Project 1: Squam Contaminants Photo: Lee Grenier Population and - - PDF document

Project 1: Squam Contaminants

Photo: Lee Grenier

Population and productivity trends

0.0 2.0 4.0 6.0 8.0 10.0 12.0 14.0 16.0 Territorial Pairs Nesting Pairs C hicks Hatched C hicks S urviving

S q u a m L a k e L

• on

s O v e r T im e

Avg 1995-2004 Avg 2005-2007 Avg 2008-2012

Organic Pollutants

Inorganic Pollutants Student Data

Project Possibilities: Field Based

• Sediment sampling
• Water sampling
• Possible Sources

– Squam – Reference Lakes – **Streams flowing into Squam

**Useful focus for the LPC

Project Possibilities: Instrument Based

• **Analysis of Zinc by flame AA and IC

**Useful focus for the LPC

Project 2: Project 2: Affects of Aquatic Plants on Affects of Aquatic Plants on Water / Sediment Chemistry Water / Sediment Chemistry

Figures from: http://www.missouriplants.com/Others/Myriophyllum_heterophyllum_plant.jpg https://c53a19f696-custmedia.vresp.com/library/1306354496/e2b7d3b0d8/uconn_ipane_myriospica_03c-reduced.JPG

Native & Non-Native Native & Non-Native Aquatic Plants Aquatic Plants

Natives Bladderwort Coontail Fanwort Native Water Milfoil Pondweed Waterweed Non-Natives Brazillian elodea Euarasion Water-Milfoil Variable Water-Milfoil

Lab-based Project Lab-based Project

• Plants grown in tanks

Plants grown in tanks

• Control and Experimental tanks

Control and Experimental tanks

• Monitor water quality

Monitor water quality

• Analyze water, sediment, plants

Analyze water, sediment, plants

CH 3600 / ESP 5090: Environmental Chemistry, Plymouth State University

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Water Quality Alkalinity Conductivity Dissolved Oxygen Hardness pH, pE Total Dissolved Solids

Class Research Project Analyses

Analytes Anions: Chloride, Fluoride, Nitrate, Phosphate, etc Cations: Calcium, Sodium, Potassium, Magnesium, etc. Metals: Lead, Cadmium, Strontium, Aluminum, Zinc Techniques Ion Chromatography Ion Selective Electrodes UV-Vis Spectrophotometry Titration Atomic Absorption

CH 3600 / ESP 5090: Environmental Chemistry, Plymouth State University

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Project Timeline

• Mar 24: Time in lab
• April 6: Project Proposal due
• April 14-May 5: Time in lab
• Finals Week: Project Presentation

CH 3600 / ESP 5090: Environmental Chemistry, Plymouth State University

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Quality Assurance of Results

CH 3600 / ESP 5090: Environmental Chemistry, Plymouth State University

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Quality Assurance

• Quality Assurance: Set of operating principles that, if

strictly followed during sample collection and analysis, will produce data of known and defensible quality

• How do you do it?
• Eliminate contaminants and interferences during sample

collection, prep, and analysis

• Prepare samples and standards to highest precision

possible

• Use best equipment possible from start to finish
• Calibrate instruments to highest precision possible
• Run Quality Control checks during analysis
• Take care with data—significant figures, statistical analysis

Make sure your answers are correct!

CH 3600 / ESP 5090: Environmental Chemistry, Plymouth State University

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Eliminating contaminants and interferences

• Use clean equipment
• Water should not “bead” on glassware
• If you do not know where it has been, it is dirty
• Final rinse of everything should be with Ultra-pure water
• Use purest reagents possible
• Check purity on bottle.
• Use Ultra-pure water for sample/standard prep

Quality Assurance

CH 3600 / ESP 5090: Environmental Chemistry, Plymouth State University

17

Highest Precision Samples/Standards

• Use best equipment

Quality Assurance

Yes! No!

Measuring Volume Volumetric flasks Volumetric pipettes Auto-pipettes Burets Beakers Graduated cylinder Measuring Mass 4-digit Analytical balance RECORD ALL DIGITS Anything less than 4 digits Rounding last digit or 2

CH 3600 / ESP 5090: Environmental Chemistry, Plymouth State University

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Quality Control Checks

• Blanks: prepare/analyze ultra-pure water same as samples.
• Should yield instrument response essentially equal to zero. If

not, subtract this value from sample values

• Duplicates: Run all samples and standards at least twice.
• Should vary by no more than 5%
• Difference in duplicates governs precision of answer (sig figs)
• Average of values used in final analysis
• Internal Standards: Create/Analyze knowns same as sample
• Separate from standards
• Run periodically throughout a long set of analyses
• Spike recovery: Add a known amount of analyte to a sample

Quality Assurance