This study examines the influence of planting mixture variations on the quality of the percolated water of the rain garden with and without plants. Six planting mixtures in experimental rain gardens have been used. It has been noted that pollutant removal efficiency of RG can exhibit variations based on specific parameters. Notably, RG6, utilizing a planting mix of 75% topsoil and 25% compost, demonstrated the highest performance. These results draw attention to the critical role of the specific planting mixtures in influencing the performance of vital parameters related to pollutant removal. The observation shows that RG5 exhibits exceptional removal efficiency in pH, Total Suspended Solids (TSS), Biological Oxygen Demand (BOD), and Chemical Oxygen Demand (COD), and RG6 performs best in electrical conductivity (EC), Total Dissolved Solids (TDS), Total Nitrogen (TN), and Total Phosphorus (TP) removal. In particular, when analyzing pollutant removal on a surface with Madagascar periwinkle plants, RG6 emerges as the most effective, achieving an impressive efficiency of approximately 49%. For the bare surface, pollutant removal efficiency is 40%. The study outcome will be useful in deciding the composition of the planting mixture, which will keep the rain garden to improve quality and quantitatively hydrological performance, lowering urban flooding magnitude.

  • The rain garden planting mixture influences pollutant removal efficiency.

  • The performance of rain garden with plant is superior to a bare rain garden.

  • The rain garden having a planting mixture of topsoil in the range of 75–80% and compost in the range of 20–25% performance is best.

Urbanization is a primary factor that is responsible for urban flooding. Urban flooding is increasing day by day due to an increase in urbanization and extreme rainfall events. Recently, urban flooding has been the biggest challenge for all planners, policymakers, and administrators. Due to urban flooding, lots of capital or financial and health losses occur (Coffman et al. 1994; Roy-Poirier et al. 2010; Kumar & Singh 2023b). Poor drainage networks and unplanned drainage systems cause urban flooding. Nowadays, a traditional drainage system is limited in capacity to drain out present urban floods due to an increase in impervious surfaces. The runoff volume has enormously increased and the current draining system is inadequate to drain the water conveniently. Improving the capacity of the existing system is highly uneconomical. So, it's time to adopt some new BMPs (Best management practices) in addition to the existing drainage system. Urbanization increases the imperviousness of the area, and the response is that the runoff rate of the area increases. After a mild rainfall in city or urban areas, an urban flood situation is created, and heavy runoff runs everywhere (Nguyen et al. 2019; Osheen & Singh 2019; Kumar & Singh 2023a). The runoff water quality in urban areas is also alarming. Increased runoff from roads accelerates the mobilization and transport of pollutants, resulting in the degradation of the quality of water bodies that receive such pollutant-loaded surface runoff. Hence, runoff from impervious surfaces is the major contributor to the collapse of healthy freshwater ecosystems in urban areas. These problems eventually lead to increased pollutant load in the receiving water bodies, impairment of the hydrological characteristics of urban watersheds, ecosystem damage, and public health threats (Mamun & Nuruzzaman 2020; Oral et al. 2020; Iqbal et al. 2022; Rentachintala et al. 2022; Putri et al. 2023; Raspati et al. 2023). All these problems are not only because of the dramatic increase in impermeable ground surfaces and the encroachment of stream areas, but also changed rainfall patterns, and insufficient sewer network capacity (Malaviya et al. 2019).

Besides these consequences, stormwater has the potential to provide a non-potable water supply. If stormwater (Putri et al. 2023) is adequately treated, we can exploit this wastewater for numerous non-potable uses. It requires less treatment as compared to municipal wastewater treatment. However, the perceived risks, particularly those associated with public health, must be addressed appropriately before utilizing stormwater (Coffman et al. 1994; Barbosa et al. 2012; Tirpak et al. 2019, 2021). Thus, structural BMPs such as rain gardens (RGs), infiltration systems, storage facilities, and alternative road structures (Davis et al. 2006; Yuan et al. 2017; Kumar & Sihag 2019) are preferred over these traditional technologies for stormwater treatment. RG removes most of the pollutants and bacteria. The RG is one of the most essential aspects that helps to recharge local groundwater. A RG collects and filtrates the polluted water while absorbing it in the ground. RGs are aesthetically pleasing and help in managing stormwater without disturbing our ecosystem (Kumar & Singh 2021). Its plants and flowers attract the fauna. RGs also reduce mosquito breeding and increase the number of beneficial insects that reduce pests. RGs, once fabricated, require very little maintenance. Hence, they are very economical compared to conventional stormwater management systems. The RG is an attempt to ‘maximize all available physical, chemical, and biological pollutant removal processes found in the soil and plant complex of a terrestrial forested community’ (Coffman et al. 1994).

