Extreme value analysis of Munich air pollution data

We present three different approaches to modelling extreme values of daily air pollution data. We fitted a generalized extreme value distribution to the monthly maxima of daily concentration measures. For the exceedances of a high threshold depending on the data, the parameters of the generalized Pareto distribution were estimated. Accounting for autocorrelation, clusters of exceedances were used. To obtain information about the relationship of the exceedance of the air quality standard and possible predictors we applied logistic regression. Results and their interpretation are given for daily average concentrations of ozone and nitrogen dioxide at two monitoring sites within the city of Munich.     

Empirical Bayes calculations of concordance between endpoints in environmental toxicity experiments

Hierarchical models are considered for estimating the probability of agreement between two outcomes or endpoints from an environmental toxicity experiment. Emphasis is placed on generalized regression models, under which the prior mean is related to a linear combination of explanatory variables via a monotone function. This function defines the scale over which the systematic effects are modelled as additive. Specific illustration is provided for the logistic link function. The hierarchical model employs a conjugate beta prior that leads to parametric empirical Bayes estimators of the individual agreement parameters. An example from environmental carcinogenesis illustrates the methods, with motivation derived from estimation of the concordance between two species carcinogenicity outcomes. Based on a large database of carcinogenicity studies, the inter-species concordance is seen to be reasonably informative, i.e. in the range 67–84%. Stratification into pertinent potency-related sub-groups via the logistic model is seen to improve concordance estimation: for environmental stimuli at the extremes of the potency spectrum, concordance can reach well above 90%.     

地震应急救援后勤保障质量评价体系的探讨

建立科学的地震应急救援后勤服务质量评价体系，客观评价地震应救援急后勤保障服务水平，是规范管理地震应急救援后勤保障质量的重要手段。本文提出在进行地震应急救援后勤保障质量评价时，调查项目必须根据地震现场后勤工作的需求特点合理设计，取样大小、统计方法必须科学规范。     

Analysis of risk factors affecting driver injury and crash injury with drivers under the influence of alcohol (DUI) and non-DUI

Objective: The objective of this research was to study risk factors that significantly influence the severity of crashes for drivers both under and not under the influence of alcohol.

Methods: Ordinal logistic regression was applied to analyze a crash data set involving drivers under and not under the influence of alcohol in China from January 2011 to December 2014.

Results: Four risk factors were found to be significantly associated with the severity of driver injury, including crash partner and intersection type. Age group was found to be significantly associated with the severity of crashes involving drivers under the influence of alcohol. Crash partner, intersection type, lighting conditions, gender, and time of day were found to be significantly associated with severe driver injuries, the last of which was also significantly associated with severe crashes involving drivers not under the influence of alcohol.

Conclusions: This study found that pedestrian involvement decreases the odds of severe driver injury when a driver is under the influence of alcohol, with a relative risk of 0.05 compared to the vehicle-to-vehicle group. The odds of severe driver injury at T-intersections were higher than those for traveling along straight roads. Age was shown to be an important factor, with drivers 50–60 years of age having higher odds of being involved in severe crashes compared to 20- to 30-year-olds when the driver was under the influence of alcohol.

When the driver was not under the influence of alcohol, drivers suffered more severe injuries between midnight and early morning compared to early nighttime. The vehicle-to-motorcycle and vehicle-to-pedestrian groups experienced less severe driver injuries, and vehicle collisions with fixed objects exhibited higher odds of severe driver injury than did vehicle-to-vehicle impacts. The odds of severe driver injury at cross intersections were 0.29 compared to travel along straight roads. The odds of severe driver injury when street lighting was not available at night were 3.20 compared to daylight. The study indicated that female drivers are more likely to experience severe injury than male drivers when not under the influence of alcohol. Crashes between midnight and early morning exhibited higher odds of severe injury compared to those occurring at other times of day.

The identification of risk factors and a discussion on the odds ratio between levels of the impact of the driver injury and crash severity may benefit road safety stakeholders when developing initiatives to reduce the severity of crashes.     

Modeling resource selection using polytomous logistic regression and kernel density estimates

Wildlife resource selection studies typically compare used to available resources; selection or avoidance occurs when use is disproportionately greater or less than availability. Comparing used to available resources is problematic because results are often greatly influenced by what is considered available to the animal. Moreover, placing relocation points within resource units is often difficult due to radiotelemetry and mapping errors. Given these problems, we suggest that an animal’s resource use be summarized at the scale of the home range (i.e., the spatial distribution of all point locations of an animal) rather than by individual points that are considered used or available. To account for differences in use-intensity throughout an animal’s home range, we model resource selection using kernel density estimates and polytomous logistic regression. We present a case study of elk (Cervus elaphus) resource selection in South Dakota to illustrate the procedure. There are several advantages of our proposed approach. First, resource availability goes undefined by the investigator, which is a difficult and often arbitrary decision. Instead, the technique compares the intensity of animal use throughout the home range. This technique also avoids problems with classifying locations rigidly as used or unused. Second, location coordinates do not need to be placed within mapped resource units, which is problematic given mapping and telemetry error. Finally, resource use is considered at an appropriate scale for management because most wildlife resource decisions are made at the level of the patch. Despite the advantages of this use-intensity procedure, future research should address spatial autocorrelation and develop spatial models for ordered categorical variables.     

