Sensitivity of veliger larvae of Mytilus edulis and mussel of various sizes to chlorination

The blue mussel Mytilus edulis is one of the dominant fouling organisms in cooling water systems. In this work, how veliger larvae and different size groups of the mussels responded against chlorine dosage was examined. Veliger larvae mortality was studied at different residual chlorine concentrations (0.05–0.5 mg L^?1), and it was found that a chlorine dose of 0.5 mg L^?1 is 4 times as effective as 0.05 mg L^?1 and twice as effective as 0.1 mg L^?1. Mortality of 100% for three size groups (1.4, 14, and 25 mm) and relative physiological activities of two size groups (14 and 25 mm) were observed. The exposure duration for 100% mortality of mussels decreased with the increasing residual chlorine concentration (0.1–4.0 mg L^?1). Mussel size was also found to be an important factor, considering that the continuation times for mussel mortality were 28 h for the 1.4 mm and 410 h for the 25 mm size groups. All size groups showed progressive reduction in physiological activities, such as oxygen consumption, foot activity, and byssus thread production with increasing chlorine dose (0.05–1.0 mg L^?1); the two data-sets were strongly correlated with each other. The results of this study should be of significance for optimizing the chlorine content, and minimize the environmental threat to industries where mussels are the dominant fouling organisms.     

Infant and Under-five Mortality in Bangladesh: Discriminant Analysis

Abstract

Bangladesh is on target for achieving the Millennium Development Goal 4 relating to infant and under-five mortality because of very rapid reduction in mortality in recent years. But this rate of reduction may be difficult to sustain and may hamper the achievement of Millennium Development Goal 4. Therefore, the main objective of this paper is to discuss and compare the different covariates of infant and under-five mortality in the context of overall country, urban and rural levels of Bangladesh using discriminant analysis. For this, the data are taken from Bangladesh Demographic and Health Survey, 2004. In discriminant analysis, the stepwise procedure has been picked up and only the significant variables are ranked according to the rank of Wilk’s Lambda values. The canonical discriminant function coefficients (unstandardized and standardized) for the predictor variables have also been calculated. Both the results show that breastfeeding is the most important variable in discriminating the two groups of mothers, i.e., mothers experiencing to infant mortality or not and mothers experiencing to under-five mortality or not. The related results of discriminant function also indicate that the discriminant function is statistically significant and discriminates well. Therefore, improvements in the health system are essential for promoting the breastfeeding practices (both inclusive and exclusive), which may be the effective strategies to reach families and communities with targeted messages and information.     

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.     

Assessing groundwater quality and its sustainability in Joypurhat district of Bangladesh using GIS and multivariate statistical approaches

Sustainable groundwater quality is a key global concern and has become a major issue of disquiets in most parts of the world including Bangladesh. Hence, the assessment of groundwater quality is an important study to ensure its sustainability for various uses. In this study, a combination of multivariate statistics, geographical information system (GIS) and geochemical approaches was employed to evaluate the groundwater quality and its sustainability in Joypurhat district of Bangladesh. The results showed that the groundwater samples are mainly Ca–Mg–HCO₃ type. Principal component analysis (PCA) results revealed that geogenic sources (rock weathering and cation exchange) followed by anthropogenic activities (domestic sewage and agro-chemicals) were the major factors governing the groundwater quality of the study area. Furthermore, the results of PCA are validated using the cluster analysis and correlation matrix analysis. Based on the groundwater quality index (GWQI), it is found that all the groundwater samples belong to excellent to good water quality domains for human consumption, although iron, fluoride and iodide contaminated to the groundwater, which do not pose any significant health hazard according to World Health Organization’s and Bangladesh’s guideline values. The results of irrigation water quality index including sodium adsorption ratio (SAR), permeability index and sodium percentage (Na %) suggested that most of the groundwater samples are good quality water for agricultural uses. The spatial distribution of the measured values of GWQI, SAR, Fe (iron), EC (electrical conductivity) and TH (total hardness) were spatially mapped using the GIS tool in the study area.     

Modeling of agricultural drought risk pattern using Markov chain and GIS in the western part of Bangladesh

The aim of the study is to assess the agricultural drought risk condition in the context of global climate change in the western part of Bangladesh that covers about 45% area of the country for the period of 1960–2011. Drought Index (DI) and Drought Hazard Index (DHI) have been calculated by Markov Chain analysis and that of Drought Vulnerability Index (DVI) from socioeconomic and physical indicators. The DI values show that the northern part in general is more drought-prone, having less crops prospect, whereas the southern part is less drought-prone with high crop potentiality. The probability of extreme drought occurrence increases in recent decades in some parts as a result the drought events become more frequent in the areas. The DHI ranges from 15 to 32, and northern part suffers from more extreme drought hazards than that of southern part. DVI also indicates that northern part is exposed to high to very high drought vulnerability as higher percentage of illiterate people are involved in agricultural practices and high percentage of irrigation to cultivable land, but southern part exposed to moderate to low vulnerability because of low values of vulnerability indicators. Finally, agricultural drought exists at high risk condition in northern part and low in southern parts and 21.63, 26.54 and 29.68% of the area poses very high, high and moderate risk, respectively. So, immediate adaptation measures are needed keeping in mind climate features like rainfall and temperature variability, drought risk and risk ranking to make viable adaptation measures.     

H∞ loop-shaping controller design for a grid-connected single-phase photovoltaic system

This paper presents a H_∞ loop-shaping controller for controlling dc-link voltage by regulating the switching signal of the inverter associated with a grid-connected single-phase photovoltaic system. To facilitate a robust control design, state-space realisation of the system model is made with uncertainties represented by linear fractional transformation. The controller is achieved through H_∞ synthesis followed by obtaining desired loop shapes through the choice of the proper weighting functions. The controller order is reduced by Henkel-norm method for facilitating its practical implementation. Controller performance is evaluated through carrying out simulations on MATLAB/Simulink platform under standard and changing atmospheric conditions, and fault condition.     

Effect of induction hydroxy and hydrogen along with algal biodiesel blend in a CI engine: a comparison of performance and emission characteristics

Environmental Science and Pollution Research - Gaseous fuel as a combustion enhancer with a pilot fuel offers significant benefits in improving engine efficiency. Hydrogen and hydroxy are the two...     

Changes in Hydrology of the Ganges Delta of Bangladesh and Corresponding Impacts on Water Resources

The Ganges Delta in Bangladesh is an example of water‐related catastrophes in a major rural river basin where limitations in quantity, quality, and timing of available water are producing disastrous conditions. Water availability limitations are modifying the hydrologic characteristics especially when water allocation is controlled from the upstream Farakka Barrage. This study presents the changes and consequences in the hydrologic regime due to climate‐ and human‐induced stresses. Flow duration curves (FDCs), rainfall elasticity, and temperature sensitivity were used to assess the pre‐ and post‐barrage water flow patterns. Hydrologic and climate indices were computed to provide insight on hydro‐climatic variability and trend. Significant increases in temperature, evapotranspiration, hot days, heating, and cooling degree days indicate the region is heading toward a warmer climate. Moreover, increase in high‐intensity rainfall of short duration is making the region prone to extreme floods. FDCs depict a large reduction in river flows between pre‐ and post‐barrage periods, resulting in lower water storage capacity. The reduction in freshwater flow increased the extent and intensity of salinity intrusion. This freshwater scarcity is reducing livelihood options considerably and indirectly forcing population migration from the delta region. Understanding the causes and directions of hydrologic changes is essential to formulate improve water resources management in the region.     

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.