Abstract

This study investigated the physical and chemical properties of a single or combination of permeable materials which can be used as fillers in the Sponge City program in China. Four types of fillers, perlite, coral sand, vermiculite and ceramsite, were selected from six alternative fillers by an analytic hierarchy process. The optimal city sponge, which consists of vermiculite (10 cm), ceramsite (15 cm), perlite (15 cm), coral sand (20 cm) and Canna indica L, was found by the orthogonal experiment (L16(45)). The results of the simulated rainwater experiment of the optimal sponge showed that the permeability coefficient K10, NH3-N, total phosphorus (TP) and chemical oxygen demand (COD) removal rate were 1.20 ± 0.23 mm/s, 96.6 ± 0.2%, 36.8 ± 0.07% and 9.6 ± 0.07% respectively. The results suggested that the optimal sponge had an excellent treatment effect on NH3-N in rainwater while ensuring rapid infiltration. It provided a simple, economical and effective method for rainwater treatment and the Sponge City program in the future.

INTRODUCTION

In recent decades in China, about ten million people have entered cities (Qiu 2015) every year. As a result of urbanization, a large number of buildings were built, resulting in decreased green areas and an increase of impervious zones, which increased the risks of urban flooding. In China, the economic loss and casualties in cities affected by flooding have increased significantly (Liao et al. 2016). In 2011, the worst urban waterlogging occurred in China, which caused nearly 400 billion yuan of economic losses (Wu et al. 2016). In 2016, 28 provinces in China experienced severe flooding during weeks of torrential rainfall (Jiang et al. 2018). Urban rain water management has become a serious challenge.

Meanwhile, many strategies promoting integrated management of urban water have been practiced among industrialized nations. Such efforts include the Low-Impact Development (LID) in the United States (Pyke et al. 2011), the British Sustainable Urban Drainage System (SUDS) (Mitchell 2005) and the Australia Water-Sensitive Urban Design (WSUD) (Morison & Brown 2011), as well as the LID in New Zealand, and the Low-Impact Urban Design and Active, Beautiful, Clean-water Program (ABC) in Singapore, to name just a few (Voyde et al. 2010). After studying these practices, the concept of ‘sponge city’ was formally introduced by the Chinese government in December 2013 (Wang et al. 2018). Since then, the concept of sponge cities has been actively promoted by the Ministry of Housing and Urban-Rural Construction and the Ministry of Water Resources have actively promoted sponge cities. The purposes of ‘the Sponge City’ are as follows: (1) Improving effective control of urban peak runoff, and improving the abilities of temporarily storing, recycling and purifying storm water; (2) Upgrading the traditional drainage system and improving the existing drainage protection standards; (3) Integrating natural drainage systems and water bodies (Chan et al. 2018).

There are a few types of LID, such as biological drainage systems, rain water gardens, permeable sidewalks and green roofs etc. (Everett et al. 2015). Among these LID designs, the rain water garden was considered as the main solution to urban flooding problems in China (Wang et al. 2009). A rain water garden is a biological detention facility, which can effectively achieve natural purification of rain water. Compared with a large-scale municipal transformation, utilizing rain water gardens to improve waterlogging and runoff pollution is more practical. Rain water gardens can effectively reduce peak runoff and relieve the pressure of municipal drainage, and can also purify water quality, improve regional climate and the livability of the city. Meanwhile, some studies on rain water gardens focus on the filler performance. Li et al. (2007) reported that low cost filler materials, such as gravel and blast furnace slag, achieved great performance in terms of pollutant removal. Wu et al. (2010) studied the decontamination ability of zeolite, anthracite, shale and vermiculite in the vertical flow constructed wetland, and found that the treatment effect is better under high load. In addition, Deng et al. (2018) developed an integrated stormwater system on a GIS platform and a Storm Water Management Model (SWMM), to assist the visual design of LID facilities. Li et al. (2018) indicated that LID can mitigate the thermal impacts of urban stormwater runoff and alleviate resulting ecological problems. Based on these policies and studies, many cities started sponge city demonstration projects. Wuhan (Hubei Province) has completed a sponge city construction in Qingshan District and Hanyang New District, where the ‘breathing’ function of the sponge city was initially demonstrated (Wen & Liu 2018).

However, a rain garden on red soil has been rarely studied. The red soil areas in China usually have the largest annual rainfall, and red soil has poor permeability, agglomeration difficulty, low base saturation and low nutrients levels (He et al. 2008). Furthermore, red soil is the most widely distributed soil type in China, with a total area of 56.9 million hm2, mostly in the vast low hills of the central Asian tropics between 25 and 31° north latitude. So it is important to study the performance of a rain garden constructed on red soil.

