Influence of north-easterly monsoon on carbonaceous particles and polycyclic aromatic hydrocarbons in PM2.5 in the City of Kuala Lumpur, Malaysia

With increasing interest in understanding contribution of secondary organic aerosol (SOA) to particulate air pollution in urban areas, an exploratory study was carried out to determine levels of carbonaceous aerosols and polycyclic aromatic hydrocarbons (PAHs) in the City of Kuala Lumpur, Malaysia. PM2.5 samples were collected using a highvolume sampler for 24 h in several areas in Kuala Lumpur during the north-easterly monsoon from January to March 2019. Samples were analysed for water soluble organic carbon (WSOC), organic carbon (OC), elemental carbon (EC) and secondary organic carbon (SOC) in PM2.5 was estimated. Particle-bound PAHs were analysed using gas chromatography-flame ionization detector (GC-FID). Average concentrations of WSOC, OC and EC were 2.7 ± 2.2 (range of 0.63-9.1) μg/m, 6.9 ± 4.9 (3.1-24.1) μg/m and 3.7 ± 1.6 (1.3-6.8) μg/m, respectively, with estimated average SOC of 2.3 μg/m, contributing 34% to total OC. The average of total PAHs was 1.8 ± 2.7 ng/m. Source identification methods revealed natural gas and biomass burning, and urban traffic combustion as dominant sources of PAHs in Kuala Lumpur. To understand human health risk posed by PAHs, a deterministic screening health risk assessment was also conducted for several age groups including infant, toddler, children, adolescent and adult. The total concentration of BaPeq is 3.8 ng/m , with the average of 0.29 (range of 0.001-1.6) ng/m. Carcinogenic and non-carcinogenic risk of PAH species were well below the acceptable levels recommended by the USEPA. Future work is needed using long-term monitoring data to understand the origin of PAH contributing to SOA formation and to apply sourcePreprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 18 January 2021 doi:10.20944/preprints202101.0362.v1


Introduction
Urban air pollution is a potential cause for deleterious impact on public health and the environment. Several studies reported that there are excess death or global premature death in the urban and industrial areas due to air pollution [1][2][3]. The major causes of ambient air pollution are related to the rise in population, industrial activities and number of vehicles in cities [4][5][6][7].
Being a tiny air particles, PM2.5 with an aerodynamic diameter equal to or less than 2.5 µm can easily penetrate deep into the human respiratory system [8] and showed an influence on the increase of daily mortality and morbidity to adults due to having breathing difficulties and development of lung cancer [9]. While originating from primary and secondary sources [10], meteorological factors significantly influence PM2.5 concentration at local and regional scale due to its smaller in size and ability to be transported in long range [11]. Previous studies have found north-easterly monsoon playing an important role on the high concentration of the air pollutants in Kuala Lumpur and neighbouring cities [12][13][14][15].
Particulate matter consists of different chemicals. The major chemical compositions in PM2.5 are elemental carbon (EC) and organic carbon (OC) emits predominantly from the transportation sector, ammonium sulfate and ammonium nitrate, and inorganic mineral components [10,16]. EC is originated from the primary sources via the incomplete combustion processes whereas OC can come from both primary and secondary pathways [16]. Increasing the secondary organic aerosol (SOA) contribution to urban particulate air pollution has been well reported in the literature [17][18][19]. Gas-phase low volatile PAHs are also a significant source of SOA [18,19]. Secondary organic carbon (SOC) contributes significantly to the total particulate matter in the lower atmosphere. Water soluble organic carbon (WSOC) which is a part of SOC are also form via photochemical reaction of volatile organic carbon and this WSOC influence the cloud formation [17]. All these carbonaceous compounds consist of carcinogenic pollutants such as polycyclic aromatic hydrocarbons (PAHs) have potential to cause cancer.
PAHs are environmentally persistent organic pollutants (POPs) that consisted of two or more fused benzene rings [4,5,10]. According to Liu et al. Liu, Man, Ma, Li, Wu and Peng [9], the PAHs concentration is being higher in the respirable fraction of air particles than the larger fractions. PAHs are ubiquitous and semi-volatile that are released into ambient air through the incomplete combustion of organic materials. Several studies reported that the predominant emission sources of PAHs are automobiles, industrial processes, domestic heating systems, waste incineration facilities, tobacco smoking and several natural sources including forest fires and volcanic eruptions [4,6,[20][21][22][23][24]. Previous literatures reported that vehicle emission, coal burning and biomass combustion were principal sources of PAHs in PM2.5 [25][26][27]. PAHs sources can be classified into two main groups such as pyrogenic and petrogenic. Pyrogenic PAHs are formed from incomplete combustion of organic matter such wood burning, coal combustion, natural gas and traffic related pollution, whereas petrogenic is related to direct contamination such as crude oil spillage [4,20,28]. In urban area, pyrogenic sources are the main source of PAHs especially high molecular particulate PAHs are mainly found in PM2. 5. Human exposure to PAHs may occur via inhalation, ingestion and dermal from air particles. However, the exposure of PAHs from inhalable tiny particles is significantly occurred via inhalation [29][30][31].
PAHs in PM2.5 have attracted great concerns and have been widely studied due to their toxicity and damage to human health [27,32].
Due to the limited study on PAHs in PM2.5, there is a knowledge gap on the emission, chemical profiles, toxicity effect and human exposure of this persistent class of organic pollutants. There is necessity to determine the potential sources and health risk estimation of PAHs in PM2.5 especially in urban area. The U.S.EPA screening health risk assessment have been applied by several researchers in previous studies [4,5,7,9,22,31,[33][34][35]. As seen the impact of PAHs towards human health, identifying the sources of them is crucial to mitigate the emission rate in ambient air. Principal component analysis-multiple linear regression (PCA-MLR) is one of receptor model that can be used to quantify the possible sources of PAHs in atmospheric particles by identifying the number of factors and the special profile of each sources [21,36,37]. There are many methods to determine the source apportionment that has been used by previous studies, such as positive matrix factorization (PMF) Sulong, Latif, Sahani, Khan, Fadzil, Tahir, Mohamad, Sakai, Fujii and Othman [35] and PCA Jamhari, Sahani, Latif, Chan, Tan, Khan and Tahir [37]. In this study, source apportionment was done using PCA-MLR coupled with absolute principal component scores (APCS) due to its simplicity and high reliability.
Due to concern of adverse health effect of PAHs in urban environment at fine particulate size, an exploratory study was done to understand sources and health risk of PAHs at Kuala Lumpur City. The objectives of this study are to a) determine concentrations of WSOC, OC and EC and estimate the concentration of SOC in PM2.5, b) investigate levels and potential sources of PAHs in PM2.5 in selected Kuala Lumpur urban areas, c) estimate the potential health risk posed by PAHs species.

