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Article

Seasonal and Emission Characteristics of PAHs in the Ambient Air of Industrial Complexes

by
Yong-koo Lee
1,
Ji-hwan Lee
2,
Nam-gwon Beak
3,
Kyoung-chan Kim
1 and
Jin-seok Han
1,*
1
Department of Environmental Engineering, Anyang University, Anyang 14028, Republic of Korea
2
AQA C&B Corporation, Ansan-si 15426, Republic of Korea
3
AQA Co., Corporation, Ansan-si 15426, Republic of Korea
*
Author to whom correspondence should be addressed.
Atmosphere 2024, 15(1), 30; https://doi.org/10.3390/atmos15010030
Submission received: 8 November 2023 / Revised: 15 December 2023 / Accepted: 20 December 2023 / Published: 27 December 2023
(This article belongs to the Special Issue Novel Insights into Air Pollution over East Asia)
Browse Figures
Versions Notes

Abstract

:
Particulate and gaseous polycyclic aromatic hydrocarbon (PAHs) samples (n = 108) were measured every six days from January to December 2022 at a representative point in the Korean Banwol National Industrial Complex. The measurement results revealed that the concentration of particulate Σ18 PAHs was 7.92 ± 4.04 ng/Sm3 in winter, 1.83 ± 1.99 ng/Sm3 in spring, 1.43 ± 0.95 ng/Sm3 in summer, and 2.58 ± 2.14 ng/Sm3 in autumn. The concentration of gaseous Σ18 PAHs was 3.32 ± 3.72 ng/Sm3 in winter, 6.34 ± 5.95 ng/Sm3 in spring, 8.33 ± 8.13 ng/Sm3 in summer, and 3.88 ± 1.71 ng/Sm3 in autumn. The results of the correlation analysis showed that particulate PAHs have positive relationships with PM10 and PM2.5 and negative relationships with temperature and O3. The diagnostic ratio and PAHs component slope showed that the emission source characteristics of the Banwol National Industrial Complex were dominated by biomass coal combustion over four seasons; however, the influence of petroleum combustion (automobile emissions) was not negligible. As for coal combustion, bituminous coal was the most influential, and lignite was relevant in summer and autumn.

1. Introduction

Polycyclic aromatic hydrocarbons (PAHs) are aromatic hydrocarbons with two or more fused benzene rings in various structural configurations [1,2,3]. In the environment, PAHs are mainly generated by the incomplete combustion of petroleum-based oil, coal, and wood. They are generated by automobiles, heating, power plants, and combustion processes in various industries, as well as by waste incineration [4]. Additionally, PAHs can arise from natural processes such as biomass combustion, volcanic eruptions, and diagenesis [5].
The PAHs generated from various sources are emitted as gas initially, but dust particles adsorb them as they diffuse in the atmosphere [6]. PAHs exist in both particle and gas phases. PAHs with lower molecular weight tend to exist in the gas phase, and PAHs with larger molecular weight tend to exist in particle form. The PAHs generated from high-temperature combustion processes are rapidly condensed or attached to particles during cooling to float in the atmosphere as ultra-fine particles of less than 1 μm. Such fine particles penetrate into the human respiratory system while breathing. Stable particles with HMW and a low decomposition rate are easily accumulated in the lungs [7].
The International Agency for Research on Cancer (IARC) classified benzo(a)pyren as a class 1 carcinogen [8]. Dibenz(a,h)anthracene, dibenz(a,i)pyrene, benz(a)antracen, benzo (b,j,k)fluoranthenes, benzo(c)phenanthrene, and indeno(1, 2,3-cd)pyrene) were found to be carcinogenic [9]. Therefore, the United States Environmental Protection Agency and the European Environment Agency indicated them as priority target pollutants [10]. The Ministry of Environment of Korea designated PAHs as toxic air pollutants while operating a toxic air pollutant monitoring network. In particular, seven PAH components have been monitored as major air pollutants [11].
Automobile exhaust gas constitutes 21–25% of the total PAH emissions [12]. As the effects of vehicles in large cities and neighboring areas were stronger compared to other areas [13], previous research has focused on large cities where exposure to PAHs is maximized. However, there are insufficient cases of simultaneously measuring and monitoring PAHs in industrial complexes where various fixed and mobile pollutants exist and studying the distribution of PAH concentration and identification of emission sources according to the season.
Therefore, this study investigated the seasonal, phase distribution, and source characteristics of particulate and gaseous PAHs in Banwol National Industrial Complex, where various fixed and mobile sources exist.