One of storm management practices most common goals includes removing and reducing pollutants. The results of pollutant removal may be satisfactory in some cases, but they can be misleading in some instances. These cases mainly arise when the quality of influent water is relatively good, and the concentration of pollutants is less, then the fractional removal may be low. But BMP cannot be elucidated as unsatisfactory in this case. The study aims to examine improvement in various water quality parameters of runoff when it percolates through the roots of Madagascar periwinkle plants in RGs for different planting mixes.

Experimental setup

The investigation was carried out in experimental RGs located in the nursery area of the National Institute of Technology, Kurukshetra, India. Six experimental RG modules, constructed from iron sheets (2,000 × 1,000 × 500 mm), featured a 100 mm drainage layer with 20 mm gravels, a 100 mm ponding depth, and a filter mat above the drainage layer. Each module maintained a 300 mm planting mix thickness. The collections of surface runoff and percolated water through planting mix arrangements are shown in Figure 1.
Figure 1

Typical sketch of an experimental setup (not to scale).

Figure 1

Typical sketch of an experimental setup (not to scale).

Close modal

The planting mixture in six RGs (RG1–RG6) varied in composition. RG1 planting mix consists of 100% topsoil and no compost. RG2 planting mix consists of 95% topsoil and 5% compost. RG3 planting mix consists of 90% topsoil and 10% compost. RG4 planting mix consists of 85% topsoil and 15% compost. RG5 planting mix consists of 80% topsoil and 20% compost. RG6 planting mix consists of 75% topsoil and 25% compost. The mixture, consisting of topsoil and compost with organic matter, was standardized across all RGs, where matured Madagascar periwinkle plants were planted uniformly spaced at 30 cm intervals in both longitudinal and transverse directions, with maintenance practices such as weekly raking and hand weeding observed throughout the study period.

Methodology

The experimental modules, situated on open flat ground, were elevated 300 mm above the ground surface to facilitate percolated water collection. A waterproof polythene cover kept over the module prevented the fall of natural rainfall on it. The artificial rain, mimicking natural conditions, was applied using mist nozzles until a constant infiltration rate was achieved from the module. The Sewage water is used to estimate the RG pollutant removal efficiency. STP water filled in a cylindrical storage tank of a capacity of 1,000 L is pumped to discharge through mist nozzles to mimic artificial rainfall in the RG. The discharge rate of the nozzle was 1,800 mL/min. After the water was applied to the RG, percolated water was collected at the bottom tank which was transferred to a plastic bottle of 1,000 mL capacity for laboratory study. Before laboratory analysis, STP and percolated water samples were stored in acid-washed plastic bottles at 4 °C. Samples were analyzed within the holding times recommended by the US EPA (2002). Each composite sample was analyzed for pH, EC, BOD, COD, TSS, TDS, TN, and TP. TN and TP concentrations were analyzed using Hach methods 10071 and 8190, respectively. The pH/EC meter was used to estimate the pH, and EC values. The standard methods for examining water and wastewater were used for estimating BOD, COD, TSS, TDS, TN, and TP (Sanwal 1993; APHA 1999).

Sample collection and testing

The planting mix sample was collected from all six RGs. These samples were analyzed in the laboratory for pH and Electrical Conductivity (EC) with the help of a pH/ EC meter. The acid titration method was employed for organic matter content estimation. The Permeability (K) was obtained by the falling head method. Specific Gravity (G), Bulk Density (ϒ), porosity (Φ), and Texture of Soil were analyzed by following the procedures specified in the Bureau of Indian Standards (BIS 1987) (Agriculture & India, Government of Delhi 2011). The topsoil of the RG planting mix consists of sand (51%), silt (30%), and clay (19%), respectively. It is brown in color, and the soil texture is sandy loam.

