Inexpensive substrate sampler for macroinvertebrates

Assessment of aquatic macroinvertebrates is a critical component of many watershed monitoring programs and passive samplers are often used to collect long-term site data, especially in environments where active sampling is not possible. However, standard passive samplers can be expensive and lost in extreme conditions. We developed a sampler using plastic soda bottles (PSB) filled with river rock and compared its effectiveness with standard Hester-Dendy samplers in both lotic and lentic environments. Abundance, taxa richness, and macroinvertebrate composition showed no significant differences between sampler types in either habitat type. PSB samplers, which can be constructed for less than one dollar each, collected the same number of organisms and represented the same diversity as Hester-Dendy devices that cost around $38 each. In studies where funds are limited, PSB samplers appear to be suitable for passive monitoring.     

Identification and characterization of extracted microplastics from agricultural soil near industrial area: FTIR and X-ray diffraction method

Microplastics have been found in large quantities in agricultural soil and now become a major global issue. Different types of microplastic have adverse effects on agricultural soil. The most widely used method for the extraction of microplastics in agricultural soil is the density floatation method by using saturated NaCl solution. This method includes the pre-digestion of soil samples with H₂O₂ to remove all the organic matter present in the soil. Different types of microplastic particles were extracted and identified by using ATR-FTIR viz polypropylene, polybutylene tetrapthalate, polyethylene, polystyrene, and polyethylene tetrapthalate. The crystalline nature of extracted microplastic was checked by employing XRD analytical technique. Floatation with higher density saturated sodium chloride (NaCl) solution recovered approximately 80% MPs from soil. Floatation methods were found to be effective for extracting microplastics from soils.     

Environmental life cycle assessment of different types of the municipal wastewater pipeline network

With the onset of social life, humans have considered waste disposal as essential, and they have been able to repel it through brick and clay channels. Checking sewage pipes for energy consumption and a longer lifetime than other sewage system components is important. Climate change and exploitation of industrial resources have made environmental impacts, which are important factors in decision making. The purpose of this study was to introduce the most suitable type of sewage pipe considering environmental protection. Therefore, we applied the environmental life cycle assessment (LCA) method, using Sima Pro 8.2.3 software for the one-kilometer length of concrete pipes (300 mm in diameter), Polyvinyl chloride (PVC), and polyethylene (PE) (315 mm in diameter). Also, the BEES method and sensitivity analysis were used to validate the results. The comparison between three types of municipal wastewater pipes indicated that PE pipes are a more environmentally friendly option than PVC, and concrete pipes in pipe recycling, reducing extraction from untapped resources, and inefficient extraction of resources. Electricity, diesel fuel, and sulfate resistance cement consumption for concrete production are the most pollution elements in the LCA of concrete pipes. Usage of PVC granular, sanitary landfill of PVC pipes, and using hydraulic drill in LCA of PVC pipes are the most elements of generating pollution. The usage of PE granules, PE pipes landfilling, hydraulic excavator, and electricity consumption in the LCA of the PE pipes are the greatest polluting parameters.     

Improved product selectivity of electrochemical reduction of carbon dioxide by tuning local carbon dioxide concentration with multiphysics models

Environmental Chemistry Letters - Electrochemical reduction of carbon dioxide (CO2) is promising to alleviate carbon emissions and produce fuels and materials in a circular way, yet effective...     

Sulfonated graphene nanomaterials for membrane antifouling,pollutant removal,and production of chemicals from biomass: a review

