During 40 years of industrial production, from the end of the 1890’s until the end of 1930’s, coal and wood distillation products were manufactured in AB Carbo’s tar factory along the River Gimån in Grötingen, in the county of Jämtland, Sweden. In accordance with the Swedish Environmental Protection Agency’s Methods for Inventories of contaminated sites, MIFO, the site in Grötingen has been identified as a potentially contaminated site. In order to determine whether toxic substances pose a threat to human health and ecological systems in the area, there was a need to investigate the presence of contaminants, their levels and potential for migration. Sensitivity and protection value regarding exposure of man and the environment at the site is high, since people live next to the site and River Gimån is a part of the Natura 2000 network. The initial phase of the MIFO method includes a preliminary survey. Information regarding AB Carbo’s activities and production has been obtained by studying map and archive material, as well as by interviews and site inspections. Potential point sources have been identified around the factory forge, storage cellar, distillation building and the wooden channel for the discharge of tar and other condensates into the River Gimån. Guided by the information gathered in MIFO phase 1, a preliminary site investigation was carried out at the site. It included sampling of soil, sediment, groundwater and surface water together with sample analysis of heavy metals and organic pollutants. The result of the analysis shows that very high levels of the contaminants PAH, aromatics and lead are found in both soil and groundwater. The sediment contamination level is high while that of surface water is very low. The distribution of contamination levels among the samples indicates a likely point source close to the distillation building due to very high contamination levels in soil and groundwater close to that point. The point source may represent a larger spillage or dumped waste products. Two of the contaminants present at the site, phenantrene and phenol, were studied by using the model CHEMFLO-2000. The analysis focussed on mobility in unsaturated soils under conditions that prevail at the site in Grötingen. The adsorption of phenantrene is significantly higher in comparison to that of phenol. Comprehensive assessment and risk classification results in risk class 2 for the site in Grötingen, which implies a need for further investigations and measurements. The risk of human exposure could be reduced by removing contaminated soil around the factory forge. Additional studies should focus on finding the location of the point source around the distillation building as well as contaminant migration from the wooden channel and the geographical spreading of heavy metal contamination. / Från slutet av 1890-talet och fram till slutet av 1930-talet tillverkades träkol och trädestillationsprodukter vid AB Carbos kolugnsanläggning i Grötingen, Jämtlands län. En undersökning enligt MIFO-modellen motsvarande fas 1 och fas 2 har genomförts på området med syfte att fastställa vilka föroreningar som förekommer på området, föroreningsnivå, spridningsförutsättningar och sammanfattningsvis bedöma föroreningssituationen på området utifrån de miljö- samt hälsorisker som området ger eller kan ge upphov till. Objektsområdet har hög känslighet och högt skyddsvärde då människor bor permanent alldeles intill objektsområdet samt att det intilliggande vattendraget Gimån ingår i Natura 2000-nätverket. Genom de orienterande studier som genomfördes i MIFO fas 1 har kart- och arkivstudier samt intervjuer resulterat i information om kolugnsanläggningens verksamhet samt tillverkade produkter. På fabriksområdet identifierades potentiella punktkällor vid fabrikens smedja, källare, reningsverk samt vid den träränna varigenom spilltjära släpptes ut till Gimån. Utifrån den information som erhölls i den orienterande fasen genomfördes en riktad provtagning enligt MIFO fas 2 i såväl mark och sediment som grund- och ytvatten. Bedömning av uppmätta föroreningshalter baseras på riktvärden och skattas från mindre allvarliga till mycket allvarliga. Analysresultaten påvisar allvarliga till mycket allvarliga halter av PAH, aromater och bly i jorden och grundvattnet. Andra föroreningar som påvisas i högre halter på området är alifater och koppar. Föroreningsnivån med avseende på PAH i sedimenten bedöms som allvarlig medan analysresultaten från ytvattenprovtagningen påvisar mycket låga föroreningsnivåer. Provtagningsresultaten påvisar ett mycket förorenat grundvattenflöde från reningsverket, där en punktkälla i form av större spill eller nedgrävda förvaringskärl kan vara lokaliserad. Eventuell spridning av föroreningar från trärännan går inte att fastställa då föroreningsspridning även kan ske från det intilliggande området där kolugnar och kondenseringshus har varit lokaliserade. Föroreningarna fenantren och fenol studerades genom modellering i CHEMFLO-2000 med avseende på deras rörlighet i omättad jord under de geohydrologiska förhållanden som råder på objektsområdet i Grötingen. Resultatet visar att fenantren adsorberas i marken i betydligt större grad än fenol. Koncentrationen fenantren i porvattnet minskar nästan lika mycket som den totala koncentrationen fenantren i marken, vilket tyder på att fenantren binds starkt genom adsorbtion i marken. Den samlade riskbedömningen av objektet resulterar i att objektet bedöms tillhöra riskklass 2, vilket innebär att objektet ger upphov till en stor risk för människa och miljön. Fortsatta undersökningar på området i Grötingen bör fokusera på att hitta den punktkälla vid reningsverket som bedöms vara orsaken till de höga föroreningsnivåer som uppmättes i grundvattenrör V1. Den geografiska utbredningen av de ytliga föroreningarna som påträffades där smedjan har stått bör också utredas vidare och eventuellt avlägsna förorenad jord eller vidta åtgärder för att minska exponeringen. Vidare undersökningar bör även klarlägga spridningen från trärännan samt orsakerna till de höga föroreningsnivåer av bly som uppmättes på området.

