Part 4 – Be a Citizen - Scientist

1. Part 4 – Be a Citizen - Scientist Electronic Tools Side Event UNECE Aarhus Convention 1st Meeting of the Parties Lucca, Italy October 21-23, 2002 Frances Stanley-Jones International Campaign for Responsible Technology GIS Research Group

2. Outline of this module • The scientific method • What is it and why is it useful? • Good data and why are they important? • Managing data and its importance?

3. The Scientific Method • This is important because you will be communicating with scientists and you will be more effective in proving a point if you proceed in a scientific fashion. • The Steps of the scientific method: • Identification of a problem • Form a hypothesis • How can the hypothesis be tested? = study design • Collect data • Derive conclusion

4. Start with problem and hypothesis • Problem: People living near River X are getting a specific type of cancer more than people living near River Y? • Hypothesis: People are being exposed to a chemical that causes this disease through food irrigated with River water. Crops irrigated with River X water will have more of the specific chemical we are concerned about.

5. Study design = asking questions OK - Let’s go measure the chemical in crops from fields irrigated with River X and River Y water. • What crop should we use? Yams, because make up a big portion of the diet of the villagers and they can accumulate the chemical in question.

6. And yet more questions • Where do we collect the yams? …from several portions of each of several fields, noting where in field notes • How many do we need? …depends on some estimate of the differences we expect to find. • How can we measure the contaminant? …need to find this out and the associated cost.

7. And still more… • How sensitive does the measurement have to be? … depends on expected magnitude of differences. • What are the levels of the chemical that will be dangerous in food? … need to find this out as well • How many yams do people eat? … can survey the villagers to find this out

8. And on and on and on … • Can people be exposed to the chemical in other ways, other foods? • Is there some other possible cause of the disease? • What time(s) of year should we sample? • How can we be sure that the chemical is coming from the irrigation water? • Who are we going to communicate this information to? • What sort of special equipment do we need?

9. Quality data and its management • This is important because the credibility of the campaign will depend heavily on the quality of the data collected and the methods used to communicate the data results. • Data requires documentation in order to be useful and usable. • The goal is to collect data that can speak for themselves!

10. I am the worst case scenario I have been collected in a stagnant ditch at 14:00 DO=5.6 pH=8.7

11. What are data anyway? In general, Data are … Results(outcomes of measurements and analyses) and their descriptors A Result can be numerical or verbal; It can be an individual value (e.g., pH 7.5); A calculated endpoint (e.g., 3 cubic ft per second); A verbal category (e.g., murky); A numeric range category (e.g., 25-50% canopy cover); Or a narrative statement, i.e., a sentence or a paragraph.

12. Descriptors are key to good data • In general, descriptors are ResultType and Qualifiers • Quantification Method • Field Measurement (Instrument ID) • Laboratory Analyses and Tests (Lab Batch ID) • QA/QC Results: error, sensitivity, integrity • Dataset: Study intent, Design, and Power • Project Organization

13. Spatial descriptors Station Type : creek, outfall, ditch, river Station Selection Intent: Impact assessment, source ID Reach Selection Design: systematic, directed, random, anecdotal Station Selection Design: (same options) Temporal descriptors Storm runoff flows (wet) or base flow (dry) weather Sample Timing Intent: worst case, snapshot, routine monitoring Seasonal Sampling Design: systematic, directed, random, anecdotal Diurnal Sampling Design:(same options) Season of interest: wet, dry

14. What does ‘good data’ mean? Good data: • are usable • and reliable • are valid • are of known quality • have fidelity • and are certified.

15. Usability of data • Capable of answering questions and supporting management decisions • Complete Parameter Package: all supporting parameters included • Comparable to other data sets in terms of sampling design and data quality • Adequate Measurement Quality Objectives in terms of sensitivity and tolerated error • Adequate Statistical Power of the dataset (number & replication of samples) • Scientifically defensible, including in court • Reported and documented in formats that can be easily read, understood, and transformed by others

16. Reliability (credibility) of data • High probability that the reported value indeed falls within the range of error specified for it, and • Complete documentation is provided (all the information on location, sampling design, measurement, QAQC, etc), and • Honest reporting by field operators

17. Validity of data • Compliance with data quality objectives has been confirmed, and • The test, assay, or analysis used to collect the data was valid

18. Three DISTINCT aspects of Data Quality A Quality of the measurement itself (accuracy, precision, detection limit, resolution) B Sample integrity (lack of deterioration, lack of contamination) C Representativeness of the measured value (how does the sample represent “true” conditions, across time & space: issues of sampling design and sample collection method in relation to inherent variability)

19. Fidelity of data • Consistent interpretation of "menu options" for verbal categories or numeric range categories • Correct transfer of information from observer to "scribe" • Correct recording, copying, and data entry into electronic formats

20. Certification of data • Laboratory analyses were made by a certified laboratory • Laboratory work was checked against certified Standards • Field work was done by certified operators, survey work done by certified surveyors • Field work done with or calibrated against certified instruments and Standards

21. Data? Hmm? Discussion of challenges • Are you already using the scientific method in designing your approaches to collecting data? • If not, why not? • What challenges do you face in gathering data of good quality? • What tools do you need to be more successful in this regard?

