Photoneutron activation analysis and multivariate geostatistical approach to assess the spatial occurrence of trace heavy metals in Crisuri Basin

IF 0.1 Q4 SOCIAL SCIENCES, INTERDISCIPLINARY Dialogo Pub Date : 2021-11-01 DOI:10.51917/dialogo.2021.8.1.7
C. Oprea, D. Cupșa, A. Oprea, Cosmin Tudor Ciocan, M. Gustova, O. D. Maslov, A. G. Belov, A. Teusdea, Istvan Gergely, I. Gruia, Doris Cadar
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Abstract

The Crisuri Basin, with its total length of 1093 km (among which 670 km in Romania), is subjected to an effective environmental monitoring system consisting of 18 water stations network. Their integrated information is used for the water assessment problem to understand the biogeochemical processes leading to significant pollution levels in some running water sectors. The goal of this research is to predict the most important factors causing the change of the geochemical measured parameters of some components of the Crisuri water resources. In the present paper, we developed a multivariate statistical model to estimate the spatiotemporal distribution of heavy metals in the field and to identify the contamination sources of Basin River waters. Two methods were deployed as an overall approach to fulfill the proposed objectives, namely the photoneutron activation analysis followed by high-resolution gamma-ray spectrometry and the multivariate statistical analysis. The elements analyzed by different analytical techniques and introduced in databases were As, Cd, Ca, Cu, Fe, Mg, Hg, Na, Ni, Pb, Zn, N-NH4, N-NO2, N-NO3, P-PO4, fixed residues, S-SO4, Cl, phenols and, additional oil compounds. By combining the spatially distributed geochemical data on trace heavy metals with the spatially distributed geophysical data, we obtained the most significant fingerprint factors and their associated uncertainty information concerning the water quality.
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光中子活化分析与多元地统计学方法评价Crisuri盆地痕量重金属空间赋存
Crisuri盆地全长1093公里(其中罗马尼亚境内670公里),拥有由18个水站网络组成的有效环境监测系统。它们的综合信息用于水评价问题,以了解导致某些自来水部门严重污染水平的生物地球化学过程。本研究的目的是预测导致克里苏里水资源部分组分地球化学测量参数变化的最重要因素。在本文中,我们建立了一个多元统计模型来估计野外重金属的时空分布,并确定流域河水的污染源。为了实现所提出的目标,采用了两种方法,即光子中子活化分析,然后是高分辨率伽马射线能谱分析和多元统计分析。采用不同的分析方法分析并导入数据库的元素有As、Cd、Ca、Cu、Fe、Mg、Hg、Na、Ni、Pb、Zn、N-NH4、N-NO2、N-NO3、P-PO4、固定残留物、S-SO4、Cl、酚类和其他油类化合物。将空间分布的痕量重金属地球化学数据与空间分布的地球物理数据相结合,得到了最显著的水质指纹因子及其相关不确定性信息。
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Dialogo
Dialogo SOCIAL SCIENCES, INTERDISCIPLINARY-
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