The composition and sources of water soluble ions in PM10 at an urban site in the Indo-Gangetic Plain

Journal Pre-proof The Composition and Sources of Water Soluble Ions in PM10 at an Urban Site in the Indo-Gangetic Plain Muhammad Usman Alvi, Magdalena Kistler, Tariq Mahmud, Imran Shahid, Khan Alam, Farrukh Chishtie, Riaz Hussain, Anne Kasper-Giebl PII:

S1364-6826(19)30411-0

DOI:

https://doi.org/10.1016/j.jastp.2019.105142

Reference:

ATP 105142

To appear in:

Journal of Atmospheric and Solar-Terrestrial Physics

Received Date: 1 May 2019 Revised Date:

10 August 2019

Accepted Date: 27 September 2019

Please cite this article as: Alvi, M.U., Kistler, M., Mahmud, T., Shahid, I., Alam, K., Chishtie, F., Hussain, R., Kasper-Giebl, A., The Composition and Sources of Water Soluble Ions in PM10 at an Urban Site in the Indo-Gangetic Plain, Journal of Atmospheric and Solar-Terrestrial Physics, https://doi.org/10.1016/ j.jastp.2019.105142. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Elsevier Ltd. All rights reserved.

GRAPHICAL ABSTRACT

The Composition and Sources of Water Soluble Ions in PM10 at an Urban Site in the Indo-Gangetic Plain Muhammad Usman Alvi1,2,3, Magdalena Kistler2, Tariq Mahmud1, Imran Shahid4, Khan Alam5*, Farrukh Chishtie6, Riaz Hussain3, and Anne Kasper-Giebl2 1

Institute of Chemistry, University of the Punjab, Lahore 54590, Pakistan Institute for Chemical Technologies and Analytics, Vienna University of Technonolgy, Vienna, Austria 3 Department of Chemistry, University of Okara, Okara, Pakistan 4 Institute of Space Technology, Islamabad, Pakistan 5 Department of Physics, University of Peshawar, Khyber Pakhtunkhwa, Pakistan 6 SERVIR-Mekong, Asian Disaster Preparedness Centre (ADPC), Bangkok, Thailand 2

*Corresponding author. [email protected]; [email protected] ABSTRACT Analysis of water soluble ions in PM10 in Pakistan is limited and therefore requires in-depth investigation. In this study composition and sources of ionic profile in PM10 were determined from an urban site of an Asian megacity (Faisalabad) in 2015-2016. The PM10 size fraction sampled on quartz filters was analyzed by ion chromatography for selected inorganic and organic ions. The daily mean PM10 mass concentration was found to be 744 ± 392 µgm-3, exceeding the limits proposed by Pak-EPA (150 µgm-3), US-EPA (150 µgm-3) and WHO (50 µgm-3). The ambient PM10 concentration was found to be highest in winter 2015-16 and autumn 2016, while the lowest in the monsoon 2016. The average total ion concentration was found to be 120 ± 51 µgm-3, which made about 16 % of the total PM10 mass. CO32- was the most dominant specie followed by NH4+, Ca2+, K+, NO3-, SO42-, C2O42-, CH3COO-, Na+, Cl- and Mg2+. The ratio analysis of selected ions indicated dominant contribution of biomass burning during autumn and winter and higher impact of fossil fuel burning during spring and summer seasons. Positive Matrix Factorization identified traffic induced soil erosion, biomass burning, fugitive dust from construction activities, secondary aerosol formation processes and fossil fuel emissions from traffic and industry as major sources of particulates.

Keywords: Ionic profile; Urban aerosol characterization; Air quality; Particulate matter characterization; Seasonality. 1. INTRODUCTION In the recent decades, atmospheric aerosols have remained in the spotlight owing to their involvement in deteriorating the air quality, visibility impairment and influence on climate, Earth’s radiation budget, ocean biogeochemistry and weather phenomena (Mahowald et al., 2005; Jickells and Moore, 2015). Several epidemic studies have associated long-term exposures to urban aerosols with adverse implications for human health, including cough, bronchitis, asthma, lung cancer, cardiovascular diseases and premature deaths (Marzouni et al., 2016; Ghozikali et al., 2018; Karimi et al., 2019). Urban aerosols are both primary and secondary in origin, consisting of diverse size ranges, constituents and sources (Calvo et al., 2013; Shen et al., 2011). The composition and rate of aerosol formation are strongly influenced by prevailing anthropogenic activities and meteorological conditions due to physical disparities among different geographical territories (Jimenez et al., 2009). The water-soluble ions constitute a major fraction of urban aerosols, playing a significant role in the tropospheric chemistry (Stone et al., 2011). They are associated with pH changes in environment; thus, pose a threat to ecosystems (Kulshrestha et al., 2009). These have also been linked to hazy weather and have a great influence on aerosol optical properties (Geng et al., 2011; Deshmukh et al., 2011). The relative abundance of the ions determines the hygroscopic nature of aerosols and can aggravate visibility impairment (Yuan et al., 2006). Moreover, these components can increase the toxic potential of organic compounds such as polycyclic aromatic hydrocarbons (PAHs) and n-alkanes by increasing their solubility and acting as surface active reagents (Goudarzi et al., 2018). Ionic concentrations are greatly influenced by meteorological factors, geographic conditions and emission sources (Naimabadi et al., 2018).

