Linking Water Quality to Drinking Water Treatment Costs Using Time Series Analysis: Examining the Effect of a Treatment Plant Upgrade in Ohio. Issue 5 (28th April 2022)

• Record Type: Journal Article
• Authors: Heberling, Matthew T.; Price, James I.; Nietch, Christopher T.; Elovitz, Michael; Smucker, Nathan J.; Schupp, Donald A.; Safwat, Amr; Neyer, Tim
• Is Part Of: Water resources research. Volume 58:Issue 5(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-04-28
• Subjects: source water -- water supply -- drinking water -- treatment cost -- economic analysis -- watershed management; Hydrology -- Periodicals; 333.91
• DOI: 10.1029/2021WR031257
• Languages: English
• ISSNs: 0043-1397

Abstract

Abstract: 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 m 3 ) 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. Key Points: Estimate how drinking water treatment costs change due to changes in source water quality following a plant upgrade Use time series analysis to understand short‐ and long‐run effects on treatment costs Raw water total organic carbon and harmful algal bloom toxin detections are important drivers in drinking water treatment costs