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. 2025 Sep 3;59(38):20401–20410. doi: 10.1021/acs.est.5c08046

Optimizing Small Water Bodies as a Nature-Based Solution for Mitigating Nitrogen Pollution

He Duan †,‡, Wangzheng Shen †,‡,*, Qingsong Wang †,§, Ziqi Qiang †,§, Sisi Li †,‡,*, Yanhua Zhuang †,‡, Mingquan Lv ∥, Shengjun Wu ∥, Liang Zhang †,‡,*
PMCID: PMC12490639  PMID: 40900176

Abstract

Despite the widely acknowledged importance of small water bodies (SWBs), their large-scale capacity for nitrogen (N) removal in agricultural landscapes remains poorly understood. This study assessed the N removal efficiency and potential of 1.75 million SWBs (<0.33 ha) in China’s rice-growing regions, using an N removal model incorporating key biogeochemical factors. Collectively, these SWBs potentially remove approximately 169.97 kt N y–1 from paddy runoff, equivalent to 23.62% of national crop N emissions, yielding an estimated economic benefit of 1.68 billion USD. However, a spatial mismatch between SWB distribution and N emission hotspots hampers the current efficiency, as 23.04% of paddy fields have high N loads but limited SWBs. Increasing SWBs in these critical areas shows better N removal efficiency than a nationwide increase strategy under land resource constraints. Specifically, increasing SWBs from the current 1.08–1.23% of the rice region achieves the most cost-effective 20.96% increase in N removal. Increasing macrophyte coverage in these SWBs to 25–50% could further augment N removal by 5.98–10.58%. This study highlights SWB spatial optimization and macrophyte manipulation as viable strategies to maximize ecological and economic benefits under resource constraints, offering a nature-based solution for N pollution.

Keywords: paddy field, nonpoint source pollution, nitrogen reduction, small wetland, vegetation

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Introduction

Excessive nitrogen (N) inputs have been a major cause of water quality degradation worldwide, posing serious threats to aquatic ecosystems and drinking water safety through eutrophication. , Paddy fields, producing the staple food for half of the world’s population, are considered important sources of nonpoint source N pollution due to intensive fertilization coupled with frequent irrigation and drainage. − Their N loads exhibit significant spatiotemporal variability and uncertainty, as well as complex pollutant migration pathways, creating substantial challenges for pollution control and management. − As the world’s largest rice producer, China faces excessive N fertilizer application, low N use efficiency, and high N surplus rate, resulting in more severe N pollution in its paddy fields. ,

N loss from paddy fields occurs mainly through surface runoff, with these N loads often moving through multiple small water bodies (SWBs), like ponds, before eventually entering receiving waters. SWBs are regarded as critical biogeochemical hotspots, accumulating N from upstream inputs and promoting its removal through various transformation mechanisms, including plant assimilation, sediment adsorption, and microbial-mediated transformations such as denitrification. − Compared to larger water bodies, SWBs have a higher sediment area-to-volume ratio and N removal rate constants, and play a disproportionately larger role in N removal. − For example, Yan et al. reported that SWBs in the Dongting Lake basin removed 68% of the N load within the basin.

Despite the recognized importance of SWBs in N removal, their N removal potential at large spatial scales remains underexplored, particularly in agricultural landscapes. Previous research has predominantly concentrated on the N removal capacity of large water bodies, either as individual sites or at the watershed scale, such as Lake Taihu in China and the Mississippi River Delta in the United States. , In limited comprehensive studies, Cheng et al. , conducted a meta-analysis to estimate N removal performance in the United States wetlands, providing valuable insights for quantifying the N removal role of SWBs at the landscape scale. However, this method is limited as it neglects key biogeochemical factors, such as macrophytes, which substantially influence N removal rate constants. Macrophytes act as a critical interface between N sources and receiving waters, playing an integral role in enhancing the ecosystem service potential of SWBs, − especially in N removal functions facilitated by synergistic interactions with microbial communities. − Macrophytes enhance N transformation and removal efficiency by absorbing N for their growth and by creating favorable conditions, such as oxygen gradients, that support nitrification and denitrification processes. Many studies have found that N removal rates in vegetated ecosystems with moderate coverage were significantly higher than in nonvegetated systems. ,, Therefore, excluding the contribution of macrophytes can significantly compromise the accuracy of N removal estimations by SWBs at the landscape scale. Furthermore, although SWBs are important for mitigating N pollution, constrained land resources challenge their large-scale restoration or expansion. Macrophyte management may represent a nature-based solution for this challenge. , However, macrophytes are currently regarded by water management agencies as an obstacle to water flow and are routinely removed to maintain hydraulic efficiency. Therefore, it is crucial to quantify the N removal capacity of SWBs under macrophyte-preserving conditions to support better management strategies.

