基于IFS-动态加权的地下综合体深基坑施工风险评价

为控制地下综合体深基坑施工风险,提出了一种基于直觉模糊集(IFS)-动态加权的风险评价方法.从地质条件、建筑环境、岩土设计、施工方案和偶然风险5个方面分析地下综合体深基坑施工风险因素,构建了综合风险评价指标体系.采取施工风险5级划分方法,利用IFS理论确定指标风险;以偏大型正态加权函数确定指标权重,得到系统综合风险.以西安市某地下综合体深基坑施工实际为背景,进行方法应用.结果表明,该西安市地下综合体深基坑施工系统风险等级为显著风险.其中,指标"邻近建筑""地下管网"的风险等级为高度风险,"道路交通""基坑边坡稳定性""降排水实施"为显著风险,对此提出了相应的安全措施.     

Estimating and comparing greenhouse gas emissions with their uncertainties using different methods: A case study for an energy supply utility

Energy supply utilities release significant amounts of greenhouse gases (GHGs) into the atmosphere. It is essential to accurately estimate GHG emissions with their uncertainties, for reducing GHG emissions and mitigating climate change. GHG emissions can be calculated by an activity-based method (i.e., fuel consumption) and continuous emission measurement (CEM). In this study, GHG emissions such as CO₂, CH₄, and N₂O are estimated for a heat generation utility, which uses bituminous coal as fuel, by applying both the activity-based method and CEM. CO₂ emissions by the activity-based method are 12–19% less than that by the CEM, while N₂O and CH₄ emissions by the activity-based method are two orders of magnitude and 60% less than those by the CEM, respectively. Comparing GHG emissions (as CO₂ equivalent) from both methods, total GHG emissions by the activity-based methods are 12–27% lower than that by the CEM, as CO₂ and N₂O emissions are lower than those by the CEM. Results from uncertainty estimation show that uncertainties in the GHG emissions by the activity-based methods range from 3.4% to about 20%, from 67% to 900%, and from about 70% to about 200% for CO₂, N₂O, and CH₄, respectively, while uncertainties in the GHG emissions by the CEM range from 4% to 4.5%. For the activity-based methods, an uncertainty in the Intergovernmental Panel on Climate Change (IPCC) default net calorific value (NCV) is the major uncertainty contributor to CO₂ emissions, while an uncertainty in the IPCC default emission factor is the major uncertainty contributor to CH₄ and N₂O emissions. For the CEM, an uncertainty in volumetric flow measurement, especially for the distribution of the volumetric flow rate in a stack, is the major uncertainty contributor to all GHG emissions, while uncertainties in concentration measurements contribute a little to uncertainties in the GHG emissions.

Implications:Energy supply utilities contribute a significant portion of the global greenhouse gas (GHG) emissions. It is important to accurately estimate GHG emissions with their uncertainties for reducing GHG emissions and mitigating climate change. GHG emissions can be estimated by an activity-based method and by continuous emission measurement (CEM), yet little study has been done to calculate GHG emissions with uncertainty analysis. This study estimates GHG emissions and their uncertainties, and also identifies major uncertainty contributors for each method.     

Influence of filtration velocity on DON variation in BAF for micropolluted surface water treatment

Biological aerated filters (BAFs) are widely used for the treatment of micropolluted surface water. However, the biological process produces dissolved organic nitrogen (DON), which, as precursors of nitrogenous disinfection by-products, pose potential threats to drinking water safety. Therefore, to control DON in BAF effluent, it is necessary to study the influence of BAF operation parameters on DON production. In this study, the influence of filtration velocity in a BAF on DON production was investigated. Under different filtration velocity (0.5, 2, and 4 m/h) conditions, profiles of DON concentrations along the media layer were measured. The profile at a filtration velocity of 0.5 m/h showed a decreasing trend, and the ones under filtration velocities of 2 and 4 m/h fluctuated in a small range (from 0.1 to 0.4 mg/L). Moreover, the relatively high filtration velocities of 2 and 4 m/h resulted in a lower level of DON concentration. Additionally, 3D excitation-emission matrix fluorescence spectroscopy was used to characterize DON. It is found that the patterns of DON at a relatively high filtration velocity condition (4 m/h) were obviously different from the ones under low filtration velocity conditions (0.5 and 2 m/h).     

