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EVALUATION OF INHALEABLE PARTICULATE MATTER (PM10) IN THE ATMOSPHERE OF PAULÍNIA-BRAZIL W. B. de Amorim, E. Tomaz, M. G. C. da Silva1* 1
Faculdade de Engenharia Química - Universidade Estadual de Campinas
Abstract. The monitoring of inhaleable particulate matter (PM10) in the atmosphere of the industrial city of Paulínia, in Brazil, was realized during the winter of 2002 and the summer of 2003. The goal was to evaluate the variations of the PM10 concentration in the atmosphere for both periods and also to characterize the elementary composition of the sampled particulate matter. The equipment used was a small volume sampler, called Dichotometer, which divides the collected particulate matter in two fractions: a fine fraction (PM2.5) and a coarse fraction (PM2.5-10). For each season 28 saPMles were collected, 19 of those collected during winter and 26 of those collected during summer were analyzed with the Energy Dispersive X-Ray Fluorescence technique allowing the elementary characterization of the total sampled PM10. The results confirm that the concentration of PM10 in the atmosphere is higher during winter when compared to summer, and it was also observed that during winter the coarse fraction was approximately 60% higher than the fine fraction and during summer the scenario was inverse, the fine fraction was 50% higher than the coarse fraction. These results point to a larger contribution of soil resuspension during winter; caused mainly by a decrease in precipitation and also by the soil preparation for sugar cane plantation which is carried during this period of the year on surrounding fields. For the results obtained during the summer season, gas oxidation caused by photochemical reactions and also the gas-particle interaction that is influenced by the high radiation ratio are likely hypothesis for the fine fraction high measured concentration. The results of elementary composition analysis on the collected PM10 showed that almost all quantified element concentrations are higher during winter, and some elements which are derived from industrial processing and vehicle emissions showed concentrations approximately constant through the two studied seasons. Keywords: Particulate Matter, Paulinia, Atmospheric Pollution.
1. Introduction The high fine particulate matter concentration levels observed in urban areas result from the concomitance of different factors such as the large quantities of primary particles emitted in atmosphere from numerous sources, the concentration of secondary particles created by chemical and photochemical processes from gaseous precursors and also atmospheric thermodynamic conditions adverse to the vertical mixings of pollutants, Marcazzan et al, 2000. Seinfeld and Pandis (1998) give a complete review about the physio-chemical processes involving aerosols. The city of Paulínia, located 110 km far from São Paulo, has a population of 51000 inhabitants and also a large and complex industrial area that discharges daily large volumes of pollutants in the atmosphere, changing the quality of the air in the region. Among those pollutants, the inhaleable particulate matter (particles smaller than 10 µm in diameter, PM10) is of great interest since these particles penetrate the human respiratory system *
To whom all correspondence should be addressed. Address: LEA/DTF/FEQ/UNICAMP, Chemical Engineering Faculty – UNICAMP, Caixa Postal 6066 CEP 13083-970 Campinas-SP, Brazil E-mail:
[email protected]
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and can reach the lung alveoli causing serious damage to health. This work focused on the monitoring and characterization of PM10 present in the atmosphere of the João Aranha neighborhood in Paulínia through sampling, quantification and characterization of the inhaleable particulate matter. The monitoring site was chosen based on the results of the dispersion model used by Clemente (2000), which showed the highest annual averages of SO2, NOx and particulate matter. The monitoring of inhaleable particulate matter (PM10) in the atmosphere of Paulínia was realized during the winter of 2002 and the summer of 2003. The goal was to evaluate the variations of the PM10 concentration in the atmosphere for both seasons and also to characterize its elementary composition.
