Abstract

Translate

This study investigated the site-specific ionic composition and wet deposition loads of rainwater collected from eight actively cultivated agricultural regions across South Korea, with the aim of quantifying spatial and seasonal variability and interpreting how regional agricultural characteristics and surrounding site conditions influence major ion concentrations and deposition patterns. Rainfall samples were obtained using automated samplers and analyzed via high-performance ion chromatography for major cations (Na⁺, $N H_{4}^{+}$ , K⁺, Ca²⁺, Mg²⁺) and anions (Cl⁻, $N O_{3}^{-}$ , $S O_{4}^{2 -}$ , $N O_{2}^{-}$ ). The results revealed significant seasonal fluctuations in ion loads, with $N H_{4}^{+}$ (peak 1.13 kg/ha) and K⁺ (peak 0.25 kg/ha) reaching their highest levels during summer due to increased fertilizer use and crop activity. Conversely, Cl⁻ peaked in winter (2.11 kg/ha in December), particularly in coastal regions, likely influenced by de-icing salts and sea-salt aerosols. Correlation analysis showed a strong positive association among $N H_{4}^{+}$ , $N O_{3}^{-}$ , and $S O_{4}^{2 -}$ (r = 0.89 and r = 0.84, respectively), indicating shared atmospheric transformation pathways from agricultural emissions. Ternary diagram analysis further revealed regional distinctions: coastal regions such as Gimhae and Muan exhibited Na⁺ and Cl⁻ dominance, while inland areas like Danyang and Hongcheon showed higher proportions of Ca²⁺ and Mg²⁺, reflecting differences in aerosol sources, land use, and local meteorological conditions. These findings underscore the complex interactions between agricultural practices, atmospheric processes, and local geography in shaping rainwater chemistry. The study provides quantitative baseline data for evaluating non-point source pollution and developing region-specific nutrient and soil management strategies in agricultural ecosystems.

Full text

Turn on search term navigation

Translate

1. Introduction

Climate change and atmospheric pollution have become significant global environmental challenges in the 21st century. Global greenhouse gas emissions and expanding industrial activities have altered atmospheric chemical compositions. These changes impact human health as well as terrestrial ecosystems, water resources, and soil environments in various ways. Primary atmospheric pollutants, such as nitrogen oxides (NOx), sulfur dioxide (SO₂), ammonia (NH₃), and volatile organic compounds (VOCs), undergo photochemical oxidation processes.

Through these processes, secondary pollutants including nitrate ( $N O_{3}^{-}$ ), sulfate ( $S O_{4}^{2 -}$ ), and ammonium ( $N H_{4}^{+}$ ) are formed [1,2,3]. These secondary ionic compounds are dissolved in rainfall and deposited onto terrestrial surfaces. Rainfall serves as a primary pathway, facilitating the transfer of atmospheric substances to terrestrial surfaces. It is thus considered a critical route for surface deposition [4,5]. Historically, rainfall was perceived merely as a water source. However, recent studies have increased attention on the chemical composition of rainfall, particularly concerning ionic constituents. These ionic components affect soils, aquatic systems, and biological communities. In particular, acid rain, salt accumulation, and ionic imbalances pose significant threats to ecosystem stability and nutrient cycling [6,7,8,9]. Consequently, analyzing the chemical composition of rainfall has become an essential research topic across diverse environmental disciplines.

Moreover, rainfall is an important medium from the perspective of non-point source pollution. Deposited ions from rainfall not only influence soil properties but also can be transported into nearby rivers, groundwater, and aquatic ecosystems through surface runoff. This transport potentially results in secondary impacts such as water quality degradation and disturbances in adjacent ecosystems [10,11]. In particular, nitrogen-based ions ( $N H_{4}^{+}$ , $N O_{3}^{-}$ ) contained in rainfall runoff can flow into water bodies [12,13,14,15]. This can subsequently cause eutrophication, algal blooms, and disruptions in aquatic ecosystems.

These environmental changes play a particularly significant role in agricultural environments. Agricultural regions are complex areas where natural ecosystems and anthropogenic activities coexist. Thus, these regions simultaneously function as receptors and emitters of atmospheric pollutants. Additionally, these pollutants can serve as nutrient sources necessary for crop growth but also negatively affect soil and crop conditions. In particular, agricultural areas significantly contribute to atmospheric emissions of ammonia (NH₃) and nitrogen oxides (NO_x) through fertilizer application and livestock activities [16,17,18,19,20]. These emitted pollutants undergo atmospheric oxidation and chemical reactions, converting into ionic substances such as ammonium ( $N H_{4}^{+}$ ), nitrate ( $N O_{3}^{-}$ ), and sulfate ( $S O_{4}^{2 -}$ ). Subsequently, these ions are incorporated into rainfall and redeposited onto agricultural land.

Ionic components delivered via rainfall have multifaceted impacts on soil chemistry, crop physiology, microbial communities, and nutrient cycling within agricultural fields. Ammonium ( $N H_{4}^{+}$ ) acts as a nitrogen source essential for plant growth [21,22]. However, its deficiency can impair plant growth, photosynthetic efficiency, and subsequently reduce crop yield and quality. Conversely, excessive $N H_{4}^{+}$ inputs lead to the accumulation of H⁺ ions, causing soil acidification, excessive vegetative growth, lodging, increased susceptibility to pests and diseases, elevated greenhouse gas emissions, and eutrophication of aquatic systems [23,24,25,26,27,28]. Nitrate ( $N O_{3}^{-}$ ), a highly mobile anion, is a major contributor to non-point source pollution and can induce nitrogen imbalance within crops [29,30,31,32]. Sulfate ( $S O_{4}^{2 -}$ ) serves as an important sulfur source, enhancing cellular structural stability and disease resistance in crops [33,34,35]. However, as a strongly acidic anion, elevated sulfate deposition exacerbates soil acidification and promotes the leaching of essential cations [36,37,38]. Chloride (Cl⁻), although an essential micronutrient, can cause salt stress and impair crop water uptake when present in excess [39,40,41,42]. Sodium (Na⁺), a non-essential cation, can deteriorate soil structure and competitively inhibit potassium (K⁺) uptake [43,44,45,46]. Conversely, calcium (Ca²⁺) and magnesium (Mg²⁺) are vital cations for maintaining crop physiological functions and soil stability [47,48]. Under acidic conditions, these ions are easily leached, and competitive losses frequently occur within the ion-exchange complex [49,50]. Potassium (K⁺) is critical for crop osmotic regulation, stomatal function, and yield formation [51,52,53,54]. Due to strong competitive interactions with ammonium ( $N H_{4}^{+}$ ) and sodium (Na⁺), it is essential to determine optimal potassium application rates based on nutrient loading [46].

Therefore, in agricultural regions, rainfall acts as a crucial transporter of atmospheric pollutants and significantly contributes to ion cycling between soils and plants. Consequently, a precise understanding and detailed analysis of rainfall ionic composition are essential for effective agricultural environmental management. Previous studies on rainfall ionic composition and its variations have been extensively conducted.

Most of these studies focused on acid rain phenomena, atmospheric aerosol compositions, and climate change indicators. However, the majority of research has been conducted in urban areas, forest ecosystems, or high-altitude observatories. In contrast, quantitative analyses in active agricultural areas have been relatively limited. Research on air quality in urban regions often emphasizes emissions from transportation and industrial sources [55,56,57,58]. Studies predominantly concentrate on chemical characteristics of acid rain and ionic composition within PM_2.5 particles [59,60,61]. However, urban air chemistry substantially differs from that of agricultural areas regarding environmental conditions, pollutant sources, and geographical characteristics. For instance, while the primary nitrogen pollutant in urban atmospheres typically originates from vehicle exhaust emissions (NOx), agricultural regions mainly emit ammonia (NH₃) through volatilization from nitrogen-based fertilizers. Oxidation products of ammonia serve as key precursors for nitrogen-based ions in these rural environments. Conversely, in rural agricultural areas, SO₂ emissions may arise from multiple localized sources, including wintertime agricultural heating systems, small combustion appliances, and anaerobic decomposition of livestock manure. These sulfur compounds subsequently undergo atmospheric oxidation, transforming into sulfate ( $S O_{4}^{2 -}$ ) and becoming incorporated into rainfall. Consequently, this mechanism can significantly elevate sulfate ion concentrations in rainfall within agricultural regions.

