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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 811 records · Page 45
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

CO 2 geological storage: Critical insights on plume dynamics and storage efficiency during long-term injection and post-injection periods

In the quest for net-zero greenhouse gas emission, CO 2 capture and storage technologies play a prominent role. Estimates suggest a potential of sequestering up to several thousand metric gigatons of CO 2 . However, some of the main challenges in its development are an assessment of reservoir-specific storage capacities due to variabilities and heterogeneities in the underlying properties (structure, formations, facies, petrophysics, reservoir architecture, and others) and understanding the migration of CO 2 in the subsurface. How much CO 2 can be stored in a reservoir, and how quickly can it be injected? What will be the dominant storing (trapping) mechanisms? These questions, among others, remain as challenges to the commercial deployment of this technology. This article critically investigates CO 2 plume characteristics and determines the evolving contributions of different trapping mechanisms during a 100-year injection period. Here, simulation results on the CO 2 plume dynamics indicate that lateral propagation is much larger than vertical propagation. The ratio of average lateral to vertical dispersion ranged between 6.3 and 18.8 for the cases investigated. On average, for all the scenarios investigated, the free phase (mobile supercritical CO 2 ) is the dominant trapping mechanism (in terms of the amount of CO 2 stored) during the injection period, accounting up to 45% of the CO 2 injected. Both residual (30%) and solubility (25%) trapping storage ratios decline with time during injection, and no significant mineral trapping appeared during the 100-year injection period. However, the behavior of CaCO 3 suggests a predominant dissolution of CaCO 3 , especially in the vicinity of the injection well.

03 NATURAL GAS↗
DOE OSTI2026

Evaluation of corrosion coupons exposed to molten-chloride electrorefiner salts long term

Corrosion mitigation has long been considered a challenge in long-term service of both pyrochemical reprocessing and molten salt reactor applications. Corrosion coupons consisting of 2.25Cr 1Mo ASME SA387 Grade 22 Class 2 steel, were installed in a uranium electrorefiner containing molten chloride salt at 500°C. This electrorefiner went into service at Idaho National Laboratory in 1994. Seven of these coupons were removed in 2019 (i.e., submerged in molten salt for 25 years) for analysis and characterization including neutron imaging, optical microscopy, scanning electron microscopy, and wave/energy dispersive x-ray spectroscopy. The results showed that all seven coupons experienced between 30 to 175 µm of surface degradation without evidence of brittle fracture. Meaning there was no evidence of accelerated corrosion or fractures, and the average steel surface loss was of less than 5 µm per year. All indications were that the environmental conditions inside the electrorefiner were favorable for the steel alloy.

36 - MATERIALS SCIENCE↗
DOE OSTI2022

The energy system transformation needed to achieve the US long-term strategy

The authors designed and executed the integrated assessment modeling for the United States long-term strategy. They bring diverse expertise to the modeling and analysis of United States decarbonization. Russell Horowitz, Matthew Binsted, and Haewon McJeon are scientists at the Joint Global Change Research Institute, a partnership between Pacific Northwest National Laboratory and the University of Maryland. Allen Fawcett, James McFarland, and Morgan Browning are economists at the Environmental Protection Agency’s Climate Economics Branch. Claire Henly is White House Fellow at the Office of the US Special Presidential Envoy for Climate. Nathan Hultman is the Director of the Center for Global Sustainability at the University of Maryland.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Consequences of long-term water exposure for bulk crystal structure and surface composition/chemistry of nickel-rich layered oxide materials for Li-ion batteries

Water exposure of layered nickel-rich transition metal oxide electrodes, widely used in high-energy lithium-ion batteries, has detrimental effects on the electrochemical performance, which complicates electrode handling and prevents implementation of environmentally benign aqueous processing procedures. Elucidating the degradation mechanisms in play may help rationally mitigate/circumvent key challenges. Here, the bulk structural consequences of long-term (>2.5 years) deuterated water (D2O) exposure of intercalation materials with compositions Li x Ni 0.5 Co 0.2 Mn 0.3 O 2 (NCM523) and Li x Ni 0.8 Co 0.1 Mn 0.1 O 2 (NCM811) are studied by neutron powder diffraction (NPD). Detailed inspection of the NPD data reveals gradual formation of a secondary crystalline phase in all exposed samples, not previously reported for this system. This unknown phase forms faster in liquid- compared to vapor-exposed compounds. Structural modelling of the NPD data shows a stable level of Li/Ni anti-site defects and does not indicate any significant changes in lattice parameters or hydrogen-lithium (D + /Li + ) exchange in the structure. Consequently, the secondary phase formation must take place via transformation rather than modification of the parent material. X-ray photoelectron spectroscopy data indicate formation of LiHCO 3 /Li 2 CO 3 at the surface and a Li-deficient oxide in the sub-surface region of the pristine compounds, and the presence of adsorbed water and transition metal hydroxides at the exposed sample surfaces.

