Spatiotemporal Distribution Characteristics of PM2.5 Components in the Yeosu and Gwangyang Industrial Complexes

Abstract

Particulate matter (PM) composition and distribution heavily rely on the spatiotemporal characteristics of its emission sources. This study analyzed the distribution characteristics and sources of fine PM (PM_2.5) and its chemical components at five locations near the Yeosu and Gwangyang industrial complexes. Samples were chemically analyzed, considering their ionic, carbonaceous, and trace elements, from August 2020 to July 2023. Spatial characteristics were examined using the coefficient of divergence and Pearson correlation coefficient, and temporal characteristics were investigated using monthly, seasonal, and annual distribution patterns. The range of average PM_2.5 concentrations at the five measurement sites was 17.2–20.3 μg/m³. The time-series analysis indicated that ionic species consistently comprised the largest proportion of PM_2.5 across all seasons and years (51.4–60.3%), with the highest proportion observed in winter. Trace elements and carbonaceous species were present in higher proportions in spring (19.4%) and autumn (35.7%), respectively. High concentrations of PM_2.5 and its chemical components were identified by temporal variations and emission sources. Principal component analysis showed that the chemical components of PM_2.5 were mainly from soil dust, vehicular emissions, the steel industry, and other pollution sources. This study provides foundational data for future research aimed at air pollutant emissions management and PM_2.5 source apportionment.

1. Introduction

The health effects of particulate matter (PM) are determined by various factors, such as particle size, surface area, and chemical composition. In particular, fine particulate matter (PM_2.5, particulate matter < 2.5 µm in aerodynamic diameter) is extremely small and can be more easily absorbed into the human body [1,2,3]. Therefore, PM_2.5 is more closely associated with adverse health effects than larger particles [4]. It is directly associated with the onset and exacerbation of diseases such as asthma and chronic obstructive pulmonary disease [5,6,7,8,9]. Epidemiological studies have shown that short-term exposure to PM_2.5 increases mortality rates due to respiratory (1.51% per 10 µg/m³) and cardiovascular (0.85% per 10 µg/m³) causes, and furthermore, long-term exposure is also associated with an increased risk of mortality from cerebrovascular disease [10,11].

PM_2.5 emitted from industrial complexes exhibits distinct chemical differences from that emitted from typical urban areas owing to variations in their emission source characteristics [12,13,14,15]. Owing to industrial activities, such as fossil fuel combustion, waste incineration, and transportation, the concentration of PM_2.5 and the chemicals adsorbed onto PM_2.5 in industrial complexes are significantly higher than those in the general atmospheric environment [16,17]. Residents living near industrial complexes have consistently filed complaints about the air quality and odors in their environment [18], underscoring the need for efforts to address air pollutants that can negatively impact health.

The petrochemical and steel industries comprise the largest industrial complexes in South Korea. As of 2023, the Yeosu National Industrial Complex accommodates 644 industrial facilities, with petrochemicals (45.4%) and machinery (25.3%) as representative industries. Similarly, the Gwangyang National Industrial Complex, which includes the Yulchon Industrial Complex, Gwangyang Seonghwang Industrial Complex, and Steel Plant Complex, houses 285 industrial facilities, with machinery (17.4%) and steel (23.2%) as representative industries [19]. Certain air pollutants emitted from the Yeosu National Industrial Complex, including volatile organic compounds and hazardous air pollutants (HAPs), are highly correlated with the air quality in Yeosu City [20]. Air pollutants emitted from the Yeosu and Gwangyang industrial complexes have been found to affect the pulmonary function of local residents [21,22].

PM_2.5 is a complex mixture consisting of various compounds, and its characteristics vary depending on temporal distribution and emission sources. In urban areas with traffic-related sources, NO₃⁻, Zn, and Cu are predominantly emitted [23,24,25]. In steel-related industries, Mn, Fe, Ni, and Cr are the dominant components [26,27], while SO₄²⁻, As, and Pb are associated with coal combustion [28,29]. Additionally, during winter, heating activities lead to high emissions of SO₄²⁻, Cd, As, Ti, and Zn [30,31], whereas in spring, yellow dust contributes to the dominance of crustal elements such as Al, Ti, Si, and Fe [32]. The major secondary aerosols, including NH₄⁺, SO₄²⁻, and NO₃⁻, are influenced by meteorological conditions such as temperature and relative humidity (RH) [33,34]. Thus, evaluating the types and concentrations of air pollutants within these areas is essential for improving the air quality in industrial complexes. However, studies analyzing PM_2.5 and its chemical components in the Yeosu and Gwangyang national industrial complexes remain insufficient despite their necessity.

In this context, the present study aimed to investigate the spatiotemporal concentration and distribution characteristics of PM_2.5 and its chemical components, focusing on five locations near the Yeosu and Gwangyang national industrial complexes over approximately three years. By examining the spatial distribution characteristics using the coefficient of divergence (COD) and Pearson correlation coefficients, this study identified the locations with different emission sources. The temporal distribution characteristics of the PM_2.5 components were analyzed based on emission sources. Furthermore, Principal Component Analysis (PCA) was conducted for source identification to provide foundational data for future emission source management strategies.

