ASTM D7390-2007(2012) 1834 Standard Guide for Evaluating Asbestos in Dust on Surfaces by Comparison Between Two Environments《通过两个环境比较对石棉表面灰尘评估的标准指南》.pdf

《ASTM D7390-2007(2012) 1834 Standard Guide for Evaluating Asbestos in Dust on Surfaces by Comparison Between Two Environments《通过两个环境比较对石棉表面灰尘评估的标准指南》.pdf》由会员分享，可在线阅读，更多相关《ASTM D7390-2007(2012) 1834 Standard Guide for Evaluating Asbestos in Dust on Surfaces by Comparison Between Two Environments《通过两个环境比较对石棉表面灰尘评估的标准指南》.pdf（15页珍藏版）》请在麦多课文档分享上搜索。

1、Designation: D7390 07 (Reapproved 2012)Standard Guide forEvaluating Asbestos in Dust on Surfaces by ComparisonBetween Two Environments1This standard is issued under the fixed designation D7390; the number immediately following the designation indicates the year oforiginal adoption or, in the case of

2、 revision, the year of last revision. A number in parentheses indicates the year of last reapproval. Asuperscript epsilon () indicates an editorial change since the last revision or reapproval.1. Scope1.1 There are multiple purposes for determining the loadingof asbestos in dust on surfaces. Each pa

3、rticular purpose mayrequire unique sampling strategies, analytical methods, andprocedures for data interpretation. Procedures are provided tofacilitate application of available methods for determiningasbestos surface loadings and/or asbestos loadings in surfacedust for comparison between two environ

4、ments. At present,this guide addresses one application of the ASTM surface dustmethods. It is anticipated that additional areas will be added inthe future. It is not intended that the discussion of oneapplication should limit use of the methods in other areas.1.2 This standard does not purport to ad

5、dress all of thesafety concerns, if any, associated with its use. It is theresponsibility of the user of this standard to establish appro-priate safety and health practices and determine the applica-bility of regulatory limitations prior to use. For specificwarning statements, see 5.7.2. Referenced

6、Documents2.1 ASTM Standards:2D5755 Test Method for Microvacuum Sampling and IndirectAnalysis of Dust by Transmission Electron Microscopyfor Asbestos Structure Number Surface LoadingD5756 Test Method for Microvacuum Sampling and IndirectAnalysis of Dust by Transmission Electron Microscopyfor Asbestos

7、 Mass Surface LoadingD6480 Test Method for Wipe Sampling of Surfaces, IndirectPreparation, and Analysis for Asbestos Structure NumberSurface Loading by Transmission Electron MicroscopyD6620 Practice for Asbestos Detection Limit Based onCountsE105 Practice for Probability Sampling of MaterialsE122 Pr

8、actice for Calculating Sample Size to Estimate, WithSpecified Precision, the Average for a Characteristic of aLot or ProcessE456 Terminology Relating to Quality and StatisticsE2356 Practice for Comprehensive Building Asbestos Sur-veys2.2 Other Document:Environmental Protection Agency, U.S. (EPA), (P

9、inkBook) Asbestos in Buildings: Simplified SamplingScheme for Surfacing Materials, EPA 560/5/85/030A,U.S. Environmental Protection Agency, Washington, DC,198533. Terminology3.1 DefinitionsUnless otherwise noted all statistical termsare as defined in Terminology E456.3.1.1 activity generated aerosola

10、 dispersion of particles inair that have become airborne due to physical disturbancessuch as human activity, sweeping, airflow, etc.3.1.2 background samplessamples taken from surfacesthat are considered to have concentrations of asbestos insurface dust that are representative of conditions that exis

11、t in anenvironment that is affected by only prevailing conditions andhas not experienced events, disturbances or activities unusualfor the environment.3.1.3 controlan area that is used as the basis for acomparison. This could be an area where the dust has beenpreviously characterized, an area though

