Removal of Volatile Organic Compounds from polluted air

Volatile Organic Compounds (VOCs) are among the most common air pollutants emitted from chemical, petrochemical, and allied industries. VOCs are one of the main sources of photochemical reaction in the atmosphere leading to various environmental hazards; on the other hand, these VOCs have good commercial value. Growing environmental awareness has put up stringent regulations to control the VOCs emissions. In such circumstances, it becomes mandatory for each VOCs emitting industry or facility to opt for proper VOCs control measures. There are many techniques available to control VOCs emission (destruction based and recovery based) with many advantages and limitations. Therefore, deciding on a particular technique becomes a difficult task. This article illustrates various available options for VOCs control. It further details the merits, demerits and applicability of each option. The authors hope that this article will help in critically analysing the requirements and accordingly decide on the appropriate strategy to control VOCs.     

Synergistic effects of chromium and copper on photosynthetic inhibition,subcellular distribution,and related gene expression in Brassica napus cultivars

Environmental Science and Pollution Research - Nowadays, modern plant physiology focuses on complex behavior of metal co-contaminants in agrosystems. Keeping this in view, the current study was...     

Soil salinity,household wealth and food insecurity in tropical deltas: evidence from south-west coast of Bangladesh

As a creeping process, salinisation represents a significant long-term environmental risk in coastal and deltaic environments. Excess soil salinity may exacerbate existing risks of food insecurity in densely populated tropical deltas, which is likely to have a negative effect on human and ecological sustainability of these regions and beyond. This study focuses on the coastal regions of the Ganges–Brahmaputra delta in Bangladesh, and uses data from the 2010 Household Income and Expenditure Survey and the Soil Resource Development Institute to investigate the effect of soil salinity and wealth on household food security. The outcome variables are two widely used measures of food security: calorie availability and household expenditure on food items. The main explanatory variables tested include indicators of soil salinity and household-level socio-economic characteristics. The results of logistic regression show that in unadjusted models, soil salinisation has a significant negative effect on household food security. However, this impact becomes statistically insignificant when households’ wealth is taken into account. The results further suggest that education and remittance flows, but not gender or working status of the household head, are significant predictors of food insecurity in the study area. The findings indicate the need to focus scholarly and policy attention on reducing wealth inequalities in tropical deltas in the context of the global sustainable deltas initiative and the proposed Sustainable Development Goals.     

Evaluating of arsenic(V) removal from water by weak-base anion exchange adsorbents

Arsenic contamination of groundwater has been called the largest mass poisoning calamity in human history and creates severe health problems. The effective adsorbents are imperative in response to the widespread removal of toxic arsenic exposure through drinking water. Evaluation of arsenic(V) removal from water by weak-base anion exchange adsorbents was studied in this paper, aiming at the determination of the effects of pH, competing anions, and feed flow rates to improvement on remediation. Two types of weak-base adsorbents were used to evaluate arsenic(V) removal efficiency both in batch and column approaches. Anion selectivity was determined by both adsorbents in batch method as equilibrium As(V) adsorption capacities. Column studies were performed in fixed-bed experiments using both adsorbent packed columns, and kinetic performance was dependent on the feed flow rate and competing anions. The weak-base adsorbents clarified that these are selective to arsenic(V) over competition of chloride, nitrate, and sulfate anions. The solution pH played an important role in arsenic(V) removal, and a higher pH can cause lower adsorption capacities. A low concentration level of arsenic(V) was also removed by these adsorbents even at a high flow rate of 250–350 h^?1. Adsorbed arsenic(V) was quantitatively eluted with 1 M HCl acid and regenerated into hydrochloride form simultaneously for the next adsorption operation after rinsing with water. The weak-base anion exchange adsorbents are to be an effective means to remove arsenic(V) from drinking water. The fast adsorption rate and the excellent adsorption capacity in the neutral pH range will render this removal technique attractive in practical use in chemical industry.     

Clinical solid waste management practices and its impact on human health and environment--A review

The management of clinical solid waste (CSW) continues to be a major challenge, particularly, in most healthcare facilities of the developing world. Poor conduct and inappropriate disposal methods exercised during handling and disposal of CSW is increasing significant health hazards and environmental pollution due to the infectious nature of the waste. This article summarises a literature review into existing CSW management practices in the healthcare centers. The information gathered in this paper has been derived from the desk study of open literature survey. Numerous researches have been conducted on the management of CSW. Although, significant steps have been taken on matters related to safe handling and disposal of the clinical waste, but improper management practice is evident from the point of initial collection to the final disposal. In most cases, the main reasons of the mismanagement of CSW are the lack of appropriate legislation, lack of specialized clinical staffs, lack of awareness and effective control. Furthermore, most of the healthcare centers of the developing world have faced financial difficulties and therefore looking for cost effective disposal methods of clinical waste. This paper emphasizes to continue the recycle-reuse program of CSW materials after sterilization by using supercritical fluid carbon dioxide (SF-CO2) sterilization technology at the point of initial collection. Emphasis is on the priority to inactivate the infectious micro-organisms in CSW. In that case, waste would not pose any threat to healthcare workers. The recycling-reuse program would be carried out successfully with the non-specialized clinical staffs. Therefore, the adoption of SF-CO2 sterilization technology in management of clinical solid waste can reduce exposure to infectious waste, decrease labor, lower costs, and yield better compliance with regulatory. Thus healthcare facilities can both save money and provide a safe environment for patients, healthcare staffs and clinical staffs.     

