Supported cobalt catalysts - preparation, characterization and reaction studies

The aim of this work was to understand on the effect of thermal treatments, precursor and support on the interaction between the support and cobalt species, and further how the interaction affects the reducibility and dispersion of the catalyst. Silica and alumina supported cobalt catalysts were prepared, characterised and tested for catalytic activity. The catalysts were prepared by gas phase deposition techniques from cobalt acetylacetonate and cobalt carbonyl and by incipient wetness impregnation from cobalt nitrate. One of the goals was to investigate whether atomic layer deposition (ALD) using cobalt acetylacetonate precursors can produce well dispersed reducible cobalt catalysts. The cobalt acetylacetonates, Co(acac)2 and Co(acac)3, were found suitable for ALD. Silica supported catalysts were prepared by chemisorption of Co(acac)3, while Co(acac)2 was used on alumina. The main mode of interaction on silica was the ligand exchange reaction with OH groups. On alumina both the ligand exchange reaction and dissociative adsorption occurred. Steric hindrance limited the amount of precursor on the support. The acac ligands were removed through calcination at 450 °C. The cobalt loading was increased by repeating the precursor addition and air calcination steps up to five times; samples with about 2 to 8 Co atoms per nm² were achieved on both silica and alumina. Calcination of the cobalt acetylacetonate modified samples led to the formation of silicate- or aluminate-type species, which decreased the reducibility of the catalysts. The reducibility was enhanced when the calcination step after the last precursor reaction step was omitted ('uncalcined' catalysts). High reduction temperatures were still needed: the maximum metal surface area was obtained after reduction at 500-600 °C. The cobalt dispersion on the uncalcined ALD catalysts was, in general, higher than on the calcined catalysts. Furthermore, the dispersion was higher on the alumina supported catalysts than on corresponding silica supported ones. The interaction between cobalt and silica on the nitrate based catalysts was found to be weak, which led to high reducibility but modest dispersion. The main cobalt species on the catalysts was Co3O4. The use of reduction temperatures above 400 °C induced sintering or migration of silica, which decreased the cobalt surface area significantly. Dicobalt octacarbonyl, Co2(CO)8, was adsorbed on silica by vapour-phase adsorption in a fluidised bed reactor under CO. The carbonyl interacted through hydrogen bonding and rearranged to Co4(CO)12 on the support. The amount of precursor that adsorbed on the support was limited by steric hindrance. Decarbonylation was achieved by heat treatment, and higher cobalt loadings were obtained by repeating the deposition and decarbonylation steps. Chemisorption of hydrogen on cobalt was found to be activated and highly reversible. The effect was stronger on alumina than on silica supported samples. The ALD and nitrate based catalysts were tested for gas phase hydrogenation of toluene, and the activity was found to correlate with the available surface area of metallic cobalt

Calibrating the sqHIMMELI v1.0 wetland methane emission model with hierarchical modeling and adaptive MCMC

Estimating methane (CH4) emissions from natural wetlands is complex, and the estimates contain large uncertainties. The models used for the task are typically heavily parameterized and the parameter values are not well known. In this study, we perform a Bayesian model calibration for a new wetland CH4 emission model to improve the quality of the predictions and to understand the limitations of such models. The detailed process model that we analyze contains descriptions for CH4 production from anaerobic respiration, CH4 oxidation, and gas transportation by diffusion, ebullition, and the aerenchyma cells of vascular plants. The processes are controlled by several tunable parameters. We use a hierarchical statistical model to describe the parameters and obtain the posterior distributions of the parameters and uncertainties in the processes with adaptive Markov chain Monte Carlo (MCMC), importance resampling, and time series analysis techniques. For the estimation, the analysis utilizes measurement data from the Siikaneva flux measurement site in southern Finland. The uncertainties related to the parameters and the modeled processes are described quantitatively. At the process level, the flux measurement data are able to constrain the CH4 production processes, methane oxidation, and the different gas transport processes. The posterior covariance structures explain how the parameters and the processes are related. Additionally, the flux and flux component uncertain-ties are analyzed both at the annual and daily levels. The parameter posterior densities obtained provide information regarding importance of the different processes, which is also useful for development of wetland methane emission models other than the square root HelsinkI Model of MEthane buiLd- up and emIssion for peatlands (sqHIMMELI). The hierarchical modeling allows us to assess the effects of some of the parameters on an annual basis. The results of the calibration and the cross validation suggest that the early spring net primary production could be used to predict parameters affecting the annual methane production. Even though the calibration is specific to the Siikaneva site, the hierarchical modeling approach is well suited for larger-scale studies and the results of the estimation pave way for a regional or global- scale Bayesian calibration of wetland emission models.Peer reviewe