神经网络在低压脱扣装置选择上的应用

低压脱扣装置应用于环境复杂的场合, 选型不当易使得电力事故频发,不利于电力系统的可靠运行。通过分析影响低压脱扣选择的因素,利用Back Propagation神经网络进行建模,提出了一种低压脱扣装置自动选择预测方法,结果表明:该方法在准确性与可重复性.上与传统方法相比具有较大优势,在工程应用中有一定的参考作用。     

Fenton试剂处理汽车油漆废渣

采用Fenton试剂处理汽车油漆废渣。考察了FeSO_4用量、H_2O_2用量、反应温度、反应时间对处理效果的影响。实验结果表明,在FeSO_4用量为20.5 mg/g、H_2O_2用量为0.6 g/g、反应温度为80 ℃、反应时间为3 h的最优条件下,清漆渣、色漆渣、底漆渣3种漆渣的干基减量率均为20%左右,湿基减量率分别为67.9%、48.2%和64.2%,3种产物的脱水性能均有不同程度的提高,处理后废液的COD均为2 000 mg/L左右。采用Fenton试剂处理后的清漆渣产物粒径与原料相似,产物颗粒球形度高,表面粗糙,内部为多孔结构,产物作为固体燃料时燃烧更充分。Fenton试剂处理未对漆渣中的树脂主体产生较大的破坏。     

加速溶剂萃取-固相萃取/超高效液相色谱-串联质谱法测定沉积物中5种微囊藻毒素

建立了加速溶剂萃取-固相萃取/超高效液相色谱-串联质谱法同时测定沉积物中5种微囊藻毒素(MC-LR、MC-RR、MC-LW、MC-LF、MC-YR)的方法。选择甲醇-水(1∶4,V/V)为ASE萃取溶剂,萃取温度和萃取压力分别为80℃和13.1 MPa,固相萃取过程以HLB小柱为萃取柱,采用液相色谱-串联质谱分析。方法在5种微囊藻毒素质量浓度5~100μg/L内线性良好(r0.995),回收率为76.0%~118%,相对标准偏差为1.9%~12.0%,检出限为2~3μg/kg,定量下限为8~12μg/kg。该方法已用于西太湖沉积物的检测,具有较好的方法适用性。     

三峡库区重庆段土壤保持服务时空分布格局研究

土壤保持是生态系统服务与功能的重要组成,在防止土壤侵蚀、减少径流泥沙与农业面源污染等方面具有至关重要的作用。以对国家生态安全具有重要作用的土壤保持生态功能区——三峡库区重庆段为研究区域,研究得到了2000~2013年时间序列区域土壤保持服务"流量"结果,结果表明:(1)三峡库区重庆段多年平均土壤保持量为604.39 t/hm~2·a,沿长江干流自西向东逐渐增强,区域差异显著;(2)三峡库区的土壤保持服务存在明显的垂直分异特征,随着高程的增加,以300 m与900 m为节点,出现递减-递增-递减的分段规律,与人类活动存在明显的相关关系;(3)增加森林覆被面积是改善区域土壤保持、减少水土流失的重要举措。同时,在三峡库区开展坡改梯工程,减少坡耕地的数量能够有效控制区域水土流失;     

典型湖泊水华特征及相关影响因素分析

通过2011-2015年对太湖、巢湖和滇池水华高发季节的连续监测，以藻类密度和水华面积为判据评价了3个湖体的水华情况及变化趋势，探讨了水华发生的主要影响因素。结果表明：太湖水华程度以"轻度水华"为主，巢湖水华程度以"轻微水华"为主，滇池水华程度以"中度水华"为主；太湖、巢湖和滇池水华规模均以"零星性水华"为主；太湖和巢湖藻类密度与水温、pH、溶解氧、总氮、总磷和高锰酸盐指数均呈显著正相关，与透明度呈显著负相关，与氨氮无显著相关性；滇池藻类密度与水温、总磷和高锰酸盐指数均呈显著正相关，与透明度和氨氮呈显著负相关，与pH、溶解氧和总氮无显著相关性。     

未来30a凉山州水稻盛夏低温危害风险分析

根据(BCC)气候系统模式第五阶段试验计划(CMIP5-RCP6.0)预测的2006~2050年各格点逐日平均温度,通过订正反演出2021~2050年凉山州各站点逐日平均温度,结合凉山州不同区域水稻抽穗杨花期低温指标,统计了各站水稻盛夏低温频次、水稻安全播种期、安全齐穗期以及安全生长季差,构建了水稻盛夏低温危害风险指数I=exp(Id+ip-2),并以此进行低温危害风险的区域划分,结果表明:(1)凉山州水稻盛夏低温频次为0.1~3.0次/a,在区域和年际之间差异很大;(2)与近30a比较,凉山州水稻安全播种期普遍提前10 d左右,提前最多的达20 d以上。安全齐穗期只有少数站点推迟,大多数站点都有提前的趋势;(3)海拔1 500 m以下为无风险区,1 500~2 000 m的区域为低风险区,2 000~2 500 m为中风险区,2 500~2 600 m为高风险区,分区结果与实际情况相符,为凉山州未来水稻生产布局、应对气候变化的影响提供科学依据。