Water,sanitation, hygiene and waste disposal practices as COVID-19 response strategy: insights from Bangladesh

Environment, Development and Sustainability - The COVID-19 pandemic has caused a global emergence, and the absence of a proven vaccine or medicine has led to the implementation of measures to...     

GCM-based analysis of water availability along the Tone River and Tokyo Metropolitan Area under climatic changes

Tone River supplies most of the water requirements of the Tokyo Metropolitan Area (TMA). Lowering of Tone flow and yearly fluctuation, however, is causing water shortage along TMA nowadays. This study investigated the future water availability scenarios under climatic changes. A state-of-the-art approach to utilize the output of several GCM has been demonstrated to investigate the future water availability scenarios for TMA from the Tone River. An integrated modeling approach for water balance considering several hydrological risk indices was adopted to quantify the future changes in this case. It is observed that the future summer precipitation along the Tone basin is going to be increased considerably, while an almost constant or decreasing trend is observed for winter season. Natural flow availability for winter or spring seasons thus can be crucial under future scenarios. After reservoir routing, the hydrological risk indices estimated, however, were not found to be changed significantly due to the presence of a robust reservoir system at the upstream.     

Potential protective effects of Spirulina platensis on liver,kidney, and brain acrylamide toxicity in rats

Environmental Science and Pollution Research - Acrylamide (AA) is a hazardous chemical that is widely used in industrial practices. Spirulina platensis (SP) is a blue green alga that is rich in...     

Sugarcane monoculture drives microbial community composition,activity and abundance of agricultural-related microorganisms

Environmental Science and Pollution Research - Sugarcane monoculture (SM) often leads to soil problems, like soil acidification, degradation, and soil-borne diseases, which ultimately pose a...     

Impact of invasive ant species on native fauna across similar habitats under global environmental changes

Environmental Science and Pollution Research - Biotic invasions can predominantly alter the dynamics, composition, functions, and structure of natural ecosystems. Social insects, particularly ants,...     

Waste battery disposal and recycling behavior: a study on the Australian perspective

Environmental Science and Pollution Research - Consumer behavior is a critical consideration for the development of sustainable waste management systems, including waste batteries, which pose a...     

Influence of polymethyl acrylate additive on the formation of particulate matter and NOX emission of a biodiesel–diesel-fueled engine

Environmental Science and Pollution Research - The aim of this study is to investigate the effect of the polymethyl acrylate (PMA) additive on the formation of particulate matter (PM) and nitrogen...     

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.     

Catalytic degradation of toxic organic dye using an aluminum-doped Cu0.4Zn0.6-xAlxFe2O4 (x = 0.0, 0.2, 0.4, 0.6) spinel ferrite in a photo-Fenton system

Catalytic activity of spinel ferrite in breaking down toxic dye materials are promising due to their uniqueness. In this study, aluminum-doped copper zinc ferrite, Cu_0.4Zn_0.6-xAl_xFe₂O₄ (x = 0.0, 0.2, 0.4, 0.6), a catalyst for toxic dye degradation is synthesized through chemical co-precipitation route. The formation of the spinel ferrite catalyst is initially confirmed by Fourier transform infrared spectra, which shows the frequency of metal-oxygen bond vibration at 539 and 427 cm⁻¹ attributed to the tetrahedral and octahedral sites respectively. Higher intensity sharp peak of X-ray diffraction for (311) plane is the evidence for the phase purity and the formation of spinel ferrite. The crystallite size is found to decrease with the increase of Al³⁺ ion. The surface structure of the obtained particles is investigated using a scanning electron microscope. Analyses of the material's magnetic characteristics using a vibrating sample magnetometer (VSM) revealed that it is, in fact, a soft magnet, as evidenced by the loop of its hysteresis, which is narrow. The catalytic degradation of methylene blue dye under the mechanism of the photo-Fenton process is studied with the obtained spinel ferrites and the result is found to be as high as 96.5%. The process follows pseudo-second order kinetics and the Langmuir isotherm.     

Multi‐strainer model concept for conjunctive pumping of saline and non‐saline groundwater from coastal aquifers

The current paper discusses the multi‐strainer technique developed to augment usable water by the combined use of saline and non‐saline aquifers in locations where a freshwater aquifer is underlain or overlain by a saline water aquifer. The multi‐strainer technique evaluates design criteria for the formulation of multi‐strainer schemes to supply water at an acceptable salinity limit by combined use of the saline and non‐saline aquifers. The design ratio of discharges can be maintained by adjusting the strainers’ lengths in the saline and non‐saline aquifers. The multi‐strainer scheme has been applied in the coastal aquifers of Bangladesh and found to be effective at lowering the water salinity concentrations to acceptable levels, thus increasing the availability of water for sustainable use. The multi‐strainer scheme should be designed based on the thickness of the aquifer layers to be screened, the salinity concentrations of the screened layers, and the level of salinity concentration to be maintained.