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Calibration and inter-comparisons between instruments – WQM FLNTU’s

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Page 1: Calibration and inter-comparisons between instruments ... · Detected by users via telemetry. Quality Control – Fuzzy logic – look at the other ... Perform a detailed characterization

Calibration and inter-comparisons between instruments – WQM FLNTU’s

Page 2: Calibration and inter-comparisons between instruments ... · Detected by users via telemetry. Quality Control – Fuzzy logic – look at the other ... Perform a detailed characterization

National Reference Stations – time series dataSensors and Sampling

Key* - long term site# - infrastructure deployed+ - Telemetry^ BGC sampling

Page 3: Calibration and inter-comparisons between instruments ... · Detected by users via telemetry. Quality Control – Fuzzy logic – look at the other ... Perform a detailed characterization

QC sensors vs. BGC water samplingCombine for cross validation

Sensors• 9 QC tests• Scientist per site• Matlab toolbox• Methods papers• NATA cal lab

BGC• 4 central processing laboratories

• Scientist per lab• Training• Sampling/lab guides• Taxonomic guides

Page 4: Calibration and inter-comparisons between instruments ... · Detected by users via telemetry. Quality Control – Fuzzy logic – look at the other ... Perform a detailed characterization

NRS design– Many FLNTUs

Page 5: Calibration and inter-comparisons between instruments ... · Detected by users via telemetry. Quality Control – Fuzzy logic – look at the other ... Perform a detailed characterization

CSIRO. Insert presentation title, do not remove CSIRO from start of footer

FLNTU – Fluorometer and backscatter

Page 6: Calibration and inter-comparisons between instruments ... · Detected by users via telemetry. Quality Control – Fuzzy logic – look at the other ... Perform a detailed characterization

Lesson Learnt – Bio-fouling of sensors after 6 monthsDetected by users via telemetry

Page 7: Calibration and inter-comparisons between instruments ... · Detected by users via telemetry. Quality Control – Fuzzy logic – look at the other ... Perform a detailed characterization

Quality Control – Fuzzy logic – look at the other sensor – take photograph of the sensor

Page 8: Calibration and inter-comparisons between instruments ... · Detected by users via telemetry. Quality Control – Fuzzy logic – look at the other ... Perform a detailed characterization

Perform a detailed characterization to determine the actual zero point and scale factor

Determine accurate blank, equivalent phytoplankton types and similar physiological conditions for calculating the scale factor

FLNTU – owners manual

Page 9: Calibration and inter-comparisons between instruments ... · Detected by users via telemetry. Quality Control – Fuzzy logic – look at the other ... Perform a detailed characterization

Occurs singly, groups and embedded in a gelatinous matrix

Used in aquaculture

Asexual phase involves cell division with each of the new individuals receiving one of the valves. This means that the offspring are of unequal sizes and successive generations tend to decrease in size.

Large individuals can also reproduce sexually

Thalassiosira weissflogii - factory set scale factorSingle point solid standard – factory set calibration

Page 10: Calibration and inter-comparisons between instruments ... · Detected by users via telemetry. Quality Control – Fuzzy logic – look at the other ... Perform a detailed characterization

Key lesson learnt for sustained observing QC – inter sensor comparability

• Spiking (physicists vs. biologists)• Change of calibration method from

single to multiple point

• Changing of sensors replacedduring servicing of instrument

Page 11: Calibration and inter-comparisons between instruments ... · Detected by users via telemetry. Quality Control – Fuzzy logic – look at the other ... Perform a detailed characterization

y = 0.6506x + 0.3566R² = 0.8285

0

0.5

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1.5

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2.5

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0 0.5 1 1.5 2 2.5 3

WQ

M

Bottle sample

Comparisons sensor to bottle @ 20 m

mean CPHL

Linear (mean CPHL)

Cross validation of sensor to monthly BGC samples – Chlorophyll 20m Maria Island NRS

Page 12: Calibration and inter-comparisons between instruments ... · Detected by users via telemetry. Quality Control – Fuzzy logic – look at the other ... Perform a detailed characterization

Chlorophyll 80m Maria Island NRS

y = 1.1654x + 0.0824R² = 0.8994

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0.1

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WQ

M

Bottle sample

Comparisons sensor to bottle @ 80 m

mean CPHL

Linear (mean CPHL)

Page 13: Calibration and inter-comparisons between instruments ... · Detected by users via telemetry. Quality Control – Fuzzy logic – look at the other ... Perform a detailed characterization

y = 0.3618x + 0.0431R² = 0.9782

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Mea

n on

WQ

M 3

nea

rest

bur

sts

Bottle sample

Comparisons sensor to bottle @ 25m

Chlorophyll 25m Port Hacking NRS –mean of 3 bursts

Page 14: Calibration and inter-comparisons between instruments ... · Detected by users via telemetry. Quality Control – Fuzzy logic – look at the other ... Perform a detailed characterization

Back to the lab

NATA Certified Calibration lab

Australian Algae collection

Many FLNTUs on hand

Highly skilled staff (not me!)