In this study we designed a series of experiments based on rainfall and soil conditions in Nanchang (Jiangxi Province), with an emphasis on red soil and local plants in rain garden designs. The study aims to obtain optimum rain garden design parameters, including a combination of filler ratio and plant type. Physical and chemical parameters including the permeability coefficient K10 of the sponge and the pollutant removal rate were measured. These parameters were analyzed by using an analytic hierarchy process and comprehensive scoring method of orthogonal tests. Finally, an optimum sponge structure suitable for red soil was obtained, with pollutant removal rate quantified, which can provide guidance for the construction of a sponge city in red soil regions in China.

MATERIALS AND METHODS

The study area

The city of Nanchang is the capital city of Jiangxi Province. It is located in the north of central Jiangxi, on the southwest bank of Poyang Lake between 115°89′ and 116°35′ east longitude and between 28°10′ and 29°11′ north latitude (Figure 1). The whole area is dominated by plains, accounting for 35.8%. The southeast is relatively flat, the northwest hills are undulating, the water network is dense, with many lakes. The water area is 2,204.37 km2, accounting for 29.78%. The annual rainfall is 1,600–1,700 mm with 147–157 precipitation days, an annual average of 5.6 rainstorm days and an annual average relative humidity of 78.5%. The city is dominated by red soil. Nanchang is a typical southern city that often experiences urban disasters, especially flood.

Figure 1

Sketch of the study area in Jiangxi province.

Figure 1

Sketch of the study area in Jiangxi province.

Reagents

Potassium dichromate (99.8%, AR), sodium hydroxide (96%, AR) and zinc sulfate heptahydrate (99.5%, AR), mercuric biniodide (99.5%, AR), L-ascorbic acid (99.7%, AR), potassium iodode (99.0%, AR), ammonium chloride (99.5%, AR), ammonium molybdate (99.0%, AR), silver sulfate (99.7%, AR), potassium dihydrogen phosphate (99.5%, AR), potassium sodium tartrate (99.0%, AR), potassium antimonyl tartrate (99.0%, AR), potassium peroxydisulfate (99.5%, AR), potassium hydrogen phthalate (99.8%, AR), ammonium iron (II) and sulfate hexahydrate (99.5%, AR), were all purchased from XiLong Scientific (Shantou, Guangdong, China).

The experimental device and measurement method

The main experimental device is shown in Figure 2. This device consists of an aquifer (50 mm), cover layer (50 mm), planting layer (150 mm), filler layer (650 mm) and drainage layer (100 mm), and it is separated by geotextiles between each layer. Simulation rain water flows out of the tank, through each layer, and finally flows out of the drain layer. Then the water sample is collected and determined by standard methods. In this study, permeability coefficient K10, chemical oxygen demand (COD), NH3-N concentration and total phosphorus (TP) concentration were measured as representative physical and chemical indicators respectively. In order to improve the accuracy of the data, these indexes were analyzed using standard methods. GB7788-87 Determination of Forest Soil Permeability was used to measure the K10 for every sample, and Nessler Reagent Spectrophotometry GB7479-87 was used to obtain the NH3-N concentration. TP concentration and COD were measured by Ammonium Molybdate Spectrophotometry GB11893-89 and Rapid Oven Method respectively.

Figure 2

The experimental main device.

Figure 2

The experimental main device.

Preparation of simulated rain water

The water quality of road runoff rain water in Qianhu campus of Nanchang University were reported as follows: 4.13–557.38 mg/L of COD, 0.01–2.41 mg/L of TP, and 0.04–5.29 mg/L of NH3-N (Jiang 2015). Since the selected area had less pollution than the area in the literature, lower concentrations were used for simulated rainwater with 160 mg/L of COD, 1.20 mg/L of TP and 1.40 mg/L of NH3-N by dissolving 1.2754 g potassium hydrogen phthalate, 0.5235 g ammonium chloride and 0.5308 g potassium dihydrogen phosphate were dissolved in 1 L water respectively.

In order to simulate true rainfall, we used Nanchang rainstorm intensity formula:
formula
(1)
where the value of P (recurrence interval) is 5. If A is defined as 9.57, then the value of A is 14.19 mm/h. Rainwater runoff was estimated using the following (Li et al. 2007):
formula
(2)
where A represents the rain force value when P is five years in Nanchang, a represents the instrument floor area of 600 cm2, and k represents the confluence area ratio of 20 (Zhang & Xu 2017). Then, Qsimulation of 4.73 mL/s is obtained and used in subsequent tests.

