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Source apportionment of suspended particulate matter at two traffic junctions in Mumbai, India
Atmospheric Environment 35 (2001) 4245–4251
Source apportionment of suspended particulate matter at two traffic junctions in Mumbai, India A.Vinod Kumara,*, R.S. Patilb, K.S.V. Nambic a b
Environmental Assessment Division, Bhabha Atomic Research Centre, Trombay, Mumbai 400 085, India Centre for Environmental Science and Engineering, Indian Institute of Technology, Mumbai 400 076, India c E-50, Shakti Illam, 8th Cross Street, Maharaja Nagar, Tirunelveli 627 011, India Received 20 April 2000; accepted 22 April 2001
Abstract Very high concentration of suspended particulate matter (SPM) is observed at traffic junctions in India. Factor analysis-multiple regression (FA-MR), a receptor modelling technique has been used for quantitative apportionment of the sources contributing to the SPM at two traffic junctions (Sakinaka and Gandhinagar) in Mumbai, India. Varimax rotated factor analysis identified (qualitative) five possible sources; road dust, vehicular emissions, marine aerosols, metal industries and coal combustion. A quantitative estimation by FA-MR model indicated that road dust contributed to 41%, vehicular emissions to 15%, marine aerosols to 15%, metal industries to 6% and coal combustion to 6% of the SPM observed at Sakinaka traffic junction. The corresponding figures for Gandhinagar traffic junction are 33%, 18%, 15%, 8% and 11%, respectively. Due to limitation in source marker elements analysed about 16% of the remaining SPM at these two traffic junctions could not be apportioned to any possible sources by this technique. Of the observed lead in the SPM, FA-MR apportioned 62% to vehicular emissions, 17% to road dust, 11% to metal industries, 7% to coal combustion and 3% to marine aerosols at Gandhinagar traffic junction and about a similar apportionment for lead in SPM at Sakinaka traffic junction. r 2001 Elsevier Science Ltd. All rights reserved. Keywords: Traffic junctions; Suspended particulate matter; Lead; Factor analysis; Multiple regression
1. Introduction Problem of vehicular pollution is very prominent at traffic junctions, due to high density of vehicles of various types and different operating modes like idling, stopping, accelerating, decelerating, etc. Traffic junctions in city centres are generally surrounded by shops and commercial complexes. The footpaths around traffic junctions are occupied by pedestrians in city centres and also used by vendors in suburbs. As such, these sites represent the ‘‘hot spots’’ of local importance with high air pollution exposure to public. Detailed air pollution studies at traffic junctions in India or in any other *Corresponding author. Fax: +91-22-550-5151. E-mail address: [email protected] (A.V. Kumar).
country are limited. Lot of work has been carried on gaseous pollutant concentrations at roadways and traffic junctions worldwide (Hamilton et al., 1990; Joumard, 1993; Hamilton and Harrison, 1996; Harrison and Hamilton, 1999) however, work related to particulate matter concentrations near roadways are limited (Namdeo et al., 1999). This is more specific to literature related to Indian traffic junctions (Luhar and Patil, 1989; URBAIR, 1994). Values of ambient air suspended particulate matter (SPM) reported for developed countries are generally less than or about 100 mg m3 (Rojas et al., 1990; Nicholas and Rashed, 1991; Camuffe and Bernardi, 1996), whereas in India the SPM values are in general very high with an annual average of 200– 500 mg m3 for an urban environment like Mumbai (Sadasivan and Negi, 1990; Sharma and Patil, 1992; URBAIR, 1994). Namdeo et al. (1999) have reported
1352-2310/01/$ - see front matter r 2001 Elsevier Science Ltd. All rights reserved. PII: S 1 3 5 2 - 2 3 1 0 ( 0 1 ) 0 0 2 5 8 - 8
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SPM concentrations of about 100 mg m3 in an urban street canyon in Nottingham, UK. In India significantly much higher concentrations are observed near roadways and at traffic junctions (Patil and Vinod Kumar, 1994; Vinod Kumar and Patil, 1994; Satyanarayana et al., 1997, 1998). Qualitatively it is known that road dust and vehicular emissions are the main sources for these SPM concentrations. In this study, an attempt has been made to quantitatively apportion the sources leading to the high SPM values observed at traffic junctions in India using factor analysis-multiple regression (FA-MR), a receptor model. Various ambient air quality parameters measured at two traffic junctions Sakinaka and Gandhinagar in Mumbai, over a period of one year has been used for this purpose.