2. Materials and Methods

2.1. Sampling Method

The Banwol National Industrial Complex, located in Ansan city, Gyeonggi-do, Korea, is a large industrial complex adjacent to the Sihwa National Industrial Complex to the west. It is a representative industrial complex with 6964 manufacturing industries (food and beverage, textile and clothing, wood and paper, petrochemicals, non-metals, steel, machinery, electronics, and transport equipment [14].
In this study, PAH samples were collected from the atmosphere on the rooftop of an apartment-type factory located in the Banwol National Industrial Complex (point A: 37.3235° N, 126.7850° E, approximately 80 m from the round). The information on the sampling point is presented in Figure 1.
Sampling was performed for 24 h from 10 a.m. to the same time on the next day, every six days, from January to December 2022. Sampling was not performed during heavy rainfall. Korea has four distinct seasons, which are distinguished as follows: winter for January, February, and December; spring from March to May; summer from June to August; and autumn from September to November [15]. Both particulate and gaseous samples were collected. A total of 108 samples were collected, including the samples in each season as follows: 16 particulate and 16 gaseous samples in winter; 10 particulate and 10 gaseous samples in spring; 13 particulate and 13 gaseous samples in summer, and 15 particulate and 15 gaseous samples in autumn.
Sampling was carried out using a large-capacity air sampler (HV-RW, Sibata Scientific Technology, Ltd., Saitama, Japan) equipped with a quartz fiber filter (QFF) and polyurethane foam + adsorption resin (PUF + XAD-2) to collect both particulate and gaseous PAH samples as shown in Figure 2. It was fixed using a stainless steel net to prevent PUF + XAD-2 from leaving. During sampling, the sampler performed high-capacity sampling of 720 Sm3 for 24 h under 500 L/m. Table 1 shows the temperature, humidity, and wind direction during the sampling period.

2.2. Sample Extraction and Analysis

Samples were preprocessed in order of extraction, refinement, and concentration. As for sample extraction, 18 PAH components were analyzed after preprocessing the particulate and gaseous samples.
Extraction was performed for 18 h by adjusting the time to circulate approximately three times per hour with dichloromethane (DCM) 400 mL using the Soxhlet extractor. To check the recovery rate of the sample during extraction, the extraction was performed by injecting laboratory surrogate (LSS); D10-fluoren, and D10-pyren by 1 μg, respectively. The extracted sample was concentrated using a rotary evaporator (EYELA N-1000S-W, Tokyo Rikakikai Co., Tokyo, Japan). Subsequently, it was refined (DCM 125 mL + hexane 125 mL) into a column and concentrated again using the rotary evaporator. The final sample of 1 mL was obtained with hexane through nitrogen purging of less than 1 mL. For quantification, five internal standard substances (D8-naphthalene, D10-acenaphthene, D10-phenanthrene, D12-chrysene, and D12-perylene) were injected by 1 μg, respectively. A gas chromatography–mass spectrometer (GC-MSD, 7890/5975 Agilent Technologies Inc., Santa Clara, CA 95051, USA) was used for the analysis, and the GC column was analyzed using DB-5MS (30 m × 0.25 mm, I.D. 0.25 m film thickness). As for the operating temperature conditions of the GC oven, an initial temperature of 100 °C was maintained for two minutes and increased to 320 °C at a rate of 10 °C/min. The final temperature was maintained for four minutes. Helium gas (99.999%) was used as a carrier gas. At a sample inlet temperature of 300 °C, 1 μL was injected in a splitless mode 2:1. Chromacograms for standards and samples are provided in the Supplementary Materials.