Preparation of filler materials and plants

Since filter materials are usually used in large quantities in the green space around roads, there should be sufficient supplies which are low cost and environmentally friendly. The six filler materials selected include perlite, coral sand, maifan stone, vermiculite, ceramsite and bio-charcoal (Ying & Zhao 2017). They were all purchased from a garden product market in Nanchang, and the physical properties of these fillers are listed in Table 1.

Table 1

Physical properities of fillers

ExteriorParticle size (mm)Density (g/cm3)Porosity (%)
Ceramsite Spherical 0.5–32 0.19 48.69 
Coral sand Amorphous 1–50 – 
Maifan stone Granular 1–6 2.55 – 
Vermiculite Sheet – 0.52 64.38 
Perlite Granular 0.1–5 0.15 – 
Bio-charcoal Granular 0.8–6 0.8–0.85 – 
ExteriorParticle size (mm)Density (g/cm3)Porosity (%)
Ceramsite Spherical 0.5–32 0.19 48.69 
Coral sand Amorphous 1–50 – 
Maifan stone Granular 1–6 2.55 – 
Vermiculite Sheet – 0.52 64.38 
Perlite Granular 0.1–5 0.15 – 
Bio-charcoal Granular 0.8–6 0.8–0.85 – 

In addition, Coreopsis drummondii Torr et Gray, Conyza Canadensis(L.) Cronq, Canna indica L and Arachis duranensis were selected as tested plants. They were collected from Nanchang Hangkong University. Especially, in order to improve the reliability of this experiment, the plants were planted in prepared soil, which consisted of 65% sand, 30% red soil and 5% nutritious soil, a month in advance.

Methods for data analysis

Analytic hierarchy process

Multiple factors need to be considered during the screening of single fillers, however, the quality and importance of each factor is different. Such a multi-objective decision-making case can be solved by the analytic hierarchy process (Gao 2014). The analytic hierarchy process is a combination of qualitative and quantitative, systematic and hierarchical analysis methods with the following six steps:

  • (1)

    Divide the factors involved into several levels and establish a multi-level hierarchical model.

  • (2)

    According to the evaluation scale, the initial relative importance of each element of the same level is determined, and quantified, then the evaluation matrix A is established.

  • (3)

    Calculate the eigenvectors of the evaluation matrix and the corresponding maximum eigenvalues to determine the relative importance of each layer of elements.

  • (4)

    The consistency check of the matrix is performed, including the calculation of the consistency index (CI), the average random consistency index (RI), and the consistency ratio (CR). If CR <0.1, then the consistency of the evaluation matrix is considered acceptable.

  • (5)

    If the above steps are passed and the consistency test is passed, the obtained feature vector is the weight of each factor.

  • (6)

    Combine the weights of various factors, and finally determine the total weight of the layer elements of the scheme to the target layer, and select the optimal filler according to the weight.

In the single filler screening experiment in the first step, the analytic hierarchy process was used to analyze the results, and the four fillers used in the combined experiment were screened, and then the next experiment was carried out.

Comprehensive scoring method for multi-index orthogonal experiment

The experiment of combined fillers was carried out by the Taguchi's orthogonal array experiment with a five-factor four-level orthogonal experimental design, and the experimental results include four indexes as follows: permeability coefficient K10, COD removal rate, TP removal rate and NH3-N removal rate. The determination of the optimal combination of experiments depends on the interaction of the four indicators. This experiment used the ‘comprehensive scoring method’ to analyze the results (Yuan 2005).

The comprehensive scoring method includes conversion from multiple indicators to a single indicator and the application of extremum difference analysis. Specifically, consider the importance of each indicator and the measured values of the experiment, and then evaluate the results of each group of experiments as a comprehensive indicator. The key to this method is scoring. The scoring should not only take into account the importance of each indicator, but also consider the pros and cons of each indicator.

Generally speaking, the comprehensive scoring method is similar to the standardization process in the analytic hierarchy process, that is, the best value of each indicator is firstly set to the full score, the highest value is determined as the lowest score, and the other values are scored proportionally to the difference from the best value. Then add all the indicators of each group of experiments to the comprehensive score of the group of experiments. The method is simple and suitable for situations where the importance of each indicator is substantially equal.

