3.1 Physico-Chemical Variables of Danro River
Table 3 presents a statistical overview of the analysed quality of river water. Water temperature, a critical parameter influencing biota activities, exhibited values ranging from 22.03°C to 25.03°C in non-monsoon and 22°C to 24.77°C in the monsoon season, with minimal seasonal variation. pH values were 8.53 and 8.10 for non-monsoon and monsoon seasons, respectively, indicating an alkaline nature at selected sites (S1–S9). Electrical conductivity (EC) showed concentrations of 122.53 µS/cm and 267.28 µS/cm for non-monsoon and monsoon seasons, indicating high ionic activity. Turbidity values exhibited variations, reaching a peak at 271.55 NTU during the monsoon and a minimum of 1.08 NTU in the non-monsoon season. Dissolved oxygen (DO) showed fluctuations within the range of 0.6 to 9.3 mg/l, with average values of 8.73 mg/l during the non-monsoon season and 1.10 mg/l in the monsoon. Biochemical oxygen demand (BOD) averaged 4.93 mg/l in non-monsoon and 5.52 mg/l during the monsoon. Chemical oxygen demand (COD) recorded a mean value of 122.22 mg/l. Alkalinity averaged 43.75 mg/l during the non-monsoon season and increased to 91.50 mg/l during the monsoon, while hardness displayed values of 133.3 mg/l and 708.33 mg/l, respectively. Total dissolved solids (TDS) remained within permissible limits; however, concentrations of calcium (Ca2+), magnesium (Mg2+), sodium (Na+), and potassium (K+) exceeded recommended levels, attributed to pollution from sewage and agricultural runoff. Elevated levels of total alkalinity, total hardness, and turbidity indicated human interventions and increased organic matter in the water.
Table 3
Descriptive statistics for water quality parameters of Danro River (Mean ± S.D.).
Parameters | Non-Monsoon | Monsoon |
Temperature (°C) | 23.79 ± 1.19 | 23.23 ± 1.00 |
pH | 8.53 ± 0.51 | 8.1 ± 0.12 |
EC (µS/cm) | 122.53 ± 4.41 | 267.28 ± 74.17 |
Turbidity (NTU) | 1.076 ± 0.14 | 271.54 ± 16.93 |
TDS (mg/) | 123.38 ± 1.62 | 231.57 ± 45.30 |
DO (mg/l) | 8.73 ± 0.52 | 1.1 ± 0.65 |
BOD (mg/l) | 4.93 ± 0.46 | 5.51 ± 0.49 |
COD (mg/l) | 99.55 ± 20.39 | 144.9 ± 7.58 |
Sodium (mg/l) | 3.31 ± 0.22 | 7.71 ± 0.50 |
Potassium (mg/l) | 2.14 ± 0.25 | 4.23 ± 1.93 |
Total Hardness (mg/l) | 133.33 ± 24.83 | 708.33 ± 91.54 |
Alkalinity (mg/l) | 43.75 ± 15.93 | 91.5 ± 5.99 |
Ca (mg/l) | 62.06 ± 2.07 | 63.18 ± 0.58 |
Mg (mg/l) | 18.25 ± 0.52 | 18.10 ± 0.39 |
3.2 Description and comparison of the heavy metals with BIS guidelines
There were no substantial differences in heavy metal concentrations among the sampling stations. Mean values of heavy metals for River Danro are presented in Table 4, showcasing the following trend: Al > Fe > Mn > Cd > Cr > Cu > Zn > Ni > B > As > Pb, with the highest average value for Al and the lowest for Pb.
Table 4
Descriptive statistics for heavy metals in Danro River (Mean ± S.D.).
