Polluted aquifers: identification and characterisation by statistical analysis

Area of Science:

• Hydrogeology
• Environmental Chemistry
• Data Analysis

Background:

• Aquifer characterization is crucial for water resource management.
• Understanding groundwater quality, especially pollutant distribution, is essential.
• Lerma Valley's groundwater system, with a history of boron pollution, presents a unique case study.

Purpose of the Study:

• To apply multivariate statistical methods for aquifer identification and characterization.
• To assess groundwater quality and boron pollution dynamics in the Lerma Valley.
• To evaluate the effectiveness of hierarchical clustering and principal component analysis in hydrogeological studies.

Main Methods:

• Sampling and analysis of physical and chemical properties of 46 wells in Lerma Valley.
• Application of Hierarchical Clustering (HC) to group sampling sites based on water properties.
• Utilisation of Principal Component Analysis (PCA) to identify pollution patterns and relationships between variables.

Main Results:

• Hierarchical clustering successfully delineated two distinct aquifer clusters corresponding to the two geographical areas.
• Boron concentrations generally decreased in the eastern area post-remediation, but some wells showed increases, indicating slow aquifer recovery.
• Principal component analysis indicated early-stage boron pollution in the western area's aquifer.

Conclusions:

• Multivariate statistical techniques (HC and PCA) are effective tools for aquifer identification and characterization.
• The study highlights the complex dynamics of boron pollution and aquifer self-recovery in the Lerma Valley.
• Findings support the consideration of distinct aquifer management strategies for the identified groundwater entities.