2. Materials and Methods

2.1. Sampling Sites

This study collected samples over three years (August 2020–July 2023) at five locations: one site each near the Gwangyang Steel Complex (F1), Gwangyang–Seonghwang General Industrial Complex (F2), and Yulchon Industrial Complex (F3), and two sites within the Yeosu National Industrial Complex (F4, F5) (Table S1 and Figure 1). These five locations were chosen to reflect the emission source characteristics of the representative industries within each industrial complex. The locations were also selected based on their proximity to residential areas (within 2.0–5.5 km) that could be affected by them.

Sampling sites were selected based on the Guidelines for Installation and Operation of Air Pollution Monitoring Networks, ensuring the lack of obstructions, such as buildings or trees, and that the sites represent the pollution levels of their respective areas [35]. At the center of the Yeosu and Gwangyang industrial complexes lies Gwangyang Port, an international container port along the coastline, which is characterized by emission sources influenced by anthropogenic (industrial complexes, traffic, heating combustion) and natural (sea breeze, sea salt, and soil) factors [36].

2.2. Sampling Collection and Data Analysis

The sample collection reflected seasonal variations; spring (March to May) and winter (December to February) were reported to have high PM_2.5 levels due to yellow dust and heating activities, respectively [37,38]. Samples were collected twice a week (Monday and Wednesday) during these seasons. In contrast, in summer (June to August) and autumn (September to November), during which lower PM_2.5 levels were reported [39], samples were collected once a week (Monday). Each sampling session began at 8 AM and lasted 24 h. PM_2.5 samples were collected using a low-volume air sampler (PMS-204, APM Co., Bucheon-si, Gyeonggi-do, Republic of Korea) designed according to the US Environmental Protection Agency Federal Reference Method at a flow rate of 16.7 L/min.

For the analysis of ionic and trace element concentrations, PTFE filters (2.0 µm, Ø27 mm) were used. Meanwhile, quartz fiber filters (Pured Quartz fiber, Ø47 mm) were used for carbonaceous components, such as organic carbon (OC) and elemental carbon (EC). Samples lost due to extreme weather events (e.g., typhoons and heavy rain) or equipment maintenance were excluded from the analysis. Over the entire sampling period, 1305 PTFE filters and 1318 quartz fiber filters were collected across the five sites (Table S1). The collected PM_2.5 samples were transported at temperatures below 4 °C using refrigerants and insulated ice boxes to minimize particle loss. The collected PM_2.5 samples were analyzed by the Korea Testing & Research Institute (KTR, Gwacheon-si, Gyeonggi-do, Republic of Korea). To correct the mass concentration of PM_2.5, laboratory blanks (LAB) and field blanks (FB) were used. LAB is required for each weighing process of PM_2.5 filter batches, with a new LAB needed for each batch. During the sampling period, LAB filters were stored in the laboratory storage and reweighed after sampling to assess quality control. Similarly, FB is required for each filter batch weighing process. It must be transported to the sampling site but is not subjected to the sampling process. Instead, it is retrieved and reweighed for quality control purposes.

2.3. Component Analysis

This study analyzed eight ionic components, two carbonaceous components, and 19 trace elements from the collected PM_2.5. For ionic components, the filters were placed in sterilized Petri dishes and sealed with Parafilm before refrigeration. Subsequently, the filters were immersed in 200 µL ethanol and placed in a beaker with the sampling side facing down. Then, 20 mL of ultrapure water (resistance >18 MΩ) was added, and the solution was stirred at 120 rpm for 120 min. After stirring, the extract was filtered using syringe filters with a 0.2 µm pore size and analyzed with ion chromatography (ICS-3000, DIONEX, Sunnyvale, CA, USA) for three anions (SO₄²⁻, NO₃⁻, Cl⁻) and five cations (Na⁺, NH₄⁺, K⁺, Mg²⁺, Ca²⁺). For carbonaceous components, filters were preheated on a quartz plate at >550 °C for over 4 h to remove impurities. OC and EC concentrations were analyzed using a semi-continuous OC/EC analyzer (Model-5, Sunset Lab, Tigard, OR, USA). An energy-dispersive X-ray fluorescence analyzer (Epsilon 4, Malvern Panalytical, Worcestershire, UK) was used to analyze the 19 trace elements: Al, Ti, V, Mn, Fe, Ni, Co, Cu, Zn, As, Sr, Mo, Cd, Ba, Pb, P, S, Cr, and Si.

2.4. Quality Assurance and Quality Control (QA/QC)

Annual QA/QC procedures were performed to ensure accurate measurements. In accordance with the Air Pollution Standard Test Methods, the method detection limit (MDL), relative standard deviation (RSD), and calibration curve were calculated [40]. Calibration curves were established using four standard solutions for ions and trace elements and three standard solutions for carbon. The linearity coefficient (R²) was required to be ≥0.99, and the RSD was maintained within 10%. If these criteria were not met, the calibration curve was re-established. The R² values for all the components exceeded 0.999. The method detection limit and relative standard deviation values are presented in Table S2.

2.5. Statistical Analysis

To evaluate the temporal distribution characteristics of PM_2.5 and its components based on emission sources in Yeosu and Gwangyang, descriptive statistical analyses (mean, SD) were performed. Analysis of variance (ANOVA) was used to identify significant concentration differences among the sampling sites at a significance level of 0.05. To minimize errors during the statistical analysis, undetected values were excluded.

