On the Use of Dimension Reduction or Signal Separation Methods for Nitrogen River Pollution Source Identification

TL;DR

This paper evaluates the effectiveness of dimension reduction and signal separation methods, including PCA, ICA, and Factor Analysis, in identifying nitrogen pollution sources in rivers, analyzing their theoretical foundations and practical applicability.

Contribution

It critically assesses whether PCA and related techniques are suitable for nitrogen source identification considering their assumptions and theoretical basis.

Findings

PCA's effectiveness depends on its assumptions being met in pollution data.

ICA and Factor Analysis offer alternative approaches with different assumptions.

The paper highlights limitations and potential of these methods for environmental source identification.

Abstract

Identification of the current and expected future pollution sources to rivers is crucial for sound environmental management. For this purpose numerous approaches were proposed that can be clustered under physical based models, stable isotope analysis and mixing methods, mass balance methods, time series analysis, land cover analysis, and spatial statistics. Another extremely common method is Principal Component Analysis, as well as its modifications, such as Absolute Principal Component Score. they have been applied to the source identification problems for nitrogen entry to rivers. This manuscript is checking whether PCA can really be a powerful method to uncover nitrogen pollution sources considering its theoretical background and assumptions. Moreover, slightly similar techniques, Independent Component Analysis and Factor Analysis will also be considered.