Characteristics of total gaseous mercury ( TGM ) concentrations in an 1 industrial complex in southern Korea : Impacts from local sources 2 3

Abstract. Total gaseous mercury (TGM) concentrations were measured every 5 min in Pohang, Gyeongsangbuk-do, Korea, during summer (17–23 August 2012), fall (9–17 October 2012), winter (22–29 January 2013), and spring (26 March–3 April 2013) to (1) characterize the hourly and seasonal variations of atmospheric TGM concentrations; (2) identify the relationships between TGM and co-pollutants; and (3) identify likely source directions and locations of TGM using the conditional probability function (CPF), conditional bivariate probability function (CBPF) and total potential source contribution function (TPSCF). The TGM concentration was statistically significantly highest in fall (6.7 ± 6.4 ng m−3), followed by spring (4.8 ± 4.0 ng m−3), winter (4.5 ± 3.2 ng m−3) and summer (3.8 ± 3.9 ng m−3). There was a weak but statistically significant negative correlation between the TGM concentration and ambient air temperature (r = −0.08, p


Introduction
Mercury (Hg) in the atmosphere exists in three major inorganic forms including gaseous elemental mercury (GEM, Hg 0 ), gaseous oxidized mercury (GOM, Hg 2+ ) and particulate bound mercury (PBM, Hg(p)).GEM which is the dominant form of Hg in ambient air, (>95%) has a relatively long residence time (0.5~2 years) due to its low reactivity and solubility Schroeder and Munthe (1998).However, GOM has high water solubility and relatively strong surface adhesion properties (Han et al., 2005), so it has a short atmospheric residence time (~days).PBM is associated with airborne particles such as dust, soot, sea-salt aerosols, and ice crystals (Lu and Schroeder, 2004) and is likely produced, in part, by adsorption of GOM species such as HgCl2 onto atmospheric particles (Gauchard et al., 2005;Lu and Schroeder, 2004;Sakata and Marumoto, 2005;Seo et al., 2015).
Atmospheric Hg released from natural and anthropogenic sources when introduced into terrestrial and aquatic ecosystem through wet and dry deposition (Mason and Sheu, 2002) can undergo various physical and chemical transformations before being deposited.Its lifetime in the atmosphere depends on its reactivity and solubility so that depending on its form it can have impacts on local, regional and global scales (Lin and Pehkonen, 1999;Lindberg et al., 2007).A portion of the Hg deposited in terrestrial environments through direct industrial discharge or atmospheric deposition is transported to aquatic system through groundwater and surface water runoff (Miller et al., 2013).
A previous study also reported that Hg directly released into terrestrial and aquatic ecosystems from industrial effluent has influenced surface water, sediment and biological tissue (Flanders et al., 2010).
Significant spatial variations in atmospheric Hg deposition near urban and industrial areas were due to local anthropogenic sources including municipal waste incinerators, medical waste incinerators, electric power generating facilities and cement kilns (Dvonch et al., 1998), ferrous and non-ferrous metal processing, iron and steel manufacturing facilities, and oil and coal combustion (Hoyer et al., 1995).Miller et al. (2013) also reported that local sources of elemental Hg are typically industrial processes including retort facilities used in the mercury mining industry to convert Hg containing minerals to elemental Hg and chloralkali facilities.
Receptor models are often used to identify sources of air pollutants and are focused on the pollutants behavior in the ambient environment at the point of impact (Hopke, 2003).In previous studies, conditional probability function (CPF), which utilizes the local wind direction, and potential source contribution function (PSCF), which utilizes longer backward trajectories (typically 3-5 days), combined with concentration data were used to identify possible transport pathways and source locations (Hopke, 2003).While PSCF has been used 6 primarily to identify regional sources, it has also been used to identify local sources (Hsu et al., 2003).The objectives of this study were to characterize the hourly and seasonal variations of atmospheric TGM (the sum of the GEM and the GOM) concentrations, to identify the relationships between TGM and co-pollutant concentrations, and to identify likely source directions and locations of TGM using CPF, conditional bivariate probability function (CBPF) and total PSCF (TPSCF).