Study areas
Kuala Lumpur is the federal capital of Malaysia that has high density of population and is a centre of commercial activities and surrounded with industrial activities with high traffic density. The volume of road traffic is higher during peak hours every day and this traffic increases rapidly during public holiday. Yet, its population has exceed one million (1,453,975 S1. KKKL is a public medical center in Kuala Lumpur, and this area is near with schools.
WISMA is a building that is surrounded with shops and DBKL is the city council which administers the city of Kuala Lumpur in Malaysia. All these sampling areas are located near to the main routes that are very busy and have high density of traffic. In addition, these areas are places that all age group of people visits and therefore, they are exposed to PAHs pollution.

Local meteorology and transport of air mass
Kuala Lumpur is located at 56 m above sea level. In the middle of March 2019, the change of ambient temperature was higher compared to January and February as shown in Fig.   S2. Interestingly, relative humidity inversely changes to ambient temperature. The local meteorology data was taken from Subang Airport (www.wunderground.com) located at the about 20 km from Kuala Lumpur. The synoptic level of wind impacts greatly the ambient level of pollutants over Malaysia. As shown in Fig. 1, a stronger wind during January was blowing from the South China Sea (north-easterly monsoon) compared to February and March. This strong wind carries higher pollutants as well as water vapor from the ocean to Malaysia and causes heavy rainfall. The synoptic level wind vector of the assimilated data was downloaded from ECMWF data repository (European Centre for Medium-Range Weather Forecasts) and plotted using Grid Analysis and Display System (GrADS) software. Fig. 2 shows the monthly cluster of backward trajectories (BTs) constructed using Hybrid Single-Particle Lagrangian Integrated Trajectory version 4.9 (Hysplit 4.9). The BTs were calculated using a set of reanalysis data by NCEP/NCAR (ftp://arlftp.arlhq.noaa.gov/pub/archives/reanalysis) calculated daily on 00, 06, 12, 18 UTC, 500 m as releasing height, 6 h interval and 120 h as total travel time. Then, the estimated cluster data were plotted in an Igor Pro platform (WaveMetrics, OR, USA).   where OP (the amount of pyrolyzed OC) is defined as the carbon content measured after the introduction of O2 until reflectance returns to its initial value at the start of analysis.
By using the lowest ratio value of OC to EC (OC/EC), the secondary organic carbon (SOC) concentrations can be estimated. The SOC concentrations were calculated as follow [42,43] in Eq. 4 : where the minimum OC/EC ratio ((OC/EC)min) is believed to be the value for primary PM2.5 emission source and season dependent, which has significant ranges of uncertainty.
The sampling was conducted strategically on 24 h basis and the north-easterly monsoon which potentially reduce the uncertainty in the concentration. Khan et al. Khan, Sulong, Latif, Nadzir, Amil, Hussain, Lee, Hosaini, Shaharom and Yusoff [41],Khan, et al. [44] stated that, the lowest value of EC/OC was used to represent the primary combustion source.
A 2×2 cm of filter sample was cut and placed inside a beaker. Then, 50 mL of ultrapure water are added into the beaker and the sample with ultrapure water was sonicated for 20 minutes. After sonication, the mixture was filtered using Whatmann filter paper 100 mm in diameter. For analysis, the filter paper was cut into one quarter or half of the filter sample and placed inside a beaker. The exposed filter was extracted using ultrasonic agitation (10 minutes sonication time) using 25 mL of dichloromethane (DCM) as solvent. Then, modified magnetic nanoparticles (C8MNPs) were added immediately into the extraction solution and then sonicated for five minutes. The detailed information of C8MNPs has been described in the supplementary file. Theoretically, the PAHs that were extracted out clean up via adsorption on the C8MNPs. The adsorption efficiency has been tested using the recovery analysis of spiking known standard of PAHs. After that, with the help of an external magnetic field, the solution was settled down until its clear. The DCM was thrown away and washed with DCM three times. Next, a 200 µL of n-hexane was added to the PAHs adsorbed on C8MNPs and sonicated for 10 minutes. PAHs were dissolved in n-hexane. Then, the n-hexane was separated from the magnetic nanoparticles with the help of an external magnetic field, collected and placed inside GC vial. The method reported here is slightly modified from the study published by Soo et al. [46].  In PCA, the input data was carefully screened and clean up the outliers, data below detection limit and the missing data was replaced with geometric mean of each variables.