Nevertheless, agricultural areas have not been sufficiently recognized as distinct spaces with unique atmospheric chemical characteristics in rainfall ionic composition analyses. Instead, these areas remain relatively neglected in atmospheric monitoring. Moreover, comparative structural analyses of ionic compositions between geographically and meteorologically distinct agricultural areas—such as coastal, inland, and peri-urban regions—are exceedingly rare.

This study not only focuses specifically on agricultural regions but also involves collecting rainfall samples directly within actively cultivated agricultural sites. Thus, this research considers locations where emissions and re-deposition from agricultural activities occur simultaneously. Consequently, the collected data reflect realistic, field-based agricultural environments. Instead of the conventional concentration-based analysis of ions, this study calculated and utilized the ion loading per unit area (kg/ha), accounting for rainfall amount.

This approach enables a quantitative assessment of the actual physical magnitude of ion loads applied to each agricultural site. Therefore, this methodology provides practical baseline data, facilitating the evaluation of regional differences, changes in soil chemistry, and predictions of non-point source pollution. To examine structural correlations among ionic components and differences in regional ionic composition, correlation matrix analysis and ternary diagrams were employed concurrently. Additionally, it provided insights into the influences of geographical characteristics, industrial features, agricultural activities, and their combined impacts, thereby enhancing the understanding of rainfall ionic structure within agricultural areas.

This study aims to quantitatively characterize both the major-ion composition (concentrations) and wet deposition loads in precipitation across representative agricultural regions of South Korea by collecting rainwater from eight actively cultivated sites (four rice paddy sites and four upland field sites). Specifically, we (i) summarize site-wise and seasonal variations in major cations and anions, (ii) estimate ion deposition loads by combining measured ion concentrations with rainfall amounts, and (iii) interpret regional differences in rainwater chemistry in relation to agricultural land-use type and site characteristics.

2. Materials and Methods

2.1. Study Areas and Sampling Sites

This study selected eight sampling sites in representative agricultural regions of South Korea, focusing on actively cultivated farmlands, to collect rainfall samples and analyze their ionic compositions. The selected sites consisted of four paddy fields and four upland fields, chosen based on agricultural types and major cultivated crops in each region of South Korea (Figure 1). To ensure the representativeness of the paddy field regions, large-scale rice-growing plains were selected by considering cultivated area and production volume. Upland field sites were selected by considering each region’s well-known specialty crops along with crop-specific production area and yield. Sampling points were located within farmlands where crop cultivation and growth activities were continuously taking place, even within rural areas. Details on the sampling sites—agricultural land use (farming type), cultivated crops, typical cropping schedules, and standard inorganic fertilization rates—are provided in Table 1.

Rainfall samples were collected from the rooftop of a small container structure (air quality monitoring station) installed at a height of approximately 3 m (Figure 2). Sufficient horizontal distance was maintained from surrounding obstacles to ensure the representativeness of the samples. The installation conditions were reviewed to minimize potential interference from non-agricultural sources such as factories, industrial facilities, and major roads. All sampling sites were selected within agriculture-dominated landscapes to reduce interference from non-agricultural emission sources. Sites located near major non-agricultural sources (e.g., industrial complexes, composting facilities, dense residential areas, and major roads/highways) were excluded during site selection.

2.2. Rainwater Sampling and Analytical Methods

In this study, an automatic rain sampler was used to collect rainfall samples in order to analyze the ionic composition of precipitation in agricultural areas. The sampling device was designed to automatically open its lid at the onset of rainfall and close it upon cessation, thereby minimizing contamination and ensuring the collection of clean precipitation samples. The collection area was fixed at 30 × 40 cm (0.135 m²), and a rainfall collection box was used for sample acquisition. Samples were collected in polyethylene (PE) bottles. The equipment was thoroughly cleaned and dried before and after each rainfall event. After collection, the samples were immediately sealed, stored under refrigerated conditions, and analyzed as soon as possible to prevent degradation (Figure 2). Rainfall sampling was conducted from May 2023 to December 2024, during which samples were collected whenever rainfall events occurred. However, in some rainfall events, sample collection was not possible due to mechanical malfunctions or sample loss.

Prior to analysis, each sample was filtered using a 0.45 μm membrane filter to remove suspended solids. Ion analysis was performed using high-performance ion chromatography (IC; 925 Compact IC Flex, Metrohm AG, Herisau, Switzerland). Target analytes included cations ( $N H_{4}^{+}$ , Na⁺, K⁺, Ca²⁺, Mg²⁺) and anions ( $N O_{3}^{-}$ , $S O_{4}^{2 -}$ , Cl⁻). Anion analysis was conducted using a Metrosep A Supp 17—100/4.0 column, with a mobile phase consisting of 5.0 mmol/L Na₂CO₃ and 0.2 mmol/L NaHCO₃. The flow rate was set at 0.6 mL/min, and the injection volume was 20 μL. For cation analysis, a Metrosep C4—150/4.0 column was used, with a mobile phase of 1.7 mmol/L HNO₃ and 0.7 mmol/L dipicolinic acid. The flow rate was set at 0.9 mL/min, and the injection volume was 10 μL. For quality assurance, standard solutions were injected periodically to verify instrument performance and evaluate analytical accuracy and precision. The concentrations of ionic components were reported in ppb (μg/L) and subsequently converted to areal deposition loads (kg/ha) based on corresponding rainfall data for interpretation.

In this study, rainfall depth was estimated based on the volume (mL) of precipitation collected by the sampling device. The collected sample volume and corresponding ion concentrations were used to calculate areal deposition loads (kg/ha). The calculation formula is presented below.

Ionic Load (kg/ha) = (C × V)/A

C: Ion concentration in the rainwater sample (ug/L)

V: Sampled rainwater volume (L)

A: Sampling surface area of the collection device (m²)

To analyze the interrelationships among ionic species, a correlation matrix was constructed using concentration data for each ion. This allowed for the evaluation of ionic correlations and similarities in ionization behavior, facilitating the interpretation of ionic composition characteristics in agricultural rainfall. To visualize regional differences and distributions in ionic composition, ternary diagrams were constructed for major cations and anions and used for comparative analysis. These diagrams enabled the visualization of relative proportions (%) of ionic components in each agricultural region, thereby illustrating regional ionic characteristics in rainfall.

3. Results and Discussion

3.1. Spatial Characteristics of Rainwater Chemistry Across Agricultural Sites

Table 2 indicates pronounced event-to-event and site-to-site variability in rainwater characteristics across the eight agricultural regions. The wide ranges in collected rainfall volume imply that wet deposition loads are strongly driven by episodic large-rainfall events, and thus flux comparisons should account for rainfall heterogeneity rather than relying on mean conditions alone. Rainwater pH was generally slightly acidic to near-neutral (site means: 5.19–6.31), consistent with regional inputs of acidifying species, while occasional strongly acidic (min 3.84) and alkaline (max 8.29) events suggest intermittent shifts in the balance between acidic inputs and neutralization by basic constituents likely relevant in agricultural environments. EC also showed substantial variability (means: 9.89–23.78 µS/cm, maxima up to ~112 µS/cm), indicating episodic ionic enrichment that can disproportionately influence cumulative deposition. Overall, these results highlight that precipitation chemistry at agricultural sites is governed by both background atmospheric conditions and short-lived enrichment/neutralization episodes.

Table 3 summarizes site-specific concentrations of major ions in rainwater. Across the eight sites, $N O_{3}^{-}$ was the dominant anion at most locations (mean 1.06–2.50 ppm), followed by $S O_{4}^{2 -}$ (mean 0.76–1.77 ppm), indicating that nitrate and sulfate largely control the ionic strength of precipitation in these agricultural regions. In contrast, Muan showed a distinct pattern with Cl⁻ as the dominant anion (mean 2.45 ppm), together with elevated Na⁺ (mean 0.79 ppm), suggesting a stronger influence of marine/sea-salt inputs relative to inland sites. Coastal-influenced sites also exhibited pronounced event-to-event variability, with very high maxima for Cl⁻ (e.g., up to 28.09 ppm at Muan), indicating episodic enrichment during specific meteorological conditions. For cations, $N H_{4}^{+}$ was the dominant species at seven of the eight sites (mean 0.57–1.74 ppm), consistent with strong agricultural relevance (e.g., NH₃ emission and subsequent neutralization of acidic species). Yuju recorded the highest mean $N H_{4}^{+}$ (1.74 ppm) and $N O_{3}^{-}$ (2.50 ppm) and also the highest mean Ca²⁺ (0.91 ppm), implying that both agricultural nitrogen inputs and soil/dust-related neutralization may contribute to rainwater chemistry at this site. Overall, the wide Min–Max ranges for several ions (notably $N O_{3}^{-}$ , $S O_{4}^{2 -}$ , and Cl⁻) highlight that precipitation chemistry is shaped not only by background conditions but also by episodic concentration peaks, which can disproportionately affect wet deposition loads.

