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Article

Mitigation of Sugar Industry Wastewater Pollution: Efficiency of Lab-Scale Horizontal Subsurface Flow Wetlands

1
Department of Microbiology, Quaid-i-Azam University Islamabad, Islamabad 45320, Pakistan
2
Department of Chemistry and Biology, Toronto Metropolitan University, Toronto, ON M5B 2K3, Canada
3
Department of Biological Sciences, National University of Medical Sciences, Rawalpindi 44000, Pakistan
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Processes 2024, 12(7), 1400; https://doi.org/10.3390/pr12071400
Submission received: 6 June 2024 / Revised: 26 June 2024 / Accepted: 3 July 2024 / Published: 4 July 2024

Abstract

:
Sugarcane accounts for around 80% of global sugar production. However, the sugar industry is known for producing significant amounts of organic wastewater with a high COD (5000–8000 mg/L) that severely pollutes the environment. A lab-scale trial was conducted to evaluate the efficacy of a horizontal subsurface flow wetland planted with Typha latifolia and Phragmites australis in removing pollutants from sugar industry wastewater. The wetland system was subjected to rigorous testing, operating at a high flow rate of 2.166 gallons per day and exposed to a high organic loading rate (3800 mg/L COD and 2470 mg/L BOD), as well as elevated levels of inorganic nitrogen, sulfate, and phosphate (100 mg/L, 80 mg/L, and 10 mg/L, respectively). Our findings indicate significant removal efficiencies, with the wetland system achieving removal rates of 88% for COD, 97% for BOD, 96% for total nitrogen, and 95% for sulfate. Remarkably, the system exhibited enhanced removal efficiency when exposed to domestic wastewater compared to tap water, owing to the abundance of microbial populations. Moreover, toxicity assessments conducted on the treated water revealed no adverse effects on the germination of wheat seeds and on the survival of fish over a week-long observation period. In conclusion, our study underscores the promising potential of horizontal subsurface flow wetlands as an effective and sustainable approach for mitigating the adverse environmental impacts associated with sugar industry wastewater. The findings offer valuable insights for policymakers and stakeholders in devising strategies to promote environmental sustainability and safeguard vital ecosystems in the Sindh region of Pakistan and beyond.

