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The reaction mechanism of Cit-nZVI@BC includes adsorption, reduction, and co-precipitation, whereas CMC-nZVI@BC also involves surface complexation reactions. The kinetic studies revealed that the removal of Cr(VI) by Cit-nZVI@BC and CMC-nZVI@BC followed the second-order reaction kinetic model, and the reaction rates of Cit-nZVI@BC and CMC-nZVI@BC were both higher than that of nZVI. The results indicate that the prepared systems are promising for Cr(VI) remediation in contaminated environments.Accurate prediction of the concentration of heavy metals is of great significance for assessing the quality of agricultural products and reducing health risks. However, the complexity and interconnectivity of the farmland ecosystem restricts the improvement of the prediction accuracy of traditional methods. This research explored the application potential of graph neural network (GNN) technology, which can extract and learn information in large-scale networks in detail, in the field of heavy metal prediction for the first time. In this study, a heavy metal prediction model for rice, CoNet-GNN, was proposed with 17 environmental factors as input variables using the co-occurrence network and GNN. Experimental results using a dataset from a field study showed that the R2 of CoNet-GNN for predicting Cd, Pb, Cr, As, and Hg had outstanding values of 0.872, 0.711, 0.683, 0.489, and 0.824, respectively. Sensitivity analysis further indicated that CoNet-GNN had good stability and robustness. Compared with random forest, gradient boosting, and multilayer perceptron, CoNet-GNN made a remarkable improvement to the prediction accuracy of all studied heavy metals. Therefore, CoNet-GNN can effectively simulate the rich relationships and laws between various factors in the soil-rice system and effectively characterize the influence diffusion path. Furthermore, it provides new ideas for heavy metal prediction based on network research methods and expands the technical scope of heavy metal evaluation.Herbicides are used extensively in Australian grain cropping systems. Despite occasional observations of herbicide-induced phytotoxicity, there is little information on the persistence and carryover of multiple herbicide classes in cropping soils and the risk to subsequent crops. Two soil surveys were conducted, in 2015 (n = 40) and 2016 (n = 42), across different Australian grain cropping fields prior to sowing of winter crops, and soil samples analysed for herbicide residues (16 analytes in 2015 and 22 analytes in 2016). Samples in 2015 were taken at two depths (0-10 cm and 10-30 cm), whilst samples in 2016 were taken in topsoil (0-10 cm) only, but from two discrete locations in each field. Our research in both years found at least one herbicide (or herbicide metabolite) residue at all sites, with a median of 6 analytes detected in 2015 and 7 analytes detected in 2016. selleck chemicals The most frequently detected residues were glyphosate and its primary breakdown product aminomethylphosphonic acid (AMPA), in 87 and 100%, re spatial distribution, bioavailability and phytotoxicity of residues and residue mixtures to enable a more accurate agronomic risk assessment.Hydrodynamic regulation is widely used to improve the water quality of urban rivers. However, it is yet to explore substantially whether hydrodynamics could regulate the metabolic activity of biofilm in such aquatic systems. Herein, the pilot experiment of hydrodynamics in the rotation tanks was designed, including two experiment phases, namely constant flow and adjusting flow for 21 days and 14 days, respectively. In constant flow phase, biofilms grew in five shear stress gradients (R1-R5, 0.0044- 0.12 Pa). The carbon metabolic rate (k) of mature biofilms evaluated by BIOLOG ECO microplates showed a hump-shaped relationship with increasing shear stress, with R3 (0.049 Pa) the highest, while R5 (0.12 Pa) the lowest. To verify whether the metabolic activity of biofilm cultured at constant flow phase can be regulated by shear stress, we initiated the adjusting flow phase, and shear stress in reactors was reset uniformly at 0.049 Pa (with the highest k). Results showed the carbon metabolic activity of biofilm in reactor R4 and R5 increased rapidly by day 3, and there was no significant difference between the carbon metabolic rates among the five treatments by day 14. Meanwhile, the utilization levels of polymers and carbohydrates by biofilms were significantly different among the five treatments after hydrodynamic regulations. These results suggested that the total carbon metabolic activity of biofilm can be regulated by hydrodynamics, while the divergent changes of the specific carbon source category might affect the biofilm-mediated carbon biogeochemical processes, which should be considered for the application of hydrodynamic regulation in river ecological restoration projects.Pioneering investigations on the effects of introduced populations on community structure, ecosystem functioning and services have focused on the effects of invaders on taxonomic diversity. However, taxonomic-based diversity metrics overlook the heterogeneity of species roles within and among communities. As the homogenizing effects of biological invasions on community and ecosystem processes can be subtle, they may require the use of functional diversity indices to be properly evidenced. Starting from the listing of major functional diversity indices, alongside the presentation of their strengths and limitations, we focus on studies pertaining to the effects of invasive species on native communities and recipient ecosystems using functional diversity indices. By doing so, we reveal that functional diversity of the recipient community may strongly vary at the onset of the invasion process, while