2.3. Quality Control (QA/QC)

This study analyzed the 18 particulate PAHs collected in the QFF and the 18 gaseous PAHs adsorbed onto the PUF + XAD-2 for the collected samples. The method detection limit, accuracy, and precision for each component of 18 PAHs were evaluated for quality control. The internal standard analysis method was applied, and calibration curves were prepared in the 0.05–1 mg/L range using 18 types of PAH compound standards. Both the particulate and gaseous PAHs showed linearity of R2 = 0.99 or higher. When 10 μL (10 mg/L) of 18 types of PAHs mix STD is added, the concentration is 100 ng. Here, a sample collection volume of 720 Sm3 (500 L/min, 24 h) at 0 °C and 1 atm was calculated, and repeated tests were conducted 7 times for 18 PAHs with a final concentration of 0.139 ng/Sm3. For the particulate and gaseous PAHs, the method detection limit ranged from 0.008 to 0.037 ng/Sm3 and 0.003 to 0.023 ng/Sm3, respectively. The accuracy was within ±30% as it ranged from 81.5 to 128.6% and 86.5 to 120.4%, respectively. The precision was within 10%, ranging from 1.7 to 4.9% and 0.1 to 4.5%, respectively. Table 2 shows the linearity, method detection limit, accuracy, and precision results for 18 PAHS.

3. Results

3.1. Concentration Distribution of PAHs by Season

Table 3 shows the analysis results for 18 PAHs by season. The concentration of particulate Σ18 PAHs was the highest in winter (7.92 ± 4.04 ng/Sm3), followed by autumn (2.58 ± 2.14 ng/Sm3), spring (1.83 ± 1.99 ng/Sm3), and summer (1.43 ± 0.95 ng/Sm3). This result may be due to the increased consumption of fossil fuels in winter, which led to a higher concentration of particulate PAHs in the atmosphere [16,17,18]. In summer, instead, with the highest temperature and the lowest fossil fuel consumption, there was the lowest concentration of particulate PAHs. Autumn showed the second highest concentration of particulate PAHs as it is close to winter; thus, the consumption of fossil fuels increased. Spring showed the third-highest concentration of particulate PAHs as fossil fuel consumption decreased.
In spring (March to May), the PM10 and PM2.5 concentrations are sensitively affected by various weather conditions under the combined effects of yellow dust, precipitation, and wind [19,20]. In particular, the PM2.5/PM10 concentration ratio is the lowest in spring due to the inflow of coarse particles caused by yellow dust [21]. As 90–95% of the PAHs adsorbed onto the particulate matter in the atmosphere are adsorbed onto fine particles of 3 μm or less [22], the concentration of particulate PAHs was low in spring compared to winter and autumn, although there were cases when the concentration in spring (5.63 ng/Sm3 on 6 March and 4.79 ng/Sm3 on 17 April) was twice as high as the average concentration of spring (1.93 ± 1.99 ng/Sm3).
The highest concentration of gaseous Σ18 PAHs was in summer (8.33 ± 8.13 ng/Sm3), followed by spring (6.34 ± 5.95 ng/Sm3), autumn (3.88 ± 1.71 ng/Sm3), and winter (3.32 ± 3.72 ng/Sm3).
Gaseous PAHs are dissolved in clouds and raindrops. They are removed by rainfall as they are adsorbed onto particles with high dry deposition [23,24]. Thus, it was expected that the concentration of gaseous PAHs would be the lowest in summer due to heavy rainfall and lower fossil fuel consumption due to high temperatures. However, in this study, the concentration of gaseous PAHs was the highest in summer. Among the 13 samples in summer, the Phen concentration was high (26.67 ng/Sm3) in the measurement results on 4 June. As the average concentration was overestimated due to this case, it was not easy to see the average concentration of PAHs in summer as a representative concentration and to judge that the concentration of gaseous PAHs was highest in summer. Additionally, the G/P ratio of Phen with three rings changed from 1.13 in winter to 10.69 in summer, and those of Flt, Pyr, and Chry with four rings changed by season, indicating that the influence of the phase distribution change is the cause of the concentration distribution in summer. The phase distribution change is further examined in the next section.

3.2. Phase Distribution of PAHs by Season

Figure 3 shows the phase distribution of particulate and gaseous PAHs by season. As it is difficult to identify the phase distribution of components (Anthr, Per, and DBghA) that exhibited not detected (ND) for both particulate and gaseous PAHs, such components were excluded. The figure reveals that the PAH components (Acy, Ace, Flu, Phe, and Anthr) with LMW (128 to 178 g/mol) mostly existed in the gas phase in winter. The PAH components (Flt, Pyr, BaA, and Chry) with moderate molecular weight (202 to 228 g/mol) existed as particles in winter, and Phen with LMW and some of the PAHs with MMW (Flt and Pyr) changed to the gas phase in spring. In summer, PAHs with MMW (BaA and Chry) and some of the PAHs with HMW (>252 g/mol) and five rings (BbF, BkF, BeP, and BaP) changed to the gas phase. Further research using particle and gas partitioning models on these phase distribution changes will also be needed.
In autumn, the PAHs with MMW and HMW changed to the particle phase as in winter. This indicates that PAHs with MMW change the phase distribution according to seasonal characteristics.

