Characteristics of Atmospheric PM 2 . 5 in a Densely Populated City with Multi-Emission Sources

Fine particulate matters (PM2.5) have been identified as one of the major air pollutants in urban areas, which are responsible for the deterioration of the atmospheric air quality as well as adverse effects on public health. In this study, the mass concentration, water-soluble ionic component, trace metal component, carbon component and modeling the contribution source for PM2.5 was characterized for Chiayi City, which has high population density and surrounded by agricultural area. The lowest PM2.5 mass concentrations were registered in the summer (9–22 μg m), while for the spring, autumn, and winter were well above the healthy level suggested by World Health Organization (WHO). For all seasons, the dominants were the sulfate (SO4), nitrate (NO3) and followed closely by ammonium (NH4). Those secondary aerosols were transformed from SO2 and NO2 into particulate NO3 and SO4 during spring, autumn and winter. Lower carbon mass concentrations were observed for summer (2.03–2.49 μg m) corresponding to the highest carbon content in PM2.5 mass concentrations in terms of percentages (average 18.1%). Using the Chemical Mass Balance receptor model, the secondary nitrate (NO3), primary traffic source, secondary sulfate (SO4), re-suspending soil particle, and petrochemical industry were identified as the major sources of PM2.5 in Chiayi City. Consequently, the PM2.5 contributions were complicated in a small but various seasons and geological distributing area. The air quality control strategies were thus seasonal and periodical dependent.


INTRODUCTION
Atmospheric aerosols or particulate matter can be described as particles emitted directly from the anthropogenic and natural sources as well as the secondary aerosols formed from gaseous precursors, such as nitrogen oxides (NO x ), sulfur dioxide (SO 2 ), and volatile organic carbons (VOCs).Particulate matter, with an aerodynamic diameter less than 2.5 µm (PM 2.5 ), has been categorized as fine particulate matter.This category of particulate matter has attracted the interest of researchers, scientists and regulators due to its possible effects on the human health and the environment especially visibility and climate change (Ho et al., 2006, Wang et al., 2015a;Wang et al., 2015b;Li et al., 2016).Due to its size, the PM 2.5 can easily penetrate the human respiratory systems a as well as across the circulatory system posing great risk in form of respiratory diseases and cardiovascular complications including lung cancer (Bell, 2012;Xu et al., 2012).Additionally, the PM 2.5 can affect visibility thus altering the climate of a region (Xu et al., 2012).
It is important to determine the PM 2.5 compositions, which ultimately determines the health effect, atmospheric visibility and conversions of the aerosols (Shen et al., 2009;Pipal et al., 2014).The compositions of PM 2.5 are usually water soluble ions, metal elements and carbonaceous species (Xu et al., 2012) as well as semi-volatile organics such as polycyclic aromatic hydrocarbons (Cheruiyot et al., 2015).Previously reported that the composition of PM 2.5 varies spatially, seasonally and with the source (Bell, 2012).Similarly, the effect on human health and environment varies depending on the composition, season, region and exposure amount (Bell et al., 2009;Bell, 2012;Chang et al., 2013).From various focusing on the chemical composition of PM 2.5 , the dominant ions include SO 4 2-, NH 4 + , and NO 3 - (Xu et al., 2012;Chang et al., 2013), which are important in determining the hygroscopicity of aerosols (Shen et al., 2009).Another parameter of interest in the composition of PM 2.5 is the organic carbon (OC) and elemental carbon (EC) (Ho et al., 2006;Chang et al., 2013).
The sources of PM 2.5 are both anthropogenic as well as natural with the compositions depending on the sources (Xu et al., 2012).Both regional transport of dust and local emission can greatly contribute to the PM 2.5 in the atmosphere (Lin et al., 2015, Lv et al., 2015).Previously chemical mass balance model (CMB) has been used to identify the sources of PM 2.5 components the (Watson et al., 2001).The CMB model is receptor model gives a least squares solution to set of linear equations expressing the concentration of a chemical component as a summation of the product of source profile species and source contributions (Watson et al., 1994, Watson et al., 2001).It is useful to evaluate a general source apportionment for local control strategy.
In Taiwan, there are several dense cities with heavy population, traffic, and industry.Most of them are nearby or close to the coast area for better transportation, such as Taichung, Tainan, and Kaohsiung, and Chiayi.The seasonal variation of weather would affect these cities in term of their air quality.Chiayi city (as shown in Fig. 1) was reported to has the worst record among the cities in Taiwan.It is the smallest but the most dese area, when there are 270 thousand people live in a 60 km 2 downtown, meaning that the population density is over 45,000 people km -3 .It is much more crowded than most of the cities around the world and even close to the dense area in Tokyo, New York, and Shanghai.Additionally, there are approximately 4,500 automobiles km -3 (motorcycles, cars, buses, and trucks), which is also a significantly high value in the world.Heavy traffic could be then concluded as the most contributive source of pollutions in Chiayi city.However, the air pollutants could be transported, especially by the monsoon winds.The strong north wind usually brings the primary coarse and secondary fine particle from China and Southeast Asia.
Nevertheless, the open burning is another potential source, since a wide agricultural region are around Chiayi city.
This study investigates the characteristics of seasonal atmospheric PM 2.5 in Chiayi city by the source apportionment tool, CMB model.The major constituents of PM 2.5 , such as EC, OC, water-soluble anions including Cl -, NO 3 -, and SO 4 2-and cations including Na + , NH 4 + , K + , Mg 2+ , and Ca 2+ , and 14 trace metal elements were determined and analyzed for their contribution to PM 2.5 mass.Therefore, the study aims to provide useful information for air quality improvement in a densely populated and multi-emission city.