Page 15: Calibration and inter-comparisons between instruments ... · Detected by users via telemetry. Quality Control – Fuzzy logic – look at the other ... Perform a detailed characterization

Compare multiple algal mono-cultures to one FLNTU

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Wet

labs

Chl

Time (sec)

WQM-040 (Chl scale factor 0.007)

#1 Thalassiosira oceanica #2 Tetraselmis sp. #3 Hetrocapsa niei#4 Synechococcus sp. #5 Ditylum brightwelli #6 Nannochloropsis

Page 16: Calibration and inter-comparisons between instruments ... · Detected by users via telemetry. Quality Control – Fuzzy logic – look at the other ... Perform a detailed characterization

Bio-optical instrument characterization HPLC vs. single FLNTU across 3 x 3 replicates of representative algae

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chl-a

(ug/

L)

Instrument and algae type and replication

Page 17: Calibration and inter-comparisons between instruments ... · Detected by users via telemetry. Quality Control – Fuzzy logic – look at the other ... Perform a detailed characterization

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1 11 21 31 41 51 61 71 81

WQM chl‐a (u

g/L)

Reading number

Nannochloropsis oculata Wetlabs

129#6_rep1

127#6_rep1

96#6_rep1

41#6_rep1

40#6_rep1

Compare multiple instruments – factory settingsRandom design

Page 18: Calibration and inter-comparisons between instruments ... · Detected by users via telemetry. Quality Control – Fuzzy logic – look at the other ... Perform a detailed characterization

Move to a new calibration standard

Factory WQM - Single solid standard

CSIRO - Multipoint Fluorescein calibration

Page 19: Calibration and inter-comparisons between instruments ... · Detected by users via telemetry. Quality Control – Fuzzy logic – look at the other ... Perform a detailed characterization

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1 11 21 31 41 51 61 71 81

WQ

M c

hl-a

(ug\

L)

Reading number

Nannochloropsis oculata CMAR

129#6_rep3

127#6_rep3 CMAR

96#6_end run CMAR

41#6_rep3CMAR

40#6_rep3CMAR

New multi-point fluorescence calibration – notescale change, as not scaled to Thalassiosira weissflogii

Page 20: Calibration and inter-comparisons between instruments ... · Detected by users via telemetry. Quality Control – Fuzzy logic – look at the other ... Perform a detailed characterization

y = 0.0216x - 3.3255R² = 0.9802

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0 500 1000 1500 2000 2500 3000 3500 4000 4500

Raw

Chl

Cou

nts

Concentration (μg/L)

WQM Chl-a Calibration Curve Using Fluorescein:

WQM 127 Fluroescein calibration

Page 21: Calibration and inter-comparisons between instruments ... · Detected by users via telemetry. Quality Control – Fuzzy logic – look at the other ... Perform a detailed characterization

y = 0.0243x - 0.8938R² = 0.9999

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Raw

Chl

Cou

nts

Concentration (μg/L)

WQM Chl-a Calibration Curve Using Fluorescein:

WQM 41 Fluorescein calibration

Page 22: Calibration and inter-comparisons between instruments ... · Detected by users via telemetry. Quality Control – Fuzzy logic – look at the other ... Perform a detailed characterization

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WQ

M c

hl-a

(ug\

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Reading Number

Nannochloropsis oculata CMAR remove WQM 127 and 96

129#6_rep1 CMAR

41#6_rep2CMAR

40#6_rep3CMAR

Remove the bogus instruments – starting to get repeatable

Page 23: Calibration and inter-comparisons between instruments ... · Detected by users via telemetry. Quality Control – Fuzzy logic – look at the other ... Perform a detailed characterization

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HPLC output WQM CMAR HPLC WQM Wetlabs Wetlabs WQM CMAR None

Ave

rage

with

SD

Chl

a (u

g/L)

Sensor, Calibration and Characterisations type

Nannochloropsis #6 (high)

Tetraselmis sp. #2 (medium)

Synechoccoccus sp. # 4 (low)

Characterize against the HPLC standard and nowboth precise and accurate across 3 species of algae

Page 24: Calibration and inter-comparisons between instruments ... · Detected by users via telemetry. Quality Control – Fuzzy logic – look at the other ... Perform a detailed characterization

