Source Apportionment Study

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1 6.0 INTRODUCTION Airborne particulate matter (PM), which is composed of a broad class of physically and chemically diverse substances are variable in size, formation, origin and concentration, across space and time. Among the inorganic compounds, most important are the trace elements, which are emitted by various natural and anthropogenic sources, such as, crustal materials, road dust, construction activities, motor vehicle emissions, coal and oil combustion, incineration and other industrial emissions (Shah and Shaheen, 2010; Shah et al., 2006; Arditsoglou and Samara, 2005; Quiterio et al., 2004; Watson et al., 2002). The major sources of air pollution in the study area are expected to be from various coal mining activities, mine fire, captive thermal power plants, coal washery, road traffic, resuspended road dust, open burning of coal, etc. The airborne particulates and related trace elements have been linked with both acute and chronic adverse health effects which mostly include respiratory diseases, lung cancer, heart diseases and damage to other organs (Callen et al., 2009;Wild et al., 2009; Magas et al., 2007; Prieditis and Adamson, 2002). Numerous epidemiological studies have shown a correlation between elevated levels of airborne particulates and increased rate of morbidity and mortality (Shah, 2009; Pope, 2000). Hence it is necessary to identify various sources of pollution which helps in regulating effective abatement strategies to reduce PM at the source level. A number of studies conducted in the coal mining area showed higher ambient particulate concentration (Huertas et al., 2012; Suman et al., 2007; Reddy and Ruj, 2003; Ghose and Majee, 2002; Sinha and Sreekesh, 2002). Results from Kuopio also showed that resuspended particulate matter could contain considerable amounts of trace elements from anthropogenic sources. The major cause for elevated concentrations of resuspended dust in study area seems to be the turbulence and tyre stress related to traffic. Keeping in view the environmental significance of the potential influence of atmospheric particulates and trace elements, the present investigation was undertaken to assess, firstly, the current status of eight trace metals (Pb, Cu, Zn, Mn, Cr, Fe, Ni and Cd) contents (as discussed in Chapter 5) in study area and their source apportionment study. 127

2 6.1 METHODOLOGY Univariate (Correlation Study) and Multivariate analysis viz., Factor analysis (FA) and Enrichment Factor analysis (EFA) were employed for source apportionment of the trace elements in the atmospheric particulates as successfully undertaken by others for similar purposes (Cocker, 2009; Shah, 2009). 6.2 RESULTS AND DISCUSSION Univariate analysis Correlation Study Correlation is a measure of association between two variables. The value of a correlation coefficient can vary from minus one to plus one. A minus one indicates a perfect negative correlation, while a plus one indicates a perfect positive correlation. A correlation of zero means there is no relationship between the two variables. When there is a negative correlation between two variables, as the value of one variable increases, the value of the other variable decreases, and vice versa. In other words, for a negative correlation, the variables work opposite to each other. When there is a positive correlation between two variables, as the value of one variable increases, the value of the other variable also increases. The annual correlation matrix of the study area is shown in Table 6.1 with significant r, varies from to and to at the 0.05 and 0.01 levels. Accordingly, a positive correlation between Fe and Mn (r=0.670), obviously indicated some common source for the two elements in PM 10. A further aspect of Fe with significant correlation with Zn (r=0.484).this situation reflected that Fe distribution goes collectively in parallel with Mn and Zn. These arise due to earth crust, resuspended soil dust etc. On similar grounds, PM 10 is positively correlated with Mn, Cd and Cr with r values (at 0.01 level), (0.05 significance level) and (0.01 significance level) respectively. 128

3 Table 6.1: Annual Correlation Matrix of the Study Area (Pearson Correlation) PM 10 Pb Ni Cu Mn Fe Zn Cd Cr PM * ** * Pb ** Ni Cu * Mn ** Fe * Zn Cd ** Cr 1 **Correlation is significant at the 0.01 level * Correlation is significant at the 0.05 level On the other hand, the negative correlation between Cu and Mn with r value signified no apparent common source of their distribution in PM 10. Therefore, negative correlation of this kind was not explicitly taken up for element source identification. Overall, the correlation data shown in correlation matrix helped to isolate some element pairs that could be used as pointers to common sources of their distribution in aerosols. Highly significant correlations such as those between Pb and Ni (r=0.815), Fe and Mn (r=0.670), Cu and Zn (r=0.785) and Cd and Cr (r=0.616) suggested likely contribution from vehicular exhaust, earth crust and industrial sources towards the bulk of these elements in the study area. Although, a linear, univariate correlation study of this kind does not licence ultimate evidence on the sources of element levels, the present correlation data simply afforded a probable explanation of an overall complex element distribution phenomenon. Additional means for comparative source identification by other sophisticated statistical methods was, therefore, highly imperative. The multivariate Factor analysis (FA) and Enrichment factor analysis (EFA) have been included in the present data treatment to adequately explain and answer some basic questions regarding the observed distribution behaviour of elements in PM 10 samples, especially with reference to their source identification. 129