Many studies have ranked Asian countries at the top as far as emission of SO2, NOx, NH3 and mineral dust is concerned (Kim et al., 2008; Maleki et al., 2016). These emissions are the main sources of inorganic salts namely, ammonium nitrate and sulfate in the atmosphere (Chou et al., 2008). According to UN statistical estimates, Pakistan having a population of about 195.4 million is ranked 6th in the world with 39.2 % of inhabitants living in the urban areas and these numbers are expected to be double by 2045 (Greene, 2011). According to WHO, ambient air pollution database 2016, Karachi, Lahore, Rawalpindi and Peshawar cities are included among the top fifty most polluted cities of the world. Numerous reports (Pak-EPA, 2006; World Bank, 2006) have mentioned enhanced traffic volumes, industrial pollutants and particulate matter as major factors involved in deteriorating the regional air quality. Although the government of Pakistan has taken initiatives such as Pakistan Clean Air Program (PCAP) and drafting of the National Environmental Quality Standards (NEQS) but the specified limits in these drafts are still higher than the guidelines provided by WHO as given in Table 1S. In view of the above discussed facts, the characterization of particulate matter is necessary in order to trace down their emission inventories, source identification, climate modeling, evaluation of possible human health effects and for pollution control strategies. In this article, a systematic characterization of the water soluble ionic fraction of PM10 is discussed along with seasonal variations and investigation of possible emission sources in Faisalabad, Pakistan. 2. Experimental Methods 2.1. Site Description Being the third largest metropolitan in Pakistan, Faisalabad is the prime industrial hub located in the North-West part of the Indo-Gangetic Plain (Latitude 30°31.5' N; Longitude 73°74' E) at 184 m a.s.l. Faisalabad is situated on the main shipping route leading from the

capital Islamabad and China border to the harbor of Karachi. Fig. 1 shows the location of Faisalabad and the sampling site used in this study. The city is situated in the subtropical zone having extreme climate. Faisalabad is an industrial city with production plants for the food sector, cotton, silk, paper, hosiery, fertilizers, chemicals and agricultural equipment. Just like other Asian mega cities, the air quality is influenced by high traffic & industrial emissions. Moreover, there is no continuous air quality monitoring in the city. 2.2. Sampling Sampling was conducted in the close proximity of the Clock Tower (city center) located on the Kotwali road Faisalabad. PM10 size fraction was sampled on 47 mm quartz fiber filters (Tissuquartz, Pall Life Sciences) using an automatic sampling device equipped with a PM10 pre-separation head (Leckel, Germany) having air intake capacity of 2.3 m3h-1. Collected aerosol samples were kept in parafilm sealed petri dishes and refrigerated until further analysis. In total, 117 samples were collected from September 2015 till December 2016 with the sampling frequency of two samples per week and sampling duration of 24 hours. Field blanks were also collected in order to consider any sampling artifacts by exposing the filters inside the sampler. 2.3. Chemical Analysis Water soluble ions were extracted from 10 mm quartz filter discs with ultra-pure water (Millipore, 18.2 MΩ) by ultrasonificaion for 30 minutes at room temperature. The ultrasonificated mixture was then centrifuged at 4000 rpm for 10 minutes and filtered through 0.45 mm syringe filters (Pall-Acrodisc®) in order to remove suspended insoluble particles. The cations (Na+, NH4+, K+, Mg2+ and Ca2+) were analyzed on Dionex ICS 3000 by using the IonPac CS12A analytical column with 38 mM solution of methane sulphonic acid as an eluent having flow rate of 1 mL min-1. The anions (Cl-, NO3-, SO42-, CH3COO- and C2O42-) were analyzed on Dionex ICS 1100 by using IonPac AS22A analytical column.

A solution of 4.5 mM of Na2CO3 and 1.4 mM of NaHCO3 was used as an eluent with a flow rate of 1 mLmin-1. The amount of CO32- ions was estimated indirectly from carbonate carbon (CC), which was obtained during carbon fraction analysis using a thermo optical method. This manual method is known to have relatively high uncertainty, especially when the concentrations of carbonate carbon are either very low or extremely high. For this purpose SUNSET OCEC laboratory instrument was used and results were extracted by integration of the high & sharp CC peak at the highest temperature (870 °C) during the inert-gas (He) phase. The temperature protocol of the National Institute for Occupational Safety and Health (Birch and Carry, 1996) and transmission mode were used. 2.4. Meteorology Parameters such as relative humidity, temperature, precipitation, surface pressure and aerosol optical depth were recorded from September 2015 to December 2016 as summarized in Table 2S. The winter season starts from 15 November and lasts up till mid of February with average temperature ranges from 6‒21 °C. The lowest temperature (up to -1 °C) was recorded from December to February along with dense fog spells. In between winter and summer, there is spring season, having a moderate temperature of about 23-25 °C. The summer was the dry season; spanning from mid of April to the end of June with average temperature ranges from 27‒39 °C. The highest temperature (up to 48 °C) was observed in the months of May and June along with dust storms and speedy winds. An average rainfall of about 250 mm was recorded per year with almost one half occurs in the monsoon season starting from July to mid-September. Autumn season starts from the mid of September and lasts till mid of November. Long episodes of heavy smog were observed during autumn of 2016. In order to trace the origin of air masses, the backward trajectories during different seasons at the study site were simulated by using the Hybrid Single Particle Lagrangian

Integrated (HYSPLIT) model (Draxler and Rolph, 2003). In total three air mass regimes were distinguished on the basis of direction and the regions of their passages as illustrated in Fig. 1S. The backward trajectory on September 2015 (autumn) and June 2016 (summer) was considered as the first air mass regime having air mass flow from the Arabian Sea and the south eastern border of the country, which mainly consist of Cholistan and Thar Desert areas. The back trajectories computed during February (winter), March (spring) and November (autumn) of 2016 constituted the second air mass regime, having the air mass flow from Afghanistan and Iran along with locally derived air masses. The third air mass regime was considered during August 2016 (monsoon), when the air mass flow was from the eastern Indian side having lowest concentrations due to the scavenging effect of rainfall influenced by the eastern monsoon. The back trajectories with the similar patterns were also computed in other regional studies (Alam et al., 2010; Shahid et al., 2016). 3. RESULTS AND DISCUSSION 3.1. PM10 Concentration The means, standard deviations and ranges for PM10 concentrations measured during different seasons have been summarized in Table 1. The daily mean PM10 mass concentration was found to be 744 ± 392 µgm-3 during the study period. This average is quite high as compared to the concentrations recorded in other Asian cities such as Islamabad (280 µgm-3), Karachi (302 µgm-3), Quetta (331 µgm-3), Peshawar (638 µgm-3), Lahore (406 µgm-3), Sanandaj (472 µgm-3), Baghdad (163 µgm-3), Beijing (140 µgm-3), Hangzhou (119 µgm-3) and Indian Punjab (116 µgm-3) (Awasthi et al., 2011; Cao et al., 2009; Rasheed et al., 2015; Hosseini et al., 2015; Chaichan et al., 2018; Zeb et al., 2018). However the reports from Ahvaz (1492 µgm-3) and Jeddah (909 µgm-3) mentioned tremendously high PM10 mass concentrations as compared to the current study due to the impact of Middle-East dust storms (Goudarzi et al., 2014; Alghamdi et al., 2015; Farsani et al., 2018).

