Data Collection Quality Assurance / Quality Control

Transcription

1 Data Cllectin Quality Assurance / Quality Cntrl When embarking n a mnitring prgram, it is imprtant t identify wh is ging t use the data and fr what purpse. Yur chices f methds fr mnitring shuld be guided by the purpse f yur mnitring prgram and the quality f data yu need. The end use f the data will determine what level f quality assurance and quality cntrl measures shuld be taken t ensure data can be reliably used fr its prpsed purpse. Fr sme mnitring grups, the main bjective is educatin fr the lcal cmmunity r schl, where the fcus is n drawing awareness t an issue rather than prducing high quality data. Hwever, grups that cllect data t infrm lcal management decisins r as part f an integrated mnitring prgram with the lcal gvernment, research rganizatins, reginal bdies and state agencies must take measures t ensure the data are credible and reliable. When apprpriate quality assurance and quality cntrl measures are implemented, yu can be cnfident that management decisins are based n sund and reliable data. Is QA/QC imprtant fr yur mnitring? There is ne imprtant questin t answer t determine whether QA/QC is imprtant fr yur mnitring prgram: Will the results f yur mnitring prgram be used t infrm natural resurce management decisins? N Yes Quality assurance and quality cntrl measures are nt a must fr yur purpses. Yur data cllectin can be a valuable educatinal exercise fr raising envirnmental awareness and learning abut the science f measuring trends in natural resurce cnditin. Fantastic! It is imprtant that yu implement a quality assurance and quality cntrl plan. This can be added t yur mnitring plan (if develped). If yu implement a QA/QC plan, yur data will be f a knwn quality and yu can cnfidently use the data t infrm management decisins QA and QC are implemented t help yu prduce data f knwn quality and these will enhance the credibility f yur grup in reprting mnitring results. Implementing QA/QC measures with yur cmmunity based mnitring prjects will ultimately save yu time and mney as it will prevent cllectin f data that des nt fit yur gals. Hwever, a gd QA/QC prgram is nly successful if all the mnitring prgram participants fllw the QA/QC measures and if all cmpnents f yur QA/QC plan are dcumented and available t data cllectrs and data users. QA/QC Checklists T help yu prduce data f knwn quality and enhance the credibility f yur grup in reprting mnitring results, QA/ QC checklists have been develped fr a range f mnitring activities (see later): Birds, fish using nets, frgs, macr invertebrates,trtises, small reptiles and mammals Grundwater and surface water Vegetatin using line transect, pht pints and weed mapping. These checklists cver items fr the verall management system f yur mnitring prject. There is an extensive range f items, which can be integrated ver time int yur prject t make it manageable. What d we mean by QA/QC? Quality Assurance (QA) is the verall management system which includes the rganizatin, planning, data cllectin, quality cntrl, dcumentatin, evaluatin, and reprting activities f yur grup. QA prvides the infrmatin yu need t ascertain the quality f yur data and whether it meets the requirements f yur prject. QA culd als be called mnitring cnfidence. Quality Cntrl (QC) is the rutine technical activities that are in place t cntrl errr. Since errrs can ccur in either the field, the labratry r in the ffice, QC must be part f each f these functins. QC shuld include bth internal and external measures. QC culd als be called data cnfidence. Figure 1 shws the place f QA /QC in the prject management cycle. 21

2 Figure 1 The place f quality assurance and quality cntrl in the prject management cycle Prject Aims What are the aims f prject? Management Actin Wider Audience Wh else besides the prject managers need t knw abut the results? Expected Changes What are the expected changes? Indicatrs f Change What indicatrs f change will yu measure? Cmmunicatin Wh needs t knw abut the results and hw will they be cmmunicated? Quality Assurance Mnitring Techniques Feedback fr Adaptive Management Hw will mnitring results influence future activities? Data Cllectin Quality Cntrl Data Management Hw will the results be recrded and stred? Imprving the quality f data In natural systems variability is a part f the natural rder f things. Changes in temperature, flw, sunlight, and many ther factrs affect these systems and the vegetatin and animals that inhabit them. Variability in mnitring data can be a result f this natural variatin but may als result frm differences in the way we read, measure and interpret infrmatin. We may als apply different levels f effrt t ur measurement and the equipment we use may be cntaminated, brken r incrrectly calibrated. These and many ther differences can lead t variability in mnitring results. Measures f precisin, accuracy, representativeness, cmparability and sensitivity help us evaluate the surces f variability Recgnizing surces f variability in the data Maintaining quality assurance requires that surces f variability in the data be identified: What are the causes f bias and imprecisin? Can these be minimized? Can we quantify the level f bias and/r imprecisin in the data? and errr. Cnsideratin f these aspects leads t increased cnfidence in ur data. These and ther key terms are described n the fllwing pages. Precisin Precisin is the range f variatin in repeated measurements f the same characteristic. Precisin may be determined by calculating the standard deviatin, r relative percent difference, amng samples taken frm the same place at the same time. Repeated measurements can tell yu hw cnsistent and reprducible yur field r labratry methds are by shwing yu hw clse yur measurements are t each ther. It des nt mean that the sample results actually reflect the "true" value. By cnventin, 68% f measured values are within ne standard deviatin (see Figure 3) frm the mean f all the measured values. If the standard deviatin is small, mst f the measured values must be clse t each ther. 22

