Source Apportionment of PM10 at an Urban Site of a South Asian Mega City

Imran Shahid1*, Muhammad Usman Alvi2,3, Muhammad Zeeshaan Shahid4, Khan Alam5, Farrukh Chishtie6

1 Institute of Space Technology, Islamabad 44000, Pakistan 2 Institute of Chemistry, University of the Punjab, Lahore 54590, Pakistan 3 University of Education, Okara Campus, Okara 57000, Pakistan 4 King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia 5 Department of Physics, University of Peshawar, Peshawar 25120, Pakistan 6 SERVIR-Mekong, Asian Disaster Preparedness Center, Bangkok 10400, Thailand

ABSTRACT

In the present study, analysis and source apportionment of the elemental composition of PM10 was conducted in the urban atmosphere of Karachi. Trace elements such as Ni, Ba, Cd, Ca, Mg, Cr, Mn, Fe, Co, Cu, Sr and Ti were measured. The PM10 concentration ranged from 255 μg m–3 to 793 μg m–3, with an average of 438 ± 161 μg m–3. Among the various elements analyzed, concentrations of Ca, Al and Fe were the highest (> 10000 ng m–3), followed by Mg and S (> 1000 ng m–3). Elements such as Zn, P, Cu, Pb, Mn, Ti, Sr and Ba demonstrated medium concentrations (> 100 ng m–3), whereas the lowest concentrations were found for elements such as Cr, Ni and Se (> 10 ng m–3). The Positive Matrix Factorization (PMF) model identified five possible factors that contributed to PM10, namely, biomass burning, coal combustion, resuspended road/soil dust, vehicular emissions and industrial dust. Industrial dust was the highest contributor (23.2%) to PM10 followed by Biomass burning (23%), Vehicular emissions (22.2%), Coal combustion (21.7%) and Re-suspended 2 dust (9.9%). A strong positive correlation (R = 0.98) was observed between the model predicted PM10 mass and the gravimetrically measured mass collected on filters.

Keywords: Particulate matter; Air pollution; Urban air quality; Elemental analysis; Source apportionment; Positive Matrix Factorization.

INTRODUCTION PM2.5 and ozone concentration in India and Chate et al. (2013) has assessed the population exposure in India due to Air pollutants is turning out to be highly problematic in air pollutants during Common Wealth games in 2010. developing countries like China, India and Pakistan, Atmospheric PM that has aerodynamic diameter less than especially in mega cities like Beijing, Shanghai, Mumbai, or equal to 10 µm (PM10) has been studied intensively over Calcutta, Delhi, Lahore and Karachi. There has been a the past few decades, because this size fraction is respirable growing concern on almost all levels regarding airborne is trapped by the alveoli of the lungs (Perez et al., 2008). Particulate Matter (PM) in recent years. Anthoropogeic According to the reports of World Health Organization sources which contribute to PM consist mainly of fossil fuel (WHO), there is a strong association between air pollution and biomass burning, industries and construction activities and cardiovascular and cancerous diseases, beyond respiratory (Parekh et al., 2001). PM not only influences global climate diseases. Some regional festivals like new year Diwali also change, but also has been associated with adverse health deteriorate the air quality in South Asian cities. Parkhi et and enviromnetal impacts as reported in previous studies al. (2016) and Beig et al. (2013) has reported the large (Gwynn et al., 2000; Husain et al., 2007; Lodhi et al., 2009; variation in air quality during Diwali festivals and also Khan et al., 2010; Stone et al., 2010; Kaushar et al., 2013; estimate population exposure during these festivals. Trace Kim et al., 2015; Pope et al., 2015). Ghude et al. (2016) elemental profiles have been studied extensively due to the has reported premature mortality rate due to exposure of numerous adverse effects of elements on human health (Sarnat et al., 2006; Liu et al., 2009; Freitas et al., 2010; Mavroidis and Chaloulakou, 2010; Anderson and Thundiyil, 2012). Like other developing countries, there has been a *Corresponding author. deep concern on airborne particulate matter levels in Pakistan. E-mail address: [email protected] There are limited number studies that have discussed air