4 6.2.2 Multivariate analysis In principle, the multivariate methods are normally used to identify the combined effect of several measured variables and the influence of external parameters on given distributions. As reported in section, 6.1 two multivariate techniques (e.g., Factor Analysis and Enrichment Factor analysis) were applied to the data obtained during the present study (Breed et al., 2002; Hair Jr et al., 1998; Jalkanen and Mannine, 1996) Factor analysis Factor analysis (FA) is a multi regression technique. This is done by using SPSS software version This is a widely used and useful receptor model which does not require prior information on expected sources and their composition (Park et al., 2011; Ramadan, 2000; Henry et al., 1984). This is commonly used in environmental studies to deduce sources from data (Gullu et al., 2005; Salvador et al., 2004; Thurston and Spengler, 1985). The FA model applied is expressed as: C it = N j= 1 L S ij jt + E it Where C it =the normalized value of concentration of i th species for t th sample, N = the total number of sources, S jt = the factor score of the j th common factor (j th source) for t th sample, L ij = the factor loading of the i th species of the j th source and E it =the residual of i th species in the t th sample not accounted by the j sources or factors. 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 eigen value eigenvector decomposition problem. A detailed description of the FA-MR can be seen in Okamoto et al. (1990), and Thurston and Spengler (1985). The principal application of factor analysis is to reduce the number of variables. Therefore, factor analysis can be applied as a data reduction method. In this regard, Principal Component Analysis 130

5 (PCA) was performed by the Varimax Rotated Factor Matrix method, based on the orthogonal rotation criterion which maximizes the variance of the squared metals in the column of a factor matrix. This method focuses on cleaning up the factors. It produces factors that have high correlations with one smaller set of variables and little or no correlation with another set of variables (Stevens, 1996). Table 6.2 presents the Principal Component (PC) loadings for the metal data of the study period with corresponding eigen values and variances. Table 6.2: Principal Component Loadings of the Trace Elements for the Study Area Factors Components PC1 PC2 PC3 PC4 PM Pb Ni Cu Mn Fe Zn Cd Cr Eigen Value %Variance 22.99% 21.12% 20.05% 15.34% %Cumulative 22.99% 44.11% 64.13% 79.45% Variance The data for PM 10 were interpreted on the basis of four factors. Four PCs with Eigen values greater than 1.0 were extracted with 79.45% cumulative variance for the study area. The PC 1 with 23.00% total variance shows highest loadings for PM 10, Cd and Cr with significant contributions from Coal mining and allied activities, crude oil combustion and metallurgical units housed in the industrial area and vehicular emissions. One of the most important sources of cadmium, chromium in the urban environment is road traffic as suggested by various researchers (Ahumada et al., 2007; Cetin et al., 2007; Querol et al., 2007; Ayras and Kashulina, 2000). Other contributors include waste incinerators, coal fired power plants, geogenic dust, and construction debris (Chandler, 1996). The second factor with 21.12% revealed close association of Fe and Mn, in PM 10 in the atmosphere, mainly contributed by earth crust/wind blown soil 131

6 (Chakraborty and Gupta, 2009; Shah et al., 2006; Quiterio et al., 2004; Ragosta et al., 2002). The third factor (PC3) with 20.05% indicated high loadings of Pb and Ni which is representative of emission from vehicular exhaust (Shah et al., 2006; Hafner, 2004; Ragosta et al., 2002; Kim et al., 2002;Weckwerth, 2001; Ramadan et al., 2000). The fourth factor (PC4) with 15.34% represented high loadings of Cu and Zn and hence may be attributed to industrial emission (Manoli et al., 2002; Ragosta et al., 2002) for the coal mining area. Hence, it can be concluded that the major source of pollution in the study area are earth crust (first),vehicular emission (second), dust through unpaved road and coal mining activities (third) and other associated industrial activities (fourth) Enrichment Factor Analysis The concept of enrichment factor was initially introduced by Rahn (1971) to determine whether a particular metallic element is found in greater abundance in air or expected to be crustal in origin. Enrichment factor is used to calculate the concentration of the metallic element in air in a sample with that of a selected reference metallic element which is almost entirely crustal in origin and the corresponding ratio in the average composition of the earth s crust and to gage the extent of anthropogenic influence (Cocker, 2009; Al-Momani et al., 2005). Usually Na, K, Al, Mg, Ca, Mn and Fe are used as the reference metals. In this study, annual mean of percent composition of metals detected in PM 10 samples were correlated and Fe shown the best correlation with all other element and hence, selected as reference element (Nazir et al., 2011). The enrichment factor is calculated through the equation X Fe EF( X ) = X Fe Sample Where EF(X) =the ratio of element to reference material, [X/Fe]Sample and [X/Fe] Crust refers to the mean concentrations of the target element and Fe in atmospheric particulate matter and continental crust, respectively. EFs are calculated on the basis of earth crust mean abundance of the metals given in CRC handbook (Lide, 2005). The enrichment factor for various locations of the study area were calculated and shown in Table Crust