1. Introduction

The sugar industry stands as a cornerstone of agricultural and socio-economic development in numerous countries, with far-reaching impacts on various sectors. In Pakistan, for instance, sugar production significantly contributes to the nation’s economy, with annual production exceeding 7 million tons [1]. For every ton of cane crushed, considerable amounts of wastewater, ranging from 0.16 to 0.76 m3, are generated, containing a mixture of pollutants such as soluble salts and heavy metals. The indiscriminate discharge of untreated effluents not only contaminates water bodies but also jeopardizes soil health and the productivity of agricultural plants [2].
In Pakistan, the geographical proximity of numerous sugar mills to the Left Bank Outfall Drain (LBOD) system exacerbates the environmental repercussions of sugar production. The LBOD drainage canal, which spans four major districts in Sindh and covers over two million hectares of land, gathers saline water, industrial effluents, and floodwater from the Indus River Basin and discharges it into the Arabian Sea. The canal was constructed with funding from the World Bank [3]. During the intensive crushing and distillation procedures inherent to sugar milling, substantial volumes of fresh water are utilized, ultimately transforming into effluents loaded with hazardous compounds. These harmful substances include process wastes such as bagasse, molasses, filter mud, grease oil, and plant sewage [4]. Regrettably, most of these harmful effluents from sugar mills are discharged into the LBOD system without prior treatment, leading to the contamination of surface and groundwater resources, the degradation of agricultural land, and the disruption of natural ecosystems.
Available treatment technologies include both physical (adsorption, coagulation/sedimentation, ozonation, membrane filtration, photolysis, integrated adsorption-membrane filtration, and ultraviolet-ozone disinfection) and biological methods (an anaerobic batch reactor, anaerobic fixed bed reactor, anaerobic baffled reactor, activated sludge, and trickling filters) [5,6,7]. However, these technologies have disadvantages such as low removal capacities, difficult separations, potential for secondary environmental pollution, high equipment and energy costs, rapid membrane clogging, long start-up times, membrane fouling, large reactor volumes, and issues such as biofilm thickness causing reactor clogging and bed recycling [8].
The reuse of treated sugar wastewater holds significant promise, particularly in agricultural and aquaculture applications. Reclaiming and repurposing wastewater not only reduces the environmental burden but also unlocks economic benefits of water reuse [9]. However, ensuring the safety and viability of reclaimed wastewater for such purposes necessitates rigorous assessment, including toxicity assessment. The discharge of effluents from the sugar industry can induce alterations in both aquatic fauna and flora, posing a threat to human health for those who directly rely on river water for domestic and agricultural needs [10]. Assessing the negative impact of industrial wastewater on aquatic environments requires more than just physical and chemical parameters [11]. Despite meeting physicochemical standards, studies have demonstrated that industrial discharges often exhibit high toxicity to aquatic life [12]. Hence, it is crucial to employ biological systems for toxicity tests using living organisms since they interact with pollutants and provide an effective response to water quality [13,14].
Constructed wetlands (CWs) have emerged as a promising, cost-effective, and environmentally sustainable approach for wastewater treatment, gaining significant attention in recent years [15,16]. CWs can be built with greater control than natural systems. Additional advantages include flexibility in sizing, site selection, and control over hydraulic pathways and retention time. CWs can be classified by their application (such as habitat creation, flood control, and wastewater treatment), but from a technical perspective, they can be divided into three basic types: free water surface flow CWs (FWSCWs), subsurface flow CWs (SSFCWs), and floating CWs (FCWs). The horizontal flow-constructed wetland subtype of SSFCW is selected for treatment due to its widespread global use, ability to saturate, production of aerobic zones around roots and rhizomes releasing oxygen into the substrate, smaller area requirement, lower sensitivity to cold, and high purification performance. In contrast, vertical flow constructed wetlands (VFCWs), another SSFCW subtype, are commonly used for treating on-site sewage from small communities, industrial wastewaters, and stormwater runoff. FWSCWs are primarily employed for purifying tertiary and secondary effluents from wastewater treatment plants (WWTPs), as well as stormwater and agricultural runoff. FCWs are mainly utilized for improving water quality in situ in rivers, lakes, and ponds [17]. CWs operate on the principle of employing gravel, sand, wetland vegetation, and microbial processes to remediate wastewater. The usage of CWs for decentralized wastewater treatment is growing due to their straightforward design and their often having cheaper operation and maintenance costs than conventional systems. Furthermore, wetlands produce minimal sludge and necessitate no chemical additions [18,19]. Additionally, CWs have been shown to decrease the amount of antibiotic-resistance genes and organisms present in wastewater [20].
The major aim of our study is to address critical gaps in the understanding and application of CWs for treating sugar wastewater. Our specific objectives are to bring the physicochemical and microbiological parameters within national environmental quality standards (NEQS). Additionally, after achieving these results, evaluating their toxicity potential for seed germination and fish mortality is another primary objective. We aim to explore the feasibility and efficacy of utilizing CWs for reclaiming sugar industry effluents through the establishment of a lab-scale treatment trial unit employing horizontal subsurface flow-constructed wetlands. By elucidating the potential of CWs in detoxifying sugar wastewater and facilitating its safe reuse for agriculture and fish farming, this study contributes to advancing sustainable water management practices in the sugar industry.

2. Materials and Methods

2.1. Study Site Description

Wastewater was collected from a sugar industry at the final discharge located in Sanghar (Sindh province), Pakistan, and transported to the experimental site. The wastewater was stored in a plastic container for one week before being discharged into the wetland system. This lab-scale wetland (Figure 1) was operated in a greenhouse protected from rainfall. The site was selected to receive no precipitation but the maximum light intensity. The system was operated under varying temperature ranges from 18 °C to 35 °C due to seasonal temperature variations from July to December.

2.2. System Design

The area of the CW cell was calculated using the K-C Model [21] design parameters with the following Equation (1):
A r e a = 0.0365 Q K A l n C i C * C e C *
Q = Discharge flow rate (m3/day) = 0.008 m3/day = 8.2 L/day = 2.166 gallons/day
K = First order areal rate constant = For HSSF = 72.2
Ci = Inlet concentration of the pollutant in mg/L = 3792 mg/L
Ce = Outlet concentration of the pollutant in mg/L and can be considered as 150 mg/L for COD (NEQS standard) [22].
For the HSSF-CW, C* = 30
Putting values in Equation (1),
Area = 0.00001426 hectare = 0.1426 m2 = 222 square inch
Applying this to the current design,
Area of rectangle = length × width = 37 inch × 6 inch = 222 square inch

Experimental Set-up

A total of two 3-month trials were conducted, each consisting of three compartments having dimensions of approximately 37 × 6 × 6 inches (length, width, and height). These three pots were sequentially placed and interconnected by polyvinyl chloride (PVC) pipes, positioned at natural heights to facilitate the gravitational flow of wastewater through the lab-scale wetlands. The distance between each pot was 1 foot and the distance between the first and last pot was 5 feet. The first pot was 4 feet higher than the last pot. The total vertical distance of the first pot was 4.5 feet from the ground, while the last pot was on the ground. The total horizontal distance from the inlet to the outlet was 11 feet (Figure 1). The inlet for water in each pot was 1 inch below the top, while the outlet was 1 inch above the bottom. The first and second units were HSSF (horizontal subsurface flow)-constructed wetlands. The first pot, receiving water from the septic tank, was filled with washed gravel (0.6 inches) up to 2 inches from the bottom. Above that, fine gravel (pebbles) was placed up to 3 inches. After that, Typha latiofila plants were planted in the first and second pots. The third pot was filled only with sand. Under the media, a plastic sheet was placed in each pot to prevent leakage. Before operation, all units were soaked with fresh water for 3 to 4 weeks to acclimate the Typha latiofila and associated microbial community in the rhizosphere, sand, and gravel bed. This aided in developing a compact bed appropriate for the reclamation of wastewater. After the initial 3 months, the compartments were planted with 50% Typha latiofolia and 50% Phragmites australis for a second three-month period.

