Bioretention systems are a low-impact development (LID) measure to effectively control stormwater runoff and reduce pollutant concentrations. In this paper, three groups of bioretention cells with different filling materials (1# bioretention soil media (BSM), 2# BSM + 5% biochar, and 3# BSM +5% biochar +biological filler) were constructed to analyze the pollutant removal characteristics and microbial action under different simulated rainfall conditions. Results showed that the overall pollutant removal capacity of systems 2# and 3# was higher than that of system 1#, with system 3# having the lowest effluent concentrations of 2.71 mg/L for total nitrogen (TN) and 64.3 mg/L for chemical oxygen demand (COD). The load reduction effect for heavy metals of the three systems was ranked as 2# > 1# > 3#, and average load reduction rates were 80.3, 75.1, and 84.8% for Cu, Pb, and Zn in 2#. Microbial community analysis indicated that Proteobacteria and Firmicutes were the absolute dominant bacteria of the three bioretention systems, and the dominant genera included Bacillus, Hyphomicrobium, Micrococcaceae, and Nitrospira. In addition, the total number of denitrifying functional bacteria genera in systems 2# and 3# was increased by 1.39 and 52.1% compared to system 1#.

  • The bioretention system of composite filler coupled with the water storage area was constructed for advanced treatment of stormwater runoff.

  • The overall pollutant removal capacity of the modified bioretention systems 2# (BSM + 5%BC) and 3# (BSM + 5%BC + BM) was improved.

  • The total number of denitrifying functional bacteria genera in system 3# (BSM + 5%BC + BM) was increased by 52.1% compared to system 1# (BSM).

The number of impervious pavements in urban areas has increased dramatically with rapid urbanization in China, reducing soil rainwater infiltration and increasing stormwater runoff (Skorobogatov et al. 2020; Thom et al. 2020). Rainfall incorporates pollutants from roads, green spaces, and roofs into stormwater runoff, resulting in high concentrations of particulate matter, nitrogen and phosphorus, heavy metals, pathogenic bacteria (bacteria, viruses, and protozoa), and organic pollutants (herbicides, polycyclic aromatic hydrocarbons, phthalates, etc.) (Järlskog et al. 2021). Most unpurified stormwater runoff enters rivers, lakes, and bays through the urban drainage system, affecting the quality of stowed water bodies and leading to the eutrophication of water bodies and increased risks to urban water ecology (Payne et al. 2019). Among the various stormwater treatment systems dedicated to runoff management, bioretention systems are green infrastructures fully integrated into the urban environment (Gong et al. 2023; Técher & Berthier. 2023). The bioretention system reduces rainfall-runoff and purifies water quality through physical, chemical, and biological mechanisms such as the adsorption, absorption, and degradation by soil, plants, and microorganisms in the system, to achieve source control of runoff pollution and sustainable use of water resources (Li et al. 2020).

As a typical urban stormwater runoff control measure, the bioretention system can effectively control water quantity and quality (Song et al. 2020). Shrestha et al. (2018) discovered that bioretention facilities may lower overall runoff and peak flow by 75 and 91%, respectively. Furthermore, bioretention facilities have shown a good removal impact on nitrogen (N), phosphorus (P), heavy metals, microplastics, and other contaminants in stormwater runoff (Smyth et al. 2021). For instance, the green bioretention facilities constructed by Caldelas et al. (2021) retained up to 73% Zn, 66% Cu, and >99% Pb. However, the control effects of traditional bioretention systems on nitrogen, nitrate nitrogen, and phosphorus are highly variable (Jiang et al. 2018). It was reported that total nitrogen (TN) removal efficiencies were reported to vary between −630 and 46%, and nitrate removal efficiencies ranged from −477 to 87% for the common bioretention (Zhang et al. 2021a; Li et al. 2022). Therefore, it is essential to improve the stability and effectiveness of bioretention systems to remove pollutants such as nitrogen, phosphorus, organic matter, and heavy metals (Zhang et al. 2019).

Various modified bioretention systems have been constructed by researchers such as changing the outflow and flow forms, replacing media type, or adding electron donor media. Among them, packing carbon sources in the internal water storage zone (IWSZ) through heterotrophic denitrification to reduce nitrate nitrogen () was always considered. The removal efficiency could be enhanced from 29 to 81% with wood chips (Lopez-Ponnada et al. 2020). Qiu et al. (2019) found that a bioretention system with 40 cm IWSZ can substantially enhance the removal efficiencies of multiple contaminants, with the average rate of TN and increased to 75 and 85%, respectively. However, the heterotrophic denitrification effect in bioretention is also significantly affected by the persistence of carbon sources, as time extends, the heterotrophic denitrification effect becomes worse due to the consumption of carbon sources (Li et al. 2022). There is a lack of enhanced research on target pollutants such as in current bioretention systems. Therefore, research on modified substrates for bioretention systems has shifted from soil-only to bioretention soil media (BSM, consisting of 30–60% sand, 20–30% soil, and 20–40% organic matter), and then improved to the modified substrate which added modifiers to either the soil or the BSM (Jiang et al. 2019a; Zhang et al. 2021b; Biswal et al. 2022). However, most conventional amendments cannot achieve comprehensive dissolved nutrient removal, so biochar and biological fillers are introduced as filling materials (Tirpak et al. 2021). Luo et al. (2020) added straw biochar to bioretention filler, the porosity of the filler increased by about 28% and the ammonia nitrogen removal increased from −26 to 77%. Therefore, further exploration of modified substrates incorporating water storage areas in bioretention systems is needed to improve pollutant removal.

According to the characteristics of rainwater quality and quantity, a new double-layer bioretention system was established in this research by setting functional areas, water storage areas, denitrification areas, and large-gap fillers. Three groups of bioretention systems (1#, 2#, 3#) were set up with different filler layers, in which bioretention systems 2# and 3# were equipped with water storage areas. Simulated rainfall experiments were also conducted to compare the adsorption characteristics, adsorption capacity, longevity, nitrification, and denitrification characteristics of the substrates of the three systems, as well as the reduction capacity of different retention systems for runoff pollutants. Besides, the microbial community composition, abundance and diversity, and functional flora differences in bioretention cells under different structures were also analyzed to lay a theoretical foundation for the action mechanism of bioretention pollutants.

Experimental system

The experimental system was made of plexiglass with a height of 80 cm, diameter of 10 cm, and thickness of 0.5 cm. The structures of the three groups of test columns from bottom to top were gravel drainage layer 10 cm, filler layer 60 cm, bark covering layer 5 cm, and overflow layer 5 cm. The filler layers of the three bioretention systems were 1# BSM, 2# BSM +5% biochar (BSM+ 5% BC), and 3# BSM+ 5% biochar +biological filler (BSM + 5% BC + BM), respectively. There was a perforated drainage pipe at the bottom of the bioretention system, and a layer of geotextile was placed on the gravel drainage layer to separate the filler to prevent the facilities in the perforated drainage pipe from being blocked. In this experiment, with reference to the previous research, the depth of the water storage area was 30 cm in the 2# and 3#, and the rainwater sampling points of three groups of bioretention systems were set 30 cm away from the bottom. The packing combination of small-scale devices is shown in Table 1, and the structure diagram is shown in Figure 1.
Table 1

Composition of the packing material of the small-scale devices

StructurePacking thicknessComposition (v/v)
Tectonic features
1#2#3#1#2#3#
Drainage layer 10 cm Broken bark (30–40 mm) for all devices    
Filler layer 60 cm BSM BSM + 5%BC BSM + 5%BC + BM No water storage area in 1#, 30 cm water storage area in 2# and 3# 
Covering layer 5 cm Gravel (30–50 mm) for all devices    
Overflow layer 10 cm    
StructurePacking thicknessComposition (v/v)
Tectonic features
1#2#3#1#2#3#
Drainage layer 10 cm Broken bark (30–40 mm) for all devices    
Filler layer 60 cm BSM BSM + 5%BC BSM + 5%BC + BM No water storage area in 1#, 30 cm water storage area in 2# and 3# 
Covering layer 5 cm Gravel (30–50 mm) for all devices    
Overflow layer 10 cm    
Figure 1

Structure diagram of small-scale devices.

