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A multivariate chemical classification of rainwater samples
Original
Research
Paper
m
Chemometrics and Intelligent Laboratory Systems, 3 (1988) 99-109 Elsevier
Science Publishers
B.V., Amsterdam
-
Printed
in The Netherlands
A Multivariate Chemical Classification of Rainwater Samples RICHARD
Department
of Civil Engineering,
J. VONG
* and TIMOTHY
WILLIAM
Department
of Chemistry,
V. LARSON
University of Washington FX-IO, Seattle,
WA 98195 (U.S.A.)
H. ZOLLER
University of Washington BG-IO, Seattle, WA 98195 (U.S.A.)
ABSTRACT
Vong, R.J., Larson, T.V. and Zoller, Chemometrics and Intelligent Laboratory
W.H., 1988. A multivariate Systems, 3: 99-109.
chemical
classification
of rainwater
samples.
An experiment was conducted during February and March of 1985 and 1986 to collect rainwater both upwind and downwind, before and after the permanent closure of a copper smelter located in Tacoma, WA to determine the effect of the smelter on downwind precipitation chemistry. The results of two storm events sampled by a 38 site network in Western Washington are reported here. Two collectors were deployed at each site. This produced information on the spatial and temporal variability in the composition of the rainwater as well as the experimental uncertainty. Principal component analysis (PCA) revealed the influence of a factor which is similar to the atmospheric emissions from the smelter (As, Sb, Cu, Pb, excess SO,‘-). Two different PCA software packages produced similar results. A classification of rainwater samples according to the influence of the copper smelter was performed using PCA models for three geographical regions with separate meteorological and source emission characteristics. Ninety percent of the samples were correctly classified into one of three regions of varying smelter influence on rainwater chemistry, demonstrating that meteorology and source location are consistent with the PCA analysis of rainwater chemistry.
steady southwesterly air flow aloft during winter rain. Therefore, the influence of a single air pollutant emission source, a copper smelter, on rainwater chemistry is potentially easier to observe in Western Washington than elsewhere. This paper presents observations of rainwater chemistry for two storms sampled on 14-15 February and 19-21 March 1985 near a large copper smelter located in Tacoma, WA, U.S.A. We discuss sampling and analysis procedures, statistical screening of the data based on measured uncertainties, spatial variability, principal compo-
INTRODUCTION
Rain chemistry source-receptor relationships are difficult to examine in the heavily impacted areas of the northeastern United States and central Europe due to the contributions from a large and variable number of local and distant emissions. In contrast, within the Puget Sound area of Washington State, clean background air [l] moves inland from the Pacific Ocean past a relatively small number of emission sources. The meteorology associated with cyclonic frontal systems results in 0169-7439/88/$03.50
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nent analysis (PCA) of the rainwater concentrations, and multivariate classification of rain samples into regions of differing smelter influence. The data set presented here is unique in the physical simplifications afforded by a single large source located in an environment where the background air is very clean.
EXPERIMENTAL
A network of 38 rainwater collectors was operated both upwind and downwind of the smelter. Fig. 1 shows the location of the rainwater sampling sites and the copper smelter. Sites are coded according to the classification scheme employed in this analysis as: (a) upwind, class 1, (b) downwind/Vashon area, class 2, and (c) downwind/ N.Seattle area, class 3. The remaining sites were considered to be of unknown influence and were tested for similarity to the three a priori classes which reflect separate meteorological and source emission characteristics. A decision to sample a given storm was based on synoptic meteorological information in an attempt to collect cyclonic frontal rain that was fairly uniform over the mesoscale extent (80 km)
0
4
06
d
SAMPLING NETWORK CLASSES CLASS 1 CUSS 2 CLASS 3 CLASSES TEST
= UPWIND = VASHON = N.SEATTLE 4+6 = SET (UNKNOWN)
Fig. 1. Map of Western Washington State, U.S.A., water sampling sites coded by meteorological/source
100
with rainclass.
of our network. The sampling protocol required that good atmospheric ventilation proceed the 24 or 48 hour sampling period. Dry deposition was not separately evaluated but was expected to be small compared to wet deposition because the sites were located in grassy areas to minimize dust and, also, due to the low ground level ambient concentrations of SO, and aerosol which are associated with rainy days in Western Washington. A total of 14 storms were sampled during February and March of 1985-86, seven with the copper smelter operating. Surface wind speed and direction data indicate that winds in the Tacoma-Seattle area were southwesterly for the two storms discussed here. An average of 0.5 cm of rain fell over 3 and 6 hour periods for the February and March events, respectively. The latter event included one day of windy, dry weather followed by a day of rain. Fig. 2 presents the 3 hour average 10 meter surface winds observed over the sampling network during the precipitation sampling period of the 19-21 March event. Rawindsondes confirmed southwesterly flow aloft at the expected height (500 m) of the copper smelter plume. Hydrogen ion concentration was computed from pH measurements taken as soon as possible after the end of the sampling period (usually within 6-8 hours). The samples were then filtered and the concentrations of Na, Mg, Ca, K, Fe, and Zn were measured on the filtrate by inductively coupled plasma emission spectroscopy (ICP). Measurements of NO;, Cl-, SO:-, Na+, and NH: were made via ion chromatography (IC) and Pb was measured by flameless atomic absorption spectroscopy (AAS). Separate sample aliquots were stored (a) frozen and acidified with Ultrex HNO, for trace metal analyses and (b) refrigerated for IC analysis. Both the soluble and insoluble fractions of Na, Ca, Al, Ti, Cu, V, Mn, Br, Sb, and As were measured by instrumental neutron activation analysis (INAA) after irradiation with thermal neutrons [2]. The filtrate was freeze-dried prior to INAA. Na+ from the IC measurements is presented here although very similar results were obtained from INAA or ICP analyses on the same samples (r > 0.93). In reporting the data, the soluble and insoluble fractions As and Sb have been
Original
‘... :
.,
:
:
‘.:‘x
; .,,,
..,.
::
__,,, y;
:
./ .j:. i/ ,,,..,, .’
_,P ,......
DATA
Surface winds .J J
in the Seattle-Tacoma
Paper
H
used to determine the combined sampling and analytical uncertainties. These measurements quantify the overall experimental uncertainties due to the rain sampler preparation, handling, and transport to the field site, rainwater collection, sample filtration and storage, and analysis. For H+, SO:-, Mg, As, Sb, Cl-, and NO;, these measured experimental uncertainties from this ‘ten-sampler’ subset represented 4-13% (average of the relative standard deviations) of the measured concentrations while Na+, Pb, and Ca had typical uncertainties of 22-26% of the measured concentrations for the three storms where the tensampler experiment was conducted [2].