RESULTS AND DISCUSSION

Screening experiment and data processing analysis of a single filler in rain gardens

The analysis of permeability coefficient K10

Stable permeability coefficient K10 of six alternative fillers was measured according to GB7788-87 Determination of Soil Permeability in Forests. The stable permeability coefficient curve of a single filler is shown in Figure 3 which indicated that each filler has reached saturation during the measurement, so the results are considered as representative. After removing the large deviation values in each set of data, the average value of the permeability coefficient K10 of each filler was obtained and compared, as shown in Figure 4. It can be seen from Figure 4 that the permeability coefficient from large to small is as follows: perlite > coral sand > maifan stone > vermiculite > ceramsite > bio-charcoal. The high permeability coefficient of perlite is mainly due its relatively uniform particle size (between 2 and 3 mm), porous surface lighter than water density. Coral sand has a large porosity and a porous surface, which may correlate to its penetration ability. Maifan stone is hard and does not swell after immersion, and its small porosity is the reason for the low permeability coefficient. Comparing vermiculite to ceramsite, vermiculite is smaller, its bulk density is larger but its permeability coefficient is greater. Mainly because the ceramsite texture is hard and light, a very strong extrusion phenomenon between the ceramsite was observed when it floats on the water surface after full immersion. It causes the porosity to decrease more sharply than in the partially immersed state, and results in a decrease in the permeability coefficient. Bio-charcoal has the lowest permeability coefficient, in spite of a strong adsorption capacity and great filtration effect.

Figure 3

Permeability coefficient K10 curve of single filler.

Figure 3

Permeability coefficient K10 curve of single filler.

Figure 4

Comparison of average permeability coefficients K10 of various fillers.

Figure 4

Comparison of average permeability coefficients K10 of various fillers.

The analysis of pollutant removal effect

In order to analyze the removal effect of COD, NH3-N and TP by each filler, water samples were collected after 60 minutes and various chemical indicators were measured. The experimental data of each indicator is shown in Table 2.

Table 2

Data of single filler removal results

SpeciesPerliteCoral sandMaifan stoneVermiculiteCeramsiteBio-charcoal
COD removal rate (%) 4.6 9.6 9.6 4.6 4.6 9.6 
NH3-N removal rate (%) 31.6 40.2 57.3 65.8 31.6 31.6 
TP removal rate (%) 2.3 2.3 –12.8 2.3 2.3 9.8 
SpeciesPerliteCoral sandMaifan stoneVermiculiteCeramsiteBio-charcoal
COD removal rate (%) 4.6 9.6 9.6 4.6 4.6 9.6 
NH3-N removal rate (%) 31.6 40.2 57.3 65.8 31.6 31.6 
TP removal rate (%) 2.3 2.3 –12.8 2.3 2.3 9.8 

All six fillers have certain purification abilities for simulating road rainwater. The removal rate of NH3-N by various fillers is generally higher than that of TP and COD. This is consistent with previous reports, mainly attributed to the good adsorption properties of these materials for NH3-N (Fu et al. 2011; Tian et al. 2012; Zhang 2012; Zheng et al. 2015).

Screening and evaluation of fillers

In this experiment, permeability coefficient, price and pollutant removal rate were selected for evaluation and analysis. Since the units of each indicator were different, it was difficult to compare directly, so it was necessary to standardize before comparison. The actual data of each indicator and the standardized results are shown in Tables 3 and 4.

Table 3

Actual data of each evaluation index

Permeability coefficient K10 (mm/s)1/price (1/¥)COD removal rate (%)NH3-N removal rate (%)TP removal rate (%)
Ceramsite 1.64 1/567 4.6 31.6 2.3 
Coral sand 2.16 1/1600 9.6 40.2 2.3 
Maifan stone 2.01 1/8000 9.6 57.3 –12.8 
Vermiculite 1.74 1/650 4.6 65.8 2.3 
Perlite 2.17 1/400 4.6 31.6 2.3 
Bio-charcoal 1.47 1/6000 9.6 31.6 9.8 
Permeability coefficient K10 (mm/s)1/price (1/¥)COD removal rate (%)NH3-N removal rate (%)TP removal rate (%)
Ceramsite 1.64 1/567 4.6 31.6 2.3 
Coral sand 2.16 1/1600 9.6 40.2 2.3 
Maifan stone 2.01 1/8000 9.6 57.3 –12.8 
Vermiculite 1.74 1/650 4.6 65.8 2.3 
Perlite 2.17 1/400 4.6 31.6 2.3 
Bio-charcoal 1.47 1/6000 9.6 31.6 9.8 
Table 4

