A Bayesian hierarchical model for monitoring harbor seal changes in Prince William Sound, Alaska

Bayesian hierarchical models were used to assess trends of harbor seals, Phoca vitulina richardsi, in Prince William Sound, Alaska, following the 1989 Exxon Valdez oil spill. Data consisted of 4–10 replicate observations per year at 25 sites over 10 years. We had multiple objectives, including estimating the effects of covariates on seal counts, and estimating trend and abundance, both per site and overall. We considered a Bayesian hierarchical model to meet our objectives. The model consists of a Poisson regression model for each site. For each observation the logarithm of the mean of the Poisson distribution was a linear model with the following factors: (1) intercept for each site and year, (2) time of year, (3) time of day, (4) time relative to low tide, and (5) tide height. The intercept for each site was then given a linear trend model for year. As part of the hierarchical model, parameters for each site were given a prior distribution to summarize overall effects. Results showed that at most sites, (1) trend is down; counts decreased yearly, (2) counts decrease throughout August, (3) counts decrease throughout the day, (4) counts are at a maximum very near to low tide, and (5) counts decrease as the height of the low tide increases; however, there was considerable variation among sites. To get overall trend we used a weighted average of the trend at each site, where the weights depended on the overall abundance of a site. Results indicate a 3.3% decrease per year over the time period.     

污水塘自净作用及环境因素的影响

本文研究了在自然状况下污水塘的净化作用及其效率,通过对总氮、总磷、COD_(Cr)和BOD_5等在进、出水中的含量与水温进行回归分析,建立了三者之间的相关性方程。同时讨论了水温对污水塘的自净效率的影响。     

Learning in participatory environmental governance – its antecedents and effects. Findings from a case survey meta-analysis

ABSTRACT

Theory on participatory and collaborative governance maintains that learning is essential to achieve good environmental outcomes. Empirical research has mostly produced individual case studies, and reliable evidence on both antecedents and environmental outcomes of learning remains sparse. Given conceptual ambiguities in the literature, we define governance-related learning in a threefold way: learning as deliberation; as knowledge- and capacity-building; and as informing environmental outputs. We develop nine propositions that explain learning through factors characterizing governance process and context, and three propositions explaining environmental outcomes of learning. We test these propositions drawing on the ‘SCAPE’ database of 307 published case studies of environmental decision-making, using multiple regression models. Results show that learning in all three modes is explained to some extent by a combination of process- and context-related factors. Most factors matter for learning, but with stark differences across the three modes of learning, thus demonstrating the relevance of this differentiated approach. Learning modes build on one another: Deliberation is seen to explain both capacity building and informed outputs, while informed outputs are also explained by capacity building. Contrary to our expectations, none of the learning variables was found to significantly affect environmental outcomes when considered alongside the process- and context-related variables.     

Modelling the relationship of driver license and offense history with fatal and serious injury (FSI) crash involvement

Introduction: Previous research has indicated that increases in traffic offenses are linked to increased crash involvement rates, making reductions in offending an appropriate measure for evaluating road safety interventions in the short-term. However, the extent to which traffic offending predicts fatal and serious injury (FSI) crash involvement risk is not well established, prompting this new Victorian (Australia) study. Method: A preliminary cluster analysis was performed to describe the offense data and assess FSI crash involvement risk for each cluster. While controlling demographic and licensing variables, the key traffic offenses that predict future FSI crash involvement were then identified. The large sample size allowed the use of machine learning methods such as random forests, gradient boosting, and Least Absolute Shrinkage and Selection Operator (LASSO) regression. This was done for the ‘all driver’ sample and five sometimes overlapping groups of drivers; the young, the elderly, and those with a motorcycle license, a heavy vehicle license endorsement and/or a history of license bans. Results: With the exception of the group of drivers who had a history of bans, offense history significantly improved the accuracy of models predicting future FSI crash involvement using demographic and licensing data, suggesting that traffic offenses may be an important factor to consider when analyzing FSI crash involvement risk and the effects of road safety countermeasures. Conclusions: The results are helpful for identifying driver groups to target with further road safety countermeasures, and for showing that machine learning methods have an important role to play in research of this nature. Practical Application: This research indicates with whom road safety interventions should particularly be applied. Changes to driver demerit policies to better target offenses related to FSI crash involvement and repeat traffic offenders, who are at greater risk of FSI crash involvement, are recommended.     

