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Article

Vertical Distribution Patterns of Nitrogen and Phosphorus in Soil Solution: Insights from a Wetland Trial Site in the Li River Basin

by
Chunjin Gong
1,
Junlei Han
1,
Junfeng Dai
2,3,*,
Rui Xia
4,
Zupeng Wan
2,
Shuaipu Zhang
2 and
Jingxuan Xu
1,4,5
1
College of Environmental Science and Engineering, Guilin University of Technology, Guilin 541000, China
2
Guangxi Key Laboratory of Environmental Pollution Control Theory and Technology, Guilin University of Technology, Guilin 541000, China
3
Collaborative Innovation Center for Water Pollution Control and Water Security in Karst Region, Guilin University of Technology, Guilin 541000, China
4
Chinese Research Academy of Environmental Sciences, Beijing 100000, China
5
Guilin Water and Resources Bureau, Guilin 541000, China
*
Author to whom correspondence should be addressed.
Water 2024, 16(13), 1830; https://doi.org/10.3390/w16131830
Submission received: 4 May 2024 / Revised: 15 June 2024 / Accepted: 24 June 2024 / Published: 27 June 2024
(This article belongs to the Section Water Quality and Contamination)
Browse Figures
Versions Notes

Abstract

:
Agricultural activities contribute significantly to the pollution of groundwater through the incomplete absorption of nitrogen (N) and phosphorus (P). Understanding the vertical migration patterns of N and P in soil solution is crucial for controlling groundwater quality. This study is based on monitoring data of soil solution nitrogen and phosphorus at different depths (30 cm, 60 cm, 100 cm) in the Huixian Wetland Experimental Area in the Li River Basin from March to December 2021. The vertical distribution patterns of nitrogen and phosphorus in soil solution in the study area are elucidated from three aspects: seasonal variations, karst types, and land use. The results indicate that the following: (1) NO3−-N is the predominant form of nitrogen, generally decreasing with increasing soil depth, while NH4+-N concentrations show slight increases and TP concentrations remain relatively stable. Overall, NO3−-N and TN concentrations tend to accumulate at 30 cm and 60 cm depths during both irrigation and non-irrigation seasons, with no distinct distribution patterns observed for NH4+-N and TP. (2) During the irrigation season, the migration distance of NO3−-N in non-karst landforms is mainly at 60 cm, while in peak forest plains, it is mainly at 100 cm, with no clear trend observed in NO3−-N concentrations in peak cluster depressions. In non-irrigation seasons, the distribution of NO3−-N content in non-karst landforms and peak cluster depressions is mainly 30 cm > 60 cm > 100 cm. The downward migration distance of NH4+-N generally follows the order of peak cluster depressions > peak forest plains > non-karst landforms. (3) During the irrigation season, NO3−-N concentrations in paddy fields remain relatively high at 100 cm, while in drylands, NO3−-N concentrations generally follow the pattern of 30 cm > 60 cm > 100 cm but may exhibit anomalous increases or decreases at 60 cm and 100 cm depths during heavy rainfall.