This study aims to conduct a comprehensive quantitative assessment of the N removal potential of SWBs in China’s major rice-growing regions and to explore strategies for maximizing N removal benefits under land resource constraints. The specific goals of this study are to (i) determine the current distribution of SWBs in the main rice-growing regions of China, and estimate their N removal percentage at the municipal resolution; (ii) quantify the total N removal amounts by SWBs and their associated environmental and economic benefits at the national scale; and (iii) explore how to optimize the spatial distribution of SWBs and their macrophyte coverage to maximize N removal benefits.

Materials and Methods

Study Area

The study area includes 16 major rice-growing provinces in China, located in the Northeast Plain, the Yangtze River Basin (Upper Yangtze Basin and Mid-Lower Yangtze Basin), and the Southeast Coastal Region (Figure ). These regions account for approximately 92% of China’s total paddy field area. Specifically, the Northeast Plain includes Heilongjiang, Jilin, and Liaoning provinces; the Upper Yangtze Basin comprises Yunnan, Guizhou, and Sichuan provinces; the Mid-Lower Yangtze Basin covers Hubei, Hunan, Jiangxi, Jiangsu, and Anhui provinces; and the Southeast Coastal Region includes Guangdong, Guangxi, Zhejiang, Fujian, and Hainan provinces.

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Spatial distribution of the main rice-growing regions and their respective contributions to the national paddy field area in China. (a) Spatial distribution of the main rice-growing regions. Proportions of the national paddy field area in the (b) Northeast Plain, (c) Upper Yangtze River Basin, (d) Mid-Lower Yangtze River Basin, and (e) Southeast Coastal Region.

The rice growth period is typically divided into three fertilization stages including the basal, tillering, and panicle stages. Each stage is characterized by distinct agricultural management practices, particularly regarding fertilizer application and water management. These differences significantly affect N load in paddy runoff and the corresponding N removal by SWBs. Therefore, this study evaluated N removal by SWBs separately for the three stages and subsequently summarized them to estimate total N removal during the entire rice growing season.

Calculation of Nitrogen Removal by Small Water Bodies

N removal processes in aquatic systems encompass various mechanisms, including plant uptake, sediment adsorption, and microbial transformation. The first-order kinetic equation is used to model these processes in SWBs. , The general form of the first-order kinetic equation is

C(t)=C0e−k·t 1

where C 0 (mg L–1) is the initial concentration, C(t) (mg L–1) is the concentration at time t, k (d–1) is the first-order reaction rate constant, and t (d) is the hydraulic retention time.

The calculation procedure included (1) estimating the N removal rate constant, k (d–1), using an empirical model developed by Duan et al., which considers the impacts of N concentration, water temperature, and macrophyte coverage (eq ); (2) calculating the N removal percentage, ρ (%), based on the N removal rate constant using a first-order kinetics equation (eq ); (3) computing the N removal amount, R (mg), by combining removal percentage with N load (eq ); and (4) deriving the N removal rate, r (mg m–2 d–1), as N removal amount per unit paddy area and per time (eq ). All computations were conducted at the municipal level for each fertilization stage.

The N removal rate constant, k i (d–1), was calculated as

ki=−ln(1−0.624covi−0.532cov2i+0.017coni+0.003tempi+0.0754.39)×136 2

where cov i is the macrophyte coverage in SWBs during the rice-growing season (May–October); con i (mg L–1) is the input N concentration to SWBs; and temp i (°C) is the average water temperature of SWBs (May to October).

The N removal percentage, ρ i (%), was calculated using a first-order kinetic equation, and the hydraulic retention time, t i (d), was estimated from the SWB volume, V i (m3), and the average flow rate of paddy drainage, q i (m3 d–1):

ρi=(1−e−ki·ti)×100 3
ti=Viqi=Si·hiqi 4

where S i (m2) and h i (m) are the surface area and depth of the SWB.

The N removal amount, R i (mg), was determined as

Ri=Mi×ρi100 5

where M i (mg) is the N load from paddy runoff.