Strong seasonal variations were observed for PM10 concentrations during the study period as illustrated in the Fig. 2. Almost all observed concentrations exceeded the limits proposed by Pakistan Environmental Protection Agency (Pak-EPA) (150 µgm-3), United States Environmental Protection Agency (US-EPA) (150 µgm-3) and World Health Organization (WHO) (50 µgm-3). The highest concentrations occurred in the autumn 2016 (1098 ± 454 ugm-3) and winter 2015-16 (966 ± 253 ugm-3). In the region during autumn, there is crop residue burning activity in order to clear fields for cultivation of next crops, which increases particulate concentrations during this season (Singh and Kaskaoutis, 2014; Alam et al., 2018). Similarly, higher PM10 values in winter 2015-16 may be attributed to biomass burning (dung cake, wooden blocks and vegetation debris) on massive scale in houses as well as in industries (Chakraborty and Gupta, 2010; Pakbin et al., 2010). In winter season there is also low mixing height, as a result of which particulates remain restricted to a particular area rather than to disperse to other areas (Sharma et al., 2014; Shahid et al., 2015b). Moreover stagnant air masses in the presence of fog favour the secondary aerosol formation processes during this season (Biswas et al., 2008). The recorded concentrations were relatively moderate in autumn 2015 (622 ± 277 ugm-3), spring 2016 (612 ± 338 ugm-3) and summer 2016 (757 ± 301 ugm-3). In comparison to autumn 2015, about 1.7 times higher PM10 concentrations were observed during autumn 2016 due to smog episodes during this period. In spring 2016 and summer 2016, elevated PM10 mass levels were observed due to enhanced crustal and road dust re-suspension favored by high temperatures, speedy winds, less rain falls, low humidity and heavy dust storms (Stone et al., 2010). Additionally, brick kilns were fully operational during these seasons (Pakbin et al., 2010). However in the monsoon 2016, the PM10 concentrations noted were 2.6 times less than the daily average (283 ± 76 ugm-3) due to scavenging effects of rains, reduced re-suspension of crustal and road dust due to higher relative humidity, wet surfaces and shutdown of brick kiln operation during this season (Budhavant et al., 2009; Shah et al., 2012). Various studies have suggested

that industrial activities, two stroke vehicles, diesel emissions, coal combustion and biomass burning have a major contribution towards airborne particle emissions in the urban environments (Stone et al., 2010; Goel and Kumar, 2014). So in the coming years, the situation may get worse due to increasing traffic volumes and rapid increase of residential areas around the big cities and industrial centers such as Faisalabad. 3.2. Water-Soluble Ions in PM10 The seasonal variations of average ionic concentrations during the study period are shown in the Fig. 2, whereas the statistical parameters have been summarized in Table 1. The seasonal disparity in the ionic masses appeared due to different emission sources and meteorological factors in different seasons. On daily average basis, the total concentration of water soluble cations and anions was 120 ± 51 µgm-3, which represented about 16 % of the total PM10 mass. The daily average of total ionic species was found to be highest in autumn 2016 (167 ± 52 µgm-3) followed by winter 2015-16 (147 ± 45 µgm-3) and summer 2016 (124 ± 36 µgm-3). Almost similar daily averages were observed during autumn 2015 (100 ± 25 µgm-3) and spring 2016 (104 ± 42 µgm-3), whereas the lowest daily average (55 ± 28 µgm-3) was found in the monsoon 2016. A similar trend was found in the studies conducted by Wang et al. (2005) and Kumar et al. (2007). The higher ionic concentrations during winter 2015-16 and autumn 2016 and were due to ionic transformations from gas to particle phase at lower temperature coupled with increased anthropogenic contributions, calm winds and lower mixing layer height, which inhibited the particulate dispersions during these seasons (Wang et al., 2005; Shakya et al., 2010). Comparatively higher ionic concentrations were observed in autumn 2016 due to substantial smog episodes during this season than autumn 2015. Lowest ionic concentrations during the monsoon 2016 can be attributed to the washout effect of rains, whereas for spring 2016 and summer 2016; the probable reason for ionic

concentrations was ventilation caused by high wind movements (Bhaskar and Mehta, 2010; Ram et al., 2012). The average concentrations of major cations and anions along with their respective percentages measured during different seasons have been displayed in Fig. 3, whereas the summary of the ionic concentrations from nationwide and international studies have been given in Table 2. For every ionic specie, distinct seasonal fashions were observed. Among the anions, CO32- (40 ± 18 µgm-3) is the most abundant specie contributing 34 % to the total ionic fraction, while NO3- (8 ± 6 µgm-3), SO42- (7 ± 6 µgm-3), C2O42- (6 ± 8 µgm-3) have collectively major contribution of 17 % and CH3COO- (4 ± 5 µgm-3), Cl- (3 ± 2 µgm-3) have minor contribution of 6 % towards total ionic concentration. Similarly, among the cationic species, NH4+ (18 ± 22 µgm-3) is by far the most prominent cation; contributing 15 % to the total ionic fraction, while Ca2+ (16 ± 11 µgm-3) and K+ (10 ± 7 µgm-3) together contribute 22 % and Na+ (3 ± 2 µgm-3) along with Mg2+ (2 ± 1 µgm-3) have minor contribution of 4 % towards total ionic concentration. NO3- represents mainly mobile pollution sources such as vehicular exhaust, whereas SO42- represents stationary sources such as coal combustion, which is mainly used in small industrial units such as brick kilns (Sharma et al., 2014). Various reports have mentioned brick kilns as major contributors of oxides of nitrogen, sulphur and particulates (Iqbal and Oanh, 2011; Begum et al., 2013). Moreover; fossil fuels, biomass combustion and transport are also prominent sources of SO42- in the atmosphere (Kuniyal et al., 2015). The daily average concentrations of NO3- and SO42- were high in spring 2016 (8 ± 5 µgm-3 and 6.8 ± 6 µgm-3) and summer 2016 (12 ± 7 µgm-3 and 7.7 ± 7 µgm-3) as compared to autumn 2015 (6 ± 5 µgm-3 and 4.8 ± 4 µgm-3) and winter 2015-16 (5 ± 4 µgm-3 and 4.6 ± 3 µgm-3) respectively, due to high temperature and enhanced radiations during dry season which favor the chemical reaction rates of SO2 and NO2 and thus formation of SO42- and NO3(Wang et al., 2005b). Various studies have suggested that NH4+, NO3- and SO42- are