1. Introduction

Nitrogen (N) and phosphorus (P) are indispensable resources for plant growth and constitute the primary components of agricultural nonpoint source pollution [1]. The excessive input of N and P leads to water degradation, which is a major concern for drinking water quality and ecosystem health [2,3]. Excessive and improper use of fertilizers in agricultural production is one of the primary causes of N and P pollution in groundwater. Unused N and P nutrients accumulate in the soil and are leached into surface and groundwater through rainfall runoff and irrigation drainage, resulting in water pollution [4]. Studies have indicated that in China, the utilization rate of nitrogen fertilizer during the growing season is only 30% to 35% [5], and for phosphorus fertilizer, it is only 10% to 25%. After nitrogen fertilizer is applied to the soil, approximately 10% to 40% is leached into groundwater, resulting in groundwater pollution [6]. According to the National Bureau of Statistics and the Ministry of Agriculture and Rural Affairs of the People’s Republic of China, as reported in the “Second National Pollution Source Census Bulletin” released in June 2020, the emissions of agricultural source water pollutants in 2017 were 216,200 tons of ammonia nitrogen, 1,414,900 tons of total nitrogen, and 212,000 tons of total phosphorus [7]. In agricultural fields, artificial synthetic nitrogen fertilizers are the primary source of soil nitrogen [8], whereas in other land use types, nitrogen derived from organic matter decomposition predominates [9]. Wang et al. [10] and Zhu et al. [11] investigated the relationship between land use patterns and soil quality in karst rocky desertification areas in Guizhou and Guangxi, revealing variations in soil total nitrogen, available nitrogen, and alkali-hydrolyzable nitrogen content due to land use changes. Yang et al. [12] further demonstrated that significant positive correlations existed between total nitrogen and P:K ratios across different land use conditions, while significant negative correlations were found with C:N and C:P ratios, indicating an inhibitory effect of total nitrogen on C:N and C:P ratios. In summary, nitrogen and phosphorus have become the primary sources of water pollutants.
Soil solution, often referred to as the “blood” of soil, denotes the dissolved or colloidal substances in soil bound with water molecules, encompassing inorganic salts, organic compounds, and microorganisms [13]. By analyzing the dynamic changes in soil solution chemical composition, one can grasp the processes, mechanisms, and relationships with the surrounding environment of various biochemical reactions in soil [14]. Furthermore, soil solution serves as a mediator for various biochemical reactions in soil, swiftly responding to environmental changes before other soil indicators exhibit alterations [15]. Since soil formation processes and biochemical reactions in soil mostly occur in soil solution, changes in the chemical composition of soil solution can to some extent reflect the latest status of soil [13]. Therefore, investigating the dynamic changes in the chemical composition of soil solution will contribute to a deeper understanding of the impact and mechanisms of N and P migration on soil ecosystems.
Karst regions, characterized by intense karstification and unique geological conditions, exhibit ecological fragility similar to that of desert margins [16,17,18]. As one of the countries with the largest karst areas in the world, China’s karst landforms cover 13.5% of its total land area, mainly distributed in the southwestern regions dominated by Yunnan, Guizhou, and Guangxi [19,20]. The presence of fractures in karst regions promotes the infiltration of soil solution, leading to the loss of N and P. Moreover, due to the humid climate and abundant rainfall in the southwestern region, N and P are more prone to migrate downward through karst fissures with the infiltration of soil solution, especially in exposed surface fissures, further increasing the leaching of soil N and P [21,22,23]. Ren et al. [24] concluded based on short-term monitoring results that shorter retention times (<5 days) favor microbial nitrification, thereby increasing nitrate nitrogen concentrations in karst conduits. In this study area, due to the special nature of parent materials for soil formation, karst and non-karst landforms coexist. In areas with exposed limestone and discontinuous soil layers, natural impermeable and filtering layers are lacking or insufficient, resulting in weak adsorption and purification capacities for pollutants. This shortens the retention time of pollutants, making excessive N and P in soil solution prone to leach into shallow karst groundwater, exacerbating karst water environment pollution [25,26].
While the importance of soil nutrient research has been recognized, research directions have been primarily limited to comparing the effects of different types of soil and terrain slopes on the distribution of nitrogen and phosphorus nutrients in the soil [27,28,29]. Additionally, most studies have utilized centrifugation or water/salt solution extraction methods to collect soil solution samples [30,31,32,33,34,35], which cannot provide long-term positional research data. These methods can disrupt soil structure, thereby affecting the concentration of certain chemical components in the solution and failing to clearly reflect the variation patterns of nitrogen and phosphorus distribution in soil solution at different depths in their natural state. Moreover, research on soil nutrient loss has mainly focused on the loss of soil nutrients through runoff [36,37,38,39]. However, in karst landscapes, the vertical transport of nitrogen and phosphorus elements in soil solution also has significant impacts and practical applications in agriculture and the environment. This process is not only a crucial part of nitrogen and phosphorus cycling in soil but also an important factor affecting plant growth and water quality protection. Tian et al. [40] studied the vertical variations of soil available phosphorus (SAP) in the critical zone of the Loess Plateau (50–200 m). Their findings indicated a decreasing trend of SAP throughout the entire profile. Zhu et al. [41] assessed the impact of climate factors on the concentration and distribution of total soil phosphorus across different forest ecosystems in China. The results showed that the concentration of total soil phosphorus significantly decreases with increasing soil depth. Qiao et al. [42] investigated the vertical distribution of soil total nitrogen (STN) and soil total phosphorus (STP) in deep soil profiles of the Loess Plateau. They found that, except for the Shenmu site, STN exhibited a pattern of initially decreasing and then increasing with soil depth, while the vertical distribution of STP consistently showed fluctuating changes. Furthermore, most studies tend to simulate this process through indoor soil column experiments [43,44,45], whereas the lack of research involving the regular collection of field soil solution samples and analysis of the vertical migration of N and P restricts understanding of the actual impact of N and P release from soil solution on water quality.
In conclusion, nitrogen and phosphorus have emerged as major contributors to water pollution. This issue is particularly acute in fragile karst regions, where thin soil layers and extensive cultivation make the soil prone to degradation and the loss of nitrogen and phosphorus. Soil solutions, acting as intermediaries in various biochemical reactions within the soil, respond swiftly to environmental changes, providing a valuable avenue for studying the impact of nitrogen and phosphorus migration on soil ecosystems. Investigating the vertical dynamics of nitrogen and phosphorus in soil solutions across different karst landscapes will thus enhance our understanding of the mechanisms behind their migration and their effects on soil ecosystems. This research is crucial for ensuring water environment safety in the study area and promoting ecological sustainability. Considering these factors, continuous in-situ monitoring of the vertical distribution of nitrogen and phosphorus in soil solution at various depths was conducted in the Hui Xian Wetland in 2021. The primary objectives of this comprehensive analysis were as follows: (1) to quantify the forms and contents of N and P in soil solution at different depths in the study area; (2) to reveal the distribution patterns of N and P in soil solution under different karst landscape and land use conditions; and (3) to explore the factors influencing the migration and transformation of N and P in soil solution, aiming to provide theoretical support for agricultural nonpoint source pollution control in the study area.

2. Materials and Methods

2.1. Study Site

The study area is located in the karst wetlands of Huixian, Guilin City, Guangxi Zhuang Autonomous Region, China, situated at the watershed divide between the primary tributaries of the Pearl River Basin, the Li River (Gui River Basin), and the Liu River Basin [46]. Its geographical coordinates range from 110°08′46″ E to 110°14′40″ E and 25°03′07″ N to 25°08′57″ N, with elevations ranging from 150 to 160 m above sea level. The climate in this area is warm and humid, classified as a subtropical monsoon climate. The annual average temperature is 20 °C, with abundant rainfall throughout the year. The annual precipitation reaches 1894.4 mm, with the rainy season occurring from April to September, representing 70% of the total annual rainfall [47]. The annual average evaporation is 1569.7 mm. Land use types mainly consist of farmland, orchards, forests, grasslands, and residential areas. The hydrogeological system in the study area is primarily composed of loose rock aquifers from the Quaternary System, Devonian, and Carboniferous carbonate aquifers [48]. The main surface rivers include the Xiangsi River, Mudong River, and Huixian River. The Xiangsi River and Huixian River flow from the northern and southern non-carbonate highlands, respectively, toward the central low-lying karst peak-forest plain (basin), where they converge with the Mudong River originating from karst springs in the northern peak-cluster depression. Double-season rice is cultivated in the study area. For early rice, land preparation, puddling, and seedling raising typically occur in late March, with transplanting in early April. For late rice, these activities take place in late June and early July. Irrigation methods mainly include pumping and canal irrigation, with the water depth and irrigation volume during the rice growing season detailed in Table 1. Fertilization mainly consists of nitrogen fertilizer and compound fertilizer, with urea (containing 46% nitrogen) being the primary nitrogen fertilizer, and compound fertilizer containing 15% each of nitrogen, phosphorus, and potassium. Generally, fertilization is applied three times per season, with basal fertilizer mainly consisting of 10 kg/ha urea and 15 kg/ha compound fertilizer. Additional fertilizer is applied twice later in the season, with urea at 10–20 kg/ha and compound fertilizer at 15–30 kg/ha each time, depending on the rice growth conditions [47]. An overview map of the study area is shown in Figure 1