12、t to be suitable foroccupancy, an area that has not experienced a disturbance ofasbestos-containing materials, or that is for some other reasondeemed to be suitable as the basis for a comparison.3.1.4 control samplessamples collected for comparison tothe study samples. These differ from background s

13、amples inthat they are collected: either: in an area where the dust hasbeen previously characterized, or in an area that has notexperienced a disturbance of asbestos-containing materials, or1This guide is under the jurisdiction of ASTM Committee D22 on Air Qualityand is the direct responsibility of

14、Subcommittee D22.07 on Sampling and Analysisof Asbestos.Current edition approved Oct. 1, 2012. Published November 2012. Originallyapproved in 2007. Last previous edition approved in 2007 as D7390 07. DOI:10.1520/D7390-07R12.2For referenced ASTM standards, visit the ASTM website, www.astm.org, orcont

15、act ASTM Customer Service at serviceastm.org. For Annual Book of ASTMStandards volume information, refer to the standards Document Summary page onthe ASTM website.3Available from United States Environmental Protection Agency (EPA), ArielRios Bldg., 1200 Pennsylvania Ave., NW, Washington, DC 20460, h

16、ttp:/www.epa.gov.Copyright ASTM International, 100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959. United States1in an area that is for some other reason deemed to be suitableas the basis for comparison.3.1.5 dustany material composed of particles in a sizerange of 0,(CHIINV(/2,2(A

17、2+1)/2),(CHIINV(,2)/2),where the value in cell A2 is the observed count of structures.(b) To obtain the lower 1- confidence limit: =IF(A20,(CHIINV(1-/2,2A2)/2),0), where the value in cell A2 is theobserved count of structures. Table A1.1 provides an exampleof the formulae in an Excel spreadsheet nec

18、essary to calculatethe lower and upper 95 % confidence limits.(2) The confidence limits associated with the significancelevel is equal to 1-. As such, Table A1.2 gives the forvarious confidence limits.(3) The number of structures at the upper and lowerconfidence limit is multiplied by the sensitivit

19、y of the mea-surement to obtain the upper and lower 1- confidence limitsfor asbestos structure loading based on one sample.A1.2.4 Interpretation of Estimate and Confidence Limits:A1.2.4.1 The value computed in A1.2.1 is an estimate of themean (expected value of the Poisson distribution) of asbestoss

20、tructure loading for the homogeneous area where the samplewas collected. The values calculated in A1.2.2 are confidencelimits for the mean (expected value of the Poisson distribution)of asbestos structure loading for the homogeneous area wherethe sample was collected.A1.3 Asbestos Surface Loading Es

21、timated from MultipleSamples Collected by Test Method D5755:A1.3.1 The measurements for multiple samples, say nsamples, collected from a homogeneous area may be combinedto produce an estimate of asbestos surface loading for thehomogeneous area that is more precise than an estimate ofasbestos surface

22、 loading based on one sample. The individualmeasurements are averaged using a weighted average wherethe sensitivities of the individual samples determine theweights.A1.3.1.1 Given n measurements (Si, Xi, Wi):i=1,2,n, the structure loadings are Yi = SiXi; the mass loadingsare Yi = SiWi . (Here, the m

23、ass, Wi, is the total massmeasured for the ith sample.) The “weights” in the weightedaverage are the reciprocals of the sensitivities (1/Si). Theweighted average has a numerator and a denominator. Thenumerator is the sum of “weight multiplied times measure-ment” for all measurements. The denominator

24、 is the sum of theweights used in the numerator. Therefore, for structure loading,the weighted average is (Xi)/ (1/Si); for mass loading, theweighted average is (Wi)/ (1/Si). Note when sensitivity isa constant, Si = S, the answers are simple averages S(Xi/n) for structure loading; S(Wi/n) for mass l

25、oading.A1.3.2 Data for Multiple Samples:A1.3.2.1 STRi, Si:I=1,2, , n are the structure countsand sensitivities of the n samples.A1.3.3 Estimate:STR/cm25 (STi#/(1/Si!# (A1.3)A1.3.3.1 Note that if the sensitivities for all measurementsare the same value, S, then the estimate is computed as theaverage