Cr, Fe, Ca, Al, Mg, K, Na, Cu, Mn and Ni were determined in these acid digested samples using flame atomic absorption spectrophotometer (AAS) (Model GBC 904AA). Hg and As were determined using hydride generator (Model GBC HG3000) coupled with AAS. Remaining half portion of SPM filter papers were treated in deionized water at 1001C and were analysed for water soluble sulphates using spectrophotometric method (UV-VIS spectrophotometer, model Shimadzu UV-2100). Quality assurance of trace metal analysis was done by analysing standard reference materials, Hay V10 and milk powder A-11, supplied by Analytical Quality Control Services (AQCS), International Atomic Energy Agency (IAEA), Vienna. The results agree within 77% of the certified values (Table 1). 2.3. Receptor models
2. Monitoring and modelling 2.1. Sampling location and sample collection The traffic junctions, Sakinaka and Gandhinagar are 4 way intersections with 14–20 m wide roads, located in mixed zone comprising of industries and residences. Average traffic density during day time (0800–2000 h) is around 5000 vehicles per hour and 2800 vehicles per hour at Sakinaka and Gandhinagar, respectively. The measurements were carried out for 1 yr, starting from April 1991 to February 1992, with a frequency of once in a week sampling. Each sample represents a continuous SPM collection of 24 h duration. Rains in India (specifically, Mumbai) are limited to four months of monsoon period with intense and continuous rainfalls (Sharma et al., 1990). Hence, during this season, the samples were collected only during dry spell days. SPM were collected on glass fibre filter paper (size 20.3 25.4 cm), using a high volume sampler, operated at a flow rate of 1.0 m3 min1. The sampling height was 1.5 m above the ground level (breathing level). NO2 and SO2 were collected by aspirating ambient air through absorbent solutions; sodium tetrachloromercurate for SO2 and a mixture of sodium hydroxide and sodium arsenite for NO2, with an aspiration rate of 1.0 l min1. 2.2. Sample processing and analysis SO2 and NO2 were analysed colorimetrically using the method of West and Gaeke and modified method of Jacob and Hochheiser, respectively (APHA, 1977). Half of the SPM filter papers were wet digested with a mixture of electronic grade nitric acid, hydrochloric acid and perchloric acid. After gentle heating a colourless solution was obtained which was evaporated to near dryness. The residue was taken up in 50 ml of 0.25% nitric acid. Filter paper blanks and acid blanks were also taken through the same procedure simultaneously. Pb,
There has been no investigation providing source composition library of particulate matter for Mumbai metropolitan city (Sharma and Patil, 1994). This limits the usage of widely accepted chemical mass balance model (CMB) for source apportionment of SPM. Hence, a multivariate analysis (MVA) technique, Factor analysis-multiple regression (FA-MR) which does not require a priori information on source composition (Hopke, 2000; Henry et al., 1984) is used in this study. The FA model applied in the field of air pollution is expressed as: Cit ¼
N X
Lij Sjt þ Eit ;
j¼1
where Cit is the normalized value of concentration of ith species for tth sample, N is the total number of sources, Sjt is the factor score of the jth common factor (jth source) for tth sample, Lij is the factor loading of the ith species of the jth source and Eit is the residual of ith species in the tth sample not accounted by the j sources or factors (Harman, 1968; Negi et al., 1988). The two vectors L and E are unknown in FA and are obtained by assuming various covariance relationships between the vectors S and E (Johnson and Wichern, 1992) and finally solving the covariance matrix as an eigenvalue– eigenvector decomposition problem. Computer softwares like SPSSt and STATGRAPHICSt are available in the market and have made factor analysis more accurate and easy to use. A detailed description of the FA-MR can be seen in Okamoto et al. (1990), and Thurston and Spengler (1985).
3. Results and discussions Table 2 presents the 24 h annual average ambient air concentration of various species monitored at Gandhinagar and Sakinaka traffic junctions.
A.V. Kumar et al. / Atmospheric Environment 35 (2001) 4245–4251
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Table 1 Concentrations of trace metals in standard reference materials from IAEA Reference material
Metal
Unit
Certified values
Observed values
Hay (V-10)
Ca Cd Cr Cu Fe Ni Pb Zn Cu K Mg Mn Pb Zn
mg/g mg/g mg/g mg/g mg/g mg/g mg/g mg/g ng/g mg/g mg/g ng/g ng/g mg/g
21.6 (21–22.2) 0.03 (0.02–0.05) 6.5 (5.6–7.1) 9.4 (8.8–9.7) 185 (177–190) 4 (3.8–4.9) 1.6 (0.8–1.9) 24 (21–27) 378 (354–402) 17.2 (16.2–18.2) 1.1 (1.02–1.18) 257 (248–266) 54 (29–79) 38.9 (36.6–41.2)
20.4 (20.1–21.8) 0.027 (0.02–0.03) 6.3 ( 6.1–7.2) 9.3 (9.1–9.5) 189 (183–192) 3.8 (3.7–4.2) 1.6 (1.45–1.73) 24.6 (23.6–25.8) 365 (362–368) 18 (17.8–18.9) 1.13 (0.97–1.16) 262 (259–265) 55 (53–57) 41 (38.6–44.3)
Milk powder (A-11)
Table 2 Twenty four hour annual average ambient air concentrations (mg m3) at Sakinaka and Gandhinagar traffic junctions Species
Sakinaka
Gandhinagar
Al Asa Ca Cr Cu Fe Hga K Mg Mn Na Ni NO2 Pb SO2 SO4 SPM
56.1727.6 871 46.2721.6 0.1570.06 0.3770.04 165.5745.7 1174 2.4970.57 10.272.4 0.8570.22 20.772.5 0.1670.04 39.374.0 1.0670.30 32.572.9 3.7071.01 1176.07245.1
58.0717.2 472 35.9717.4 0.0570.03 1.5570.72 265.5774.2 24713 5.1473.08 21.679.1 1.4770.88 18.276.6 0.1070.05 44.5710.4 0.8270.43 35.5713.3 2.7571.20 1031.97235.5
Concentration in ng m3; number of samples=45 at each junctions. Uncertainty is one standard deviation of day to day concentration. a
3.1. Factor analysis Henry et al. (1984) suggested that the minimum number of samples (n) for FA should be such that n > 30 þ ðV þ 3Þ=2; where V represents the number of variables. An important aspect in any MVA is the selection of proper data set. Unusual events or errors in sampling and analysis results in very high or very low values (outliers) of one or more variables. These outliers,
