Zinc removal from synthetic waters using magnetite/graphene oxide composites

The removal of heavy metals from wastewater has become a global challenge, which demands the continuous study of efficient and low-cost treatment alternatives such as adsorption. In this research, the removal of zinc was evaluated using batch adsorption processes with nonconventional materials such as graphene oxide (GO), magnetite (MG), and their composites (GO:MG), formulated with three weight ratios (2:1, 1:1, and 1:2). Graphene was synthesized by the modified Marcano method, using pencil lead graphite as a precursor. MG and the composites were synthesized by chemical coprecipitation of ferrous sulfate and ferric chloride. The materials were characterized by Raman and Fourier transform infrared spectroscopies, scanning electron microscopy, X-ray diffraction, and the Brunauer–Emmett–Teller method to determine the functional groups, microstructural and morphological characteristics, and specific surface area. Batch adsorption tests were carried out to optimize the adsorbent dose and contact time with zinc solutions of 10 ppm. Zinc adsorption reached equilibrium at 2 h, with an optimal dose between 0.25 and 1.0 g/L. The maximum zinc removal efficiencies/adsorption capacities were 98.6%/165.6, 83.4%/47.6, 83.5%/21.9, 72.8%/19.9, and 82.2%/9.25 mg/g using GO, 2GO:1MG, 1GO:1MG, 1GO:2MG, and MG, respectively. Furthermore, the analysis of the isotherm and adsorption kinetics models determined that the adsorption processes using MG and the composites fit the Sips and pseudo-second-order models.     

Revalorization of Macadamia nutshell residue as a filler in eco-friendly castor polyol-based polyurethane foam

Journal of Material Cycles and Waste Management - The use of lignocellulosic fibers as fillers in polymer matrices has aroused the interest of the scientific community and industrial sectors. In...     

Production of new concrete with leather tannery waste used as partial replacement of the natural sand

Journal of Material Cycles and Waste Management - Leather production is a technology that boosts the economy because of its versatility and durability. However, the wastes generated throughout the...     

Face mask and medical waste generation in the City of Baguio,Philippines: its current management and GHG footprint

Journal of Material Cycles and Waste Management - The daily use of facemask to prevent virus transmission increases the negative effect on the environment because of improper waste disposal. Due to...     

Long-term Dynamics of Pike Infection by Parasites Triaenophorus crassus and T. nodulosus (Plathelminthes,Cestoda) in the Rybinsk Reservoir: Effect of Hydraulic Construction and Climate Warming

Russian Journal of Ecology - The long-term dynamics (over more than 70 years) of pike infection by cestodes Triaenophorus crassus and T. nodulosus in the Rybinsk Reservoir (the Volga River) has...     

Effect of Anthropogenic Factors on the Ability of Narrow-skulled Voles (Lasiopodomys gregalis) to Adapt to Captive Conditions

Russian Journal of Ecology - To test the hypothesis that animals from habitats exposed to high anthropogenic pressure are more successful in adapting to captivity, immature narrow-skulled voles...     

Spatiotemporal Variability of Modeled Watershed Scale Surface‐Depression Storage and Runoff for the Conterminous United States

This study explores the viability of using simulated monthly runoff as a proxy for landscape‐scale surface‐depression storage processes simulated by the United States Geological Survey’s National Hydrologic Model (NHM) infrastructure across the conterminous United States (CONUS). Two different temporal resolution model codes (daily and monthly) were run in the NHM with the same spatial discretization. Simulated values of daily surface‐depression storage (treated as a decimal fraction of maximum volume) as computed by the daily Precipitation‐Runoff Modeling System (NHM‐PRMS) and normalized runoff (0 to 1) as computed by the Monthly Water Balance Model (NHM‐MWBM) were aggregated to monthly and annual values for each hydrologic response unit (HRU) in the CONUS geospatial fabric (HRU; n = 109,951) and analyzed using Spearman’s rank correlation test. Correlations between simulated runoff and surface‐depression storage aggregated to monthly and annual values were compared to identify where which time scale had relatively higher correlation values across the CONUS. Results show Spearman’s rank values >0.75 (highly correlated) for the monthly time scale in 28,279 HRUs (53.35%) compared to the annual time scale in 41,655 HRUs (78.58%). The geographic distribution of HRUs with highly correlated monthly values show areas where surface‐depression storage features are known to be common (e.g., Prairie Pothole Region, Florida).     

Use of Predictive Weather Uncertainties in an Irrigation Scheduling Tool Part II: An Application of Metrics and Adjoints

We apply predictive weather metrics and land model sensitivities to improve the Colorado State University Water Irrigation Scheduler for Efficient Application (WISE). WISE is an irrigation decision aid that integrates environmental and user information for optimizing water use. Rainfall forecasts and verification performance metrics are used to estimate predictive rainfall probabilities that are used as input data within the irrigation decision aid. These input data errors are also used within a land model sensitivity study to diagnose important prognostic water movement behaviors for irrigation tool development purposes simultaneously performing the analysis in space and time. Thus, important questions such as “how long can a crop water application be delayed while maintaining crop yield production?” are addressed by evaluating crop growth stage interactions as a function of soil depth (i.e., space), rainfall events (i.e., time), and their probabilistic uncertainties. Editor’s note : This paper is part of the featured series on Optimizing Ogallala Aquifer Water Use to Sustain Food Systems. See the February 2019 issue for the introduction and background to the series.     

Comparison and Evaluation of Gridded Precipitation Datasets in a Kansas Agricultural Watershed Using SWAT

Gridded precipitation datasets are becoming a convenient substitute for gauge measurements in hydrological modeling; however, these data have not been fully evaluated across a range of conditions. We compared four gridded datasets (Daily Surface Weather and Climatological Summaries [DAYMET], North American Land Data Assimilation System [NLDAS], Global Land Data Assimilation System [GLDAS], and Parameter‐elevation Regressions on Independent Slopes Model [PRISM]) as precipitation data sources and evaluated how they affected hydrologic model performance when compared with a gauged dataset, Global Historical Climatology Network‐Daily (GHCN‐D). Analyses were performed for the Delaware Watershed at Perry Lake in eastern Kansas. Precipitation indices for DAYMET and PRISM precipitation closely matched GHCN‐D, whereas NLDAS and GLDAS showed weaker correlations. We also used these precipitation data as input to the Soil and Water Assessment Tool (SWAT) model that confirmed similar trends in streamflow simulation. For stations with complete data, GHCN‐D based SWAT‐simulated streamflow variability better than gridded precipitation data. During low flow periods we found PRISM performed better, whereas both DAYMET and NLDAS performed better in high flow years. Our results demonstrate that combining gridded precipitation sources with gauge‐based measurements can improve hydrologic model performance, especially for extreme events.