Standardization of target data

Permeability coefficient K10 (mm/s)1/price (1/¥)COD removal rate (%)NH3-N removal rate (%)TP removal rate (%)
Best value 2.17 (1) 1/400 (1) 9.6 (1) 65.8 (1) 9.8 (1) 
Worst value 1.47 (0) 1/8000 (0) 4.6 (0) 31.6 (0) –12.8 (0) 
Ceramsite 0.24 0.69 0.00 0.00 0.67 
Coral sand 0.99 0.21 1.00 0.25 0.67 
Maifan stone 0.77 0.00 1.00 0.75 0.00 
Vermiculite 0.39 0.60 0.00 1.00 0.67 
Perlite 1.00 1.00 0.00 0.00 0.67 
Bio-charcoal 0.00 0.18 1.00 0.00 1.00 
Permeability coefficient K10 (mm/s)1/price (1/¥)COD removal rate (%)NH3-N removal rate (%)TP removal rate (%)
Best value 2.17 (1) 1/400 (1) 9.6 (1) 65.8 (1) 9.8 (1) 
Worst value 1.47 (0) 1/8000 (0) 4.6 (0) 31.6 (0) –12.8 (0) 
Ceramsite 0.24 0.69 0.00 0.00 0.67 
Coral sand 0.99 0.21 1.00 0.25 0.67 
Maifan stone 0.77 0.00 1.00 0.75 0.00 
Vermiculite 0.39 0.60 0.00 1.00 0.67 
Perlite 1.00 1.00 0.00 0.00 0.67 
Bio-charcoal 0.00 0.18 1.00 0.00 1.00 

The target layer is the ideal filler (A). The factors of the criterion layer are the permeability coefficient K10 (B1), the price (B2), and the pollutant removal efficiency (B3). The three factors of the scheme layer are COD removal rate (C1), NH3-N removal rate (C2) and TP removal rate (C3).

First, the hierarchical model was built, as shown in Figure 5. Then the relative importance of permeability coefficient K10, price and pollutant removal efficiency were determined according to the 1–9 ratio scale method, as shown in Table 5.

Table 5

1–9 ratio scale method

ScalingDefinition (comparing factors i and j)
Factors i and j are equally important 
Factor i is slightly more important than j 
Factor i is more important than j 
Factor i is strongly important than j 
Factor i is absolutely more important than j 
2, 4, 6,8 Intermediate value of two adjacent degrees 
Reciprocal Comparison factors j and i 
ScalingDefinition (comparing factors i and j)
Factors i and j are equally important 
Factor i is slightly more important than j 
Factor i is more important than j 
Factor i is strongly important than j 
Factor i is absolutely more important than j 
2, 4, 6,8 Intermediate value of two adjacent degrees 
Reciprocal Comparison factors j and i 
Figure 5

Hierarchical model.

Figure 5

Hierarchical model.

So, a pairwise comparison matrix of three indexes (permeability coefficient K10, price and pollutant removal efficiency) was obtained according to Table 5, as shown in Table 6.

Table 6

Comparison of the criteria layer factors to the target pairwise comparison matrix

For AB1B2B3
B1 1/3 
B2 
B3 1/2 1/5 
For AB1B2B3
B1 1/3 
B2 
B3 1/2 1/5 
Table 6 can be converted into an evaluation matrix:
formula
Add the three columns of the matrix to obtain the sum matrix:
formula
An eigenvector W of the matrix A (approximate) can be obtained by nominalizing the above matrix with its sum:
formula
The evaluation matrix A and the eigenvector W product were:
formula
Since W is an approximate eigenvector of A, AW is not a multiple of W. The average of the sum of the ratios of the corresponding elements of matrix AW and matrix A is taken as the approximate eigenvalue, the maximum eigenvalue of matrix A was obtained:
formula

After λmax is calculated, the consistency check of evaluation matrix A can be performed.

First, the consistency indicator (CI) is calculated:
formula
Then, since the order of A was 3, the average random consistency index (RI) is obtained according to Table 7.
formula
Table 7

Average random consistency indicator

Order34567891011121314
RI 0.58 0.89 1.12 1.26 1.36 1.41 1.46 1.49 1.52 1.54 1.56 1.58 
Order34567891011121314
RI 0.58 0.89 1.12 1.26 1.36 1.41 1.46 1.49 1.52 1.54 1.56 1.58 
Finally, calculate the consistency ratio based on CI and RI:
formula

Since CR is <0.1, the consistency of the evaluation matrix passed the test, meaning feature vector W represented the weight of the three factors: permeability coefficient K10, price and pollutant removal efficiency.

After completing the weight analysis of the criterion layer to the target layer, the weight of the pollutant removal efficiency of the three factors in the scheme layer is analyzed. The evaluation matrix is shown in Table 8.

Table 8

Comparison matrix of solution layer factors

C1C2C3
C1 
C2 1/7 
C3 1/8 1/2 
C1C2C3
C1 
C2 1/7 
C3 1/8 1/2 
Similarly:
formula
Find the eigenvectors of the evaluation matrix B (the calculation process is the same as before):
formula
The product of the evaluation matrix B and the eigenvector W1 is:
formula
The maximum eigenvalue of matrix B is obtained as follows:
formula
As before, after the λmax is calculated, a consistency check of evaluation matrix B can be performed:
formula

Utilize Table 7 to obtain the average consistency index RI = 0.58.

