Earlier rice phenology as a result of climate change can increase the risk of cold damage during reproductive growth in northern Japan

The present study analyzed the impact of earlier rice phenology as a result of climate change on the risk of cold damage during reproductive growth using the historical temperature record from 1961 to 2010 at four locations in northern Japan. During this period, heading date has become 0.7-1.9 days earlier per 10 years. Air temperatures during the booting stage (6-15 days before heading) decreased by 0.18 °C per 10 years even though the air temperature on a given calendar date has increased slightly. The estimated potential yield losses caused by cold damage have increased since the start of the study period. Thus, the advance in phonological events as a result of global warming is likely to increase the risk of future yield losses, and this has important implications for future adaptation strategies (breeding new cultivars and changing crop management strategies) to reduce the risk of cold damage.     

自来水厂液氯贮罐氯气泄漏事故风险评价初探

针对自来水厂液氯贮罐发生泄露的事故风险,应用烟团模式进行了液氯泄露后污染物浓度预测,并根据预测浓度从环境标准和化学物质危险度两方面进行了事故风险评价探讨,得出致死风险.     

磁湖污染现状调查与分析

结合磁湖污染源调查对近5年水质变化进行分析,结果表明磁湖水体已达到了产生水华的营养物水平,水体的自净能力出现下降趋势,现在环境状况下不适宜再疏浚。提出进一步治理应调整水体功能定位,加大环境监管力度,推行植物修复生态治理技术,注重对生态的保护。     

Nonylphenol and octylphenol in human breast milk

Human milk is the most important form of nourishment for newborn children. Its consumption is strongly recommended by health authorities also for other important advantages. Unfortunately, in the last three decades a great number of investigations have shown the occurrence of several environmental contaminants in human milk, especially those with lipophilic properties. This study investigates the presence of nonylphenol, octylphenol (OP), nonylphenol monoethoxylate (NP1EO) and two octylphenol ethoxylates (OPEOs) (namely OP1EO and OP2EO), in human breast milk of Italian women. NP was the contaminant found at the highest levels with mean concentrations of 32 ng/mL, about two orders of magnitude higher than OP (0.08 ng/mL), OP1EO (0.07 ng/mL) and OP2EO (0.16 ng/mL). In the group of study a positive correlation among fish consumption and levels of NP in the milk was observed, in accordance with the evidence that seafood represents one of the most important sources of exposure to this group of contaminants in Italy. On the basis of the concentrations found in the breast milk samples, a maximum NP daily intake of 3.94 mug/kg/day can be calculated, which is close to the Tolerable Daily Intake (TDI) of 5 mug/kg body weight (bw) proposed by the Danish Institute of Safety and Toxicology. In the cases of OP no TDI is available, but its intake is at least six orders of magnitude lower than the NOAEL of 10 mg/kg/day derived from a two generation study on rats.     

A fuzzy expert system for soil characterization

As soil is a natural resource not always renewable, the risk characterization of contaminated soils is an issue of great interest. Artificial Intelligence (AI), based on Decision Support Systems (DSSs), has been developed for a wide range of applications in contaminated soil management. Decision trees have already shown to be easy to interpret and able to treat large scale applications. Fuzzy logic gives an improvement in the perturbations and the variance of the training data, due to the elasticity of fuzzy set formalism. In this study, we have developed a classificatory tool applied to characterize contaminated soil in function of human and environmental risks. Knowledge engineering for constructing the Soil Risk Characterization Decision Support System (SRC-DSS) involves three stages: knowledge acquisition, conceptual design and system implementation. A total of 26 parameters were divided into three groups to facilitate the configuration of the expert system: source attributes, transfer vector attributes, and local properties. Sixteen case studies were evaluated with the SRC-DSS. In comparison with other techniques, the results of the current study have shown that SRC-DDS is an excellent tool to classify and characterize soils according to the associated risk.     

Enhancing regional process safety management

As industrial operations expand, major incidents continue to affect people, damage facilities, and impact the environment. In the last 20 years, about 50% of these incidents occurred in facilities that had implemented some form of Process Safety Management (PSM) and 50% came about in smaller facilities that did not include such planning (Demichela et al., 2004). The objective of this article is to use PSM principles to create practical recommendations at the regional level, to complement those previously developed for singular facilities. This article compares Strathcona County Emergency Service (SCES) in Alberta with Technical Standards & Safety Authority (TSSA) in Ontario, with respect to safety, facility licensing, permit requirements, risk assessment procedures and land use planning aspects to determine PSM enhancements for SCES. Furthermore, for a better overview, two supplemental provincial organisations in Alberta, namely Alberta Boiler Safety Association (ABSA) and Safety Codes Council (SCC), were also considered. We proposed that SCES could develop more detailed facility-specific licensing procedures, auditing, and inspection. SCES could also provide details of accredited organisations that carry out inspections and audits on their behalf. When reviewing the quantitative risk assessment processes for SCES and TSSA, we recommend that SCES should update their probability data sources used in their cumulative risk assessment study. Based on the authors’ experience and gathered data, the use of additional facility practices such as safety management system, internal audits, and checklists can enhance incident prevention.     

Risky scenario identification in a risk perception scale for construction workers in Thailand

Introduction: Construction sites exhibit unique hazardous characteristics. Hence, investigating the causes of fatal and nonfatal accidents is extremely important to promote safety on construction sites. Literature shows a risk perception (RP) scale for construction workers; to expand the existing research, this study aims to identify risky scenarios for assessing the RP of Thai construction workers, with the goal of reducing workplace incidents. Method: The scale development process consists of four phases. Item development was also performed, followed by factor analysis. Reliability and validity assessments were finalized in the process. A survey of 500 construction workers in Thailand was conducted to investigate risky scenarios. Through the process, a total of 17 items remained in the final RP scale, and the reliability and validity of these items were confirmed. Results: The RP scale was affirmed to have four dimensions (probability, severity, worry, and unsafe). These dimensions of workers’ RP were negatively correlated with the workers’ risk-taking behaviors. This study further showed that participants with a high level of affective RP were more likely to have a high level of cognitive RP. Hence, those with a high level of RP tended to be involved in fewer risk-taking behaviors. Practical Applications: The final version of the scale was reliable and valid in determining the RP and risk-taking behaviors of construction workers in Thailand.     

Decision-making on process risk of Arctic route for LNG carrier via dynamic Bayesian network modeling

An increasing number of ships have chosen the suitable route to transport in Arctic waters during summer. Seeking a suitable model for risk decision-making in route planning is a necessary research topic at present. Due to its complex natural environment, there is significant uncertainty regarding ship navigation safety in Arctic waters. The process risk-based decision-making method to support route planning is established based on the dynamic Bayesian network (DBN) risk assessment model for LNG carrier collision with ice or obstacles in Arctic waters. The decision-making process for ship navigation is dynamically associated with time. Therefore, a Markov Chain (MC) is built for each dynamic node in Bayesian belief network (BBN) to realize DBN associated risk assessment, which is called process risk and is applied to decision-making. Three possible routes for ships sailing from the Vikitsky Strait to the Long Strait in Arctic waters were selected in conjunction with the objective daily change data of wind speed, temperature, wave height, and ice condition. Simulations for risk decision-making in the ship navigation process are performed. Application examples show that the ship selected either ROUTE2 (Vikitsky Strait – Laptev Sea – Sannikov Strait – Eastern Siberian Sea – Long Strait) or ROUTE3 (Vikitsky Strait – Laptev Sea – Proliv Dmitriya Lapteva – Eastern Siberian Sea – Long Strait) in August as the best navigable route.