Amendment of straw biochar increased molecular diversity and enhanced preservation of plant derived organic matter in extracted fractions of a rice paddy

Modeling suction of unsaturated granular soil treated with biochar in plant microbial fuel cell bioelectricity system

There is an initiative driven by the carbon-neutrality nature of biochar in recent times, where various countries across Europe and North America have introduced perks to encourage the production of biochar for construction purposes. This objective aligns with the zero greenhouse emission targets set by COP27 for 2050. This research work seeks to assess the effectiveness of biochar in soils with varying grain size distributions in enhancing the soil-water characteristic curve (SWCC). This work further explores the effect of different combinations of biochar content (0 to 15 mass %) on the bioelectricity generation from biochar-improved plant microbial fuel cells (BPMFC). Additionally, different machine learning models such as the "Gradient Boosting (GB)", "CN2 Rule Induction (CN2)", "Naive Bayes (NB)", "Support vector machine (SVM), "Stochastic Gradient Descent (SGD)", "K-Nearest Neighbors (KNN)", "Tree Decision (Tree)", "Random Forest (RF)", and "Response Surface Methodology" (RSM), have been developed to predict SWCC based on soil suction, electric current, electrical potential, volumetric water content, temperature, and bulk density. The newly established model demonstrates a reasonable ability to predict SWCC and a cheaper technology in predicting the suction of unsaturated soils in relation to the studied bioelectric factors of the BPMFC. Overall, in this research paper, the GB, SVM and CN2 outclassed the other regression techniques in this order thereby proposing the cheapest technology with the highest performance index to predict the SWCC behavior of unsaturated soils in a BPMFC system.

Dissolved organic matter evolution and straw decomposition rate characterization under different water and fertilizer conditions based on three-dimensional fluorescence spectrum and deep learning

Straw returning is a sustainable way to utilize agricultural solid waste resources. However, incomplete decomposition of straw will cause harm to crop growth and soil quality. Currently, there is a lack of technology to timely monitor the rate of straw decomposition. Dissolved organic matter (DOM) is the most active organic matter in soil and straw is mainly immersed in the soil in the form of DOM. In order to formulate reasonable straw returning management measures , a timely monitoring method of straw decomposition rate was developed in the study. Three water treatment (60%-65%, 70%-75% and 80%-85% maximum field capacity) and two fertilizer (organic fertilizer and chemical fertilizer) were set up in the management of straw returning to the field. Litterbag method was used to monitor the weight loss rate of straw decomposition under different water and fertilizer conditions in strawberry growth stage. The changes of DOM components were determined by three-dimensional fluorescence spectroscopy (3D-EEM). From the faster decomposition period to the slower decomposition period, the main components of DOM changed from protein-like components to humus-like components. At the end of the experiment, the relative content of humus-like components under the treatment of organic fertilizer and moderate water was the highest. Convolutional neural network (CNN) combined with 3D-EEM was used to identify the decomposition speed of straw. The classification precision of neural network validation set and test are 85.7% and 81.2%, respectively. In order to predict the decomposition rate of straw under different water and fertilizer conditions, 3D-EEM data of DOM were used as the input of CNN, parallel factor analysis (PARAFAC) and fluorescence region integral (FRI), and dissolved organic carbon data were used as the input of dissolved organic carbon linear prediction. The prediction model based on CNN had the best effect (R2 = 0.987). The results show that this method can effectively identify the spectral characteristics and predict the decomposition rate of straw under different conditions of water and fertilizer, which is helpful to promote the efficient decomposition of straw.

Remediation of copper and lead contaminated sediments using iron-based granule biochar: mechanisms and enzyme activity

In recent years, there has been a growing concern about heavy metal contamination in sediments. In this study, iron-based granular biochar (MGB) is prepared to remediate Cu and Pb contaminated sediments. Characterizations via scanning electron microscopy (SEM) and Fourier transform infrared spectroscopy (FTIR) show that the rough surface of MGB with abundant pore structures and a large number of oxygen-containing functional groups that facilitate stabilization of Cu and Pb in sediments. Potential mobility and bioavailability of Cu and Pb are investigated using BCR sequential extraction in the 35 day remediation experiment. The XPS results indicate that FeOOH and C-OH play a crucial part in stabilizing heavy metals. Large affinity of FeOOH for Pb allows it to occupy a proportion in F₂ while C-OH is attractive to Cu. Changes of pH, organic matter (OM), and available phosphorus (AP) in sediments after adding MGB as well as the relationship between changes and the stable solidification of Cu and Pb are explored. The stable solidification of heavy metals effectively reduces the available phosphorus in sediments. Magnetic and particle properties of the material are used to reduce the impact of MGB aging on sediment environment and separate it from the remediated sediment. Finally, 3% of MGB significantly enhanced the sediment catalase activity in the biological enzyme activity experiment. All findings indicate that MGB is a green and environmentally friendly sediment remediation material with satisfactory potential in synergistically stabilizing heavy metals and phosphorus.Highlights The complexation of FeOOH with Pb on the surface of MGB fixes it to the reduced stateThe C-OH on the surface of MGB is more attractive to Cu than PbMGB effectively mitigates the release of bioavailable phosphorus from sediments to overlying water.

Characterization of Different Molecular Size Fractions of Glomalin-Related Soil Protein From Forest Soil and Their Interaction With Phenanthrene

As a natural organic compound secreted by arbuscular mycorrhizal fungi (AMF), glomalin-related soil protein (GRSP) is an important part in soil, affecting the bioavailability of polycyclic aromatic hydrocarbons (PAHs) in it. Previous research have demonstrated that GRSP could enhance the availability of PAHs in the soil and favor their accumulation in plant roots. However, a scarcity of research exists on the different molecular weights of GRSP interacting with PAHs due to their complexation and heterogeneity. In this research, the extracted GRSP in soil was divided into three molecular weight (Mw) fractions of GRSP (<3,000, 3,000-10,000, and >10,000 Da), whose characteristics and binding capacity of PAHs were conducted by using UV-visible absorption, quenching fluorometry and, Fourier transform infrared spectroscopy. The results showed that the GRSP was composed of abundant compounds, it has a wide distribution of molecular weight, and the >10,000 Da Mw fraction was dominant. For three Mw fractions of GRSP, they have some difference in spectral features, for example, the >10,000 Da fraction showed higher dissolved organic carbon (DOC) contents, more phenolic hydroxyl groups, and stronger UV adsorption capacity than the low and middle Mw fractions. In addition, the interaction between GRSP and phenanthrene is related to the characteristics of the Mw fractions, especially the phenolic hydroxyl group, which has a significantly positive correlation with a binding coefficient of K A (k = 0.992, p < 0.01). Simultaneously, hydrophobic, NH-π, and H-bound also played roles in the complexation of phenanthrene with GRSP. These findings suggested that different GRSP Mw fractions could influence the fate, availability, and toxicity of PAHs in soil by their interaction.