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Fs in samples of digested sewage sludge
Chemosphere 45 (2001) 1139±1150
www.elsevier.com/locate/chemosphere
Interlaboratory comparison exercise for the analysis of PCDD/Fs in samples of digested sewage sludge Joanna L. Stevens a
a,*
, Nicholas J.L. Green a, Russell J. Bowater b, Kevin C. Jones a
Department of Environmental Science, Institute of Environmental and Natural Sciences, Lancaster University, Lancaster LA1 4YQ, UK b Centre for Applied Statistics, Lancaster University, Lancaster LA1 4YF, UK Received 30 January 2001; received in revised form 2 July 2001; accepted 4 July 2001
Abstract Five UK laboratories participated in a study designed to explore the principal sources of interlaboratory variation in the analysis of PCDD/Fs in sewage sludge. Samples of wet sludge, dry sludge, toluene extract of sludge and cleaned extract of sludge were prepared by an organising laboratory. The samples were analysed in duplicate by each laboratory along with a solution of PCDD/F standards and reference sediment. Mean coecients of variation between laboratories were 45% for the wet sludge, 33% for the dry sludge, 32% for the extract of sludge, 36% for the cleaned extract of sludge, 32% for the reference sediment and 28% for the standard solution. The results were subjected to statistical analysis, which showed that there was no speci®c part of the analysis that introduced a dominant part of the variation. The spread of data generated from the analysis of wet sludge samples was not appreciably greater than the spread for the analysis of cleaned extracts. Thus the drying, extraction and clean up processes in the PCDD/F analysis of wet sludge did not have a dramatic eect on the interlaboratory variation. Ó 2001 Elsevier Science Ltd. All rights reserved. Keywords: Polychlorinated dibenzo-p-dioxins; Polychlorinated dibenzofurans; Round robin
1. Introduction With the potential for European Union limit values for PCDD/Fs in sludge to be introduced in the future there is a need to expand the dataset of PCDD/F concentrations in UK sewage sludges. There have been few studies on PCDD/Fs in UK sludge to date (DoE, 1994; Sewart et al., 1995; Stevens et al., 2001) and it is therefore anticipated that a large amount of monitoring work will be undertaken in the next few years. Sewage sludge is a challenging matrix to analyse. Its composition is complex and highly variable and there are a great number of possible interfering compounds that
*
Corresponding author. Tel.: +44-1524-593-974; fax: +441524-593-985. E-mail address: [email protected] (J.L. Stevens).
must be removed before PCDD/F analysis can take place. It is therefore important to be certain of an acceptable degree of intra and interlaboratory consistency in the results generated from such monitoring, to ensure that any proposed standards can be properly complied with. Interlaboratory studies on PCDD/F analysis in the literature show a large amount of variation between laboratories. There have been a number of such exercises published in recent years on many dierent matrices. These include air, incinerator ash, standard solutions, paper industry waste, water, sediment, milk and blood (Bradley et al., 1990; Tashiro et al., 1990a,b; Stephens et al., 1992; Bruckmann et al., 1993; Lao et al., 1993). Such studies have rarely examined the possible major sources of variation with a view to improving interlaboratory consistency. The aim of this study was to evaluate the comparability of data generated by UK
0045-6535/01/$ - see front matter Ó 2001 Elsevier Science Ltd. All rights reserved. PII: S 0 0 4 5 - 6 5 3 5 ( 0 1 ) 0 0 1 6 4 - 3
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J.L. Stevens et al. / Chemosphere 45 (2001) 1139±1150
laboratories for the analysis of PCDD/Fs in sewage sludge, and to try to identify the largest sources of variation to the data set. 2. Materials and methods Each laboratory analysed four separate samples derived from a single batch of sewage sludge, a sample of reference sediment and a standard solution. The four sludge media were prepared at the Lancaster University laboratory from 15 l of digested sewage sludge, collected from the digester holding tank at a small, mainly domestic UK wastewater treatment plant with a population equivalent of 10,000. Sub-samples of the same initial sludge were used throughout to remove potential uncertainties due to matrix eects, and to facilitate statistical analysis. However the results of the dierent sample types (i±iv) were not assumed to be directly comparable with one another as losses of analytes, or contamination, during preparation of such a large amount of material could not be ruled out. 2.1. Preparation of samples for the round robin (i) Wet sludge: The crude sludge was thoroughly mixed and a 400 g wet weight portion was sent to each laboratory. 10 g portions of sludge from each sample were removed and analysed for lead concentration by atomic absorption spectrometry (AAS), which was used as a simple check for homogeneity. The mean lead concentration n 7 was found to be 5.1 lg=g wet sludge, with a standard deviation of 0.3 (RSD 6%).
(ii) Dried sludge: The remainder of the sludge was centrifuged for 20 min at 12,000 rpm, the supernatant liquid discarded and the remaining solids were air dried for 7 days on the bench top. The dried sludge was ground to <500 mesh and mixed thoroughly, then divided into aliquots. The homogeneity of the sample was checked by taking 0.2 g sub-samples of these aliquots and analysing the copper content by AAS. The mean copper concentration n 7 was found to be 660 lg=g dried sludge, with a standard deviation of 43 (RSD 7%). (iii) Uncleaned extract: 120 g of dried sewage sludge was divided into 10 portions and each was Soxhlet extracted with toluene for 16 h. A laboratory blank and a sample of a reference sediment were extracted at the same time, and these were taken through the remainder of the analysis procedure to check the extraction eciency. The extracts were concentrated then combined, and made up to 75 ml toluene gravimetrically. The solution was homogenised by sonication. 5 ml aliquots of the extract were measured into crimptop vials and submitted along with their weight to each laboratory. (iv) Cleaned extract: 25 ml of extract was spiked with 13 C12 -labelled 1,2,7,8-TCDF and 13 C6 -labelled 1,2,3,4,6,7,8-HpCDF to allow the distributing laboratory to assess the eciency of the following clean-up. The extract was concentrated to 1 ml nonane, made up to 50 ml with hexane, and then divided into 10, 5 ml portions. Each portion was subjected to the clean-up detailed in Fig. 1. The cleaned extracts were combined, concentrated to 0.5 ml nonane and 50 ll aliquots were sent to each laboratory.
Fig. 1. Clean-up method for cleaned extract (cf. Behnisch, 1997).
J.L. Stevens et al. / Chemosphere 45 (2001) 1139±1150
1141
Table 1 Summary of sample preparation and clean-up methods used by the participating laboratories Laboratory number
Drying method for wet sludge prior to extraction
Extraction method
1 2
3
Air dried to constant weight at ambient temperature
Soxhlet extraction for 8 h minimum with toluene
4
Air dried to constant weight at 30°C, ground and sieved
ASE with toluene
5
Centrifugation at 12,000 rpm and supernatant discarded, followed by grinding with 50 g sodium sulphate
Soxhlet extraction for 16 h with 300 ml toluene
(v) Reference sediment: No reference material is available for the assessment of PCDD/Fs in sewage sludge. The closest matrix that is available is a lake sediment. 4 g DX-2 (Promochem UK) was analysed by each laboratory. (vi) Standard solution: A solution containing the 17 unlabelled 2,3,7,8-substituted PCDD/Fs at concentrations between 2 and 20 pg=ll was prepared by dilution of a commercially available standard (CAMPRO Scienti®c, Veenendaal, NL). The vial weight for samples (iii), (iv), and (vi) were checked on arrival at each participating laboratory. 2.2. Summary of procedures used by each laboratory Each laboratory used their preferred methods for sample preparation, extraction and clean-up where appropriate. These are summarised in Table 1. Samples (i)± (iv) were analysed and reported in duplicate to assess within-laboratory variability and to decrease the statistical uncertainty in any conclusions that may be drawn. 3. Results 3.1. General comments Five UK laboratories participated in the exercise. Each laboratory reported the concentrations of the 17
Clean-up summary
GC column/s used
Basic alumina column Mixed silica column 3 Conc. Sulfuric acid wash Mixed silica column
Db5-ms