3 Accuracy Accuracy measures hw clse yur results are t a true r expected value. Fr mnitring water quality this be determined by cmparing yur analysis f a standard r reference sample t its actual value. The smaller the difference between the measurement f a parameter and its "true" r expected value, the mre accurate the measurement. Accuracy = Average value true value Where the average value is the average f x replicates, and the true value is the value f standard reference sample (e.g. ph slutin ph 7.0) Increasingly, the term "bias" is being used t reflect errr in the measurement system and "accuracy" is used t indicate bth the degree f precisin and bias (see Figure 2). Fr sme measurements reference samples can be used t test the accuracy f yur measurement. Fr example, the difference between an expert analyst s measurement f a mystery sample and yur measurement indicates yur ability t btain an accurate measurement. Fr many parameters such as species abundance, n standard reference r perfrmance evaluatin samples exist. In these cases, the expert r trainer's results may be cnsidered as the reference value. Representativeness Representativeness is the extent t which measurements actually represent the true state f the resurce at the time a sample was cllected. A number f factrs may affect the representativeness f yur data. Are yur sampling/ mnitring lcatins indicative f the regins Figure 2 An illustratin f the terms precisin, bias and accuracy (after Hunt et al. (1996)) 1 Bias Precisn, Bias, and Accuracy High Lw High Inaccurate Precisin Lw Inaccurate Figure 3 Explanatin f standard deviatin STANDARD DEVIATION The Vlunteer Sil Mnitring Prject wants t determine the precisin f its sil phsphrus assessment prcedure. They have taken 4 replicate samples: Replicate 1 (X 1 ) = 21.1 ppm Replicate 2 (X 2 ) = 21.1 ppm Replicate 3 (X 3 ) = 20.5 ppm Replicate 4 (X 4 ) = 20.0 ppm T determine the Standard Deviatin (s), use the fllwing frmula: s n i1 ( X i n X ) 1 where X i = measured value f the replicate, X = mean f replicate measurements, n = number f replicates, = the sum f the calculatins fr each measurement value - in this case, X 1 thrugh X 4. First, figure ut the mean, r average f the sample measurements. Mean = (X 1 + X 2 + X 3 + X 4 ) 4. In this example, the mean is equal t ppm. Then, fr each sample measurement (X 1 thrugh X i ), calculate the next part f the frmula. Fr X 1 and X 2, the calculatin wuld lk like this: ( ) 2 = 2 (0.42) 2 = = Fr X 3 the calculatin wuld be ; and fr X 4 it wuld be Finally, add tgether the calculatins fr each measurement and find the square rt f the sum: = The square rt f is S, the standard deviatin fr phsphrus is (runded ff). That is, 68% f measured values will be within apprximately 0.5 ppm f the mean value. in yur management prject? Fr example, data cllected just belw a pipe utflw is nt representative f an entire creek. Similarly, chsing sites that are htspts fr birds will prduce results that represent the chsen htspts but nt ther areas f management interest. These variatins shuld be cnsidered and their impacts minimized when develping yur sampling design. Accurate Inaccurate 23

4 Cmparability Cmparability is the extent t which data can be cmpared between sample lcatins r perids f time within a prject, r between prjects. Fr example, yu may wish t cmpare tw seasns f summer data frm yur prject r cmpare yur summer data set t ne cllected 10 years ag. Using standardized sampling and analytical methds, units f reprting and site selectin prcedures helps ensure cmparability. Keeping gd recrds abut the time f year, day, weather cnditins and having ther mnitring activities n the same day crss-referenced fr easy access will help yu t judge when measurements are cmparable. Detectin limit Detectin limit is a term that can apply t mnitring and analytical instruments as well as t methds. Fr example, detectin limit is defined as the lwest cncentratin f a given pllutant yur methds r equipment can detect and reprt as greater than zer. Readings that fall belw the detectin limit are t unreliable t use in yur data set. Furthermre, as readings apprach the detectin limit, i.e. as they g frm higher and easy-t-detect cncentratins t lwer and hard-t-detect cncentratins, they becme less and less reliable. Manufacturers generally prvide detectin limit infrmatin with high-grade mnitring equipment such as meters. Detectability is als an issue when carrying ut bilgical surveys. If a survey fails t uncver the presence f a rare species, hw many times d yu have t survey befre yu can say that the species is truly nt there? Measurement range Measurement range is the range f reliable measurements f an instrument r measuring device. Pre-assembled kits usually cme with infrmatin indicating the measurement range that applies. Fr example, yu might purchase a kit that is capable f detecting ph falling between 6.1 and 8.1. Hwever, ph can theretically range frm 0.0 t If acidic cnditins (belw ph 6) are a prblem in the waters yu are mnitring, yu will need t use a kit r meter that is sensitive t the lwer ph ranges Quality Cntrl (QC) Samples. Quality cntrl sampling Cntaminatin and bserver errr are a cmmn surce Spiked samples are samples t which a knwn cncentratin f the analyte f interest has been added. f errr in bth sampling and analytical prcedures. QC samples help yu identify when and hw cntaminatin might ccur. Fr mst prjects, there is n set number f field r labratry QC samples r bservatins which must be taken. The general rule is that 10% f samples shuld be QC samples. This means that if 20 samples are cllected, at least tw additinal samples must be added as a QC sample. The decisin t accept data, reject it r accept nly a prtin f it shuld be made after analysis f all QC data. Quality cntrl samples can include field blanks, equipment r slutin blanks, replicate r duplicate samples, spiked samples and mystery slutins. Quality cntrl samples fr water quality A field blank is a clean sample, prduced in the field, used t detect analytical prblems during the whle prcess (sampling, transprt, and lab analysis). T create a field blank, take a clean sampling cntainer with "clean" water t the sampling site. Clean water is distilled r deinized water that des nt cntain any f the substance yu are analyzing. Other sampling cntainers will be filled with water frm the site. Except fr the type f water in them, the field blank and all site samples shuld be handled and treated in the same way. Fr example, if yur methd calls fr the additin f a preservative, this shuld be added t the field blank in the same manner as in the ther samples. When the field blank is analyzed, it shuld read as analyte-free r, at a minimum, the reading shuld be a factr f 5 belw all sample results. An equipment r rinsate blank is a clean sample used t check the cleanliness f sample cllectin equipment. This type f blank is used t evaluate if there is carryver cntaminatin frm reuse f the same sampling equipment. A sample f distilled water is cllected in a sample cntainer using regular cllectin equipment and analyzed as a sample. A split sample is ne sample that is divided equally int tw r mre sample cntainers and then analyzed by different analysts r labs. Split samples are used t measure precisin. Samples shuld be thrughly mixed befre they are divided. Large errrs can ccur if the analyte is nt equally distributed int the tw cntainers. Replicate samples are btained when tw r mre samples are taken frm the same site, at the same time, using the same methd, and independently analyzed in the same manner. Replicates (r duplicates) can be used t detect bth the natural variability in the envirnment and the errr frm field sampling methds, including differences intrduced by different bservers. Spiked samples are used t measure accuracy. If this is dne in the field, the results reflect the effects f 24