Sampling Equipment and Methods
The PM 2.5 and PM 10 sampling in this study followed the standard procedure (NIEA A205.11C) announced by TWEPA.Particulate samples were collected by two parallel low-volume air samplers with a flowrate of 16.7 L min -1 ± 5% at 1 atm, which consisted of one-stage filter packs placed after very sharp cut cyclones (VSCCs, BGI) with particle size 10 and 2.5 µm each (PQ200 series).The polytetrafluoroethene (PTFE, Teflon ® ) fiber filter, with 46.7 mm diameter, was used to collect the accurate mass of PM 2.5 by using an electronic microbalance (METTLER TOLEDO Model XP2U) with sensitivity of ± 1 µg after 24 h equilibration at 23 ± 1°C with relative humidity at 40 ± 5%.Each filter sample was pre-and post-weighed three times to ensure the variance for each one was less than 10 µg.The net mass was thus calculated by subtracting initial weight from final weight, and further divided by sampling volume (about 24 m 3 ) to obtain PM 2.5 concentrations.

Chemical Analyses for PM 2.5 Compositions Water-soluble Ions
The National Environmental Analysis (NIEA) method W415.52B was used to identify the following four watersoluble anions (Cl -, F -, NO 3 -, and SO 4 2-) and while five cations (Na + , NH 4 + , K + , Mg 2+ , and Ca 2+ ) were analyzed using an Ion Chromatography (IC).For sample pretreatment, 1/2 of each collected PTFE filter was shaken for 30 minutes in a 15 mL ultrapure deionized water and then extracted ultrasonically for another 90 minutes in order to completely release the water soluble species.Anions were analyzed using a Dionex ICS-1000 with ASRS-ULTRA suppressor, Ion Pac AS4A-SC column and a Na 2 CO 3 /NaHCO 3 eluent with a flowrate of 2 mL min -1 .Cations were analyzed by a Dionex DX-900 with CSRS-ULTRA suppressor, Ion Pac CS12, using a 0.1 M sulfuric acid (H 2 SO 4 ) eluent.The standards of anions and cations were used in different calibrations to control the quality of analysis.The concentrations in blank sample filters were below limit of detection (LOD) of each ion.Thus, no subtraction was done for data calibration.