Figure 1

Structure diagram of small-scale devices.

Close modal

The height of the water storage area has a certain influence on the water accumulation on the surface of the bioretention cell and the runoff of the perforated pipe. The fillers in the water storage areas of 2# and 3# were domesticated with sludge in the early stage, and the sludge from the oxidation ditch of the sewage treatment plant in Jiangning District of Nanjing was used as the inoculum. 200 mL mixed solution was taken into the water storage area, and the culture medium was continuously pumped into the water storage area in a continuous flow mode. From the 1st day to the 10th day of domestication culture, the effluent from the bioretention tank is continuously circulated to the top of the water storage area to enhance biological membrane growth. The concentration of N ( and ) was measured daily to gain insight into the transformation of N. When the continuous effluent concentration of was similar and the differences in and TN concentrations were small over 2 days, the acclimatization of the system had been completed.

Rainfall simulation and runoff calculation

Based on the empirical model of rainfall intensity in Nanjing (Jiangsu Province, China) in Equation (1), we simulated the rainfall patterns with the Chicago rain pattern using Equations (2) and (3). According to the ‘Technical Guidelines for Sponge City Construction – Construction of Rainwater System for Low-impact Development’, the confluence ratio was 20:1, the confluence coefficient was 0.9, and the rainfall time was 120 min. The influent water volume of each retention system is designed according to Equation (4).
formula
(1)
formula
(2)
formula
(3)
formula
(4)
where i is the rainfall intensity, mm/min; t is the rainfall duration, min; q is the design flow rate of rainwater, mL/min; P is the rainfall recurrence period, a; A, b, and n are the Chicago model parameters, and r is the rain peak coefficient.

The simulated rainfall in this experiment was formulated with reference to the method of Zhao et al. (2018) and historical rainfall data of Nanjing, the specific concentration of pollutants in simulated rainfall is shown in Table 2.

Table 2

Pollutant concentration in simulated rainfall experiments

IndexTPTN -NCODPbZnCu
Low concentration 0.71 ± 0.13 3.2 ± 0.27 1.5 ± 0.15 80 ± 7.4 0.1 ± 0.14 0.3 ± 0.20 0.2 ± 0.08 
High concentration 1.8 ± 0.47 8.0 ± 0.72 5.5 ± 0.35 200 ± 15.7 0.3 ± 0.41 0.72 ± 0.34 0.86 ± 0.12 
IndexTPTN -NCODPbZnCu
Low concentration 0.71 ± 0.13 3.2 ± 0.27 1.5 ± 0.15 80 ± 7.4 0.1 ± 0.14 0.3 ± 0.20 0.2 ± 0.08 
High concentration 1.8 ± 0.47 8.0 ± 0.72 5.5 ± 0.35 200 ± 15.7 0.3 ± 0.41 0.72 ± 0.34 0.86 ± 0.12 

Matrix adsorption characteristic test

The adsorption properties of sand (S), biochar (BC), clay (C), sand/clay (SC), and sand/biochar/clay (SBC) for , , TP, COD, Cu, Pb, Zn, Cd, and Ni were evaluated by static isothermal adsorption experiments. Firstly, the substrate material was soaked in deionized water and washed and dried, then 2 g of the substrate was put into 100 mL of rainwater solution (the concentrations of , , TP, COD, Cu, Pb, Zn, Cd, and Ni were 3.4, 3.1, 1.1, 191.6, 0.61, 0.62, 1.1, 0.062, and 0.068 mg/L, respectively), the conical flask was placed on a constant temperature shaker at 150 r/min and 23 °C for 24 h.

Adsorption saturation test: The adsorption capacity of different retention tank fillers under dynamic operation was investigated through column experiments. The column was 25 cm in height, 3 cm in internal diameter, and the packing was placed at a height of 18 cm. The synthetic stormwater runoff concentrations were referenced to the average stormwater runoff concentrations of the underlayment in Nanjing (Table 2). The annual rainfall in Nanjing is about 1,200 mm, and the total water inflow (V) is calculated according to the following equation.
formula
(5)
where n is the design life, 10 years; A is the catchment area, m2; m is the mass of substrate in the column, g; M is the mass of substrate per unit area of bioretention in the test, g; ρ is the density measured in BSM, g/m2, and H is the filling height and is calculated by 0.7 m.

Water sampling

Before starting the simulated rainfall test, the original water sample was measured from the inlet bucket by a 100 mL polyethylene plastic bottle, and the pH and temperature of the water sample were also measured and recorded; after the test started, the outflow runoff water quality samples were collected immediately when water droplets appeared at the outlet for the first time, and thereafter, considering the initial scouring effect of rainfall and the principle of the first dense and then sparse, the water samples were collected at 30, 45, 60, 90, and 120 min of the artificial rainfall simulation test, and the sampling records were prepared and labeled.

Nitrification and denitrification intensity of lower substrate

Two samples (20 g) were taken from the lower layer of the bioretention system (20 cm from the bottom) and placed in 200 mL conical flasks. Ammonium chloride was added to one conical flask to make the initial concentration of the water samples 100 mg/L, and then the concentration of and in the water samples was determined after aeration at 25°C for 24 h; In another conical flask, potassium nitrate was added to make the initial concentration of water samples 100 mg/L, the appropriate amount of carbon source was injected, and after anoxic incubation for 24 h, the concentrations of and in water samples were tested. The intensity of substrate nitrification and denitrification was calculated from the increase in per unit time in the nitrification reaction and the decrease in per unit time in the denitrification reaction.

Microbial community analysis

Microbial samples were collected from the lower filler (30 cm from the bottom) of three bioretention systems after 4 months of stable operation, and microbial characteristics and denitrification intensity were tested at the same time. All microbial samples taken included filler and interstitial water at different heights, and three samples were collected from each sample area for mixing; 60 g of each sample after mixing was weighed and added to 100 mL of sterile water, mixed well and sonicated for 5 min, then vortexed and shaken for 1 min; the suspensions taken on three occasions were mixed, and the mixed suspensions were centrifuged; the solid samples obtained after centrifugation were stored in a −80 °C refrigerator, and the microbial samples were sequenced and analyzed by Shanghai Meiji Biomedical Technology Company.

Analysis of substrate decontamination characteristics of bioretention facilities

Analysis of substrate adsorption characteristics

To more intuitively understand the physicochemical properties of substrates, the basic characteristics of each sample including bulk weight (), specific surface area (SSA), particle size (), void ratio (n), and price were tested, and the results of the tests are shown in Table 3. The specific surface areas of clay and biochar were relatively large, both exceeding 15 m2/g, indicating that both had more voids with strong adsorption potential, but the particle size of biochar was small and the void ratio was very low, so it should be appropriately blended in the preparation of the substrate. The porosity of the biological filler exceeded 90%, and it had a strong water storage capacity, which could be peak-cut and retained. At the same time, it had a large space for bio-filming, which was conducive to the cultivation of nitrification and denitrification.