7 Table 6.3: Enrichment Factors Values for Trace Elements in Air Borne Dust Samples Sl. Name of the Pb Ni Cu Mn Fe Zn Cd Cr NO. location 1 A1 (Steel Gate) A2 (Main Gate) A3 (Bus Stand) A4 (Court More) A5(Railway Station) A6 (Bank More) A7 (Kusunda) A8 (East Bassuriya) A9 (Tetulmari) A10 (Sijua) A11 (Katras) A12 (Muraidih) A13 (Baghmara) A14 (Kharkharee) A15(MohudaMore) A16 (Murulidih) A17 (Bastacola) A18 (Jamadoba) A19 (Tisra) A20 (Barari) A21 (Sudamdih) A22 (Chasnala) A23 (BIT-Sindri) A24 (ISM-Campus) A25 (Madhuband) A26 (Lohapati) A27 (Bhatdih) A28 (Singra) A29 (Jarma) A30 (Lodna) A31 (Pateherdih) The analysis of enrichment factor revealed that though the concentration of certain elements could be high, it may not, necessarily, be enriched. On the basis of annual Enrichment Factors (EF) value, trace elements were grouped under three categories. They are described as: Little enriched- EFs is < 10 Moderately enriched-efs is Highly enriched- EFs is

8 Figure 6.1 shows the annual EFs for Ni, Mn and Cr at various locations of the study area. Among all trace elements, Nickel (Ni) Chromium (Cr) and Manganese (Mn) were little enriched in aerosol with mean EFs of 1.15, 2.4 and 4.0 at various locations of the study area. Manganese is typically dominated by crustal contributions, the form found in soil largely being MnO 2 (Von Schneidemesser et al., 2010). Location A11 (Katras) followed by A2(ISM-Main Gate) reported higher EFs of 3.64 and 3.12, respectively for Nickel (Ni). Due to vehicular pollution and other commercial activities. Similarly, highest EFs for Mn were obtained for A17 (Bastacola) i.e., this is mainly originates from airborne soil dust. In the same way, highest value for Cr was obtained for A22 (Chasnala), being industrial area with EFs value of Figure 6.2 shows the second group of elements comprising of EFs between and shows moderately-enriched elements. This group of elements are Zn and Pb with mean EFs 55 and 96, respectively. This is in agreement with published literature as all the elements belonging to this group have a known crustal source in addition to various emission sources (Khan et al., 2010; Salvador et al., 2004). Among all locations A5 (Railway Station) represented higher EFs value, i.e., 230, while lowest was shown by A20 (Barari). Figure 6.1 : Enrichment Factor Analysis of Trace Elements (Mn,Cr,Ni and Zn) in the Study Area 134

9 Figure 6.2: Enrichment Factor Analysis of Zn and Pb in the Study Area Figure 6.3: Enrichment Factor Analysis of Cd and Cu in the Study Area The third group (EFs ) represented by Cu & Cd and shown in Figure 6.3 with their annual mean value and , respectively and were anomalously enriched in aerosol. The highest EF value for Cu and Cd were shown by location Bank more (A6) and Bastacola (A16) while lowest were reported for A28 (Singra) and A23 (BIT-Sindri), respectively. Vehicular tire wear, battery 135

10 manufacture, pigments, metal plating and smelting industries are important sources of these elements (Khillare et al., 2012; Querol et al., 2007; Birmili et al., 2006) in the study area. 6.3 SUMMARY This chapter deals with source contribution of particulate matter with respect to trace elements at various locations of the study area. Among all trace elements Fe depicted higher concentration due to its origin from earth crust. The main source of pollution in the study area is thus earth crust, air borne soil dust due to the movement of vehicles over unpaved road. Next category of pollution source is coal mining activities which leads to fugitive dust emission from the adjacent open cast mining projects. Third pollution sources are coming from vehicular exhaust as indicated by high correlation between Pb and Ni. The last possible pollution source is other industrial activities which lead to emission of Zn, Fe and Cd. Source contribution estimates for the more complex sources of particulate matter, such as local combustion and long-range transported air pollution, differed substantially between the two methods. The major cause for elevated concentrations of resuspended dust in ambient environments seems to be the turbulence and tyre stress related to traffic. 136

Trace Metals in fine and coarse aerosols: Emerging Results from SPARTAN. Graydon Snider CSC June 8, 2016

Trace Metals in fine and coarse aerosols: Emerging Results from SPARTAN Graydon Snider CSC June 8, 2016 1 Global significance of PM2.5 Dhaka Edmonton Rehovot Hanoi *Forouzanfar et al.: Lancet, 2015. Beijing