QA/QC for Water Quality Data DIVE INTO DATA November 26 | 12-1pm Eastern Mary Kruk With Webinar Series Water Data Specialist The Gordon Foundation Megan Thompson Aquatic Ecologist, Limnologist Thompson Aquatic Consulting
The Gordon Foundation is a 55- year old charitable organization with a long history of protecting Canada’s waters and promoting citizen engagement in policy- making and action.
Overview ● Error: precision and accuracy ● Definition of QA/QC ● Quality Assurance ● Importance of study design ● Standard procedures ● Quality Control ● Measurement Limits ● Blanks ● Replicates ● Tips for Data Inspection
Precision and Accuracy Taken from “Scientia Plus Conscientia ” blog, 2020
What causes errors in water quality data? Causes Solutions Sampling Design errors: Sampling Design errors: ● ● Samples not representative of system Properly define your background Systematic errors : Systematic errors : ● ● Calibrate and care for equipment Instrument is not accurate ● ● Follow standard procedures Incorrect measurement techniques ● ● Lack of observation Take field notes Random errors : Random errors : ● ● Unexpected shifts in sampling Replication ● environment Can never eliminate, but can reduce by a ● thoughtful study design Personal bias https://manoa.hawaii.edu/exploringourfluidearth/physical/world-ocean/map-distortion/practices-science-scientific-error
How Things Go Wrong Public Domain, https://commons.wikimedia.org/w/index.php?curid=2740821 https://agriculture.vic.gov.au/livestock-and- https://universe.byu.edu/2018/03/07/byu-students-community- animals/livestock-health-and-welfare/caring-for-animals- participate-in-citizen-science-project-to-improve-utah-lake1/ during-extreme-heat
How Things Go Wrong Goldberg and Strickler 2014 http://www.soest.hawaii.edu/S-LAB/equipment/slab_autoanalyzer.htm
What is QA/QC? Quality Assurance (QA) = Prevent error Precautions, standard protocols, sensor calibration Are you measuring what you think you’re measuring? Quality Control (QC) = Detect error Test blanks, duplicates, and standards, examine data Is the data you’re looking at reporting what it says it’s reporting?
Quality Assurance
Importance of sampling program design Carefully plan monitoring programs – address bias and improve accuracy ● Are control sites really unaffected? Are there other impacts that you are not accounting for? ● Are impacted sites impacted in ways other than what you are monitoring for? What are those impacts, and how might they be reflected in your data? ● How will conditions change over time, with seasons and over the years? How could this affect your sampling plan? And how might these variations be reflected in your data? Many additional, important considerations….
Standard procedures In the field... PEI ELJ Environment Division 2011 https://3.bp.blogspot.com/_YLEBrXdXjB4/S9MtWoKDn9I/AAAAAAAAAQ4/_DHa4KMsCw8/ ALS Limited s1600/scan0002.jpg
Standard procedures In the lab...
Quality Control
Measurement limits in water quality Marine Pollution Studies Laboratory at the Moss Landing Marine Laboratories, 2017 Detection limits refer to the ability to DETECT a constituent in water. Quantification limits refer to the ability to QUANTIFY a constituent in water. Reporting limits are often developed by each laboratory. Detection, quantification and reporting limits can be different for the same parameter using the same method.
Non-detects in water data (<DL, <MDL, <RL) What does it mean that a water quality parameter concentration is below detection limit? ● It does not necessarily mean zero or not present, although it could mean that. ● It represents a range of unknown concentration. Why is this important? ● Very high measurement limits (especially historical) can reduce the usefulness of data. ● Differences in measurement limits can reduce the ability to compare different datasets.