3.3. Correlations between Particulate/Gaseous PAHs and PM10/PM2.5/O3/Temperature

The correlation analysis of particulate and gaseous PAHs was conducted using the Pearson correlation coefficient with the results of particulate and gaseous PAHs, the monthly data of PM10, PM2.5, O3, and temperature collected from Air Korea of the Korea Environment Corporation of ME, and the data collected [25,26] using the weather data open information portal of the Korea Meteorological Administration of ME. Table 4 shows the analysis results. Figure 4 and Figure 5 compare particulate and gaseous PAHs with the PM10, PM2.5, O3, and temperature data by season.
As shown in Figure 4, due to the seasonal characteristics of Korea, the temperature was lowest in winter. It increased in spring, reached its peak in summer, and again decreased in autumn. The concentration of O3 was highest in May and then decreased gradually. The PM10 and PM2.5 were highest in winter and decreased toward summer as the temperatures rose. This phenomenon was caused by the increased use of fossil fuel for heating in winter.
Additionally, the PM10 concentration temporarily increased in spring under the influence of the yellow dust. When the results of weather conditions and particulate PAHs were compared, it was found that the concentration of particulate PAHs increased when the temperature and O3 concentrations fell, and the PM10 and PM2.5 concentrations increased. The Pearson correlation analysis shown in Table 4 reveals that particulate PAHs showed strong negative relationships with temperature (−0.87661) and O3 (−0.75763) and strong positive relationships with PM10 (0.71681) and PM2.5 (0.81017).
Gaseous PAHs, however, showed no significant correlation with PM10, PM2.5, O3, and temperature. However, as shown in Figure 5, gaseous PAHs did not show significant correlations with PM10, PM2.5, O3, and temperature.