Heavy Metals | Mean ± S.D. |
Al | 0.930 ± 0.832 |
As | -0.047 ± 0.049 |
B | -0.028 ± 0.002 |
Ca | 27.143 ± 7.225 |
Cd | 0.002 ± 0.001 |
Co | -0.006 ± 0.002 |
Cr | -0.009 ± 0.002 |
Cu | -0.012 ± 0.001 |
Fe | 0.573 ± 0.545 |
Mg | 7.212 ± 1.752 |
K | 3.364 ± 0.460 |
Mn | 0.006 ± 0.011 |
Na | 21.446 ± 8.045 |
Ni | -0.021 ± 0.004 |
Pb | -0.122 ± 0.023 |
Zn | -0.018 ± 0.004 |
The Al content in the Danro River exceeded the BIS limit for surface waters at all sampling sites. Nevertheless, the concentrations of Cu, Cr, Zn, Mn, B, Pb, and Ni remained within the maximum permissible limits as defined by the BIS (2012) standards. For As, concentrations exceeded the BIS (2012) guidelines for drinking water at sites S6 and S9, while Cd surpassed the BIS (2012) guidelines at sites S1, S2, and S6. Elevated Fe content exceeded permissible limits for drinking water at sites S4, S5, S6, S8, and S9. The concentrations of dissolved heavy metals at the remaining three sites were low, suggesting no additional sources of contamination near the river.
3.3 Water Quality Analysis Using Overall Index of Pollution
The evaluation of water quality was carried out through the utilization of the Overall Index of Pollution (OIP), involving the scrutiny of 10 physicochemical parameters: pH, electrical conductivity, turbidity, total dissolved solids (TDS), dissolved oxygen (DO), biochemical oxygen demand (BOD), total hardness, calcium, magnesium, and alkalinity. This analysis was carried out at specific sites along the Danro River during both non-monsoon and monsoon seasons. The OIP scores highlighted that electrical conductivity, turbidity, and total hardness were the predominant parameters at all the sampling sites. The summarized OIP values for water samples from six selected sites during each season are presented in Table 5. The overall findings revealed an excellent water quality status (1 < OIP < 1.9) during the non-monsoon season and slightly polluted conditions (4 < OIP < 7.9) during the monsoon season in the Danro River.
Table 5
Summary of Overall Index of Pollution (OIP) values of the Danro River.
| Non-Monsoon | Monsoon |
Sampling Site | OIP | Class | Water Quality Status | OIP | Class | Water Quality Status |
S1 | 0.734 | C1 | Excellent | 6.156 | C3 | Slightly Polluted |
S2 | 0.683 | C1 | Excellent | 6.835 | C3 | Slightly Polluted |
S3 | 0.715 | C1 | Excellent | 6.622 | C3 | Slightly Polluted |
S4 | 0.714 | C1 | Excellent | 6.035 | C3 | Slightly Polluted |
S5 | 0.722 | C1 | Excellent | 5.715 | C3 | Slightly Polluted |
S6 | 0.729 | C1 | Excellent | 6.319 | C3 | Slightly Polluted |
Average | 0.716 | C1 | Excellent | 6.280 | C3 | Slightly Polluted |
Based on the OIP index scores, the health status during the non-monsoon season was excellent at all sampling sites (S1: 0.734, S2: 0.683, S3: 0.715, S4: 0.714, S5: 0.722, S6: 0.729). Conversely, the monsoon season recorded higher OIP values ranging from 5.715 at S5 to 6.835 at S2, with an average value of 6.280, indicating a slightly polluted water quality. This could be attributed to significant sediment runoff, bank erosion, the convergence of waters from various points (sewage water discharge), and non-point sources (agricultural runoff) during the monsoon season.
The overall assessment classified the water quality of the Danro River as Class C1 during the non-monsoon season and Class C3 during the monsoon season across all sampling sites. Temporary turbidity was notably elevated at all sites, attributed to sediment removal from the riverbed, resulting in increased parameters such as TDS, conductivity, and hardness. A similar study by (Shukla et al., 2017) on the Ganga River, utilizing the OIP index, revealed acceptable water quality in summer and winter, with pollution observed in the monsoon season due to significant sediment runoff, debris, and bank erosion caused by elevated stream flow.
