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Identification of airborne particulate sources, of samples collected in Ticomán, Mexico, using PIXE and multivariate analysis
Nuclear Instruments and Methods in Physics Research B 189 (2002) 249–253 www.elsevier.com/locate/nimb
Identification of airborne particulate sources, of samples collected in Ticom an, Mexico, using PIXE and multivariate analysis R.V. Dıaz *, F. Aldape, J. Flores M. Instituto Nacional de Investigaciones Nucleares (ININ), Apartado Postal 18-1027, Col. Escand on, CP 11801 M exico D.F., Mexico
Abstract A set of samples of airborne particulate matter collected during February and March 1997 in Ticom an, a place located in the northern part of Mexico City’s Metropolitan Area, were analysed by PIXE and the concentrations of ten main elements were determined. The fundamental information provided by PIXE was used to build up the data base needed to apply statistical tools such as absolute principal factor analysis. The VARIMAX rotated component matrix of the data was obtained, and the first three principal factors were assigned to soil, industry, and sulfates pollution sources. The quantitative source apportionments of the atmospheric trace element concentrations for the day time and the night time were determined by means of absolute principal factor scores. Ó 2002 Published by Elsevier Science B.V. PACS: 0785; 86.70.G Keywords: PIXE; Airborne particulate matter
1. Introduction Mexico City is near to being the most polluted city in the world. At present, the problem expands across the country since many of the major cities such as Guadalajara, Monterrey and others have registered high rates of pollution as a consequence of contaminated residual fumes from neighbour industrial areas. It is of interest to concentrate attention on Mexico City’s Metropolitan Area (ZMCM). Since
*
Corresponding author. Tel.: +52-5-3297200x2632; fax +525-3297332. E-mail address: [email protected] (R.V. Dıaz).
1940 this area has shown an accelerated and steady increase in its population. 20% (approximately 20 million inhabitants) of the total population of the country is concentrated in only 1% of the national territory; the population density is about 5494 inhabitants per square kilometre. Moreover, 50% of the industrial activity [1] is also concentrated into the same percentage of the territory. Several government institutions as well as the research and academic community have made a great effort to contribute to decrease the pollution problem [2–6]. However, the actions have not been enough and have only mitigated the situation. At present, more studies are needed in order to create new strategies that permit to control and diminish the problem. In this respect, it is worth mentioning
0168-583X/02/$ - see front matter Ó 2002 Published by Elsevier Science B.V. PII: S 0 1 6 8 - 5 8 3 X ( 0 1 ) 0 1 0 7 3 - 4
250
R.V. Dıaz et al. / Nucl. Instr. and Meth. in Phys. Res. B 189 (2002) 249–253
that identification of the air pollutant sources can help to reduce the problem. The determination of the composition and sources of urban air particulate matter is becoming increasingly important to establish air pollution control programs. Once the sources are identified, especially those of undesirable elements, steps can be taken to control their release to the environment. Multivariate analysis has been shown to be a powerful technique for connecting a large body of elemental concentrations of atmospheric particles to their possible sources. In this respect, the fundamental and unique role played by PIXE in providing the basic information to build up the data base needed to apply these statistical tools must be pointed out. One of the areas within the ZMCM which has been detected to have a poor air quality is Ticom an [7] and it has been chosen to apply the multivariate analysis. Partial results of the PIXE analysis performed on a set of samples taken in this place has already been presented [7]. However, the number of analysed samples was significantly increased in order to carry out this study, with a total of 73 samples taken during night and day periods. Ten elements were identified: S, K, Ca, Ti, V, Mn, Fe, Cu, Zn and Pb appeared recurringly while others appeared with less frequency, for instance Cl, Ni, As and Br. Cl, Ni and Br appeared mainly during the night. In order to provide new insights into the air pollution problem in this area, absolute principal factor analysis (APFA) and absolute principal factor scores (APFS) were applied to the new data base using SPSSâ software [8] version 10.0 by SPSS Inc. It was thus possible to identify soil, industry and sulfates as emission sources for the day and night sampling periods, and to determine the apportionment of each source for each element.