The N removal rate, r i (mg m–2 d–1), by SWBs from paddy runoff was calculated as

ri=RiAi·D 6

where A i (m2) is the area of paddy fields, and D (d) is the number of days in the fertilization stage.

After calculating the above N removal values at the municipal level for each fertilization stage, the N removal amounts for the three fertilization stages were summed up to generate the values for the entire rice-growing season for the rice region level or the country level. The N removal rate for the entire season at the rice region level was calculated by dividing the total N removal amount of that region by the corresponding paddy area and season duration. The N removal percentage for the entire season at the rice region level was determined as the ratio of the total N removal amount to the total N load into SWBs for that region.

Calculation of Nitrogen Load of Paddy Runoff

The N load of paddy runoff, M i (mg), was calculated as

Mi=Qi·coni 7

where Q i (L) and con i (mg L–1) are the volume and N concentration of paddy runoff.

The N loading rate, m i (mg m–2 d–1), was estimated as

mi=MiAi·D 8

where A i (m2) is the area of paddy fields, and D (d) is the number of days in the fertilization stage.

Scenario Simulations of Small Water Body Restoration and Management

To explore the N removal potential of SWB restoration, two restoration scenarios were simulated with the same restored area. In the Nationwide-Increase Scenario (SC-Nationwide), the SWB area was proportionally restored across the main rice-growing regions of China. In the Critical-Area-Increase Scenario (SC-Critical Area), SWB was increased only in municipalities with high N load and low SWB area, using the ratio of N load to SWB area as a coefficient. Furthermore, for each SWB restoration scenario, the macrophyte coverage of SWBs was increased to 25–50% to assess the additional N reduction potential by SWB management.

Estimation of Economic Benefit from Small Water Bodies

The economic value of N removal by SWBs, E i (USD), was estimated by comparing the N removal performance with the conventional wastewater treatment cost:

Ei=Tcost·RiCin−Cout×1000 9

where R i (mg) is the N removal amount; C in and C out (mg L–1) are the influent and effluent N concentrations in wastewater treatment plants, respectively; and T cost (USD m–3) represents the unit cost of wastewater treatment.

Uncertainty and Sensitivity Analysis

To account for parameter uncertainty and its impact on the results, a sensitivity analysis was conducted using the Sobol index. Subsequently, Monte Carlo simulations were performed on the sensitive parameters to capture the uncertainty in model predictions. Sobol sensitivity analysis is a widely used global sensitivity analysis method based on variance decomposition, particularly suitable for nonlinear models. The Total Sobol Index integrates both first-order and higher-order interaction effects to comprehensively assess the total sensitivity of individual parameters, including their interactions with other parameters. Based on the sensitivity analysis results (Table S1), influent N concentration in SWBs, paddy drainage volume, and SWB area were selected as the key sensitive parameters for Monte Carlo simulations of the removal rate constant, removal percentage, removal rate, and removal amount (Figure S1, S4, and S6). A total of 1000 Monte Carlo simulations were conducted, and results were presented as mean ± standard deviation (SD).

Data Sources

The spatial distribution of SWBs was identified based on a recently published data set of SWBs in China. The data set was derived from 2-m resolution RGB imagery acquired from Google Earth between May and September 2018. From this data set, a total of 1.75 million SWBs with surface areas of less than 0.33 ha were extracted in the rice-growing regions. Larger SWBs were excluded as they are typically used for aquaculture or have collective ownership, making them less likely to be involved in rice irrigation and drainage.

Data used to calculate the N removal rate constants for SWBs included water temperature, macrophyte coverage, and N input concentration. Water temperature data were obtained from the National Surface Water Quality Automatic Monitoring Real-time Data Publishing System (https://szzdjc.cnemc.cn:8070/GJZ/Business/Publish/Main.html), China National Environmental Monitoring Centre. Monthly water temperature data from 2021 to 2023 were collected from various stations in each municipal region within China’s major rice-growing regions. The average water temperature from May to October over the three years was used for model simulations. Macrophyte coverage in SWBs was determined via visual interpretation of GF-2 remote sensing images with 1-m resolution, collected from May to October, for 1236 representative SWBs across 16 provinces in the major rice-growing regions. The seventh-day N concentration of paddy runoff during each fertilization period was used as the N input concentration for SWBs in each municipal region. This concentration was estimated based on relationships with fertilizer application rates, soil pH, and soil organic matter, extracted from 76 studies and 3486 data points.

