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Application of a multivariate chemical balance method to milan aerosol, fog and wet deposition data
J . A..,OJoi Sd.• Vol. 21. Suppl. 1. pp. S59}-S594. \996 Copyright 0 1996 Ebevier Science Ltd
~ Pergamon
PII: 80021-8502(96)00369-2
Printed in 0 ....1 Britain . All "8hU .....rved 002\.8502/96 515.00 + 0.00
APPLICATION OF A MULTIVARIATE CHEMICAL BALANCE METHOD TO MILAN AEROSOL, FOG AND WET DEPOSITION DATA M.MAUGERI Istituto di Fisica GeneraJe Applicata- University of Milan - via Brera,28 - 20121 Milan -Italy
A.NOVO ENEUCRAM - via Rubattino, 54 - 20100 - Milan - Italy
KEYWORDS Receptormodel, Multivariate Chemical Balance,Milan Receptor models have been applied to compositional data of dust samples on filters for more than twenty years. Usually these models are divided in Chemical Element Balance models (CEB) and multivariate models (Hopke, 1991). CEB models employ a least square calculation to quantitatively apportion the contribution of atmospheric aerosol sources under the assumption that their number and composition is known. Multivariate methods analyse the element correlation (or covariance) matrix without assumptions on the sources, however they allow only a qualitatively identification of their composition and of the apportionment ofthe observed data to the identified sources. BesideCEB and multivariate models, some other methods that joint the peculiarity of the models of the two principal groups have been proposed in the last years. The best known among these methods is Target Transformation Factor Analysis, but also other interesting approaches have been proposed. One of these is Tsurumi's method (1982). In this method. as in all the other receptor models, the observed data matrix, D, is expressed by D = C'S, where S is a source profile matrix and C is a source contribution one. The peculiarity ofTsurumi's method is that at first tentative values are used for S and with these data C is determined by least squares; then, with the calculated C matrix, new sources profiles(S) are determined (again by least squares) and so on till the matrix product of C and S is closeenough to the observed data matrix D. In additionapplying the model to ionicsolutions it is possibleto set a link amongcations and anions forcingtheir sums to be equal. Even if Tsurumi used his model also for source apportionment of ions in wet deposition samples (1990), its application to Milan data required some changes as here, and generally in Italy, the alkaline source contributions are often strong. Therefore acidity has to be calculated as a weighted (by source contributions) mean of terms (acidities in source profiles) that can be positive (acid sources) or negative (alkaline sources).
Acidity Ca++
Mg++ Na+ K+ NH: SO.NO)' Cl-
SOz
NOs
Soil
Sea salt
SOz
NOx
Soil
Sea salt
1 0 0 0 0 0 1 0 0
0 0 0 0 0 0.5 0.25 0.25 0
-I
0 0 0 0.5 0 0 0 0
0.74 0.07 0.Q3 0.03 0.01 0 0.67 0.20 0
0.02 0.02 0 0 0 0.47 0.23 0.26 0.02
-0.69 0.55 0.11 0.03 0.01 0.14 0.16 0 0
0.01 0.02 0.09 0.35 0.03 0 0
1 0 0 0 0 0 0 0
O.S
O.OS
0.45
Table 1: column 1-4: initial source profiles (tentative values); column 5-8: model calculatedprofiles SS93
Abstracts of the 1996 European Aerosol Conference
8594
With the applicationofthis new version ofTsurumi's model, ion concentrations in 158 weekly Milan wet deposition samples collected in the period 1987/1994 was best explained by the four sources of table 1. It is interesting to observe that the application of usual methods as Principal Component Analysisdoesn'tallow the identification of these sourcesas strong alkalinesource contributions cause low correlationof acidity with S04- and NO,'. Also the application of the model to elemental composition data of aerosol samples required a few changes. These data are more difficultto analyse as the number of variables is greater and there are elements with concentrations of the' order of 10 ng/m' beside other ones with concentrations 1000 times greater. As a consequence of these problems the application of Tsurumi's method with only tentative initial source profiles is very difficult and it is better to use literature profiles as initial data for some sources (the best known) and tentative values for the other ones, but also with these initial profiles the application of the model is often difficult as the literature source profiles are oftenstrongly modified by the iterative process. In order to avoid these problems a new version of the model has been developed that allows to keep constant one or more source profiles during the iterative process. With these version of the model the identification of the unknown sources is more easy. After the identification of the unknown source profiles the model can be applied again with no constantprofiles in order to optimise all the source profilesto the observeddata. With the applicationofthese modified versionofthe model, elemental composition data of 60 aerosol samples collected in Milan in autumn 1991 are best explained by 5 sources: conversion of SOz to sulphate, soil, motor vehicles, oil combustion and a source, mostly characterised by Zn, Fe, Ca, Pb and MJ\ which includes probably several antropic sources as iron and steel industry and refuse incineration. The profiles obtained for these sourcesare in good agreement with the ones obtained by the application ofTTFA to the same data (Manini et al., 1994; 1995).
