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

Health & safety training
Health & safety trainingHealth & safety training
Health & safety trainingHarvey Allen
Β 
Qhse objective &amp; target 2015
Qhse objective &amp; target 2015Qhse objective &amp; target 2015
Qhse objective &amp; target 2015Fitri Ifony
Β 
Occupational health surveillence
Occupational health surveillenceOccupational health surveillence
Occupational health surveillenceDalia El-Shafei
Β 
Occupational cancer what you need to know
Occupational cancer   what you need to knowOccupational cancer   what you need to know
Occupational cancer what you need to knowMike Slater
Β 
A Part 1 Safety Induction
A  Part 1 Safety InductionA  Part 1 Safety Induction
A Part 1 Safety InductionJames McCann
Β 
Management system of health and safety.
Management system of health and safety.Management system of health and safety.
Management system of health and safety.Prince Mello
Β 
HSE-INDUCTION-TRAINING.ppt
HSE-INDUCTION-TRAINING.pptHSE-INDUCTION-TRAINING.ppt
HSE-INDUCTION-TRAINING.pptssuser0ebdc3
Β 
Occupational Safety And Health
Occupational Safety And HealthOccupational Safety And Health
Occupational Safety And HealthBhawna Gupta
Β 
Health And Safety Induction Training
Health And Safety Induction TrainingHealth And Safety Induction Training
Health And Safety Induction Trainingedale07
Β 
training near miss program
training near miss programtraining near miss program
training near miss programoscar anell
Β 
HSE Annual Performance
HSE Annual PerformanceHSE Annual Performance
HSE Annual PerformanceArun Kumar
Β 
EHs management concept & realities
EHs management concept & realitiesEHs management concept & realities
EHs management concept & realitiesArvind Kumar
Β 
How to prevent accidents in a workplace
How to prevent accidents in a workplaceHow to prevent accidents in a workplace
How to prevent accidents in a workplacessuser438d6f
Β 
Risk Assessment Training | JCH Safety
Risk Assessment Training | JCH SafetyRisk Assessment Training | JCH Safety
Risk Assessment Training | JCH Safetyjchsafety
Β 
Workplace hazards
Workplace hazardsWorkplace hazards
Workplace hazardsNavneet Maan
Β 

What's hot (20)

Health & safety training
Health & safety trainingHealth & safety training
Health & safety training
Β 
Qhse objective &amp; target 2015
Qhse objective &amp; target 2015Qhse objective &amp; target 2015
Qhse objective &amp; target 2015
Β 
Occupational health surveillence
Occupational health surveillenceOccupational health surveillence
Occupational health surveillence
Β 
Occupational cancer what you need to know
Occupational cancer   what you need to knowOccupational cancer   what you need to know
Occupational cancer what you need to know
Β 
A Part 1 Safety Induction
A  Part 1 Safety InductionA  Part 1 Safety Induction
A Part 1 Safety Induction
Β 
Management system of health and safety.
Management system of health and safety.Management system of health and safety.
Management system of health and safety.
Β 
Induction QHSE
Induction QHSEInduction QHSE
Induction QHSE
Β 
HSE-INDUCTION-TRAINING.ppt
HSE-INDUCTION-TRAINING.pptHSE-INDUCTION-TRAINING.ppt
HSE-INDUCTION-TRAINING.ppt
Β 
Occupational Safety And Health
Occupational Safety And HealthOccupational Safety And Health
Occupational Safety And Health
Β 
Health And Safety Induction Training
Health And Safety Induction TrainingHealth And Safety Induction Training
Health And Safety Induction Training
Β 
Workplace risk management
Workplace risk managementWorkplace risk management
Workplace risk management
Β 
training near miss program
training near miss programtraining near miss program
training near miss program
Β 
HSE Annual Performance
HSE Annual PerformanceHSE Annual Performance
HSE Annual Performance
Β 
Staff safety hand book
Staff   safety hand bookStaff   safety hand book
Staff safety hand book
Β 
EHs management concept & realities
EHs management concept & realitiesEHs management concept & realities
EHs management concept & realities
Β 
General HSE Training (Level 1 & 2)
General HSE Training (Level 1 & 2)General HSE Training (Level 1 & 2)
General HSE Training (Level 1 & 2)
Β 
How to prevent accidents in a workplace
How to prevent accidents in a workplaceHow to prevent accidents in a workplace
How to prevent accidents in a workplace
Β 
Risk Assessment Training | JCH Safety
Risk Assessment Training | JCH SafetyRisk Assessment Training | JCH Safety
Risk Assessment Training | JCH Safety
Β 
Workplace hazards
Workplace hazardsWorkplace hazards
Workplace hazards
Β 
Hse inspection presentation
Hse inspection presentationHse inspection presentation
Hse inspection presentation
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