..
3
Fig. 2. Ten meter wind direction for 20 March 1985.
Research
area
added to form their total concentrations in rainwater because the two fractions were correlated in the samples. About 80% of the As and Sb were in the soluble fraction of our samples. Two pre-washed (HNO,, then repeated distilled, deionized water rinses until conductivity was less than 1 @/cm) funnel and bottle collectors were deployed at each site. For three storms at one site near the center of our collection network (behind a locked gate on a large grassy field which was well removed from traffic and combustion sources), ten samplers were placed 3 meters apart and collected a subset of samples that were
SCREENING
The results of the ten-sampler experiment were used to determine whether or not the observed difference in the pH, nitrate (NO;), and excess sulfate ion (SO:-xs) concentrations between any pair of samples at a given site was unexpectedly large. (Excess SOi- is defined as the non-seasalt portion of SOi- and is calculated as the mean that would be predicted from seasalt composition and observed rainwater Na+ and Mg concentrations. For samples where Ca > 10 peq/l only Na+ was used to avoid bias due to crustal Mg.) An assumption in the screening approach is that the ten-sampler experiment accurately reflects the sampling and analysis errors for all uncontaminated sites in the network. The relationship between the variance and the mean for the three sets (three different storms at the same site) of ten-sampler results indicated a variance stabilizing transformation to the square root of the original values [3]. The ten-sampler residual variance was determined from an one-way analysis of variance (ANOVA) of the square root transformed concentrations. For each sample pair in the network, the variance is exactly twice the residual mean square, s,. Thus, in order to reject only 0.5% of the pairs by chance, the confidence limits for outlier identification were set at the 0.25% level. Thus a t statistic of t(df= 27)0.25%3.0 was chosen so that any acceptable observed 101
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value of the pair difference (for paired samples A and B) in transformed units would be: SQRT( cA) - SQRT( cn) < 3.0 x (2 x s;)‘.~ A measurement that failed this pair difference criteria was eliminated only if its sample had a charge balance ratio (sum of cations/sum of anions) which was outside the range also defined by the ten-sampler experiment (0.88 < ratio < 1.20). In all cases, when one measurement was rejected for failing the charge balance criteria, the measurement which was retained had to significantly improve the sample charge balance when combined with the pair-mean values for the other species in the sample. Four, nine, and sixteen percent of the SO:-xs, NO;, and pH measurements were identified as probable outliers based on the pair difference criteria with one-half of those outliers rejected from the data based on the charge balance criteria. This procedure was intended to ensure than experimental uncertainties did not contribute to the observed variation in composition or the correlation between species.
RESULTS
AND DISCUSSION
Table 1 presents the overall mean concentrations for 24 chemical species for 56 samples for the two storms and for each of the three classes which are later used in the multivariate data analysis. All species except for Na+, Cl-, and Mg have much lower concentrations on the Pacific coast of Washington State [l] than reported here. The species SO:-xs, As, Sb, Cu, and Pb which have been identified as present in the smelter plume [4] and/or rain samples collected immediately downwind of the smelter [5-81, are highest for class 2 in these data. Class 2 is located downwind of the smelter but upwind of other potential sources of SO:-, V, and Pb in Seattle. The smelter is considered to be by far the dominant source of As and Sb in the region. Class 3 was chosen as the geographical region most likely to be affected by automobile traffic, industrial fuel burning, cement manufacturing, and secondary Pb refining under the southwesterly wind flow patterns associated with rain (see Figs. 1 and 2). The highest Ca, Al, 102
and Ti concentrations occurred for class 3. Class 1 is considered to be upwind of the Seattle-Tacoma urban-industrial area and has generally lower concentrations of all species in these two rain storms except for Na+ and Mg, two elements which are generally derived from seasalt in Western Washington rains [1,8]. Fig. 3 presents a geographical mapping of rainwater concentrations for the storm collected 19-21 March 1985 demonstrating a clear enhancement of H+, SO:-xs, As,, and Pb downwind of the smelter (to the northeast), consistent with the hypothesis that the smelter was an important source of these species. Similar geographical distributions for the 14-15 February storm were observed in maps of these species [2]. Fig. 4 depicts the spatial variation in Al, (subscript i refers to the insoluble fraction, subscript t refers to the sum of soluble and insoluble fractions, unsubscripted chemical species are the soluble fraction only), Mn, Ca, and V. These species have a different geographical distribution than the ‘smelter fingerprint’ species with generally the highest values of Ca, Al, and V located northeast of the Seattle industrial district, consistent with a cement plant and oil burning processes as potential sources. Other localized fuel burning sources may be responsible for elevated V concentrations elsewhere. Mn concentrations are highest to the east of our network, possibly due to the influence of soil or a coal-fired power plant located 80 km upwind of our sampling network. We have analyzed the rainwater chemical data for the two storms with PCA and cluster analysis to identify sources influencing the area covered by our sampling network. Table 2 presents the PCA results for the training set (classes l-3), i.e. the sites with the most regional emission source characteristics under the prevailing southwesterly winds associated with rain. The calculations were performed using the ARTHUR [9] and SIMCA-3X [lO,ll] software. Since the concentrations of each species over the network were more nearly lognormal than normal, the data were log-transformed before PCA and cluster analysis. The data were also preprocessed (‘autoscaled’) to give each variable equal weight in the analysis [5,9,12]. The number of significant components in the
Original
TABLE
Research
Paper
W
1
Rainwater chemical (std. deviation)
composition
for storms
