Vertical distribution and contamination assessment of heavy metals in sediment cores of ship breaking area of Bangladesh

Environmental Geochemistry and Health - Vertical heavy metal profiling reflects the history of the deposition of metals and helps to understand the characteristics of accumulation in various layers...     

Semiconducting oxide photocatalysts for reduction of CO<Subscript>2</Subscript> to methanol

The explosive growth in anthropogenic energy consumption, coupled with the consequent environmental pollution, have been acknowledged as two impending challenges confronting humanity. Photocatalytic CO₂ reduction to produce value-added hydrocarbon fuels, by using abundant solar energy and redundant atmospheric CO₂, is an innovative way to satisfy global energy requirements whilst simultaneously reducing atmospheric CO₂ levels. Although this notion is several decades old, it has unfortunately been lingering in a state of infancy due to inherently poor CO₂-to-fuel conversion efficiencies, and the generation of low-value products (e.g., CO, HCHO). These pitfalls hamper this process from any potential commercial breakthrough and are primarily fuelled by the lack of progress in developing high-performance photocatalytic materials. Fortunately, the advent of nanotechnology has recently introduced many promising novel materials for this purpose. Here, we review photocatalysts with proven potential for converting CO₂ into methanol, a high-value, energy-dense hydrocarbon fuel that is easily transported using existing pipeline infrastructure. Methanol possesses multifarious applications in the automobile, industrial and petrochemical sector. In addition, the development of direct methanol fuel cells (DMFCs) has introduced the tantalizing prospect of using methanol as a medium for storing solar energy that is easily converted to electricity via DMFCs. As such, methanol is an ideal fuel, with numerous advantages over its counterparts. This article reviews several photocatalysts that have been reported for this environmentally sustainable process of converting CO₂ into methanol by photocatalysis. Specifically, the performance enhancement effected by adding dopant atoms, forming heterostructured composites and nanostructures, is investigated in terms of four key areas: (1) enhanced visible light sensitivity, (2) improved adsorption of reactants on the catalytic surface, (3) lowered electron–hole recombination and (4) increased CO₂ reduction kinetics. The trends deduced therein are invaluable for researchers developing novel photocatalytic materials, which will utilize sunlight to convert CO₂ into methanol with enhanced efficiency, thus ushering in the era of a green methanol-based economy.     

Phosphorous in the environment: characteristics with distribution and effects,removal mechanisms,treatment technologies,and factors affecting recovery as minerals in natural and engineered systems

Environmental Science and Pollution Research - Phosphorus (P), an essential element for living cells, is present in different soluble and adsorbed chemical forms found in soil, sediment, and water....     

Rice bran: A prospective resource for biodiesel production in Bangladesh

The increasing demand of renewable energy sources has pressed the need to search for biofuels. The world is not only thrusting for potential sources of biofuels but also surveilling not to hamper the food supply, particularly in the Third World countries, such as Bangladesh. Rice bran oil is a prominent source of biofuels. Rice, the main cereal in Bangladesh, is cultivated all the year round. Rice hull containing bran is mostly wasted and merely used as feedstock for cattle and for cooking purposes. This study considered rice bran as a prospective source of biodiesel in Bangladesh. The properties of oil collected from rice bran were investigated to ensure the production of biodiesel by transesterification. An economic analysis relative to Bangladesh was conducted, and the production rate of biodiesel under different percentage of catalyst was investigated.     

Integrated groundwater resource management in Indus Basin using satellite gravimetry and physical modeling tools

Reliable and frequent information on groundwater behavior and dynamics is very important for effective groundwater resource management at appropriate spatial scales. This information is rarely available in developing countries and thus poses a challenge for groundwater managers. The in situ data and groundwater modeling tools are limited in their ability to cover large domains. Remote sensing technology can now be used to continuously collect information on hydrological cycle in a cost-effective way. This study evaluates the effectiveness of a remote sensing integrated physical modeling approach for groundwater management in Indus Basin. The Gravity Recovery and Climate Experiment Satellite (GRACE)-based gravity anomalies from 2003 to 2010 were processed to generate monthly groundwater storage changes using the Variable Infiltration Capacity (VIC) hydrologic model. The groundwater storage is the key parameter of interest for groundwater resource management. The spatial and temporal patterns in groundwater storage (GWS) are useful for devising the appropriate groundwater management strategies. GRACE-estimated GWS information with large-scale coverage is valuable for basin-scale monitoring and decision making. This frequently available information is found useful for the identification of groundwater recharge areas, groundwater storage depletion, and pinpointing of the areas where groundwater sustainability is at risk. The GWS anomalies were found to favorably agree with groundwater model simulations from Visual MODFLOW and in situ data. Mostly, a moderate to severe GWS depletion is observed causing a vulnerable situation to the sustainability of this groundwater resource. For the sustainable groundwater management, the region needs to implement groundwater policies and adopt water conservation techniques.     