5 preservatin, shipping, labratry preparatin, and analysis. If dne in the labratry, they reflect the effects f the analysis frm the pint when the cmpund is added, e.g. just prir t the measurement step. Percent recvery f the spike material is used t calculate analytical accuracy. Sme Waterwatch grups in Australia use the terms mystery samples and shadw testing fr quality cntrl checks in their vlunteer prgram. Vlunteers are given a mystery sample t test and their results are tested fr precisin and accuracy, against the measurements f expert analysts. Shadw testing is where an expert tests the same field sample as yu, allwing the expert t test the accuracy f the testing equipment as well as assessing technique and methdlgy f the mnitring participant QA/QC prfrmas Different mnitring prgrams will have different needs fr cntrlling and assuring quality f the prgram and the resulting data. Pages prvide example prfrmas fr areas f QA/QC cmmn t many prgrams and include: calibrating recrding instruments checking that training sessins cver all the necessary tpics recrding details f vlunteer training checking mystery samples checking testing prtcls and equipment thrugh shadw testing. Quality cntrl samples fr bilgical surveys It is pssible t measure the precisin and bias f bservers using techniques fr vegetatin, bird, and macrinvertebrate surveys. Quality cntrl measures include strictly fllwing standardised prtcls, including having an expert carry ut the survey at the same site fr cmparisn. Anther technique is t take samples (r call recrdings) and have identificatin verified by an expert. There are many ways in which quality assurance measures can help t increase the credibility f the data. These include measures generally applicable t mst mnitring techniques, such as: using standardised techniques clearly dcumented in a handbk. using sites which are thrughly characterised and dcumented in a site flder. taking adequate recrds in the field n standardized data sheets. crss-referencing fr easy access t ther data cllected in the field at the same time. Cmpiling a QA/QC plan Just as yur mnitring plan is imprtant fr yur prject plan, having a quality assurance and quality cntrl plan is imprtant fr yur mnitring plan. Cmpiling a quality assurance and quality cntrl plan will help t ensure time and mney spent n mnitring is nt wasted in btaining data f unknwn quality that will nt be credible enugh t frm the basis f decisins. Table 3 prvides sme prmpt questins that will guide yu in cmpiling a sund quality assurance and quality cntrl plan fr yur mnitring prject. 25

6 Table 3 Prmpt questins fr cmpiling a sund QA/QC cntrl plan COMPONENT List key persnnel and rganizatins invlved in yur prgram Descriptin f mnitring prgram INCLUDE Wh will verify samples/data? Wh will maintain/stre data? Wh will undertake analysis and interpretatin? Wh are the end users f the results? What respnsibilities d these peple/rganisatins have? (frm the mnitring plan) Data quality bjectives - the quantitative and qualitative terms yu use t describe hw gd yur data need t be t meet yur prject's bjectives. Training requirements r certificatin list training needs, hw they will be met, details f training undertaken (number f participants, type f training and level) Dcumentatin and recrds - identify the field and labratry infrmatin and recrds yu need fr this prject. Cpies f frms and datasheets used can be attached t the QA/QC plan. Sampling design - utline the experimental design f the prject. Yu may refer t the relevant sectins f yur prgram's standardised prcedures which detail the sampling design f the prject, in place f extensive discussin. Sampling methds (standard prtcls can be cited) Hw precise des the data need t be Hw accurate des the data need t be? Hw representative f the system des the data need t be? Hw cmparable t data frm ther sites, times, prjects des the data need t be? Is the measurement range f the equipment r design adequate fr the range f data t be cllected? Wh needs what training? Hw will the training be delivered and by whm? What recrds f the training need t be kept (eg n. participants, date, scpe), by whm, where? What level f cmpetency has been reached? When will re-training r refresher training be needed? What raw data will be kept? What QC checks will be used? What data sheets will be used? What labratry r vucher sheets will be used? Where and fr hw lng will recrds be kept? Hw is the mnitring data and assciated infrmatin made accessible t stakehlders and end users? What types f sampling/surveys are required? Hw frequently will samples/surveys be undertaken? Hw is seasnality etc being accunted fr? Hw are sample sites selected? Are there any issues which may limit prpsed sampling activities (eg. site access, seasnal cnstraints)? What parameters will be sampled? What prtcls fr sampling are being used? What equipment is being used? Hw are samples r vuchers preserved and stred, and what are the hlding times fr samples? Hw will equipment be cleaned and decntaminated (eg. dipnets need t be thrughly rinsed and examined fr clinging rganisms between sampling events)? 26