Table 3

Basic characteristics of substrates

NumberFillerρ (g/cm3)SSA (m2/g) (mm)N (%)Price ($/t)
Clay 1.64 17.6 <0.05 
Biochar 0.53 82.7 <0.05 1,000–4,000 
Sand 2.07 1.03 1–3 37.1 200–450 
Biofiller 1.12 2.17 10 94.2 9,800–24,000 
NumberFillerρ (g/cm3)SSA (m2/g) (mm)N (%)Price ($/t)
Clay 1.64 17.6 <0.05 
Biochar 0.53 82.7 <0.05 1,000–4,000 
Sand 2.07 1.03 1–3 37.1 200–450 
Biofiller 1.12 2.17 10 94.2 9,800–24,000 

The results of the physisorption of N, P, and COD by three substrates (S, C, and BC) and combinations (SC and SBC) are shown in Figure 2. The biochar (0.63 mg/g) adsorbed significantly more than sand (0.32 mg/g) and soil (0.25 mg/g) because of its high SSA, however, the difference between SC and SBC was unremarkable. In contrast, clay, biochar, and sand all had low adsorption capacity, and the adsorption capacity of biochar (0.13 mg/g) was slightly higher than that of other materials (0.09 mg/g). Previous studies also showed that the adsorption of by biochar is limited, which depends on the type of biomass and the pyrolysis temperature. Rahman et al. (2020) found that washing biochar with acid and deionized water reduced biomass carbon ash content and produced more adsorption sites due to surface protonation. The differences in the adsorption capacity of the three single substrates on P were small (Figure 2(c)), with the clay having the largest adsorption capacity (0.166 mg/g), and the adsorption capacity of the three substrates was as follows: C > BC > S; the adsorption characteristics of combined substrates for P were similar to those of single substrate, and the overall adsorption capacity of SBC (0.126 mg/g) was slightly higher than that of SC (0.11 mg/g). This result is consistent with the findings of Liu (2020).
Figure 2

Static adsorption capacity of substrates on N, P, and COD in stormwater runoff.

Figure 2

Static adsorption capacity of substrates on N, P, and COD in stormwater runoff.

Close modal

The adsorption capacity of COD (Figure 2(d)) was higher than that of N and P, mainly due to the high influent concentration of COD; the adsorption capacity of clay was the greatest (6.5 mg/g), mainly because the clay itself has a lower organic matter content and a larger SSA, making it easier to adsorb P and COD; the adsorption capacity of the two combinations for COD is relatively close. From the results of the isothermal adsorption test, the adsorption of N, P, and COD by the three substrates did not differ much, and the overall adsorption capacity was as follows: BC > C > S; SBC > SC. In addition, it was found that SBC was more effective than BC in adsorbing both P and COD. This may be because BC is subject to the leaching of nitrogen, phosphorus, and organic matter in stormwater, while sand is an inert material and does not have this effect.

The results of static adsorption of heavy metals by different substrates are shown in Figure 3. Unlike the adsorption characteristics of N, P, and COD, the removal of five heavy metals by substrates varied greatly. Biochar showed a high removal rate (>90%) of all heavy metals, which showed a strong adsorption capacity. The order of adsorption capacity of biochar for different heavy metals was Zn > Pb > Cu > Cd > Ni. The adsorption capacity of clay for heavy metals was slightly lower than that of biochar, with the highest adsorption capacity of 2.79 mg/g for Pb, and the weakest adsorption capacity of 0.152 mg/g for Cd. Sand had the lowest adsorption capacity for heavy metals which was 1/10–1/5 of the adsorption capacity of biochar. Sand is mainly composed of silica, with large particle size, small SSA, and high stacking voids and small surface voids, resulting in the weakest adsorption capacity (Jiang et al. 2019b). The adsorption capacity of SBC for the five heavy metals was slightly higher than that of BC, so mixing a certain percentage of biochar in the bioretention system was beneficial to improve the removal rate of heavy metals. Overall, the order of adsorption capacity of substrates and their combinations for different heavy metals was Pb > Zn > Cu > Ni > Cd, and the order of substrates for the average removal capacity of heavy metals was B > C > S > SBC > BC, which also proved that mixing the appropriate amount of biochar into substrate could effectively improve the retention of heavy metals in the bioretention system.
Figure 3

Static adsorption capacity of substrates on heavy metal in stormwater runoff.

Figure 3

Static adsorption capacity of substrates on heavy metal in stormwater runoff.

Close modal

Analysis of substrate adsorption capacity and lifetime

According to the different filling materials in bioretention systems, small-scale devices were constructed to carry out adsorption saturation tests, and it was considered that the packing was saturated when Cout > 0.9 Cin. The outflow concentration of different filling materials is shown in Figure 4. The effluent concentration of systems 2# and 3# was higher than that of 1# for the same total amount of influent water, and had a greater effect on the heavy metals Pb and Zn, which was consistent with the results of the static adsorption test; when the total amount of infiltration was only 15 L, the TP effluent concentration was close to its influent concentration, indicating that the adsorption saturation amount of P by substrate was small, while for Pb and Zn, the total amount of influent approached the saturation point at about 30 L, which indicated that the adsorption saturation amount of the substrate on the heavy metals was large, and the removal effect was better. The pollutant adsorption potential of the different small-scale device was in the following order: Pb > Zn > > TP.
Figure 4

Outflow concentration of bioretention systems with different filling materials.

Figure 4

Outflow concentration of bioretention systems with different filling materials.

Close modal

A polynomial was fitted to the discrete points of the effluent stormwater concentration, and the intersection of the effluent concentration line and 0.9 times the influent concentration (0.9 Cin) was assumed to be the saturated adsorption point of the packing material based on the empirical values of the actual engineering (Jiang et al. 2018). The cumulative rainfall at the time of adsorption saturation of the substrate was obtained, and the cumulative rainfall was divided by the annual rainfall (4.43 L/year), which is the operational life of the system, and the results of the operational life estimation are shown in Table 4. When ammonia nitrogen was the target pollutant, the SBC combined packing still did not reach the adsorption saturation point after 6 years of total water intake, and the SC combined packing was close to adsorption saturation when the total amount of water intake was equivalent to about 4 years of rainfall. When TP is the target pollutant, the saturation point is reached when the adsorption capacity of SBC combined packing is slightly larger, and the water intake is equivalent to the rainfall amount of 3.9 years when it is close to saturation, compared with SC packing whose adsorption saturation time point is delayed by nearly 1 year. When Pb was the target pollutant, the SBC substrate approached adsorption saturation after 7 years, which was higher than the 4.2 years for the SC substrate. More consistent with the adsorption characteristics of Pb, the saturation point of Zn adsorption by SBC was reached at 6.5 years, which was higher than that of SC substrate at 5.4 years. In the filter column test system, the incoming simulated stormwater is all dissolved pollutants, and SS is a significant pollutant in actual stormwater runoff.

Table 4

Operational life estimation of different substrates

PollutantSubstrateStormwater outflow concentration fitting curveLifespan (years)
TP SC R2 = 0.88 2.9 
SBC R2 = 0.87 3.9 
-N SC R2 = 0.98 4.1 
SBC R2 = 0.94 6.9 
Pb SC R2 = 0.94 5.9 
SBC R2 = 0.95 7.2 
Zn SC R2 = 0.97 5.4 
SBC R2 = 0.96 6.5 
PollutantSubstrateStormwater outflow concentration fitting curveLifespan (years)
TP SC R2 = 0.88 2.9 
SBC R2 = 0.87 3.9 
-N SC R2 = 0.98 4.1 
SBC R2 = 0.94 6.9 
Pb SC R2 = 0.94 5.9 
SBC R2 = 0.95 7.2 
Zn SC R2 = 0.97 5.4 
SBC R2 = 0.96 6.5 

Analysis of the intensity of nitrification and denitrification of substrates in water storage areas

