Characteristics of Atmospheric Pollutants in Paddy and Dry Field Regions: Analyzing the Oxidative Potential of Biomass Burning

Abstract

This study aimed to identify the characteristics of atmospheric pollutants emitted by agricultural activities and to evaluate factors that may cause harm to human health. For the research, atmospheric pollutants were measured over the course of a year in representative rice farming and field crop farming areas in South Korea. The results confirmed that the characteristics of atmospheric pollutants in agricultural areas are influenced by the nature of agricultural activities. Specifically, when comparing rice paddies and field crop areas, during summer, the correlation between oxidative potential and levoglucosan—a marker for biomass burning—weakens due to less burning activity in the rice-growing season, leading to lower oxidative potential despite different PM_2.5 across areas. The study also finds that methyl sulfonic acid, indicating marine influence, plays a big role in keeping oxidative potential low in summer. This suggests that the main causes of PM_2.5-related health risks in the area are from biomass burning and external sources, with burning being a significant factor in increasing oxidative potential. Based on these results, it is hoped that measures can be taken in the future to reduce atmospheric pollutants in agricultural areas.

1. Introduction

Atmospheric pollutants can be emitted by various natural activities, but anthropogenic activities are considered the main cause of the increase in atmospheric pollutants [1]. The rise in atmospheric pollutants can adversely affect human health, a well-known fact evidenced by the increase in respiratory diseases and deaths due to the rise in particulate matter [2,3]. Thus, the increase in atmospheric pollutants is recognized as a significant social factor that can reduce human life expectancy and cause various economic losses [4]. For these reasons, efforts to reduce atmospheric pollutants are deemed essential in modern society, particularly the reduction of pollutants stemming from anthropogenic activities.

Recent studies have identified agricultural activities as a significant contributor to atmospheric pollution [4], attributed to the substantial volume of pollutants emitted by agriculture and their potential harm to human health [5,6]. Agricultural emissions include not only particulate matter but also various precursor gases necessary for the formation of PM_2.5. Previous research presented that agricultural activities are among the largest sources of particulate matter emissions across Europe. In China, 92.2% of NH₃ emissions come from agriculture, along with the production of various volatile organic compounds (VOCs) [7,8,9]. From a human health perspective, PM_2.5 (particulate matter with a diameter of less than 2.5 μm) emitted by agricultural activities is known to be harmful even at very low concentrations, with agriculture being a significant factor in deaths attributed to atmospheric pollutants in Europe, Russia, the United States, Japan, and Canada [10,11,12,13]. Controlling pollutants from agricultural activities could potentially reduce a fifth of the global deaths caused by PM_2.5 [5]. To mitigate the PM_2.5 emitted by agricultural activities, it is necessary to first examine the characteristics of these emissions. However, the emission characteristics of PM_2.5 from agricultural activities can vary even for the same crops, depending on local soil characteristics, water conditions, cropping systems, and differences in agricultural practices [14,15,16]. Therefore, to understand the characteristics of PM_2.5 emitted from agricultural activities, it is crucial to consider variables such as soil characteristics, cropping systems, climate, and agricultural practices on a regional basis. However, accounting for all variables for each region, crop type, land characteristics, and agricultural activity is realistically impossible. This is why current studies on the emission characteristics of atmospheric pollutants from agricultural activities rely on bottom-up tracing methods based on field observations, necessitating observations of atmospheric pollutants specific to different crops and agricultural characteristics [14,17,18].

This research was conducted to identify the characteristics of PM_2.5 emitted by agricultural activities. Representative rice and field crop farming areas in South Korea were selected for observation over a year. Based on the observations, the study presents the characteristics of PM_2.5 emissions related to rice and field crop activities and identifies factors of oxidative potential from agricultural-area pollutants. The results are expected to serve as data for future studies on the emission characteristics of atmospheric pollutants from agricultural activities and to be utilized for managing pollutants emitted by such activities.

2. Materials and Methods

2.1. Measurement Locations and Sampling Period

The observation of atmospheric pollutants in agricultural areas was conducted in farmlands located in Buan (35°46′21″ N 126°40′05″ E) and Gochang (35°20′58″ N 126°35′50″ E), South Korea (Figure 1). The observation sites in Buan and Gochang are distinguished as rice and field crop farmlands, respectively, and are agricultural areas with no residential buildings or industrial complexes within a 1 km radius of the observation points. However, residential areas are located approximately 1.5 km away, and the sea is within 5 km to the west. The measurement period spanned one year, from 1 September 2022 to 31 August 2023, with atmospheric pollutants being observed from the 1st to the 24th of each month for quartz and Teflon filter sampling for 48 h. The time following the observation period each month was allocated for analysis and maintenance of the collection devices.

