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Carbon stored in soils represents the largest terrestrial carbon pool and factors affecting this will be vital in the understanding of future atmospheric CO2 concentrations. This book provides an integrated view on measuring and modelling soil carbon dynamics.

An improved identification of the environmental variables that can be used to predict the content of soil organic carbon (SOC) stored belowground is required to reduce uncertainties in estimating the response of the largest terrestrial carbon reservoir to environmental change. Recent studies indicate that some metal cations can have an active role in the stabilization of SOC, primarily by coordinating the interaction between soil minerals and organic matter through cation bridging and by creating complexes with organic molecules when their hydration shells are displaced. The effective cation exchange capacity (CEC eff.) is a measure that integrates information about available soil surfaces to which metal cations are retained. Therefore, we critically tested the relationship between CEC eff. and SOC content using regression analyses for more than 1000 forest sites across Switzerland, spanning a unique gradient of mean annual precipitation (640–2500 mm), elevation (277–2207 m a.s.l), pH (2.8–8.1) and covering different geologies and vegetation types. Within these sites, SOC content is significantly related to CEC eff., in both topsoils and subsoils. Our results demonstrate that, on a pH-class average, in Swiss forest topsoils (<30 cm depth) there is a strong confounding effect of soil organic matter contributing between 35 and 50% to the total CEC eff. In subsoils, soil organic matter has a negligible contribution to CEC eff., and the variation of CEC eff. is associated to the presence of inorganic surfaces such as clay content as well as iron- and aluminum- oxides and hydroxides. At pH > 5.5, between 59 and 83% of subsoil CEC eff. originates from exchangeable calcium, whereas in acidic soils exchangeable aluminum contributes between 21 and 44% of the CEC eff. Exchangeable iron contributes to less than 1% of the variability in CEC eff. Overall this study indicates that in Swiss forests subsoils, CEC eff. strongly reflects the surface of soil minerals to which SOC can be bound by metal cations. The strength of the relationship between CEC eff. and SOC content depends on the pH of the soil, with the highest amount of variation of SOC content explained by CEC eff. in subsoils with pH > 5.5.

Today, plastic materials are mostly made from fossil resources, and they are characterized by their long lifetime and pronounced persistence in the open environment. These attributes of plastics are one cause of the ubiquitous pollution we see in our environment. When plastics end up in the environment, most of this pollution can be attributed to a lack of infrastructure for appropriately collecting and recycling plastic waste, mainly due to mismanagement. Because of the huge production volumes of plastics, their merits of being cheap to produce and process and their recalcitrance have turned into a huge disadvantage, since plastic waste has become the end point of our linear economic usage model, and massive amounts have started to accumulate in the environment, leading to microplastics pollution and other detrimental effects. A possible solution to this is offered by “bioplastics”, which are materials that are either (partly) biobased and/or degradable under defined conditions. With the rise of bioplastics in the marketplace, several standards and test protocols have been developed to assess, certify, and advertise their properties in this respect. This article summarizes and critically discusses different views on bioplastics, mainly related to the properties of biodegradability and biobased carbon content; this shall allow us to find a common ground for clearly addressing and categorizing bioplastic materials, which could become an essential building block in a circular economy. Today, bioplastics account for only 1–2% of all plastics, while technically, they could replace up to 90% of all fossil-based plastics, particularly in short-lived goods and packaging, the single most important area of use for conventional plastics. Their replacement potential not only applies to thermoplastics but also to thermosets and elastomers. Bioplastics can be recycled through different means, and they can be made from renewable sources, with (bio)degradability being an option for the mismanaged fraction and special applications with an intended end of life in nature (such as in seed coatings and bite protection for trees). Bioplastics can be used in composites and differ in their properties, similarly to conventional plastics. Clear definitions for “biobased” and “biodegradable” are needed to allow stakeholders of (bio)plastics to make fact-based decisions regarding material selection, application, and end-of-life options; the same level of clarity is needed for terms like “renewable carbon” and “bio-attributed” carbon, definitions of which are summarized and discussed in this paper.

Aims Lakeshore wetlands are global carbon (C) hotspots, but their role in C sequestration has been largely overlooked. The rhizosphere has a complex interaction of microbiota and metabolites, which plays an important role in wetland C cycling. This study aims to understand how the rhizospheric interactions affect harvested aboveground C and soil C of lakeshore wetlands in a subtropical region. Methods An investigation of five lakeshore reed ( Phragmites australis ) wetlands at the similar latitudes of the Lower Yangtse Valley in China was carried out to explore the relationship of rhizospheric interactions with harvested aboveground C and soil C. The plant traits and soil physicochemical properties were determined due to their important role in affecting rhizosphere interactions. Results Plant traits and soil physicochemical properties significantly differed among the sites, while aboveground C fixation did not significantly differ. The soil organic C (SOC) content of the topsoil was accounting for the majority of the soil total C at most sites, except for the wetland at the Yangtze River estuary with higher soil pH and conductivity, whose soil inorganic C (SIC) accounted for almost half. Bacterial community and metabolite composition were significantly partitioned across the region. Structural equation modeling revealed the rhizospheric interactions positively affected aboveground C and SOC, but negatively affected SIC. Their effects on soil C content were stronger than those on aboveground C fixation. Conclusions The rhizosphere exhibited the direct and indirect effects on harvested aboveground C and soil C by altering microbial community structure and metabolite composition.

