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“AMORE” Decision Support System for probabilistic Ecological Risk Assessment - Part I: Exposure and risk assessment of the case study on cyanide

Workshop report

Occupational exposure limits have traditionally focused on preventing morbidity and mortality arising from inhalation exposures to individual chemical stressors in the workplace. While central to occupational risk assessment, occupational exposure limits have limited application as a refined disease prevention tool because they do not account for all of the complexities of the work and non-occupational environments and are based on varying health endpoints. To be of greater utility, occupational exposure limits and other risk management tools could integrate broader consideration of risks from multiple exposure pathways and routes (aggregate risk) as well as the combined risk from exposure to both chemical and non-chemical stressors, within and beyond the workplace, including the possibility that such exposures may cause interactions or modify the toxic effects observed (cumulative risk). Although still at a rudimentary stage in many cases, a variety of methods and tools have been developed or are being used in allied risk assessment fields to incorporate such considerations in the risk assessment process. These approaches, which are collectively referred to as cumulative risk assessment, have potential to be adapted or modified for occupational scenarios and provide a tangible path forward for occupational risk assessment. Accounting for complex exposures in the workplace and the broader risks faced by the individual also requires a more complete consideration of the composite effects of occupational and non-occupational risk factors to fully assess and manage worker health problems. Barriers to integrating these different factors remain, but new and ongoing community-based and worker health-related initiatives may provide mechanisms for identifying and integrating risk from aggregate exposures and cumulative risks from all relevant sources, be they occupational or non-occupational.

Occupational exposure limits for 30 organophosphate pesticides based on inhibition of red blood cell acetylcholinesterase

Working on brownfields, the workers who have to carry out these jobs are exposed to hazardous substances (chemicals and/or micro-organisms). This paper proposes a method of risk assessment and specification of measures for occupational safety and health protection, consisting of two steps: danger analysis and exposure assessment. Danger analysis means the investigation of the hazardous substances, of their physical, chemical and hazardous properties and the states of matter, being responsible for their mobility and emission behavior. Exposure assessment starts with the so-called “working area analysis” – the investigation of the jobs that have to be carried out and of the working area conditions, which have a serious influence on the chance and level of exposure. Taking into account the possible absorption routes and limiting values used in occupational safety and health protection, the combination of the danger analysis data with the results of the exposure assessment leads to the risk assessment, the assessment of the possible health risk, and finally to the specification of safety measures. Risk assessment saves money: the less you know about the hazardous properties of a contaminated site, the higher the level of safety measures has to be, and the higher the costs of safety and health protection become.

A method is proposed for integrated probabilistic risk assessment where exposure assessment and hazard characterization are both included in a probabilistic way. The aim is to specify the probability that a random individual from a defined (sub)population will have an exposure high enough to cause a particular health effect of a predefined magnitude, the critical effect size (CES). The exposure level that results in exactly that CES in a particular person is that person's individual critical effect dose (ICED). Individuals in a population typically show variation, both in their individual exposure (IEXP) and in their ICED. Both the variation in IEXP and the variation in ICED are quantified in the form of probability distributions. Assuming independence between both distributions, they are combined (by Monte Carlo) into a distribution of the individual margin of exposure (IMoE). The proportion of the IMoE distribution below unity is the probability of critical exposure (PoCE) in the particular (sub)population. Uncertainties involved in the overall risk assessment (i.e., both regarding exposure and effect assessment) are quantified using Monte Carlo and bootstrap methods. This results in an uncertainty distribution for any statistic of interest, such as the probability of critical exposure (PoCE). The method is illustrated based on data for the case of dietary exposure to the organophosphate acephate. We present plots that concisely summarize the probabilistic results, retaining the distinction between variability and uncertainty. We show how the relative contributions from the various sources of uncertainty involved may be quantified.