In order to compare the characteristics of the soil and cultivated crops in the study areas of Buan and Gochang, an examination was conducted. The soil in the Buan area is composed of approximately 74% loamy sand (46%) and sandy loam (24%), with specific soil textures including 19.45% sand, 31% loamy, and 21% silty loam. In contrast, the Gochang area exhibits a deeper effective soil depth with 0–20 cm at 15%, 20–50 cm at 15%, 50–100 cm at 28%, and over 100 cm at 34%, and the soil texture comprises 61% sandy clay loam, 9% sandy loam, and 7% loamy sand (Republic of Korea Land Information, Soil Portal: http://soil.rda.go.kr/soil/chart/chartSoilType.jsp accessed on 10 March 2024). Regarding the primary agricultural crops, the Buan region predominantly engages in paddy farming, with rice and rice barley accounting for approximately 85% of all cultivated crops. Meanwhile, Gochang’s agricultural activities are more varied, with paddy farming representing about 12% of the total agricultural activities and field farming accounting for 85% (Buan County Office: https://www.buan.go.kr accessed on 10 March 2024). Specifically, the field crops in the Gochang area include watermelons (32%), cabbages (13%), radishes (19%), and onions (12%), totaling 22 different types of field crops (Gochang County Office: https://www.gochang.go.kr accessed on 10 March 2024). Additionally, both regions engage in orchard operations, albeit at a minimal level (<2%) compared to paddy and field farming activities. Given that the observational sites in Buan and Gochang are approximately 60 km apart, it is presumed that there are no significant differences in agricultural activities attributable to climate variations. The distinctions in agricultural practices are primarily due to the differences between paddy and field farming and the varying planting and harvesting times of the crops. Notably, South Korea experiences four distinct seasons, with paddy farming in the Buan area involving planting in April, followed by harvesting in September to October. In contrast, Gochang’s cultivation cycles vary depending on the crop, with watermelons planted in April to May and harvested in June to August, onions planted in January to March and harvested in May to June, and radishes planted in August to September and harvested in November to December. Therefore, while the Buan area maintains a consistent planting and harvesting schedule, the Gochang area exhibits diversity in agricultural activities, conducting 2 or 3 farming operations annually based on the characteristics of the cultivated crops.

2.2. Real-Time Measurements

Real-time measurements included the concentrations of PM₁₀ and PM_2.5, as well as gaseous compounds such as NH₃, NO, NO₂, and O₃. PM₁₀ and PM_2.5 were observed using a beta-ray measurement device (Spirant BAM 1020, Met One Instruments Inc., Grants Pass, OR, USA). Gaseous substances were measured in real-time using equipment for NH3 (cavity ring-down spectroscopy, CRDS G2013), NO and NO₂ (Serinus^® 40, Ecotech ACOEM Group, Melbourne, Australia), SO₂ (Serinus^® 50, Ecotech ACOEM Group, Melbourne, Australia), and O₃ (Serinus^® 10, Ecotech ACOEM Group, Melbourne, Australia). All equipment for measuring gaseous compounds was calibrated using a three-point calibration before measurement. After calibration, a different concentration was injected to ensure measurements were within 5% of the target concentration.

2.3. Chemical Speciation

Sample collection for PM_2.5 was conducted at 48-h intervals. Specifically, PM_2.5 collection was performed at a flow rate of 42.0 L/min onto a 90 mm quartz filter, allowing for the acquisition of 12 samples on a monthly basis. Analyzed components of PM_2.5 included organic carbon (OC), elemental carbon (EC) and water-soluble ionic components (NH₄⁺, NO₃⁻, SO₄²⁻, Cl⁻, Na⁺, and K⁺). In addition, collected PM_2.5 samples were analyzed for organic molecular markers such as levoglucosan, 1,2-benzenecarboxylic acid (1,2-BCA), 1,4-benzenecarboxylic acid (1,4-BCA), and methanesulfonic acid (MSA), and a hazard evaluation was performed using the quinone dithiothreitol-based oxidative potential (QDTT-OP) analysis method.