3.4 Water Quality Analysis using Nemerow Pollution Index
Based on Eq. (3), the Nemerow Pollution Index values were computed and are presented in Table 6. A lower Nemerow Pollution Index value indicates higher water quality. The assessment results reveal that the water quality pollution index for the Danro River ranged between 0.10 and 1.74 during the non-monsoon season and between 0.22 and 27.15 in the monsoon season. Generally, the water exhibits acceptable quality, with pH levels close to the standard during both seasons. However, turbidity significantly increases during the monsoon, indicating potential pollution sources. Conductivity remains within acceptable limits for both seasons. Dissolved oxygen levels, though generally acceptable, decrease notably during the monsoon. Biochemical Oxygen Demand is slightly higher during the monsoon, suggesting increased organic load. Total Dissolved Solids, alkalinity, calcium, and magnesium levels are within standards, indicating overall good water quality. Total Hardness exceeds the standard during the monsoon, highlighting a potential concern. These results emphasize the need for targeted interventions, especially during the monsoon, to address specific parameters and maintain a consistently high water quality standard.
Table 6
Numero’s Pollution Index (NPI) values of the Danro River.
Sl. No. | Parameters | Standard | NPI Values (Non-Monsoon) | NPI Values (Monsoon) |
1 | pH | 8.5 | 1.004 | 0.953 |
2 | Turbidity, NTU | 10 | 0.108 | 27.155 |
3 | Conductivity, µs/cm | 300 | 0.408 | 0.891 |
4 | Dissolved Oxygen, mg/L, Min. | 5 | 1.746 | 0.220 |
5 | Biochemical Oxygen Demand, mg/L, Max. | 5 | 0.986 | 1.104 |
6 | Total Dissolved Solids, mg/L, Max. | 500 | 0.247 | 0.463 |
7 | Alkalinity | 120 | 0.365 | 0.763 |
8 | Total Hardness, mg/L | 300 | 0.444 | 2.361 |
9 | Calcium (as Ca), mg/L | 75 | 0.560 | 0.842 |
10 | Magnesium (as Mg), mg/L | 30 | 0.667 | 0.603 |
The outcomes derived from the Nemerow Pollution Index (NPI) and the Overall Index of Pollution (OIP) contribute complementary perspectives on water quality. The NPI values for specific parameters offer a nuanced insight into the distinct factors influencing water quality, whereas the OIP provides a comprehensive overview by assigning an overall pollution index. By comparing the two indices, it becomes evident that the monsoon season has a notable impact on overall water quality, as both indices show an increase in pollution levels during this period. This correlation suggests a potential link between the individual parameters measured by the NPI and the overall water quality status reflected in the OIP.
3.5 HPI index
The Heavy Metal Pollution Index (HPI) was computed individually for each sampling location to compare pollution loads and evaluate water quality at the selected stations and during different seasons (refer to Table 7). These values represent the cumulative impact of various heavy metals. According to the guidelines for drinking water by BIS (2012) (Balan et al., 2012), the HPI findings suggest that the surface water bodies investigated in our study are extensively contaminated by heavy metals and are unsuitable for potable purposes, with HPI values surpassing 100.
Table 7
Heavy Metal Pollution Index (HPI) values of the Danro River.
Site | HMI value |
1 | 245.9 |
2 | 202.9 |
3 | 316.4 |
4 | 330.4 |
5 | 256.9 |
6 | 331.0 |
7 | 267.9 |
8 | 307.0 |
9 | 329.6 |
Noteworthy variations in HPI values are evident across different sampling sites. Furthermore, the mean HPI values for each sampling site indicate that the pollution load is most pronounced at sampling site 6 (HPI 331.04). Elevated HPI values are attributed to industrial wastewater, domestic sewage, landfill leachate, and agricultural runoff. Consequently, it is affirmed that water pollution poses a significant concern, yet no solutions have been proposed as of now.