Metal Elements
The NIEA method M105.01B was used for analysis of metal elements, including Na, Mg, Al, K, Ca, V, Cr, Mn, Fe, Ni, Cu, Zn, Cd, and Pb, utilized the other 1/2 PTFE sample filter for acid digestion and instrument analysis.The digestion took place in Teflon vessels with concentrated nitric acid (HNO 3 ), hydrochloric acid (HCl), and hydrofluoric acid (HF).Determination of sample concentration was performed by high-resolution inductively coupled plasma in series with a mass spectrometer (ICP-MS, Jobin Yvon ULTIMA 2000).The calibration lines were checked by standard solution with the absolute error < 10%.The recovery of method was checked every 10 samples by an extra standard sample solution with the value in rage of 80-120%.The methodological blank sample were quantified and found less than LOD.

Elemental and Organic Carbon
On the other hand, the parallel quartz fiber filters were used to capture and determine the elementary carbon (EC) and organic carbon (OC) contents and determined by using Thermal Optical Reflectance (TOR) analysis, Interagency Monitoring Protection Visual Environment (IMPROVE).The TOR carbon analyzer consists of a thermal system and an optical system.The thermal system consists of a quartz tube installed inside a coiled heater.Current through the heater is controlled to approach and maintain pre-set temperatures for certain time periods.A part of the quartz filter sample is placed in the heating zone and heated to different temperatures under non-oxidizing and oxidizing atmospheres.Additionally, the optical system is composed of a He-Ne laser, a fiber optic transmitter and receiver, and a photocell.The filter deposit should face a quartz light tube, resulting to that the reflected laser intensity can be monitored throughout the analysis.The OC were volatilized from the filter in a non-oxidizing condition, while the EC is not oxidized, as the temperature first increases to 580°C.Furthermore, the oxygen was added to the He when temperatures greater than 580°C, and caused the EC burns and released to the sample stream.The gas stream then passed through a heated MnO 2 bed, where they were oxidized to CO 2 , and further were reduced to CH 4 by crossing a heated Ni catalyst.Finally, the methane is then quantified with a flame ionization detector (FID).Please refer to DRI Standard Operating Procedure for more detail information of TOR analysis (DRI, 2012).

Chemical Mass Balance Receptor Model
The Chemical Mass Balance (CMB) receptor model use the chemical and physical characteristics of PM 2.5 measured at sources (reference data) and receptor (samples in this study) to both identify the presence of and to quantify the source contributions to receptor concentrations.This model was first used in the early 19070's (Schauer et al., 2005) provides a least-square solutions to sets of linear equations (Watson et al., 1994) that represents for each receptor chemical concentration as a linear sum of products of source profiles and source contributions.The model fits speciated data from a source group to corresponding receptor data (sample) and the output data consists of the amount contributed by each emitter type represented by a profile to the total mass concentration (Begum et al., 2007).The model requires a sufficient number of samples at receptor sites, analysis of receptor sites samples for chemical species present in the source emissions, identification of potential sources and their profiles and the number of noncollinear sources be less than the number of measured species (Watson et al., 1994).The mass balance equation is shown as follows.
where C i is the concentration of species i measured at the receptor site in µg m -3 .a ij is the mass fraction of species i in the profile of the source j (%).n is the number of species.S j is the mass concentration at the receptor site of all species assigned to the source j (µg m -3 ).
Some of the assumptions of the CMB model include: (i) the compositions of the source emissions are constant over the sampling duration, (ii) the chemical species do not react with each other, (iii) all sources are identified, (iv) the number of sources are less than the chemical species, (v) the source profiles are linearly independent and (vi) any uncertainties are uncorrelated and normally distributed (Watson et al., 1994).

