SlideShare a Scribd company logo
1 of 28
Unit 9Supplementary hygiene Sampling and compliance information
Basic description of variables used in hygiene calculations and sampling considerations
Flow rate is the rate of which air is being pulled through the sampling device Typically reported as liters/min (l/min) Calculate average between pre and post calibration measures π‘“π‘™π‘œπ‘€π‘Ÿπ‘Žπ‘‘π‘’=(π‘π‘Ÿπ‘’Β π‘“π‘™π‘œπ‘€π‘Ÿπ‘Žπ‘‘π‘’+π‘π‘œπ‘ π‘‘Β π‘“π‘™π‘œπ‘€π‘Ÿπ‘Žπ‘‘π‘’)2 NOTE on calibration: Pre and post measurements must be within 10% or sample is invalid and should be thrown out If >5% but <10%, sample may be considered with caution Β  Flow Rate
Sample duration is the total length of time the sample was collected  Typically this is reported in minutes (min) but can also be reported in seconds, hours, days, or weeks During measurement record the (1) start time and date when sampling begun, (2) the end time and date when sampling ceased Take the difference to calculate duration π‘‘π‘’π‘Ÿπ‘Žπ‘‘π‘–π‘œπ‘›=Β π‘’π‘›π‘‘Β π‘‘π‘–π‘šπ‘’Β βˆ’π‘ π‘‘π‘Žπ‘Ÿπ‘‘Β π‘‘π‘–π‘šπ‘’ Β  Sample duration
The volume collected can be determined by using the sample flow rate and sample duration π‘£π‘œπ‘™π‘’π‘šπ‘’=π‘“π‘™π‘œπ‘€Β π‘Ÿπ‘Žπ‘‘π‘’Β βˆ—π‘‘π‘’π‘Ÿπ‘Žπ‘‘π‘–π‘œπ‘› π‘£π‘œπ‘™π‘’π‘šπ‘’Β π‘™π‘–π‘‘π‘’π‘Ÿπ‘ =π‘™π‘–π‘‘π‘’π‘Ÿπ‘ π‘šπ‘–π‘›π‘’π‘‘π‘’βˆ—π‘šπ‘–π‘›π‘’π‘‘π‘’π‘  π‘£π‘œπ‘™π‘’π‘šπ‘’Β π‘™π‘–π‘‘π‘’π‘Ÿπ‘ =π‘™π‘–π‘‘π‘’π‘Ÿπ‘ π‘šπ‘–π‘›π‘’π‘‘π‘’βˆ—π‘šπ‘–π‘›π‘’π‘‘π‘’π‘  NOTE:  Volume will most likely need to be converted to m3, which can be done either before entering into concentration equation or after Β  Volume Collected If we multiply the flow rate by duration we can see that we cancel out minutes and are left with liters
For most analytical methods we will be provided with a mass value from the analytical laboratory that conducted the analysis of the samples The units will depend on the measurement method Common unit values would include: grams (g) milligrams (mg) micrograms (Β΅g) nanograms (ng) Mass of substance
Concentration of a substance is calculated using the volume collected (previously calculated) and the mass reported by the laboratory πΆπ‘œπ‘›π‘π‘’π‘›π‘‘π‘Ÿπ‘Žπ‘‘π‘–π‘œπ‘›=π‘šπ‘Žπ‘ π‘ π‘£π‘œπ‘™π‘’π‘šπ‘’=π‘šπ‘”π‘™π‘–π‘‘π‘’π‘Ÿ Incorporating flow-rate formula we get an overall formula: πΆπ‘œπ‘›π‘π‘’π‘›π‘‘π‘Ÿπ‘Žπ‘‘π‘–π‘œπ‘›=π‘šπ‘Žπ‘ π‘ π‘“π‘™π‘œπ‘€π‘Ÿπ‘Žπ‘‘π‘’βˆ—π‘‘π‘’π‘Ÿπ‘Žπ‘‘π‘–π‘œπ‘›=π‘šπ‘”π‘™π‘–π‘‘π‘’π‘Ÿπ‘ π‘šπ‘–π‘›π‘’π‘‘π‘’βˆ—π‘šπ‘–π‘›π‘’π‘‘π‘’π‘  Β  Concentration
Sample calculation (step 1: Calculate sample duration/flow rate) π‘¬π’™π’‚π’Žπ’‘π’π’†Β π‘Ίπ’‚π’Žπ’‘π’π’†Β π‘°π’…Β πŸπŸŽπŸŽπŸ π‘‘π‘’π‘Ÿπ‘Žπ‘‘π‘–π‘œπ‘›=Β π‘’π‘›π‘‘Β π‘‘π‘–π‘šπ‘’Β βˆ’π‘ π‘‘π‘Žπ‘Ÿπ‘‘Β π‘‘π‘–π‘šπ‘’  =  (4:20 pm – 8:02 am)   =  (16:20 – 8:02)  =   8 hours + 18 min   =  480 min + 18 min   =  498 minutes Β  Where, 8 hours *  (60 min/hour) =  480 min
Sample calculation (step 1: Calculate sample duration/flow rate) π‘¬π’™π’‚π’Žπ’‘π’π’†Β π‘Ίπ’‚π’Žπ’‘π’π’†Β π‘°π’…Β πŸπŸŽπŸŽπŸ 					=  (1.998  l/min   +   1.967 l/min) 2 = (3.965 l/min) / 2 = 1.982  l/min Β  π‘“π‘™π‘œπ‘€π‘Ÿπ‘Žπ‘‘π‘’=(π‘π‘Ÿπ‘’Β π‘“π‘™π‘œπ‘€π‘Ÿπ‘Žπ‘‘π‘’+π‘π‘œπ‘ π‘‘Β π‘“π‘™π‘œπ‘€π‘Ÿπ‘Žπ‘‘π‘’)2 Β 
π‘¬π’™π’‚π’Žπ’‘π’π’†Β π‘Ίπ’‚π’Žπ’‘π’π’†Β π‘°π’…Β πŸπŸŽπŸŽπŸ Take smaller flow rate and multiply by 10%/5%: 1.967 l/min * 0.1 = 0.197 l/min Check to ensure other flow rate is within 10% 1.967 l/min + 0.197 l/min =  2.164 l/min  (OK) Check flow rate within 5% 1.967 l/min * 0.05 =  0.098 l/min + 1.967 l/min = 2.065 l/min (OK) Β  Sample calculation (step 2:  Check flow rates within 10 & 5 %)
Pre and post flow rates for samples 2001  and 2053  are within 5% of each other  οƒ  Valid Samples Pre and post flow rates for sample 2051 are not within 10% of each other οƒ  invalid sample (Throw out) Sample calculation (step 2:  Check flow rates within 10 & 5 %)
π‘¬π’™π’‚π’Žπ’‘π’π’†Β π‘Ίπ’‚π’Žπ’‘π’π’†Β π‘°π’…Β πŸπŸŽπŸŽπŸ π‘£π‘œπ‘™π‘’π‘šπ‘’=π‘“π‘™π‘œπ‘€Β π‘Ÿπ‘Žπ‘‘π‘’Β βˆ—π‘‘π‘’π‘Ÿπ‘Žπ‘‘π‘–π‘œπ‘›	=  (1.982 l/min  *  498 min) 			 	=  (1.982 l/min  *  498 min) 				=  987 liters Convert to m3 = 987 liters *  (1 m3/1000 l) 	 = 0.987 m3 Β  Sample calculation (step 3: Calculate volume m3)
π‘¬π’™π’‚π’Žπ’‘π’π’†Β π‘Ίπ’‚π’Žπ’‘π’π’†Β π‘°π’…Β πŸπŸŽπŸŽπŸ πΆπ‘œπ‘›π‘π‘’π‘›π‘‘π‘Ÿπ‘Žπ‘‘π‘–π‘œπ‘›=π‘šπ‘Žπ‘ π‘ π‘£π‘œπ‘™π‘’π‘šπ‘’Β Β Β Β =π‘šπ‘”π‘š3 = (2.54 mg)/(0.987 m3) = 2.57 mg/m3 Β  Sample calculation (step 4: Calculate concentration mg/m3)
*Na = Not applicable Sample calculation (Final concentrations)
Field blanks
Field blanks are samples that are sent out during sampling that are opened and closed without pulling air through them What is the purpose of field blanks? To test for contamination of samples during transportation, handling, and storage How many field blanks should you use? It depends but recommended practice is 10% of your number of samples   Do we have to analyze the samples?  YES you must!  Best practice Field blanks
What do you do if mass is reported on field blanks?   Throw the samples out for that sampling period Good option if contamination is limited to small number of samples or if contamination levels were high  Adjust for the contamination Acceptable if contamination levels are not too high If small batch is contaminated we can adjust only those samples from the contaminated batch by the field blank value If contamination is on multiple blanks during a sampling project we can adjust for each batch or we can apply an adjustment to all samples using average field blank value Ignore contamination and include all samples  It is recommended not to use this option οƒ  bad practice How to treat Field blank results