7 Table 3 cntinued COMPONENT INCLUDE Analysis/identificatin methds (standard prtcls can be cited) Quality cntrl QC checks can be described narratively and if apprpriate, shuld include discussin f replicate sample cllectin, crss checks by different field crews, peridic srting checks f samples, and maintenance f vucher and reference cllectins. Equipment/instrument testing, inspectin and What methds and equipment are needed fr the analysis/identificatin? Have any changes been made t standard prtcls? What types and number f quality cntrl samples will be cllected? If yu are sending samples t an expert/labratry, d yu have a cpy f their QA/QC plan? What actins will yu take if the QC samples reveal a sampling r analytical prblem? What is yur plan fr rutine inspectin and preventative maintenance f equipment? maintenance What spare parts and replacement equipment needs t be kept n hand? Instrument calibratin Hw, when and against what standards will yu calibrate sampling and analytical instruments? What recrds f calibratin f instruments will be kept? Inspectin/acceptance f supplies Hw will yu check the quality and apprpriateness f supplies such as sample bttles, nets, chemicals, equipment? Data acquisitin Hw will yu check that data yu are using frm ther surces (eg. State gvernment database) is quality assured? Data management this invlves tracing the path yur data takes frm the field cllectin t analysis, strage and use. Data review, verificatin and validatin. This can include cmparing field datasheets t entered data, checking fr data gaps, checking the QC dcumentatin, checking calculatins, checking fr extreme values, reviewing graphs, tables and written reprts. Evaluatin and management this cmpnent helps yu t take an verview f what is wrking well and what needs imprvement. Hw will yu check fr accuracy and cmpleteness f field and labratry datasheets and frms? Hw will yu decide when t accept, reject r qualify data? Hw will yu minimise and crrect errrs in data entry, calculatins and reprts? Hw will data users be infrmed f any crrectins? What cmputer sftware are yu ging t use t stre and analyse yur data? Hw will different versins f databases be managed t ensure everyne has valid data? Hw will yu evaluate the effectiveness and efficiency f field, lab and data management activities, grups and rganisatins (eg analysis labs) in the curse f yur prject? Hw will yu crrect any prblems identified thrugh audits r assessments (eg. it may be decided that equipment needs t be calibrated mre frequently, r refresher training is required mre regularly)? Hw will psitive feedback be prvided t participants? Reprts What type, frequency, cntent will reprts t data users, spnsrs and partner rganisatins take? Wh will reprts be sent t? Data recnciliatin and usefulness Des the data help us t measure prgress twards the prject bjectives? Hw effective is the QA/QC prgram in prducing precise, accurate, cmplete, representative and cmparable data? What imprvements can be made in the QA/QC prgram? 27

8 Figure 4 Calibratin Recrd Sheet Calibratin Recrd (fr EC r ph) ***Recrd reading (measured value) befre adjusting calibratin*** Grup name: Equipment Type: Date Purchased: Crdinatr: Supplier: Equipment N. Prpsed calibratin frequency: The fllwing table can be used t recrd up t a three-pint calibratin. Date Calibratin Standard Expiry date Expected value Measured Value Expected value Calibratin results Measured value Expected value Measured value 28

9 Figure 5 Example Training Checklist The fllwing tpics shuld be cvered in all training sessins fr each level f mnitring. Date:.. Trainer Participants: Sampling and Strage f Samples Cleaning f sampling cntainer; Labelling f sampling cntainers; Crrect sampling prcedures; Strage f samples nt analysed in situ. Testing Prcedures Variety f parameters available fr testing; Reasns fr parameter selectin; Methdlgies fr selected parameters; Safety; Quality cntrl. Equipment Cleaning f equipment; Servicing and maintenance f equipment; Strage f equipment; Limitatins f equipment; Calibratin f equipment. Recrding f Data Recrd sheets; Reprting units; Recrding f equipment calibratin; Catchment database. 29

10 Figure 6 Mnitring Training Lg Date Name f trainer Name f Individual Training Aspect Level 30

11 Figure 7 QA/QC Mystery Samples Site: Date: Name Number f years Water Quality Mnitring experience: QA/QC Cde: Parameter Equipment Type/N. Mystery Sample N Sample 1 Mystery Sample N Sample 2 ph EC (μs/cm) Turbidity (NTU) Reactive Phsphapte - as P (mg/l) Cmments (eg. calibratin ntes, dilutin suspect equipment) Return Sheet t: 31

12 Figure 8 QA/QC Shadw Testing Date: Name f Mnitr 1 Name f Shadw Mnitr Meter 1 (Mnitr) Meter 2 (Shadw tester) Cmments: (serviced, calibrated etc.) Equipment Type and Cde Reading and unit Equipment Type and Cde Reading and Unit Ntes: Return Sheet t: 32