Analysis of PAHs using Gas
To reduce the variability in the concentration, a normalization procedure was followed through the deduction of the concentration from the average value and then divided with the standard deviation. The rescaled and normalized new database has been used for PCA procedure. Next, the key challenge is to obtain a suitable set of principal components (PC) factors. As for this step, we applied several options such as increase or decrease of the number of the factors, threshold of Eigen value, variance (%) and rotation of the PC loadings. An Eigen value is a mathematical term and to set a threshold of Eigen value 1 helps to know the variability to the PC factors. The variance (%) of the PCs may help to know the significance of the PCs. To obtained a minimum set of PCs with the largest of the variability, we applied all the steps described above [20,22,37]. Suitability of the PAHs samples for PCA analysis was tested using the Kaiser-Meyer-Olkin (KMO). A large value of KMO (close to one) generally reflected that the data set is suitable to carry out PCA analysis while lower value of KMO indicates the less suitability of the data set for PCA analysis. Jamhari et al. Jamhari, Sahani, Latif, Chan, Tan, Khan and Tahir [37] stated that the KMO value of greater than 0.6 is required for the suitability of data set in PCA procedure. The value of KMO in this study is 0.650. This value is close to one, therefore, the data set is suitable for PCA analysis. Thus, PCA analysis was performed to identify the potential sources of the PAHs samples. To obtain the quantitative contribution of the identified sources, the PCA scores were corrected using absolute principal component scores (APCS) suggested by Thurston and Spengler (1985). Then, the APCS were regressed against the total PAHs concentration detected by GC-FID.

US EPA Health risk modelling
Health risk of PAHs exposure can be estimated via the pathways of ingestion, inhalation, or dermal exposure as suggested by USEPA [53]. However, in this study, inhalation of air particles contaminated with PAHs was considered for health risk assessment as PM2.5 is aerodynamically very tiny and can penetrate the human respiratory system to the alveolar level. The Benzo(a)Pyrene equivalent health risk as B[a]Peq was determined using the equation below: Where C is the concentration of individual compound in each sample and TEF is toxic equivalency factors which has different value for each compound.
The excess lifetime cancer risk (ELCR) in humans and hazard quotient (HQ) can be determined by calculating the lifetime average daily dose (LADD) for carcinogenic and average daily dose for non-carcinogenic exposure of PAHs according to USEPA guidelines [54].
For LADD, the equation is: ELCR was calculate as follow: Where, C is concentration in air particles (mg m -3 ), IR is the air inhalation rate, EF is the exposure frequency, ED is the lifetime exposure duration, BW is the body weight, ALT is the averaging lifetime for carcinogens and SF is the slope factor (mg kg -1 day -1 ) -1 . SF can be determined as; where IUR is inhalation unit risk.
In this study, the health risk assessments are calculated for five group age; infant, toddler, children, adolescent and adult because these group are exposed to the PAHs pollution.
The reference values for all the constant above shows in the Table S3 and Table S4 for IUR values. For non-carcinogenic exposure, the Hazard quotient (HQ) was calculated using ADD and reference dose (R f D). ADD are calculated using based on Eq. (6).
The value of R f D are different for every PAHs compound and has been referred in Table   S5.