To be successful, individuals that are susceptible to predation have to optimize the trade-offs between predator avoidance and other fitness related activities such as foraging or reproduction. One challenge for prey is to identify which species pose a threat and should be avoided, and which species should be ignored. The goal of this study was to investigate whether minnows can generalize recognition of predators and non-predators using visual cues. I conducted experiments in both clear and turbid conditions to test whether the level of turbidity affects the quality of visual information available to the prey and hence the ability of prey to generalize. Latent inhibition and learned irrelevance are mechanisms of learning that can be used by prey to recognize stimuli as non-risky. Repeated exposure to an unknown stimulus in the absence of risk leads to the stimulus being categorized as non-risky. Fathead minnows were pre-exposed to the sight of brook trout or control water to provide minnows the opportunity to learn to recognize the trout as a non-predator. Following this the fish were conditioned with alarm cues (AC) to the sight of each predator paired and then their responses to the sight of brook trout, rainbow trout, and yellow perch were tested either in clear or turbid water. In clear water, minnows conditioned to recognize one of the trout species generalized their response to the other species. However, when the minnows were pre-exposed to the sight of a brook trout, they were inhibited from subsequently recognizing the sight of brook trout as threat and generalized this non-predator recognition to the sight of rainbow trout but not to yellow perch. In turbid water, however, minnows that were pre-exposed to the sight of brook trout had impaired responses to all predators while those pre-exposed to water showed an intermediate intensity anti-predator response toward each predator. Overall, my results demonstrate that minnows were able to distinguish between predators and non-predators in the clear environment but turbidity influences the visual information used by minnows and hence impaired the minnow’s ability to recognize and generalize the sight of predators and non-predator species.

Family history (FH) is a risk factor for many diseases. Disease guidelines often include family history as important in assessing chronic disease risks, but the empirical evidence base to inform the routine use of family history in primary care in practice appears largely lacking. An environmental scan of how family history is represented in prevention guidelines for five conditions showed that, while family history is often included in guidelines, there is variation in the definition used, recommendation given and evidence cited. A dataset on cardiovascular health in women was analyzed to examine whether family history offers useful discrimination value above standard risk factors. Regression results showed that family history is an independent risk predictor for coronary heart disease which improves discrimination beyond classical clinical factors. However, the absolute amount of discriminatory ability alone or with other factors is moderate at best, raising issues regarding clinical utility.