Common Reasons people do not take Field blanks Don’t know they should Many people taking hygiene samples lack training on proper sampling collection procedures and best practices Don’t want to risk having to throw out samples Perceived risk of job Can be regarded as throwing money away in eyes of management Risk of reputationοƒ  viewed as doing β€œbad job”/inadequate performance Feel like all the work was done for nothing οƒ  not completing tasks Budget restraints Often budgets for hygiene sampling is very limited and people do not want to allocate a significant proportion (~10%) to β€œblanks”
What does it mean if we find contamination in our blanks? We may potentially have contamination in our samples Our reported results may be higher than the actual exposure levels By having blanks we are aware of contamination and can adjust accordingly  What does it mean if we had contamination and do not know (i.e. we don’t have field blanks) We can overestimate exposures May lead to: Additional sampling (probably more costly than including 10% blanks) Implementation of potentially unnecessary controls (very costly) Workers’ compensation orders for non-compliance In summary, field blanks: Increases our confidence in our measurements Saves time and money How to β€˜sell’ field blanks
Limit of detection
What is LOD? LOD stands for the Limit Of Detection This is the lowest level (e.g. concentration) measureable by an analytical method or sampling device Why is this important Measurements under the LOD do not give us much information on the hazard but they cannot be ignored/omitted from analysis or the discussion of results Having multiple LOD measurements often results in skewed or lognormal data distributions  They can be difficult to deal with and interpret LOD Definition
Several methods have been proposed, most important thing to remember is you cannot omit them from determining the average concentrations.  Two most commonly used: Method 1 Multiply the LOD by 0.5 (i.e. LOD/2)  for each data point that was <LOD For example if the LOD reported is 2 ppm then you would input (2ppm*0.5 = 1ppm)  Only use when the data are highly skewed (GSD approximately 3.0 or greater) Method 2 Multiply the LOD by 0.707 (i.e. LOD/√2) for each data point that was <LOD For example if the LOD reported is 2 ppm then you would input (2ppm*0.707 = 1.4 ppm)  Use when data not highly skewed Methods to deal with <lod measurements
Determining compliance from exposure data
Now that we have conducted sampling how do we determine if we are compliant with the regulations? Do we compare each reading/sample with limits? Do we calculate the % of samples over the limits? Do we compare the average of the readings/samples with the limits? Although these methods are commonly used compliance is a bit more complex and methods for determining compliance are under debate For this class we are going to review a method frequently used and accepted in North America using confidence limits For this topic please recall readings from last week that covered confidence limits and determination of compliance (pg. 510-512 of text) and also readings from this week (pg. 516-517) Determining compliance
The first step to determine compliance is to calculate the upper and lower confidence limits of the mean Why do we do this? When we take samples we introduce uncertainty/error into our measurement This comes from error in our measurement, instruments, and analysis This means the measurement we take is not the β€œtrue” value of the exposure The true value is the measured exposure +/- error  Calculating confidence limits (or the confidence interval) allows us to account for some of the error/uncertainty in our measurements Determining compliance using confidence limits
Confidence limits are limits placed around the mean (i.e. average) that represents the amount of uncertainty in our samples The confidence limits include an upper and a lower bound estimate: LCL = lower confidence limit, the lower bound limit UCL = upper confidence limit, the upper bound limit This interval (upper confidence limit ↔ lower confidence limit) specifies the range of values in which the true exposure mean may lie at a specified confidence level  (95% most common) More narrow the interval, the more precise our measurements are More wide the interval, the less precise our measurements are Confidence limits
The confidence limit method used to determine compliance compares the mean, upper and lower confidence limits to the exposure limit If the upper confidence limit is below the exposure limit we can say that we are complaint β€œon average” If the lower confidence limit is above the exposure limit we can say that we are not compliant β€œon average” If the lower and upper confidence limit crosses the exposure limit it is unclear if we are compliant or not and require further testing Using confidence limits to determine compliance The next slide graphically displays the concept where: Upper Confidence Limit Mean Lower Confidence Limit
Compliance chart Exposure Limit Concentration     Compliant	     Possibly non-compliant	        Non-Compliant