The spatial distribution characteristics of the five sampling sites were analyzed using the COD and Pearson correlation coefficient. COD, which was initially used in biology to determine species diversity [41], assesses spatial heterogeneity based on concentration differences between two sampling locations. The COD formula is given by Equation (1),

(1)$\mathrm{C}\mathrm{O}\mathrm{D}=\sqrt{{\displaystyle \frac{1}{n}}{\sum }_{i=1}^n({\displaystyle \frac{X_{ij}-X_{ik}}{X_{ij}+X_{ik}}}{)}^2}$

where $X_{ij}$ represents the average concentration of the j-th component at location i and n is the number of measured components. A COD close to 0 indicates similar emission sources, whereas a COD close to 1 reflects significant differences in emission sources. A threshold of 0.3 was adopted as the criterion for COD [42,43,44], with values > 0.3 suggesting different emission sources between the two sampling sites.

Pearson’s correlation coefficient was used to indicate homogeneity between sampling sites, in which correlations closer to 1 signify similarities in component concentrations and emission sources [45,46].

To identify possible sources of PM_2.5 and its chemical components, Principal Component Analysis (PCA) was conducted with Varimax rotation. PCA is a method that reduces dimensionality and extracts factors for source apportionment [47,48]. The analysis results showed that all factors had eigenvalues exceeding 1.0, and the process was terminated when the cumulative variance explained more than 70% of the total variance. The PCA protocol used in this study can be referenced from our previous study [49].

The Pearson’s correlation coefficient and PCA normalized all data between 0 and 1 using z-score standardization before calculation. All statistical analyses were performed using SPSS software (version 21.0).

2.6. Backward Trajectory Analysis

The backward trajectory model was conducted using the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT 4.7) model to determine the transportation route of the air mass analyze potential source of PM_2.5 [50,51]. The backward trajectory analysis was set for the day with the highest PM_2.5 concentration during the study period (27 January 2022) and the day with the lowest PM_2.5 concentration (12 October 2021). The analysis used data from the Global Data Assimilation System (GDAS), with a simulation period of 72 h.

The wind direction at altitudes of 100 m, 500 m, and 1000 m above ground level (AGL) was utilized to analyze the movement of the lower, middle, and upper atmosphere [52,53]. The analysis started at 00 UTC (09:00 am Korea Standard Time). The target site was selected to represent the receptor in Yeosu and Gwangyang (34°52′30.00″ N, 127°36′00.00″ E).

3. Results and Discussion

3.1. Distribution Characteristics of PM_2.5 and Its Components

The PM_2.5 within the Yeosu and Gwangyang industrial complexes revealed an average concentration of 18.50 ± 9.86 µg/m³ (Table 1). Among the individual sites, F3 exhibited the highest concentration at 20.28 ± 10.27 µg/m³, followed by F5, F4, F1, and F2. Statistically significant differences in the concentrations were observed among the sites (p < 0.05).

Analysis of the ionic components in PM_2.5 showed that SO₄²⁻ had the highest concentration at 3.18 ± 2.45 µg/m³ across all five sampling sites, followed by NO₃⁻, NH₄⁺, Na⁺, Cl⁻, K⁺, Ca²⁺, and Mg²⁺. However, except for SO₄²⁻ and NH₄⁺, the concentration differences for all the other ionic components were not statistically significant across the sites (p > 0.05). SO₄²⁻ is predominantly formed as a secondary sulfate from SO₂ emissions resulting from industrial activities, coal combustion, and mobile sources [54]. The consistently high SO₄²⁻ levels at all sites suggest the widespread impact of coal combustion-related industrial activities. Natural sources, such as sea salt, likely contribute to the concentrations of Na⁺, Cl⁻, Ca²⁺, and Mg²⁺ [55,56]. Given the coastal locations of the Yeosu and Gwangyang industrial complexes, all five sites were likely influenced by sea breezes.

The distribution of carbonaceous components showed that both EC and OC concentrations were statistically higher at F3 (p < 0.05). However, no considerable differences were observed in emissions across the sites. This suggests that the highest EC and OC concentrations at F3 were likely influenced by primary sources such as anthropogenic emissions and traffic, as well as meteorological factors, including high concentrations of volatile organic compounds (VOCs) that contribute to secondary aerosol formation [57].

Analysis of trace element concentrations revealed that S had the highest concentration across all five sites at 1317.13 ± 1035.66 ng/m³, followed by Si > Al > Fe > Zn > Mn > Ba > Ti > Pb > P > Cd > Cu > Mo > As > Cr > Ni > V > Sr > Co. Except for S, Fe, Zn, Mn, P, Mo, As, Cr, Ni, V, and Co, all trace elements showed no statistically significant differences in concentration across sites (p > 0.05). Elements such as S, Si, Al, Fe, and Ti are representative crustal elements that are typically found in resuspended dust and soil [32]. This suggests that all five sites were affected by similar soil-related sources.

An analysis of Fe concentrations among the five sites revealed its wide range of concentrations (141.19 ng/m³ at F5 to 278.96 ng/m³ at F1), indicating significant differences among sites. The highest Fe concentration in F1 was attributed to the steel complex located in the industrial area. This result is consistent with the elevated levels of V and Ni observed in F1, which are commonly associated with metallurgical processes, steel alloys, plating, and oil combustion [58,59,60]. Similarly, Mn, associated with the steel industry and gasoline combustion [61,62], and Co, linked to the steel industry [63], further suggest that emissions from steel-related facilities within the industrial complex significantly influenced F1.