Assessing carbon stocks in tropical forests is crucial for understanding their role in mitigating climate change. Researchers have previously underestimated key factors contributing to carbon dynamics in tropical forests. This study aims to address this knowledge gap. This study collected soil samples and made physical measurements of buttressed, control, and non-buttressed trees in a tropical forest from 2020 to 2022. Our findings reveal that a significant proportion of trees (69.57%) had 3 to 5 buttress roots per tree. The total average biomass of the buttress roots and the above-ground portion of the trees with buttress roots was calculated to be 8.5 tonnes/ha for buttress roots and 44.04 tonnes/ha for above-ground biomass. The buttress root biomass accounted for 16.18% of the total tree biomass. It was observed that the presence of buttress roots was associated with a higher soil organic carbon content by an average of 20.8% in the upslope areas with buttress roots regardless of the season. Tree species with buttress roots had on average 20% higher organic carbon content. The upslope area of trees with buttress roots had lower soil temperature and higher soil moisture when compared to the other sectors measured in the study. Regardless of the season, the soil respiration rate in the areas without buttress roots and the control areas was higher than in those with buttress roots. The presence of buttress roots positively affected soil nutrient concentration throughout the study period. This research shows that buttress roots play a crucial role in carbon storage. By integrating buttress roots into carbon accounting models, we can obtain more accurate estimates of carbon stock potential and develop more effective conservation and restoration strategies for tropical forests.

AimsThe aim of study was to quantify the temporal change of soil organic carbon content in relation to agricultural management for a dry sandy arable soil and to derive the C sequestration potential.MethodsWe analyzed data from a long-term field experiment with three crop rotations of different cereal proportions, with two levels of straw application (removal/return) in combination with four mineral nitrogen rates (40 … 160 kg ha-1 yr-1). Treatments are arranged in a two-factorial block design with two replicates for each rotation. During the 24-year study period, grain and straw yield of two cereal test crops and soil organic carbon content in topsoil were determined annually from each plot.ResultsSoil organic carbon content was positively influenced by removing non-cereal crops from the rotation and – to a smaller extent – by straw application. Increasing mineral N-fertilization from 40 kg ha-1 yr-1 to higher rates increased grain yield of rye but not barley, increased straw yield of both cereals more, with no effect of higher straw yields on soil organic carbon content.ConclusionsDespite the overall soil organic carbon content of the sandy soil under study is comparatively low, the results indicate that agricultural management has a relevant impact on soil carbon stocks. Straw return contributes to carbon sequestration even in rotations with a low potential for reproduction of organic matter. High mineral N-fertilization is not an adequate measure to sequester carbon in these soils.

Rapid and accurate measurement of the soil organic carbon (SOC) content is a pre-condition for sustainable grain production and land development, and contributes to carbon neutrality in the agricultural industry. To provide technical support for the development and utilization of land resources, the SOC content can be estimated using Vis-NIR diffuse reflectance spectroscopy. However, the spectral redundancy and co-linearity issues of Vis-NIR spectra pose extreme challenges for spectral analysis and model construction. This study compared the effects of different pre-processing methods and feature variable algorithms on the estimation of the SOC content. To this end, in situ hyperspectral data and soil samples were collected from the lakeside oasis of Bosten Lake in Xinjiang, China. The results showed that the combination of continuous wavelet transform (CWT)-random frog could rapidly estimate the SOC content with excellent estimation accuracy (R2 of 0.65–0.86). The feature variable selection algorithm effectively improved the estimation accuracy (average improvement of (0.30–0.48); based on their ability to improve model estimation on average, the algorithms can be ranked as follows: particle swarm optimization (PSO) > ant colony optimization (ACO) > random frog > Boruta > simulated annealing (SA) > successive projections algorithm (SPA). The CWT-XGBoost model based on random frog showed the best results, with R2 = 0.86, RMSE = 2.44, and RPD = 2.78. The feature bands accounted for only 0.57% of the Vis-NIR bands, and the most important sensitive bands were distributed at 755–1195 nm, 1602 nm, 1673 nm, and 2213 nm. These findings are of significance for the extraction of precise information on lakeside oases in arid areas, which would aid in achieving human–land sustainability.

Current calculation methods for the carbon content as received (Car) of coal rely on multiple instruments, leading to high costs for enterprises. There is a need for a cost-effective model that maintains accuracy in CO2 emission accounting. This study introduces an MISM model using key parameters identified through correlation and ablation analyses. An Improved State-Space Model (ISSM) and an IS-Mamba module are integrated into a Multi-Layer Perceptron (MLP) framework, enhancing information flow and regression accuracy. The MISM model demonstrates superior performance over traditional methods, reducing the Root Mean Square Error (RMSE) by 22.36% compared to MLP, and by 9.65% compared to Mamba. Using only six selected parameters, the MISM model achieves a precision of 0.27% for the discrepancy between the calculated CO2 emissions and the actual measurements. An ablation analysis confirms the importance of certain parameters and the effectiveness of the IS-Mamba module at improving model performance. This paper offers an innovative solution for accurate and cost-effective carbon accounting in the thermal power sector, supporting China’s carbon peaking and carbon neutrality goals.

The carbon content as received (Car) of coal is essential for the emission factor method in IPCC methodology. The traditional carbon measurement mechanism relies on detection equipment, resulting in significant detection costs. To reduce detection costs and provide precise predictions of Cars even in the absence of measurements, this paper proposes a neural network combining MLP with an attention mechanism (MSA-Net). In this model, the Attention Module is proposed to extract important and potential features. The Skip-Connections are utilized for feature reuse. The Huber loss is used to reduce the error between predicted Car values and actual values. The experimental results show that when the input includes eight measured parameters, the MAPE of MSA-Net is only 0.83%, which is better than the state-of-the-art Gaussian Process Regression (GPR) method. MSA-Net exhibits better predictive performance compared to MLP, RNN, LSTM, and Transformer. Moreover, this article provides two measurement solutions for thermal power enterprises to reduce detection costs.