Water Quality Index and Wastewater Quality Index

The Water Quality Index (WQI) and Wastewater Quality Index (WWQI) are numerical metrics that were established in the early 1970s to offer an accurate and simple assessment of water and waste water qualities. These indices are meant to serve as essential tools and aid in decision-makers' clear public communications (Canadian et al. 2001). The WWQI is a helpful tool for figuring out the extent of wastewater pollution caused by humans and the necessary remediation steps to achieve preset goals since it provides a thorough evaluation of the previously mentioned criteria. The Sequential Batch Reactor (SBR) operating procedures are greatly impacted by the influent wastewater characteristics, which include pH, BOD, COD, TSS, TDS, TN, and TP.

Canadian Water Quality Index (CWQI):

The CWQI consists of three factors

  • Factor 1: Scope

F1 (Scope) represents the extent of water quality guideline non-compliance over the time period of interest. It has been adopted directly from the British Columbia Index:
formula
  • Factor 2: Frequency

F2 (Frequency) represents the percentage of individual tests that do not meet objectives (‘failed tests’):
formula

The formulation of this factor is drawn directly from the British Columbia Water Quality Index.

  • Factor 3: Amplitude

F3 (Amplitude) represents the amount by which failed test values do not meet their objectives. F3 is calculated in three steps. The formulation of the third factor is drawn from work done under the auspices of the Alberta Agriculture, Food and Rural Development

  • (i) The number of times by which an individual concentration is greater than (or less than, when the objective is a minimum) the objective is termed an ‘excursion’ and is expressed as follows. When the test value must not exceed the objective:

  • (ii)

For the cases in which the test value must not fall below the objective:
formula
  • (i) The collective amount by which individual tests are out of compliance is calculated by summing the excursions of individual tests from their objectives and dividing by the total number of tests (both those meeting objectives and those not meeting objectives). This variable, referred to as the normalized sum of excursions, or nse, is calculated as:

  • (ii) F3 is then calculated by an asymptotic function that scales the normalized sum of the excursions from objectives (nse) to yield a range between 0 and 100.
    formula
The CWQI is calculated as [R] :
formula

Wastewater Quality Index

The quality of wastewater may be characterized as a numerical number that is inherently related to its constituent factors. The Canadian Council of Ministers of the Environment (CCME) WQI comprises two components, viz. percentile100 and the other varying factor ((F12 + F22 + F32)0.5)/1.732. This factor can be called pollution index (PI), when PI reaches 0, CWQI reaches 100 and water quality is considered perfect. On the other hand, when PI reaches 100, CWQI becomes 0 and water quality is considered poor. In other words, the amount of pollution is a determining factor in assessing the characteristics and quality of water. This concept of WQI is applied to wastewater and the quality of wastewater can be determined based on PI. Thus, the degree of contamination in wastewater will be assessed and referred to as the WWQI (Vijayan et al. 2016). Treatment processes can be adjusted by adjusting the number and duration of cycles based on the WWQI, and aeration times can be adjusted based on the calculated organic load or other component parameters.

Once the CWQI value has been determined, water quality is categorized as Excellent, Good, Fair, Marginal and Poor in Table 1.

Table 1

WWQI water quality is categorized (Singh et al. 2011)

WWQIWater quality
0–44 Poor 
45–64 Marginal 
64–79 Fair 
80–94 Good 
95–100 Excellent 
WWQIWater quality
0–44 Poor 
45–64 Marginal 
64–79 Fair 
80–94 Good 
95–100 Excellent 

The waste water quality parameters, viz. pH, EC, BOD, COD, TSS, TDS, TN, and TP, of influent and effluent waste water of RGs are evaluated for assessing the effectiveness of pollutant removal.

Table 2 shows influent and effluent waste water quality parameters of various RGs with bare surfaces. It is seen that the maximum reduction in pH, EC, BOD, COD, TSS, TDS, TN, and TP values is RG3, RG4, RG4 & 5, RG6, RG6, RG5, RG6 and RG6, respectively. These reductions in values lead to conclusion the compost has a major role in reduction. In general, with an increase in compost percentage in planting mix in the RG overall removal efficiency increases. The possible reason is chemical and physical processes taking place in planting a mix of RGs due to adsorption, reduction, sedimentation, cation-exchange capacity, and complexation.