OC and EC were analyzed using a carbon analyzer (Lab-based OCEC Carbon Aerosol Analyzer, Sunset Laboratory Inc., Tigard, OR, USA), based on the National Institute of Occupational Safety & Health (NIOSH 5040) protocol. Water-soluble ions were quantitatively analyzed using ion chromatography for anions (Metrohm 930 Switzerland, Metrosep A Supp 150/4.0 column, 3.7 mM Na₂CO₃ & 1.0 mM NaHCO₃) and cations (Metrohm 930 Switzerland, Metrosep C4-250/4.0 column, 5 mM HNO₃) after thermostatic extraction for 2 h using an ultrasonic cleaner (8800, Branson, St. Louis, MO, USA) connected to a thermostatic circulator (CA-111, Eyela, Tokyo, Japan) with the sectioned collection filter and 10 mL of distilled water. Organic marker substances such as levoglucosan, 1,2-BCA, 1,4-BCA, and MSA were analyzed using an LC-QToF (LC: Agilent Infinity II, QToF: SCIEX X500r) after extraction with a water and methanol 1:1 solution using an SB-C18 column (Agilent Poroshell 120, Santa Clara, CA, USA).

The QDTT-OP experimental method was determined by normalizing to quinone. Briefly, the redox reaction-based DTT-OP method is performed by reacting PM_2.5 chemical components with DTT, utilizing the rate of DTT reduction induced by various chemical components in PM_2.5, including reactive oxygen species (ROS) [19,20]. However, the DTT reduction rate in the DTT-OP method can vary depending on the initial DTT concentration used in the analysis, making it challenging to quantitatively assess human health risks using DTT-OP [21]. The QDTT-OP method, an improvement over the traditional DTT-OP, normalizes the DTT reduction rate based on the concentration of 9,10-Phenanthrenequinone and then evaluates the DTT reduction rate of PM_2.5 samples [22]. The experiment involved injecting 0.2 mM DTT, 2.41 mM 5,5-dithio-bis (2-nitrobenzoic acid) (DTNB), and 500 mM potassium phosphate monobasic into the PM_2.5 extract solution and adjusting the pH to 7.4, followed by the addition of 100 mM potassium phosphate dibasic. The mixture was then processed using a dispenser (Multiflo FX, Multi-Mode Dispenser, Agilent Technologies, Santa Clara, CA, USA) and reacted under 37 °C, with constant stirring. The absorbance at 412 nm was measured four times over 40 min to assess TNB. Every 15 samples were re-analyzed, and quinone samples were injected to maintain analysis accuracy and precision within 5%.

2.4. SOR, NOR, and NHR

The calculated values for the Sulfate Oxidation Ratio (SOR), Nitrate Oxidation Ratio (NOR), and Nitrogen Homologation Ratio (NHR) utilized the analyzed water-soluble ions NH₄⁺, NO₃⁻, and SO₄²⁻, along with observed gaseous results for NOx, SO₂, and NH₃. SOR, NOR, and NHR are defined as the molar ratios of SO₄²⁻, NO₃⁻, and NH₄⁺ to the total oxidized and reduced forms of sulfur (S) and nitrogen (N), respectively [23,24]. The NOR, SOR, and NHR were calculated using the formulas below.

NOR = [NO₃⁻]/([NO_x] + [NO₃⁻])

(1)

SOR = [SO₄²⁻]/([SO₂] + [SO₄²⁻])

(2)

NHR = [NH₄⁺]/([NH₃] + [NH₄⁺])

(3)

3. Results and Discussions

3.1. Characteristics of Atmospheric Pollutants in Agricultural Areas

The results of the chemical analysis in Buan by paddy fields, and Gochang by dry fields, are presented in

Table 1. Summary statistics of average concentrations of measured species in rice field areas of Buan and in Gochang areas of Jeollabuk-do during the measurement period.

