The impact of mining and smelting activities on property values : a study of Mount Isa city, Queensland, Australia

Much publicity has been given to the problem of high levels of environmental contaminants, most notably high blood lead concentration levels among children in the city of Mount Isa because of mining and smelting activities. The health impacts from mining-related pollutants are now well documented. This includes published research being discussed in an editorial of the Medical Journal of Australia (see Munksgaard et al. 2010). On the other hand, negative impacts on property prices, although mentioned, have not been examined to date. This study rectifies this research gap. This study uses a hedonic property price approach to examine the impact of mining- and smelting-related pollution on nearby property prices. The hypothesis is that those properties closer to the lead and copper smelters have lower property (house) prices than those farther away. The results of the study show that the marginal willingness to pay to be farther from the pollution source is AUS $13 947 per kilometre within the 4 km radius selected. The study has several policy implications, which are discussed briefly. We used ordinary least squares, geographically weighted regression, spatial error and spatial autoregressive or spatial lag models for this analysis.

Personalised ontology learning and mining for web information gathering

Over the last decade, the rapid growth and adoption of the World Wide Web has further exacerbated user needs for e±cient mechanisms for information and knowledge location, selection, and retrieval. How to gather useful and meaningful information from the Web becomes challenging to users. The capture of user information needs is key to delivering users' desired information, and user pro¯les can help to capture information needs. However, e®ectively acquiring user pro¯les is di±cult. It is argued that if user background knowledge can be speci¯ed by ontolo- gies, more accurate user pro¯les can be acquired and thus information needs can be captured e®ectively. Web users implicitly possess concept models that are obtained from their experience and education, and use the concept models in information gathering. Prior to this work, much research has attempted to use ontologies to specify user background knowledge and user concept models. However, these works have a drawback in that they cannot move beyond the subsumption of super - and sub-class structure to emphasising the speci¯c se- mantic relations in a single computational model. This has also been a challenge for years in the knowledge engineering community. Thus, using ontologies to represent user concept models and to acquire user pro¯les remains an unsolved problem in personalised Web information gathering and knowledge engineering. In this thesis, an ontology learning and mining model is proposed to acquire user pro¯les for personalised Web information gathering. The proposed compu- tational model emphasises the speci¯c is-a and part-of semantic relations in one computational model. The world knowledge and users' Local Instance Reposito- ries are used to attempt to discover and specify user background knowledge. From a world knowledge base, personalised ontologies are constructed by adopting au- tomatic or semi-automatic techniques to extract user interest concepts, focusing on user information needs. A multidimensional ontology mining method, Speci- ¯city and Exhaustivity, is also introduced in this thesis for analysing the user background knowledge discovered and speci¯ed in user personalised ontologies. The ontology learning and mining model is evaluated by comparing with human- based and state-of-the-art computational models in experiments, using a large, standard data set. The experimental results are promising for evaluation. The proposed ontology learning and mining model in this thesis helps to develop a better understanding of user pro¯le acquisition, thus providing better design of personalised Web information gathering systems. The contributions are increasingly signi¯cant, given both the rapid explosion of Web information in recent years and today's accessibility to the Internet and the full text world.

Raman microscopic study of the Li amphibole holmquistite, from the Martin Marietta Quarry, Bessemer City, NC, USA

The Raman spectrum of holmquistite, a Li-containing orthorhombic amphibole from Bessemer City, USA has been measured. The OH-stretching region is characterized by bands at 3661, 3646, 3634 and 3614 cm–1 assigned to 3 Mg–OH, 2 Mg + Fe2+–OH, Mg + 2Fe2+–OH and 3 Fe2+–OH, respectively. These Mg and Fe2+ cations are located at the M1 and M3 sites and have a Fe2+/(Fe2+ + Mg) ratio of 0.35. The 960–1110 cm–1 region represents the antisymmetric Si–O–Si and O–Si–O stretching vibrations. For holmquistite, strong bands are observed around 1022 and 1085 cm–1 with a shoulder at 1127 cm–1 and minor bands at 1045 and 1102 cm–1. In the region 650–800 cm–1 bands are observed at 679, 753 and 791 cm–1 with a minor band around 694 cm–1 attributed to the symmetrical Si–O–Si and Si–O vibrations. The region below 625 cm–1 is characterized by 14 vibrations related to the deformation modes of the silicate double chain and vibrations involving Mg, Fe, Al and Li in the various M sites. The 502 cm–1 band is a Li–O deformation mode while the 456, 551 and 565 cm–1 bands are Al–O deformation modes.