As shown in Figure 5, the order of magnitude of the average nitrification intensity of different substrates was: 3# > 2# > 1#. The nitrification intensity of biological fillers is much higher than that of fillers such as soil, sand, and biochar, which is mainly due to the high porosity (>90%) and high SSA of biological fillers providing a larger space and a good environment for the growth of nitrifying bacteria, thus promoting nitrification. However, since the lower substrate could not directly contact the air, the dissolved oxygen concentration was low compared to the upper layer, resulting in poorer growth and metabolic conditions for nitrifying bacteria in the lower substrate (Purvis et al. 2018; Jiang et al. 2018). When the carbon source is sufficient, the denitrification rate is proportional to the content of denitrifying bacteria, the denitrification intensity can reflect the substrate suitable for the growth degree of denitrifying bacteria, the average denitrification intensity of the substrate in the lower layer ranked as follows: 3# > 2# > 1#. The storage of water in the lower substrate facilitates the formation of a local anaerobic environment so that denitrifying bacteria attach to the surface to form a film, and the high surface area and pore structure of the substrate is beneficial to denitrification, resulting in the denitrification strength of the biological filler being significantly higher than that of a single substrate. In addition, the biochar has a certain electrical conductivity, the long-term anaerobic digestion process releases electrons that can be transferred to the denitrifying bacteria through the biochar to promote the denitrification process, resulting in the denitrification intensity of the SBC filler being higher than that of SC. This result was similar to the findings of Chen (2018), Han (2018), Song & Song (2019), and Wang et al. (2019) in ecological facilities such as ditches and wetlands.
Figure 5

Nitrification and denitrification intensity of lower substrates.

Figure 5

Nitrification and denitrification intensity of lower substrates.

Close modal

Research on the effect and mechanism of bioretention system on the regulation of stormwater runoff

Characteristics of runoff pollutant removal by different types of bioretention facilities

Figure 6 shows the effluent concentration of pollutants in three groups of bioretention systems tested for simulated rainfall (P = 1a). The average reduction rate of in system 2# was the highest (77.2%) with an average effluent concentration of 1.36 mg/L, which was below the threshold of Class V for surface water (Figure 6(a)). The overall effluent quality of system 2# was better than the other two systems, probably due to the higher ion exchange capacity of straw biochar, which in turn can adsorb ammonium root ions through electrostatic gravitational attraction. Besides, the large SSA was a major advantage of 2# for ammonia nitrogen interception.
Figure 6

The effluent concentration of pollutants in different bioretention systems.

Figure 6

The effluent concentration of pollutants in different bioretention systems.

Close modal

The effluent concentration in system 1# ranged from 2.74 to 3.81 mg/L, with an average effluent concentration of 3.22 mg/L, the range of load reduction rate was 24.2–45.7%, and the overall removal effect of was poor. The range of load reductions for in 3# was 60.5–74.1%, with an average effluent concentration of 1.2–2.2 mg/L (Figure 6(b)). The nitrifying bacteria of the substrate in the water storage area played an important role in the process of removal and the straw biochar in the inundation area provided the denitrifying bacteria with an appropriate amount of carbon source to promote the occurrence of denitrification and the stable removal of . According to the nitrification and denitrification intensity characteristics of the substrate in the storage area of section 3.1.3, it was clear that the denitrification potential of 3# was significantly higher than that of 2#, resulting in a higher reduction load of . In addition, part of the could be intercepted in the bioretention facility through substrate adsorption, which was gradually absorbed by the root of grasses and promoted their growth.

Nitrogen removal from stormwater typically involves biodegradation, vegetative uptake, and substrate adsorption. Nitrification and denitrification by microorganisms are the primary pathways for nitrogen removal from stormwater and are highly dependent on dissolved oxygen. The TN concentrations in system 1# ranged from 4.1 to 5.4 mg/L, with an average concentration of 4.7 mg/L, which was higher than the threshold of Class V for surface water, and was unable to meet the requirements for surface water discharges; the load reduction rate of TN in system 3# ranged from 69.1 to 77.3%, with an average effluent concentration of only 2.71 mg/L (Figure 6(c)). Corn stover biochar can not only adsorb ammonia nitrogen in large quantities but also provide a carbon source for microorganisms on the substrate to promote denitrification. In addition, its large SSA allows for the loading of more microorganisms, resulting in better removal effects of systems 2# and 3# than 1#. The results agreed with those of Liu (2020) and Rahman et al. (2020).

Phosphorus in stormwater runoff includes particulate phosphorus and dissolved phosphorus. Particulate phosphorus is mainly attached to the surface of SS and is easily trapped on the surface by permeable pavement and bioretention facilities, and the removal of dissolved phosphorus relies mainly on substrate adsorption. The average TP concentration in system 1# (0.18 mg/L) was lower than in system 3# (0.37 mg/L), which provided a high removal rate compared to other low-impact facilities (grass-planted swales, undercuts, and permeable pavements) with stable effects. The load reduction of TP in 2# ranged from 72.1 to 84.5% with an average value of 78.1%, and the addition of biochar modifier promoted phosphorus removal compared to the bioretention system with conventional filler (Figure 6(d)). The phosphorus pollution load reduction rate of the 3# was slightly lower than that of systems 1# and 2#, which could be attributed to poorer ability to adsorb and utilize phosphorus by biological filler in comparison to biochar, clay, and sand. However, the effluent quality also met the surface water standards, indicating that a combination of biochar amendments was necessary as a bioretention filler to enhance pollutant removal. For COD, the average effluent concentration of 1# was 97.25 mg/L and was higher than that of 2# (67.7 mg/L) and 3# (64.3 mg/L), which in turn verified the characteristics of corn stover biochar in terms of high SSA and high adsorption (Figure 6(e)).

Figure 6(f)–6(h) shows the effluent concentration of heavy metals in bioretention systems. Calculation results of the load reduction rates of heavy metals by the three groups of bioretention systems indicated that the load reduction effect for different heavy metals was in the following order: 2# > 1# > 3#. The average load reduction rate of Cu, Pb, and Zn in system 2# was 80.3, 75.1, and 84.8%, respectively. The load reductions of Cu, Pb, and Zn in 2# were improved by approximately 11.3, 15.9, and 13.5% over 1#.

Effect of rainfall intensity on the purification efficiency of bioretention systems

As shown in Figure 7(a), the TP concentration in the effluent of the three groups of bioretention systems gradually increased as the rainfall intensity increased from light to heavy rainfall. The average removal rate decreased from 82.55 to 64.32% in 1#, 90.91 to 72.73% in 2#, and 3# had the lowest average removal rate for TP from 81.82 to 52.64%. Bioretention systems remove TP primarily through physical adsorption and chemical precipitation by fillers, and polyphosphate bacteria can also take up dissolved phosphate from stormwater. Smaller rainfall intensity increases the retention time of the stormwater, the contact time with the filler, and the uptake time of phosphorus by the phosphorus-colonizing bacteria and plants, allowing the system to remove phosphorus more efficiently. Therefore, when the rainfall intensity was raised, the retention time of rainwater was short, and the physical retention and adsorption were weak (Pucher & Langergraber 2019); at the same time, the total amount of runoff generated during heavy rainfall was large, the total pollution load was the largest, and the phosphorus adsorption by the substrate was prone to saturation within a short period, so TP removal by three systems during heavy rainfall was low. The optimal removal effect of system 2# was mainly related to the adsorption capacity of the substrate, and the incorporation of biochar effectively strengthened the phosphorus removal capacity of 2#. Compared with the research results of domestic and international scholars, the results are similar to those of Huo (2018), and slightly lower than the actual engineering monitoring results of Jiang et al. (2018), mainly due to the different heights of packing materials.
Figure 7

The effluent concentration of pollutants under different rainfall intensities.

Figure 7

The effluent concentration of pollutants under different rainfall intensities.