have to be removed before analysis to avoid propagation of errors, since they do not represent the normally observed situation (Hopke, 2000). In this study 17 variables and 45 samples (after removing outliers) were considered for FA. Results of varimax rotated factor analysis carried out on various ambient air components using SPSS software (SPSS, 1988) at Gandhinagar and Sakinaka and the corresponding possible sources are depicted in Tables 3 and 4, respectively. Five factors at each site were selected based primarily on the following criteria. Firstly, the number of factors were selected such that the cumulative percentage variance explained by all the chosen factors is more than 80%. Secondly, only the factors with eigenvalue more than one were chosen. The argument is made that the normalized variables each carry one unit of variance. Thus, if an eigenvalue is less than one, then it carries less information than one of the initial variables and is therefore not needed (Maenhaut and Cafmeyer, 1987). Since higher factor loading of particular elements (marker elements) in a factor can help in identifying the possible sources (Henry et al., 1984), the number of factors selected (sources identified) should represent the sources which are relevant in the receptor domain. Varimax rotated factor analysis showed five possible groups/factors (based on factor loading of marker elements greater than 0.7) indicating five different contributing sources for the trace metals at this sampling location. The total percentage variance explained by five factors were about 85% at Gandhinagar and 83% at Sakinaka. The first factor was heavily loaded (factor loading >0.8) with Fe, Ca and Al at Gandhinagar and at Sakinaka. These being crustal elements, factor 1 can be identified as originating from crustal contribution. A study of the aerosol sources in the receptor domain
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Table 3 Varimax rotated factor matrix for Gandhinagar Species
Al As Ca Cr Cu Fe Hg K Mg Mn Na Ni NO2 Pb SO2 SO4 Eigenvalue % variance Cumulative % variance
Possible emission sources Road dust
Vehicular emissions
Metal emissions
Marine
Coal combustion
Communality
0.83 0.08 0.85 0.33 0.19 0.85 0.64 0.24 0.35 0.07 0.08 0.21 0.17 0.22 0.04 0.85 6.3 39.1 39.1
0.19 0.21 0.11 0.86 0.18 0.26 0.29 0.09 0.24 0.02 0.07 0.09 0.94 0.92 0.17 0.16 2.5 15.7 54.8
0.08 0.07 0.19 0.05 0.86 0.20 0.25 0.04 0.07 0.89 0.07 0.86 0.08 0.16 0.11 0.17 2.1 13 67.8
0.39 0.09 0.12 0.04 0.19 0.21 0.18 0.91 0.72 0.03 0.88 0.22 0.07 0.05 0.13 0.01 1.7 10.8 78.6
0.18 0.90 0.20 0.21 -0.13 -0.04 0.14 0.13 0.29 0.12 0.01 0.02 0.14 0.13 0.91 0.17 1.1 6.8 85.4
0.93 0.88 0.84 0.89 0.86 0.87 0.61 0.92 0.79 0.82 0.81 0.85 0.95 0.94 0.88 0.81
Table 4 Varimax rotated factor matrix for Sakinaka Species
Al As Ca Cr Cu Fe Hg K Mg Mn Na Ni NO2 Pb SO2 SO4 Eigenvalue % variance Cumulative % variance
Possible emission sources Road dust
Vehicular emissions
Marine
Coal combustion
Metal emissions
Communality
0.89 0.06 0.89 0.17 0.31 0.87 0.57 0.07 0.14 0.08 0.29 0.31 0.34 0.41 0.02 0.61 7.0 43.6 43.6
0.23 0.31 0.24 0.74 0.14 0.27 0.31 0.06 0.08 0.02 0.09 0.21 0.86 0.87 0.07 0.23 2.1 13.3 56.9
0.19 0.31 0.18 0.06 0.13 0.13 0.33 0.92 0.92 0.01 0.69 0.07 0.11 0.09 0.19 0.04 1.6 10.9 67.8
0.03 0.83 0.11 0.38 0.53 0.00 0.29 0.17 0.06 0.00 0.31 0.00 0.12 0.09 0.89 0.54 1.5 8.8 76.6
0.08 0.06 0.09 0.22 0.57 0.14 0.37 0.00 0.03 0.89 0.11 0.77 0.00 0.09 0.00 0.07 1.0 6.1 82.7
0.89 0.89 0.91 0.78 0.74 0.86 0.75 0.89 0.88 0.79 0.69 0.74 0.89 0.95 0.83 0.73
indicates that there is no separate crustal source, other than the natural wind blown crustal component of ambient aerosol at both the sampling locations. However, a significant amount of road dust is present on the
road and also on the road shoulders and is kept in suspension by vehicular movement. Hence, this source can be identified as the road dust component. Major portion of the percentage variance (39% in case of
A.V. Kumar et al. / Atmospheric Environment 35 (2001) 4245–4251
Gandhinagar and 43.6% in case of Sakinaka) was explained by factor one (crustal origin). A high factor loading of sulphate is seen in factor 1 at Gandhinagar and a moderate loading (0.6) at Sakinaka. Sulphate in the atmosphere is generally assumed to be a secondary fine particle aerosol component produced from gas to particle conversion of SO2 and hence its association with crustal elements is questionable and should have been identified as a separate source. The probable reasons for its association with crustal elements can be the chemical binding of sulphate with crustal elements in the road dust, masking of the fine sulphate aerosol component variation by the large resupended dust load contribution variation or inability of the FA-MR model to separate this component in absence of sufficient marker elements. The second factor explained percentage variance of 15.7 and 13.3 for Gandhinagar and Sakinaka, respectively with moderate to high loading of Pb, NO2 and Cr. Pb is associated with leaded gasoline vehicular emissions. During the sample collection period, 0.155 g Pb l1 gasoline was added to the gasoline distributed in Mumbai region (URBAIR, 1994) as an antiknocking agent. Main sources of NO2 at traffic junction are direct emission of NO2 which is about 2–5% of total NOx in gasoline powered and about 30–40% of total NOx in diesel powered automobile exhausts, conversion of NO emitted by the vehicles to NO2 by reacting with O3 or by the low temperature thermal oxidation in presence of O2 in the atmosphere (Lenner et al., 1983). Hence, this factor comprising of Pb and NO2 can be identified with vehicular emissions. Chromium is also associated with this group at both the traffic junctions, which is slightly