Crude oil and petroleum products are widespread water and soil pollutants resulting from marine and terrestrial spillages. International statistics of oil spill sizes for all incidents indicate that the majority of oil spills are small (less than 7 tonnes). The major accidents that happen in the oil industry contribute only a small fraction of the total oil which enters the environment. However, the nature of accidental releases is that they highly pollute small areas and have the potential to devastate the biota locally. There are several routes by which oil can get back to humans from accidental spills, e.g. through accumulation in fish and shellfish, through consumption of contaminated groundwater. Although advances have been made in the prevention of accidents, this does not apply in all countries, and by the random nature of oil spill events, total prevention is not feasible. Therefore, considerable world-wide effort has gone into strategies for minimising accidental spills and the design of new remedial technologies. This paper summarizes new knowledge as well as research and technology gaps essential for developing appropriate decision-making tools in actual spill scenarios. Since oil exploration is being driven into deeper waters and more remote, fragile environments, the risk of future accidents becomes much higher. The innovative safety and accident prevention approaches summarized in this paper are currently important for a range of stakeholders, including the oil industry, the scientific community and the public. Ultimately an integrated approach to prevention and remediation that accelerates an early warning protocol in the event of a spill would get the most appropriate technology selected and implemented as early as possible – the first few hours after a spill are crucial to the outcome of the remedial effort. A particular focus is made on bioremediation as environmentally harmless, cost-effective and relatively inexpensive technology. Greater penetration into the remedial technologies market depends on the harmonization of environment legislation and the application of modern laboratory techniques, e.g. ecogenomics, to improve the predictability of bioremediation.

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Oil spill problems and sustainable response strategies through new technologies

https://www.pure.ed.ac.uk/ws/files/79828523/79828403._Masek._AAM.pdf

A critical review of clay-based composites with enhanced adsorption performance for metal and organic pollutants.

Offshore oil spill response practices and emerging challenges.

Ammonium is an important nutrient in primary production; however, high ammonium loads can cause eutrophication of natural waterways, contributing to undesirable changes in water quality and ecosystem structure. While ammonium pollution comes from diffuse agricultural sources, making control difficult, industrial or municipal point sources such as wastewater treatment plants also contribute significantly to overall ammonium pollution. These latter sources can be targeted more readily to control ammonium release into water systems. To assist policy makers and researchers in understanding the diversity of treatment options and the best option for their circumstance, this paper produces a comprehensive review of existing treatment options for ammonium removal with a particular focus on those technologies which offer the highest rates of removal and cost-effectiveness. Ion exchange and adsorption material methods are simple to apply, cost-effective, environmentally friendly technologies which are quite efficient at removing ammonium from treated water. The review presents a list of adsorbents from the literature, their adsorption capacities and other parameters needed for ammonium removal. Further, the preparation of adsorbents with high ammonium removal capacities and new adsorbents is discussed in the context of their relative cost, removal efficiencies, and limitations. Efficient, cost-effective, and environmental friendly adsorbents for the removal of ammonium on a large scale for commercial or water treatment plants are provided. In addition, future perspectives on removing ammonium using adsorbents are presented.

Removing ammonium from water and wastewater using cost-effective adsorbents: A review.

Assessing cost-effectiveness of specific LID practice designs in response to large storm events

Emergency preparedness enables us to effectively handle sudden environmental events. In previous research, we have proposed to develop environmental emergency preparedness systems employing Case-Based Reasoning (CBR) technology, though developing such a CBR system has been stifled by a deficiency of cases and difficulties of case adaptation. In this paper, an Improved Genetic Algorithm (IGA) is put forward to resolve the issue of adaptability, and thus simultaneously overcoming the deficiency of cases. First we introduce the Frame method, which creates a case representation in accordance with the characteristics of, for instance, a sudden chemical leakage event and the system's preparedness for treating this case. Then we present the principle of genetic algorithm (GA) for case adaptation. Next, we introduce an Improved Genetic Algorithm (IGA) that achieves case adaptation in the CBR system. The IGA overcomes simplex GA (SGA)'s defects including premature and slow convergence rate, and also enhances search efficiency for globally optimal solutions. The IGA employs technologies including the Multi-Factor Integrated Fitness Function, the Multi-Parameter Cascade Code method, the Small Section method for generation of an initial population, and Niche technology for genetic operations including selection, crossover, and mutation. The results of SGA and IGA performance testing are also presented. A prototype CBR-IGA environmental emergency preparedness system is developed and introduced, and its applicability is tested using a hypothetical ammonia leakage emergency in one district of Shanghai. The results indicate that the proposed IGA methodology can resolve the adaptation issue and expand the case base effectively in CBR systems for environmental emergency preparedness. Future research opportunities are discussed, including the potential to combine CBR, GA, and Back Propagation-Artificial Neural Network (BP-ANN) to alleviate additional challenges, such as the ''speed and accuracy'' of environmental emergency response.

Adaptation methodology of CBR for environmental emergency preparedness system based on an Improved Genetic Algorithm

Analysis on LID for highly urbanized areas' waterlogging control: demonstrated on the example of Caohejing in Shanghai.

Environmental emergency decision support system based on Artificial Neural Network

Studies on the cost–effectiveness analysis on low impact development (LID) measures are demanded for guiding the plan, design, and construction of LID in rapidly and highly urbanized regions, such as many cities in China. In this paper, five single LID measures are given, including bio-retention (BR), infiltration trench (IT), porous pavement (PP), rain barrels (RB), and grass swale (GS), and three composite measures including IT + PP, IT + RB, and PP + RB. A detailed cost–effectiveness analysis on LID is presented using the case of Caohejing in Shanghai, China. Life cycle cost method is adopted to calculate the costs of these measures. Storm recurrence interval, without waterlogging of the drainage system, is applied as the effectiveness of a LID. In consideration of adding LID measures, an approach to calculate the storm recurrence interval without waterlogging is constructed. The aftermath indicates that RB, IT and their combination, IT + RB, are the three most suitable cost–effective measures ...

Zhenliang Liao

Papers

Adaptation methodology of CBR for environmental emergency preparedness system based on an Improved Genetic Algorithm

Analysis on LID for highly urbanized areas' waterlogging control: demonstrated on the example of Caohejing in Shanghai.

Environmental emergency decision support system based on Artificial Neural Network

Cost–effectiveness analysis on LID measures of a highly urbanized area

Urban flood risk assessment and analysis with a 3D visualization method coupling the PP-PSO algorithm and building data.