A stochastic approach for risk analysis in vapor cloud explosion

A stochastic approach for evaluating the risk of vapor cloud explosions is proposed in this work. The proposed methodology aims to incorporate the effect of uncertainty into the risk analysis to produce a better overall view for the risk. Some stochastic variables are used to estimate the probability of vapor cloud explosions: frequency of the release, the probability of not having an immediate ignition, the probability of delayed ignition and the probability of a vapor cloud explosion given a delayed ignition, as well as different possible meteorological conditions. These stochastic variables are represented with probability distribution curves. Different curves for the frequencies of releases from process equipment types (steel process pipes, flanges, manual valves, actuated valves, etc.), different equipment diameters and different leak sizes are also used in this analysis. Monte Carlo simulation is performed to obtain the risk as a probability distribution using the Analytic Solver Platform. Then the risk distribution curve obtained by Monte Carlo simulation is used to estimate the probability of satisfying the risk tolerance criterion.     

关于制定连云港市水土保持生态建设规划的思考

连云港市地处欧亚大陆桥的东桥头堡,是沿海十四个开放城市之一,国家定位是将其建设成为连接太平洋沿岸与中亚地区的国际商贸中心和全国性的旅游城市。而目前的山丘区水土流失严重,植被稀少,生态环境恶化,与国家定位目标和连云港市发展目标相差甚远。因此在制定水土保持生态环境建设规划时,必须紧紧围绕改善生态环境这条主线,以增加农民收入和改善生存条件为突破口,因地制宜,坚持长期综合治理,方能实现“山川秀美”之目标。     

ENVIRONMENTAL AUDITING: An Approach for Characterizing Tropospheric Ozone Risk to Forests

/ The risk tropospheric ozone poses to forests in the United States is dependent on the variation in ozone exposure across the distribution of the forests in question and the various environmental and climate factors predominant in the region. All these factors have a spatial nature, and consequently an approach to characterization of ozone risk is presented that places ozone exposure-response functions for species as seedlings and model-simulated tree and stand responses in a spatial context using a geographical information systems (GIS). The GIS is used to aggregate factors considered important in a risk characterization, including: (1) estimated ozone exposures over forested regions, (2) measures of ozone effects on species' and stand growth, and (3) spatially distributed environmental, genetic, and exposure influences on species' response to ozone. The GIS-based risk characterization provides an estimation of the extent and magnitude of the potential ozone impact on forests. A preliminary risk characterization demonstrating this approach considered only the eastern United States and only the limited empirical data quantifying the effect of ozone exposures on forest tree species as seedlings. The area-weighted response of the annual seedling biomass loss formed the basis for a sensitivity ranking: sensitive-aspen and black cherry (14%-33% biomass loss over 50% of their distribution); moderately sensitive-tulip popular, loblolly pine, eastern white pine, and sugar maple (5%-13% biomass loss); insensitive-Virginia pine and red maple (0%-1% loss). In the future, the GIS-based risk characterization will include process-based model simulations of the three- to 5-year growth response of individual species as large trees with relevant environmental interactions and model simulated response of mixed stands. The interactive nature of GIS provides a tool to explore consequences of the range of climate conditions across a species' distribution, forest management practices, changing ozone precursors, regulatory control strategies, and other factors influencing the spatial distribution of ozone over time as more information becomes available.KEY WORDS: Ecological risk assessment; GIS; Ozone; Risk characterization; Forests; Trees     

生态经济论

本文按生态发展历史进程，论述了，论述了生态经济发展的必然性。     

浙江省生态环境可持续发展评价指标体系的建立

生态环境的可持续发展评价是生态安全与生态建设中的重要内容。本研究应用主成分分析法，收集浙江省1990～2000年的数据，对评价指标进行初步筛选．同时采用德尔斐法进行专家咨询，得到评价指标的分层排序。最后从初选的54个评价指标中，根据主成分分析法、专家咨询法的指标筛选结果，结合有关国家及地方生态环境建设规划，建立了由40个指标组成的浙江省生态环境可持续发展评价指标体系。     

《大气污染防治行动计划》对郑州市冬季PM2.5浓度及化学组成影响

基于《大气污染防治行动计划》实施前后,在污染严重的冬季（2013年和2018年12月同期）,采集郑州市监测站的PM_2.5样品,分析PM_2.5化学组成,通过对比分析PM_2.5中的EC、OC、水溶性离子和金属元素的浓度变化来评估PM_2.5浓度及化学组成的变化,同时选取不同阶段重污染过程,探究PM_2.5浓度及组成的变化. 结果表明：①郑州市冬季ρ（PM_2.5）平均值由2013年的（215.38 ±107.28） μg·m^-3 下降至2018年的（77.45 ±49.81） μg·m^-3,下降率高达64%. ②PM_2.5中EC、K⁺、SO₄^2-和Cl^-,分别下降了85%、80%、78%和72%;OC、NH₄⁺和NO₃^-下降幅度较小,分别为50%、41%和32%. ③与2013年冬季相比,2018年冬季OC/EC的值升高了2.6倍,二次有机碳在OC中占比升高至57%;同时,硫氧化率和氮氧化率的值分别升高了1.5倍和1.0倍,表明郑州市二次污染较为严重,二次转化程度升高. ④NO₃^-/SO₄^2-（质量比）由2013年的0.8 ±0.2升高至2018年2.5 ±1.0,表明郑州市移动源贡献上升并且超过固定源成为冬季大气污染的主要来源. ⑤不同阶段重污染过程对比结果显示,与2013年相比,2018年重污染过程中PM_2.5浓度下降显著,峰值浓度下降了61%,主要化学组成由OC、NO₃^-、SO₄^2-和NH₄⁺变为OC、NO₃^-和NH₄⁺. 研究结果表明郑州市一次排放源管控取得了显著的成效,但二次生成对PM_2.5贡献呈现升高趋势,因此未来需要关注二次生成的影响.     

水稻生产气象灾害危险性分析：—以湖南省常德市水稻生产为例

本文通过对研究区水稻气象灾害规律的研究和危险性评价,计算了不同等级的水稻综合风险率,为保险部门制定水稻保险费率提供了科学依据.     

Risk assessment of safety level in university laboratories using questionnaire and Bayesian network

Accidents in university laboratories not only create a great threat to students’ safety but bring significant negative social impact. This paper investigates the university laboratory safety in China using questionnaire and Bayesian network (BN) analysis. Sixteen influencing factors for building the Bayesian net were firstly identified. A questionnaire was distributed to graduate students at 60 universities in China to acquire the probability of safe/unsafe conditions for sixteen influencing factors, based on which the conditional probability of four key factors (human, equipment and material, environment, and management) was calculated using the fuzzy triangular theory and expert judgment. The determined conditional probability was used to develop a Bayesian network model for the risk analysis of university laboratory safety and identification of the main reasons behind the accidents. Questionnaire results showed that management problems are prominent due to insufficient safety education training and weak management level of management personnel. The calculated unsafe state probability was found to be 65.2%. In the BN analysis, the human factor was found to play the most important role, followed by equipment and material factor. Sensitive and inferential analysis showed that the most sensitive factors are personnel incorrect operation, illegal operation, and experiment equipment failure. Based on the analysis, countermeasures were proposed to improve the safe management and operation of university laboratories.