Seasonal Variation of PM 2.5 in Chiayi City
The air quality guideline values recommended by WHO acknowledge the heterogeneity and, in particular, recognize that when formulating policy targets, governments should consider their own local circumstances carefully before adopting the guidelines directly as legally based standards.In Taiwan, the 24-hour standard was set as 35 µg m -3 , when the annual average standard was 15 µg m -3 , which were suitable to identify the atmospheric PM 2.5 condition in local area in this study.
Fig. 2 shows the comparison of PM 10 and PM 2.5 vis-à-vis the Taiwan PM 2.5 and PM 10 daily standards.The results show that summer registered the lowest PM 2.5 mass concentrations in all the sampling sites.The PM 2.5 concentrations were in the range of 33-61 µg m -3 , 9-22 µg m -3 , 21-76 µg m -3 and 29-69 µg m -3 .For the whole of Chiayi City, the average summer concentrations were 12.3 µg m -3 while the average for PY, CYEPB, LT, and HM sites were 1.8 µg m -3 , 12.6 µg m -3 , 11.8 µg m -3 and 11.8 µg m -3 , respectively.For all the Chiai area, the average for autumn, winter and spring were 44.9 µg m -3 , 50.0 µg m -3 and 45.4 µg m -3 , respectively.Similarly, according to Fig. 2 it is only in summer were the PM 2.5 levels below the daily standards while for the other seasons they were above the set standards of 35 µg m -3 .Fig. 2 also shows that for most of the sampling period the LT station recorded the highest PM 2.5 levels amongst all the sites.LT site is in the suburbs and human activities maybe the reason for observed high PM 2.5 levels.
The mass concentration levels PM 2.5 and PM 10 in summer were well below the Taiwan PM 2.5 and PM 10 standards of 35 µg m -3 and 60 µg m -3 , respectively, while for the rest of seasons the levels were mostly over and above the standards.Additionally, the ratios of PM 2.5 /PM 10 were lower in summer than all other seasons showing that the contribution of PM 2.5 was more significant in spring, winter and autumn.The possible cause of lower levels in summer is the intermittent precipitation which removes the primary PM 2.5 and secondary precursors such as SO 2 , NO x and VOCs.Similar observation has been reported elsewhere by (Li et al., 2016).
The Fig. 2 also shows the levels of the PM 2.5 for the PY, CYEB and LT sites relative to the TWEPA reported values.The trend is similar for all the sites for each sampling day and there were no significant differences between the sites on any particular day.The figure also shows that for all the sites the lowest PM 2.5 levels were observed in summer and the highest in spring season.Similar observations were made by (Liu et al., 2014) who reported that PM 2.5 levels were highest in spring in Beijing