Esperance

peridinin = dinoflagellatesalloxanthin = chryptophytes19 but ~ pelagophytesfucoxanthin ~ diatoms19 hex ~ coccolithophoridszeaxanthin ~ SynechococcusDV chla = ProchlorococcusChlb ~ greensneoxanthin ~ greensprasinoxanthin ~ greens

Rottnest IslandNingaloo

Yongala

North Stradbroke Island

Port Hacking

Maria Island

Kangaroo Island

Darwin

Across the system now need to recalibrate instruments, characterize against the HPLC results and reprocess

Page 25: Calibration and inter-comparisons between instruments ... · Detected by users via telemetry. Quality Control – Fuzzy logic – look at the other ... Perform a detailed characterization

History of FLNTUSB Loggers at AIMS• Purchased first 3 instruments in 2006• Routine use started Oct 2007 at 14 sites deployed on inshore reefs• Problems with negative Chl values - 2006• Logger step changes noticed and discussions with company in ~2008• Internal calibrations developed:

• Pontoon deployments with regular direct water sampling (since August 2010) - natural water

• Tank deployments with plankton bloom initiated by nutrient addition• Tank deployments with plankton culture added

• After discussions with WET Labs loggers sequentially sent to WET Labs for Uranine cal as they were retrieved from the field for recalibration

• Meeting with WET Labs after ICRS (mid July) and discussed multiplication by a ratio: Uranine pre-cal / old-cal as easiest adjustment solution.

• Adjustment results showing varied success• Solution still required for problem loggers and those with negative

values

Page 26: Calibration and inter-comparisons between instruments ... · Detected by users via telemetry. Quality Control – Fuzzy logic – look at the other ... Perform a detailed characterization
Page 27: Calibration and inter-comparisons between instruments ... · Detected by users via telemetry. Quality Control – Fuzzy logic – look at the other ... Perform a detailed characterization

AIMS pontoon co-deploymentsfor chlorophyll QC 2010

Chlorophyll (µg L-1):•High inter-logger variability apparent, range ~0.5 µg L-1•Only instrument 816 shows good agreement with results from water sampling for chlorophyll.

Page 28: Calibration and inter-comparisons between instruments ... · Detected by users via telemetry. Quality Control – Fuzzy logic – look at the other ... Perform a detailed characterization

AIMS pontoon co-deployments for turbidity QC 2010

Turbidity (NTU):•Good agreement between loggers, apart from a few spikes

•Suspended solids data from water sampling (mg L-1) included for reference,.

Page 29: Calibration and inter-comparisons between instruments ... · Detected by users via telemetry. Quality Control – Fuzzy logic – look at the other ... Perform a detailed characterization

Test deployment with ratio adjustment applied

Page 30: Calibration and inter-comparisons between instruments ... · Detected by users via telemetry. Quality Control – Fuzzy logic – look at the other ... Perform a detailed characterization

Ratio adjustment with ref samples

Page 31: Calibration and inter-comparisons between instruments ... · Detected by users via telemetry. Quality Control – Fuzzy logic – look at the other ... Perform a detailed characterization

Performance of new uranine cal

Page 32: Calibration and inter-comparisons between instruments ... · Detected by users via telemetry. Quality Control – Fuzzy logic – look at the other ... Perform a detailed characterization
Page 33: Calibration and inter-comparisons between instruments ... · Detected by users via telemetry. Quality Control – Fuzzy logic – look at the other ... Perform a detailed characterization
Page 34: Calibration and inter-comparisons between instruments ... · Detected by users via telemetry. Quality Control – Fuzzy logic – look at the other ... Perform a detailed characterization

Conclusions

•Some FLNTUs produce poor quality data

•FLNTU’s can return good field data

•FLNTU’s need categorisation

•FLNTU’s require multi-point calibrations

Page 35: Calibration and inter-comparisons between instruments ... · Detected by users via telemetry. Quality Control – Fuzzy logic – look at the other ... Perform a detailed characterization

Authors:Tim Lynch, Lesley Clementson, Robert Kay, Guillaume Galibert, Irena Zagorskis

NRS teams:Anthony Richardson and his team

Pru Bonham and Peter Thompson

John Middleton and Charles James

Ming Feng and Liejun Zhong

Craig Steinberg, Vittorio Brando, Martina Doblin

Brad Morris, Moninya Roughan

Ken Ridgeway, Darren Moore

Acknowledgements

Page 36: Calibration and inter-comparisons between instruments ... · Detected by users via telemetry. Quality Control – Fuzzy logic – look at the other ... Perform a detailed characterization

Contact UsPhone: 1300 363 400 or +61 3 9545 2176

Email: [email protected] Web: www.csiro.au

Thank you