3.6 Principal Component Analysis
Principal Component Analysis (PCA) was conducted on the correlation matrix of rearranged data for the Danro River. The variance/covariance and factor loadings of variables with eigenvalues were calculated. A combination of correlation matrix, factor analysis, and cluster analysis was employed to evaluate contamination levels, identify chemical processes, and trace diffusion paths. Varimax rotated factor analyses were performed on 21 parameters from the PCA, and factor loadings were computed. Four major components, with eigenvalues exceeding one, were selected, explaining 92.34% of the total variance. Features with factor loadings greater than 0.5 were considered significant for interpreting each component. Communalities for all metals ranged between 0.87 and 1, indicating satisfactory allocation to identified factors. The physical interpretation of each factor or source was based on its association with the strong loading of marker elements typically emitted from that source. The first component (factor 1), associated with pH, EC, DO, BOD, Ca, Mg, Cd, Pb, and turbidity, explained 41.23% of the total variance, indicating the presence of high organic content in the water and pollution from electrical conductivity due to riverbank erosion from dredging activities. Elevated levels of Pb were linked to highways, road dust, traffic activities, and major roads (Abidin et al., 2017; Hudson et al., 2008; Kumari et al., 2021). The second component (factor 2), primarily linked to TDS, DO, Cr, Mn, and Zn, explained 21.78% of the total variance, suggesting possible industrial discharges. Elevated concentrations of Cr, Mn, and Zn may indicate activities such as metal manufacturing, electroplating, and mining, releasing metals into the water (J. Liu et al., 2011). High TDS levels were associated with agricultural runoff, especially in areas with fertilizer and pesticide use, while urban areas contributed to increased TDS and metal levels in surface water through stormwater runoff. The third component (factor 3), including B and Ni, explained 12.36% of the total variance, suggesting potential sources like industrial discharges, agricultural runoff, mining activities, urban stormwater runoff, natural geological processes, wastewater treatment plant effluents, atmospheric deposition, and waste disposal sites near the river (Cempel & Nikel, 2006). The fourth component (factor 4), associated with turbidity, Al, Cd, and Fe, explained 11.07% of the total variance, indicating influences from industrial discharges, agricultural runoff, natural weathering, urban runoff, mining activities, wastewater discharges, and atmospheric deposition (Muhammad, 2017; Sithole, 2018; Sun et al., 2018). Ultimately, the fifth component (factor 5), primarily linked to Cr, elucidated 5.88% of the total variance, indicating inputs from human activities, particularly agricultural practices such as the application of pesticides and fertilizers, along with lithogenic sources (Proshad et al., 2021; Yuanan et al., 2020).
3.7 Land Use Change and WQ
The studied area has undergone significant changes in land use, primarily driven by public activities such as deforestation, construction, and cultivation. This dynamic land use transformation was overlaid and correlated with the Water Quality Index (WQI) to assess the extent to which human activities contributed to the degradation of river water quality, as depicted in Fig. 3. Land use changes exert diverse impacts on local temperature, natural ecosystems, socio-economic factors, and policy formulation and implementation (Huber et al., 2013; Y. Liu et al., 2016; Rounsevell et al., 2014). Numerous studies have highlighted the connection between land use changes and seasonal variations in water quality (Chang, 2004; de Souza Pereira et al., 2019; Huang et al., 2016; Lee et al., 2009; J. Liu et al., 2017; Rothenberger et al., 2009). Water quality exhibits variations between the non-monsoon and monsoon seasons, providing direct evidence of the influence of anthropogenic activities. The quality of water is poorer during the monsoon season due to increased river flow, making it more susceptible to non-point source pollutants mobilized by the higher velocity of the river water during this period (M. R. Kumar et al., 2022). Physicochemical analysis of water parameters revealed unsatisfactory water quality in areas with extensive human intervention. Natural forest cover serves as a nutrient retention system, fostering a biologically rich environment conducive to water and aquatic life (Bassi et al., 2014). Conversely, areas heavily impacted by human activities exhibit adverse effects (Adegbeye et al., 2020; Osman, 2018). Additionally, the disruption of natural systems and land use changes significantly contribute to total dissolved solids, nitrogen and phosphorous deposition, influenced by both point and non-point source pollution (Delkash et al., 2018). High concentrations of Mg2+, Na+, K+, Cl−, F−, and Fe2 + suggest the discharge of significant amounts of untreated sewage and agricultural waste into the river at the study sites. This aligns with previous studies indicating that water quality is substantially influenced by untreated waste (Akhtar et al., 2021; Anh et al., 2023; Mamun et al., 2021; Mamun & An, 2021). The predominant factor influencing the presence of heavy metals in water samples is associated with the land use pattern. Specifically, built-up areas, concentrated in the southern part of the study area, exhibit a notable correlation with the composition of heavy metals in water samples. These built-up areas can act as non-point sources of heavy metals due to diverse activities, including small-scale industries (such as leather and textile) and human settlements where the discharge of wastewater introduces various heavy metals like Fe, Zn, Mn, etc., into river water bodies. Simultaneously, activities like sand mining, natural factors like rock weathering, and other domestic effluents in the upstream region further contribute to elevated concentrations of heavy metals like Ti, Cu, Cr, Ni (Molekoa et al., 2021). The mean Heavy Metal Pollution Index (HPI) was found to be above 100, rendering the water unsuitable for human use and irrigation, as contamination can propagate through the food chain, causing long-term health issues. Table 8 outlines the contaminants of concern, their sources, impacts, and management techniques.