There are many situations where a linguistic description of complex phenomena allows better assessments. It is well known that the assessment of water quality continues depending heavily upon subjective judgments and interpretation, despite the huge datasets available nowadays. In that sense, the aim of this study has been to introduce intelligent linguistic operations to analyze databases, and produce self interpretable water quality indicators, which tolerate both imprecision and linguistic uncertainty. Such imprecision typically reflects the ambiguity of human thinking when perceptions need to be expressed. Environmental management concepts such as: "water quality", "level of risk", or "ecological status" are ideally dealt with linguistic variables. In the present Thesis, the flexibility of computing with words offered by fuzzy logic has been considered in these management issues. Firstly, a multipurpose hierarchical water quality index has been designed with fuzzy reasoning. It integrates a wide set of indicators including: organic pollution, nutrients, pathogens, physicochemical macro-variables, and priority micro-contaminants. Likewise, the relative importance of the water quality indicators has been dealt with the analytic hierarchy process, a decision-aiding method. Secondly, a methodology based on a hybrid approach that combines fuzzy inference systems and artificial neural networks has been used to classify ecological status in surface waters according to the Water Framework Directive. This methodology has allowed dealing efficiently with the non-linearity and subjective nature of variables involved in this classification problem. The complexity of inference systems, the appropriate choice of linguistic rules, and the influence of the functions that transform numerical variables into linguistic variables have been studied. Thirdly, a concurrent neuro-fuzzy model based on screening ecological risk assessment has been developed. It has considered the presence of hazardous substances in rivers, and incorporates an innovative ranking and scoring system, based on a self-organizing map, to account for the likely ecological hazards posed by the presence of chemical substances in freshwater ecosystems. Hazard factors are combined with environmental concentrations within fuzzy inference systems to compute ecological risk potentials under linguistic uncertainty. The estimation of ecological risk potentials allows identifying those substances requiring stricter controls and further rigorous risk assessment. Likewise, the aggregation of ecological risk potentials, by means of empirical cumulative distribution functions, has allowed estimating changes in water quality over time. The neuro-fuzzy approach has been validated by comparison with biological monitoring. Finally, a hierarchical fuzzy inference system to deal with sediment based ecological risk assessment has been designed. The study was centered in sediments, since they produce complementary findings to water quality analysis, especially when temporal trends are required. Results from chemical and eco-toxicological analyses have been used as inputs to two parallel inference systems which assess levels of contamination and toxicity, respectively. Results from both inference engines are then treated in a third inference engine which provides a final risk characterization, where the risk is provided in linguistic terms, with their respective degrees of certitude. Inputs to the risk system have been the levels of potentially toxic substances, mainly metals and chlorinated organic compounds, and the toxicity measured with a screening test which uses the photo-luminescent bacteria Vibrio fischeri. The Ebro river basin has been selected as case study, although the methodologies here explained can easily be applied to other rivers. In conclusion, this study has broadly demonstrated that the design of water quality indexes, based on fuzzy logic, emerges as suitable and alternative tool to support decision makers involved in effective sustainable river basin management plans. / Existen diversas situaciones en las cuales la descripción en términos lingüísticos de fenómenos complejos permite mejores resultados. A pesar de los volúmenes de información cuantitativa que se manejan actualmente, es bien sabido que la gestión de la calidad del agua todavía obedece a juicios subjetivos y de interpretación de los expertos. Por tanto, el reto en este trabajo ha sido la introducción de operaciones lógicas que computen con palabras durante el análisis de los datos, para la elaboración de indicadores auto-interpretables de calidad del agua, que toleren la imprecisión e incertidumbre lingüística. Esta imprecisión típicamente refleja la ambigüedad del pensamiento humano para expresar percepciones. De allí que las variables lingüísticas se presenten como muy atractivas para el manejo de conceptos de la gestión medioambiental, como es el caso de la "calidad del agua", el "nivel de riesgo" o el "estado ecológico". Por tanto, en la presente Tesis, la flexibilidad de la lógica difusa para computar con palabras se ha adaptado