Evaluating atmospheric methane inversion model results for Pallas, northern Finland

A state-of-the-art inverse model, CarbonTracker Data Assimilation Shell (CTDAS), was used to optimize estimates of methane (CH4) surface fluxes using atmospheric observations of CH4 as a constraint. The model consists of the latest version of the TM5 atmospheric chemistry-transport model and an ensemble Kalman filter based data assimilation system. The model was constrained by atmospheric methane surface concentrations, obtained from the World Data Centre for Greenhouse Gases (WDCGG). Prior methane emissions were specified for five sources: biosphere, anthropogenic, fire, termites and ocean, of which bio-sphere and anthropogenic emissions were optimized. Atmospheric CH 4 mole fractions for 2007 from northern Finland calculated from prior and optimized emissions were compared with observations. It was found that the root mean squared errors of the posterior esti - mates were more than halved. Furthermore, inclusion of NOAA observations of CH 4 from weekly discrete air samples collected at Pallas improved agreement between posterior CH 4 mole fraction estimates and continuous observations, and resulted in reducing optimized biosphere emissions and their uncertainties in northern Finland

CH4_4 Fluxes Derived from Assimilation of TROPOMI XCH4_4 in CarbonTracker Europe-CH4_4: Evaluation of Seasonality and Spatial Distribution in the Northern High Latitudes

Recent advances in satellite observations of methane provide increased opportunities for inverse modeling. However, challenges exist in the satellite observation optimization and retrievals for high latitudes. In this study, we examine possibilities and challenges in the use of the total column averaged dry-air mole fractions of methane (XCH$_4$) data over land from the TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel 5 Precursor satellite in the estimation of CH4 fluxes using the CarbonTracker Europe-CH$_4$ (CTE-CH$_4$) atmospheric inverse model. We carry out simulations assimilating two retrieval products: Netherlands Institute for Space Research’s (SRON) operational and University of Bremen’s Weighting Function Modified Differential Optical Absorption Spectroscopy (WFM-DOAS). For comparison, we also carry out a simulation assimilating the ground-based surface data. Our results show smaller regional emissions in the TROPOMI inversions compared to the prior and surface inversion, although they are roughly within the range of the previous studies. The wetland emissions in summer and anthropogenic emissions in spring are lesser. The inversion results based on the two satellite datasets show many similarities in terms of spatial distribution and time series but also clear differences, especially in Canada, where CH$_4$ emission maximum is later, when the SRON’s operational data are assimilated. The TROPOMI inversions show higher CH$_4$ emissions from oil and gas production and coal mining from Russia and Kazakhstan. The location of hotspots in the TROPOMI inversions did not change compared to the prior, but all inversions indicated spatially more homogeneous high wetland emissions in northern Fennoscandia. In addition, we find that the regional monthly wetland emissions in the TROPOMI inversions do not correlate with the anthropogenic emissions as strongly as those in the surface inversion. The uncertainty estimates in the TROPOMI inversions are more homogeneous in space, and the regional uncertainties are comparable to the surface inversion. This indicates the potential of the TROPOMI data to better separately estimate wetland and anthropogenic emissions, as well as constrain spatial distributions. This study emphasizes the importance of quantifying and taking into account the model and retrieval uncertainties in regional levels in order to improve and derive more robust emission estimates

HIMMELI v1.0 : HelsinkI Model of MEthane buiLd-up and emIssion for peatlands

Wetlands are one of the most significant natural sources of methane (CH4) to the atmosphere. They emit CH4 because decomposition of soil organic matter in waterlogged anoxic conditions produces CH4, in addition to carbon dioxide (CO2). Production of CH4 and how much of it escapes to the atmosphere depend on a multitude of environmental drivers. Models simulating the processes leading to CH4 emissions are thus needed for upscaling observations to estimate present CH4 emissions and for producing scenarios of future atmospheric CH4 concentrations. Aiming at a CH4 model that can be added to models describing peatland carbon cycling, we composed a model called HIMMELI that describes CH4 build-up in and emissions from peatland soils. It is not a full peatland carbon cycle model but it requires the rate of anoxic soil respiration as input. Driven by soil temperature, leaf area index (LAI) of aerenchymatous peat-land vegetation, and water table depth (WTD), it simulates the concentrations and transport of CH4, CO2, and oxygen (O-2) in a layered one-dimensional peat column. Here, we present the HIMMELI model structure and results of tests on the model sensitivity to the input data and to the description of the peat column (peat depth and layer thickness), and demonstrate that HIMMELI outputs realistic fluxes by comparing modeled and measured fluxes at two peatland sites. As HIMMELI describes only the CH4-related processes, not the full carbon cycle, our analysis revealed mechanisms and dependencies that may remain hidden when testing CH4 models connected to complete peatland carbon models, which is usually the case. Our results indicated that (1) the model is flexible and robust and thus suitable for different environments; (2) the simulated CH4 emissions largely depend on the prescribed rate of anoxic respiration; (3) the sensitivity of the total CH4 emission to other input variables is mainly mediated via the concentrations of dissolved gases, in particular, the O-2 concentrations that affect the CH4 production and oxidation rates; (4) with given input respiration, the peat column description does not significantly affect the simulated CH4 emissions in this model version.Peer reviewe

