Characteristics of roadside air pollution in Korean metropolitan city (Daegu) over last 5 to 6 years: temporal variations, standard exceedances, and dependence on meteorological conditions

The present study performed a roadside data analysis to provide baseline data for exploring associations between environmental exposure to four gaseous pollutants and health effects on residents living near roadways. The yearly roadside concentrations of CO and SO₂ showed a well-defined decreasing trend, whereas those of NO₂ and O₃ exhibited the reverse trend. In most cases, the diurnal trends of the roadside concentrations were well-defined for all seasons, plus the daytime concentrations were higher than the nighttime concentrations. In contrast to the other target pollutants, the daytime O₃ concentrations observed at the roadside sites were lower than those observed at the residential site, likely due to high-levels of fresh NO from traffic emissions that rapidly react with O₃, thereby reducing the O₃ roadside level. The Sunday roadside concentrations of CO, NO₂, and SO₂ were similar to or somewhat lower than the weekday concentrations. Conversely, for O_3, the Sunday roadside concentrations were similar to or somewhat higher than the weekday concentrations. The higher O₃ concentrations on Sunday may be due to the reduced titration from a decrease in NO_x emissions under VOC-limited conditions (low VOC/NO_x conditions). The monthly averages of O₃ concentrations exhibited the reverse seasonal variation to the other target compounds, with peak O₃ concentrations between April and June, and the second peak between August and October. It is also suggested that for O₃, the 8-h standard is more stringent than the 1-h standard, while for NO₂ and SO₂, the 1-h standard is more stringent than the 24-h standard. The multiple regression equations obtained from the relationship between the concentrations and five meteorological parameters indicated that the number and type of meteorological variables in the equations varied according to the pollutant, monitoring station, or season.     

示踪技术用于气体泄漏扩散模拟的多元线性回归分析

采用江苏工业学院油气储运安全综合实验模型平台,利用示踪技术(以CO2为示踪剂),模拟了有害气体瞬时泄漏扩散的整个过程.并结合多元线性回归分析的基本方法,定量分析了气体泄漏扩散过程,建立了多元线性回归模型,初步确定了泄漏气体浓度与相关物理量的变化关系,在此基础上得到了2个定量评价指标:确定性系数R=0.991,相关系数r=0.995.研究表明,在实验室建立的试验模型能较好地反映现实情况,模型仿真度较高,数据拟合效果较好.研究可为评价模型的相似性提供理论参考,对有害气体泄漏扩散事故应急救援、实施有效的现场控制,具有一定的科学参考价值.     

油品真实蒸汽压多元非线性回归

油品蒸气压是从事油品蒸发损耗研究、计算油品蒸发损耗量的重要参数,但是仅有美国石油学会给出的诺模图,给利用计算机来计算储罐蒸发损耗带来了很大困难.为了利用计算机来计算储罐的蒸发损耗,运用Excel中的回归功能,对原始数据进行必要的转换,进行了真实蒸气压的多元非线性回归,回归出了油品真实蒸气压计算公式.结果显示,回归的方程具有很高的精度,利用回归方程编制了计算软件,为储罐蒸发损耗计算提供帮助.     

体育馆建筑内青年学生疏散预动作时间调查和分析

通过组织火灾应急疏散逃生演习、现场问卷调查等方式,统计了体育馆内人员的疏散预动作时间,进而建立多分变量逻辑斯蒂回归模型对该统计数据进行了研究.发现:男生选择疏散预动作时间为＜30 s的可能性是女生的0.886倍,选择疏散预动作时间为30-60 s的可能性是女生的2.818倍.预测人员疏散预动作时间为＜30 s、30 -60 s、＞60s的概率,男生分别为15.45％,75.61％、8.94％,而女生分别为24.14％,39.72％、36.14％;研究结果表明:人员性别的不同对疏散预动作时间有一定的影响,在体育馆类建筑中,青少年人员疏散预动作时间基本上小于60 s.     

建筑企业员工职业病危害认知影响因素的调查分析

随着建筑行业的持续快速发展,建筑企业作业场所中的职业病危害呈现出高发性、复杂性和持续性等特点.通过针对建筑企业员工的实际状况设计调查问卷获取第一手资料,并运用SPSS17.0统计软件建立相关分析、回归分析等模型进行数据分析,可以得知建筑企业作业场所员工遭受职业病危害的可能性与对安全知识的了解程度、从业时间、性别及文化背景这四个因素的线性相关性.通过对建筑企业作业人员职业病危害的调查与分析,辨识出影响职业病危害的人为因素,提出相关的建议措施以改善建筑企业作业场所职业病危害严重的局面,提高建筑企业员工自我保护意识,增加其职业病危害防护知识,减少职业病危害事故的发生.     