secondary inorganic constituents and mostly exist in the form of NH4NO3 and NH4HSO4 and (NH4)2SO4 particles in the environment (Zhong et al., 2014). Oxidation of NOx produces NO3- which subsequently reacts with ammonia to generate NH4NO3 particles (Wang et al., 2005c). It is well established that the ubiquitous dominance of NH4+ and K+ can be attributed to biomass combustion and soil emissions from agriculture (Deka and Hoque, 2014a), so high NH4+ and K+ concentrations were observed in autumn 2016 (41 ± 35 µgm-3 and 15 ± 7 µgm-3) and winter 2015-16 (28 ± 19 µgm-3 and 15 ± 6 µgm-3) respectively having biomass burning (such as straw, cornstalk and vegetation) at its peak during these seasons, while in dry seasons these might have originated from earth’s crust. Ca2+ and Mg2+ are typical crustal elements and higher concentrations of Ca2+ and Mg2+ in summer 2016 (29 ± 13 µgm-3 and 3 ± 1 µgm-3) and comparatively lower values respectively in monsoon 2016 (8 ± 3 µgm-3 and 0.8 ± 0.4 µgm-3) confirm the crustal input of these ions to the total ionic mass due to strong winds and intensive dust events in the dry season (Rastogi and Sarin, 2005). Apart from mineral dust, these are emitted from ceramic, coal fly ash, construction, demolition activities and cement production (Khawaja et al., 2009). The concentration of CO32- remained approximately same in all seasons (except monsoon), suggesting a steady source of CO32-. It is mainly emitted from rock deposits in the nearby area, earth’s crust and traffic-induced soil erosion which could be the steady source. The concentration of Na+ and Cl- appeared due to anthropogenic input such as waste incineration and biomass burning during winter, and due to dust re-suspension, construction activities and agricultural erosion by turbulent winds during summer (Zhao et al., 2011). This is reflected by high concentrations of these ions during winter 2015-16 (4.5 ± 2 µgm-3 and 2.5 ± 0.2 µgm-3) and summer 2016 (4.4 ± 1.5 µgm-3 and 4.3 ± 2 µgm-3), respectively. During monsoon 2016 (1.34 ± 1.28 µgm-3 and 1.82 ± 1.40 µgm-3), these ions appeared mainly due to marine contribution in the form of monsoonal wind from the Arabian Sea.

Oxalate

has

been

revealed

to

be

originated

from

biomass

emissions

(Yamasoe et al., 2000) and from conversion of natural and anthropogenic precursor gases into aerosols of secondary nature (Legrand et al., 2007). Many studies from UK and East Asia

have

indicated

secondary

processes

as

major

oxalate

sources

(Laongsri and Harrison, 2013). This is why highest daily average value of 13 ± 16 µgm-3 was noted in autumn 2016 due to the presence of visible smog spells during this season. Low concentration of acetate was obtained due to the prevalence of it in the gas-phase (Bardouki et al., 2003). It is documented in the literature that biomass burning, vegetation and fossil fuel combustion are responsible for acetic acid proportions (Servant et al., 1991). It is summarized that although very high concentrations of water-soluble ions were recorded and their trend is practically in line with PM10 trend, they are not the main species responsible for causing the extreme PM10 concentration peaks. However, the ionic concentrations and their seasonal variations allowed general conclusions about an origin of observed air pollution. It is also evident from the HYSPLIT model that the concentrations of ions emitted due to anthropogenic activities were comparatively less when the back trajectories were from the Arabian Sea because of comparatively less polluted marine air. On the other hand, concentrations of soil derived ions were higher when the passages of air mass flows were over the desert areas. 3.3. Chemistry of Water-Soluble Ions 3.3.1. Interionic Correlations In order to understand the impact of seasonal changes in the aerosol composition, seasonal correlation among different ionic species was performed as given in Table 3. C2O42- was correlated with NH4+ in autumn 2015 (r = 0.49) and winter 2015-16 (r = 0.65), while in spring 2016 it was correlated with NH4+ (r = 0.73), K+ (r = 0.69) and Na+ (r = 0.62).

