PM2.5 samples were collected in Suncheon during the summer (June 2-11, 2023) and winter (January 15-21, 2024). The chemical composition analysis included carbonaceous components (OC, EC), secondary ionic components (NH4+, NO3-, SO42-), dithiothreitol - oxidative potential (QDTT-OP), and volatile organic compounds. Results showed higher summer PM2.5 concentrations due to photochemical reactions and higher winter concentrations from heating and stable atmospheric conditions. The OC/EC ratio indicated greater secondary organic aerosol formation in summer. Oxidative potential (QDTT-OPv) was higher in summer (0.12 µM/m³) than winter (0.09 µM/m³), correlating strongly with OC in summer. Health risk assessment of BTEX revealed higher concentrations in winter, with benzene as the primary contributor to lifetime cancer risk (LTCR). The cumulative hazard quotient (HQ) was higher in winter, indicating increased non-carcinogenic risk. The study highlighted that oxidative potential is more influenced by chemical composition than physical characteristics, suggesting that regulating PM2.5 concentration alone may be insufficient. VOCs, as precursors of SOA, showed a positive correlation with QDTT-OPv, with benzene exhibiting the strongest correlation in winter. These findings emphasize the need for targeted management of specific PM2.5 components to mitigate health risks effectively.
Research Article
Association Between Time of Day and Carbonaceous PM2.5 and Oxidative Potential in Summer and Winter in the Suncheon Industrial Area, Republic of Korea
https://doi.org/10.21203/rs.3.rs-5062142/v1
This work is licensed under a CC BY 4.0 License
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DTT-OP
Health risk
BTEX
Industrial complexes, typically housing various enterprises such as chemical manufacturing, fossil fuel processing, and waste incineration facilities, can emit large quantities of air pollutants, thereby impacting the surrounding area's air quality. In addition, depending on the types of chemicals handled at each site, hazardous substances such as volatile organic compounds (VOCs) and polycyclic aromatic hydrocarbons (PAHs) are also released (Marquès et al., 2020). Thus, meticulous management of air pollutants emitted from industrial complexes is essential. In Changzhou, China, during the period from October to December 2016, the average fine particulate matter (PM2.5) concentration in industrial complexes (113.1 µg/m3) was higher than that in suburban areas (85.3 µg/m3), while the OC/EC ratio was higher in suburban areas (5.0) than in industrial complexes (4.9) (Tao et al., 2021). Zheng et al. (2017) investigated VOC emissions from Chinese industrial complexes between 2011 and 2013, finding a 38.3% increase from 15.3 Tg in 2011 to 29.4 Tg in 2013. VOC levels from petrochemical complexes were 54.8 (5.6 ~ 541) ppbv, with vehicular emissions (35.6%) and industrial emissions (38.5%) identified as major sources (Zheng et al., 2020). According to Li et al. (2023), the PM2.5 measured in industrial complexes was 63.2 µg/m3, with OC and EC accounting for 13.4 and 4.0 µg/m3, respectively, representing 21% and 6% of PM2.5.