Compound	Unit	Buan	Gochang
Temperature	Celsius	14.04 ± 10.00	14.14 ± 10.00
Humidity	%	78.70 ± 8.81	77.43 ± 9.57
NH3	ppb	23.18 ± 11.08	30.18 ± 11.57
NO	ppb	1.48 ± 0.71	1.62 ± 0.50
NO2	ppb	4.46 ± 2.58	4.12 ± 1.99
SO2	ppb	2.80 ± 0.97	1.34 ± 0.59
O3	ppb	34.65 ± 10.94	32.98 ± 9.03
PM10	μg/m3	39.41 ± 23.21	34.57 ± 23.48
PM2.5	μg/m3	22.47 ± 12.24	15.97 ± 9.78
OC	μg/m3	4.23 ± 2.46	3.80 ± 2.12
EC	μg/m3	0.41 ± 0.22	0.42 ± 0.21
Cl−	μg/m3	0.23 ± 0.27	0.28 ± 0.26
NO3−	μg/m3	4.05 ± 5.97	3.81 ± 5.51
SO42−	μg/m3	3.27 ± 1.89	3.05 ± 1.82
Na+	μg/m3	0.07 ± 0.06	0.01 ± 0.10
NH4+	μg/m3	2.57 ± 1.93	2.53 ± 1.84
K+	μg/m3	0.13 ± 0.13	0.12 ± 0.11
Levoglucosan	μg/m3	0.042 ± 0.044	0.057 ± 0.070
1,2-BCA 1)	μg/m3	0.004 ± 0.003	0.005 ± 0.003
1,4-BCA 2)	μg/m3	0.015 ± 0.013	0.016 ± 0.011
MSA 3)	μg/m3	0.029 ± 0.020	0.023 ± 0.016
QDTT-OP	μM/m3	0.130 ± 0.061	0.121 ± 0.057
NHR	-	0.098 ± 0.048	0.077 ± 0.039
NOR	-	0.207 ± 0.174	0.205 ± 0.173
SOR	-	0.419 ± 0.148	0.594 ± 0.181
[NH4+]/[SO42−]	-	4.53 ± 3.20	4.67 ± 2.23
[NO3−]/[SO42−]	-	1.98 ± 2.69	1.83 ± 1.91

¹⁾ 1,2-benzenecarboxylic acid; ²⁾ 1,4-benzenecarboxylic acid; ³⁾ methanesulfonic acid.

. As shown in the table, the temperature and humidity in both Buan and Gochang were similar, averaging 14 ± 10 °C and 78 ± 10% RH, respectively. The concentrations of gaseous compounds such as NO, NO₂, SO₂, and O₃ were found to be at levels where no significant differences could be attributed, with concentrations of approximately 1.5 ppb for NO, 4.3 ppb for NO₂, 2 ppb for SO₂, and 33 ppb for O₃, respectively. However, the concentration of NH₃ was significantly different, with about 23 ppb in Buan and 30 ppb in Gochang, indicating that the ammonia levels in the dry farming region were over 30% higher than in the paddy farming region. This difference is presumed to be due to variations in the amounts of fertilizers and compost used in crop cultivation, as well as the timing of biomass-burning activities between paddy and dry field agriculture. Generally, the primary nutrients required for crop growth include N, P, and K, with fertilizers and composts mainly containing these elements [25]. N, in particular, is released into the atmosphere as NH₃ following application to the soil, influenced by temperature and evaporation processes [26]. Fertilizer application in agricultural practices occurs primarily during soil tilling before seeding and in large quantities immediately after seeding, followed by intermittent applications (https://www.nongsaro.go.kr accessed on 10 March 2024; Rural Development Administration, Wanju, Republic of Korea). Thus, the timing of fertilizer and compost application is concentrated around the soil-tilling and seeding periods, correlating with higher atmospheric concentrations of NH₃. Furthermore, the emission sources of NH₃ in agricultural areas include not only the use of fertilizers and composts but also the burning of agricultural residues after harvest [27]. Buan, being an area that exclusively cultivates rice in paddy fields, has a single crop cultivation cycle annually, whereas Gochang cultivates a variety of crops, such as watermelons, chili peppers, and radishes, sequentially throughout the year. Therefore, the timing of fertilizer application and the burning of agricultural residues in Buan is less frequent compared to Gochang, where these activities occur more regularly prior to the cultivation of each crop and after the harvest. Consequently, compared to the paddy farming region of Buan, the dry farming region of Gochang has higher frequencies of fertilizer and compost use and agricultural residue burning, leading to higher concentrations of atmospheric NH₃.

The difference in characteristics between single-crop paddy farming regions and multi-crop dry farming regions is also evident in the ratio of PM₁₀ and PM_2.5 concentrations. PM emissions from agricultural activities are categorized into particles arising from mechanical factors, such as soil-cultivation, crop-harvesting, and grain-handling processes, and secondary precursor emissions from the use of compost and pesticides [14,28,29,30]. Particles from mechanical factors tend to be larger, with mineral matter as the primary component and soil dust as the main cause [31,32,33]. Thus, PM₁₀ in agricultural activities is primarily generated from dust raised by soil cultivation and other mechanical factors, while PM_2.5 is more influenced by secondary precursors, such as compost and pesticides. Since PM_2.5 is inhalable and smaller in size, it is considered more dangerous for human health compared to PM₁₀, which is merely respirable.

