Impact identification methods

Additionally, some of the impact identification methods previously discussed may also be of value in impact prediction (i.e. checklists, assessment matrix and networks). 

EIA Preparation is the scientific and objective analysis of the scale, significance and importance of impacts identified. Various methods have been developed, in relation to baseline studies impact identification prediction evaluation and mitigation, to execute this task. 

The specific properties of ABPP experiments had a significant impact on the design of postlabeling detection and identification methods, which will be summarized below. All methods represent complementary approaches that display individual strengths and weaknesses in important proteomic disciplines such as throughput, sensitivity, and sample conservation. 

These methods usually require chemical derivatization to produce volatile species with sufficient vapour pressure to form a gas in the MS sample compartment. The sample may be introduced either by itself, for electron impact (El) methods, or mixed with a large excess of another gas for chemical ionization (Cl). The volatile sample mixture is bombarded with a beam of electrons with energies (typically up to 100 eV) which may be captured to produce negatively charged species (M ), or where electron impact is sufficient to displace electrons and produce positive ions (M" ). This may also result in fragmentation of the unstable radicals produced from the sample molecules, and the characteristic fragmentation patterns are frequently useful in structural identification. 

Park J, Chang F-K. System identification method for monitoring impact events. In SPIE 2005 smart structures and NDE symposium 2005. pp. 189—200. 

A number of EIA methods and tools are available for identifying impacts. Some are also useful for scoping and/or presenting the results of the EIA or assigning significance. Methods for impact identification can be positioned on a continuum, from simple to advanced methods, often involving the application of mathematical models. 

The most advanced and sophisticated methods for impact identification rely on expert systems (Rodriguez-Bachiller and Glasson, 2003), defined as computer systems that emulate the decision-making abUity of a human expert (Jackson, 1998). The basic idea behind expert systems is that expertise, which is the vast body of task-specific knowledge, is transferred from a human to a computer and then stored in the computer and users call upon the computer for specific advice as needed (Liao, 2005). Several expert systems have been proposed in the hterature (Liu and Lai, 2009). Among them, two categories are noteworthy. The use of analytic hierarchy... 

In general, the simpler methods for impact identification are easier to use, more reliable and more effective in presenting information in the EIS, but their coverage of impact significance, indirect impacts or alternatives is very limited. More complex models incorporate these aspects, but at the cost of immediacy. 

As for impact identification, several methods are used for predicting the characteristics of impacts. 

Types of methods in EIA Impact identification Impact prediction Impact significance evaluation... 

Analytical applications Mass spectrometry has been applied to a variety of analytical problems related to expls, some of which have already been mentioned. Identification of the principal constituents of expls has been attempted from electron impact cracking patterns (Refs 34, 50 58), as well as chemical ionization spectra (Refs 69,70 71). Such methods necessarily include vapor species analysis and are directed to detection of buried mines (Refs 50, 58, 61,... 

Karam, L.R and Simic, M.G. (1986). Methods for the identification of irradiated chicken meat. Presented at the WHO Working Group on Health Impact and Control of Irradiated Foods, Neuherberg, Germany, November. 

Alternative approaches consist in heat extraction by means of thermal analysis, thermal volatilisation and (laser) desorption techniques, or pyrolysis. In most cases mass spectrometric detection modes are used. Early MS work has focused on thermal desorption of the additives from the bulk polymer, followed by electron impact ionisation (El) [98,100], Cl [100,107] and field ionisation (FI) [100]. These methods are limited in that the polymer additives must be both stable and volatile at the higher temperatures, which is not always the case since many additives are thermally labile. More recently, soft ionisation methods have been applied to the analysis of additives from bulk polymeric material. These ionisation methods include FAB [100] and LD [97,108], which may provide qualitative information with minimal sample pretreatment. A comparison with FAB [97] has shown that LD Fourier transform ion cyclotron resonance (LD-FTTCR) is superior for polymer additive identification by giving less molecular ion fragmentation. While PyGC-MS is a much-used tool for the analysis of rubber compounds (both for the characterisation of the polymer and additives), as shown in Section 2.2, its usefulness for the in situ in-polymer additive analysis is equally acknowledged. 

Mass spectrometry (MS) in its various forms, and with various procedures for vaporization and ionization, contributes to the identification and characterization of complex species by their isotopomer pattern of the intact ions (usually cation) and by their fragmentation pattern. Upon ionization by the rough electron impact (El) the molecular peak often does not appear, in contrast to the more gentle field desorption (FD) or fast-atom bombardment (FAB) techniques. An even more gentle way is provided by the electrospray (ES) method, which allows all ionic species (optionally cationic or anionic) present in solution to be detected. Descriptions of ESMS and its application to selected problems are published 45-47 also a representative application of this method in a study of phosphine-mercury complexes in solution is reported.48... 

Logic Model Methods The following tools are most commonly used in quantitative risk analysis, but can also be useful qualitatively to understand the combinations of events which can cause an accident. The logic models can also be useful in understanding how protective systems impact various potential accident scenarios. These methods will be thoroughly discussed in the Risk Analysis subsection. Also, hazard identification and evaluation tools discussed in this section are valuable precursors to a quantitative risk analysis (QRA). Generally a QRA quantifies the risk of hazard scenarios which have been identified by using tools such as those discussed above. 

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