a diversos tópicos en la gestión de la calidad del agua. Primero, se desarrolló un índice jerárquico multipropósito de calidad del agua que se obtuvo mediante razonamiento difuso. El índice integra un extenso grupo de indicadores que incluyen: contaminación orgánica, nutrientes, patógenos, variables macroscópicas, así como sustancias prioritarias micro-contaminantes. La importancia relativa de los indicadores al interior del sistema de inferencia se estimó con un método de análisis de decisiones, llamado proceso jerárquico analítico. En una segunda fase, se utilizó una metodología híbrida que combina los sistemas de inferencia difusos y las redes neuronales artificiales, conocida como neuro-fuzzy, para el estudio de la clasificación del estado ecológico de los ríos, de acuerdo con los lineamientos de la Directiva Marco de Aguas. Esta metodología permitió un manejo adecuado de la no-linealidad y naturaleza subjetiva de las variables involucradas en este problema clasificatorio. Con ella, se estudió la complejidad de los sistemas de inferencia, la selección apropiada de reglas lingüísticas y la influencia de las funciones que transforman las variables numéricas en lingüísticas. En una tercera fase, se desarrolló un modelo conceptual neuro-fuzzy concurrente basado en la metodología de evaluación de riesgo ecológico preliminar. Este modelo consideró la presencia de sustancias peligrosas en los ríos, e incorporó un mapa auto-organizativo para clasificar las sustancias químicas, en términos de su peligrosidad hacia los ecosistemas acuáticos. Con este modelo se estimaron potenciales de riesgo ecológico por combinación de factores de peligrosidad y de concentraciones de las sustancias químicas en el agua. Debido a la alta imprecisión e incertidumbre lingüística, estos potenciales se obtuvieron mediante sistemas de inferencia difusos, y se integraron por medio de distribuciones empíricas acumuladas, con las cuales se pueden analizar cambios espacio-temporales en la calidad del agua. Finalmente, se diseñó un sistema jerárquico de inferencia difuso para la evaluación del riesgo ecológico en sedimentos de ribera. Este sistema estima los grados de contaminación, toxicidad y riesgo en los sedimentos en términos lingüísticos, con sus respectivos niveles de certeza. El sistema se alimenta con información proveniente de análisis químicos, que detectan la presencia de sustancias micro-contaminantes, y de ensayos eco-toxicológicos tipo "screening" que usan la bacteria Vibrio fischeri. Como caso de estudio se seleccionó la cuenca del río Ebro, aunque las metodologías aquí desarrolladas pueden aplicarse fácilmente a otros ríos. En conclusión, este trabajo demuestra ampliamente que el diseño y aplicación de indicadores de calidad de las aguas, basados en la metodología de la lógica difusa, constituyen una herramienta sencilla y útil para los tomadores de decisiones encargados de la gestión sostenible de las cuencas hidrográficas.

Contaminated areas resulting from anthropogenic activities have, for the most part, concentrations of contaminants that exceed Tier 1 standards below which the risk is considered acceptable. However, contaminants that have been in soil for a prolonged period can become recalcitrant over time, due to various physico-chemical and biological processes. Sequestered and recalcitrant contaminants are not readily biologically available to living organisms. However, they are easily measured analytically because of the strong acid extractions that are used in the analytical methodologies. Because toxicity is a function of exposure concentration(s), exposure duration, and bioavailability, contaminants in soil can be present at concentrations that exceed established standards but they represent minimal risk to ecological receptors because the contaminants are not fully available. To predict toxicity and estimate risk, it is imperative that an accurate and reliable measure of bioavailability be available. Several surrogate measures of bioavailability were compared to the results of a battery of toxicity tests using Cu, Pb, and Zn-contaminated soils collected from a former industrial area and Cu and Zn-contaminated soils collected from a former mining site. CaCl₂extractions, hydroxypropyl-β-cyclodextrin (cyclodextrin) extractions, Simulated Earthworm Gut (SEG) tests, and bioaccumulation tests were performed using the soils. Overall, SEG-extractable Cu was most predictive of adverse effects in industrial soils, likely due to enzymatic activity and/or increased ionic strength of the solution. For the mining soils, all chemical measures of bioavailability correlated with several biological responses; however, CaCl₂-extractable Cu and SEG-extractable Cu and Zn best predicted earthworm responses. Total Cu concentrations in soil correlated best with adverse effects to plants. No method was a good predictor of all biological effects for a single organism when data from the two sites were combined. The SEG test may provide a good indication of metal toxicity at contaminated sites with varying soil physico-chemical characteristics but further validation is required.