3.4. Source Characteristics Based on the Diagnostic Ratios of PAHs

Based on the results of previous studies, the sources of PAHs were evaluated using the diagnostic ratio. In addition, emission sources are identified through statistical approaches using principal component analysis (PCA), but in this study, emission source identification was conducted with a priority on the diagnosis rate. The PAHs diagnosis rate of Banwol National Industrial Complex is presented in Figure 6. In addition, in order to interpret the characteristics of the industrial complex, we also analyzed the PAHs results of the Sihwa National Industrial Complex (Sihueng Jungwang). Sihwa National Industrial Complex is a national industrial complex adjacent to Banwol National Industrial Complex, the sample collection point for this study. The PAH diagnosis rate of Sihwa National Industrial Complex was interpreted using the PAH results provided by Air Korea of the Ministry of Environment. (https://www.airkorea.or.kr/, accessed on 15 March 2023). The source of PAHs was petroleum for Flt/(Flt + Pyr) < 0.4, petroleum combustion for 0.4 < Flt/(Flt + Pyr) < 0.5, and biomass coal combustion for Flt/(Flt + Pyr) > 0.5. It was also petroleum for Ind/(Ind+ BghiP) < 0.2, petroleum combustion for 0.2 < Ind/(Ind + BghiP) < 0.5, and biomass coal combustion for Ind/(lnd + BghiP) ≥ 0.5 [18,27,28].
Figure 6 shows that the Flt/(Flt + Pyr) ratio was higher than 0.5 in winter, spring, and autumn, according to the PAH results of this study, indicating the dominant influence of biomass coal combustion. The Ind/(lnd + BghiP) ratio was higher than 0.35 in winter, spring, and autumn for the Banwol National Industrial Complex, indicating the dominant impact of biomass coal combustion. However, in summer, there was the effect of petroleum combustion due to 0.2 < Ind/(lnd + BghiP) < 0.5. As the Ind and BghiP components were ND in many cases, although it is difficult to determine that the emission characteristics in summer were affected by petroleum combustion. In the case of the Sihwa National Industrial Complex, the impact of coal combustion was significant in winter. Nevertheless, the effect of petroleum combustion was observed in summer and spring. Some of the autumn data were found to be affected by petroleum combustion. Additionally, 0.35 < Ind/(lnd + BghiP) < 0.7 was considered the effect of diesel sources in some studies. Therefore, it was difficult to distinguish diesel and biomass emissions using the proposed single diagnostic ratio (Ind/(lnd + BghiP)) [24,29,30]. Based on this, for the Banwol National Industrial Complex, the influence of biomass coal combustion dominates in winter, spring, and autumn; nonetheless, the impact of petroleum combustion is not negligible. Although the impact of petroleum combustion was observed in particular in summer, it is difficult to determine if it was dominant. Regarding the Sihwa National Industrial Complex, it was found that the influence of biomass coal combustion was dominant in winter, but PAHs were affected by petroleum combustion in spring, autumn, and summer. The two national industrial complexes showed different results because the Siheung monitoring station of the Sihwa National Industrial Complex was adjacent to nearby residential and commercial areas.
Figure 7 shows the slope of Flt and Pyr by season to identify source characteristics. Automobile emissions affected PAHs at a Flt and Pyr slope of 1.42, biomass combustion at 0.96, coal (bituminous coal + lignite) combustion in the industrial area at 0.90, lignite combustion in the industrial area at 0.77, and bituminous coal combustion in the industrial area at 0.64 [18,31,32,33]. Figure 7a shows that in winter, the slope was 0.58 and 0.57 for the PAHs results of this study and those of the Siheung Jungwang monitoring station, respectively, confirming the impact of coal combustion (bituminous coal). Figure 7b shows that in spring, the slope was 0.47 and 0.40, respectively, confirming the impact of coal combustion (bituminous coal) as in winter. Figure 7c reveals that in summer, the impact of coal combustion (bituminous coal) was observed from the Siheung Jungwang monitoring station, and in winter, it had a slope of 0.61. However, the impact of coal combustion (bituminous coal + lignite) was observed from the results of this study with a slope of 0.89.
Figure 7d shows that in autumn, the impact of coal combustion (bituminous coal) was observed from the Siheung Jungwang monitoring station with a slope of 0.57. Nevertheless, the results of this study showed the influence of coal combustion (lignite) with a slope of 0.89. When examining the emission characteristics of the four seasons using the slope of Flt and Pyr, it was found that the Banwol National Industrial Complex was affected by coal combustion (bituminous coal) in winter and spring, coal combustion (bituminous coal + lignite) in summer, and coal combustion (lignite) in autumn. The Sihwa National Industrial Complex was affected by coal combustion (bituminous coal) in all four seasons.
Figure 8 shows that the source characteristics were examined through the slope of Ind and BghiP. Automobile emissions affected PAHs at an Ind and BghiP slope of 3.8, biomass combustion at 1.00, coal combustion in the industrial area at 0.60, coal (bituminous coal + lignite) combustion in the industrial area at 1.45, and lignite combustion in the industrial area at 1.14 [20,31,32,33]. In Figure 8a, the slope was found to be 0.44 and 0.63 in winter for the Banwol and Sihwa National Industrial Complexes, indicating the impact of coal combustion (bituminous coal). In Figure 8b, the impact of coal combustion (bituminous coal) was observed from the Banwol National Industrial Complex with a slope of 0.55 and the impact of biomass from the Sihwa National Industrial Complex with a slope of 0.92 in the spring. In summer, the PAH results of the Banwol National Industrial Complex were insufficient to identify a certain tendency because BghiP and Ind were ND in many cases, as shown in Figure 8c. As for the Sihwa National Industrial Complex, the slope of Ind and BghiP was 1.98. As it is located between the slope for biomass (1.0) and the slope for automobile emissions (3.8), the impact of biomass, which was nearer, could be determined; nevertheless, the effect of automobile emissions was not negligible. In autumn, the impact of coal combustion (bituminous coal) was observed from the two national industrial complexes as in winter because the slopes of Ind and BghiP were 0.63 and 0.46, respectively.

