1 / 18

Assessing Ground Water Vulnerability through Statistical Methods I

Explore societal implications, monitoring techniques, and national nitrate examples for groundwater contamination using regression models and national research. Understand the concern about nitrate in shallow groundwater, risks, and mitigation costs. Learn about empirical models characterizing uncertainty, nonlinear regression, and prediction methods to identify variables influencing nitrate concentrations in groundwater. Discover the importance of using statistical models to enhance monitoring, identify vulnerable areas, and evaluate contamination scenarios.

Télécharger la présentation

Assessing Ground Water Vulnerability through Statistical Methods I

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Assessing Ground Water Vulnerability through Statistical Methods I NWQMC May 7-11, 2006 San Jose, CA

  2. Regression Model for National Assessment of Nitrate in Ground Water Tom Nolan and Kerie Hitt btnolan@usgs.gov NWQMC May 7-11, 2006 San Jose, CA

  3. Road Map • Societal implications of aquifer vulnerability • Monitoring and prediction • National examples for nitrate: (1) shallow ground water (2) drinking-water wells

  4. Ground-Water Vulnerability “The tendency or likelihood for [nonpoint- source] contaminants to reach a specified position in the GW system after introduction at some location above the uppermost aquifer.” -National Research Council, 1993 Focazio, M.J., and others (2002), Assessing ground-water vulnerability to contamination — providing scientifically defensible information for decision makers: USGS Circular 1224.

  5. Why the Concern About Shallow Ground Water? Risk

  6. Why the Concern About Nitrate? • Methemoglobinemia first reported in U.S. in 1945—rare since MCL promulgated. • Increased risk of non-Hodgkins lymphoma in Nebraska, NO3 > 4 mg/L (Ward et al., 1996) • Increased risk of bladder and ovarian cancers in Iowa, NO3 > 2.5 mg/L (Weyer et al., 2001) Ward et al., “Drinking-Water Nitrate and Health—Recent Findings and Research Needs,” EHP, June 2005 • Few well-designed studies for cancer sites • Combined effect of nitrate intake from food and water is difficult to evaluate

  7. Mitigation Cost • Treatment – Nitrate removal from public well water can exceed $2 million per system – Annual operating cost is 4 – 5 times higher (up to $6 per 1,000 gal) • Well abandonment – $600,000 for new community well typically

  8. Monitoring GIS Prediction

  9. National Example Objectives: • Use empirical models to identify variables that significantly influence nitrate concentration in ground water. • Map contamination risk for the U.S. • Characterize uncertainty.

  10. Nonlinear Regression → Concentrations Attenuation Load Transport cgwi = mean nitrate concentration (mg/L) for ground-water network i Xn,i= average N load from source n in network I Tj,i= average transport factor j for network i Zk,i = average attenuation factor k for network i βn = coefficient for N source n αj = coefficient for transport factor j δk = coefficient for attenuation factor k εi= model error for network i

  11. Two nonlinear models developed… (1) Shallow ground water (R2 = 0.80) (2) Drinking water wells (R2 = 0.77)

  12. Drinking Water Model

  13. Model Fit R2 = 0.77

  14. NO3 attenuation • High N input • High water input • Well-drained soils or fractured rocks Prediction

  15. Monte Carlo for Uncertainty

  16. Typical depth Model → Reduce risk by seeking deeper supplies. Hypothetical depth ― “shallow” Population Scenarios 1% of users

  17. Take home: National statistical models help… • Identify areas for enhanced monitoring and protection. • Identify aquifer vulnerability factors. • Evaluate contamination scenarios.

  18. Assessing Ground Water Vulnerability through Statistical Methods I NWQMC May 7-11, 2006 San Jose, CA

More Related