Citation:

Heberling, Matthew, J. Price, C. Nietch, M. Elovitz, N. Smucker, D. Schupp, A. Safwat, AND T. Neyer. Linking Water Quality to Drinking Water Treatment Costs Using Time Series Analysis: Examining the Effect of a Treatment Plant Upgrade in Ohio. WATER RESOURCES RESEARCH. American Geophysical Union, Washington, DC, 58(5):e2021WR031257, (2022). https://doi.org/10.1029/2021WR031257

Impact/Purpose:

Understanding the determinants of drinking water treatment costs can support cost-effective decisions related to technology selection, production, and the treatment process (including source water protection or wellhead protection). Economic cost functions can be used to relate treatment costs to source water conditions. Our cost function for a treatment plant in southwest Ohio, reveals benefits, in terms of avoided treatment costs, for reducing raw water total organic carbon (a precursor for disinfection byproducts) and harmful algal bloom toxins. These water quality variables are not typically included in cost functions for drinking water treatment plants. Conducting research to evaluate how changes in source water quality affect drinking water treatment costs, not only supports local decisions, but can support policy makers who are analyzing national water quality regulations.

Description:

  We estimate a cost function for a water treatment plant in Ohio to assess the avoided-treatment costs resulting from improved source water quality. Regulations and source water concerns motivated the treatment plant to upgrade its treatment process by adding a granular activated carbon building in 2012. The cost function uses daily observations from 2013 to 2016; this allows us to compare the results to a cost function estimated for 2007–2011 for the same plant. Both models focus on understanding the relationship between treatment costs per 1,000 gallons (per 3.79 m3) of produced drinking water and predictor variables such as turbidity, pH, total organic carbon, deviations from target pool elevation, final production, and seasonal variables. Different from the 2007–2011 model, the 2013–2016 model includes a harmful algal bloom toxin variable. We find that the new treatment process leads to a different cost model than the one that covers 2007–2011. Both total organic carbon and algal toxin are important drivers for the 2013–2016 treatment costs. This reflects a significant increase in cyanobacteria cell densities capable of producing toxins in the source water between time periods. The 2013–2016 model also reveals that positive and negative shocks to treatment costs affect volatility, the changes in the variance of costs through time, differently. Positive shocks, or increased costs, lead to higher volatility compared to negative shocks, or decreased costs, of similar magnitude. After quantifying the changes in treatment costs due to changes in source water quality, we discuss how the study results inform policy-relevant decisions.

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