浅谈万宝矿产有限公司海外项目相关方安全管理

随着国家“一带一路”倡议的推进,越来越多中国企业“走出去”,拓展海外市场。相关方作为海外项目安全管理的重点,存在数量多、安全管理能力参差不齐、安全管理难度大等特点。万宝矿产有限公司从相关方准入审核、过程管控、考核淘汰等全生命周期对海外项目各相关方进行规范化管理,提升了相关方安全管理水平,避免了生产安全事故,保障了项目顺利进行。     

A composite receptor and dispersion model approach for estimation of effective emission factors for vehicles

It is difficult to estimate vehicular emission factors at traffic junctions for use in dispersion modelling studies. Firstly, because the vehicles are in various modes of operation and secondly, it is difficult to delineate the effects of other contributing sources, mainly the effects of road dust and deposited constituents, which are very prominent at traffic junctions in India. Factor analysis-multiple regression (FA-MR), a receptor modelling technique has been used in this study for apportioning the contributing sources. The measurement data consist of one year's temporal variation of suspended particulate matter (SPM), analysed for its trace metal constituents, and two gaseous components NO₂ and SO₂ at two traffic junctions in Mumbai (India). FA-MR apportioned 40% of the observed SPM to road dust and 15% to vehicular sources. Of the total Pb observed in the SPM, FA-MR apportioned 60% to vehicular sources and 20% to road dust. The field-observed vehicular counts, meteorological parameters and road geometry were used in California line source dispersion model to estimate the effective vehicular emission factor for Pb at one traffic junction. This derived emission factor was used to predict the Pb concentration at second (independent observation) traffic junction. The result was found to be more satisfactory than using default emission factors obtained from literature. Similarly, effective vehicular emission factor for NO₂ was also evaluated for one site and tested for predicting concentrations at the other site.     

城市道路两侧土壤重金属分布特征及生态风险评价

通过采集吕梁市G209线公路两侧土壤,研究土壤中锌、铜、镍、铅4种重金属的分布特征,并采用单因子污染指数法和内梅罗综合指数法进行生态风险评价。结果表明:采样点距离公路直线距离越大,土壤中重金属含量越低,且主要富集在0~20m可耕层,表现出明显的垂直递减规律。土壤重金属污染程度为:Pb(中度污染)>Cu(轻微污染)>Ni(轻微污染)>Zn(未污染),生态风险评价等级为Ⅴ,属于重污染。汽车尾气的排放是主要污染来源,对于农业用地,农药、化肥的施用也是主要污染源。     

经济全球化能够有效抑制“一带一路”沿线国家的生态足迹吗?——来自动态门槛面板模型的经验证据

基于1993-2013年“一带一路”沿线55个国家的平行面板数据,运用Hansen动态面板门槛模型,以经济发展水平（人均GDP）为门槛变量,检验经济全球化与生态足迹之间的关系。研究结果表明：在不同的经济门槛范围内,经济全球化对生态足迹存在不同的影响。当经济发展水平小于等于3905美元时,经济全球化水平的提高能够显著降低生态足迹;当经济发展水平超过第一门槛值3905美元时,经济全球化水平对生态足迹的影响从显著抑制转变为促进作用,但是不显著;而当经济发展水平跨越第二门槛值8778美元时,经济全球化水平的提高能够显著刺激生态足迹的提高。当前“一带一路”沿线国家主要集中在第一门槛区间和第三门槛区间,经济全球化对生态足迹的影响出现两极分化态势,其中在第三门槛区间的国家在参与全球化的过程中应对资源的可持续利用引起充分重视,这关系到全球化的可持续发展和“一带一路”建设的顺利推进。     

Weighted road density: A simple way of assigning traffic-related air pollution exposure

BackgroundExisting traffic variables used for predicting NO₂ in epidemiological studies are either difficult to acquire or explain only a small proportion of the variance. The purpose of this study was to develop and validate a new predictor, weighted road density, which combines the maximum amount of information related to traffic into a single variable without the requirement of obtaining traffic counts for a given area.MethodTwo week NO₂ samples were collected using the readings of up to 32 passive samplers on 3 separate rounds between September and December 2006 and again in 2007. Several types of traffic related explanatory variables based on traffic counts, distance to main road and the proposed weighted road density were constructed using GIS software, and tested for association with the NO₂ samplers. Assessment of the best model was based on R² values, as well as leave-one-out cross validation.ResultsThe weighted road density variable and the density variable based on traffic counts resulted in a similar R² (0.59) for predicting NO₂, although weighted road density was much easier to construct and outperformed other variables such as distance to main road.ConclusionAs well as being a powerful predictor for use in a land use regression model, weighted road density can be used as a proxy for exposure to traffic-related pollution, for use in circumstances where direct measurements of pollutant levels are not feasible or are not required.     