Shahid et al., Aerosol and Air Quality Research, 18: 2498–2509, 2018 2499 quality in Karachi and some of these have been summarized the risk of hyperhomocysteinemia due to lead (Pb) level in as follows: blood of a low income urban population in Karachi. Shahid According to Husain et al. (2007) and Lodhi et al. (2009) et al. (2016) reported chemical characterization of PM10 some key anthropogenic activities such as transportation, and PM2.5 that included Elemental Carbon (EC), Organic agricultural actvities, industries, biomass burning for cooking Carbon (OC), soluble ions, anyhdro sugars and mass closure. and construction activities are progressively deteriorating All the studies mentioned above reported PM10 and TSP air quality in Karachi due to rapid and excessive contributions concentrations only one Mansha et al. (2012) has used of particulate matter. Khwaja et al. (2012) have described PMF model for PM2.5 This study is an attempt to estimate the impact of bad air quality on the morbidity rate in in the the contribution of five differnet sectors in PM10 mass using metropolitan city of an estimated population of 15 million. trace metals concentrations in PMF model. Another study by Khwaja et al. (2009) features measurements of the composition of urban aerosol using electron MATERIALS AND METHODS microscopy. They report that seven different types of sources namely, sea salt, cement, steel mill, fossil fuel, soil, Study Area biological, and mobile sources contributed to ambient air, Karachi is situated on coast of Arbain Sea in south of whereas vehicular traffic emission was the major contributor. Pakistanwith geographic coordinates 24°51′N 67°02′E. The Shahid et al. (2016) have reported PM2.5 concentration of sampling site for this study is situated on the north western 75 µg m–3 in Karachi. Mansha et al. (2012) has described region of Karachi airport i.e., SUPARCO Headquarters as PM2.5 concentration and corresponding source apportionment shown in Fig. 1. There are four different seasons in the using PMF model at an urban site in Karachi and showed region which inclues pre-monsoon usually from April– that the sea salt originated from the Arabian Sea while road June, monsoon from July to September, post-monsoon from dust, transport and inudustrial emissions are major October to December and winter from January–March. The contributors to secondary aerosols. Parekh et al. (2001) annual average rainfall in Karachi is about 170 mm, and reported TSP loading in the ambient air of Karachi and approximately 80% of the precipitation occurring between observed an averaged TSP concentration of 668 µg m–3. July to September. The air is usualy hot and humid and Ghauri et al. (2007) has established baseline levels in major dew point temperatures are usually above 21°C. Daytime urban cities including Karachi and reported TSP concentration temperatures can exceed 27°C throughout the year. The at 410 µg m–3 in Karachi. Yakub and Iqbal (2010) studied monthly mean wind magnitude and vectors (m s–1) from

Fig. 1. Location map.

2500 Shahid et al., Aerosol and Air Quality Research, 18: 2498–2509, 2018

MERRA 2 satellite data at 10 m height during the study using a PM10 high volume sampler. The sampling was done peried i.e., March and April of year 2010 are provided in during the pre-monsoon season in the months of March– Fig. 2(a). According to Dutkiewicz et al. (2009) in Karachi April 2009. The field blanks samples were also collected and winds are comparatively calm in winter, but there is a strong considered for calculation procedures. In order to reduce the influence of northeasterly continental air. The daily mean losses due to evaporation and volatization the filter samples temperature at 2m height over Karachi, during March and were kept in refrigeration prior to chemical analysis. April of 2010 have been given in Fig. 2(b). Chemical Analysis Sampling Trace metals Ni, Ba, Cd, Ca, Mg, Cr, Mn, Fe, Co, Cu, PM samples were collected on Quartz fiber filters (Pall Sr, and Ti were measured using 5% HNO3 standard Corporation) at an urban site in Karachi as shown in Fig. 1 solution and Al, Pb, Zn, Se, P and S, 1000 mg L–1 in 1%

Fig. 2(a). Monthly mean wind magnitude and vectors (m s–1) from MERRA 2 satellite data at 10 m height during March and April of 2010.