2. Experimental
the Mexico City’s Metropolitan Area (19°300 N, 99°320 W) and it represents 5.9% of the total area occupied by the Federal District. It has a high population density and counts with several sites of major human activity. Some examples of these are: the Indios Verdes Bus and Metro Station visited everyday by 500–700 thousand commuters, North Central Bus Station, the area of Hospitals, the industrial zones of San Juan Ixhuatepec and Xalostoc influencing the area through the prevailing winds coming from north and northeast towards the city, and two other neighbouring industrial zones Atzcapotzalco and Tlalnepanta, all these influencing the area. There is an additional contribution to the local pollution derived from a large number of industries of varying size [9]. All this together makes a deteriorated place that must be observed and studied carefully. 2.2. Sampling The samples were collected from 17 February to 25 March 1997 in 12 h periods, starting in the morning at 7:00 [11]. This study has been focussed on fine mode aerosol due to its importance in human health. The samplers used were stacked filter units (SFU) [10] which separate the particles into two main fractions: the fine mode made of particles under 2.5 lm diameter, deposited on Teflonâ filters, and the coarse mode composed of particles of aerodynamic diameter between 2.5 and 15 lm deposited on Nucleporeâ polycarbonate membrane filters. The amount of mass deposited on the samples were obtained by gravimetric analysis. The filters were weighed before and after sampling in a E. Mettler Zurich electronic microbalance with a 1 lg sensitivity. Before weighing, all filters were conditioned during 24 h at 45% relative humidity and 22 °C temperature. fine particulate matter (FPM) mass concentrations (units lg/m3 ), as presented in Tables 1 and 2, were derived.
2.1. The sampling site 2.3. PIXE analysis Land use in Ticom an is distributed in residential, industrial and commercial sectors among others. Ticom an is located in the northern part of
PIXE analysis of aerosol samples was performed at the ININ PIXE facility following the protocol
R.V. Dıaz et al. / Nucl. Instr. and Meth. in Phys. Res. B 189 (2002) 249–253
251
Table 1 VARIMAX rotated component matrix of the fine mode aerosol from Ticoman, Mexico Industry
Soil
Communality
Percent of variance
Cumulative
0.225 0.099 0.223 0.341
0.896 0.757 0.779 0.838
51.933
51.933
0.943 0.924 0.725 0.707
0.034 0.117 0.491 0.043
0.890 0.878 0.789 0.943
20.667
72.601
0.061 0.202
0.944 0.864
0.936 0.904
13.496
86.096
Day Mn Cu Pb Zn
0.879 0.863 0.851 0.848
0.272 0.042 0.069 0.047
Ca Ti K Fe
0.021 0.103 0.146 0.664
S FPM
0.201 0.340 Soil
Sulfates
Industry
Night Ti Ca Fe K
0.912 0.903 0.770 0.699
0.122 0.191 0.529 0.166
0.041 0.131 0.215 0.382
0.849 0.869 0.918 0.662
47.360
17.360
Cu Pb
0.130 0.219
0.863 0.844
0.083 0.066
0.769 0.764
16.945
64.305
0.072 0.317 0.316 0.203
0.085 0.156 0.565 0.515
0.908 0.815 0.586 0.516
0.836 0.789 0.763 0.573
13.616
77.921
S FPM Mn Zn
Extraction method: principal component analysis. Rotation method: VARIMAX, with Kaiser normalization.
described in [12]. The samples were bombarded by 2.5 MeV protons using ion beam currents at the sample of typically a few nanoamperes. Beam homogeneity at the sample was achieved by a beam diffuser foil. The characteristic X-rays were detected with a Si(Li) detector located outside the irradiation chamber at a 90° angle with respect to the ion beam direction. Details of the experimental set-up and calibration of the analysis system have also been given in the same reference. The analysis permitted us to identify and to quantify the chemical elements of the airborne particulate matter (APM) deposited on the membrane filters.