Previous studies have shown that increased concentrations and inhalation of PM2.5 in the atmosphere have adverse effects on human health (Gao et al., 2020). PM2.5, emitted from various sources such as vehicle emissions, industrial activities, and biomass combustion, is associated with millions of premature deaths, cardiovascular, and respiratory diseases worldwide each year (Jiang et al., 2019). A general hypothesis regarding the toxicological mechanisms of PM2.5 is that it interacts with reduced biomolecules to generate reactive oxygen species (ROS), which can subsequently induce oxidative stress in the body. To measure PM2.5 ability to oxidize these target molecules, oxidative potential (OP) has been proposed as an intrinsic property representing PM2.5 capacity to generate ROS. The DTT (Dithiothreitol) assay is one of the widely used methods to quantify the OP of various PM2.5 sources, including primary emissions and secondary aerosols (Bates et al., 2018; Weber et al., 2018). The DTT assay is particularly utilized as an OP metric because it mirrors the redox cycling of quinones that generate ROS within the human body. The rate of DTT loss serves as an OP measure for diverse PM2.5 sources, such as primary emissions and secondary aerosols (Bates et al., 2018; Weber et al., 2018; Cho et al., 2005). Table 1 summarizes previous studies related to the DTT assay. Gao et al. (2020) reported that both soluble and insoluble components of PM2.5 collected in Atlanta's urban area during 2017 contributed to oxidative potential. In urban areas of Japan, oxidative potential varied due to coal combustion and biomass burning particles (Kurihar et al., 2022). The DTT-OP observed in industrial complexes and urban areas in Italy was approximately 2.5 times higher in industrial areas compared to urban areas (Pietrogrande et al., 2019). Choe et al. (2022) analyzed DTT-OP normalized to Quinone (QDDT-OP) for PM2.5 collected in a tunnel, finding that oxidative potential was higher in summer (2.07 µM/m³) than in winter (1.64 µM/m³). Song et al. (2023) observed seasonal differences in gaseous pollutant characteristics from the Gwangyang steel industrial complex, with higher QDTT-OP in summer compared to winter. Air pollutants from domestic industrial complexes are suspected to significantly contribute to PM2.5 formation. Major industrial complexes in Korea are located in Ulsan, Banwol, Yeosu, Gwangyang, Changwon, and Ansan. Notably, the Yeosu National Industrial Complex in Jeollanam-do is the largest petrochemical complex. Although there are many studies on air pollution related to large industrial complexes, no measurements have been taken in Suncheon, Republic of Korea.
| Country | City | Type of environment | DTT-OPv | DTT-OPm | Reference | |
|---|---|---|---|---|---|---|
| Portugal | Seixal | Suburban, Industrial | 0.21 ± 0.17 (nmol/min) | 12.9 ± 6.6 (pmol/min·µg) | Canha et al., 2024 | |
| Japan | Yokohama | Urban | 153 ± 74 (pmol/ min·m3) | 14.9 ± 5.0 (pmol/ min·µg) | Kurihara et al., 2022 | |
| Noto | Rural | 119 ± 96 (pmol/ min·m3) | 22.6 ± 16.8 (pmol/ min·µg) | |||
| Italy | Trentino | Urban, Industrial | 0.61 ± 0.2 (nmol/min·m3) | Pietrogrande et al., 2019 | ||
| Lecce | Urban | 0.24 ± 0.9 (nmol/min·m3) | ||||
| Trento | Industrial | 0.61 ± 0.23 (nmol/min·m3) | 0.06 ± 0.03 (nmol/ min·µg) | Pietrogrande et al., 2018 | ||
| France | Dunkerque | Urban Industrial | 0.36 (nmol/min·m3) | 17.7 (pmol/ min·µg) | Moufarrej et al., 2020 | |
| U.S.A | Atlanta | Suburban | 0.22 ± 0.07 (nmol/min·m3) | 0.024 ± 0.008 (nmol/ min·µg) | Gao et al., 2020 | |
| China | Huangshi | Urban | 2.14 ± 1.31 (nmol/min·m3) | Wang et al., 2024 | ||
| Lebanon | Beirut | Traffic Industrial | 3.51 ± 0.54 (nmol/min·m3) | 8.22 ± 1.27 (nmol/ min·mg) | Farahani et al., 2022 | |
Therefore, this study quantified particulate and gaseous components in the atmosphere using a mobile trailer at the Suncheon industrial complex located between the Yeosu and Gwangyang industrial complexes. In addition, the study assessed the OP of PM2.5 and identified the contribution of its chemical components. The results of this study can serve as foundational data for future air pollutant reduction measures and human health risk management in industrial complexes.
2.1 Sampling location
In this study, sample collection for the analysis of PM2.5 and gaseous components was conducted using a mobile measurement vehicle within the Yulchon 1st Industrial Complex located in Suncheon, Jeollanam-do. The Yulchon 1st Industrial Complex spans an area of 9.17 km² (Nguyen et al., 2015). The sampling site is located in Jeonnam Technopark in the Yulchon 1st Industrial Complex (34.8977°, 127.5805°) (Fig. 1). It has been designated as a specialized complex for foundational industries essential to all manufacturing sectors, including casting, molding, and welding. Additionally, the measurement site is adjacent to an eight-lane road with high traffic volumes of large vehicles. The observation periods were 10 days in the summer (from June 2, 2023, to June 11, 2023) and 7 days in the winter (from January 15, 2024, to January 21, 2024).