To calculate the N removal percentage, rate, and amount from paddy runoff by SWBs, daily paddy field drainage volumes from 2008 to 2017 were simulated using the WQQM-PIDU model. This model was developed based on hydrological and nutrient cycling processes, as well as two-year observations in China, and calibrated by comparing the simulated result with published literature values. Average paddy field drainage volumes for each fertility period over a ten-year span in each municipality were used for model simulations. The basal, tiller, and panicle fertilization periods for each municipality were determined through field surveys.

Results and Discussion

Characteristics of Small Water Bodies and Nitrogen Load from Paddy Runoff

A total of 1.75 million SWBs (<0.33 ha) were identified in China’s main rice-growing regions. These SWBs cover a total area of 5098.42 km2, accounting for 1.08% of the total land area in China’s main rice-growing regions, with a clear decreasing trend from south to north (Figure ). The Mid-Lower Yangtze Basin contains the largest SWB area, totaling 3424.30 km2, which accounts for 1.70% of the total area of rice regions. The Southeast Coastal Region follows with 1025.38 km2, accounting for 1.34%. These two regions account for 87.28% of the total SWB area in China’s rice-growing regions. In contrast, the Upper Yangtze Basin and the Northeast Plain have smaller SWB areas, with 642.17 km2 (0.70%) and 6.57 km2 (0.02%), respectively. Since SWBs in rice regions are usually used for irrigation, drainage, or flooding, their macrophytes are regularly cleared. Macrophyte coverage in these SWBs is generally low, ranging from 1.51 to 8.43% (Figure S2). Macrophytes not only directly remove N from water bodies through uptake but also provide habitats for microorganisms, facilitating microbial transformation and N removal. The low proportion of SWB area relative to paddy fields, coupled with limited macrophyte coverage, may restrict their contribution to N removal from paddy runoff.

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Current status of small water bodies and N load from paddy runoff in China’s main rice-growing regions. NE, UYZ, MLYZ, and SE represent the Northeast Plain, Upper Yangtze Basin, Mid-Lower Yangtze Basin, and Southeast Coastal Region, respectively.

In China’s main rice-growing regions, the N load from paddy runoff during the entire rice-growing period is estimated at 289.88 ± 26.01 kt annually, with an N load rate of 7.84 ± 0.74 mg m–2 d–1, exhibiting a similar south-to-north decreasing pattern to the SWB area (Figure and S8). These estimates are consistent with previous studies. The Mid-Lower Yangtze Basin and Southeast Coastal Region have higher N loads, at 137.08 ± 19.58 and 89.26 ± 10.17 kt annually, and corresponding higher N load rates of 8.08 ± 1.15 and 14.05 ± 1.55 mg m–2 d–1. These two regions together account for 78.08% of the total N load. Their N loads are 4.04 and 2.63 times greater than those in the Upper Yangtze River Basin, and 4.63 and 3.01 times greater than those in the Northeast Plain, respectively (Figure S7). Temporally, the N load rate is higher during the basal and tillering (15.94 ± 1.57 and 14.83 ± 2.29 mg m–2 d–1) stages, while the total N load peaks during the tillering stage (190.89 ± 22.19 kt) (Figure S7 and S8c). The tillering stage accounts for 65.85% of the total N load, which is 2.59 and 7.54 times higher than the loads during the basal and panicle stages, respectively.

Spatiotemporal variation in N loads and load rates is largely driven by differences in water management and fertilization intensity. In the Mid-Lower Yangtze Basin and Southeast Coastal Regions, heavy and frequent precipitation leads to excess water in paddy fields, necessitating substantial drainage to prevent rice root damage from flooding , (Figure S5). Additionally, intensive double-cropping systems in these regions result in higher fertilizer inputs, further amplifying N export (Figure S7). Temporally, basal and tillering fertilization together account for 60–80% of total fertilizer application to meet the strong nutrient demand of rice, resulting in high N concentrations and consequently elevated N load rates in runoff (Figure S3 and S8c). Furthermore, the tillering stage has an extended duration and substantial drainage aimed at controlling excessive tillering (Figure S5), and thus contributes most to the total N load (Figure S7). The combination of hydrological and agricultural factors highlights the Mid-Lower Yangtze Basin and Southeast Coastal Region as critical areas for N removal management, particularly during the tillering stage.

