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Bacterial Contamination in Texas Coastal Bays: Data Characterization

Bacterial Contamination in Texas Coastal Bays: Data Characterization. James Seppi CE397 – Statistics in Water Resources Spring 2009. Background. CWA mandates classification of impaired water bodies.

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Bacterial Contamination in Texas Coastal Bays: Data Characterization

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  1. Bacterial Contamination in Texas Coastal Bays:Data Characterization James SeppiCE397 – Statistics in Water Resources Spring 2009

  2. Background • CWA mandates classification of impaired water bodies. • Median fecal coliform concentration in bay and gulf waters, exclusive of buffer zones, shall not exceed 14 colonies per 100 ml, with not more than 10% of all samples exceeding 43 colonies per 100 ml. - TAC, Title 30, Part 1, Chapter 307, Rule §307.7 • Future work at the CRWR – modeling for determination of TMDL

  3. Background - Bays • East Matagorda Bay • Cedar Lakes • Tres Palacios/Turtle Bays • Lavaca/Chocolate Bays • Cox Bay • Crancahua Bay • San Antonio/ Hynes/ Guadalupe Bays • Copano Bay • Matagorda Bay

  4. Data • TCEQ Surface Water Quality Monitoring – accessible online • Fecal Colony Forming Units / 100 mL • ~1972-2005 • Detection Limit of 2 cfu/100mL • Censored Data – “Less Thans” • Ex: <10 cfu/100mL • Measured at multiple stations per bay

  5. Data

  6. Statistics - Project Goals • Confirm Data are LogNormally-Distributed • Calculate Median and 90th Percentiles • Calculate Confidence Intervals • For period of record, for last 5 years, and for last 7 years • Calculate Prediction Intervals

  7. Statistics • How to deal with all the censored data and those at the detection limit? • Best method of estimation? • Large data sets (mostly)

  8. Statistics - NADA • Underused in the field, even though we have lots of nondetects in environmental data. • Very important!

  9. Statistics – NADA • Three approaches detailed • Substitution • Maximum Likelihood Estimation • Regression on Order Statistics

  10. Statistics – NADA • Three approaches detailed • Substitution • Maximum Likelihood Estimation • Regression on Order Statistics

  11. Statistics – NADA MLE • Three approaches detailed • Substitution • Maximum Likelihood Estimation • 50-80% censored data • Large number of data points • Regression on Order Statistics

  12. Statistics – NADA MLE • These don’t look so good… • MLE might be overestimating SD

  13. Results – NADA MLE Plots

  14. Results – NADA MLE

  15. Statistics – NADA ROS • Three approaches detailed • Substitution • Maximum Likelihood Estimation • [Robust] Regression on Order Statistics • Regression equation on probability plot • Use sample data where we have it • Assume distribution only for censored data • Impute values for censored points • Best for small data sets

  16. Results – NADA ROS Plots

  17. Results – NADA ROS

  18. Results – Prediction Intervals • Prediction Interval – “bracket the range of locations for … observations not currently in the data set.” • Finding a value outside should happen only 1-0.95 = 5% of the time • Used MLE method to get params

  19. Future Work • Repeat for last 5-years and last 7-years of data • Is water quality in bays improving/declining? • Use method/findings in Copano Bay project to predict median/90th %ile given geomean from model • Look at spatial variation in each bay • Though regulation is not done this way

  20. Thanks & Questions • Thanks to: • Stephanie Johnson • Grace Chen • Sammy Sandoval • Dr. Maidment

  21. Results without NADA

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