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Comparing drinking water treatment costs to source water protection costs using time series analysis.
Citation:
Heberling, Matt, C. Nietch, H. Thurston, M. Elovitz, K. Birkenhauer, S. Panguluri, B. Ramakrishnan, E. Heiser, AND T. Neyer. Comparing drinking water treatment costs to source water protection costs using time series analysis. WATER RESOURCES RESEARCH. American Geophysical Union, Washington, DC, 51(11):8741-8756, (2015).
Impact/Purpose:
Our clients want water quality trading to work. Unfortunately, there typically are not enough traditional participants (point sources and agricultural producers) for the market to function properly. The motivation for the study is to determine whether a drinking water treatment plant (DWTP) would have incentive to participate in a water quality trading program as a demander for pollution reduction credits from agricultural producers. In addition, the analysis provides decision support for the DWTP to invest in source water protection and conservation in the form of comparing drinking water treatment costs to source water protection costs. Highlights research in SSWR project 1.2.
Description:
We present a framework to compare water treatment costs to source water protection costs, an important knowledge gap for drinking water treatment plants (DWTPs). This trade-off helps to determine what incentives a DWTP has to invest in natural infrastructure or pollution reduction in the watershed rather than pay for treatment on site. To illustrate, we use daily observations from 2007 to 2011 for the Bob McEwen Water Treatment Plant, Clermont County, Ohio, to understand the relationship between treatment costs and water quality and operational variables (e.g., turbidity, total organic carbon [TOC], pool elevation, and production volume). Part of our contribution to understanding drinking water treatment costs is examining both long-run and short-run relationships using error correction models (ECMs). Treatment costs per 1000 gallons (per 3.79 m3) were based on chemical, pumping, and granular activated carbon costs. Results from the ECM suggest that a 1% decrease in turbidity decreases treatment costs by 0.02% immediately and an additional 0.1% over future days. Using mean values for the plant, a 1% decrease in turbidity leads to $1123/year decrease in treatment costs. To compare these costs with source water protection costs, we use a polynomial distributed lag model to link total phosphorus loads, a source water quality parameter affected by land use changes, to turbidity at the plant. We find the costs for source water protection to reduce loads much greater than the reduction in treatment costs during these years. Although we find no incentive to protect source water in our case study, this framework can help DWTPs quantify the trade-offs.