3.2. Spatial Variability Analysis

Spatial variability was calculated for the entire study period and seasons to identify temporal differences. The COD values for the entire study period ranged from 0.07 to 0.16 (Figure 2). Seasonal COD values were calculated as follows: spring (0.05–0.18), summer (0.10–0.15), autumn (0.07–0.24), and winter (0.08–0.17). All values were below the heterogeneity threshold of 0.3, indicating no significant spatial differences among the sampling sites during any period.

For a comprehensive understanding of spatial variability across the five sampling sites, Pearson correlation coefficients were calculated. The seasonal Pearson correlation coefficients (Table 2) were as follows: spring (0.835–0.940), summer (0.731–0.944), autumn (0.846–0.943), and winter (0.674–0.942). For the entire study period, the correlation coefficients between each pair of sites ranged from 0.797 to 0.942, indicating strong correlations. These results supported the findings of the COD analysis, which suggested no significant spatial differences among the sites. Although the industrial complexes in Yeosu and Gwangyang are distinct in nature, their close proximity appears to result in similar spatial concentration distributions of pollutants emitted from the various industrial complexes.

3.3. Temporal Characteristics of PM_2.5 and Its Components

Based on the results of the COD and Pearson correlation coefficient analyses, the emission sources among the sampling sites did not significantly differ. Therefore, a temporal analysis of PM_2.5 and its components was conducted to examine concentration variations over the study period (Figure 3). The time-series distribution of PM_2.5 concentrations during the study period (4.22–62.94 µg/m³) showed a wide range of variability. The PM_2.5 24 h guideline of 35 µg/m³, set by the Ministry of Environment, was exceeded during approximately 5% of the study period, with a maximum exceedance of up to 1.8 times. According to previous studies, this exceedance rate is higher than that reported for Seongbuk (3.61%) but lower than that for Jungnang (11.11%), two districts in Seoul, Korea [64]. Although the mean PM_2.5 concentration (18.50 µg/m³) did not exceed the guideline, short-term exposure to high PM_2.5 concentrations has been associated with increased all-cause and cardiovascular mortality [65]. An analysis of the temporal changes in components revealed that total carbon (r = 0.740) and total ions (r = 0.847) exhibited similar patterns of increase and decrease, corresponding to changes in PM_2.5 concentrations. Total trace elements (r = 0.712) showed a similar trend, although deviations were observed during certain periods. The red circle indicates the period with distinct trends among the ∑ components. The composition ratios of the PM_2.5 components were found to vary with temporal characteristics, prompting further analysis of annual, seasonal, and monthly concentration trends for PM_2.5 and its components.

Analysis of the composition ratios of ionic, carbonaceous, and trace elemental components in PM_2.5 showed that ionic components accounted for the highest proportion, ranging from 51.4% to 60.3% across all seasons and years, followed by carbonaceous components (27.9–35.7%) and trace elements (9.0–19.4%). These results are consistent with those of previous studies on the chemical composition of PM_2.5 [66,67]. The seasonal composition ratios (Figure 4a) revealed clear variations in the proportion of trace elements, which were the highest in spring (19.4%), subsequently decreasing in summer (14.7%) and autumn (9.8%), and increasing again in winter (11.8%). The ionic components were most abundant in winter, whereas the carbonaceous components were most abundant in autumn. The annual composition ratios (Figure 4b) showed that the proportion of trace elements increased from 9.0% in 2020 to 18.0% in 2023. In contrast, the carbonaceous components maintained a consistent composition ratio of 28.3–31.5%. Meanwhile, no clear increasing or decreasing trends were observed for ionic components.

The monthly average PM_2.5 concentration patterns across all five sampling sites are shown in Figure 5. High PM_2.5 concentrations were observed at all sites from December to March, which is consistent with Korea’s seasonal characteristics, where higher PM_2.5 concentrations are observed in winter and spring due to heating and yellow dust, compared with the lower concentrations in summer and autumn [68,69].

The monthly concentration trends of PM_2.5 (Figure 6) show that among the ionic components, Cl⁻, NO₃⁻, NH₄⁺, K⁺, and Ca²⁺ exhibited high concentrations in winter (December–February), similar to PM_2.5. NO₃⁻ showed the highest concentrations in December and January, likely due to reactions forming ammonium nitrate, which is promoted at relatively low temperatures [70]. In contrast, SO₄²⁻ was the highest in August, likely due to photochemical reactions during summer, which promoted the formation of secondary sulfate [71]. The monthly concentration trends of carbonaceous components showed that EC and OC were the highest in winter (December–January), similar to PM_2.5. EC and OC are known as representative indicators of vehicle emissions [72,73,74]. These findings are consistent with studies reporting that mobile emission sources contribute significantly to high concentrations in industrial complexes during winter [75]. Furthermore, the condensation of vehicle exhaust into smaller particles owing to low winter temperatures [76] contributed to high concentrations during this period.

