STATISTICS ON THREE REGULATED ASBESTIFORM AMPHIBOLES: LENGTH, WIDTH AND ASPECT RATIO B. LI, PhD N.C. BATTA, MS, RPIH S. C. SU, PhD N. K. BATTA, PE BATTA.

Slides:



Advertisements
Similar presentations
A core course on Modeling kees van Overveld Week-by-week summary.
Advertisements

DATA & STATISTICS 101 Presented by Stu Nagourney NJDEP, OQA.
It is very difficult to measure the small change in volume of the mercury. If the mercury had the shape of a sphere, the change in diameter would be very.
Role of Physicochemical Aspects of Fibrous Particles in
A Survey of Fibrosity of Fibrous Amphiboles Larry S. Pierce Fiberquant Analytical Services Phoenix, AZ
Unit 5 Counting matter. Challenge Question (first correct answer gets a sticker) The plastic vial on the front desk contains a certain number of beads.
Fundamentals of Data Analysis Lecture 12 Methods of parametric estimation.
Office of Research and Development National Exposure Research Laboratory Photo image area measures 2” H x 6.93” W and can be masked by a collage strip.
NSSGA Mineral Identification and Management Guide Employee Training Module Revision date: June 2011.
EVALUATING BIOASSAY UNCERTANITY USING GUM WORKBENCH ® HPS TECHNICAL SEMINAR April 15, 2011 Brian K. Culligan Fellow Scientist Bioassay Laboratory Savannah.
The Central Limit Theorem
Confidence Interval Estimation
 Crystal size distribution (CSD) is measured with a series of standard screens.  The size of a crystal is taken to be the average of the screen openings.
Hypothesis Testing After 2 hours of frustration trying to fill out an IRS form, you are skeptical about the IRS claim that the form takes 15 minutes on.
Maximizing Classifier Utility when Training Data is Costly Gary M. Weiss Ye Tian Fordham University.
Evaluating Hypotheses
CHAPTER 6 Statistical Analysis of Experimental Data
A Statistical Approach to Method Validation and Out of Specification Data.
Lehrstuhl für Informatik 2 Gabriella Kókai: Maschine Learning 1 Evaluating Hypotheses.
Statistics Lecture 22. Last Day…completed 5.1 Today Parts of Section 5.3 and 5.4.
Quantitative Genetics
Uncertainty in Measurement
Course No: CE 4000 INVESTIGATION ON THE PERFORMANCE OF BAMBOO REINFORCED CONCRETE BEAMS Supervised By: MUHAMMAD HARUNUR RASHID Presented By: MOHAMMAD TAREQ.
Chapter 9 Two-Sample Tests Part II: Introduction to Hypothesis Testing Renee R. Ha, Ph.D. James C. Ha, Ph.D Integrative Statistics for the Social & Behavioral.
Copyright ©2011 Nelson Education Limited Large-Sample Estimation CHAPTER 8.
The value of microscopy analysis Garry Burdett Health and Safety Laboratory Harpur Hill, Buxton, UK, SK17 9JN.
© 2002 Prentice-Hall, Inc.Chap 6-1 Statistics for Managers using Microsoft Excel 3 rd Edition Chapter 6 Confidence Interval Estimation.
© 2003 Prentice-Hall, Inc.Chap 6-1 Business Statistics: A First Course (3 rd Edition) Chapter 6 Sampling Distributions and Confidence Interval Estimation.
Exploiting Clustering Techniques for Web Session Inference A.Bianco, G. Mardente, M. Mellia, M.Munafò, L. Muscariello (Politecnico di Torino)
Metrology Adapted from Introduction to Metrology from the Madison Area Technical College, Biotechnology Project (Lisa Seidman)
AGGREGATES.
Biostatistics Class 1 1/25/2000 Introduction Descriptive Statistics.
Luis Avila Isabelle Vu Trieu Intensive General Chemistry Uncertainty Analysis Wet Techniques.
Chap 1 Matter and Change Honors Chemistry. 1.0:Chemistry Chemistry – the study of the composition of substances and the changes they undergo Five major.
Current Asbestos Related Issues Aparna Koppikar, M.D., Ph. D May 21, 2003 National Center for Environmental Assessment.
CHEMISTRY ANALYTICAL CHEMISTRY Fall Lecture 6.
General concept about machines and equipment.
CHEMISTRY ANALYTICAL CHEMISTRY Fall

Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Section 7-4 Estimating a Population Mean:  Not Known.
1 Sampling Distribution of Arithmetic Mean Dr. T. T. Kachwala.
 2006 National Council on Compensation Insurance, Inc. Slide 1 of 17 A Claim Counts Model for Discerning the Rate of Inflation from Raw Claims Data Spring.
Korea University User Interface Lab Copyright 2008 by User Interface Lab Human Action Laws in Electronic Virtual Worlds – An Empirical Study of Path Steering.
© 2002 Prentice-Hall, Inc.Chap 8-1 Basic Business Statistics (8 th Edition) Chapter 8 Confidence Interval Estimation.
Lecture 4 Confidence Intervals. Lecture Summary Last lecture, we talked about summary statistics and how “good” they were in estimating the parameters.
Section 1–2: Measurements in Experiments Physics Pages 10–20.
CHAPTER- 3.1 ERROR ANALYSIS.  Now we shall further consider  how to estimate uncertainties in our measurements,  the sources of the uncertainties,
Measuring and Calculating Chapter 2. n Scientific method- a logical approach to solving problems n -Observation often involves making measurements and.
Evaluating Hypotheses. Outline Empirically evaluating the accuracy of hypotheses is fundamental to machine learning – How well does this estimate its.
Objective  To develop methods for analysis of compounds in organic aerosol particles Why is this important?  Environmental impact  Alternative fuels.
Copyright © 2009 Pearson Education, Inc t LEARNING GOAL Understand when it is appropriate to use the Student t distribution rather than the normal.
DATA MINING: CLUSTER ANALYSIS (3) Instructor: Dr. Chun Yu School of Statistics Jiangxi University of Finance and Economics Fall 2015.
A Method to Approximate the Bayesian Posterior Distribution in Singular Learning Machines Kenji Nagata, Sumio Watanabe Tokyo Institute of Technology.
ICHEP2002, Amsterdam Zhengguo Zhao, Weiguo Li1 Test of QCD in 2-5 GeV with BESII Weiguo Li, Zhengguo Zhao (Representing BES Collaboration) IHEP of CAS,
Fundamentals of Data Analysis Lecture 11 Methods of parametric estimation.
Chapter 2: Measurements and Calculations
Inference about Comparing Two Populations
Technique for the Measurement of Mechanical Strength and Fracture Characteristics of Micron Diamond Engis R&D.
PSIE Pasca Sarjana Unsri
Central Limit Theorem, z-tests, & t-tests
Measurement of Chrysotile Fiber Retention Efficiencies on MCE Filters to Support Exposure Assessments Daniel A. Vallero, U.S. EPA/NERL, RTP, NC John R.
NSSGA Mineral Identification and Management Guide
Chapter 1: The Nature of Analytical Chemistry
Measurements and Their Uncertainty 3.1
Measurements and Their Uncertainty
Evaluating Hypotheses
SP 225 Lecture 11 Confidence Intervals.
Chapter 13: Inferences about Comparing Two Populations Lecture 7a
Chapter Outline Inferences About the Difference Between Two Population Means: s 1 and s 2 Known.
Introduction to Analytical Chemistry
Presentation transcript:

STATISTICS ON THREE REGULATED ASBESTIFORM AMPHIBOLES: LENGTH, WIDTH AND ASPECT RATIO B. LI, PhD N.C. BATTA, MS, RPIH S. C. SU, PhD N. K. BATTA, PE BATTA LABORATORIES, INC.

2 SIGNIFICANT PHYSICAL PROPERTY OF ALL AMPHIBOLES: CLEAVAGE

3 AMPHIBOLE CRYSTAL FRAGMENTS BREAK PREFERABLY ALONG CLEAVAGE PLANES BY: Crushing Milling Grinding

4 HOW BIG A ROLE CAN CLEAVAGE PLAY IN DEFINING FIBER DIMENSIONS IN TERMS OF: LENGTH WIDTH ASPECT RATIO

5 AND MOST IMPORTANTLY: SAMPLE PREPARATION FIBER COUNT WEIGHT ESTIMATE

6 EXPERIMENTAL DESIGN MATERIAL NIST STANDARD – ANTHOPHYLLITE AND A MIXTURE OF TREMOLITE AND ACTINOLITE USING THE MIXTURE RATHER THAN EACH OF TREMOLITE AND ACTINOLITE WAS DETERMINED BASED ON THEIR CLOSE SIMILARITY IN PHYSICAL PROPERTY AND STATISTICAL BEHAVIOR OF FIBER DIMENSIONS AT THE BEGINNING OF THIS STUDY

