Multivariate Statistical Assessment of Surface Water Quality

Multivariate statistical assessment integrates a suite of analytical techniques to interpret complex water quality datasets by analysing multiple parameters simultaneously. This approach deploys methods such as principal component analysis, cluster analysis, discriminant analysis and factor analysis, often complemented by indices like the Water Quality Index and Trophic State Index or machine-learning tools such as self-organising maps. By reducing data dimensionality, revealing hidden relationships among variables and grouping sampling sites by similarity, these methods elucidate spatio-temporal trends, pinpoint key pollutant sources and optimise monitoring networks. Applied across diverse basins—rivers, reservoirs and groundwater systems—multivariate assessment supports evidence-based decision-making for pollution control, ecosystem protection and sustainable water-resource management. Its global adoption underpins strategies to address challenges posed by climate variability, urbanisation and agricultural runoff, ensuring safe water for human and ecological health.

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Multivariate Statistical Assessment of Surface Water Quality publication trend

The graph below shows the total number of articles in multivariate statistical assessment of surface water quality across all publications each year (not limited to Nature Index journals).

Technical terms

Multivariate statistical assessment: Application of multiple statistical methods to analyse complex datasets with numerous interrelated variables.

Principal component analysis (PCA): Technique that transforms correlated variables into a smaller number of uncorrelated principal components explaining data variance.

Cluster analysis: Method for grouping sampling sites or variables into clusters based on similarity in their characteristics.

Discriminant analysis: Statistical approach to identify which variables best differentiate predefined groups.

Factor analysis: Approach to uncover underlying factors that explain correlations among observed variables.

Water Quality Index (WQI): Aggregated numerical measure that summarises multiple water quality parameters into a single score.

Trophic State Index (TSI): Index that quantifies the nutrient enrichment and productivity level of a water body.

Self-organising map (SOM): Artificial neural network used to visualise and cluster high-dimensional data through unsupervised learning.

References

1. Assessment of water quality using multivariate statistics and geographical information systems (GIS) of Wadi Aldabab, Taiz, Yemen. Applied Water Science (2023).
2. Water quality assessment of the Nam River, Korea, using multivariate statistical analysis and WQI. International Journal of Environmental Science and Technology (2023).