事故前期发展过程浅析

任何事故发生之前都有一段发展演变的过程.当失误不断积累,超过系统或组织自身的自适应能力以后,一旦激发事件出现,就有可能导致事故的发生.本文在分析事故致灾条件的基础上,将其前期发展过程分为了7个阶段,得出了事故前期发展过程流程图.     

液化气运输过程中的危险分析

应用各种爆炸后果估计模型对液化气运输过程中的爆炸后果进行了评估，并用ETA分析方法对存在的危险进行了分析，指出了控制措施。     

NITRATE CONCENTRATIONS IN ILLINOIS RIVERS 1967 TO 1974: COMPARISON AMONG REPORTING AGENCIES1

ABSTRACT: Long term data on surface water quality can sometimes be assembled by combining data collected by different agencies at different times and assuming that between agency differences in data quality are insignificant. The objective of this paper was to assess the quality of riverine nitrate (NO₃) concentrations in Illinois measured and reported by four agencies from 1967 to 1974 by comparing median values for similar sampling locations and periods. A total of 17 river reaches were identified for which two agencies reported NO₃ concentrations during similar periods. Nonparametric comparison of median values and analysis of covariance with discharge as a covariant produced similar results. Nitrate concentrations reported by the U.S. Geological Survey (USGS) from 1967 to 1971 were not statistically (P > 0.05) different from values reported by the Illinois State Water Survey (ISWS) for two of three river reaches. Additionally, NO₃ concentrations reported by USGS from 1972 to 1974 were not statistically different than concentrations reported by the Illinois Environmental Protection Agency (IEPA) for four of five river reaches. From 1969 to 1971, NO₃ concentrations reported by the Illinois Department of Public Heath and the Illinois Environmental Protection Agency (IDPH/IEPA) were less than one‐fourth the magnitude of values reported by ISWS. The median NO₃ concentrations measured by the Central Illinois Public Service (CIPS) were significantly greater than those measured by USGS and IDPH/IEPA in the three comparable sampling locations. The use of NO₃ concentrations measured by CIPS and IDPH/IEPA prior to 1972 is not recommended.     

AN EVALUATION OF THE AVAILABLE TECHNIQUES FOR ESTIMATING MISSING FECAL COLIFORM DATA1

ABSTRACT: This paper presents the findings of a study aimed at evaluating the available techniques for estimating missing fecal coliform (FC) data on a temporal basis. The techniques investigated include: linear and nonlinear regression analysis and interpolation functions, and the use of artificial neural networks (ANNs). In all, seven interpolation, two regression, and one ANN model structures were investigated. This paper also investigates the validity of a hypothesis that estimating missing FC data by developing different models using different data corresponding to different dynamics associated with different trends in the FC data may result in a better model performance. The FC data (counts/100 ml) derived from the North Fork of the Kentucky River in Kentucky were employed to calibrate and validate various models. The performance of various models was evaluated using a wide variety of standard statistical measures. The results obtained in this study are able to demonstrate that the ANNs can be preferred over the conventional techniques in estimating missing FC data in a watershed. The regression technique was not found suitable in estimating missing FC data on a temporal basis. Further, it has been found that it is possible to achieve a better model performance by first decomposing the whole data set into different categories corresponding to different dynamics and then developing separate models for separate categories rather than developing a single model for the composite data set.     

污染物排放相关指标计算方法的研究

针对传统的污染排放数据指标计算方法存在的问题,提出污染物排放指标应根据排污强度和经济指标确定的新思路,同时提出污染物排放指标数据的快速测算模型设计.建议通过环境监测数据或物料衡算数据确定每个排污单位的平均排污强度,作为计算机处理的参数,再由该排污单位申报每月的经济指标(产量或产值),输入计算机计算其产污量、排污量和去除量.实施计算机管理的关键是选取适当的数据计算模型.新思路应该能够规范计算程序,建立污染源动态数据库,对排污数据实行有效的统计分析.     