Nitrogen Removal Rate Constants and Removal Percentage of Small Water Bodies

Significant variations in N removal rate constants were observed for SWBs across different regions and growth stages in China’s rice-growing regions, with a mean of 3.98 × 10–2 ± 8.29 × 10–3 d–1 (Figure a). Our findings show that the N removal rate constant for SWBs is 1.73 times as high as the previously reported average for all Chinese water bodies (2.3 × 10–2 ± 1.7 × 10–2 d–1), demonstrating the importance of SWBs in N removal. Regionally, the Northeast Plain shows the highest N removal rate constant, at 4.73 × 10–2 ± 1.42 × 10–2 d–1, followed by the Southeast Coastal Region with 4.10 × 10–2 ± 1.07 × 10–2 d–1, while the Yangtze River Basin exhibits the lowest value, at 3.72 × 10–2 ± 2.93 × 10–3 d–1 (Figure c). Among the different growth stages, the basal and tillering periods exhibit higher N removal rate constants (4.32 × 10–2 ± 1.22 × 10–2 and 4.00 × 10–2 ± 4.77 × 10–3 d–1) compared to the panicle stage (3.48 × 10–2 ± 2.85 × 10–3 d–1) (Figure d). Spatiotemporal variation in N removal rate constants is primarily driven by differences in input N concentrations across regions and growth stages. Temporally, N concentrations are elevated during the basal and tillering stages due to intensive fertilization (Figure S3). Spatially, the Northeast Plain, characterized by nutrient-rich soils and lower precipitation, exhibits significantly higher N concentrations in paddy runoff compared to the Mid-Lower Yangtze Basin. These elevated concentrations enhance microbial and plant-mediated N removal processes, leading to higher rate constants.

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Potential N removal rate constants and removal percentage of small water bodies in China’s main rice-growing regions. The spatial distribution of (a) potential N removal rate constants and (b) potential N removal percentage during the entire rice growing season. The potential N removal rate constants (c) across different regions and (d) during different periods, and the potential N removal percentage (e) across different regions and (f) during different periods. NE, UYZ, MLYZ, and SE represent the Northeast Plain, Upper Yangtze Basin, Mid-Lower Yangtze Basin, and Southeast Coastal Region, respectively. BFS, TFS, and PFS denote the basal, tillering, and panicle fertilization stages, respectively. *, **, and *** indicate significant differences at the confidence levels of 0.05, 0.01, and 0.001, respectively.

Similarly, significant spatial variations in potential N removal percentage were observed across different rice-growing regions in China, with 58.63% ± 7.80% (Figure b). These findings are also higher than the medium and large water bodies in previous studies, which reported N removal percentages between 32 and 49%. ,, However, the spatial distribution of the N removal percentage differs from that of the N removal rate constants (Figure c–f). The Yangtze Basin and Southeast Coastal Region exhibit significantly higher potential N removal percentages (70.39% ± 12.24% and 54.38% ± 9.69%) than the Northeast Plain (3.55% ± 2.40%) (Figure e). The spatial differences are primarily driven by the combined influence of N removal rate constants and hydraulic retention time. A longer hydraulic retention time enables extended interaction of N within the water bodies, enhancing microbial and plant-mediated N removal. Despite a high potential N removal rate constant in the Northeast Plain (Figure a), the limited SWB area results in shorter hydraulic retention time (1.77 ± 0.18 d) (Figure ), substantially limiting the potential N removal percentage in this region (Figure b). The potential N removal percentage varies significantly across growth stages, with values of 61.01% ± 12.34%, 60.06% ± 10.58%, and 41.00% ± 16.77% for the basal, tillering, and panicle stages, respectively (Figure f). This trend is consistent with that of the potential N removal rate constant across different fertilization stages.

Nitrogen Removal Amount and Associated Economic Benefits of Small Water Bodies

In China’s main rice-growing regions, SWBs are estimated to remove approximately 169.97 ± 16.71 kt N annually from paddy runoff, with potential removal rate of 4.59 ± 0.47 mg m–2 d–1, accounting for 23.62% of the total N emissions from national crop production (Figure ). Spatially, the Mid-Lower Yangtze Basin and Southeast Coastal Region exhibit higher potential removal amounts of 102.41 ± 15.00 and 48.54 ± 6.65 kt annually, along with higher potential N removal rate of 6.08 ± 0.91 and 7.70 ± 1.02 mg m–2 d–1. These two regions account for 88.81% of the total N removal amount. In contrast, the Upper Yangtze Basin and Northeast Plain have lower potential N removal amounts (17.97 ± 3.12 and 1.05 ± 0.55 kt annually) and removal rates (3.49 ± 0.61 and 0.15 ± 0.08 mg m–2 d–1), respectively. The N removal capacity of SWBs is jointly determined by SWB area, N removal percentage, and N load from paddy runoff. Regions with the highest potential N removal amount typically have larger SWB areas and higher N loads (Figure and S7). In these areas, high N loads, extensive SWB coverage, and high removal percentages together result in substantial N reduction by SWBs.