Fig. 2(b). Daily mean Temperature at 2 m height over Karachi, during March and April of 2010.

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HNO3 standard solution using Inductively coupled plasma Eq. (1) can be defined as atomic emission spectroscopy (ICP-AES), spectrometer. The method was described by Mukhtar and Limbeck (2012). X = GF + E (3) The used methods were assessed for accuracy and the applicability by investigation of the certified reference where X is Matrix of Measured data with dimension “no. material SRM 2709® (San Joaquin Soil) from National of samples” M “no. of species”. Institute of Standards and Technology (NIST, Gaithersburg, G is Contributions Matrix with dimension “no. of samples” MD, USA) which was processed and analyzed in the same M “no. of factors”. way as the collected aerosol samples. The uncertainty in F is Source profiles Matrix with dimension “no. of species” chemical analysis has been described in detail by Mukhtar M “no. of factors”. and Limbeck (2012). E is Matrix of residuals with dimension “no. of samples” M “no. of species”. Source Apportionment and Data Analysis The uncertainty matrix ‘S’ and Matrix of the measured Receptor models are widely used in various research concentrations ‘M’ are inputs for PMF model, matrices areas because of their capability to handle large datasets. ‘G’, ‘F’ and ‘E’ are attained as output data. The source These models are applied so as to reduce the original datasets contribution matrix “G” was applied for source apportionment into lower dimensions, in order to analyze hidden information by considering the measured PM10 mass. For quantitative and to clarify the inconsistency of the measured variables. source apportionment, a scaling coefficient, yk, is presented Particularly, in the field of environmental studies, the goals in the model Eq. (1) as follows, of receptor modelling are to estimate the number and composition of sources, to highlight any trends and/or p yk x  gfe  (4) correlations among observations and to identify potential ij  iky kj ij tracer/indicator for pollutant sources. The positive matrix k 1 k factorization (PMF) is a mathematical approach developed by Paatero and Tapper in (1994), and used for quantification of so that yk was measured by multi-linear regression of source contributions to the samples based on the composition calculated source contribution against measured particulate or markers of the sources. PMF is a non data-sensitive matter mass. The constant of linear regression was assumed method that can resolve inhomogeneous datasets without to be zero. Moreover, any univariate analysis. An error estimates (or weight) related m to the data can be introduced in PMF e.g., in difficult data mygikik  (5) sets outliers or below-detection limit can be introduced in l 1 the model with some weight. The composition is determined by analytical techniques suitable for the particular medium This approach was implemented in the EPA-PMF version and important species that are required to separate impacts. 3.1.1, and applied to the analytical results of the collected A speciated data set can be used as a data matrix X of i by j PM10 samples to classify their sources and contributions to the dimensions, where i is number of samples and j is chemical receptor's site. After several runs, EPA-PMF 3.1.1 revealed species that are measured with certain uncertainties sij The the five most appropiate sources that have a minimum Q aim of this receptor model is to resolve the chemical mass value. balance (CMB) between source profiles and measured species concentrations, (Eq. (1)), with number of factors p, the RESULTS AND DISCUSSION species profile f of each source, and the amount of mass g contributed by each factor to each sample. Particulate Matter The average PM10 concentration at the sampling site p varied from 255 µg m–3 to 793 µg m–3 with an average of –3 x  gf  e (1) ij  ik kj ij 438 ± 161 µg m . The minimum value of PM10 is much k 1 higher than WHO limits (150 µg m–3) and also prescribed by Pakistan National Environmental Quality Standards (NEQS) To analyze source profiles (fkj) and contributions (gik), for Ambient Air (Pak-EPA). The PM10 concentrationa at PMF uses a least squares method. The purpose of EPA Karachi has been shown in Fig. 3. The observed PM10 PMF is to minimizes the sum of squares of standardized concentration of this study is quite high as compared to the (residual divided by corresponding uncertainty value) studies conducted in most of the European cities. In Milan- residuals (Q). –3 –3 Italy PM10 levels of 81.4 µg m and 91.9 µg m were observed during day and night times respectively (Vecchi 2 ab eij et al., 2007). Limbeck et al. (2009) reported PM10 levels in Q   (2) s Vienna-Austria at three different sites of Schafberg (20.4 ij11ij µg m–3), Kendlerstraße (27.7 µg m–3) and Rinnbockstraße –3 –3 (32.5 µg m ). A mean PM10 concentration of 34.4 µg m was where, a = Total number of samples, b = Total number of observed in an urban environment of Zaragoza city of Spain th th species and sij = Uncertainty for j species in the i sample. by Callen et al. (2013). In Ulsan-Korea PM10 concentration of