Table 2

Summary of effluent water quality parameters of RGs with bare surface

ParameterInfluent waste water qualityRG1RG2RG3RG4RG5RG6
pH 9.26 6.97 7.01 6.52 6.84 6.92 6.99 
EC (μs/cm) 6,387 5,852 5,560 5,362 4,840 5,776 5,254 
BOD (mg/L) 38 28 24 18 16 16 20 
TSS (mg/L) 120 68 65 60 63 57 50 
TDS (mg/L) 705.2 694.4 642.9 669.6 635.04 628.9 626.4 
COD (mg/L) 250 96 90 84 84 60 76 
TN(mg/L) 64 58 49 44 42 39 35 
TP(mg/L) 1.9 1.5 1.6 1.8 1.4 1.5 0.9 
ParameterInfluent waste water qualityRG1RG2RG3RG4RG5RG6
pH 9.26 6.97 7.01 6.52 6.84 6.92 6.99 
EC (μs/cm) 6,387 5,852 5,560 5,362 4,840 5,776 5,254 
BOD (mg/L) 38 28 24 18 16 16 20 
TSS (mg/L) 120 68 65 60 63 57 50 
TDS (mg/L) 705.2 694.4 642.9 669.6 635.04 628.9 626.4 
COD (mg/L) 250 96 90 84 84 60 76 
TN(mg/L) 64 58 49 44 42 39 35 
TP(mg/L) 1.9 1.5 1.6 1.8 1.4 1.5 0.9 

Table 3 shows influent and effluent waste water quality parameters of various RGs with Madagascar periwinkle plant. It is seen that maximum reduction in pH, EC, BOD, COD, TSS, TDS, TN, and TP values is RG5, RG6, RG6, RG6, RG6, RG5, RG6, and RG6, respectively. These reductions in values lead to similar outcomes the compost has a major role in reduction. In general, with an increase in compost percentage in planting mix in the RG overall removal efficiency increases. The possible reason is chemical and physical processes taking place in planting a mix of RGs due to adsorption, reduction, sedimentation, cation-exchange capacity, and complexation. The Madagascar periwinkle plant is quite effective in lessoning EC, BOD and COD values (Libutti et al. 2023). Madagascar periwinkle can absorb and assimilate salts and other dissolved solids through its root system. The uptake of these ions by the plant reduces their concentration in the water, thereby lowering the EC. The roots of the periwinkle release oxygen into the rhizosphere (root zone), promoting aerobic microbial activity. This aerobic decomposition is more efficient, reducing the organic load and consequently the BOD. The root exudates of the periwinkle support diverse microbial communities that can break down complex organic compounds, reducing the overall COD. These suggest that the Madagascar periwinkle plant is a good bio-absorbent of these parameters.

Table 3

Summary of effluent water quality parameters of RGs with Madagascar periwinkle plant

ParameterInfluent waste water qualityRG1RG2RG3RG4RG5RG6
pH 10.09 8.79 7.21 6.95 7.02 6.92 6.99 
EC (μs/cm) 6,835 6,692 5,987 5,236 4,960 4,286 3,894 
BOD (mg/L) 39.8 34 29 25 20 20 18 
TSS (mg/L) 130 82 72 79 81 77 63 
TDS (mg/L) 752.09 734.05 685.22 632.03 589.11 528.19 509.24 
COD (mg/L) 293 120 105 92 84 69 82 
TN (mg/L) 69 58 47 46 40 43 32 
TP (mg/L) 2.1 1.9 1.8 1.5 1.4 1.3 0.9 
ParameterInfluent waste water qualityRG1RG2RG3RG4RG5RG6
pH 10.09 8.79 7.21 6.95 7.02 6.92 6.99 
EC (μs/cm) 6,835 6,692 5,987 5,236 4,960 4,286 3,894 
BOD (mg/L) 39.8 34 29 25 20 20 18 
TSS (mg/L) 130 82 72 79 81 77 63 
TDS (mg/L) 752.09 734.05 685.22 632.03 589.11 528.19 509.24 
COD (mg/L) 293 120 105 92 84 69 82 
TN (mg/L) 69 58 47 46 40 43 32 
TP (mg/L) 2.1 1.9 1.8 1.5 1.4 1.3 0.9 

Table 4 shows influent WWQI of wastewater sample is 30.00 which is below 40% i.e. poor quality. After the treatment through the bare surface RGs (RG1–RG6) WWQI values improves to 76.51, 88.11, 88.15, 88.21, 88.26, and 100, respectively, as shown in Table 5. These WWQI values correspond to fair, good, good, good, good, and excellent quality.