2.2. Experimental Field Layout and Sampling Methods

Within the Huixian experimental area, both non-karst and karst landforms (peak forest plain, peak-cluster depression) were selected for paddy fields and dryland, totaling six underlying surface types, with two experimental fields chosen for each type, making a total of twelve experimental fields. Considering the soil profile characteristics and the specific conditions of the study area, the sampling depth was determined to be 100 cm. Sampling points were established in each experimental field, and soil samples were collected using a soil auger for layer-by-layer sampling at depths near 30 cm, 60 cm, and 100 cm. Sampling was conducted under rain-free conditions, with 6 soil samples collected per borehole at different depths, totaling 72 soil samples. Among them, 36 samples were collected using a ring knife for the measurement of soil bulk density and moisture content. The samples from the same layer were mixed uniformly to obtain test samples using the quartering method. After air-drying the samples indoors, they were ground, sieved (0.25 mm), and stored for the analysis of soil particle composition (particle size distribution), organic matter, nitrogen, and phosphorus content.
Soil solution samples were collected using an in-situ soil solution extraction method, extracting soil solutions at different depths (30 cm, 60 cm, and 100 cm) by negative pressure. The sampling device consisted of a microporous ceramic head sampling bottle, connected plastic tubing, and outlet rubber tubing, with a diameter of 31 mm and a length of approximately 7 cm for the ceramic head sampler. During installation, holes were drilled to the required depth, and the sampler was vertically inserted into the hole, allowing soil solution to infiltrate into the sampling tube under negative pressure. To extract the solution, a hand-operated air pump interface was inserted into the air pressure tube port and pressurized to allow the soil solution to flow back into a 50 mL polyethylene bottle. The sampling period was from March 2021 to December 2021 with monthly monitoring of NO3−-N, NH4+-N, TN, and TP concentrations in the soil solution, and additional samples were collected 1–2 days after heavy rainfall and irrigation; a total of 14 samples were collected, 10 during the irrigation season, and 4 during the non-irrigation season. Prior to sampling, the polyethylene bottles were cleaned with distilled water and then all samples were sealed and stored in the laboratory in a dark place at 5°.

2.3. Sample Collection and Data Analysis

Soil pH was measured according to the standards set by the Ministry of Ecology and Environment of the People’s Republic of China (HJ 962-2018). This involved using the potentiometric method on a soil-water suspension with a ratio of 2.5:1. Soil moisture content was determined based on the standard HJ 613-2011, by calculating the mass difference of soil samples before and after drying at (105 ± 5) °C. Soil bulk density was measured following the Ministry of Agriculture standard (NY/T 1121.4-2006), using a ring knife to collect soil samples in their natural state and then calculating the mass of the dried soil per unit volume. Soil particle size distribution was assessed using the forestry industry standard (LY/T 1225-1999), employing a soil hydrometer to directly read the mass of particles in the soil suspension and calculate their content. Soil organic matter (SOM) content was determined according to standard F-HZ-DZ-TR-0046. Soil samples were subjected to digestion with potassium dichromate under heating conditions, which oxidized the carbon in the organic matter to carbon dioxide. During this process, the dichromate ions were reduced to trivalent chromium ions. The remaining potassium dichromate was then titrated with a standard solution of ferrous ammonium sulfate. The content of organic matter in the soil was calculated based on the change in the amount of dichromate ions before and after the oxidation of organic carbon. TN in the soil was measured following the forestry industry standard (LY/T 1228-2015). This process involved digesting the soil samples with concentrated sulfuric acid in the presence of a catalyst, converting organic nitrogen to inorganic ammonium salts. These were then converted to ammonia under alkaline conditions, distilled with water vapor, absorbed by an excess of the boric acid solution, and finally titrated with standard hydrochloric acid to calculate the nitrogen content. TP in the soil was measured according to the Ministry of Ecology and Environment standard (HJ 623-2011). The soil samples were fused with sodium hydroxide to convert phosphorus-containing minerals and organic phosphorus compounds to soluble orthophosphate. The orthophosphate then reacted with molybdenum-antimony-ascorbic acid color reagent under acidic conditions to form molybdenum blue, and absorbance was measured at 700 nm. Soil NO3−-N and NH4+-N concentrations were determined according to the agricultural industry standard (LY/T 1228-2015). The soil samples were extracted using a 2 mol/L KCl solution and analyzed using a continuous flow injection analyzer (SKALAR SAN++ from the Netherlands) to measure the respective concentrations. The collection of soil samples and the determination of their physical and chemical properties are conducted to assist in the analysis of the soil solution. Therefore, sampling is only carried out once at 12 locations before sampling the soil solution. Concentrations of TN, NH4+-N, NO3−-N, and TP in soil solution samples were measured using a continuous flow injection analyzer (SKALAR SAN++ from the Netherlands), with each sample analyzed in triplicate. IBM SPSS Statistic 26 was used to perform a Kruskal–Wallis one-way ANOVA on the data as the data did not fulfill the conditions of normal distribution and Homogeneity of variance test. Spearman correlation plot as well as other plots using origin 2018.

3. Results

3.1. Physico-Chemical Properties of Soils in the Study Area

The physicochemical properties of soil layers are detailed in Table 2. A1, A2; B2, B3; and C3, C4 represent three different terrain types: non-karst area, peak forest plain, and peak-cluster depression, respectively. The soil in the study area can be roughly divided into the topsoil layer, subsoil layer, and substratum. The topsoil layer, located at the soil surface, typically ranges from 15 to 30 cm in thickness. Influenced by agricultural activities and surface biota, the topsoil layer is loose in texture, well-drained, and characterized by rapid material turnover rates and abundant available nutrients. The subsoil layer, located below the topsoil layer, is approximately 20–30 cm thick and serves as a crucial layer for water and nutrient retention, supplying water and nutrients to plants during the later stages of growth. The substratum is less influenced by tillage and maintains the characteristics of the parent material [49].