collected
on 14-15
February
and 19-21
March
1985, presented
as mean concentration
Major ions are presented as peq/l and trace species as pg/l (ppb). i = insoluble component as determined from ram filters. t = sum of soluble and insoluble components. All other species are soluble fraction. nd = not detected, Cu < 0.4 ppb. Analyte Major ions Na NH, Cl NO3
so, xs H+ K Ca Mg Trace species Zn Fe Pb As-t Sb-t Al-i Ti-i V-i V Cu-i cu Mn-i Mn Br-i Br Number of samples
All 56 samples
33.7 9.1 39.9 11.5 25.2 15.9 12.5 14.9 10.9
(32.3) (9.7) (33.5) (12.5) (11.8) (12.6) (25.0) (12.2) (9.2)
34.0 (34.0) 5.5 (3.7) 6.1 (14.2) 4.2 (14.9) 0.5 (1.3) 186.0 (243.0) 13.2 (18.1) 0.4 (0.4) 0.3 (0.2) 2.9 (7.5) 5.6 (22.3) 2.6 (4.9) 4.7 (7.0) 0.2 (0.4) 2.3 (3.8)
56
Class 1
Class 2
53.3 (42.1) 8.7 (4.6) 61.0 (38.0) 6.8 (3.5) 17.1 (5.4) 17.7 (9.8) 7.3 (3.9) 9.4 (5.3) 16.0 (11.5)
41.3 (28.0) 8.3 (3.8) 38.0 (20.5) 8.8 (5.1) 29.5 (8.5) 30.1 (10.6) 5.6 (4.9) 11.0 (7.9) 13.1 (9.1)
46.0 (68.0) 3.4 (2.2) 2.3 (2.3) 0.2 (0.2) 0.03 (0.04) 67.0 (75.0) 4.7 (7.6) 0.2 (0.2) 0.2 (0.1) nd nd 0.7 (0.7) 2.6 (4.0) 0.2 (0.2) 1.7 (2.2)
21.0 5.8 14.7 13.4 1.3 83.0 5.7 0.2 0.4 7.4 18.6 0.8 2.9 0.1 2.4
10
13
rainwater data for combined classes 1, 2, and 3 for the two storms was between 3 and 5 based on the Malinowski indicator function criteria [13] and was 3 based on cross-validation [14]. Table 2 presents the first five principal components as calculated with the ARTHUR software with all factor loadings of at least 0.25 indicated. SIMCA3X determined three principal components which are very similar to components 1-3 in Table 2. Hierarchical clustering using the complete ,link method [9] produced a similar grouping of chemical species as did the PCA. Components 4 and 5 were retained for informative purposes. The principal components were examined in
(15.0) (4.3) (27.7) (29.7) (2.6) (85.0) (7.1) (0.2) (0.1) (14.0) (44.5) (0.8) (2.8) (0.3) (3.1)
Class 3
26.0 5.4 34.5 9.0 23.7 16.6 19.5 19.6 8.1
(29.4) (4.7) (33.0) (6.2) (7.4) (10.8) (44.7) (14.5) (5.8)
38.0 (18.0) 6.1 (4.1) 5.6 (5.6) 1.9 (1.4) 0.3 (0.2) 249.0 (333.0) 19.7 (26.3) 0.5 (0.6) 0.4 (0.2) 3.6 (4.4) 0.7 (1.6) 2.9 (3.6) 4.4 (3.1) 0.3 (0.5) 1.9 (4.4)
14
terms of the loadings of each chemical species and the scores of each sample [5,6,12] which were interpreted to represent the source chemical signatures and their variation among samples, respectively. Soil with NO;, metals and sulfur from the copper smelter, and seasalt, are the hypothesized sources producing the first three components. Components 4 and 5 are less obvious. The five unrotated components describe 75% of the variance in the original data set. A varimax rotation of the PCA solution produced similar results. The PCA ‘factor scores’ for component 2 can be thought of as the relative degree of influence of the smelter’s sulfur and trace metal emissions at a 103
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Pb
Fig. 3. Spatial variation of rainwater H+, SOa-xs, As,, and Pb concentrations for a storm dashed lines define constant concentrations with levels and units indicated on the maps.
given site in our rain sampling network. Figs. 5 and 6 present these scores (from the PCA results from Table 2) for storms collected 14-15 February and 19-21 March 1985 plotted over the geographic extent of our network. As expected, the lowest factor scores for component 2 correspond to the meteorologically upwind area of our network (south of the smelter) and the highest factor scores occur immediately downwind of the smelter (to the northeast). These scores describe an area resembling that anticipated from a ‘plume’ of emissions from the smelter and are entirely consistent with the wind direction and location of the smelter (see Figs. 1 and 2). 104
sampled
19-21
March
1985. Solid and
Fig. 7 presents the results of a principal components (SIMCA) classification of rainwater samples [10,11,15-171. The samples were originally assigned to classes 1-3 based on wind direction and the location of emission sources in Tacoma and to the south of Seattle (see Fig. 1). Principal component models with two cross-validated principal components per class were formed with As, Sb, Cu, and Ca providing the largest discrimination power between classes (class 1 had only one cross-validated principal component although two were used in the classification of samples). Ninety percent of the rain samples were correctly classified by the SIMCA models, i.e., meteorology and
Original
Fig. 4. Spatial variation of rainwater Ca, Mn, Ali and V concentrations for a storm sampled lines define constant concentrations with levels and units indicated on the maps.
rainwater chemistry were consistent. Samples from two sites in the northwest portion of our sampling network were reclassified as ‘upwind’ after the original SIMCA modeling. Reexamination of the location of these two sampling sites indicates that they are also correctly considered upwind. Therefore, the SIMCA analysis of rainwater chemical composition is considered to be in complete agreement with site location, smelter location, and wind direction. Fig. 8 indicates the variation of sample principal component scores for the ‘smelter’ and ‘seasalt’ principal components (PC 2 vs. PC 3). Samples are coded according to the originally
19-21
March
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Paper
1985. Solid and dashed
assigned class numbers l-3 with the test set labeled 4 or 6. The ‘smelter influenced’ class 2 samples lie to the left (higher As, Pb, Sb, Cu, Fe, H+, SOi-xs) of the samples from the ‘upwind’ class 1. Since the distance in N-space of the class 1 samples to the class 2 model is greater than two standard deviations (class 2 residual standard deviation), the ‘upwind’ class 1 samples are clearly different from the class 2 samples collected immediately downwind of the copper smelter [10,11,17]. Many of the samples from class 3 are close to samples from class 2 on the ‘smelter influenced X-axis of Fig. 8. However, class 3 (North Seattle) samples have much higher Ca concentrations than 105
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Principal
components
calculated
for classes 1-3,
N = 37
Rainwater samples collected 14-15 February and 19-21 March 1985 near Olympia, Tacoma, and Seattle, representing 75% of the variance (24 species) in the data set were selected based on the Malinowski indicator calculations were performed using the ARTHUR software program. Similar results were obtained using components being significant based on a cross-validation criterion.