questionable. Ward (1990) has attributed high chromium in road dust and near by vegetation to vehicular emissions. Also, Hopke (1980), Sadasivan and Negi (1990) have associated Cr with the wear and tear of brake lining, tire and rust particles of the vehicles. These contributions which appears insignificant can be a significant factor at traffic junction on a long term basis, if the Cr is getting associated with the road dust. At Gandhinagar, the third factor was heavily loaded with Cu, Mn and Ni with percentage variance of 13. Manganese is normally associated with ferrous metal processing. Ni can be a component of specialty steels. Hence, this cluster can be traced as a complex mixture of metal working emissions. There are several small and medium scale industries located in the receptor domain of both the sampling sites. Hence, this factor can be termed as contribution from metal industries. The fourth factor was heavily loaded with Na and K and moderately loaded with Mg with percentage variance of 10.8. These being major components of marine sea salts can be attributed to contribution from marine aerosols. Mumbai is an island city in the Arabian sea on the western margin of Deccan trap country and a significant amount of SPM in the Mumbai urban atmosphere is
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apportioned to marine aerosols (Sharma and Patil, 1994; Negi et al., 1997). The fifth factor showed a heavy loading of Arsenic and SO2 with percentage variance of 6.8. These two are emitted during combustion of coal, firewood and refuse burning (Sadasivan and Negi, 1990). Since, the sampling sites as mentioned above are located in a mixed zone comprising of small scale industries and residences, there are many sources of coal, firewood combustion and refuse burning. Hence, this factor can be identified as combustion source and is clubbed together by the name coal combustion in this study. Similar type of elemental clustering was observed at Sakinaka for factors 3, 4 and 5, however there was marginal difference in the percentage variance explained and the sequence of clusters (sources). Here, the third factor indicating marine source was loaded with Na, K and Mg with percentage variance of 10.9. The fourth factor indicating coal combustion was loaded with As and SO2 with a percentage variance of 8.8. The fifth factor indicating contribution from metal industries was loaded with Mn, Ni and a moderate loading of Cu with a percentage variance explanation of 6.1. 3.2. Factor analysis-multiple regression Factor scores available as output from factor analysis (SPSS, 1988) is unscaled and uncentred to produce absolute factor scores (AFS) using the procedure outlined by Thurston and Spengler (1985). This technique known as absolute principal component analysis (APCA) has been successively applied in a number of cases (Maenhaut and Cafmeyer, 1987; Andrade et al., 1993). In this technique the AFS are regressed on the observed suspended particulate matter concentration to apportion the suspended particulate matter to each sample (Okamoto et al., 1990). Source apportionment to the 24 h average SPM is shown in Table 5 for Sakinaka and Gandhinagar, respectively. For the 24 h average SPM concentration, 41.4% is contributed by road dust, 14.3% is contributed from vehicular emissions, 15.2% marine aerosols, 6% from coal combustion, 6.2% by metal industries and 16.9% remains as unexplained contributions, at Gandhinagar. At Sakinaka for the 24 h average SPM concentration 32.9% is contributed by road dust, 18.3% is contributed from vehicular emissions, 14.8% marine aerosols, 10.8% from coal combustion, 8% by metal industries and 15.2% remains as unexplained contributions. Source apportionment to elemental lead observed at both the sites were obtained by regressing AFS on the observed elemental lead concentration. It was seen that at both the sites about 60% of lead in the SPM is coming from vehicular sources, 20% from road dust and remaining from other sources as shown in Table 5. The contribution of lead from other sources identified by FA-MR can be mainly
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Table 5 Source apportionment of SPM and Lead at traffic junctions Possible Sources
Road dust Vehicular emissions Marine aerosols Coal combustion Metal industries Unexplained
Percentage contribution to SPM
Percentage contribution to lead
Sakinaka
Gandhinagar
Sakinaka
Gandhinagar
33 18 15 11 8 15
41 15 15 6 6 17
18 59 6 10 7
17 62 3 7 11
due to the marker element of a particular source being chemically bound to the vehicular source marker (say Pb) in the road dust (Hopke, 1980).
4. Conclusions Very high concentrations of SPM are observed at traffic junctions in India. Qualitatively it is known that road dust and vehicular emissions are the main sources for these SPM concentrations. In this study receptor modelling technique was applied to quantitatively apportion the SPM sources. FA-MR technique apportioned five sources for the SPM observed, namely road dust, vehicular emissions, marine aerosols, metal industries and coal combustion. The major source for the SPM observed at traffic junction is the road dust (40% of the SPM). This dust is mainly of crustal nature and is likely to have its origin from the road shoulders which is later kept in suspension/resuspended by vehicular movements. Vehicular emissions are responsible for 15%– 18% of the SPM observed at traffic junctions. About 60% of the lead in the SPM at traffic junction is originating from vehicular emissions and 20% from the road dust.
Acknowledgements The authors acknowledge the cooperation extended by the traffic police department of Mumbai police, M/S India Tube Mills, Gandhinagar and M/S Gujral Auto Parts, Sakinaka during the sampling period and to Dr. R.M. Tripathi, Dr. Radha Raghunath and Shri. A.P. Sathe for their useful discussion and help during the trace metal analysis.