26、structure count over the samples multiplied by S:STR/cm25 S(STi#/n! (A1.4)A1.3.4 Confidence Limits:A1.3.4.1 Upper and lower confidence limits are obtainedusing the formulas in A1.2.2 with B2 set equal to the totalnumber of structures counted in the n samples, STRi.A1.4 Compare Two Environments :A1.4

27、.1 Compare Two Environments Using Confidence In-tervals:A1.4.1.1 Compute separate confidence limits based onsamples collected from Homogeneous Area 1 and Homoge-neous Area 2. Apply the following decision rule: If theconfidence intervals based on these limits overlap, concludethat the asbestos struct

28、ure loadings in the two homogeneousareas are the same; if the confidence intervals do not overlap,conclude that the asbestos structure loadings in the twohomogeneous areas are different. Overlap occurs when theupper confidence limit of the interval with the smaller esti-mated mean is larger than the

29、 lower confidence limit of theinterval with the larger estimated mean.A1.4.2 Interpretation of Confidence Interval Test:A1.4.2.1 If 95 % confidence intervals are used to conductthe statistical test described in A1.4.1, the significance level forthe test is approximately 0.05. In general, if 100(1-)%

30、confidence intervals are used for the test described in A1.4.1,the significance level for the test is approximately . Theconfidence interval test is an approximate test that yieldsreliable results where the overlap or separation of the intervalsis large. For example, data where the confidence interv

31、als havea small overlap indicating no statistically significant differencemay show a statistically significant difference if a more precisestatistical test were used. See for example “Testing the equalityof two Poisson means using the rate ratio,” Hon Keung TonyNg and Man-Lai Tang, Statistics in Med

32、icine, 24, 2005, pp.955-965.TABLE A1.1 Spreadsheet Formulae to Calculate Upper and Lower 95 % Confidence LimitsAB C1Number ofStructures Counted95 % LCL (structures) 95 % UCL (structures)2 1 =(IF(A20,(CHIINV(0.975,2A2)/2),0) =(IF(A20,(CHIINV(0.025,2(A2+1)/2),(CHIINV(0.05,2)/2)TABLE A1.2Confidence Lim

33、it a90 % 0.1095 % 0.0599 % 0.01D7390 07 (2012)8A1.4.3 Compare Two Environments Using Normal Distri-bution Approximation for Poisson Count Data:A1.4.3.1 One Sample from Each Environment:(1) The square root of a structure count has an approximateNormal distribution with mean equal to the square root o

34、f thecount mean and variance equal to 0.25. Let STR1and STR2bethe structure counts for two samples with sensitivities S1andS2respectively. The Z-value for testing the equality of theasbestos surface loadings for the two environments where thesamples were collected is:Z 5 ST1!1/22 ST2!1/2#/0.5S11S2!1

35、/2# (A1.5)(2) To test the null hypothesis of “no difference betweenmean asbestos surface loadings in the two environments”compare Z to test value 1.96 for a test with approximatesignificance level equal to 0.05; compare Z to 2.58 for a testwith approximate significance level equal to 0.01. Reject th

36、enull hypothesis if Z is larger than the test value.A1.4.3.2 Multiple Samples from Each Environment:Z 5 ST1/cm2!1/22 ST2/cm2!1/2#/$0.51/(1/S1i!#11/(1/S2i!#!1/2% (A1.6)where STRi/cm25 (STij#/(1/Sij!# i 5 1, 2; j 5 1, 2, , ni(1) The subscripts “1” and “2” indicate measurements forsamples from the two

37、different environments that are com-pared. (Refer to A1.3 for definitions of the notation.) Z is usedto test the null hypothesis of “no difference between meanasbestos surface loadings in the two environments” as de-scribed in A1.3.1.A1.4.3.3 ExampleTest described in A1.4.3.2 applied toExample 2 in

