Identification of Geochemical Anomaly by Multifractal Analysis

The separation of anomalies from geochemical background is an important part of data analysis because lack of such identifications might have profound influence on or even distort the final analysis results. In this article, 1 672 geochemical analytical data of 11 elements, including Cu, Mo, Ag, Sn, and others, from a region within Tibet, South China, are used as one example. Together with the traditional anomaly recognition method of using the iterative mean ±2σ local multifractality theory has been utilized to delineate the ranges of geochemical anomalies of the elements. To different degrees, on the basis of original data mapping, C-A fractal analysis and singularity exponents, Sn differs from the other 10 elements. Moreover, geochemical mapping results based on values of the multifractal asymmetry index for all elements delineate the highly anomalous area. Similar to other 10 elements, the anomalous areas of Sn delineated by the asymmetry index distribute along the main structure orientations. According to the asymmetry indexes, the 11 elements could be classified into 3 groups： （1） Ag and Au, （2） As-Sb-Cu-Pb-Zn-Mo, and （3） Sn-Bi-W. This parageneflc association of elements can be used to interpret possible origins of mineralization, which is in agreement with petrological analysis and field survey results.

Decomposition analysis of energy-related carbon dioxide emissions in the iron and steel industry in China

This work aims to identify the main factors influencing the energy-related carbon dioxide （CO2） emissions from the iron and steel industry in China during the period of 1995-2007. The logarithmic mean divisia index （LMDI） technique was applied with period-wise analysis and time-series analysis. Changes in energy- related CO2 emissions were decomposed into four factors： emission factor effect, energy structure effect, energy consumption effect, and the steel production effect. The results show that steel production is the major factor responsible for the rise in CO2 emissions during the sampling period; on the other hand the energy consump- tion is the largest contributor to the decrease in C02 emissions. To a lesser extent, the emission factor and energy structure effects have both negative and positive contributions to C02 emissions, respectively. Policy implications are provided regarding the reduction of C02 emissions from the iron and steel industry in China, such as controlling the overgrowth of steel production, improving energy-saving technologies, and introducing low-carbon energy sources into the iron and steel industry.