Search

Assessments of spatiotemporal fine particulate matter (PM2.5) concentrations are crucial for establishing risk maps and maintaining human health. This study spatiotemporally assessed PM2.5 concentrations in Taiwan by using multivariate indicator kriging (MVIK) according to current Taiwanese and US regulatory standards for annual average PM2.5 concentrations (15 and 12 μg/m3, respectively). First, multivariate integration was implemented to analyze data on PM2.5 concentrations for 2019–2021 and 2020–2022 because of no statistical difference of the 3-year PM2.5 data. MVIK was then used for modeling probabilities according to the two standards. Finally, quantile estimates on the basis of the occurrence probabilities of analyzing PM2.5 concentrations were employed to determine the optimal classifications for establishing risk maps according to the two PM2.5 standards. The study results indicated that the multivariate integration of temporal PM2.5 data in MVIK can effectively streamline the analytic process. The multivariate integration of 3-year PM2.5 data was suitable for assessing the risk categories of the regulatory standards for annual average PM2.5. The greatest estimated difference between the 2019–2021 and 2020–2022 multivariate integrations was in the Northern and Chumiao air quality regions. Because many air quality regions were in the PM2.5 categories of exceeding 12 μg/m3, the regulatory standard for annual average PM2.5 of 12 μg/m3 was inappropriate in Taiwan at this point in time according to assessing the 3-year spatiotemporal variability of PM2.5 concentrations. [ABSTRACT FROM AUTHOR]

Scientific and methodological aspects of ensuring the safety of offshore subsea pipelines according to accident risk criteria are considered. A conceptual approach to assessing the hazard of defects according to risk criteria is formulated by analyzing modern norms and requirements on pipeline design. A probabilistic model for assessing the risk of accidents at offshore subsea pipelines considering the randomness of defects and damage from accidents is developed. The criterial conditions for the permissibility of defects in pipelines are formulated. Two conceptual directions for developing a procedural framework for calculating the permissible defects versus risk criteria are proposed on this basis. The first direction consists in elaborating semi-probabilistic calculation procedures using differentiated margin factors, considering the accident risk level. The second direction consists in solving the problem of the probability of pipeline failure, taking into account the restrictions in the form of a preset risk value. The probability and scale of accidents are linked through a risk matrix. The calculation methodology is developed using a semi-probabilistic concept for the most typical defects in offshore subsea pipelines. The suitability of the pipeline for operation after in-line diagnostics is determined using a three-level assessment of permissible defect sizes. The first level is basic and determines permissible defect sizes according to the strength criteria for pipelines exposed to the main loads, i.e., internal overpressure and external hydrostatic external head. The second level is extended and determines permissible defect sizes according to the strength criteria, taking into account the impact of additional longitudinal and bending loads on pipelines. The third level is special and determines permissible dimensions of cracks and cracklike defects according to the crack resistance characteristics of the pipeline metal. The novelty of the method consists in the justification of the margin factors through the failure probability levels corresponding to a preset class of damage and losses. A scheme for making decisions on the permissibility of defects according to risk criteria has been developed. An example of the assessment of the hazard of defects in offshore subsea pipelines is presented. [ABSTRACT FROM AUTHOR]

Probabilistic information is used increasingly, from medical research to weather forecasting. The relationship between probability and causality requires an acceptable philosophical account. Social work, which contributes to healthy wellbeing, increasingly uses language of probabilistic causal relationships between harms and subsequent limitations to healthy functioning. This paper explores causal understandings of probabilistic knowledge using concepts of the theologian, Thomas Aquinas. Social welfare terminology regarding risk (such as factors that are 'causative of' child abuse) is explored using epistemological concepts from scholastic philosophy. Aquinas' anthropological concepts related to modern 'risk science' and his concepts of rationality, harm and prudence are applied to contemporary social welfare. [ABSTRACT FROM AUTHOR]

Precautionary Principles are often said to be appropriate for decision-making in contexts of uncertainty such as climate policy. Contexts of uncertainty are contrasted to contexts of risk depending on whether we have probabilities or not. Against this view, I argue that the risk-uncertainty distinction is practically irrelevant. I start by noting that the history of the distinction between risk and uncertainty is more varied than is sometimes assumed. In order to examine the distinction, I unpack the idea of having probabilities, in particular by distinguishing three interpretations of probability: objective, epistemic, and subjective probability. I then claim that if we are concerned with whether we have probabilities at all-regardless of how low their epistemic credentials are-then we almost always have probabilities for policy-making. The reason is that subjective and epistemic probability are the relevant interpretations of probability and we almost always have subjective and epistemic probabilities. In contrast, if we are only concerned with probabilities that have sufficiently high epistemic credentials, then we obviously do not always have probabilities. Climate policy, for example, would then be a case of decision-making under uncertainty. But, so I argue, we should not dismiss probabilities with low epistemic credentials. Rather, when they are the best available probabilities our decision principles should make use of them. And, since they are almost always available, the risk-uncertainty distinction remains irrelevant. [ABSTRACT FROM AUTHOR]

Flooding hazard evaluation is the basis of flooding risk assessment which has significances to natural environment, human life and social economy. This study develops a spatial framework integrating naïve Bayes (NB) and geographic information system (GIS) to assess flooding hazard at regional scale. The methodology was demonstrated in the Bowen Basin in Australia as a case study. The inputs into the framework are five indices: elevation, slope, soil water retention, drainage proximity and density. They were derived from spatial data processed in ArcGIS. NB as a simplified and efficient type of Bayesian methods was used, with the assistance of remotely sensed flood inundation extent in the sampling process, to infer flooding probability on a cell-by-cell basis over the study area. A likelihood-based flooding hazard map was output from the GIS-based framework. The results reveal elevation and slope have more significant impacts on evaluation than other input indices. Area of high likelihood of flooding hazard is mainly located in the west and the southwest where there is a high water channel density, and along the water channels in the east of the study area. High likelihood of flooding hazard covers 45 % of the total area, medium likelihood accounts for about 12 %, low and very low likelihood represents 19 and 24 %, respectively. The results provide baseline information to identify and assess flooding hazard when making adaptation strategies and implementing mitigation measures in future. The framework and methodology developed in the study offer an integrated approach in evaluation of flooding hazard with spatial distributions and indicative uncertainties. It can also be applied to other hazard assessments. [ABSTRACT FROM AUTHOR]