2.2. Experimental methods
In Fig. 1, the interior of the measurement vehicle is equipped with two digital high-volume aerosol samplers for PM2.5 collection (High Volume Aerosol Sampler DHA-80, Digitel Elektronik AG, Switzerland) and a beta-ray monitor for real-time analysis of PM2.5 mass (5014i Continuous Particulate Monitor, Thermo Scientific, USA). A static weather sensor (EOLOS-IND, LAMBRECHT meteo, Germany) was installed to observe meteorological conditions (temperature, relative humidity, wind speed, wind direction). PM2.5 was collected using the DHA-80 at 3-hour intervals, with eight samples collected per day on quartz filters (Pallflex, 2500QATUP, Pall Corp., USA) throughout the entire study period. The collected PM2.5 was analyzed for carbonaceous components, Organic Carbon (OC) and Elemental Carbon (EC), using a carbon analyzer (Lab-based OCEC Carbon Aerosol Analyzer, Sunset Laboratory Inc., USA) based on the National Institute of Occupational Safety & Health (NIOSH 5040) protocol (Choe et al., 2023; Song et al., 2024). Water-soluble ionic components, such as NO3−, SO42−, and NH4+, were analyzed using anion (Metrohm 930, Switzerland, Metrosep A Supp 150/4.0 column, 3.7 mM Na2CO3 & 1.0 mM NaHCO3) and cation ion chromatography (Metrohm 930, Switzerland, Metrosep C4-250/4.0 column, 5 mM HNO3) (Song et al., 2022; Yu et al., 2024).
2.3 Volatile Organic Compounds using GC/MS-TD
To collect samples for benzene, toluene, ethylbenzene, and xylene (BTEX) analysis, a custom-made near-real-time sampler and a 2-bed TD (C2-CAXX-5149, Markes International Ltd, UK) were used. BTEX samples were collected at a rate of 50 mL/min at 3-hour intervals, with eight samples collected per day concurrently with the DHA-80. The BTEX analysis was performed using thermal desorption (Unity2, Markes International, Ltd, UK) - gas chromatography (GC Agilent 8890, MS Agilent 5977B) - mass spectrometry (MS, Agilent 5975C). The column used for the analysis was Agilent DB-1 60 m, and the oven conditions were as follows: held at 35°C for 22 minutes, ramped to 50°C at 5°C/min, then to 160°C at 10°C/min, to 260°C at 10°C/min, and finally to 320°C at 20°C/min. The analysis was conducted using GC-MS SIM mode (Song et al., 2023; Song et al., 2021).
2.4 Exposure concentration and lifetime cancer risk for BTEX
Carcinogenic and non-carcinogenic risks due to BTEX exposure were evaluated using a model developed and updated by the United States Environmental Protection Agency (USEPA-540-R-070-002) (Hajizadeh et al., 2018). The risk assessment was based on the exposure concentration (ECi, µg/m³) of BTEX, calculated using Eq. 1:
\(\:{EC}_{i}=\frac{\left(PC\:\times\:ET\:\times\:EF\:\times\:ED\right)}{AT}\) Eq. 1
where PC (mg/m3) is the concentration of BTEX, ET (hr/day) is the exposure time per day, EF (days/year) is the exposure frequency, ED (years) is the exposure duration, and AT (hr) is the average lifespan. In this study, each index was based on the 2022 Korean Statistical Information Service, reflecting average working hours, lifespan, and working duration (https://kosis.kr/). EF was set at 252 days, the exposure duration was 30 years, and the average lifespan was 82 years. ET was set at 3 hours based on BTEX measurement time to calculate ECi values for 3-hour intervals during day and night, and the results are presented accordingly.
The carcinogenic risk assessment for BTEX focused on benzene and ethylbenzene, which are known carcinogens, using Eq. 2:
\(\:\text{L}\text{T}\text{C}\text{R}=\text{E}\text{C}\left(\frac{ug}{{m}^{3}}\right)/\:UR\left(\frac{ug}{{m}^{3}}\right)\) Eq. 2
where lifetime cancer risk (LTCR) represents the average inhalation lifetime cancer risk of benzene and ethylbenzene, and UR is the unit risk factor for benzene and ethylbenzene. Based on previous studies, the UR used was 2.5×10− 6 for benzene and 7.8×10− 6 for ethylbenzene (Ghaffari et al., 2021; Hajizadeh et al., 2018; Heidari et al., 2021).