In summer 2016; C2O42- was only correlated with K+ (r = 0.75). The gradual replacement of NH4+ with other cations suggests the formation of NH4+ only in low temperature months. It has been revealed that at high temperature NH4+ is converted back to NH3 (Seinfeld, 2004). Moreover, in winter 2015-16 ammonia dissolves in water yielding NH4+ and OH- ions. However, in dry conditions NH4+ will not be formed due to the absence of this reaction. CH3COO- was not correlated to any cation in winter 2015-16, autumn 2015 and autumn 2016 seasons, but was significantly correlated to Na+ (r = 0.76), Mg2+ (r = 0.75) and Ca2+ (r = 0.67) in monsoon 2016. Similarly CO32- was not correlated to any cation in low temperature months such as autumn 2015 and winter 2015-16, while correlated (p < 0.05) with Na+ (r = 0.49) and K+ (r = 0.58) in autumn 2016. In spring 2016 it was strongly correlated to K+ (r = 0.91), Na+ (r = 0.89), NH4+ (r = 0.87), Mg2+ (r = 0.81) and additionally with Ca2+ (r = 0.70) along with other cations in summer 2016. However, in monsoon 2016 it was only correlated with K+ (r = 0.69). This fact confirms the entry of CO32- into the environment due to soil erosion, which is most extensive in dry season. SO42- was correlated with NH4+ (r = 0.53) only in winter 2015-16, implying the formation of NH4HSO4 and (NH4)2SO4 due to anthropogenic activities (Li et al., 2013). Similarly NO3- was negatively correlated with cations associated with mineral dust such as Na+ (r = -0.58) and Mg2+ (r = -0.65) in spring 2016, correlated to NH4+ (r = 0.51), Na+ (r = 0.50), Mg2+ (r = 0.50) and Ca2+ (r = 0.49) at a significance level of 0.05 in summer 2016 and strongly correlated to Na+ (r = 0.694) in the monsoon 2016. Both SO42- and NO3- are assumed as either secondary aerosols constituted by oxidation of SO2 and NOx emitted from fossil fuels and biomass combustion or have imparted by soil abrasion by wind. In this case since both ions are not strongly correlated to mineral ions, so in autumn 2015, autumn 2016 and winter 2015-16, these are mostly formed via oxidation of precursors such as SO2 and NOx emitted from anthropogenic origins. Cl- showed significant correlations (p<0.01) with NH4+ (r = 0.57) in winter 2015-16 and K+ (r = 0.68) in autumn 2016 indicating the emissions from

anthropogenic sources. K+ has been used for tracing biomass combustion in many source identification studies (Saud et al., 2013 and references therein). The positive correlation of K+ with both NH4+ and Cl- depicts the biomass burning influence at the sampling site as burning of crops residue is a common and easy practice in the study area in order to prepare the fields for the summer crops (Jain et al., 2014). During monsoon 2016, Cl- was correlated with Na+ (r = 0.65) showing the origin from marine salt (Sen et al., 2014). 3.3.2. Ionic Balance The acidity/alkalinity of aerosol particles and information about potentially missing ions was approximated by determining the ionic balance between cationic and anionic species. The scatter plot of cationic and anionic micro-equivalents has been shown in Fig. 4. The slope of the line revealed a notable anionic deficiency in the PM10 samples. The anionic deficiency might have appeared due to underestimation of CO32-; because it was not measured directly by ion chromatography, but was estimated indirectly from Carbonate carbon (CC) measured by thermo-optical method. In the study of Karnasiou et al., 2011, NIOSH-like protocols were tested and a tendency for underestimation was found for CC loadings above 200 µg per sample. In this study the loadings were much above this value. The presence of CO32- implied that aerosol particles were alkalescent (Cai and Xie, 2011), as a result of which they were not only capable to absorb semi-volatile species (nitric, carboxylic and hydrochloric acids), but also provided surface area for heterogeneous reactions involving SO2 and NO2 gases (Dentener et al., 1996). There was also no correlation found between Ca2+, Mg2+ and SO42-, this also suggests calcite and dolomite as a major form of CO32- (Cao et al., 2005). Moreover; certain organic acids, inorganic anions such as Br-, I-, S2-, SiO32- and humic-like species were not analyzed that were included in other similar studies (Javed et al., 2015), thus causing significant anionic deficiencies.

3.3.3. Ratio Analysis In many studies, the mass ratios of certain specific ions have been used as indicators of the relevant ion sources. For example, in regions where there is a predominance of vehicular exhaust over coal based burning, always a higher mass ratio of NO3-/SO42- was found (Wang et al., 2006). Similarly the contributions from soil erosion were estimated from the mass ratio of Mg2+/Ca2+, because these ions are also emitted from stack gases during coal combustion (Zhang et al., 2002). Many reports based on source profiling have linked the high K+ levels to the burning of vegetation and wood combustion on a residential scale (Watson et al., 2001), so low mass ratios of Cl-/K+ and SO42-/K+ might differentiate the biomass combustion from other events of particulate contributions (Shen et al., 2009). The time series of the certain ionic ratios have been shown in Fig. 5. In low temperature months (autumn 2015, winter 2015-16, autumn 2016) there is more practice of biomass burning for cooking and heating, so lower mass ratios of Cl-/K+ and SO42-/K+ were observed due to high K+ levels, whereas these ratios were started to rise with increase in temperature from spring 2016 up till monsoon 2016, because of lesser dependence on biomass and more on fossil fuels during these months. Similarly, NO3-/SO42- ratios were also higher in spring 2016 and summer 2016 due to more consumption of fossil fuels than biomass burning (Wang et al., 2006). Moreover, high NO3-/SO42- ratios starting from spring 2016 reflect the dominance of pollution from mobile sources over the stationary ones (Arimoto et al., 1996). In high temperature months (spring 2016 and summer 2016) besides the contribution from fossil fuels, there was strong photochemical activity of atmospheric SO2 conversion to SO42(Chiwa, 2010), as a result of which higher SO42-/K+ were observed during this time. The Mg2+/Ca2+ ratios were comparatively high in autumn 2015, winter 2015-16 and autumn 2016 due to low contribution of Ca2+ from soil dust as compared to spring 2016, summer 2016 and monsoon 2016. Moreover, high Mg2+/Ca2+ ratios during autumn 2015,