Finally, calculate the consistency ratio based on CI and RI:
formula
Since CR is <0.1, the consistency of the evaluation matrix passed the test, so the eigenvector W1 is weights of COD, NH3-N and TP.

Next, the total weight of the plan layer factor is calculated, as shown in Table 9.

Table 9

Total weight of scheme layer factor to target layer

B3 for A: 0.121C for B3C for AOrder
C1 0.770 0.093 
C2 0.151 0.018 
C3 0.079 0.010 
B3 for A: 0.121C for B3C for AOrder
C1 0.770 0.093 
C2 0.151 0.018 
C3 0.079 0.010 

Multiply the scores of the permeability coefficients K10, price, COD removal rate, NH3-N removal rate and TP removal rate of the six fillers by their respective total weights and obtain the total scores of six fillers, see Table 10.

Table 10

Single filler total score table

ProjectsPermeability coefficient K10PriceCOD removal rateTP removal rateNH3-H removal rateTotal score
Total weight 0.238 0.641 0.093 0.010 0.018 
Ceramsite 0.24 0.69 0.67 0.506 
Coral sand 0.99 0.21 0.67 0.25 0.474 
Maifan stone 0.77 0.75 0.290 
Vermiculite 0.39 0.60 0.67 0.502 
Perlite 0.67 0.886 
Bio-charcoal 0.18 0.218 
ProjectsPermeability coefficient K10PriceCOD removal rateTP removal rateNH3-H removal rateTotal score
Total weight 0.238 0.641 0.093 0.010 0.018 
Ceramsite 0.24 0.69 0.67 0.506 
Coral sand 0.99 0.21 0.67 0.25 0.474 
Maifan stone 0.77 0.75 0.290 
Vermiculite 0.39 0.60 0.67 0.502 
Perlite 0.67 0.886 
Bio-charcoal 0.18 0.218 

The total score of the materials is ranked from high to low as perlite > ceramsite > vermiculite > coral sand > maifan stone > bio-charcoal. The top four fillers are used as fillers for combined experiments to find the combination of high permeability and pollutant removal capacity.

Screening experiment and data processing analysis of combined filler experiments

In order to obtain objective and accurate experimental results, we designed a five-factor four-level orthogonal experiment, L16(45), with factors and levels of the experiment shown in Table 11, and the experimental arrangement is shown in Table 12. The orthogonal experiment is an effective way to determine the optimum conditions among many factors (Shao et al. 2019).

Table 11

Factor level table

Factors
LevelCeramsiteVermiculitePerliteCoral sandPlant species
Coreopsis drummondii Torr et Gray 
10 10 10 10 Conyza Canadensis (L.) Cronq 
15 15 15 15 Canna indica L 
20 20 20 20 Arachis duranensis 
Factors
LevelCeramsiteVermiculitePerliteCoral sandPlant species
Coreopsis drummondii Torr et Gray 
10 10 10 10 Conyza Canadensis (L.) Cronq 
15 15 15 15 Canna indica L 
20 20 20 20 Arachis duranensis 

The units of factors are ‘cm’, except the plant species.

Table 12

Orthogonal experiment combination table

Factor/level
Group numberCeramsiteVermiculitePerliteCoral sandPlant species
10 
11 
12 
13 
14 
15 
16 
Factor/level
Group numberCeramsiteVermiculitePerliteCoral sandPlant species
10 
11 
12 
13 
14 
15 
16 

Composite filler design and its permeability coefficient and removal effect

When the permeability coefficient K10 was measured, plant factors were not considered. The experimental procedure was consistent with the determination method of the permeability coefficient K10 of single filler material. Photographs of the experiment are shown in Figure 6.

Figure 6

(a) Determination of the permeability coefficient K10 of Group 7 and (b) Determination of the removal rate of Group 7.

Figure 6

(a) Determination of the permeability coefficient K10 of Group 7 and (b) Determination of the removal rate of Group 7.

The filler was cleaned and dried before each experiment, then all experiments were carried out according to Table 12, an orthogonal test table, which is based on the orthogonality to select some representative points from the comprehensive test. Then, according to Equations (1) and (2), rainwater runoff simulation flow q is 4.73 mL/s. All experiments were carried out in the device shown in Figure 2. Finally, in each experiment, a 1 L water sample was collected and measured after 60 minutes.

