受限水域船舶积压风险测度方法研究

为了系统、客观地评估受限水域船舶积压衍生风险,实现对船舶积压风险的防控,进行船舶积压风险测度方法的研究。阐述了受限水域船舶积压风险的3种表现形式,提炼了风险表现形式的测评指标,提出了受限水域船舶积压指数,并建立了以二元Logistic回归为核心的风险测度模型,对积压风险测评指标进行量化处理,最后通过对三峡-葛洲坝枢纽船舶积压风险的实证分析验证测度方法的可行性。结果表明,受限水域船舶积压风险测度方法可以根据水域特征筛选积压风险主要测评指标,并最终得到积压衍生风险发生概率预测公式,预测并判断相应水域某一时期的船舶积压衍生风险严重程度,对实时测度和降低受限水域船舶积压风险、完善受限水域船舶通航安全预警和相关软件开发运用具有重要意义。     

Identifying factors affecting the safety of mid-block bicycle lanes considering mixed 2-wheeled traffic flow

Objective: Electric bikes (e-bikes) have been one of the fastest growing trip modes in Southeast Asia over the past 2 decades. The increasing popularity of e-bikes raised some safety concerns regarding urban transport systems. The primary objective of this study was to identify whether and how the generalized linear regression model (GLM) could be used to relate cyclists' safety with various contributing factors when riding in a mid-block bike lane. The types of 2-wheeled vehicles in the study included bicycle-style electric bicycles (BSEBs), scooter-style electric bicycles (SSEBs), and regular bicycles (RBs).

Methods: Traffic conflict technology was applied as a surrogate measure to evaluate the safety of 2-wheeled vehicles. The safety performance model was developed by adopting a generalized linear regression model for relating the frequency of rear-end conflicts between e-bikes and regular bikes to the operating speeds of BSEBs, SSEBs, and RBs in mid-block bike lanes.

Results: The frequency of rear-end conflicts between e-bikes and bikes increased with an increase in the operating speeds of e-bikes and the volume of e-bikes and bikes and decreased with an increase in the width of bike lanes. The large speed difference between e-bikes and bikes increased the frequency of rear-end conflicts between e-bikes and bikes in mid-block bike lanes. A 1% increase in the average operating speed of e-bikes would increase the expected number of rear-end conflicts between e-bikes and bikes by 1.48%. A 1% increase in the speed difference between e-bikes and bikes would increase the expected number of rear-end conflicts between e-bikes/bikes by 0.16%.

Conclusions: The conflict frequency in mid-block bike lanes can be modeled using generalized linear regression models. The factors that significantly affected the frequency of rear-end conflicts included the operating speeds of e-bikes, the speed difference between e-bikes and regular bikes, the volume of e-bikes, the volume of bikes, and the width of bike lanes. The safety performance model can help better understand the causes of crash occurrences in mid-block bike lanes.     

Can variations in visual behavior measures be good predictors of driver sleepiness? A real driving test study

Objective: The primary purpose of this study was to examine the association between variations in visual behavior measures and subjective sleepiness levels across age groups over time to determine a quantitative method of measuring drivers' sleepiness levels.

Method: A total of 128 volunteer drivers in 4 age groups were asked to finish 2-, 3-, and 4-h continuous driving tasks on expressways, during which the driver's fixation, saccade, and blink measures were recorded by an eye-tracking system and the subjective sleepiness level was measured through the Stanford Sleepiness Scale. Two-way repeated measures analysis of variance was then used to examine the change in visual behavior measures across age groups over time and compare the interactive effects of these 2 factors on the dependent visual measures.

Results: Drivers' visual behavior measures and subjective sleepiness levels vary significantly over time but not across age groups. A statistically significant interaction between age group and driving duration was found in drivers' pupil diameter, deviation of search angle, saccade amplitude, blink frequency, blink duration, and closure duration. Additionally, change in a driver's subjective sleepiness level is positively or negatively associated with variation in visual behavior measures, and such relationships can be expressed in regression models for different period of driving duration.

Conclusions: Driving duration affects drivers' sleepiness significantly, so the amount of continuous driving time should be strictly controlled. Moreover, driving sleepiness can be quantified through the change rate of drivers' visual behavior measures to alert drivers of sleepiness risk and to encourage rest periods. These results provide insight into potential strategies for reducing and preventing traffic accidents and injuries.     