Chemical Compositions Water Soluble Ions
Fig. 3 shows the arithmetic mean contributions of various ions in the PM 2.5 mass concentrations.Except for spring, the anions were dominant than the cations.The average anion/ cation ratios were 1.09, 1.06 and 1.08 in summer, autumn and winter while in spring they were averaged at 0.915.This shows that the PM 2.5 in Chiayi area is mostly acidic in nature during summer, autumn and winter since not all anions could be neutralized (He et al., 2012).The hygroscopicity of particulate and the potential to form secondary aerosols is influenced much by the acidity (Park et al., 2015).
The results in Fig. 3 the major water-soluble ions were the sulfate (SO 4 2- ) and nitrate (NO 3 -) and followed closely by ammonium (NH 4 + ) which might explain why in summer, autumn and winter the ratio of anion to cation were greater than unity.The trend in spring and winter the trend was NO 3 -> SO 4 2-> NH 4 + > Cl -> Na + > K + > Ca 2+ > Mg 2+ ~Fwhile in summer and autumn the trends was SO 4 2-> NO 3 -> NH 4 + > Cl -> Na + > K + > Ca 2+ > Mg 2+ ~F-.The contributions of NO 3 -were 19%, 38.3%, 34% and 33% in summer, spring, winter and autumn while that of SO 4 2-was 55%, 28.7%, 32% and 38%.The contribution of SO 4 2-in PM 2.5 spiked to more than 50% in summer and it reduces in autumn, winter and spring in that order similar to the study of (Shen et al., 2008).The reverse is true for NO 3 -fractions.The SO 4 2-in aerosol would increase because of temperature of ambient air increased.The reason is SO 2 has higher potential to transform SO 4 2-because of solar radiation and further neutralized by ammonium and eventually form secondary aerosols.The ammonium-rich condition around Chiayi agricultural area lead the transformation of SO 2 to predominate the formation of sulfur-PM 2.5 .In the other hand, NO 3 -and NH 4 + would decrease because of temperature increased.The reason is NO 3 -and NH 4 + will volatilize from surface of PM.Similarly, other studies have shown that NO 3 -, SO 4 2-and NH 4 + as the dominant water soluble ions (Shen et al., 2009;Deshmukh et al., 2011;Wang et al., 2015a;Lu et al., 2016).The higher contribution of NO 3 -and SO 4 2-signifies the occurrence of atmospheric oxidation process for NO 2 and SO 2 (Lin, 2002).
The ratio of sulfate/sulfur (or nitrate/nitrogen) to total sulfur (or total nitrogen) can be used to understand the extent of atmospheric conversion from SO 2 to SO 4 2-and from NO 2 to NO 3 - (Kaneyasu et al., 1995, Shen et al., 2008).The SOR indicates the degree of oxidation of sulfur in terms of the ratio of sulfate to total sulfur in SO 4 2-+ SO 2 .The NOR indicates the degree of oxidation of nitrogen in terms of the ratio of nitrate to total nitrogen in NO 3 -and NO x and if values of SOR and NOR are 0.25 and 0.1 is an indication of high potential for the oxidation leading to secondary inorganic aerosol formation in the atmosphere (Li et al., 2016).
Table 1 shows the values calculated for the potential of SOR and NOR.The SOR values for spring, summer, autumn and winter were in the range of 0.36-066, 0.04-0.13,0.35-0.49and 0.31-0.61,respectively.On the other side, the NOR values were 0.16-0.24,0.0002-0.0014,0.08-0.29 and 0.11-0.19for spring, summer, autumn and winter, respectively.The low values of NOR and SOR for summer show that the potential for secondary formation of NO 3 -and SO 4 2-were low and mostly were sourced from local, pollution while it was high for spring, autumn and winter which shows the potential to convert SO 2 and NO x to particulate form via secondary transformation (Zhou et al., 2009;Li et al., 2016).This can also be attributed to lack of favorable conditions such as low PM 2.5 concentrations reported earlier or low abundance of nitrogen due to the loss of NH 4 NO 3 at high temperatures (Zhou et al., 2009;Wu et al., 2015).
The dominant components for secondary inorganic salts are NH 4 + , NO 3 -and SO 4 2-in the ambient air.It would be existed in NH 4 NO 3 , NH 4 HSO 4 and (NH 4 ) 2 SO 4 etc. (Steinfeld and Pandis, 1998) determined that the "I" index equals to the equivalent mole ratio of NH 4 + (eq) and SO 4 2-(eq) as Eq.(3).Different I indicated that different sulfate components in PM 2.5 .There are NH 4 SO 4 existed when I > 2.0, partial NH 4 HSO 4 existed when I < 2.0 (Chu, 2004) determined that the index "J" as the equivalent ratio of average NH 4 + (eq) during 24 hrs over (2•SO 4 2-(eq) + NO 3 -(eq) ) as Eq. ( 4), and it should be balanced.It means that NH 4 + are not enough to balance NO 3 -and SO 4 2-when J < 1.0.According to the results of ion concentrations, the I and J value were determined (as shown in Fig. 4. I was higher than 2.0 and J was higher than 1.0 during spring.It means that most of sulfates and nitrates were balanced; I was higher than 2.0 or nearby 2.0 and J was less than 1.0 during summer and autumn.It means that most of sulfates were balanced, but nitrates were not balanced.Nitrates would be controlled more easily; I was higher than 2.0 and J was higher than 1.0.It means that most of sulfates and nitrates were balanced.There is abundant NH 4 + , from the potential sources of NH 4 + fermentation products of livestock operation, agricultural waste and plant decomposition, biomass burning, and natural soils around Chiayi area.Thus, controlling NO x is better than SO 2 to reduce secondary PM 2.5 level.

