Application of multivariate analysis on naphthalene adsorption in aqueous solutions

• Published in: Environmental science and pollution research international Vol. 27; no. 3; pp. 3329 - 3337
• Main Authors: Frescura, Lucas Mironuk, de Menezes, Bryan Brummelhaus, Duarte, Rafael, da Rosa, Marcelo Barcellos
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 2020; Springer Nature B.V
• Subjects: Activated carbon; Activated charcoal; Adsorbents; Adsorption; Air pollution; Aquatic Pollution; Aqueous solutions; Atmospheric Protection/Air Quality Control/Air Pollution; Charcoal; Cluster analysis; Correlation analysis; Data analysis; Earth and Environmental Science; Ecotoxicology; Environment; Environmental Chemistry; Environmental Health; Environmental science; Euclidean geometry; Multivariate analysis; Naphthalene; Organic chemistry; Parameters; Pollutants; Principal components analysis; Research Article; Sediment pollution; Soil pollution; Soil water; Waste Water Technology; Water Management; Water pollution; Water Pollution Control; Naphthalene; Polycyclic aromatic hydrocarbons; Adsorption; Multivariate analysis; Hierarchical cluster analysis; Principal component analysis
• ISSN: 0944-1344; 1614-7499
• DOI: 10.1007/s11356-019-07278-1

Naphthalene (NAP) is found as a pollutant in water, soil, and air, and adsorption is the most prominent removal process of this compound, among the methods studied. A study concerning the types of adsorbents and the parameters with the greatest influence on the adsorption process is interesting to direct future works on new adsorbents. The use of multivariate data analysis tools becomes an appealing way to compile data obtained from bibliographic reviews and to establish a behavior in NAP adsorption. This work aims to evaluate the parameters with greater influence on NAP adsorption process regarding adsorption capacity (qe exp ) with the principal component analysis (PCA), and to group common NAP adsorbents by chemical characteristics through hierarchical cluster analysis (HCA). The variables qe exp , S, [NAP] 0 , T, CT, and [Ads] were used to perform PCA with correlation matrix. For the HCA, the variables S, [NAP] 0 , T, CT, and [Ads] with average linkage method (UPGMA) and Euclidean distance were used. Through PCA, it is possible to infer that S and [NAP] 0 are the factors with greater influence in qe exp of NAP, while T, CT, and [Ads] have little correlation. PCA also shows that activated charcoal is the adsorbent with higher qe exp . HCA grouped the adsorbents into four groups by their chemical classes, except group A. Both PCA and HCA methods show themselves as potential tools to evaluate a data set of NAP adsorption processes.