Sampling and analysis
TGM concentrations were measured on the roof of the Korean Federation of Community Credit Cooperatives (KFCCC) building (latitude: 35.992°, longitude: 129.404°, ~10 m above ground) in Pohang city, in Gyeongsangbuk-do, a province in eastern South Korea.Gyeongsangbuk-do has a population of 2.7 million (5% of the total population and the third most populated province in South Korea) and an area of 19,030 km 2 (19% of the total area of South Korea and the largest province geographically in South Korea).Pohang city has a population of 500,000 (1% of the total population in South Korea) and an area of 605.4 km 2 (1.1% of the total area in South Korea).It is heavily industrialized with the third largest steel manufacturing facility in Asia and the fifth largest in the world.There are several iron and steel manufacturing facilities including electric and sintering furnaces using coking in Gyeongsangbuk-do including Pohang.In addition, there are several coke plants around the sampling site.The Hyungsan River divides the city into a residential area and the steel complex.Hg emissions data from iron and steel manufacturing, and a hazardous waste incinerator were estimated based on a previous study (Kim et al., 2010) (Fig. 1).

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TGM concentrations were measured every 5 min during summer (17 August~23 August 2012), fall (9 October~17 October 2012), winter (22 January ~29 January 2013), and spring (26 March~3 April 2013) using a mercury vapor analyzer (Tekran 2537B) which has two gold cartridges that alternately collect and thermally desorb mercury.Ambient air at a flow rate of 1.5 L min -1 was transported through a 3 m-long heated sampling line (1/4" OD Teflon) in to the analyzer.The sampling line was heated at about 50 ºC using heat tape to prevent water condensation in the gold traps because moisture on gold surfaces interferes with the amalgamation of Hg (Keeler and Barres, 1999).Particulate matter was removed from the sampling line by a 47 mm Teflon filter.

Meteorological data
Hourly meteorological data (air temperature, relative humidity, and wind speed and direction) were obtained from the Automatic Weather Station (AWS) operated by the Korea Meteorological Administration (KMA) (http://www.kma.go.kr) (6 km from the site).Hourly concentrations of NO2, O3, CO, PM10 and SO2 were obtained from the National Air Quality Monitoring Network (NAQMN) (3 km from the site) (Fig. 1).

QA/QC
Automated daily calibrations were carried out for the Tekran 2537B using an internal permeation source.Two-point calibrations (zero and span) were separately performed for each gold cartridge.Manual injections were performed prior to every field sampling campaign to evaluate these automated calibrations using a saturated mercury vapor standard.
The relative percent difference (RPD) between automated calibrations and manual injections was less than 2%.The recovery measured by directly injecting known amounts of four mercury vapor standards when the sample line was connected to zero air ranged from 92 to 110% (99.4 ± 5.2% in average).

Conditional Probability Function (CPF)
CPF was originally performed to determine which wind directions dominate during high concentration events to evaluate local source impacts (Ashbaugh et al., 1985).It has been successfully used in many previous studies (Begum et al., 2004;Kim et al., 2003a;Kim et al., 2003b;Xie and Berkowitz, 2006;Zhao et al., 2004;Zhou et al., 2004).CPF estimates the probability that the measured concentration will exceed the threshold criterion for a given wind direction.The CPF is defined as follows Eq. (1).
where, m Δθ is the number of samples from the wind sector θ having concentration C greater than or equal to a threshold value x, and n Δθ is the total number of samples from wind sector Δθ.In this study, 16 sectors (Δθ = 22.5º) were used and calm winds (≤ 1 m s -1 ) were excluded from the analysis.The threshold criterion was set at above the overall average TGM concentration (5.0 ng m -3 ).Thus, CPF indicates the potential for winds from a specific direction to contribute to high air pollution concentrations.

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CBPF couples ordinary CPF with wind speed as a third variable, allocating the measured concentration of pollutant to cells defined by ranges of wind direction and wind speed rather than to only wind direction sectors.
where,  ∆,∆ is the number of samples in the wind sector Δθ with wind speed interval ∆ having concentration C greater than a threshold value x, and nΔθΔu is the total number of samples in that wind direction-speed interval.The threshold criterion was set at above the overall average TGM concentration (5.0 ng m -3 ).The extension to the bivariate case can provide more information on the nature of the sources because different source types such as stack emission sources and ground-level sources can have different wind speed dependencies (prominent at low and high wind speed).More detailed information is described in a previous study (Uria-Tellaetxe and Carslaw, 2014).