Detection of the toughest: Pedestrian injury risk as a smooth function of age

Objective: Though it is common to refer to age-specific groups (e.g., children, adults, elderly), smooth trends conditional on age are mainly ignored in the literature. The present study examines the pedestrian injury risk in full-frontal pedestrian-to–passenger car accidents and incorporates age—in addition to collision speed and injury severity—as a plug-in parameter.

Methods: Recent work introduced a model for pedestrian injury risk functions using explicit formulae with easily interpretable model parameters. This model is expanded by pedestrian age as another model parameter. Using the German In-Depth Accident Study (GIDAS) to obtain age-specific risk proportions, the model parameters are fitted to the raw data and then smoothed by broken-line regression.

Results: The approach supplies explicit probabilities for pedestrian injury risk conditional on pedestrian age, collision speed, and injury severity under investigation. All results yield consistency to each other in the sense that risks for more severe injuries are less probable than those for less severe injuries. As a side product, the approach indicates specific ages at which the risk behavior fundamentally changes. These threshold values can be interpreted as the most robust ages for pedestrians.

Conclusions: The obtained age-wise risk functions can be aggregated and adapted to any population. The presented approach is formulated in such general terms that in can be directly used for other data sets or additional parameters; for example, the pedestrian's sex. Thus far, no other study using age as a plug-in parameter can be found.     

中国交通碳排放及影响因素时空异质性

选取30个省级行政单位作为空间单元,采用探索性空间数据分析（ESDA）方法对交通碳排放时空分布格局进行研究,同时考虑空间单元的差异性,构建地理加权回归（GWR）模型分析交通碳排放影响因素的时空异质性.研究发现：2000~2015年交通碳排放量呈现显著的空间聚类特征,聚类趋势逐年加强.双变量空间自相关指数为0.165~0.274,显著性水平介于0.016~0.045,表明交通碳排放同机动车保有量、GDP、货运周转量及客运周转量之间存在显著的空间正相关关系.GWR模型的R²在0.783~0.865之间,而OLS模型的R²在0.675~0.844之间,且GWR模型的AICc值均低于OLS模型的,说明GWR模型的拟合结果明显优于OLS模型,可以更好地解释交通碳排放的影响机制.GWR的回归结果表明碳排放的影响因素存在明显的时空异质性特征,其中GDP是主要的推动因素,部分地区回归系数高达0.91,2000年影响程度由东向西递减,而2005、2010和2015年由北向南递减.客运周转量起到关键的抑制作用,影响程度由东北向西南递减.因此建议应当充分考虑碳排放影响因素的时空异质性特征,制定差异化的碳减排政策.     

汉江下游河流型水华暴发的多影响要素特征识别

水体富营养化通常发生于湖泊和水库等闭合水域,在大型流动性河流中通常较少发生.然而,随着近年来人类活动的干扰,汉江中下游水华事件频繁暴发,给沿岸居民的饮用水安全带来了严重隐患.科学辨识可能导致汉江下游水华暴发的多要素变化特征,是揭示河流水华成因和优化上游水利工程调度的重要依据.基于收集的汉江流域下游气象、水文、水环境和水生态长序列数据,系统检测了可能导致汉江河流型水华暴发的多要素特征和不同时期的变化差异.结果表明:①汉江中下游降雨量（1961-2015年）下降、气温（1961-2012年）显著上升,汉江流域近50年气候逐渐呈现暖干的变化趋势.②1992-2013年汉江中下游径流变化呈显著下降趋势,在水华严重暴发的2008-2011年,汉江干流中下游主要断面年均流量和水位均处于历史上相对较低的一段时期.③2004-2014年汉江下游主要水环境指标变化趋势不显著,但总体水质状况较差,水华年ρ（COD_Mn）和ρ（TP）明显高于非水华年.④汉江水华暴发季节时间自2008年后有明显前移趋势,每年春季的2月中下旬-3月中旬将是水华暴发的重点防控时段.研究显示,汉江下游藻密度的变化相对于营养盐和水文情势要素更加敏感,在不同水华暴发时期的差异也最为显著,是导致河流型水华暴发的主要驱动因素.      

合肥市O3污染时空变化特征及影响因素分析

为掌握合肥市大气环境中O_3的时空变化特征及其影响因素,对合肥市2013—2015年空气质量监测数据和气象参数进行系统的分析.结果表明,合肥市O_3污染在夏、秋季节有逐渐加重的趋势,尤其是城市中心区域,呈显著的季节特征和日单峰性;O_3易在风速小于3.3 m·s-1、相对湿度介于50%~60%、地面气压低于1020 h Pa和日平均气温介于25~28℃的气象条件下出现浓度峰值;合肥市O_3浓度与NO2和VOCs分别存在显著的负相关和正相关关系,其中,烯烃、烷烃、芳香烃和炔烃对O_3生成潜势(OFP)的贡献值依次增大.O_3浓度可通过前一日O_3浓度、相对湿度、平均气温、PM2.5、当日8:00风速、平均气压及NO2等参数建立良好的统计预报模型,非标准化可决系数R2高达0.559.     