Close modal

Figure 7(b) shows the removal effect of the bioretention system for TN in stormwater runoff, the removal rate of systems 2# and 3# showed a positive correlation trend with the rainfall intensity, and system 1# showed a negative correlation, but the difference of the removal rate of the three systems under different rainfall intensities was small, only about 20%. With the gradual increase in rainfall intensity, the TN removal rate of system #1 decreased from 57.7 to 42.7%, mainly because there was no water storage area in system #1, the removal of N relied only on the substrate adsorption, the contact time between rainwater and substrate was long when the rainfall intensity became lower, and the removal rate was high; the contact time between rainwater and substrate was short and removal effect became poor when rainfall intensity was higher. The upper aerobic area of the bioretention system was more so was easily converted to ; however, the water storage area at the bottom of systems 2# and 3# provided certain anaerobic conditions, and the rainwater contained a more sufficient carbon source, which promoted denitrification, resulting in higher removal rate, and the water storage area was higher when the rainfall intensity rose. The denitrifying biomass may be relatively high in the substrate of system 3# compared to 2#, resulting in superior nitrogen removal in 2#. The COD removal of bioretention system decreased with increasing rainfall intensity (Figure 7(c)), and when the influent concentration was 182 mg/L, the effluent concentrations of systems 2# and 3# were slightly above 40 mg/L under light rainfall conditions, close to reaching the threshold of Class V for surface water. When the rainfall intensity was heavy, medium, and light rainfall, the average COD removal rates of the three groups of devices ranged from 58.6 to 71.5%, 46.5 to 65.7%, and 37.2 to 43.3%, respectively. The reduction of COD by the bioretention system was a combination of microbial degradation, plant uptake, and adsorption by the packing layer. During the rainfall-runoff period, due to the short residence time of rainwater in the system 1#, the removal of COD by the bioretention cell was mainly through the adsorption of the substrate sand, and the removal capacity was limited; the water storage area was designed in system 2# to increase the contact time between the substrate and the rainwater, and the adsorption capacity of biochar in 2# was significantly higher than that of sandy soil, resulting in the best purification effect. The biological substrate in the lower layer of system 3#, despite the presence of larger biomass, requires more contact time for microbial utilization, and thus the removal efficiency was lower in the short term compared to system 2#.

The removal effect of heavy metals by three groups of bioretention systems is shown in Figure 7(d)–7(f). Under different rainfall intensities, the differences in the removal effect of Cu among the three groups of systems were more obvious, and the effluent concentrations were lower than 0.25 mg/L; the overall removal effect was significantly higher in system 2# than in systems 1# and 3#, mainly due to the water storage area and the adsorption of biochar. The removal efficiency and amount of Zn by three systems under the same rainfall characteristics were better than that of Cu and Pb, the order of removal of Zn by three systems was 2# > 3# > 1#. The degradation efficiency of heavy metals by different systems was consistent with the static adsorption characteristics of the substrate. The degradation ability of heavy metals was positively correlated with rain intensity, the higher the rainfall, the higher the pollution load, and the faster the flow rate so that rainwater runoff would pass through the substrate layer quicker, reducing the hydraulic residence time of the rainwater runoff, lowering the rapid adsorption and filtration of heavy metals by the substrate, and cutting down on the treatment effect.

Impact of runoff pollution loads on the purification efficacy of bioretention systems

The average removal rate of pollutants under two pollution loads is shown in Figure 8. System 3# had the largest elevation of up to 6.3%, correlating with the adsorption capacity of the biochar, where higher phosphorus loads were able to rapidly occupy the limited number of sequestration sites on the substrate, resulting in a decrease in phosphorus removal with increasing phosphorus concentration at high loads. For TN removal, all three groups of systems showed opposite trends to TP, with high pollutant loading stormwater being treated less efficiently than low loading stormwater, and despite the increase in the concentration of pollutants, the bioretention systems 2# and 3# were still able to reduce a significant amount of TN in a short period, especially with the biologically active retention area 3# system. According to You et al. (2019), who reported that removal efficiencies could be improved by approximately 30% at higher nutrient inflows compared to lower infiltration concentrations, the modified systems 2# and 3# could remove both nitrogen and phosphorus from runoff. The removal pattern of COD was similar to that of TN; the higher the influent concentration, the lower the removal rate, and the worst effect of system 1#, mainly related to the adsorption saturation amount of its substrate, and system 3# was slightly better than system 2# due to the biological action in its water storage area.
Figure 8

The average removal rate of pollutants under two pollution loads.

Figure 8

The average removal rate of pollutants under two pollution loads.

Close modal

For heavy metals (Figure 8(b)), three heavy metals showed higher pollutant removal efficiencies at high concentrations (extreme rainfall) than under normal rainfall conditions, and despite the increase in heavy metal concentrations in the simulated rainwater, the modified bioretention systems 2# and 3# were able to treat the excess pollutants in a short period. For Cu, system 2# had the best removal effect, which was mainly related to the high adsorption capacity of the biochar in the substrate, and system #3 had the lowest removal rate due to the low adsorption capacity of the lower biological filler. For Pb, system 2# still showed better resistance to load impact, and the removal rate of pollution load was higher than 70%; when the influent load increased, the removal rates of the three groups of systems were 81.1, 90.2, and 74.3%, respectively, with an overall increase of 14.3–21.4% compared with that of the low concentration. The variation characteristics of Zn were consistent with those of Cu and Pb, and the removal of Zn increased by 3.5 to 10.1% when the influent load was increased, respectively.

Analysis of microbial mechanisms of action in bioretention systems

Microbial community abundance and diversity

Next-generation sequencing (NGS) was applied to determine the microbial community structure within three groups of bioretention systems, and a total of 179,032 high-quality sequences were obtained by controlling and filtering the sequencing data, with an average sequencing length of 415.2 bp. To facilitate comparisons in subsequent analyses, Amplicon sequencing variant (ASV), coverage index (Coverage), diversity index (Shannon), and richness index (Abundance-based Coverage Estimator (ACE) and Chao) were calculated for each sample after normalizing the sequence similarity clustering for each sample, and the results were shown in Table 5. The coverage of the sequenced samples ranged from 99.4 to 99.9%, indicating that the sequence libraries constructed by this sequencing covered almost all microbial species, and the sequencing results represented the real situation of microorganisms in the samples.

Table 5

Statistical table of sequencing information for bioretention system samples

SamplesSequencesASVChaoShannonSimpsonCoverage
1# 60,180 1,749 1,763 8.73096 0.989934 0.994404 
2# 52,015 1,205 1,207 8.4374 0.981909 0.999593 
3# 66,837 908 912 6.91947 0.960706 0.999333 
SamplesSequencesASVChaoShannonSimpsonCoverage
1# 60,180 1,749 1,763 8.73096 0.989934 0.994404 
2# 52,015 1,205 1,207 8.4374 0.981909 0.999593 
3# 66,837 908 912 6.91947 0.960706 0.999333 

The ACE and Chao indices reflect microbial abundance, with higher values indicating higher microbial abundance in the samples. The abundance of the samples fluctuated between 908 and 1,763 in the three groups of bioretention systems, with the highest abundance values of 1,749 and 1,763 in system 1#, respectively. The lowest microbial abundance was in system 3# with ASV and Chao indices of 908 and 912, respectively, and system 2# had microbial abundance between. The Shannon and Simpson indices reflect the diversity of the microbial community, and the Shannon index ranged from 6.9 to 8.7, similar to the Simpson index, with relatively large variations, which indicated that there were significant differences in the microbial community structure in the samples within three systems. The relationship between the above four indices was consistent across samples, with 3# < 2# < 1#. The Shannon index within this sequencing bioretention system was higher than those reported in the literature for wastewater treatment plants, water pipelines, and subsurface flow constructed wetlands (Luo 2016; Chen 2019; Bai 2020), which indicated that the diversity of the bioretention system was relatively high, and the community structure of the microorganisms was more enriched.