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Potential (a) N removal amount and (b) N removal rate by small water bodies in China’s main rice-growing regions. NE, UYZ, MLYZ, and SE are the Northeast Plain, the Upper Yangtze Basin, the Mid-Lower Yangtze Basin, and the Southeast Coastal Region, respectively.

Temporally, the potential N removal rate by SWBs is elevated during the basal and tillering stages, reaching 10.10 ± 1.08 and 8.82 ± 1.50 mg m–2 d–1, respectively (Figure b), while the potential N removal amount peaks during the tillering stage at 114.64 ± 15.18 kt (Figure a). The temporal variation in N removal by SWBs is largely driven by fluctuations in N discharge from paddy runoff, following a similar seasonal pattern (Figure S7 and S8).

The potential economic value of SWBs was assessed by comparing their N removal capacity with the costs of conventional wastewater treatment. If traditional wastewater treatment plants were used to achieve the same N removal as SWBs in China’s main rice-growing regions, the cost would reach approximately 1.68 ± 0.17 billion USD, equivalent to 65.40% ± 6.46% of China’s 2022 budget for water pollution control (2.57 billion USD). Additionally, achieving the same level of N reduction as SWBs would require the construction of approximately 630 new wastewater treatment plants. However, the value of SWBs extends beyond their economic impact. Their ability to reduce N under low-concentration conditions provides a distinct advantage in water quality management. In contrast, existing wastewater treatment plants primarily treat high-concentration wastewater, and the costs associated with treating low-concentration wastewater are significantly higher. Therefore, the economic valuation of SWBs based on wastewater treatment costs may underestimate their true contribution to N removal. Moreover, current wastewater treatment technologies cannot fully replicate the ecological functions of SWBs, which play an irreplaceable role in maintaining ecological balance, protecting biodiversity, and promoting sustainable development.

Spatial Mismatch between Small Water Body and Nitrogen Load

Regions with high N loads require more SWB areas to achieve water quality goals. However, our analysis reveals a spatial mismatch between the distribution of SWBs and the N loads from paddy runoff (Figure a). We classified municipalities in China’s main rice-growing regions based on SWB area and N load from paddy runoff. The results show that approximately 5.91% of the regions, although also characterized by low N loads (<1.38 kt y–1, mean), exhibit higher SWB areas (>24.4 km2, mean). Notably, 16.58% of the regions have N loads exceeding the mean, while SWB areas fall below the average. These regions encompass approximately 50551.23 km2 of paddy fields, representing 23.04% of the total paddy field area in China’s main rice-growing regions, and contribute 20.74% of the total N load, despite limited SWB areas.

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Spatial relationship between N load and small waterbody area. Gray dots indicate regions with low N load and low water body area. Blue dots denote regions with low N load and high small water body area. Yellow dots indicate regions with high N load and high water body area. Red dots represent regions with a high N load and a low small water body area. The horizontal dashed line corresponds to the mean small water body area (24.4 km2), while the vertical dashed line corresponds to the mean N load (1.38 kt y–1). NE, UYZ, MLYZ, and SE represent the Northeast Plain, Upper Yangtze Basin, Mid-Lower Yangtze Basin, and Southeast Coastal Region, respectively.

These “critical areas” limited by SWB are mainly distributed in the Northeast Plain and Southeast Coastal Region, which account for 46.05 and 36.23% of the total SWB-limited areas, respectively (Figure b). In recent years, accelerated urbanization and agricultural intensification have led to the filling or conversion of substantial SWB areas into agricultural land, resulting in a significant reduction in SWBs. This is particularly evident in the Northeast Plain, where agricultural intensification is more pronounced (Figure b). In the Southeast Coastal Region, although the SWB area is relatively large, the significantly greater N load from paddy runoff imposes excessive pressure on these SWBs (Figure S7). These combined factors have resulted in insufficient SWB buffer zones in some high N load areas, which consequently face more severe challenges in managing N pollution.

