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微波消解-氢化物发生原子荧光光谱法测定粉煤灰中的硒

粉煤灰中含有的硒元素较高,有综合利用价值.由于基体的复杂性,并未形成成熟可靠的测定技术.本文参考煤炭样品和土壤样品中硒的原子荧光光谱分析方法,采用氢化物发生-原子荧光光谱法测定粉煤灰中的硒.样品经氢氟酸-硝酸-双氧水体系微波消解得到清亮、透明的消解液,消解液经浓盐酸还原后,加入三价铁盐消除共存元素的干扰,所加的铁盐量不干扰硒的测定.方法检出限为0.020 μg/g;平行测定5份粉煤灰样品,相对标准偏差低于4.2％;加标回收率为94.0％～105.0％.应用建立的方法对煤飞灰标准物质进行分析,测定值与标准值基本相符;对不同地区的实际粉煤灰样品进行分析,证实方法可靠,可用于粉煤灰中硒元素的测定.     

湖北省不同资料反演大气可降水量的误差分析

利用常规探空、秒级原始探空、GPS/MET、微波辐射计、GFS再分析资料以及区域中尺度WRF模式的预报场资料计算整层可降水量,对多种资料计算的整层可降水量进行误差特征和原因分析,结果表明:秒级探空和常规探空计算的整层可降水量基本一致。GPS/MET、微波辐射计、GFS以及WRF计算的整层可降水量与常规(秒级)探空的相关系数分别为0.94、0.92、0.93、0.80,有降水时GPS/MET和微波辐射计与常规探空的相关系数分别下降到0.85和0.81,但有降水时GPS/MET误差分布较集中,而有降水时微波辐射计误差显著增大,主要由于1~2 km处水汽密度误差异常增大。除微波辐射计和GFS宜昌站计算的整层可降水量为相对常规探空偏高,其他资料均为偏低,GPS/MET宜昌和恩施站平均偏低3 mm,GFS武汉和恩施站分别偏低1和7 mm,WRF恩施平均偏低2 mm,WRF武汉和宜昌平均偏低6~8 mm。GFS恩施站可降水量偏低是由于GFS资料中恩施地面气压比实际偏低,但其露点温度整层均比常规探空偏高。除GFS恩施站外,GFS武汉、GFS宜昌和WRF 3站的露点温度相对常规探空资料露点温度均表现为:850 hPa以下偏低,850 hPa以上偏高。WRF 12 h预报场的整层可降水量与常规探空整层可降水量的相关性和误差均优于24 h预报场。     

微波消解-氢化物发生原子荧光光谱法测定茶园土壤中的铅

样品用王水微波消解浸提,氢化物发生-原子荧光光谱法测定茶园土壤中痕量铅的含量。对样品浸取方法、实验条件、增感剂和共存元素进行了条件实验。结果表明,王水微波消解浸取,铅浸出量最大,减少了试剂用量和环境污染;铁氰化钾-盐酸羟胺体系有显著的增感作用,铁氰化钾在配制溶液时用米糠除去试剂中可能存在的铅,降低了空白;钴、锌、砷、镉等共存离子不干扰铅的测定,通过加入邻菲啰啉-硫氰酸钠消除铁和铜的干扰,提高了铅的回收率。方法检出限为0.65 μg/L,精密度(RSD,n=10)为1.89%,回收率在86.8%~110.4%之间。用土壤标准物质验证,测定值与标准值相符,方法快速准确,适合于大批量样品的分析检测。     

The effects of scene heterogeneity on soil moisture retrieval from passive microwave data

The τ–ω model of microwave emission from soil and vegetation layers is widely used to estimate soil moisture content from passive microwave observations. Its application to prospective satellite-based observations aggregating several thousand square kilometres requires understanding of the effects of scene heterogeneity. The effects of heterogeneity in soil surface roughness, soil moisture, water area and vegetation density on the retrieval of soil moisture from simulated single- and multi-angle observing systems were tested. Uncertainty in water area proved the most serious problem for both systems, causing errors of a few percent in soil moisture retrieval. Single-angle retrieval was largely unaffected by the other factors studied here. Multiple-angle retrievals errors around one percent arose from heterogeneity in either soil roughness or soil moisture. Errors of a few percent were caused by vegetation heterogeneity. A simple extension of the model vegetation representation was shown to reduce this error substantially for scenes containing a range of vegetation types.     

