Evaluation of ammonia and nitrate distribution and reduction within stormwater green infrastructure with different woody plants under multiple influencing factors.

The impact of stormwater green infrastructures (GIs) with different woody plants on nitrogen (N) distribution is still poorly understood. Laboratory experiments were conducted for GIs without or with Sophora japonica and Malus baccata to investigate the distribution of NH-N and NO-N. The test data was utilized to calibrate and validate the HYDRUS-2D. The validated model was subsequently used to analyze the distribution of NH-N and NO-N within the different GIs under three different rainfall conditions: inflow/runoff pollutant concentration, rainfall recurrence interval (runoff amount of a rainfall event), and number of dry days (during which no rainwater infiltrates into the soil). The average NH-N and NO-N concentrations in the upper soil (0-30 cm) of the GIs were about 4.8 and 2.4 times those of the lower layer (30-60 cm). Compared to the control (V), the average NH-N concentrations in soil with Sophora japonica (V) and Malus baccata (V) decreased by 15.8% and 35.1% while those of NO-N decreased by 15.5% and 27.2%, respectively. Degrees of influence by the three factors on the average soil NH-N and NO-N concentrations were inflow concentration > number of dry days > recurrence interval. The number of dry days was the smallest influence factor for the overflow N load while the inflow concentration was the most significant influence factor for the outflow, bio-utilization, and soil nitrogen loads. Compared to the control, outflow (groundwater recharge) loads of NO-N from the V and V increased by 14.0-16.6% and 3.7-6.8%, respectively under different conditions. The overflow (runoff) loads from V and V decreased by 16.8-36.3% and 6.6%-8.4%, respectively. A multiple regression equation was used to establish a quantitative coupling relationship between N pollutant load reduction rates and influence factors (R ≥ 0.83). This relationship can be used to estimate the runoff treatment effectiveness of green infrastructure on target pollutants.