3.2. Distribution Characteristics of Nitrogen and Phosphorus Content in Soil Solution

During the monitoring period (March 2021 to December 2021), the median concentrations of NO3−-N at depths of 30 cm, 60 cm, and 100 cm were 0.7321 mg/L, 0.4123 mg/L, and 0.2013 mg/L, respectively, with ranges of variation from 0.0010 to 17.9261 mg/L. The median concentrations of NH4+-N at the same depths were 0.230 mg/L, 0.233 mg/L, and 0.255 mg/L, respectively, with ranges of variation from 0.0010 to 12.0600 mg/L. For TN concentrations, the medians at depths of 30 cm, 60 cm, and 100 cm were 4.083 mg/L, 4.0334 mg/L, and 3.59 mg/L, respectively, with ranges of variation from 0.0013 to 36.8916 mg/L. As for TP concentrations, the medians at depths of 30 cm, 60 cm, and 100 cm were 0.017 mg/L, 0.0467 mg/L, and 0.017 mg/L, respectively, with ranges of variation from 0.0002 to 0.0145 mg/L. Referring to the groundwater quality standard of the People’s Republic of China (GB/T∙14848-2017), the NO3−-N levels meet Class II standards, with 87.1%, 83.1%, and 90% of samples from each layer meeting Class II standards, respectively. Additionally, the average ratio of nitrate nitrogen to total nitrogen concentrations in each layer exceeds 48%, indicating that NO3−-N is the predominant form within the TN component. The overall trend of soil solution in the study area is depicted in Figure 2. Generally, both NO3−-N and TN concentrations exhibit a decreasing trend with increasing depth, while NH4+-N concentration shows a slight increase with depth, albeit with a small magnitude. TP concentration remains relatively stable, fluctuating within a narrow range.

3.3. Temporal Variation Characteristics of Nitrogen and Phosphorus in Soil Solution

3.3.1. Overall Variation in Nitrogen and Phosphorus in Soil Solution between Irrigation and Non-Irrigation Seasons

The monitoring period was divided into two periods: irrigation season (April to September) and non-irrigation season (October to December). Descriptive statistical analysis was conducted on soil solution NO3−-N, NH4+-N, TN, and TP during these periods (Table 3). The results indicated that NO3−-N and TN showed low variability (Cv < 10%) during the irrigation season and moderate variability (10% < Cv < 100%) during the non-irrigation season. The concentrations of NO3−-N and TN were significantly higher during the non-irrigation season compared to the irrigation season. NH4+-N and TP exhibited strong variability during both the irrigation and non-irrigation seasons, with TP showing a much higher coefficient of variation during the irrigation season than during the non-irrigation season. The comparison of differences in nitrogen and phosphorus concentrations between irrigated and non-irrigated seasons was based on the Kruskal–Wallis ANOVA test (Figure 3). The results showed that there was a significant difference between NO3−-N and NH4+-N in both irrigated and non-irrigated seasons. Specifically, NH4+-N and TP concentrations remained relatively stable, while NO3−-N and TN concentrations were significantly higher during the non-irrigation season compared to the irrigation season.

3.3.2. Nitrogen and Phosphorus Changes in Soil Solutions at Different Depths during the Irrigation Season and Non-Irrigation Season

Kruskal–Wallis ANOVA test was conducted on the monitoring data of irrigation and non-irrigation seasons for each depth separately. As shown in Table 4, significant differences were observed in NO3−-N and NH4+-N concentrations at all depths between the irrigation and non-irrigation seasons. TN concentration showed significant differences at the 100 cm depth between the irrigation and non-irrigation seasons. However, there were no significant differences in TP concentrations at any depth.
The trends in nitrogen and phosphorus concentrations at various soil layers during the irrigation and non-irrigation seasons are depicted in Figure 4. Influenced by downward soil moisture movement, during the irrigation season, the depth of NO3−-N migration increases, primarily accumulating at a depth of 60 cm. In the non-irrigation season, due to lower soil moisture content, NO3−-N and TN concentrations are predominantly distributed at a depth of 30 cm, gradually decreasing with increasing depth. The adsorption of NH4+-N by soil colloids results in relatively small variations in concentration with depth during both irrigation and non-irrigation seasons. TP concentrations are generally low and exhibit no significant changes. Under the influence of rainfall and irrigation infiltration, there is a risk of downward migration of NO3−-N, NH4+-N, and TN during the irrigation season, with vertical migration observed more prominently for NO3−-N and TN compared to NH4+-N.

3.4. Nitrogen Concentration Variability in Soil Solutions across Periods, Landforms, and Land Use Types

Soil solution data were categorized into three variables, different periods (irrigation season, non-irrigation season), various landform types (non-karst landform, peak forest plain, peak-cluster depression), and diverse land use types (paddy fields, dryland), to analyze the differences in nitrogen concentration within these variables (Table 5). Significant differences were observed in NO3−-N concentration across periods and landform type–land use type categories (p = 1.18 × 10−8, 8.36 × 10−11), while TN showed significant differences across periods, landform types, landform type–land use type interactions, and period-landform type–land use type interactions (p = 0.026, 0.038, 2.35 × 10−6, 0.013). Moreover, NH4+-N exhibited significant differences among landform types (p = 3 × 10−6).