More Related Content

What's hot

HIRA TRAINING PPT.pptx
HIRA  TRAINING PPT.pptxHIRA  TRAINING PPT.pptx
HIRA TRAINING PPT.pptxMoolRaj3
Β 
Dow Fire and Explosion Index (Dow F&EI) and Mond Index
Dow Fire and Explosion Index (Dow F&EI) and Mond IndexDow Fire and Explosion Index (Dow F&EI) and Mond Index
Dow Fire and Explosion Index (Dow F&EI) and Mond IndexEvonne MunYee
Β 
Nebosh International Diploma unit A questions matrix
Nebosh International Diploma unit A questions matrixNebosh International Diploma unit A questions matrix
Nebosh International Diploma unit A questions matrixCiske Berrington
Β 
Nebosh IGC overview world most benchmarking certification
Nebosh IGC overview world most benchmarking certificationNebosh IGC overview world most benchmarking certification
Nebosh IGC overview world most benchmarking certificationISDTIS KENNIS TRAINING INSTITUTE
Β 
OSHA 30 hour General Industry.PDF
OSHA 30 hour General Industry.PDFOSHA 30 hour General Industry.PDF
OSHA 30 hour General Industry.PDFKenneth Kaftan
Β 
Osha 30 hour General Industry Outreach Training
Osha 30 hour General Industry Outreach TrainingOsha 30 hour General Industry Outreach Training
Osha 30 hour General Industry Outreach TrainingFarhan Jaffry
Β 
OISD STD 114.pptx
OISD STD 114.pptxOISD STD 114.pptx
OISD STD 114.pptxVarun Sharma
Β 
Lecture 4 part ii
Lecture 4 part iiLecture 4 part ii
Lecture 4 part iiYusof Omar
Β 
Hazard Communication Training by LAUSD
Hazard Communication Training by LAUSDHazard Communication Training by LAUSD
Hazard Communication Training by LAUSDAtlantic Training, LLC.
Β 
Nebosh International Diploma Unit C Question Matrix
Nebosh International Diploma Unit C Question MatrixNebosh International Diploma Unit C Question Matrix
Nebosh International Diploma Unit C Question MatrixCiske Berrington
Β 
Workplace and Environmental Dust Monitoring seminar BSRIA and TSI HSE present...
Workplace and Environmental Dust Monitoring seminar BSRIA and TSI HSE present...Workplace and Environmental Dust Monitoring seminar BSRIA and TSI HSE present...
Workplace and Environmental Dust Monitoring seminar BSRIA and TSI HSE present...BSRIA
Β 
Industrial safety
Industrial safetyIndustrial safety
Industrial safetypriyaprabbu
Β 
Noise Monitoring
Noise Monitoring  Noise Monitoring
Noise Monitoring Hardik Kalal
Β 
INDUSTRIAL SAFETY
INDUSTRIAL SAFETYINDUSTRIAL SAFETY
INDUSTRIAL SAFETYprakash shinde
Β 
HEALT & SAFETY IN THE OIL & GAS INDUSTRY
HEALT & SAFETY IN THE OIL & GAS INDUSTRYHEALT & SAFETY IN THE OIL & GAS INDUSTRY
HEALT & SAFETY IN THE OIL & GAS INDUSTRYAlex TX
Β 
Ind hygiene β„– 27
Ind hygiene β„– 27Ind hygiene β„– 27
Ind hygiene β„– 27Jasmine John
Β 

What's hot (20)

HIRA TRAINING PPT.pptx
HIRA  TRAINING PPT.pptxHIRA  TRAINING PPT.pptx
HIRA TRAINING PPT.pptx
Β 
Dow Fire and Explosion Index (Dow F&EI) and Mond Index
Dow Fire and Explosion Index (Dow F&EI) and Mond IndexDow Fire and Explosion Index (Dow F&EI) and Mond Index
Dow Fire and Explosion Index (Dow F&EI) and Mond Index
Β 
Nebosh International Diploma unit A questions matrix
Nebosh International Diploma unit A questions matrixNebosh International Diploma unit A questions matrix
Nebosh International Diploma unit A questions matrix
Β 
Presentation hazop introduction
Presentation hazop introductionPresentation hazop introduction
Presentation hazop introduction
Β 
Nebosh IGC overview world most benchmarking certification
Nebosh IGC overview world most benchmarking certificationNebosh IGC overview world most benchmarking certification
Nebosh IGC overview world most benchmarking certification
Β 
Occupational Health and Safety Training Courses
Occupational Health and Safety Training CoursesOccupational Health and Safety Training Courses
Occupational Health and Safety Training Courses
Β 
Occupational Noise Exposure
Occupational Noise ExposureOccupational Noise Exposure
Occupational Noise Exposure
Β 
OSHA 30 hour General Industry.PDF
OSHA 30 hour General Industry.PDFOSHA 30 hour General Industry.PDF
OSHA 30 hour General Industry.PDF
Β 
Osha 30 hour General Industry Outreach Training
Osha 30 hour General Industry Outreach TrainingOsha 30 hour General Industry Outreach Training
Osha 30 hour General Industry Outreach Training
Β 
OISD STD 114.pptx
OISD STD 114.pptxOISD STD 114.pptx
OISD STD 114.pptx
Β 
Lecture 4 part ii
Lecture 4 part iiLecture 4 part ii
Lecture 4 part ii
Β 
Hazard Communication Training by LAUSD
Hazard Communication Training by LAUSDHazard Communication Training by LAUSD
Hazard Communication Training by LAUSD
Β 
Nebosh International Diploma Unit C Question Matrix
Nebosh International Diploma Unit C Question MatrixNebosh International Diploma Unit C Question Matrix
Nebosh International Diploma Unit C Question Matrix
Β 
Workplace and Environmental Dust Monitoring seminar BSRIA and TSI HSE present...
Workplace and Environmental Dust Monitoring seminar BSRIA and TSI HSE present...Workplace and Environmental Dust Monitoring seminar BSRIA and TSI HSE present...
Workplace and Environmental Dust Monitoring seminar BSRIA and TSI HSE present...
Β 
Industrial safety
Industrial safetyIndustrial safety
Industrial safety
Β 
Noise Monitoring
Noise Monitoring  Noise Monitoring
Noise Monitoring
Β 
INDUSTRIAL SAFETY
INDUSTRIAL SAFETYINDUSTRIAL SAFETY
INDUSTRIAL SAFETY
Β 
HEALT & SAFETY IN THE OIL & GAS INDUSTRY
HEALT & SAFETY IN THE OIL & GAS INDUSTRYHEALT & SAFETY IN THE OIL & GAS INDUSTRY
HEALT & SAFETY IN THE OIL & GAS INDUSTRY
Β 
Occupational Noise
Occupational NoiseOccupational Noise
Occupational Noise
Β 
Ind hygiene β„– 27
Ind hygiene β„– 27Ind hygiene β„– 27
Ind hygiene β„– 27
Β 