The Estimated Nitrogen Removal under Small Water Body Restoration Scenarios

The spatial mismatch between the distribution of SWBs and N load hotspots in China’s rice-growing regions indicates significant potential for improving water quality through targeted SWB restoration in critical areas with high N load and low SWB area. Under SC-Nationwide, N removal increases linearly with increasing SWB area (R 2 = 0.95), whereas SC-Critical Area exhibits a power-law relationship between N removal and SWB area proportion in rice regions (R 2 = 0.85) (Figure a). SC-Critical Area has a higher N removal than SC-Nationwide when the SWB area proportion is less than 2.37%. As the SWB area increases, the relative advantage of SC-Critical Area over SC-Nationwide initially increases and then declines, reaching its maximum at an SWB area proportion of approximately 1.23%. This point represents a threshold beyond which further investment in SC-Critical Area provides no added efficiency over SC-Nationwide. Considering the limited availability of land resources, a large-scale increase in SWBs may not be practical. Therefore, the 1.23% area proportion may serve as a cost-effective target for SWB increase under resource constraints.

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Scenario simulation analysis of restoration and management of small water bodies. (a) Relationship between the area increase of small water bodies and enhanced N removal amount under SC-Nationwide and SC-Critical Area. (b) Increased N removal amount under SC-Nationwide and SC-Critical Area. Spatial distribution of increased small water body area under (c) SC-Nationwide and (d) SC-Critical Area. SC-Nationwide and SC-Critical Area are scenarios under proportional amplification across all regions and prioritization of regions with high N load and low SWB area. NE, UYZ, MLYZ, and SE represent the Northeast Plain, Upper Yangtze Basin, Mid-Lower Yangtze Basin, and Southeast Coastal Region, respectively.

Our scenario simulation shows that the spatial distribution of SWB restoration varies significantly across the two scenarios (Figure c,d). In SC-Nationwide, the additional SWBs are primarily concentrated in the Mid-Lower Yangtze Basin and Guangdong and Guangxi provinces within the Southeast Coastal Region, accounting for 79.94% of the total increase in SWB area. In SC-Critical Area, the restoration focus is distributed among municipalities with high N loads and low SWB areas, such as Jiamusi in Heilongjiang, Nanping in Fujian, Foshan in Guangdong, and Baise in Guangxi.

By expanding the SWB area proportion to 1.23% (713.78 km2 of SWBs, by an additional 0.15% of paddy area), SC-Critical Area demonstrates the highest N removal, increasing the N removal amount by 20.96% (35.62 kt annually) compared to current levels. SC-Critical Area is 3.98 times more effective than SC-Nationwide (8.95 kt annually, a 5.26% increase) (Figure b and Table S2). Similarly, SC-Critical Area also yields the highest economic benefits, reaching 298.11 million USD annually (a 17.73% increase) (Table S3). Prioritizing SWB restoration in regions with high N loads and low SWB areas (SC-Critical Area) significantly enhances both N removal amount and economic benefits. This result is consistent with recent studies that indicate spatially optimized wetland restoration can maximize ecological benefits, particularly under limited resource conditions. ,, Additionally, increasing the macrophyte coverage of SWBs to 25–50% further enhances N removal amount by 10.17–17.98 kt annually across the two scenarios, representing a 5.98–10.58% increase (Figure b and Table S2), and boosting economic benefits increase by 96.25–171.86 million annually (Table S3).

In the two SWB restoration scenarios, increased N removal through area increase and macrophyte enhancement is primarily concentrated during the period and in regions with high N discharge (Figure S7 and S11). Temporally, the increased N removal is highest during the tillering stage with the highest discharge, which accounts for 61.45–67.20% of the total increased N removal amount (Figure S11b,d). Spatially, the most significant increase in N removal is observed primarily in the regions with higher discharge, including the Mid-Lower Yangtze Basin and the Southeast Coastal Region, which together account for 56.68–85.36% of the total increased N removal amount (Figure S11a,c). Enhancing SWB’s area and their macrophyte coverage can significantly boost N removal rate and economic benefits, especially in the high N discharge regions of the Mid-Lower Yangtze Basin and the Southeast Coastal Region during the high N discharge period of the tillering stage.