Similar to PM_2.5, the trace elements Cu, Sr, Cd, Ba, Pb, and S exhibited high concentrations in winter (December–January). Except for V, Ni, Cd, and S, most elements had their lowest concentrations in summer (June–August). As, Zn, Mn, and Cr showed high concentrations in winter and spring, which is likely attributed to heating activities driven by fuel combustion [30,31,77,78]. Si, Al, Fe, and Ti, which are known markers of soil and crustal materials, and P, which is recognized as a bioaerosol indicator [79,80], exhibited high concentrations in spring (March–May), reflecting the seasonal influence of yellow dust and pollen. Therefore, the temporal variations in the components of PM_2.5 are influenced by monthly and seasonal climatic factors and the characteristics of the emission sources.

3.4. Potential Sources and Formations of PM_2.5

Backward trajectory analysis was conducted using the HYSPLIT model to identify the air mass transport affecting Yeosu and Gwangyang, assessing potential PM_2.5 sources (Figure 7). During the study period, significant differences in air mass transport were observed between the days with the highest (Figure 7a) and lowest (Figure 7b) PM_2.5 concentrations. On the day with the highest PM_2.5 concentration, upper AGL (1000 m) originating from central Mongolia traveled through western China and the Liaodong Peninsula before reaching Yeosu and Gwangyang. Meanwhile, middle AGL (500 m) and lower AGL (100 m) air masses originating from Liaoning Province exhibited strong stagnation in the surrounding region before moving across the West Sea and arriving at Yeosu and Gwangyang. These patterns suggest the influence of northwesterly winds, which are characteristic of winter atmospheric circulation. On the day with the lowest PM_2.5 concentration, the upper, middle, and lower air masses were found to originate from different air masses (central China, eastern Russia, and western Mongolia, respectively) and were transported by strong westerly winds to the East Sea before arriving in Yeosu–Gwangyang.

Carbonaceous aerosol represents various primary emission sources and secondary organic carbon (SOC) formed through atmospheric reactions. The OC/EC mass ratio serves as a crucial indicator of both emission and formation processes. An OC/EC mass ratio exceeding 2 suggests the possible presence of SOC [81]. During the study period, the average OC/EC ratio was determined to be 8.74 ± 2.53, indicating SOC formation. This value is significantly higher than the measurements in Seoul (2.16) [82] and Guangzhou (2.3–4.5) [83]. Potential emission sources with similar mass ratios include coal combustion (16.8–40.0) [84] and biomass burning (7.7) [85]. However, due to the difficulty in quantitatively distinguishing primary organic carbon (POC) from SOC, we estimate SOC using the minimum OC/EC ratio [86]. The calculations are given by Equations (2) and (3):

(2)$\mathrm{S}\mathrm{O}\mathrm{C}={\mathrm{O}\mathrm{C}}_{\mathrm{t}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l}}-\mathrm{P}\mathrm{O}\mathrm{C}$

(3)$\mathrm{P}\mathrm{O}\mathrm{C}=\mathrm{E}\mathrm{C}-(\mathrm{O}\mathrm{C}/\mathrm{E}\mathrm{C}{)}_{\mathrm{m}\mathrm{i}\mathrm{n}}$

During the study period, the (OC/EC)_min ratio was 2.19, and the SOC concentration was calculated to be 2.95 ± 1.50. SOC accounted for 72.81% of OC (Table 3), which is significantly higher than the values reported in Guangzhou (12.6–54.7%) [87] and Shanghai (14.3–47.1%) [88]. This suggests that the diverse meteorological conditions in Yeosu and Gwangyang contribute significantly to SOC formation [88].

3.5. Source Identification

The characteristics of PM_2.5 and its chemical components vary depending on emission sources. The mass ratio of NO₃⁻/SO₄²⁻ can reflect the relative contribution of mobile and stationary sources [89]. During the study period, the NO₃⁻/SO₄²⁻ ratio was found to be 0.85 ± 0.87, which was lower than those reported in Ansan and Siheung (2.08) [49] and Shanghai (0.86) [90]. This suggests that stationary sources, including coal combustion, were more dominant than mobile sources [91]. The seasonal variation in the mass ratio of NO₃⁻/SO₄²⁻ (Figure 8) followed the order Winter (1.32) > Spring (0.89) > Autumn (0.57) > Summer (0.17). However, such distinct seasonal differences in the NO₃⁻/SO₄²⁻ ratio can be influenced by various factors, including temperature and relative humidity [33,34]. Summer exhibited the lowest mass ratio, consistent with previous findings [92], which is likely due to seasonal characteristics that hinder NO₃⁻ formation under high temperatures and elevated RH conditions [93].