Table 4

Influent WWQI for bare surface RGs

ParameterpHBOD (mg/L)TDS (mg/L)TSS (mg/L)COD (mg/L)TN (mg/L)TP (mg/L)WWQIQuality Type
Wastewater 9.26 38 705.2 120 250 64 1.9 30.00 Poor 
ParameterpHBOD (mg/L)TDS (mg/L)TSS (mg/L)COD (mg/L)TN (mg/L)TP (mg/L)WWQIQuality Type
Wastewater 9.26 38 705.2 120 250 64 1.9 30.00 Poor 
Table 5

Effluent WWQI from RGs for bare surface

ParameterpHBOD (mg/L)TDS (mg/L)TSS (mg/L)COD (mg/L)TN (mg/L)TP (mg/L)WWQIQuality Type
RG1 6.97 28 674.4 68 96 58 1.5 76.51 Fair 
RG2 7.01 24 642.9 65 90 49 1.6 88.11 Good 
RG3 6.52 18 629.6 60 84 44 1.8 88.15 Good 
RG4 6.84 16 605.04 63 84 42 1.4 88.21 Good 
RG5 6.92 16 578.9 57 60 39 1.5 88.26 Good 
RG6 6.99 20 486.4 50 76 35 0.9 100 Excellent 
ParameterpHBOD (mg/L)TDS (mg/L)TSS (mg/L)COD (mg/L)TN (mg/L)TP (mg/L)WWQIQuality Type
RG1 6.97 28 674.4 68 96 58 1.5 76.51 Fair 
RG2 7.01 24 642.9 65 90 49 1.6 88.11 Good 
RG3 6.52 18 629.6 60 84 44 1.8 88.15 Good 
RG4 6.84 16 605.04 63 84 42 1.4 88.21 Good 
RG5 6.92 16 578.9 57 60 39 1.5 88.26 Good 
RG6 6.99 20 486.4 50 76 35 0.9 100 Excellent 

Table 6 shows influent WWQI of wastewater sample is 18.34% which is less than 40% i.e. poor quality. After the treatment through the RGs with plants WWQI improves to 64.89, 88.15, 88.25, 88.33, 88.33, and 88.34 respectively in RG1–RG6 as shown in Table 7. These values reflect fair, good, good, good, good and good quality respectively. The WWQI suggests that planting mix composition has a major role in the improvement of wastewater quality.

Table 6

Influent WWQI for RGs with plants

ParameterpHBOD (mg/L)TDS (mg/L)TSS (mg/L)COD (mg/L)TN (mg/L)TP (mg/L)CWQIQuality Type
Waste water 10.09 39.8 752.09 130 293 69 2.1 18.34 Poor 
ParameterpHBOD (mg/L)TDS (mg/L)TSS (mg/L)COD (mg/L)TN (mg/L)TP (mg/L)CWQIQuality Type
Waste water 10.09 39.8 752.09 130 293 69 2.1 18.34 Poor 
Table 7

Effluent WWQI of RGs with plants

ParameterpHBODTDSTSSCODTN (mg/L)TP (mg/L)WWQIQuality Type
RG1 8.79 34 734.05 82 120 58 1.9 64.89 Fair 
RG2 7.21 29 685.22 72 105 47 1.8 88.15 Good 
RG3 6.95 25 632.03 79 92 46 1.5 88.25 Good 
RG4 7.02 20 589.11 81 84 40 1.4 88.33 Good 
RG5 6.92 20 528.19 77 69 43 1.3 88.33 Good 
RG6 6.99 18 509.24 63 82 32 0.9 88.34 Good 
ParameterpHBODTDSTSSCODTN (mg/L)TP (mg/L)WWQIQuality Type
RG1 8.79 34 734.05 82 120 58 1.9 64.89 Fair 
RG2 7.21 29 685.22 72 105 47 1.8 88.15 Good 
RG3 6.95 25 632.03 79 92 46 1.5 88.25 Good 
RG4 7.02 20 589.11 81 84 40 1.4 88.33 Good 
RG5 6.92 20 528.19 77 69 43 1.3 88.33 Good 
RG6 6.99 18 509.24 63 82 32 0.9 88.34 Good 