3. Multivariate analysis In this study, the data set with the elemental concentrations as derived from the bulk analysis was subjected to multivariate analysis. APFA was
applied to this data set in an attempt to identify the major particle sources, to determine the aerosol source profiles, and to obtain the quantitative source apportionments of the aerosol total mass. APFS were calculated for each sample and its elemental concentrations were regressed on the APFS to obtain the concentrations of the elements for each source. Finally, these source profiles can be used to obtain the source apportionment of the atmospheric trace element concentrations [13–17].
4. Results The VARIMAX rotated component matrix of the data presented in Table 1 shows the factors for both the day and the night in the fine particle mode. Only three factors were statistically significant for both day and night. These factors (F1, F2 and F3) were associated with industry, soil and
252
R.V. Dıaz et al. / Nucl. Instr. and Meth. in Phys. Res. B 189 (2002) 249–253
Table 2 APFA of the fine mode atmospheric trace element concentrations, day time and night time, from Ticoman, Mexico Elem
Descriptive statistics (N ¼ 37)
APFS
Elemental concentrations percentage
n
Mean
SD
1.34 (Ind.)
2.57 (Soil)
1.53 (Sulf.)
Sum
Mean
Ratio
Ind.
Soil
Day S Cl K Ca Ti V Mn Fe Ni Cu Zn Br Pb
37 2 37 37 37 24 37 37 9 37 37 8 37
2521.41 42.00 230.30 288.31 26.57 25.71 20.57 262.11 8.00 21.98 251.33 17.25 119.14
1959.28 0.00 74.48 106.56 8.60 28.68 11.45 84.70 5.17 11.66 215.42 4.89 49.52
340.92 0.00 0.00 0.00 0.00 0.00 12.96 84.80 0.00 14.63 224.81 0.00 64.52
392.50 22.44 149.20 274.43 22.73 0.00 4.76 174.43 0.00 3.72 0.00 0.00 25.42
2610.13 14.14 60.60 0.00 1.76 27.87 2.83 0.00 3.84 0.00 84.82 0.00 23.68
2558.55 36.57 209.80 274.43 24.49 27.87 20.55 259.23 3.84 18.35 309.62 0.00 113.63
2521.41 42.00 230.30 288.31 26.57 25.71 20.57 262.11 8.00 21.98 251.33 17.25 119.14
1.01 0.87 0.91 0.95 0.92 1.08 1.00 0.99 0.48 0.84 1.23 0.00 0.95
13.52 0.00 0.00 0.00 0.00 0.00 62.98 32.35 0.00 66.58 89.45 0.00 54.15
15.57 53.42 64.78 95.19 85.53 0.00 23.13 66.55 0.00 16.93 0.00 0.00 21.34
103.52 33.66 26.32 0.00 6.64 108.40 13.74 0.00 47.99 0.00 33.75 0.00 19.88
FPM
37
22.33
5.69
4.03
7.05
10.13
21.21
22.33 0.95
18.07
31.56
45.36
Soil
Ind.
1.04 0.91 0.89 0.95 0.96 1.01 0.99 0.00 0.79 0.91 0.67 0.41 0.95
0.00 53.45 80.62 88.26 0.00 18.76 60.97 0.00 28.16 0.00 67.26 41.37 28.83
0.00 0.00 8.47 7.12 0.00 27.57 22.95 0.00 50.84 31.43 0.00 0.00 42.99
104.27 37.17 0.00 0.00 95.77 54.95 14.89 58.32 0.00 59.90 0.00 0.00 22.87
21.05 0.95
26.51
0.00
68.19
Descriptive statistics (N ¼ 36)
1.87 (Soil)
1.00 (Ind.)
2.39 (Sulf.)
