Pipe loop studies are used to evaluate corrosion control treatment, and updated regulatory guidance will ensure that they remain important for water quality management. But the data they generate are difficult to analyze: non-linear time-trends, non-detects, 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 model for pipe loop data that accommodates many of these difficult-to-model attributes: a robust Bayesian generalized additive model with continuous-time autoregressive errors. Our approach facilitates corrosion control treatment comparisons without the need for imputing non-detects 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 four-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 tradeoffs in applying orthophosphate for corrosion control, and better models of pipe loop data can help inform the decision-making process.
Revisions after the first round of peer review