3.2. Temporal Variations in Cation and Anion Deposition in Agricultural Areas

An analysis of the areal deposition loads of major cations (Na⁺, $N H_{4}^{+}$ , K⁺, Ca²⁺, Mg²⁺) in rainfall samples collected from eight agricultural regions in South Korea between May 2023 and December 2024 revealed distinct monthly variability (Figure 3). No samples are shown for January 2024 because no rainfall events occurred during this period. The total cation load ranged from a minimum of 0.30 kg ha⁻¹ to a maximum of 2.23 kg ha⁻¹, with a mean value of 1.04 kg ha⁻¹ and a standard deviation of 0.49 kg ha⁻¹. The highest total cation load was observed in August 2024, during which the recorded rainfall amount was also the highest at 19,017 mL. During this period, the concentrations of key cations—including $N H_{4}^{+}$ (1.13 kg ha⁻¹), Ca²⁺ (0.30 kg ha⁻¹), and K⁺ (0.25 kg ha⁻¹)—also reached their respective peak values. Although agricultural schedules in Korea vary by crop type, they are generally classified into four phases: sowing in spring (March–May), growing in summer (June–August), harvesting in autumn (September–November), and fallow in winter (December–February). During the summer months, the cultivation of various crops such as rice, corn, and vegetables becomes active, and both the frequency and intensity of fertilizer application increase. In this study, a noticeable increase in the deposition loads of $N H_{4}^{+}$ and K⁺ was observed during the summer period from June to September. Ammonium ( $N H_{4}^{+}$ ) exhibited the highest average load across the entire sampling period at 0.45 kg ha⁻¹, reaching a peak of 1.13 kg ha⁻¹ in August. This pattern is likely attributed to the volatilization of NH₃ from nitrogen fertilizers under high summer temperatures, followed by its atmospheric accumulation and subsequent deposition via rainfall. Additionally, nitrogenous volatile compounds released from compost application and farming operations in agricultural areas are presumed to have contributed significantly. Sodium (Na⁺) showed the second-highest average load at 0.29 kg ha⁻¹ and exhibited the greatest monthly variability among the analyzed cations. Notably, in December 2024, despite relatively low rainfall of 5200 mL, the Na⁺ load sharply increased to 1.17 kg ha⁻¹. This may suggest that salts accumulated through dry deposition during rainless periods were flushed during initial rainfall events, or that regional characteristics—such as the proximity of sampling sites to coastal areas like Gimhae and Muan—facilitated the influx of sea-salt-derived sodium aerosols. Calcium (Ca²⁺) and magnesium (Mg²⁺) recorded average deposition loads of 0.16 kg ha⁻¹ and 0.06 kg ha⁻¹, respectively, showing an increasing trend from summer to early autumn. This trend is interpreted as being influenced by crustal aerosols such as soil dust, resuspended road particles, and mineral components of terrestrial origin. In particular, Ca²⁺ concentrations peaked in August and March 2024, which may reflect dry meteorological conditions and the influence of local soil characteristics. Potassium (K⁺) maintained a relatively low average load of 0.074 kg ha⁻¹ but increased to 0.15 kg ha⁻¹ and 0.25 kg ha⁻¹ in July and August 2024, respectively. This may be attributed to potassium leached from plant residues by summer rainfall, with additional contributions potentially arising from agricultural residues or the application of organic compost.

A linear regression analysis between total cation load and rainfall amount revealed a significant positive correlation (r = 0.7192, p < 0.001). As confirmed by the scatter plot analysis (Figure 4), total deposition load showed a clear tendency to increase with higher rainfall events. However, instances such as in December 2024, where Na⁺ load spiked despite low rainfall, suggest that factors beyond precipitation amount—such as regional atmospheric conditions, coastal proximity, and industrial emissions—may have played a combined role. Analysis of scatter plot distributions by region showed a general trend of increasing total load with rainfall, though slight deviations were observed between locations. In regions such as Gimhae, Muan, and Nonsan, data points with elevated loads were clustered, which may be attributed to regional differences in farming practices, fertilizer application intensity, and the maritime versus continental nature of atmospheric composition.

In agricultural regions, the total monthly anion deposition load varied seasonally, ranging from a minimum of 0.68 kg ha⁻¹ in May 2023 to a maximum of 3.56 kg ha⁻¹ in December 2024 (Figure 5). Notably, despite relatively low precipitation (5200 mL) in December 2024, the total anion load peaked during this month, primarily due to a sharp increase in Cl⁻ concentration (2.11 kg ha⁻¹), which accounted for 59.3% of the total load. This pattern is likely influenced by the application of de-icing salts (e.g., NaCl, CaCl₂) on rural roads and farm machinery access routes during winter, in combination with sea-salt aerosols transported inland by prevailing northwesterly winds from the western coastline. The second-highest total anion deposition load was observed in August 2024 (3.54 kg ha⁻¹), which coincided with the month of highest rainfall (19,017 mL). During this period, the deposition loads of $N O_{3}^{-}$ (1.65 kg ha⁻¹) and $S O_{4}^{2 -}$ (1.02 kg ha⁻¹) reached their annual peaks, suggesting extensive wet deposition of atmospheric pollutants during summer rainfall. In Korea, the summer season is characterized by frequent monsoon rains and heavy downpours, during which herbicide application, nitrogen fertilization, and intensive machinery use are common in agricultural areas. These agricultural activities increase the release of precursor compounds such as NH₃, NO, NO_x, and SO₂ into the atmosphere, which are subsequently transformed into nitrate and sulfate through photochemical oxidation and secondary aerosol formation, and then redeposited onto farmland via rainfall. Anion composition analysis showed that nitrate ( $N O_{3}^{-}$ ) accounted for the largest share of total load, with an average of 40.4%, followed by sulfate ( $S O_{4}^{2 -}$ ) at 31.3%, chloride (Cl⁻) at 24.8%, and nitrite ( $N O_{2}^{-}$ ) at 3.5%. The high contribution of $N O_{3}^{-}$ is likely due to excessive nitrogen fertilizer use, ammonia volatilization, and the subsequent formation and wet deposition of secondary pollutants through photochemical oxidation in agricultural areas. $S O_{4}^{2 -}$ is interpreted to originate from the oxidation of SO₂ released by fuel combustion in agricultural machinery, heating devices, and the open burning of crop residues. The elevated Cl⁻ levels reflect regional and seasonal characteristics, likely influenced by the use of de-icing salts and the influx of sea-salt particles from nearby coastal areas. Due to its high reactivity, $N O_{2}^{-}$ is rapidly converted into compounds such as $N O_{3}^{-}$ in the atmosphere, resulting in relatively low concentrations at the time of deposition.

A statistically significant positive correlation was found between total anion deposition load and rainfall amount (Pearson’s r = 0.5389, p < 0.001). The coefficient of determination (R² = 0.2904) suggests that rainfall is one of the primary drivers of anion input in agricultural areas (Figure 6). However, notable differences in anion loads were observed across sites and time periods, even under similar rainfall conditions. This indicates that additional factors—such as fertilizer application intensity, use of de-icing salts, local weather patterns, and topographic features—also influence ion deposition in complex ways. These findings are significant because they quantitatively reflect the material cycling processes between the atmosphere and soil that occur specifically in agricultural regions. In particular, high concentrations of acidifying anions such as $N O_{3}^{-}$ and $S O_{4}^{2 -}$ can reduce soil pH and accelerate cation leaching. These effects may disrupt crop growth and threaten overall agricultural sustainability.

In agricultural regions, atmospheric ion composition can vary depending on land-use practices such as fertilization, crop cultivation, and soil disturbance. In coastal areas, sea-salt aerosol input is a relevant factor, while in areas near cities, emissions from industry and traffic should also be considered. Therefore, even with equivalent rainfall amounts, the quantity and composition of deposited ions may differ based on regional characteristics. This highlights the need to incorporate spatial heterogeneity into future assessments of atmospheric deposition and environmental monitoring efforts.