水源水消毒副产物生成势与UV254/DOC的相关性研究

使用XAD-4和XAD-8树脂对宜兴3个水源原水中DOM进行亲疏水性分类,采用优化方法测定分类水样的DBPFP,并进行DBPFP与DOM量化指标之间的相关性分析,以确定实际生产过程DBPs生成量的主要影响因素.由三维荧光光谱图得到3个水源原水DOM组成差异较大,水质区别明显.由亲疏水性分类分析发现原水中疏水性和亲水性DOM含量较高,疏水性DOM水样的DBPFP大于原水,得到水样的DBPFP主要取决于DOM与消毒剂反应产生DBPs的效率,而不是DOM的量.引入UV_(254)/DOC这个指标,发现不同分类水样UV_(254)/DOC的差异情况与其DBPFP相吻合,对两者进行回归分析,发现水样THMFP和DHAAFP与UV_(254)/DOC之间存在明显的线性关系,线性可决系数R2分别为0.89和0.85.因此研究减小原水UV_(254)/DOC的技术工艺对于控制给水中DBPs浓度具有重要意义.     

气象要素对气溶胶光学厚度估算PM2.5的影响

地面监测得到的近地面细颗粒物PM_(2.5)浓度较为精确,但数据覆盖范围相对较小,卫星遥感反演的气溶胶光学厚度(AOD)数据可以反映污染物浓度分布,具有范围大且速度快的特点,因此,大多数学者通过建立PM_(2.5)-AOD模型来实现卫星遥感监测PM_(2.5)浓度,并通过引入气象要素来优化模型.然而,气象要素的选择与引入往往对模型的精度有较大的影响,如何有效地选择对PM_(2.5)浓度影响较大的气象要素一直是PM_(2.5)-AOD模型中的关键问题.因此,本文基于华东地区2014—2015年的MODIS AOD和地面监测站的PM_(2.5)浓度数据,结合再分析气象资料,利用多元逐步线性回归方法建立PM_(2.5)-AOD模型,从由特定时刻、高度上的气象要素与随时间、高度变化的气象要素组成的气象要素集中,筛选出对因变量PM_(2.5)浓度有显著影响的关键气象要素.结果表明:在地域与季节双重尺度下的PM_(2.5)-AOD模型精度更高;相较于特定时刻高度的气象要素,随时间和高度变化的气象要素对PM_(2.5)-AOD模型的影响更为显著;在地域与季节双重尺度下,1000～850 hPa经向风速差、世界时0:00—6:00近地面温度差、850～600 hPa温度差、6:00边界层高度、12:00—18:00近地面压强差、1000～850 hPa温度差对模型影响较大,但应依据不同季节和不同地区的具体影响程度作为选择标准.     

Source apportionment of PM2.5 light extinction in an urban atmosphere in China

Haze in China is primarily caused by high pollution of atmospheric fine particulates (PM_2.5). However, the detailed source structures of PM_2.5 light extinction have not been well established, especially for the roles of various organic aerosols, which makes haze management lack specified targets. This study obtained the mass concentrations of the chemical compositions and the light extinction coefficients of fine particles in the winter in Dongguan, Guangdong Province, using high time resolution aerosol observation instruments. We combined the positive matrix factor (PMF) analysis model of organic aerosols and the multiple linear regression method to establish a quantitative relationship model between the main chemical components, in particular the different sources of organic aerosols and the extinction coefficients of fine particles with a high goodness of fit (R² = 0.953). The results show that the contribution rates of ammonium sulphate, ammonium nitrate, biomass burning organic aerosol (BBOA), secondary organic aerosol (SOA) and black carbon (BC) were 48.1%, 20.7%, 15.0%, 10.6%, and 5.6%, respectively. It can be seen that the contribution of the secondary aerosols is much higher than that of the primary aerosols (79.4% versus 20.6%) and are a major factor in the visibility decline. BBOA is found to have a high visibility destroying potential, with a high mass extinction coefficient, and was the largest contributor during some high pollution periods. A more detailed analysis indicates that the contribution of the enhanced absorption caused by BC mixing state was approximately 37.7% of the total particle absorption and should not be neglected.