Faculty of Chemistry and Pharmacy, University of Sofia, 1 James Bourchier Blvd., Sofia 1164, Bulgaria.
Molecules. 2019 Mar 2;24(5):883. doi: 10.3390/molecules24050883.
The present study deals with the assessment of pollution caused by a large industrial facility using multivariate statistical methods. The primary goal is to classify specific pollution sources and to apportion their involvement in the formation of the total concentration of the chemical parameters being monitored. This aim is accomplished by intelligent data analysis based on cluster analysis, principal component analysis and principal component regression analysis. Five latent factors are found to explain over 80% of the total variance of the system being conditionally named "organic", "non-ferrous smelter", "acidic", "secondary anthropogenic contribution" and "natural" factor. The apportionment models designate the contribution of the identified sources quantitatively and help in the interpretation of risk assessment and management actions. Since the study takes into account pollution uptake from soil to a cabbage plant, the data interpretation could help in introducing biomonitoring aspects of the assessment. The chemometric expertise helps in revealing hidden relationships between the objects and the variables involved to achieve a better understanding of specific pollution events in the soil of a severely industrially impacted region.
本研究采用多元统计方法评估大型工业设施造成的污染。主要目标是对特定污染源进行分类，并分配它们在监测的化学参数总浓度形成中的作用。这一目标是通过基于聚类分析、主成分分析和主成分回归分析的智能数据分析来实现的。发现五个潜在因素可以解释系统总方差的 80%以上，这些因素被条件命名为“有机”、“有色冶炼厂”、“酸性”、“二次人为贡献”和“自然”因素。分配模型定量指定了已确定来源的贡献，并有助于解释风险评估和管理行动。由于本研究考虑了从土壤到卷心菜植物的污染吸收，因此数据解释可以帮助引入评估的生物监测方面。化学计量学专业知识有助于揭示对象之间和所涉及变量之间的隐藏关系，以更好地理解严重受工业影响地区土壤中的特定污染事件。
