Risk assessment quantification

RISK ASSESSMENT Quantification to the risk relating to one or several hazards (including the process of determining these) (CEN/TR 15419). 

The detection and analysis, including quantification, of cyanobacterial toxins are essential for monitoring their occurrence in natural and controlled waters used for agricultural purposes, potable supplies, recreation and aquaculture. Risk assessment of the cyanobacterial toxins for the protection of human and animal health, and fundamental research, are also dependent on efficient methods of detection and analysis. In this article we discuss the methods developed and used to detect and analyse cyanobacterial toxins in bloom and scum material, water and animal/clinical specimens, and the progress being made in the risk assessment of the toxins. 

Chu, T. L. et al., 1990, Quantification of the Probabilistic Risk Assessment of the High Flux Beam Reactoi (HFBR) at Brookhaven National Laboratory, ANS Topical Meeting, The Safety, Siauis and Future of Non-Commercial Reactors and Irradiation Facilities, Boise, ID, Sepiember Ol - October 4, 1990. 

Chapter 4 focuses on techniques which are applied to a new or existing system to optimize human performance or qualitatively predict errors. Chapter 5 shows how these teclmiques are applied to risk assessment, and also describes other techniques for the quantification of human error probabilities. Chapters 6 and 7 provide an overview of techniques for analyzing the underlying causes of incidents and accidents that have already occurred. 

Performance-influencing factors analysis is an important part of the human reliability aspects of risk assessment. It can be applied in two areas. The first of these is the qualitative prediction of possible errors that could have a major impact on plant or personnel safety. The second is the evaluation of the operational conditions under which tasks are performed. These conditions will have a major impact in determining the probability that a particular error will be committed, and hence need to be systematically assessed as part of the quantification process. This application of PIFs will be described in Chapters 4 and 5. 

In addition, the chapter will provide an overview of htunan reliability quantification techniques, and the relationship between these techniques and qualitative modeling. The chapter will also describe how human reliability is integrated into chemical process quantitative risk assessment (CPQRA). Both qualitative and quantitative techniques will be integrated within a framework called SPEAR (System for Predictive Error Analysis and Reduction). 

Because most research effort in the human reliability domain has focused on the quantification of error probabilities, a large number of techniques exist. However, a relatively small number of these techniques have actually been applied in practical risk assessments, and even fewer have been used in the CPI. For this reason, in this section only three techniques will be described in detail. More extensive reviews are available from other sources (e.g., Kirwan et al., 1988 Kirwan, 1990 Meister, 1984). Following a brief description of each technique, a case study will be provided to illustrate the application of the technique in practice. As emphasized in the early part of this chapter, quantification has to be preceded by a rigorous qualitative analysis in order to ensure that all errors with significant consequences are identified. If the qualitative analysis is incomplete, then quanhfication will be inaccurate. It is also important to be aware of the limitations of the accuracy of the data generally available... 

This chapter has provided an overview of a recommended framework for the assessment of human error in chemical process risk assessments. The main emphasis has been on the importance of a systematic approach to the qualitative modeling of human error. This leads to the identification and possible reduction of the human sources of risk. This process is of considerable value in its own right, and does not necessarily have to be accompanied by the quantification of error probabilities. 

The definitions of method detection and quantification limits should be reliable and applicable to a variety of extraction procedures and analytical methods. The issue is of particular importance to the US Environmental Protection Agency (EPA) and also pesticide regulatory and health agencies around the world in risk assessment. The critical question central to risk assessment is assessing the risk posed to a human being from the consumption of foods treated with pesticides, when the amount of the residue present in the food product is reported nondetect (ND) or no detectable residues . 