winter 2015-16, and autumn 2016 were clearly demonstrating the anthropogenic contributions (such as coal and industry) during these seasons (Zhang et al., 2002). 3.4. Source Identification Positive Matrix Factorization (PMF) model was used for the quantification and identification of emission sources of aerosol over the Faisalabad city. It is an advanced tool for factor analysis with flexible modeling approach, whose detailed description has been given elsewhere (Paatero and Tapper, 1994). This model has been used extensively in many source apportionment studies and aerosol emission control programs (Ulbrich et al., 2009; Guo et al., 2010). Concentrations of ions and PM10 were used as an input and the data points for the modeling were treated according to the guidelines given by Polissar et al. (1998). In the current study, EPA-PMF version 5.0 software was used and 5 interpretable factors depending on the expected source profile and Q value were obtained. On the basis of certain tracer species that are assumed to be belonging to a particular emission source, the identified source profiles included mineral dust having crustal origin (red), biomass burning and fugitive dust from construction activities (blue), fuel combustion from traffic and industry (green), inorganic secondary aerosol formation processes (yellow) and mixed sources (orange). The contributions of the identified factors to the individual ionic species have been shown in Fig. 6. Factor 1 was identified as mineral dust from crustal origin; having high loadings of Ca2+, Mg2+ and Na+ (Gugamsetty et al., 2012; Watson et al., 2001). The mineral dust is primarily derived due to the traffic induced soil erosion from broken and unpaved roads having no vegetation at the edges. Many studies conducted from the other mega cities of Pakistan such as Lahore and Karachi have mentioned the major contribution of dust towards total PM10 mass (Alam et al., 2015; Alam et al., 2014; Mansha et al., 2012). Factor 2 was defined as biomass combustion along with fugitive dust from construction activities. The ions

such as K+, NH4+, C2O42- and CH3COO- have high loadings in this factor which have been considered as the products of biomass burning (Du et al., 2014). Due to the shortage of natural gas supply in the recent years, much of the dependence for domestic cooking and heating is on biomass burning. Moreover, biomass is also used as a fuel in the brick kilns present in the study area. The high loading of CO32- in this factor is an indication of fugitive dust from activities at under construction buildings and stone crushing/grinding units in the surrounding areas for the manufacturing of the building material (Lodhi et al., 2009). Factor 3 was characterized as fuel combustion from urban traffic and industrial units, as reflected by loadings of Cl-, NO3-, CH3COO- and C2O42- (Liu et al., 2017). Both vehicular and industrial emissions are the major causes of air pollution. The traffic in the city is quite congested. Vehicles are not only overloaded and poorly maintained, but also use low quality fuel, thus aggravating the situation. Similarly, there is no control over industrial emissions. Factor 4 was related to inorganic secondary aerosol formation processes, which was confirmed by the loadings of NH4+, SO42-, C2O42- and NO3- in this factor (Suresh et al., 2017). These species are produced indirectly from the gaseous precursors; emitted from industrial and vehicular emissions. Factor 5 was associated with the mixed source profile having high loadings of Na+ and Cl-. Both these ions are emitted from various sources such as sea salt, building construction, waste incineration and agricultural erosion (Zhao et al., 2011). It is demonstrated that biomass burning along with fugitive dust from construction activities were major contributors of aerosol (30 %), followed by mineral dust (29 %), secondary transformations (23 %), mixed sources (10 %) and anthropogenic emissions (8 %). A comparison between the measured (gravimetric mass measurement on the filter) and the predicted (from the values of scaled source contributions) PM10 mass has been given in the Fig. 2S. The value of correlation coefficient (R) between the measured and predicted PM10 mass was 0.81, indicating not only a good result for the ionic data utilized in the model,

but also an effective accounting of the resolved factors for most of the PM10 mass concentration variations. 4. CONCLUSION A rigorous study of water soluble ionic constituents of PM10 size fraction was conducted for more than a year in an urban site of Faisalabad, Pakistan. The highest ionic concentrations were observed during the winter 2015-16 and autumn 2016, whereas the lowest concentrations were recorded during monsoon 2016. Among the cations, NH4+, Ca2+ and K+ were the major constituents, while CO32-, NO3- and SO42- were the prominent anionic species. The scatter plot of cationic and anionic equivalents revealed a notable anionic deficiency in the PM10 samples, interpreting the alkaline nature of aerosol. The lower mass ratios of Cl-/K+ and SO42-/K+ during autumn 2015, winter 2015-16 and autumn 2016 indicated biomass burning contribution to PM10. The higher NO3-/SO42- ratios in spring 2016 and summer 2016, indicated the preference of fossil fuels over biomass burning. The Mg2+/Ca2+ ratios were comparatively higher in autumn 2015, winter 2015-16 and autumn 2016, not only due to low contribution of Ca2+ from the soil but also due to anthropogenic contributions during these seasons. The sampling site is located on the major arterial road near the city center having high volumes of vehicular traffic (motorbikes, rickshaws, cars, vans, buses and trucks) circulating in the city each business day. Besides this, the particulate contents are contributed by various other sources including industrial activities, biomass burning, dust re-suspension & abrasion, and secondary transformations. The major contribution of particulate emissions is from the earth’s crust, fugitive and re-suspended dust from unpaved road shoulders having no vegetation (Javed et al., 2014; Shakeel et al., 2015). In industry, wood and coal based fuel combustion, metallurgical operations, construction activities, brick kilns, heavy duty diesel based power generation, gas to particle conversions and solid waste incineration are major

contributors towards particulate emissions in air. On a smaller scale, improper waste disposal also contributed in particulates, whereby waste matter is either dumped or incinerated in open areas without any protective cover (Niaz et al., 2015). In order to improve the air quality in Faisalabad and surrounding areas, following control strategies have been suggested. The safety limits and guidelines for the air pollutants should be revised regularly along with the inclusion of emerging air pollutants. Only those vehicles having proper maintenance and fitness certification should be allowed to travel on roads. A relief in taxation policies should be provided by the government regarding the import of hybrid vehicles. Moreover, instead of fossil fuels, there should be more reliance on wind, solar and ocean based power generation. There should be installation of catalytic converters on vehicles as well as on industrial exhaust systems. In order to capture the particulates from the industrial metallurgical operations, installation of cyclone & electrostatic separators, wet scrubbers and fabric filters is required. In order to control soil emissions, there is an urgent need of fully paved and green road belts along with the restoration of forests. Instead of open dumping and incineration, waste matter should either be recycled or converted to other useful products such as fertilizers or methane gas. If incineration is unavoidable, then proper incineration units per international standards are advised. This study may provide better visualization about the nature and sources of aerosols, such that appropriate steps should be taken in order to improve the ambient air quality of the study area, which is worsening day by day. Although some air quality related research has been carried in the study area, but still there are certain areas which are needed to be addressed. For example, there is an emergent need of linking the air quality related information to the health status of the local permanent residents along with the bioaccessibility studies. Moreover, deployment of efficient, continuous and reliable air