The ratios of PM₁₀/PM_2.5 in Buan and Gochang were 1.73 and 2.14, respectively, with Buan showing seasonal ratios of 2.26 in spring, 1.63 in summer, 1.50 in fall, and 1.53 in winter, and Gochang showing 2.42 in spring, 2.27 in summer, 2.25 in fall, and 1.62 in winter. This indicates that Buan experiences an increase in the PM₁₀/PM_2.5 ratio during the spring due to soil-tilling activities in paddy farming but does not show a continuous increase. In contrast, Gochang demonstrates a persistent increase in the PM₁₀/PM_2.5 ratio following spring, attributed to the sequential cultivation of different crops requiring soil tilling for each, resulting in a continuous increase in PM₁₀.

The study examined the concentration of PM_2.5 and its compositional components in the research area. The average concentration of PM_2.5 was 22.47 μg/m³ in Buan and 15.97 μg/m³ in Gochang, indicating that the paddy farming region of Buan had higher PM_2.5 concentrations compared to the dry farming region of Gochang. An analysis of the PM_2.5 components in Buan showed that approximately 20.65% were composed of carbonaceous material (OC and EC), with soluble ions accounting for about 45.93%. In Gochang, carbonaceous components comprised 26.42%, and soluble ions comprised 61.37% of PM_2.5. Therefore, over 60% of PM_2.5 in both agricultural regions was identified as being composed of carbonaceous material and soluble ions, highlighting the significant contribution of these components to agricultural-area PM_2.5. However, the paddy farming region of Buan had a combined carbonaceous and soluble ion content of approximately 66.58%, compared to 87.79% in the dry farming region of Gochang. This difference is presumed to be influenced by the emission of biogenic volatile organic compounds (BVOCs) such as isoprene and monoterpenes from rice cultivation areas. These components, based on their high reactivity, contribute to the formation of secondary organic aerosols (SOAs) and O₃, with previous studies indicating the emission of monoterpenes from areas cultivating rice [34,35,36]. The observation results from Buan and Gochang indicated that the OC/EC ratio, used to estimate the SOC ratio, was 10.32 in Buan and 9.05 in Gochang, showing a higher SOC ratio in Buan. In addition, the O₃ concentrations during spring and summer, when BVOCs are predominantly emitted, were 44 ppb in Buan and 41 ppb in Gochang during spring, and 37 ppb in Buan and 33 ppb in Gochang during summer, indicating differences. Comparing PM₁₀ and PM_2.5 concentrations, as shown in Table 1, Buan had higher PM₁₀ concentrations, i.e., 39.41 μg/m³, compared to 34.57 μg/m³ in Gochang, a difference of approximately 14%. Conversely, the difference in PM_2.5 concentrations was significantly higher in Buan than in Gochang by about 41%, indicating a higher generation of secondary particles in Buan compared to Gochang.

The soluble ions that make up PM_2.5 were analyzed, focusing on NO₃⁻, SO₄²⁻, NH₄⁺, Cl⁻, Na⁺, and K⁺. Among these, NO₃⁻, SO₄²⁻, and NH₄⁺, (known as SNA), which are secondary inorganic aerosols, accounted for 97% of the analyzed soluble ions in both Buan and Gochang. Thus, a majority of soluble ions were confirmed to be from SNA, indicating a significant influence of SNA on PM_2.5 mass. The research evaluated the formation characteristics of secondary inorganic aerosols (SIAs) based on SNA, examining ammonium-rich conditions and ratios of NHR, NOR, and SOR. The ammonium-rich conditions, assessed using the molar ratio of [NH₄⁺]/[SO₄²⁻], suggest an environment conducive to SIA formation through SNA if the ratio is above 1.5, and less conducive (ammonium-poor) if below 1.5 [23,30,37]. The molar ratio of [NO₃⁻]/[SO₄²⁻] was also evaluated, with a higher ratio indicating a greater contribution of NO₃⁻ to SIA formation due to the sequential neutralization reactions of atmospheric NH₃ with SOx products followed by NOx products [30,38,39]. The [NH₄⁺]/[SO₄²⁻] ratio was 4.53 in Buan and 4.67 in Gochang, with [NO₃⁻]/[SO₄²⁻] ratios of 1.98 in Buan and 1.83 in Gochang. Therefore, both regions, irrespective of paddy or dry field characteristics, were evaluated as ammonium-rich, indicating an environment conducive to SIA formation through SNA, with NO₃⁻ contributing significantly to SIA formation compared to SO₄²⁻. In addition, NH₃ concentrations at the observation points were more than tenfold higher than those measured in the capital of South Korea from 2015 to 2017 (2.26 ppb) [40], indicating particle formation from abundant atmospheric NH₃ in an ammonium-rich environment. The correlation between gaseous SO₂, NO₂, and SO₄²⁻ and NO₃⁻ in PM_2.5 was also examined. The results showed a correlation coefficient (r²) between SO₂ and SO₄²⁻ of 0.008 in Buan and 0.002 in Gochang, and between NO₂ and NO₃⁻ of 0.601 in Buan and 0.537 in Gochang. These results suggest that SO₄²⁻ in PM_2.5 could be influenced rather than local SO₂ emissions, while NO₃⁻ in PM_2.5 is generated from local NOx emissions.