Trace Metal Elements
The Fig. 5 shows the compositions of the trace metal elements from various sampling sites in Chiayi.The trace metal elements contributed about 4.0-6.9%,10.2-15.3%,4.6-10.9%,and 4.6-10.9% of the total PM 2.5 mass in spring, summer, autumn and winter, respectively.The major trace elements were Na + and K + .The Na + is majorly derived from sea salt spray while K + is from biomes burning.Thus it can be seen that the contribution of K + was greatly reduced in summer when there is minimal biomass burning in Chiayi.The amount of K + increased in the PM 2.5 in spring as more agricultural waste was burnt.On the other hand other metals such as lead and zinc maybe sourced from road dust and gasoline emissions, respectively, transported through the atmosphere from the city.
Mg, Al, Fe and Zn were the most abundant trace elements in the PM 2.5 in this study similar to the study by who identified Al, Fe, Si, Mg, and Cu as the dominant trace elements.Additionally, Ni, Fe, Cu and Zn are reported basically from anthropogenic activities, while Cr, Mg and Mn maybe from crustal sources (Pipalatkar et al., 2014).

Elemental and Organic Carbons
The contributions of the elemental and organic carbon in the PM 2.5 as well as the total carbon content in the PM 2.5 and the OC/EC ratio were shown in Fig. 6.These carbonaceous constituents are important because they influence the potential for aerosols in terms of health impacts, climate change and visibility effect (Huang et al., 2014).The elemental carbon concentration ranged between 2.09-2.29 µg m -3 , 0.86-0.97µg m -3 , 1.62-1.72µg m -3 and 1.77-2.05µg m -3 for spring, summer, autumn and winter respectively.On the other side, the concentrations for organic carbon were in the range of 4.26-4.63µg m -3 , 1.17-1.34µg m -3 , 2.77-2.97µg m -3 , and 4.58-4.80µg m -3 in spring, summer, autumn and winter, respectively.As for total carbon the concentrations were in the range of 6.35-6.92µg m -3 , 2.03-2.49µg m -3 , 4.39-4.69µg m -3 and 6.35-6.81µg m -3 , for spring, summer, autumn and winter respectively.The low concentration of organic carbon in summer can be attributed to scavenging effect due to high precipitation, higher temperatures which shift the gas-particle equilibrium of SVOCs and higher mixing heights experienced in summer (Yang et al., 2011).
The contributions of EC to the PM 2.5 mass concentrations averaged at 4.78%, 7,46%, 6.22% and 3.95% while that of OC averaged at 9.85%,10.6%,10.6%, and 9.49%, for spring, summer, autumn and winter respectively.This gave a total carbon contribution to the PM 2.5 mass concentration of about 14.6%, 18.1%, 16.8% and 13.5% for spring, summer, autumn and winter respectively which was lower compared to the 23.3%, 25.5% and 19.2% reported by (Li et al., 2009) for urban, industrial and coastal sites in China.In a study done in Tianjin , China, the EC contributions to PM 2.5 mass were 4.9%, 7.0%, 5.1 and 5.2% for spring, summer, autumn and winter respectively while those of OC were 13.95, 13.0%, 15.2% and 19.8% for spring, summer, autumn and winter, respectively (Gu et al., 2010).The results show that even though the concentrations of EC, OC and TC were lowest in summer they had higher contribution to the PM 2.5 mass concentration compared to other seasons.
Some studies have shown that the ratio OC/EC of about 2.2 or OC/TC value of 0.67 indicates generation of the secondary aerosol (Turpin and Huntzicker, 1995).The OC/EC ratios in this study were in the range of 2.02-2.09 in the spring, 1.37-1.56 in the summer, 1.66-1.71 in the autumn and 2.31-2.57in the winter.Secondary aerosol formation was more likely in the winter and spring seasons based on these observations (Li et al., 2009).These ratios were comparable to the average 2.4 OC/EC ratios reported for Pearl River Delta in China by (Cao et al., 2003).The ratios reported in this study are lower than 4.8, 5.9 and 4.5 reported for urban, industrial and coastal sites by (Li et al., 2009) and those in northeastern China which varied between 3.9 and 12.9 and averaged at 7.5 (Zhang et al., 2012).
Additionally the secondary organic carbon, which can be formed from secondary atmospheric photochemical reactions (Zhang et al., 2012) was determined using the equation proposed by (Castro et al., 1999) which uses the minimum OC/EC ratio and EC tracer which is resistant to chemical reactions and a good indicator of anthropogenic pollutants (Zhang et al., 2012;Huang et al., 2014) .The equation used to estimate for SOC is as following Eq.5: where SOC is the concentration of secondary OC (µg m -3 ), OC Total is the concentration of total OC (µg m -3 ), (OC/EC) min is the minimum OC/EC ratio in the ambient aerosol during the sampling priod and EC is the elemental carbon content in (µg m -3 ).The results are presented in Fig. 6.The concentrations of secondary organic carbon were in the range of 0.189-0.422µg m -3 in the spring, 0.02-0.13µg m -3 in    the summer, 0.082-0.36µg m -3 in the autumn and 0.04-0.037µg m -3 in the winter, respectively.The corresponding fraction of SOC in the total OC (SOC/OC) was 4-9% in the spring, 1-11% in the summer, 3-12% in the autumn and 1-8% in the winter.These values were lower than the 50.3%, 45.7% and 37.2% reported by (Li et al., 2009) for urban, industrial and coastal sites or the 61.7% annual average reported by (Gu et al., 2010) in Tianjin, as well as 42% reported in northeastern China (Zhang et al., 2012).Similarly, (Cao et al., 2003) reported the contribution of SOC to PM 2.5 mass to be in the range of 25.5-59.9%for cities in the Pearl Dealta Region.The lower SOC in Chiayi area might be due to the higher EC composition, which also known as black carbon (BC) and produced from heavy traffic local environment.The supporting source contributions were reported in the following section.