Night S K Ca Ti V Mn Fe Ni Cu Zn As Br Pb
36 36 36 36 27 36 36 13 36 36 6 13 36
2352.77 272.47 254.21 24.62 37.73 25.30 291.40 9.43 25.01 380.95 9.69 17.26 124.17
1130.36 104.19 121.87 11.71 51.76 13.70 127.41 6.87 12.95 262.10 2.98 4.95 56.21
0.00 145.64 204.96 21.73 0.00 4.75 177.66 0.00 7.04 0.00 6.52 7.14 35.80
0.00 0.00 21.54 1.75 0.00 6.97 66.88 0.00 12.71 119.72 0.00 0.00 53.38
2453.18 101.28 0.00 0.00 36.14 13.90 43.38 5.50 0.00 228.19 0.00 0.00 28.39
2453.18 246.92 226.49 23.49 36.14 25.62 287.92 5.50 19.75 347.91 6.52 7.14 117.58
FPM
36
21.05
4.09
5.58
0.00
14.35
19.93
2352.77 272.47 254.21 24.62 37.73 25.30 291.40 9.43 25.01 380.95 9.69 17.26 124.17
Sulf.
Number of samples (N); number of appearances (n); standard deviation (SD); industry (Ind.); sulfates (Sulf.); measured (Meas.); ratio (Sum/Meas.); fine particulate matter (FPM); units (ng/m3 ); FPM units (lg/m3 ).
sulfates for the daytime data variability, and they explain 86.1% of this parameter. For the nighttime data variability, 77.9% can be explained with these three factors, although in this case F1, F2 and F3 correspond to soil dust, industry and sulfates respectively. During daytime, the first factor F1, highly correlated to the elements Mn, Cu, Pb and Zn, is associated with industrial sources, and it explains 51.9% of the total variability. F2 is highly
correlated to Ca, Ti, K and Fe, which defines soil dust, and it explains 20.7% of the variability. Finally, F3 is highly correlated to S and FPM, corresponding to sulfates, and it explains 13.5% of the variability. During the night, F1, F2 and F3 explain 47.4%, 16.9% and 13.6% of the variability, although in this case the associated sources are respectively soil dust, industry and sulfates (S þ FPM þ Mn þ Zn). The factor analysis was subject
R.V. Dıaz et al. / Nucl. Instr. and Meth. in Phys. Res. B 189 (2002) 249–253
to the Kaiser criteria and to the Kaiser–Meyer– Olkin (KMO) and Bartlett’s factor analysis tests, all these showing that the statistical significance is appropriate, thus indicating a real change in the elemental structure of the factors. This observation also reflects the real changes in atmospheric pollution due to different anthropogenic activities conducted during the day respect to the night. The results of the APFA showing the quantitative source apportionments of the atmospheric trace element concentrations for the day time and the night time are presented in Table 2. Fig. 1 shows a graphic representation of the FPM source apportionment (%) for the day time and night time. It can be seen that sulfates are the major
253
pollution source for both the day and the night sampling periods. During the night, the FPM apportionment from industry goes down to zero, reflecting a major reduction in industrial activity.
5. Conclusions The combination of PIXE analysis, providing the fundamental information on elemental composition, and APFA–APFS is of considerable value in helping to interpret the sources of APM. Multivariate analysis is a powerful technique for relating a large data base of elemental concentrations of atmospheric particles and their possible sources. The determination of the elemental composition of urban air particulate matter and its sources is important to establish air pollution control programs which can help to alleviate the atmospheric pollution problem.
References
Fig. 1. FPM source apportionment (%) of the fine mode aerosol from Ticom an, Mexico.
