The nanocrystalline minerals contents predominantly and positively controlled the sizes of intermediate and stable SOC pools, and the mean residence time of intermediate SOC pool. Biotic and climatic factors (i.e., microbial biomass, available N for microbes, and excess precipitation) controlled the labile SOC pool. Compared with topsoil, stabilized organic matters were more in the intermediate rather than in the stable SOC pool, and the temperature had a more significant effect on the stable SOC pool in subsoil than in topsoil. Available N for microbes partially controlled the labile and intermediate SOC pools in subsoil (more limited available N for microbes), but not in topsoil. Thus, subsoil SOC would be more sensitive to climate change than topsoil SOC. This study helped to understand the SOC stabilization mechanism and emphasized the high climate- and mineral-dependence of SOC in subsoil of tropical volcanic regions.The aim of this study was to investigate reported cases of honeybee mortality incidents and the potential association to pesticide exposure and to their metabolites. The same honeybee samples were also assessed for Varroa mites, and Nosema microsporidia provoked infections to provide an integrated picture of all observable stressors that may impact bees' survival. Thus, honeybee samples from different areas of Greece (2014-2018) were analyzed for the presence of pesticide residues and metabolites. In this context, an existing LC-ESI-QqQ-MS multiresidue method of analytes of different chemical classes such as neonicotinoids, organophosphates, triazoles, carbamates, was enriched with additional active substances, developed and validated. A complementary GC-EI-QqQ-MS method was also exploited for the same scope covering pyrethroid compounds. Both methods monitored more than 150 active substances and metabolites and presented acceptable linearity over the ranges assayed. The calculated recoveries ranged from 65 to 120% for the three concentration levels, while the precision (RSD%) values ranged between 4 and 15%. Therefore, this approach proved sufficient to act as a monitoring tool for the determination of pesticide residues in cases of suspected honeybee poisoning incidents. From the analysis of 320 samples, the presence of 70 active substances and metabolites was confirmed with concentrations varying from 1.4 ng/g to 166 μg/g. Predominant detections were the acaricide coumaphos, several neonicotinoids exemplified by clothianidin, organophosporous compounds dimethoate and chlorpyrifos, and some pyrethroids. Metabolites of imidacloprid, chlorpyrifos, coumaphos, acetamiprid, fenthion and amitraz were also identified. Concerning Nosema and Varroa they were identified in 27 and 22% of samples examined, respectively, verifying their prevalence and coexistence with pesticides and their metabolites in honeybees.The development of carbon-based materials to catalyze two-electron (2e-) pathway of oxygen reduction reaction (ORR) offers great potential for hydrogen peroxide (H2O2) production.  https://www.selleckchem.com/products/nik-smi1.html  As a class of novel two-dimensional (2D) carbon materials, graphene and its derivatives have raised increasing attention as excellent noble-metal-free catalysts in 2e ORR due to their unique structure, physical and chemical properties. This review focuses on the synthesis of main graphene family members and graphene based electrodes, as well as their applications for H2O2 generation in electrochemical systems. We describe the functions of the graphene family in electrochemical systems, such as accelerating electron transfer and increasing oxygen transfer for cathodes in electrochemical systems, aiming to reveal the enhancement mechanisms of graphene and its derivatives on H2O2 production. Furthermore, the challenges and prospects for graphene family used as catalyst for H2O2 production in the future are also proposed.Agricultural water demand, groundwater extraction, surface water delivery and climate have complex nonlinear relationships with groundwater storage in agricultural regions. As an alternative to elaborate computationally intensive physical models, machine learning methods are often adopted as surrogate to capture such complex relationships due to their high computational efficiency. Inevitably, using only one machine learning model is prone to underestimate prediction uncertainty and subjected to poor accuracy. This study presents a novel machine learning-based groundwater ensemble modeling framework in conjunction with a Bayesian model averaging approach to predict groundwater storage change and improve overall model predicting reliability. Three different machine learning models have been developed namely artificial neural network, support vector machine and response surface regression. To explicitly quantify uncertainty from machine learning model parameter and structure, Bayesian model averaging is employeg framework can serve as an alternative approach to simulating groundwater response, especially in those agricultural regions where lack of subsurface data hinders physically-based modeling.Marine shipping emissions exert important air quality and climate impacts. This study characterized the ambient pollutants predominant by emissions from a variety of marine vessel types near the mid-latitude East China Sea. Two discernible primary shipping emissions were identified by factorization analysis on detailed mass spectra of organic aerosol (OA), as emissions in maneuvering and cruise, highly linked with NOx (and less oxidized OA, black carbon, BC) or CO (and more oxidized OA), respectively. Using radio-recorded quantities and activities of 3566 vessels mixed with slow and high-speed diesel engines, we found emission of NOx or BC per vessel was positively correlated with vessel speed, while CO emission peaked at moderate speed. The approach here based on vessel operation mode directly linked the vessel activities to ambient concentrations of pollutants from marine shipping emission, and may synthesize the complex vessel types in shipping emission inventory.To reduce cadmium (Cd) pollution of food chains, screening and breeding of low-Cd-accumulating genotypes have received increasing attention. However, the mechanisms involving Cd tolerance and accumulation are not fully understood. Here, we investigated the physiological responses and metabolomics profiling on two wheat (Triticum aestivum L.) genotypes, a low-Cd-accumulating genotype in grains (Aikang58, AK58) and a high-Cd-accumulating genotype in grains (Zhenmai10, ZM10), in hydroponic culture treated without/with Cd for 7 days. The results showed that AK58 was a Cd tolerant genotype with higher capacity of antioxidant systems in root. In addition, the concentrations of Cd bound to root cell walls were higher in AK58 than ZM10, of which pectin and hemicellulose played important roles in Cd binding. Moreover, subcellular distribution manifested that Cd sequestrated in the vacuoles was another tolerance mechanism in AK58. Simultaneously, metabolomics profiling showed that, in AK58, phenylalanine metabolism, alanine, aspartate and glutamate metabolism, isoquinoline alkaloid biosynthesis, arginine and proline metabolism, arginine biosynthesis and glyoxylate and dicarboxylate metabolism are highly related to antioxidant defense system, cell wall biosynthesis and metabolisms of phytochelatins together with other organic ligands, playing crucial roles in Cd tolerance and Cd fixation mechanisms in roots.