Assessment of Water Quality in the Panama Canal Watershed Using Multivariate Analysis of Physicochemical and Biological Parameters DOI Open Access
Mitzi Cubilla‐Montilla, Glòria Carrasco,

Marisela Del C. Castillo

и другие.

Water, Год журнала: 2025, Номер 17(7), С. 979 - 979

Опубликована: Март 27, 2025

In the hydrometeorological context of watersheds, water quality is strongly related to its physical, chemical, and biological characteristics. this regard, joint analysis these parameters at watershed level highly important. The objective study was analyze a total twenty-three (23) physicochemical in Panama Canal with aim determining interrelationships among them, explaining their clustering simultaneously identifying homogeneous hydrological stations. Multivariate statistical techniques were used for data analysis. principal component revealed that can be grouped into two dimensions, suggesting potential temporal or spatial patterns quality. Furthermore, not across various stations reservoir. cluster fourteen (14) sampling similar characteristics three groups clusters. future research, established precedent interpretation complex river basins. Finally, research great significance those responsible environmental management, as results have direct impact on management areas.

Язык: Английский

Assessment of Water Quality in the Panama Canal Watershed Using Multivariate Analysis of Physicochemical and Biological Parameters DOI Open Access
Mitzi Cubilla‐Montilla, Glòria Carrasco,

Marisela Del C. Castillo

и другие.

Water, Год журнала: 2025, Номер 17(7), С. 979 - 979

Опубликована: Март 27, 2025

In the hydrometeorological context of watersheds, water quality is strongly related to its physical, chemical, and biological characteristics. this regard, joint analysis these parameters at watershed level highly important. The objective study was analyze a total twenty-three (23) physicochemical in Panama Canal with aim determining interrelationships among them, explaining their clustering simultaneously identifying homogeneous hydrological stations. Multivariate statistical techniques were used for data analysis. principal component revealed that can be grouped into two dimensions, suggesting potential temporal or spatial patterns quality. Furthermore, not across various stations reservoir. cluster fourteen (14) sampling similar characteristics three groups clusters. future research, established precedent interpretation complex river basins. Finally, research great significance those responsible environmental management, as results have direct impact on management areas.

Язык: Английский

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