Environmental Impact and Significance

Despite SWBs being increasingly recognized as biogeochemical hotspots with significant roles in N dynamics, quantitative assessments of their capacity to reduce agricultural N surpluses and their effectiveness as a management tool remain limited. , Our study indicates that the average area ratio of SWBs in China’s rice-growing region is only 1.08%, yet they contributes 58.63% of the N removal from paddy runoff. These findings confirm that SWBs play a crucial role in N management, offering an effective and low-cost solution for mitigating N emissions from paddy runoff.

However, amid a global wetland area decline of approximately 35% between 1970 and 2015, the protection of SWBs has often been neglected in comparison to larger wetlands. , Conservation initiatives have primarily targeted larger wetlands, resulting in the limited inclusion of SWBs in conservation frameworks, such as resource inventories and protection registries. For example, the “no net loss” policy in the United States has prioritized the restoration of a few large wetlands. This imbalance has led to the neglect and replacement of SWBs, whose essential ecosystem services are often undervalued, rendering them increasingly vulnerable to unnoticed degradation and gradual disappearance. The degradation of SWBs diminishes nutrient processing capacity at the landscape scale, leading to significant environmental degradation and considerable economic losses. ,, Therefore, greater emphasis should be placed on the protection and restoration of SWBs to achieve effective N management and sustainable environmental governance.

A significant spatial mismatch between regions with high N emissions and extensive SWB areas has been detected through municipal resolution assessment in this study, which underscores the need for targeted protection and restoration efforts to optimize N reduction at proper locations. Prioritizing SWB restoration in high N loads and low SWB areas regions provides the most favorable environmental and economic outcomes. However, this approach faces practical challenges, including land use conflicts, high implementation costs, and variability in local policy support. Moreover, rice-growing areas in the world are predominantly located in developing regions that face multiple pressures from economic development, food security, and environmental protection. In these regions and other intensively developed agricultural landscapes, SWB expansion is often constrained, limiting N mitigation through water body enlargement alone. These complexities necessitate a comprehensive approach that integrates ecological, economic, social, and policy considerations to ensure feasible and sustainable SWB restoration. Enhancing and maintaining macrophyte coverage in SWBs offers a promising complementary strategy, particularly for artificial wetland designs that are frequently limited by space and budget constraints. − Increasing macrophyte coverage, rather than expanding SWB areas alone, is a cost-effective method to enhance N removal, reduce agricultural N loads, and improve water quality in land-scarce regions. Additionally, as an integral component of wetland ecosystems, maintaining moderate macrophyte coverage improves ecosystem structure and function in SWBs, contributing to broader ecological benefits. For example, moderate macrophyte coverage promotes biodiversity by offering diverse microhabitats and enhancing ecological stability. It also enhances the carbon sink capacity of SWBs by increasing biomass and promoting photosynthetic carbon fixation. Furthermore, the negative impacts associated with SWB wastewater purificationsuch as the emission of greenhouse gases like methane and nitrous oxidecan potentially be mitigated through macrophyte maintenance. , This dual strategyspatial optimization and macrophyte managementprovide a cost-effective SWB management strategy under resource constraints.

In the context of climate change, the increasing frequency of extreme weather events presents growing challenges for nonpoint source pollution control globally. A combined strategy of reduction of nutrients at the source and ecological interceptionsuch as optimizing networks of SWBs between pollution sources and receiving watersoffers one of the most effective means of controlling nonpoint source pollution. This study underscores the critical need for targeted SWB protection and restoration and offers a robust framework for optimizing SWB management to maximize both ecological and economic outcomes. Given the regional variability, such efforts face numerous practical challenges and require tailored municipal-scale solutions.

Supplementary Material

es5c08046_si_001.pdf (1.5MB, pdf)

Acknowledgments

This study was financially supported by the National Natural Science Foundation of China (U21A2025), and the Hubei Provincial Natural Science Foundation of China (2024AFA020, 2025AFA108, 2025AFB407).

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.est.5c08046.

  • Sensitivity analysis of nitrogen removal parameters for small water bodies; fluctuation range of small water body area, nitrogen concentration, and drainage volume in paddy runoff for Monte Carlo simulation; nitrogen concentration, drainage volume, nitrogen load, and nitrogen loading rate in paddy runoff; macrophyte coverage in small water bodies; potential nitrogen removal amount, nitrogen removal rate, and economic benefits of nitrogen removal by small water bodies; and nitrogen removal potential of small water bodies under restoration scenarios (PDF)

The authors declare no competing financial interest.

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