3.3. Correlation Analysis of Major Ions in Rainwater from Agricultural Areas

In this study, Pearson correlation analysis was conducted on nine major ions—Na⁺, $N H_{4}^{+}$ , K⁺, Ca²⁺, Mg²⁺, Cl⁻, $N O_{2}^{-}$ , $N O_{3}^{-}$ , and $S O_{4}^{2 -}$ —to investigate interrelationships among ionic species in rainwater collected from agricultural areas (Figure 7). The analysis revealed statistically significant positive correlations among many ion pairs, suggesting that they may originate from common sources or share simultaneous atmospheric transformation and deposition pathways. In particular, $N H_{4}^{+}$ showed very strong correlations with $N O_{3}^{-}$ (r = 0.89) and $S O_{4}^{2 -}$ (r = 0.84). This correlation may reflect a nitrogen cycling process in which ammonium-based fertilizers are applied to farmland, volatilized as ammonia (NH₃), oxidized in the atmosphere, and subsequently converted into ionic forms that are deposited through rainfall. In agricultural areas, large quantities of highly volatile nitrogen fertilizers such as urea (CO(NH₂)₂), ammonium sulfate ((NH₄)₂SO₄), and ammonium nitrate (NH₄NO₃) are commonly applied. These compounds exist as $N H_{4}^{+}$ in the soil but can be converted to NH₃ and released into the atmosphere depending on pH and temperature conditions. The atmospheric NH₃ then reacts with oxidants such as OH, O₃, NOₓ, and SO₂, forming $N O_{3}^{-}$ and $S O_{4}^{2 -}$ , which are deposited back onto agricultural land via precipitation. These results provide evidence that fertilizer application in agricultural areas has a direct impact on the formation of nitrogen- and sulfur-based ionic species in the atmosphere.

A strong correlation was also observed between Mg²⁺ and Cl⁻ (r = 0.85). This is likely due to their common role as major components of sea-salt aerosols. Seawater contains high concentrations of Na⁺, Cl⁻, and Mg²⁺. In coastal areas, wind and wave action can generate airborne sea spray that forms particulate aerosols (Gupta et al., 2015 [62]; Oddie, 1959 [63]; Wada & Kokubu, 1973 [64]). These aerosols can be transported inland by prevailing winds and, during rainfall events, are incorporated into precipitation through wet deposition. In fact, a simultaneous increase in Mg²⁺ and a sharp spike in Cl⁻ concentration were observed in December 2024, supporting the co-influence of marine aerosol input.

The correlation coefficient between Ca²⁺ and $N O_{3}^{-}$ was r = 0.64, reflecting both fertilizer application types and atmospheric chemical interactions. In agricultural areas, calcium nitrate [Ca(NO₃)₂] fertilizers are commonly applied (Soltani et al., 2014 [65]), which may explain the co-occurrence of these ions. Additionally, gaseous HNO₃ in the atmosphere can react with Ca-containing particles to form particulate calcium nitrate aerosols. These are highly soluble and are easily incorporated into precipitation (Mahalinganathan & Thamban, 2016 [66]). A statistically significant correlation was also found between Mg²⁺ and Ca²⁺ (r = 0.58), indicating a common origin from weathered minerals such as carbonates and silicates. These ions may have been released as soil dust during dry-season agricultural activities (e.g., tilling, weeding, machinery operation) and subsequently deposited via rainfall (Novak et al., 2024 [67]; Yang et al., 2022 [68]). These analytical results demonstrate that ion composition in agricultural rainwater is shaped by various environmental factors, including fertilizer input, marine aerosol transport, and soil-derived particulates. Therefore, the ionic composition and distribution patterns may vary significantly depending on local agricultural practices and meteorological conditions, highlighting the need for region-specific analysis.

3.4. Spatial Distribution of Rainwater Ions According to Agricultural Regional Characteristics

In this study, a ternary diagram was used to compare the major cation composition ( $N H_{4}^{+}$ , Na⁺, K⁺, Ca²⁺, Mg²⁺) of rainwater samples collected from agricultural areas (Figure 8). The results showed clear regional differences in ionic composition. Clusters with a high relative proportion of Na⁺ + K⁺ were primarily observed in coastal sites. The elevated proportions of Na and K in these areas are attributed to the incorporation of sea-salt particles into rainfall. Na⁺ and K⁺ ions originate from sea-salt aerosols, which are transported inland by wind and dissolve into precipitation. This dominance of Na⁺ + K⁺ has also been reported in previous studies as a typical chemical feature of marine-influenced rain. Continuous deposition of such marine-influenced rainfall may lead to salt accumulation in the soil and increased salinity stress on crops, suggesting the need for salinity management in these regions.

In the coastal regions of Gimhae and Muan, the average proportion of Na⁺ + K⁺ was 43.9% and 37.8%, respectively, significantly higher than the overall average of 29.3% across the eight regions. These differences were statistically significant in Gimhae (p < 0.001) and Muan (p < 0.05). $N H_{4}^{+}$ ions showed clearly higher proportions in most of the agricultural sites where active farming takes place. $N H_{4}^{+}$ is primarily formed through atmospheric chemical transformations of ammonia (NH₃) emitted from fertilizer use and livestock activities. These findings suggest that intensive agricultural activities and heavy nitrogen fertilizer application are directly reflected in the cation composition of rainfall. Continuous input of $N H_{4}^{+}$ through rainfall may lead to excessive nitrogen loading in soil, acidification, and potential groundwater contamination. Therefore, appropriate nitrogen fertilizer management and non-point source pollution control strategies are essential in agricultural areas.

Regions with a higher concentration of Ca²⁺ + Mg²⁺ were mainly observed in inland and peri-urban areas such as Hongcheon and Danyang. These ions are commonly found in areas rich in soil dust and crustal material, and their increase has been reported in previous studies as being derived from terrestrial sources. Elevated levels of Ca and Mg may raise soil pH and cause micronutrient imbalance. Therefore, appropriate management is required to prevent excess or deficiency of these ions in the soil. Detailed analysis revealed that Danyang had a significantly higher average proportion of Ca²⁺ + Mg²⁺ (29.1%) compared to the average of other regions (21.3%) (p < 0.01). Meanwhile, Hongcheon also showed a relatively high average of 24.7%, but the difference was not statistically significant (p = 0.445). In summary, the ionic composition of rainfall collected from different regions reflects the influence of distinct agricultural practices and geographical characteristics, resulting in marked regional variations in rainwater chemistry.

Analysis of the ternary diagram for major anions (Cl⁻, $N O_{3}^{-}$ , $S O_{4}^{2 -}$ ) in rainwater samples collected from agricultural areas revealed that nitrate ( $N O_{3}^{-}$ ) was the dominant component overall (Figure 9). In particular, samples from Yeoju, Nonsan, and Sangju were mostly distributed in the area where $N O_{3}^{-}$ accounted for more than 50%, indicating a strong influence from nitrogen-based air pollutants or nitrogen fertilizers in these regions. Although chloride (Cl⁻) generally showed a lower proportion, several samples from Muan, Gimhae, and Sangju exhibited relatively high Cl⁻ fractions. This is likely due to Muan’s coastal location, which allows sea-salt aerosols to be transported inland and incorporated into rainfall. Rainwater samples from Gimhae were broadly distributed between $N O_{3}^{-}$ and $S O_{4}^{2 -}$ , suggesting a mixed origin from both fertilizer use and industrial emissions (e.g., nearby airport and factory complexes). Due to the compositional nature of a ternary plot, an increase in the proportion of one ion necessarily leads to a relative decrease in the other two. As a result, most $N O_{3}^{-}$ -dominant samples had lower proportions of Cl⁻ and $S O_{4}^{2 -}$ , while Cl⁻-dominant samples from Muan showed low levels of $N O_{3}^{-}$ and $S O_{4}^{2 -}$ . These distribution patterns reflect differences in ion formation mechanisms, emission sources, and reactivity, and can serve as a useful basis for identifying pollution origins in rainfall samples. Furthermore, the presence of many samples with moderately high to high levels of both $N O_{3}^{-}$ and $S O_{4}^{2 -}$ indicates that ammonia, nitrogen oxides, and sulfur dioxide likely underwent atmospheric oxidation and neutralization to form nitrate and sulfate before being deposited via rainfall. This suggests that rather than a single source, a combination of agricultural activities and other external emissions may be shaping the atmospheric composition in agricultural regions.

