Rapid degradation of butachlor in wheat rhizosphere soil

The degradative characteristics of butachlor in non-rhizosphere, wheat rhizosphere, and inoculated rhizosphere soils were measured. The rate constants for the degradation of butachlor in non-rhizosphere, rhizosphere, and inoculated rhizosphere soils were measured to be 0.0385, 0.0902, 0.1091 at 1 mg/kg, 0.0348, 0.0629, 0.2355 at 10 mg/kg, and 0.0299, 0.0386, 0.0642 at 100 mg/kg, respectively. The corresponding half-lives for butachlor in the soils were calculated to be 18.0, 7.7, 6.3 days at 1 mg/kg, 19.9, 11.0, 2.9 days at 10 mg/kg, and 23.2, 18.0, 10.8 days at 100 mg/kg, respectively. The experimental results show that the degradation of butachlor can be enhanced greatly in wheat rhizosphere, and especially in the rhizosphere inoculated with the bacterial community designated HD which is capable of degrading butachlor. It could be concluded that rhizosphere soil inoculated with microorganisms-degrading target herbicides is a useful pathway to achieve rapid degradation of the herbicides in soil.     

Spatial and temporal organic and heavy metal pollution at Mai Po Marshes Nature Reserve,Hong Kong

An intensive monthly sampling of water and sediments from 12 sites over 8 months covering wet and dry seasons at Mai Po Marshes Nature Reserve was conducted during June 1997-February 1998. Major organic (C, N and P) and heavy metal pollutants (Cd, Cr, Cu, Ni, Pb, Zn) water and sediment samples were examined. The results showed that Mai Po Marshes were severely polluted by organic matter and heavy metals, and the water from Deep Bay appeared to be the source of pollution. Up to 13-55% chance that the sediments of Mai Po Marshes were classified as moderately to seriously metal contaminated materials, according to the guideline set by Hong Kong Government. Empirical models describing organic matter and heavy metal spatial and seasonal dynamics in the water and sediments were formulated, based on data analysis. During wet season (June-October), more than 58% variations of total P can be explained by ortho-P in water, while ammonia-N explained up to 90% variations of total Kjeldahl nitrogen in water. Throughout the whole sampling period (June-February), there were significant correlations (p<0.01) between total organic C in water. pH in the sediments and salinity in water appeared to be important factors determining heavy metal mobility in sediments, while potential metal release from the sediments is a concern when any oxidizing processes such as flooding or dredging are imposed on sediments.     

HUMAN BEHAVIOR AND RESPONSE TOWARDS STORM HAZARD IN WEST VANCOUVER,BRITISH COLUMBIA1

ABSTRACT: Human behavior and response towards storm hazard are examined in this study. One-hundred and twenty flood victims in West Vancouver, British Columbia, were administered a sentence completion test to ascertain how these victims behaved and responded to the flash flood of July 12, 1972. The sentence completion test consisted of 11 sentence stems. Seven of these dealt with the respondent's behavior toward the storm experience, and four with the issue of internal vs. external locus of control. Completions of the seven sentence stems at three time situations showed that West Vancouverites expressed the normal reactions of fear and anxiety before the storm; showed negative emotions during the storm; and displayed a feeling of mutual cooperation after the storm. Completions of the remaining four sentence stems revealed that West Vancouverites emphasized the importance of internal factors, viz., autonomy, drive and hard work, in controlling their lives. They recognized God as a benevolent - protective and almighty - powerful figure. Forty percent of the respondents did not believe in luck, while 8 percent regarded it as important in their lives. One might conclude that West Vancouverites could cope with natural hazardous threats better than those who rely on external factors in directing their lives.     

Environmental impacts of iron ore tailings—The case of Tolo Harbour,Hong Kong

Disposal of iron ore tailings along the shore of Tolo Harbour, Hong Kong has altered the adjacent environment. Due to the ever-expanding population, the vast development of various industries, and the lack of sanitary control, the existing pollution problem of Tolo Harbour is serious. The iron ore tailings consist of a moderate amount of various heavy metals, e.g., copper, iron, manganese, lead, zinc, and a lower level of macronutrients. A few living organisms have been found colonizing this manmade habitat. Higher metal contents were also found in the tissue ofPaphia sp. (clam);Scopimera intermedia (crab);Chaetomorpha brychagona (green alga);Enteromorpha crinita (green alga); andNeyraudia reynaudiana (grass). The area can be reclaimed by surface amelioration using inert materials, soils, or organic substrates, and by direct seeding, using nontolerant and tolerant plant materials. Reclamation of the tailings would improve the amenity of the adjacent environment and also mitigate pollution escaping to the sea.     

Modelling and Prediction of Diesel Engine Performance using Relevance Vector Machine

Diesel engines are being increasingly adopted by many car manufacturers today, yet no exact mathematical diesel engine model exists due to its highly nonlinear nature. In the current literature, black-box identification has been widely used for diesel engine modelling and many artificial neural network (ANN) based models have been developed. However, ANN has many drawbacks such as multiple local minima, user burden on selection of optimal network structure, large training data size, and over-fitting risk. To overcome these drawbacks, this article proposes to apply an emerging machine learning technique, relevance vector machine (RVM), to model and predict the diesel engine performance. The property of global optimal solution of RVM allows the model to be trained using only a few experimental data sets. In this study, the inputs of the model are engine speed, load, and cooling water temperature, while the output parameters are the brake-specific fuel consumption and the amount of exhaust emissions like nitrogen oxides and carbon dioxide. Experimental results show that the model accuracy is satisfactory even the training data is scarce. Moreover, the model accuracy is compared with that using typical ANN. Evaluation results also show that RVM is superior to typical ANN approach.