Similar to Unit 9 hygiene calculations sampling issues compliance

Method Development and Validation : Laser diffraction particle size analyzer ...
Method Development and Validation : Laser diffraction particle size analyzer ...Method Development and Validation : Laser diffraction particle size analyzer ...
Method Development and Validation : Laser diffraction particle size analyzer ...Md. Saddam Nawaz
Β 
2 lab qaqc-fall2013
2 lab qaqc-fall20132 lab qaqc-fall2013
2 lab qaqc-fall2013TAMUK
Β 
Errors-Analysis-MNN-RN.pptx
Errors-Analysis-MNN-RN.pptxErrors-Analysis-MNN-RN.pptx
Errors-Analysis-MNN-RN.pptxRishabhNath3
Β 
Calculating Uncertainties
Calculating UncertaintiesCalculating Uncertainties
Calculating Uncertaintiesmrjdfield
Β 
Data analysis ( Bio-statistic )
Data analysis ( Bio-statistic )Data analysis ( Bio-statistic )
Data analysis ( Bio-statistic )Amany Elsayed
Β 
Introduction to analysis- Pharmaceutical Analysis
Introduction to analysis- Pharmaceutical AnalysisIntroduction to analysis- Pharmaceutical Analysis
Introduction to analysis- Pharmaceutical AnalysisSanchit Dhankhar
Β 
Practical Work In Biology
Practical Work In BiologyPractical Work In Biology
Practical Work In BiologyGerryC
Β 
study metarial-DOE-13-12 (1).pptx
study metarial-DOE-13-12 (1).pptxstudy metarial-DOE-13-12 (1).pptx
study metarial-DOE-13-12 (1).pptxParthaPratimPal12
Β 
Errors in Chemistry ANALYTICAL CHEMISTRY (Errors in Chemical Analysis).pptx
Errors in Chemistry ANALYTICAL CHEMISTRY (Errors in Chemical Analysis).pptxErrors in Chemistry ANALYTICAL CHEMISTRY (Errors in Chemical Analysis).pptx
Errors in Chemistry ANALYTICAL CHEMISTRY (Errors in Chemical Analysis).pptxsppatel44435
Β 
Uncertainties & Error.ppt
Uncertainties & Error.pptUncertainties & Error.ppt
Uncertainties & Error.pptKhalil Alhatab
Β 
Quality assurance part_2
Quality assurance part_2Quality assurance part_2
Quality assurance part_2ThorikulHuda2
Β 
Chap 9 A Process Capability & Spc Hk
Chap 9 A Process Capability & Spc HkChap 9 A Process Capability & Spc Hk
Chap 9 A Process Capability & Spc Hkajithsrc
Β 
Application of microbiological data
Application of microbiological dataApplication of microbiological data
Application of microbiological dataTim Sandle, Ph.D.
Β 
Basic QC Statistics - Improving Laboratory Performance Through Quality Contro...
Basic QC Statistics - Improving Laboratory Performance Through Quality Contro...Basic QC Statistics - Improving Laboratory Performance Through Quality Contro...
Basic QC Statistics - Improving Laboratory Performance Through Quality Contro...Randox
Β 
Introduction to Analytical Analysis Instrumentation
Introduction to Analytical Analysis InstrumentationIntroduction to Analytical Analysis Instrumentation
Introduction to Analytical Analysis InstrumentationM.T.H Group
Β 
Lecture 1 - System of Measurements, SI Units
Lecture 1 - System of Measurements, SI UnitsLecture 1 - System of Measurements, SI Units
Lecture 1 - System of Measurements, SI UnitsMarjorieJeanAnog
Β 

Similar to Unit 9 hygiene calculations sampling issues compliance (20)

Method Development and Validation : Laser diffraction particle size analyzer ...
Method Development and Validation : Laser diffraction particle size analyzer ...Method Development and Validation : Laser diffraction particle size analyzer ...
Method Development and Validation : Laser diffraction particle size analyzer ...
Β 
2 lab qaqc-fall2013
2 lab qaqc-fall20132 lab qaqc-fall2013
2 lab qaqc-fall2013
Β 
Errors-Analysis-MNN-RN.pptx
Errors-Analysis-MNN-RN.pptxErrors-Analysis-MNN-RN.pptx
Errors-Analysis-MNN-RN.pptx
Β 
Analytical control strategy 3
Analytical control strategy 3Analytical control strategy 3
Analytical control strategy 3
Β 
Calculating Uncertainties
Calculating UncertaintiesCalculating Uncertainties
Calculating Uncertainties
Β 
Tests of significance
Tests of significance  Tests of significance
Tests of significance
Β 
Data analysis ( Bio-statistic )
Data analysis ( Bio-statistic )Data analysis ( Bio-statistic )
Data analysis ( Bio-statistic )
Β 
Introduction to analysis- Pharmaceutical Analysis
Introduction to analysis- Pharmaceutical AnalysisIntroduction to analysis- Pharmaceutical Analysis
Introduction to analysis- Pharmaceutical Analysis
Β 
Practical Work In Biology
Practical Work In BiologyPractical Work In Biology
Practical Work In Biology
Β 
Representative sampling
Representative samplingRepresentative sampling
Representative sampling
Β 
study metarial-DOE-13-12 (1).pptx
study metarial-DOE-13-12 (1).pptxstudy metarial-DOE-13-12 (1).pptx
study metarial-DOE-13-12 (1).pptx
Β 
Errors in Chemistry ANALYTICAL CHEMISTRY (Errors in Chemical Analysis).pptx
Errors in Chemistry ANALYTICAL CHEMISTRY (Errors in Chemical Analysis).pptxErrors in Chemistry ANALYTICAL CHEMISTRY (Errors in Chemical Analysis).pptx
Errors in Chemistry ANALYTICAL CHEMISTRY (Errors in Chemical Analysis).pptx
Β 
Uncertainties & Error.ppt
Uncertainties & Error.pptUncertainties & Error.ppt
Uncertainties & Error.ppt
Β 
Quality assurance part_2
Quality assurance part_2Quality assurance part_2
Quality assurance part_2
Β 
Errors.pptx
Errors.pptxErrors.pptx
Errors.pptx
Β 
Chap 9 A Process Capability & Spc Hk
Chap 9 A Process Capability & Spc HkChap 9 A Process Capability & Spc Hk
Chap 9 A Process Capability & Spc Hk
Β 
Application of microbiological data
Application of microbiological dataApplication of microbiological data
Application of microbiological data
Β 
Basic QC Statistics - Improving Laboratory Performance Through Quality Contro...
Basic QC Statistics - Improving Laboratory Performance Through Quality Contro...Basic QC Statistics - Improving Laboratory Performance Through Quality Contro...
Basic QC Statistics - Improving Laboratory Performance Through Quality Contro...
Β 
Introduction to Analytical Analysis Instrumentation
Introduction to Analytical Analysis InstrumentationIntroduction to Analytical Analysis Instrumentation
Introduction to Analytical Analysis Instrumentation
Β 
Lecture 1 - System of Measurements, SI Units
Lecture 1 - System of Measurements, SI UnitsLecture 1 - System of Measurements, SI Units
Lecture 1 - System of Measurements, SI Units
Β 

