Water Quality Monitoring of Maryland’s Tidal Waterways
UMBC REU Site: Interdisciplinary Program in High Performance Computing
1234Rosemary K. Le , Christopher V. Rackauckas , Anne S. Ross , Nehemias Ulloa
556Graduate assistant: Sai K. Popuri , Faculty mentor: Dr. Nagaraj K. Neerchal , Client: Dr. Brian R. Smith
1234Brown University Oberlin College Colorado State University California State University, Bakersfield
56University of Maryland, Baltimore County Maryland Department of Natural Resources

Chesapeake BayOverview
Our research determined areas of water quality concern in the Chesapeake Bay and is split into two projects:
Project 1• Compute and compare failure rates for the stations• Assess stations’ statuses using the Wilcoxon Signed-Rank Test
• Preform a simulation to assess the validity of the above test
• Rank the stations using multiple comparison tests
Project 2• Conducted trend analyses at five stations in the Corsica River
To complement our research, we have developed a data-driven
software in R to analyze and display results in a GUI.
This poster covers Project 1.

Stations’ Statuses

Courtesy - Maryland DNR Eyes on the Bay webpage• Home to more than 3,600 species of plants and animals• Valuable commercial and recreational resource• Maryland Department of Natural Resources (DNR) monitors various parameters such as dissolved oxygen, turbidity
and chlorophyll• We determined areas of water quality concern by assessing parameters against respective DNR-provided thresholdlevelsParameter
Dissolved Oxygen
Dissolved Oxygen
Chlorophyll
Turbidity

Failure Threshold< 3mg/L< 5mg/L> 30µg/L> 7 NTU

Left graph serves as an example of the skewed parameters, specifically chlorophyll. Right map shows the stations’statuses utilizing the Wilcoxon test on the log-transformed data with the Benjamini-Hochberg rejection method.

Simulation and Adjustmentm hi
XRi · sign(xi − thresh)• Test Statistic: S =i=1

where Ri = rank of |xi − thresh|• The Wilcoxon Signed-Rank Test assumes the data is symmetric.
For non-symmetric data, the true Type 1 Error may be inflated.
• Our simulation study (using Γ(α, β) on a range of skewness
values) shows that a log-transformation of the data substantiallyreduces the Type I Error but the error is still large for data withskews in the range we see in our data.

References and Acknowledgments• Technical report for Project 1 , Project 2, and GUI: HPCF–2012–12
www.umbc.edu/hpcf > Publications.
• For more information on the data, visit the Eyes on the Bay websitewww.eyesonthebay.net• REU Site: www.umbc.edu/hpcreu, funded jointly by NSF and NSA• DNR, UMBC, HPCF, CIRC

Ranking
Oxygen (5mg) — Ranks of monitoring stations with respect to percent
failure: the Tukey Test (TT), the Bonferroni Test (Bonf), and theBenjamini–Hochberg Method (BH)
Station NameBetterton
Havre de Grace
Flats..
.
Little Monie
Masonville (bottom)
Goose (bottom)% Fail000.0086..
.
80.2075
80.4072
89.7545TT
111.
..
363638

Bonf
111.
..
363638BH% FailMean

113.
..
363638452.
..
363738

Ranking• Ranking methods used multiple comparison tests to control for
Type I Error• Bonferonni Adjustment was the most conservative and thus created the largest groupings. Benjamini-Hochberg was the leastconservative.