同化和共代谢降解氯苯菌株的筛选与特性研究

基于污染场地,筛选了一株可降解氯苯（CB）的微生物,经鉴定该菌株属于粘质沙雷氏菌属（Serratia marcescans）,命名为Serratia marcescans TF-1.同化降解结果表明,该菌株能够在有氧的条件下以CB为唯一碳源和能源,菌体平均增长速率为0.0063~0.022g_cell/（mol_CB·h）,最大比生长速率（μ_max）为0.015~0.42h^-1,CB降解速率（V_CB）为1.35~4.47mol/（g_cell·h）,菌株对CB最高耐受浓度高于200mg/L.共代谢降解结果显示,TF-1可以琥珀酸钠和柠檬酸钠为底物共代谢降解CB;氯苯浓度（c_CB）<80mg/L时,μ_{max（柠檬酸钠）}（0.21~0.87h^-1）>μ_{max（琥珀酸钠）}（0.20~0.81h^-1）,V_{CB（柠檬酸钠）}（0.15~0.47mol/（g_cell·h））<V_{CB（琥珀酸钠）}（0.17~0.48mol/（g_cell·h））;c_CB>80mg/L时,μ_{max（柠檬酸钠）}（0.086~0.21h^-1）<μ_{max（琥珀酸钠）}（0.17~0.25h^-1）,V_{CB（柠檬酸钠）}（0.61~1.11mol/（g_cell·h））>V_{CB（琥珀酸钠）}（0.56~0.95mol/（g_cell·h））,表明共代谢降解过程中,CB浓度,底物种类是调控污染降解的重要因素.最后考察了温度、pH值和接种量对TF-1降解CB的影响,结果发现,该菌株适宜生长的温度范围为20~35℃,最适温度为30℃;适宜生长pH值为5~9,最适pH值为7;最适接种量为5%.与现有菌株比较发现TF-1的温度和pH值适用范围更广,降解能力更强,污染物耐受浓度更高,既能同化又能共代谢降解CB,在贫营养和富营养污染场地中应用潜力更大.本研究可为原位CB污染场地修复提供有效的生物资源.     

Effectiveness of the policy of circular economy in China: A DEA-based analysis for the period of 11th five-year-plan

As a policy instrument for sustainable development, nationwide circular economy (CE) mode has been implemented for 7 years by the Chinese government, to overcome the dilemma among economic depression, energy shortage and environmental pollution. Unfortunately, few literatures contribute to checking the efficiency of regional CE and exploring the potential reasons in China, which will be practically helpful in guiding China's future development and providing reference for other developing countries that will adopt CE mode. Therefore, this paper puts forward the method of super-efficiency DEA window analysis to dynamically evaluate CE efficiency of 30 regions in China covering the period of 2005–2010. In line with the features of CE, the specific efficiency of three sub-systems, namely resource saving and pollutant reducing (RSPR) sub-system, waste reusing and resource recycling (WRRR) sub-system and pollution controlling and waste disposing (PCWD) sub-system, was assessed and compared regarding time series trend and spatial distribution, based on which, the comprehensive CE efficiency was totally ranked. The results show that, on the one hand, during the period of 2005–2010, Chinese CE efficiency slightly increased, implying a significant policy effect of CE; on the other hand, the efficiency of different sub-systems varies, with RSPR sub-system the lowest, and WRRR and PCWD sub-systems relatively higher. In terms of regional distribution, the efficiency of RSPR sub-system in the east area dominates during the whole period. However, for the other two sub-systems, from 2005 to 2007, the efficiency scores of the east fluctuate, then after 2007, the east area became more efficient than the central and the west areas. Throughout the study period, the RSPR sub-system in the central area stably maintains low efficiency, and so does the WRRR sub-system in the west area. The difference in regional efficiency of three sub-systems reflects the difficulty of carrying out CE policy in China comprehensively, which means that, promoting the adaptability of regional policy for local government and enhancing the coordination among various policies for China's central government are the key concerns for realizing sustainable development.     

Challenges to quantitative applications of Landsat observations for the urban thermal environment

Since the launch of its first satellite in 1972, the Landsat program has operated continuously for more than forty years. A large data archive collected by the Landsat program sig-nificantly benefits both the academic community and society. Thermal imagery from Landsat sensors, provided with relatively high spatial resolution, is suitable for monitoring urban thermal environment. Growing use of Landsat data in monitoring urban thermal environment is demonstrated by increasing publications on this subject, especially over the last decade. Urban thermal environment is usually delineated by land surface temperature (LST). However, the quantitative and accurate estimation of LST from Landsat data is still a challenge, especially for urban areas. This paper will discuss the main challenges for urban LST retrieval, including urban surface emissivity, atmospheric correction, radiometric calibration, and validation. In addition, we will discuss general challenges confronting the continuity of quantitative applications of Landsat observations. These challenges arise mainly from the scan line corrector failure of the Landsat 7 ETM + and channel differences among sensors. Based on these investigations, the concerns are to: (1) show general users the limitation and possible uncertainty of the retrieved urban LST from the single thermal channel of Landsat sensors; (2) emphasize efforts which should be done for the quantitative applications of Landsat data; and (3) understand the potential challenges for the continuity of Landsat observation (i.e., thermal infrared) for global change monitoring, while several climate data record programs being in progress.     