Florisil column Mixed column (sodium sulfate, celite/sulfuric acid, sodium sulfate/sodium bicarbonate, silica) Alumina column Re¯ux with acid silica GPC column Alumina fractionation column 3 Conc. Sulfuric acid wash Mixed silica column GPC column Activated copper Basic alumina column Carbon column
Db5-ms Sp 2331 for PeCDD, 2,3,4,7,8-PeCDF and 1,2,3,7,8,9-HxCDF Db5-ms
Db5-ms
Db5-ms for homologues, HpCDFs, HpCDD, OCDF, OCDD Sp2331 for other congeners
2,3,7,8-substituted PCDD/Fs and the total homologue concentrations of the tetra- to octa-PCDD/Fs. The water content of the wet sludge sample as % w/w was also reported. Tables 2 and 3 shows the mean, median, minimum and maximum value for each homologue/ congener in the samples of sewage sludge, certi®ed reference sediment and standard solution, respectively. The variation between data is presented as the relative standard deviation (RSD) or coecient of variation. The sludge sample values were all corrected to pg/g dry weight, to allow comparison of the data obtained from the dierent samples. In Tables 2 and 3, congeners/ homologues that were below the limits of detection have been treated as unreported data and the detection limits are not included in the mean or median values. 3.2. Interlaboratory variation versus Intralaboratory variation Since each laboratory analysed two samples, it is possible to make some comparison between the variation of results within laboratories and the variation of results between laboratories. For each of the compounds, a separate analysis of variance was performed on the results obtained for the wet sludge. For a large majority of the compounds (20 out of the 24 studied) it
60 3 105
170 341
193
2±11 1±8 35±205
1±4 29±145
5±11 2±5 2±10 2 30±126
1±4 4±14 1±8 37±100
45±86 1±4 47±169
1300±2910 1930
1,2,3,7,8-PeCDD PeCDD's
1,2,3,4,7,8-HxCDF 1,2,3,6,7,8-HxCDF 2,3,4,6,7,8-HxCDF 1,2,3,7,8,9-HxCDF HxCDF's
1,2,3,4,7,8-HxCDD 1,2,3,6,7,8-HxCDD 1,2,3,7,8,9-HxCDD HxCDD's
1,2,3,4,6,7,8-HpCDF 1,2,3,4,7,8,9-HpCDF HpCDF's
1,2,3,4,6,7,8-HpCDD 90±266 HpCDD's 190±519
115±306
1,2,3,7,8-PeCDF 2,3,4,7,8-PeCDF PeCDF's
OCDF
OCDD
2 9 4 68
8 4 6 2 69
3 49
4 5 69
1 33
1 12±135
2,3,7,8-TCDD TCDD's
14 80
10±31 55±130
2,3,7,8-TCDF TCDF's
1820
204
171 344
58 3 106
2 9 5 72
7 4 6 2 65
3 40
3 6 58
1 18
11 71
29
35
33 31
23 40 34
49 33 44 32
25 29 49 * 40
48 70
79 51 72
28 124
46 29
11 000± 22 300
350±770
460±1500 960±3010
130±190 7±11 300±575
3±6 8±37 3±19 170±414
7±16 3±9 3±15 2±3 74±170
5±7 38±200
4±9 2±10 51±160
1±4 24±130
7±11 52±168
15 200
526
920 2000
155 9 370
5 24 11 303
10 6 8 3 137
6 109
6 6 77
2 48
10 95
Mean
Dried sludge Range
Median RSD
Range
Mean
Wet sludge
14 000
542
869 2030
165 9 339
5 26 12 300
11 7 9 3 150
6 98
5 7 66
2 33
10 76
23
27
37 32
15 16 23
18 37 45 25
23 42 49 13 21
14 48
34 50 42
46 77
13 44
Median RSD
14 400± 18 100
456±1000
519±1060 1250±2360
144±223 3±24 250±456
2±8 10±38 3±19 138±404
8±23 3±13 4±19 3±4 108±331
3±10 55±206
3±9 2±14 47±150
1±3 31±94
9±146 47±184
Range
15 800
592
866 1970
177 10 392
5 25 11 318
13 8 10 4 184
6 117
6 7 84
2 53
11 105
Mean
Sludge extract
15 700
548
883 2070
168 9 408
5 26 10 338
10 8 8 4 175
6 109
7 7 69
2 41
10 80
7
28
19 18
15 58 15
34 33 39 26
36 36 51 20 31
38 36
33 48 37
23 44
22 50
Median RSD
Table 2 Range, median, mean values (pg/g dry sludge) and relative standard deviation n 10 of sludge samples analysed by the participants
4440±8000
153± 300
238±500 563±1140
85±150 2±9 150±313
1±5 5±24 1±9 88±250
4±11 2±7 1±14 2 63±150
1±4 12±63
2±4 1±7 20±74
0.4±1 3±12
3±8 6±41
6250
221
416 943
112 4 205
2 13 5 162
5 4 5 2 89
2 40
2 3 34
1 7
4 21
Mean
Clean extract Range
6430
219
475 1060
111 3 193
2 12 5 156
5 3 4 2 83
2 39
2 3 28
1 7
4 18
17
24
24 22
17 49 24
52 43 40 29
40 43 73 * 31
32 35
40 59 52
27 42
41 50
Median RSD
1142 J.L. Stevens et al. / Chemosphere 45 (2001) 1139±1150
29±91 531±1200
210±369 360±521
9±145 28±120 160±1010
7.80±37 168±391
330±854 52±172 12±100 4.0±75 710±2650
16±36 40±100 26±69 630±1170
1600±3460 51±190 2000±6040
350±786 700±1550
5900±7760
2900±4710
2,3,7,8-TCDF TCDF's
2,3,7,8-TCDD TCDD's
1,2,3,7,8-PeCDF 2,3,4,7,8-PeCDF PeCDF's
1,2,3,7,8-PeCDD PeCDD's
1,2,3,4,7,8-HxCDF 1,2,3,6,7,8-HxCDF 2,3,4,6,7,8-HxCDF 1,2,3,7,8,9-HxCDF HxCDF's
1,2,3,4,7,8-HxCDD 1,2,3,6,7,8-HxCDD 1,2,3,7,8,9-HxCDD HxCDD's
1,2,3,4,6,7,8-HpCDF 1,2,3,4,7,8,9-HpCDF HpCDF's
1,2,3,4,6,7,8-HpCDD HpCDD's
OCDF
OCDD
4206
7038
675 1327
2866 161 3890
29 87 52 846
649 122 65 40 1810
27 280
60 89 751
273 467
71 783
4289
7167
700 1415
3025 180 3810
29 95 56 857
688 130 72 41 1940
30 270
42 104 816
254 478
76 809
14
9
20 20
20 28 30
21 24 27 19
29 36 48 92 35
37 28
83 40 36
25 11
29 28
18±31
17±32
6±16
9±15 7±17
8±16 5±17 6±16
8±15 6±15 6±19 5±17
8±12
8±16 9±16
1±4
2±4
22
22
11
11 11
11 11 11
10 10 10 10
9
10 11
2
2
Mean
Standard solution (pg/ll) RSD
Range
Median
Range
Mean
Reference sediment (pg/g dw)
Table 3 Range, median, mean and relative standard deviation of reference sediment and standard solution analysed by the participants
20
20
10
10 10
10 10 10
10 9 9 10
9
9 10
2
2
Median
20
23
27
22 31
24 31 29
22 28 41 40
15
25 23
35
34
RSD
J.L. Stevens et al. / Chemosphere 45 (2001) 1139±1150 1143
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J.L. Stevens et al. / Chemosphere 45 (2001) 1139±1150
was found that the mean square error between laboratories was greater than the mean square error within laboratories. Using an F-test these dierences were generally extremely signi®cant (the F-value for 12 out of the 20 compounds concerned was signi®cant at the 1% level) even though there were only two replicates from only ®ve laboratories. Moreover, it is likely that the between laboratory variation is substantially greater than the within laboratory variation. Therefore, the following statistical analysis will concentrate on the between laboratory variation. 3.3. Standard solution For a standard solution, we can regard the concentrations quoted by the supplier as the `true' concentrations and so for this test substance, deviations around the true values can in some sense be calculated. Results from the analysis of the standard solution provide a measure of the variation caused by dierences in instrument operating conditions and the individual laboratories' standards and quantitation method. Fig. 2 shows the mean values from each laboratory compared with the values of the congeners in the standard, based on those quoted by the supplier. RSDs ranged between 15% and 41% of the `quoted' values. These results are comparable with those of two studies by Tashiro et al. (1990a,b). They reported RSDs ranging 18±61% (mean 27, n 7) and ranging 8±43% (mean 23, n 6) for round robins on standard solutions that were 5±50 times more concentrated than the one used in this study. Bradley et al. (1990) reported a higher degree of interlaboratory consistency with standards
containing congeners at concentrations 1000 times higher than those used here. 3.4. Reference sediment A reference sediment was analysed to enable the data generated by the ®ve laboratories to be compared directly with an internationally accepted data set. The 95% con®dence intervals provided by the supplier of the reference sediment correspond to a range of RSDs equivalent to 10±64% with a mean of 21% and a TEQ RSD of 15%. The data from this study are presented in Tables 2 and 3 and, with the exception of two problem congeners, show a range of RSDs of 9±48%. The mean RSD including these two congeners was 32%. Fig. 3(a) and (b) shows the mean values for each laboratory, along with the reference value and 95% con®dence limit values for the sediment. 3.5. Sludge samples Table 4 shows the range of RSDs for each of the sludge samples and compares the results of this study to other studies in the literature. The interlaboratory variation in this study is in a similar range to that of others. The study on sewage sludge by Lindig (1998) and the reference sediment from the Van Bavel (1999) study oer the most direct comparison. The mean and range of RSD values for the sludge samples are similar, and do not appear to implicate any one step of the analysis as being the predominant source of the overall uncertainty. However, mean RSDs are a crude summary of the data and a detailed statistical analysis is performed in the following section.
Fig. 2. Congener concentrations in the standard solution returned by the individual laboratories compared with the quoted values.
J.L. Stevens et al. / Chemosphere 45 (2001) 1139±1150
1145
Fig. 3. (a) Homologue group concentrations in the certi®ed reference sediment returned by the individual laboratories compared with the reference values and con®dence limits. (b) Congener concentrations in the certi®ed reference sediment returned by the individual laboratories compared with the reference values and con®dence limits.