4. Conclusions

In this study, 18 types of particle and gaseous PAHs were measured and characterized by season at 6-day intervals for one year in 2022 in the Banwol National Industrial Complex in Ansan, a representative industrial area in Korea. The seasonal concentration distribution of particle-phase Σ18 PAHs appeared in the order of winter > fall > spring > summer due to the increased use of fossil fuels in winter. The concentration of gaseous Σ18 PAHs is believed to be relatively high in summer due to changes in the phase distribution of LMW PAHs and MMW PAHs in the order of summer > spring > fall > winter.
Second, particulate PAHs had a high correlation with PM10, PM2.5, O3, and gas temperature through correlation analysis. In the case of gaseous PAHs, there was no correlation with PM10, PM2.5, O3, and gas temperature.
Third, through the diagnosis ratio, in the case of the Banwol National Industrial Complex, the influence of biomass and coal combustion is dominant over the four seasons, but the influence of oil combustion (vehicle emissions) cannot be ignored, and in the case of coal combustion, the influence of bituminous coal is the majority, but lignite There was also an impact in the summer and fall seasons. In the case of the nearby Sihwa National Industrial Complex, it is affected by biomass and coal combustion in winter out of the four seasons but is affected by oil combustion in spring, fall, and summer. This was different from the source emission characteristics of the Banwol National Industrial Complex because residential and commercial areas are concentrated in the surrounding area due to the location of the sample collection point of the Sihwa National Industrial Complex.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/atmos15010030/s1, chromatograms for standard materials and samples analyzed by GC-MSD.

Author Contributions

Conceptualization, J.-s.H.; data curation, Y.-k.L. and J.-s.H.; formal analysis, Y.-k.L., J.-h.L., K.-c.K., and N.-g.B.; supervision, J.-s.H.; writing—original draft, Y.-k.L.; writing—review and editing, Y.-k.L. and J.-s.H. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available in Supplementary Materials.

Acknowledgments

This research was supported by the Particulate Matter Management Specialized Graduate Program through the Korea Environmental Industry and Technology Institute (KEITI) funded by the Ministry of Environment (MOE).

Conflicts of Interest

The authors declare no conflict of interest. Y.-k.L, J.-h.L. and N.-g.B are employees of AQA C&B Corp. and AQA Corp. Y.-k.L, J.-h.L. and N.-g.B was funded by AQA C&B Corp. and AQA Corp. The company had no roles in the design of the study; in the collection, analysis, or interpretation of data; in the writing of the manuscript, or in the decision to publish the articles. The paper reflects the views of the scientists and not the company.