13 QA/QC Bird Checklist Bird Mnitring Using Fixed Area Searches Quality Assurance Reasns fr selecting the mnitring technique are dcumented. Methds fr analysing the mnitring data, including analysis tls and cmputer prgrams are selected and dcumented. Methd fr selecting the mnitring sites and reasns why they are representative f the prject area are dcumented. If apprpriate, a pilt study t identify the number and/r size f samples required has been cnducted. Fr example this may invlve pltting a species accumulatin curve t identify the number r size f samples required t ensure at least 90% f the relevant species present will be detected by the mnitring technique. Yur standardized mnitring prtcl is dcumented and is easy t interpret and readily accessible t mnitring participants. Refer t any existing prtcls and dcument any variatin frm the standard such as hw t deal with the prblems f using the prtcl at the site. Mnitring participants are trained in the standardized methdlgy, cmpletin and management f datasheets and species identificatin. Cmpetency levels that the trainees shuld achieve and hw these are assessed are clearly defined and dcumented. Refresher and ther training sessins are held t ensure mnitring participants are trained in any new methdlgies intrduced t the prject and t maintain standards and cnsistency between participants. Infrmatin regarding the site lcalities is recrded and includes directins and maps t ensure sites are easy fr participants t lcate. An initial species list has been generated in cnsultatin with a recgnized expert. Standardized data cllectin sheets are used fr recrding data in the field. Datasheets are checked by a mnitring crdinatr after each mnitring sessin is cmpleted. Field datasheets are cpied, and cpies are stred in safe, accessible and separate strage systems with ther relevant infrmatin. Questinable r unknwn species identificatins are verified by recgnized experts. Quality Cntrl The database is regularly maintained. Questinable r unreliable data are clearly identified with links t a descriptin f the issues cncerned and invalid data are remved. Cmparisns are nly made between data cllected using cnsistent methdlgies. Results frm tw different methds may nt be cmparable. Seasnal and sampling differences are identified and separated frm ther differences when interpreting the data. Bird identificatin is regularly checked with a recgnised expert. Prtcls are strictly fllwed. Fr example, d nt include data cllected frm utside the defined survey area utside the define survey time. Lcatins f survey sites are recrded including GPS crdinates. Observer errr is identified thrugh cmparing the results frm multiple bservers fr the same sample, surveyed at the same time. (This may include an expert bserver and less experienced prject participants.) Entered data are crss-checked with field datasheets after data entry. indicates that this item invlves a requirement t check decisins r infrmatin with anther prject participant r a recgnized expert 33

14 QA/QC Fish Checklist Fish Mnitring Using Nets Quality Assurance Reasns fr selecting the mnitring technique are dcumented. Methds fr analysing the mnitring data, including analysis tls and cmputer prgrams are selected and dcumented. Methd fr selecting the mnitring sites and reasns why they are representative f the prject area are dcumented. If apprpriate, a pilt study t identify the number and/r size f samples required has been cnducted. Fr example this may invlve pltting a species accumulatin curve t identify the number r size f samples required t ensure at least 90% f the relevant species present will be detected by the mnitring technique. Yur standardized mnitring prtcl is dcumented and is easy t interpret and readily accessible t mnitring participants. Refer t any existing prtcls and dcument any variatin frm the standard such as hw t deal with the prblems f using the prtcl at the site. Quality Cntrl Identificatin f fish specimens is regularly checked with a recgnised expert r the museum. Prtcls are strictly fllwed. Observer errr is identified thrugh cmparing the results frm multiple bservers fr the same sample, surveyed at the same time. (This may include an expert bserver and less experienced prject participants.) This is particularly imprtant fr taking measurements f fish, which is difficult t d accurately. Entered data are crss-checked with field datasheets after data entry. Mnitring participants are trained in the standardized methdlgy, cmpletin and management f datasheets and species identificatin. Cmpetency levels that the trainees shuld achieve and hw these are assessed are clearly defined and dcumented. Refresher and ther training sessins are held t ensure mnitring participants are trained in any new methdlgies intrduced t the prject and t maintain standards and cnsistency between participants. Infrmatin regarding the site lcalities is recrded and includes directins and maps t ensure sites are easy fr participants t lcate. An initial species list has been generated in cnsultatin with a recgnized expert. Standardized data cllectin sheets are used fr recrding data in the field. Fish are released as clse as pssible t the site where they were captured. Datasheets are checked by a mnitring crdinatr after each mnitring sessin is cmpleted. Field datasheets are cpied, and cpies are stred in safe, accessible and separate strage systems with ther relevant infrmatin. Questinable r unknwn species identificatins are verified by recgnized experts r the museum. The database is regularly maintained. Questinable r unreliable data are clearly identified with links t a descriptin f the issues cncerned and invalid data are remved. Cmparisns are nly made between data cllected using cnsistent methdlgies. Results frm tw different methds may nt be cmparable. Seasnal and sampling differences are identified and separated frm ther differences when interpreting the data. indicates that this item invlves a requirement t check decisins r infrmatin with anther prject participant r a recgnized expert 34