Hair burning and liming in tanneries is a source of pollution by arsenic,lead, zinc,manganese and iron

Heavy metals in the environment may be toxic for human and animals. Tanneries are a source of pollution by heavy metals. There is little information on heavy metals pollution in tanneries, especially on metals produced by the process of hair burning and liming. Liming is the first stage of chemical treatment where animal hair or wool is removed with sodium sulphide and calcium oxide. Here we studied cow, goat, buffalo and sheep hair, conventional liming agents and liming wastewaters from several sources. Samples were acid-digested and aliquots were analysed by atomic absorption spectroscopy following APHA standard method to measure concentrations of arsenic, lead, cadmium, zinc, manganese and iron. Results show that the range of metal contents in hair or wool and liming agents are 1.3–8.2 mg/kg for arsenic, 0.02–21.8 mg/kg for lead, 17.7–121.0 mg/kg for manganese, 7.3–141.1 mg/kg for zinc and 119.6–10613.8 mg/kg for iron. Liming wastewaters contain 1.9–5.6 µg/L arsenic, 0.03–6.05 µg/L lead, 38.6–139.0 µg/L manganese, 144.0–171.5 µg/L zinc and 399.5–1069.0 µg/L iron. Cadmium was below detection limits. This is the first investigation that reveals that hair burning liming operation is a potential source of heavy metals in the environment.     

Bubble size distribution in a laboratory-scale electroflotation study

The performance of electroflotation (EF) is strongly influenced by the size of O₂ and H₂ bubbles. Therefore, in this study, the bubble sizes are measured in a lab-scale EF cell using a high-speed camera. The mean bubble size is found to vary in the range of 32.7–68.6 μm under different operating conditions. This study shows that the electrode material, current density, water pH, ionic strength, and frother (Tennafroth 250) concentration are important factors in controlling the bubble size. Furthermore, four mathematical distributions (normal, log-normal, Weibull, and gamma distributions) are fitted to the experimental data, among which the log-normal distribution is found to be the best fit based on the lower Anderson-Darling (AD) value.     

Increasing trend of primary NO2 exhaust emission fraction in Hong Kong

Despite the successful reduction in roadside NO( x ) levels, no such decrease has been detected in roadside NO(2) concentration in Hong Kong. One underlying cause could be the rising primary NO(2) fraction of the total emission of NO( x ). Primary NO(2) can be particularly detrimental to Hong Kong because a large fraction of the population are exposed to the traffic-related primary pollutants in the street canyons formed by congested high-rise buildings. In this study, hourly mean concentration data for roadside nitrogen oxides (NO( x )), nitrogen dioxide (NO(2)), and background ozone (O(3)) were used to estimate the mean primary NO(2) fraction from vehicle exhausts in Hong Kong. An overall increasing trend was observed for the primary NO(2) fraction (f-NO(2)) values in all the three roadside air monitoring sites. The primary NO(2) as a fraction of total NO( x ) (f-NO(2)) increased approximately from 2% in 1998 to 13% in 2008 in Hong Kong. The two particular periods of rising f-NO(2) coincided with the two implementation periods of the diesel retrofit programs for the light-duty vehicles and heavy-duty vehicles. Future vehicle emission control strategies should target not only total NO( x ) but also primary NO(2). Health benefit or disease burden estimates should be taken into account and updated in the process of policy planning and evaluation.     

Space-time Bayesian small area disease risk models: development and evaluation with a focus on cluster detection

This paper extends the spatial local-likelihood model and the spatial mixture model to the space-time (ST) domain. For comparison, a standard random effect space-time (SREST) model is examined to allow evaluation of each model’s ability in relation to cluster detection. To pursue this evaluation, we use the ST counterparts of spatial cluster detection diagnostics. The proposed criteria are based on posterior estimates (e.g., misclassification rate) and some are based on post-hoc analysis of posterior samples (e.g., exceedance probability). In addition, we examine more conventional model fit criteria including mean square error (MSE). We illustrate the methodology with a real ST dataset, Georgia throat cancer mortality data for the years 1994–2005, and a simulated dataset where different levels and shapes of clusters are embedded. Overall, it is found that conventional SREST models fair well in ST cluster detection and in goodness-of-fit, while for extreme risk detection the local likelihood ST model does best.