25 ENERGY STORAGE↗
DOE OSTI2023

Long–short-term memory encoder–decoder with regularized hidden dynamics for fault detection in industrial processes

The ability of recurrent neural networks (RNN) to model nonlinear dynamics of high dimensional process data has enabled data-driven RNN-based fault detection algorithms. Previous studies have focused on detecting faults by identifying the discrepancies in data distribution between the faulty and normal data, as reflected in prediction errors generated by RNN models. However, in industrial processes, variations in data distribution can also result from changes in normal control setpoints and compensatory control adjustments in response to disturbances, making it hard to differentiate between normal and faulty conditions. This paper proposes a fault detection method utilizing a long short-term memory (LSTM) encoder–decoder structure with regularized hidden dynamics and reversible instance normalization (RevIN) to compactly represent high-dimensional measurements for effective monitoring. During training, the hidden states of the model are regularized to form a low-dimensional latent space representation of the original multivariate time series data. As a result, the prediction errors of the latent states can be used to monitor the abnormal dynamic variations, while the reconstruction errors of the measured variables are used to monitor the abnormal static variations. Furthermore, the proposed indices can reflect operating conditions, even when the distribution of test data changes, which helps distinguish faults from normal adjustments and disturbances that controllers can settle. Here, data from numerical simulation and the Tennessee Eastman process are used to illustrate the effectiveness of the proposed fault detection method.

42 ENGINEERING↗
DOE OSTI2021

Ride-hailing and taxi versus walking: Long term forecasts and implications from large-scale behavioral data

Introduction: Although ride-hailing and taxi trips can potentially reduce single-occupant vehicle trips and auto ownership, they can also replace pedestrian trips. Because physical activity is associated with improved health outcomes, the extent to which ride-hailing and taxi travel captures walking's mode share is of interest to policymakers. Methods: Based on large-scale behavioral data from the 2017 U.S. National Household Travel Survey, this paper reports on the development of a full Bayesian logistic regression model for determining the mode split between (1) ride-hailing and taxi and (2) walk while accounting for unobserved heterogeneity. The results from the stand-alone model inform two longer-term travel forecasting scenarios: a) higher risk of walk trips converting to ride-hailing and taxi, specifically in the future with high prevalence of automated vehicles, b) higher probability of such trips remaining as walking. Results: The results revealed that some of the important characteristics that increase the likelihood of a traveler using the ride-hailing and taxi mode versus walking include having a longer trip, using a smartphone to access the internet, having an interest in technologies, having a medical condition, and living in a metropolitan area with rail access. Further, the results from the first scenario suggest that an overall increase of up to 2.9% in the ride-hailing and taxi mode share may be expected. The second scenario shows that between 68% and 76% of ride-hailing and taxi trips could be diverted to walking if supportive pedestrian infrastructure were provided in the case study locations. The planning process can be adapted to consider not only congestion, crash, and emissions impacts of such shifts but also the effects of a loss of physical activity. Conclusions: The study findings show how the ride-hailing and taxi mode competes with walking. Further, the findings enable planners to update their regional travel forecasting models; policy makers can thus encourage active travel by prioritizing pedestrian infrastructure investments that may divert ride-hailing and taxi trips to walking. However, equity should be a key consideration to ensure that addressing the competition between these two modal choices does not hinder the provision of pedestrian facilities in communities that depend on walking.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Ultra-selective CANAL Polymers for Hydrogen-based Membrane Separations after Long-Term Aging