In addition, the non-carcinogenic hazard quotient (HQ) was calculated using Eq. 3 as the ratio of ECi to the reference concentration (Rfc):
\(\:\text{H}\text{Q}=\:\frac{{EC}_{i}\:\left(\frac{ug}{{m}^{3}}\right)}{Rfc\:\left(\frac{ug}{{m}^{3}}\right)}\) Eq. 3
where Hazard Quotient (HQ) is the non-carcinogenic risk index for BTEX. The Rfc values used were 0.03 mg/m3 for benzene, 5 mg/m3 for toluene, 1 mg/m3 for ethylbenzene, and 0.1 mg/m3 for xylene (Cerón Bretón et al., 2020). The sum of HQs for BTEX was presented as the Hazard Index (HI).
2.5 Quantitative analysis for dithiothreitol - oxidative potential
During the study period, the quinone dithiothreitol-oxidative potential (QDTT-OP) analysis was conducted to evaluate the toxicity of the collected PM2.5. Following the method of Choe et al. (2022), the solvent was prepared and mixed using a Multiflo FX Multi-Mode Dispenser (Agilent Technologies, USA). The mixture was then incubated at 37°C with isothermal shaking, and absorbance was measured at 412 nm using a microplate reader (Multiskan SkyHigh, Thermo Scientific, USA) at 10-minute intervals for 40 minutes, totaling four readings. As shown in Figs. 2a and 2b, the final DTT consumption rate (nmol/min) was calculated by correcting the slope of absorbance decrease (σABS) of the collected sample for the background sample and normalizing it to the collected air volume to yield the dithiothreitol-oxidative potential (DTT-OP). The QDTT-OP was then calculated by applying a calibration curve to the slope of the quinone concentration analyzed by DTT-OP (Figs. 2c and 2d) (Choe et al., 2022; Song et al., 2023).
Equations 4 and 5 represent the formulas for QDTT-OPv and QDTT-OPm. These formulas were cited from Wang et al. (2019). Here, rs and rb represent the DTT consumption rates for the sample and the blank, respectively. Vt and Mt represent the total sampled air volume and the total particle mass corrected for the filter blank, respectively. Ah and At represent the punch area and the total filter area, respectively. Vs and Ve represent the sample volume and the extraction volume participating in the reaction, respectively. The additional variable Qslope was used to standardize the DTT-OP values. Qslope is the slope calculated from the linear regression of the DTT consumption rate of 9,10-phenanthraquinone (9,10-PQ) at concentrations ranging from 0.0544 µmol/L to 0.321 µmol/L, measured at 10-minute intervals over a 40-minute reaction time, and applied to calculate the standardized QDTT-OPv and QDTT-OPm (Fig. 2).
3.1 Results of chemical compositions
PM2.5 was collected in Suncheon during the summer (June 2, 2023, to June 11, 2023) and winter (January 15, 2024, to January 21, 2024). The analysis results of the carbonaceous components (OC, EC), secondary ionic components (NH4+, NO3−, SO42−), QDTT-OP, and VOCs in the collected PM2.5 are summarized in Table 2. The time series of temperature, relative humidity, PM2.5, OC, QDTT-OPv, and QDTT-OPm in summer and winter are shown in Fig. 3. Meteorological conditions were observed during the measurement period, with average summer temperatures and relative humidity of 23.6°C and 58.3%, respectively, and winter values of 6.5°C and 62.9%. The average PM2.5 concentrations in summer and winter were 19.54 (3.88–53.35) µg/m3 and 18.76 (1.32–90.76) µg/m3, respectively, with slightly higher concentrations in summer. Generally, summer PM2.5 is formed through active photochemical oxidation processes involving high concentrations of gaseous precursors, while winter PM2.5 is produced by increased air pollutants from heating and stable atmospheric conditions (Cheng et al., 2016; Elser et al., 2016). As shown in Figs. 5a and 5g, summer PM2.5 concentrations tended to increase steadily in the afternoon until sunset due to photochemical reactions, whereas winter concentrations peaked during morning and evening rush hours. PM2.5 levels gradually increased from 3 AM on January 17 to 9 PM the following day and then decreased from the early morning of January 18. High relative humidity generally plays a crucial role in the formation of secondary pollutants through heterogeneous reaction processes in the atmosphere (Yu et al., 2018; Liu et al., 2015). Ma et al. (2017) observed that higher PM2.5 concentrations in winter were associated with increased relative humidity and decreased wind speed. On January 17, the average wind speed and relative humidity were 0.5 m/s and 71%, respectively, suggesting that low wind speeds led to the stagnation and increase of PM2.5.