2. Materials and Methods In order to accomplish the PM10 sampling, a Dichotometer was used; this equippement divides the sampled PM10 in two fractions: a fine fraction (PM2.5, particles of diameters < 2.5 µm) and a coarse fraction (PM2.5-10, particles of diameters between 2.5 µm and 10 µm). The concentrations of PM10, PM2.5 and PM2.5-10 fractions were determined through gravimetrical analysis, using a 1 µg sensitivity electronic microbalance. One sample was collected for each 24 hour period every 3 days during the winter of 2002 (July 2nd until September 21st) and the summer of 2003 (December 20th until March 20th). The sampler was installed at the parking lot of the municipal gymnasium of Paulinia, which is located approximately 3 Km northwest of a moderate traffic highway (SP332) which is mainly used for the transportation of the refinery products. In front of the gymnasium there is a paved road that does not present intense traffic and in the area nearby there are fields and non paved roads that contribute to the soil resuspension of particulate matter. The samples were collected with 37 mm diameter Teflon filters, those filters were then analyzed through Energy Dispersive X-Ray Fluorescence in order to determine the elementary composition of the collected particulate matter. A total of 19 elements were searched in this analysis: Al, Si, P, S, Sr, Rb, Br, Pb, Cl, K, Ca, Ti, V, Cr, Mn, Fe, Ni, Cu and Zn. The equipment used for this analysis consists of a Philips X-Ray tube, model PW 2215/20, with a Mo target and a Philips Tension generator, model PW 1830, which produces the current and tension combinations, varying in the 10 to 60 mA and 10 to 60 keV ranges, respectively. Each sample was analyzed under two conditions or excitation modes, in the air to quantify the elements with higher atomic numbers and in the vacuum to avoid the X-Ray absorption by lower atomic number elements. For both excitation modes the excitation time was 200 s, the used detection thresholds were set for this excitation time. The spectra were interpreted through the software AXIL (Analysis of X-Ray spectra by Interactive Least Squares fitting), which provided the liquid areas for the characteristic X-Rays belonging to the elements present in the samples and standards. The one-way ANOVA was applied to analyze the results of PM2.5 and PM2.5-10 considering two different inputs of season, winter and summer. The one-way ANOVA performs a comparison of the means of a number of replications of experiments performed where a single input factor is varied at different settings or levels. The object of this comparison is to determine the proportion of the variability of the data that is due to the different treatment levels as opposed to variability due to random error. The model deals with specific treatment levels and 2
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is involved with testing the null hypothesis H0: µ 1 = µ 2 = … µ . where µ i represents the level mean. Basically, rejection of the null hypothesis indicates that variation in the output is due to variation between the treatment levels and not due to random error. If the null hypothesis is rejected, there is a difference in the output of the GLIIHUHQW OHYHOV DW D VLJQLILFDQFH
. DQG LW UHPDLQV WR EH GHWHUPLQHG EHWZHHQ ZKLFK WUHDWPHQW OHYHOV WKH DFWXDO
differences lie.
3. Results and Discussion 3.1 Variations of Particulate Matter Concentrations in the Atmosphere The obtained results show that the concentration of particulate matter is higher during winter, the dry season, when compared to summer, as can be seen in the Figure 1. During winter, the unfavorable dispersion conditions, a lower thermal inversion height and a higher atmospheric stability favor an increase in pollution, not necessarily due to an increase in the emissions. An increase in the emissions can, however, happen due to two factors: the soil resuspension during the driest -3
seasons and sugar cane field burns, common in the region of this study. The daily standard threshold (65 µg.m ) established by EPA (US Environmental Protection Agency) for PM2.5 was violated once, the concentration 66.4 -3
th
µg.m was measured on July 11 2002. It was also observed that during winter the coarse fraction was approximately 60% higher than the fine fraction and during summer the scenario was inverse, the fine fraction was 50% higher than the coarse fraction. These results point to a large contribution of soil resuspension during winter; caused mainly by a decrease of precipitation and also by soil preparation for sugar cane plantation which is carried during this period of the year on surrounding areas. For the results obtained during the summer season, gas oxidation caused by photochemical reactions and also the gas-particle interaction that is influenced by the high radiation ratio are likely hypothesis for the higher fine fraction concentration measured.
-3
Particulate Concentration µ ( g.m )
40 35 30 25 20 15 10 5 0 Winter
Summer Season Fine Fraction
Coarse Fraction
Fig. 1. Comparison between the average values of fine and coarse fractions in the particulate matter for the two studied seasons 3
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Castanho and Artaxo (2001) in a similar study in São Paulo, verified when comparing winter and summer periods that the fine fraction showed a reduction of about 50 % while the coarse fraction showed an even greater reduction of 60 %. This was explained due to the difference of deposition efficiency between the coarse and fine modes in the summer when there are more frequent rainy periods. MATSUMOTO (2000) studied two monitoring sites in the Campinas region (neighbor city of Paulínia) during three seasons: fall, winter and summer and observed that the concentration of coarse fraction was approximately 3 times higher than the fine fraction, and that ratio did not change with the time of the year or the location of the site studied. It was verified that the concentration of PM10 in the atmosphere is related to the following variables: rainfall rate, humidity and wind speed as shown in Figure 2. The precipitation rate is inversely proportional to PM10 concentration. The reduction of PM10 concentration due to rain is caused by the following processes: reduction of soil resuspension due to particle fixation and direct interaction of the aerosol with the rain through washing and nucleation mechanisms which are efficient for larger particles, i.e., the coarse fraction of PM10. There is also an inverse relationship between PM10 concentration, wind speed and humidity, as shown in Figure 2. These
6
20
4
10
2
0
0
Precipitation rate (mm)
30
-1
8
Wind speed (m.s ),
10
40
March-03
12
50
February-03
14
60
January-03
70
December-02
16
September-02
18
80
August-02
90
July-02
Humidity (%)
-3
PM10 concentrations (mg.m ),
results agree with studies such as Pillai et al. (2002), Marcazzan et al. (2001) and Statheropoulos et al. (1998).