3.4.1. Vertical Transport of Nitrogen in Soil Solutions among Different Karst Types during the Irrigation Season

Figure 5 illustrates the variations in concentrations of NO3−-N and NH4+-N in paddy fields during the irrigation season across different karst landscapes. The results indicate that under similar rainfall conditions, the concentrations of NO3−-N are relatively higher at depths of 30 cm and 60 cm in non-karst paddy fields during the irrigation season. In contrast, in the peak forest plain, the concentration of NO3−-N shows an upward trend at a depth of 100 cm, suggesting a potential risk of downward leaching. Regarding the peak cluster depressions, NO3−-N consistently migrates downward across all depths, although no clear pattern of change is observed.
Overall, the NH4+-N concentrations across different landscape types do not show significant differences and exhibit a consistent trend. Specifically, in non-karst landscapes and peak-cluster depressions, NH4+-N concentrations peak in May and then gradually decrease. Additionally, on May 2021, a decline in NH4+-N concentrations was observed across all layers in the three landscape types, followed by a subsequent increase. The magnitude of this decline was greatest in peak-cluster depressions, followed by peak-forest plains, and was least pronounced in non-karst landscapes.

3.4.2. Vertical Transport of Nitrogen in Soil Solutions among Different Karst Types during the Non-Irrigation Season

Figure 6 illustrates the variations in NO3−-N and NH4+-N concentrations in these non-irrigated rice fields. In non-karst landforms and peak-cluster depressions, the NO3−-N concentrations at each sampling layer follow the pattern of 30 cm > 60 cm > 100 cm. However, in peak-forest plains, the pattern shifts to 100 cm > 30 cm > 60 cm. Notably, the NO3−-N concentrations at a depth of 60 cm exhibit substantial temporal variation in non-karst landforms and peak-forest plains, whereas in peak-cluster depressions, significant fluctuations are observed at a depth of 100 cm.
In non-karst landscapes, the concentration of NH4+-N at a depth of 30 cm exhibits significant temporal variation, initially rising and then falling. In contrast, within the peak forest plains, the concentration of NH4+-N at a depth of 100 cm remains at its highest levels and continues to increase, reaching a peak concentration of up to 10 mg/L. The migration of NH4+-N occurs through the fissures between soil and rock, with the migration distance being greatest in the peak forest plains, followed by the peak cluster depressions, and least in non-karst landforms.

3.4.3. Vertical Transport of Nitrogen in Soil Solutions among Different Land Use Types during the Irrigation Season

Figure 7 illustrates the variation in NO3−-N and NH4+-N concentrations across different land use types in the peak-forest plains during the irrigation season. Prior to May 2021, there were no significant differences in NO3−-N concentrations at various depths in paddy fields. However, in June 2021, NO3−-N concentrations at a depth of 30 cm initially increased and then slightly decreased, while the concentration at a depth of 60 cm showed the opposite trend. In contrast, the NO3−-N concentrations in dryland exhibited substantial variability without a consistent pattern. Generally, the concentrations at a depth of 30 cm were higher than those at 60 cm and 100 cm during most periods.
The NH4+-N concentrations in different layers of the paddy field showed no significant differences and exhibited similar rising and falling trends. In May, June, July and August 2021, NH4+-N concentrations at three depths all decreased. Rainfall data indicated cumulative precipitation during the sampling intervals as follows: 70 mm, 48 mm, 28 mm, and 30.5 mm, respectively. In contrast, NH4+-N concentrations in the upland area displayed more pronounced variations. In June 2021, NH4+-N concentrations at three depths were roughly equal. However, thereafter, concentrations at 60 cm depth gradually increased, while those at 30 cm and 100 cm depths declined gradually. By September 2021, NH4+-N concentration at 60 cm depth plummeted sharply, while concentrations at 30 cm and 100 cm depths surged abruptly. This suggests downward migration of NH4+-N across the layers, resulting in reduced NH4+-N content in upper layers and increased content in lower layers.

4. Effect of Rainfall on Soil Solution Nitrogen and Phosphorus

Spearman correlation analysis was conducted between the nitrogen and phosphorus concentrations in soil solution at different depths and rainfall amounts in Figure 8. The results are presented in the table below: NO3−-N concentrations in soil solution at depths of 30 cm and 60 cm showed negative correlations with rainfall, with correlation coefficients of −0.51 and −0.39, respectively. As rainfall increased, the NO3−-N concentrations decreased in the shallow soil layers (30 cm and 60 cm), with the strength of correlation weakening with depth. TN concentrations at 30 cm and 60 cm also exhibited negative correlations with rainfall, with correlation coefficients of −0.49 and −0.38, respectively, and the strength of correlation weakened with increasing soil depth as well.

5. Discussion

5.1. Variation Analysis of Nitrogen and Phosphorus Content in Soil Solution in Irrigation Season and Non-Irrigation Season