4. Discussion 4.1. Dierences between sludge samples The wet sludge and the clean extract have consistently low mean values compared to the dry sludge and extract (see Tables 2 and 3). For the clean extract this may possibly be explained by less than 100% recovery of the analytes during the bulk clean-up process. For the wet sludge, this could indicate loss of analytes on drying
due to volatilisation or inecient extraction due to heterogeneous particle size. This is unlikely, however, as all the sludges were dried and ground by the laboratories prior to extraction. Another possible source of error is the reporting of % solids for conversion to pg/g dry sludge. Some laboratories reported this to the nearest percent, which implies a possible error of 0.5%. As the wet sludge had a water content of 98% such an error could make a dierence of 20% to the dry weight value. This calculation error could still not explain all of
1146
J.L. Stevens et al. / Chemosphere 45 (2001) 1139±1150
Table 4 Results of this study compared with other selected studies Reference
Matrix
Range of %RSDs
Mean %RSD
Lao et al. (1993)
Paper mill euents
0±86 (congeners only)
28
Stephens et al. (1992)
Milk Blood
15±310 25±180 (congeners only)
Data not available
19
Bruckmann et al. (1993) Lindig (1998)
Air Sewage sludge
12±81 Sample A: 17±68 Sample B: 19±89
36 31 34
4 60
Van Bavel (1999)
Fly ash (2) Spiked extract Sediment Industrial sludge Soil extract
46±212, 112±402 17±161 28±218 34±128 15±286 (congeners only)
68, 250 37 72 71 60
74
This study (2000)
(a) Wet sludge (b) Dry sludge (c) Extract (d) Clean extract (e) Sediment
23±124 13±77 7±58 17±73 9±92
45 33 32 36 32
the low bias, but may explain some of the high variation in the wet sludge. Whatever the cause of the dierences between the matrices, the error appears to be systematic and is likely to have been incurred during preparation of the bulk materials. The preparation of materials required handling much greater amounts of sludge than a normal analytical procedure is designed and validated for. To accommodate the excess material several modi®cations were adopted which might have altered the PCDD/F content of each bulk sample. For example, the drying of centrifuged solids on a laboratory bench, and the splitting of the dried sludge into 10 Soxhlet bodies for extraction without recourse to recovery standards for each one. The preparation process was not designed to ensure absolutely that the integrity of the original sludge was retained for each matrix, rather that each matrix was homogeneous before sub-sampling. In this way, the interlaboratory variability could be compared from one matrix to another, even if a comparison of the actual PCDD/F concentrations between each matrix was not appropriate. 4.2. Statistical dierences between samples Since the statistical analysis is based on the results of only ®ve laboratories, any conclusions drawn need to seriously take into account statistical uncertainty. In order to do this we will imagine that there is a large collection of N laboratories from which the ®ve laboratories have been randomly drawn. We will then draw conclusions about the kind of results that would have been obtained had we sent samples to all the laboratories in this large collection and asked them to return the
No of laboratories 7
5
average result of two separate analyses. In other words, it will be this set of N averages that will be the population of interest. Since the sample size of ®ve laboratories is very small, it was considered inappropriate to rely on traditional parametric statistical methods which rely on the assumption of a normal distribution. Therefore, the population median rather the population mean has been made the main target of our analysis, since non-parametric con®dence intervals for the population median can easily be constructed. Given the high degree of statistical uncertainty that needs to be accounted for, any conclusions that are drawn about the population median on the basis of this data set can be viewed as closely related to conclusions that could be made about the population mean. Table 5 gives the results of calculations that are relevant to the more detailed statistical analysis that was carried out. For samples of size ®ve, the minimum and maximum values of the sample provide a 94% con®dence interval for the population median. Therefore the 94% con®dence intervals in columns wb, wc, cb, and cc, of Table 5 are the minimum and maximum over a set of ®ve readings produced by each laboratory averaging its two readings. Note that before these averages were formed, where a compound was below the limits of detection, the value of half the detection limit has been used. However, due to diculties in quantifying 1,2,3,7,8,9-HxCDF experienced by the laboratories this compound was excluded from the statistical analysis. To measure deviation around the population mean, the following quantity, namely the average percentage deviation (APD), has been used in place of the standard
40.8 49.8
1.53
67.6 60.4
2.67
340 193 1930
193 120 1300
53.5 90.0
1.10
3.91
105 170
4.50
9.00
wd
24.3
15.9 17.6 91.0 67.8 60.8 49.2 33.2 28.0 35.1 18.5 23.6 50.6 19.7 35.6
20.9 17.1
486 280 2820
159 251 22.8 29.8 22.4 32.7
22.6 22.0
4.11 27.8
83.3 83.9
5.73 29.6
12.3
16.2 115 4.75 73.3 10.1 7.25 126 4.02 92.3 9.50 4.89 8.59 84.4 3.64
wc
1.98 5.47 3.93 20.6
2.98 0.20
3.07
6.95 3.87
2.06
2.47
4.91 5.44 42.7 1.54 25.5 10.3 9.19 13.8 0.25 6.19 3.34 13.8 12.4 10.4
we
43.5 44.8 46.4 44.4
51.4 47.2
53.9
39.7 38.8
71.8
50.0
26.1 44.0 227 170 152 72.5 83.1 54.1 87.5 25.4 49.0 71.5 28.4 89.0
wf
wg
25.5 33.3 25.0 36.6
25.2 24.6
31.0
23.4 19.1
33.1
27.2
17.8 19.7 102 75.9 67.9 55.0 37.2 31.3 39.3 20.7 26.4 56.5 22.0 39.8
ca 4.71 20.6 1.54 8.17 2.69 3.11 33.5 2.54 41.5 5.66 3.62 5.48 89.4 2.65
cb
912 223 6160
207 403
4.08
161 111
4.93
12.9
588 157 4520
169 250
2.69
100 86.0
1.88
5.63
2.79 10.0 .64 3.69 1.56 1.25 20.0 1.75 24.5 4.31 2.31 1.88 62.5 1.67
cd
32.6
36.3 44.9 83.7 39.5 30.8 45.9 42.8 23.1 26.0 29.8 32.5 55.8 24.3 37.1
22.3 12.2
1120 300 7810
306 494
20.3 21.4 14.5 32.5
19.1 22.8
7.81 36.7
244 144
8.06 26.6
23.1
6.88 38.1 4.75 12.5 4.06 6.69 69.4 4.00 56.3 9.88 6.56 13.1 144 4.94
cc
10.5 62.4 0.50 2.50 2.24 1.25 38.0 1.75 30.8 5.75 2.00 2.00 49.7 1.26
12.8 79.8 1.45 27.2 4.03 4.54 68.8 2.61 49.2 7.71 3.28 5.01 69.4 1.92
wb
wa
ce
12.3 3.84 1.48 20.3
4.01 14.9
12.5
2.06 0.06
2.26
1.88
20.4 21.3 43.0 5.13 7.10 5.58 17.6 1.41 2.43 11.7 2.42 6.01 4.27 6.28
35.6 34.7 26.9 58.6
47.7 37.9
91.7
51.1 29.9
63.7
79.5
46.0 84.8 209 54.9 51.0 115 107 57.8 41.0 74.5 81.4 140 60.7 86.6
cf
cg
22.7 24.0 16.2 36.4
21.3 25.5
41.0
24.9 13.6
29.7
36.4
40.6 50.2 93.6 44.2 34.5 51.4 47.9 25.8 29.1 33.3 36.4 62.4 27.2 41.5 10.0 10.0 10.0 10.0 10.0 10.0 10.0
10.4 10.7 9.43 10.1 9.19 9.89 11.3
23.7 22.3
10.2
10.3
10.8
10.4
20.0 20.0
10.0
10.0
10.0
10.0
10.0
2.00
2.11
10.5
2.00
sb
2.24
sa
24.3 16.4
21.7
24.6
16.3
25.6
27.1
20.9
17.3 24.1 33.1
13.7
17.8 15.1
25.6
23.02
sc
Standard solution
wa, ca, sa ± Mean of all values submitted by all labs for the wet sludge, cleaned extract and standard solution, respectively. wb, cb ± lower 94% con®dence limit for the population median of the wet sludge and cleaned extract, respectively. wc, cc ± upper 94% con®dence limit for the population median of the wet sludge and cleaned extract, respectively. wd, cd ± average percentage deviation (APD) for the wet sludge and cleaned extract, respectively. sd ± concentrations in the standard solution as quoted by the supplier. we, ce ± lower 94% con®dence limit for the MPDP of the wet sludge and cleaned extract, respectively. wf, cf ± upper 94% con®dence limit for the MPDP of the wet sludge and cleaned extract, respectively. sc ± average percentage deviation (APD) for the standard solution calculated using the true values instead of the sample means. wg, cg ± estimates of APD in relation to the population mean for the wet sludge and cleaned extract, respectively.
2,3,7,8-TCDF TCDF's 2,3,7,8-TCDD TCDD's 1,2,3,7,8-PeCDF 2,3,4,7,8-PeCDF PeCDF's 1,2,3,7,8-PeCDD PeCDD's 1,2,3,4,7,8-HxCDF 1,2,3,6,7,8-HxCDF 2,3,4,6,7,8-HxCDF HxCDF's 1,2,3,4,7,8HxCDD 1,2,3,6,7,8HxCDD 1,2,3,7,8,9HxCDD HxCDD's 1,2,3,4,6,7,8HpCDF 1,2,3,4,7,8,9HpCDF HpCDF's 1,2,3,4,6,7,8HpCDD HpCDD's OCDF OCDD Av APD/Q
Clean extract
Wet sludge
Table 5 Statistical data for the wet sludge, clean extract and standard solution
J.L. Stevens et al. / Chemosphere 45 (2001) 1139±1150 1147
1148
J.L. Stevens et al. / Chemosphere 45 (2001) 1139±1150
deviation or RSD as it is easier to use APD when we wish to make aggregate inferences about deviations over many compounds. APD
5 100 X jxi 5x i1
xj;
1
where xi is the average of the two concentration readings for laboratory i. This quantity is closely related to the following quantity, the median percentage deviation of the population (MPDP): ( MPDP median
100jxi x
xj
) : i 1; 2; . . . ; N :
2
However unlike APD this quantity can be easily made the target of a non-parametric style of inference. Therefore in the following discussion this quantity has also been given some consideration. 4.2.1. Comparison of the wet sludge with the clean extract If the variation in laboratory readings for the wet sludge is similar to the variations in readings for the clean extract, then there are strong grounds for suggesting that the processing required in transforming the wet sludge into the clean extract has a negligible impact on the overall variation between readings. Column wd and cd of Table 5 shows the APD value for all the compounds calculated on the basis of Eq. (1). The ®nal row of the two columns gives the average APD value over all the compounds. The values in columns we, wf, ce and cf of Table 5 are non-parametric 94% con®dence limits for the MPDP. The fact that these con®dence intervals for the wet sludge overlap with the corresponding intervals for the clean extract for all of the compounds is evidence that the processing of the wet sludge does not have a dramatic eect on the interlaboratory variation. However, since the sample size is small these con®dence intervals are wide, so a substantial eect cannot be ruled out.
