Long-term temporal trends (1992-2008) of imposex status associated with organotin contamination in the dogwhelk Nucella lapillus along the Icelandic coast

Using the dogwhelk Nucella lapillus as a biomonitor, we evaluated the organotin contamination along the Icelandic coast over nearly two decades. In 2008, adult dogwhelks collected from 30 locations were examined for imposex status. In 2009, tissue concentrations of six organotin species in dogwhelks from 16 of the 30 locations were analysed using gas chromatography-mass spectrometry. Tributyltin was the most predominant residue ranging from 2.07 to 70.38 μg kg(-1) dw, while triphenyltin was also detected at concentrations up to 22.79 μg kg(-1) dw. Higher total organotin concentrations and imposex indices were observed at locations near large harbours (Reykjavík and Hafnarfj?reur). By comparing the current imposex results with those of previous surveys (1992-2003), we found that recovery was slow near the large harbours, but more apparent near the smaller harbours. We also observed a notable increase in imposex at several northwest sites implying incessant input of organotins.     

The effectiveness of double hulls in reducing vessel-accident oil spillage

While much work has been done in investigating determinants of oil spillage attributed to vessel accidents, little research has been conducted on the effectiveness of ship hull design in reducing marine pollution. This paper addresses whether the double-hull requirement reduces vessel-accident oil spillage. The volume of oil spillage due to oil-cargo vessel accidents was investigated using tobit regressions and an empirical data set of individual vessel accident pollution incidents investigated by the US Coast Guard from 2001 to 2008. The results indicate that the double hull design on average reduces the size of oil spills by 20% and 62% in tank barge and tanker ship accidents, respectively.     

Artificial neural network for AOA estimation in a multipathenvironment over the sea

In this paper, we use a neural network to carry out angle-of-arrival (AOA) estimation in a multipath oceanic environment. In particular, the AOA problem is considered as a mapping from the space of AOA to the space of the sensor output. A neural network is used to determine the inverse mapping from the sensor output space to the space of AOA and this inversion is realized using a radial basis function (RBF) network. We will present the development of the RBF approach for AOA estimation. Simulations are carried out to understand the efficiency and performance of this method. Furthermore, real data are used to evaluate the RBF approach and the results demonstrate the robustness and effectiveness of this neural network method     

Turbulence and Fossil Turbulence in Oceans and Lakes

Turbulence is defined as an eddy-like state of fluid motion where the inertial-vortex forces of the eddies are larger than any of the other forces that tend to damp the eddies out. Energy cascades of irrotational flows from large scales to small are non-turbulent, even if they supply energy to turbulence. Turbulent flows are rotational and cascade from small scales to large, with feedback. Viscous forces limit the smallest turbulent eddy size to the Kolmogorov scale. In stratified fluids, buoyancy forces limit large vertical overturns to the Ozmidov scale and convert the largest turbulent eddies into a unique class of saturated, non-propagating, internal waves, termed fossil-vorticity-turbulence. These waves have the same energy but different properties and spectral forms than the original turbulence patch. The Gibson (1980, 1986) theory of fossil turbulence applies universal similarity theories of turbulence and turbulent mixing to the vertical evolution of an isolated patch of turbulence in a stratified fluid as its growth is constrained and fossilized by buoyancy forces. Quantitative hydrodynamic-phase-diagrams (HPDs) from the theory are used to classify microstructure patches according to their hydrodynamic states. When analyzed in HPD space, previously published oceanic datasets showed their dominant microstructure patches are fossilized at large scales in all layers. Laboratory and field measurements suggested phytoplankton species with different swimming abilities adjust their growth strategies by pattern recognition of tur-bulence-fossil-turbulence dissipation and persistence times that predict survival-relevant surface layer sea changes. New data collected near a Honolulu waste-water outfall showed the small-to-large evolution of oceanic turbulence microstructure from active to fossil states, and revealed the ability of fossil-density-turbulence patches to absorb, and vertically radiate, internal wave energy, information, and enhanced turbulent-mixing-rates toward the sea surface so that the submerged waste-field could be detected from a space satellite (Bondur and Filatov, 2003).     

Seasonal movement and groundwater flow mechanism in an unsaturated saprolitic hillslope

Numerous field monitoring programs have been conducted to investigate the performance of an unsaturated soil slope subjected to rainfalls in wet seasons. Most case histories focus on the response of matric suction, which is one of the two stress-state variables governing unsaturated soil behaviour. However, effects due to another variable, net normal stress, are often ignored. Also, slope performance under alternative wet and dry seasons is rarely reported and analysed. In this study, a saprolitic hillslope situated in Hong Kong was instrumented heavily to investigate its seasonal movement due to changes of the two variables and also groundwater flow mechanism. Two-year seasonal variations of matric suction and net normal stress were monitored by tensiometers together with heat dissipation matric water potential sensors and earth pressure cells, respectively. During heavy rainstorms in wet season, there was a substantial recharge of the main groundwater table, causing a significant increase of positive pore-water pressure in deeper depths. Rupture surface likely developed at depths between 5.5 and 6 m, hence resulting in a “deep-seated” mode of downslope movement. The downslope movement resulted in a peak increase of horizontal stress. In dry seasons, matric suction of up to 190 kPa was recorded, and the associated soil shrinkage led to substantial upslope rebounds. The stress built up in wet seasons hence reduced. After monitoring period of 2 years, downslope ratcheting is identified. Up to 40 % of the downslope displacements were recovered by the upslope rebounds.     