Potential Source Contribution Function (PSCF)
The PSCF model has been extensively and successfully used in the previous studies to identify the likely source areas (Cheng et al., 1993;Han et al., 2004;Hopke et al., 2005;Lai et al., 2007;Lim et al., 2001;Poissant, 1999;Zeng and Hopke, 1989).The PSCF is a simple method that links residence time in upwind areas with high concentrations through a conditional probability field and was originally developed by Ashbaugh et al. (1985).PSCFij is the conditional probability that an air parcel that passed through the ijth cell had a high concentration upon arrival at the monitoring site and is defined as the following Eq.( 3).
where, nij is the number of trajectory segment endpoints that fall into the ij-th cell, and mij is the number of segment endpoints in the same grid cell (ij-th cell) when the concentrations are higher than a criterion value as measured at the sampling site.
High PSCF values in those grid cells are regarded as possible source locations.Cells including emission sources can be identified with conditional probabilities close to one if trajectories that have crossed the cells efficiently transport the released pollutant to the receptor site.Therefore, the PSCF model provides a tool to map the source potentials of geographical areas.
The criterion value of PSCF for TGM concentration was set at above the overall average concentration (5.0 ng m -3 ) to identify the emission sources associated with high TGM concentrations and provide a better estimation and resolution of source locations during the sampling periods.The geographic area covered by the computed trajectories was divided into an array of 0.05º latitude by 0.05º longitude grid cells.As will be discussed in Section 5.4, 24 h backward trajectories starting at every hour at a height of 10, 50, and 100 m above ground level were computed using the vertical velocity model because local sources are more important than that of long-range transport in this study (It should be noted that PSCF results using 48 h backward trajectories had similar results as the 24 h backward trajectories).Each trajectory was terminated if they exit the model top (5,000m), but advection continues along the surface if trajectories intersect the ground.To generate horizontally highly resolved meteorological inputs for trajectory calculations, the Weather Research and Forecast (WRF) model was used to generate a coarse domain at a resolution of 27 km and a nested domain at a horizontal resolution of 9 km, which geographically covers northeast Asia and the southern part of the Korean Peninsula, respectively.The nested domain has 174 columns in the eastwest direction and 114 rows in the north-south direction.PSCF was calculated with 9 km meteorological data.
In this study, TPSCF which incorporates probability from above different starting heights was calculated since backward trajectories starting at different heights traverse different distances and pathways, thus providing information that cannot be obtained from a single starting height (Cheng et al., 1993).
Previous studies suggest that there are increasing uncertainties as backward trajectory distances increase (Stohl et al., 2002) and that PSCF modeling is prone to the trailing effect is which locations upwind of sources are also identified as potential sources (Han et al., 2004).
An alternative to back trajectory calculations in the interpretation of atmospheric trace substance measurements (Stohl et al., 2002) although this technique does not provide much information on source locations.
Generally, PSCF results show that the potential sources covered wide areas instead of indicating individual sources due to the trailing effect.The trailing effect appears since PSCF distributes a constant weight along the path of the trajectories.To minimize the effect of small nij (the number of trajectory segment endpoints that fall into the ij-th cell) values, resulting in high TPSCF values with high uncertainties, an arbitrary weight function W (nij) was applied to down-weight the PSCF values for the cell in which the total number of end points was less than three times the average value of the end points (Choi et al., 2011;Heo et al., 2009;Hopke et al., 1995;Polissar et al., 2001).The TPSCF value for a grid cell was defined with following Eq.(4). where,

Clean Air Policy Support System (CAPSS) data
In this study, the Korean National Emission Inventory estimated using Clean Air Policy Support System (CAPSS) data developed by the National Institute of Environmental Research (NIER) were used (http://airemiss.nier.go.kr/main.jsp(accessed December 09,

2015)
).The CAPSS is the national emission inventory system for the air pollutants (CO, NOx, SOx, TSP, PM10, PM2.5, VOCs and NH3) which utilizes various national, regional and local statistical data collected from about 150 organizations in Korea.In CAPSS, the Source Classification Category (SCC) excluding fugitive dust and biomass burning based on the European Environment Agency's (EEA) CORe Inventory of AIR emissions was classified into the following four levels (EMEP/CORINAIR) (NIER, 2011).More detailed information about SCCs in CAPSS is described in Table S1.