The values in the ®nal rows of columns we, wf, ce and cf of Table 5 are 94% con®dence limits for the following quantity: ( ) 24 100 X jxij xj j Q median : i 1; 2; . . . ; N 3 xj 24 j1 (where xij is the average of the two readings for compound j for laboratory i and xj is the average reading for compound j over all laboratories). Knowledge of Q would allow us to reach a conclusion about the overall variability in readings over all laboratories in the population and averaged over all compounds. The con®dence interval for Q for the wet sludge overlaps substantially with the corresponding interval for the clean extract which again indicates that the processing of the wet sludge does not have a dramatic eect on the interlaboratory variation. However, since the upper limit for the wet sludge is 44.4 and lower limit for the clean extract is 20.3, on the basis of the data we can only be sure that the processing contributes no more than about half to the overall error margin. The quantity, APD, as de®ned in Eq. (1) provides a measure of deviation around the sample mean. However the values for APD in columns wd and cd of Table 5 would be greater if we had based our APD measure around the unknown population mean l i.e. if x in Eq. (1) was replaced with l. Under the normality assumption, the values in columns wg and cg of Table 6 are an estimate of APD under this alternative de®nition. These estimates are achieved by simply multiplying the ®gures p in column wd and cd by 5=4. A comparison of columns wb and wc of Table 5 with those of columns cb and cc shows that 7 of the con®dence intervals for the median concentration fail to overlap, which is far higher than one would expect by random chance. This suggests that the conversion of the wet sludge to the clean extract before distribution had a non-negligible eect on the level of PCDD/Fs in the sludge samples. However, using a similar line of reasoning to one used earlier, it can be argued that since the
Table 6 Comparison of variation in WHO TEQs with Van Bavel (1999) Reference
Matrix
Relative standard deviation of WHO PCDD/F TEQ (%)
Mean WHO PCDD/F TEQ (pg TEQ/g dw)
Van Bavel (1999)
Ash (2) Spiked extract Sediment Industrial sludge Soil extract
49, 141 16 34 34 17
760, 137 9750 30 6200 7300
This study
(a)Wet sludge (b) Dry sludge (c) Extract (d) Clean extract
41 36 31 28
12 28 29 15
J.L. Stevens et al. / Chemosphere 45 (2001) 1139±1150
conversion process was carried out on an unrepresentatively large quantity of wet sludge the errors introduced by the process are unrepresentative of errors that may typically occur in the course of a normal PCDD/F analysis of sewage sludge. 4.2.2. Comparison of the clean extract with the standard solution Whilst the quantity APD can tell us about variations around the population mean it cannot tell us about variations around the so-called `true value' i.e. there may be an overall bias in the data. For a standard solution, the concentrations quoted by the supplier can be known and so deviations around these values can be calculated. By comparing these deviations with the deviations around the population mean for the clean extract, we can get an idea of the overall bias, if any, that may be present in our readings. The values in column sc of Table 5 have been calculated using Eq. (1) except that the sample means have been replaced by the quoted concentrations given in column sb. The APD values in column sc are lower in general than the adjusted APD values in column cg. At ®rst glance this may appear to suggest that far more accurate measurements can be made on a standard solution rather than a clean extract. However, the concentrations of most of the compounds involved are far lower in the clean extract than the standard solution. In the concentration range concerned, readings for compounds/homologues in low concentration are more inaccurate than for compounds in high concentrations. Four compounds namely 1,2,3,4,6,7,8-HpCDF, 1,2,3,4,6,7,8-HpCDD, OCDF and OCDD are present either in a similar concentration in both the clean extract and the standard solution or have a higher concentration in the clean extract. For these four compounds the adjusted APD value in column cg of Table 5 matches closely with the APD values in column sc. Unless we believe that the biasing mechanism has little eect on the distribution of readings around the population mean, which is not a highly probable event, then we should conclude that there is no evidence from the data that the concentration readings for the clean extract or the wet sludge are biased. However, the possibility that the biasing mechanism acts very unpredictably should not be overlooked. In summary, the dierences in variation between samples and the number of participating laboratories are not large enough to show a major source of variation at any single point within the analytical process that can be statistically proven. 4.3. WHO TEQs of sludge samples The mean WHO PCDD/F TEQ (Van den Berg et al., 1998) values for each sample re¯ected the bias of the
1149
individual results with the wet sludge and cleaned extract, having mean TEQs of 12 and 15 pg/g dw respectively, compared to the dry sludge and uncleaned extract with mean TEQs of 28 and 29 pg/g dw, respectively. The RSDs show a decrease with 41% for the wet sludge, 36% for the dry sludge, 31% for the extract and 28% for the clean extract. However, considering the conclusions of the statistical analysis above, this decreasing trend may be a coincidence of the congeners that make up the predominant part of the TEQ. Table 6 compares the variation of the TEQs of this study with those of Van Bavel (1999). The concentration variation within this study compares well with the sediment, sludge and ash samples. There is slightly more variation in the concentrations for the uncleaned extract of this study than van Bavel's extracts, but it should be noted that the WHO TEQs of that study are 10±500 times those measured in this study. In summary, sewage sludge is a complex matrix for PCDD/F analysis, and the one chosen for this study contained relatively low concentrations of PCDD/Fs. Nevertheless, the interlaboratory variation obtained was similar to other studies for PCDD/Fs in various matrices reported in the literature. There was not sucient dierence in the interlaboratory variation of dierent samples to statistically identify a particular step in the analytical process which gave rise to a disproportionate amount of the overall variation. This may in part be due to small number of participating laboratories. The variation observed here and in other studies should therefore be viewed as inherent to the PCDD/F analysis of sludge. This should be recognised and taken into account when setting and enforcing regulatory limits for PCDD/Fs in sewage sludge. Acknowledgements The authors would like to thank the laboratories that participated in this study. We also thank Joanne Jones for help with sample analysis and distribution, The UK Environment Agency for funding this study and UKWIR and the UK DETR for funding other work on organics in sewage sludge. References Behnisch, P., 1997. Nicht-, mono- und di-ortho-chlorierte ber Biphenyle (PCB): Isomerenspezi®sche Untersuchungen u Eintrag. Verbleib und Risikoabschatzung in der Umwelt, Ph.D. Thesis. Bradley, J., Nichols, A., Bonaparte, K., Campana, J., Clement, R., Czuczwa, J., DeRoos, F., Lamparski, L., Nestrick, T., Patterson, D., Phillips, D., Stanley, J., Tondeur, Y., Wehler, J., 1990. Interlaboratory testing study on 2,3,7,8-substituted polychlorinated dibenzo-p-dioxin and polychlorinated
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dibenzofuran isomer standard solutions. Chemosphere 20, 487±493. Bruckmann, P., Hackhe, K., Konig, J., Theisen, J., Ball, M., Papke, O., Kirschmer, P., Mulder, W., Rappe, C., Kjeller, L-O., 1993. A comparative study for the determination of polychlorinated dibenzofurans and dibenzo-p-dioxins in ambient air. Chemosphere 27, 707±720. Department of the Environment, 1994. Working party on organic environmental contaminants in food: sub-group on sewage sludge. Report on the examination of sewage sludges for PCDDs and PCDFs, DoE report SS/26/94. Lao, R., Shu, Y., Poole, G., Thomas, R., Chiu, C., Turle, R., 1993. Interlaboratory variability on the determination of PCDD/PCDF in pulp and paper mill euents. Organohalogen Compd. 11, 53±56. Lindig, C., 1998. Pro®ciency testing for dioxin laboratories determination of polychlorinated dibenzo-p-dioxins and dibenzofurans in sewage sludge. Chemosphere 37, 405±420. Sewart, A., Harrad, S., McLachlan, M., McGrath, S., Jones, K., 1995. PCDD/Fs and non-o-PCBs in digested UK sewage sludges. Chemosphere 30, 51±67.