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Atmosphere 15 00030 g001
Figure 1. Sampling point for measuring PAHs in the Banwol National Industrial Complex.
Figure 1. Sampling point for measuring PAHs in the Banwol National Industrial Complex.
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Figure 2. The High volume air sampler and instrument for PAHs.
Figure 2. The High volume air sampler and instrument for PAHs.
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Figure 3. The seasonal phase distribution of PAHs.
Figure 3. The seasonal phase distribution of PAHs.
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Figure 4. Seasonal comparison of particulate PAHs, PM10, PM2.5, O3, and temperature.
Figure 4. Seasonal comparison of particulate PAHs, PM10, PM2.5, O3, and temperature.
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Figure 5. Seasonal comparison of gaseous PAHs, PM10, PM2.5, O3, and temperature.
Figure 5. Seasonal comparison of gaseous PAHs, PM10, PM2.5, O3, and temperature.
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Figure 6. Diagnostic ratios for the source of PAHs; Flt/(Flt + Pyr) versus Ind/(Ind + BghiP).
Figure 6. Diagnostic ratios for the source of PAHs; Flt/(Flt + Pyr) versus Ind/(Ind + BghiP).
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Figure 7. A scatter plot calculated for the components of PAHs; Pyr versus Flt.
Figure 7. A scatter plot calculated for the components of PAHs; Pyr versus Flt.
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Figure 8. A scatter plot calculated for the components of PAHs; BghiP versus Ind.
Figure 8. A scatter plot calculated for the components of PAHs; BghiP versus Ind.
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Table 1. Meteorological data during the sampling period.
Table 1. Meteorological data during the sampling period.
Sampling
Period		Mean
Temp.
(°C)		Mean
Max Temp.
(°C)		Mean
Min Temp.
(°C)		Mean
Humidity
(%)		Mean
Wind
Direction		Mean
Wind
Speed
(m/s)
Winter
(Jan–Feb,
Dec 2022)		−3.04.8−1165.8NW0.9
Spring
(Mar–May
2022)		11.323.6−1.370.8NW, SW1.0
Summer
(Jun–Aug
2022)		23.830.716.783.1SSW0.9
Autumn
(Sep–Nov
2022)		13.327.10.678.8NW0.7
Source: KMA, Open MET Data Portal, https://data.kma.go.kr/cmmn/main.do (accessed on 13 March 2023)
Table 2. Method detection limit, accuracy, and precision of particulate and gaseous PAHs.
Table 2. Method detection limit, accuracy, and precision of particulate and gaseous PAHs.
PAHs CompoundsAbb 1Calibration
Curve
(R2)		MDL
(ng/Sm3)		Accuracy
(%)		Precision
(%)
P 2G 3PGPGPG
AcenaphthyleneAcy0.99950.99930.0110.00689.590.81.90.4
AcenaphtheneAce0.99920.99920.0130.00689.490.11.70.8
FluoreneFlu0.99970.99960.0090.00591.891.72.21.2
PhenanthrenePhen0.99950.99940.0110.00388.289.61.80.6
AnthraceneAnthr0.99870.99880.0140.00697.295.62.60.1
FluorantheneFlt0.99870.99890.0120.004101.4101.22.20.7
PyrenePyr0.99990.99990.0110.00493.993.81.90.9
Benzo(a)anthraceneBaA0.99790.99810.0140.007115.8109.82.11.1
ChryseneChry0.99960.99970.0080.01288.189.33.01.5
Benzo(b)fluorantheneBbF0.99680.99710.0230.011123.4113.02.91.0
Benzo(k)fluorantheneBkF0.99860.99880.0320.023107.8115.54.01.1
Benzo(e)pyreneBeP0.99950.99950.0080.00781.586.51.82.4
Benzo(a)pyreneBaP0.99600.99720.0370.008109.9114.43.34.5
PerylenePer0.99890.99870.0180.00886.591.02.53.2
Indeno[1,2,3-cd]pyreneInd0.99040.99120.0300.012128.6120.44.91.3
Dibenz[a,h]anthraceneDBahA0.99230.99480.0240.011124.4107.92.01.6
Benzo[ghi]peryleneBghiP0.99430.99590.0210.011113.4107.93.02.5
CoroeneCor0.99390.99350.0190.012117.0110.34.61.4
1 Abb: abbreviation, 2 P: particulate PAHs, and 3 G: gaseous PAHs.
