In statistics, the identification of environmental criticalities, one of the primary goals of environmental monitoring and management, translates into the detection of spatial outliers. Detected in relation to purposely defined sets of indicators, both global and local outliers are pivotal in the identification not only of the severity and spread of criticalities, but also of their nature and causes. The present research exemplifies a procedural framework to identify environmental criticalities, using two different approaches for the detection of spatial outliers in river ecosystems related to several sets of parameters (organic C, inorganic C, Ca, Co, Cr, Fe, K, Mg, Mn, N, Na, P, S, Si, V, Zn, Cl−, F−, NO3−, SO42−, chlorophyll a, chlorophyll b, pheophytin a, pheophytin b, total carotenoids, pH, and electrical conductivity), including emerging contaminants. To this end, indicator sets diagnostic for specific criticalities, derived from an empirical dataset of water quality parameters, were employed, using detection techniques based on geographically weighted principal component analysis and a modified pairwise Mahalanobis distance–based algorithm. Clear and accurate criticality scenarios were derived, highlighting both the strengths and the limitations of the proposed approach, especially in relation to the classic threshold-based methods.

Multivariate spatial analysis for the identification of criticalities and of the subtended causes in river ecosystems

Bellino A.;Baldantoni D.
2020

Abstract

In statistics, the identification of environmental criticalities, one of the primary goals of environmental monitoring and management, translates into the detection of spatial outliers. Detected in relation to purposely defined sets of indicators, both global and local outliers are pivotal in the identification not only of the severity and spread of criticalities, but also of their nature and causes. The present research exemplifies a procedural framework to identify environmental criticalities, using two different approaches for the detection of spatial outliers in river ecosystems related to several sets of parameters (organic C, inorganic C, Ca, Co, Cr, Fe, K, Mg, Mn, N, Na, P, S, Si, V, Zn, Cl−, F−, NO3−, SO42−, chlorophyll a, chlorophyll b, pheophytin a, pheophytin b, total carotenoids, pH, and electrical conductivity), including emerging contaminants. To this end, indicator sets diagnostic for specific criticalities, derived from an empirical dataset of water quality parameters, were employed, using detection techniques based on geographically weighted principal component analysis and a modified pairwise Mahalanobis distance–based algorithm. Clear and accurate criticality scenarios were derived, highlighting both the strengths and the limitations of the proposed approach, especially in relation to the classic threshold-based methods.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11386/4734509
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