Factors associated with driving under the influence of alcohol

Objective: This study examined the risk factors of driving under the influence of alcohol (DUI) among drivers of specific vehicle categories (DSC). On the basis of this research, the variables related to DUI and involvement in traffic crashes were defined. The analysis was conducted for car drivers, bicyclists, motorcyclists, bus drivers, and truck drivers.

Method: The research sample included drivers involved in traffic crashes on the territory of Serbia in 2016 (60,666). Two types of analyses were conducted in this study. Logistic regression established the correlation between DUI and DSC and the The Technique for Order of Preference by Similarity to Ideal Solution (Multi-criteria decision making) method was applied to consider the scoring and explore the potential for the prevalence of DUI on the basis of 2 data sets (DUI and non DUI).

Results: The study results showed that driver error and male drivers were the 2 most significant risk factors for DUI, with the highest scores and potential for prevalence. The nonuse of restraint systems, driver experience, and driver age are the factors with a significant prediction of involvement in an accident and an insignificant prediction of DUI.

Conclusions: Following the development of the logistic prediction models for DUI drivers, testing of the model was conducted for 3 control driver groups: Car, motorcycle, and bicycle. The prediction model with a probability greater than 50% showed that 77% of car drivers were under the influence of alcohol. Similarly, the prediction percentage for motorcyclists and bicyclists amounted to 71 and 67%, respectively. The recommendation of the study is that drivers whose DUI probability is above 50% should be potentially suspected of DUI. The results of this study can help to understand the problem of DUI among specific driver categories and detect DUI drivers, with the aim of creating successful traffic safety policy.     

基于GPR-TOPSIS方法的充填配比多目标多属性优化

为获得某金矿尾砂胶结充填材料最优配比,基于试验结果,以海水比例、灰砂比和料浆质量浓度为输入参数,以充填体强度、塌落度及泌水率为输出参数,建立了充填配比与其响应量的高斯过程回归模型,分析了不同因素对充填性能的影响程度;采用遗传算法对高斯过程回归模型进行多目标参数优化,获得了Pareto非劣解,在此基础上,引入多属性决策的TOPSIS法对Pareto非劣解进行方案优选,确定了充填最优配比。研究结果表明:高斯过程回归模型相对误差值均小于6%,可靠性高;灰砂比及料浆质量浓度对充填性能影响较为显著,采用海水作为充填水源将降低充填体的强度;经优化后的充填配比与试验结果相符。     

公路隧道火灾发生位置与人群疏散通道仿真研究

利用EXODUS建立隧道的仿真场景,确定疏散人群仿真参数,并将出口工效OPS作为评价疏散效率的指标,分析不同火灾发生位置对疏散时间的影响。采用K-means算法分别对火灾发生位置和人群疏散通道位置进行聚类,并建立基于两者影响下的出口工效模型,通过对该模型求偏导,得出隧道火灾发生位置和人群疏散通道选择之间的关系。研究结果表明:火灾发生位置越接近人行横通道,疏散时间波动越大,人行横通道和隧道入口的疏散时间随人群疏散区域分界线变化,当火灾发生距人行横通道20~110 m时,变化明显,当距140~350 m时,变化平稳;出口工效OPS总体呈现不稳定的阶梯状变化趋势;火灾发生位置和人群疏散通道的最优位置呈线性递减关系。研究结果可应用于隧道应急指挥中心管理人员制定有效疏散路径和采取诱导分流人群措施。     

Regression analysis of forest inventory data with time and space dependencies

In this paper the data of a forest health inventory are analyzed. Since 1983 the degree of defoliation, together with various explanatory variables (covariates) concerning stand, site, soil and weather, are recorded by the second of the two authors, in the forest district of Rothenbuch (Spessart, Bavaria). The focus is on the space and time dependencies of the data. The mutual relationship of space-time functions and the set of covariates is evaluated. For this we use generalized linear models (GLMs) for ordinal response variables and semiparametric estimation approaches. By using goodness-of-fit measures it turns out that (i) the contribution of space-time functions is quantitatively comparable with that of the set of covariates, (ii) the contribution of space-time functions is small compared with the contribution of a set of variables describing the last-year and neighboring response values. By applying appropriate residual methods a detailed analysis of the individual sites in the area can be carried out. This analysis reveals where the predictive power of the covariates fail to explain the observed defoliation.