To further investigate the correlation between gaseous precursors (NH₃, NOx, and SO₂) and secondary inorganic aerosol particles, the formation rates of secondary inorganic aerosol particles were calculated. NHR, NOR, and SOR represent the degree of oxidation and reduction when atmospheric NH₃, NOx, and SO₂ form particulate matter. Higher values of NHR, NOR, and SOR indicate active oxidation and reduction processes, generating more secondary inorganic aerosol particles in the atmosphere. Typically, when NHR, NOR, and SOR are below 0.1, primary emission sources dominate, and values above 0.1 indicate chemically induced generation [41,42,43]. The calculated seasonal results for NHR, NOR, and SOR in the research area are presented in Figure 2. NHR averaged 0.098 in Buan and 0.077 in Gochang, indicating more active chemical reactions of gaseous precursors in Buan compared to Gochang. Seasonally, both Buan and Gochang exhibited NHR values above 0.1 during winter, suggesting more active chemical reactions in winter compared to other seasons. Typically, SNA’s SIA reactions, driven by photochemical reactions, would result in higher NHR values in summer compared to winter. However, the study found the lowest NHR values in summer (0.074 in Buan; 0.052 in Gochang) and the highest in winter. This discrepancy may be attributed to the interaction between atmospheric NH₃ and NOx. Nitrate formed from the reaction of NH₃ and NOx is relatively stable at low temperatures and unstable at high temperatures [44,45], making the conversion of atmospheric NOx into particulate nitrate more challenging during summer. Indeed, the concentration of NO₃⁻ in PM_2.5 averaged 4.05 μg/m³ in Buan and 3.81 μg/m³ in Gochang, but in summer, NO₃⁻ concentrations were 0.26 μg/m³ in Buan and 0.42 μg/m³ in Gochang, indicating lower nitrate formation during summer. Additionally, based on the correlation between [NO₃⁻]/[SO₄²⁻] and gaseous SO₂ and NO₂ with SO₄²⁻ and NO₃⁻, the research area was confirmed to have stronger local reactions of NO₃⁻ in SIA formation compared to SO₄²⁻. Therefore, the higher NHR in winter compared to summer is likely influenced by temperature-dependent nitrate formation.

Examining NOR in the research area, we found that Buan had average values of 0.205 in spring, 0.040 in summer, 0.219 in fall, and 0.364 in winter, averaging 0.207, while Gochang had 0.219 in spring, 0.055 in summer, 0.171 in fall, and 0.375 in winter, averaging 0.205. Thus, NOR indicated predominance of chemically generated particles except in summer, where lower results were likely influenced by temperature effects hindering nitrate formation. SOR in Buan was 0.366 in spring, 0.396 in summer, 0.443 in fall, and 0.472 in winter, averaging 0.419, while Gochang had 0.494 in spring, 0.711 in summer, 0.612 in fall, and 0.546 in winter, averaging 0.594. This shows that both Buan and Gochang exhibited efficient photochemical reactions of gaseous precursors, with Gochang displaying a wider seasonal variance. Our seasonal examination of SOR revealed winter as the highest season in Buan, contrary to summer in Gochang. Various factors may contribute, but the influence of biomass-burning emissions from agricultural activities is considered significant. Biomass burning is a major source of SO₂ emissions in agricultural areas, with some regions reporting an 80–90% contribution to SO₂ emissions from biomass burning [46,47]. Biomass burning typically occurs after crop harvest and before soil tilling in Buan, a paddy farming region, primarily in autumn and winter, whereas Gochang, with its sequential cultivation of various crops, experiences biomass burning not only in autumn and winter but also in spring and summer. Therefore, SOR in Buan peaks during autumn and winter with active biomass burning, while Gochang sees a summer peak due to both biomass burning and the most active photochemical reactions. In additional, as previously mentioned, the correlation between atmospheric SO₂ concentrations and SO₄²⁻ was found to be very low in the research area, suggesting that SOR is likely generated from widespread SOx undergoing atmospheric chemical reactions during regional transport.