| Compounds | unit | summer | winter | ||
|---|---|---|---|---|---|
| average | S.D | average | S.D | ||
| PM2.5 | µg/m3 | 19.54 | 10.63 | 18.76 | 19.04 |
| OC | µg/m3 | 5.10 | 1.72 | 4.49 | 3.84 |
| EC | µg/m3 | 0.20 | 0.17 | 0.82 | 0.71 |
| NO3− | µg/m3 | 1.41 | 0.82 | 2.97 | 2.97 |
| SO42− | µg/m3 | 3.80 | 1.48 | 1.91 | 1.93 |
| NH4+ | µg/m3 | 1.49 | 0.50 | 1.43 | 1.21 |
| QDTT-OPv | µM/m3 | 0.12 | 0.04 | 0.09 | 0.06 |
| Benzene | ppb | 0.30 | 0.05 | 0.40 | 0.22 |
| Toluene | ppb | 1.47 | 0.52 | 0.67 | 0.49 |
| Ethylbenzene | ppb | 0.25 | 0.09 | 0.74 | 0.66 |
| m&p-Xylene | ppb | 0.45 | 0.15 | 2.18 | 1.91 |
| Styrene | ppb | 0.10 | 0.03 | 0.01 | 0.01 |
| o-Xylene | ppb | 0.18 | 0.06 | 0.79 | 0.79 |
During the measurement period, OC and EC showed contrasting results between winter and summer (Figs. 5d and 5j). The average OC concentration was approximately 1.1 times higher in summer (5.10 µg/m3) than in winter (4.49 µg/m3), while the average EC concentration was about 4.0 times lower in summer (0.20 µg/m3) than in winter (0.82 µg/m3). Summer OC was highest during the evening hours (18:00–21:00), while winter OC peaked during the morning hours (06:00–09:00). EC increased during rush hours in summer and peaked in the morning and afternoon in winter. EC acts as a primary emission tracer from incomplete combustion processes based on fossil fuels used in heating activities, vehicles, and industrial processes, while OC can be emitted directly or formed secondarily (Wu et al., 2016; Karanasiou et al., 2015). The correlation between OC and EC was higher in winter (r2 = 0.77) than in summer (r2 = 0.45). The lower r2 value between OC and EC in summer suggests that other formation processes, such as secondary OC formation, contributed to the increase in OC concentration. In contrast, the higher r2 value in winter indicates that significant amounts of measured OC and EC were closely related to primary combustion sources. The OC/EC ratio is used to infer sources of carbonaceous aerosols and to determine the contributions of POA and SOA, with higher OC/EC ratios indicating greater SOA formation (Paraskevopoulou et al., 2014). The observed OC/EC ratio was higher in summer than in winter and increased gradually in the afternoon during summer, while it decreased in the afternoon during winter. The high OC/EC ratio in summer indicates that more secondary organic aerosols were generated due to active photochemical reactions, which is characteristic of the season.
In the atmosphere, NH4+, NO3−, and SO42− in PM2.5 are secondary products formed from the homogeneous and heterogeneous oxidation of large amounts of gaseous precursors (NH3, NOx, SO2) emitted from industrial complexes (Lin, 2002; Chow et al., 1996). The average concentrations of NH4+, NO3−, and SO42− in summer were 1.49 (0.63–2.39), 1.41 (0.39–3.99), and 3.80 (1.45–7.26) µg/m3, respectively, while in winter, they were 1.43 (0.04–5.32), 2.97 (0.07–12.24), and 1.91 (0.05–10.51) µg/m3, respectively. NH4+ showed no seasonal difference in this study, while NO3− and SO42− exhibited contrasting results. On January 17, 2024, the peak concentrations of secondary ionic components were observed, indicating their significant contribution to the high PM2.5 levels.
