Nitrogen removal by different riparian vegetation buffer strips with different stand densities and widths

The migration of nitrogen (N) from farmland to lake aggravates eutrophication. Riparian buffer strips (RBSs) are crucial in alleviating nitrogen into water bodies. This study examined the impacts of different RBS patterns on nitrogen removal. The effects of different RBSs of various widths (5, 15, 30, and 40 m), with different vegetation types (Taxodium hybrid ‘Zhongshanshan’, poplar (Nanlin-95), and a mixed forest of T. hybrid ‘Zhongshanshan’ and poplar) and at different densities (400, 1,000, and 1,600 plants·hm ) on the TN, NH4 þ-N and NO3 -N removal rates in different depths of runoff water were studied. The results showed that the 15 m-wide RBS removed nitrogen the most effectively, with average removal rates of NH4 þ-N, NO3 -N and TN reaching 67.79%, 65.93% and 65.08%, respectively. Among the RBSs with different vegetation types, the poplar forest RBS removed the most NH4 þ-N (74.28%) and NO3 -N (61.71%). The mixed-forest RBS removed the most TN (65.57%). The RBS with 1,000 plants·hm 2 was more suitable in terms of the removal of NH4 þ-N (74.25%), NO3 -N (71.08%) and TN (62.67%). The conclusion can provide the basis of vegetation and width optimization for the design and construction of an RBS for maximum eutrophication nutrient removal.


GRAPHICAL ABSTRACT INTRODUCTION
Due to the rapid development of industry and agriculture and increase in the usage of chemical fertilizers, pollution of surface waters such as lakes and rivers with nitrogen pollutants has become increasingly severe (Zhang et al. ). The RBS, a banded vegetation zone between polluted water bodies and pollution sources, serves as a buffer transition zone closely connected with aquatic ecosystems and terrestrial ecosystems (Glenn ). The RBS is also an important link for the exchange of energy, material and information between river ecosystems and terrestrial ecosystems (Casey & Klaine ). Currently, construction of RBSs is emphasized in lake eutrophication management to reduce the nitrogen content of lake water that occurs through surface runoff and underground runoff (Hefting et al. ). To protect river banks and water bodies, the New Zealand government has established regulations, including the restoration of river banks using riparian vegetation buffer strips (RBSs) (Yuan et al. ). RBSs have been considered the best management measure for soil and water in Canada (Lorion & Kennedy ), and other European countries are initiating research on RBS technology and actively promoting the application of this technology (Phillips ; Smith ).
In China, the level of eutrophication of lakes and reservoirs has exceeded 66%, of which approximately 22% of lakes experienced severe eutrophication and ultra-eutrophication, indicating that eutrophication of lakes and rivers has been a significant aquatic environmental problem for a long time (Guo et al. ). Water eutrophication not only severely affects the environment and health but also restricts the utilization of water resources (Abu-Zreig et al. ; Vymazal & Kröpfelová ). Taihu Lake, the third largest freshwater lake in China, plays a significant role in the economy and daily life of its surrounding residents (Qin et al. ). In May 2007, a large-scale outbreak of cyanobacteria in Taihu Lake caused widespread concern in society and among scholars, which resulted in the problem of eutrophication of lakes becoming recognized by the public as an urgent issue of the aquatic environment (Qin et al. ).
Due to the universally wide range of agricultural production activities, agricultural nonpoint source pollution has become an important cause of the deteriorating water quality of lakes and rivers (Hazlett et al. ). The study showed that the most important form of water pollution in Taihu Lake is agricultural nonpoint source pollution, in which nitrogen pollution is up to 56% (Borin et al. ).
The width of an RBS determines whether it can fully exert its ecological service function (Lee et

Study area
This study was conducted in Zhoutie town adjacent to Taihu Lake, southeastern Yixing City, Jiangsu Province, China The rainwater in the area was concentrated in spring and summer and the early summer was the rainy season. The annual average number of rainy days in the area was 136.6 d, and the annual average precipitation was 1,177.1 mm. The heavy rain in June and September was more frequent. The soil had a pH of 6.8, 24.1 g·kg À1 organic matter, 0.2 g·kg À1 TP and 9.7 g·kg À1 TN, respectively (http://vdb3.soil.csdb.cn).
The average slope of the soil surface was 2%.

Plot setting
There one week apart from the sampling time. The rainfall during the period is shown in Table 3 (www.yixing.gov.cn).

