Evaluation of approaches for terrestrial hazard classification

Hazard assessment is an essential element in the evaluation of the potential effects of chemical substances on the environment. To date, most work has focused on hazard assessment schemes for the aquatic environment, but in recent years, a number of proposals have been developed for other environmental compartments. Due to limited datasets, the suitability of the toxicity cut-off values in these schemes has not been fully determined and the practicalities associated with using these approaches have not been fully established. This study, which focused on the soil compartment, was performed to examine cut-off values proposed by two terrestrial hazard assessment schemes and establish the availability of data. Data on earthworms indicated that current proposals for toxicity cut-off values are appropriate. However, analysis of IUCLID (International Uniform ChemicaL Information Database) indicates that even for commonly used high production volume chemicals, insufficient data are available to enable classification. Whilst the necessary data may already be available for selected groups of substances (e.g. pesticides and veterinary medicines), a significant experimental testing programme would therefore be required before a terrestrial classification system could be applied widely. Such data may become available in the future as a result of initiatives such as REACH.     

标准类别指数法在地表水环境质量评价中的应用

在确定水体主要污染物的基础上，以相应水体主要污染物项目为评价项目，以地表水环境质量标准的类别分级为依据，应用模糊数学理论对地表水环境质量或污染程度予以分级。能客观、直观地反映相应地表水体环境质量或污染程度，计算过程简便，是一种可采纳的实用水质评价方法。     

Review of sustainability terms and their definitions

Terminology in the field of sustainable development is becoming increasingly important because the number of terms continues to increase along with the rapid increase in awareness of the importance of sustainability. Various definitions of terms are used by different authors and organizations, for example, green chemistry, cleaner production, pollution prevention, etc. The importance of this topic has stimulated research into the problems of clarifying ambiguity and classifying terms used in the sustainability field. This paper provides results of the literature survey and summarizes the definitions of the terms, focusing on the environmental engineering field. In some cases, it proposes an improved definition. A hierarchical classification of the terms and their relationships has been based on a layer format that is presented graphically.     

基于PCA-BP神经网络的多股铜导线 熔痕定量金相识别方法研究

为实现火灾现场中多股铜导线熔痕的自动识别,采用主成分分析(PCA)和反向传播(BP)神经网络算法对四种多股铜导线熔痕(一次短路熔痕、二次短路熔痕、过负荷熔痕和火烧熔痕)的金相组织进行了识别研究。利用Image-Pro Plus 6.0和Axio-Imaging软件获取每种熔痕30组17维金相组织参数数据,采用PCA对四种熔痕共120组数据降维,获得前6个主成分得分矩阵,建立具有6个输入层节点,10个隐层节点和4个输出节点的神经网络模式识别模型。随机抽取每种熔痕的20组样品的主成分得分矩阵作为训练集,将每种熔痕的剩余10组主成分得分为测试数据,输入最终训练完成的模型进行识别,其识别准确率达到92.5%。实验结果表明采用PCA+BP神经网络的算法,可以较好地实现多股铜导线熔痕识别,为火灾物证鉴定工作提供了有力的工具。     

Human and organizational factors in Chinese hazardous chemical accidents: a case study of the ‘8.12’ Tianjin Port fire and explosion using the HFACS-HC

Human and organizational factors have been proven to be the prime causes of Chinese hazardous chemical accidents (HCAs). A modified version of the Human Factors Analysis and Classification System (HFACS), namely the HFACS-Hazardous Chemicals (HC), was developed to identify the human factors involved in Chinese HCAs. The ‘8.12’ Tianjin Port fire and explosion, the costliest HCA in recent years, was reanalyzed using this framework, and the results were compared with the official accident inquiry report to determine their differences related to the identification of human and organizational factors. The study revealed that interacting human factors from different levels in Ruihai Company led to this catastrophe, and the inquiry report had limitations in the identification of human factors and the guidance for similar accident prevention. This study showed the applicability of the HFACS-HC in HCA analyses as well as the necessity to recommend this approach for future HCA investigations.     

Predicting categories of drugs used by suspected drug-impaired drivers using the Drug Evaluation and Classification Program tests

Objective: The purpose of this study was to statistically determine which combination(s) of drug-related signs and symptoms from the Drug Evaluation and Classification (DEC) protocol best predict the drug category used by the suspected drug-impaired driver.

Methods: Data from 1,512 completed DEC evaluations of suspected impaired drivers subsequently found to have ingested central nervous system (CNS) depressants, CNS stimulants, narcotic analgesics, and cannabis were analyzed using a multinomial logistic regression procedure. A set of evaluations completed on drug-free subjects was also included. The relative importance of clinical, behavioral, and observational measures in predicting drug categories responsible for impairment was also examined.

Results: Thirteen drug-related indicators were found to significantly contribute to the prediction of drug category, including being under the care of a doctor or dentist, condition of the eyes, condition of the eyelids, mean pulse rate, assessment of horizontal gaze nystagmus (HGN), convergence, performance on the One Leg Stand (OLS) Test, eyelid tremors, pupil size in darkness, reaction to light, presence of visible injection sites, systolic blood pressure, and muscle tone. Indicators related to the appearance and physiological response of the eye contributed the most to the prediction of drug category, followed closely by clinical indicators and performance on the psychophysical tests.

Conclusions: The findings from this study suggest that drug recognition experts (DREs) should be careful to review a set of key signs and symptoms when determining the category of drug used by suspected drug-impaired drivers. Drug use indicators related to the appearance and physiological response of the eye were found to contribute the most to the prediction of the drug category responsible for the impairment. These results could help form the basis of a core set of indicators that DREs could initially consult to form their opinion of drug influence. This in turn may enhance the validity, effectiveness, and efficiency of drug detection and identification by DREs and lead to a more effective and efficient DEC program, improved enforcement of drug-impaired driving, and greater acceptance of the DEC program by the courts.     