PCA for source identification resulted in the extraction of eight factors, explaining 71.53% of the total variance (Table 4). PC1 was characterized by high amounts of Al, Ti, Fe, Co, P, and Si, with an eigenvalue of 5.00, accounting for 17.25% of the total variance. The high loadings of cluster elements such as Al, Ti, and Si indicate that PC1 represents soil dust. PC2 was characterized by high amounts of NO₃⁻, EC, OC, Mn, Cu, and Zn, with an eigenvalue of 3.67, accounting for 12.66% of the total variance. NO₃⁻, EC, and OC are commonly emitted from vehicular sources [72,73,74,94], while Mn, Cu, and Zn are known to be released during the steel smelting process [26,27,95,96]. Thus, PC2 was classified as vehicular emissions and steel industry. PC3 was characterized by high amounts of Ni and Cr, with an eigenvalue of 2.69, accounting for 9.28% of the total variance. Ni and Cr serve as indicators of fuel oil combustion for residential heating [59,77,97], leading to the classification of PC3 as oil combustion. PC4 was characterized by high amounts of SO₄²⁻, NH₄⁺, and S, with an eigenvalue of 2.52, accounting for 8.69% of the total variance. Given that SO₄²⁻ and NH₄⁺ are the main components of secondary aerosols, PC4 was classified as a secondary aerosol. PC5 was characterized by high amounts of Cl⁻, Na⁺, K⁺, and Mg²⁺, with an eigenvalue of 2.24, accounting for 7.73% of the total variance. Yeosu and Gwangyang are coastal areas that are directly influenced by sea breeze. Therefore, PC5 was interpreted as sea salt [32,33]. PC6 was characterized by high amounts of Sr, Mo, and Ba, with an eigenvalue of 2.00, accounting for 6.89% of the total variance. Since Ba and Sr are primarily used in the electronics industry [98,99], PC6 was classified as industrial sources. PC7 was characterized solely by high amounts of Ca²⁺, with an eigenvalue of 1.35, accounting for 4.65% of the total variance. Ca²⁺ can originate from CaO, a primary component of cement, and serves as an indicator of construction dust [100,101]. Therefore, PC7 was classified as construction. PC8 was characterized solely by high amounts of Pb, with an eigenvalue of 1.27, accounting for 4.38% of the total variance. Pb can be emitted from coal combustion [102], leading to the classification of PC8 as coal combustion.

The limitations acknowledged in this study are the absence of meteorological factors such as wind direction, temperature, relative humidity, and solar radiation in analyzing PM_2.5 and its chemical components. These diurnal meteorological factors can affect atmospheric conditions and are also strongly associated with the formation, decomposition, and suppression of chemical components [103,104,105,106,107]. A further limitation is that the number of sampling sites for investigating spatial distribution was limited to five, which requires thorough consideration. Therefore, future advanced studies must incorporate various factors influencing PM_2.5 and include a greater number of sampling sites to ensure a comprehensive analysis.

4. Conclusions

This study provided foundational data for source identification by analyzing the temporal and spatial characteristics of PM_2.5 and its components emitted from industrial complexes. To this end, the distribution characteristics of PM_2.5 and its components were investigated from August 2020 to July 2023 at five sites within the Yeosu and Gwangyang industrial complexes, where diverse air pollutant sources exist.

ANOVA indicated statistically significant concentration differences for certain components, including PM_2.5 (p < 0.05). Spatial variability analysis using COD and the Pearson correlation coefficient revealed no spatially significant differences among the sites. Time-series analysis showed that PM_2.5 concentrations varied widely. Total carbon and total ions displayed similar trends to the increases and decreases in PM_2.5, whereas total trace elements showed discrepancies during certain periods.

Ionic components accounted for the largest proportion of PM_2.5 across all seasons and years, particularly in winter. Trace elements exhibited the highest proportion in spring, followed by a decreasing trend in summer and autumn, and an increasing trend observed over the years. Substances with high concentrations of PM_2.5 and its components were influenced by temporal changes (e.g., temperature, yellow dust) and differences in emission source characteristics (e.g., fossil fuel combustion).

Source identification through PCA resulted in the extraction of eight factors. The PCA results indicate that the chemical components of PM_2.5 are mainly from soil dust, vehicular emissions, the steel industry, and other pollution sources.

Through an analysis of concentration distributions, spatiotemporal characteristics, and source identification, this study provides evidence for understanding the air pollution status in the Yeosu and Gwangyang industrial complexes. This is expected to contribute to future air pollutant emission management and air quality policy development. Furthermore, these findings are expected to serve as foundational data for future studies aimed at source identification and estimation.

Author Contributions

Conceptualization, methodology, software, writing—original draft preparation, H.J.; validation, formal analysis, investigation, writing—review and editing, S.-Y.P.; resources, data curation, visualization, Y.-H.K.; supervision, project administration, C.-M.L. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The authors do not have permission to share data.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:

PM	Particulate matter
PM2.5	Particulate matter < 2.5 µm in aerodynamic diameter
COD	Coefficient of divergence
PCA	Principal component analysis
OC	Organic carbon
EC	Elemental carbon
RH	Relative humidity
LAB	Laboratory blank
FB	Field blank
QA/QC	Quality assurance and quality control
HYSPLIT	Hybrid single-particle lagrangian integrated trajectory
AGL	Above ground level
ANOVA	Analysis of variance
POC	Primary organic carbon
SOC	Secondary organic carbon

Footnotes

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Figures and Tables

Figure 1. Sampling sites in Yeosu and Gwangyang.

Figure 2. Coefficient of divergence (COD) of PM2.5 among the sampling sites in Yeosu and Gwangyang.

Figure 3. Time-series variations of summed concentrations of the (a) carbonaceous, (b) ionic, and (c) trace elemental components of PM2.5 during the sampling period. The red circle indicates distinct trends among the ∑ components. The red dotted line represents the Ministry of Environment PM2.5 24 h limit.

Figure 4. Composition ratios of the PM2.5 chemical components: (a) seasonal and (b) annual.

Figure 5. Monthly variations in the average concentration of PM2.5.

Figure 6. Monthly variations in the concentrations of (a) ionic, (b) carbonaceous, and (c) trace elemental components of PM2.5.