More from University of Victoria - Distance Education Services

More from University of Victoria - Distance Education Services (20)

The one pageproposal-revisedfordl
The one pageproposal-revisedfordlThe one pageproposal-revisedfordl
The one pageproposal-revisedfordl
Β 
Unit 9 introduction to ih stat
Unit 9 introduction to ih statUnit 9 introduction to ih stat
Unit 9 introduction to ih stat
Β 
Unit 6 - Physical Hazards
Unit 6 - Physical HazardsUnit 6 - Physical Hazards
Unit 6 - Physical Hazards
Β 
Unit 5-calculation-example
Unit 5-calculation-exampleUnit 5-calculation-example
Unit 5-calculation-example
Β 
Unit 6-physical-hazards-2
Unit 6-physical-hazards-2Unit 6-physical-hazards-2
Unit 6-physical-hazards-2
Β 
Unit 3-calculations
Unit 3-calculationsUnit 3-calculations
Unit 3-calculations
Β 
Unit 2-industrial-toxicology
Unit 2-industrial-toxicologyUnit 2-industrial-toxicology
Unit 2-industrial-toxicology
Β 
HPEO 403 Unit 1 Presentation 2
HPEO 403 Unit 1 Presentation 2HPEO 403 Unit 1 Presentation 2
HPEO 403 Unit 1 Presentation 2
Β 
HPEO 403 Unit 2
HPEO 403 Unit 2HPEO 403 Unit 2
HPEO 403 Unit 2
Β 
HPEO 408 Unit 1 Presentation 2
HPEO 408 Unit 1 Presentation 2HPEO 408 Unit 1 Presentation 2
HPEO 408 Unit 1 Presentation 2
Β 
HPEO 408 Unit 1 Presentation 1
HPEO 408 Unit 1 Presentation 1HPEO 408 Unit 1 Presentation 1
HPEO 408 Unit 1 Presentation 1
Β 
HPEO 403 RMP (Part A)
HPEO 403 RMP (Part A)HPEO 403 RMP (Part A)
HPEO 403 RMP (Part A)
Β 
HPEO 403 Unit 1
HPEO 403 Unit 1HPEO 403 Unit 1
HPEO 403 Unit 1
Β 
HPPR404 Unit 10
HPPR404 Unit 10HPPR404 Unit 10
HPPR404 Unit 10
Β 
UVic MACD Orientation | Welcome to the Program
UVic MACD Orientation | Welcome to the ProgramUVic MACD Orientation | Welcome to the Program
UVic MACD Orientation | Welcome to the Program
Β 
HPPR404 Unit8
HPPR404 Unit8HPPR404 Unit8
HPPR404 Unit8
Β 
HPPR404 Unit7
HPPR404 Unit7HPPR404 Unit7
HPPR404 Unit7
Β 
HPPR404 Unit 5
HPPR404 Unit 5HPPR404 Unit 5
HPPR404 Unit 5
Β 
UVic School of Public Administration - Student Groups
UVic School of Public Administration - Student GroupsUVic School of Public Administration - Student Groups
UVic School of Public Administration - Student Groups
Β 
Hand and Power Tools General Safety Lecture 22
Hand and Power Tools General Safety Lecture 22Hand and Power Tools General Safety Lecture 22
Hand and Power Tools General Safety Lecture 22
Β 