[1] http://www.df.gob.mx. [2] F. Aldape, J. Flores M., R.V. Dıaz, J.R. Morales, T.A. Cahill, L. Saravia, Int. J. PIXE 1 (1991) 355. [3] F. Aldape, J. Flores M., R.V. Dıaz, J. Miranda, T.A. Cahill, J.R. Morales, Int. J. PIXE 1 (1991) 373. [4] A. Baez, M. Reyes, I. Rosas, P. Mosi~ no, Atm osfera 1 (1988) 87. [5] A.H. Bravo, R. Camacho, M.I. Saavedra, R. Sosa, R. Torres, Proceedings of the 82nd Annual Meeting of the Air and Waste Management Association, 1989. [6] A.H. Bravo, Proceedings of the APCA International Conference on Particulate Matter and Fugitive Dusts, Tucson Az., Vol. 36, 1986, p. 113. [7] J. Flores M., F. Aldape, Int. J. PIXE, in press. [8] SPSSâ software version 10.0 by SPSS Inc., 2000. [9] Cuaderno Estadıstico Delegacional, Gustavo A. Madero, INEGI, Gobierno del Distrito Federal, 2000. [10] T.A. Cahill, R.A. Eldred, P.J. Feeney, P.J. Beveridge, L.K. Wilkinson, Trans. Air Waste Manage. Assoc. (1990) 213. [11] Dise~ no de estrategias de muestreo, Informe Tecnico ININ, Mexico, 1999. [12] F. Aldape, J. Flores M., R.V. Dıaz, D. Crumpton, Nucl. Instr. and Meth. B 75 (1993) 304. [13] R.A. Johnson, in: Applied Multivariate Statistical Analysis, 3rd ed., Prentice Hall, New Jersey, 1992, p. 219. [14] R.C. Henry, G.M. Hidy, Atmos. Env. 16 (1982) 929. [15] D.J. Alpert, P.K. Hopke, Atmos. Env. 14 (1980) 1137. [16] P.K. Hopke, Atmos. Env. 10 (1976) 1015. [17] R.C. Henry, G.M. Hidy, Atmos. Env. 13 (1979) 1581.
Identification of airborne particulate sources, of samples collected in Ticom an, Mexico, using PIXE and multivariate analysis R.V. Dıaz *, F. Aldape, J. Flores M. Instituto Nacional de Investigaciones Nucleares (ININ), Apartado Postal 18-1027, Col. Escand on, CP 11801 M exico D.F., Mexico
Abstract A set of samples of airborne particulate matter collected during February and March 1997 in Ticom an, a place located in the northern part of Mexico City’s Metropolitan Area, were analysed by PIXE and the concentrations of ten main elements were determined. The fundamental information provided by PIXE was used to build up the data base needed to apply statistical tools such as absolute principal factor analysis. The VARIMAX rotated component matrix of the data was obtained, and the first three principal factors were assigned to soil, industry, and sulfates pollution sources. The quantitative source apportionments of the atmospheric trace element concentrations for the day time and the night time were determined by means of absolute principal factor scores. Ó 2002 Published by Elsevier Science B.V. PACS: 0785; 86.70.G Keywords: PIXE; Airborne particulate matter
1. Introduction Mexico City is near to being the most polluted city in the world. At present, the problem expands across the country since many of the major cities such as Guadalajara, Monterrey and others have registered high rates of pollution as a consequence of contaminated residual fumes from neighbour industrial areas. It is of interest to concentrate attention on Mexico City’s Metropolitan Area (ZMCM). Since
*
Corresponding author. Tel.: +52-5-3297200x2632; fax +525-3297332. E-mail address: [email protected] (R.V. Dıaz).
1940 this area has shown an accelerated and steady increase in its population. 20% (approximately 20 million inhabitants) of the total population of the country is concentrated in only 1% of the national territory; the population density is about 5494 inhabitants per square kilometre. Moreover, 50% of the industrial activity [1] is also concentrated into the same percentage of the territory. Several government institutions as well as the research and academic community have made a great effort to contribute to decrease the pollution problem [2–6]. However, the actions have not been enough and have only mitigated the situation. At present, more studies are needed in order to create new strategies that permit to control and diminish the problem. In this respect, it is worth mentioning
0168-583X/02/$ - see front matter Ó 2002 Published by Elsevier Science B.V. PII: S 0 1 6 8 - 5 8 3 X ( 0 1 ) 0 1 0 7 3 - 4
250
R.V. Dıaz et al. / Nucl. Instr. and Meth. in Phys. Res. B 189 (2002) 249–253
that identification of the air pollutant sources can help to reduce the problem. The determination of the composition and sources of urban air particulate matter is becoming increasingly important to establish air pollution control programs. Once the sources are identified, especially those of undesirable elements, steps can be taken to control their release to the environment. Multivariate analysis has been shown to be a powerful technique for connecting a large body of elemental concentrations of atmospheric particles to their possible sources. In this respect, the fundamental and unique role played by PIXE in providing the basic information to build up the data base needed to apply these statistical tools must be pointed out. One of the areas within the ZMCM which has been detected to have a poor air quality is Ticom an [7] and it has been chosen to apply the multivariate analysis. Partial results of the PIXE analysis performed on a set of samples taken in this place has already been presented [7]. However, the number of analysed samples was significantly increased in order to carry out this study, with a total of 73 samples taken during night and day periods. Ten elements were identified: S, K, Ca, Ti, V, Mn, Fe, Cu, Zn and Pb appeared recurringly while others appeared with less frequency, for instance Cl, Ni, As and Br. Cl, Ni and Br appeared mainly during the night. In order to provide new insights into the air pollution problem in this area, absolute principal factor analysis (APFA) and absolute principal factor scores (APFS) were applied to the new data base using SPSSâ software [8] version 10.0 by SPSS Inc. It was thus possible to identify soil, industry and sulfates as emission sources for the day and night sampling periods, and to determine the apportionment of each source for each element.