15 QA/QC Frg Checklist Frg Mnitring Using Frg Call Recrdings Quality Assurance Reasns fr selecting the mnitring technique are dcumented. Methds fr analysing the mnitring data, including analysis tls and cmputer prgrams are selected and dcumented. Methd fr selecting the mnitring sites and reasns why they are representative f the prject area are dcumented. If apprpriate, a pilt study t identify the number and/r size f samples required has been cnducted. Fr example this may invlve pltting a species accumulatin curve t identify the number r size f samples required t ensure at least 90% f the relevant species present will be detected by the mnitring technique. Yur standardized mnitring prtcl is dcumented and is easy t interpret and readily accessible t mnitring participants. Refer t any existing prtcls and dcument any variatin frm the standard such as hw t deal with the prblems f using the prtcl at the site. Quality Cntrl Identificatin f frg calls regularly checked by a recgnised expert. Prtcls are strictly fllwed. Fr example, d nt include data cllected frm utside the defined survey time r area. GPS lcatin f survey sites are recrded. Observer errr is identified thrugh cmparing the results frm multiple bservers fr the same sample. (This may include an expert bserver and less experienced prject participants.) Entered data are crss-checked with field datasheets after data entry. Mnitring participants are trained in the standardized methdlgy, cmpletin and management f datasheets and species identificatin. Cmpetency levels that the trainees shuld achieve and hw these are assessed are clearly defined and dcumented. Refresher and ther training sessins are held t ensure mnitring participants are trained in any new methdlgies intrduced t the prject and t maintain standards and cnsistency between participants. Infrmatin regarding the site lcalities is recrded and includes directins and maps t ensure sites are easy fr participants t lcate. An initial species list has been generated in cnsultatin with a recgnized expert. Standardized data cllectin sheets are used fr recrding data in the field. Datasheets are checked by a mnitring crdinatr after each mnitring sessin is cmpleted. Field datasheets are cpied, and cpies are stred in safe, accessible and separate strage systems with ther relevant infrmatin. Questinable r unknwn species identificatins are verified by recgnized experts. The database is regularly maintained. Questinable r unreliable data are clearly identified with links t a descriptin f the issues cncerned and invalid data are remved. Cmparisns are nly made between data cllected using cnsistent methdlgies. Results frm tw different methds may nt be cmparable. Seasnal and sampling differences are identified and separated frm ther differences when interpreting the data. indicates that this item invlves a requirement t check decisins r infrmatin with anther prject participant r a recgnized expert 35

16 QA/QC Water Checklist Grundwater Quality Mnitring Quality Assurance Reasns fr selecting the mnitring techniques are dcumented. Methds fr analysing the mnitring data, including analysis tls and cmputer prgrams are selected and dcumented. Methd fr selecting the mnitring sites and reasns why they are representative f the prject area are dcumented. Yur standardized mnitring prtcl is dcumented and is easy t interpret and readily accessible t mnitring participants. Refer t any existing prtcls and dcument any variatin frm the standard such as hw t deal with the prblems f using the prtcl at the site. Mnitring participants are trained in the standardized methdlgy and cmpletin and management f datasheets. Cmpetency levels that the trainees shuld achieve and hw these are assessed are clearly defined and dcumented. Refresher and ther training sessins are held t ensure mnitring participants are trained in any new methdlgies intrduced t the prject and t maintain standards and cnsistency between participants. Infrmatin regarding the site lcalities is recrded and includes directins and maps t ensure sites are easy fr participants t lcate. Lgs are maintained fr field instruments. Lgs shuld include recrds f usage, dates fr scheduled calibratin and diagnstic tests and recrds f repairs and replacements. Standardized data cllectin sheets are used fr recrding data in the field. Chain f custdy is adequately dcumented t identify samples and trace sample cllectin, transprt, analysis and strage. Datasheets are checked by a mnitring crdinatr after each mnitring sessin is cmpleted. Field datasheets are cpied, and cpies are stred in safe, accessible and separate strage systems with ther relevant infrmatin. The database is regularly maintained. Questinable r unreliable data are clearly identified with links t a descriptin f the issues cncerned and invalid data are remved. Cmparisns are nly made between data cllected using cnsistent methdlgies. Results frm tw different methds may nt be cmparable. Seasnal and sampling differences are identified and separated frm ther differences when interpreting the data. Quality Cntrl Standardized equipment is used, maintained and calibrated apprpriately. Observer errr is identified thrugh cmparing the results frm multiple bservers fr the same sample. (This may include an expert bserver and less experienced prject participants.) Bres are purged, and details f purge recrded befre water quality samples are taken. Blanks, duplicates and spikes are used t identify errrs in sampling and sample analysis. A minimum f 5% blind samples are prcessed by the labratry. Reagents are stred and transprted apprpriately and replaced at apprpriate intervals. All prtcls are strictly fllwed. Lcatins f survey sites are permanently marked and GPS lcatins are recrded. Entered data are crss-checked with field data sheets after data entry. indicates that this item invlves a requirement t check decisins r infrmatin with anther prject participant r a recgnized expert 36

17 QA/QC Macr Invertebrate Checklist Macr Invertebrate Mnitring Quality Assurance Reasns fr selecting the mnitring technique are dcumented. Methds fr analysing the mnitring data, including analysis tls and cmputer prgrams are selected and dcumented. Methd fr selecting the mnitring sites and reasns why they are representative f the prject area are dcumented. If apprpriate, a pilt study t identify the number and/r size f samples required has been cnducted. Fr example this may invlve pltting a species accumulatin curve t identify the number r size f samples required t ensure at least 90% f the relevant species present will be detected by the mnitring technique. Yur standardized mnitring prtcl is dcumented and is easy t interpret and readily accessible t mnitring participants. Refer t any existing prtcls and dcument any variatin frm the standard such as hw t deal with the prblems f using the prtcl at the site. Mnitring participants are trained in the standardized methdlgy, cmpletin and management f datasheets and species identificatin. Cmpetency levels that the trainees shuld achieve and hw these are assessed are clearly defined and dcumented. Refresher and ther training sessins are held t ensure mnitring participants are trained in any new methdlgies intrduced t the prject and t maintain standards and cnsistency between participants. Infrmatin regarding the site lcalities is recrded and includes directins and maps t ensure sites are easy fr participants t lcate. An initial species list has been generated in cnsultatin with a recgnized expert. Standard equipment is maintained and used cnsistently. Fr example: hles in nets are repaired befre sampling Standardized data cllectin sheets are used fr recrding data. Datasheets are checked by a mnitring crdinatr after each mnitring sessin is cmpleted. Datasheets are cpied, and cpies are stred in safe, accessible and separate strage systems with ther relevant infrmatin. Questinable r unknwn species identificatins are verified by recgnized experts. Quality Cntrl Identificatin f specimens is regularly checked with a recgnised expert. The database is regularly maintained. Questinable r unreliable data are clearly identified with links t a descriptin f the issues cncerned and invalid data are remved. Cmparisns are nly made between data cllected using cnsistent methdlgies. Results frm tw different methds may nt be cmparable. Seasnal and sampling differences are identified and separated frm ther differences when interpreting the data. Prtcls are strictly fllwed. Fr example, d nt include data cllected frm utside the defined survey area r pint. Lcatin f the survey site is recrded with GPS crdinates. Standard equipment is maintained and used cnsistently. Fr example: hles in nets are repaired befre sampling Observer errr is identified thrugh cmparing the results frm multiple bservers fr the same sample. (This may include an expert bserver and less experienced prject participants.) Entered data are crss-checked with field datasheets after data entry. indicates that this item invlves a requirement t check decisins r infrmatin with anther prject participant r a recgnized expert 37