Chemical analysis
Three different depths of water samples were collected at different widths in each plot. The leaching water was extracted with a small plastic water pump and filled a 250 mL plastic bottle. Samples were brought back to the laboratory and stored in a À4-0 C refrigerator. The different forms of nitrogen in the water samples were determined as soon as possible. After each sampling was completed, the   The rate of nitrogen removal from runoff is based on Equation (1): where i is the RBS width (5, 15, 30 and 40 m), rN i is the cumulative removal rate of nitrogen at different widths (%), N i is the concentration of nitrogen at the width of i (mg·L À1 ), and N 0 is the concentration of nitrogen in runoff water at the 0 m width (mg·L À1 ). If rN i % is greater than zero, the concentration of nitrogen at i width is higher than that at the initial width. If rN i % is less than zero, the concentration of nitrogen at i width is lower than that at the initial width.

Statistical analyses
All data were expressed as the observed mean, followed by its standard error (±SE). Statistical analyses were performed using SPSS Version 19.0 software (SPSS Inc., Chicago, USA). Means were compared using the least significant difference (LSD) determined by a one-way ANOVA, and differences were considered statistically significant at p < 0.05.

Differences in nitrogen interception and absorption in the RBS with different widths
The effect of the RBSs with different widths on runoff NH 4 þ -N removal In the case of NH 4 þ -N, the mass concentration of runoff    and lower by 16.7% and 9.3% than that of the RBSs with 1,000 stem·hm À2 and 1,600 stem·hm À2 , respectively. At a width of 15 m, the RBS with a density of 1,000 stem·hm À2 (81.00%) had a higher removal efficiency than that of the RBS with 1,600 stem·hm À2 (67.55%), and the difference was significant (p < 0.05). At the other widths, the removal efficiencies were almost the same. In the four RBSs with different widths, the RBS with a density of 1,000 stem·hm À2 had a significantly higher removal efficiency than that of the RBS with 400 stem·hm À2 (p < 0.05, except at a width of 30 m).
The effect of the RBSs with different stand densities on runoff NO 3 À -N removal The three RBSs with different stand densities showed high and different NO 3 À -N removal efficiencies from runoff ( Figure 5(b)). The RBS with 1,000 stem·hm À2 showed the highest average removal efficiency of NO 3 À -N (71.08%).
The average removal efficiency of the RBSs with 1,600 stem·hm À2 (70.08%) and 400 stem·hm À2 (61.37%) was lower by 1.4% and 15.8% than that of the RBS with 1,000 stem·hm À2 . At a width of 30 m, the RBS with a density of 1,000 stem·hm À2 (70.66%) had a lower removal efficiency of NO 3 À -N than that of the RBS with 1,600 stem·hm À2 (75.19%); while at other widths, the RBS with a density of 1,000 stem·hm À2 showed a higher NO 3 À -N removal efficiency than those of the RBSs with different densities, and the difference was not obvious. Of the four RBSs with different widths, the RBS with 1,000 stem·hm À2 had a higher average removal efficiency than that of the RBS with 400 stem·hm À2 , and the difference was significant at widths of 5 and 15 m (p < 0.05).
The effect of the RBSs with different stand densities on runoff TN removal  With the increase in stand density, the amount of nitrogen entering the RBSs decreased. However, there was no significant difference in the removal rates of different fractions of nitrogen between the RBS with 1,600 stem·hm À2 and the RBS with 1,000 stem·hm À2 , which may have been because although the stand density was high enough to absorb nitrogen, there was more vegetation litter in the RBS with high stand density and more nitrogen recycling to the soil through senescence and decay of leaves (Rutherford & Nguyen ). Studies have shown that approximately 80% of the nitrogen absorbed by a deciduous forest RBS will return nitrogen to the soil as plant litter and decay (Nilsson & Svedmark ).

Difference in nitrogen interception and absorption in the RBSs with various types of vegetation
The effect of the RBSs with various vegetation types on runoff NH 4 þ -N removal The removal effects of RBSs with the different vegetation types (T. hybrid 'Zhongshanshan' forest, poplar forest, and mixed forests) on NH 4 þ -N in runoff are shown in Figure 6(a).
After passing through these RBSs buffers, the NH 4 þ -N concentration in runoff was significantly reduced, and the The effect of the RBSs with various vegetation types on runoff NO 3 À -N removal The removal effects of the RBSs with various vegetation types on NO 3 À -N in runoff are shown in Figure 6(b). The NO 3 À -N removal efficiency of RBSs with different vegetation types was significantly higher than that of the    (Figure 6(c)).
The TN removal efficiency of mixed forests (65.57%) and poplar forest (62.67%) were higher than that of T. hybrid

CONCLUSIONS
The RBSs had a better removal capacity for N pollutants than strips without any tree cover (wasteland) and the removal capacity could be improved in the aspects of the width, vegetation type and density. In summary, the 15 mwide RBS attained the desired goal in terms of nitrogen removal. The poplar forest RBSs were more suitable for nitrogen removal. The RBS with 1,000 stem·hm À2 was the optimal density in terms of nitrogen removal.
This study for optimizing RBS patterns not only suggests that the widths, vegetation types and stand densities are very important in influencing N removal, but also provides essential scientific and technological support for the prevention