Applications of Sensors in Surface Water Quality Assessment Neuse River, Estuary, and Pamlico Sound

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Applications of Sensors in Surface Water Quality
Assessment Neuse River, Estuary, and Pamlico
Sound Kenneth H. Reckhow Nicholas School of the
Environment Earth Sciences Duke University
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Facts About the Neuse River

• 3rd Largest River Basin in NC (6,234 mi2)
• 200 miles long, 3000 stream miles
• Estuary in lower 50 miles
• 1.5 million people in basin, mostly near
  headwaters

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Sensors Three Application Areas

• Long-term Can we use sensors to assess if
  compliance has been achieved with the TMDL for
  nitrogen and with the water quality criterion for
  chlorophyll?

• Short-term Can we monitor flow and pollutant
  concentration during extreme floods and hurricane
  stormflows?

• Real-time Can sensors provide an effective
  early warning system associated with wastewater
  spills, lagoon breaches, and hypoxia events?

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Modeling and Environmental Forecasting
Proposed Goal Using a combination of in situ
measurements, remote sensing, and modeling,
achieve a predictive understanding of pollutant
flux and environmental response over a broad
range of spatial and temporal scales.
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Distributed Sensor Nets
DATABASE AND VISUALIZATION
MODELING AND SIMULATION
Sensor Network
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A. Sanderson, RPI Duke CLEANER Workshop
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Dependencies are described by conditional
probability distributions.
p(R)
p(CN)
p(CA)
p(AN,R)
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p(Health Poor
N inputs X)
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Adaptive Implementation Bayesian Analysis
Criterion Concentration
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Challenges

• Development In situ monitors, or sensors, are
  needed for total nutrient (nitrogen phosphorus)
  concentration.

• Design Models and/or program objectives should
  provide the basis for space/time design (data
  acquisition) of the sensor network.

• Applications Methods are needed to effectively
  integrate data from sensor networks into models
  to improve forecasts and scientific assessment.

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Challenges From a Water Quality Modeling and
Assessment Perspective

• Can we develop in situ monitors or sensors to
  assess total nitrogen phosphorus (as opposed to
  the inorganic fractions only)?

• Can we develop the (probabilistic)
  modeling/monitoring framework so that data from
  the sensor network is effectively used in
  forecasting and assessment?

• Can we design and deploy a sensor network that
  will function during hurricanes and provide data
  to estimate flows and pollutant fluxes?