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Ficure 1: obaerved V. calcuted valuCi Cor Fe (lcCt) .lad Pb (rcicbt) - tbe valuCi are exprcued in p.rJmc.
The error of the reconstructed data, expressed by the mean value (over the 60 samples) of the absolute values of the ratios of the differences between calculated and observed data and observed data, depends on the elements, ranging from values under 10% for Si, S, V, Fe and Ni to values over 50 % for Cl. The average error (i.e. the mean value of the errors ofall the considered elements) is 16 %. Figure I shows, as an example calculated Vs observed concentrations for Fe and Pb. The data reconstructed with Tsurumi method are in better agreement with observed data than the one obtained by TTFA. In fact, applyingthese model to the same data the mean error is 22 %.
REFERENCES • • • • • •
Tsurumi M. (l982)Anal. Chim. Acta 138,177-182. Tsurumi M.,Takahasi A. and Ichikuni M. (1990) Atmospheric Environment, 24A, 1493-1500. Hopke P.K. (1991) Receptor Modeling for Air Quality Managment, Elsevier, Amsterdam. Maugeri, M. Valentini M. and Novo A. (1994) Life Chemistry Reports, 10,237.247. Manini G.•Maugeri M., Rocca A. and Novo A. (1994) J Aerosol Sci., 25 SUPllll, S12S-S126. Manini G., Maugeri M. and Novo A. (1995) Ingegneria Ambientale, XXIV, 410-419.
~ Pergamon
PII: 80021-8502(96)00369-2
Printed in 0 ....1 Britain . All "8hU .....rved 002\.8502/96 515.00 + 0.00
APPLICATION OF A MULTIVARIATE CHEMICAL BALANCE METHOD TO MILAN AEROSOL, FOG AND WET DEPOSITION DATA M.MAUGERI Istituto di Fisica GeneraJe Applicata- University of Milan - via Brera,28 - 20121 Milan -Italy
A.NOVO ENEUCRAM - via Rubattino, 54 - 20100 - Milan - Italy
KEYWORDS Receptormodel, Multivariate Chemical Balance,Milan Receptor models have been applied to compositional data of dust samples on filters for more than twenty years. Usually these models are divided in Chemical Element Balance models (CEB) and multivariate models (Hopke, 1991). CEB models employ a least square calculation to quantitatively apportion the contribution of atmospheric aerosol sources under the assumption that their number and composition is known. Multivariate methods analyse the element correlation (or covariance) matrix without assumptions on the sources, however they allow only a qualitatively identification of their composition and of the apportionment ofthe observed data to the identified sources. BesideCEB and multivariate models, some other methods that joint the peculiarity of the models of the two principal groups have been proposed in the last years. The best known among these methods is Target Transformation Factor Analysis, but also other interesting approaches have been proposed. One of these is Tsurumi's method (1982). In this method. as in all the other receptor models, the observed data matrix, D, is expressed by D = C'S, where S is a source profile matrix and C is a source contribution one. The peculiarity ofTsurumi's method is that at first tentative values are used for S and with these data C is determined by least squares; then, with the calculated C matrix, new sources profiles(S) are determined (again by least squares) and so on till the matrix product of C and S is closeenough to the observed data matrix D. In additionapplying the model to ionicsolutions it is possibleto set a link amongcations and anions forcingtheir sums to be equal. Even if Tsurumi used his model also for source apportionment of ions in wet deposition samples (1990), its application to Milan data required some changes as here, and generally in Italy, the alkaline source contributions are often strong. Therefore acidity has to be calculated as a weighted (by source contributions) mean of terms (acidities in source profiles) that can be positive (acid sources) or negative (alkaline sources).