4. Conclusions

This study quantitatively analyzed the ionic composition of rainwater collected directly from eight active agricultural areas within four distinct regions of South Korea, focusing on spatial and seasonal variations in key cations (Na⁺, $N H_{4}^{+}$ , K⁺, Ca²⁺, Mg²⁺) and anions (Cl⁻, $N O_{3}^{-}$ , $S O_{4}^{2 -}$ ). By calculating the ion deposition loads per unit area (kg/ha) on a monthly basis, the study identified seasonal characteristics of ionic input in agricultural regions; by conducting correlation analysis among rainfall ions, it evaluated interrelationships between ionic species; and through ternary diagram analysis, it assessed the distribution patterns of ions according to regional characteristics.

The analysis results indicate that the monthly ion deposition loads per unit area in agricultural regions exhibited distinct seasonal patterns. $N H_{4}^{+}$ and $N O_{3}^{-}$ emerged as the most dominant ions, with particularly high $N H_{4}^{+}$ and K⁺ loads observed during the summer when fertilizer application and crop activity peaked. In contrast, Cl⁻ loads were notably elevated during the winter in coastal and peri-urban regions, likely due to the use of de-icing salts and the inland transport of sea-salt aerosols. While total ion loads generally increased with rainfall volume, the data also highlighted the influence of localized emission sources and differing land-use practices.

In addition, correlation analysis among major ionic species revealed strong positive relationships between $N H_{4}^{+}$ and both $N O_{3}^{-}$ (r = 0.89) and $S O_{4}^{2 -}$ (r = 0.84), supporting the idea that these ions commonly form through the atmospheric oxidation of volatilized ammonia. Cl⁻ and Mg²⁺ showed a similarly high correlation (r = 0.85), suggesting a shared marine origin, while Ca²⁺ and $N O_{3}^{-}$ were moderately correlated, possibly due to fertilizer sources and atmospheric chemical interactions. These associations confirm that the ionic composition of rainwater is shaped by both environmental processes and human activities within agricultural landscapes.

The ternary diagram analysis of cation and anion composition further revealed regional differences in ionic distributions. Coastal regions such as Gimhae and Muan displayed higher proportions of Na⁺ and K⁺, characteristic of sea-salt aerosol influence, whereas inland areas like Danyang had elevated levels of Ca²⁺ and Mg²⁺, likely reflecting soil dust and mineral sources. $N H_{4}^{+}$ dominated in most areas with active farming, indicating a strong agricultural influence. For anions, $N O_{3}^{-}$ was generally dominant, while Cl⁻ appeared more frequently in coastal samples, illustrating the spatial variability of emission sources and deposition characteristics across different agricultural regions. These findings collectively illustrate the multifactorial influences on rainwater ion chemistry and emphasize the need for region-specific management and monitoring strategies. Overall, this study highlights the importance of recognizing agricultural areas as unique atmospheric environments, where ion composition in rainfall is shaped by a combination of natural and anthropogenic factors. The findings underscore the necessity for site-specific environmental monitoring and fertilizer management strategies to mitigate non-point source pollution and sustain soil health.

Unlike urban areas, agricultural regions exhibit distinct rainwater ionic compositions due to fertilizer usage, livestock emissions, and proximity to natural and marine sources. Moreover, the characteristics of rainfall chemistry vary further depending on the geographical location of the agricultural area. Therefore, analyzing rainfall in agricultural regions is crucial for understanding localized environmental impacts and for developing tailored management approaches. Future research should aim to expand temporal coverage and include additional pollutant types, while also considering the influence of different agricultural land-use types—such as paddy fields versus upland fields—and specific farming activities on rainwater ion composition, to further refine our understanding of ion cycling in agroecosystems.

Author Contributions

B.W.O. led the overall conception, design, and execution of the study. He conducted the analysis, interpreted the results, and drafted the manuscript. J.H.K. contributed essential data resources and provided domain-specific expertise critical to the study. He supported data validation and offered technical guidance throughout the research process. Y.E.N. served as a corresponding author and was responsible for the comprehensive review, critical revision, and quality control of the manuscript. I.H.S. provided analytical support and academic supervision, offering expert insights that strengthened the study’s methodological framework and interpretation of findings. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Data Availability Statement

The data presented in this study are available on reasonable request from the corresponding author due to legal and institutional restrictions associated with their use within a national public institution-funded project. The original contributions presented in this study are included in the article.

Conflicts of Interest

The authors declare no conflicts of interest.

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Figures and Tables

Figure 1 Geographical distribution of rainwater sampling sites across agricultural regions in South Korea.

Figure 2 Installation Location and Components of the Automatic rainwater sampler ((left): Installation site; (right): Structural components).

Figure 3 Monthly Rainfall and Cation Deposition Load (displays the monthly trends in rainfall amount and the areal deposition loads [kg/ha] of five major cations—Na⁺, ${NH}_{4}^{+}$ , K⁺, Ca²⁺, Mg²⁺—collected from eight agricultural sites in Korea from May 2023 to December 2024).

Figure 4 Relationship Between Rainfall and Total Cation Load (shows the correlation between total cation deposition load and rainfall amount using scatter plots. The gray shaded area represents the 95% confidence interval of the linear regression model).

Figure 5 Monthly rainfall and anion deposition load (displays the monthly trends in rainfall amount and the areal deposition loads [kg/ha] of four major cations—Cl⁻, $N O_{2}^{-}$ , $N O_{3}^{-}$ , $S O_{4}^{2 -}$ —collected from eight agricultural sites in Korea from May 2023 to December 2024.).

Figure 6 Relationship between rainfall and total anion load (shows the correlation between total cation deposition load and rainfall amount using scatter plots). The gray shaded area represents the 95% confidence interval of the linear regression model.

Figure 7 Pearson correlation coefficients among major cations and anions in rainfall samples collected from agricultural areas.

Figure 8 Ternary diagram illustrating the relative proportions of $N H_{4}^{+}$ , Na⁺ + K⁺, and Ca²⁺ + Mg²⁺ in rainwater samples collected from eight agricultural regions in South Korea.

Figure 9 Ternary diagram illustrating the relative composition of major anions (Cl⁻, $N O_{3}^{-}$ , ${S O}_{4}^{2 -}$ ) in rainwater samples collected from eight agricultural regions in South Korea.

Table 1

Cropping system and standard inorganic fertilizer recommendations (nutrient basis) for each rainwater sampling site.

Site	Agricultural Type	Cultivated Crop	Typical Farming Schedule in Korea	Standard Fertilization Rate(N–P₂O₅–K₂O, kg/10 a)
Hongcheon	Upland	Maize (corn)	Sowing: April–May; Harvest: August–September	15–3–6
Danyang	Upland	Garlic	Planting: September–October; Harvest: May–June	24.8–7.8–20.0
Sangju	Upland	Soybean	Sowing: May–June; Harvest: October–November	3.0–3.0–3.4
Muan	Upland	Onion	Nursery: August–September; Transplanting: October–November; Harvest: April–June	24–7.7–15.4
Gimhae	Paddy	Rice	Sowing: April–May; Transplanting: May–June; Harvest: September–October	9.0–4.5–8.7
Yuju	Paddy	Rice	Sowing: April–May; Transplanting: May–June; Harvest: September–October	9.0–4.5–8.7
Nonsan	Paddy	Rice	Sowing: April-May; Transplanting: May–June; Harvest: September–October	9.0–4.5–8.7
Naju	Paddy	Rice	Sowing: April–May; Transplanting: May–June; Harvest: September–October	9.0–4.5–8.7

Table 2

Summary of sampling events, collected rainfall volume (mL), pH, and electrical conductivity (EC) at each rainwater sampling site.

Sampling Sites	Sampling Events (n)	Collected Rainfall Volume (mL)	pH	EC (µS/cm)
Hongcheon	15	12,939 (920–34,000)	5.85 (5.15–7.33)	9.89 (4.14–34.10)
Danyang	21	13,967 (480–32,100)	6.29 (5.40–6.76)	13.47 (3.91–60.40)
Sangju	22	11,762 (295–32,650)	6.19 (5.15–7.01)	15.78 (4.48–110.70)
Gimhae	47	6163 (270–18,520)	5.19 (3.84–6.60)	16.28 (3.27–78.50)
Yuju	14	12,563 (620–72,100)	6.31 (5.59–7.09)	23.78 (6.24–109.00)
Nonsan	38	7974 (36–28,160)	6.16 (4.53–6.92)	15.35 (2.74–65.40)
Naju	26	9263 (250–29,630)	5.95 (4.32–8.29)	16.95 (4.02–83.60)
Muan	25	8473 (270–20,750)	6.16 (5.12–8.17)	20.40 (4.05–111.80)

Values are presented as mean (minimum–maximum). NA indicates missing values.