Stephens, R., Rappe, C., Hayward, D., Nygren, M., Startin, J., Esboll, A., Carle, J., Yrjanheikki, E., 1992. World Health Organization international intercalibration study on dioxins and furans in human milk and blood. Anal. Chem. 64, 3109±3117. Stevens, J., Green, N., Jones, K., 2001. Survey of PCDD/Fs and non-ortho PCBs in UK sewage sludges. Chemosphere 44, 1455±1462. Tashiro, C., Clement, R., Davies, S., Dann, T., Steer, P., Bumbaco, M., Oliver, B., Munshaw, T., Fenwick, J., Chittim, B., Foster, M., 1990a. Ambient air analysis round robin. Chemosphere 20, 1319±1324. Tashiro, C., Clement, R., Davies, S., Oliver, B., Munshaw, T., Fenwick, J., Chittim, B., Foster, M., 1990b. Water round robin for parts-per-quadrillion determination of PCDDs and PCDFs. Chemosphere 20, 1313±1317. Van Bavel, B., 1999. Final report fourth round of the international intercalibration study. University of Umea. Van den Berg, M. et al., 1998. Toxic equivalency factors (TEFs) for PCBs PCDDs PCDFs for humans and wildlife. Environ. Health Perspect. 106, 775±791.
www.elsevier.com/locate/chemosphere
Interlaboratory comparison exercise for the analysis of PCDD/Fs in samples of digested sewage sludge Joanna L. Stevens a
a,*
, Nicholas J.L. Green a, Russell J. Bowater b, Kevin C. Jones a
Department of Environmental Science, Institute of Environmental and Natural Sciences, Lancaster University, Lancaster LA1 4YQ, UK b Centre for Applied Statistics, Lancaster University, Lancaster LA1 4YF, UK Received 30 January 2001; received in revised form 2 July 2001; accepted 4 July 2001
Abstract Five UK laboratories participated in a study designed to explore the principal sources of interlaboratory variation in the analysis of PCDD/Fs in sewage sludge. Samples of wet sludge, dry sludge, toluene extract of sludge and cleaned extract of sludge were prepared by an organising laboratory. The samples were analysed in duplicate by each laboratory along with a solution of PCDD/F standards and reference sediment. Mean coecients of variation between laboratories were 45% for the wet sludge, 33% for the dry sludge, 32% for the extract of sludge, 36% for the cleaned extract of sludge, 32% for the reference sediment and 28% for the standard solution. The results were subjected to statistical analysis, which showed that there was no speci®c part of the analysis that introduced a dominant part of the variation. The spread of data generated from the analysis of wet sludge samples was not appreciably greater than the spread for the analysis of cleaned extracts. Thus the drying, extraction and clean up processes in the PCDD/F analysis of wet sludge did not have a dramatic eect on the interlaboratory variation. Ó 2001 Elsevier Science Ltd. All rights reserved. Keywords: Polychlorinated dibenzo-p-dioxins; Polychlorinated dibenzofurans; Round robin
1. Introduction With the potential for European Union limit values for PCDD/Fs in sludge to be introduced in the future there is a need to expand the dataset of PCDD/F concentrations in UK sewage sludges. There have been few studies on PCDD/Fs in UK sludge to date (DoE, 1994; Sewart et al., 1995; Stevens et al., 2001) and it is therefore anticipated that a large amount of monitoring work will be undertaken in the next few years. Sewage sludge is a challenging matrix to analyse. Its composition is complex and highly variable and there are a great number of possible interfering compounds that
*
Corresponding author. Tel.: +44-1524-593-974; fax: +441524-593-985. E-mail address: [email protected] (J.L. Stevens).
must be removed before PCDD/F analysis can take place. It is therefore important to be certain of an acceptable degree of intra and interlaboratory consistency in the results generated from such monitoring, to ensure that any proposed standards can be properly complied with. Interlaboratory studies on PCDD/F analysis in the literature show a large amount of variation between laboratories. There have been a number of such exercises published in recent years on many dierent matrices. These include air, incinerator ash, standard solutions, paper industry waste, water, sediment, milk and blood (Bradley et al., 1990; Tashiro et al., 1990a,b; Stephens et al., 1992; Bruckmann et al., 1993; Lao et al., 1993). Such studies have rarely examined the possible major sources of variation with a view to improving interlaboratory consistency. The aim of this study was to evaluate the comparability of data generated by UK
0045-6535/01/$ - see front matter Ó 2001 Elsevier Science Ltd. All rights reserved. PII: S 0 0 4 5 - 6 5 3 5 ( 0 1 ) 0 0 1 6 4 - 3
1140
J.L. Stevens et al. / Chemosphere 45 (2001) 1139±1150
laboratories for the analysis of PCDD/Fs in sewage sludge, and to try to identify the largest sources of variation to the data set. 2. Materials and methods Each laboratory analysed four separate samples derived from a single batch of sewage sludge, a sample of reference sediment and a standard solution. The four sludge media were prepared at the Lancaster University laboratory from 15 l of digested sewage sludge, collected from the digester holding tank at a small, mainly domestic UK wastewater treatment plant with a population equivalent of 10,000. Sub-samples of the same initial sludge were used throughout to remove potential uncertainties due to matrix eects, and to facilitate statistical analysis. However the results of the dierent sample types (i±iv) were not assumed to be directly comparable with one another as losses of analytes, or contamination, during preparation of such a large amount of material could not be ruled out. 2.1. Preparation of samples for the round robin (i) Wet sludge: The crude sludge was thoroughly mixed and a 400 g wet weight portion was sent to each laboratory. 10 g portions of sludge from each sample were removed and analysed for lead concentration by atomic absorption spectrometry (AAS), which was used as a simple check for homogeneity. The mean lead concentration n 7 was found to be 5.1 lg=g wet sludge, with a standard deviation of 0.3 (RSD 6%).
(ii) Dried sludge: The remainder of the sludge was centrifuged for 20 min at 12,000 rpm, the supernatant liquid discarded and the remaining solids were air dried for 7 days on the bench top. The dried sludge was ground to <500 mesh and mixed thoroughly, then divided into aliquots. The homogeneity of the sample was checked by taking 0.2 g sub-samples of these aliquots and analysing the copper content by AAS. The mean copper concentration n 7 was found to be 660 lg=g dried sludge, with a standard deviation of 43 (RSD 7%). (iii) Uncleaned extract: 120 g of dried sewage sludge was divided into 10 portions and each was Soxhlet extracted with toluene for 16 h. A laboratory blank and a sample of a reference sediment were extracted at the same time, and these were taken through the remainder of the analysis procedure to check the extraction eciency. The extracts were concentrated then combined, and made up to 75 ml toluene gravimetrically. The solution was homogenised by sonication. 5 ml aliquots of the extract were measured into crimptop vials and submitted along with their weight to each laboratory. (iv) Cleaned extract: 25 ml of extract was spiked with 13 C12 -labelled 1,2,7,8-TCDF and 13 C6 -labelled 1,2,3,4,6,7,8-HpCDF to allow the distributing laboratory to assess the eciency of the following clean-up. The extract was concentrated to 1 ml nonane, made up to 50 ml with hexane, and then divided into 10, 5 ml portions. Each portion was subjected to the clean-up detailed in Fig. 1. The cleaned extracts were combined, concentrated to 0.5 ml nonane and 50 ll aliquots were sent to each laboratory.
Fig. 1. Clean-up method for cleaned extract (cf. Behnisch, 1997).
J.L. Stevens et al. / Chemosphere 45 (2001) 1139±1150
1141
Table 1 Summary of sample preparation and clean-up methods used by the participating laboratories Laboratory number
Drying method for wet sludge prior to extraction
Extraction method
1 2
3
Air dried to constant weight at ambient temperature
Soxhlet extraction for 8 h minimum with toluene
4
Air dried to constant weight at 30°C, ground and sieved
ASE with toluene
5
Centrifugation at 12,000 rpm and supernatant discarded, followed by grinding with 50 g sodium sulphate
Soxhlet extraction for 16 h with 300 ml toluene
(v) Reference sediment: No reference material is available for the assessment of PCDD/Fs in sewage sludge. The closest matrix that is available is a lake sediment. 4 g DX-2 (Promochem UK) was analysed by each laboratory. (vi) Standard solution: A solution containing the 17 unlabelled 2,3,7,8-substituted PCDD/Fs at concentrations between 2 and 20 pg=ll was prepared by dilution of a commercially available standard (CAMPRO Scienti®c, Veenendaal, NL). The vial weight for samples (iii), (iv), and (vi) were checked on arrival at each participating laboratory. 2.2. Summary of procedures used by each laboratory Each laboratory used their preferred methods for sample preparation, extraction and clean-up where appropriate. These are summarised in Table 1. Samples (i)± (iv) were analysed and reported in duplicate to assess within-laboratory variability and to decrease the statistical uncertainty in any conclusions that may be drawn. 3. Results 3.1. General comments Five UK laboratories participated in the exercise. Each laboratory reported the concentrations of the 17
