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1.
采用大肠杆菌吸附-化学还原法,以大肠杆菌(ECCs)为模板、十六烷基三甲基溴化铵为保护剂、抗坏血酸为还原剂,由废含金催化剂制备金纳米线(AuNWs)。采用XRD,SEM,TEM等技术对AuNWs进行表征。研究了AuNWs对罗丹明6G(R6G)和4-巯基苯甲酸(4-MBA)的拉曼散射信号的增强效果。实验结果表明:在制备过程中加入微生物ECCs,可使金回收率提高约20百分点;当溶液pH小于4时,反应2 h后,有大量呈线状的AuNWs聚集沉降,金回收率可达99%1以上。表征结果显示,AuNWs呈多晶结构,晶格间距为0.23 nm。表面增强拉曼散射分析表明,AuNWs对R6G和4-MBA具有良好的拉曼光谱增强性能。 相似文献
2.
In order to study a new leak detection and location method for oil and natural gas pipelines based on acoustic waves, the propagation model is established and modified. Firstly, the propagation law in theory is obtained by analyzing the damping impact factors which cause the attenuation. Then, the dominant-energy frequency bands of leakage acoustic waves are obtained through experiments by wavelet transform analysis. Thirdly, the actual propagation model is modified by the correction factor based on the dominant-energy frequency bands. Then a new leak detection and location method is proposed based on the propagation law which is validated by the experiments for oil pipelines. Finally, the conclusions and the method are applied to the gas pipelines in experiments. The results indicate: the modified propagation model can be established by the experimental method; the new leak location method is effective and can be applied to both oil and gas pipelines and it has advantages over the traditional location method based on the velocity and the time difference. Conclusions can be drawn that the new leak detection and location method can effectively and accurately detect and locate the leakages in oil and natural gas pipelines. 相似文献
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绿潮作为一种新型的海洋灾害,已经引起了各个国家的重视.依据2012年南黄海海域浒苔遥感监测分布面积数据,选取了温度、天气状况、风向、风力、浪高5种影响浒苔扩散的气候因子,建立了基于SVR的浒苔分布面积预测模型,并与经典的最近邻点插值模型、线性插值模型、3次样条函数插值模型和分段3次Hermite插值模型进行了回归效果的对比.分析结果表明,基于SVR的浒苔分布面积预测模型能够为浒苔遥感数据的插补提供一种方法,且回归效果优于传统的回归方法,为浒苔的防治提供辅助决策信息. 相似文献
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以杨梅单宁(BT)为模板,水热法合成纳米二氧化钛(BT-NTO)。采用XRD、FTIR、SEM和TEM技术对BT-NTO的结构及形貌进行了表征。将BT-NTO用于吸附溶液中的Th~(4+),在溶液pH为3.5、温度为25℃、Th~(4+)初始浓度为0.5 mmol/L的条件下,BT-NTO对Th~(4+)的吸附量为0.905 8 mmol/g;吸附过程符合准二级动力学方程和Langmuir等温吸附方程;溶液中共存的Zn~(2+)、Ni~(2+)、Co~(2+)、Sr~(2+)、Yb~(3+)、Nd~(3+)、Sm~(3+)、Gd~(3+)、La~(3+)、Cl-、ClO4-离子对Th~(4+)吸附量影响较小;BT-NTO的重复使用性能较好。 相似文献
8.
Chou CJ 《Environmental monitoring and assessment》2006,119(1-3):571-598
Statistical analyses were applied at the Hanford Site, USA, to assess groundwater contamination problems that included (1) determining local backgrounds to ascertain whether a facility is affecting the groundwater quality and (2) determining a ‘pre-Hanford' groundwater background to allow formulation of background-based cleanup standards. The primary purpose of this paper is to extend the random effects models for (1) assessing the spatial, temporal, and analytical variability of groundwater background measurements; (2) demonstrating that the usual variance estimate s
2, which ignores the variance components, is a biased estimator; (3) providing formulas for calculating the amount of bias; and (4) recommending monitoring strategies to reduce the uncertainty in estimating the average background concentrations. A case study is provided. Results indicate that (1) without considering spatial and temporal variability, there is a high probability of false positives, resulting in unnecessary remediation and/or monitoring expenses; (2) the most effective way to reduce the uncertainty in estimating the average background, and enhance the power of the statistical tests in general, is to increase the number of background wells; and (3) background for a specific constituent should be considered as a statistical distribution, not as a single value or threshold. The methods and the related analysis of variance tables discussed in this paper can be used as diagnostic tools in documenting the extent of inherent spatial and/or temporal variation and to help select an appropriate statistical method for testing purposes. 相似文献
9.
