THE GENERAL AN INDEX AND ITS APPLICATION .4. THE APPLICATION OF GENERAL AN INDEX TO HYDROCARBONS

The general a(N) index (GAI) was used to characterize the cis, trans isomers of hydrocarbons. The best one-variable equations were obtained with GAI for several physicochemical properties of seven pairs of olefin cis, trans isomers. The linear correlation coefficients are in the range of 0.975 to 0.997. GAI was also compared with the other five topological indices, Randic connectivity index chi, Wiener number W, Hosoya index Z, the average distance sum connectivity J proposed by Balaban and a(N) index introduced by Yang, in correlating with the octane number (MON) of heptanes and octanes.

GENERAL ALPHA-N INDEX AND ITS APPLICATION .2. THE CORRELATION BETWEEN THE GENERAL ALPHA-N INDEX AND PHYSICAL-PROPERTIES OF NEUTRAL PHOSPHORUS-COMPOUNDS

The relationship between the alpha-N index and physical properties of neutral phosphorus extractants is studied. Using the general alpha-N index which could describe extractants with minute difference in structure, the good correlation between it and various physical properties of the neutral phosphorus extractants (e.g., densities, refractive index, shift ratio of paper chromatography and IR frequencies of bond P = O) is obtained. The result indicates that general alpha-N index is a good topological index of organic compounds.

GENERAL ALPHA-N INDEX AND ITS APPLICATION .1. GENERAL ALPHA-N INDEX AND ITS APPLICATION IN THE EXTRACTION OF Y, CE, U, AND TH BY NEUTRAL PHOSPHORUS EXTRACTANCTS

The general a(N) index is established for molecules containing heteroatoms, rings, and multiple bonds. The general a(N) index is able to describe molecules with minute differences in structure and can also reveal the properties of molecules. This theory is successfully applied to the case of neutral phosphorus extractants. Both the molecular polarity and steric effect are characterized by the general a(N) index. The relationships between these properties and the distribution ratios for extracting Y, Ce, U, and Th are also shown by the general a(N) index.

Pervasive autocorrelation of the chemical index of alteration in sedimentary profiles and its palaeoenvironmental implications

The chemical index of alteration has been used widely for reconstruction of the palaeoclimate. However, the mechanisms and environmental factors controlling the chemical index of alteration of sediments are not yet fully understood. In this study, autocorrelations of the chemical index of alteration in nine sedimentary profiles, from both the land and the sea, spanning different geological times, are discussed. The sediments of these profiles have different origins (dust, fluvial or ocean sediments) and are from various climate situations and sedimentary environments. Autocorrelations of chemical index of alteration series are ubiquitously evident in all profiles. It is suggested here that autocorrelations may be caused by post-depositional changes such as persistent weathering and diagenesis. As a result, the chemical index of alteration may not reflect climatic conditions during the time of sediment deposition. This study strongly recommends the confirmation of the reliability and veracity of the chemical index of alteration before it is adopted to evaluate the weathering degree of parent rocks and to reconstruct the past climate. Significant autocorrelations in loess profiles were specifically observed, suggesting that the existing understanding of loess deposition in terms of climate conditions requires re-examination, and that previous reconstructions of rapid climate changes (for example, in centennial-millennial scales) should be treated with caution.

Feeding habits of Calanus sinicus (Crustacea : Copepoda) during spring and autumn in the Bohai Sea studied with the herbivore index

Pigment ingestion rate (PIR) and egg production rate (EPR) of the dominant copepod Calanus sinicus, as well as chlorophyll-a concentration and phytoplankton assemblages were measured in the Bohai Sea, North China in June 1997, October 1998 and May 1999. A herbivore index (H) was also calculated as the carbon specific ratio of PIR and EPR, in order to investigate its feeding habits in the spring and autumn phytoplankton bloom respectively. On average, chlorophyll-a concentration was relatively similar (1-1.34 mg m(-3)) in the three cruises, but PIR was quite different. It was 3.24 mu g C female(-1) d(-1) in October, equivalent to one half of the PIR for June and one third of the PIR for May. Average EPR was highest in May, and quite similar during the other two months. According to H values, herbivorous feeding contributed 100% of the egg production of C. sinicus in June, 82.5% in May, but only 47.8% in October. It is possible that omnivorous feeding of C. sinicus in October was induced by a prevalence of large-sized diatoms and sufficient non-phytoplankton food resources during the autumn bloom period.

Assessing the potential of VEGETATION sensor data for mapping snow and ice cover: a Normalized Difference Snow and Ice Index

The VEGETATION (VGT) sensor in SPOT 4 has four spectral bands that are equivalent to Landsat Thematic Mapper (TM) bands (blue, red, near-infrared and mid-infrared spectral bands) and provides daily images of the global land surface at a 1-km spatial resolution. We propose a new index for identifying and mapping of snow ice cover, namely the Normalized Difference Snow/Ice Index (NDSII), which uses reflectance values of red and mid-infrared spectral bands of Landsat TM and VGT. For Landsat TM data, NDSII is calculated as NDSIITM =(TM3 -TM5)/(TM3 +TM5); for VGT data, NDSII is calculated as NDSIIVGT =(B2- MIR)/(B2 + MIR). As a case study we used a Landsat TM image that covers the eastern part of the Qilian mountain range in the Qinghai-Xizang (Tibetan) plateau of China. NDSIITM gave similar estimates of the area and spatial distribution of snow/ice cover to the Normalized Difference Snow Index (NDSI=(TM2-TM5)/(TM2+TM5)) which has been proposed by Hall et al. The results indicated that the VGT sensor might have the potential for operational monitoring and mapping of snow/ice cover from regional to global scales, when using NDSIIVGT.