Microbial community compositions and similarities analysis

The relative abundance of the microbial community at the phylum level is presented in Figure 9(a). The dominant phyla in systems 1# and 2# was Proteobacteria (37.9 and 26.6%), followed by Actinobacteria (14.3–20.7%), Chloroflexi (13.7–26.1%), Bacteroidetes (7.3–10.1%), Acidobacteria (7.2–10.1%), and Nitrospirae (0.6–2.9%); Firmicutes were the dominant phyla in system 3# with 65.6%, followed by Actinobacteria (13.5%) and Chloroflexi (7.9%).
Figure 9

Microbial compositions in different bioretention systems: (a) phylum level, (b) genus level, and (c) Venn diagram of the microbial community.

Figure 9

Microbial compositions in different bioretention systems: (a) phylum level, (b) genus level, and (c) Venn diagram of the microbial community.

Close modal

Previous studies have found that Proteobacteria occupies a central role in degradation processes such as biological nitrogen and phosphorus removal (Zhong et al. 2018; Rajan et al. 2019; Cheng et al. 2021), which is partially attributed to Bacteroidetes (Zhu et al. 2017; Wang 2018). In terms of abundance variation at the phylum level, Chloroflexi also occupies a higher proportion in system 2#, which was shown to be the dominant organism in anaerobic sludge reactors in related research (Liu et al. 2016), and it also played a crucial role in denitrification, which also proved the denitrification capacity of the water storage area in system 2#. Meanwhile, Nitrospirae was the main bacteria directly associated with stormwater runoff pollution treatment, and its high relative abundance indicated a high abundance of nitrifying bacteria.

Firmicutes were the absolute dominant functional microorganism of system 3#. Bacillus in Firmicutes could promote nitrification to degrade ammonia nitrate and nitrogen in stormwater runoff, as well as produce protease and amylase enzymes for effective degradation of organic matter in stormwater. Relevant research also revealed that the presence of Bacillus accelerates the conversion of nitrite and significantly facilitates nitrate transport, which can enhance nitrification (Yang et al. 2020; Jiang et al. 2022). Therefore, it had a greater contribution to the removal of nutrients in the runoff as well as maintaining the stability of the system, which may be the main reason why the TN removal effect in system 3# was better than the other two groups.

The differences in the composition of microorganisms in different retention systems at the genus level were shown in Figure 9(b), where Bacillus, Hyphomicrobium, Micrococcaceae, and Nitrospira were the dominant genera in three bioretention systems. In system 1#, Micrococcaceae, Azospirillum, and SBR1031 accounted for 12.1, 3.7, and 3.4% of the total microorganisms, respectively; system 2# had the highest percentage of SBR1031 (8.7%), followed by Saccharimonadaceae (3.6%), Nitrospira (2.9%), Tetrasphaera (2.5%), and Bdellovibrio (2.3%). Bacillus (51.9%), Hyphomicrobium (2.4%), and Peptostreptococcus (2.1%) were the main dominant genera in system 3#. In contrast to the overall difference between system 1# and 2#, system 3# had a prominent proportion of Bacillus, which was reported to be the main genera of denitrifying bacteria in the wetland, with organic matter degradation and denitrification functions, and was crucial in the microbial transformation of the system.

The Venn diagram of ASV distribution provided a more intuitive representation of the similarity and overlap in the composition of the number of ASVs in different samples. As shown in Figure 9(c), the number of ASVs contained in 1# and 2# together was 610, accounting for 34.9 and 50.6% of the total number of 1# and 2#, respectively; the number of ASVs contained in 1# and 3# together was only 108 ASVs, accounting for only 6.2% of 1#; the three groups had only 90 identical ASV values, accounting for 5.1, 7.5, and 9.9%, respectively, which indicated that each system had less similarity and more variability in community structure.

Analysis of functional microbial genera in bioretention systems

It was found that the bacteria of Nitrobacter and Nitrospira have nitrification functions; Nitrosomonas, Nitrosospira, and Nitrosococcus have ammonia-oxidizing functions; Pseudomonas, Bacillus, Thiobacillus, and Thermomonas are denitrifying and participate in the process of nitrogen removal (Sun 2018; Pan et al. 2019). Therefore, this section focuses on the total relative abundance of such related functional bacterial genera.

As shown in Figure 10(a), microorganisms with nitrification function detected in the three groups of bioretention systems with relative abundance greater than 0.1% were mainly distributed in five genera, including Nitrococcus, Nitrospira, Nitrosospira, Nitrosomonas, and Nitrobacter. The total number of nitrifying bacteria from each of the bioretention cells varied considerably, with relative abundance ranging from about 0.41 to 4.22% of the total of all bacteria, with 2# > 1# > 3#. The structural composition of nitrifying bacteria in each retention system was found to be highly variable among the groups. The nitrifying population structures of systems 1# and 2# were relatively consistent, and system 3# only contained a small part of Nitrosospira. In terms of the structure of the bioretention systems, system 1# had a higher porosity inside the substrate due to its poor water retention, which would be beneficial to aerobic bacterial growth; Nitrospira was more diverse in its nitrogen metabolism (Han et al. 2021), being able to reduce to in anoxic or anaerobic environments.
Figure 10

The relative abundance of functional genera related to nitrifying and denitrifying in bioretention systems.

Figure 10

The relative abundance of functional genera related to nitrifying and denitrifying in bioretention systems.

Close modal

As shown in Figure 10(b), the microorganisms detected in the bioretention system with relative abundance greater than 0.1% and with denitrifying function were mainly distributed in four genera. The highest average relative abundance in three bioretention systems was Bacillus, followed by Pseudomonas with Thermomonas. The abundance of Bacillus in system 3# was 51.8%, which was significantly higher than that of common bioretention facilities, indicating that the nitrogen removal capacity of system 3# had a greater advantage compared with 1# and 2#. The nitrogen removal effect during the operation of three bioretention systems was closely related to the relative abundance of denitrifying functional bacterial genera, compared with system1#, the total amount of denitrifying functional bacterial genera in systems 2# and 3# increased by 1.39 and 52.1%, respectively, and their TN removal rates increased by 29.8 and 67.4%, respectively, which also further proved that the denitrification process of microorganisms occupies an important position in the transformation of nitrogen in bioretention systems. As denitrifying bacteria were mostly anaerobic bacteria and the lower water storage area of systems 2# and 3# was in the inundation state for a long time in the operation process, they were prone to the formation of an anaerobic environment. Multiple results of effluents also indicated that these two systems were more conducive to the proliferation of microorganisms, which could effectively promote denitrification. Wu (2019) found that Pseudomonas was a common species in the flooded area of the bioretention cell with an abundance of about 1.39%, which was close to the abundance level of systems 1# and 2# of this research.

This paper developed a methodology to improve the removal of typical pollutants from stormwater runoff, proposing a double-layer substrate system with modifications for different types of pollutant removal pathways. The addition of biochar and large void filler as filtration media improved the treatment capacity and efficiency of the bioretention system for nutrient removal from stormwater.

  • (1)

    Substrate adsorption tests showed that the overall pollutant removal capacity of systems 2# (BSM + 5%BC) and 3# (BSM + 5%BC + BM) was higher than that of system 1# (BSM), which indicated that the biochar modifier as a filler of the retention facility was effective for the enhancement of the pollutant removal. The load reduction effect for heavy metals of the three systems was ranked as 2# > 1# > 3#, and average load reduction rates were 80.3, 75.1, and 84.8% for Cu, Pb, and Zn in 2#.

  • (2)

    Simulated rainfall experiments revealed that rainfall intensity may cause a significant impact on the removal of pollutants from bioretention systems. The overall pollutant reduction capacity of the three retention systems was as follows: 2# (BSM + 5%BC) > 3# (BSM + 5%BC + BM) > 1# (BSM), which verified the high surface area and high adsorption characteristics of the 2# system with the addition of biochar.