Table 3. Seasonal concentration particulate and gaseous PAHs.
Table 3. Seasonal concentration particulate and gaseous PAHs.
Compounds
/Ring		Winter
Jan–Feb, Dec 2002 (n = 16 × 2)		Spring
Mar–May 2022 (n = 10 × 2)		Summer
Jun–Aug, 2022 (n = 13 × 2)		Autumn
Sep–Nov, 2022 (n = 15 × 2)
PGG/PPGG/PPGG/PPGG/P
Acy2ND0.34 ± 0.42 ND0.08 ± 0.16 ND0.20 ± 0.46 ND0.07 ± 0.10
Ace2ND0.42 ± 0.19 ND0.54 ± 0.36 0.08 ± 0.140.72 ± 0.498.78ND0.62 ± 0.24
Flu30.01 ± 0.020.98 ± 0.9193.30ND1.36 ± 1.07 0.15 ± 0.221.18 ± 0.838.060.03 ± 0.051.32 ± 0.5448.28
Phen30.99 ± 0.521.12 ± 1.741.130.17 ± 0.242.88 ± 3.2117.230.45 ± 0.554.84 ± 6.9310.690.25 ± 0.131.59 ± 0.826.31
Anthr3ND0.07 ± 0.25 NDND ND0.04 ± 0.15 NDND
Flt41.57 ± 0.740.24 ± 0.510.150.30 ± 0.350.79 ± 0.822.640.16 ± 0.170.66 ± 0.684.060.39 ± 0.530.17 ± 0.260.43
Pyr40.90 ± 0.530.11 ± 0.250.120.11 ± 0.210.56 ± 0.535.000.22 ± 0.350.58 ± 0.642.640.37 ± 0.840.12 ± 0.230.32
BaA40.30 ± 0.17ND 0.05 ± 0.10ND ND0.01 ± 0.02 0.07 ± 0.06ND
Chry40.81 ± 0.41ND 0.29 ± 0.310.02 ± 0.060.080.02 ± 0.040.02 ± 0.030.730.18 ± 0.13ND
BbF50.88 ± 0.61ND 0.26 ± 0.29ND 0.08 ± 0.110.07 ± 0.100.840.35 ± 0.25ND
BkF50.47 ± 0.250.02 ± 0.020.030.14 ± 0.17ND 0.02 ± 0.03ND 0.15 ± 0.11ND
BeP50.31 ± 0.160.00 ± 0.010.020.11 ± 0.14ND 0.04 ± 0.060.02 ± 0.040.540.12 ± 0.09ND
BaP50.11 ± 0.12ND NDND NDND 0.06 ± 0.07ND
Per50.02 ± 0.030.01 ± 0.020.49ND0.11 ± 0.17 0.02 ± 0.03ND NDND
Ind60.82 ± 0.70ND 0.16 ± 0.22ND 0.04 ± 0.06ND 0.25 ± 0.22ND
DBahA60.02 ± 0.020.01 ± 0.040.870.02 ± 0.02ND NDND NDND
BghiP60.44 ± 0.31ND 0.15 ± 0.19ND 0.06 ± 0.09ND 0.18 ± 0.16ND
Cor60.30 ± 0.21ND 0.12 ± 0.14ND 0.08 ± 0.10ND 0.17 ± 0.12ND
∑18 PAHs7.92 ± 4.043.32 ± 3.720.421.83 ± 1.996.34 ± 5.953.461.43 ± 0.958.33 ± 8.135.382.58 ± 2.143.88 ± 1.711.50
SS95.65 ± 23.4186.5 ± 22.12 87.83 ± 25.0784.68 ± 19.43 72.33 ± 5.8682.97 ± 19.97 79.47 ± 8.3792.45 ± 18.34
LSS189.45 ± 4.9392.96 ± 14.86 88.76 ± 6.7290.32 ± 10.62 90.17 ± 7.0187.52 ± 7.91 104.05 ± 5.0299.79 ± 5.16
LSS290.97 ± 11.7491.28 ± 12.54 96.54 ± 6.34100.45 ± 9.71 103.14 ± 8.23102.35 ± 6.49 110.46 ± 11.15114.71 ± 14.74
ND: not detected, P: particulate PAHs, G: gaseous PAHs, SS: field-surrogate standard ( d12-Benzo(e)pyrene), LSS1: laboratory surrogate standard (d10-fluorene) and LSS2: d10-pyrene.
Table 4. Correlation analysis using Pearson’s correlation coefficient.
Table 4. Correlation analysis using Pearson’s correlation coefficient.
Temp.Particulate
Σ18 PAHs		Gaseous
Σ18 PAHs		O3PM10PM2.5
Temp.1
Particulate Σ18 PAHs−0.876611
Gaseous Σ18 PAHs0.24210−0.343611
O30.66329−0.757630.310981
PM10−0.848030.71681−0.24893−0.437231
PM2.5−0.854620.81017−0.25412−0.493090.921381
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Lee, Y.-k.; Lee, J.-h.; Beak, N.-g.; Kim, K.-c.; Han, J.-s. Seasonal and Emission Characteristics of PAHs in the Ambient Air of Industrial Complexes. Atmosphere 2024, 15, 30. https://doi.org/10.3390/atmos15010030

AMA Style

Lee Y-k, Lee J-h, Beak N-g, Kim K-c, Han J-s. Seasonal and Emission Characteristics of PAHs in the Ambient Air of Industrial Complexes. Atmosphere. 2024; 15(1):30. https://doi.org/10.3390/atmos15010030

Chicago/Turabian Style

Lee, Yong-koo, Ji-hwan Lee, Nam-gwon Beak, Kyoung-chan Kim, and Jin-seok Han. 2024. "Seasonal and Emission Characteristics of PAHs in the Ambient Air of Industrial Complexes" Atmosphere 15, no. 1: 30. https://doi.org/10.3390/atmos15010030

APA Style

Lee, Y. -k., Lee, J. -h., Beak, N. -g., Kim, K. -c., & Han, J. -s. (2024). Seasonal and Emission Characteristics of PAHs in the Ambient Air of Industrial Complexes. Atmosphere, 15(1), 30. https://doi.org/10.3390/atmos15010030

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