Annoyance due to aircraft noise has increased over the years—Results of the HYENA study

In the HYENA study (HYpertension and Exposure to Noise near Airports) noise annoyances due to aircraft and road traffic noise were assessed in subjects that lived in the vicinity of 6 major European airports using the 11-point ICBEN scale (International Commission on Biological Effects of Noise). A distinction was made between the annoyance during the day and during the night. L_den and L_night were considered as indicators of noise exposure. Pooled data analyses showed clear exposure–response relationships between the noise level and the noise annoyance for both exposures. The exposure–response curves for road noise were congruent with the EU standard curves used for predicting the number of highly noise annoyed subjects in European communities. Annoyance ratings due to aircraft noise, however, were higher than predicted by the EU standard curves. The data supports other findings suggesting that the people's attitude towards aircraft noise has changed over the years, and that the EU standard curve for aircraft noise should be modified.     

Evaluating Aggregate Terrestrial Impacts of Road Construction Projects for Advanced Regional Mitigation

This study presents a GIS-based database framework used to assess aggregate terrestrial habitat impacts from multiple highway construction projects in California, USA. Transportation planners need such impact assessment tools to effectively address additive biological mitigation obligations. Such assessments can reduce costly delays due to protracted environmental review. This project incorporated the best available statewide natural resource data into early project planning and preliminary environmental assessments for single and multiple highway construction projects, and provides an assessment of the 10-year state-wide mitigation obligations for the California Department of Transportation. Incorporation of these assessments will facilitate early and more strategic identification of mitigation opportunities, for single-project and regional mitigation efforts. The data architecture format uses eight spatial scales: six nested watersheds, counties, and transportation planning districts, which were intersected. This resulted in 8058 map planning units statewide, which were used to summarize all subsequent analyses. Range maps and georeferenced locations of federally and state-listed plants and animals and a 55-class landcover map were spatially intersected with the planning units and the buffered spatial footprint of 967 funded projects. Projected impacts were summarized and output to the database. Queries written in the database can sum expected impacts and provide summaries by individual construction project, or by watershed, county, transportation district or highway. The data architecture allows easy incorporation of new information and results in a tool usable without GIS by a wide variety of agency biologists and planners. The data architecture format would be useful for other types of regional planning.     

A Comparison of Data Sets Varying in Spatial Accuracy Used to Predict the Occurrence of Wildlife-Vehicle Collisions

Wildlife-vehicle collisions (WVCs) pose a significant safety and conservation concern in areas where high-traffic roads are situated adjacent to wildlife habitat. Improving transportation safety, accurately planning highway mitigation, and identifying key habitat linkage areas may all depend on the quality of WVC data collection. Two common approaches to describe the location of WVCs are spatially accurate data derived from global positioning systems (GPS) or vehicle odometer measurements and less accurate road-marker data derived from reference points (e.g., mile-markers or landmarks) along the roadside. In addition, there are two common variable types used to predict WVC locations: (1) field-derived, site-specific measurements and (2) geographic information system (GIS)-derived information. It is unclear whether these different approaches produce similar results when attempting to identify and explain the location of WVCs. Our first objective was to determine and compare the spatial error found in road-marker data (in our case the closest mile-marker) and landmark-referenced data. Our second objective was to evaluate the performance of models explaining high- and low-probability WVC locations, using congruent, spatially accurate (<3-m) and road-marker (<800-m) response variables in combination with field- and GIS-derived explanatory variables. Our WVC data sets were comprised of ungulate collisions and were located along five major roads in the central Canadian Rocky Mountains. We found that spatial error (mean ± SD) was higher for WVC data referenced to nearby landmarks (516 ± 808 m) than for data referenced to the closest mile-marker data (401 ± 219 m). The top-performing model using the spatially accurate WVC locations contained all explanatory variable types, whereas GIS-derived variables were only influential in the best road-marker model and the spatially accurate reduced model. Our study showed that spatial error and sample size, using road-marker data for ungulate species, are important to consider for model output interpretation, which will impact the appropriate scale on which to apply modeling results. Using road-marker references <1.6 km or GPS-derived data locations may represent an optimal compromise between data acquisition costs and analytical performance. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

入境旅游流空间转移与旅游经济联系研究——以北京向丝绸之路转移为例

旅游流和当地旅游经济发展有着重要的联系。旅游流量对增强旅游经济联系强度有极大的促进作用,而旅游经济强度增强又会带来更多的旅游转移流量。以北京入境旅游流向丝绸之路沿线省区转移为例,利用SPSS16.0统计分析软件,分析了1998—2007年北京入境旅游流向丝绸之路沿线省区转移规模和旅游经济联系强度之间的关系。结果表明,北京入境旅游流向陕西转移流量最大,其次为新疆,甘肃最小;转移客流量和旅游经济联系强度存在强相关关系;北京转移入境旅游流对丝绸之路各省旅游经济发展有促进作用,并为跨区域旅游合作研究提供理论参考。     

Road Traffic Noise Prediction Model under Interrupted Traffic Flow Condition

The objective of this study is to develop an empirical traffic noise prediction model under interrupted traffic flow conditions using two analytical the approaches, the first being the acceleration lane approach and second being the deceleration approach. The urban road network of Bangalore city has been selected as the study area. Sixteen locations are chosen in major traffic junctions of the study area. The traffic noise data collected from the study locations were analyzed separately for both acceleration and deceleration lanes when vehicles leave an intersection on a green traffic light and come to a stop on red traffic light. Based on the study, a regression noise prediction model has been developed for both acceleration and deceleration lanes.