Water pollution and the unsustainable use of fossil fuel derivatives require advanced catalytic methods to clean waters and to produce fine chemicals from modern biomass. Classical homogeneous catalysts such as sulfuric, phosphoric, and hydrochloric acid are highly corrosive and non-recyclable, whereas heterogeneous catalysts appear promising for lignocellulosic waste depolymerization, pollutant degradation, and membrane antifouling. Here, we review the use of sulfonated graphene and sulfonated graphene oxide nanomaterials for improving membranes, pollutant adsorption and degradation, depolymerization of lignocellulosic waste, liquefaction of biomass, and production of fine chemicals. We also discuss the economy of oil production from biomass. Sulfonated graphene and sulfonated graphene oxide display an unusual large theoretical specific surface area of 2630 m²/g, allowing the reactants to easily enter the internal surface of graphene nanosheets and to reach active acid sites. Sulfonated graphene oxide is hydrophobic and has hydrophilic groups, such as hydroxyl, carboxyl, and epoxy, thus creating cavities on the graphene nanosheet’s surface. The adsorption capacity approached 2.3–2.4 mmol per gram for naphthalene and 1-naphthol. Concerning membranes, we observe an improvement of hydrophilicity, salt rejection, water flux, antifouling properties, and pollutant removal. The nanomaterials can be reused several times without losing catalytic activity due to the high stability originating from the stable carbon–sulfur bond between graphene and the sulfonic group.

     

Special issue MinWat2020, third international multidisciplinary conference on mineral and thermal waters

Environmental Geochemistry and Health -     

AI-based runoff simulation based on remote sensing observations: A case study of two river basins in the United States and Canada

Data-driven techniques are used extensively for hydrologic time-series prediction. We created various data-driven models (DDMs) based on machine learning: long short-term memory (LSTM), support vector regression (SVR), extreme learning machines, and an artificial neural network with backpropagation, to define the optimal approach to predicting streamflow time series in the Carson River (California, USA) and Montmorency (Canada) catchments. The moderate resolution imaging spectroradiometer (MODIS) snow-coverage dataset was applied to improve the streamflow estimate. In addition to the DDMs, the conceptual snowmelt runoff model was applied to simulate and forecast daily streamflow. The four main predictor variables, namely snow-coverage (S-C), precipitation (P), maximum temperature (T_max), and minimum temperature (T_min), and their corresponding values for each river basin, were obtained from National Climatic Data Center and National Snow and Ice Data Center to develop the model. The most relevant predictor variable was chosen using the support vector machine-recursive feature elimination feature selection approach. The results show that incorporating the MODIS snow-coverage dataset improves the models' prediction accuracies in the snowmelt-dominated basin. SVR and LSTM exhibited the best performances (root mean square error = 8.63 and 9.80) using monthly and daily snowmelt time series, respectively. In summary, machine learning is a reliable method to forecast runoff as it can be employed in global climate forecasts that require high-volume data processing.     

China’s sponge cities alleviate urban flooding and water shortage: a review

Environmental Chemistry Letters - About one tenth of humans are impacted by water shortages around the globe. Water resilience is worsening under climate change because intensifying weather...     

Effects of Amazonian flying rivers on frog biodiversity and populations in the Atlantic rainforest

Given the speed at which humans are changing the climate, species with high degrees of endemism may not have time to avoid extinction through adaptation. We investigated through teleconnection analysis the origin of rainfall that determines the phylogenetic diversity of rainforest frogs and the effects of microclimate differences in shaping the morphological traits of isolated populations (which contribute to greater phylogenetic diversity and speciation). We also investigated through teleconnection analysis how deforestation in Amazonia can affect ecosystem services that are fundamental to maintaining the climate of the Atlantic rainforest biodiversity hotspot. Seasonal winds known as flying rivers carry water vapor from Amazonia to the Atlantic Forest, and the breaking of this ecosystem service could lead Atlantic Forest species to population decline and extinction in the short term. Our results suggest that the selection of morphological traits that shape Atlantic Forest frog diversity and their population dynamics are influenced by the Amazonian flying rivers. Our results also suggest that the increases of temperature anomalies in the Atlantic Ocean due to global warming and in the Amazon forest due to deforestation are already breaking this cycle and threaten the biodiversity of the Atlantic Forest hotspot.     

Mechanism of complexation of toxic arsenate,selenate, and molybdate with hydrotalcites

Environmental Chemistry Letters - Wastewater from the uranium mining industry contains toxic arsenate (AsO43–), selenate (SeO42–), and molybdate (MoO42–) that can be removed by...