Clean-up summary
GC column/s used
Basic alumina column Mixed silica column 3 Conc. Sulfuric acid wash Mixed silica column
Db5-ms
Florisil column Mixed column (sodium sulfate, celite/sulfuric acid, sodium sulfate/sodium bicarbonate, silica) Alumina column Re¯ux with acid silica GPC column Alumina fractionation column 3 Conc. Sulfuric acid wash Mixed silica column GPC column Activated copper Basic alumina column Carbon column
Db5-ms Sp 2331 for PeCDD, 2,3,4,7,8-PeCDF and 1,2,3,7,8,9-HxCDF Db5-ms
Db5-ms
Db5-ms for homologues, HpCDFs, HpCDD, OCDF, OCDD Sp2331 for other congeners
2,3,7,8-substituted PCDD/Fs and the total homologue concentrations of the tetra- to octa-PCDD/Fs. The water content of the wet sludge sample as % w/w was also reported. Tables 2 and 3 shows the mean, median, minimum and maximum value for each homologue/ congener in the samples of sewage sludge, certi®ed reference sediment and standard solution, respectively. The variation between data is presented as the relative standard deviation (RSD) or coecient of variation. The sludge sample values were all corrected to pg/g dry weight, to allow comparison of the data obtained from the dierent samples. In Tables 2 and 3, congeners/ homologues that were below the limits of detection have been treated as unreported data and the detection limits are not included in the mean or median values. 3.2. Interlaboratory variation versus Intralaboratory variation Since each laboratory analysed two samples, it is possible to make some comparison between the variation of results within laboratories and the variation of results between laboratories. For each of the compounds, a separate analysis of variance was performed on the results obtained for the wet sludge. For a large majority of the compounds (20 out of the 24 studied) it
60 3 105
170 341
193
2±11 1±8 35±205
1±4 29±145
5±11 2±5 2±10 2 30±126
1±4 4±14 1±8 37±100
45±86 1±4 47±169
1300±2910 1930
1,2,3,7,8-PeCDD PeCDD's
1,2,3,4,7,8-HxCDF 1,2,3,6,7,8-HxCDF 2,3,4,6,7,8-HxCDF 1,2,3,7,8,9-HxCDF HxCDF's
1,2,3,4,7,8-HxCDD 1,2,3,6,7,8-HxCDD 1,2,3,7,8,9-HxCDD HxCDD's
1,2,3,4,6,7,8-HpCDF 1,2,3,4,7,8,9-HpCDF HpCDF's
1,2,3,4,6,7,8-HpCDD 90±266 HpCDD's 190±519
115±306
1,2,3,7,8-PeCDF 2,3,4,7,8-PeCDF PeCDF's
OCDF
OCDD
2 9 4 68
8 4 6 2 69
3 49
4 5 69
1 33
1 12±135
2,3,7,8-TCDD TCDD's
14 80
10±31 55±130
2,3,7,8-TCDF TCDF's
1820
204
171 344
58 3 106
2 9 5 72
7 4 6 2 65
3 40
3 6 58
1 18
11 71
29
35
33 31
23 40 34
49 33 44 32
25 29 49 * 40
48 70
79 51 72
28 124
46 29
11 000± 22 300
350±770
460±1500 960±3010
130±190 7±11 300±575
3±6 8±37 3±19 170±414
7±16 3±9 3±15 2±3 74±170
5±7 38±200
4±9 2±10 51±160
1±4 24±130
7±11 52±168
15 200
526
920 2000
155 9 370
5 24 11 303
10 6 8 3 137
6 109
6 6 77
2 48
10 95
Mean
Dried sludge Range
Median RSD
Range
Mean
Wet sludge
14 000
542
869 2030
165 9 339
5 26 12 300
11 7 9 3 150
6 98
5 7 66
2 33
10 76
23
27
37 32
15 16 23
18 37 45 25
23 42 49 13 21
14 48
34 50 42
46 77
13 44
Median RSD
14 400± 18 100
456±1000
519±1060 1250±2360
144±223 3±24 250±456
2±8 10±38 3±19 138±404
8±23 3±13 4±19 3±4 108±331
3±10 55±206
3±9 2±14 47±150
1±3 31±94
9±146 47±184
Range
15 800
592
866 1970
177 10 392
5 25 11 318
13 8 10 4 184
6 117
6 7 84
2 53
11 105
Mean
Sludge extract
15 700
548
883 2070
168 9 408
5 26 10 338
10 8 8 4 175
6 109
7 7 69
2 41
10 80
7
28
19 18
15 58 15
34 33 39 26
36 36 51 20 31
38 36
33 48 37
23 44
22 50
Median RSD
Table 2 Range, median, mean values (pg/g dry sludge) and relative standard deviation n 10 of sludge samples analysed by the participants
4440±8000
153± 300
238±500 563±1140
85±150 2±9 150±313
1±5 5±24 1±9 88±250
4±11 2±7 1±14 2 63±150
1±4 12±63
2±4 1±7 20±74
0.4±1 3±12
3±8 6±41
6250
221
416 943
112 4 205
2 13 5 162
5 4 5 2 89
2 40
2 3 34
1 7
4 21
Mean
Clean extract Range
6430
219
475 1060
111 3 193
2 12 5 156
5 3 4 2 83
2 39
2 3 28
1 7
4 18
17
24
24 22
17 49 24
52 43 40 29
40 43 73 * 31
32 35
40 59 52
27 42
41 50
Median RSD
1142 J.L. Stevens et al. / Chemosphere 45 (2001) 1139±1150
29±91 531±1200
210±369 360±521
9±145 28±120 160±1010
7.80±37 168±391
330±854 52±172 12±100 4.0±75 710±2650
16±36 40±100 26±69 630±1170
1600±3460 51±190 2000±6040
350±786 700±1550
5900±7760
2900±4710
2,3,7,8-TCDF TCDF's
2,3,7,8-TCDD TCDD's
1,2,3,7,8-PeCDF 2,3,4,7,8-PeCDF PeCDF's
1,2,3,7,8-PeCDD PeCDD's
1,2,3,4,7,8-HxCDF 1,2,3,6,7,8-HxCDF 2,3,4,6,7,8-HxCDF 1,2,3,7,8,9-HxCDF HxCDF's
1,2,3,4,7,8-HxCDD 1,2,3,6,7,8-HxCDD 1,2,3,7,8,9-HxCDD HxCDD's
1,2,3,4,6,7,8-HpCDF 1,2,3,4,7,8,9-HpCDF HpCDF's
1,2,3,4,6,7,8-HpCDD HpCDD's
OCDF
OCDD
4206
7038
675 1327
2866 161 3890
29 87 52 846
649 122 65 40 1810
27 280
60 89 751
273 467
71 783
4289
7167
700 1415
3025 180 3810
29 95 56 857
688 130 72 41 1940
30 270
42 104 816
254 478
76 809
14
9
20 20
20 28 30
21 24 27 19
29 36 48 92 35
37 28
83 40 36
25 11
29 28
18±31
17±32
6±16
9±15 7±17
8±16 5±17 6±16
8±15 6±15 6±19 5±17
8±12
8±16 9±16
1±4
2±4
22
22
11
11 11
11 11 11
10 10 10 10
9
10 11
2
2
Mean
Standard solution (pg/ll) RSD
Range
Median
Range
Mean
Reference sediment (pg/g dw)
Table 3 Range, median, mean and relative standard deviation of reference sediment and standard solution analysed by the participants
20
20
10
10 10
10 10 10
10 9 9 10
9
9 10
2
2
Median
20
23
27
22 31
24 31 29
22 28 41 40
15
25 23
35
34
RSD
J.L. Stevens et al. / Chemosphere 45 (2001) 1139±1150 1143
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J.L. Stevens et al. / Chemosphere 45 (2001) 1139±1150
was found that the mean square error between laboratories was greater than the mean square error within laboratories. Using an F-test these dierences were generally extremely signi®cant (the F-value for 12 out of the 20 compounds concerned was signi®cant at the 1% level) even though there were only two replicates from only ®ve laboratories. Moreover, it is likely that the between laboratory variation is substantially greater than the within laboratory variation. Therefore, the following statistical analysis will concentrate on the between laboratory variation. 3.3. Standard solution For a standard solution, we can regard the concentrations quoted by the supplier as the `true' concentrations and so for this test substance, deviations around the true values can in some sense be calculated. Results from the analysis of the standard solution provide a measure of the variation caused by dierences in instrument operating conditions and the individual laboratories' standards and quantitation method. Fig. 2 shows the mean values from each laboratory compared with the values of the congeners in the standard, based on those quoted by the supplier. RSDs ranged between 15% and 41% of the `quoted' values. These results are comparable with those of two studies by Tashiro et al. (1990a,b). They reported RSDs ranging 18±61% (mean 27, n 7) and ranging 8±43% (mean 23, n 6) for round robins on standard solutions that were 5±50 times more concentrated than the one used in this study. Bradley et al. (1990) reported a higher degree of interlaboratory consistency with standards
containing congeners at concentrations 1000 times higher than those used here. 3.4. Reference sediment A reference sediment was analysed to enable the data generated by the ®ve laboratories to be compared directly with an internationally accepted data set. The 95% con®dence intervals provided by the supplier of the reference sediment correspond to a range of RSDs equivalent to 10±64% with a mean of 21% and a TEQ RSD of 15%. The data from this study are presented in Tables 2 and 3 and, with the exception of two problem congeners, show a range of RSDs of 9±48%. The mean RSD including these two congeners was 32%. Fig. 3(a) and (b) shows the mean values for each laboratory, along with the reference value and 95% con®dence limit values for the sediment. 3.5. Sludge samples Table 4 shows the range of RSDs for each of the sludge samples and compares the results of this study to other studies in the literature. The interlaboratory variation in this study is in a similar range to that of others. The study on sewage sludge by Lindig (1998) and the reference sediment from the Van Bavel (1999) study oer the most direct comparison. The mean and range of RSD values for the sludge samples are similar, and do not appear to implicate any one step of the analysis as being the predominant source of the overall uncertainty. However, mean RSDs are a crude summary of the data and a detailed statistical analysis is performed in the following section.
Fig. 2. Congener concentrations in the standard solution returned by the individual laboratories compared with the quoted values.
J.L. Stevens et al. / Chemosphere 45 (2001) 1139±1150
1145
Fig. 3. (a) Homologue group concentrations in the certi®ed reference sediment returned by the individual laboratories compared with the reference values and con®dence limits. (b) Congener concentrations in the certi®ed reference sediment returned by the individual laboratories compared with the reference values and con®dence limits.