The National Vegetation Classification Standard applied to the remote sensing classification of two semiarid environments

The National Vegetation Classification Standard (NVCS) was implemented at two US National Park Service (NPS) sites in Texas, the Padre Island National Seashore (PINS) and the Lake Meredith National Recreation Area (LMNRA), to provide information for NPS oil and gas management plans. Because NVCS landcover classifications did not exist for these two areas prior to this study, we created landcover classes, through intensive ground and aerial reconnaissance, that characterized the general landscape features and at the same time complied with NVCS guidelines. The created landcover classes were useful for the resource management and were conducive to classification with optical remote sensing systems, such as the Landsat Thematic Mapper (TM). In the LMNRA, topographic elevation data were added to the TM data to reduce confusion between cliff, high plains, and forest classes. Classification accuracies (kappa statistics) of 89.9% (0.89) and 88.2% (0.87) in PINS and LMNRA, respectively, verified that the two NPS landholdings were adequately mapped with TM data. Improved sensor systems with higher spectral and spatial resolutions will ultimately refine the broad classes defined in this classification; however, the landcover classifications created in this study have already provided valuable information for the management of both NPS lands. Habitat information provided by the classifications has aided in the placement of inventory and monitoring plots, has assisted oil and gas operators by providing information on sensitive habitats, and has allowed park managers to better use resources when fighting wildland fires and in protecting visitors and the infrastructure of NPS lands.     

Land cover mapping of wetland areas in an agricultural landscape using SAR and Landsat imagery

Saline wetlands in the Monegros Desert, NE Spain, are situated in an agricultural landscape which is undergoing significant changes. Agricultural intensification in recent decades and current installation of new irrigation systems threaten these valuable habitats, set to be included in the Natura2000 network. Their preservation and successful management depend on the information available regarding the transformation of surrounding areas. When soil and vegetation maps at adequate scale are not available, remote sensing is an alternative means to obtain needed data. We have used SAR data, taking advantage of the soil surface characteristics perceived in SAR images. The objective of this work is to explore the capability of multitemporal SAR data to characterize the land covers of these wetlands and their environment. We have developed specific contextual classifications which take into account the statistical properties of the radar distribution. Moreover, we tested the contribution of radar in Landsat classification.     

Predicting Sustained Fire Spread in Tasmanian Native Grasslands

Fire is widely used in conservation management of native grasslands. Burning is often carried out under conditions that are marginal for sustained fire spread, and therefore it would be useful to be able to predict fire sustainability. There is currently no model allowing such prediction in temperate grasslands. This study aims to identify the environmental variables that determine whether fires will sustain in native grasslands in Tasmania, Australia, and develop a model for predicting fire sustainability in this vegetation. Fuel characteristics and weather conditions were recorded for 111 test fires. Logistic regression modeling identified dead fuel moisture content, fuel load, and percentage dead fuel as predictors of fire sustainability. Classification tree modeling identified dead fuel moisture and fuel load threshold values for sustaining fires. There was also evidence indicating a percentage dead fuel threshold. The logistic regression model and a model combining the results of the classification tree and the percentage dead fuel threshold accurately predicted the outcomes of a small set of experimental fires. These models are likely to have utility in predicting fire sustainability in Tasmanian grasslands and are also likely to be applicable to similar grasslands elsewhere.     

Nest selection by snow petrels Pagodroma nivea in East Antarctica: Validating predictive habitat selection models at the continental scale

Little is known on the factors controlling distribution and abundance of snow petrels in Antarctica. Studying habitat selection through modeling may provide useful information on the relationships between this species and its environment, especially relevant in a climate change context, where habitat availability may change. Validating the predictive capability of habitat selection models with independent data is a vital step in assessing the performance of such models and their potential for predicting species’ distribution in poorly documented areas.From the results of ground surveys conducted in the Casey region (2002–2003, Wilkes Land, East Antarctica), habitat selection models based on a dataset of 4000 nests were created to predict the nesting distribution of snow petrels as a function of topography and substrate. In this study, the Casey models were tested at Mawson, 3800 km away from Casey. The location and characteristics of approximately 7700 snow petrel nests were collected during ground surveys (Summer 2004–2005). Using GIS, predictive maps of nest distribution were produced for the Mawson region with the models derived from the Casey datasets and predictions were compared to the observed data. Models performance was assessed using classification matrixes and Receiver operating characteristic (ROC) curves. Overall correct classification rates for the Casey models varied from 57% to 90%. However, two geomorphologically different sub-regions (coastal islands and inland mountains) were clearly distinguished in terms of habitat selection by Casey model predictions but also by the specific variations in coefficients of terms in new models, derived from the Mawson data sets. Observed variations in the snow petrel aggregations were found to be related to local habitat availability.We discuss the applicability of various types of models (GLM, CT) and investigate the effect of scale on the prediction of snow petrel habitats. While the Casey models created with data collected at the nest scale did not perform well at Mawson due to regional variations in nest micro-characteristics, the predictive performance of models created with data compiled at a coarser scale (habitat units) was satisfactory. Substrate type was the most robust predictor of nest presence between Casey and Mawson. This study demonstrate that it is possible to predict at the large scale the presence of snow petrel nests based on simple predictors such as topography and substrate, which can be obtained from aerial photography. Such methodologies have valuable applications in the management and conservation of this top predator and associated resources and may be applied to other Antarctic, Sub-Antarctic and lower latitudes species and in a variety of habitats.