3.2. Assessment of Health Hazard Factors in PM_2.5 from Agricultural Areas

To identify the oxidative potential in PM_2.5 collected from the study area that could potentially induce human health hazards, an analysis using the QDTT-OP method was conducted. The results revealed that, in the Buan area, the concentrations of QDTT-OP were 0.145 μM/m³ in spring, 0.072 μM/m³ in summer, 0.132 μM/m³ in autumn, and 0.172 μM/m³ in winter, with an average of 0.130 μM/m³. In the Gochang area, the concentrations were 0.144 μM/m³ in spring, 0.075 μM/m³ in summer, 0.164 μM/m³ in autumn, and 0.100 μM/m³ in winter, averaging 0.121 μM/m³. When evaluating the hazard based solely on QDTT-OP concentrations, both Buan and Gochang areas exhibited higher levels during winter and spring compared to summer and autumn.

To determine the PM_2.5 components affecting QDTT-OP, a daily time-series analysis, as depicted in Figure 3, was performed to correlate QDTT-OP with PM_2.5 concentration and its constituents, including OC, EC, and soluble ions. The analysis confirmed a correlation between QDTT-OP and the concentrations of PM_2.5, OC, EC, and soluble ions. Specifically, when examining the correlation between QDTT-OP and analyzed components based on the r², PM_2.5 showed a correlation of 0.69 in Buan and 0.67 in Gochang. For OC, the correlation was 0.67 in Buan and 0.55 in Gochang; for EC, 0.51 in Buan and 0.65 in Gochang; and for soluble ions, 0.66 in Buan and 0.53 in Gochang. However, a closer examination of autumn and winter, the seasons with the highest QDTT-OP concentrations, revealed that QDTT-OP had the strongest correlation with OC (Buan r² > 0.90, Gochang r² > 0.89). This indicates that, while the human health hazard assessed based on QDTT-OP is not independent of PM_2.5 concentrations and soluble ions, the high QDTT-OP concentrations are relatively more influenced by OC.

To further investigate OC as the influencer of high concentrations of QDTT-OP in Buan and Gochang, a detailed analysis of OC components was conducted. This analysis aimed to infer the sources impacting human health hazards based on the content of OC. The organic components identified within OC were levoglucosan, 1,2-BCA, 1,4-BCA, and MSA. Levoglucosan, a substance produced exclusively through the decomposition of cellulose and hemicellulose at combustion temperatures above 300 °C, serves as a representative marker of biomass burning. Meanwhile, 1,2-BCA and 1,4-BCA are recognized as water-soluble organic acids involved extensively in various atmospheric chemical processes and serve as molecular markers. MSA is utilized as an indicator for secondary production influenced by precursors such as SO₂, associated with long-range transport related to marine influences [28,48,49]. The results for identifying the components affecting QDTT-OP, as shown in

Table 2. PM_2.5 composition in rice field areas of Buan and Gochang in Jeollabuk-do by season.

Compound		QDTT	OC	EC	LEVOG	1,2-BCA	1,4-BCA	MSA
Unit		μM/m3	μg/m3	μg/m3	μg/m3	μg/m3	μg/m3	μg/m3
Fall	Buan	0.132 ±0.066	4.645 ±2.726	0.288 ±0.141	0.029 ±0.022	0.005 ±0.002	0.018 ±0.015	0.025 ±0.01
	Gochang	0.100 ±0.053	4.871 ±2.544	0.35 ±0.162	0.029 ±0.02	0.005 ±0.002	0.015 ±0.011	0.022 ±0.009
Winter	Buan	0.172 ±0.047	5.14 ±2.402	0.542 ±0.232	0.091 ±0.05	0.005 ±0.002	0.023 ±0.015	0.02 ±0.01
	Gochang	0.164 ±0.045	4.36 ±1.997	0.514 ±0.263	0.115 ±0.096	0.006 ±0.003	0.020 ±0.012	0.014 ±0.008
Spring	Buan	0.145 ±0.045	4.392 ±2.708	0.517 ±0.222	0.037 ±0.035	0.004 ±0.003	0.012 ±0.007	0.037 ±0.025
	Gochang	0.144 ±0.043	3.65 ±1.702	0.519 ±0.193	0.066 ±0.062	0.005 ±0.003	0.018 ±0.01	0.032 ±0.023
Summer	Buan	0.072 ±0.034	2.75 ±0.984	0.305 ±0.102	0.012 ±0.012	0.001 ±0.001	0.006 ±0.004	0.035 ±0.026
	Gochang	0.075 ±0.041	2.272 ±1.013	0.308 ±0.118	0.018 ±0.018	0.002 ±0.002	0.011 ±0.008	0.023 ±0.014