Date Humidity
PM Concentration
Wind speed
Precipitation rate
Fig. 2. PM10 concentration, humidity, wind speed and precipitation rate for the sampling period.
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To confirm statistically that the different seasons influence the concentrations of particulate matter in the atmosphere a study of Analysis of Variance (ANOVA) was applied to the database. The results are shown in the section 3.1.1. 3.1.1 Results of Analysis of Variance Tables 1 and 2 show the ANOVA analysis for PM2.5 and PM2.5-10 , respectively. This study was carried with the goal of showing that the two different seasons have an effect on the particulate matter present in the atmosphere. As mentioned before, the experiments were realized during the winter of 2002 and the summer of 2003 and 28 samples were taken in each season. The objective is to know if different concentrations of particulate matter vary due to season grouping, at a 5% significance level. Table 1. Analysis of Variance for PM2.5 Group
Winter
Variance
185.00
Source of
Sum of
Degrees
Mean
Variation
Squares
of Freedom
Square
Between
1476.00
1
1476.00
7641.63
54
141.512
9117.63
55
F0
10.43
P
0.0021
FCRIT
4.02
levels Summer
98.00
Within levels
Total
On Table 1, it can be seen that F0 > FCRIT , the null hypothesis must be rejected and there is a difference in the treatment levels at a significancH RI .
IRU WKH ILQH IUDFWLRQ FRQFHQWUDWLRQV 7KLV HQGRUVHV WKH
hypothesis that the concentrations observed in the PM2.5 results vary due to season when compared to random error. Table 2. Analysis of Variance for PM2.5-10 Group
Winter
Variance
696.60
Source of
Sum of
Degrees
Variation
Squares
Between
13188.72
1
13188.72
19882.92
54
368.20
33071.66
55
of Freedom
Mean
F0
P
FCRIT
1.8E-7
4.02
Square 35.82
levels Summer
39.80
Within levels
Total
As shown on Table 2 and similarly to the PM2.5 case, F0 > FCRIT for PM2.5-10 and the null hypothesis must also be rejected. The F0 value obtained for the coarse fraction is significantly larger than the F0 value obtained 5
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for the fine fraction; this corroborates the fact that the season change influences more the concentration of coarse fraction than fine fraction as explained in section 3.1. 3.2 Results for Energy Dispersive X-Ray Fluorescence Analysis (ED-XRF) The results of elementary composition analysis on the collected particulate matter showed that almost all quantified element concentrations are higher during winter, and some elements which are derived from industrial processing (Sr and Rb) and vehicle emissions, e.g., Cu, showed concentrations approximately constant through the two studied seasons. Table 3 shows the average concentration for the samples collected in the seasons studied for the 19 quantified elements. The elements Al, Si, Ti and Fe showed the highest concentrations in the coarse fraction of the particulate matter which are strongly related to soil resuspension and the element S showed the highest concentrations in the fine fraction, which is originated as a secondary aerosol formed in the atmosphere through reactions between SO2 and volatile organic compounds which are emitted in combustion activities and fugitive emissions. Similar results were observed by Ho et al. (2003). Table 3. Variation of elemental composition to PM2.5 and PM2.5-10 PM2.5 Element -3
(ng.m )
Winter Mean
StDev
PM2.5-10 Summer
Mean
StDev
Al
550.34
298.40
168.77
105.60
Si
663.34
358.80
204.04
126.20
P
156.11
90.70
80.12
S
1164.88
543.0
Cl
59.15
Winter Mean 3210.44
Summer StDev
Mean
StDev
1965.0
902.58
763.0
3889.67
2255.0
1196.11
945.0
64.70
37.36
37.40
23.71
6.40
613.28
386.70
221.72
98.40
120.56
53.90
146.50
7.64
0.24
80.94
62.40
24.19
22.00
K
540.73
234.90
110.60
104.00
341.96
132.90
131.17
48.10
Ca
63.19
44.40
24.93
18.90
734.35
380.70
214.43
141.10
Ti
41.97
29.10
10.17
8.86
548.95
348.10
153.89
126.50
V
