Assessing Indices Tracking Changes in River Geochemistry and Implications for Monitoring

Abstract

In geochemical data analysis, assessing the potential of new techniques to identify compositional time–space changes is of great interest for monitoring purposes. This work aims to evaluate, in the light of the compositional data analysis perspective, the performance of different statistical indices in tracing the evolution of a geochemical composition and the relationships among its parts. To reach this goal, source-to-sink chemical changes in water and stream sediment composition of the Tiber river (central Italy) are analyzed using three indices: (i) the cumulative sum of unclosed perturbation factors of each composition (row sum) with respect to a reference composition; (ii) the robust Mahalanobis distance, describing the compositional differences from the same reference and, (iii) the geometric mean of each composition as a measure able to capture the interactions among the parts. The results highlight the major compositional changes downriver, allowing to explore geochemical footprints’ propagation and their natural or anthropogenic origin. The tested indices are consistent in most cases, particularly if high-variability species are treated separately and low values are rare. Under this latter condition, the geometric mean of the composition shows a close connection with the cumulative sum of unclosed perturbation factors. This indicates that both indices inherit the complex history of the changes, well capturing the interactions among the parts under the influence of environmental drivers. With this awareness, the application of these methods in monitoring and applied geochemical studies could offer new insights into the inner workings of river systems and their resilience to environmental pressures.

Application of best subset method for river water quality modeling: A study on Godavari River, India

“…The spectral reflectance from the ground and satellite platforms is used by Hirtle and Rencz (2003) to develop regression models for the prediction of dissolved organic carbon. Gozzi and Buccianti (2022) have conducted geochemical data analysis to evaluate the performance of different statistical indices to trace the evolution of a geochemical composition. Correlation analysis can determine the closeness involved in the relationship between dependent and vari-ous independent variables (Jain & Sharma, 2002;Sharma & Jain, 2005;Singanan et al, 1995).…”

“…River water quality parameters undergo dynamic changes during monsoon and post‐monsoon periods due to factors such as distribution, solubility, exchange capacity, selectivity of minerals, and their interdependence. Researchers worldwide have conducted numerous studies to investigate the hydrochemical and geochemical characteristics of river water as a means to assess water quality on a global scale (Boral et al., 2020; Gozzi & Buccianti, 2022; Gupta et al., 2020, 2023; Khan et al., 2022; Sharma et al., 2022; Vuba et al., 2015).…”

“…Researchers worldwide have conducted numerous studies to investigate the hydrochemical and geochemical characteristics of river water as a means to assess water quality on a global scale (Boral et al, 2020;Gozzi & Buccianti, 2022;Gupta et al, 2020Gupta et al, , 2023Khan et al, 2022;Sharma et al, 2022;Vuba et al, 2015).…”

A new version of the Langelier-Ludwig square diagram under a compositional perspective

“…It is worth noting that products and related geometric means can be highly affected by parts with small relative values, possibly causing misinterpretations (e.g. Rock, 1988;Gozzi and Buccianti, 2022). For example, imagine a composition with 4 parts with the values (0.001,2,5,7.5), then your geometric mean is 0.523.…”