Hydrogen is a critically important molecule in the chemicals and energy industries, driving the need for efficient purification technologies such as membrane-based separations. However, polymer membranes often suffer from physical aging, leading to decreased gas permeability over time. This study leverages the unique aging behavior of contorted microporous polymers, synthesized via catalytic arene-norbornene annulation (CANAL) polymerization, to enhance molecular sieving for H 2 separations. While prior studies demonstrated a 1000% increase in H 2 /CH 4 selectivity with minimal permeability loss over 150 days, we extend this investigation to hyperaged (∼1 year) CANAL polymers and evaluate their performance under various conditions of industrial relevance. We report a remarkable 6100% increase in H 2 /CH 4 selectivity and a 2400% improvement in H 2 /N 2 selectivity, with only a 55% decline in H 2 permeability. These membranes exhibit excellent long-term stability in binary and ternary gas mixtures and at elevated temperatures. Additionally, extended aging enhances H 2 /CO 2 selectivity beyond the 2008 upper bound due to improved size-sieving. High-temperature permeation experiments and grand canonical Monte Carlo simulations reveal that H 2 permeability increases from 570 to 3500 barrer as the temperature rises from 35 °C to 190 °C, while H 2 /CO 2 selectivity declines from 14 to 3 due to the higher diffusion activation energy of CO 2 . Despite this tradeoff, hyperaged CANAL membranes maintain performance beyond the 200 °C upper bound, having selectivity–permeability performance comparable to many state-of-the-art membranes for H 2 /CO 2 separations. As a result, this study highlights the robustness and industrial viability of ultra-selective CANAL polymers for hydrogen purification, contributing to advancements in clean energy and sustainable separation technologies.

36 MATERIALS SCIENCE↗
DOE OSTI2023

High yield, large-scale synthesis of calcium-based microporous metal-organic framework and examination of the long-term stability for xenon adsorption applications

Here, scale-up synthesis of calcium-based microporous metal-organic framework (SBMOF-1) up to ~400 g in a batch with a yield of >90 % was achieved by a solvothermal reaction of sulfonyldibenzoic acid (SDB) with an excess of calcium chloride. Here, we observed that recrystallization of unreacted SDB at a solvothermal condition caused a moderate reaction yield (40–50 %) at the reference condition of $C_{CaCl_2}$/$C_{SDB}$=1 and $C_{SDB}$/$C_{SDB(ref)}$=1. Simply adding more reagents to the reactor did not increase the mass of product formed per unit volume due to a more pronounced loss of the yield at those conditions. By simultaneously changing the molar ratio of CaCl 2 to SDB, $C_{CaCl_2}$/$C_{SDB}$, and the molar concentration of the SDB reagent, $C_{SDB}$/$C_{SDB(ref)}$, we explored %yield of the reaction. Interestingly, a linear improvement in the yield was observed from 21% (at $C_{CaCl_2}$/$C_{SDB}$=0.5) up to 78% (at $C_{CaCl_2}$/$C_{SDB}$=6) at a fixed ratio of $C_{SDB}$/$C_{SDB(ref)}$=2 and the yield leveled off after further addition. Unlike those at $C_{CaCl_2}$/$C_{SDB}$=1, the yields at a high CaCl 2 excess continued to improve with increasing the $C_{SDB}$/$C_{SDB(ref)}$. When a large pressure vessel (2500 mL EtOH, $C_{CaCl_2}$/$C_{SDB}$=6, $C_{SDB}$/$C_{SDB(ref)}$=8) was used, about 415 g of SBMOF-1 with a yield of 92.3% was produced, indicating 16 × the space yield improvement. The ability to synthesize SBMOF-1 on a large scale allowed us to examine the long-term stability of SBMOF-1 for almost 200 days in the presence of varying levels of relative humidity.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Superior long-term cycling of high-voltage lithium-ion batteries enabled by single-solvent electrolyte

A new single-solvent electrolyte system comprising lithium bis(fluorosuflonyl) imide (LiFSI) and beta-fluorinated sulfone (TFPMS) was designed to enable very stable long-term cycling of high-voltage lithium-ion batteries. Compared to other fluorinated solvents such as alpha-fluorinated sulfone (FMES) and fluorinated carbonate (FEMC), which are prone to reduction on the graphite anode, the LiFSI-TFPMS system displayed outstanding compatibility with graphite. While regular carbonate and sulfone from the LiFSI electrolyte system are compatible with the graphite anode, their high solvating power not only induces severe corrosion on the aluminum cathode current collector at high voltage, but also renders a low aggregation level at a normal salt concentration (about 1.0 M), resulting in the formation of an unstable solid-electrolyte interphase (SEI) on the graphite anode. Owing to the low solvating power of TFPMS, the aggregation level of the LiFSI-TFPMS system is relatively high even at normal salt concentration, which not only facilitates the formation of a robust SEI by the sacrificial decomposition of LiFSI, but also suppresses the aluminum corrosion of the LiFSI electrolyte system at high voltage. Together with the high intrinsic anodic stability of TFPMS, the superior cycling performance of graphite parallel to LiNi 0.6 Co 0.2 Mn 0.2 O 2 cells was achieved by employing the non-flammable LiFSI-TFPMS single-solvent electrolyte system.