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Source apportionment of suspended particulate matter at two traffic junctions in Mumbai, India A.Vinod Kumara,*, R.S. Patilb, K.S.V. Nambic a b
Environmental Assessment Division, Bhabha Atomic Research Centre, Trombay, Mumbai 400 085, India Centre for Environmental Science and Engineering, Indian Institute of Technology, Mumbai 400 076, India c E-50, Shakti Illam, 8th Cross Street, Maharaja Nagar, Tirunelveli 627 011, India Received 20 April 2000; accepted 22 April 2001
Abstract Very high concentration of suspended particulate matter (SPM) is observed at traffic junctions in India. Factor analysis-multiple regression (FA-MR), a receptor modelling technique has been used for quantitative apportionment of the sources contributing to the SPM at two traffic junctions (Sakinaka and Gandhinagar) in Mumbai, India. Varimax rotated factor analysis identified (qualitative) five possible sources; road dust, vehicular emissions, marine aerosols, metal industries and coal combustion. A quantitative estimation by FA-MR model indicated that road dust contributed to 41%, vehicular emissions to 15%, marine aerosols to 15%, metal industries to 6% and coal combustion to 6% of the SPM observed at Sakinaka traffic junction. The corresponding figures for Gandhinagar traffic junction are 33%, 18%, 15%, 8% and 11%, respectively. Due to limitation in source marker elements analysed about 16% of the remaining SPM at these two traffic junctions could not be apportioned to any possible sources by this technique. Of the observed lead in the SPM, FA-MR apportioned 62% to vehicular emissions, 17% to road dust, 11% to metal industries, 7% to coal combustion and 3% to marine aerosols at Gandhinagar traffic junction and about a similar apportionment for lead in SPM at Sakinaka traffic junction. r 2001 Elsevier Science Ltd. All rights reserved. Keywords: Traffic junctions; Suspended particulate matter; Lead; Factor analysis; Multiple regression
1. Introduction Problem of vehicular pollution is very prominent at traffic junctions, due to high density of vehicles of various types and different operating modes like idling, stopping, accelerating, decelerating, etc. Traffic junctions in city centres are generally surrounded by shops and commercial complexes. The footpaths around traffic junctions are occupied by pedestrians in city centres and also used by vendors in suburbs. As such, these sites represent the ‘‘hot spots’’ of local importance with high air pollution exposure to public. Detailed air pollution studies at traffic junctions in India or in any other *Corresponding author. Fax: +91-22-550-5151. E-mail address: [email protected] (A.V. Kumar).
country are limited. Lot of work has been carried on gaseous pollutant concentrations at roadways and traffic junctions worldwide (Hamilton et al., 1990; Joumard, 1993; Hamilton and Harrison, 1996; Harrison and Hamilton, 1999) however, work related to particulate matter concentrations near roadways are limited (Namdeo et al., 1999). This is more specific to literature related to Indian traffic junctions (Luhar and Patil, 1989; URBAIR, 1994). Values of ambient air suspended particulate matter (SPM) reported for developed countries are generally less than or about 100 mg m3 (Rojas et al., 1990; Nicholas and Rashed, 1991; Camuffe and Bernardi, 1996), whereas in India the SPM values are in general very high with an annual average of 200– 500 mg m3 for an urban environment like Mumbai (Sadasivan and Negi, 1990; Sharma and Patil, 1992; URBAIR, 1994). Namdeo et al. (1999) have reported
1352-2310/01/$ - see front matter r 2001 Elsevier Science Ltd. All rights reserved. PII: S 1 3 5 2 - 2 3 1 0 ( 0 1 ) 0 0 2 5 8 - 8
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SPM concentrations of about 100 mg m3 in an urban street canyon in Nottingham, UK. In India significantly much higher concentrations are observed near roadways and at traffic junctions (Patil and Vinod Kumar, 1994; Vinod Kumar and Patil, 1994; Satyanarayana et al., 1997, 1998). Qualitatively it is known that road dust and vehicular emissions are the main sources for these SPM concentrations. In this study, an attempt has been made to quantitatively apportion the sources leading to the high SPM values observed at traffic junctions in India using factor analysis-multiple regression (FA-MR), a receptor model. Various ambient air quality parameters measured at two traffic junctions Sakinaka and Gandhinagar in Mumbai, over a period of one year has been used for this purpose.