2. Experimental
the Mexico City’s Metropolitan Area (19°300 N, 99°320 W) and it represents 5.9% of the total area occupied by the Federal District. It has a high population density and counts with several sites of major human activity. Some examples of these are: the Indios Verdes Bus and Metro Station visited everyday by 500–700 thousand commuters, North Central Bus Station, the area of Hospitals, the industrial zones of San Juan Ixhuatepec and Xalostoc influencing the area through the prevailing winds coming from north and northeast towards the city, and two other neighbouring industrial zones Atzcapotzalco and Tlalnepanta, all these influencing the area. There is an additional contribution to the local pollution derived from a large number of industries of varying size [9]. All this together makes a deteriorated place that must be observed and studied carefully. 2.2. Sampling The samples were collected from 17 February to 25 March 1997 in 12 h periods, starting in the morning at 7:00 [11]. This study has been focussed on fine mode aerosol due to its importance in human health. The samplers used were stacked filter units (SFU) [10] which separate the particles into two main fractions: the fine mode made of particles under 2.5 lm diameter, deposited on Teflonâ filters, and the coarse mode composed of particles of aerodynamic diameter between 2.5 and 15 lm deposited on Nucleporeâ polycarbonate membrane filters. The amount of mass deposited on the samples were obtained by gravimetric analysis. The filters were weighed before and after sampling in a E. Mettler Zurich electronic microbalance with a 1 lg sensitivity. Before weighing, all filters were conditioned during 24 h at 45% relative humidity and 22 °C temperature. fine particulate matter (FPM) mass concentrations (units lg/m3 ), as presented in Tables 1 and 2, were derived.
2.1. The sampling site 2.3. PIXE analysis Land use in Ticom an is distributed in residential, industrial and commercial sectors among others. Ticom an is located in the northern part of
PIXE analysis of aerosol samples was performed at the ININ PIXE facility following the protocol
R.V. Dıaz et al. / Nucl. Instr. and Meth. in Phys. Res. B 189 (2002) 249–253
251
Table 1 VARIMAX rotated component matrix of the fine mode aerosol from Ticoman, Mexico Industry
Soil
Communality
Percent of variance
Cumulative
0.225 0.099 0.223 0.341
0.896 0.757 0.779 0.838
51.933
51.933
0.943 0.924 0.725 0.707
0.034 0.117 0.491 0.043
0.890 0.878 0.789 0.943
20.667
72.601
0.061 0.202
0.944 0.864
0.936 0.904
13.496
86.096
Day Mn Cu Pb Zn
0.879 0.863 0.851 0.848
0.272 0.042 0.069 0.047
Ca Ti K Fe
0.021 0.103 0.146 0.664
S FPM
0.201 0.340 Soil
Sulfates
Industry
Night Ti Ca Fe K
0.912 0.903 0.770 0.699
0.122 0.191 0.529 0.166
0.041 0.131 0.215 0.382
0.849 0.869 0.918 0.662
47.360
17.360
Cu Pb
0.130 0.219
0.863 0.844
0.083 0.066
0.769 0.764
16.945
64.305
0.072 0.317 0.316 0.203
0.085 0.156 0.565 0.515
0.908 0.815 0.586 0.516
0.836 0.789 0.763 0.573
13.616
77.921
S FPM Mn Zn
Extraction method: principal component analysis. Rotation method: VARIMAX, with Kaiser normalization.
