Literature overview: Emerging organic contaminants in water and their remediation

Emerging organic contaminants (EOCs) are widespread in the environment, and concentrations above the acceptable limits are being detected due to improved detection methods, technologies, and regulations for their monitoring. Increasing use of new consumer, pharmaceutical, and scientific products has introduced EOCs, such as nanomaterials and siloxanes, into the environment. The releases of EOCs into the environment can lead to surface‐water, soil, and groundwater contamination. The presence of pharmaceuticals and their enantiomers can lead to resistant algal strains and, thus, more persistent algal toxins. This review discusses some of the prominent EOCs and the methods for their remediation. This is useful for assessing current remediation technologies and for developing new technologies to treat EOCs in water. © 2008 Wiley Periodicals, Inc.     

Effective environmental management through life cycle assessment

Unplanned and unsustainable development (particularly rapid industrialisation) has placed great pressure in every dimension of the environment (air, water, soil, health, etc.). The resulting disturbance in the natural ecological balance is a serious concern. Sustainable development is the need of the hour; it can only be achieved through effective environmental management. Environmental management will become indispensable in the future as regulatory restrictions tighten and public expectations of environmental performance increase. The day is not far away when a customer will prefer to buy products produced by an environmentally committed organisation. In short, the environmental commitment of an organisation will become a market strategy.

Environmental management is a set of actions based on a structured methodology to ensure that an organisation is committed to the environment and that the production process has minimal/no adverse affect on it. This article emphasises environmental management in the real engineering sense of the term, and discusses how to develop an effective environmental management system through life cycle assessment. It further demonstrates through a real life case study how an industry has achieved landmark success in managing its environment, production, as well as winning the good faith of society.     

Nanoparticles Addition in Coir‐Basalt‐Innegra Fibers Reinforced Bio-synthetic Epoxy Composites

Journal of Polymers and the Environment - In the present investigation, the influence of coir micro-particles and titanium carbide (TiC) nanofillers on mechanical characteristics and thermal...     

Use of chemical coagulants to control fouling potential for wastewater membrane bioreactor processes.

Chemical coagulation with ferric chloride, alum, and an organic polymer were used to control the fouling potential of mixed liquors for submerged membrane bioreactor (MBR) processes in treating municipal wastewater. Their filterability was evaluated using a submerged hollow fiber ultrafiltration apparatus operated in constant permeate flux mode. The collected transmembrane pressures over filtration time were used to calculate the membrane fouling rates. The results showed that coagulation pretreatment can reduce fouling rates when MBRs were operated above the critical flux. Even though coagulation with the organic polymer formed larger mixed liquor suspended solids particles and had shorter time-to-filtration than those with ferric chloride and alum, the filterability for membrane filtration were similar, indicating that the membrane fouling in MBR systems was mainly controlled by the concentration of smaller colloidal particles.     

Design and cognitive evaluation of 6 dynamic lane departure warning symbols

Objective: Lane departure, caused by inattention, distraction, drowsiness, or any unusual driver behavior, is a typical risk threatening the driver as well as other road users. Accurate perception of such situations through effective warnings would help drivers to avoid serious consequences. With regard to critical functions of warning symbols for risk communication, the present study focused on providing effective and easily perceivable symbols, compatible with human cognitive capabilities. Thus, the main purpose of the present study was to design and cognitively appraise 6 newly designed dynamic symbols, candidates for a new type of lane departure warning system.

Methods: Simplicity, familiarity, concreteness, meaningfulness, and semantic closeness were the major assessment criteria, defining cognitive features by the earlier researchers in the field. A total number of 187 driving license applicants, with a mean age of 20.58 years (SD = 3.20), participated in the present survey. The participants rated cognitive features of the 6 dynamic symbols along a 0–100 scale.

Results: Significant main effect of the element factor type of the designed symbols on rating cognitive features revealed that the existence of car element was the best predictor for illustrating lane departure. The interaction of both element factor and location of element factor significantly affected the ratings. However, the location of element factor did not solely have any strong effect on the ratings. The results also demonstrated that semantic closeness received the highest overall mean score across symbols (M = 61.80), especially within the symbols that include the car element (M = 75.67). Moreover, a significant difference was observed between the average ratings of the cognitive features, despite the fact that a significant correlation was found between cognitive features.

Conclusion: The most considerable result of the current study was the match between the symbol with the highest ratings and the International Organization for Standardization (ISO)-related icon in appearance. Because previous studies demonstrated a strong correlation between comprehension scores of the symbol and both semantic closeness and meaningfulness, high-level comprehensibility of the best ranked symbol is expected.     

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.     

Spatial and temporal variability of fluoride concentrations in groundwater resources of Larestan and Gerash regions in Iran from 2003 to 2010

Environmental Geochemistry and Health - There is discrepancy about intervals of fluoride monitoring in groundwater resources by Iranian authorities. Spatial and temporal variability of fluoride in...