22. Glossary of terms – data quality Accuracy: The extent of agreement between an observed value (measurement result) and the accepted, or true, value of the parameter being measured. Blank (Sample): A sample that contains pure water and is analyzed concomitantly with a set of environmental samples. Blanks usually include field blanks and trip blanks to assure that there was not contamination during sampling and shipping, as well as method blanks and reagent blanks tested within the analytical procedures Calibration: The action of adjusting the readings of an instrument to have them match a “true” value as represented by known natural conditions (e.g., freezing point) or a laboratory standard. Contamination: Inadvertent addition of an analyte or interfering compounds to a sample from the sampling equipment, sample container, etc. Contamination may cause false positive results or higher result values. Concomitant (sampling): accompanying; one activity related to a given spot is happening at the same time as another activity at the same spot, e.g., a sample for analysis of Ammonia is collected from the creek at the same time the pH is measured in creek water. Data: bits of information. The core of DATA is the RESULT. Descriptors (see Metadata): A myriad of descriptive items that provide information about the data. There are about 200 descriptors needed to make one result "bit" usable, reliable, and of known quality. Endpoint: (1) A numerical value representing the result of a measured parameter that has been calculated from a number of individual measurements (e.g.: flow discharge in cubic feet per second, or bacterial concentration in MPN/100 ml.). (2) That stage in titration at which an effect, such as a color change, occurs, indicating that a desired point in the titration has been reached.

23. Glossary of terms – data quality Measurement: (1) one of the sample/measurement/observation (SMO) entities, conducted in situ (with probe) or in a sampling device, immediately (not hauled away). (2) generic term for any quantitation activity Measurement Quality Objectives: Statements about the tolerated error and desired sensitivity of a measurement. They include extent of values for the measures of precision, accuracy, detection limit, and resolution. MQOs are a subset of Data Quality Objectives (DQOs). Metadata: "Data about data." Information that describes the result of each measurement (i.e., the “how much”) in terms of what, when, where, why, how and by whom that result was collected. This information is essential for data validation and helps others understand exactly how the data was obtained. Observation: one of the sample/measurement/observation (SMO) entities, an on-site estimate or evaluation of a parameter not measured. Results are expressed categorically (word from menu, e.g., "murky", or numeric range category, e.g., "1 to 20 l/sec”) Parameter: A property or substance to be measured within a medium. Parameters include properties such acidity (pH) or electrical conductivity, particulates such as suspended solids or bacteria, and analytes such as ammonia or heavy metals. Precision: A measure of how close repeated trials are to each other Quality Assurance/Quality Control (QA/QC): The total integrated program for assuring the validity of monitoring and measurement data.

24. Glossary of terms – data quality Representativeness: A data quality indicator, representativeness is the degree to which data portray the actual or true environmental condition measured. Resolution: The smallest increment that can be discerned on the scale of a measuring device, or the capability of a method to discriminate between measurement responses. Result: the outcome of a measurement, analysis, or observation. Results are always linked to a parameter, and are linked to units in most situations. A result for a specific parameter is what the collector produces and what the data user will use. Sample: one of the sample/measurement/observation (SMO) entities, a sample is collected in a container, hauled away, and analyzed elsewhere Sample (SMO) ID: a unique identifier for a sample/measurement/observation (SMO) entity. Water Quality Parameters: Any of the measurable properties, qualities or contents of water.

25. IHEAL Community Monitoring Presented by Frances Stanley-Jones, Lead Technical Support Engineer, Tele Atlas N.A. (http://www.teleatlas.com/) and International Campaign for Responsible Technology GIS Research Group, San Jose, California (http://www.svtc.org/icrt/) Presentation prepared by Richard Looker and Michael Stanley-Jones for the International POPs Elimination Network (http://ipen.ecn.cz/) and Interactive Health Ecology Access Links (http://www.iheal.org) Digital cartography by Michael Meuser, Clary Meuser Research Network (http://www.mapcruzin.com/) and Frances Stanley-Jones.