors and to interpret their data for dark matter searches. Low-activity shielding, and the depth of SNOLAB, will reduce most backgrounds, but cosmogenically produced H-3 and naturally occurring Si-32 will be present in the detectors at some level. Even if these backgrounds are 10 times higher than expected, the science reach of the HV detectors would be over 3 orders of magnitude beyond current results for a dark matter mass of 1 GeV/c^2. The iZIP detectors are relatively insensitive to variations in detector response and backgrounds, and will provide better sensitivity for dark matter particles with masses ≳5 GeV/c^2. The mix of detector types (HV and iZIP), and targets (germanium and silicon), planned for the experiment, as well as flexibility in how the detectors are operated, will allow us to maximize the low-mass reach, and understand the backgrounds that the experiment will encounter. Upgrades to the experiment, perhaps with a variety of ultra-low-background cryogenic detectors, will extend dark matter sensitivity down to the “neutrino floor,” where coherent scatters of solar neutrinos become a limiting background.

Agnese, R.↗
DOE OSTI2017

Adjoint-Based Sensitivity and Uncertainty Analysis for Density and Composition: A User’s Guide

The ability to perform sensitivity analyses using adjoint-based first-order sensitivity theory has existed for decades. This paper provides guidance on how adjoint sensitivity methods can be used to predict the effect of material density and composition uncertainties in critical experiments, including when these uncertain parameters are correlated or constrained. Two widely used Monte Carlo codes, MCNP6 (Ref. 2) and SCALE 6.2 (Ref. 3), are both capable of computing isotopic density sensitivities in continuous energy and angle. Additionally, Perkó et al. have shown how individual isotope density sensitivities, easily computed using adjoint methods, can be combined to compute constrained first-order sensitivities that may be used in the uncertainty analysis. This paper provides details on how the codes are used to compute first-order sensitivities and how the sensitivities are used in an uncertainty analysis. Constrained first-order sensitivities are computed in a simple example problem.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2016

Dye-Sensitized Core/Active Shell Upconversion Nanoparticles for Optogenetics and Bioimaging Applications

Near Infrared (NIR) dye-sensitized upconversion nanoparticles (UCNPs) have recently been proposed in order to broaden the absorption range and to boost upconversion efficiency. However, implementing this strategy has been limited only to bare core UCNP structures that are faintly luminescent. Herein, we report on an approach to achieve significantly enhanced upconversion luminescence in dye-sensitized core-active shell UCNPs with a broadened absorption range via the doping of ytterbium ions in the UCNP shell in order to bridge the energy transfer from the dye to the UCNP core. As a result, we have been able to synergize the two most practical upconversion booster effectors (dye-sensitizing and core/shell enhancement). The absolute quantum yield of our dye-sensitized core/active shell UCNPs at 800 nm was determined to be ~6% at 2 W/cm2, about 33 times larger than the highest value reported to date for existing 800 nm excitable UCNPs. Moreover, for the first time, by using dye-sensitized core/active shell UCNP embedded poly(methyl methacrylate) polymer implantable systems, we successfully shifted the optogenetic neuron excitation window to a wavelength that is compatible with deep tissue penetrable near the infrared wavelength at 800 nm. Finally, amphiphilic triblock copolymer, Pluronic F127 coatings permit the transfer of hydrophobic UCNPs into water, resulting in water-soluble nanoparticles with well-preserved optical property in aqueous solution. We believe that this research offers a new solution to enhance upconversion efficiency for photonic and biophotonic purposes and opens up new opportunities to use UCNPs as a NIR relay for optogenetic applications.

Wu, Xiang↗
DOE OSTI2016

Exploring the Relationship Between Octane Sensitivity and Heat-of-Vaporization

The latent heat-of-vaporization (HoV) of blends of biofuel and hydrocarbon components into gasolines has recently experienced expanded interest because of the potential for increased HoV to increase fuel knock resistance in direct-injection (DI) engines. Several studies have been conducted, with some studies identifying an additional anti-knock benefit from HoV and others failing to arrive at the same conclusion. Consideration of these studies holistically shows that they can be grouped according to the level of fuel octane sensitivity variation within their fuel matrices. When comparing fuels of different octane sensitivity significant additional anti-knock benefits associated with HoV are sometimes observed. Studies that fix the octane sensitivity find that HoV does not produce additional anti-knock benefit. New studies were performed at ORNL and NREL to further investigate the relationship between HoV and octane sensitivity. Three fuels were formulated for the ORNL study with matched RON and octane sensitivity, but with differing HoV. Experiments with these fuels in a 1.6-liter GTDI engine showed that the fuels exhibited very similar combustion phasing under knock-limited spark advance (KLSA) conditions. Fuels having a range of RON, octane sensitivity, and HoV were tested at NREL in a single-cylinder GDI engine under conditions where octane sensitivity has little effect on knock resistance. KLSA was found to be well correlated with RON. These results reinforce the concept that HoV anti-knock effects can be viewed as a contributor to octane sensitivity. From this viewpoint, HoV effects manifest themselves as increases in octane sensitivity.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2018

Temperature sensitivity of soil organic carbon decomposition increased with mean carbon residence time: Field incubation and data assimilation