Assessing the likelihood of secondary crashes on freeways with Adaptive Signal Control System deployed on alternate routes

Introduction: Reducing the likelihood of freeway secondary crashes will provide significant safety, operational and environmental benefits. This paper presents a method for assessing the likelihood of freeway secondary crashes with Adaptive Signal Control Systems (ASCS) deployed on alternate routes that are typically used by diverted freeway traffic to avoid any delay or congestion due to a freeway primary crash. Method: The method includes four steps: (1) identification of secondary crashes, (2) verification of alternate routes, (3) assessment of the likelihood of secondary crashes for freeways with ASCS deployed on alternate routes and non-ASCS (i.e. pre-timed, semi- or fully-actuated) alternate routes, and (4) investigation of unobserved heterogeneity of the likelihood of freeway secondary crashes. Four freeway sections (i.e., two with ASCS deployed on alternate routes and two non-ASCS alternate routes) in South Carolina are considered. Results and Conclusions: Findings from the logistic regression modeling reveal significant reduction in the likelihood of secondary crashes for one freeway section (i.e., Charleston I-26 E) with ASCS deployed on alternate route. Other factors such as rear-end crash, dark or limited light, peak period, and annual average daily traffic contribute to the likelihood of freeway secondary crashes. Furthermore, random-parameter logistic regression model results for Charleston I-26 E reveal that unobserved heterogeneity of ASCS effect exists across the observations and ASCS are associated with the reduction of the likelihood of freeway secondary crashes for 84% of the observations (i.e., primary crashes). Location of the primary crash on the freeway is observed to affect the benefit of ASCS toward freeway secondary crash reduction as the primary crash’s location determines how many upstream freeway vehicles will be able to take the alternate route. Practical Applications: Based on the findings, it is recommended that the South Carolina Department of Transportation (SCDOT) considers deploying ASCS on alternate routes parallel to freeway sections where high percentages of secondary crashes are found.     

基于感知失衡的外卖配送交通安全研究*

为降低外卖配送交通事故发生概率,通过有序多分类Logistic回归分析个体属性与交通事故关联性,构建交通安全风险感知与配送效率感知失衡模型,分析外卖配送交通安全影响因素及路径关系。结果表明:配送员年龄、入行渠道对交通事故影响显著;COVID-19健康成本感知对交通安全风险感知和配送效率感知的失衡有显著中介调节作用。要实现交通感知平衡状态,互联网平台企业有必要按配送员年龄段、就业渠道实施差异化管理,树立良好交通安全意识。研究结果可为破解外卖配送“交通文明洼地”提供启示。     

Primary and secondary components of PM2.5 in Milan (Italy)

In sampling campaigns--carried out by means of a high-volume gravimetric sampler--performed between August 2002 and December 2003, 24-h PM2.5 samples have been collected at an urban background site in downtown Milan and analyzed for elemental and organic carbon, ionic species (i.e., chloride, nitrates, sulfates and ammonium) and some elemental species. Chemical speciation data are evaluated also in terms of primary and secondary components of fine particulate matter: in particular, the contribution of secondary organic aerosols (SOA) and of the primary contribution from traffic to observed PM2.5 concentration levels are evaluated by means of the EC tracer method.     

电子产品中有毒有害物质评价分析

随着电子产品产量的不断增长,电子产品的污染问题日益成为关系国计民生的重大问题。应用主成分分析原理对电子产品有毒有害物质进行评价分析。建立评价指标体系,选取具有代表性的主成分指标代替原来的评价指标,对电子产品有毒有害物质进行主成分分析,所得结果合理可信。     

基于零膨胀负二项回归模型的内河水上交通人命安全分析

运用零膨胀负二项回归模型,分析水上交通事故中人员死亡失踪数量的各影响因素的影响程度。通过Pearson相关分析得到与水上交通事故中人员死亡失踪人数密切相关的因素,建立ZINB回归模型得到各因素的参数估计值。运用弹性分析方法对不同影响因素的影响程度进行评估,根据影响程度对各影响因素进行排序。将该方法应用于长江海事局辖区范围内的水上交通事故人员死亡失踪分析,取得了理想效果。     

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。