成都市生活垃圾分类支付意愿及影响因素的问卷调查分析

为了为成都市垃圾分类政策制定提供科学依据,文章基于问卷调研,运用条件价值评估法结合二元Logistic回归模型、多元线性回归模型,分析了成都市居民对城市生活垃圾分类的支付意愿(WTP)及其影响因素。调研发现:问卷填报者中95.3%的人支持强制垃圾分类,但仅21.0%人深入掌握垃圾分类知识;造成成都市垃圾分类失效的原因,73.8%的人认为政策不到位,需要采取强制监管措施,67.6%的人认为垃圾分类桶等基础硬件设施建设不到位,69.2%的人认为居民自身环保意识差,需要加强环保宣传教育。成都市居民生活垃圾治理的WTP金额为12.65元/月/户。年龄、有无住房、户口类型和月收入4个因素对支付额度有显著影响。     

淮南市淮河水域水源保护与防治对策

根据淮河水环境的特点，对水域现状和污染源现状进行了分析，通过对水源保护功能的划分，提出了防治对策和综合整治措施。     

Air classification of blast furnace dust collected in a fabric filter for recycling to the sinter process

Totally dry cleaning has become a common technology for top gas cleaning in blast furnaces in recent years. A significant advantage of totally dry gas cleaning is that the dust collected is obtained as dry powder, thus simplifying the recycling of the dust in the sinter plant and avoiding aqueous emissions. The concentration of some heavy metals, especially zinc, in the collected dust is usually higher than the maximum tolerable concentration for recycling to the sinter process. Therefore, a process for separation of dust with a low level of contamination from the rest is necessary to make partial recycling possible. This is possible because the limited components are more volatile and accumulate in the finer dust fraction. In wet blast furnace top gas cleaning, hydrocyclones are well established for this separation. For the separation of dry powder from the dry dedusting process air classification can be used. Dust from the top gas of a blast furnace with a fabric filter for dry top gas cleaning was split into several size fractions using a laboratory air classifier. The concentration of Ca, Cd, Cl, Cu, Fe, K, Na, Pb and Zn was analysed for each particle class and the loss on ignition was determined. A strong dependence of the concentration on the particle size was found for the more volatile metals, whereas the Fe and Ca concentration and the loss on ignition were quite evenly distributed. With the calculated recovery–removal-functions the possible recycling rate can be estimated for a given removal rate for the limited components.     

Habitat mapping using machine learning-extended kernel-based reclassification of an Ikonos satellite image

The spatial resolution of satellite imagery suitable for earth resources studies has improved from 80 m (Landsat-MSS, launched in 1972) to 0.6 m (QuickBird, launched in 2001). The conventional pixel-based methods developed for medium resolution satellite images are not suitable for classification of very high spatial resolution images, because the spectral responses of particular habitat classes are much more variable. On the other hand, in the original Barnsley–Barr kernel-based reclassification algorithm not only the spectral information of a pixel but also the textural information in the vicinity of the pixel is used when the pixel labeling decision is made. The first step of the kernel reclassification algorithm is to perform an initial classification of the original image. In the second step, the adjacency-event matrices are computed for each pixel according to co-occurrence frequencies of the initial classes in the kernel window. The degree of matching between an adjacency-event matrix corresponding to specific pixel and the set of class-specific template matrices produced during training is the criterion for pixel re-labeling. We extend the original kernel-based reclassification algorithm with a decision tree-based reclassification, simultaneously taking into account the class-specific similarity images, which are a side-product of the original algorithm. The advantage of decision tree-extended approach over the original approach seems to be the ability of the former to consider more input information, thus increasing the Kappa classification accuracy for an Ikonos image of our study area from 0.56 to 0.60, using a nomenclature containing 10 habitat classes.     

Effects of sample survey design on the accuracy of classification tree models in species distribution models

We evaluated the effects of probabilistic (hereafter DESIGN) and non-probabilistic (PURPOSIVE) sample surveys on resultant classification tree models for predicting the presence of four lichen species in the Pacific Northwest, USA. Models derived from both survey forms were assessed using an independent data set (EVALUATION). Measures of accuracy as gauged by resubstitution rates were similar for each lichen species irrespective of the underlying sample survey form. Cross-validation estimates of prediction accuracies were lower than resubstitution accuracies for all species and both design types, and in all cases were closer to the true prediction accuracies based on the EVALUATION data set. We argue that greater emphasis should be placed on calculating and reporting cross-validation accuracy rates rather than simple resubstitution accuracy rates. Evaluation of the DESIGN and PURPOSIVE tree models on the EVALUATION data set shows significantly lower prediction accuracy for the PURPOSIVE tree models relative to the DESIGN models, indicating that non-probabilistic sample surveys may generate models with limited predictive capability. These differences were consistent across all four lichen species, with 11 of the 12 possible species and sample survey type comparisons having significantly lower accuracy rates. Some differences in accuracy were as large as 50%. The classification tree structures also differed considerably both among and within the modelled species, depending on the sample survey form. Overlap in the predictor variables selected by the DESIGN and PURPOSIVE tree models ranged from only 20% to 38%, indicating the classification trees fit the two evaluated survey forms on different sets of predictor variables. The magnitude of these differences in predictor variables throws doubt on ecological interpretation derived from prediction models based on non-probabilistic sample surveys.     