Global Atmospheric δ13CH4 and CH4 Trends for 2000–2020 from the Atmospheric Transport Model TM5 Using CH4 from Carbon Tracker Europe–CH4 Inversions

This study investigates atmospheric δ13CH4 trends, as produced by a global atmospheric transport model using CH4 inversions from CarbonTracker-Europe CH4 for 2000–2020, and compares them to observations. The CH4 inversions include the grouping of the emissions both by δ13CH4 isotopic signatures and process type to investigate the effect, and to estimate the CH4 magnitudes and model CH4 and δ13CH4 trends. In addition to inversion results, simulations of the global atmospheric transport model were performed with modified emissions. The estimated global CH4 trends for oil and gas were found to increase more than coal compared to the priors from 2000–2006 to 2007–2020. Estimated trends for coal emissions at 30∘ N–60∘ N are less than 50% of those from priors. Estimated global CH4 rice emissions trends are opposite to priors, with the largest contribution from the EQ to 60∘ N. The results of this study indicate that optimizing wetland emissions separately produces better agreement with the observed δ13CH4 trend than optimizing all biogenic emissions simultaneously. This study recommends optimizing separately biogenic emissions with similar isotopic signature to wetland emissions. In addition, this study suggests that fossil-based emissions were overestimated by 9% after 2012 and biogenic emissions are underestimated by 8% in the inversion using EDGAR v6.0 as priors

Cingulate cortex hypoperfusion predicts Alzheimer's disease in mild cognitive impairment

BACKGROUND: Mild cognitive impairment (MCI) was recently described as a heterogeneous group with a variety of clinical outcomes and high risk to develop Alzheimer's disease (AD). Regional cerebral blood flow (rCBF) as measured by single photon emission computed tomography (SPECT) was used to study the heterogeneity of MCI and to look for predictors of future development of AD. METHODS: rCBF was investigated in 54 MCI subjects using Tc-99m hexamethylpropyleneamine oxime (HMPAO). An automated analysis software (BRASS) was applied to analyze the relative blood flow (cerebellar ratios) of 24 cortical regions. After the baseline examination, the subjects were followed clinically for an average of two years. 17 subjects progressed to Alzheimer's disease (PMCI) and 37 subjects remained stable (SMCI). The baseline SPECT ratio values were compared between PMCI and SMCI. Receiver operating characteristic (ROC) analysis was applied for the discrimination of the two subgroups at baseline. RESULTS: The conversion rate of MCI to AD was 13.7% per year. PMCI had a significantly decreased rCBF in the left posterior cingulate cortex, as compared to SMCI. Left posterior cingulate rCBF ratios were entered into a logistic regression model for ROC curve calculation. The area under the ROC curve was 74%–76%, which indicates an acceptable discrimination between PMCI and SMCI at baseline. CONCLUSION: A reduced relative blood flow of the posterior cingulate gyrus could be found at least two years before the patients met the clinical diagnostic criteria of AD

Development of CarbonTracker Europe-CH4 – part 1 : system set-up and sensitivity analyses

CarbonTracker Europe-CH4 (CTE-CH4) inverse model versions 1.0 and 1.1 are presented. The model optimizes global surface methane emissions from biosphere and anthropogenic sources using an ensemble Kalman filter (EnKF) based optimization method, using the TM5 chemistry transport model as an observation operator, and assimilating global in-situ atmospheric methane mole fraction observations. In this study, we examine sensitivity of our CH4 emission estimates on the ensemble size, covariance matrix, prior estimates, observations to be assimilated, assimilation window length, convection scheme in TM5, and model structure in the emission estimates by performing CTE-CH4 with several set-ups. The analyses show that the model is sensitive to most of the parameters and inputs that were examined. Firstly, using a large enough ensemble size stabilises the results. Secondly, using an informative covariance matrix reduces uncertainty estimates. Thirdly, agreement with discrete observations became better when assimilating continuous observations. Finally, the posterior emissions were found sensitive to the choice of prior estimates, convection scheme and model structure, particularly to their spatial distribution. The distribution of posterior mole fractions derived from posterior emissions is consistent with the observations to the extent prescribed in the various covariance estimates, indicating a satisfactory performance of our system.peer-reviewe