Recently uploaded

Arizona Broadband Policy Past, Present, and Future Presentation 3/25/24
Arizona Broadband Policy Past, Present, and Future Presentation 3/25/24Arizona Broadband Policy Past, Present, and Future Presentation 3/25/24
Arizona Broadband Policy Past, Present, and Future Presentation 3/25/24Mark Goldstein
Β 
Long journey of Ruby standard library at RubyConf AU 2024
Long journey of Ruby standard library at RubyConf AU 2024Long journey of Ruby standard library at RubyConf AU 2024
Long journey of Ruby standard library at RubyConf AU 2024Hiroshi SHIBATA
Β 
New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024BookNet Canada
Β 
UiPath Community: Communication Mining from Zero to Hero
UiPath Community: Communication Mining from Zero to HeroUiPath Community: Communication Mining from Zero to Hero
UiPath Community: Communication Mining from Zero to HeroUiPathCommunity
Β 
The Ultimate Guide to Choosing WordPress Pros and Cons
The Ultimate Guide to Choosing WordPress Pros and ConsThe Ultimate Guide to Choosing WordPress Pros and Cons
The Ultimate Guide to Choosing WordPress Pros and ConsPixlogix Infotech
Β 
Time Series Foundation Models - current state and future directions
Time Series Foundation Models - current state and future directionsTime Series Foundation Models - current state and future directions
Time Series Foundation Models - current state and future directionsNathaniel Shimoni
Β 
TeamStation AI System Report LATAM IT Salaries 2024
TeamStation AI System Report LATAM IT Salaries 2024TeamStation AI System Report LATAM IT Salaries 2024
TeamStation AI System Report LATAM IT Salaries 2024Lonnie McRorey
Β 
TrustArc Webinar - How to Build Consumer Trust Through Data Privacy
TrustArc Webinar - How to Build Consumer Trust Through Data PrivacyTrustArc Webinar - How to Build Consumer Trust Through Data Privacy
TrustArc Webinar - How to Build Consumer Trust Through Data PrivacyTrustArc
Β 
(How to Program) Paul Deitel, Harvey Deitel-Java How to Program, Early Object...
(How to Program) Paul Deitel, Harvey Deitel-Java How to Program, Early Object...(How to Program) Paul Deitel, Harvey Deitel-Java How to Program, Early Object...
(How to Program) Paul Deitel, Harvey Deitel-Java How to Program, Early Object...AliaaTarek5
Β 
Generative AI for Technical Writer or Information Developers
Generative AI for Technical Writer or Information DevelopersGenerative AI for Technical Writer or Information Developers
Generative AI for Technical Writer or Information DevelopersRaghuram Pandurangan
Β 
Digital Identity is Under Attack: FIDO Paris Seminar.pptx
Digital Identity is Under Attack: FIDO Paris Seminar.pptxDigital Identity is Under Attack: FIDO Paris Seminar.pptx
Digital Identity is Under Attack: FIDO Paris Seminar.pptxLoriGlavin3
Β 
Why device, WIFI, and ISP insights are crucial to supporting remote Microsoft...
Why device, WIFI, and ISP insights are crucial to supporting remote Microsoft...Why device, WIFI, and ISP insights are crucial to supporting remote Microsoft...
Why device, WIFI, and ISP insights are crucial to supporting remote Microsoft...panagenda
Β 
So einfach geht modernes Roaming fuer Notes und Nomad.pdf
So einfach geht modernes Roaming fuer Notes und Nomad.pdfSo einfach geht modernes Roaming fuer Notes und Nomad.pdf
So einfach geht modernes Roaming fuer Notes und Nomad.pdfpanagenda
Β 
Generative Artificial Intelligence: How generative AI works.pdf
Generative Artificial Intelligence: How generative AI works.pdfGenerative Artificial Intelligence: How generative AI works.pdf
Generative Artificial Intelligence: How generative AI works.pdfIngrid Airi GonzΓ‘lez
Β 
Use of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptx
Use of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptxUse of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptx
Use of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptxLoriGlavin3
Β 
Decarbonising Buildings: Making a net-zero built environment a reality
Decarbonising Buildings: Making a net-zero built environment a realityDecarbonising Buildings: Making a net-zero built environment a reality
Decarbonising Buildings: Making a net-zero built environment a realityIES VE
Β 
The State of Passkeys with FIDO Alliance.pptx
The State of Passkeys with FIDO Alliance.pptxThe State of Passkeys with FIDO Alliance.pptx
The State of Passkeys with FIDO Alliance.pptxLoriGlavin3
Β 
From Family Reminiscence to Scholarly Archive .
From Family Reminiscence to Scholarly Archive .From Family Reminiscence to Scholarly Archive .
From Family Reminiscence to Scholarly Archive .Alan Dix
Β 
Scale your database traffic with Read & Write split using MySQL Router
Scale your database traffic with Read & Write split using MySQL RouterScale your database traffic with Read & Write split using MySQL Router
Scale your database traffic with Read & Write split using MySQL RouterMydbops
Β 
Assure Ecommerce and Retail Operations Uptime with ThousandEyes
Assure Ecommerce and Retail Operations Uptime with ThousandEyesAssure Ecommerce and Retail Operations Uptime with ThousandEyes
Assure Ecommerce and Retail Operations Uptime with ThousandEyesThousandEyes
Β 

Recently uploaded (20)

Arizona Broadband Policy Past, Present, and Future Presentation 3/25/24
Arizona Broadband Policy Past, Present, and Future Presentation 3/25/24Arizona Broadband Policy Past, Present, and Future Presentation 3/25/24
Arizona Broadband Policy Past, Present, and Future Presentation 3/25/24
Β 
Long journey of Ruby standard library at RubyConf AU 2024
Long journey of Ruby standard library at RubyConf AU 2024Long journey of Ruby standard library at RubyConf AU 2024
Long journey of Ruby standard library at RubyConf AU 2024
Β 
New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
Β 
UiPath Community: Communication Mining from Zero to Hero
UiPath Community: Communication Mining from Zero to HeroUiPath Community: Communication Mining from Zero to Hero
UiPath Community: Communication Mining from Zero to Hero
Β 
The Ultimate Guide to Choosing WordPress Pros and Cons
The Ultimate Guide to Choosing WordPress Pros and ConsThe Ultimate Guide to Choosing WordPress Pros and Cons
The Ultimate Guide to Choosing WordPress Pros and Cons
Β 
Time Series Foundation Models - current state and future directions
Time Series Foundation Models - current state and future directionsTime Series Foundation Models - current state and future directions
Time Series Foundation Models - current state and future directions
Β 
TeamStation AI System Report LATAM IT Salaries 2024
TeamStation AI System Report LATAM IT Salaries 2024TeamStation AI System Report LATAM IT Salaries 2024
TeamStation AI System Report LATAM IT Salaries 2024
Β 
TrustArc Webinar - How to Build Consumer Trust Through Data Privacy
TrustArc Webinar - How to Build Consumer Trust Through Data PrivacyTrustArc Webinar - How to Build Consumer Trust Through Data Privacy
TrustArc Webinar - How to Build Consumer Trust Through Data Privacy
Β 
(How to Program) Paul Deitel, Harvey Deitel-Java How to Program, Early Object...
(How to Program) Paul Deitel, Harvey Deitel-Java How to Program, Early Object...(How to Program) Paul Deitel, Harvey Deitel-Java How to Program, Early Object...
(How to Program) Paul Deitel, Harvey Deitel-Java How to Program, Early Object...
Β 
Generative AI for Technical Writer or Information Developers
Generative AI for Technical Writer or Information DevelopersGenerative AI for Technical Writer or Information Developers
Generative AI for Technical Writer or Information Developers
Β 
Digital Identity is Under Attack: FIDO Paris Seminar.pptx
Digital Identity is Under Attack: FIDO Paris Seminar.pptxDigital Identity is Under Attack: FIDO Paris Seminar.pptx
Digital Identity is Under Attack: FIDO Paris Seminar.pptx
Β 
Why device, WIFI, and ISP insights are crucial to supporting remote Microsoft...
Why device, WIFI, and ISP insights are crucial to supporting remote Microsoft...Why device, WIFI, and ISP insights are crucial to supporting remote Microsoft...
Why device, WIFI, and ISP insights are crucial to supporting remote Microsoft...
Β 
So einfach geht modernes Roaming fuer Notes und Nomad.pdf
So einfach geht modernes Roaming fuer Notes und Nomad.pdfSo einfach geht modernes Roaming fuer Notes und Nomad.pdf
So einfach geht modernes Roaming fuer Notes und Nomad.pdf
Β 
Generative Artificial Intelligence: How generative AI works.pdf
Generative Artificial Intelligence: How generative AI works.pdfGenerative Artificial Intelligence: How generative AI works.pdf
Generative Artificial Intelligence: How generative AI works.pdf
Β 
Use of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptx
Use of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptxUse of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptx
Use of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptx
Β 
Decarbonising Buildings: Making a net-zero built environment a reality
Decarbonising Buildings: Making a net-zero built environment a realityDecarbonising Buildings: Making a net-zero built environment a reality
Decarbonising Buildings: Making a net-zero built environment a reality
Β 
The State of Passkeys with FIDO Alliance.pptx
The State of Passkeys with FIDO Alliance.pptxThe State of Passkeys with FIDO Alliance.pptx
The State of Passkeys with FIDO Alliance.pptx
Β 
From Family Reminiscence to Scholarly Archive .
From Family Reminiscence to Scholarly Archive .From Family Reminiscence to Scholarly Archive .
From Family Reminiscence to Scholarly Archive .
Β 
Scale your database traffic with Read & Write split using MySQL Router
Scale your database traffic with Read & Write split using MySQL RouterScale your database traffic with Read & Write split using MySQL Router
Scale your database traffic with Read & Write split using MySQL Router
Β 
Assure Ecommerce and Retail Operations Uptime with ThousandEyes
Assure Ecommerce and Retail Operations Uptime with ThousandEyesAssure Ecommerce and Retail Operations Uptime with ThousandEyes
Assure Ecommerce and Retail Operations Uptime with ThousandEyes
Β 