18 QA/QC Small Mammal & Reptile Checklist Using Pitfall & Ellitt Traps Quality Assurance Reasns fr selecting the mnitring technique are dcumented. Methds fr analysing the mnitring data, including analysis tls and cmputer prgrams are selected and dcumented. Methd fr selecting the mnitring sites and reasns why they are representative f the prject area are dcumented. Yur standardized mnitring prtcl is dcumented and is easy t interpret and readily accessible t mnitring participants. Refer t any existing prtcls and dcument any variatin frm the standard such as hw t deal with the prblems f using the prtcl at the site. Quality Cntrl Identificatin f specimens is regularly checked with a recgnised expert. Prtcls are strictly fllwed. Lcatins f survey sites are permanently marked and GPS lcatins are recrded. Standard equipment and standard measures are used. Fr example: depth f and size f pitfall traps is cnsistent. Mnitring participants are trained in the standardized methdlgy, cmpletin and management f datasheets and species identificatin. Cmpetency levels that the trainees shuld achieve and hw these are assessed are clearly defined and dcumented. Refresher and ther training sessins are held t ensure mnitring participants are trained in any new methdlgies intrduced t the prject and t maintain standards and cnsistency between participants. Infrmatin regarding the site lcalities is recrded and includes directins and maps t ensure sites are easy fr participants t lcate. An initial species list has been generated in cnsultatin with recgnized experts. Standardized data cllectin sheets are used fr recrding data in the field. Datasheets are checked by a mnitring crdinatr after each mnitring sessin is cmpleted. Field datasheets are cpied, and cpies are stred in safe, accessible and separate strage systems with ther relevant infrmatin. Questinable r unknwn species identificatins are verified by recgnized experts r the museum. Observer errr identified thrugh cmparing the results frm multiple bservers fr the same sample. (This may include an expert bserver and less experienced prject participants.) The database is regularly maintained. Questinable r unreliable data are clearly identified with links t a descriptin f the issues cncerned and invalid data are remved. Cmparisns are nly made between data cllected using cnsistent methdlgies. Results frm tw different methds may nt be cmparable. Seasnal and sampling differences (including differences in trapping effrt) are identified and separated frm ther differences when interpreting the data. Entered data are crss-checked with field datasheets after data entry. indicates that this item invlves a requirement t check decisins r infrmatin with anther prject participant r a recgnized expert 38

19 QA/QC Water Checklist Surface Water Quality Mnitring Quality Assurance Reasns fr selecting the mnitring techniques are dcumented. Methds fr analysing the mnitring data, including analysis tls and cmputer prgrams are selected and dcumented. Methd fr selecting the mnitring sites and reasns why they are representative f the prject area are dcumented. Yur standardized mnitring prtcl is dcumented and is easy t interpret and readily accessible t mnitring participants. Refer t any existing prtcls and dcument any variatin frm the standard such as hw t deal with the prblems f using the prtcl at the site. Mnitring participants are trained in the standardized methdlgy and cmpletin and management f datasheets. Cmpetency levels that the trainees shuld achieve and hw these are assessed are clearly defined and dcumented. Refresher and ther training sessins are held t ensure mnitring participants are trained in any new methdlgies intrduced t the prject and t maintain standards and cnsistency between participants. Infrmatin regarding the site lcalities is recrded and includes directins and maps t ensure sites are easy fr participants t lcate. Lgs are maintained fr field instruments. Lgs shuld include recrds f usage, dates fr scheduled calibratin and diagnstic tests and recrds f repairs and replacements. Standardized data cllectin sheets are used fr recrding data in the field. Datasheets are checked by a mnitring crdinatr after each mnitring sessin is cmpleted. Quality Cntrl Standardized equipment is used, maintained and calibrated apprpriately. Observer errr is identified thrugh cmparing the results frm multiple bservers fr the same sample. (This may include an expert bserver and less experienced prject participants.) At least ne field and ne transprt blank are included in every sampling run. At least ne cntainer blank is used fr every batch f cntainers. Mystery samples are tested every 6 mnths. Field replicates are tested every 10 samples. Spikes are used t identify errr in sampling and analysing samples. Reagents are stred and transprted apprpriately and replaced at apprpriate intervals. All prtcls are strictly fllwed. Lcatins f survey sites are permanently marked and GPS lcatins are recrded. Entered data are crss-checked with field data sheets after data entry. Chain f custdy is adequately dcumented t identify samples and trace sample cllectin, transprt, analysis and strage. Field datasheets are cpied, and cpies are stred in safe, accessible and separate strage systems with ther relevant infrmatin. The database is regularly maintained. Questinable r unreliable data are clearly identified with links t a descriptin f the issues cncerned and invalid data are remved. Cmparisns are nly made between data cllected using cnsistent methdlgies. Results frm tw different methds may nt be cmparable. Seasnal and sampling differences are identified and separated frm ther differences when interpreting the data. indicates that this item invlves a requirement t check decisins r infrmatin with anther prject participant r a recgnized expert 39