In this study, the NO3−-N/TN ratio of soil solution at various depths exceeded 48%, indicating that nitrate nitrogen is the primary form of nitrogen emission in this area. We believe that aquifer thickness affects nitrate nitrogen concentration, as evidenced by an increase in nitrate concentration with a decrease in aquifer thickness [50,51]. This may be attributed to the fact that the study area is characterized by typical karst topography, with a shallow groundwater table and a thin vadose zone, which are conducive to nitrification reactions that convert ammonium nitrogen into nitrate nitrogen. A study has indicated that [50] areas with a thicker vadose zone and a greater saturated thickness of the aquifer have relatively lower nitrate concentrations. Moreover, nitrate nitrogen, carrying a negative charge, exhibits high mobility in soil solution, allowing NO3−-N to migrate with water flow within the soil. Previous research [52,53] has shown that nitrate nitrogen in the soil gradually moves downward to deeper soil layers along with moisture. In this study, the downward migration distance of NO3−-N during the irrigation season increased, mainly accumulating at a depth of 60 cm, while during the non-irrigation season, the soil moisture content was lower, and NO3−-N and TN concentrations were mainly distributed at a depth of 30 cm.
It was observed that during the irrigation season, the variability of NO3−-N and TN content in soil solution was relatively low, while in the non-irrigation season, the variability was moderate. Furthermore, significant differences were observed between the irrigation and non-irrigation seasons, with significantly higher NO3−-N and TN content during the non-irrigation season. It has been shown that [54,55,56] increased soil water content and reduced soil aeration promote denitrification processes, resulting in the loss of NO3−-N. Additionally, heavy rainfall during the irrigation season raises the groundwater level, intensifying the interaction between soil substances and groundwater, influencing the vertical distribution of water nitrogen in the soil. Moreover, prolonged flooding during the rice planting period in the study area results in relatively stable soil moisture content across soil layers, leading to minimal fluctuations in NO3−-N and TN concentrations. Some studies showed [57,58] that just a 10 cm decrease in water table depth led to a reduction in denitrification and a corresponding increase in soil nitrogen content; with increasing shallow groundwater depth, NO3−-N concentrations in the shallow groundwater decreased exponentially.
NH4+-N and TP showed strong variability in both the irrigation and non-irrigation seasons in this study, with no significant differences in concentration at various depths. This may be because during the non-irrigation season, the soil moisture content is lower, and NH4+-N, which carries a positive charge, is easily adsorbed by soil colloids, making it less likely to migrate in the soil solution [59]. However, during the irrigation season, due to the higher soil moisture content, the long-term flooding, and high groundwater levels in the paddy fields of the study area, the soil is often in a reduced state [60]. The anaerobic environment inhibits the activity of autotrophic nitrifying bacteria, resulting in limited soil nitrification. Research has shown [61] that TP is mainly lost through surface runoff and moves relatively slowly in the soil. However, during the irrigation season, increased dissolution and particle-bound phosphorus loss occur due to rainfall and irrigation, resulting in greater variability in TP during this season [62,63].

5.2. Factors Affecting the Vertical Distribution of Soil Solution N and P in Different Karst Landforms

During the irrigation season in paddy fields, NO3−-N tends to accumulate more at a depth of 60 cm in non-karst and karst landforms (peak forest plain, peak-cluster depression). Long-term flooding of rice fields can lead to poor drainage in the upper soil layers and increased evaporation, which in turn reduces the mobility of nitrate nitrogen in the soil. Simultaneously, the irrigation season coincides with the rainy season in the study area, and the rise in groundwater level (less than 1 m) enhances denitrification under partially or fully saturated conditions in the lower soil layers [55], resulting in a decrease in nitrate nitrogen concentration in the lower layers. Consequently, nitrate nitrogen concentrations are mostly concentrated in the middle layer. In the peak forest plain, there is little variation in NO3−-N concentration in soil solutions across different layers. Conversely, in the peak-cluster depression, NO3−-N migrates downwards across all layers. A study [64] has indicated that karst-covered areas, such as the peak forest plain, are prone to surface runoff and increased pore water pressure between soil layers during heavy rainfall. In May 2021, a decrease in NO3−-N concentration was observed at depths of 30 cm and 60 cm in the peak-cluster depression, while an abnormal increase occurred at 100 cm. This indicates the migration of NO3−-N concentration from depths of 30 cm and 60 cm to 100 cm in soil solutions, where it accumulates. The observed changes are attributed to the relatively low-lying terrain of the peak-cluster depression, characterized by the development of sinkholes or vertical wells at the bottom. The depth of groundwater varies from 2 to 5 m, while near the transition zone between surface water and groundwater, it ranges from 1 to 3 m. During rainfall events, the groundwater level rises to less than 1 m. Surrounding the area are limestone formations resulting from intense dissolution, forming a collective of peaks. Some studies [65,66] have shown that nitrate migration and transformation are closely linked to the degree of karst media development, the transformation process of nitrogen in the conduit and surface water is dominated by nitrification, and the HNO3 produced from NH4+ via microbial nitrification facilitated carbonate weathering, thereby controlling NO3 enrichment in karst groundwater. Rainwater can directly reach the water table through karst fissures and conduits, leading to significant NO3−-N loss from the soil. This phenomenon intensifies with greater rainfall, resulting in more severe NO3−-N loss from the soil. In irrigated paddy fields, NH4+-N concentrations are generally low across all layers in the three types of karst landforms. This can be attributed to the positive charge of NH4+-N, which makes it prone to adsorption by soil colloids [67]. During periods of heavy rainfall, NH4+-N concentrations in different karst landforms do not exhibit sharp declines immediately. Instead, redistribution of water within the soil leads to NH4+-N migration and accumulation in deeper soil layers [68].
Overall, this study investigates the vertical dynamics of nitrogen and phosphorus in soil solutions across different karst landforms, contributing to our understanding of the underlying mechanisms and influencing factors of their migration. However, due to the complexity of karst soil, accurately calculating the factors affecting nitrogen and phosphorus migration is challenging. Future research should consider a broader range of environmental variables and employ more refined and multi-scale methods to enhance the accuracy and applicability of the results.

6. Conclusions

The main form of TN component in the soil solution in the study area was NO3−-N, and the concentrations of NO3−-N and TN generally decreased with depth, while TP and NH4+-N did not change significantly with depth.
During the irrigation season, NO3−-N exhibits its highest concentrations in the deep layers of both peak forest plains and peak-cluster depressions, The risk of downward transport of unused NO3−-N and TN from agricultural activities is higher during irrigation and rainfall. Conversely, during non-irrigation periods, NO3−-N predominantly resides in the shallow soil layers. NH4+-N concentrations across different landform types show minimal variation among soil layers, maintaining consistent trends.
Within paddy fields, the concentrations of NO3−-N and NH4+-N remain relatively stable during irrigation seasons, whereas in dryland areas, their concentrations exhibit greater fluctuations.

Author Contributions

Conceptualization, C.G. and J.H.; methodology, J.H.; software, C.G.; validation, C.G. and J.D.; formal analysis, R.X.; investigation, C.G., Z.W., S.Z., and J.X.; resources, J.D.; data curation, C.G.; writing—original draft preparation, C.G. and J.H.; writing—review and editing, C.G. and J.D.; visualization, J.H. and J.D.; supervision, Z.W. and J.X.; project administration, S.Z. and R.X. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China (No. 51979046), the Science and Technology Planning Project of Guangxi, China (No. AB 23026045), the National Natural Science Foundation of China (No. 52269010), and the Science and Technology Plan Project of Guilin (No. 20220114-2).