4. Discussion 4.1. Dierences between sludge samples The wet sludge and the clean extract have consistently low mean values compared to the dry sludge and extract (see Tables 2 and 3). For the clean extract this may possibly be explained by less than 100% recovery of the analytes during the bulk clean-up process. For the wet sludge, this could indicate loss of analytes on drying
due to volatilisation or inecient extraction due to heterogeneous particle size. This is unlikely, however, as all the sludges were dried and ground by the laboratories prior to extraction. Another possible source of error is the reporting of % solids for conversion to pg/g dry sludge. Some laboratories reported this to the nearest percent, which implies a possible error of 0.5%. As the wet sludge had a water content of 98% such an error could make a dierence of 20% to the dry weight value. This calculation error could still not explain all of
1146
J.L. Stevens et al. / Chemosphere 45 (2001) 1139±1150
Table 4 Results of this study compared with other selected studies Reference
Matrix
Range of %RSDs
Mean %RSD
Lao et al. (1993)
Paper mill euents
0±86 (congeners only)
28
Stephens et al. (1992)
Milk Blood
15±310 25±180 (congeners only)
Data not available
19
Bruckmann et al. (1993) Lindig (1998)
Air Sewage sludge
12±81 Sample A: 17±68 Sample B: 19±89
36 31 34
4 60
Van Bavel (1999)
Fly ash (2) Spiked extract Sediment Industrial sludge Soil extract
46±212, 112±402 17±161 28±218 34±128 15±286 (congeners only)
68, 250 37 72 71 60
74
This study (2000)
(a) Wet sludge (b) Dry sludge (c) Extract (d) Clean extract (e) Sediment
23±124 13±77 7±58 17±73 9±92
45 33 32 36 32
the low bias, but may explain some of the high variation in the wet sludge. Whatever the cause of the dierences between the matrices, the error appears to be systematic and is likely to have been incurred during preparation of the bulk materials. The preparation of materials required handling much greater amounts of sludge than a normal analytical procedure is designed and validated for. To accommodate the excess material several modi®cations were adopted which might have altered the PCDD/F content of each bulk sample. For example, the drying of centrifuged solids on a laboratory bench, and the splitting of the dried sludge into 10 Soxhlet bodies for extraction without recourse to recovery standards for each one. The preparation process was not designed to ensure absolutely that the integrity of the original sludge was retained for each matrix, rather that each matrix was homogeneous before sub-sampling. In this way, the interlaboratory variability could be compared from one matrix to another, even if a comparison of the actual PCDD/F concentrations between each matrix was not appropriate. 4.2. Statistical dierences between samples Since the statistical analysis is based on the results of only ®ve laboratories, any conclusions drawn need to seriously take into account statistical uncertainty. In order to do this we will imagine that there is a large collection of N laboratories from which the ®ve laboratories have been randomly drawn. We will then draw conclusions about the kind of results that would have been obtained had we sent samples to all the laboratories in this large collection and asked them to return the
No of laboratories 7
5
average result of two separate analyses. In other words, it will be this set of N averages that will be the population of interest. Since the sample size of ®ve laboratories is very small, it was considered inappropriate to rely on traditional parametric statistical methods which rely on the assumption of a normal distribution. Therefore, the population median rather the population mean has been made the main target of our analysis, since non-parametric con®dence intervals for the population median can easily be constructed. Given the high degree of statistical uncertainty that needs to be accounted for, any conclusions that are drawn about the population median on the basis of this data set can be viewed as closely related to conclusions that could be made about the population mean. Table 5 gives the results of calculations that are relevant to the more detailed statistical analysis that was carried out. For samples of size ®ve, the minimum and maximum values of the sample provide a 94% con®dence interval for the population median. Therefore the 94% con®dence intervals in columns wb, wc, cb, and cc, of Table 5 are the minimum and maximum over a set of ®ve readings produced by each laboratory averaging its two readings. Note that before these averages were formed, where a compound was below the limits of detection, the value of half the detection limit has been used. However, due to diculties in quantifying 1,2,3,7,8,9-HxCDF experienced by the laboratories this compound was excluded from the statistical analysis. To measure deviation around the population mean, the following quantity, namely the average percentage deviation (APD), has been used in place of the standard
40.8 49.8
1.53
67.6 60.4
2.67
340 193 1930
193 120 1300
53.5 90.0
1.10
3.91
105 170
4.50
9.00
wd
24.3
15.9 17.6 91.0 67.8 60.8 49.2 33.2 28.0 35.1 18.5 23.6 50.6 19.7 35.6
20.9 17.1
486 280 2820
159 251 22.8 29.8 22.4 32.7
22.6 22.0
4.11 27.8
83.3 83.9
5.73 29.6
12.3
16.2 115 4.75 73.3 10.1 7.25 126 4.02 92.3 9.50 4.89 8.59 84.4 3.64
wc
1.98 5.47 3.93 20.6
2.98 0.20
3.07
6.95 3.87
2.06
2.47
4.91 5.44 42.7 1.54 25.5 10.3 9.19 13.8 0.25 6.19 3.34 13.8 12.4 10.4
we
43.5 44.8 46.4 44.4
51.4 47.2
53.9
39.7 38.8
71.8
50.0
26.1 44.0 227 170 152 72.5 83.1 54.1 87.5 25.4 49.0 71.5 28.4 89.0
wf
wg
25.5 33.3 25.0 36.6
25.2 24.6
31.0
23.4 19.1
33.1
27.2
17.8 19.7 102 75.9 67.9 55.0 37.2 31.3 39.3 20.7 26.4 56.5 22.0 39.8
ca 4.71 20.6 1.54 8.17 2.69 3.11 33.5 2.54 41.5 5.66 3.62 5.48 89.4 2.65
cb
912 223 6160
207 403
4.08
161 111
4.93
12.9
588 157 4520
169 250
2.69
100 86.0
1.88
5.63
2.79 10.0 .64 3.69 1.56 1.25 20.0 1.75 24.5 4.31 2.31 1.88 62.5 1.67
cd
32.6
36.3 44.9 83.7 39.5 30.8 45.9 42.8 23.1 26.0 29.8 32.5 55.8 24.3 37.1
22.3 12.2
1120 300 7810
306 494
20.3 21.4 14.5 32.5
19.1 22.8
7.81 36.7
244 144
8.06 26.6
23.1
6.88 38.1 4.75 12.5 4.06 6.69 69.4 4.00 56.3 9.88 6.56 13.1 144 4.94
cc
10.5 62.4 0.50 2.50 2.24 1.25 38.0 1.75 30.8 5.75 2.00 2.00 49.7 1.26
12.8 79.8 1.45 27.2 4.03 4.54 68.8 2.61 49.2 7.71 3.28 5.01 69.4 1.92
wb
wa
ce
12.3 3.84 1.48 20.3
4.01 14.9
12.5
2.06 0.06
2.26
1.88
20.4 21.3 43.0 5.13 7.10 5.58 17.6 1.41 2.43 11.7 2.42 6.01 4.27 6.28
35.6 34.7 26.9 58.6
47.7 37.9
91.7
51.1 29.9
63.7
79.5
46.0 84.8 209 54.9 51.0 115 107 57.8 41.0 74.5 81.4 140 60.7 86.6
cf
cg
22.7 24.0 16.2 36.4
21.3 25.5
41.0
24.9 13.6
29.7
36.4
40.6 50.2 93.6 44.2 34.5 51.4 47.9 25.8 29.1 33.3 36.4 62.4 27.2 41.5 10.0 10.0 10.0 10.0 10.0 10.0 10.0
10.4 10.7 9.43 10.1 9.19 9.89 11.3
23.7 22.3
10.2
10.3
10.8
10.4
20.0 20.0
10.0
10.0
10.0
10.0
10.0
2.00
2.11
10.5
2.00
sb
2.24
sa
24.3 16.4
21.7
24.6
16.3
25.6
27.1
20.9
17.3 24.1 33.1
13.7
17.8 15.1
25.6
23.02
sc
Standard solution
wa, ca, sa ± Mean of all values submitted by all labs for the wet sludge, cleaned extract and standard solution, respectively. wb, cb ± lower 94% con®dence limit for the population median of the wet sludge and cleaned extract, respectively. wc, cc ± upper 94% con®dence limit for the population median of the wet sludge and cleaned extract, respectively. wd, cd ± average percentage deviation (APD) for the wet sludge and cleaned extract, respectively. sd ± concentrations in the standard solution as quoted by the supplier. we, ce ± lower 94% con®dence limit for the MPDP of the wet sludge and cleaned extract, respectively. wf, cf ± upper 94% con®dence limit for the MPDP of the wet sludge and cleaned extract, respectively. sc ± average percentage deviation (APD) for the standard solution calculated using the true values instead of the sample means. wg, cg ± estimates of APD in relation to the population mean for the wet sludge and cleaned extract, respectively.
2,3,7,8-TCDF TCDF's 2,3,7,8-TCDD TCDD's 1,2,3,7,8-PeCDF 2,3,4,7,8-PeCDF PeCDF's 1,2,3,7,8-PeCDD PeCDD's 1,2,3,4,7,8-HxCDF 1,2,3,6,7,8-HxCDF 2,3,4,6,7,8-HxCDF HxCDF's 1,2,3,4,7,8HxCDD 1,2,3,6,7,8HxCDD 1,2,3,7,8,9HxCDD HxCDD's 1,2,3,4,6,7,8HpCDF 1,2,3,4,7,8,9HpCDF HpCDF's 1,2,3,4,6,7,8HpCDD HpCDD's OCDF OCDD Av APD/Q
Clean extract
Wet sludge
Table 5 Statistical data for the wet sludge, clean extract and standard solution
J.L. Stevens et al. / Chemosphere 45 (2001) 1139±1150 1147
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J.L. Stevens et al. / Chemosphere 45 (2001) 1139±1150
deviation or RSD as it is easier to use APD when we wish to make aggregate inferences about deviations over many compounds. APD
5 100 X jxi 5x i1
xj;
1
where xi is the average of the two concentration readings for laboratory i. This quantity is closely related to the following quantity, the median percentage deviation of the population (MPDP): ( MPDP median
100jxi x
xj
) : i 1; 2; . . . ; N :
2
However unlike APD this quantity can be easily made the target of a non-parametric style of inference. Therefore in the following discussion this quantity has also been given some consideration. 4.2.1. Comparison of the wet sludge with the clean extract If the variation in laboratory readings for the wet sludge is similar to the variations in readings for the clean extract, then there are strong grounds for suggesting that the processing required in transforming the wet sludge into the clean extract has a negligible impact on the overall variation between readings. Column wd and cd of Table 5 shows the APD value for all the compounds calculated on the basis of Eq. (1). The ®nal row of the two columns gives the average APD value over all the compounds. The values in columns we, wf, ce and cf of Table 5 are non-parametric 94% con®dence limits for the MPDP. The fact that these con®dence intervals for the wet sludge overlap with the corresponding intervals for the clean extract for all of the compounds is evidence that the processing of the wet sludge does not have a dramatic eect on the interlaboratory variation. However, since the sample size is small these con®dence intervals are wide, so a substantial eect cannot be ruled out.