Neighborhood effects in bird distributions,Navarre, Spain

This study examines the role of neighborhood effects in the spatial distributions of selected bird species in Navarre, Spain. We employed a geographic information system (GIS) to organize the data on bird distributions and relevant environmental variables and to analyze their spatial patterns. Three bird species were selected for analysis: the European honey-buzzard (Pernis apivorus), the Eurasian hobby (Falco subbuteo), and the European pied flycatcher (Ficedula hypoleuca). Selected environmental variables of the study area were digitized to create a comprehensive data base and logistic regression models were used to evaluate the significance of each variable in the spatial distribution. The spatial patterns of bird distributions were used to extract topological relationships and to identify neighborhood effects. Although all the selected species illustrate a pattern of positive spatial autocorrelation in their distributions, the significance of neighborhood effects varies from species to species. Among the selected species, neighborhood effects are most evident in the distribution of the European pied flycatcher and are significant for the Eurasian hobby. The distribution of the European honey-buzzard is not much affected by neighborhood effects. The results suggest that examination of neighborhood effects is a prerequisite for modeling bird distributions.     

A GIS analysis of suitability for construction aggregate recycling sites using regional transportation network and population density features

Aggregate is used in road and building construction to provide bulk, strength, support, and wear resistance. Reclaimed asphalt pavement (RAP) and reclaimed Portland cement concrete (RPCC) are abundant and available sources of recycled aggregate. In this paper, current aggregate production operations in Virginia, Maryland, and the District of Columbia are used to develop spatial association models for the recycled aggregate industry with regional transportation network and population density features.The cost of construction aggregate to the end user is strongly influenced by the cost of transporting processed aggregate from the production site to the construction site. More than 60% of operations recycling aggregate in the mid-Atlantic study area are located within 4.8 km (3 miles) of an interstate highway. Transportation corridors provide both sites of likely road construction where aggregate is used and an efficient means to move both materials and on-site processing equipment back and forth from various work sites to the recycling operations.Urban and developing areas provide a high market demand for aggregate and a ready source of construction debris that may be processed into recycled aggregate. Most aggregate recycling operators in the study area are sited in counties with population densities exceeding 77 people/km² (200 people/mile²). No aggregate recycling operations are sited in counties with less than 19 people/km² (50 people/mile²), reflecting the lack of sufficient long-term sources of construction debris to be used as an aggregate source, as well as the lack of a sufficient market demand for aggregate in most rural areas to locate a recycling operation there or justify the required investment in the equipment to process and produce recycled aggregate.Weights of evidence analyses (WofE), measuring correlation on an area-normalized basis, and weighted logistic regression (WLR), are used to model the distribution of RAP and RPCC operations relative to transportation network and population distribution data. The models can be used on a regional scale to quickly map the relative site suitability for a RAP or RPCC aggregate recycling operation in a particular area based on transportation network and population parameters. The results can be used to identify general areas to be further evaluated on a site-specific basis using more detailed marketplace information. As transportation or population features change due to planning or actual development, the models can be easily revised to reflect these changes.     

Assessing Potential for Recovery of Biotic Richness and Indicator Species due to Changes in Acidic Deposition and Lake ph in Five Areas of Southeastern Canada

Biological damage to sensitive aquatic ecosystems is among the most recognisable, deleterious effects of acidic deposition. We compiled a large spatial database of over 2000 waterbodies across southeastern Canada from various federal, provincial and academic sources. Data for zooplankton, fish, macroinvertebrate (benthos) and loon species richness and occurrence were used to construct statistical models for lakes with varying pH, dissolved organic carbon content and lake size. pH changes, as described and predicted using the Integrated Assessment Model (Lam et al., 1998; Jeffries et al., 2000), were based on the range of emission reductions set forth in the Canada/US Air Quality Agreement (AQA). The scenarios tested include 1983, 1990, 1994 and 2010 sulphate deposition levels. Biotic models were developed for five regions in southeastern Canada (Algoma, Muskoka, and Sudbury, Ontario, southcentral Québec, and Kejimkujik, Nova Scotia) using regression tree, multiple linear regression and logistic regression analyses to make predictions about recovery after emission reductions. The analyses produced different indicator species in different regions, although some species showed consistent trends across regions. Generally, the greatest predicted recovery occurred during the final phase of emission reductions between 1994 and 2010 across all taxonomic groups and regions. The Ontario regions, on average, were predicted to recover to a greater extent than either southcentral Québec or the Kejimkujik area of Nova Scotia. Our results reconfirm that pH 5.5–6.0 is an important threshold below which damage to aquatic biota will remain a major local and regional environmental problem. This damage to biodiversity across trophic levels will persist well into the future if no further reductions in sulphate deposition are implemented.     

不同类型农户对宅基地退出的响应

利用重庆市梁平县农户的调查数据,运用Logistic模型,探讨了户籍制度改革下不同类型农户宅基地退出意愿及其影响因素。结果表明：(1)在现有退地补偿政策下农户意愿普遍偏低,不同类型农户退地意愿存在一定的差异性;(2)影响纯农户宅基地退出的因素有户主年龄、家庭拥有的房屋数量、对政策的支持度和退地收益;影响Ⅰ兼业户宅基地退出的因素有家庭宅基地面积大小、对政策的支持度和退地收益;影响Ⅱ兼业户宅基地退出的因素有家庭小孩数、对政策的支持度和退地收益;影响非农户宅基地退出的因素有户主年龄、户主文化程度、家庭人口数、宅基地面积和退地收益;其中,退地收益是影响各类农户退地决策最显著的因素,对政策的支持度是农户退地决策影响力最大的因素。研究认为,影响各类农户退地决策的因素存在差异,其核心问题还在于退地政策。政府应根据不同类型农户的特征完善目前的农村宅基地退出政策,这有助于提高农户的参与意愿,顺利推进户籍制度和农村土地制度的改革