Data Availability Statement

Data are contained within the article.

Conflicts of Interest

No potential conflicts of interest were reported by the authors.

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Water 16 01830 g001
Figure 1. Information on land use types and sampling sites in the study area.
Figure 1. Information on land use types and sampling sites in the study area.
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Figure 2. The general characteristics of nitrogen and phosphorus concentration in soil solution.
Figure 2. The general characteristics of nitrogen and phosphorus concentration in soil solution.
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Figure 3. Characteristics and variability of soil solution nitrogen and phosphorus concentrations during irrigated and non-irrigated seasons.
Figure 3. Characteristics and variability of soil solution nitrogen and phosphorus concentrations during irrigated and non-irrigated seasons.
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Figure 4. Variations in nitrogen and phosphorus concentrations at different soil depths during the irrigation and non-irrigation seasons.
Figure 4. Variations in nitrogen and phosphorus concentrations at different soil depths during the irrigation and non-irrigation seasons.
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Figure 5. The variations in NO3−-N and NH4+-N concentrations in paddy fields during the irrigation season.
Figure 5. The variations in NO3−-N and NH4+-N concentrations in paddy fields during the irrigation season.
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Figure 6. The variations in NO3−-N and NH4+-N concentration in paddy fields during the non-irrigation season.
Figure 6. The variations in NO3−-N and NH4+-N concentration in paddy fields during the non-irrigation season.
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Figure 7. The variations in NO3−-N and NH4+-N concentration in both paddy fields and upland areas.
Figure 7. The variations in NO3−-N and NH4+-N concentration in both paddy fields and upland areas.
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Figure 8. Correlation between N and P content and rainfall at each depth.
Figure 8. Correlation between N and P content and rainfall at each depth.
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Table 1. The water layer depth and irrigation amount during the rice growing period in the Huixian experimental area.
Table 1. The water layer depth and irrigation amount during the rice growing period in the Huixian experimental area.
Growth PeriodNursery
Stage 		Transplanting
Stage 		Tillering
Stage 		Panicle Initiation
Stage		Heading
Stage 		Milk Ripening
Stage
Early riceWater depth
(mm)		3010~2020~3020~3010~2010~20
Irrigation volume
(m3/ha)		300150250250150150
Late riceWater depth
(mm)		30~5010~2020~3020~3010~2010~20
Irrigation volume
(m3/ha)		400150250250150150
Table 2. Physical and chemical properties of soil at different depths.
Table 2. Physical and chemical properties of soil at different depths.
Landform TypesPlot NumberLand Use MethodspHMoisture Content
%		Bulk Density g/cm3Particle Size Classification (%)SOM g/kgNO3−-Nmg/kgNH4+-N mg/kgTN g/kgTP g/kg
<0.002 mm2.0–0.05 mm0.05–0.002 mm
non-karst landformsA1-1paddy fields7.7624.71.5646.75 32.21 21.04 8.616.047.820.4110.379
A1-27.6233.51.4647.97 29.43 22.60 8.161.885.830.4160.39
A1-37.59321.5547.87 34.94 17.20 5.732.262.390.3910.321
A2-1paddy fields7.2328.91.3938.22 34.94 26.84 27.41.492.421.270.68
A2-27.2233.71.443.66 25.30 31.04 34.22.52.461.480.35
A2-37.430.71.5233.49 46.34 20.17 10.22.333.080.4860.329
A3-1dryland6.89291.4157.41 22.91 19.68 12.39.082.710.6560.402
A3-26.41331.4560.12 24.13 15.76 9.171.752.470.5340.184
A3-35.7334.21.3956.69 25.90 17.41 9.121.92.120.4890.413
A4-1dryland6.8335.61.3955.57 13.93 30.51 24.77.31.271.180.385
A4-26.8532.71.3652.84 13.07 34.09 24.85.671.381.30.584
A4-37.0932.81.4955.32 22.96 21.72 20.81.411.010.8360.458
peak-forest plainsB1-1dryland7.3716.51.1116.79 37.55 45.67 20.33.411.221.020.427
B1-27.74231.1827.45 26.73 45.82 16.51.881.270.9580.327
B1-37.8238.81.2317.24 35.86 46.90 43.24.815.672.220.626
B2-1paddy fields7.73361.2238.73 27.59 33.68 21.40.411.831.130.679
B2-27.5936.61.3357.06 18.19 24.75 7.72.162.030.5060.587
B2-37.7637.31.2654.99 23.18 21.83 8.253.312.120.5760.527
B3-1paddy fields7.2442.51.2826.14 28.47 45.39 36.31.596.121.680.546
B3-27.3837.51.335.69 25.02 39.29 17.91.192.190.9580.507
B3-37.3433.31.5132.70 35.29 32.01 12.21.073.790.6060.359
B4-1dryland7.8939.91.4421.94 34.79 43.27 22.75.173.490.9850.559
B4-27.9631.61.523.48 47.34 29.19 19.70.45.340.9610.641
B4-37.8731.21.4329.51 39.62 30.86 15.60.596.040.7410.561
peak-cluster depressionsC1-1dryland5.8628.81.3340.99 13.24 45.77 2413.63.671.410.585
C1-26.2828.41.1346.21 6.26 47.53 19.24.771.721.130.397
C1-36.3431.61.3246.36 18.66 34.98 1813.21.541.130.454
C2-1dryland5.429.91.2742.10 15.59 42.32 22.211.21.051.480.826
C2-26.634.41.1942.76 25.48 31.76 13.13.41.491.040.766
C2-37.6533.61.3944.12 27.53 28.35 16.32.927.281.190.976
C3-1paddy fields7.9531.21.5325.77 45.03 29.21 25.35.861.261.120.827
C3-27.8534.61.4751.57 26.21 22.22 90.961.650.6110.45
C3-37.71391.4252.51 23.06 24.42 11.11.441.250.7460.641
C4-1paddy fields7.8930.71.4233.05 32.61 34.34 14.60.662.910.8540.612
C4-27.8636.11.4338.19 27.34 34.48 8.350.822.520.630.51
C4-37.8532.81.3939.21 31.34 29.45 9.160.672.40.590.435
Note(s): The -1, -2, and -3 after each sampling point in the table represent points at 30 cm, 60 cm, and 100 cm depths.
Table 3. Descriptive statistics of nitrogen and phosphorus concentrations in soil solution during the irrigation season and non-irrigation season.
Table 3. Descriptive statistics of nitrogen and phosphorus concentrations in soil solution during the irrigation season and non-irrigation season.
Detector
Indicators		Irrigation-SeasonNon-Irrigation-Season
Concentration/(mg·L−1)Concentration/(mg·L−1)
Sample QuantityMinimum Value–Maximum ValueMean Value ± Standard DeviationCoefficient of Variation
(%)		Sample QuantityMinimum Value–Maximum ValueMean Value ± Standard DeviationCoefficient of Variation
(%)
NO3−-N3040.0010 ± 11.40531.5663 ± 0.13428.571030.0010 ± 17.92613.3355 ± 0.378111.34
NH4+-N3070.0010 ± 11.96350.5438 ± 1.4093259.16900.0010 ± 12.06000.6070 ± 1.5966263.03
TN2970.0013 ± 21.89384.3795 ± 0.19714.501010.1612 ± 36.89165.4843 ± 0.662212.07
TP2990.0002 ± 0.97650.0647 ± 0.1401216.54930.0010 ± 0.21500.0167 ± 0.0221132.34
Table 4. Results of Kruskal–Wallis ANOVA test for nitrogen and phosphorus concentrations at various depths during the irrigation season and non-irrigation season.
Table 4. Results of Kruskal–Wallis ANOVA test for nitrogen and phosphorus concentrations at various depths during the irrigation season and non-irrigation season.
Detector
Indicators		DepthIrrigation-SeasonNon-Irrigation-SeasonHDegrees of Freedomp
Concentration
(mg·L−1)		Concentration
(mg·L−1)
Sample QuantityMean
Value		Sample QuantityMean
Value
NO3−-N301061.6047 ± 2.22334.3739 ± 4.4719.30210 **
601101.7394 ± 2.58383.2888 ± 3.3119.2910.001 **
100821.3035 ± 2.16322.3203 ± 3.544.41510.036 *
NH4+-N301080.4750 ± 1.19270.4516 ± 3.345.25210.022 *
601090.5975 ± 1.34370.5512 ± 1.118.52310.004 **
100900.5576 ± 1.71280.8267 ± 2.405.12410.024 *
TN301044.3800 ± 3.34337.2708 ± 7.951.23210.267
601044.6101 ± 3.45375.1120 ± 4.700.13910.709
100894.1095 ± 3.41314.0271 ± 6.926.54410.011 *
TP301050.0730 ± 0.16300.0221 ± 0.040.00210.968
601060.0573 ± 0.13340.0138 ± 0.010.00410.952
100880.0637 ± 0.13290.0140 ± 0.010.54210.462
Note(s): * indicates that the difference is significant at the p = 0.05 level (two-sided),** indicates that the difference is significant at the p = 0.01 level (two-sided).
Table 5. Differences in soil solution nitrogen between variables.
Table 5. Differences in soil solution nitrogen between variables.
NO3−-NNH4+-NTN
FSignificancePartial Eta
Squared		FSignificancePartial Eta SquaredFSignificancePartial Eta Squared
Ss33.9371.18 × 10−8 **0.0790.0370.470.0000974.9880.026 *0.013
Lt0.9050.4050.00513.0353 × 10−6 **0.0633.3080.038 *0.017
Lm0.0820.7740.0002080.6180.4320.0020.210.5710.001
Ss-Lt0.7390.4780.0040.9790.3770.0051.6640.1910.009
Ss-Lm0.7480.3880.0021.1410.2860.0030.7370.3910.002
Lt-Lm24.6238.36 × 10−11 **0.1112.5790.0770.01313.4052.35 × 10−6 **0.065
Ss-Lt-Lm0.8300.4370.0041.3070.2720.0074.3650.013 *0.022
Note(s): * indicates that the difference is significant at the p = 0.05 level (two-sided), ** indicates that the difference is significant at the p = 0.01 level (two-sided). Ss: periods; Lt: land use type; Lm: landform types.
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MDPI and ACS Style