  • (3)

    Microbial community analysis indicated that Proteobacteria and Firmicutes were the absolute dominant bacteria of the three bioretention systems, and the dominant genera included Bacillus, Hyphomicrobium, Micrococcaceae, and Nitrospira. In addition, the total number of denitrifying functional bacteria genera in systems 2# and 3# was increased by 1.39 and 52.1% compared to system 1#, which further proved the anaerobic environment formed in the water storage area effectively promotes microbial proliferation and denitrification.

  • (4)

    This research provides an important reference for the effectiveness of long-term operation of bioretention systems and their mechanisms (e.g., adsorption and biotransformation), as well as the management of improved bioretention systems to address stormwater quality and quantity.

This research was funded by General Project of Philosophy and Social Sciences Research in Jiangsu Education Department, grant number 2023SJYB1812; Postgraduate Research & Practice Innovation Program of Jiangsu Province, grant numbers SJCX23-1814 and SJCX23-1816.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.

Bai
X.
2020
Research and Practice of Horizontal Submerged Flow Artificial Wetland for Deep Nitrogen Removal From Tailwater of Urban Sewage Plant
.
Dissertation
,
Northeast Normal University
.
Biswal
B.
,
Vijayaraghavan
K.
,
Tsen-Tieng
D.
&
Balasubramanian
R.
2022
Biochar-based bioretention systems for removal of chemical and microbial pollutants from stormwater: A critical review
.
Journal of Hazardous Materials
422
,
126886
.
https://doi.org/10.1016/j.jhazmat.2021.126886
.
Caldelas
C.
,
Gurí
R.
,
Araus
J.
&
Sorolla
A.
2021
Effect of ZnO nanoparticles on Zn, Cu, and Pb dissolution in a green bioretention system for urban stormwater remediation
.
Chemosphere
282
,
131045
.
https://doi.org/10.1016/j.chemosphere.2021.131045
.
Chen
T.
2018
Study on the Purification Characteristics of Aquatic Vegetable-Based Artificial Wetlands on Village Discharged Sewage
.
Dissertation
,
Southeast University
.
Chen
Y.
2019
Optimized Operation and Micro-Ecological Mechanism of Deep Nitrogen Removal in Integrated Wastewater Treatment Plant
.
Dissertation
,
Nanjing University
.
Cheng
R.
,
Zhu
H.
,
Shutes
B.
&
Yan
B.
2021
Treatment of microcystin (MC-LR) and nutrients in eutrophic water by constructed wetlands: Performance and microbial community
.
Chemosphere
263
,
128139
.
https://doi.org/10.1016/j.chemosphere.2020.128139
.
Gong
Y.
,
Li
X.
,
Xie
P.
,
Fu
H.
,
Nie
L.
,
Li
J.
&
Li
Y.
2023
The migration and accumulation of typical pollutants in the growing media layer of bioretention facilities
.
Environmental Science and Pollution Research
30
,
44591
44606
.
https://doi.org/10.1007/s11356-023-25305-0
.
Han
Q.
2018
Study of a Novel Submerged Artificial Wetland for Treatment of Irrigation Tailwater From Rural Domestic Wastewater
.
Dissertation
,
Southeast University
.
Han
S.
,
Luo
X.
,
Hao
X.
,
Ouyang
Y.
,
Zeng
L.
,
Wang
L.
,
Wen
S.
,
Wang
B.
,
Van Nostrand
J.
,
Chen
W.
,
Zhou
J.
&
Huang
Q.
2021
Microscale heterogeneity of the soil nitrogen cycling microbial functional structure and potential metabolism
.
Environmental Microbiology
23
(
2
),
1199
1209
.
https://doi.org/10.1111/1462-2920.15348
.
Huo
X.
2018
Research on the Removal Efficacy of Pollutants From Stormwater and Snowmelt Runoff in Bioretention Ponds
.
Dissertation
,
Harbin Institute of Technology
.
Järlskog
I.
,
Strömvall
A. M.
,
Magnusson
K.
,
Galfi
H.
,
Björklund
K.
,
Polukarova
M.
,
Garção
R.
,
Markiewicz
A.
,
Aronsson
M.
,
Gustafsson
M.
,
Norin
M.
,
Blom
L.
&
Andersson-Sköld
Y.
2021
Traffic-related microplastic particles, metals, and organic pollutants in an urban area under reconstruction
.
Science of the Total Environment
774
,
145503
.
https://doi.org/10.1016/j.scitotenv.2021.145503
.
Jiang
C.
,
Li
J.
,
Zhang
B.
,
Ruan
T.
,
Li
H.
&
Dong
W.
2018
Design parameters and treatment efficiency of a retrofit bioretention system on runoff nitrogen removal
.
Environmental Science and Pollution Research
25
,
33298
33308
.
https://doi.org/10.1007/s11356-018-3267-5
.
Jiang
C.
,
Li
J.
,
Li
H.
&
Li
Y.
2019a
An improved approach to design bioretention system media
.
Ecological Engineering
136
,
125
133
.
https://doi.org/10.1016/j.ecoleng.2019.06.014
.
Jiang
C.
,
Li
J.
,
Li
H.
&
Li
Y.
2019b
Nitrogen retention and purification efficiency from rainfall runoff via retrofitted bioretention cells
.
Separation and Purification Technology
220
,
25
32
.
https://doi.org/10.1016/j.seppur.2019.03.036
.
Jiang
M.
,
Li
Q.
,
Hu
S.
,
He
P.
,
Chen
Y.
,
Cai
D.
,
Wu
Y.
&
Chen
S.
2022
Enhanced aerobic denitrification performance with Bacillus licheniformis via secreting lipopeptide biosurfactant lichenysin
.
Chemical Engineering Journal
434
,
134686
.
https://doi.org/10.1016/j.cej.2022.134686
.
Li
J.
,
Zhao
R.
,
Li
Y.
&
Li
H.
2020
Simulation and optimization of layered bioretention facilities by HYDRUS-1D model and response surface methodology
.
Journal of Hydrology
586
,
124813
.
https://doi.org/10.1016/j.jhydrol.2020.124813
.
Li
H.
,
Liu
Z.
,
Tan
C.
,
Zhang
X.
,
Zhang
Z.
,
Bai
X.
,
Wu
L.
&
Yang
H.
2022
Efficient nitrogen removal from stormwater runoff by bioretention system: The construction of plant carbon source-based heterotrophic and sulfur autotrophic denitrification process
.
Bioresource Technology
349
,
126803
.
https://doi.org/10.1016/j.biortech.2022.126803
.
Liu
F.
2020
Development and Parameter Optimization of Bioretention Modified Filler for Straw Biochar
.
Dissertation
,
Xi'an University of Technology
.
Lopez-Ponnada
E.
,
Lynn
T.
,
Ergas
S.
&
Mihelcic
J.
2020
Long-term field performance of a conventional and modified bioretention system for removing dissolved nitrogen species in stormwater runoff
.
Water Research
170
,
115336
.
https://doi.org/10.1016/j.watres.2019.115336
.
Luo
J.
2016
Water Quality Simulation of Large-Scale Raw Water Transmission Pipeline and Research on Biofilm Water Purification Function
.
Dissertation
,
Harbin Institute of Technology
.
Luo
Y.
,
Yue
X.
,
Duan
Y.
,
Zhou
A.
,
Gao
Y.
&
Zhang
X.
2020
A bilayer media bioretention system for enhanced nitrogen removal from road runoff
.
Science of the Total Environment
705
,
135893
.
https://doi.org/10.1016/j.scitotenv.2019.135893
.
Pan
A.
,
Zhang
Z.
,
Sun
L.
,
Yu
L.
&