, revealed seasonal concentration variations. As indicated in Table 2, QDTT-OP concentrations were affected by variations in OC concentrations, with a significant correlation found with levoglucosan. To examine this relationship more closely, the correlation between QDTT-OP, OC, and levoglucosan was analyzed, yielding the results presented in Figure 4. As demonstrated in Figure 4, QDTT-OP was correlated with OC throughout the research period, and the correlation with levoglucosan increased during spring and winter when QDTT-OP concentrations were high. Considering that levoglucosan is an indicator of biomass burning and that biomass burning in agricultural areas primarily occurs after crop harvest and before soil tilling in winter and spring, it can be inferred that the source of high QDTT-OP concentrations is biomass burning. Thus, it is evident that the source of increased human health hazards in PM_2.5 from agricultural areas is attributed to emissions from biomass burning.

Meanwhile, during summer, when QDTT-OP concentrations were lower, as illustrated in Figure 4, the correlation with levoglucosan appeared relatively diminished. Considering the rice cultivation period in Buan, we see that the summer season corresponds to the growth phase of rice, during which biomass-burning activities are virtually nonexistent. Indeed, as Table 2 indicates, the concentration of levoglucosan during summer was the lowest compared to autumn, winter, and spring, suggesting minimal biomass-burning activities during this time. In additional, as shown in Figure 3, QDTT-OP demonstrates a strong correlation with PM_2.5 concentration (r² > 0.67). However, despite significant differences in PM_2.5 concentrations between Buan (14.67 μg/m³) and Gochang (8.75 μg/m³) during summer, the QDTT-OP concentrations remained similar at 0.072 μM/m³ and 0.075 μM/m³, respectively. This result implies the existence of a common source persistently causing low QDTT-OP concentrations, aside from biomass-burning sources that induce high concentrations of QDTT-OP.

To identify the components affecting QDTT-OP during the summer, when the influence of biomass-burning emissions is minimal, an investigation was conducted, leading to the findings presented in Figure 5. As Figure 5 demonstrates, the low QDTT-OP concentrations in summer were correlated with MSA, with an r² of 0.43 or higher (0.43 in Buan and 0.45 in Gochang), indicating a relation between summer QDTT-OP concentrations and MSA. Considering MSA as an indicator for long-range transport related to marine influences, its role in affecting the generation of secondary products from precursors such as SO₂, and the previously mentioned correlation between SO₄²⁻ and external SO₂ inputs, it can be inferred that the research area, being adjacent to the western coast of South Korea, is influenced by long-range marine influx. It can be concluded that the low QDTT-OP concentrations in the study area are influenced by long-range transport related to marine impacts. In essence, the research identifies two main sources of substances inducing human health hazards in PM_2.5 from the study area: external influx components and biomass-burning emissions, with high concentrations of QDTT-OP significantly increased by biomass burning. Although the current research does not encompass all elements affecting the human health hazards of PM_2.5 in the study area, it clearly indicates the influence of both biomass-burning sources and long-range marine influx components on human health hazards.

4. Conclusions

This study aimed to examine the characteristics of air pollutants in agricultural areas, focusing on paddy and dry field farming regions, and to estimate the sources of human health hazards in these agricultural settings. During the summer, the correlation between QDTT-OP and levoglucosan significantly drops, reflecting minimal biomass burning due to the rice cultivation period in Buan. This period sees the lowest levoglucosan, indicating reduced biomass-burning activities and, consequently, lower QDTT-OP, despite varying PM_2.5 between locations. The study identifies MSA as a key influencer of low QDTT-OP concentrations during summer, suggesting long-range transport from marine sources as a significant factor. This finding points to two primary sources of PM_2.5 human health hazards in the area: biomass-burning emissions and external influx components, with biomass burning predominantly raising QDTT-OP concentrations. The results of this research imply two significant insights. First, understanding the characteristics of air pollutants emitted due to differences in paddy and dry farming activities allows for the prediction of air pollutant emissions based on the timing of agricultural activities and the characteristics of cultivated crops. Second, from a human health hazard perspective, the study provides a basis for managing hazardous emissions sources, such as biomass burning. It is hoped that the outcomes of this research will the enable effective management of air pollutants emitted from agricultural areas in the future.