This work investigates a statistical approach analysing data from monitoring activities on marine-coastal areas for environmental quality determination and surveillance. Analyses were performed on a database of the Environmental Protection and Prevention Agency of the Puglia Region. As, Cr, Ni, and Pb concentration values in marine sediments and biota from 2013 to 2015 and 2017 were processed to investigate different contaminant characteristics. Hierarchical cluster analysis identified three contaminant distribution classes with (1) highest Cr, Ni, and Pb concentrations, (2) highest As concentration, and (3) lowest contaminants concentration. The Kruskal-Wallis and Friedman tests showed that contaminant distributions were statistically different when considering the monitoring years and classes. However, statistical similarities resulted during the 2013–2017 and 2014–2015 periods. Spearman’s coefficients displayed positive correlations among the pollutants in each matrix and mainly negative correlations for matrices comparison. This methodology aims to provide a practical support for monitoring to identify potential environmental deterioration over time and correlations with specific contamination sources.
Characterising contaminants distribution in marine-coastal sediments through multivariate and nonparametric statistical analyses: a complementary strategy supporting environmental monitoring and control
Race M.;
2023-01-01
Abstract
This work investigates a statistical approach analysing data from monitoring activities on marine-coastal areas for environmental quality determination and surveillance. Analyses were performed on a database of the Environmental Protection and Prevention Agency of the Puglia Region. As, Cr, Ni, and Pb concentration values in marine sediments and biota from 2013 to 2015 and 2017 were processed to investigate different contaminant characteristics. Hierarchical cluster analysis identified three contaminant distribution classes with (1) highest Cr, Ni, and Pb concentrations, (2) highest As concentration, and (3) lowest contaminants concentration. The Kruskal-Wallis and Friedman tests showed that contaminant distributions were statistically different when considering the monitoring years and classes. However, statistical similarities resulted during the 2013–2017 and 2014–2015 periods. Spearman’s coefficients displayed positive correlations among the pollutants in each matrix and mainly negative correlations for matrices comparison. This methodology aims to provide a practical support for monitoring to identify potential environmental deterioration over time and correlations with specific contamination sources.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
