Phillipsbeatty1537

Many areas around the world are affected by Groundwater Level rising (GWLr). One of the most severe consequences of this phenomenon is Groundwater Flooding (GF), with serious impacts for the human and natural environment. E-7386 inhibitor In Europe, GF has recently received specific attention with Directive 2007/60/EC, which requires Member States to map GF hazard and propose measures for risk mitigation. In this paper a methodology has been developed for Groundwater Flooding Susceptibility (GFS) assessment, using for the first time Spatial Distribution Models. These Machine Learning techniques connect occurrence data to predisposing factors (PFs) to estimate their distributions. The implemented methodology employs aquifer type, depth of piezometric level, thickness and hydraulic conductivity of unsaturated zone, drainage density and land-use as PFs, and a GF observations inventory as occurrences. The algorithms adopted to perform the analysis are Generalized Boosting Model, Artificial Neural Network and Maximum Entropy. Ensemble Models are carried out to reduce the uncertainty associated with each algorithm and increase its reliability. GFS is mapped by choosing the ensemble model with the best predictivity performance and dividing occurrence probability values into five classes, from very low to very high susceptibility, using Natural Breaks classification. The methodology has been tested and statistically validated in an area of 14,3 km2 located in the Metropolitan City of Naples (Italy), affected by GWLr since 1990 and GF in buildings and agricultural soils since 2007. The results of modeling show that about 93% of the inventoried points fall in the high and very high GFS classes, and piezometric level depth, thickness of unsaturated zone and drainage density are the most influencing PFs, in accordance with field observations and the triggering mechanism of GF. The outcomes provide a first step in the assessment of GF hazard and a decision support tool to local authorities for GF risk management.Pesticides make indispensable contributions to agricultural productivity. However, the residues after their excessive use may be harmful to crop production, food safety and human health. Although the ability of plants (especially crops) to accumulate and metabolize pesticides has been intensively investigated, data describing the chemical and metabolic processes in plants are limited. Understanding how pesticides are metabolized is a key step toward developing cleaner crops with minimal pesticides in crops, creating new green pesticides (or safeners), and building up the engineered plants for environmental remediation. In this review, we describe the recently discovered mechanistic insights into pesticide metabolic pathways, and development of improved plant genotypes that break down pesticides more effectively. We highlight the identification of biological features and functions of major pesticide-metabolized enzymes such as laccases, glycosyltransferases, methyltransferases and ATP binding cassette (ABC) transporters, and discuss their chemical reactions involved in diverse pathways including the formation of pesticide S-conjugates. The recent findings for some signal molecules (phytohomormes) like salicylic acid, jasmonic acid and brassinosteroids involved in metabolism and detoxification of pesticides are summarized. In particular, the emerging research on the epigenetic mechanisms such DNA methylation and histone modification for pesticide metabolism is emphasized. The review would broaden our understanding of the regulatory networks of the pesticide metabolic pathways in higher plants.The impacts of climate change on the water environment have aroused widespread concern. With global warming, mountainous basins are facing serious water supply situations. However, there are limited meteorological stations on mountains, which thus creates a challenge in terms of accurate simulation of streamflow and water resources. To solve this problem, this study developed a method to model streamflow in data-scarce mountainous basins. Selecting the two head waters originating in the Tienshan mountains, Aksu and Kaidu Rivers, we firstly reconstructed precipitation and temperature dynamics based on Earth system data products, and then integrated the radial basis function artificial neural network and complete ensemble empirical mode decomposition with adaptive noise to model streamflow. Comparison with the observed streamflow according to hydrological stations indicated that the proposed approach was highly accurate. The modeling results showed that the El-Niño Southern Oscillation, temperature, precipitation, and the North Atlantic Oscillation are the main factors driving streamflow, and the streamflow decreased in both the Aksu River and Kaidu River between 2000 and 2017.Eutrophication in freshwater environments may be enhanced by the elevation of sulfate in waters, through the release of internal phosphorus (P) from anoxic sediments. However, the influence of increasing but modest sulfate concentrations (less than 3000 μM) on P release under oxic conditions across the sediment-water interface (SWI) in eutrophic freshwater is poorly understood. In this study, the profiles of P, iron (Fe), sulfur (S) and physicochemical parameters were measured in a simulated lacustrine system with varying concentrations of sulfate (970-2600 μM) in overlying water. The results indicated that elevated concentrations of sulfate increased the soluble reactive P in overlying waters under oxic conditions across the SWI. A 100 μM increase of sulfate was found to induce a 0.128 mgm-2d-1 increase of P flux from surface sediments into overlying waters under oxic conditions. Higher sulfate concentrations in the overlying waters increased the concentrations of labile S(-II) in the deep sediments, due to sulfate penetration and subsequent reduction to S(-II). We also found the fluxes of labile Fe (10.34 to 18.33 mgm-2d-1) and P (2.70 to 1.33 mgm-2d-1) from deep to surface sediment were both positive and greater than the corresponding fluxes (Fe, 2.2 to 3.51 and P, 2.6 to 0.39 mgm-2d-1, respectively) from surface sediment to the overlying water, suggesting that reduction of P-bearing Fe(III)(oxyhydr)oxides in deep anoxic sediment acted as a major source of internal P release. In addition, the upward flux of Fe(II) was significantly lower under higher sulfate conditions, indicating that the Fe(II) flux could be mitigated by formation of Fe(II) sulfides in the deep sediment. Under these conditions, less Fe(II) from deep sediments could be re-oxidized and combine with P in the surface, oxic sediment, thereby reducing the retention capacity for P and leading to higher release of internal P to the water column.