WA. Five components function criteria. These SIMCA-3X with three
Principal component
Eigenvalue
Percent variance
Species
Loading
Interpretation
1
8.7
36.4
NO, V-i Ca Al-i Ti-i Mn
-0.30 -0.30 - 0.29 - 0.28 - 0.25 - 0.24
Soil/NO,
3.3
13.9
Sb-t Pb As-t Fe cu H+ so,xs
-0.36 - 0.35 -0.35 -0.33 -0.30 - 0.27 - 0.27
Copper
2.7
11.3
Na
- 0.41 - 0.39 - 0.39 0.32 0.29
Seasalt/-soil
0.55 - 0.45 0.37 0.28 0.26
Unknown
NH, Fe K Ca V H+ so,xs Br-i Pb
- 0.40 - 0.39 0.28 0.24
Mg Cl Mn-i Al-i 1.9
1.4
7.7
5.7
class 2 (probably a cement plant influence) with As and Sb concentrations in class 3 samples at levels consistent with their downwind distance from the smelter. Comparison of the distance in N-space from the class 3 samples to the principal component model for class 2 (smelter influence) reveals that many of the class 3 samples are about one standard ‘deviation (class 2 residual standard deviation) away from class 2. This indicates that these samples are members of class 2 as well as their own class 3 and that those two class models are not entirely separated. 106
Zn
0.63
smelter
Fuel/auto/-SO,
The distance of all the test set samples (see Figs. 1, 7, and 8) to the class 2 model were more than twice the residual standard deviation of the class 2 model indicating that the test set was not smelter influenced. Two sites to the extreme northeast of our network were classified as upwind based on their distance to the class 1 model and the residual standard deviation of the upwind, class 1, model. This suggests that either the smelter plume was transported more north or east of those sites or that the level of smelter influence at those locations was below the ability of the analytical
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Fig. 5. Variation in rainwater sample scores for principal component 2 (see Table 2) composed of As,, Sb,, Pb, Fe, Cu, SO:-xs, and Hf. Solid and dashed lines define scores which are average and high, respectively, with three regions labeled as “I-I” for scores above average, “M” for scores nearly average, and “L” for scores below average for the two storm rainwater data. Sampled 14-15 February 1985.
Fig. 7. Class membership for rainwater February and 19-21 March 1985 based ing. Sites are labeled as belonging to unlabeled sites not a member of any p = 0.10. PC
x z:
As.
sb,
Cu.
PL,
36$5 1 4 64
633
114 3 ?32
collected 14-15 SIMCA model1, 2, or 3 with three classes at
SC&,Ii+. Fe
-4 34% 2
samples on the classes of the
6
6
3 1
3 66
f
2 32
2’ 2
2
2 1 6
2 2
1
1
1
2 PC SCORES FROM CLRSSES
Fig. 6. Variation in rainwater sample scores for principal component 2 (see Table 2) composed of As,, Sb,, Pb, Fe, Cu, SOa-xs, and H+. Coding is the same as for Fig. 5. Sampled 19-21 March 1985.
l-3.
3
TESTSETz4.6
Fig. 8. Principal component (PC) score plot for the rainwater samples collected 14-15 February and 19-21 March 1985 (variances and loadings are given in Table 2). Individual samples are coded according to the a priori classes defined in Fig. 1, i.e. class 1 = upwind, class 2 = Vashon, class 3 = N. Seattle, and classes 4 and 6 are a test set of unknown class membership. 107
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techniques and the principal component models to resolve.
CONCLUSIONS
Samples collected in the upwind area of our sampling network contain considerably less As, Pb, Sb, Cu, H+, and SOi-xs than in the downwind samples. Measurement of the experimental uncertainties in our rain sampling procedures and the application of these uncertainties in a screening procedure to eliminate questionable samples from the data set increases confidence in the interpretation of spatial variations in rainwater composition. The multivariate data analysis has identified at least three factors which contribute to the composition of. rainwater near the copper smelter. Both the SIMCA-3X and ARTHUR software identified the same principal components. Seasalt and crustal material are two important contributors to the soluble and insoluble fractions, respectively, of our rain samples. The remaining component has been interpreted to result from the emissions from the copper smelter. Its chemical composition and geographical variation are consistent with meteorology and the location of the copper smelter. Given that the smelter was the largest source of airborne arsenic in the United States, the existence of a single principal component composed of As, Cu, Pb, Sb, SOi-xs, and H+, the variation of the smelter signature (PC 2) can reasonably be interpreted as the variation in the smelter’s influence. Based on these principal components, samples were classified into one of three groups according to the extent of influence of the copper smelter. Using this multivariate classification procedure, the smelter’s contribution to rainwater pH and SO,2Yxs will be further evaluated using rainwater samples collected at the same three groups of sites after the permanent closure of the copper smelter.
ACKNOWLEDGEMENTS
This work was supported by USEPA and Battelle PNL as part of the National Acid Precipita108
tion Assessment program but has not been reviewed by them and the findings are those of the authors and not the USEPA. The authors are grateful to the Seattle Water Department, Evergreen State College, Green River and Highline Community Colleges, Vashon Island, Tacoma, Olympia, King County, and Mt. Vernon School Districts for providing sampling sites and for sample collection. M. Stevenson performed the IC chemical analysis, J.O’Loughlin performed the ICP analysis, and R. Peterson performed the INAA analysis. The authors thank R.J. Charlson, L. Moseholm, and B.R. Kowalski of the University of Washington for their suggestions.
REFERENCES 1 R.J. Vong, Simultaneous observations of rainwater and aerosol chemistry at a remote mid-latitude site, Ph.D. Dissertation, University of Washington, Seattle, WA, 1985. 2 R.J. Vong, T.V. Larson, W.H. Zoller, D.S. Covert, R.J. Charlson, I. Sweet, R. Peterson, T. Miller, J.F. O’Loughlin and M.N. Stevenson, Rainwater chemistry near an isolated SO, emission source, in R.W. Johnson (Editor), Acid Rain: I. Sources and Atmospheric Processes, ACS Symposium Series, American Chemical Society, Washington, DC, 1987, in press. 3 G.E.P. Box, W.G. Hunter and J.S. Hunter, Statistics for Experimenters, Wiley, New York, 1978. 4 A.J. Alkezweeny, R.E. Peterson and N.S. Laulainen, unpublished aircraft data, Battelle PNL, Richland, WA, 1986. 5 R.J. Vong, I.E. Frank, R.J. Charlson and B.R. Kowalski, Exploratory data analysis of rainwater composition, in J.J. Breen and P.E. Robinson (Editors), Environmental Applications of Chemometrics, ACS Symposium Series 292, American Chemical Society, Washington, DC, 1985, pp. 34-52. 6 E.J. Knudson, D.L. Duewer, G.D. Christian and T.V. Larson, Application of factor analysis to rain chemistry in the Puget Sound region, in B.R. Kowalski (Editor), Chemometrics, Theory and Application, ACS Symposium Series 52, American Chemical Society, Washington, DC, 1977, pp. 80-116. 7 T.V. Larson, R.J. Charlson, E.J. Knudson, G.D. Christian and H. Harrison, The influence of a sulfur dioxide point source on the rain chemistry of a single storm in the Puget Sound region, Water, Air, and Soil Pollution, 4 (1974) 319-328. 8 R.J. Vong, T.V. Larson, D.S. Covert and A.P. Waggoner, Measurement and modeling of Western Washington precipitation chemistry, Water, Air, and Soil Pollution, 26 (1985) 71-84. 9 D.L. Duewer, A.M. Harper, J.R. Koskinen, J.L. Fasching and B.R. Kowalski, ARTHUR, version 7-l-79, Infometrics, Inc., Seattle, WA, 1979.