The values in the ®nal rows of columns we, wf, ce and cf of Table 5 are 94% con®dence limits for the following quantity: ( ) 24 100 X jxij xj j Q median : i 1; 2; . . . ; N 3 xj 24 j1 (where xij is the average of the two readings for compound j for laboratory i and xj is the average reading for compound j over all laboratories). Knowledge of Q would allow us to reach a conclusion about the overall variability in readings over all laboratories in the population and averaged over all compounds. The con®dence interval for Q for the wet sludge overlaps substantially with the corresponding interval for the clean extract which again indicates that the processing of the wet sludge does not have a dramatic eect on the interlaboratory variation. However, since the upper limit for the wet sludge is 44.4 and lower limit for the clean extract is 20.3, on the basis of the data we can only be sure that the processing contributes no more than about half to the overall error margin. The quantity, APD, as de®ned in Eq. (1) provides a measure of deviation around the sample mean. However the values for APD in columns wd and cd of Table 5 would be greater if we had based our APD measure around the unknown population mean l i.e. if x in Eq. (1) was replaced with l. Under the normality assumption, the values in columns wg and cg of Table 6 are an estimate of APD under this alternative de®nition. These estimates are achieved by simply multiplying the ®gures p in column wd and cd by 5=4. A comparison of columns wb and wc of Table 5 with those of columns cb and cc shows that 7 of the con®dence intervals for the median concentration fail to overlap, which is far higher than one would expect by random chance. This suggests that the conversion of the wet sludge to the clean extract before distribution had a non-negligible eect on the level of PCDD/Fs in the sludge samples. However, using a similar line of reasoning to one used earlier, it can be argued that since the
Table 6 Comparison of variation in WHO TEQs with Van Bavel (1999) Reference
Matrix
Relative standard deviation of WHO PCDD/F TEQ (%)
Mean WHO PCDD/F TEQ (pg TEQ/g dw)
Van Bavel (1999)
Ash (2) Spiked extract Sediment Industrial sludge Soil extract
49, 141 16 34 34 17
760, 137 9750 30 6200 7300
This study
(a)Wet sludge (b) Dry sludge (c) Extract (d) Clean extract
41 36 31 28
12 28 29 15
J.L. Stevens et al. / Chemosphere 45 (2001) 1139±1150
conversion process was carried out on an unrepresentatively large quantity of wet sludge the errors introduced by the process are unrepresentative of errors that may typically occur in the course of a normal PCDD/F analysis of sewage sludge. 4.2.2. Comparison of the clean extract with the standard solution Whilst the quantity APD can tell us about variations around the population mean it cannot tell us about variations around the so-called `true value' i.e. there may be an overall bias in the data. For a standard solution, the concentrations quoted by the supplier can be known and so deviations around these values can be calculated. By comparing these deviations with the deviations around the population mean for the clean extract, we can get an idea of the overall bias, if any, that may be present in our readings. The values in column sc of Table 5 have been calculated using Eq. (1) except that the sample means have been replaced by the quoted concentrations given in column sb. The APD values in column sc are lower in general than the adjusted APD values in column cg. At ®rst glance this may appear to suggest that far more accurate measurements can be made on a standard solution rather than a clean extract. However, the concentrations of most of the compounds involved are far lower in the clean extract than the standard solution. In the concentration range concerned, readings for compounds/homologues in low concentration are more inaccurate than for compounds in high concentrations. Four compounds namely 1,2,3,4,6,7,8-HpCDF, 1,2,3,4,6,7,8-HpCDD, OCDF and OCDD are present either in a similar concentration in both the clean extract and the standard solution or have a higher concentration in the clean extract. For these four compounds the adjusted APD value in column cg of Table 5 matches closely with the APD values in column sc. Unless we believe that the biasing mechanism has little eect on the distribution of readings around the population mean, which is not a highly probable event, then we should conclude that there is no evidence from the data that the concentration readings for the clean extract or the wet sludge are biased. However, the possibility that the biasing mechanism acts very unpredictably should not be overlooked. In summary, the dierences in variation between samples and the number of participating laboratories are not large enough to show a major source of variation at any single point within the analytical process that can be statistically proven. 4.3. WHO TEQs of sludge samples The mean WHO PCDD/F TEQ (Van den Berg et al., 1998) values for each sample re¯ected the bias of the
1149
individual results with the wet sludge and cleaned extract, having mean TEQs of 12 and 15 pg/g dw respectively, compared to the dry sludge and uncleaned extract with mean TEQs of 28 and 29 pg/g dw, respectively. The RSDs show a decrease with 41% for the wet sludge, 36% for the dry sludge, 31% for the extract and 28% for the clean extract. However, considering the conclusions of the statistical analysis above, this decreasing trend may be a coincidence of the congeners that make up the predominant part of the TEQ. Table 6 compares the variation of the TEQs of this study with those of Van Bavel (1999). The concentration variation within this study compares well with the sediment, sludge and ash samples. There is slightly more variation in the concentrations for the uncleaned extract of this study than van Bavel's extracts, but it should be noted that the WHO TEQs of that study are 10±500 times those measured in this study. In summary, sewage sludge is a complex matrix for PCDD/F analysis, and the one chosen for this study contained relatively low concentrations of PCDD/Fs. Nevertheless, the interlaboratory variation obtained was similar to other studies for PCDD/Fs in various matrices reported in the literature. There was not sucient dierence in the interlaboratory variation of dierent samples to statistically identify a particular step in the analytical process which gave rise to a disproportionate amount of the overall variation. This may in part be due to small number of participating laboratories. The variation observed here and in other studies should therefore be viewed as inherent to the PCDD/F analysis of sludge. This should be recognised and taken into account when setting and enforcing regulatory limits for PCDD/Fs in sewage sludge. Acknowledgements The authors would like to thank the laboratories that participated in this study. We also thank Joanne Jones for help with sample analysis and distribution, The UK Environment Agency for funding this study and UKWIR and the UK DETR for funding other work on organics in sewage sludge. References Behnisch, P., 1997. Nicht-, mono- und di-ortho-chlorierte ber Biphenyle (PCB): Isomerenspezi®sche Untersuchungen u Eintrag. Verbleib und Risikoabschatzung in der Umwelt, Ph.D. Thesis. Bradley, J., Nichols, A., Bonaparte, K., Campana, J., Clement, R., Czuczwa, J., DeRoos, F., Lamparski, L., Nestrick, T., Patterson, D., Phillips, D., Stanley, J., Tondeur, Y., Wehler, J., 1990. Interlaboratory testing study on 2,3,7,8-substituted polychlorinated dibenzo-p-dioxin and polychlorinated
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dibenzofuran isomer standard solutions. Chemosphere 20, 487±493. Bruckmann, P., Hackhe, K., Konig, J., Theisen, J., Ball, M., Papke, O., Kirschmer, P., Mulder, W., Rappe, C., Kjeller, L-O., 1993. A comparative study for the determination of polychlorinated dibenzofurans and dibenzo-p-dioxins in ambient air. Chemosphere 27, 707±720. Department of the Environment, 1994. Working party on organic environmental contaminants in food: sub-group on sewage sludge. Report on the examination of sewage sludges for PCDDs and PCDFs, DoE report SS/26/94. Lao, R., Shu, Y., Poole, G., Thomas, R., Chiu, C., Turle, R., 1993. Interlaboratory variability on the determination of PCDD/PCDF in pulp and paper mill euents. Organohalogen Compd. 11, 53±56. Lindig, C., 1998. Pro®ciency testing for dioxin laboratories determination of polychlorinated dibenzo-p-dioxins and dibenzofurans in sewage sludge. Chemosphere 37, 405±420. Sewart, A., Harrad, S., McLachlan, M., McGrath, S., Jones, K., 1995. PCDD/Fs and non-o-PCBs in digested UK sewage sludges. Chemosphere 30, 51±67.
Stephens, R., Rappe, C., Hayward, D., Nygren, M., Startin, J., Esboll, A., Carle, J., Yrjanheikki, E., 1992. World Health Organization international intercalibration study on dioxins and furans in human milk and blood. Anal. Chem. 64, 3109±3117. Stevens, J., Green, N., Jones, K., 2001. Survey of PCDD/Fs and non-ortho PCBs in UK sewage sludges. Chemosphere 44, 1455±1462. Tashiro, C., Clement, R., Davies, S., Dann, T., Steer, P., Bumbaco, M., Oliver, B., Munshaw, T., Fenwick, J., Chittim, B., Foster, M., 1990a. Ambient air analysis round robin. Chemosphere 20, 1319±1324. Tashiro, C., Clement, R., Davies, S., Oliver, B., Munshaw, T., Fenwick, J., Chittim, B., Foster, M., 1990b. Water round robin for parts-per-quadrillion determination of PCDDs and PCDFs. Chemosphere 20, 1313±1317. Van Bavel, B., 1999. Final report fourth round of the international intercalibration study. University of Umea. Van den Berg, M. et al., 1998. Toxic equivalency factors (TEFs) for PCBs PCDDs PCDFs for humans and wildlife. Environ. Health Perspect. 106, 775±791.