PlumX Metrics
Embed PlumX Metrics

Performance of Conditional Random Forest and Regression Models at Predicting Human Fecal Contamination of Produce Irrigation Ponds in the Southeastern United States

ACS ES and T Water, ISSN: 2690-0637, Vol: 4, Issue: 12, Page: 5844-5855
2024
  • 0
    Citations
  • 0
    Usage
  • 1
    Captures
  • 0
    Mentions
  • 0
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

Article Description

Irrigating fresh produce with contaminated water contributes to the burden of foodborne illness. Identifying fecal contamination of irrigation waters and characterizing fecal sources and associated environmental factors can help inform fresh produce safety and health hazard management. Using two previously collected data sets, we developed and evaluated the performance of logistic regression and conditional random forest models for predicting general and human-specific fecal contamination of ponds in southwest Georgia used for fresh produce irrigation. Generic Escherichia coli served as a general fecal indicator, and human-associated Bacteroides (HF183), crAssphage, and F+ coliphage genogroup II were used as indicators of human fecal contamination. Increased rainfall in the previous 7 days and the presence of a building within 152 m (a proxy for proximity to septic systems) were associated with increased odds of human fecal contamination in the training data set. However, the models did not accurately predict the presence of human-associated fecal indicators in a second data set collected from nearby irrigation ponds in different years. Predictive statistical models should be used with caution to assess produce irrigation water quality as models may not reliably predict fecal contamination at other locations and times, even within the same growing region.

Bibliographic Details

Hofstetter, Jessica; Holcomb, David A; Kahler, Amy M; Rodrigues, Camila; da Silva, Andre Luiz Biscaia Ribeiro; Mattioli, Mia C

American Chemical Society (ACS)

Chemistry; Chemical Engineering; Environmental Science

Provide Feedback

Have ideas for a new metric? Would you like to see something else here?Let us know