Original
10 S. Wold, SIMCA-3X, computer software available from Principal Data Components, Columbia, MO, 1986. 11 S. Wold and M. Sjostrom, SIMCA: a method of analyzing chemical data in terms of similarity and analogy, in B.R. Kowalski (Editor), Chemometrics, Theory and Applicafion, ACS Symposium Series 52, American Chemical Society, Washington, DC, 1977, pp. 243-282. 12 B.R. Kowalski and C.F. Bender, Pattern recognition. A powerful approach to interpreting chemical data, Journal of the American Chemical Society, 94 (1972) 5632-5639. 13 E.R. Malinowski, Abstract factor analysis - a theory of error and its application to analytical chemistry, in B.R. Kowalski, Chemometrics, Theory and Application, ACS Symposium Series 52, American Chemical Soceity, Washington, DC, 1977. pp. 50-79.
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14 S. Wold, Cross validatory estimation of the number of components in factor and principal component models, Technometrics, 20 (1978) 397-405. 15 P.C. Jurs, Pattern recognition used to investigate multivariate data in analytical chemistry, Science, 232 (1986) 1219-1224. 16 S. Wold, Pattern recognition by means of disjoint principal component models, Pattern Recognition, 8 (1976) 127-139. 17 S. Wold, C. Albano, W.J. Dunn, K. Esbensen, S. Hellberg, E. Johansson and M. Sjostrom, Pattern recognition: finding and using regularities in multivariate data, in H. Martens and H. Russwurm (Editors), Food Research and Data Analysis, Applied Science Publishers, London, 1983, pp. 147-188.
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Chemometrics and Intelligent Laboratory Systems, 3 (1988) 99-109 Elsevier
Science Publishers
B.V., Amsterdam
-
Printed
in The Netherlands
A Multivariate Chemical Classification of Rainwater Samples RICHARD
Department
of Civil Engineering,
J. VONG
* and TIMOTHY
WILLIAM
Department
of Chemistry,
V. LARSON
University of Washington FX-IO, Seattle,
WA 98195 (U.S.A.)
H. ZOLLER
University of Washington BG-IO, Seattle, WA 98195 (U.S.A.)
ABSTRACT
Vong, R.J., Larson, T.V. and Zoller, Chemometrics and Intelligent Laboratory
W.H., 1988. A multivariate Systems, 3: 99-109.
chemical
classification
of rainwater
samples.
An experiment was conducted during February and March of 1985 and 1986 to collect rainwater both upwind and downwind, before and after the permanent closure of a copper smelter located in Tacoma, WA to determine the effect of the smelter on downwind precipitation chemistry. The results of two storm events sampled by a 38 site network in Western Washington are reported here. Two collectors were deployed at each site. This produced information on the spatial and temporal variability in the composition of the rainwater as well as the experimental uncertainty. Principal component analysis (PCA) revealed the influence of a factor which is similar to the atmospheric emissions from the smelter (As, Sb, Cu, Pb, excess SO,‘-). Two different PCA software packages produced similar results. A classification of rainwater samples according to the influence of the copper smelter was performed using PCA models for three geographical regions with separate meteorological and source emission characteristics. Ninety percent of the samples were correctly classified into one of three regions of varying smelter influence on rainwater chemistry, demonstrating that meteorology and source location are consistent with the PCA analysis of rainwater chemistry.
steady southwesterly air flow aloft during winter rain. Therefore, the influence of a single air pollutant emission source, a copper smelter, on rainwater chemistry is potentially easier to observe in Western Washington than elsewhere. This paper presents observations of rainwater chemistry for two storms sampled on 14-15 February and 19-21 March 1985 near a large copper smelter located in Tacoma, WA, U.S.A. We discuss sampling and analysis procedures, statistical screening of the data based on measured uncertainties, spatial variability, principal compo-
INTRODUCTION
Rain chemistry source-receptor relationships are difficult to examine in the heavily impacted areas of the northeastern United States and central Europe due to the contributions from a large and variable number of local and distant emissions. In contrast, within the Puget Sound area of Washington State, clean background air [l] moves inland from the Pacific Ocean past a relatively small number of emission sources. The meteorology associated with cyclonic frontal systems results in 0169-7439/88/$03.50
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B.V.
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nent analysis (PCA) of the rainwater concentrations, and multivariate classification of rain samples into regions of differing smelter influence. The data set presented here is unique in the physical simplifications afforded by a single large source located in an environment where the background air is very clean.
EXPERIMENTAL
A network of 38 rainwater collectors was operated both upwind and downwind of the smelter. Fig. 1 shows the location of the rainwater sampling sites and the copper smelter. Sites are coded according to the classification scheme employed in this analysis as: (a) upwind, class 1, (b) downwind/Vashon area, class 2, and (c) downwind/ N.Seattle area, class 3. The remaining sites were considered to be of unknown influence and were tested for similarity to the three a priori classes which reflect separate meteorological and source emission characteristics. A decision to sample a given storm was based on synoptic meteorological information in an attempt to collect cyclonic frontal rain that was fairly uniform over the mesoscale extent (80 km)
0
4
06
d
SAMPLING NETWORK CLASSES CLASS 1 CUSS 2 CLASS 3 CLASSES TEST
= UPWIND = VASHON = N.SEATTLE 4+6 = SET (UNKNOWN)
Fig. 1. Map of Western Washington State, U.S.A., water sampling sites coded by meteorological/source
100
with rainclass.