Purpose: The purpose of this dissertation was to examine the determinants of unplanned emergency department (ED) use by home care clients, the profile of older ED patients, their transitions from the ED, as well as the determinants of post discharge outcomes among older ED patients. The goal of this dissertation was to create theoretically driven, evidence-based, and practical risk identification methods for home care and the ED. Methods: First, a multi-year, census-level cohort study was conducted on home care clients in two Canadian provinces (N=617,035). Census-level data from RAI-HC assessments were linked to census-level ED records. A needs-based decision tree model – the ED Model – informed by the Andersen Behavioural Model, was created using decision tree analyses. The final model was validated on a separate data partition and compared to the ERA Index and the CARS. Multilevel analyses were conducted to test regional variation in model performance. Disease stratified analyses were also conducted to test model generalizability across common disease classes. Regression analyses determined the effect of predisposing and enabling factors within ED Model strata. Second, a multi-site, multi-province prospective cohort study was conducted, termed the Management of Older Persons in Emergency Departments (MOPED) Study, using a clinically representative sample of 2,101 older ED patients. The interRAI ED-CA was used to assess older ED patients, and a 90-day disposition was collected. The profile of older ED patents was examined. Best-subset regression analyses identified person-level determinants of acute inpatient admission. Two needs-based decision tree models – the ALC/LTC and ED Revisit Models – were created using decision tree analyses to determine the risk of ALC designation or LTC placement, and unplanned repeat ED visits, respectively. Both models were validated on separate data partitions. Multilevel analyses were conducted to test site-level variation in the models’ performance. Results: Overall, 41.2% of home care clients had at least one unplanned emergency department visit within 6 months of an assessment. Previous ED use, cardio-respiratory symptoms, cardiac conditions, and mood symptoms featured heavily in the ED Model. The ED Model provided moderate risk differentiation and clinical utility. It achieved an area under the curve of 0.62 (95% CI: 0.61-0.62) and showed clear differentiation in Kaplan-Meier plots using validation data. Multi-level analyses showed no regional variation. The ED Model significantly outperformed the similar tools specific to primary care with respect to overall accuracy and perceived clinical utility. Predisposing and enabling characteristics provided little added differentiation beyond evaluated need. The majority of older ED patients were dependent on others for basic tasks of daily living, and many had fragile informal care or lived alone. Triage acuity generally did not differentiate chronic geriatric disabilities and conditions. Previous ED or hospital use was associated with chronic geriatric disabilities and conditions as well as informal caregiver distress. The Admission Model found that multiple factors were associated with admission to inpatient acute care, including: acuity, instability, changes in ADL function, cognition, nutrition, and anhedonia. Overall, 20.7% of older ED patients admitted to acute care were designated ALC or discharged to LTC; whereas 39.5% of older ED patients discharged home had one or more repeat ED visits within 90 days. Cognitive, functional, and informal care indicators were predictive of ALC/LTC; whereas functional status and symptoms were predictive of repeat ED use. The ALC/LTC and ED Revisit Models provided good risk differentiation, achieving AUC’s of 0.74 (95% CI: 0.69-0.79) and 0.69 (95% CI: 0.63-0.74), respectively. The ALC/LTC and ED Revisit Models showed clear differentiation in Kaplan-Meier plots. Multi-level analyses showed no site-level variation in each models’ performance. Conclusions: This dissertation produced tangible and empirically-based risk assessment models for clinical practice in home care and the ED. The models developed in this dissertation can support the targeting of preventative services as well as better communication strategies between the ED and community supportive care, primary care, and inpatient acute care. Key questions related to the prevention of the risk pathways identified in each risk assessment model remain unanswered, and should be a focus of future research.

The present research was archival in nature. It was divided into three phases involving a comprehensive psychometric examination of a newly developed risk assessment measure designed to assess risk for sexual violence, guide treatment planning, assess readiness to change, and evaluate whether positive changes in risk are linked to reductions in recidivism risk among sexually offending youth–the Violence Risk Scale: Youth Sexual Offender version (VRS:YSO). Phase I focused on an examination of the basic psychometric properties of a young offender version of the Violence Risk Scale–Sexual Offender Version (VRS–SO; Wong et al., 2003), subsequently referred to as the VRS:YSO. The tool revision was based on a thorough review of the literature on male youth and adults who have sexually offended and selected readings on youth violence, as well as a review of a measure designed to assess risk for violence among violent youth (i.e., the VRS:YV). The developers of the VRS–SO completed the structural revisions to the tool. The resulting risk assessment measure included six static and 17 dynamic risk variables that are empirically and/or theoretically associated with an increased risk for sexual violence among youth. As part of its initial psychometric examination, the VRS:YSO was rated retrospectively from the comprehensive files of 99 male sexually offending youth who had received outpatient sex offender services (i.e., assessment and/or treatment) from the Saskatoon Health Region, Child and Youth Services (CYS)–Young Offender Program (YOP) from 1995 to 2008. Overall, the VRS:YSO showed good-to-excellent interrater reliability, sound item properties (i.e., internal consistency and item-total correlations), and a factor structure that is consistent with research on sexually offending youth and adults, as well as other measures designed to assess risk for sexual violence in youth. Phase II focused on the validation of the VRS:YSO through examining the concurrent, postdictive, predictive, and incremental validity