基于水足迹强度的江苏省水资源利用效率分析

水足迹强度是可以量化水资源利用效率的指标,文章在计算江苏省水足迹的基础上,选取6项水足迹强度指标,分部门比较各地市水资源利用效率的差异,并基于ESDA研究水足迹强度的空间自相关性,构建空间集聚图。研究表明:2002~2014年江苏省各市水足迹总体呈现上升趋势,水资源压力增大;总体上苏南和苏北地区的农业水足迹强度都偏高,农业水资源利用效率低;苏南地区工业产值高但耗水量大,因此工业水足迹强度也偏高;苏北地区的灰水足迹强度偏高,水资源压力较大;江苏省水足迹强度具有不断增强的全局自相关性,地市出现集聚现象,不同地市的集聚分布也日益明显;大部分苏北地区属于高高集聚并且向外扩张,低低集聚主要分布在苏南地区。研究结果为政府部门分析地市间水资源利用效率差异,制定高效的水资源利用政策等提供依据。     

A method for simplifying the analysis of traffic accidents injury severity on two-lane highways using Bayesian networks

Introduction

This study describes a method for reducing the number of variables frequently considered in modeling the severity of traffic accidents. The method's efficiency is assessed by constructing Bayesian networks (BN).

Method

It is based on a two stage selection process. Several variable selection algorithms, commonly used in data mining, are applied in order to select subsets of variables. BNs are built using the selected subsets and their performance is compared with the original BN (with all the variables) using five indicators. The BNs that improve the indicators’ values are further analyzed for identifying the most significant variables (accident type, age, atmospheric factors, gender, lighting, number of injured, and occupant involved). A new BN is built using these variables, where the results of the indicators indicate, in most of the cases, a statistically significant improvement with respect to the original BN.

Conclusions

It is possible to reduce the number of variables used to model traffic accidents injury severity through BNs without reducing the performance of the model.

Impact on Industry

The study provides the safety analysts a methodology that could be used to minimize the number of variables used in order to determine efficiently the injury severity of traffic accidents without reducing the performance of the model.     

遥感指数在湖泊叶绿素a反演研究中的应用

借鉴陆地植被指数的算法,利用MODIS数据的波段组合对太湖叶绿素8浓度进行反演。探讨了差值植被指数DVI、比值植被指数RVI、归一化植被指数NDVI这三种遥感指数形式的反演效果及各自特点。MODIS数据蓝光波段和红外波段的组合是反演太湖叶绿素浓度的最佳波段组合,DVI适合于反演叶绿素浓度高的情况,RVI的反演结果两极分化明显而普适性较差,NDVI适合于反演浓度较低的情况。     

Development of novel combustion risk index for flammable liquids based on unsupervised clustering algorithms

Current liquid flammability classification mainly relies on flash point and its risk is largely dependent on consequence and probability. However, combustions of liquefied marine fuels have their uniqueness, leading to a less consistent with the common classification. This work aims at classifying flammable liquids in compression ignition engines for further safety evaluation. Besides liquid flammability characteristics, flame propagation and aerosol formulation are considered. Two unsupervised machine learning clustering algorithms, k-means and spectral clustering, are applied to the collected liquid compounds database. To consider both cluster cohesion and separation, the global mean silhouette value is used to find the optimal number of clusters and to evaluate the clustering performance. The results show that the spectral clustering outperforms k-means on classifying the risk ratings for all proposed models, while the clustering accuracy of the optimal model has been doubled by employing spectral clustering algorithm. Moreover, principal component analysis and star coordinate diagrams are presented to visualize high dimensional data to 2-D graphs. Finally, the overall liquid safety performance is evaluated by a novel combustion risk index via the weight values determined by the information entropy approach. This index can be used to explore inherently safer fuels in the process industries.     

Collaborative activity with business partners for improvement of product environmental performance using LCA

Collaborative activities with business partners have potential to improve environmental performance of product and life cycle assessment (LCA) is an essential technique to implement eco-design of product and production process. However, collection of LCA data from supply chain is a major issue for LCA practitioners. We propose a Supply Chain Collaboration Model (SCCM), which is a framework for collecting producer-specific LCA data from business partners and for promoting improvement activity of product environmental performance. We demonstrate the practicability of the SCCM using three case studies. In each case study, two or three partner companies organized a product improvement project and carried out process analysis techniques such as LCA and material flow cost accounting (MFCA). As a result, improvement activities from the economical standpoint could provide an incentive for business partners to collect LCA data, and thus the SCCM is an effective framework for eco-design.     

基于DEA的江苏省环境效率研究

运用DEA方法中SBM模型对江苏省13个地级市2001～2007年间环境效率及其变化率进行分析.结果显示江苏省各地级市间环境效率差异明显,苏州、无锡、常州、盐城和宿迁5市处于前沿面上,其他市均是DEA非有效或弱有效.Mahnquist指数计算显示各地级市在研究期内变化率不大,但扬州市近年来环境效率持续降低.综合经济产出...     

Using data mining technology to explore causes of inaccurate reliability data and suggestions for maintenance management

Reliability data reflects equipment safety and provides a reference for setting inspection period, thereby serving as crucial information for the implementation of equipment integrity management policies. The calculation foundation of reliability data is maintenance records of adequate data quality. However, maintenance records of doubtful quality are common. Despite excluding poor quality recodes and using only the remaining maintenance recodes to calculate the reliability data, the calculated results generally lack a sufficient degree of confidence. This study applied data mining technology, including quality metrics, the association rule, and clustering, to explore the cause of low-quality maintenance data. The results revealed that the low data quality of maintenance records was due to ineffective maintenance policies, the low integrity of key system columns, nonadherence to the policy, and misunderstanding of column definitions. The proposed method successfully identified the causes of low-quality maintenance records. By incorporating the method into the function module of a CMMS, operators can equip the system with self-diagnosis, self-supervision, and continuous optimization functions.