of our network. The sampling protocol required that good atmospheric ventilation proceed the 24 or 48 hour sampling period. Dry deposition was not separately evaluated but was expected to be small compared to wet deposition because the sites were located in grassy areas to minimize dust and, also, due to the low ground level ambient concentrations of SO, and aerosol which are associated with rainy days in Western Washington. A total of 14 storms were sampled during February and March of 1985-86, seven with the copper smelter operating. Surface wind speed and direction data indicate that winds in the Tacoma-Seattle area were southwesterly for the two storms discussed here. An average of 0.5 cm of rain fell over 3 and 6 hour periods for the February and March events, respectively. The latter event included one day of windy, dry weather followed by a day of rain. Fig. 2 presents the 3 hour average 10 meter surface winds observed over the sampling network during the precipitation sampling period of the 19-21 March event. Rawindsondes confirmed southwesterly flow aloft at the expected height (500 m) of the copper smelter plume. Hydrogen ion concentration was computed from pH measurements taken as soon as possible after the end of the sampling period (usually within 6-8 hours). The samples were then filtered and the concentrations of Na, Mg, Ca, K, Fe, and Zn were measured on the filtrate by inductively coupled plasma emission spectroscopy (ICP). Measurements of NO;, Cl-, SO:-, Na+, and NH: were made via ion chromatography (IC) and Pb was measured by flameless atomic absorption spectroscopy (AAS). Separate sample aliquots were stored (a) frozen and acidified with Ultrex HNO, for trace metal analyses and (b) refrigerated for IC analysis. Both the soluble and insoluble fractions of Na, Ca, Al, Ti, Cu, V, Mn, Br, Sb, and As were measured by instrumental neutron activation analysis (INAA) after irradiation with thermal neutrons [2]. The filtrate was freeze-dried prior to INAA. Na+ from the IC measurements is presented here although very similar results were obtained from INAA or ICP analyses on the same samples (r > 0.93). In reporting the data, the soluble and insoluble fractions As and Sb have been
Original
‘... :
.,
:
:
‘.:‘x
; .,,,
..,.
::
__,,, y;
:
./ .j:. i/ ,,,..,, .’
_,P ,......
DATA
Surface winds .J J
in the Seattle-Tacoma
Paper
H
used to determine the combined sampling and analytical uncertainties. These measurements quantify the overall experimental uncertainties due to the rain sampler preparation, handling, and transport to the field site, rainwater collection, sample filtration and storage, and analysis. For H+, SO:-, Mg, As, Sb, Cl-, and NO;, these measured experimental uncertainties from this ‘ten-sampler’ subset represented 4-13% (average of the relative standard deviations) of the measured concentrations while Na+, Pb, and Ca had typical uncertainties of 22-26% of the measured concentrations for the three storms where the tensampler experiment was conducted [2].
..
3
Fig. 2. Ten meter wind direction for 20 March 1985.
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area
added to form their total concentrations in rainwater because the two fractions were correlated in the samples. About 80% of the As and Sb were in the soluble fraction of our samples. Two pre-washed (HNO,, then repeated distilled, deionized water rinses until conductivity was less than 1 @/cm) funnel and bottle collectors were deployed at each site. For three storms at one site near the center of our collection network (behind a locked gate on a large grassy field which was well removed from traffic and combustion sources), ten samplers were placed 3 meters apart and collected a subset of samples that were
SCREENING
The results of the ten-sampler experiment were used to determine whether or not the observed difference in the pH, nitrate (NO;), and excess sulfate ion (SO:-xs) concentrations between any pair of samples at a given site was unexpectedly large. (Excess SOi- is defined as the non-seasalt portion of SOi- and is calculated as the mean that would be predicted from seasalt composition and observed rainwater Na+ and Mg concentrations. For samples where Ca > 10 peq/l only Na+ was used to avoid bias due to crustal Mg.) An assumption in the screening approach is that the ten-sampler experiment accurately reflects the sampling and analysis errors for all uncontaminated sites in the network. The relationship between the variance and the mean for the three sets (three different storms at the same site) of ten-sampler results indicated a variance stabilizing transformation to the square root of the original values [3]. The ten-sampler residual variance was determined from an one-way analysis of variance (ANOVA) of the square root transformed concentrations. For each sample pair in the network, the variance is exactly twice the residual mean square, s,. Thus, in order to reject only 0.5% of the pairs by chance, the confidence limits for outlier identification were set at the 0.25% level. Thus a t statistic of t(df= 27)0.25%3.0 was chosen so that any acceptable observed 101
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value of the pair difference (for paired samples A and B) in transformed units would be: SQRT( cA) - SQRT( cn) < 3.0 x (2 x s;)‘.~ A measurement that failed this pair difference criteria was eliminated only if its sample had a charge balance ratio (sum of cations/sum of anions) which was outside the range also defined by the ten-sampler experiment (0.88 < ratio < 1.20). In all cases, when one measurement was rejected for failing the charge balance criteria, the measurement which was retained had to significantly improve the sample charge balance when combined with the pair-mean values for the other species in the sample. Four, nine, and sixteen percent of the SO:-xs, NO;, and pH measurements were identified as probable outliers based on the pair difference criteria with one-half of those outliers rejected from the data based on the charge balance criteria. This procedure was intended to ensure than experimental uncertainties did not contribute to the observed variation in composition or the correlation between species.
RESULTS
AND DISCUSSION
Table 1 presents the overall mean concentrations for 24 chemical species for 56 samples for the two storms and for each of the three classes which are later used in the multivariate data analysis. All species except for Na+, Cl-, and Mg have much lower concentrations on the Pacific coast of Washington State [l] than reported here. The species SO:-xs, As, Sb, Cu, and Pb which have been identified as present in the smelter plume [4] and/or rain samples collected immediately downwind of the smelter [5-81, are highest for class 2 in these data. Class 2 is located downwind of the smelter but upwind of other potential sources of SO:-, V, and Pb in Seattle. The smelter is considered to be by far the dominant source of As and Sb in the region. Class 3 was chosen as the geographical region most likely to be affected by automobile traffic, industrial fuel burning, cement manufacturing, and secondary Pb refining under the southwesterly wind flow patterns associated with rain (see Figs. 1 and 2). The highest Ca, Al, 102