Figure 2(a) shows pH removal efficiency of RGs with and without plants. It is seen that RG5 and RG3 are the most effective among the RGs with and without plants, respectively. RG3 and RG5 ‘s ability to achieve a substantial reduction of approximately 30% in pH, ensuring compliance with the IS Standard's specifications, accentuates its superiority in managing pH levels effectively. This underscores the importance of strategic RG design and emphasizes RG3 and RG5 as a promising choice for achieving optimal pH removal in RG systems. Compost increases the cation-exchange capacity of the soil, allowing it to retain more positively charged ions (cations) such as ammonium, calcium, and magnesium. This retention prevents these alkaline cations from leaching into water, thus reducing the overall pH of runoff and percolating water in the RG. Compost also improves the soil structure by increasing its porosity and water-holding capacity. This improved structure allows for better infiltration and retention of water, which can dilute alkaline substances and facilitate the action of organic acids produced by decomposing compost, leading to a reduction in pH. This outcome highlights the significance of choosing an appropriate RG configuration, particularly in achieving optimal pH removal.
Figure 2

(a) Histogram of pH in all RGs with and without plants. (b) Histogram of EC in all RGs with and without plants. (c) Histogram of BOD in all RGs with bare surfaces. (d) Histogram of TDS in all RGs with and without plants. (e) Histogram of COD in all RGs with and without plants. (f) Histogram of TN in all RGs with and without plants. (g) Histogram of TP in all RGs with bare surfaces. (h) Histogram of TSS in all RGs with and without plants.

Figure 2

(a) Histogram of pH in all RGs with and without plants. (b) Histogram of EC in all RGs with and without plants. (c) Histogram of BOD in all RGs with bare surfaces. (d) Histogram of TDS in all RGs with and without plants. (e) Histogram of COD in all RGs with and without plants. (f) Histogram of TN in all RGs with and without plants. (g) Histogram of TP in all RGs with bare surfaces. (h) Histogram of TSS in all RGs with and without plants.

Close modal

Figure 2(b) represents EC values for all the RGs under examination. From the Histogram shown in Figure 2(b), the performances of RG6 and RG4 are the best as compared to the other RGs with and without plants. Here, RG4 demonstrates a reduction of up to 24%, and RG6 shows an impressive reduction of 41% in EC levels.

The Biological Oxygen Demand (BOD), is shown in Figure 2(c). The data represented in Figure 2(c) show that reductions of BOD in RG4 and RG5 are maximum for bare surface and RG6 for planted surface. RG4 and RG5 effectively reduce BOD levels up to 58% and RG6 reduce the BOD level up to 54%. It is essential to emphasize that the considerable reduction in BOD levels observed in RG4, RG5 and RG6 shows their outstanding efficiency in enhancing water quality.

As shown in Figure 2(d), it is evident that RG6 is performing best for reducing total dissolved solids (TDS) with and without plants. RG6 effectively reduce TDS levels up to 11% for bare surfaces and 32% for planted surfaces. RG1 shows least reduction, indicating poor performance. As this RG is devoid of compost, there will be no microbial activity in the soil. Lack of microorganisms also do not produce acidic byproducts which contribute to the lowering of soil and water pH. Additionally, due to the absence of a microbial community, the transformation of nutrients and the neutralization of alkaline compounds do not happen. These results show the varying efficiency of RGs and underscore the importance of optimizing their design and composition for improved water quality management.

As per the data represented in Figure 2(e), it is evident that RG5 shows a substantial reduction of 76% for bare surface and 77% for planted surface in the concentration of COD. This figure specifies a highly effective performance in reducing this particular pollutant. This significant outcome emphasizes the potential of RG5 as an influential component in mitigating the presence of COD in the examined context. The substantial 76 and 77% reduction in COD concentration is a promising indicator of the RG's effectiveness in treating and purifying the water. It is an important factor in applications aimed at water quality improvement and environmental sustainability.

Figure 2(f) shows an interesting trend that emerges in the reduction of total nitrogen (TN) within the various RGs (RGs). RG6 exhibits maximum reduction with and without plants, with plants 49 and 45% is for without plants decrease in TN levels, followed by RG5, RG4, and RG3, which show considerable reductions in TN content.