CONCLUSIONS
The Higher atmospheric PM 2.5 level occurred in in autumn (44.9 µg m -3 ), winter (50.0 µg m -3 ) and spring (45.4 µg m -3 ), when the lowest one, were registered in the summer (9-22 µg m -3 ) around Chiayi City.The major water-soluble ions were the sulfate (SO 4 2- ) and nitrate (NO 3 -), and followed closely by ammonium (NH 4 + ).Low values of NOR and SOR for summer indicated decreased potential for secondary formation of NO 3 -and SO 4 2-thus the ions were more sourced from local pollution.As for spring, autumn and winter, the potential to convert SO 2 and NO x to particulate form via secondary transformation were shown.Additionally, the total carbon contribution to the PM 2.5 mass were about 14.6%, 18.1%, 16.8% and 13.5% for spring, summer, autumn, and winter respectively.The OC/EC ratios in this study were in the range of 2. 02-2.09, 1.37-1.56, 1.66-1.71, and 2.31-2.57for four seasons, respectively, indicating that secondary aerosol formation was more likely in the winter and spring seasons.As the results from CMB modelling, the main contribution sources for atmospheric PM 2.5 were majorly the secondary nitrate (13.7-20.1%),traffic source (11.2-21.6%),secondary sulfate (12.3-19.0%),re-suspended soil particle (8.10-12.4%)and petrochemical industry (7.46-10.9%).Other sources included agricultural open burning, metallurgical industry, cement industry, and sea salt.Generally, the source apportionment is employed to derive the air quality control strategies.In this local study, the PM 2.5 contributions were complicated in a small but various seasons and geological distributing area.The traffic emission controls, such as more utilization of greener transportation, replacement of highly emitting and aged vehicles, and carpooling system, were first suggested.Furthermore, the prohibition of open combustion and engine idling operation, optimization of road sweeper, and environmental education should be implemented.Finally, the air quality forecasting and announcement could be followed by the protection of particle-sensitive people to reduce the exposure.The air quality control strategies and their sensitivity were suggested to be further tested by 3D Eulerian grid air pollution modelling, which were thus seasonal and periodical dependent.Real-time inverse or diffusion modeling were also suggested.

Fig. 1 .
Fig. 1.Locations of the sampling sites and nearby area.

Fig. 5 .
Fig. 5. Metal compositions in PM 2.5 in Chiayi City during various seasons.

Fig. 6 .
Fig. 6.Carbon components in PM 2.5 in Chiayi City during various seasons.

Fig. 7 .
Fig. 7.The average seasonal source contributions to PM 2.5 at TWEPA site.

Table 1 .
The potential for secondary PM 2.5 in Chiayi City during four seasons.