Gong, C.; Han, J.; Dai, J.; Xia, R.; Wan, Z.; Zhang, S.; Xu, J. Vertical Distribution Patterns of Nitrogen and Phosphorus in Soil Solution: Insights from a Wetland Trial Site in the Li River Basin. Water 2024, 16, 1830. https://doi.org/10.3390/w16131830

AMA Style

Gong C, Han J, Dai J, Xia R, Wan Z, Zhang S, Xu J. Vertical Distribution Patterns of Nitrogen and Phosphorus in Soil Solution: Insights from a Wetland Trial Site in the Li River Basin. Water. 2024; 16(13):1830. https://doi.org/10.3390/w16131830

Chicago/Turabian Style

Gong, Chunjin, Junlei Han, Junfeng Dai, Rui Xia, Zupeng Wan, Shuaipu Zhang, and Jingxuan Xu. 2024. "Vertical Distribution Patterns of Nitrogen and Phosphorus in Soil Solution: Insights from a Wetland Trial Site in the Li River Basin" Water 16, no. 13: 1830. https://doi.org/10.3390/w16131830

APA Style

Gong, C., Han, J., Dai, J., Xia, R., Wan, Z., Zhang, S., & Xu, J. (2024). Vertical Distribution Patterns of Nitrogen and Phosphorus in Soil Solution: Insights from a Wetland Trial Site in the Li River Basin. Water, 16(13), 1830. https://doi.org/10.3390/w16131830

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