2502 Shahid et al., Aerosol and Air Quality Research, 18: 2498–2509, 2018

Fig. 3. PM10 Obsvered and predicted concentrations at Karachi.

50.5 µg m–3 was reported by Hieu and Lee (2010). Similarly, samples collected from an urban area of Karachi. According from Istanbul-Turkey the annual mean concentration of to the observed concentration ranges elements have been –3 PM10 of 39.1 µg m was noted by Theodosi et al. (2010). divided into four groups as summarized in Table 1. In the –3 The measured PM10 level of 438 µg m at the sampling site first group three elements: Ca, Al and Fe have been place. is comparable to the other reports from the Asian countries. Among this group, the concentration of Ca was highest –3 Duan et al. (2007) reported PM10 at an urban location of with an average amount of 57260 ng m . It may originate Guangzhou that ranged from 80 µg m–3 to 397 µg m–3. from soil sources and sea salt spray. After Ca, Al and Fe –3 Karar and Gupta (2007) measured PM10 mass concentrations have the highest average concentrations of 14280 ng m ranged from 68.2 µg m–3 to 280.6 µg m–3 and 62.4 µg m–3 and 13730 ng m–3 respectively. Both these elements are the to 401 µg m–3 at the residential and industrial sites of Kolkata dominant constituent of mineral dust. Apart from that, these respectively. The PM10 concentration levels measured in elements can come from vehicular emissions, constructional other cities of Pakistan (inclding Karachi) were also in the activities and industrial emissions. Limbeck et al. (2009) range of observation mentioned in the present study. Alam has reported trace metals concentrations in Vienna that has –3 –3 et al. (2015) reported PM10 mean concentration of of less than 0.1 ng m (Cd), approximately 200 ng m (Zn), –3 –3 480 µg m in the Peshawar city that is very close to PM10 and mineral components ranging from 0.01 ng m (Ca) to concentration in presented study. At Lahore, Pakistan an 16.3 ng m–3 (Si). Hieu et al. (2010) has reported Ca (3025 –3 –3 –3 –3 annual average PM10 mass concentration of 340 µg m ng m ) followed by Al (166 ng m ) and Na (1523 ng m ) was observed by Schneidemesser et al. (2010). Similarly, in Ulsan, South Korea. Venkataraman et al. (2002) reported –3 respirable PM10 annual average concentration of 336 µg m the size and chemical characteristics of aerosols measured were reported by Stone et al. (2010). Likewise Alam et al. at Mumbai and measured concentration of various elements –3 –3 –3 (2014) reported that PM10 concentration at an urban site in like Al (3330 ng m ), Si (70 ng m ), Fe (1920 ng m ), Lahore varied from 254 µg m–3 to 555 µg m–3 with an Mn (40 ng m–3) and Zn (770 ng m–3). Mansha et al. (2012) –3 average of 406 µg m . In Faisalabad city average PM10 examined the source apportionment of PM in Karachi. The concentrations of 372 µg m–3, 283 µg m–3, 223 µg m–3 and chemical analysis of these samples indicated that Al 150 µg m–3 were observed at designated sites by Zafar et al. (51440 ng m–3), Ca (43910 ng m–3) and K (51360 ng m–3) (2015). In a comparative study; PM10 concentrations were as the major components of the samples during the summer assessed from four large cities (Karachi, Lahore, Rawalpindi season (Mansha et al., 2012). and Peshawar) in Pakistan. From all these above cited Mg and S constitute the second group of elements with –3 –3 studies it is obivious that PM10 concentraiton in Karachi is concentrations of 7350 ng m and 6390 ng m , respectively. comparable to other cities in the region but far higher thatn Mg may have its origin from earth crest, sea salt spray and WHO limits and European cities. industrial activities, whereas S may come from vehicular emissions and burning of fossil fuels. Trace Elements Elements like Zn, P, Cu, Pb, Mn, Ti, Sr and Ba have been The total eighteen elements were analyzed in PM10 placed in the third group. The average concentrations of

