Multivariate statistical analysis of water chemistry conditions in three wastewater stabilization ponds with algae blooms and pH fluctuations

• Characterization of water chemistry in three wastewater stabilization ponds (WSPs).
• WSPs have experienced excessive algae growth and elevated pH in the summer.
• Principal Components Analysis (PCA) conducted on full water chemistry dataset.
• PCA identified key parameters affecting pH and quantified their inter-relationships.
• Results to be used for long term system modifications and understanding pH issues.

The wastewater stabilization ponds (WSPs) at a wastewater treatment facility in eastern Ontario, Canada, have experienced excessive algae growth and high pH levels in the summer months. A full range of parameters were sampled from the system and the chemical dynamics in the three WSPs were assessed through multivariate statistical analysis. The study presents a novel approach for exploratory analysis of a comprehensive water chemistry dataset, incorporating principal components analysis (PCA) and principal components (PC) and partial least squares (PLS) regressions. The analyses showed strong correlations between chl-a and sunlight, temperature, organic matter, and nutrients, and weak and negative correlations between chl-a and pH and chl-a and DO. PCA reduced the data from 19 to 8 variables, with a good fit to the original data matrix (similarity measure of 0.73). Multivariate regressions to model system pH in terms of these key parameters were performed on the reduced variable set and the PCs generated, for which strong fits (R2 > 0.79 with all data) were observed. The methodologies presented in this study are applicable to a wide range of natural and engineered systems where a large number of water chemistry parameters are monitored resulting in the generation of large data sets.

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