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ABBREVIATIONS AGL

=

Above Ground Level

CC

=

Carbonate carbon

GDAS

=

Global Data Assimilation System

HYSPLIT

=

Hybrid Single Particle Lagrangian Integrated

KMO

=

Kaiser-Meyer-Olkin

NEQS

=

National Environmental Quality Standards

NIOSH

=

National Institute for Occupational Safety and Health

NOAA

=

National Oceanic and Atmospheric Administration

PAHs

=

Polycyclic Aromatic Hydrocarbons

Pak-EPA

=

Pakistan Environmental Protection Agency

PCAP

=

Pakistan Clean Air Program

PMF

=

Positive Matrix Factorization

US-EPA

=

United States Environmental Protection Agency

WHO

=

World Health Organization

CAPTIONS OF TABLES Table 1 Mean mass concentration of PM10 and water soluble ions (µgm-3) during the study period and in different seasons. Table 2 Summary of the ionic concentrations (µgm-3) from nationwide and international studies. Table 3 Seasonal inter-specie correlations of water soluble ions in PM10.

CAPTIONS OF FIGURES Fig. 1 Location of Faisalabad within the political map of Pakistan and sampling place within the city. Fig. 2 The seasonal variations of PM10 and ionic concentrations during the sampling period.

Fig. 3. The average concentrations of major cations and anions along with their respective percentages measured during different seasons. Fig. 4 Scatter plot drawn between cationic and anionic equivalents. Fig. 5 The time series of the certain ionic ratios. Fig. 6 Positive matrix factorization (PMF) model-resolved source profiles for the four factors

1

Table 1 Specie

Overall

Autumn 2015

Winter 2015-16

Spring 2016

Summer 2016

Monsoon 2016

Autumn 2016

Avg±SD Range Avg±SD Range Avg±SD Range Avg±SD Range Avg±SD Range Avg±SD Range Avg±SD Range PM10

744± 392177-2064 622±277

2011138

966±253

5051413

612±338

1771340

757±301 270-1284 283±76 207-4751098±454 550-2064

Cations Na+

3.36±1.790.47-8.242.95±1.241.04-5.064.57±1.531.70-8.242.49±1.24 0.99-5.334.45±1.511.70-7.381.34±1.280.47-6.013.60±1.591.12-8.14

28.20±18.7 18.87±19.3 NH4+ 18.17±22.86 0.46-110.35 11.59±9.49 0.81-27.43 1.43-78.72 0.68-68.411.68±0.950.68-4.623.15±1.780.46-7.56 41.88±35.38 2.71-110.35 9 6 K+

10.30±6.62 1.27-30.30 10.43±5.04 1.95-19.42 15.45±6.02 1.27-23.208.51±4.882.43-20.266.98±1.954.12-10.822.29±0.701.27-3.6115.06±6.94 7.28-30.30

Mg2+

1.89±1.030.39-6.141.57±0.520.39-2.362.26±0.621.14-3.321.35±0.53 0.56-2.753.14±1.400.94-6.140.88±0.380.44-2.141.86±0.650.86-3.23

28.76±13.3 Ca2+ 16.08±11.44 2.05-65.97 14.61±12.57 3.50-65.97 18.85±8.27 2.05-34.419.84±4.622.57-24.02 9.81-58.228.39±2.933.29-16.22 13.24±8.43 3.99-32.69 8 Anions Cl-

2.53±1.830.12-11.081.90±1.130.33-4.592.50±0.260.70-4.132.40±2.470.40-11.084.36±2.180.27-7.701.82±1.400.12-5.732.09±1.140.61-4.59

NO3-

7.80±6.070.79-31.345.97±5.191.32-20.475.07±3.621.45-16.658.20±5.111.97-19.51 12.70±6.48 0.79-28.286.76±3.453.01-16.599.23±8.271.63-31.34

SO42-

6.78±6.960.72-33.264.81±4.761.31-18.424.69±3.071.67-14.636.89±6.610.80-25.907.77±8.411.33-25.289.71±8.910.72-22.048.56±8.371.37-33.26

CH3CO2- 4.45±5.420.13-28.181.23±1.190.13-5.465.63±7.610.50-28.183.30±3.310.20-10.919.05±3.653.79-18.961.78±0.670.88-3.545.15±6.120.71-21.62 13.29±16.9 C2O42- 5.87±8.050.41-76.444.45±2.290.41-9.296.25±6.241.66-27.814.41±2.800.80-10.094.45±2.791.09-9.902.41±1.590.81-6.99 1.58-76.44 2 CO32- 40.26±18.76 8.77-98.29 38.99±11.13 12.50-62.42 52.19±19.63 30.23-89.21 37.00±15.3 15.63-67.32 39.13±11.4 15.88- 15.17±4.20 8.77-21.94 50.64±19.0 22.52-98.29

0 2 3 4 5 6 7 8 9 10

BDL = Below Detection Limit

11

Table 2 Area and Place

Study PM10 Na+ Period Sep Faisalabad, 3.3 2015744 Pakistan 7 Dec 2016 Nov Lahore, 1.0 2005-Jan 190 Pakistan 5 2006 Mar Karachi, 2009-Apr 437 3.7 Pakistan 2009 Jan 20138.6 233 Delhi, India Dec 2014 0 Apr Shanghai, 20121.7 210 China May 3 2012 Sichuan, Oct 121 0.3

