Bayesian calibration of a soil organic carbon model using Δ¹⁴C measurements of soil organic carbon and heterotrophic respiration as joint constraints

Soils of temperate forests store significant amounts of organic matter and are considered to be net sinks of atmospheric CO₂. Soil organic carbon (SOC) turnover has been studied using the Δ¹⁴C values of bulk SOC or different SOC fractions as observational constraints in SOC models. Further, the Δ¹⁴C values of CO₂ that evolved during the incubation of soil and roots have been widely used together with Δ¹⁴C of total soil respiration to partition soil respiration into heterotrophic respiration (HR) and rhizosphere respiration. However, these data have not been used as joint observational constraints to determine SOC turnover times. Thus, we focus on (1) how different combinations of observational constraints help to narrow estimates of turnover times and other parameters of a simple two-pool model, the Introductory Carbon Balance Model (ICBM); (2) whether relaxing the steady-state assumption in a multiple constraints approach allows the source/sink strength of the soil to be determined while estimating turnover times at the same time. To this end ICBM was adapted to model SOC and SO¹⁴C in parallel with litterfall and the Δ¹⁴C of litterfall as driving variables. The Δ¹⁴C of the atmosphere with its prominent bomb peak was used as a proxy for the Δ¹⁴C of litterfall. Data from three spruce-dominated temperate forests in Germany and the USA (Coulissenhieb II, Solling D0 and Howland Tower site) were used to estimate the parameters of ICBM via Bayesian calibration. Key findings are as follows: (1) the joint use of all four observational constraints (SOC stock and its Δ¹⁴C, HR flux and its Δ¹⁴C) helped to considerably narrow turnover times of the young pool (primarily by Δ¹⁴C of HR) and the old pool (primarily by Δ¹⁴C of SOC). Furthermore, the joint use of all observational constraints made it possible to constrain the humification factor in ICBM, which describes the fraction of the annual outflux from the young pool that enters the old pool. The Bayesian parameter estimation yielded the following turnover times (mean ± standard deviation) for SOC in the young pool: Coulissenhieb II 1.1 ± 0.5 years, Solling D0 5.7 ± 0.8 years and Howland Tower 0.8 ± 0.4 years. Turnover times for the old pool were 377 ± 61 years (Coulissenhieb II), 313 ± 66 years (Solling D0) and 184 ± 42 years (Howland Tower), respectively. (2) At all three sites the multiple constraints approach was not able to determine if the soil has been losing or storing carbon. Nevertheless, the relaxed steady-state assumption hardly introduced any additional uncertainty for the other parameter estimates. Overall the results suggest that using Δ¹⁴C data from more than one carbon pool or flux helps to better constrain SOC models

Reviewing the Carbonation Resistance of Concrete

The paper reviews the studies on one of the important durability properties of concrete i.e. Carbonation. One of the main causes of deterioration of concrete is carbonation, which occurs when carbon dioxide (CO2) penetrates the concrete’s porous system to create an environment with lower pH around the reinforcement in which corrosion can proceed. Carbonation is a major cause of degradation of concrete structures leading to expensive maintenance and conservation operations. Herein, the importance, process and effect of various parameters such as water/cement ratio, water/binder ratio, curing conditions, concrete cover, super plasticizers, type of aggregates, grade of concrete, porosity, contaminants, compaction, gas permeability, supplementary cementitious materials (SCMs)/ admixtures on the carbonation of concrete has been reviewed. Various methods for estimating the carbonation depth are also reported briefl

Relationship between ecosystem productivity and photosynthetically-active radiation for northern peatlands

We analyzed the relationship between net ecosystem exchange of carbon dioxide (NEE) and irradiance (as photosynthetic photon flux density or PPFD), using published and unpublished data that have been collected during midgrowing season for carbon balance studies at seven peatlands in North America and Europe. NEE measurements included both eddy-correlation tower and clear, static chamber methods, which gave very similar results. Data were analyzed by site, as aggregated data sets by peatland type (bog, poor fen, rich fen, and all fens) and as a single aggregated data set for all peatlands. In all cases, a fit with a rectangular hyperbola (NEE = α PPFD Pmax/(α PPFD + Pmax) + R) better described the NEE-PPFD relationship than did a linear fit (NEE = β PPFD + R). Poor and rich fens generally had similar NEE-PPFD relationships, while bogs had lower respiration rates (R = −2.0μmol m−2s−1 for bogs and −2.7 μmol m−2s−1 for fens) and lower NEE at moderate and high light levels (Pmax = 5.2 μmol m−2s−1 for bogs and 10.8 μmol m−2s−1 for fens). As a single class, northern peatlands had much smaller ecosystem respiration (R = −2.4 μmol m−2s−1) and NEE rates (α = 0.020 and Pmax = 9.2μmol m−2s−1) than the upland ecosystems (closed canopy forest, grassland, and cropland) summarized by Ruimy et al. [1995]. Despite this low productivity, northern peatland soil carbon pools are generally 5–50 times larger than upland ecosystems because of slow rates of decomposition caused by litter quality and anaerobic, cold soils