25 ENERGY STORAGE↗
DOE OSTI2022

Origin for retained activity in Pr 2 NiO 4 while undergoing substantial phase transformation in a long-term solid oxide cell operation

Pr 2 NiO 4 (PNO) is a known active oxygen electrode for solid oxide cells but undergoes phase transformation at high temperatures. An in-situ synchrotron study on PNO electrodes show that phase transformation reaches nearly 100% in a long-term operation. Such significant phase transformation is expected to play a detrimental role on the cell performance. However, PNO retains the activity in an enduring operation. The origin for this dichotomy has remained obscure. Here, the aim of this article is to investigate the origin for retained activity and performance stability in PNO electrode. High-resolution transmission electron microcopy analysis shows the presence of a large number of nanoclusters (~ 5 nm) through the entire electrode bulk, which are localized in the 20–60 nm region. In-situ synchrotron studies show that those nanoclusters are nickelates that account for the retained activity during phase transformation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Quantifying the generalization error in deep learning in terms of data distribution and neural network smoothness

We report the accuracy of deep learning, i.e., deep neural networks, can be characterized by dividing the total error into three main types: approximation error, optimization error, and generalization error. Whereas there are some satisfactory answers to the problems of approximation and optimization, much less is known about the theory of generalization. Most existing theoretical works for generalization fail to explain the performance of neural networks in practice. To derive a meaningful bound, we study the generalization error of neural networks for classification problems in terms of data distribution and neural network smoothness. We introduce the cover complexity (CC) to measure the difficulty of learning a data set and the inverse of the modulus of continuity to quantify neural network smoothness. A quantitative bound for expected accuracy/error is derived by considering both the CC and neural network smoothness. Although most of the analysis is general and not specific to neural networks, we validate our theoretical assumptions and results numerically for neural networks by several data sets of images. The numerical results confirm that the expected error of trained networks scaled with the square root of the number of classes has a linear relationship with respect to the CC. We also observe a clear consistency between test loss and neural network smoothness during the training process. In addition, we demonstrate empirically that the neural network smoothness decreases when the network size increases whereas the smoothness is insensitive to training dataset size.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

A structural model of the long-term degradation of the concrete biological shield

The concrete biological shield (CBS) of light water reactors is exposed to high neutron radiation dose in the long term, which may lead to the degradation of the concrete’s mechanical properties. Given the important shielding role of the CBS, it is necessary to investigate the irradiation effects at the structural scale and provide estimates of the damage extent from the wall’s inner surface to study potential license renewals. For this purpose, we developed a mechanical model accounting for radiation-induced expansion, creep, and damage in concrete using the Grizzly finite element code, informed by ex-core neutron flux calculations using the VERA tool. The model was applied to a 3D CBS structure represented by the CBS wall, a steel liner, reinforcement bars, and a concrete base mat and evaluated damage at 40, 60, and 80 years of operation. The VERA model predicted a maximum fluence of approximately 2 x 10 19 ncm -2 at 80 years of operation. The results showed that damage is highest at the inner surface of the CBS wall and gradually decreases with depth. It extends beyond the rebar after 60 years and reaches a depth of approximately 12 cm at 80 years.