Figure 6. Monthly variations in the concentrations of (a) ionic, (b) carbonaceous, and (c) trace elemental components of PM2.5.

Figure 6. Monthly variations in the concentrations of (a) ionic, (b) carbonaceous, and (c) trace elemental components of PM2.5.

Figure 7. Backward trajectory analysis results on (a) 27 January 2022 with the highest PM2.5 concentration and (b) 12 October 2021 with the lowest PM2.5 concentration.

Figure 8. Mass ratio of NO3− and SO42−.

Table 1

Concentrations of PM_2.5, carbon, ions, and elements across all sampling sites.

Component												Total
		F1		F2		F3		F4		F5
		Mean	SD	Mean	SD	Mean	SD	Mean	SD	Mean	SD	N	Mean	SD	p-Value a
PM2.5 (μg/m3)		17.72	9.34	17.17	10.06	20.28	10.28	18.32	9.43	18.98	9.96	1304	18.50	9.81	0.003 *
Ion	Cl−	0.38	0.37	0.39	0.40	0.43	0.45	0.43	0.47	0.42	0.42	1246	0.41	0.42	0.570
(μg/m3)	NO3−	2.50	3.02	2.43	2.97	2.87	3.40	2.52	2.97	2.73	3.25	1176	2.61	3.12	0.510
	SO42−	3.03	2.70	3.00	2.41	3.08	2.30	2.97	2.18	3.82	2.65	1244	3.18	2.45	0.000 *
	Na+	0.41	0.34	0.41	0.32	0.42	0.33	0.45	0.55	0.43	0.34	1181	0.42	0.38	0.825
	NH4+	1.75	1.60	1.80	1.48	1.94	1.63	1.73	1.49	2.13	1.55	1214	1.87	1.55	0.023 *
	K+	0.22	0.16	0.22	0.21	0.25	0.30	0.22	0.16	0.23	0.16	1091	0.23	0.20	0.412
	Mg2+	0.11	0.08	0.11	0.08	0.11	0.08	0.10	0.08	0.11	0.08	730	0.11	0.08	0.912
	Ca2+	0.14	0.12	0.17	0.53	0.13	0.10	0.13	0.10	0.15	0.12	1105	0.14	0.20	0.340
Carbon	EC	0.48	0.24	0.48	0.45	0.52	0.57	0.47	0.23	0.43	0.19	1314	0.48	0.34	0.049 *
(μg/m3)	OC	4.12	2.37	3.51	1.68	4.20	2.03	3.94	2.01	4.17	2.00	1315	3.99	2.02	0.000 *
Elements	Al	321.17	408.58	391.64	565.68	386.44	559.51	380.18	578.87	421.84	591.78	290	380.25	540.88	0.893
(ng/m3)	Ti	8.82	11.54	11.66	17.31	11.08	15.07	10.25	14.36	10.50	15.22	1209	10.46	14.70	0.271
	V	1.70	1.41	1.43	1.12	1.29	0.97	1.33	1.05	1.70	1.23	1176	1.49	1.16	0.000 *
	Mn	14.49	8.09	13.22	10.16	17.76	14.71	10.87	8.24	9.73	6.62	1311	13.22	9.56	0.000 *
	Fe	278.96	167.39	185.10	172.77	189.82	160.20	163.74	153.02	141.19	150.78	1311	191.76	161.83	0.000 *
	Ni	2.78	3.78	1.57	1.14	1.83	1.31	1.86	3.84	1.79	1.36	1287	1.96	2.29	0.000 *
	Co	1.62	1.00	1.08	1.02	1.12	0.94	0.99	0.89	0.93	0.88	1162	1.15	0.95	0.000 *
	Cu	3.94	3.24	4.07	4.35	3.89	2.58	3.80	3.59	4.10	2.89	1309	3.96	3.33	0.844
	Zn	53.27	27.96	40.06	25.74	48.60	34.69	41.58	29.84	36.80	24.41	1311	44.06	28.53	0.000 *
	As	4.53	2.89	2.45	1.85	2.72	2.21	2.39	1.87	2.33	1.83	1234	2.88	2.13	0.000 *
	Sr	1.34	1.17	1.74	3.88	1.46	1.30	1.31	1.22	1.51	1.28	755	1.47	1.77	0.381
	Mo	2.71	2.98	3.01	3.40	3.03	3.33	3.61	4.01	5.73	6.10	744	3.62	3.96	0.000 *
	Cd	3.90	3.80	4.70	4.85	4.25	4.15	3.97	3.82	4.08	4.00	825	4.18	4.13	0.429
	Ba	9.98	9.13	12.39	10.93	11.90	10.76	12.16	11.80	13.66	14.96	830	12.02	11.51	0.076
	Pb	10.13	7.36	8.18	5.75	11.57	28.71	9.32	6.64	8.97	6.38	1263	9.64	10.97	0.078
	P	8.20	6.87	9.17	10.06	8.02	8.43	7.61	9.08	13.51	13.09	934	9.30	9.51	0.000 *
	S	1289.15	1029.48	1222.95	988.60	1317.64	1030.25	1194.89	968.82	1564.03	1161.16	1298	1317.13	1035.66	0.000 *
	Cr	3.49	3.68	2.25	1.56	2.64	1.72	2.45	2.22	2.09	1.43	1284	2.59	2.12	0.000 *
	Si	351.49	525.97	433.35	787.66	369.48	587.08	359.07	613.09	423.16	660.44	1289	387.31	634.85	0.457

^a ANOVA results among the sites, * p < 0.05. OC: organic carbon, EC: elemental carbon, SD: standard deviation.