Install Stable Diffusion in windows machine
Install Stable Diffusion in windows machineInstall Stable Diffusion in windows machine
Install Stable Diffusion in windows machinePadma Pradeep
Β 
Artificial intelligence in cctv survelliance.pptx
Artificial intelligence in cctv survelliance.pptxArtificial intelligence in cctv survelliance.pptx
Artificial intelligence in cctv survelliance.pptxhariprasad279825
Β 
SAP Build Work Zone - Overview L2-L3.pptx
SAP Build Work Zone - Overview L2-L3.pptxSAP Build Work Zone - Overview L2-L3.pptx
SAP Build Work Zone - Overview L2-L3.pptxNavinnSomaal
Β 
Leverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage Cost
Leverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage CostLeverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage Cost
Leverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage CostZilliz
Β 
Dev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio WebDev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio WebUiPathCommunity
Β 
CloudStudio User manual (basic edition):
CloudStudio User manual (basic edition):CloudStudio User manual (basic edition):
CloudStudio User manual (basic edition):comworks
Β 
"Federated learning: out of reach no matter how close",Oleksandr Lapshyn
"Federated learning: out of reach no matter how close",Oleksandr Lapshyn"Federated learning: out of reach no matter how close",Oleksandr Lapshyn
"Federated learning: out of reach no matter how close",Oleksandr LapshynFwdays
Β 
Training state-of-the-art general text embedding
Training state-of-the-art general text embeddingTraining state-of-the-art general text embedding
Training state-of-the-art general text embeddingZilliz
Β 
DevoxxFR 2024 Reproducible Builds with Apache Maven
DevoxxFR 2024 Reproducible Builds with Apache MavenDevoxxFR 2024 Reproducible Builds with Apache Maven
DevoxxFR 2024 Reproducible Builds with Apache MavenHervΓ© Boutemy
Β 
Developer Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQLDeveloper Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQLScyllaDB
Β 
Gen AI in Business - Global Trends Report 2024.pdf
Gen AI in Business - Global Trends Report 2024.pdfGen AI in Business - Global Trends Report 2024.pdf
Gen AI in Business - Global Trends Report 2024.pdfAddepto
Β 
DevEX - reference for building teams, processes, and platforms
DevEX - reference for building teams, processes, and platformsDevEX - reference for building teams, processes, and platforms
DevEX - reference for building teams, processes, and platformsSergiu Bodiu
Β 
Unleash Your Potential - Namagunga Girls Coding Club
Unleash Your Potential - Namagunga Girls Coding ClubUnleash Your Potential - Namagunga Girls Coding Club
Unleash Your Potential - Namagunga Girls Coding ClubKalema Edgar
Β 
Designing IA for AI - Information Architecture Conference 2024
Designing IA for AI - Information Architecture Conference 2024Designing IA for AI - Information Architecture Conference 2024
Designing IA for AI - Information Architecture Conference 2024Enterprise Knowledge
Β 
Search Engine Optimization SEO PDF for 2024.pdf
Search Engine Optimization SEO PDF for 2024.pdfSearch Engine Optimization SEO PDF for 2024.pdf
Search Engine Optimization SEO PDF for 2024.pdfRankYa
Β 
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek SchlawackFwdays
Β 
Streamlining Python Development: A Guide to a Modern Project Setup
Streamlining Python Development: A Guide to a Modern Project SetupStreamlining Python Development: A Guide to a Modern Project Setup
Streamlining Python Development: A Guide to a Modern Project SetupFlorian Wilhelm
Β 
Human Factors of XR: Using Human Factors to Design XR Systems
Human Factors of XR: Using Human Factors to Design XR SystemsHuman Factors of XR: Using Human Factors to Design XR Systems
Human Factors of XR: Using Human Factors to Design XR SystemsMark Billinghurst
Β 
Scanning the Internet for External Cloud Exposures via SSL Certs
Scanning the Internet for External Cloud Exposures via SSL CertsScanning the Internet for External Cloud Exposures via SSL Certs
Scanning the Internet for External Cloud Exposures via SSL CertsRizwan Syed
Β 