Abstract Temperature sensitivity of soil organic carbon ( SOC ) decomposition is one of the major uncertainties in predicting climate‐carbon (C) cycle feedback. Results from previous studies are highly contradictory with old soil C decomposition being more, similarly, or less sensitive to temperature than decomposition of young fractions. The contradictory results are partly from difficulties in distinguishing old from young SOC and their changes over time in the experiments with or without isotopic techniques. In this study, we have conducted a long‐term field incubation experiment with deep soil collars (0–70 cm in depth, 10 cm in diameter of PVC tubes) for excluding root C input to examine apparent temperature sensitivity of SOC decomposition under ambient and warming treatments from 2002 to 2008. The data from the experiment were infused into a multi‐pool soil C model to estimate intrinsic temperature sensitivity of SOC decomposition and C residence times of three SOC fractions (i.e., active, slow, and passive) using a data assimilation ( DA ) technique. As active SOC with the short C residence time was progressively depleted in the deep soil collars under both ambient and warming treatments, the residences times of the whole SOC became longer over time. Concomitantly, the estimated apparent and intrinsic temperature sensitivity of SOC decomposition also became gradually higher over time as more than 50% of active SOC was depleted. Thus, the temperature sensitivity of soil C decomposition in deep soil collars was positively correlated with the mean C residence times. However, the regression slope of the temperature sensitivity against the residence time was lower under the warming treatment than under ambient temperature, indicating that other processes also regulated temperature sensitivity of SOC decomposition. These results indicate that old SOC decomposition is more sensitive to temperature than young components, making the old C more vulnerable to future warmer climate.

Zhou, Xuhui↗
DOE OSTI2018

Fine-scale application of WRF-CAM5 during a dust storm episode over East Asia: Sensitivity to grid resolutions and aerosol activation parameterizations

An advanced online-coupled meteorology and chemistry model WRF-CAM5 has been applied to East Asia using triple-nested domains at different grid resolutions (i.e., 36-, 12-, and 4-km) to simulate a severe dust storm period in spring 2010. Analyses are performed to evaluate the model performance and investigate model sensitivity to different horizontal grid sizes and aerosol activation parameterizations and to examine aerosol-cloud interactions and their impacts on the air quality. A comprehensive model evaluation of the baseline simulations using the default Abdul-Razzak and Ghan (AG) aerosol activation scheme shows that the model can well predict major meteorological variables such as 2-m temperature (T2), water vapor mixing ratio (Q2), 10-m wind speed (WS10) and wind direction (WD10), and shortwave and longwave radiation across different resolutions with domain-average normalized mean biases typically within±15%. The baseline simulations also show moderate biases for precipitation and moderate-to-large underpredictions for other major variables associated with aerosol-cloud interactions such as cloud droplet number concentration (CDNC), cloud optical thickness (COT), and cloud liquid water path (LWP) due to uncertainties or limitations in the aerosol-cloud treatments. The model performance is sensitive to grid resolutions, especially for surface meteorological variables such as T2, Q2, WS10, and WD10, with the performance generally improving at finer grid resolutions for those variables. Comparison of the sensitivity simulations with an alternative (i.e., the Fountoukis and Nenes (FN) series scheme) and the default (i.e., AG scheme) aerosol activation scheme shows that the former predicts larger values for cloud variables such as CDNC and COT across all grid resolutions and improves the overall domain-average model performance for many cloud/radiation variables and precipitation. Sensitivity simulations using the FN series scheme also have large impacts on radiations, T2, precipitation, and air quality (e.g., decreasing O3) through complex aerosol-radiation-cloud-chemistry feedbacks. The inclusion of adsorptive activation of dust particles in the FN series scheme has similar impacts on the meteorology and air quality but to lesser extent as compared to differences between the FN series and AG schemes. Compared to the overall differences between the FN series and AG schemes, impacts of adsorptive activation of dust particles can contribute significantly to the increase of total CDNC (~45%) during dust storm events and indicate their importance in modulating regional climate over East Asia.

WRF-CAM5, Aerosol activation, adsorptive activatio↗
DOE OSTI2017

Higher climatological temperature sensitivity of soil carbon in cold than warm climates

The projected loss of soil carbon to the atmosphere resulting from climate change is a potentially large but highly uncertain feedback to warming. The magnitude of this feedback is poorly constrained by observations and theory, and is disparately represented in Earth system models (ESMs). In this study, to assess the climatological temperature sensitivity of soil carbon, we calculate apparent soil carbon turnover times that reflect long-term and broad-scale rates of decomposition. We show that the climatological temperature control on carbon turnover in the top metre of global soils is more sensitive in cold climates than in warm climates and argue that it is critical to capture this emergent ecosystem property in global-scale models. We present a simplified model that explains the observed high cold-climate sensitivity using only the physical scaling of soil freeze-thaw state across climate gradients. Current ESMs fail to capture this pattern, except in an ESM that explicitly resolves vertical gradients in soil climate and carbon turnover. An observed weak tropical temperature sensitivity emerges in a different model that explicitly resolves mineralogical control on decomposition. Lastly, these results support projections of strong carbon-climate feedbacks from northern soils and demonstrate a method for ESMs to capture this emergent behaviour.

54 ENVIRONMENTAL SCIENCES↗