研究了几种典型的城市给水管网优化设计数学模型,并给出了求解方法,指出在给水管网系统的优化设计中进一步完善广义简约(GRG)算法的必要性. 相似文献
10.
Background, Aim and Scope Air quality is an field of major concern in large cities. This problem has led administrations to introduce plans and regulations
to reduce pollutant emissions. The analysis of variations in the concentration of pollutants is useful when evaluating the
effectiveness of these plans. However, such an analysis cannot be undertaken using standard statistical techniques, due to
the fact that concentrations of atmospheric pollutants often exhibit a lack of normality and are autocorrelated. On the other
hand, if long-term trends of any pollutant’s emissions are to be detected, meteorological effects must be removed from the
time series analysed, due to their strong masking effects.
Materials and Methods The application of statistical methods to analyse temporal variations is illustrated using monthly carbon monoxide (CO) concentrations
observed at an urban site. The sampling site is located at a street intersection in central Valencia (Spain) with a high traffic
density. Valencia is the third largest city in Spain. It is a typical Mediterranean city in terms of its urban structure and
climatology. The sampling site started operation in January 1994 and monitored CO ground level concentrations until February
2002. Its geographic coordinates are W0°22′52″ N39°28′05″ and its altitude is 11 m. Two nonparametric trend tests are applied.
One of these is robust against serial correlation with regards to the false rejection rate, when observations have a strong
persistence or when the sample size per month is small. A nonparametric analysis of the homogeneity of trends between seasons
is also discussed. A multiple linear regression model is used with the transformed data, including the effect of meteorological
variables. The method of generalized least squares is applied to estimate the model parameters to take into account the serial
dependence of the residuals of this model. This study also assesses temporal changes using the Kolmogorov-Zurbenko (KZ) filter.
The KZ filter has been shown to be an effective way to remove the influence of meteorological conditions on O3 and PM to examine underlying trends.
Results The nonparametric tests indicate a decreasing, significant trend in the sampled site. The application of the linear model
yields a significant decrease every twelve months of 15.8% for the average monthly CO concentration. The 95% confidence interval
for the trend ranges from 13.9% to 17.7%. The seasonal cycle also provides significant results. There are no differences in
trends throughout the months. The percentage of CO variance explained by the linear model is 90.3%. The KZ filter separates
out long, short-term and seasonal variations in the CO series. The estimated, significant, long-term trend every year results
in 10.3% with this method. The 95% confidence interval ranges from 8.8% to 11.9%. This approach explains 89.9% of the CO temporal
variations.
Discussion The differences between the linear model and KZ filter trend estimations are due to the fact that the KZ filter performs the
analysis on the smoothed data rather than the original data. In the KZ filter trend estimation, the effect of meteorological
conditions has been removed. The CO short-term componentis attributable to weather and short-term fluctuations in emissions.
There is a significant seasonal cycle. This component is a result of changes in the traffic, the yearly meteorological cycle
and the interactions between these two factors. There are peaks during the autumn and winter months, which have more traffic
density in the sampled site. There is a minimum during the month of August, reflecting the very low level of vehicle emissions
which is a direct consequence of the holiday period.
Conclusions The significant, decreasing trend implies to a certain extent that the urban environment in the area is improving. This trend
results from changes in overall emissions, pollutant transport, climate, policy and economics. It is also due to the effect
of introducing reformulated gasoline. The additives enable vehicles to burn fuel with a higher air/fuel ratio, thereby lowering
the emission of CO. The KZ filter has been the most effective method to separate the CO series components and to obtain an
estimate of the long-term trend due to changes in emissions, removing the effect of meteorological conditions.
Recommendations and Perspectives Air quality managers and policy-makers must understand the link between climate and pollutants to select optimal pollutant
reduction strategies and avoid exceeding emission directives. This paper analyses eight years of ambient CO data at a site
with a high traffic density, and provides results that are useful for decision-making. The assessment of long-term changes
in air pollutants to evaluate reduction strategies has to be done while taking into account meteorological variability 相似文献