BASIN SCALE WATER MANAGEMENT AND FORECASTING USING ARTIFICIAL NEURAL NETWORKS1

ABSTRACT: Water scarcity in the Sevier River Basin in south‐central Utah has led water managers to seek advanced techniques for identifying optimal forecasting and management measures. To more efficiently use the limited quantity of water in the basin, better methods for control and forecasting are imperative. Basin scale management requires advanced forecasts of the availability of water. Information about long term water availability is important for decision making in terms of how much land to plant and what crops to grow; advanced daily predictions of streamflows and hydraulic characteristics of irrigation canals are of importance for managing water delivery and reservoir releases; and hourly forecasts of flows in tributary streams to account for diurnal fluctuations are vital to more precisely meet the day‐to‐day expectations of downstream farmers. A priori streamflow information and exogenous climate data have been used to predict future streamflows and required reservoir releases at different timescales. Data on snow water equivalent, sea surface temperatures, temperature, total solar radiation, and precipitation are fused by applying artificial neural networks to enhance long term and real time basin scale water management information. This approach has not previously been used in water resources management at the basin‐scale and could be valuable to water users in semi‐arid areas to more efficiently utilize and manage scarce water resources.     

官方媒体参与下不同风险感知群体竞争模型及其演化规律

为识别公众风险感知的演化规律和官方媒体参与的作用机制，首先，根据公众的行为表现划分不同风险感知群体，并分析群体间的相互竞争和官方媒体的扰动作用；然后，通过扩展Lotka Volterra模型建立群体间的相互竞争模型；在此基础上，通过推导模型平衡点及其稳定性分析模型演化规律；最后，运用数值仿真考查了官方媒体的参与对模型演化的影响。研究结果表明:模型演化具有3种结果情景，官方媒体参与能够显著改变模型演化的速度和结果。     

Connecting the Dots: Public Engagement with Environmental Data

Air pollution and other environmental hazards are often imperceptible and need to be made publicly visible. The paper argues for the importance of visualizations in drawing public attention to imperceptible hazards and in providing the public with access to empirical data describing the risks. It also argues for critical inquiry into hazards’ selective visibility as it is produced by visualizations. The impact of visualizations and their selective visibility are considered through the example of a public art project called Particle Falls installed in 2014 in Pittsburgh, a city with a long history of both ignoring air pollution and working to ameliorate this problem. I examine the impact and selective visibility of Particle Falls by considering the underlying production of data, as well as context and support systems for this visualization, and by comparing it with other visualizations of local air quality.     

Quantifying population declines based on presence‐only records for red‐list assessments

Accurate trend estimates are necessary for understanding which species are declining and which are most in need of conservation action. Imperfect species detection may result in unreliable trend estimates because this may lead to the overestimation of declines. Because many management decisions are based on population trend estimates, such biases could have severe consequences for conservation policy. We used an occupancy‐modeling framework to estimate detectability and calculate nationwide population trends for 14 Swiss amphibian species both accounting for and ignoring imperfect detection. Through the application of International Union for Conservation of Nature Red List criteria to the different trend estimates, we assessed whether ignoring imperfect detection could affect conservation policy. Imperfect detection occurred for all species and detection varied substantially among species, which led to the overestimation of population declines when detectability was ignored. Consequently, accounting for imperfect detection lowered the red‐list risk category for 5 of the 14 species assessed. We demonstrate that failing to consider species detectability can have serious consequences for species management and that occupancy modeling provides a flexible framework to account for observation bias and improve assessments of conservation status. A problem inherent to most historical records is that they contain presence‐only data from which only relative declines can be estimated. A move toward the routine recording of nonobservation and absence data is essential if conservation practitioners are to move beyond this toward accurate population trend estimation.     

The Role of Disaggregation of Asset Values in Flood Loss Estimation: A Comparison of Different Modeling Approaches at the Mulde River,Germany

In loss estimation there is a spatial mismatch of hazard data that are commonly modeled on an explicit raster level and exposure data that are often available only for aggregated administrative units. Usually disaggregation methods that use ancillary information to distribute lumped exposure data in a finer spatial resolution help to bridge this gap. However, the actual influence of different mapping techniques and ancillary data on the final loss estimation has not been analyzed yet. In this paper three methods are applied to disaggregate residential building assets using two kinds of land use/land cover (LULC) data. The resulting disaggregated assets are validated and compared using census data of the residential building number on the community and constituency level. In addition, the disaggregated assets are taken to estimate residential building losses due to the flood in August 2002 in 21 municipalities on the River Mulde in Saxony, Germany. Losses are calculated with the help of four loss models. In general, disaggregation helps to decrease the error variance within the loss estimation. It must, however, be stated that the application of sophisticated disaggregation methods does not lead to significant improvements compared to the straightforward binary method. Therefore more effort should instead be put into the provision of high-resolution LULC data. Finally, the remaining uncertainties in loss estimation are high and demand further improvements in all modeling aspects.