Passive L-band microwave soil moisture retrieval error arising from topography in otherwise uniform scenes

Soil moisture is an important component of the water cycle and will be measured for the first time on a global scale by a dedicated passive L-band microwave radiometer that is planned for launch in 2008. Here, the contribution of topography to the error budget is examined for a vegetated scene with uniform microwave emission. Dual-polarization brightness temperature curves were generated over a range of look angles for 1-D scenes with simple geometrical features, and the soil moisture was retrieved assuming a flat surface. The errors were small for the scenarios considered. Theoretical errors were tested for realistic topography with a DEM transect of a mountainous region, and were found to be comparable. Knowledge of the mean slope from high-resolution DEM data can be used to improve the accuracy of the retrieval.     

On the sampling errors from raingauges and microwave attenuation measurements

The sampling error formalism by North and Nakamoto (1989) has been widely referenced in research papers on sampling using space-borne sensors or ground-borne sensors. However, their formalism is found to not only underestimate the sampling error, especially for the raingauge network case, but also not be applicable for the cases of using a line of raingauges or microwave attenuation measurements. In this paper, the sampling error formalism has been revised and applied to the same sampling design and the same rainrate model as in North and Nakamoto (1989) for the comparison. The sampling error estimated using the revised formula was found to be more than 50% higher than that by North and Nakamoto (1989). For the case of using a line of raingauges we found that the sampling error converges to a certain value, not zero as in North and Nakamoto formalism, as the number of gauges increases. The microwave attenuation measurements case, which is the same as the case of using a line of infinite raingauges, also gives non-zero sampling errors. Finally, the combined sampling using both satellite and ground-borne sensors (e.g., raingauge network, a line of raingauges, or microwave attenuation measurements) was reviewed to check their design orthogonality and estimated the sampling errors for the combination of satellite and raingauge network case to see its behavior depending on various settings of these two different measurements.     

Thermal and kinematic corrections to the microwave background polarization induced by galaxy clusters along the line of sight

We derive analytic expressions for the leading-order corrections to the polarization induced in the cosmic microwave background (CMB) owing to scattering of photons off hot electrons in galaxy clusters along the line of sight. For a thermal distribution of electrons with kinetic temperature k _B T _e∼10 keV and bulk peculiar velocity V ∼1000 km s⁻¹, the dominant corrections to the polarization induced by the primordial CMB quadrupole and the cluster peculiar velocity arise from electron thermal motion and are at the level of ∼10 per cent in each case, near the peak of the polarization signal. When more sensitive measurements become feasible, these effects will be significant for the determination of transverse peculiar velocities, and the value of the CMB quadrupole at the cluster redshift, via the cluster polarization route.     

Simultaneous estimation of noise and signal in cosmic microwave background experiments

To correctly analyse data sets from current microwave detection technology, one is forced to estimate the sky signal and experimental noise simultaneously. Given a time-ordered data set we propose a formalism and method for estimating the signal and associated errors without prior knowledge of the noise power spectrum. We derive the method using a Bayesian formalism and relate it to the standard methods; in particular we show how this leads to a change in the estimate of the noise covariance matrix of the sky signal. We study the convergence and accuracy of the method on two mock observational strategies and discuss its application to a currently-favoured calibration procedure.     

Large-scale structure, the cosmic microwave background and primordial non-Gaussianity

Cosmic microwave background and large-scale structure data will shortly improve dramatically with the Microwave Anisotropy Probe and Planck Surveyor , and the Anglo-Australian 2-Degree Field and Sloan Digital Sky Survey. It is therefore timely to ask which of the microwave background and large-scale structure will provide a better probe of primordial non-Gaussianity. In this paper we consider this question, using the bispectrum as a discriminating statistic. We consider several non-Gaussian models and find that in each case the microwave background will provide a better probe of primordial non-Gaussianity. Our results suggest that if microwave background maps appear Gaussian, then apparent deviations from Gaussian initial conditions in galaxy surveys can be attributed with confidence to the effects of biasing. We demonstrate this precisely for the spatial bispectrum induced by local non-linear biasing.