3.2 Stagnation and Long-Range Transport Period on January 17, 2024
Currently, the Korea Air Quality Forecasting Center operates a high-performance air quality forecasting modeling system at the level of a supercomputer, with numerical models run four times a day (www.airkorea.or.kr). The air quality forecasting modeling system is divided into the modeling process and the processing and refining of the driven model data. The modeling process is subdivided into the weather model (Weather Research and Forecasting, WRF), the emissions model (Sparse Matrix Operator Kernel Emissions, SMOKE), and the air quality model (Community Multiscale Air Quality, CMAQ). According to the results, on January 17, high concentrations of fine particulate matter in the measurement area were analyzed to be due to the stagnation of residual fine particulate matter from the previous day along with the influx of externally generated fine particulate matter (Fig. 4).
3.3 Results of benzene, toluene, ethylbenzene, and xylene
During the summer observation period, the concentrations were as follows: benzene 0.30 ppb, toluene 1.47 ppb, ethylbenzene 0.25 ppb, m&p-xylene 0.45 ppb, and o-xylene 0.18 ppb. When comparing the total concentrations of BTEX between seasons, the summer period recorded 2.63 ppb, while the winter period recorded 4.79 ppb, indicating higher concentrations in winter. Generally, the concentrations of VOCs, including BTEX, are influenced by seasonal temperature and atmospheric boundary layer conditions. In detail, the atmospheric boundary layer expands during the unstable summer atmosphere due to higher temperatures, leading to the diffusion of BTEX, which results in lower BTEX concentrations despite the same emission amount (Latif et al., 2019; Ghaffari et al., 2021). Conversely, higher temperatures in summer enhance the evaporation of BTEX, increasing its atmospheric concentration (Yurdakul et al., 2018; Ghaffari et al., 2021). Therefore, to understand the seasonal differences in BTEX concentrations, it is essential to examine the emission characteristics of the major BTEX sources.
To investigate the emission characteristics of BTEX, the ratios of m,p-xylene/ethylbenzene (X/E) and toluene/benzene (T/B) as well as the correlation between toluene and ethylbenzene (T/E) were analyzed. The X/E ratio provides insight into the photochemical aging of BTEX. Specifically, m,p-xylene and ethylbenzene, when emitted from the same source, undergo different degradation rates due to varying oxidation rates by OH radicals in the atmosphere, leading to a decreasing X/E ratio over time (Monod et al., 2001). An X/E ratio of 2 or more indicates local emissions (Nelson and Quigley, 1983). The T/B ratio is used to infer emission sources: T/B ≤ 1 suggests biomass burning, 0.1–5 indicates coal combustion, 0.5–10 suggests vehicle emissions from gasoline and diesel, and 1–10,000 implies industrial processes and solvent use (Zhang et al., 2016). The T/E correlation helps trace emission sources, with high correlations (> 0.9) suggesting combustion sources such as vehicles and heating, and low correlations (< 0.5) indicating evaporative sources like solvents (Monod et al., 2001; Sigsby et al., 1987). The X/E ratio in the study area averaged 2.53 in summer and 4.30 in winter, indicating that BTEX emissions were influenced by local sources rather than external transport. The T/B and T/E correlations are presented in Fig. 6. The T/B ratio was used to estimate BTEX sources under the assumption that toluene and benzene share the same sources. This was validated by their correlation, which varied over time. During the summer observation period, the correlation coefficient (r²) between toluene and benzene was significant (> 0.5) from 09:00 to 24:00, enabling source estimation. The average T/B ratio from 09:00 to 21:00 was 6.16, suggesting BTEX emissions from coal, diesel, gasoline combustion, and industrial processes during these hours. In winter, significant correlations were observed from 03:00 to 24:00, with a T/B ratio of 1.77, indicating identical local sources.