Quality Control Samples - Blanks For water samples, blanks are used to account for potential contamination. Blank Type How is it collected? What does it identify? Field Blank Container filled with Contamination in analyte-free water in the transport, storage, field field handling Equipment/Rinsate Blank Container filled with Contaminated field analyte-free water that has equipment been passed through https://extension.usu.edu/utahwat erwatch/monitoring/field- collection equipment instructions/ecoli/idexxmethod Trip Blank Container filled with Contamination during analyte-free water in the transport lab, taken to sample site, and returned to lab un- opened Lab Blank Analyte-free solution Contamination in lab prepared in lab and https://clu- analyzed with other in.org/conf/tio/rcraexpert_100516/slides/Data- Review-Manual-110114.pdf samples
Quality Control Samples – Replicates For field measures and water samples, replicates are used to assess reproducibility and variability. Replicate Type How is it collected? What does it identify? Field Replicate- Multiple field Used to assess the Measurement/Observation measurements or reproducibility and observations taken at the variability of the sampling same time, location, and technique. with same controlled variables. Field Replicate – Data A field measurement or Used to assess the quality Logger observation verified by of data taken by data alternate method. logger. Field Replicate – Grab Multiple samples collected Used to assess the Sample at the same time, location, reproducibility and and with same controlled variability of the sampling variables. technique and lab analysis. Lab Duplicate A sample that is split into Used to assess the quality subsamples in the lab. of data obtained by lab analysis.
Tips for Quality Control
Plotting Data ● Helps you compare: ○ Monitoring locations → spatial variation ○ Dates → temporal variation ○ Months and seasons → seasonal variation ○ Time of day → diurnal variation ● Also can detect outliers It is important to plot water quality data before you use it! DataStream can help with this.
Scatter Plots
Scatter Plots
Box Plots Outlier (Max) 95 th Percentile Q3 (75 th Percentile) Median (50 th Percentile) Q1 (25 th Percentile) 5 th Percentile
Multiple Box Plots
Outliers What does it mean when there are outliers in your data? ● Rare event (e.g. flood, contaminant spill) ● Contamination during sample collection or analysis ● Measurement error ● Recording or data handling error “Does it represent a reasonably accurate observation of an unusual situation?” – Helsel et al. 2020, Statistical Methods in Water Resources
Outliers – Box Plots
Outliers – Scatter Plots
Unrealistic outliers indicate human error Temperature, water ( ° C ) 160 140 120 100 80 60 40 20 0 2002-01-01 2004-09-27 2007-06-24 2010-03-20 2012-12-14 2015-09-10 2018-06-06
Identify Data Below Detection Limits
Check Probe vs Lab Results 9 8 7 pH (lab) 6 5 4 3 3 4 5 6 7 8 pH (field)
Sample Fraction ● Describes the portion of the characteristic being analyzed ● Metals or Ions : Total or Dissolved ● Nutrients : Filtered or Unfiltered Method Speciation ● Identifies the chemical speciation, where applicable ● Mainly important for nutrients ● E.g. Ammonia as N or as NH 4 will have a different concentration value This is important to distinguish if you are cross-comparing datasets.
Sample Fraction: Total vs Dissolved Non- Filterable (Suspended Total Fraction Solids) Dissolved Fraction
Sample Fraction: Check Dissolved vs Total Concentrations Total Copper Dissolved Copper 45 40 CONCENTRATION (UG/L) 35 30 25 20 15 10 5 0 2002-09-01 2004-01-14 2005-05-28 2006-10-10 2008-02-22 2009-07-06
Check Method Speciation prior to comparison
Data Tables Quality Control Data – Duplicates/Replicates How different are ○ they? What type of ○ replicate is it? Rule out data ○ handling error. Is it a mislabeled sample?
RPD for Duplicates RPD = Relative Percent Difference The difference (%) between duplicate samples 𝐸 1 − 𝐸 2 𝑆𝑄𝐸 = (𝐸 1 + 𝐸 2 )/2 x 100 Large RPD indicates low reproducibility and precision in your results.
Data Tables Quality Control Data - Blanks How many are above measurement limit? ○ What type of blank is it? Field, travel, lab ○ ○ Rule out data handling error. Is it a mislabeled sample?
Quality Quality Control Assurance
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