Fine-root turnover rates of European forests revisited: an analysis of data from sequential coring and ingrowth cores

Background and Aims Forest trees directly contribute to carbon cycling in forest soils through the turnover of their fine roots. In this study we aimed to calculate root turnover rates of common European forest tree species and to compare them with most frequently published values. Methods We compiled available European data and applied various turnover rate calculation methods to the resulting database. We used Decision Matrix and Maximum-Minimum formula as suggested in the literature. Results Mean turnover rates obtained by the combination of sequential coring and Decision Matrix were 0.86 yr−1 for Fagus sylvatica and 0.88 yr−1 for Picea abies when maximum biomass data were used for the calculation, and 1.11 yr−1 for both species when mean biomass data were used. Using mean biomass rather than maximum resulted in about 30 % higher values of root turnover. Using the Decision Matrix to calculate turnover rate doubled the rates when compared to the Maximum-Minimum formula. The Decision Matrix, however, makes use of more input information than the Maximum-Minimum formula. Conclusions We propose that calculations using the Decision Matrix with mean biomass give the most reliable estimates of root turnover rates in European forests and should preferentially be used in models and C reporting

The C:N:P:S stoichiometry of soil organic matter

The formation and turnover of soil organic matter (SOM) includes the biogeochemical processing of the macronutrient elements nitrogen (N), phosphorus (P) and sulphur (S), which alters their stoichiometric relationships to carbon (C) and to each other. We sought patterns among soil organic C, N, P and S in data for c. 2000 globally distributed soil samples, covering all soil horizons. For non-peat soils, strong negative correlations (p < 0.001) were found between N:C, P:C and S:C ratios and % organic carbon (OC), showing that SOM of soils with low OC concentrations (high in mineral matter) is rich in N, P and S. The results can be described approximately with a simple mixing model in which nutrient-poor SOM (NPSOM) has N:C, P:C and S:C ratios of 0.039, 0.0011 and 0.0054, while nutrient-rich SOM (NRSOM) has corresponding ratios of 0.12, 0.016 and 0.016, so that P is especially enriched in NRSOM compared to NPSOM. The trends hold across a range of ecosystems, for topsoils, including O horizons, and subsoils, and across different soil classes. The major exception is that tropical soils tend to have low P:C ratios especially at low N:C. We suggest that NRSOM comprises compounds selected by their strong adsorption to mineral matter. The stoichiometric patterns established here offer a new quantitative framework for SOM classification and characterisation, and provide important constraints to dynamic soil and ecosystem models of carbon turnover and nutrient dynamics

Single-cell analysis: visualizing pharmaceutical and metabolite uptake in cells with label-free 3D mass spectrometry imaging

Detecting metabolites and parent compound within a cell type is now a priority for pharmaceutical development. In this context, three-dimensional secondary ion mass spectrometry (SIMS) imaging was used to investigate the cellular uptake of the antiarrhythmic agent amiodarone, a phospholipidosis-inducing pharmaceutical compound. The high lateral resolution and 3D imaging capabilities of SIMS combined with the multiplex capabilities of ToF mass spectrometric detection allows for the visualization of pharmaceutical compound and metabolites in single cells. The intact, unlabeled drug compound was successfully detected at therapeutic dosages in macrophages (cell line: NR8383). Chemical information from endogenous biomolecules was used to correlate drug distributions with morphological features. From this spatial analysis, amiodarone was detected throughout the cell with the majority of the compound found in the membrane and subsurface regions and absent in the nuclear regions. Similar results were obtained when the macrophages were doped with amiodarone metabolite, desethylamiodarone. The FWHM lateral resolution measured across an intracellular interface in a high lateral resolution ion images was approximately 550 nm. Overall, this approach provides the basis for studying cellular uptake of pharmaceutical compounds and their metabolites on the single cell level