Cr, Fe, Ca, Al, Mg, K, Na, Cu, Mn and Ni were determined in these acid digested samples using flame atomic absorption spectrophotometer (AAS) (Model GBC 904AA). Hg and As were determined using hydride generator (Model GBC HG3000) coupled with AAS. Remaining half portion of SPM filter papers were treated in deionized water at 1001C and were analysed for water soluble sulphates using spectrophotometric method (UV-VIS spectrophotometer, model Shimadzu UV-2100). Quality assurance of trace metal analysis was done by analysing standard reference materials, Hay V10 and milk powder A-11, supplied by Analytical Quality Control Services (AQCS), International Atomic Energy Agency (IAEA), Vienna. The results agree within 77% of the certified values (Table 1). 2.3. Receptor models
2. Monitoring and modelling 2.1. Sampling location and sample collection The traffic junctions, Sakinaka and Gandhinagar are 4 way intersections with 14–20 m wide roads, located in mixed zone comprising of industries and residences. Average traffic density during day time (0800–2000 h) is around 5000 vehicles per hour and 2800 vehicles per hour at Sakinaka and Gandhinagar, respectively. The measurements were carried out for 1 yr, starting from April 1991 to February 1992, with a frequency of once in a week sampling. Each sample represents a continuous SPM collection of 24 h duration. Rains in India (specifically, Mumbai) are limited to four months of monsoon period with intense and continuous rainfalls (Sharma et al., 1990). Hence, during this season, the samples were collected only during dry spell days. SPM were collected on glass fibre filter paper (size 20.3 25.4 cm), using a high volume sampler, operated at a flow rate of 1.0 m3 min1. The sampling height was 1.5 m above the ground level (breathing level). NO2 and SO2 were collected by aspirating ambient air through absorbent solutions; sodium tetrachloromercurate for SO2 and a mixture of sodium hydroxide and sodium arsenite for NO2, with an aspiration rate of 1.0 l min1. 2.2. Sample processing and analysis SO2 and NO2 were analysed colorimetrically using the method of West and Gaeke and modified method of Jacob and Hochheiser, respectively (APHA, 1977). Half of the SPM filter papers were wet digested with a mixture of electronic grade nitric acid, hydrochloric acid and perchloric acid. After gentle heating a colourless solution was obtained which was evaporated to near dryness. The residue was taken up in 50 ml of 0.25% nitric acid. Filter paper blanks and acid blanks were also taken through the same procedure simultaneously. Pb,
There has been no investigation providing source composition library of particulate matter for Mumbai metropolitan city (Sharma and Patil, 1994). This limits the usage of widely accepted chemical mass balance model (CMB) for source apportionment of SPM. Hence, a multivariate analysis (MVA) technique, Factor analysis-multiple regression (FA-MR) which does not require a priori information on source composition (Hopke, 2000; Henry et al., 1984) is used in this study. The FA model applied in the field of air pollution is expressed as: Cit ¼
N X
Lij Sjt þ Eit ;
j¼1
where Cit is the normalized value of concentration of ith species for tth sample, N is the total number of sources, Sjt is the factor score of the jth common factor (jth source) for tth sample, Lij is the factor loading of the ith species of the jth source and Eit is the residual of ith species in the tth sample not accounted by the j sources or factors (Harman, 1968; Negi et al., 1988). The two vectors L and E are unknown in FA and are obtained by assuming various covariance relationships between the vectors S and E (Johnson and Wichern, 1992) and finally solving the covariance matrix as an eigenvalue– eigenvector decomposition problem. Computer softwares like SPSSt and STATGRAPHICSt are available in the market and have made factor analysis more accurate and easy to use. A detailed description of the FA-MR can be seen in Okamoto et al. (1990), and Thurston and Spengler (1985).
3. Results and discussions Table 2 presents the 24 h annual average ambient air concentration of various species monitored at Gandhinagar and Sakinaka traffic junctions.
A.V. Kumar et al. / Atmospheric Environment 35 (2001) 4245–4251
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Table 1 Concentrations of trace metals in standard reference materials from IAEA Reference material
Metal
Unit
Certified values
Observed values
Hay (V-10)
Ca Cd Cr Cu Fe Ni Pb Zn Cu K Mg Mn Pb Zn
mg/g mg/g mg/g mg/g mg/g mg/g mg/g mg/g ng/g mg/g mg/g ng/g ng/g mg/g
21.6 (21–22.2) 0.03 (0.02–0.05) 6.5 (5.6–7.1) 9.4 (8.8–9.7) 185 (177–190) 4 (3.8–4.9) 1.6 (0.8–1.9) 24 (21–27) 378 (354–402) 17.2 (16.2–18.2) 1.1 (1.02–1.18) 257 (248–266) 54 (29–79) 38.9 (36.6–41.2)
20.4 (20.1–21.8) 0.027 (0.02–0.03) 6.3 ( 6.1–7.2) 9.3 (9.1–9.5) 189 (183–192) 3.8 (3.7–4.2) 1.6 (1.45–1.73) 24.6 (23.6–25.8) 365 (362–368) 18 (17.8–18.9) 1.13 (0.97–1.16) 262 (259–265) 55 (53–57) 41 (38.6–44.3)
Milk powder (A-11)
Table 2 Twenty four hour annual average ambient air concentrations (mg m3) at Sakinaka and Gandhinagar traffic junctions Species
Sakinaka
Gandhinagar
Al Asa Ca Cr Cu Fe Hga K Mg Mn Na Ni NO2 Pb SO2 SO4 SPM
56.1727.6 871 46.2721.6 0.1570.06 0.3770.04 165.5745.7 1174 2.4970.57 10.272.4 0.8570.22 20.772.5 0.1670.04 39.374.0 1.0670.30 32.572.9 3.7071.01 1176.07245.1
58.0717.2 472 35.9717.4 0.0570.03 1.5570.72 265.5774.2 24713 5.1473.08 21.679.1 1.4770.88 18.276.6 0.1070.05 44.5710.4 0.8270.43 35.5713.3 2.7571.20 1031.97235.5
Concentration in ng m3; number of samples=45 at each junctions. Uncertainty is one standard deviation of day to day concentration. a
3.1. Factor analysis Henry et al. (1984) suggested that the minimum number of samples (n) for FA should be such that n > 30 þ ðV þ 3Þ=2; where V represents the number of variables. An important aspect in any MVA is the selection of proper data set. Unusual events or errors in sampling and analysis results in very high or very low values (outliers) of one or more variables. These outliers,