Risk assessments have assumed an increasingly important role in the management of risks in this country. The determination of which pollutants or public health issues are to be regulated, the degree and extent of regulation, and the priority assigned to particular problems are all areas of risk assessment that influence the country's $100 billion annual investment in environmental protection. Recent trends in public policy have brought the practice of risk assessment under greater scrutiny. As policy makers increasingly insist that specific numerical risk levels (so-called bright lines) be incorporated into regulatory decisions, the stakes for good risk assessment practice, already high, are raised even further. Enhancing the scientific basis of risk assessments was a major goal of the Workshop on Exposure Databases. In this article, we present the Risk Assessment Work Group's evaluation of the use of exposurerelated databases in risk assessment and the group's recommendations for improvement. The work group's discussion focused on the availability, suitability, and quality of data that underly exposure assessments, a critical component of risk assessment. The work group established a framework for evaluation, based on exposure scenarios typically used in regulatory decisions. The scenarios included examples from Superfund, the Clean Air Act, the Toxic Substances Control Act, and other regulatory programs. These scenarios were used to illustrate current use of exposure data, to highlight gaps in existing data sources, and to discuss how improved exposure information can improve risk assessments. The work group concluded that many of the databases available are designed for purposes that do not meet exposure and risk assessment needs. Substantial gaps exist in measurements of actual human exposure and in the data necessary to model exposures, to characterize distributions of exposure, to identify high-risk groups, and to identify possible environmental inequities in exposure. The work group, on the basis of its findings, made both short-term and longer-term recommendations for improving the collection of exposure data in the future.

One of the main problems that may put people's safety in danger is the lack of real-time detection, evaluation, and recognition of predictable safety risks. Current real-time risk identification solutions are limited to proximity sensing, which lack providing the exposed person with risk-specific information in real-time. Combined values of concurrently presented risks are either unrecognized or underestimated. This study goes beyond the proximity sensing state-of-the-art by envisioning, planning, designing, developing, assembling, and examining an automated intelligent real-time risk (AIR) assessment system. A holistic safety assessment approach is followed to include identification, prioritization, detection, evaluation, and control at risk exposure time. Multi-sensor technologies based on RFID are integrated with a risk assessment intelligent system. System prototype is developed and examined to prove the concept for on-foot building construction workers. The evaluation of AIR assessment system's performance proved its validity, significance, simplicity, representation, accuracy, precision, and timeliness. The reliability of providing quantitative proximity values of risk can be limited due to the signal attenuation; however, it can be reliable in providing risk proximity in a subjective linguistic fashion (Near/Far). The main contributions of the AIR assessment system are that the mobile wearable device can provide a linguistic meaningful risk assessment resultant value, the value represents the combined evaluation of concurrently presented risks, and can be sound delivered to the exposed person in real-time of exposure. Therefore, AIR system can be used as an effective prognostic risk assessment tool that can empower workers with real-time recognition and measurability of risk exposure.

In recent years, ammonia leakage accidents occur frequently, which causes a great concern in China. Based on traditional risk analysis methods and the equipment characteristics, a risk evaluation method for small ammonia refrigeration units is proposed. The method includes a risk assessment model based on area division, a failure probability assessment method based on expert scoring system, a failure consequence assessment method based on regional population density and consequence mitigation measures, and a HAZOP risk correction method. Based on the proposed method, a risk assessment system of small ammonia refrigeration units is developed. This risk assessment method and system will provide a scientific basis to carry out the cold storage management in China.

Risk assessment for human health and ecosystems by exposure to chemicals is an important process to aid in the mitigation of affected areas. Generally, this process is carried out in isolated spots and therefore may be ineffective in mitigating. This chapter describes an architecture of a multi-agent system for environmental risk assessment in areas contaminated as often occur in mining, oil exploration, intensive agriculture and others. Plan multiple points in space-time matrix where each agent carries out exposure assessment and the exchange of information on toxicity, to characterize and classify risk in real time. Therefore, it is an architecture model with multi-agent that integrates ontology by semantic representation, classifies risks by decision rules by support vectors machines with multidimensional data. The result is an environment to exchange information that provides knowledge about the chemical contamination, which can assist in the planning and management of mitigation of the affected area.

In the risk assessment of chemical substances, aggregation of exposure to a substance from different sources via different pathways is not common practice. Focusing the exposure assessment on a substance from a single source can lead to a significant underestimation of the risk. To gain more insight on how to perform an aggregate exposure assessment, we applied a deterministic (tier 1) and a person-oriented probabilistic approach (tier 2) for exposure to the four most common parabens through personal care products in children between 0 and 3 years old. Following a deterministic approach, a worst-case exposure estimate is calculated for methyl-, ethyl-, propyl- and butylparaben. As an illustration for risk assessment, Margins of Exposure (MoE) are calculated. These are 991 and 4966 for methyl- and ethylparaben, and 8 and 10 for propyl- and butylparaben, respectively. In tier 2, more detailed information on product use has been obtained from a small survey on product use of consumers. A probabilistic exposure assessment is performed to estimate the variability and uncertainty of exposure in a population. Results show that the internal exposure for each paraben is below the level determined in tier 1. However, for propyl- and butylparaben, the percentile of the population with an exposure probability above the assumed “safe” MoE of 100, is 13% and 7%, respectively. In conclusion, a tier 1 approach can be performed using simple equations and default point estimates, and serves as a starting point for exposure and risk assessment. If refinement is warranted, the more data demanding person-oriented probabilistic approach should be used. This probabilistic approach results in a more realistic exposure estimate, including the uncertainty, and allows determining the main drivers of exposure. Furthermore, it allows to estimate the percentage of the population for which the exposure is likely to be above a specific value.