Today, when a pesticide with no detectable residues is registered for use, a Tolerance or maximum residue limit (MRL) is established at the lowest concentration level at which the method was validated. However, for risk assessment purposes it would be wrong to use this number in calculating the risk posed to humans by exposure to the pesticide from the consumption of the food product. This would be assuming that the amount of the pesticide present in all food products treated with the pesticide and for which no detectable residues were found is just less than the lowest level of method validation (LLMV). The assumption is wrong, but there is no better way of performing a risk assessment calculation unless the limit of detection (LOD) and limit of quantification (LOQ) of the method were clearly defined in a uniformly acceptable manner. 

In contrast to traditional methods for total petroleum hydrocarbons that report a single concentration number for complex mixtures, the fractionation methods report separate concentrations for discrete aliphatic and aromatic fractions. The petroleum fraction methods available are GC-based and are thus sensitive to a broad range of hydrocarbons. Identification and quantification of aliphatic and aromatic fractions allows one to identify petroleum products and evaluate the extent of product weathering. These fraction data also can be used in risk assessment. 

A more recent Dutch report (Vermeire et al. 2001) provides a practical guide for the application of probabilistic distributions of default assessment factors in human health risk assessments, and it is stated that the proposed distributions will be applied in risk assessments of new and existing substances and biocides prepared at RIVM (the National Institute of Public Health and the Environment) and TNO. The report concentrated on the quantification of default distributions of the assessment factors related to interspecies extrapolation (animal-to-human), intraspecies extrapolation (human-to-human), and exposure duration extrapolation. 

There seems to be a desire among the workshop participants to develop a series of standard distributions, or distribution parameters, for exposure and effects variables that are generally used in risk assessments. In the case of toxicity data, for example, investigations leading to the quantification of a generic variance for between-species variation from pooled data for many pesticides may be useful (Luttik and Aldenberg 1997). 

Woo, Y.-T. and Lai, D.Y., (2010) QSAR analysis of genotoxic and nongenotoxic carcinogens a state-of-the-art overview, in Cancer Risk Assessment Chemical Carcinogenesis from Biology to Standards Quantification (eds G. Hsu and T. Stedeford), John Wiley and Sons, Ltd., Hoboken, N.J., pp. 517-556. 

The fundamental question of risk assessment for potential human carcinogens requires definition of substances that exceed an evidentiary threshold. Once the scientific evidence establishes a substantial basis for conclusion of known or potential human cancer, it is then in order to determine a procedure for risk quantification. Quantitative risk assessments must always be read with the qualitative evidence of the likelihood of carcinogenicity. 

The extent of accommodation and characterization of uncertainty in exposure assessment must necessarily be balanced against similar considerations with respect to hazard, since the outcome of any risk assessment is a function of comparison of the two. If, for example, there is limited information to inform quantitatively on hazard and, as a result, a need to rely on defaults, there is limited benefit to be gained in developing the exposure analysis such that any increase in certainty is cancelled by uncertainties of greater magnitude associated with quantification of critical hazard, as a basis for a complete risk assessment. 

The extent of development and characterization of uncertainty associated with estimating exposure should take into account the nature of quantification of hazard in any risk assessment to ensure comparability of the two. 

Quantification of the uncertainty in the measured transformation rates is crucial if the data are to be used as inputs to models for risk assessment. Without knowledge of the accuracy, precision, and... 

Noninvasive validation of tobacco smoking behavior is necessary for large population health studies. Moreover, a main problem in the risk assessment of passive smoking is the lack of a suitable methodology for the quantification of exposure. Measurements of nicotine in hair could prove to be a reliable marker for passive exposure. Several reports have presented data on nicotine in hair. ° Some have included the monitoring of cotinine, the major metabolite of nicotine. 

Risk management decisions follow the identification and quantification of risk, and they are determined by risk assessments. During the regulatory process, risk managers may request that additional risk assessments be conducted to justify the risk management decisions—the risk assessment and risk management processes are therefore intimately related. 

Cumulative risk assessment An analysis, characterization and possible quantification of the combined risks to humans or the environment from multiple agents or stressors (USEPA, 2003). 

As an example of a quantitative risk assessment we will consider angina attacks caused by CO-exposure. The model for the quantification is given in Fig. 8. 

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