An integrated SOM-based multivariate approach for spatio-temporal patterns identification and source apportionment of...

Area of Science:

• Environmental Science
• Water Quality Assessment
• Pollution Source Apportionment

Background:

• Understanding spatial water pollution levels is crucial for effective environmental management.
• Identifying temporal water quality response delay phenomena (WQRDP) aids in predicting pollution impacts.
• Differentiating pollution sources (point, urban non-point, agricultural non-point) is key to targeted mitigation.

Purpose of the Study:

• To classify spatial water pollution levels in rivers.
• To identify temporal water quality response delay phenomena (WQRDP).
• To determine source pollution types and perform quantitative source apportionment.

Main Methods:

• Applied classification techniques: self-organizing maps, hierarchical cluster analysis, and discriminant analysis.
• Utilized multivariate models: principal components analysis (PCA) and positive matrix factorization (PMF) for source apportionment.
• Categorized 27 inflow rivers into three spatial pollution levels (A: high, B: medium, C: low).

Main Results:

• Rivers were spatially divided into high (A), medium (B), and low (C) pollution levels.
• Primary pollution patterns correlated with land use: point sources in Cluster A, urban non-point in B, and agricultural non-point in C.
• Source apportionment identified five factors for Cluster A and six factors for Clusters B and C, explaining 80%-90% of data variance.

Conclusions:

• The study successfully classified spatial water pollution and identified dominant source types.
• Integrated classification and source apportionment models provide a robust framework for water quality assessment.
• Findings support targeted pollution control strategies based on identified source types and pollution levels.