NH4 +

K+

Mg2+

Ca2+

18.1 7

10.3 0

1.89

16.0 8

14.1 1

4.02

0.07

0.9

1.2

14.1 5

Cl-

4

NO -

NO3-

2.53 6

-

1.17

9.91

1.8

52.0

6.87

1.03

7.02

0.57

6.3

2.7

56.67

SO42 -

F-

7.80

6.78

0.0 7

19.8 0

6.6

-

8.07

13.4 8

0.29

4.00

0.2

0.5

0

C2O42 HCO2

CH3CO

-

-

5.87

-

4.45

13.0 3

0.08

0.58

-

-

Raja et al., 2010

5.7

14.0

-

-

-

-

Shahid et al., 2016

-

11.9 5

17.4 1

0.99

-

-

-

Saxena et al., 2017

1.55

-

11.4 9

21.1 7

0.16

-

-

-

Tao et al., 2014

2.0

-

10.2

21.6

0.1

-

-

-

Zhou et al.,

2

-

Reference

-

2

Present Study

China Kathmandu , Nepal Sistan, Iran Makkah, S. Arabia

Hong Kong Mt. Fuji, Japan Singapore Pingtung, Taiwan Jeju Island, Korea Genoa, Italy Oporto, Portugal Auckland, New Zealand

2012Aug 2013 Dec 2007-Jan 2008 Jun 2014Oct 2014 Nov 2010Nov 2011 April 1995May 1996 Jul 2001Aug 2002 Jan 2000Jan 2001 2004 April 2001 May 2009May 2010 Jan 2013Jan 2014 Apr 2000May 2000

2016

-

-

0.81

0.32

0.07

1.16

1.70

0.0 2

1.80

3.16

-

0.12

-

-

Shakya et al., 2010

433

3.5

9.0

8.8

1.2

13.9

3.4

0.7

2.4

6.5

-

-

-

-

Behrooz et al., 2017

145

-

2.0

-

-

-

-

-

5.5

21.8

-

-

-

-

Mohammed et al., 2015

-

4.7 7

1.30

0.58

0.61

0.55

6.44

-

2.70

8.57

-

-

-

-

Cheng et al., 2000

22.0

-

0.53

7.34

0.67

-

1.82

15.0 2

-

-

-

-

1.62

0.53

0.07

0.29

0.6

-

0.9

5.0

-

0.30

0.08

0.06

17.9

2.56

1.60

3.48

3.89

-

20.2

18.3

-

-

-

Lin et al., 2007

1.95

0.57

0.59

3.47

2.64

-

4.14

6.85

-

-

-

-

Park et al., 2004

-

-

Cuccia et al., 2013

27 192 108

2.0 6 0.6 0 4.9 4 1.6 8

22

0.6 5

0.99

0.21

0.16

0.58

0.59

-

2.10

2.93

-

26.7

0.5 4

0.62

0.21

0.06

0.14

0.61

-

1.14

1.95

0.00 5

0.17

0.04

0.01

-

2.2 6

0.01

0.11

0.24

0.16

3.37

-

0.73

1.29

-

-

-

-

Suzuki et al., 2008 Karthikeyan et al., 2006

Custódio et al., 2016 Wang et al., 2001

Table 3 NO3SO42Variable ClNa+ 0.285 -0.273 0.300 NH4+ -0.095 -0.038 0.104 Autumn K+ -0.021 -0.150 0.455 2015 2+ 0.463 -0.462 0.279 Mg 2+ 0.206 -0.179 0.058 Ca + -0.175 0.340 0.458* Na + 0.251 0.571** 0.535* NH4 Winter + -0.336 0.138 0.535* K 2015-16 2+ 0.264 -0.139 0.106 Mg 2+ 0.147 -0.332 -0.356 Ca + 0.132 -0.192 -0.584** Na + 0.189 -0.017 -0.509* NH4 Spring + 0.117 -0.204 -0.473* K 2016 2+ 0.021 -0.120 -0.653** Mg 2+ 0.014 -0.209 0.033 Ca + 0.360 -0.186 0.501* Na + -0.174 -0.072 0.517* NH4 Summer + 0.343 -0.221 0.546* K 2016 2+ 0.213 -0.090 0.509* Mg 2+ 0.089 -0.197 0.498* Ca + -0.299 0.656** 0.694** Na + 0.212 0.292 -0.299 NH4 Monsoon + 0.147 0.075 -0.041 K 2016 2+ 0.513 -0.338 0.568* Mg 2+ 0.269 0.325 -0.287 Ca + -0.394 -0.157 0.514* Na + -0.132 0.122 0.552* NH4 Autumn + -0.027 0.688** -0.529* K 2016 2+ 0.015 -0.177 -0.241 Mg 2+ -0.490* -0.070 -0.371 Ca ** Correlation is significant at the 0.01 level (2-tailed) * Correlation is significant at the 0.05 level (2-tailed) Season

CH3CO20.456 -0.273 0.146 0.118 0.354 .241 0.141 0.418 0.229 0.303 0.229 0.028 0.199 0.155 -0.003 0.628** -0.193 0.306 0.654** 0.482* 0.764** -0.299 0.456 0.750** 0.673** 0.113 -0.059 0.060 0.007 0.114

C2O420.101 0.491* 0.177 0.265 0.253 -.020 0.655** -0.040 -0.210 -0.470* 0.623** 0.738** 0.693** 0.441 -0.211 0.310 0.139 0.750** 0.124 0.183 0.027 0.412 -0.392 -0.101 -0.249 -0.002 0.198 0.159 -0.214 -0.259

CO320.329 -0.138 -0.022 0.258 0.199 .296 0.354 0.221 0.136 -0.134 0.898** 0.875** 0.916** 0.815** 0.405 0.608** 0.189 0.493* 0.691** 0.702** 0.305 -0.474 0.695** 0.318 0.374 0.499* 0.231 0.587* 0.246 -0.121

Fig. 1.

Fig. 2

Fig. 3.

1 2 3

Fig. 4.

Fig. 5.

Fig. 6.

HIGHLIGHTS •

Ions & their sources in PM10 were evaluated from a site in the Indo-Gangetic Plain

•

The PM10 values were exceeding the limits fixed by Pak-EPA, US-EPA and WHO

•

The total ionic concentration constituted about 16 % of the total PM10 mass

•

CO32- > NH4+ > Ca2+ > K+ > NO3- > SO42- > C2O42- > CH3CO2- > Na+ > Cl- > Mg2+

•

Source apportionment was carried out by using PMF model