Exposure and risk assessment can be based on direct epidemiological investigations that study exposed individuals or on models. Examples from situations in Germany for both approaches are presented and discussed here. Epidemiological studies for the health risk assessment of contaminated soil typically have one or more of the following problems: the number of exposed persons is too small: critical confounders cannot be measured and controlled for appropriately; or the expected effects are not large enough to be detected. Therefore, at least for most situations in Germany, epidemiological investigations designed to measure health risks infrequently provide clear answers. In contrast, direct epidemiological investigations are more likely to be able to quantify exposure. If the soil concentrations are high, one is able to identify the location of relevant “hot spots” using biological and environmental monitoring. Further, epidemiology can be used to get information about the distribution of exposure in specific populations, such as young children or amateur gardeners. Exposure assessment by modeling provides answers in situations in which no reasonable answers can be expected using epidemiological means. The model gives quantitative estimates for average and reasonable worst-case situations. The latter can be determined by modeling probability distributions.

Dairy and dairy processing industries are included in the group of food products and high-risk industries. Decision making in relation to risk management in dairy industry supply chain is significant. This study aimed at designing a Intelligent decision support system (DSS) for risk assessment of dairy agroindustry supply chain and the estimation of dairy price in the risk-based farmer level. The risk assessment is analyzed by fuzzy logic approach which is Fuzzy Inference System (FIS) and Fuzzy Assosiative Memories (FAMs). The basic knowledge of this system is obtained through the preparation of rule base of risk assessment and the relation of production cost and risks at the farm based on the expert arguments variables. There are six outputs yielded from RSDA, that is risk assessment in accordance with priority issue, risk assessment for delivery activity, risk source exploration, risk performance, risk management partially and the estimation of production cost and price with risks. The system provides several alternatives which will help decision making in preparing risk management in dairy agroindustry supply chain. Moreover, this system also provides several scenarios of dairy price estimation at the level of farmer who includes risk factor in the farmer. By this system, it is expected that the opportunity of risk and risk impact of dairy agroindustry supply chain can be minimized.

A risk assessment framework for self-management of poorly soluble low toxic nanomaterials

PTT Exploration and Production Public Company Limited (PTTEP), the E&P Company of Thailand has applied the principle of Health Risk Assessment (HRA) to all the operating assets since 2006 by using the Risk Assessment Matrix (RAM) as a tool for risk assessment. The RAM applies the 5 scaled health hazard rating and either 5 scaled occupational exposure limits (OEL), or 5 scaled Frequency of Occurrences (FO), in case hazards are not quantitatively measurable, to define the level of risk into 3 levels. The FO is graded according to how often the condition has been known within the E&P industry. When applied to health condition, the FO could be arbitrary and may not provide the accurate assessment. In 2012 PTTEP has developed the Health Risk Exposure Score (HRES) system which can replace the FO and OEL in the RAM. The HRES comprises the number of exposed persons, the frequency and exposed duration, the industrial hygiene results, proper occupational health trainings, and the availability of PPE/existing control. Each item is weighed and calculated by three points scale. The result is averaged and distributed into 5 ranges to replace the FO and OEL in RAM. The HRES is tested against the conventional HRA done within PTTEP in 2012. The result showed significant difference of risk assessment level between the two methods, while the HRES provides more objective assessment and requires less specialist’s opinion in HRA process. Thus the HRA process and finding can become standardized among the PTTEP operating assets. The PTTEP HRA improving process via the application of HRES will be implemented in both domestic and international assets. The more user friendly software to support the process of HRA is being developed. Then the result of HRA and the feedback from HRA team will be collected for further study as continuous PTTEP HRA process improvement.