Table 3

Summary statistics of major ion concentrations in rainwater at each site (ppm). Values are presented as mean (minimum–maximum).

Ions	Site	Na⁺ (ppm)	$N H_{4}^{+}$ (ppm)	K⁺ (ppm)	Ca²⁺ (ppm)	Mg²⁺ (ppm)
Cations	Hongcheon	0.27(0.05–1.19)	0.67(0.13–1.91)	0.11(0.02–0.43)	0.35(0.06–2.50)	0.07(0.01–0.48)
	Danyang	0.28(0.07–1.25)	0.86(0.14–2.99)	0.11(0.02–0.47)	0.54(0.07–3.56)	0.09(0.00–0.41)
	Sangju	0.28(0.05–1.44)	1.15(0.20–7.62)	0.14(0.00–0.48)	0.36(0.03–1.89)	0.10(0.00–0.50)
	Gimhae	0.60(0.08–3.05)	0.57(0.06–4.02)	0.11(0.01–0.35)	0.28(0.03–1.27)	0.11(0.00–0.57)
	Yuju	0.45(0.10–2.50)	1.74(0.38–4.90)	0.18(0.02–0.47)	0.91(0.10–7.25)	0.12(0.01–0.65)
	Nonsan	0.32(0.05–1.25)	1.12(0.14–4.96)	0.21(0.01–3.38)	0.38(0.03–1.84)	0.09(0.00–0.43)
	Naju	0.61(0.06–6.04)	1.03(0.19–7.31)	0.13(0.01–0.47)	0.22(0.02–1.04)	0.12(0.01–0.80)
	Muan	0.79(0.12–4.45)	1.31(0.18–7.32)	0.34(0.05–2.61)	0.42(0.08–2.18)	0.19(0.02–1.63)
	Site	Cl⁻ (ppm)	${NO}_{2}^{-}$ (ppm)	${NO}_{3}^{-}$ (ppm)	${SO}_{4}^{2 -}$ (ppm)
Anions	Hongcheon	0.46(0.14–2.06)	0.07(0.06–0.11)	1.06(0.35–2.30)	0.76(0.21–2.20)
	Danyang	0.38(0.15–1.73)	0.08(0.06–0.16)	1.43(0.35–6.64)	0.96(0.21–4.37)
	Sangju	0.44(0.14–2.01)	0.09(0.02–0.26)	1.55(0.03–11.43)	1.01(0.02–6.02)
	Gimhae	0.95(0.21–5.47)	0.09(0.01–0.83)	1.46(0.11–12.00)	1.18(0.10–6.96)
	Yuju	0.65(0.21–3.35)	0.12(0.07–0.42)	2.50(0.65–13.97)	1.66(0.48–9.01)
	Nonsan	0.59(0.14–3.11)	0.09(0.02–0.36)	1.84(0.04–9.60)	1.02(0.02–5.68)
	Naju	1.12(0.18–12.40)	0.08(0.04–0.15)	1.66(0.37–12.40)	1.38(0.03–12.01)
	Muan	2.45(0.22–28.09)	0.09(0.04–0.16)	2.01(0.35–12.40)	1.77(0.29–12.02)

References

1. Chu, B.; Zhang, X.; Liu, Y.; He, H.; Sun, Y.; Jiang, J.; Li, J.; Hao, J. Synergetic Formation of Secondary Inorganic and Organic Aerosol: Effect of SO₂ and NH₃ on Particle Formation and Growth. Atmos. Chem. Phys.; 2016; 16, pp. 14219-14230. [DOI: https://dx.doi.org/10.5194/acp-16-14219-2016]

2. Volkamer, R.; Jimenez, J.L.; San Martini, F.; Dzepina, K.; Zhang, Q.; Salcedo, D.; Molina, L.T.; Worsnop, D.R.; Molina, M.J. Secondary Organic Aerosol Formation from Anthropogenic Air Pollution: Rapid and Higher than Expected. Geophys. Res. Lett.; 2006; 33, L17811. [DOI: https://dx.doi.org/10.1029/2006GL026899]

3. Jenkin, M.E.; Clemitshaw, K.C. Ozone and Other Secondary Photochemical Pollutants: Chemical Processes Governing Their Formation in the Planetary Boundary Layer. Atmos. Environ.; 2000; 34, pp. 2499-2527. [DOI: https://dx.doi.org/10.1016/S1352-2310(99)00478-1]

4. Tiwari, S.; Hopke, P.K.; Thimmaiah, D.; Dumka, U.C.; Srivastava, A.K.; Bisht, D.S.; Rao, P.S.P.; Chate, D.M.; Srivastava, M.K.; Tripathi, S.N. Nature and Sources of Ionic Species in Precipitation across the Indo-Gangetic Plains, India. Aerosol Air Qual. Res.; 2016; 16, pp. 943-957. [DOI: https://dx.doi.org/10.4209/aaqr.2015.06.0423]

5. Lesack, L.F.W.; Melack, J.M. The Deposition, Composition, and Potential Sources of Major Ionic Solutes in Rain of the Central Amazon Basin. Water Resour. Res.; 1991; 27, pp. 2953-2977. [DOI: https://dx.doi.org/10.1029/91WR01946]

6. Bhargava, S.; Bhargava, S. Ecological Consequences of the Acid Rain. IOSR J. Appl. Chem.; 2013; 5, pp. 19-24. [DOI: https://dx.doi.org/10.9790/5736-0541924]

7. Ling, D.J.; Zhang, J.E.; Ouyang, Y. Advancements in Research on Impact of Acid Rain on Soil Ecosystem: A Review. Soils; 2007; 39, pp. 514-521.

8. Singh, A.; Agrawal, M. Acid Rain and Its Ecological Consequences. J. Environ. Biol.; 2007; 29, 15.

9. Haines, B.; Waide, J. Predicting Potential Impacts of Acid Rain on Elemental Cycling in a Southern Appalachian Deciduous Forest at Coweeta. Effects of Acid Precipitation on Terrestrial Ecosystems; Springer: Berlin/Heidelberg, Germany, 1980; pp. 335-340.

10. Dzhamalov, R.G.; Zlobina, V.L. The Role of Precipitation in Groundwater Pollution. Advances in Groundwater Pollution Control and Remediation; Springer: Berlin/Heidelberg, Germany, 1996; pp. 493-507.

11. Park, J.; Lee, D.; Kim, H.; Woo, N.C. Effects of Water-Table Changes Following Rainfall Events on Arsenic Fate and Transport in Groundwater–Surface Water Mixing Zones. Sci. Total Environ.; 2024; 933, 173200. [DOI: https://dx.doi.org/10.1016/j.scitotenv.2024.173200]

12. Camargo, J.A.; Alonso, Á. Ecological and Toxicological Effects of Inorganic Nitrogen Pollution in Aquatic Ecosystems: A Global Assessment. Environ. Int.; 2006; 32, pp. 831-849. [DOI: https://dx.doi.org/10.1016/j.envint.2006.05.002] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/16781774]

13. Smith, V.H. Eutrophication of Freshwater and Coastal Marine Ecosystems a Global Problem. Environ. Sci. Pollut. Res.; 2003; 10, pp. 126-139. [DOI: https://dx.doi.org/10.1065/espr2002.12.142]

14. Xu, Y.; Yuan, Q.; Zhao, C.; Wang, L.; Li, Y.; Ma, X.; Guo, J.; Yang, H. Identification of Nitrate Sources in Rivers in a Complex Catchment Using a Dual Isotopic Approach. Water; 2021; 13, 83. [DOI: https://dx.doi.org/10.3390/w13010083]

15. Wang, J.; Li, X.; Li, Y.; Shi, Y.; Xiao, H.; Wang, L.; Yin, W.; Zhu, Z.; Bian, H.; Li, H. Transport Pathways of Nitrate in Stormwater Runoff Inferred from High-Frequency Sampling and Stable Water Isotopes. Environ. Sci. Technol.; 2024; 58, pp. 17026-17035. [DOI: https://dx.doi.org/10.1021/acs.est.4c02495] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/39152914]

16. Zhang, L.; Liu, X. Nitrogen Transformations Associated with N₂O Emissions in Agricultural Soils. Nitrogen in Agriculture—Updates; InTech Rijeka: Rijeka, Croatia, 2018.