Table 8
Contaminants of concern, their source, impact and management techniques.
Contaminant of concern (Sampling Locations) | Observed LULC | Natural Sources | Anthropogenic Sources | Adverse effects on Humans | Ecological Impact | Technical Solution | Ecological Solution (Phytoremediation) |
Al (S1-S9) | Agriculture and semi-urban area with Danro River | Acid rain, Acidic rocks and soils (Hydes & Liss, 1976; Senze et al., 2021; Tria et al., 2007) | Infiltration of wastewater from towns (Huang et al., 2023) | Loss of memory, severe trembling, dementia (Mardare & Horhogea, 2019) | Habitat disruption, Greenhouse gas emissions, air and water pollution (Botté et al., 2022; Olivieri et al., 2006) | Active carbon absorbtion (Hicran et al., 2022; Zongo et al., 2009) | Water Hyacinth, Moringa oleifera seeds and Boscia senegelensis seeds (Hanafiah et al., 2020; Hegazy et al., 2011) |
Ca (S1-S9) | Agriculture and semi-urban area with Danro River | Limestone, dolomite, gypsum, and other calcium-containing rocks and minerals (Potasznik & Szymczyk, 2015) | Originates from the weathering of carbonate rocks (Weyhenmeyer et al., 2019) | Stomach upset, nausea, vomiting and constipation (Kataria et al., 2011; Kumar & Puri, 2012) | Making things from calcium, like limestone, can harm nature due to changes in habitats and using a lot of energy, depending on how it's done (Jeziorski & Smol, 2017) | Reverse Osmosis (Hammes et al., 2003) | Schoenoplectus litoralis and Hordeum vulgare (Farid et al., 2014; Parwin & Karar Paul, 2019; Walsh et al., 2020) |
Cd (S1, S2, S6) | Covering rural built-up and mainly crop grown region | Volcanic eruptions, weathering, natural fires, and dust storms (Cullen & Maldonado, 2013; Gardiner, 1989) | Welding, electroplating, pesticides, fertilizer, batteries, nuclear fission plant (Yin et al., 2021; Yuan et al., 2019) | Psychological disorders, diarrhoea and damage of immune system (Genchi et al., 2020) | Soil and water pollution, bioaccumulation in organisms, disruption of aquatic ecosystems, and potential threats to human health through the food chain (Haider et al., 2021; Wright & Welbourn, 1994) | Nanocomposite adsorbents (Rao et al., 2010) | Water Hyacinth, Cattail (Li et al., 2023; Yapoga et al., 2013) |
Fe (S4, S5, S6, S8, S9) | Rural built-up and agriculture-dominated area | Mafic rocks, limestones and shales (Xiao et al., 2004) | Erosion of soil, runoff from agricultural field, construction sites, deforested area, urban runoff and decaying organic matter (J.-B. Chen et al., 2014) | Staining of cloths and plumbing material (Viana et al., 2021) | Habitat disruption, water and air pollution, and significant ecological impacts on landscapes and ecosystems (Lei et al., 2018) | Chemical oxidation, filtration (Zongo et al., 2009) | Mango leaf, guava leaf, Typha domingensis and duckweed Lemna minor (Hanafiah et al., 2020) |
Mg (S1-S9) | Agriculture and semi-urban area with Danro River | Mafic rocks, limestones and shales (Bolou-Bi et al., 2009) | Fertilizer, cattle feed (Connor et al., 2014; Shin et al., 2014) | Diarrhea that can be accompanied by nausea and abdominal cramping (Abdul-Kareem et al., 2011; Kumar & Puri, 2012) | Making things from magnesium, like limestone, can harm nature due to changes in habitats and using a lot of energy, depending on how it's done (Gomes & Asaeda, 2010; Zhang et al., 2021) | Water softening (Semerjian & Ayoub, 2003) | Schoenoplectus litoralis and Hordeum vulgare (Walsh et al., 2020) |
K (S1-S9) | Agriculture and semi-urban area with Danro River | Weathering of rocks (Handa, 1975; K. Wang et al., 2021) | Municipal and industrial sewage discharges and agricultural runoff (Skowron et al., 2018) | Heart palpitations, shortness of breath, chest pain, nausea, or vomiting (Belton et al., 2020) | Natural potassium is eco-friendly, but environmental issues arise from the extraction and use of potassium-based fertilizers, causing habitat disruption, energy use, and possible water pollution, depending on industrial practices (Sardans & Peñuelas, 2021) | Coagulation (Grobelak et al., 2019) | Azolla caroliniana (Parwin & Karar Paul, 2019) |