Meteorological data analysis
Fig. S2 shows the frequency of counts of measured wind direction occurrence by season during the sampling period.The predominant wind direction at the sampling site was W (20.9%) and WS (19.2%), and calm conditions of wind speed less than 1 m s -1 occurred 7.6% of the time.Compared to other seasons, however, the prevailing winds in summer were N (17.0%),NE (16.4%), S (16.4%), and SW (15.8%).

General characteristics of TGM
The seasonal distributions of TGM were characterized by large variability during each sampling period (Fig. 2).The average concentration of TGM during the complete sampling period was 5.0 ± 4.7 ng m -3 (range: 1.0-79.6ng m -3 ).This is significantly higher than the Northern Hemisphere background concentration (~1.5 ng m -3 ) (Sprovieri et al., 2010) and those measured in China, in Japan and other locations in Korea, however considerably lower than those measured near large Hg sources in Guangzhou, China (Table 1).The median TGM concentration was 3.6 ng m -3 which was much lower than that of the average, suggesting that there were some extreme pollution episodes with very high TGM concentrations.
The TGM concentration follows a typical log-normal distribution (Fig. S3).The range of 2 to 5 ng m -3 dominated the distribution, accounting for more than half of the total number of samples (60.8%).The maximum frequency of 28.1% occurred between 2 and 3 ng m -3 .

Relationship between TGM and CO
CO has a significant anthropogenic source and is considered to be an indicator of anthropogenic emissions (Mao et al., 2008).Previous studies reported that TGM and CO have a strong correlation because they have similar emission sources (combustion processes) and similar long atmospheric residence times (Weiss-Penzias et al., 2003).
There was a weak positive correlation between TGM and CO in this study (r = 0.04) (p = 0.27).However there was a statistically significant correlation between TGM and CO in winter (r = 0.25) (p < 0.05), suggesting that TGM and CO were affected by similar, possibly distant, anthropogenic emission sources in winter.
On the other hand, there were no statistically significant correlations between TGM and CO in spring (r = 0.02) (p = 0.78), in summer (r = 0.13) (p = 0.08), or in fall (r = -0.03)(p = 0.69), indicating that TGM and CO were affected by different anthropogenic emission sources in these seasons.

