Comparing drinking water treatment costs to source water protection costs using time series analysis

Abstract

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.

Two centuries of nitrogen dynamics: Legacy sources and sinks in the Mississippi and Susquehanna River Basins

“…At the local level, high nutrient concentrations in agricultural runoff have increased the costs of drinking water treatment [Heberling et al, 2015] and, recently, have led to litigation calling for greater regulation of agricultural nutrient sources [Stowe, 2016]. At larger scales, nutrient loading to near-shore coastal waters has fed the growth of large hypoxic zones, decreasing marine biodiversity and altering ecosystem structures.…”

“…Over the last century, intensive agricultural practices and increasing fossil fuel consumption have led to high levels of nonpoint source nutrient pollution, threatening drinking water quality, and contributing to the destruction of aquatic ecosystems from the local to the global scale [Rockström et al, 2009;Howarth et al, 2011;Carpenter et al, 2012;Beusen et al, 2016]. At the local level, high nutrient concentrations in agricultural runoff have increased the costs of drinking water treatment [Heberling et al, 2015] and, recently, have led to litigation calling for greater regulation of agricultural nutrient sources [Stowe, 2016]. At larger scales, nutrient loading to near-shore coastal waters has fed the growth of large hypoxic zones, decreasing marine biodiversity and altering ecosystem structures.…”

Economic Support for Decisions on Source Water Protection

“…Among recent US‐based studies, a 1% decline in TOC was associated with a 0.46 and 0.77% reduction in chemical costs (Warziniack et al , Freeman et al ). However, Heberling et al () found that TOC has no appreciable effect on variable costs. A 1% decline in nitrate concentrations, sediment loading, and phosphorus loading is associated with 0.02, 0.26, and 0.02–0.19% reductions in variable costs, respectively (Mosheim & Ribaudo , Heberling et al , McDonald & Shemie ).…”

“…Studies show that marginal improvements in source water quality lead to modest reductions in treatment costs. Evidence of this effect is most robust for turbidity, where recent US‐ and Canada‐based studies showed that a 1% decline in turbidity led to a 0.09–0.11% reduction in variable costs (Price et al , Heberling et al , Mosheim ). For example, with a $1 million annual‐treatment‐cost budget, a PWS could save $1,100 per year if a 1% reduction in turbidity led to a 0.11% change in treatment costs.…”

“…This latter issue is crucial, as evidence suggests that SWP programs are cost‐effective only for a fraction of PWSs (Vincent et al , Heberling et al , McDonald & Shemie , Hudak et al , Pyke et al ), except when program costs can be sufficiently distributed across multiple stakeholders (Gartner et al ). Where initial estimates for SWP are projected to be cost‐effective, PWSs can pursue more precise economic and hydrologic modeling specific to their treatment system and watershed to better inform the decision‐making process.…”