Pipe loop studies are used to evaluate corrosion control
treatment,
and updated regulatory guidance will ensure that they remain important
for drinking water quality management. But the data they generate
are difficult to analyze: nonlinear time trends, nondetects, extreme
values, and autocorrelation are common attributes that make popular
methods, such as the t- or rank-sum tests, poor descriptive models.
Here, we propose a framework for describing pipe loop data that accommodates
all of these challenging attributes: a robust Bayesian generalized
additive model with continuous-time autoregressive errors. Our approach
facilitates corrosion control treatment comparisons without the need
for imputing nondetects or special handling of outliers. It is well
suited to describing nonlinear trends without overfitting, and it
accounts for the reduced information content in autocorrelated time
series. We demonstrate it using a 4-year pipe loop study, with multiple
pipe configurations and orthophosphate dosing protocols, finding that
an initially high dose of orthophosphate (2 mg P L–1) that is subsequently lowered (0.75 mg P L–1)
can yield lower lead release than an intermediate dose (1 mg P L–1) in the long term. Water utilities face difficult
trade-offs in applying orthophosphate for corrosion control, and better
models of pipe loop data can help inform the decision-making process.