OC, EC and SOC
Concentrations of OC1, OC2, OC3, OC4, OP, EC1-OP, EC2 and EC3 are shown in Fig.   S6a. Concentrations of OC, EC and TC in each filter samples are shown in Fig. S6b and OC to EC ratio for each filter samples are illustrated in Fig. S5.

Diagnostic Ratio (DR)
Application of Ant / (Ant + Phe) was not possible because no phenanthrene was detected in the samples. From Sylvestre [49]. The PAHs are estimated to come mainly from coal, grass and wood burning activities also from traffic emission. The high density of traffic in the sampling site is a potential cause in this regard and due to the wind speed and flow that brought the PAHs from coal and open burning near the sampling site.

PCA-MLR
PCA factor loadings obtained via the varimax rotation are shown in  [22]. Flt, Pyr, B[a]A are tracers for the emission of natural gas [20,37]. Sulong, Latif, Sahani, Khan, Fadzil, Tahir, Mohamad, Sakai, Fujii and Othman [35] reported that Flu, Ant, Flt, Pyr are emission from natural gas and biomass burning. B[a]P is specific marker for vehicle and gasoline emission [21,22,35,37]. However, Factor 1 shows more tracer for emission from natural gas, coal and biomass burning. Factor 2 (20.27% of the total variance) was loaded with B[k]F and B[a]P, indicating that the emission come from vehicle and gasoline combustion. Higher level of B[k]F is suggested to indicate diesel vehicles [28] and B[a]P is specific marker for vehicle and gasoline emissions [21,22,35,37]. These emissions can be classified as urban traffic combustion. Factor 3 (8.93% of the total variance) was dominated by B[b]F (HMW PAH) are expelled from the combustion of heavy oil [21,35].
We also believed that these pollutants may be influent from neighboring country that has been emitted and carried by the wind to Malaysia. As reported, a major portion (about 70%) of the energy in China has been obtained from coal (https://www.worldatlas.com/articles/15-countries-most-dependent-on-coal-forenergy.html). Coal was also identified as the largest source of PM2.5 bound -PAHs in Beijing [23]. Biomass burning also occurred potentially in the East Asian region e.g. Hong Kong and reported as a predominant source of PAHs in PM2.5 [60]. From Fig. S3 Fig. 2 depicts that the Northeast monsoon wind arrives in three months (Fig. 2 (a) January, Fig. 2  respectively and leaving 57% of the PAHs concentration as undefined. The percentage of undefined PAHs is higher because some of the variables were excluded, because the data is ≤ 30% from overall variables.

Excess lifetime cancer risk (ELCR)
From  [37] also reported that adult has the highest ILCR value and infant has the lowest value of ILCR among the age groups. Adult has highest health risk compared to others because adult is exposed to the carcinogen for long period of time compared to other age groups. It is possibly due to the longer of the exposure time as the concentration of carcinogenic element can accumulate in the body during such extended lifetime.
However,the estimated cancer risk that have been calculated were considered insignificant and safe since the values in less than 1×10 -6 .
From Comparing to other compound, HQ for B[a]P for all age groups is the highest, with value of ×10 -3 to ×10 -4 . Khpalwak, Jadoon, Abdel-dayem and Sakugawa [22] observed HQ via inhalation route for the road and aerial dusts from Kabul and Jalalabad cities, Afghanistan. HQ estimated from three types of air particles such as road-dust Kabul, road-dust Jalalabad and aerial-dust Jalalabad were 2.99×10 -9 (children) and 5.12×10 -9 (adult), 1.42×10 -9 (children) and 2.44×10 -9 (adult), and 2.36×10 -9 (children) and 1.38×10 -9 (children) respectively. From From the current study, HI for infant is the highest with value of 0.0043, followed with toddler, adolescent, children and adult with values of 0.0034, 0.0023, 0.0022 and 0.0013 respectively. The trend is also similar with the trend of HQ for each age group. Our results showed that none of the age group category were the risk of HI>1. All age groups showed HI values of 10 -3 , which is lower than one (1). In addition, for every age groups, DBKL has the lowest HI values which is ×10 -6 compared to Wisma and KKKL. Thus, noncarcinogenic health effect was less prominent at the studies areas.

Conclusion
In this study, concentration of OC1, OC2, OC3, OC4, OP, EC1, EC2 and EC3 were determined by using IMPROVE_A protocol and then concentration of SOC was Hazard Index (HI) for all age groups were less than one (<1), means that no substantial non-cancerous risk is present. Although the health risk assessment that have been done shows no significant health risk for both carcinogenic and non-carcinogenic risk, precautionary measures must be taken to reduce the PAHs exposure. To better elucidate potential PAH emission sources, the use of multivariate source apportionment modeling e.g. PMF with long-term monitoring data would be needed. In future study, we will consider a long term of the sampling and also the breathing zone or level for the PM2.