Na (S1-S9) | Agriculture and semi-urban area with Danro River | Precipitation and weathering of silicate minerals (Sundaray et al., 2009) | Road salt, water treatment chemicals, domestic water softeners, and sewage effluents (RamyaPriya & Elango, 2018) | Excessive thirst, bloating and blood pressure rise (Cañedo-Argüelles et al., 2013; Wu & Sun, 2016) | Natural sodium is generally eco-friendly, but the environmental impact of extracting and using sodium compounds, like table salt, may lead to soil salinity, water pollution, and harm to aquatic ecosystems, depending on industrial practices (Jouquet & Bruand, 2023) | Reverse osmosis (Imran et al., 2016; Wen et al., 2018) | Schoenoplectus litoralis and Hordeum vulgare (Donatti et al., 2017; Moogouei & Chen, 2020) |
As (S6, S9) | Rural built-up and agriculture-dominated area | Sulphide mineral deposits and sedimentary deposits deriving from volcanic rocks (Ferguson & Gavis, 1972; Garelick et al., 2009; Smedley & Kinniburgh, 2002) | Pesticides, fungicides, metal smelters, mining and burning of fossil fuel (Cui et al., 2014; Garelick et al., 2009) | Kidney, skin, blood and Liver disorders (Mandal & Suzuki, 2002; Naujokas et al., 2013) | Soil and water contamination, bioaccumulation in food chains, and detrimental effects on aquatic life, with long-term persistence and potential human health hazards (G. Chen et al., 2015; Oremland & Stolz, 2003) | Oxidation, Coagulation, precipitation, filtration, Adsorption, Ion exchange and Membrane techniques (Choong et al., 2007; Jain & Singh, 2012; Mohan & Pittman Jr, 2007; Mondal et al., 2013; Ungureanu et al., 2015) | Duckweed (Lemna minor L), water hyacinth (Eichhornia crassipes), water zinnia (Wedela trilobata Hitchc.) and water lettuce (Pistia stratiotes L.) (Mirza et al., 2010) |
Pb (S1-S9) | Agriculture and semi-urban area with Danro River | Soil erosion, volcanic eruptions, sea sprays, and bush fires (Kong et al., 2018) | Paint, pesticides, batteries, automobile emission, mining, and burning of coal (Grousset et al., 1995; Kong et al., 2018; L. Wang et al., 2011) | Lead toxicity leads to anaemia, nervous system. Gastro-intestinal respiratory and cardiovascular diseases (Ekpo et al., 2008) | Soil and water contamination and lead-based products, adverse effects on wildlife and plant life, potential bioaccumulation in food chains, and risks to human health (Besser et al., 2009; Prior et al., 2018) | Sediment dredging (Arbabi et al., 2015) | Water Hyacinth, Cattail (Singh et al., 2012) |
3.8 Treatment of river water contaminated with heavy metals
The intricate composition of wastewater, influenced by numerous coexisting compounds, poses a challenge for current technologies to precisely recognize detailed compositions. Early investigations relied on physicochemical tests like complexometric titration, ion exchange, and stripping voltammetry to evaluate complexation features, hindering the determination of exact coordination conditions of heavy metals (Caroli, 1996; Kiss et al., 2017; Plachká et al., 2023). A promising solution to address pollutants in rivers and water bodies, detrimental to marine life and human health, is the eco-friendly and cost-effective approach of phytoremediation. This method involves using plants such as Water Hyacinth, Indian Mustard, Sunflower, Vetiver Grass, Azolla, Neem Tree, Bamboo, and Spider Lily to absorb, accumulate, and detoxify heavy metals from water. In India, where water pollution is a significant concern, these plants are employed for their ability to remediate heavy metals effectively. The effectiveness of phytoremediation depends on factors like specific contaminants, environmental conditions, and plant species used, and it is often used in combination with other remediation techniques for optimal results. Researchers globally, including (X. Xia & Chen, 1997) and (Ma et al., 2016), have studied bioremediation of heavy metals.
