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Developing a Decision-making Tool for Agricultural Surface Water Decontamination Using Ultraviolet-C Light.

Olivia C Haley1, Yeqi Zhao1, Trevor Hefley2

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Ultraviolet-C (UV-C) light effectively reduces microbes in agricultural water, but particulate matter poses a challenge. This study tested two UV-C devices, informing a new web tool to help growers choose the best system for their needs.

Keywords:
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Area of Science:

  • Agricultural Water Treatment
  • Microbial Decontamination
  • UV-C Technology

Background:

  • Chemical sanitizers face challenges in agricultural surface water due to high particulate matter.
  • Ultraviolet-C (UV-C) light is a promising alternative for decontaminating agricultural water.
  • Effective UV-C implementation requires understanding device performance relative to water quality.

Purpose of the Study:

  • To evaluate the microbial reduction efficacy of two commercial UV-C water treatment devices.
  • To develop a web application aiding growers in selecting appropriate on-farm UV-C systems.
  • To assess the impact of water quality, specifically UV-transmissivity (UVT), on UV-C inactivation efficiency.

Main Methods:

  • An on-farm study compared a low power/low flow (LP/LF) and a high power/high flow (HP/HF) UV-C device.
  • Tested devices across three agricultural water sources at flow rates of 6, 7, and 9 gallons per minute (GPM).
  • Measured microbial reduction and correlated it with water's UV-transmissivity (%UVT).

Main Results:

  • A UV-transmissivity (UVT) threshold of 30% was identified for the HP/HF device; lower UVT significantly reduced microbial inactivation.
  • The LP/LF device, while less effective at lower %UVT, demonstrated lower installation, maintenance, and operational costs.
  • Developed a preliminary model predicting generic Escherichia coli reduction based on %UVT for both devices.

Conclusions:

  • UV-C light is a viable technology for reducing microbial risks in agricultural water.
  • Water transmissivity is a critical factor influencing UV-C disinfection efficacy.
  • The developed decision-making tool provides a foundation for growers, with future iterations planned for broader applicability.