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–3 Table 1. Summary of trace element levels (ng m ) in the airborne particulate matter (PM10). Minimum (ng m–3) Maximum (ng m–3) Average (ng m–3) –3 PM10 (µg m ) 255 793 438 Al 7050 32200 14280 Ba 50 190 220 Ca 28200 91360 57260 Cd < LOD < LOD < LOD Co < LOD < LOD < LOD Cr 20 100 50 Cu 190 540 420 Fe 4930 31160 13730 Mg 3320 14980 7350 Mn 130 730 370 Ni 10 50 20 P 270 730 570 Pb 120 1160 410 S 3760 10650 6390 Se 10 20 10 Sr 110 330 220 Ti 130 640 260 Zn 180 1870 830 these elements were comparable and found to be: Zn (830 Cr, Ni, Se and found to be 50 ng m–3, 20 ng m–3 and 10 ng m–3 ng m–3), P (570 ng m–3), Cu (420 ng m–3), Pb (410 ng m–3), respectively. The trace concentration of these metals were Mn (370 ng m–3), Ti (260 ng m–3), Sr (220 ng m–3) and Ba observed due to their lowest crustal abundance, while (220 ng m–3). These heavy elements affect the human health concentration of both Co and Cd were lower than the the most and are usually attributed to anthropogenic sources. detection limit. Das et al. (2015) measured elemental Salam et al. (2003) determined trace elements in Dhaka composition of particulate matter from 16 locations in Greater –3 –3 –3 Bangladesh and found that Fe (24800 ng m ), As (9.8 ng m ), Kolkata. In PM10 size fractions Fe (11242 ng m ), Na (2393 Cd (2.5 ng m–3), Cu (54 ng m–3), Pb (280 ng m–3), Zn (800 ng m–3), Al (5223 ng m–3), K (3486 ng m–3), Ca (7610 ng m–3) ng m–3), Na (1300 ng m–3), K (1600 ng m–3), Ca (6800 ng m–3) were present in high concentrations, Mn (249 ng m–3), Zn and Mg (42 ng m–3) concentration in aerosol samples (761 ng m–3) and Pb (394 ng m–3) demonstrated medium collected under pre-monsoon conditions. Alam et al. (2015) concentrations, while V (18 ng m–3), Co (4.1 ng m–3), Ni investigated the elemental profile in the aerosol samples (48 ng m–3), Mo (15 ng m–3), Cd (8.6 ng m–3), Sn (21 ng m–3) collected from Peshawar. Elements analyzed were crustal and Sb (10 ng m–3) had low concentrations (Das et al., 2015). Al (7590 ng m–3), Si (3060 ng m–3), Mg (4050 ng m–3), Fe Qadir et al. (2012) monitored geological and anthropogenic (8630 ng m–3), traffic related Cu (1750 ng m–3), Mn (190 contributions to the urban air of Rawalpindi/Islamabad by ng m–3), Pb (2200 ng m–3), Zn (1420 ng m–3), Ba (60 ng m–3) trace elemental analysis. The presence of Yb, Cs, Sc, Rb, Co, and mixed sources of soils and pollution like Sr (1110 ng m–3), Eu, La, Ba, Zn and Hf indicated their geological origin, Cr (550 ng m–3), K (2570 ng m–3) and Na (5280 ng m–3) while Sc was considered to be arising from coal burning. Alam et al. (2014) described the characterization of crustal The presence of Cr, Fe, Ce, Pb and Cd was attributed to Al (12000 ng m–3), Ca (18000 ng m–3), Mg (5690 ng m–3), anthropogenic activities (Qadir et al., 2012). Schneidemesser Fe (10200 ng m–3), S (5600 ng m–3), and Ti (210 ng m–3) and et al. (2010) measured detailed elemental composition in trace metals As (5 ng m–3), Ba (111 ng m–3), Cd (40 ng m–3), monthly composites from Lahore. Elemental analysis revealed Cr (30 ng m–3), Cu (230 ng m–3), Mn (290 ng m–3), Ni (10 extremely high concentrations of Pb (4400 ng m–3), Zn ng m–3), Pb (920 ng m–3), Sn (10 ng m–3) and Zn (3020 (12000 ng m–3), Cd (77 ng m–3) and several other toxic metals –3 ng m ) in PM10 samples collected from an urban site in (Schneidemesser et al., 2010). It is obivious from all above Lahore. Ali and Athar (2010) monitored the ambient air cited studies that elemental concentration at Karachi is much quality of Lahore and determined PM10 and Pb levels. The higher than European cities but comparable to region South concentration of Pb at different locations of the city was Asian cities like Lahore, Peshawar, Mumbai. As most of found in the range of 1900 to 7200 ng m–3. Niaz et al. (2012) the mega cities in South Asia experience worst air quality evaluated the air pollutant Lead (Pb) in the Faisalabad city. Gulia et al. (2015). The Pb contents of 4330 ng m–3 were recorded during whole study period Ghauri et al. (2007) determined lead contents Source Apportionment from various sites in the urban area of Karachiwith The Positive Matrix Factorization model (EPA PMF concentrationswhich ranged from 3030–5870 ng m–3. 3.1.1) was used for source apportionment. The analytical The least concentrations were measured for elements results of the PM10 samples collected during this study placed in the fourth group. This included heavy metals like were used in the PMF model in order to identify sources