2007).
There are also uncertainties from the potential mixing between Hg associated with longrange transported airflows and local air making it difficult to distinguish between distant and local source impacts.However, it is possible that the one-week sampling period in each season did not capture the long-range transport events, and more can be learned using a larger dataset than just using the one-week sampling period to confirm these results.
The daytime TGM concentration (5.3 ± 4.7 ng m -3 ) was higher than that in the nighttime (4.7 ± 4.7 ng m -3 ) (p < 0.01), which was similar to several previous studies (Cheng et al., 2014;Gabriel et al., 2005;Nakagawa, 1995;Stamenkovic et al., 2007) but different than another studies (Lee et al., 1998).Previous studies reported that this different is due to local sources close to the sampling site (Cheng et al., 2014;Gabriel et al., 2005), a positive correlation between TGM concentration and ambient air temperature (Nakagawa, 1995) and increased traffic (Stamenkovic et al., 2007).However, another study suggested that the higher TGM concentration during the night was due to the shallowing of the boundary layer, which concentrated the TGM near the surface (Lee et al., 1998).
In a previous study the daytime TGM concentration was relatively lower than that in the nighttime because the sea breeze transported air containing low amounts of TGM from the ocean during the daytime whereas the land breeze transported air containing relatively high concentrations of TGM from an urban area during the nighttime (Kellerhals et al., 2003).
Although it is possible that the land-sea breeze may affect diurnal variations in TGM concentrations since the sampling site was near the ocean and lower TGM were also observed during the daytime, the higher concentrations in the daytime than those in nighttime were due to local emission sources because the daytime temperature (14.7 ± 10.0 ºC) was statistically significantly higher than that in the nighttime (13.0 ± 9.8 ºC) (t-test, p < 0.05) and there was a weak but statistically significant negative correlation between TGM concentration and ambient air temperature (r = -0.08)(p < 0.05).In addition, there are several known Hg sources such as iron and steel manufacturing facilities including electric and sintering furnaces using coking between the sampling site and the ocean.
As shown in Fig. 3 and Fig. S4, there was a weak but negative relationship between the TGM concentrations and O3 concentrations (r = -0.18)(p < 0.01), suggesting that oxidation of GEM in the oxidizing atmosphere during periods of strong atmospheric mixing was partially responsible for the diurnal variations of TGM concentrations.In addition, oxidation of GEM by bromine species in the coastal area (Obrist et al., 2011) or by chloride radicals in marine boundary layer (Laurier et al., 2003) might play a significant role.If oxidation of GEM occurred, GOM concentrations would increase.However there are uncertainties on the net effects on TGM (the sum of the GEM and the GOM) since we did not measure GOM concentrations.
Significantly different diurnal patterns have been observed at many suburban sites with the daily maximum occurring in the afternoon (12:00-15:00), possibly due to local emission sources and transport (Fu et al., 2010;Fu et al., 2008;Kuo et al., 2006;Wan et al., 2009).
Other studies in Europe reported that TGM concentrations were relatively higher early in the morning or at night possibly due to mercury emissions from surface sources that accumulated in the nocturnal inversion layer (Lee et al., 1998;Schmolke et al., 1999).
TGM concentration was negatively correlated with ambient air temperature (r = -0.08)(p < 0.05) because high ambient air temperature in the daytime will increase the height of the boundary layer and dilute the TGM, and the relatively lower boundary layer at nighttime could concentrate the TGM in the atmosphere (Li et al., 2011).Although there was a statistically significant negative correlation between the TGM concentration and ambient air temperature, there was a rapid increase in TGM concentration between 06:00-09:00 when ambient temperatures also increased possibly due to local emissions related to industrial activities, increased traffic, and activation of local surface emission sources.Similar patterns were found in previous studies (Li et al., 2011;Stamenkovic et al., 2007).Nonparametric correlations revealed that there is a positive correlation between TGM and ambient air temperature (rs = 0.11, p=0.27) between 06:00-09:00.The TGM concentration was negatively correlated with O3 (rs = -0.33,p<0.01) but positively correlated with NO2 (rs = 0.21, p<0.05), suggesting that the increased traffic is the main source of TGM during these time periods.
Compared to other seasons, significantly different diurnal variations of TGM were observed in fall.The daytime TGM concentrations in fall were similar to those in other seasons, however, the nighttime TGM concentrations in fall were much higher than other seasons.As described earlier in Section 5.3, the high TGM concentrations in fall was possibly due to the relationship between other pollutants and meteorological conditions as well as different wind direction and sources.The nighttime TGM concentrations in fall were simultaneously positively correlated with PM10 (r=0.26)(p<0.05) and CO (r=0.21)(p<0.05)concentrations and wind speed (r=0.35)(p<0.01),suggesting that the combustion process is an important source during this period.
Based on the above results, the diurnal variations in TGM concentration are due to a combination of: 1) reactions with an oxidizing atmosphere, 2) changes in ambient temperature and 3) local emissions related to industrial activities.To supplement these conclusions CPF and CBPF were used to identify source directions and TPSCF was used to identify potential source locations.

