Impact of grass traits on the transport path and retention efficiency of nitrate nitrogen in vegetation filter strips
Introduction
Non-point source pollution, especially agricultural non-point source pollution, has become the primary reason for the deterioration of water quality in rivers and lakes today (Xiang et al., 2017, Wu et al., 2019). Among them, nitrate is one of the most common dissolved contaminants and causes a series of problems, including eutrophication in surface water, nitrate pollution of groundwater, and destruction of aquatic ecosystems around rivers and lakes (Penuelas et al., 2013, Wu et al., 2017, Wu et al., 2019). Therefore, the issue of how to control non-point source pollution, especially nitrogen, has become an essential and difficult problem that needs to be solved to alleviate the current water environmental problems. Vegetative filter strips (VFSs), located at the end of agricultural land as interfaces between terrestrial and aquatic ecosystems, have the potential to effectively slow down surface runoff, promote infiltration and then greatly increase the chances of dissolved pollutants being adsorbed by soil, uptake by plants or transformed (Munoz-Carpena et al., 1999, Dorioz et al., 2006, Mayer et al., 2007, Howarth et al., 2011, Baniya et al., 2020). Due to the economy and ecology of VFSs, they are thought to be the best management practice to control non-point source pollution into water bodies (Smith et al., 2008, Yuan et al., 2009).
The effects of vegetation on the pollutant reduction in VFS have been studied over the past decades, but the studies have mainly focused on the qualitative analysis of vegetation conditions (Haan et al., 1994, Ludwig et al., 2005, Lambrechts et al., 2014). Mankin et al. (2007), Hao et al. (2009), Zhang et al. (2010) found that different vegetation species and configuration methods had direct impacts on the pollutant removal. Meanwhile, the pollutant removal efficiencies varied significantly due to the dynamic changes in vegetation traits with grow stages and seasonal changes (Zhao et al., 2014, Pan et al., 2017, Kavian et al., 2018, Satchithanantham et al., 2019, Valkama et al., 2019). However, due to the specificity of the study sites and the limitations in the experimental variables, these results have not provided a consistent conclusion regarding the effects of vegetation on pollutants that can be applied to create a universal standard for vegetation VFS designs (Franklin et al., 2019, He et al., 2020).
Changes of the vegetation conditions, such as vegetation types, collocation methods, growth stages, and periods, primarily cause the variations in vegetation characteristics, as well as soil environment related to them (Clement et al., 2002, Weissteiner et al., 2013, Mekonnen et al., 2016). Therefore, it would be an effective method that using the plant traits to uniformly characterize vegetation conditions and to quantitatively explain vegetation effects on ecosystem services and, notably, pollutant removal, in VFSs (Lavorel and Garnier, 2002, Faucon et al., 2017, Kervroëdan et al., 2018, Franklin et al., 2019). Some scholars have paid attention to the relationship between vegetation characteristics and the pollutant removal efficiency of VFSs. For example, Lambrechts et al. (2014) and Pan et al. (2017) found that traits such as grass tillering number, stem spacing, and morphological structure had significant effects on the sediment retention efficiency in VFSs according to lab experiments and model simulations. Franklin et al. (2019) proposed a new framework by which plant species that are likely to maximize N remove in riparian VFSs could be selected according to plant traits with respect to N cycling, such as root forms, growth rates, and leaf characteristics. In addition, this study indicated that the vegetation effects on pollutant transport and reduction in VFSs can be explained according to scattered studies that have examined the effects of individual plant traits in VFSs or in other areas. However, it is still difficult to quantitatively compare the influences of vegetation specific traits and reveal advantageous traits for pollutant removal in a particular VFS system (Hou et al., 2020).
Additionally, the pollutant removal mechanism in VFSs can be understood only if the water flow in VFSs is roundly investigated, as it is the driving force of pollutant transport and determines the mechanisms and time of pollutant contact with the soil or vegetation (Hoffmann et al., 2009; Munoz-Carpena et al., 1999; O’Toole et al., 2018; Weissteiner et al., 2013; Yu et al., 2019). Duchemin and Hogue (2009) and Zhao et al. (2016) reported that shallow subsurface flow was the primary pathway for NO3-N loss in sloping land, which reflects the importance of studying the subsurface flow when studying the NO3-N retention in VFSs. Some studies have tried to determine the mechanism of trait effects on water flow by studying the relationships between runoff and aboveground plant characteristics (e.g., stem density, diameter, and stiffness, and leaf area and density) (Burylo et al., 2012, Lambrechts et al., 2014, Kervroëdan et al., 2018), or the relationship between the infiltration capacity and plant roots (Gyssels et al., 2005, Lambrechts et al., 2014, Wu et al., 2017). Though the results of these studies have revealed some relationships between plant traits and water flow, such as plant coverage weakening the runoff flow velocity, the mechanism of vegetation effects on the process of water flow above and below ground and their relative proportions have not yet been fully revealed.
The aim of this research is to investigate the plant trait effects on water flow and the nitrate nitrogen (NO3-N) removal efficiency in VFSs. The objectives are (1) to examine the differences in the process of water flow and NO3-N transport above and below ground between four VFSs with a high variability in plant traits under three slope gradients and (2) to analyze the contribution of plant traits to those differences and define the most important trait for NO3-N removal using the grey correlation analysis. The results could improve the understanding of vegetation effects on water flow and NO3-N transport in VFSs and provide a valuable reference for the selection of candidate vegetation species based on traits to improve NO3-N retention in VFSs.
Section snippets
Simulation experiment device, materials and execution
The experiments were conducted in the Jiangxi Eco-Science Park of Soil and Water Conservation, located in the Yangou watershed in Jiangxi Province, China (29°16′N to 29°17′N, 115°42′E to 115°43′E). An experimental device was designed to test the NO3-N retention efficiency and transport path of the VFSs (Fig. 1a), that included a soil box to construct the simulated VFS and a pressure pump system under the soil box to turn the soil box to achieve different slope gradient conditions. Each soil box
The grass traits of stems and roots
Table 2 shows the main stem and root traits as well as the total N content and uptake mass of the three grasses measured at the end of all the experiments. In general, the differences in all the characteristics between the three grasses were significant at the 0.05 level, except root depth. Specifically, the tall fescue VFS showed the lowest value of stem spacing (60.05 ± 15.12 cm) and root depth (6.20 ± 1.30 cm), but the highest value of the root length density (5.32 ± 0.42 cm cm−3), the
Discussion
The impact of grass traits on water flow and NO3-N transport above and below ground was investigated using simulated runoff experiments with three grassed VFSs and one bare VFS. The grasses were nurtured under greenhouse conditions for two months, until the vegetation coverage reached over 90%. Meanwhile, there was obvious morphological difference between the grasses, which have been shown to result in significant differences in runoff and sediment interception in VFSs (Pan et al., 2017;
Conclusions
In this study, the responses of water flow and NO3-N transport above and below ground to grass specific traits in VFSs were investigated. For the water flow, stem density and root length density had the greatest positive correlations with the surface flow and the subsurface flow, respectively, while the flexibility of vegetation may weaken the positive effect. For the NO3-N loss concentration, the effect of roots was lower than that of stems as a whole. Additionally, to reduce NO3-N loss mass,
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgments
We would like to thank the two anonymous referees for their constructive suggestions. This study was supported by the National Key Research and Development Program of China (No. 2018YFD0900805), the National Natural Science Foundation of China (No. 51879071), and the Fundamental Research Funds for the Central Universities (No. 2019B07314).
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