Analysis of results

The permeability coefficient K10 of the combined filler is shown in Figure 7. In general, the composite filler had a lower permeability coefficient K10 than the single filler, and this was due to different fillers stacked together which led to an increase in bulk density and a decrease in porosity. In addition, in the 16 orthogonal experiments, only three groups of experiments had a permeability coefficient K10 greater than 1. Interestingly, the proportion of coral sand was more than 30% in these three groups of experiments, indicating the higher the content of coral sand, the better the permeability coefficient K10 of the sponge body, but excessive coral sand can affect plant growth. Similarly, when the proportion of vermiculite and perlite was high, the permeability coefficient of the sponge was very low, however, the removal of pollutants performed better. On the other hand, the thickness of the sponge body also affected the permeability coefficient K10. The thickness of the first and fourth groups of sponges was 20 cm (thinnest) and 65 cm (thickest), respectively, and they all had poor permeability coefficients K10. When the thickness of the sponge was 45–55 cm, the permeability coefficient K10 of the sponge was high.

Figure 7

Permeability coefficient K10 of each combined filler.

Figure 7

Permeability coefficient K10 of each combined filler.

In order to obtain the optimal sponge structure, the permeability coefficient K10 and contaminant removal capacity of the sponge must be considered at the same time. In the study, a comprehensive scoring method for the multi-index orthogonal experiment was adopted.

First, according to the results of orthogonal experiments, as is shown in Table 13, we know the optimal and worst value for each indicator, and the optimal value is marked as 10 points, with the worst value marked as 1 point; other values are calculated according to their proportion of the difference between the optimal value and the worst value. Second, when calculating the composite score, stipulate the weight of the permeability coefficient K10 as 2, and other indicators are 1. So, a comprehensive score is the sum of 2× permeability coefficient K10 + COD removal rate + NH3-N removal rate + TP removal rate. The analysis results are shown in Table 14.

Table 13

Experimental data

Experimental index
Group numberPermeability coefficient K10COD removal rate (%)TP removal rate (%)NH3-H removal rate (%
0.694 24.70 49.60 70.10 
0.984 24.70 45.90 75.00 
1.075 9.61 81.20 84.60 
0.400 19.60 77.40 90.40 
0.919 9.60 23.30 70.10 
1.143 39.73 16.54 87.20 
0.640 39.70 77.40 94.20 
0.796 29.68 60.90 67.50 
0.906 19.64 57.10 84.60 
10 1.184 29.70 34.60 89.70 
11 0.560 29.70 51.10 91.80 
12 0.734 49.80 64.70 77.80 
13 0.747 29.70 45.90 89.70 
14 0.779 9.60 49.60 94.20 
15 0.580 19.64 51.10 89.70 
16 0.500 –0.45 33.10 94.20 
Experimental index
Group numberPermeability coefficient K10COD removal rate (%)TP removal rate (%)NH3-H removal rate (%
0.694 24.70 49.60 70.10 
0.984 24.70 45.90 75.00 
1.075 9.61 81.20 84.60 
0.400 19.60 77.40 90.40 
0.919 9.60 23.30 70.10 
1.143 39.73 16.54 87.20 
0.640 39.70 77.40 94.20 
0.796 29.68 60.90 67.50 
0.906 19.64 57.10 84.60 
10 1.184 29.70 34.60 89.70 
11 0.560 29.70 51.10 91.80 
12 0.734 49.80 64.70 77.80 
13 0.747 29.70 45.90 89.70 
14 0.779 9.60 49.60 94.20 
15 0.580 19.64 51.10 89.70 
16 0.500 –0.45 33.10 94.20 
Table 14

Formula scoring results

Score of experiment index
Group numberPermeability coefficient K10COD removal rate (%)TP removal rate (%)NH3-H removal rate (%)Total score
4.38 5.50 5.60 1.88 21.74 
7.70 5.50 5.09 3.53 29.52 
8.75 2.80 10.00 6.76 37.06 
1.00 4.59 9.47 8.72 27.78 
6.96 2.80 1.94 1.88 20.54 
9.53 8.20 1.00 7.64 35.90 
3.76 8.19 9.47 10.00 35.18 
5.55 6.40 7.17 1.00 25.67 
6.81 4.60 6.64 6.76 31.62 
10 10.00 6.40 3.51 8.48 38.39 
11 2.84 6.40 5.81 9.19 27.09 
12 4.83 10.00 7.70 4.47 31.83 
13 4.98 6.40 5. 09 8.48 29.93 
14 5.35 2.80 4.60 10.00 28.10 
15 3.07 4.60 5.81 8.48 25.03 
16 2.15 1.00 3.30 10.00 18.60 
Score of experiment index
Group numberPermeability coefficient K10COD removal rate (%)TP removal rate (%)NH3-H removal rate (%)Total score
4.38 5.50 5.60 1.88 21.74 
7.70 5.50 5.09 3.53 29.52 
8.75 2.80 10.00 6.76 37.06 
1.00 4.59 9.47 8.72 27.78 
6.96 2.80 1.94 1.88 20.54 
9.53 8.20 1.00 7.64 35.90 
3.76 8.19 9.47 10.00 35.18 
5.55 6.40 7.17 1.00 25.67 
6.81 4.60 6.64 6.76 31.62 
10 10.00 6.40 3.51 8.48 38.39 
11 2.84 6.40 5.81 9.19 27.09 
12 4.83 10.00 7.70 4.47 31.83 
13 4.98 6.40 5. 09 8.48 29.93 
14 5.35 2.80 4.60 10.00 28.10 
15 3.07 4.60 5.81 8.48 25.03 
16 2.15 1.00 3.30 10.00 18.60 