of the measure. Moreover, the psychometric properties of existing specialized risk assessment measures (i.e., J-SOAP-II, ERASOR, and J-SORRAT-II) were examined to inform the limited, albeit growing, literature on risk assessment with sexually offending youth. All measures were rated from the same comprehensive youth files as in Phase I. Youth were followed-up for an average of 11.83 years (SD = 3.42, range = 3.89-17.41) starting from their first contact with the community post-adjudication (i.e., release from custody or commencement of a community sentence). Overall reconviction rates were 8% for sexual, 24% for any violent (sexual and nonsexual), and 37% for general (any) offending. In sum, there was good preliminary evidence for the concurrent and, to some extent, postdictive validity of the VRS:YSO. Furthermore, there was good evidence for the predictive validity of the measure, particularly with respect to any violence (sexual and nonsexual) and general (any) recidivism. There was also encouraging evidence, albeit offset seemingly by a small post-treatment N and thus restricted power, on the potential value of the therapeutic change score in the prediction of recidivism risk. These findings supported the value of the VRS:YSO as a new specialized risk assessment measure for sexually offending youth, particularly as it uniquely includes a systematic rubric for assessing change. Moreover, there was good evidence for the predictive validity of the J-SORRAT-II and certain components of the J-SOAP-II for sexual recidivism. There was also good evidence for the predictive validity of the J-SOAP-II and the ERASOR for violent (sexual and nonsexual) and general (any) recidivism. Lastly, Phase III examined the role of psychopathy-related personality features (as measured by the PCL:YV) in the criminal and treatment outcomes of youth who have sexually offended. The PCL:YV was rated from the same comprehensive youth files (in conjunction with the other measures referenced above). Overall, the PCL:YV significantly predicted violent (sexual and nonsexual) and general (any) reoffending, particularly among offenders with any peer/adult victims, but not sexual reoffending. Increasing psychopathy-related personality features were significantly associated with non-completion of treatment. Clinical implications of research findings, along with limitations and future research directions for each of the three phases of this research were discussed.

Modern societies increasingly depend on the reliable functioning of urban infrastructure systems in the aftermath of natural disasters such as hurricane and earthquake events. Apart from a sizable capital for maintenance and expansion, the reliable performance of infrastructure systems under extreme hazards also requires strategic planning and effective resource assignment. Hence, efficient system reliability and risk assessment methods are needed to provide insights to system stakeholders to understand infrastructure performance under different hazard scenarios and accordingly make informed decisions in response to them. Moreover, efficient assignment of limited financial and human resources for maintenance and retrofit actions requires new methods to identify critical system components under extreme events. Infrastructure systems such as highway bridge networks are spatially distributed systems with many linked components. Therefore, network models describing them as mathematical graphs with nodes and links naturally apply to study their performance. Owing to their complex topology, general system reliability methods are ineffective to evaluate the reliability of large infrastructure systems. This research develops computationally efficient methods such as a modified Markov Chain Monte Carlo simulations algorithm for network reliability, and proposes a network reliability framework (BRAN: Bridge Reliability Assessment in Networks) that is applicable to large and complex highway bridge systems. Since the response of system components to hazard scenario events are often correlated, the BRAN framework enables accounting for correlated component failure probabilities stemming from different correlation sources. Failure correlations from non-hazard sources are particularly emphasized, as they potentially have a significant impact on network reliability estimates, and yet they have often been ignored or only partially considered in the literature of infrastructure system reliability. The developed network reliability framework is also used for probabilistic risk assessment, where network reliability is assigned as the network performance metric. Risk analysis studies may require prohibitively large number of simulations for large and complex infrastructure systems, as they involve evaluating the network reliability for multiple hazard scenarios. This thesis addresses this challenge by developing network surrogate models by statistical learning tools such as random forests. The surrogate models can replace network reliability simulations in a risk analysis framework, and significantly reduce computation times. Therefore, the proposed approach provides an alternative to the established methods to enhance the computational efficiency of risk assessments, by developing a surrogate model of the complex system at hand rather than reducing the number of analyzed hazard scenarios by either hazard consistent scenario generation or importance sampling. Nevertheless, the application of surrogate models can be combined with scenario reduction methods to improve even further the analysis efficiency. To address the problem of prioritizing system components for maintenance and retrofit actions, two advanced metrics are developed in this research to rank the criticality of system components. Both developed metrics combine system component fragilities with the topological characteristics of the network, and provide rankings which are either conditioned on specific hazard scenarios or probabilistic, based on the preference of infrastructure system stakeholders. Nevertheless, they both offer enhanced efficiency and practical applicability compared to the existing methods. The developed frameworks for network reliability evaluation, risk assessment, and component prioritization are intended to address important gaps in the state-of-the-art management and planning for infrastructure systems under natural hazards. Their application can enhance public safety by informing the decision making process for expansion, maintenance, and retrofit actions for infrastructure systems.

