Statistical analysis of wastewater treatment plant data
Gulhan Bourget
Abstract
Abstract In this paper, we propose to apply the linear mixed effects model (LMEM) for the first time in this discipline to evaluate wastewater treatment plant (WWTP) data. Because the most widely used statistical analyses cannot handle data dependency, as well as fixed and random factors, a new approach was essential. We used the LMEM method to analyze three groups of 19 pharmaceuticals and personal care products (PPCPs) from a California municipal WWTP. We studied the relationships between five treatments, seasons, and three groups of PPCPs. The main contributions of this paper are to investigate three-way and two-way factor interactions and propose that the autoregressive lag 1, AR (1), is a suitable variance-covariance structure for data dependency. While the seasons did not affect the mean concentration levels for the treatments and groups ( $$p=0.2540$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>p</mml:mi> <mml:mo>=</mml:mo> <mml:mn>0.2540</mml:mn> </mml:mrow> </mml:math> ), the mean concentration levels for the seasons differed by groups ( $$p<0.0001$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>p</mml:mi> <mml:mo><</mml:mo> <mml:mn>0.0001</mml:mn> </mml:mrow> </mml:math> ) and the treatments ( $$p=0.0027$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>p</mml:mi> <mml:mo>=</mml:mo> <mml:mn>0.0027</mml:mn> </mml:mrow> </mml:math> ).