Laboratory of Environmental Physics, Georgi Nadjakov Institute of Solid State Physics, Bulgarian Academy of Sciences, Sofia, Bulgaria.
J Environ Sci Health A Tox Hazard Subst Environ Eng. 2013;48(8):963-72. doi: 10.1080/10934529.2013.762743.
Maritsa River is one of the largest rivers flowing on Bulgarian territory. The quality of its waters is of substantial importance for irrigation, industrial, recreation and domestic use. Besides, part of the river is flowing on Turkish territory and the control and management of the Maritsa catchment is of mutual interst for the neighboring countires. Thus, performing interpretation and modeling of the river water quality is a major environmetric problem. Two multivariate statstical methods (Cluster analysis/CA/and Principal components analysis/PCA/) were applied for model assessment of the water quality of Maritsa River on Bulgarian territory. The study used long-term monitoring data from 21 sampling sites characterized by 8 surface water quality indicators. The application of CA to the indicators results in 3 significant clusters showing the impact of biological, anthropogenic and eutrophication sources. For further assessment of the monitoring data, PCA was implemented, which identified, again,three latent factors confirming, in principle, the clustering output. The latent factors were conditionally named "biologic", "anthropogenic" and "eutrophication" source. Their identification coinside correctly to the location of real pollution sources along the Maritsa River catchment. The linkage of the sampling sites along the river flow by CA identified four special patterns separated by specific tracers levels: biological and anthropogenic major impact for pattern 1, euthrophication major impact for pattern 2, background levels for pattern 3 and eutrophication and agricultural major impact for pattern 4. The apportionment models of the pollution determined the contribution of each one of identified pollution factors to the total concentration of each one of the water quality parameters. Thus, a better risk management of the surface water quality is achieved both on local and national level.
马里察河是流经保加利亚领土的最大河流之一。其水质对于灌溉、工业、娱乐和家庭用途具有重要意义。此外，该河流的一部分流经土耳其领土，因此对邻国来说，控制和管理马里察河流域具有共同利益。因此，对河流水质进行解释和建模是一个主要的环境问题。本研究应用了两种多元统计方法（聚类分析/CA/和主成分分析/PCA/）来评估保加利亚境内马里察河的水质模型。该研究使用了来自 21 个采样点的长期监测数据，这些采样点的 8 个地表水质量指标具有特征。CA 对指标的应用产生了 3 个显著的聚类，显示了生物、人为和富营养化源的影响。为了进一步评估监测数据，实施了 PCA，它又确定了三个潜在因素，原则上确认了聚类的结果。潜在因素被条件命名为“生物”、“人为”和“富营养化”源。它们的识别与沿马里察河流域的实际污染源的位置完全一致。CA 通过沿河流流向对采样点进行链接，确定了四个特殊模式，这些模式通过特定的示踪剂水平分隔：模式 1 主要受到生物和人为的影响，模式 2 主要受到富营养化的影响，模式 3 为背景水平，模式 4 为富营养化和农业的主要影响。污染分摊模型确定了每个已识别的污染因素对每个水质参数总浓度的贡献。因此，无论是在地方还是国家层面，都可以更好地管理地表水质量的风险。