17. Ma, R.; Zou, J.; Han, Z.; Yu, K.; Wu, S.; Li, Z.; Liu, S.; Niu, S.; Horwath, W.R.; Zhu-Barker, X. Global Soil-derived Ammonia Emissions from Agricultural Nitrogen Fertilizer Application: A Refinement Based on Regional and Crop-specific Emission Factors. Glob. Chang. Biol.; 2021; 27, pp. 855-867. [DOI: https://dx.doi.org/10.1111/gcb.15437] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/33155724]

18. Beaudor, M.; Vuichard, N.; Lathière, J.; Evangeliou, N.; Van Damme, M.; Clarisse, L.; Hauglustaine, D. Global Agricultural Ammonia Emissions Simulated with the ORCHIDEE Land Surface Model. Geosci. Model. Dev.; 2023; 16, pp. 1053-1081. [DOI: https://dx.doi.org/10.5194/gmd-16-1053-2023]

19. Singh, R.K.; Prajapati, S.; Nigam, I.M.; Zhao, R.; Gupta, T. Particulates and Gaseous Emission from Indian Agricultural Sector and Health Burden Attributable to Agricultural PM_2.5. ChemRxiv; 2024; [DOI: https://dx.doi.org/10.26434/chemrxiv-2024-hxdbt]

20. Yang, Y.; Liu, L.; Liu, P.; Ding, J.; Xu, H.; Liu, S. Improved Global Agricultural Crop-and Animal-Specific Ammonia Emissions during 1961–2018. Agric. Ecosyst. Environ.; 2023; 344, 108289. [DOI: https://dx.doi.org/10.1016/j.agee.2022.108289]

21. Liu, Y.; von Wirén, N. Ammonium as a Signal for Physiological and Morphological Responses in Plants. J. Exp. Bot.; 2017; 68, pp. 2581-2592. [DOI: https://dx.doi.org/10.1093/jxb/erx086]

22. Uddin, M.; Perrine-Walker, F.; Kaiser, B. The Role of Ammonium Transport Proteins in Improving Nitrogen Use Efficiency in Crop Production. Improving Nitrogen Use Efficiency in Crop Production; Burleigh Dodds Science Publishing: Cambridge, UK, 2024; pp. 33-66. ISBN 9781801464703

23. Wdowiak, A.; Kryzheuskaya, K.; Podgórska, A.; Paterczyk, B.; Zebrowski, J.; Archacki, R.; Szal, B. Ammonium Nutrition Modifies Cellular Calcium Distribution Influencing Ammonium-Induced Growth Inhibition. J. Plant Physiol.; 2024; 298, 154264. [DOI: https://dx.doi.org/10.1016/j.jplph.2024.154264]

24. Xiao, C.; Fang, Y.; Wang, S.; He, K. The Alleviation of Ammonium Toxicity in Plants. J. Integr. Plant Biol.; 2023; 65, pp. 1362-1368. [DOI: https://dx.doi.org/10.1111/jipb.13467]

25. Podgorska, A.; Ostaszewska, M.; Gardeström, P.; Rasmusson, A.G.; Szal, B. In Comparison with Nitrate Nutrition, Ammonium Nutrition Increases Growth of the Frostbite1 A Rabidopsis Mutant. Plant Cell Environ.; 2015; 38, pp. 224-237. [DOI: https://dx.doi.org/10.1111/pce.12404] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/25040883]

26. Ye, J.Y.; Tian, W.H.; Jin, C.W. Nitrogen in Plants: From Nutrition to the Modulation of Abiotic Stress Adaptation. Stress. Biol.; 2022; 2, 4. [DOI: https://dx.doi.org/10.1007/s44154-021-00030-1] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/37676383]

27. Qin, C.; Yi, K.; Wu, P. Ammonium Affects Cell Viability to Inhibit Root Growth in Arabidopsis. J. Zhejiang Univ. Sci. B; 2011; 12, pp. 477-484. [DOI: https://dx.doi.org/10.1631/jzus.B1000335]

28. Coleto, I.; Marín-Peña, A.J.; Urbano-Gámez, J.A.; González-Hernández, A.I.; Shi, W.; Li, G.; Marino, D. Interaction of Ammonium Nutrition with Essential Mineral Cations. J. Exp. Bot.; 2023; 74, pp. 6131-6144. [DOI: https://dx.doi.org/10.1093/jxb/erad215] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/37279530]

29. Riaz, M.; Ahmad, M.N.; Mukhtar, M.; Nawaz, N. Nitrate Contamination of Soil and Water: Implications for Ecosystem Functions and Human Health. Inorganic Contaminants and Radionuclides; Elsevier: Amsterdam, The Netherlands, 2024; pp. 351-373.

30. Toetz, D. Nitrate in Ground and Surface Waters in the Vicinity of a Concentrated Animal Feeding Operation. Arch. Hydrobiol.; 2006; 166, pp. 67-77. [DOI: https://dx.doi.org/10.1127/0003-9136/2006/0166-0067]

31. Bharti, C.; Mohapatra, A.; Maurya, R.; Maharana, C.; Maurya, A.; Malakar, P. Nitrate Pollution with Modernization of Indian Agriculture. J. Pharmacogn. Phytochem.; 2020; 9, pp. 2073-2080.

32. Hornbeck, J.H. Biological Remediation of Nitrate ( $N O_{3}^{-}$ ) Pollution at the Land/Water Interface. Stud. Online J.; 2000; 5, pp. 1-7.

33. Künstler, A.; Gullner, G.; Ádám, A.L.; Kolozsváriné Nagy, J.; Király, L. The Versatile Roles of Sulfur-Containing Biomolecules in Plant Defense—A Road to Disease Resistance. Plants; 2020; 9, 1705. [DOI: https://dx.doi.org/10.3390/plants9121705]

34. Anderson, J.W.; Fitzgerald, M.A. Sulphur Distribution and Redistribution in Vegetative and Generative Plants. Sulphur in Plants; Springer: Berlin/Heidelberg, Germany, 2003; pp. 113-134.

35. Hell, R.; Rennenberg, H. The Plant Sulphur Cycle. Sulphur in Agroecosystems; Springer: Berlin/Heidelberg, Germany, 1998; pp. 135-173.

36. Singh, S.S. Increase in Neutral Salt Extractable Cation Exchange Capacity of Some Acid Soils as Affected by CaSO₄ Applications. Can. J. Soil Sci.; 1984; 64, pp. 153-161. [DOI: https://dx.doi.org/10.4141/cjss84-017]

37. Mitchell, M.J.; Lindberg, S.E. Sulfur Chemistry, Deposition, and Cycling in Forests. Atmospheric Deposition and Forest Nutrient Cycling: A Synthesis of the Integrated Forest Study; Springer: Berlin/Heidelberg, Germany, 1992; pp. 72-149.

38. Fasth, W.J.; David, M.B.; Vance, G.F. Sulfate Retention and Cation Leaching of Forest Soils in Response to Acid Additions. Can. J. For. Res.; 1991; 21, pp. 32-41. [DOI: https://dx.doi.org/10.1139/x91-005]

39. Ulaş, F.; Ameen, H.; Ulaş, A. Salt Stress and Its Implications in Vegetable Crops with Special Reference to the Cucurbitaceae Family. Ann. Arid. Zone; 2024; 63, pp. 117-129. [DOI: https://dx.doi.org/10.56093/aaz.v63i4.155494]

40. Lindberg, S.; Premkumar, A. Ion Changes and Signaling under Salt Stress in Wheat and Other Important Crops. Plants; 2023; 13, 46. [DOI: https://dx.doi.org/10.3390/plants13010046]

41. Bernstein, N. Plants and Salt: Plant Response and Adaptations to Salinity. Model Ecosystems in Extreme Environments; Elsevier: Amsterdam, The Netherlands, 2019; pp. 101-112.

