A carbon-13 method for evaluating degradation of starch-based polymers

A new method for evaluating biodegradability of starch-based and certain other polymer blends uses the pre- and postexposure stable carbon isotope composition of material coupled with weight loss data to determine which components have degraded. The naturally occurring stable isotope of carbon.¹³C, is enriched in corn starch (¹³C, approx. –11) compared to petroleum-derived synthetic polymers (¹³C, approx. –32). Results on starch-synthetic polymer blends indicate that the ¹³C signatures of these blends are near-linear mixtures of their component ¹³C. Values of a ¹³C for starch-synthetic polymer blends exposed to biologically active laboratory soil and artificial seawater conditions are depleted in¹³C compared to unexposed samples, suggesting loss of the starch component. Combined with weight loss data for the exposed samples, the ¹³C values are statistically consistent with models requiring loss of the soluble component glycerin, followed by loss of starch, then petrochemical polymer, or simultaneous loss of starch and petrochemical polymer. Replicate ¹³C analyses of starch-synthetic polymer blends increase the statistical power of this relatively inexpensive, accessible technique to discriminate between degrading components.     

关于饮用水水源保护在广西环境保护“十三五”规划中的战略思考

在对广西当前环境形势以及"十二五"时期饮用水水源地保护问题识别的基础上,结合广西全面建设小康环境新要求,提出了"十三五"饮用水水源保护规划体系以及规划目标的设想,并从保护红线、面源治理、点源治理、环境监测、信息公开5个方面给出了基本的策略建议,可为制定广西"十三五"环境保护规划目标和重点任务提供参考。     

火电行业"十三五"主要大气污染物减排潜力情景分析研究

火电行业是总量减排的重点行业,也是主要大气污染物削减量的首要贡献者,其主要大气污染物排放量的削减抵消了其他行业的排放增长,为"十二五"全国减排任务的完成做出了重大贡献.本研究在火电行业主要大气污染物排放控制现状分析的基础上,结合火电行业技术政策措施要求,对火电行业"十三五"新增排放量进行了预测,并设置基于技术可行、排放标准以及超低排放三套减排情景,测算"十三五"减排潜力,评估火电行业"十三五"减排空间,对全国及各省火电行业减排形势提出了相应的意见和建议.     

An uncertainty based multi-criteria ranking system for open pit mining cut-off grade strategy selection

Cut-off grade strategy (COGS) is a concept that directly influences the financial, technical, economical, and environmental issues in relation to the exploitation of a mineral resource. Despite the simple definition of cut-off grade, the COGS problem is one of the complex and complicated problems in the mine planning process. From the optimization point of view, the COGS with an objective of maximizing the present value of future cash flows is a non-linear and a non-convex problem that even in its deterministic form can be solved using approximate optimization methods. This optimization problem will also be more complex and complicated under uncertainty conditions. This paper proposes an uncertainty based multi-criteria ranking system to investigate the problem of COGS selection considering metal price and geological uncertainties. The proposed system aims at selection of the best COGS among technically feasible alternative COGSs under uncertainty circumstances. Our developed system is based on integrating metal price and geological uncertainties as well as operating flexibility to close the mine early. We incorporate this operating flexibility into the proposed system using a Monte Carlo based real options (RO) valuation model. For this purpose, in addition to the expected value, other risk criteria are considered to rank the alternatives. These risk criteria include abilities of strategies in producing extra profits, minimizing losses, and achieving the predefined goals of the production. In this study, the technically possible COGSs are generated using the Lane comprehensive algorithm. To demonstrate the effectiveness of the proposed system, we utilize data of an Iranian gold mine. Results show that the proposed system outperforms conventional methods in the sense that it shows significantly lower average mis-ranking than the other methods and also selects a strategy with a higher value. The sensitivity analysis of the proposed system relative to the gold price shows that the system is highly dependent on the parameters of the stochastic process used to model the evolution of the metal price. Therefore, special consideration should be given in estimating stochastic process parameters.