This study investigates the risk of arsenic (As) exposure from staple diet to the communities in rural Bengal, even when they have been supplied with As safe drinking water. The results indicate that average accumulation of As in rice grain increases with decrease of grain size [extra-long slender (ELS): 0.04 mg kg-1; long slender (LS): 0.10 mg kg-1; medium slender (MS): 0.16 mg kg-1 and short bold (SB): 0.33 mg kg-1], however people living in the rural villages mostly prefer brown colored SB type of rice because of its lower cost. Among the vegetables generally consumed in rural villages, the accumulation of As is highest in the leafy type of vegetables (0.21 mg kg-1), compared to non-leafy (0.07 mg kg-1) and root  vegetables (0.10 mg kg-1). Arsenic predominantly accumulates in rice (&gt;90%) and vegetables (almost 100%) in inorganic species [As(III &amp; V)]. The estimates of exposure via dietary and drinking water routes show that when people are consuming water with As concentration &lt;10 μg L-1, the total daily intake of inorganic As (TDI-iAs) exceeds the previous provisional tolerable daily intake (PTDI) value of 2.1 μg day-1 kg-1 BW, recommended by World Health Organization (WHO) in 35% of the cases due to consumption of rice. Considerably high concentration of As in urine and saliva despite drinking of As safe water (&lt;10 μg L-1) further supports that dietary intake of As, mainly through consumption of rice could be alternative pathway of As exposure among the population. When the level of As concentration in drinking water is above 10 μg L-1, the TDI-iAs exceeds the previous  PTDI for all the participants. These results imply that when rice consumption is a significant contributor to the TDI-iAs, supplying water with As concentration at current national drinking water standard for India and Bangladesh (50 μg L-1) would place many people above the safety threshold of PTDI. When As concentration in drinking water exceeds 50 μg L-1 As exposure through drinking water largely predominates over the exposure through dietary intake. It is found that the consumption of vegetables in rural Bengal does not pose  significant health threat to the population independently. It is also revealed that cooking of rice with high volume of As safe (&lt;10 μg L-1) water can decrease both total and inorganic As content in cooked rice. However, the assessment of As exposure risk indicates that despite such lowering in As concentrations, still consumption of cooked rice is a significant pathway of As exposure to the population in rural Bengal. This study suggests that any effort to mitigate the As exposure of the villagers in Bengal must consider the risk of As exposure from rice consumption together with drinking water. / <p>QC 20130919</p> / EAECA-EURINDIA 2009-1665

During the last decades the problems caused by anthropogenic sound and noise in oceans have been recognized in public, by governments, and military. With the use of active sonar, different choices can be made to minimize the risk of damaging or disturbing marine mammals. For this purpose knowledge of sonar disturbance is crucial. There are methods for time or area planning, i.e. when and where to use active sonars, to avoid marine mammals. The purpose of this work is to find information in literature on marine mammal behaviour reactions to the sound of sonar pings, and to evaluate which of two different behavioural models used in risk assessment programs, the “varying response” model and the “avoidance” model, is more correct to use. Main focus is on sonars and marine mammals residing in Sweden, i.e. the harbour porpoise, grey seal, harbour seal and ringed seal. Behavioral results from other research areas such as bycatch, environmental, and strandings, together with other sound sources than sonars and other species, provide a broader picture of the situation in noisy oceans. For the harbor porpoise the “avoidance” model works well. It is a very shy species, which flees fast and far when it comes in contact with new things. With the seals however the “avoidance” model is probably less good, since their responses to sonar differ rather much. Hence, for these taxa we recommend to use the “various” model that takes into account such varying responses.