and Ti concentrations occurred for class 3. Class 1 is considered to be upwind of the Seattle-Tacoma urban-industrial area and has generally lower concentrations of all species in these two rain storms except for Na+ and Mg, two elements which are generally derived from seasalt in Western Washington rains [1,8]. Fig. 3 presents a geographical mapping of rainwater concentrations for the storm collected 19-21 March 1985 demonstrating a clear enhancement of H+, SO:-xs, As,, and Pb downwind of the smelter (to the northeast), consistent with the hypothesis that the smelter was an important source of these species. Similar geographical distributions for the 14-15 February storm were observed in maps of these species [2]. Fig. 4 depicts the spatial variation in Al, (subscript i refers to the insoluble fraction, subscript t refers to the sum of soluble and insoluble fractions, unsubscripted chemical species are the soluble fraction only), Mn, Ca, and V. These species have a different geographical distribution than the ‘smelter fingerprint’ species with generally the highest values of Ca, Al, and V located northeast of the Seattle industrial district, consistent with a cement plant and oil burning processes as potential sources. Other localized fuel burning sources may be responsible for elevated V concentrations elsewhere. Mn concentrations are highest to the east of our network, possibly due to the influence of soil or a coal-fired power plant located 80 km upwind of our sampling network. We have analyzed the rainwater chemical data for the two storms with PCA and cluster analysis to identify sources influencing the area covered by our sampling network. Table 2 presents the PCA results for the training set (classes l-3), i.e. the sites with the most regional emission source characteristics under the prevailing southwesterly winds associated with rain. The calculations were performed using the ARTHUR [9] and SIMCA-3X [lO,ll] software. Since the concentrations of each species over the network were more nearly lognormal than normal, the data were log-transformed before PCA and cluster analysis. The data were also preprocessed (‘autoscaled’) to give each variable equal weight in the analysis [5,9,12]. The number of significant components in the
Original
TABLE
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1
Rainwater chemical (std. deviation)
composition
for storms
collected
on 14-15
February
and 19-21
March
1985, presented
as mean concentration
Major ions are presented as peq/l and trace species as pg/l (ppb). i = insoluble component as determined from ram filters. t = sum of soluble and insoluble components. All other species are soluble fraction. nd = not detected, Cu < 0.4 ppb. Analyte Major ions Na NH, Cl NO3
so, xs H+ K Ca Mg Trace species Zn Fe Pb As-t Sb-t Al-i Ti-i V-i V Cu-i cu Mn-i Mn Br-i Br Number of samples
All 56 samples
33.7 9.1 39.9 11.5 25.2 15.9 12.5 14.9 10.9
(32.3) (9.7) (33.5) (12.5) (11.8) (12.6) (25.0) (12.2) (9.2)
34.0 (34.0) 5.5 (3.7) 6.1 (14.2) 4.2 (14.9) 0.5 (1.3) 186.0 (243.0) 13.2 (18.1) 0.4 (0.4) 0.3 (0.2) 2.9 (7.5) 5.6 (22.3) 2.6 (4.9) 4.7 (7.0) 0.2 (0.4) 2.3 (3.8)
56
Class 1
Class 2
53.3 (42.1) 8.7 (4.6) 61.0 (38.0) 6.8 (3.5) 17.1 (5.4) 17.7 (9.8) 7.3 (3.9) 9.4 (5.3) 16.0 (11.5)
41.3 (28.0) 8.3 (3.8) 38.0 (20.5) 8.8 (5.1) 29.5 (8.5) 30.1 (10.6) 5.6 (4.9) 11.0 (7.9) 13.1 (9.1)
46.0 (68.0) 3.4 (2.2) 2.3 (2.3) 0.2 (0.2) 0.03 (0.04) 67.0 (75.0) 4.7 (7.6) 0.2 (0.2) 0.2 (0.1) nd nd 0.7 (0.7) 2.6 (4.0) 0.2 (0.2) 1.7 (2.2)
21.0 5.8 14.7 13.4 1.3 83.0 5.7 0.2 0.4 7.4 18.6 0.8 2.9 0.1 2.4
10
13
rainwater data for combined classes 1, 2, and 3 for the two storms was between 3 and 5 based on the Malinowski indicator function criteria [13] and was 3 based on cross-validation [14]. Table 2 presents the first five principal components as calculated with the ARTHUR software with all factor loadings of at least 0.25 indicated. SIMCA3X determined three principal components which are very similar to components 1-3 in Table 2. Hierarchical clustering using the complete ,link method [9] produced a similar grouping of chemical species as did the PCA. Components 4 and 5 were retained for informative purposes. The principal components were examined in
(15.0) (4.3) (27.7) (29.7) (2.6) (85.0) (7.1) (0.2) (0.1) (14.0) (44.5) (0.8) (2.8) (0.3) (3.1)
Class 3
26.0 5.4 34.5 9.0 23.7 16.6 19.5 19.6 8.1
(29.4) (4.7) (33.0) (6.2) (7.4) (10.8) (44.7) (14.5) (5.8)
38.0 (18.0) 6.1 (4.1) 5.6 (5.6) 1.9 (1.4) 0.3 (0.2) 249.0 (333.0) 19.7 (26.3) 0.5 (0.6) 0.4 (0.2) 3.6 (4.4) 0.7 (1.6) 2.9 (3.6) 4.4 (3.1) 0.3 (0.5) 1.9 (4.4)
14
terms of the loadings of each chemical species and the scores of each sample [5,6,12] which were interpreted to represent the source chemical signatures and their variation among samples, respectively. Soil with NO;, metals and sulfur from the copper smelter, and seasalt, are the hypothesized sources producing the first three components. Components 4 and 5 are less obvious. The five unrotated components describe 75% of the variance in the original data set. A varimax rotation of the PCA solution produced similar results. The PCA ‘factor scores’ for component 2 can be thought of as the relative degree of influence of the smelter’s sulfur and trace metal emissions at a 103
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Pb
Fig. 3. Spatial variation of rainwater H+, SOa-xs, As,, and Pb concentrations for a storm dashed lines define constant concentrations with levels and units indicated on the maps.
given site in our rain sampling network. Figs. 5 and 6 present these scores (from the PCA results from Table 2) for storms collected 14-15 February and 19-21 March 1985 plotted over the geographic extent of our network. As expected, the lowest factor scores for component 2 correspond to the meteorologically upwind area of our network (south of the smelter) and the highest factor scores occur immediately downwind of the smelter (to the northeast). These scores describe an area resembling that anticipated from a ‘plume’ of emissions from the smelter and are entirely consistent with the wind direction and location of the smelter (see Figs. 1 and 2). 104
sampled
19-21
March
1985. Solid and
Fig. 7 presents the results of a principal components (SIMCA) classification of rainwater samples [10,11,15-171. The samples were originally assigned to classes 1-3 based on wind direction and the location of emission sources in Tacoma and to the south of Seattle (see Fig. 1). Principal component models with two cross-validated principal components per class were formed with As, Sb, Cu, and Ca providing the largest discrimination power between classes (class 1 had only one cross-validated principal component although two were used in the classification of samples). Ninety percent of the rain samples were correctly classified by the SIMCA models, i.e., meteorology and
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Fig. 4. Spatial variation of rainwater Ca, Mn, Ali and V concentrations for a storm sampled lines define constant concentrations with levels and units indicated on the maps.