Recently uploaded (20)

Install Stable Diffusion in windows machine
Install Stable Diffusion in windows machineInstall Stable Diffusion in windows machine
Install Stable Diffusion in windows machine
Β 
Artificial intelligence in cctv survelliance.pptx
Artificial intelligence in cctv survelliance.pptxArtificial intelligence in cctv survelliance.pptx
Artificial intelligence in cctv survelliance.pptx
Β 
SAP Build Work Zone - Overview L2-L3.pptx
SAP Build Work Zone - Overview L2-L3.pptxSAP Build Work Zone - Overview L2-L3.pptx
SAP Build Work Zone - Overview L2-L3.pptx
Β 
Leverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage Cost
Leverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage CostLeverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage Cost
Leverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage Cost
Β 
Dev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio WebDev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio Web
Β 
E-Vehicle_Hacking_by_Parul Sharma_null_owasp.pptx
E-Vehicle_Hacking_by_Parul Sharma_null_owasp.pptxE-Vehicle_Hacking_by_Parul Sharma_null_owasp.pptx
E-Vehicle_Hacking_by_Parul Sharma_null_owasp.pptx
Β 
CloudStudio User manual (basic edition):
CloudStudio User manual (basic edition):CloudStudio User manual (basic edition):
CloudStudio User manual (basic edition):
Β 
"Federated learning: out of reach no matter how close",Oleksandr Lapshyn
"Federated learning: out of reach no matter how close",Oleksandr Lapshyn"Federated learning: out of reach no matter how close",Oleksandr Lapshyn
"Federated learning: out of reach no matter how close",Oleksandr Lapshyn
Β 
Training state-of-the-art general text embedding
Training state-of-the-art general text embeddingTraining state-of-the-art general text embedding
Training state-of-the-art general text embedding
Β 
DevoxxFR 2024 Reproducible Builds with Apache Maven
DevoxxFR 2024 Reproducible Builds with Apache MavenDevoxxFR 2024 Reproducible Builds with Apache Maven
DevoxxFR 2024 Reproducible Builds with Apache Maven
Β 
Developer Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQLDeveloper Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQL
Β 
Gen AI in Business - Global Trends Report 2024.pdf
Gen AI in Business - Global Trends Report 2024.pdfGen AI in Business - Global Trends Report 2024.pdf
Gen AI in Business - Global Trends Report 2024.pdf
Β 
DevEX - reference for building teams, processes, and platforms
DevEX - reference for building teams, processes, and platformsDevEX - reference for building teams, processes, and platforms
DevEX - reference for building teams, processes, and platforms
Β 
Unleash Your Potential - Namagunga Girls Coding Club
Unleash Your Potential - Namagunga Girls Coding ClubUnleash Your Potential - Namagunga Girls Coding Club
Unleash Your Potential - Namagunga Girls Coding Club
Β 
Designing IA for AI - Information Architecture Conference 2024
Designing IA for AI - Information Architecture Conference 2024Designing IA for AI - Information Architecture Conference 2024
Designing IA for AI - Information Architecture Conference 2024
Β 
Search Engine Optimization SEO PDF for 2024.pdf
Search Engine Optimization SEO PDF for 2024.pdfSearch Engine Optimization SEO PDF for 2024.pdf
Search Engine Optimization SEO PDF for 2024.pdf
Β 
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
Β 
Streamlining Python Development: A Guide to a Modern Project Setup
Streamlining Python Development: A Guide to a Modern Project SetupStreamlining Python Development: A Guide to a Modern Project Setup
Streamlining Python Development: A Guide to a Modern Project Setup
Β 
Human Factors of XR: Using Human Factors to Design XR Systems
Human Factors of XR: Using Human Factors to Design XR SystemsHuman Factors of XR: Using Human Factors to Design XR Systems
Human Factors of XR: Using Human Factors to Design XR Systems
Β 
Scanning the Internet for External Cloud Exposures via SSL Certs
Scanning the Internet for External Cloud Exposures via SSL CertsScanning the Internet for External Cloud Exposures via SSL Certs
Scanning the Internet for External Cloud Exposures via SSL Certs
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