42 ENGINEERING↗
DOE OSTI2026

Long-term thermal stability and calibration of Type-II fiber Bragg grating array inscribed in radiation-hardened fibers

This paper investigates the long-term thermal stability of Type-II fiber Bragg grating (FBG) arrays, inscribed by femtosecond laser in radiation-hardened fiber, for potential applications as multiplexed sensors in high-temperature energy systems. The thermal stability of FBG sensors was assessed through 16 thermal cycles from room temperature (RT) to 750 ℃ about two months, involving 100 FBG sensors. The results show that the absolute temperature drift of FBG sensors can be reduced to less than 0.4 pm/day after 54 h thermal annealing process at a constant temperature of 800 ℃. As temperature sensors, the FBGs demonstrated stable performance, achieving a standard deviation (STD) of 1.8 pm (corresponding to a temperature resolution of 0.118 ℃) post-annealing. Repeated thermal cycles revealed a random drift of 2.3 pm in the FBG wavelength at RT. Polynomial fitting was explored as a calibration method to convert FBG wavelength shifts into absolute temperature measurements. By optimizing calibration temperature points (RT, 200 ℃, 400 ℃, and 750 ℃), the study shows that cubic polynomial calibration using four points yields an average R2 of 0.9997 and an RMSE of 3.58 ℃ across the entire temperature range (RT to 750 ℃). This approach represents an 11.53-fold improvement over empirical slope calibration and a 1.34-fold improvement over four-point piecewise fitting. The findings indicate that Type-II FBGs inscribed in radiation-hardened fibers can function as accurate temperature sensors, with performance on par with or exceeding that of thermocouples. With their multiplexing capability, robust signal transmission over long lead cables, and immunity to electromagnetic interference, FBG sensors offer a promising alternative to traditional electronic sensors for energy system monitoring.

Dominguez-Ontiveros, Elvis [ORNL] (ORCID:000000018↗
DOE OSTI2026

Measuring Success: A Refined Methodology for Estimating Long-term Continuous Improvement

Successful resource management systems require current and detailed feedback on operational and corporate-level performance. As corporate accountability concerns intensify, the precision and reliability of these performance metrics have become crucial. Traditional savings estimation methods can be difficult to understand, particularly linear regression, and can provide varying results. This paper reviews common efficiency metrics and highlights underlying mathematical inconsistencies when estimating total and percent savings with current methods. A refined approach to calculating long-term utility savings is proposed that simplifies current methodologies utilizing ratios to define an adjusted baseline, allowing for consistent and fair aggregation of results across multiple scales from resources to corporate performance. A simplified example demonstrates how the proposed methodology improves upon existing methods, especially in intermediate years. This paper’s major contributions are the simplified approach for converting modeled utility usage into estimated savings and the consistent roll-up methodology enabling more comparable, aggregable, and actionable results across scales.

Price, Chris [ORNL] (ORCID:0000000202007906)↗
DOE OSTI2020

Constructing a spatiotemporally coherent long-term PM2.5 concentration dataset over China during 1980–2019 using a machine learning approach

The lack of long-term observations and satellite retrievals of health-damaging fine particulate matter in China has demanded the estimates of historical PM 2.5 (particulate matter less than 2.5 µm in diameter) concentrations. This study constructs a gridded near-surface PM 2.5 concentration dataset across China covering 1980–2019 using the space-time random forest model with atmospheric visibility observations and other auxiliary data. The modeled daily PM2.5 concentrations are in excellent agreement with ground measurements during 2015–2019, with a coefficient of determination of 0.95 and mean relative error of 12%. Besides the atmospheric visibility which explains 30% of total importance of variables in the model, both emissions and meteorological conditions are also essential factors affecting PM2.5 predictions. From 1980 to 2014, the model-predicted PM2.5 concentrations increased constantly with the maximum growth rate of 5–10 µg/m3/decade over eastern China. Due to the clean air actions, PM 2.5 concentrations have decreased effectively at a rate over 50 µg/m 3 /decade in the North China Plain and 20–50 µg/m 3 /decade over eastern China during 2014–2019. The newly generated dataset of 1-degree gridded PM 2.5 concentrations for the past 40 years across China provides a useful means for investigating interannual and decadal environmental and climate impacts related to aerosols.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Climate and atmospheric deposition drive the inter-annual variability and long-term trend of dissolved organic carbon flux in the conterminous United States