38、main body of the guide. (See Table A1.3.)(1) From Table 2 in 6.10 in the main body of the guide wehave:ST1/cm25 3508; ST2/cm25 2133 (A1.7)Sum of Sensitivity Weights S15 0.014821 and S25 0.024377(2) This makes the denominator in the Z ratio = 0.5(1/0.010205)+(1/0.02439)1/2= 5.2080.(3) Therefore:Z 5 5

39、9.23 2 46.19!/5.2080 5 2.5 (A1.8)(4) Since the statistical hypothesis being tested is atwo-sided hypothesis, mathematical notation for the p-value is21 (Z), where () is the standard normal distribution.Therefore the p-value is calculated with the formula:21 2 Z!# (A1.9)(5) The p-value can be calcula

40、ted using spreadsheetfunctions. For example the following expression in MicrosoftsExcel spreadsheet program will calculate the p-value where Zis known:21 2 NORMSDISTZ,0,1,TRUE! (A1.10)(6) The p-value for the Z in this example is 0.012 and asthis p-value is less than 0.05, as is described in 6.10.2.1

41、 the twoareas are considered to be different. TableA1.4 gives Z and thep-value for various confidence intervals.A1.4.4 Additional details concerning statistical tests forPoisson data are provided in “Testing the Equality of TwoPoisson Means Using the Rate Ratio,” Hon Keung, Tony Ng,and Man-Lai Tang,

42、 Statistics in Medicine, 24, 2005, pp.955-965; and Statistical Rules of Thumb, Wiley, 2002.A1.5 Identification and Control of Sources of Variation:A1.5.1 Differences in collection efficiency which couldaffect comparisons are discussed in Appendix X1.A1.6 Sample LocationsOne method of determining whe

43、reto sample using a random number table is described below.A1.6.1 The investigator wishes to collect samples from 20metal desks. The 20 metal desks are given number 01, 02,19, 20. Beginning in the middle of a random number table,the investigator separates the numbers into 2-digit values. Thefirst si

44、x pairs might be 88, 26. 14, 06, 72, and 96. Since thenumbers 14 and 06 correspond to the numbers assigned to thedesks, two of the desks have been chosen for sampling. Thisprocess continues until 5 different desks (or the number ofsamples as determined below) have been selected.A1.6.2 This same proc

45、ess is repeated to select the locationon the top surface of each desk selected. An imaginary grid of9 equal areas is constructed on each desk top and numbered10-19. Again, from the random number table the investigatorselects 2-digit numbers until one pair of numbers matches oneof the grid numbers. I

46、f the 2-digit pairs are 66, 24, 42, and 12;then the grid corresponding to “12” is where the sample will becollected for that desk.A1.7 Sets of Samples:A1.7.1 One set of samples should be collected to character-ize the asbestos dust loadings for each different type ofhomogeneous surface being tested.

47、 For example, if the sam-pling was being conducted following a cleaning the followingcould apply.A1.7.2 If workers followed the same cleaning procedure fora group of 10 desks, 20 filing cabinets and 12 bookcases allconstructed of metal then may be grouped together as “metalfurniture.” However, if 5

48、of the desks had leather tops, these 5TABLE A1.3Number ofStructuresCounted inStudy SamplesSum ofSensitivitiesfor StudyAreaMeasurementsNumber ofStructuresCounted inBackgroundSamplesSum ofSensitivitiesfor BackgroundArea Measurements52 0.014821 52 0.024377Z = 2.5 p-value = 0.012TABLE A1.4ConfidenceInte

49、rvalZ p-value99 % 2.56 #0.0195 % 1.96 #0.0590 % 1.64 #0.10D7390 07 (2012)9would be sampled as a separate set, or could be combined withother leather surfaces.A1.7.3 If 40 desks were cleaned; 20 of which were wet-wiped, and 20 were HEPAvacuumed, these would be separatedinto two groups of 20 desks for sampling since the cleaningmethods were significantly different.A1.8 Number of SamplesThe number of samples used totest for a difference between the asbestos surface load