Research article Temporal performance assessment of wastewater treatment plants by using multivariate statistical analysis Milad Ebrahimi a, * , Erin L. Gerber b , Thomas D. Rockaway a a Center for Infrastructure Research, Civil and Environmental Engineering Department, University of Louisville, Louisville, KY, USA b Industrial Engineering Department, University of Louisville, Louisville, KY, USA article info Article history: Received 28 November 2016 Received in revised form 9 February 2017 Accepted 12 February 2017 Keywords: Municipal wastewater Wastewater Quality Index Correlation Principal component analysis Multivariate regression analysis abstract For most water treatment plants, a significant number of performance data variables are attained on a time series basis. Due to the interconnectedness of the variables, it is often difficult to assess over-arching trends and quantify operational performance. The objective of this study was to establish simple and reliable predictive models to correlate target variables with specific measured parameters. This study presents a multivariate analysis of the physicochemical parameters of municipal wastewater. Fifteen qualityand quantity parameters were analyzed using data recorded from 2010 to 2016. To determine the overall quality condition of raw and treated wastewater, a Wastewater Quality Index (WWQI) was developed. The index summarizes a large amount of measured quality parameters into a single water quality term by considering pre-established quality limitation standards. To identify treatment process performance, the interdependencies between the variables were determined by using Principal Component Analysis (PCA). The five extracted components from the 15 variables accounted for 75.25% of total dataset information and adequately represented the organic, nutrient, oxygen demanding, and ion activity loadings of influent and effluent streams. The study also utilized the model to predict quality parameters such as Biological Oxygen Demand (BOD), Total Phosphorus (TP), and WWQI. High accuracies ranging from 71% to 97% were achieved for fitting the models with the training dataset and relative prediction percentage errors less than 9% were achieved for the testing dataset. The presented tech- niques and procedures in this paper provide an assessment framework for the wastewater treatment monitoring programs. © 2017 Elsevier Ltd. All rights reserved. 1. Introduction Each year, wastewater treatment plants process billions of gal- lons of sanitary and/or combined stormwater flow. For each treat- ment plant the physicochemical properties of the influent streams are unique and dependent on factors such as the origin of discharge, type of sewer system infrastructure (combined or separate), development level of the area, climate condition, and groundwater levels. Thus, in all cases, the wastewater stream not only has a unique composition, but the organic, inorganic and nutrient loadings vary in terms of time, place and source (Avella et al., 2011; Lefkir et al., 2015). Identifying the dynamics of wastewater's constituents and their range are critical for establishing the preferred treatment system (Tchobanoglous and Burton, 1991). Process designs must be opti- mized to effectively mitigate contaminants throughout all expected ranges and combinations of flow levels (Ebrahimi et al., 2016). This is especially important when effluent is directed towards a reuse project. Depending on the reuse objective, i.e. discharge to surface or groundwater bodies, irrigation purposes, or industrial reuse, the effluent should meet established quality limitations at all times. Thus, understanding the influent variability and its impact within the treatment process is essential to prevent the adverse health and environmental impacts of reused wastewater. Appropriately characterizing wastewater streams and assessing wastewater treatment plant efficiencies is difficult due to the abundant chemical, physical, and microbiological parameters that should be considered (Bryant, 1995). Even if all necessary waste- water data are collected, it is still challenging for the operators to make decision due to the complex interrelationships of the pa- rameters (Timmerman et al., 2010). Thus, there is a need for * Corresponding author. E-mail addresses: m.ebrahimi@louisville.edu (M. Ebrahimi), e.gerber@louisville. edu (E.L. Gerber), tom.rockaway@louisville.edu (T.D. Rockaway). Contents lists available at ScienceDirect Journal of Environmental Management journal homepage: www.elsevier.com/locate/jenvman http://dx.doi.org/10.1016/j.jenvman.2017.02.027 0301-4797/© 2017 Elsevier Ltd. All rights reserved. Journal of Environmental Management 193 (2017) 234e246