CPF, CBPF and TPSCF results of TGM
Conventional CPF, CBPF and TPSCF plots for TGM concentrations higher than the average concentration show high source probabilities to the west in the direction of large steel manufacturing facilities and waste incinerators (Fig. 4).The CPF only shows high probabilities from the west and provides no further information, however, the CBPF shows 20 groups of sources with the high probabilities from the west and the northeast.CBPF shows that the high probabilities from the west occurred under high wind speed (> 3 m s -1 ) indicative of emissions from stacks as well as low wind speed ( ≤ 3 m s -1 ) indicative of nonbuoyant ground level sources (Uria-Tellaetxe and Carslaw, 2014).
As described in Section 5.4, correlations between TGM and CO revealed that TGM and CO were affected by similar anthropogenic emission sources in winter but affected by different sources in spring, summer and fall, which is supported by Fig. S6 which shows significantly different seasonal patterns of CPF and CBPF for TGM concentrations.
It is difficult to discuss about the different seasonal patterns for CPF and CBPF for TGM concentrations since there were no correlations between TGM and other pollutants in spring, summer and fall except O3.However, compared to Fig. 4, the CPF and CBPF patterns in fall were similar to those during the whole sampling periods.Especially, the nighttime TGM concentration in fall was simultaneously positively correlated with PM10 (r=0.26)(p<0.05) and CO (r=0.21)(p<0.05)concentrations and wind speed (r=0.35)(p<0.01),indicating that the combustion process from the west is an important source during this period.
Since TGM showed a significant correlation with CO (r=0.25)(p<0.05) and showed a weak positive correlation with PM10 (r=0.08)(p=0.33) in winter with high wind speed, combustion sources from the west are likely partially responsible for this result.
TPSCF identified the likely sources of TGM as the iron and manufacturing facilities and the hazardous waste incinerators which are located to the west from the sampling site.A previous study reported that the waste incinerators (9%) and iron and steel manufacturing (7%) were relatively high Hg emissions sources in Korea (Kim et al., 2010).Waste incinerators emissions were due to the high Hg content in the waste (Lee et al., 2004).
Emissions from iron and steel manufacturing are due to the numerous electric and sintering furnaces using coking which emits relatively high mercury concentrations (Lee et al., 2004) in Gyeongsangbuk-do including Pohang.There are several coke plants around the sampling site (http://www.poscoenc.com/upload/W/BUSINESS/PDF/ENG_PLANT_2_1_3_5.pdf(accessed December 09, 2015)).They are essential parts of the iron and steel manufacturing, and the major source of atmospheric mercury related to the iron and steel manufacturing is from coke production (Pacyna et al., 2006).
The coastal areas east of the sampling site where there are large ports were also identified as the likely source areas of TGM.A previous study reported that the emissions of gaseous and particulate pollutants were high during vehicular operations in port areas and from marine vessel and launches (Gupta et al., 2002).Another possibility is that significant amount of GEM are emitted from the ocean surface because of photo-chemically and microbiologically mediated photo-reduction of dissolved GOM (Amyot et al., 1994;Zhang and Lindberg, 2001).The northeast direction including the East Sea was also identified as potential source areas likely because this is an area with lots of domestic passenger ships routes.The south from the sampling was also identified as a likely source area of TGM where Ulsan Metropolitan City, South Korea's seventh largest metropolis with a population of over 1.1 million is located.It includes a large petrochemical complex known as a TGM source (Jen et al., 2013).

Conclusions
During the sampling periods, the average TGM concentration was higher than the Northern Hemisphere background concentration, however, considerably lower than those near industrial areas in China and higher than those in Japan and other locations in Korea.The median concentration of TGM was much lower than that of the average, suggesting that there were some extreme pollution episodes with very high TGM concentrations.The TGM concentration was highest in fall, followed by spring, winter and summer.The high TGM concentration in fall is due to transport from different wind directions than during the other periods.The low TGM concentration in summer is likely due to increased mixing height and gas phase oxidation at higher temperatures particularly at this sampling site which is close to the ocean (2 km) where oxidation involving halogens may be enhanced.
TGM consistently showed a diurnal variation with a maximum in the early morning (06:00-09:00) and minimum in the afternoon (14:00-17:00).Although there was a statistically significant negative correlation between the TGM concentration and ambient air temperature, the daytime TGM concentration was higher than those in the nighttime, suggesting that local emission sources are important.There was a negative relationship between the TGM concentrations and O3 concentrations, indicating that the oxidation was partially responsible for the diurnal variations of TGM concentrations.The observed ΔTGM/ΔCO was significantly lower than that indicative of Asian long-range transport, suggesting that local sources are more important than that of long-range transport.CPF only shows high probabilities to the west from the sampling site where there are large steel manufacturing facilities and waste incinerators.However, CBPF and TPSCF indicated that the dominant sources of TGM were the hazardous waste incinerators and the coastal areas in the northeast        The error bars represent standard error.

Fig. 1 .
Fig. 1.The location of sampling site in this study ((a) South Korea, (b) Gyeongsangbuk-do

Fig. 3 .
Fig. 3.The diurnal variations of TGM concentrations during the sampling periods.

Fig. 1 .
Fig. 1.The location of sampling site in this study ((a) South Korea, (b) Gyeongsangbuk-do and (c) Pohang).AWS, NAQMN and PSC represent Automatic Weather Station, National Air Quality Monitoring Network and Pohang Steel Complex, respectively.

Table List Table 1 .
Comparison with previous studies for TGM concentrations.

Table 2 .
Summary of atmospheric concentrations of TGM and co-pollutants, and meteorological data.

Table 1 .
Comparison with previous studies for TGM concentrations.537