As shown in Fig. 6 (c & d), the T/E ratio analysis during the summer observation period revealed two distinct groups: from 21:00 to 09:00, the average r² was 0.64, and from 09:00 to 21:00, the average r² was 0.02. This pattern was also observed in winter, with r² values of 0.92 from 15:00 to 09:00 and 0.32 from 09:00 to 15:00. A high T/E correlation suggests emissions from combustion activities, while a low correlation indicates evaporative emissions from solvents. These findings suggest that the study area is influenced by a mix of combustion and evaporative sources. Specifically, the summer daytime (09:00–21:00) and winter daytime (09:00–15:00) periods were more influenced by solvent evaporation, while summer nighttime (21:00–09:00) and winter nighttime (15:00–09:00) were more influenced by combustion activities. The lower T/B ratio in winter (1.67) compared to summer (4.04) suggests that winter heating activities primarily involved low T/B fuels such as coal or LNG (Alsbou and Omari, 2020; Mokammel et al., 2022; Zhang et al., 2016).
The health risks associated with BTEX exposure were evaluated using the LTCR for benzene and ethylbenzene and the HQ for non-carcinogenic risk. The ECi was calculated based on a 24-hour exposure period to align with the 3-hour BTEX measurement intervals. The results are presented in Fig. 6 (e & f). The cumulative HQ for BTEX HI was 0.017 in summer and 0.049 in winter, indicating a 2.8 times higher non-carcinogenic risk in winter compared to summer. However, all HQ values were below 1, suggesting acceptable risk levels (Dehghani et al., 2018; Mokammel et al., 2022; Rostami et al., 2021). The LTCR for benzene and ethylbenzene was 2.07×10− 6 in summer and 2.81×10− 6 in winter, showing a 1.3 times higher carcinogenic risk in winter. According to WHO, LTCR values between 1×10− 5 and 1×10− 6 are considered acceptable for humans, while USEPA recommends values below 1×10− 6 (Dehghani et al., 2018; Delikhoon et al., 2018; Nabizadeh et al., 2020). The study area's LTCR met WHO standards but exceeded USEPA recommendations. Benzene was the primary contributor to LTCR, with r² values of 0.99 for both seasons, indicating the need to manage benzene to reduce carcinogenic risk. Despite benzene's high HQ, the overall non-carcinogenic risk (HI) was more closely correlated with toluene, ethylbenzene, and xylene, especially xylene (r² > 0.94).
Human health risks were also assessed based on work hours, distinguishing between daytime (09:00–18:00) and nighttime (21:00–06:00). The study area, consisting of small-scale businesses, showed higher Hi and LTCR values during the daytime for both summer and winter observation periods. Specifically, daytime HI and LTCR were 1.17 and 1.10 times higher, respectively, in summer, and 1.35 and 1.44 times higher, respectively, in winter compared to nighttime. This indicates higher carcinogenic and non-carcinogenic risks during the day, likely due to solvent evaporation. The results suggest the need to manage BTEX emissions from solvent evaporation and to prioritize benzene for carcinogenic risk and xylene for non-carcinogenic risk.
3.4 Association with Oxidative Potential
The QDTT-OP was quantified to evaluate the health risks of PM2.5 observed during the measurement period. The average concentration of oxidative potential per unit volume (QDTT-OPv) was higher in summer (0.12 µM/m³) than in winter (0.09 µM/m³). The highest QDTT-OPv concentration in summer was observed on June 5, when OC levels were high, while in winter, it was observed on January 17, during a high PM2.5 event. The diurnal variation of QDTT-OPv showed a decreasing trend in the afternoon during summer, contrary to the high PM2.5 concentrations due to active photochemical reactions (Fig. 5b). This result differs from previous studies showing a high correlation between DTT-OP and PM2.5 mass concentration (Kurihara et al., 2022; Janssen et al., 2014). Oxidative potential per unit mass of PM2.5 considers the intrinsic properties of PM2.5 that affect its oxidative capacity. Thus, the quantified QDTT-OPv was divided by the PM2.5 mass concentration to calculate the QDTT-OPm (µM/µg) and shown in Figs. 5b and 5h. As a result, the diurnal variations of QDTT-OPv and QDTT-OPm in summer were different. Additionally, the QDTT-OPv and OC/PM2.5 ratio in summer both peaked at 9 PM, showing a very similar diurnal trend and the highest correlation (r²=0.97) among major chemical components in PM2.5 (Fig. 7a). As the contribution of OC to PM2.5 increased, the oxidative potential also increased. Borlaza et al. (2018) observed that the oxidative potential increased with OC due to the influence of secondary organic aerosols in summer, consistent with the results of this study. These findings suggest that OC significantly impacts the oxidative potential of PM2.5 in summer and can be used to assess the health risks of PM2.5 from industrial complexes. Consequently, when evaluating the concentration of reactive oxygen species per unit mass, PM2.5 health risks were highest at night, rather than during rush hours or the active photochemical reaction periods in the afternoon. Saffari et al. (2013) also found a strong correlation between oxidative potential and carbonaceous components (OC, EC), especially with primary organic matter from vehicle emissions in winter. Furthermore, they confirmed the contributions of both primary and secondary sources to the oxidative potential of PM2.5. Similarly, this study found a good correlation between winter QDTT-OPm and unit mass of OC, EC, and NO3− (r²=0.80) (Fig. 7b). Recent studies have revealed that combustion-related sources (primarily oil, coal, and biomass burning) can directly emit NO3− (Chen et al., 2010; Cui et al., 2017). Unlike in summer, the diurnal variation in winter showed increased concentrations during rush hours, indicating the influence of vehicle emissions and biomass burning, consistent with the chemical component results. Overall, these results indicate that oxidative potential is more influenced by chemical composition than physical characteristics. Additionally, from a health risk perspective, regulating only PM2.5 concentration may be insufficient.