Acidity Ca++
Mg++ Na+ K+ NH: SO.NO)' Cl-
SOz
NOs
Soil
Sea salt
SOz
NOx
Soil
Sea salt
1 0 0 0 0 0 1 0 0
0 0 0 0 0 0.5 0.25 0.25 0
-I
0 0 0 0.5 0 0 0 0
0.74 0.07 0.Q3 0.03 0.01 0 0.67 0.20 0
0.02 0.02 0 0 0 0.47 0.23 0.26 0.02
-0.69 0.55 0.11 0.03 0.01 0.14 0.16 0 0
0.01 0.02 0.09 0.35 0.03 0 0
1 0 0 0 0 0 0 0
O.S
O.OS
0.45
Table 1: column 1-4: initial source profiles (tentative values); column 5-8: model calculatedprofiles SS93
Abstracts of the 1996 European Aerosol Conference
8594
With the applicationofthis new version ofTsurumi's model, ion concentrations in 158 weekly Milan wet deposition samples collected in the period 1987/1994 was best explained by the four sources of table 1. It is interesting to observe that the application of usual methods as Principal Component Analysisdoesn'tallow the identification of these sourcesas strong alkalinesource contributions cause low correlationof acidity with S04- and NO,'. Also the application of the model to elemental composition data of aerosol samples required a few changes. These data are more difficultto analyse as the number of variables is greater and there are elements with concentrations of the' order of 10 ng/m' beside other ones with concentrations 1000 times greater. As a consequence of these problems the application of Tsurumi's method with only tentative initial source profiles is very difficult and it is better to use literature profiles as initial data for some sources (the best known) and tentative values for the other ones, but also with these initial profiles the application of the model is often difficult as the literature source profiles are oftenstrongly modified by the iterative process. In order to avoid these problems a new version of the model has been developed that allows to keep constant one or more source profiles during the iterative process. With these version of the model the identification of the unknown sources is more easy. After the identification of the unknown source profiles the model can be applied again with no constantprofiles in order to optimise all the source profilesto the observeddata. With the applicationofthese modified versionofthe model, elemental composition data of 60 aerosol samples collected in Milan in autumn 1991 are best explained by 5 sources: conversion of SOz to sulphate, soil, motor vehicles, oil combustion and a source, mostly characterised by Zn, Fe, Ca, Pb and MJ\ which includes probably several antropic sources as iron and steel industry and refuse incineration. The profiles obtained for these sourcesare in good agreement with the ones obtained by the application ofTTFA to the same data (Manini et al., 1994; 1995).
.
·
· •J ·
0 0
o • 0 0
DO
• 10.1.··
0
~"t.
0
.
0
. J•
p
a.
d' gO
•..,D 0
-
....... ,
,
.
DO
....
0
•
i·" ••
~o
.•
. .
~a·
~
.......-
u
.
Ficure 1: obaerved V. calcuted valuCi Cor Fe (lcCt) .lad Pb (rcicbt) - tbe valuCi are exprcued in p.rJmc.
The error of the reconstructed data, expressed by the mean value (over the 60 samples) of the absolute values of the ratios of the differences between calculated and observed data and observed data, depends on the elements, ranging from values under 10% for Si, S, V, Fe and Ni to values over 50 % for Cl. The average error (i.e. the mean value of the errors ofall the considered elements) is 16 %. Figure I shows, as an example calculated Vs observed concentrations for Fe and Pb. The data reconstructed with Tsurumi method are in better agreement with observed data than the one obtained by TTFA. In fact, applyingthese model to the same data the mean error is 22 %.
REFERENCES • • • • • •
Tsurumi M. (l982)Anal. Chim. Acta 138,177-182. Tsurumi M.,Takahasi A. and Ichikuni M. (1990) Atmospheric Environment, 24A, 1493-1500. Hopke P.K. (1991) Receptor Modeling for Air Quality Managment, Elsevier, Amsterdam. Maugeri, M. Valentini M. and Novo A. (1994) Life Chemistry Reports, 10,237.247. Manini G.•Maugeri M., Rocca A. and Novo A. (1994) J Aerosol Sci., 25 SUPllll, S12S-S126. Manini G., Maugeri M. and Novo A. (1995) Ingegneria Ambientale, XXIV, 410-419.