have to be removed before analysis to avoid propagation of errors, since they do not represent the normally observed situation (Hopke, 2000). In this study 17 variables and 45 samples (after removing outliers) were considered for FA. Results of varimax rotated factor analysis carried out on various ambient air components using SPSS software (SPSS, 1988) at Gandhinagar and Sakinaka and the corresponding possible sources are depicted in Tables 3 and 4, respectively. Five factors at each site were selected based primarily on the following criteria. Firstly, the number of factors were selected such that the cumulative percentage variance explained by all the chosen factors is more than 80%. Secondly, only the factors with eigenvalue more than one were chosen. The argument is made that the normalized variables each carry one unit of variance. Thus, if an eigenvalue is less than one, then it carries less information than one of the initial variables and is therefore not needed (Maenhaut and Cafmeyer, 1987). Since higher factor loading of particular elements (marker elements) in a factor can help in identifying the possible sources (Henry et al., 1984), the number of factors selected (sources identified) should represent the sources which are relevant in the receptor domain. Varimax rotated factor analysis showed five possible groups/factors (based on factor loading of marker elements greater than 0.7) indicating five different contributing sources for the trace metals at this sampling location. The total percentage variance explained by five factors were about 85% at Gandhinagar and 83% at Sakinaka. The first factor was heavily loaded (factor loading >0.8) with Fe, Ca and Al at Gandhinagar and at Sakinaka. These being crustal elements, factor 1 can be identified as originating from crustal contribution. A study of the aerosol sources in the receptor domain
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Table 3 Varimax rotated factor matrix for Gandhinagar Species
Al As Ca Cr Cu Fe Hg K Mg Mn Na Ni NO2 Pb SO2 SO4 Eigenvalue % variance Cumulative % variance
Possible emission sources Road dust
Vehicular emissions
Metal emissions
Marine
Coal combustion
Communality
0.83 0.08 0.85 0.33 0.19 0.85 0.64 0.24 0.35 0.07 0.08 0.21 0.17 0.22 0.04 0.85 6.3 39.1 39.1
0.19 0.21 0.11 0.86 0.18 0.26 0.29 0.09 0.24 0.02 0.07 0.09 0.94 0.92 0.17 0.16 2.5 15.7 54.8
0.08 0.07 0.19 0.05 0.86 0.20 0.25 0.04 0.07 0.89 0.07 0.86 0.08 0.16 0.11 0.17 2.1 13 67.8
0.39 0.09 0.12 0.04 0.19 0.21 0.18 0.91 0.72 0.03 0.88 0.22 0.07 0.05 0.13 0.01 1.7 10.8 78.6
0.18 0.90 0.20 0.21 -0.13 -0.04 0.14 0.13 0.29 0.12 0.01 0.02 0.14 0.13 0.91 0.17 1.1 6.8 85.4
0.93 0.88 0.84 0.89 0.86 0.87 0.61 0.92 0.79 0.82 0.81 0.85 0.95 0.94 0.88 0.81
Table 4 Varimax rotated factor matrix for Sakinaka Species
Al As Ca Cr Cu Fe Hg K Mg Mn Na Ni NO2 Pb SO2 SO4 Eigenvalue % variance Cumulative % variance
Possible emission sources Road dust
Vehicular emissions
Marine
Coal combustion
Metal emissions
Communality
0.89 0.06 0.89 0.17 0.31 0.87 0.57 0.07 0.14 0.08 0.29 0.31 0.34 0.41 0.02 0.61 7.0 43.6 43.6
0.23 0.31 0.24 0.74 0.14 0.27 0.31 0.06 0.08 0.02 0.09 0.21 0.86 0.87 0.07 0.23 2.1 13.3 56.9
0.19 0.31 0.18 0.06 0.13 0.13 0.33 0.92 0.92 0.01 0.69 0.07 0.11 0.09 0.19 0.04 1.6 10.9 67.8
0.03 0.83 0.11 0.38 0.53 0.00 0.29 0.17 0.06 0.00 0.31 0.00 0.12 0.09 0.89 0.54 1.5 8.8 76.6
0.08 0.06 0.09 0.22 0.57 0.14 0.37 0.00 0.03 0.89 0.11 0.77 0.00 0.09 0.00 0.07 1.0 6.1 82.7
0.89 0.89 0.91 0.78 0.74 0.86 0.75 0.89 0.88 0.79 0.69 0.74 0.89 0.95 0.83 0.73
indicates that there is no separate crustal source, other than the natural wind blown crustal component of ambient aerosol at both the sampling locations. However, a significant amount of road dust is present on the
road and also on the road shoulders and is kept in suspension by vehicular movement. Hence, this source can be identified as the road dust component. Major portion of the percentage variance (39% in case of
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Gandhinagar and 43.6% in case of Sakinaka) was explained by factor one (crustal origin). A high factor loading of sulphate is seen in factor 1 at Gandhinagar and a moderate loading (0.6) at Sakinaka. Sulphate in the atmosphere is generally assumed to be a secondary fine particle aerosol component produced from gas to particle conversion of SO2 and hence its association with crustal elements is questionable and should have been identified as a separate source. The probable reasons for its association with crustal elements can be the chemical binding of sulphate with crustal elements in the road dust, masking of the fine sulphate aerosol component variation by the large resupended dust load contribution variation or inability of the FA-MR model to separate this component in absence of sufficient marker elements. The second factor explained percentage variance of 15.7 and 13.3 for Gandhinagar and Sakinaka, respectively with moderate to high loading of Pb, NO2 and Cr. Pb is associated with leaded gasoline vehicular emissions. During the sample collection period, 0.155 g Pb l1 gasoline was added to the gasoline distributed in Mumbai region (URBAIR, 1994) as an antiknocking agent. Main sources of NO2 at traffic junction are direct emission of NO2 which is about 2–5% of total NOx in gasoline powered and about 30–40% of total NOx in diesel powered automobile exhausts, conversion of NO emitted by the vehicles to NO2 by reacting with O3 or by the low temperature thermal oxidation in presence of O2 in the atmosphere (Lenner et al., 1983). Hence, this factor comprising of Pb and NO2 can be identified with vehicular emissions. Chromium is also associated with this group at both the traffic junctions, which is slightly questionable. Ward (1990) has attributed high