Li
Y.
2019
Differential analysis of purification effect and microbial community in surface flow artificial wetland planted with different plants
.
Journal of Environmental Engineering
13
(
08
),
1918
1929
.
Payne
E.
,
McCarthy
D.
,
Deletic
A.
&
Zhang
K.
2019
Biotreatment technologies for stormwater harvesting: Critical perspectives
.
Current Opinion in Biotechnology
57
,
191
196
.
https://doi.org/10.1016/j.copbio.2019.04.005
.
Pucher
B.
&
Langergraber
G.
2019
Influence of design parameters on the treatment performance of VF wetlands – a simulation study
.
Water Science and Technology
80
(
2
),
265
273
.
https://doi.org/10.2166/wst.2019.268
.
Purvis
R. A.
,
Winston
R. J.
,
Hunt
W. F.
,
Lipscomb
B.
,
Narayanaswamy
K.
,
McDaniel
A.
,
Lauffer
M. S.
&
Libes
S.
2018
Evaluating the water quality benefits of a bioswale in Brunswick County, North Carolina (NC), USA
.
Water
10
(
2
),
134
.
https://doi.org/10.3390/w10020134
.
Qiu
F.
,
Zhao
S.
,
Zhao
D.
,
Wang
J.
&
Fu
K.
2019
Enhanced nutrient removal in bioretention systems modified with water treatment residuals and internal water storage zone
.
Environmental Science: Water Research & Technology
5
(
5
),
993
1003
.
https://doi.org/10.1039/C9EW00093C
.
Rajan
R.
,
Sudarsan
J.
&
Nithiyanantham
S.
2019
Microbial population dynamics in constructed wetlands: Review of recent advancements for wastewater treatment
.
Environmental Engineering Research
24
(
2
),
181
190
.
https://doi.org/10.4491/eer.2018.127
.
Shrestha
P.
,
Hurley
S.
&
Wemple
B.
2018
Effects of different soil media, vegetation, and hydrologic treatments on nutrient and sediment removal in roadside bioretention systems
.
Ecological Engineering
112
,
116
131
.
https://doi.org/10.1016/j.ecoleng.2017.12.004
.
Skorobogatov
A.
,
He
J.
,
Chu
A.
,
Valeo
C.
&
van Duin
B.
2020
The impact of media, plants and their interactions on bioretention performance: A review
.
Science of the Total Environment
715
,
136918
.
https://doi.org/10.1016/j.scitotenv.2020.136918
.
Smyth
K.
,
Drake
J.
,
Li
Y.
,
Rochman
C.
,
Van Seters
T.
&
Passeport
E.
2021
Bioretention cells remove microplastics from urban stormwater
.
Water Research
191
,
116785
.
https://doi.org/10.1016/j.watres.2020.116785
.
Song
Y.
&
Song
S.
2019
Migration and transformation of different phosphorus forms in rainfall runoff in bioretention system
.
Environmental Science and Pollution Research
26
,
30633
30640
.
https://doi.org/10.1007/s11356-018-2405-4
.
Song
D.
,
Yu
L.
&
Zeng
K.
2020
Analysis of the effect of in-situ ecological combination technology to improve the purification of secondary effluent in winter
.
Journal of Environmental Engineering
14
,
103
112
.
Sun
W.
2018
Nitrogen Removal Effect and Microbiological Mechanism of Surface Runoff Treatment in Bioretention Pond Submerged Area
.
Dissertation
,
Tianjin University
.
Técher
D.
&
Berthier
E.
2023
Supporting evidences for vegetation-enhanced stormwater infiltration in bioretention systems: A comprehensive review
.
Environmental Science and Pollution Research
30
,
19705
19724
.
https://doi.org/10.1007/s11356-023-25333-w
.
Thom
J.
,
Szota
C.
,
Coutts
A.
,
Fletcher
T.
&
Livesley
S.
2020
Transpiration by established trees could increase the efficiency of stormwater control measures
.
Water Research
173
,
115597
.
https://doi.org/10.1016/j.watres.2020.115597
.
Tirpak
R.
,
Afrooz
A.
,
Winston
R.
,
Valenca
R.
,
Schiff
K.
&
Mohanty
S.
2021
Conventional and amended bioretention soil media for targeted pollutant treatment: A critical review to guide the state of the practice
.
Water Research
189
,
116648
.
https://doi.org/10.1016/j.watres.2020.116648
.
Wang
Y.
2018
Effect of Organic Matter on Anaerobic Ammonia Oxidation Reaction and Its Microbial Study
.
Dissertation
,
Suzhou University of Science and Technology
.
Wang
Y.
,
Li
H.
,
Wu
Y.
,
Cai
Y.
,
Song
H.
,
Zhai
Z.
&
Yang
X.
2019
In situ nutrient removal from rural runoff by a new type aerobic/anaerobic/aerobic water spinach wetlands
.
Water
11
(
5
),
1100
.
https://doi.org/10.3390/w11051100
.
Wu
X.
2019
Study on Nitrogen and Phosphorus Removal Characteristics and Microbial Diversity in Sand-Soil-Fly Ash Matrix Stormwater Bioretention Ponds
.
Dissertation
,
Nanjing University of Information Engineering
.
Yang
T.
,
Xin
Y.
,
Zhang
L.
,
Gu
Z.
,
Li
Y.
,
Ding
Z.
&
Shi
G.
2020
Characterization on the aerobic denitrification process of Bacillus strains
.
Biomass and Bioenergy
140
,
105677
.
https://doi.org/10.1016/j.biombioe.2020.105677
.
You
Z.
,
Zhang
L.
,
Pan
S.
,
Chiang
P.
,
Pei
S.
&
Zhang
S.
2019
Performance evaluation of modified bioretention systems with alkaline solid wastes for enhanced nutrient removal from stormwater runoff
.
Water Research
161
,
61
73
.
https://doi.org/10.1016/j.watres.2019.05.105
.
Zhang
W.
,
Sang
M.
,
Che
W.
&
Sun
H.
2019
Nutrient removal from urban stormwater runoff by an up-flow and mixed-flow bioretention system
.
Environmental Science and Pollution Research
26
,
17731
17739
.
https://doi.org/10.1007/s11356-019-05091-4
.
Zhang
H.
,
Ahmad
Z.
,
Shao
Y.
,
Yang
Z.
,
Jia
Y.
&
Zhong
H.
2021a
Bioretention for removal of nitrogen: Processes, operational conditions, and strategies for improvement
.
Environmental Science and Pollution Research
28
,
10519
10535
.
https://doi.org/10.1007/s11356-020-12319-1
..
Zhang
Z.
,
Li
J.
,
Li
Y.
,
Wang
D.
,
Zhang
J.
&
Zhao
L.
2021b
Assessment on the cumulative effect of pollutants and the evolution of micro-ecosystems in bioretention systems with different media
.
Ecotoxicology and Environmental Safety
228
,
112957
.
https://doi.org/10.1016/j.ecoenv.2021.112957
.
Zhao
Y.
,
Zhou
S.
,
Zhao
C.
&
Valeo
C.
2018
The influence of geotextile type and position in a porous asphalt pavement system on Pb (II) removal from stormwater
.
Water
10
(
9
),
1205
.
https://doi.org/10.3390/w10091205
.
Zhong
L.
,
Wang
Y.
&
Li
M.
2018
Advances in the study of functional groups of nitrifying and denitrifying microorganisms in grasslands
.
Chinese Agronomy Bulletin
34
(
3
),
128
133
.
Zhu
S.
,
Huang
X.
,
Ho
S.
,
Wang
L.
&
Yang
J.
2017
Effect of plant species compositions on performance of lab-scale constructed wetland through investigating photosynthesis and microbial communities
.
Bioresource Technology
229
,
196
203
.
https://doi.org/10.1016/j.biortech.2017.01.023
.
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