Assessment of soil radon potential in Hong Kong, China, using a 10-point evaluation system

Radon and its progenies have been ranked second of being responsible for lung cancer in humans. Hong Kong has four major groups of uranium-rich plutonic and volcanic rocks and is suffering from radon emanated therefrom. However, there is a lack of radon potential maps in Hong Kong to resolve the spatial distribution of radon-prone areas. A ten-point radon potential system was developed in Germany (2005) to predict radon potential using both the in situ geogenic and geographic parameters under hierarchical ranking. Primarily, the ten-point system requires the desk study of the geological environment of sampling sites, which has an advantage of saving resources and manpower in extensive radon potential mapping over the traditional soil radon concentration sampling method. This paper presents a trial of radon potential mapping in Hong Kong to further verify the system. Despite some slight departures, the system demonstrates an acceptable correlation with soil radon concentrations (R ² = 0.62–0.66) from 768 samples of mainly intermediate radon potential. Hong Kong has a mean soil radon concentrations of 58.9 kBqm^?3, while the radon potential from the ten-point system achieves an average of 4.93 out of 10 over the territory. The vicinity of fault zone showed high soil radon concentrations and potentials, which were conducive to uranium enrichment and rapid soil-gas diffusion near faults. High uranium-238 content in soil was found to cause high soil radon concentration with a large R ², 0.84. The Jurassic granite and volcanic crystal tuff cover more than 85 % of the whole Hong Kong area, and they show relatively high radon concentrations (Geometric mean 83 and 49 kBqm^?3, respectively) which are associated with their high uranium contents (Geometric mean 234 and 197 Bqkg^?1, respectively). While indoor radon concentration is an important factor for radon risk assessment, this study has not considered the correlation between indoor radon concentration and radon potential. The reason is that almost all buildings in Hong Kong are high-rise buildings where indoor radon concentrations are governed only by the radium content in the building materials and the ventilation conditions.     

Can saltwater toxicity be predicted from freshwater data?

The regulation of substances discharged to estuarine and coastal environments relies upon data derived from ecotoxicity tests. Most such data are generated for freshwater rather than saltwater species. If freshwater toxicity data are related to saltwater toxic effects in a systematic and predictable way, the former can be used to predict the latter. This would have economic advantages due to a reduction in toxicity testing of saltwater species. If toxicity data are plotted as species sensitivity distributions, four theoretical relationships between freshwater and saltwater can be envisaged. Examples show that each one of these relationships is supported by empirical data. These examples show that although there is considerable potential for freshwater to saltwater prediction, species parity and representativeness need to be examined for each chemical substance to avoid bias.     

An environmental decision‐support system for the management of water pollution in a tidal river network

This paper is about the development of a decision‐support system for water‐pollution management and environmental planning. More specifically, the paper first presents the overall concept and the system architecture of a generic environmental decision‐support system (EDSS) and then develops an EDSS especially for analysing the tidal flow pattern and water quality of China's Pearl River Delta. The EDSS developed here employs the object‐oriented approach to design the environmental database and utilizes the system integration technology to develop the overall user‐friendly system that operates in the Windows environment. Furthermore, the system can be expanded to facilitate automated model selection and analysis. The EDSS should be of value for managing water quality of river networks with complicated flow patterns, such as that found in the Pearl River Delta.     

Using spatial analysis and Bayesian network to model the vulnerability and make insurance pricing of catastrophic risk

Vulnerability refers to the degree of an individual subject to the damage arising from a catastrophic disaster. It is affected by multiple indicators that include hazard intensity, environment, and individual characteristics. The traditional area aggregate approach does not differentiate the individuals exposed to the disaster. In this article, we propose a new solution of modeling vulnerability. Our strategy is to use spatial analysis and Bayesian network (BN) to model vulnerability and make insurance pricing in a spatially explicit manner. Spatial analysis is employed to preprocess the data, for example kernel density analysis (KDA) is employed to quantify the influence of geo-features on catastrophic risk and relate such influence to spatial distance. BN provides a consistent platform to integrate a variety of indicators including those extracted by spatial analysis techniques to model uncertainty of vulnerability. Our approach can differentiate attributes of different individuals at a finer scale, integrate quantitative indicators from multiple-sources, and evaluate the vulnerability even with missing data. In the pilot study case of seismic risk, our approach obtains a spatially located result of vulnerability and makes an insurance price at a finer scale for the insured buildings. The result obtained with our method is informative for decision-makers to make a spatially located planning of buildings and allocation of resources before, during, and after the disasters.     

Integration of air pollution data collected by mobile sensors and ground-based stations to derive a spatiotemporal air pollution profile of a city

ABSTRACT

Air pollution has become a serious environmental problem causing severe consequences in our ecology, climate, health, and urban development. Effective and efficient monitoring and mitigation of air pollution require a comprehensive understanding of the air pollution process through a reliable database carrying important information about the spatiotemporal variations of air pollutant concentrations at various spatial and temporal scales. Traditional analysis suffers from the severe insufficiency of data collected by only a few stations. In this study, we propose a rigorous framework for the integration of air pollutant concentration data coming from the ground-based stations, which are spatially sparse but temporally dense, and mobile sensors, which are spatially dense but temporally sparse. Based on the integrated database which is relatively dense in space and time, we then estimate air pollutant concentrations for given location and time by applying a two-step local regression model to the data. This study advances the frontier of basic research in air pollution monitoring via the integration of station and mobile sensors and sets up the stage for further research on other spatiotemporal problems involving multi-source and multi-scale information.