described in [12]. The samples were bombarded by 2.5 MeV protons using ion beam currents at the sample of typically a few nanoamperes. Beam homogeneity at the sample was achieved by a beam diffuser foil. The characteristic X-rays were detected with a Si(Li) detector located outside the irradiation chamber at a 90° angle with respect to the ion beam direction. Details of the experimental set-up and calibration of the analysis system have also been given in the same reference. The analysis permitted us to identify and to quantify the chemical elements of the airborne particulate matter (APM) deposited on the membrane filters.
3. Multivariate analysis In this study, the data set with the elemental concentrations as derived from the bulk analysis was subjected to multivariate analysis. APFA was
applied to this data set in an attempt to identify the major particle sources, to determine the aerosol source profiles, and to obtain the quantitative source apportionments of the aerosol total mass. APFS were calculated for each sample and its elemental concentrations were regressed on the APFS to obtain the concentrations of the elements for each source. Finally, these source profiles can be used to obtain the source apportionment of the atmospheric trace element concentrations [13–17].
4. Results The VARIMAX rotated component matrix of the data presented in Table 1 shows the factors for both the day and the night in the fine particle mode. Only three factors were statistically significant for both day and night. These factors (F1, F2 and F3) were associated with industry, soil and
252
R.V. Dıaz et al. / Nucl. Instr. and Meth. in Phys. Res. B 189 (2002) 249–253
Table 2 APFA of the fine mode atmospheric trace element concentrations, day time and night time, from Ticoman, Mexico Elem
Descriptive statistics (N ¼ 37)
APFS
Elemental concentrations percentage
n
Mean
SD
1.34 (Ind.)
2.57 (Soil)
1.53 (Sulf.)
Sum
Mean
Ratio
Ind.
Soil
Day S Cl K Ca Ti V Mn Fe Ni Cu Zn Br Pb
37 2 37 37 37 24 37 37 9 37 37 8 37
2521.41 42.00 230.30 288.31 26.57 25.71 20.57 262.11 8.00 21.98 251.33 17.25 119.14
1959.28 0.00 74.48 106.56 8.60 28.68 11.45 84.70 5.17 11.66 215.42 4.89 49.52
340.92 0.00 0.00 0.00 0.00 0.00 12.96 84.80 0.00 14.63 224.81 0.00 64.52
392.50 22.44 149.20 274.43 22.73 0.00 4.76 174.43 0.00 3.72 0.00 0.00 25.42
2610.13 14.14 60.60 0.00 1.76 27.87 2.83 0.00 3.84 0.00 84.82 0.00 23.68
2558.55 36.57 209.80 274.43 24.49 27.87 20.55 259.23 3.84 18.35 309.62 0.00 113.63
2521.41 42.00 230.30 288.31 26.57 25.71 20.57 262.11 8.00 21.98 251.33 17.25 119.14
1.01 0.87 0.91 0.95 0.92 1.08 1.00 0.99 0.48 0.84 1.23 0.00 0.95
13.52 0.00 0.00 0.00 0.00 0.00 62.98 32.35 0.00 66.58 89.45 0.00 54.15
15.57 53.42 64.78 95.19 85.53 0.00 23.13 66.55 0.00 16.93 0.00 0.00 21.34
103.52 33.66 26.32 0.00 6.64 108.40 13.74 0.00 47.99 0.00 33.75 0.00 19.88
FPM
37
22.33
5.69
4.03
7.05
10.13
21.21
22.33 0.95
18.07
31.56
45.36
Soil
Ind.
