净水厂氯气泄漏的环境风险分析

以丹阳净水厂为例,选取大气非正常排放模式,预测了液氯钢瓶阀门泄漏及爆炸对周围环境和保护目标的影响,并模拟了钢瓶爆裂事故的后果,计算出事故发生时的初始警戒区半径,提出了相应的氯气泄漏防范措施。     

室内空气污染预测方法的实证研究

在大型(30m3)、小型(1m3)环境箱实验获得数据的基础上构建释放模型,并将模拟结果和现场监测得到的浓度水平进行对比,对开发的室内空气污染预测方法进行了实证研究.以一新装修房间为例,分别利用大型和小型环境箱实验,研究了复杂整装材料和简单层状材料的释放规律,建立了相应材料的污染物释放模型;依据单元内物质守恒理论和污染物充分混合的假设将释放模型组合,建立了室内空气污染预测方法,并预测了该房间甲醛和TVOC(total volatile organic compounds)浓度的变化过程.在考虑了0.03ACH(air change per hour)的换气率之后,模型预测与现场监测的污染物浓度变化趋势基本吻合,甲醛和TVOC污染预测误差(正则化标准差)分别为2.8%和1.6%.模型分析表明,各污染源对于甲醛污染的贡献,家具＞涂料＞地板;对于TVOC污染的贡献,涂料＞地板＞家具.结论表明,该预测方法可以真实反映现场污染物浓度变化趋势,可以用来分析各污染源对于整体污染的贡献、指导装修材料的选择和作为室内污染评估和控制的有效工具.     

检验环境影响预测准确度统计方法的研究近况

开展环境影响评价工作近二十年来,国内外对预测结果准确度的检验尚无制定统一标准,而预测结果的准确与否,又直接影响到环境影响报告书的质量。为此,本文将国内外这方面的研究状况作以评论,以供从事环境影响评价和后评估的专业人员参考     

集合卡曼滤波算法对FARSITE模型林火蔓延预测的修正效果研究

针对火源位置输入偏差导致的FARSITE林火行为模型火线预测不准确的问题,提出了一种基于集合卡曼滤波算法的动态修正方法。利用FARSITE对复杂工况下的林火蔓延过程进行数值模拟,以火线位置为待修正参量,以均方根误差(RMSE)为评价指标,对算法的可行性进行了验证,并研究了算法的集合元素个数,观测数据标准差及同化频率对FARSITE预测偏差的修正效果的影响。结果表明:算法能显著提高FARSITE火线预测精度;逐时同化时:集合元素个数为5 时,算法的修正效果并不理想,随着集合元素个数增大,样本误差减小,修正效果得到改善,但增大到30以上时, 修正能力的提升就不再明显;观测数据标准差大小与RMSE值呈正相关;给定条件下当同化频率由1 h/次降低至2 h/次,整个模拟时长内的误差仍能得到较好控制,RMSE曲线并不会过快增长。     

基于链路预测的安全隐患管理研究*

为探寻安全隐患的发生规律，揭示安全隐患的演化特点，为管理人员提供建议和参考。以潞安集团司马煤业有限公司2009—2015年安全隐患记录为数据源，建立无向加权安全隐患共词网络，利用链路预测技术，寻找安全隐患的内在联系及发展趋势。结果表明:相比于共同邻居、资源分配等基于局部信息的相似性指标，资源分配指标能够更好地预测安全隐患各个关键词之间的变化联系；通过分析关键词共现关系，证实预测的准确性，预测出部分未来存在但当前未产生的节点关系。安全管理人员可基于此方法采取有针对性的预防措施，减少相关隐患的发生。     

Repeatability,Sensitivity, and Uncertainty Analyses of the BANCS Model Developed to Predict Annual Streambank Erosion Rates

Accelerated streambank erosion caused by channel instability can be the leading cause of sediment impairment of streams. Obtaining accurate streambank erosion rates for sediment budgeting and prioritizing mitigation efforts can be difficult and costly. One approach to quantifying streambank erosion rates is through the development and implementation of an empirically derived “Bank Assessment for Non‐point Source Consequences of Sediment” (BANCS) model. This study aims to improve the BANCS model application by evaluating repeatability between users and identifying sensitive and/or uncertain model inputs. Statistical analysis of streambank evaluations conducted by 10 different individuals suggests the implementation of the BANCS model may not be repeatable. This finding may be due to sensitive model inputs, such as streambank height and near‐bank stress level prediction method selection, and/or uncertain model inputs, such as bank material identification and the associated adjustment of erosion potential. Furthermore, it was found assessing streambanks as a group by obtaining a measure of central tendency from individual evaluations, as well as obtaining a higher level of training, may improve model implementation precision. Application of these suggestions may result in improved prediction of streambank erosion rates utilizing the BANCS model methodology.     

考虑随机误差的软土基坑沉降趋势分析及预测*

为准确分析基坑沉降变性规律，基于现场监测数据，通过卡尔曼滤波对趋势项及误差项进行分解，采用M-K检验对发展趋势进行评价，利用优化广义回归神经网络和差分整合移动平均自回归模型，构建基坑沉降分项预测模型，并将预测结果与发展趋势评价结果对比分析，以实现基坑沉降变形规律综合研究。结果表明:卡尔曼滤波能有效分解基坑沉降数据趋势项与误差项，相较于传统小波分解效果更佳；基坑沉降呈持续增加趋势，但趋势性逐渐减弱；预测结果相对误差均值均不大于2%，预测模型精度较高；沉降变形会进一步增加，但增加速率明显降低，与发展趋势分析结果一致，两者相互佐证分析结果准确性。研究结果为基坑沉降变形规律分析提供新思路。     

一种新的时间序列预报方法及其应用

本文叙述了作者提出的时间序列预报方法的基本原理,介绍了这一方法在应用过程中的发展及在实际部门应用的情况,其中包括异常旱涝灾害预报及方法的改进等最新成果。     

STATISTICAL CONSIDERATIONS ON THE RANDOMNESS OF ANNUAL MAXIMUM DAILY RAINFALL1

ABSTRACT: Annual maximum daily rainfall data from nine stations throughout the southern slopes of the Eastern Italian Alps with record length of 67–68 years have been analyzed with the aim of verifying if their internal structure justifies the assumption of independence and identical distribution, or the “White noise hypothesis.” The approach is to consider the hypothesis H₀ of white noise as the intersection of several sub-hypotheses, each concerning one of the characteristics of a white noise process. To this end the nine series were subjected to various statistical tests regarding randomness, independence, change-points, and predictability. The results are examined first individually and then globally. They indicate that in eight of the nine considered time series the “white noise hypothesis” was rejected.