Table 2

Pearson correlation coefficients of PM_2.5 among the sampling sites in Yeosu and Gwangyang.

Season	Site	F1	F2	F3	F4	F5	p-Value a
Spring	F1	1.000	0.938	0.835	0.868	0.910	0.000 *
	F2		1.000	0.900	0.914	0.932
	F3			1.000	0.940	0.870
	F4				1.000	0.908
	F5					1.000
Summer	F1	1.000	0.912	0.731	0.850	0.910	0.000 *
	F2		1.000	0.909	0.931	0.935
	F3			1.000	0.869	0.876
	F4				1.000	0.944
	F5					1.000
Autumn	F1	1.000	0.906	0.846	0.876	0.893	0.000 *
	F2		1.000	0.888	0.922	0.908
	F3			1.000	0.943	0.870
	F4				1.000	0.892
	F5					1.000
Winter	F1	1.000	0.742	0.888	0.876	0.922	0.000 *
	F2		1.000	0.674	0.622	0.724
	F3			1.000	0.942	0.891
	F4				1.000	0.925
	F5					1.000
Total	F1	1.000	0.842	0.857	0.891	0.904	0.000 *
	F2		1.000	0.797	0.800	0.826
	F3			1.000	0.942	0.894
	F4				1.000	0.924
	F5					1.000

^a all site pairs are significantly different, * p < 0.05.

Table 3

Average mass ratio of ionic and carbonaceous species.

Species	Mean ± SD
OC/EC	8.74 ± 2.53
SOC	2.95 ± 1.50
SOC/OC (%)	72.81 ± 8.71
NO3−/SO42−	0.85 ± 0.87

Table 4

PCA results in Yeosu and Gwangyang.

Component	PC1	PC2	PC3	PC4	PC5	PC6	PC7	PC8
Cl−	0.004	0.163	−0.091	−0.012	0.780	0.116	−0.181	0.180
NO3−	0.036	0.676	−0.084	0.467	0.059	0.076	−0.013	0.025
SO42−	−0.046	0.067	0.097	0.817	−0.073	0.021	0.099	0.114
Na+	−0.006	−0.140	0.125	−0.002	0.742	0.058	0.006	−0.178
NH4+	0.018	0.428	0.050	0.794	0.042	0.072	0.048	0.168
K+	−0.006	0.250	0.034	0.133	0.595	−0.162	0.282	0.001
Mg2+	−0.047	−0.059	0.144	0.070	0.663	−0.112	0.413	−0.109
Ca2+	0.259	0.076	−0.006	0.070	0.202	0.122	0.817	0.015
EC	0.033	0.671	−0.012	0.033	0.059	0.043	−0.080	−0.069
OC	0.074	0.755	0.129	0.259	0.093	0.091	0.016	0.202
Al	0.942	−0.056	−0.021	0.006	−0.048	−0.022	0.051	0.008
Ti	0.859	−0.031	0.124	−0.074	0.071	−0.032	0.114	−0.097
V	0.444	−0.198	0.472	0.399	−0.045	0.199	−0.069	0.047
Mn	0.384	0.545	0.500	−0.031	0.050	0.058	0.132	0.067
Fe	0.782	0.354	0.388	0.049	−0.001	0.044	0.095	0.072
Ni	0.105	0.129	0.805	0.216	0.019	0.168	−0.063	0.051
Co	0.718	0.311	0.416	0.098	−0.028	0.231	0.084	0.078
Cu	0.087	0.689	0.312	0.046	−0.034	0.154	0.076	0.039
Zn	0.196	0.602	0.561	0.086	0.026	0.017	0.073	0.190
As	−0.008	0.472	0.200	0.066	−0.064	−0.281	0.190	−0.081
Sr	0.406	0.034	0.021	0.056	−0.126	0.633	0.488	0.077
Mo	−0.034	0.132	0.131	−0.019	0.071	0.785	−0.061	−0.017
Cd	−0.008	0.122	−0.084	−0.056	0.236	0.000	−0.053	−0.752
Ba	−0.047	0.027	0.075	0.253	−0.054	0.791	0.097	0.042
Pb	0.061	0.360	0.024	0.062	0.196	0.070	−0.066	0.647
P	0.790	0.108	0.042	0.015	−0.062	−0.057	−0.023	0.107
S	0.074	0.159	0.190	0.770	0.232	0.151	−0.024	−0.169
Cr	0.181	0.218	0.790	0.032	0.170	−0.008	0.060	−0.013
Si	0.966	0.052	0.063	0.035	0.042	0.034	0.110	−0.027
Eigenvalue	5.00	3.67	2.69	2.52	2.24	2.00	1.35	1.27
% of variance	17.25	12.66	9.28	8.69	7.73	6.89	4.65	4.38
% of cumulative	17.25	29.91	39.19	47.88	55.61	62.50	67.15	71.53

Note: Factor loadings > 0.5 are shown in bold.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/atmos16030241/s1, Table S1: Information of sampling sites in this study; Table S2: QA/QC results of the chemical components analyzed in this study.

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