it stabilizes at intermediate and high levels of invasion. As functional changes occurring during the lag phase of an invasion have been poorly investigated, we show that it is still unknown whether there are consistent changes in functional diversity metrics that could indicate the end of the lag phase. Thus, we recommend providing information on the invasion stage under consideration when computing functional diversity metrics. For the existing literature, it is also surprising that very few studies explored the functional difference between organisms from the recipient communities and invaders of the same trophic levels, or assessed the effects of non-native organism establishment into a non-analogue versus an analogue community. By providing valuable tools for obtaining in-depth diagnostics of community structure and functioning, functional diversity indices can be applied for timely implementation of restoration plans and improved conservation strategies. To conclude, our work provides a first synthetic guide for their use in hypothesis testing in invasion biology.A high-resolution soil moisture prediction method has recently gained its importance in various fields such as forestry, agricultural and land management. However, accurate, robust and non- cost prohibitive spatially monitoring of soil moisture is challenging. In this research, a new approach involving the use of advance machine learning (ML) models, and multi-sensor data fusion including Sentinel-1(S1) C-band dual polarimetric synthetic aperture radar (SAR), Sentinel-2 (S2) multispectral data, and ALOS Global Digital Surface Model (ALOS DSM) to predict precisely soil moisture at 10 m spatial resolution across research areas in Australia. The total of 52 predictor variables generated from S1, S2 and ALOS DSM data fusion, including vegetation indices, soil indices, water index, SAR transformation indices, ALOS DSM derived indices like digital model elevation (DEM), slope, and topographic wetness index (TWI). The field soil data from Western Australia was employed. The performance capability of extreme gradient boosting regression (XGBR) together with the genetic algorithm (GA) optimizer for features selection and optimization for soil moisture prediction in bare lands was examined and compared with various scenarios and ML models. The proposed model (the XGBR-GA model) with 21 optimal features obtained from GA was yielded the highest performance (R2 = 0. 891; RMSE = 0.875%) compared to random forest regression (RFR), support vector machine (SVM), and CatBoost gradient boosting regression (CBR). Conclusively, the new approach using the XGBR-GA with features from combination of reliable free-of-charge remotely sensed data from Sentinel and ALOS imagery can effectively estimate the spatial variability of soil moisture. The described framework can further support precision agriculture and drought resilience programs via water use efficiency and smart irrigation management for crop production.Sludge alkaline fermentation liquid (SAFL) is an alternative to sodium acetate (NaAc) in enhancing wastewater nitrogen removal. Upon SAFL addition, dissolved organic nitrogen (DON) can be externally introduced or biologically synthesized during nitrogen removal, which is an important precursor to toxic nitrogenous disinfection by-products (N-DBPs). This study aims to evaluate the effects of different carbon source addition on effluent DON concentration, composition, and N-DBP formation potentials. A lab-scale A2O system treating real municipal wastewater was operated with NaAc or SAFL as external carbon sources. DON molecules and potential N-DBP precursors were identified by Orbitrap mass spectrometry. Subsequently, major microorganisms contributing to DON biosynthesis were suggested based on metagenomics. It was found that effluent DON was higher with SAFL as the carbon source than NaAc (1.51 ± 0.24 v.s. 0.56 ± 0.08 mg N/L, p less then 0.05). Nevertheless, dichloroacetonitrile and nitrosamine formation potentials (7.14 ± 1.02 and 1.57 ± 0.07 μg/mg DON-N, respectively) of the effluent with SAFL addition were 42.79 ± 2.42% and 54.89 ± 1.70% lower than those of NaAc. Protein- and lignin-like compounds were the most abundant DON molecules in the effluent, where alanine, glycine and tyrosine were important precursors to N-DBPs. Azonexus and Flavobacterium spp. were positively correlated with these precursors, and possessed key genes involved in precursor synthesis. SAFL is a promising carbon source, not only for achieving efficient inorganic nitrogen and DON removals, but also for reducing N-DBP formation potentials of chlorinated effluent.The urbanization of Tibetan Plateau (TP) probably results in a significant contamination of organic pollutants in the area, such as phthalate esters (PAEs). However, there is a lack of monitoring and evaluation of their occurrence and risks in the outdoor dust on TP. This study for the first time investigated the concentrations, distributions and health risk of PAEs in outdoor dust samples on TP, China. A total of 132 outdoor dust samples were collected from five different functional areas, and results showed the ubiquitous detection of all PAEs in the samples. The Σ6PAEs concentrations ranged from 0.08 to 31.49 μg·g-1 with a mean of 3.57 μg·g-1. High concentrations of Σ6PAEs in the outdoor dust were found in commercial districts, which were attributed to the heavy use of PAEs in the public commerce such as consumer products, commodities, and building materials. Di-n-butyl phthalate (DBP) and bis(2-ethylhexyl) phthalate (DEHP) were the dominant components accounting for 30.65% and 53.19% of the Σ6PAEs. Principal component analysis, positive matrix factorization, and correlation analysis were used to apportion the potential sources of PAEs in outdoor dust samples.