Unit 9 hygiene calculations sampling issues compliance

  • 1. Unit 9Supplementary hygiene Sampling and compliance information
  • 2. Basic description of variables used in hygiene calculations and sampling considerations
  • 3. Flow rate is the rate of which air is being pulled through the sampling device Typically reported as liters/min (l/min) Calculate average between pre and post calibration measures π‘“π‘™π‘œπ‘€π‘Ÿπ‘Žπ‘‘π‘’=(π‘π‘Ÿπ‘’Β π‘“π‘™π‘œπ‘€π‘Ÿπ‘Žπ‘‘π‘’+π‘π‘œπ‘ π‘‘Β π‘“π‘™π‘œπ‘€π‘Ÿπ‘Žπ‘‘π‘’)2 NOTE on calibration: Pre and post measurements must be within 10% or sample is invalid and should be thrown out If >5% but <10%, sample may be considered with caution Β  Flow Rate
  • 4. Sample duration is the total length of time the sample was collected Typically this is reported in minutes (min) but can also be reported in seconds, hours, days, or weeks During measurement record the (1) start time and date when sampling begun, (2) the end time and date when sampling ceased Take the difference to calculate duration π‘‘π‘’π‘Ÿπ‘Žπ‘‘π‘–π‘œπ‘›=Β π‘’π‘›π‘‘Β π‘‘π‘–π‘šπ‘’Β βˆ’π‘ π‘‘π‘Žπ‘Ÿπ‘‘Β π‘‘π‘–π‘šπ‘’ Β  Sample duration
  • 5. The volume collected can be determined by using the sample flow rate and sample duration π‘£π‘œπ‘™π‘’π‘šπ‘’=π‘“π‘™π‘œπ‘€Β π‘Ÿπ‘Žπ‘‘π‘’Β βˆ—π‘‘π‘’π‘Ÿπ‘Žπ‘‘π‘–π‘œπ‘› π‘£π‘œπ‘™π‘’π‘šπ‘’Β π‘™π‘–π‘‘π‘’π‘Ÿπ‘ =π‘™π‘–π‘‘π‘’π‘Ÿπ‘ π‘šπ‘–π‘›π‘’π‘‘π‘’βˆ—π‘šπ‘–π‘›π‘’π‘‘π‘’π‘  π‘£π‘œπ‘™π‘’π‘šπ‘’Β π‘™π‘–π‘‘π‘’π‘Ÿπ‘ =π‘™π‘–π‘‘π‘’π‘Ÿπ‘ π‘šπ‘–π‘›π‘’π‘‘π‘’βˆ—π‘šπ‘–π‘›π‘’π‘‘π‘’π‘  NOTE: Volume will most likely need to be converted to m3, which can be done either before entering into concentration equation or after Β  Volume Collected If we multiply the flow rate by duration we can see that we cancel out minutes and are left with liters
  • 6. For most analytical methods we will be provided with a mass value from the analytical laboratory that conducted the analysis of the samples The units will depend on the measurement method Common unit values would include: grams (g) milligrams (mg) micrograms (Β΅g) nanograms (ng) Mass of substance
  • 7. Concentration of a substance is calculated using the volume collected (previously calculated) and the mass reported by the laboratory πΆπ‘œπ‘›π‘π‘’π‘›π‘‘π‘Ÿπ‘Žπ‘‘π‘–π‘œπ‘›=π‘šπ‘Žπ‘ π‘ π‘£π‘œπ‘™π‘’π‘šπ‘’=π‘šπ‘”π‘™π‘–π‘‘π‘’π‘Ÿ Incorporating flow-rate formula we get an overall formula: πΆπ‘œπ‘›π‘π‘’π‘›π‘‘π‘Ÿπ‘Žπ‘‘π‘–π‘œπ‘›=π‘šπ‘Žπ‘ π‘ π‘“π‘™π‘œπ‘€π‘Ÿπ‘Žπ‘‘π‘’βˆ—π‘‘π‘’π‘Ÿπ‘Žπ‘‘π‘–π‘œπ‘›=π‘šπ‘”π‘™π‘–π‘‘π‘’π‘Ÿπ‘ π‘šπ‘–π‘›π‘’π‘‘π‘’βˆ—π‘šπ‘–π‘›π‘’π‘‘π‘’π‘  Β  Concentration
  • 8. Sample calculation (step 1: Calculate sample duration/flow rate) π‘¬π’™π’‚π’Žπ’‘π’π’†Β π‘Ίπ’‚π’Žπ’‘π’π’†Β π‘°π’…Β πŸπŸŽπŸŽπŸ π‘‘π‘’π‘Ÿπ‘Žπ‘‘π‘–π‘œπ‘›=Β π‘’π‘›π‘‘Β π‘‘π‘–π‘šπ‘’Β βˆ’π‘ π‘‘π‘Žπ‘Ÿπ‘‘Β π‘‘π‘–π‘šπ‘’ = (4:20 pm – 8:02 am) = (16:20 – 8:02) = 8 hours + 18 min = 480 min + 18 min = 498 minutes Β  Where, 8 hours * (60 min/hour) = 480 min
  • 9. Sample calculation (step 1: Calculate sample duration/flow rate) π‘¬π’™π’‚π’Žπ’‘π’π’†Β π‘Ίπ’‚π’Žπ’‘π’π’†Β π‘°π’…Β πŸπŸŽπŸŽπŸ = (1.998 l/min + 1.967 l/min) 2 = (3.965 l/min) / 2 = 1.982 l/min Β  π‘“π‘™π‘œπ‘€π‘Ÿπ‘Žπ‘‘π‘’=(π‘π‘Ÿπ‘’Β π‘“π‘™π‘œπ‘€π‘Ÿπ‘Žπ‘‘π‘’+π‘π‘œπ‘ π‘‘Β π‘“π‘™π‘œπ‘€π‘Ÿπ‘Žπ‘‘π‘’)2 Β 
  • 10. π‘¬π’™π’‚π’Žπ’‘π’π’†Β π‘Ίπ’‚π’Žπ’‘π’π’†Β π‘°π’…Β πŸπŸŽπŸŽπŸ Take smaller flow rate and multiply by 10%/5%: 1.967 l/min * 0.1 = 0.197 l/min Check to ensure other flow rate is within 10% 1.967 l/min + 0.197 l/min = 2.164 l/min (OK) Check flow rate within 5% 1.967 l/min * 0.05 = 0.098 l/min + 1.967 l/min = 2.065 l/min (OK) Β  Sample calculation (step 2: Check flow rates within 10 & 5 %)
  • 11. Pre and post flow rates for samples 2001 and 2053 are within 5% of each other οƒ  Valid Samples Pre and post flow rates for sample 2051 are not within 10% of each other οƒ  invalid sample (Throw out) Sample calculation (step 2: Check flow rates within 10 & 5 %)
  • 14. *Na = Not applicable Sample calculation (Final concentrations)
  • 16. Field blanks are samples that are sent out during sampling that are opened and closed without pulling air through them What is the purpose of field blanks? To test for contamination of samples during transportation, handling, and storage How many field blanks should you use? It depends but recommended practice is 10% of your number of samples Do we have to analyze the samples? YES you must! Best practice Field blanks
  • 17. What do you do if mass is reported on field blanks? Throw the samples out for that sampling period Good option if contamination is limited to small number of samples or if contamination levels were high Adjust for the contamination Acceptable if contamination levels are not too high If small batch is contaminated we can adjust only those samples from the contaminated batch by the field blank value If contamination is on multiple blanks during a sampling project we can adjust for each batch or we can apply an adjustment to all samples using average field blank value Ignore contamination and include all samples It is recommended not to use this option οƒ  bad practice How to treat Field blank results