20 Quality Assurance QA/QC Trtise Checklist Trtise Mnitring Using Nest Cunts Reasns fr selecting the mnitring technique are dcumented. Methds fr analysing the mnitring data, including analysis tls and cmputer prgrams are selected and dcumented. Methd fr selecting the mnitring sites and reasns why they are representative f the prject area are dcumented. Yur standardized mnitring prtcl is dcumented and is easy t interpret and readily accessible t mnitring participants. Refer t any existing prtcls and dcument any variatin frm the standard such as hw t deal with the prblems f using the prtcl at the site. Mnitring participants are trained in the standardized methdlgy, cmpletin and management f datasheets and identificatin f nests and ther signs. Cmpetency levels that the trainees shuld achieve and hw these are assessed are clearly defined and dcumented. Refresher and ther training sessins are held t ensure mnitring participants are trained in any new methdlgies intrduced t the prject and t maintain standards and cnsistency between participants. Infrmatin regarding the site lcalities is recrded and includes directins and maps t ensure sites are easy fr participants t lcate. Standardized data cllectin sheets are used fr recrding data in the field. Datasheets are checked by a mnitring crdinatr after each mnitring sessin is cmpleted. Field datasheets are cpied, and cpies are stred in safe, accessible and separate strage systems with ther relevant infrmatin. Questinable identificatin f nests r ther signs is verified by recgnized experts. Quality Cntrl The database is regularly maintained. Questinable r unreliable data are clearly identified with links t a descriptin f the issues cncerned and invalid data are remved. Cmparisns are nly made between data cllected using cnsistent methdlgies. Results frm tw different methds may nt be cmparable. Seasnal and sampling differences are identified and separated frm ther differences when interpreting the data. Identificatin f nests and ther signs is regularly verified by a recgnized expert. indicates that this item invlves a requirement t check decisins r infrmatin with anther prject participant r a recgnized expert Prtcls are strictly fllwed. Fr example, d nt include data cllected frm utside the defined survey area. Lcatins f survey sites are permanently marked and GPS lcatins are recrded Observer errr is identified thrugh cmparing the results frm multiple bservers fr the same sample, surveyed at the same time. (This may include an expert bserver and less experienced prject participants.) Entered data are crss-checked with field datasheets after data entry. 40

21 QA/QC Vegetatin Checklist Vegetatin Mnitring Using Line Transects Quality Assurance Reasns fr selecting the mnitring technique are dcumented. Methds fr analysing the mnitring data, including analysis tls and cmputer prgrams are selected and dcumented. Methd fr selecting the mnitring sites and reasns why they are representative f the prject area are dcumented. If apprpriate, a pilt study t identify the number and/r size f samples required has been cnducted. Fr example this may invlve pltting a species accumulatin curve t identify the number r size f samples required t ensure at least 90% f the relevant species present will be detected by the mnitring technique. Yur standardized mnitring prtcl is dcumented and is easy t interpret and readily accessible t mnitring participants. Refer t any existing prtcls and dcument any variatin frm the standard such as hw t deal with the prblems f using the prtcl at the site. Mnitring participants are trained in the standardized methdlgy, cmpletin and management f datasheets and species identificatin. Cmpetency levels that the trainees shuld achieve and hw these are assessed are clearly defined and dcumented. Refresher and ther training sessins are held t ensure mnitring participants are trained in any new methdlgies intrduced t the prject and t maintain standards and cnsistency between participants. Infrmatin regarding the site lcalities is recrded and includes directins and maps t ensure sites are easy fr participants t lcate. An initial species list has been generated in cnsultatin with a btanist/recgnized expert. Standardized data cllectin sheets are used fr recrding data in the field. Quality Cntrl Datasheets are checked by a mnitring crdinatr after each mnitring sessin is cmpleted. Field datasheets are cpied, and cpies are stred in safe, accessible and separate strage systems with ther relevant infrmatin. Questinable r unknwn species identificatins are verified by recgnized experts r the herbarium. The database is regularly maintained. Questinable r unreliable data are clearly identified with links t a descriptin f the issues cncerned and invalid data are remved. Cmparisns are nly made between data cllected using cnsistent methdlgies. Results frm tw different methds may nt be cmparable. Seasnal and sampling differences are identified and separated frm ther differences when interpreting the data. Identificatin f plant specimens regularly checked with a recgnised expert r the herbarium. Prtcls are strictly fllwed. Fr example, d nt include data cllected frm utside the defined survey area r pint. Lcatins f transects permanently marked and GPS lcatin and bearing f the transect recrded Observer errr identified thrugh cmparing the results frm multiple bservers fr the same sample/transect, surveyed at the same time. (This may include an expert bserver and less experienced prject participants.) Entered data are crss-checked with field datasheets after data entry. indicates that this item invlves a requirement t check decisins r infrmatin with anther prject participant r a recgnized expert 41