The highest total phosphorus (TP) reduction is observed in RG6 with and without plants, with plants reduction at is maximum that is 57% and without plants reduction level is 52%. As illustrated in Figure 2(g). it is seen that RG6 excels in TP removal compared to the other RGs.

With reference to Figure 2(h), The findings show that RG6 is a very successful RG in lowering TSS levels with and without plants. The reduction level is maximum without plants is 54% and with plants, the value is 52%. On the other hand, RG1 showed a less noticeable decrease in TSS, indicating that it could need to be modified or improved in order to increase its ability to reduce suspended particles.

These findings highlight the diverse pollutant removal capabilities of different RGs, emphasizing the importance of tailoring their design and composition for enhanced water quality management.

Several mechanisms help to reduce contaminants from stormwater runoff, with planting mix and plants playing important roles in these processes. The chemical and physical processes of RG planting mix help in adsorption, reduction, sedimentation, cation-exchange capacity, and complexation. As stormwater traverses through the planting mix, larger suspended particles become trapped from the runoff, and held within the pores of the media due to the influence of gravity. The filter bed substrate not only infiltrates stormwater but also fosters plant growth, which, in turn, absorbs contaminants, effectively removing pollutants and providing clean water.

The outcome of the study indicates that a specific planting mixture comprising 75–80% topsoil and 25–20% compost is the most effective pollutant removal. The balance achieved with RG6 proved best for enhancing the RG's ability to filter and purify the water effectively, making it a valuable choice for promoting cleaner groundwater quality.

The careful balance of 75–80% topsoil and 25–20% compost emerged as a prime recommendation, showcasing its potential to play a pivotal role in efficiently managing and reducing pollutants in rainwater. This insight into the ideal planting mixture composition contributes valuable knowledge for designing and implementing effective RGs to enhance water quality.

In summary, assessing RG planting mix pollutant removal efficiency reveals significant variations based on specific parameters, as observed in the bare and planted surfaces. The study highlights the significance of the planting mixture's composition in optimizing pollutant removal (such as pH, EC, BOD, TDS, TSS, COD, TN, and TP) within RGs.

  • 1. Planting mix with compost is helpful in reducing the pollutants.

  • 2. Planting mix consisting of 20–25% compost is most suitable for pollutant removal.

  • 3. RG's performance improves with the presence of plants.

  • 4. COD removal is maximum (approximately 70%) as compared to other parameters.

  • 5. TDS removal is minimum as compared to other parameters.

  • 6. The WQI indicates that the poor quality of water after passing through RGs planting mix improves fair to excellent quality.

  • 7. In RG6, the average pollutant removal efficiency is notably high, reaching approximately 40% for the bare surface and 49% for Madagascar periwinkle surface.

The removal efficiency level is satisfactory and indicates that the modified planting mixture utilized in the RG has a notable positive impact on pollutant removal. It underscores the importance of fine-tuning the planting mix to effectively enhance the RG's ability to mitigate pollutants. The significant pollutant removal efficiency observed in RG6 demonstrates the potential for further optimizing RG designs. Fine-tuning the planting mixture can lead to even more effective pollutant removal, paving the way for sustainable urban water management practices. This finding encourages continued research and implementation of tailored planting mixtures to boost the performance of RGs in mitigating environmental contaminants. These findings emphasize the importance of RG design and composition in enhancing water quality management.

The authors extend their deep appreciation to Dr Anil Kumar Dahiya, overseeing Horticulture and Nursery affairs at the National Institute of Technology, Kurukshetra (India), and Dr Dinesh Kumar Tomar, head of the Water and Soil Laboratory at Chaudhary Charan Singh Haryana Agricultural University, Hisar (India). Their invaluable support resource and data collection assistance were pivotal in completing this project.

S.K. and K.K.S. conceptualized the study; did formal analysis and investigated the study; S.K. prepared and wrote the original draft ; K.K.S. wrote, reviewed, and edited the article and supervised the study.

This work is financially supported jointly by the Ministry of Education (MOE), GOI and Director NIT Kurukshetra through PhD scholarship grant 2K19/NITK/PHD/61900082.

Data cannot be made publicly available; readers should contact the corresponding author for details.

The authors declare there is no conflict.

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