  • 18. Common Reasons people do not take Field blanks Don’t know they should Many people taking hygiene samples lack training on proper sampling collection procedures and best practices Don’t want to risk having to throw out samples Perceived risk of job Can be regarded as throwing money away in eyes of management Risk of reputationοƒ  viewed as doing β€œbad job”/inadequate performance Feel like all the work was done for nothing οƒ  not completing tasks Budget restraints Often budgets for hygiene sampling is very limited and people do not want to allocate a significant proportion (~10%) to β€œblanks”
  • 19. What does it mean if we find contamination in our blanks? We may potentially have contamination in our samples Our reported results may be higher than the actual exposure levels By having blanks we are aware of contamination and can adjust accordingly What does it mean if we had contamination and do not know (i.e. we don’t have field blanks) We can overestimate exposures May lead to: Additional sampling (probably more costly than including 10% blanks) Implementation of potentially unnecessary controls (very costly) Workers’ compensation orders for non-compliance In summary, field blanks: Increases our confidence in our measurements Saves time and money How to β€˜sell’ field blanks
  • 21. What is LOD? LOD stands for the Limit Of Detection This is the lowest level (e.g. concentration) measureable by an analytical method or sampling device Why is this important Measurements under the LOD do not give us much information on the hazard but they cannot be ignored/omitted from analysis or the discussion of results Having multiple LOD measurements often results in skewed or lognormal data distributions They can be difficult to deal with and interpret LOD Definition
  • 22. Several methods have been proposed, most important thing to remember is you cannot omit them from determining the average concentrations. Two most commonly used: Method 1 Multiply the LOD by 0.5 (i.e. LOD/2) for each data point that was <LOD For example if the LOD reported is 2 ppm then you would input (2ppm*0.5 = 1ppm) Only use when the data are highly skewed (GSD approximately 3.0 or greater) Method 2 Multiply the LOD by 0.707 (i.e. LOD/√2) for each data point that was <LOD For example if the LOD reported is 2 ppm then you would input (2ppm*0.707 = 1.4 ppm) Use when data not highly skewed Methods to deal with <lod measurements
  • 24. Now that we have conducted sampling how do we determine if we are compliant with the regulations? Do we compare each reading/sample with limits? Do we calculate the % of samples over the limits? Do we compare the average of the readings/samples with the limits? Although these methods are commonly used compliance is a bit more complex and methods for determining compliance are under debate For this class we are going to review a method frequently used and accepted in North America using confidence limits For this topic please recall readings from last week that covered confidence limits and determination of compliance (pg. 510-512 of text) and also readings from this week (pg. 516-517) Determining compliance
  • 25. The first step to determine compliance is to calculate the upper and lower confidence limits of the mean Why do we do this? When we take samples we introduce uncertainty/error into our measurement This comes from error in our measurement, instruments, and analysis This means the measurement we take is not the β€œtrue” value of the exposure The true value is the measured exposure +/- error Calculating confidence limits (or the confidence interval) allows us to account for some of the error/uncertainty in our measurements Determining compliance using confidence limits
  • 26. Confidence limits are limits placed around the mean (i.e. average) that represents the amount of uncertainty in our samples The confidence limits include an upper and a lower bound estimate: LCL = lower confidence limit, the lower bound limit UCL = upper confidence limit, the upper bound limit This interval (upper confidence limit ↔ lower confidence limit) specifies the range of values in which the true exposure mean may lie at a specified confidence level (95% most common) More narrow the interval, the more precise our measurements are More wide the interval, the less precise our measurements are Confidence limits
  • 27. The confidence limit method used to determine compliance compares the mean, upper and lower confidence limits to the exposure limit If the upper confidence limit is below the exposure limit we can say that we are complaint β€œon average” If the lower confidence limit is above the exposure limit we can say that we are not compliant β€œon average” If the lower and upper confidence limit crosses the exposure limit it is unclear if we are compliant or not and require further testing Using confidence limits to determine compliance The next slide graphically displays the concept where: Upper Confidence Limit Mean Lower Confidence Limit
  • 28. Compliance chart Exposure Limit Concentration Compliant Possibly non-compliant Non-Compliant