Now, the orthogonal experiment of multiple indicators is summarized as a single index, and the analysis results of the single index are shown in Table 15.

Table 15

Formula scoring range analysis

Factor
RangeCeramsiteVermiculitePerliteCoral sandPlant species
K1 116.11 103.83 114.63 116.85 110.83 
K2 117.29 131.91 106.92 112.22 114.92 
K3 128.93 124.36 122.45 114.59 134.72 
K4 101.66 103.88 119.98 120.33 103.51 
k1 29.03 25.96 28.66 29.21 27.71 
k2 29.32 32.98 26.73 28.06 28.73 
k3 32.23 31.09 30.61 28.65 33.68 
k4 25.42 25.97 30.00 30.08 25.88 
6.81 7.02 3.88 2.02 7.80 
Factor
RangeCeramsiteVermiculitePerliteCoral sandPlant species
K1 116.11 103.83 114.63 116.85 110.83 
K2 117.29 131.91 106.92 112.22 114.92 
K3 128.93 124.36 122.45 114.59 134.72 
K4 101.66 103.88 119.98 120.33 103.51 
k1 29.03 25.96 28.66 29.21 27.71 
k2 29.32 32.98 26.73 28.06 28.73 
k3 32.23 31.09 30.61 28.65 33.68 
k4 25.42 25.97 30.00 30.08 25.88 
6.81 7.02 3.88 2.02 7.80 

As can be seen from the above table, the R value (from high to low) is in the order R plant species > R vermiculite > R ceramsite > R perlite > R coral sand. For the plant species, K3 is the highest (33.68), suggesting Canna indica L is the best plant. Similarly, the optimal levels of filler depth are found as follows: vermiculite = 10 cm, ceramsite = 15 cm, perlite = 15 cm and coral sand = 20 cm. In summary, the optimal sponge structure is vermiculite (10 cm) + ceramsite (15 cm) + perlite (15 cm) + coral sand (20 cm) + Canna indica L. Performance tests of optimal sponge were carried out, and the results are shown in Table 16.

Table 16

Experimental data of optimal sponge structure

Permeability coefficient K10COD removal rateNH3-N removal rateTP removal rate
1.20 ± 0.23 9.6% ± 0.07 96.6% ± 0.2 36.8% ± 0.07 
Permeability coefficient K10COD removal rateNH3-N removal rateTP removal rate
1.20 ± 0.23 9.6% ± 0.07 96.6% ± 0.2 36.8% ± 0.07 

For the optimal sponge structure, as much as 96.6% of NH3-N can be removed, and the permeability coefficient K10 is up to 1.2. The results suggest that this analytical method is very reliable in selecting the factors.

CONCLUSIONS

In this paper, a high NH3-N removal urban sponge was designed for application in red soils, which consisted of 15 cm ceramsite +10 cm vermiculite +15 cm perlite +20 cm coral sand with Canna indica L. Performance indicators of the optimal sponge are as follows: the permeation coefficient, K10, of 1.20 mm/s, with an NH3-N removal rate of 96.6%, a TP removal rate of 36.8%, and a chemical oxygen demand removal rate of 9.6%. The results showed that the urban sponge designed in this experiment not only has excellent permeability, but it can also effectively remove contaminants in runoff rain water. The whole design and selection process can be very helpful for the construction of an urban sponge city in red soil areas in southern China and other red soil areas in the world.

ACKNOWLEDGEMENTS

The authors wish to thank partial financial support by the National Natural Science Foundation of China (Grants 21467018), China Scholarship Council (CSC No. 201408360050) and Foundation of Jiangxi Education Committee (GJJ170576).

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