42. Roy, S.; Chowdhury, N. Salt Stress in Plants and Amelioration Strategies: A Critical Review; IntechOpen: London, UK, 2020; ISBN 1838810625

43. Yan, S.; Zhang, T.; Zhang, B.; Zhang, T.; Cheng, Y.; Wang, C.; Luo, M.; Feng, H.; Siddique, K.H.M. The Higher Relative Concentration of K⁺ to Na⁺ in Saline Water Improves Soil Hydraulic Conductivity, Salt-Leaching Efficiency and Structural Stability. Soil; 2023; 9, pp. 339-349. [DOI: https://dx.doi.org/10.5194/soil-9-339-2023]

44. Mäser, P.; Gierth, M.; Schroeder, J.I. Molecular Mechanisms of Potassium and Sodium Uptake in Plants. Plant Soil; 2002; 247, pp. 43-54. [DOI: https://dx.doi.org/10.1023/A:1021159130729]

45. Black, A.S.; Abdul-Hakim, B.M.S. Soil Structure Effects and Leaching of Sodium Following Sodium Chloride Fertiliser Applications. New Zealand J. Agric. Res.; 1984; 27, pp. 399-403. [DOI: https://dx.doi.org/10.1080/00288233.1984.10430641]

46. Wakeel, A. Potassium–Sodium Interactions in Soil and Plant under Saline-sodic Conditions. J. Plant Nutr. Soil Sci.; 2013; 176, pp. 344-354. [DOI: https://dx.doi.org/10.1002/jpln.201200417]

47. Cakmak, I. Magnesium in Crop Production, Food Quality and Human Health. Plant Soil; 2013; 368, pp. 1-4. [DOI: https://dx.doi.org/10.1007/s11104-013-1781-2]

48. Jing, T.; Li, J.; He, Y.; Shankar, A.; Saxena, A.; Tiwari, A.; Maturi, K.C.; Solanki, M.K.; Singh, V.; Eissa, M.A. Role of Calcium Nutrition in Plant Physiology: Advances in Research and Insights into Acidic Soil Conditions-A Comprehensive Review. Plant Physiol. Biochem.; 2024; 210, 108602. [DOI: https://dx.doi.org/10.1016/j.plaphy.2024.108602] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/38608506]

49. Rufyikiri, G.; Dufey, J.E.; Achard, R.; Delvaux, B. Cation Exchange Capacity and Aluminum–Calcium–Magnesium Binding in Roots of Bananas Cultivated in Soils and in Nutrient Solutions. Commun. Soil Sci. Plant Anal.; 2002; 33, pp. 991-1009. [DOI: https://dx.doi.org/10.1081/CSS-120003079]

50. Richter, D.D.; Johnson, D.W.; Dai, K.H. Cation Exchange and A1 Mobilization in Soils. Atmospheric Deposition and Forest Nutrient Cycling: A Synthesis of the Integrated Forest Study; Springer: Berlin/Heidelberg, Germany, 1992; pp. 341-377.

51. Oosterhuis, D.M.; Loka, D.A.; Kawakami, E.M.; Pettigrew, W.T. The Physiology of Potassium in Crop Production. Adv. Agron.; 2014; 126, pp. 203-233.

52. Anil Kumar, S.; Kaniganti, S.; Hima Kumari, P.; Sudhakar Reddy, P.; Suravajhala, P.P.S.; Kishor, P.B.K. Functional and Biotechnological Cues of Potassium Homeostasis for Stress Tolerance and Plant Development. Biotechnol. Genet. Eng. Rev.; 2024; 40, pp. 3527-3570. [DOI: https://dx.doi.org/10.1080/02648725.2022.2143317] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/36469501]

53. Ragel, P.; Raddatz, N.; Leidi, E.O.; Quintero, F.J.; Pardo, J.M. Regulation of K⁺ Nutrition in Plants. Front. Plant Sci.; 2019; 10, 281. [DOI: https://dx.doi.org/10.3389/fpls.2019.00281] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/30949187]

54. Sayed, S.; Hellal, F.A.; El–Rab, N.G.; Zewainy, R.M. Ameliorative Effects of Potassium on the Salinity Stress in Plants: A Review. Asian J. Soil Sci. Plant Nutr.; 2019; 23, pp. 1-15. [DOI: https://dx.doi.org/10.9734/ajsspn/2019/v4i230040]

55. Squizzato, S.; Masiol, M.; Visin, F.; Canal, A.; Rampazzo, G.; Pavoni, B. The PM_2.5 Chemical Composition in an Industrial Zone Included in a Large Urban Settlement: Main Sources and Local Background. Environ. Sci. Process Impacts; 2014; 16, pp. 1913-1922. [DOI: https://dx.doi.org/10.1039/C4EM00111G]

56. Ren, H.; Wu, X.; Zhao, W.; Cheng, J. Potential Risks of PM_2.5 in Urban and Suburban Environments: A Dual Perspective on Chemical Constituents and Pollution Sources. Aerosol Air Qual. Res.; 2024; 24, 230315. [DOI: https://dx.doi.org/10.4209/aaqr.230315]

57. Lewis, A.C. The Changing Face of Urban Air Pollution. Science; 2018; 359, pp. 744-745. [DOI: https://dx.doi.org/10.1126/science.aar4925] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/29449479]

58. Vega, E.; López-Veneroni, D.; Ramírez, O.; Chow, J.C.; Watson, J.G. Particle-Bound PAHs and Chemical Composition, Sources and Health Risk of PM2. 5 in a Highly Industrialized Area. Aerosol Air Qual. Res.; 2021; 21, 210047. [DOI: https://dx.doi.org/10.4209/aaqr.210047]

59. Skyllakou, K.; Rivera, P.G.; Dinkelacker, B.; Karnezi, E.; Kioutsioukis, I.; Hernandez, C.; Adams, P.J.; Pandis, S.N. Changes in PM 2.5 Concentrations and Their Sources in the US from 1990 to 2010. Atmos. Chem. Phys.; 2021; 21, pp. 17115-17132. [DOI: https://dx.doi.org/10.5194/acp-21-17115-2021]

60. Galloway, J.N.; Cowling, E.B.; Gorham, E.; McFee, W.W. National Program for Assessing the Problem of Atmospheric Deposition (Acid Rain). A Report to the Council on Environmental Quality; U.S. Department of Energy Office of Scientific and Technical Information: Oak Ridge, TN, USA, 1978.

61. Bai, W.; Zhao, X.; Yin, B.; Guo, L.; Zhang, W.; Wang, X.; Yang, W. Characteristics of PM_2.5 in an Industrial City of Northern China: Mass Concentrations, Chemical Composition, Source Apportionment, and Health Risk Assessment. Int. J. Environ. Res. Public Health; 2022; 19, 5443. [DOI: https://dx.doi.org/10.3390/ijerph19095443]

62. Gupta, D.; Eom, H.-J.; Cho, H.-R.; Ro, C.-U. Hygroscopic behavior of NaCl–MgCl₂ mixture particles as nascent sea-spray aerosol surrogates and observation of efflorescence during humidification. Atmos. Chem. Phys.; 2015; 15, pp. 11273-11290. [DOI: https://dx.doi.org/10.5194/acp-15-11273-2015]

63. Oddie, B.C.V. The composition of precipitation at Lerwick, Shetland. Q. J. R. Meteorol. Soc.; 1959; 85, pp. 163-165. [DOI: https://dx.doi.org/10.1002/qj.49708536410]

64. Wada, S.; Kokubu, N. Chemical composition of maritime aerosols. Geochem. J.; 1973; 6, pp. 131-139. [DOI: https://dx.doi.org/10.2343/geochemj.6.131]

65. Soltani, S.; Azadi, H.; Mahmoudi, H.; Witlox, F. Organic Agriculture in Iran: Farmers’ Barriers to and Factors Influencing Adoption. Renew. Agric. Food Syst.; 2014; 29, pp. 126-134. [DOI: https://dx.doi.org/10.1017/S1742170513000069]

66. Mahalinganathan, K.; Thamban, M. Potential Genesis and Implications of Calcium Nitrate in Antarctic Snow. Cryosphere; 2016; 10, pp. 825-836. [DOI: https://dx.doi.org/10.5194/tc-10-825-2016]

67. Novak, A.; Šumrada, T.; Juvančič, L. Are Farmers’ Minds Really Too Elusive to Capture? A Novel Evaluation Frame-work for the Knowledge Transfer Initiatives in Agriculture. Int. J. Agric. Sustain.; 2024; 22, 2379877. [DOI: https://dx.doi.org/10.1080/14735903.2024.2379877]

68. Yang, D.; Yao, T.; Wu, G.; Zhu, M.; De, J.; Qu, D.; Shi, Y. Identifying the Natural and Agricultural Impacts from Glacio-chemistry in the Aru Ice Core on the Northwestern Tibetan Plateau. SSRN; 2022; [DOI: https://dx.doi.org/10.2139/ssrn.4514376]

© 2026 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Ionic Composition and Deposition Loads of Rainwater According to Regional Characteristics of Agricultural Areas

Content area