According to Song et al. (2023), VOCs, gaseous precursors of secondary organic aerosols, showed a positive correlation with QDTT-OPv, indicating direct and indirect impacts. Similarly, this study found a positive correlation between observed VOCs and QDTT-OPv, with winter showing relatively better correlations than summer, especially with benzene (r²=0.41). These results suggest that VOCs during the study period acted as major precursors of secondary organic aerosols, directly and indirectly influencing QDTT-OPv.
This study investigated the chemical compositions, oxidative potential, and health risks of PM2.5 collected in Suncheon during summer and winter. The analysis revealed seasonal variations in PM2.5, with higher concentrations observed in summer due to active photochemical reactions and in winter due to increased heating and stable atmospheric conditions. Meteorological conditions, including temperature and relative humidity, significantly influenced the formation of secondary pollutants. In particular, higher humidity facilitated the formation of secondary pollutants through heterogeneous reactions, contributing to elevated PM2.5 levels. The study also found seasonal differences in the concentrations of organic carbon (OC) and elemental carbon (EC), with higher OC levels in summer and higher EC levels in winter. The oxidative potential of PM2.5, measured as QDTT-OP, varied seasonally. Summer showed higher oxidative potential per unit volume (QDTT-OPv), while the highest values were recorded during periods of elevated OC and PM2.5 levels. The correlation between QDTT-OP and OC/PM2.5 was strongest in summer, indicating that OC significantly impacts the oxidative potential of PM2.5. These findings suggest that the health risks associated with PM2.5 are highest at night. In addition, the study assessed the health risks of BTEX exposure, finding higher concentrations in winter. The carcinogenic and non-carcinogenic risks were evaluated, with benzene identified as the primary contributor to lifetime cancer risk (LTCR). The results indicated that winter posed a higher non-carcinogenic and carcinogenic risk compared to summer. Overall, the study highlights the importance of considering chemical composition when assessing the oxidative potential and health risks of PM2.5. It suggests that merely regulating PM2.5 concentration may be insufficient for mitigating health risks. Instead, targeted management of specific components such as benzene and xylene are necessary to effectively reduce the associated health risks.
Author contributions
Seoyeong Choe contributed to experimental measurements, data analysis, and the preparation of the original manuscript. Geun-Hye Yu contributed to raw data analysis and manuscript preparation. Myoungki Song Jeon provided the research sample and performed chemical data analyses, along with Sea-Ho Oh, who also contributed to chemical data analysis. Dong-Hoon Ko contributed to the preparation and collection of the research sample, while Hajeong Jeon conducted research sample analyses. Min-Suk Bae was responsible for experiment planning, supervision, and final data confirmation. All authors have read and approved the published version of the manuscript.
Funding
This research was supported by the Research Funds of Mokpo National University in 2023.
Availability of data and materials
The datasets used can be accessed can be available upon request from the second author.
Ethical approval
“All authors have read, understood, and have complied as applicable with the statement on "Ethical responsibilities of Authors" as found in the Instructions for Authors.”
Competing interests
The authors declare that there is no financial or personal conflict of interest.
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No competing interests reported.