The present work focuses on evaluating wastewater remediation using constructed riverbeds containing Eichhornia crassipes (Water hyacinth) and Chrysopogon zizanioides (Vetiver grass), which are found locally. Within the study's scope, there was a need for a cost-effective, portable, and maintenance-free design model with no energy requirements. The design, shaped like the letter "L" serves as the basis for applying the phytoremediation method. The dimensions can be adjusted based on the river's structure, offering flexibility in response to factors such as wastewater discharge direction and areas with high pollution levels. The "L" form allows for diverse design combinations, adapting to variations in stream conditions or facilitating community use. The primary structure comprises a 5x5 wire cage filled successively with stone chips and a soil layer, covered by a 15x15 wire on top. The stone chips layer adds weight and diminishes surface water flow in the stream. The soil layer, crucial for plant development, accumulates water where plant roots are situated, enabling the extraction of heavy metals from the water while releasing oxygen. The finalized system is illustrated in Fig. 4. Plant species like Typha latifolia and Monochoria hastata, which are again native plant species in Jharkhand, could be used in the riparian zones along the riverbed to hold additional pollutants. The treatment efficiency for these species has been recorded by various authors and is discussed in Table 9.
Table 9
Phytoremediation Efficiency of Selected Plant Species in this study for Heavy Metals
Selected Species | Efficiency | Reference |
Eichhornia crassipes | 99.5% removal of heavy metals (such as Mn, Cd, Fe, Zn, Cu, As, and Pb) and plant nutrients (such as N, P, K, Ca, Mg) | (Abbas et al., 2021; Ajayi & Ogunbayo, 2012; Qin et al., 2016; Rezania et al., 2015; Saha et al., 2020; Victor et al., 2016; X. Xia & Chen, 1997) |
Chrysopogon zizanioides | 95% removal of N, P, Zn, Mn and Ni | (Datta et al., 2013; Davamani et al., 2021; Goren et al., 2021; Mahmoudpour et al., 2021; Masinire et al., 2021; Nakbanpote et al., 2024; Nugroho et al., 2021; Parnian & Furze, 2021) |
Monochoria hastata | Cd can be classified as moderate accumulator | (Baruah et al., 2017; Hazra et al., 2015; Islam et al., 2021; Mahmud et al., 2008; Mustafa & Hayder, 2021; Wijetunga et al., 2009; Yanuwiadi & Polii, 2013) |
Typha latifolia | Potential to remove both salts (Na, Cl, Ca, Mg) and heavy metals (Zn, Cu, Fe, Mn) | (Hejna et al., 2020; M. Kumari & Tripathi, 2015; Manios et al., 2003; Meitei & Prasad, 2021; A. Pandey et al., 2021) |
An alternative approach to address urban river pollution, stemming from primary pollutant sources and their impacts on soil, water, and living organisms, involves the use of ecological floating beds (see Fig. 5). The structure of the Plants Floating Bed is constructed using two types of thick bamboo tubes (TBT). The first type of TBT requires perforations at regular intervals for transplanting plants into the upper holes. The second type of TBT, without holes, is utilized to secure the perforated TBT and provide buoyancy. These two types of TBT are combined and fastened together, with a net drawn over them. Eichhonia crassipes and Chrysopogon zizanioides could be planted on the floating bed in turn.