2504 Shahid et al., Aerosol and Air Quality Research, 18: 2498–2509, 2018 and their contributions to the total PM10 mass. Based on sulfate, biomass burning, secondary nitrate, traffic emission the analytical results of the present study, five (05) factors and re-suspended dust. Chan et al. (2008) collected PM were identified by PMF. The major sources of PM10 were samples from Melbourne, Sydney, Brisbane and Adelaide. identified as biomass burning, coal combustion, re-suspension Chan et al. (2008) reported eight different factors for the fine road/soil dust, vehicular emission, and industrial dust as particle samples that includes combustion sources, crustal/soil shown in Fig. 4. dust, ammonium sulphates, nitrates, motor vehicles, marine Lim et al. (2010) measured PM10 concentration at aerosols, chloride depleted marine aerosols, and industry. Daejeon, Korea and identified nine different sources using While for the coarse particle four factos were recognized PMF model that includes vehicle exhaust, soil, road dust, including marine aerosols, crustal/soil dust, nitrates and cement/construction, fossil fuel combustion and field road side). Gupta et al. (2012) reported the PM sources of burning, secondary aerosol, incineration/Pb related industry various sites within Mumbai city using PMF. Industrial area and metal smelting. Xie et al. (2008) determined source of Mahul showed sources such as residual oil combustion and contributions to ambient PM10 in Beijing, China with PMF paved road dust, traffic, coal fired boiler and nitrate sources. model. Results indicated various sources of ambient PM10 On the basis of the previouse studies, as described earlier which were urban fugitive dust, crustal soil, coal combustion, five factors were designed for source apportionment in secondary sulfate and nitrate, biomass burning with PMF. These factors covers almost all aspects of available municipal incineration and vehicle emissions. Chueinta et sources in the region and these factors includes biomass al. (2000) reported fine and coarse fractions of airborne burning, coal combustion, re-suspended soil dust, vehicular particulate matter in an urban residential area of metropolitan emission and industrial combustion. The uncertainities values Bangkok. PMF revealed soil as the major source of airborne for chemical analysis used in PMF has been given in Table S1 particulate matter for all data sets while other sources were as (Supplementary Material). motor vehicles, sea-salt spray, charcoal/wood burning and Factor-1 corresponds to biomass burning, and it has a major incineration contributions. Gu et al. (2011) used PMF method contribution from Cu, Co, Cd, Mg, and Ca, which results in in order to identify the sources of ambient particles (PM10) 23% average mass of PM10 as shown in Fig. 5. Recently, in Augsburg. For particulate chemical composition data, six Alam et al. (2015) reported that household combustion factors were identified and associated with NaCl, secondary emission in Peshawar contained biomass burning, which