Influence of multi-industrial activities on trace metal contamination: an approach towards surface water body in the vicinity of Dhaka Export Processing Zone (DEPZ)

Industrial wastewater discharged into aquatic ecosystems either directly or because of inadequate treatment of process water can increase the concentrations of pollutants such as toxic metals and others, and subsequently deteriorate water quality, environmental ecology and human health in the Dhaka Export Processing Zone (DEPZ), the largest industrial belt of 6-EPZ in Bangladesh. Therefore, in order to monitor the contamination levels, this study collected water samples from composite effluent points inside DEPZ and the surrounding surface water body connected to effluent disposal sites and determined the environmental hazards by chemical analysis and statistical approach. The water samples were analysed by inductively coupled plasma mass spectrometry to determine 12 trace metals such as As, Ag, Cr, Co, Cu, Li, Ni, Pb, Se, Sr, V and Zn in order to assess the influence of multi-industrial activities on metal concentrations. The composite effluents and surface waters from lagoons were characterized by a strong colour and high concentrations of biochemical oxygen demand, chemical oxygen demand, electrical conductivity, pH, total alkalinity, total hardness, total organic carbon, Turb., Cl(-), total suspended solids and total dissolved solids, which were above the limit of Bangladesh industrial effluent standards, but dissolved oxygen concentration was lower than the standard value. The measurement of skewness and kurtosis values showed asymmetric and abnormal distribution of the elements in the respective phases. The mean trend of variation was found in a decreasing order: Zn > Cu > Sr > Pb > Ni > Cr > Li > Co > V > Se > As > Ag in composite industrial effluents and Zn > Cu > Sr > Pb > Ni > Cr > Li > V > As > Ag > Co > Se in surface waters near the DEPZ. The strong correlations between effluent and surface water metal contents indicate that industrial wastewaters discharged from DEPZ have a strong influence on the contamination of the surrounding water bodies by toxic metals. The average contamination factors were reported to be 0.70-96.57 and 2.85-1,462 for industrial effluents and surface waters, respectively. The results reveal that the surface water in the area is highly contaminated with very high concentrations of some heavy/toxic metals like Zn, Pb, Cu, Ni and Cr; their average contamination factors are 1,460, 860, 136, 74.71 and 4.9, respectively. The concentrations of the metals in effluent and surface water were much higher than the permissible limits for drinking water and the world average concentrations in surface water. Therefore, the discharged effluent and surface water may create health hazards especially for people working and living inside and in the surrounding area of DEPZ.     

An analytic network process model for municipal solid waste disposal options

The aim of this paper is to present an evaluation method that can aid decision makers in a local civic body to prioritize and select appropriate municipal solid waste disposal methods. We introduce a hierarchical network (hiernet) decision structure and apply the analytic network process (ANP) super-matrix approach to measure the relative desirability of disposal alternatives using value judgments as the input of the various stakeholders. ANP is a flexible analytical program that enables decision makers to find the best possible solution to complex problems by breaking down a problem into a systematic network of inter-relationships among the various levels and attributes. This method therefore may not only aid in selecting the best alternative but also helps decision makers to understand why an alternative is preferred over the other options.     

Additional danger of arsenic exposure through inhalation from burning of cow dung cakes laced with arsenic as a fuel in arsenic affected villages in Ganga-Meghna-Brahmaputra plain

In arsenic contaminated areas of the Ganga-Meghna-Brahmaputra (GMB) plain (area 569,749 sq. km; population over 500 million) where traditionally cow dung cake is used as a fuel in unventilated ovens for cooking purposes, people are simply exposed to 1859.2 ng arsenic per day through direct inhalation, of which 464.8 ng could be absorbed in respiratory tract.     

Revised fire consequence models for offshore quantitative risk assessment

Offshore oil and gas platforms are well known for their compact geometry, high degree of congestion, limited ventilation and difficult escape routes. A small mishap under such conditions can quickly escalate into a catastrophe. Among all the accidental process-related events occurring offshore, fire is the most frequently reported. It is, therefore, necessary to study the behavior of fires and quantity the hazards posed by them in order to complete a detailed quantitative risk assessment. While there are many consequence models available to predict fire hazards-varying from point source models to highly complex computational fluid dynamic models—only a few have been validated for the unique conditions found offshore.

In this paper, we have considered fire consequence modeling as a suite of sub-models such as individual fire models, radiation model, overpressure model, smoke and toxicity models and human impact models. This comprehensive suite of models was then revised by making the following modifications: (i) fire models: existing fire models have been reviewed and the ones most suitable for offshore conditions were selected; (ii) overpressure impact model: a model has been developed to quantify the overpressure effects from fires to investigate the possible damage from the hot combustion gases released in highly confined compartments; (iii) radiation model: instead of a point/area model, a multipoint grid-based model has been adopted for better modeling and analysis of radiation heat flux consequences. A comparison of the performance of the revised models with the ones used in a commercial software package for offshore risk assessment was also carried out and is discussed in the paper.     

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.