Corrigendum to ‘Fitting molecular weight distributions using a log-normal distribution model’ [Eur. Polym. J. 65C (2015) 197–201]

Abstract A preparative fractionation of about 23 g of a commercial cis-polybutadiene rubber is described. The method employed was a solvent elution chromatographic method with very little temperature gradient. The molecular weight distributions of the fractions obtained were determined by an analytical fractionation of 20 mg of polymer. The method was similar to the preparative fractionation and involved solvent elution chromatography. The fractions obtained were assayed for quantity, molecular weight, and molecular weight distribution by GPC. The low molecular weight fractions of the preparative fractionation had molecular weight distributions which could be closely approximated by two log normal distributions, the low molecular weight component having the narrower width. The ratio of weight to number average molecular weight was found to be about 1.1 for these samples. The higher molecular weight fractions could also be approximated by two log normal distributions; however, in these fractions the low molecular weight component had a very broad distribution but constituted only a small portion of the sample. The widths of the GPC curves of the fractions correlate satisfactorily with the molecular weight distributions found by the analytical refractionations. The GPC width is a sensitive criterion of the width of the molecular weight distribution even when only two columns are used. It is felt that the analytical fractionation procedure presented gives more detailed information on the molecular weight distribution than is easily obtainable from an ordinary GPC curve.

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Comprehensive Source Identification and Apportionment Analysis of five Heavy Metals in Soils in Wenzhou City, China

10.5194/egusphere-egu2020-2514 ◽

2020 ◽

Author(s):

Shuai Shao ◽

Bifeng Hu ◽

Yin Zhou ◽

Zhou Shi

Keyword(s):

Heavy Metals ◽

Normal Distribution ◽

Source Identification ◽

Mixture Distribution ◽

Anthropogenic Sources ◽

Distribution Model ◽

Auxiliary Variables ◽

Industrial Emissions ◽

Log Normal ◽

Log Normal Distribution

<p>Source identification and apportionment of heavy metals (HMs) has been a vital issue of soil contamination restoration. In this study, qualitive approaches (Finite mixture distribution model (FMDM) and raster based principal components analysis (RB-PCA)) as well as quantitative approach (positive matrix factorization (PMF)) were composed to identify and apportion sources of five HMs (Cd, Hg, As, Pb, Cr) with the help of several crucial auxiliary variables in Wenzhou City, China. The result of FMDM showed Cd, and Pb fitted for single log-normal distribution, while Hg fitted for double log-normal mixed distribution, and As, Cr presented triple log-normal distribution. Each element was identified and separated from natural or anthropogenic sources. An improved score interpolation map of PCA attached with corresponded auxiliary variables analysis suggested three main contribution sources including parental materials, mines exploiting and industrial emissions contributes most in the whole study area. Each element was further discussed for quantitative contributions through PMF model. Parental materials contributed to all elements (Cd, Hg, As, Pb, Cr) as 89.22%, 84.81%, 7.31%, 35.84%, 27.42%. Industrial emissions had a contribution as 2.94%, 80.77%, 15.93%, 4.79%, 25.63% for each element respectively. While Mine exploiting mixed with fertilizers inputs has dedicated for such five HMs as 7.84%,11.92%, 48.23%, 10.40% and 46.95%. Such results could efficiently be devoted to scientific decisions and strategies making regarding HMs pollution regulation in soils.</p>

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