Fig. 4. Contributions of identified sources to PM10 mass at Karachi, Pakistan.

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Fig. 5. Trace metals in aerosols samples and their major sources at Karachi during March to April, 2009.

contributed 12.8% of the total mass of PM10. Secondary al., 2009; Mansha et al., 2012). Secondary aerosols contain aerosol formation in rural areas of Lahore and Karachi nitrates and sulfates, which are emitted directly from natural could be largely from coal and biomass combustion (Lodhi et as well as anthropogenic sources. Gugamsetty et al. (2012)

2506 Shahid et al., Aerosol and Air Quality Research, 18: 2498–2509, 2018 reported that biomass burning, wood burning, and vegetative CONCLUSION burning produces high concentration of potassium and sulphate. During the last few decades, there has been a rapid increase Factor-2 is identified as coal combustion, which has a in population, urbanization, industrialization, transportation major contribution from Zn, Sn, Cd, and Co. Coal combustion and other human activities, especially in developing countries. contributed 21.7% of the total mass of PM10. As a result, a sharp increase has occurred in both the variety Factor-3, re-suspended road/soil dust contributed 9.9% and the quantity of air pollutants and their corresponding of the total mass of PM10. Re-suspended road/soil dust sources. The ambient air quality of Karachi is getting worse includes Ba, Al, Mg, Cu, Fe, Ca, Cr and Mn. Recent day by day due to a large number of factors, including studies reported that re-suspended road/soil dust includes industrialization, inefficient energy usage, tremendously high concentration of Al, Ca, Fe, Mg, Mn, Sr, Ti, and Zn high vehicular population, unobstructed solid waste burning, (Gugamsetty et al., 2012; Alam et al., 2014). and utilization of ozone depleting species. The average PM10 Factor-4 is responsible for vehicular emission, and has a concentration measured at an urban site in Karachi was major contribution from Pb, Sb, Zn, Sn, S, As and Cd, found to be many folds higher than the maximum daily and which results in 22.2% average mass of PM10. annual average concentrations of PM10 prescribed as ambient- Factor-5 was identified as industrial dust, which contributed air guidelines by Pakistan NEQS and WHO. This measured 23.2% of the total mass of PM10. Industrial dust has a value was also significantly higher than the limits given by major contribution from Ni, Ti, Fe, Al, As and Mn. The European and United States ambient air quality standards. PMF model result appears to be significantly good for the The observed and predicted values of PM10 by PMF showed elemental data of particulate matter utilized. A strong excellent correlation, with R2 = 0.98, a mean bias of 0.22 and positive correlation was observed between predicted and an RMSE value of 17.3 μg m–3, demonstrating the accuracy 2 calculated mass of PM10. The correlation (R ) between the of the model. The PMF study revealed that industrial dust measured (gravimetrically measured mass collected on is a major contributor (23.2%) to PM10 followed by filter) and the model predicted PM10 mass was 0.98 as Biomass burning (23%), Vehicular emissions (22.2%), shown in Fig. 6. Coal combustion (21.7%) and Re-suspended dust (9.9%).

–3 Fig. 6. Correlation between observed and predicted PM10 concentration. The RMSE value is in µg m .

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