Performance Assessment of Natural Wastewater Treatment Plants by Multivariate Statistical Models: A Case Study

Abstract

Waste stabilization ponds (WSPs) as natural wastewater treatment plants are commonly utilized for wastewater treatment due to their simple design, low cost, and low-skilled operator requirements. Large-scale studies assessing the performance of WSPs using multivariate statistical models are scarce. Therefore, this study was conducted to assess the performance of 16 full-scale WSPs regarding physicochemical parameters, algae, bacterial indicators, and pathogens (e.g., Cryptosporidium, Entamoeba histolytica) by using multivariate statistical models. The principal component analysis revealed that the chemical pollutants were removed significantly (p < 0.001) through the treatment stages of 16 WSPs, indicating that the treatment stages made a substantial change in the environmental parameters. The non-multidimensional scale analysis revealed that the treatment stages restructured the bacterial indicators significantly (p < 0.001) in the WSPs, implying that the bacterial indicators were removed with the progress of the treatment processes. The algal community exhibited a distinct pattern between the geographical location (i.e., upper WSPs versus lower WSPs) and different treatment stages (p < 0.001). Four out of the sixteen WSPs did not comply with the Egyptian ministerial decree 48/1982 for discharge in agriculture drainage; three of these stations are in lower Egypt (M.K., Al-Adlia, and Ezbet El-Borg), and one is in upper Egypt (Armant). The continuous monitoring of WSPs for compliance with regulatory guidelines with the aid of multivariate statistical models should be routinely performed.