The lateral flux of dissolved organic carbon (DOC) from soils to inland waters and ultimately to the ocean represents a fundamental component of the global carbon cycle. To estimate the DOC flux, we developed an empirical terrestrial-aquatic DOC fluxes model (TAF-DOC). TAF-DOC incorporates various environmental factors (e.g., meteorology, sulfur, and nitrogen deposition) that to-date have not been comprehensively considered or well-represented in existing modeling frameworks. TAF-DOC was applied to estimate spatial-temporal patterns of DOC flux and potential fates across the conterminous United States during the 1985 to 2018 time period. Our results suggest that TAF-DOC successfully characterized spatial-temporal of DOC flux. As expected, the interannual pattern of DOC flux was strongly regulated by precipitation, but the long-term trend was significantly influenced by the rate of atmospheric wet sulfur deposition. From 1985 to 2018, TAF-DOC estimated DOC loading from terrestrial to aquatic ecosystems in the conterminous United States to be 33.5 ± 2.2 TgC per year, which was roughly 0.39–0.49% of total soil organic carbon stock estimates. The dominant fate of terrestrially-derived DOC was delivery to the coastal ocean in riverine export (41%), with another 21% buried in sediment and the remaining 12.8 ± 0.4 TgC per year (38%) returned to the atmosphere through outgassing from inland waters. Finally, assuming the quantities of DOC sediment burial and export to the ocean as an annual sink of terrestrially-derived carbon, budget inventories and models that do not account for DOC flux in the conterminous United States will underestimate the net annual carbon sink by as much as 5.5–6.4%.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Long-term leaf C:N ratio change under elevated CO 2 and nitrogen deposition in China: Evidence from observations and process-based modeling

Climate change, elevating atmosphere CO 2 (eCO 2 ) and increased nitrogen deposition (iNDEP) are altering the biogeochemical interactions between plants, microbes and soils, which further modify plant leaf carbon-nitrogen (C:N) stoichiometry and their carbon assimilation capability. Many field experiments have observed large sensitivity of leaf C:N ratio to eCO 2 and iNDEP. However, the large-scale pattern of this sensitivity is still unclear, because eCO 2 and iNDEP drive leaf C:N ratio toward opposite directions, which are further compounded by the complex processes of nitrogen acquisition and plant-and-microbial nitrogen competition. Here, we attempt to map the leaf C:N ratio spatial variation in the past 5 decades in China with a combination of data-driven model and process-based modeling. These two approaches showed consistent results. Over different regions, we found that leaf C:N ratio had significant but uneven changes between 2 time periods (1960-1989 and 1990-2015): a 5% ± 8% increase for temperate grasslands in northern China, a 3% ± 6% increase for boreal grasslands in western China, and by contrast, a 7% ± 6% decrease for temperate forests in southern China, and a 3% ± 5% decrease for boreal forests in northeastern China. Additionally, the structural equation models indicated that the leaf C:N change was sensitive to ΔNDEP, ΔCO 2 and ΔMAT rather than ΔMAP and ecosystem types. In this work, process-based modeling suggested that iNDEP was the main source of soil mineral nitrogen change, dominating leaf C:N ratio change in most areas in China, while eCO 2 led to leaf C:N ratio increase in low iNDEP area. This study also indicates that the long-term leaf C:N ratio acclimation was dominated by climate constraint, especially temperature, but was constrained by soil N availability over decade scale.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Long-term dynamics of soil, tree stem and ecosystem methane fluxes in a riparian forest

The carbon (C) budgets of riparian forests are sensitive to climatic variability. Therefore, riparian forests are hot spots of C cycling in landscapes. Only a limited number of studies on continuous measurements of methane (CH 4 ) fluxes from riparian forests is available. Here, in this work, we report continuous high-frequency soil and ecosystem (eddy-covariance; EC) measurements of CH 4 fluxes with a quantum cascade laser absorption spectrometer for a 2.5-year period and measurements of CH 4 fluxes from tree stems using manual chambers for a 1.5 year period from a temperate riparian Alnus incana forest. The results demonstrate that the riparian forest is a minor net annual sink of CH 4 consuming 0.24 kg CH 4 -C ha –1 y –1 . Soil water content is the most important determinant of soil, stem, and EC fluxes, followed by soil temperature. There were significant differences in CH 4 fluxes between the wet and dry periods. During the wet period, 83% of CH 4 was emitted from the tree stems while the ecosystem-level emission was equal to the sum of soil and stem emissions. During the dry period, CH 4 was substantially consumed in the soil whereas stem emissions were very low. A significant difference between the EC fluxes and the sum of soil and stem fluxes during the dry period is most likely caused by emission from the canopy whereas at the ecosystem level the forest was a clear CH 4 sink. Our results together with past measurements of CH 4 fluxes in other riparian forests suggest that temperate riparian forests can be long-term CH 4 sinks.

54 ENVIRONMENTAL SCIENCES↗