1.04 0.91 0.89 0.95 0.96 1.01 0.99 0.00 0.79 0.91 0.67 0.41 0.95
0.00 53.45 80.62 88.26 0.00 18.76 60.97 0.00 28.16 0.00 67.26 41.37 28.83
0.00 0.00 8.47 7.12 0.00 27.57 22.95 0.00 50.84 31.43 0.00 0.00 42.99
104.27 37.17 0.00 0.00 95.77 54.95 14.89 58.32 0.00 59.90 0.00 0.00 22.87
21.05 0.95
26.51
0.00
68.19
Descriptive statistics (N ¼ 36)
1.87 (Soil)
1.00 (Ind.)
2.39 (Sulf.)
Night S K Ca Ti V Mn Fe Ni Cu Zn As Br Pb
36 36 36 36 27 36 36 13 36 36 6 13 36
2352.77 272.47 254.21 24.62 37.73 25.30 291.40 9.43 25.01 380.95 9.69 17.26 124.17
1130.36 104.19 121.87 11.71 51.76 13.70 127.41 6.87 12.95 262.10 2.98 4.95 56.21
0.00 145.64 204.96 21.73 0.00 4.75 177.66 0.00 7.04 0.00 6.52 7.14 35.80
0.00 0.00 21.54 1.75 0.00 6.97 66.88 0.00 12.71 119.72 0.00 0.00 53.38
2453.18 101.28 0.00 0.00 36.14 13.90 43.38 5.50 0.00 228.19 0.00 0.00 28.39
2453.18 246.92 226.49 23.49 36.14 25.62 287.92 5.50 19.75 347.91 6.52 7.14 117.58
FPM
36
21.05
4.09
5.58
0.00
14.35
19.93
2352.77 272.47 254.21 24.62 37.73 25.30 291.40 9.43 25.01 380.95 9.69 17.26 124.17
Sulf.
Number of samples (N); number of appearances (n); standard deviation (SD); industry (Ind.); sulfates (Sulf.); measured (Meas.); ratio (Sum/Meas.); fine particulate matter (FPM); units (ng/m3 ); FPM units (lg/m3 ).
sulfates for the daytime data variability, and they explain 86.1% of this parameter. For the nighttime data variability, 77.9% can be explained with these three factors, although in this case F1, F2 and F3 correspond to soil dust, industry and sulfates respectively. During daytime, the first factor F1, highly correlated to the elements Mn, Cu, Pb and Zn, is associated with industrial sources, and it explains 51.9% of the total variability. F2 is highly
correlated to Ca, Ti, K and Fe, which defines soil dust, and it explains 20.7% of the variability. Finally, F3 is highly correlated to S and FPM, corresponding to sulfates, and it explains 13.5% of the variability. During the night, F1, F2 and F3 explain 47.4%, 16.9% and 13.6% of the variability, although in this case the associated sources are respectively soil dust, industry and sulfates (S þ FPM þ Mn þ Zn). The factor analysis was subject
R.V. Dıaz et al. / Nucl. Instr. and Meth. in Phys. Res. B 189 (2002) 249–253
to the Kaiser criteria and to the Kaiser–Meyer– Olkin (KMO) and Bartlett’s factor analysis tests, all these showing that the statistical significance is appropriate, thus indicating a real change in the elemental structure of the factors. This observation also reflects the real changes in atmospheric pollution due to different anthropogenic activities conducted during the day respect to the night. The results of the APFA showing the quantitative source apportionments of the atmospheric trace element concentrations for the day time and the night time are presented in Table 2. Fig. 1 shows a graphic representation of the FPM source apportionment (%) for the day time and night time. It can be seen that sulfates are the major
253
pollution source for both the day and the night sampling periods. During the night, the FPM apportionment from industry goes down to zero, reflecting a major reduction in industrial activity.
5. Conclusions The combination of PIXE analysis, providing the fundamental information on elemental composition, and APFA–APFS is of considerable value in helping to interpret the sources of APM. Multivariate analysis is a powerful technique for relating a large data base of elemental concentrations of atmospheric particles and their possible sources. The determination of the elemental composition of urban air particulate matter and its sources is important to establish air pollution control programs which can help to alleviate the atmospheric pollution problem.
References
Fig. 1. FPM source apportionment (%) of the fine mode aerosol from Ticom an, Mexico.
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