chromium in road dust and near by vegetation to vehicular emissions. Also, Hopke (1980), Sadasivan and Negi (1990) have associated Cr with the wear and tear of brake lining, tire and rust particles of the vehicles. These contributions which appears insignificant can be a significant factor at traffic junction on a long term basis, if the Cr is getting associated with the road dust. At Gandhinagar, the third factor was heavily loaded with Cu, Mn and Ni with percentage variance of 13. Manganese is normally associated with ferrous metal processing. Ni can be a component of specialty steels. Hence, this cluster can be traced as a complex mixture of metal working emissions. There are several small and medium scale industries located in the receptor domain of both the sampling sites. Hence, this factor can be termed as contribution from metal industries. The fourth factor was heavily loaded with Na and K and moderately loaded with Mg with percentage variance of 10.8. These being major components of marine sea salts can be attributed to contribution from marine aerosols. Mumbai is an island city in the Arabian sea on the western margin of Deccan trap country and a significant amount of SPM in the Mumbai urban atmosphere is
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apportioned to marine aerosols (Sharma and Patil, 1994; Negi et al., 1997). The fifth factor showed a heavy loading of Arsenic and SO2 with percentage variance of 6.8. These two are emitted during combustion of coal, firewood and refuse burning (Sadasivan and Negi, 1990). Since, the sampling sites as mentioned above are located in a mixed zone comprising of small scale industries and residences, there are many sources of coal, firewood combustion and refuse burning. Hence, this factor can be identified as combustion source and is clubbed together by the name coal combustion in this study. Similar type of elemental clustering was observed at Sakinaka for factors 3, 4 and 5, however there was marginal difference in the percentage variance explained and the sequence of clusters (sources). Here, the third factor indicating marine source was loaded with Na, K and Mg with percentage variance of 10.9. The fourth factor indicating coal combustion was loaded with As and SO2 with a percentage variance of 8.8. The fifth factor indicating contribution from metal industries was loaded with Mn, Ni and a moderate loading of Cu with a percentage variance explanation of 6.1. 3.2. Factor analysis-multiple regression Factor scores available as output from factor analysis (SPSS, 1988) is unscaled and uncentred to produce absolute factor scores (AFS) using the procedure outlined by Thurston and Spengler (1985). This technique known as absolute principal component analysis (APCA) has been successively applied in a number of cases (Maenhaut and Cafmeyer, 1987; Andrade et al., 1993). In this technique the AFS are regressed on the observed suspended particulate matter concentration to apportion the suspended particulate matter to each sample (Okamoto et al., 1990). Source apportionment to the 24 h average SPM is shown in Table 5 for Sakinaka and Gandhinagar, respectively. For the 24 h average SPM concentration, 41.4% is contributed by road dust, 14.3% is contributed from vehicular emissions, 15.2% marine aerosols, 6% from coal combustion, 6.2% by metal industries and 16.9% remains as unexplained contributions, at Gandhinagar. At Sakinaka for the 24 h average SPM concentration 32.9% is contributed by road dust, 18.3% is contributed from vehicular emissions, 14.8% marine aerosols, 10.8% from coal combustion, 8% by metal industries and 15.2% remains as unexplained contributions. Source apportionment to elemental lead observed at both the sites were obtained by regressing AFS on the observed elemental lead concentration. It was seen that at both the sites about 60% of lead in the SPM is coming from vehicular sources, 20% from road dust and remaining from other sources as shown in Table 5. The contribution of lead from other sources identified by FA-MR can be mainly
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Table 5 Source apportionment of SPM and Lead at traffic junctions Possible Sources
Road dust Vehicular emissions Marine aerosols Coal combustion Metal industries Unexplained
Percentage contribution to SPM
Percentage contribution to lead
Sakinaka
Gandhinagar
Sakinaka
Gandhinagar
33 18 15 11 8 15
41 15 15 6 6 17
18 59 6 10 7
17 62 3 7 11
due to the marker element of a particular source being chemically bound to the vehicular source marker (say Pb) in the road dust (Hopke, 1980).
4. Conclusions Very high concentrations of SPM are observed at traffic junctions in India. Qualitatively it is known that road dust and vehicular emissions are the main sources for these SPM concentrations. In this study receptor modelling technique was applied to quantitatively apportion the SPM sources. FA-MR technique apportioned five sources for the SPM observed, namely road dust, vehicular emissions, marine aerosols, metal industries and coal combustion. The major source for the SPM observed at traffic junction is the road dust (40% of the SPM). This dust is mainly of crustal nature and is likely to have its origin from the road shoulders which is later kept in suspension/resuspended by vehicular movements. Vehicular emissions are responsible for 15%– 18% of the SPM observed at traffic junctions. About 60% of the lead in the SPM at traffic junction is originating from vehicular emissions and 20% from the road dust.
Acknowledgements The authors acknowledge the cooperation extended by the traffic police department of Mumbai police, M/S India Tube Mills, Gandhinagar and M/S Gujral Auto Parts, Sakinaka during the sampling period and to Dr. R.M. Tripathi, Dr. Radha Raghunath and Shri. A.P. Sathe for their useful discussion and help during the trace metal analysis.
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