17.54
13.21
10.85
6.30
30.16
17.34
8.21
4.50
Cr
6.09
4.63
6.26
3.00
24.66
16.10
8.71
6.20
Mn
14.53
9.40
6.68
3.72
72.11
43.40
19.71
13.70
Fe
287.06
164.30
96.30
53.90
2715.28
1672.0
723.39
548.0
Ni
10.31
6.43
4.03
2.50
6.43
4.94
2.08
1.05
Cu
13.50
6.70
14.28
3.28
18.21
9.23
13.39
3.70
Zn
37.50
36.25
19.71
7.73
33.05
20.00
16.78
6.90
Br
7.61
5.41
2.60
1.32
2.22
1.40
1.66
0.75
Rb
4.60
1.60
4.03
1.36
4.29
1.45
3.90
1.60 6
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Sr
3.15
1.46
3.46
1.38
6.23
2.06
4.02
1.24
Pb
16.38
9.47
9.77
4.90
9.31
4.90
6.66
2.50
The elements K and P show also higher concentrations in the fine fraction particulate matter and are probably generated by industrial processes as the sampling location is near a fertilizer industry. The observed results for the elementary concentrations measured through ED-XRF confirmed results already reported in the literature (Marcazzan et al. 2001) where the PM10 coarse fraction is composed mainly by elements present in the Earth’s crust: Al, Ca, Fe, Si and Ti, as shown on Table 3. In the fine fraction the concentration of S was verified to be the highest.
4. Conclusions The monitoring of inhaleable particulate matter (PM10) in the atmosphere of the industrial city of Paulínia. in Brazil, was realized during the winter of 2002 and the summer of 2003. The concentrations measured during summer, when there are more frequent rainy periods, were lower than the winter concentrations. The PM10 -3
average concentrations obtained were 57 and 19 µg.m for winter and summer, respectively. The daily standard -3
-3
threshold (65 µg.m ) established by EPA for PM2.5 was violated once, the concentration 66.4 µg.m , was th
measured on July 11 2002. It was also confirmed by analysis of variance (ANOVA) that the particulate concentration in the atmosphere is influenced by the two different seasons, winter and summer. The elementary concentrations were measured by ED-XRF for both fine and coarse fractions. The results confirmed that the PM10 coarse fraction composition originates mainly from the soil resuspension and that the fine fraction is mainly originated from combustion processes and secondary aerosols.
References Castanho, D. A. and Artaxo, P. (2001). Wintertime and summertime São Paulo aerosol source apportionment study. Atmospheric Environment. vol 35, pp. 4889-4902. Clemente, D. A. (2000). Estudo do Impacto ambiental das fonts industriais de poluição do ar no município de Paulínia-SP empregando o modelo ISCT3 – Campina – SP (2000), Master’s Dissertation, 179p (in Portuguese). Ho, K. F., Lee, S. C., Chan, C. K., Yu, J. C. Chow, J. C. e Yao, X. H. (2003). Characterization of chemical species in PM2.5 and PM10 aerosols en Hong Kong. Atmospheric Environment, vol 37, pp. 31-39. Marcazzan, G. M., Vaccaro. S., Valli. G. and Vecchi, R. (2001) Characterisation of PM10 and PM2.5 particulate matter in the ambient air of Milan (Italy). Atmospheric Environment, v. 35, pp. 4639-4650. Matsumoto, E. (2001). Estudo da contaminação ambiental atmosférica e de águas superficiais. empregando a fluorescência de raios X dispersiva em energia (EDXRF) e reflexão total (TXRF). PhD thesis, 150 p (in Portuguese).
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Pillai, P. S., Babu, S. S. and Moorthy, K. K. (2002). A study of PM, PM10 and PM2.5 concentration at a tropical station. Atmospheric Research, n. 61, pp.149-167. Seinfeld, J. H; Pandis. S. N. (1998). Atmospheric chemistry and physics: from air pollution to climate changes. New York: John Wiley & Sons, 1326p. Statheropoulos, M., Vasiiliadis, N. and Pappa, A. (1995). Principal component and canonical correlation analysis for examining air pollution and metereological data. Atmospheric Environment, vol 32, no 6, pp. 1087-1095.
Acknowledgments To FAPESP and FAEP for the financial support aimed at the realization of this work.
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