rainwater chemistry were consistent. Samples from two sites in the northwest portion of our sampling network were reclassified as ‘upwind’ after the original SIMCA modeling. Reexamination of the location of these two sampling sites indicates that they are also correctly considered upwind. Therefore, the SIMCA analysis of rainwater chemical composition is considered to be in complete agreement with site location, smelter location, and wind direction. Fig. 8 indicates the variation of sample principal component scores for the ‘smelter’ and ‘seasalt’ principal components (PC 2 vs. PC 3). Samples are coded according to the originally
19-21
March
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1985. Solid and dashed
assigned class numbers l-3 with the test set labeled 4 or 6. The ‘smelter influenced’ class 2 samples lie to the left (higher As, Pb, Sb, Cu, Fe, H+, SOi-xs) of the samples from the ‘upwind’ class 1. Since the distance in N-space of the class 1 samples to the class 2 model is greater than two standard deviations (class 2 residual standard deviation), the ‘upwind’ class 1 samples are clearly different from the class 2 samples collected immediately downwind of the copper smelter [10,11,17]. Many of the samples from class 3 are close to samples from class 2 on the ‘smelter influenced X-axis of Fig. 8. However, class 3 (North Seattle) samples have much higher Ca concentrations than 105
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Principal
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N = 37
Rainwater samples collected 14-15 February and 19-21 March 1985 near Olympia, Tacoma, and Seattle, representing 75% of the variance (24 species) in the data set were selected based on the Malinowski indicator calculations were performed using the ARTHUR software program. Similar results were obtained using components being significant based on a cross-validation criterion.
WA. Five components function criteria. These SIMCA-3X with three
Principal component
Eigenvalue
Percent variance
Species
Loading
Interpretation
1
8.7
36.4
NO, V-i Ca Al-i Ti-i Mn
-0.30 -0.30 - 0.29 - 0.28 - 0.25 - 0.24
Soil/NO,
3.3
13.9
Sb-t Pb As-t Fe cu H+ so,xs
-0.36 - 0.35 -0.35 -0.33 -0.30 - 0.27 - 0.27
Copper
2.7
11.3
Na
- 0.41 - 0.39 - 0.39 0.32 0.29
Seasalt/-soil
0.55 - 0.45 0.37 0.28 0.26
Unknown
NH, Fe K Ca V H+ so,xs Br-i Pb
- 0.40 - 0.39 0.28 0.24
Mg Cl Mn-i Al-i 1.9
1.4
7.7
5.7
class 2 (probably a cement plant influence) with As and Sb concentrations in class 3 samples at levels consistent with their downwind distance from the smelter. Comparison of the distance in N-space from the class 3 samples to the principal component model for class 2 (smelter influence) reveals that many of the class 3 samples are about one standard ‘deviation (class 2 residual standard deviation) away from class 2. This indicates that these samples are members of class 2 as well as their own class 3 and that those two class models are not entirely separated. 106
Zn
0.63
smelter
Fuel/auto/-SO,
The distance of all the test set samples (see Figs. 1, 7, and 8) to the class 2 model were more than twice the residual standard deviation of the class 2 model indicating that the test set was not smelter influenced. Two sites to the extreme northeast of our network were classified as upwind based on their distance to the class 1 model and the residual standard deviation of the upwind, class 1, model. This suggests that either the smelter plume was transported more north or east of those sites or that the level of smelter influence at those locations was below the ability of the analytical
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Fig. 5. Variation in rainwater sample scores for principal component 2 (see Table 2) composed of As,, Sb,, Pb, Fe, Cu, SO:-xs, and Hf. Solid and dashed lines define scores which are average and high, respectively, with three regions labeled as “I-I” for scores above average, “M” for scores nearly average, and “L” for scores below average for the two storm rainwater data. Sampled 14-15 February 1985.
Fig. 7. Class membership for rainwater February and 19-21 March 1985 based ing. Sites are labeled as belonging to unlabeled sites not a member of any p = 0.10. PC
x z:
As.
sb,
Cu.
PL,
36$5 1 4 64
633
114 3 ?32
collected 14-15 SIMCA model1, 2, or 3 with three classes at
SC&,Ii+. Fe
-4 34% 2
samples on the classes of the
6
6
3 1
3 66
f
2 32
2’ 2
2
2 1 6
2 2
1
1
1
2 PC SCORES FROM CLRSSES
Fig. 6. Variation in rainwater sample scores for principal component 2 (see Table 2) composed of As,, Sb,, Pb, Fe, Cu, SOa-xs, and H+. Coding is the same as for Fig. 5. Sampled 19-21 March 1985.
l-3.
3
TESTSETz4.6
Fig. 8. Principal component (PC) score plot for the rainwater samples collected 14-15 February and 19-21 March 1985 (variances and loadings are given in Table 2). Individual samples are coded according to the a priori classes defined in Fig. 1, i.e. class 1 = upwind, class 2 = Vashon, class 3 = N. Seattle, and classes 4 and 6 are a test set of unknown class membership. 107
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techniques and the principal component models to resolve.
CONCLUSIONS
Samples collected in the upwind area of our sampling network contain considerably less As, Pb, Sb, Cu, H+, and SOi-xs than in the downwind samples. Measurement of the experimental uncertainties in our rain sampling procedures and the application of these uncertainties in a screening procedure to eliminate questionable samples from the data set increases confidence in the interpretation of spatial variations in rainwater composition. The multivariate data analysis has identified at least three factors which contribute to the composition of. rainwater near the copper smelter. Both the SIMCA-3X and ARTHUR software identified the same principal components. Seasalt and crustal material are two important contributors to the soluble and insoluble fractions, respectively, of our rain samples. The remaining component has been interpreted to result from the emissions from the copper smelter. Its chemical composition and geographical variation are consistent with meteorology and the location of the copper smelter. Given that the smelter was the largest source of airborne arsenic in the United States, the existence of a single principal component composed of As, Cu, Pb, Sb, SOi-xs, and H+, the variation of the smelter signature (PC 2) can reasonably be interpreted as the variation in the smelter’s influence. Based on these principal components, samples were classified into one of three groups according to the extent of influence of the copper smelter. Using this multivariate classification procedure, the smelter’s contribution to rainwater pH and SO,2Yxs will be further evaluated using rainwater samples collected at the same three groups of sites after the permanent closure of the copper smelter.
ACKNOWLEDGEMENTS
This work was supported by USEPA and Battelle PNL as part of the National Acid Precipita108
tion Assessment program but has not been reviewed by them and the findings are those of the authors and not the USEPA. The authors are grateful to the Seattle Water Department, Evergreen State College, Green River and Highline Community Colleges, Vashon Island, Tacoma, Olympia, King County, and Mt. Vernon School Districts for providing sampling sites and for sample collection. M. Stevenson performed the IC chemical analysis, J.O’Loughlin performed the ICP analysis, and R. Peterson performed the INAA analysis. The authors thank R.J. Charlson, L. Moseholm, and B.R. Kowalski of the University of Washington for their suggestions.
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