Work environment risk factors for injuries in wood processing

Problem

The reported injury rate for wood product manufacturing in Maine, 1987-2004, was almost twice the state-wide average for all jobs. Method: A case-control study was conducted in wood processing plants to determine preventable risk factors for injury. A total of 157 cases with injuries reported to workers' compensation and 251 controls were interviewed. Results: In multivariable analyses, variables associated with injury risk were high physical workload, machine-paced work or inability to take a break, lack of training, absence of a lockout/tagout program, low seniority, and male gender. Different subsets of these variables were significant when acute incidents and overexertions were analyzed separately and when all injuries were stratified by industry sub-sector. Impact on industry: Generalizability may be limited somewhat by non-representative participation of workplaces and individuals. Nevertheless, these findings provide evidence that many workplace injuries occurring in wood processing could be prevented by application of ergonomics principles and improved work organization.     

Using conditional inference forests to identify the factors affecting crash severity on arterial corridors

Introduction

The study aims at identifying traffic/highway design/driver-vehicle information significantly related with fatal/severe crashes on urban arterials for different crash types. Since the data used in this study are observational (i.e., collected outside the purview of a designed experiment), an information discovery approach is adopted for this study.

Method

Random Forests, which are ensembles of individual trees grown by CART (Classification and Regression Tree) algorithm, are applied in numerous applications for this purpose. Specifically, conditional inference forests have been implemented. In each tree of the conditional inference forest, splits are based on how good the association is. Chi-square test statistics are used to measure the association. Apart from identifying the variables that improve classification accuracy, the methodology also clearly identifies the variables that are neutral to accuracy, and also those that decrease it.

Results

The methodology is quite insightful in identifying the variables of interest in the database (e.g., alcohol/ drug use and higher posted speed limits contribute to severe crashes). Failure to use safety equipment by all passengers and presence of driver/passenger in the vulnerable age group (more than 55 years or less than 3 years) increased the severity of injuries given a crash had occurred. A new variable, ‘element’ has been used in this study, which assigns crashes to segments, intersections, or access points based on the information from site location, traffic control, and presence of signals.

Impact

The authors were able to identify roadway locations where severe crashes tend to occur. For example, segments and access points were found to be riskier for single vehicle crashes. Higher skid resistance and k-factor also contributed toward increased severity of injuries in crashes.     

生活垃圾填埋场矿化垃圾分选研究

对上海市老港生活垃圾填埋场填埋龄为1～14年的矿化垃圾进行了分选研究.结果表明,填埋龄在8年以上矿化垃圾细料(粒径＜50 mm)的百分比约占60%,而填埋龄低于5年的矿化垃圾细料比例低于40%.开采出来的垃圾经分选后的产品可得到有效的资源化利用.     

Applications of artificial neural networks for patterning and predicting aquatic insect species richness in running waters

Two artificial neural networks (ANNs), unsupervised and supervised learning algorithms, were applied to suggest practical approaches for the analysis of ecological data. Four major aquatic insect orders (Ephemeroptera, Plecoptera, Trichoptera, and Coleoptera, i.e. EPTC), and four environmental variables (elevation, stream order, distance from the source, and water temperature) were used to implement the models. The data were collected and measured at 155 sampling sites on streams of the Adour–Garonne drainage basin (South-western France). The modelling procedure was carried out following two steps. First, a self-organizing map (SOM), an unsupervised ANN, was applied to classify sampling sites using EPTC richness. Second, a backpropagation algorithm (BP), a supervised ANN, was applied to predict EPTC richness using a set of four environmental variables. The trained SOM classified sampling sites according to a gradient of EPTC richness, and the groups obtained corresponded to geographic regions of the drainage basin and characteristics of their environmental variables. The SOM showed its convenience to analyze relationships among sampling sites, biological attributes, and environmental variables. After accounting for the relationships in data sets, the BP used to predict the EPTC richness with a set of four environmental variables showed a high accuracy (r=0.91 and r=0.61 for training and test data sets respectively). The prediction of EPTC richness is thus a valuable tool to assess disturbances in given areas: by knowing what the EPTC richness should be, we can determine the degree to which disturbances have altered it. The results suggested that methodologies successively using two different neural networks are helpful to understand ecological data through ordination first, and then to predict target variables.     

关于现行铁路行车事故分类的思考

为了纠正事故处理中的不正之风，促进铁路安全的科学管理，本文提出应当依照事故造成的人、财、物的损失程度进行铁路事故的分类。