METHOD METHOD: INSTANT PLM PREP AND MANUAL GRINDING WITH TIME INTERVALS OF: 0 (instant prep), 5, 15, 30, 60 AND 120 MINUTES TIME INTERVALS (i.e. 5, 15, … minutes) WERE INTENDED TO APPROXIMATE DIFFERENT SETTINGS OF MACHINE GRINDERS OR PULVERIZERS

MEASUREMENT MEASUREMENT & RECORDING: PLM (0- 15) AND TEM (15-120) FOR LENGTH, WIDTH AND FIBER END SHAPES ALL FRAGMENTS (INCLUDING BUNDLES) WERE NON-SELECTIVELY, BUT SEPARATELY MEASURED REGARDLESS WHETHER THEY ARE IN THE FORMS OF CLUSTERS OR MATRICES (NOT SO COMMON IN THIS PREP DUE TO PURITY).

9 RESULTS

10 AVERAGE LENGTH CHANGE THROUGH TIME PLM TEM

11 AVERAGE WIDTH CHANGE THROUGH TIME PLM TEM

12 ASPECT RATIO DISTRIBUTION OVER TIME - ANTHOPHYLLITE

13

14

15

16

17

18 CHANGE IN AVERAGE ASPECT RATIO THROUGH TIME PLM TEM

19 ASPECT RATIO DISTRIBUTION OVER TIME FOR TREMOLITE AND ACTINOLITE IS SIMILAR TO ANTHOPHYLLITE

20 HOW DOES CHANGE IN FIBER LENGTH AND ASPECT RATIO AFFECT WEIGHT AND COUNTS?

21 FIRST: HOW DO ASPECT RATIOS VARY WITH AMPHIBOLE SPECIES?

22 ANTHOPHYLLITE PLM TEM

23 TREMO-ACTINOLITE PLM TEM

24 SECOND: HOW DO THESE CHANGES AFFECT ASBESTOS COUNTS AND WEIGHT BY ANALYTICAL METHODS?

25 IMPACT ON COUNTS FOR ANTHOPHYLLITE

26 IMPACT ON COUNTS FOR TREMO- ACTINOLITE

27 IMPACT ON WEIGHT/MASS FOR ANTHOPHYLLITE

28 IMPACT ON WEIGHT/MASS FOR TREMO-ACTINOLITE

29 WHO MADE SUCH A “MASS”?

30 CLEAVAGE?

31 CLEAVAGE BEHAVIOR EXHIBITED BY FIBER ENDS SHAPES PLM TEM

32 What Have We Learned So Far?

33 CONCLUSION CLEAVAGE IS AN INTRINSIC PROPERTY OF ALL AMPHIBOLES DIRECTLY OR INDIRECTLY IMPACTING ON LENGTH, CLEAVAGE DICTATES ASPECT RATIO BEHAVIOR DURING SAMPLE PREP METHODS OF PREP CAN HAVE SIGNIFICANT IMPACT ON FIBER COUNTS AND WEIGHT METHOD ACCURACY IS A FUNCTION OF METHODS OF PREPARATION

34 SUGGESTED CORRECTIVE ACTIONS ACKNOWLEDGE THE EXISTENCE OF CLEAVAGE FIBERS. IN ANOTHER WORD, CLEAVAGE FIBERS ARE ASBESTOS FIBERS AS LONG AS THEY MEET THE FIBER DEFINITION VARIOUS PREPARATION METHODS HAVE TO BE EVALUATED QUANTITATIVELY USING AN APPROACH SIMILAR TO THIS STUDY FOR THEIR IMPACT ON COUNTS AND WEIGHT ESTIMATE ANALYTICAL METHODS HAVE TO BE CHOSEN BASED ON PREP METHODS FOR OPTIMAL ACCURACY CLIENTS AND THE PUBLIC HAVE A RIGHT TO KNOW WHAT PREP METHODS WERE CHOSEN AND HOW THEY AFFECT THEIR RESULTS ALTHOUGH IT WAS KNOWN THAT GRINDING OR PULVERIZING WOULD HAVE IMPACT ON FIBER COUNTS OR WEIGHT ESTIMATE, UNCERTAINTY OR A CONVERSION FACTOR HAS TO BE QUANTITATIVELY DETERMINED AND REPORTED WITH THE RESULTS ACCORDING TO THE PREP METHOD ADOPTED

35 THANK YOU ALL! WHAT WE DO AT BATTA LABORATORIES ASBESTOS ANALYSIS (PLM, PCM & TEM) CHEMISTRY (ALL REGULATED METALS) RESEARCH AND DEVELOPMENT INTERNSHIP PROGRAM PUBLIC OUTREACH PROGRAM