2.3. System Operation

After macrophytes establishment, the lab-scale wetland was fed with sugar industry wastewater for a total of 20 weeks from July to December 2022. A 12-Liter sample of sugar industry wastewater was fed in an influent container and flowed through the wetland at a flow rate of 2.16 gallons/day. This flow rate was maintained throughout the 6 months of operation because of the system design and area of the CW. According to the K-C Model design for the 222-square inch CW area, the 2.16 gallons/day flow rate is the maximum efficiency flow rate as evident from Equation (1) and Figure 1. The wastewater obtained in the effluent container was recycled into the influent container. This process was continued for 2 weeks, with samples taken every 7 days for physicochemical and microbiological analysis. After every 2 weeks, new samples were poured into the influent container with different dilution levels: 50%TW:50%SWW (TW = tap water, SWW = sugar wastewater), 25%DWW:75%SWW (DWW = domestic wastewater), 50%DWW:50%SWW, and 100%SWW), and the same cycle was repeated for another 14 days. Starting in November 2022, undiluted samples (100% sugar industry wastewater) were processed and analyzed weekly.

2.4. Sampling and Water Quality Analysis

Grab samples were taken from the inlet and outlet of the system every 7 days from July to December 2022 and analyzed on the same day of sampling. In total, 16 samples were collected and analyzed for pH, electrical conductivity (EC), dissolved oxygen (DO), total solids, total nitrogen, total sulfate, COD, BOD5, and decolorization. The values of pH, EC, and DO were measured through a pH meter (ELE 970, ELE International, Leighton Buzzard, UK) and DO probe (CRISON OXI 45+, Crison Instruments, Barcelona, Spain), respectively. Total Colony Forming Units (CFU) were measured through the serial dilution method. Total sulfate, total nitrogen, COD, and BOD were analyzed using a Merck kit (www.merckmillipore.com). Total solids were measured after filtration and drying using an oven or furnace. A 103–105 °C oven was used for suspended solids and a 650 °C furnace was used for volatile solid and dissolved solid measurements [23].
For wastewater decolorization, absorption measurements were taken of the raw sample and treated samples (across the wetland reactor) at 350 nm, 500 nm, and 600 nm through a UV-VIS spectrophotometer [24]. This technique investigates the mean absorption coefficient (decolorizing indicator) of the samples. A decrease in the absorption coefficient indicates a gradual decolorization. The absorption was calibrated for a blank (absorption = 0) before taking sample absorption.

2.5. Phytotoxicity Analysis on Seed (Wheat) Germination

The phytotoxicity of sugar industry wastewater against wheat seed germination was analyzed in both Petri dishes and pots [25].

2.5.1. In Petri Dishes

A piece of Whatman qualitative filter paper grade 1 (WHA1001090, Sigma-Aldrich, St. Louis, MO, USA) was placed in each Petri dish. Three wheat seeds were placed per dish, using three dishes per sample and control (containing tap water). Germination was conducted under UV light at a temperature of 25 °C. The dishes were wrapped with polyethylene plastic to prevent desiccation and volatile passage between samples. Germination time was tested for each treated and untreated sample weekly.

2.5.2. In Pots

Each pot was filled with clay up to the maximum mark and three wheat seeds were placed in each pot. Three pots were used per sample and control. Germination was carried out under light at a temperature of 25 °C. The germination time was calculated for each treated and untreated sample, with weekly testing.

2.6. Fish Toxicity Analysis

Weekly bioassay tests were conducted using fish as test organisms to check the toxicity of the wastewater and its potential consequences, including indirect, chronic, and delayed effects. The test organisms were selected according to the criteria approved by the United States Environmental Protection Agency (USEPA, 1979). Tilapia, specifically Oreochromis niloticus, was chosen as the test organism due to its easy availability. Healthy Tilapia individuals with lengths and weights of 1–2 inches and 20–30 g, respectively, were chosen for the experiment. The fish were acquired regularly during the experiment from a local pond having the required quality control parameters like temperature, salinity, turbidity, color, plankton, pH, CO2, ammonia, hydrogen sulfide, total alkalinity, hardness, aquatic weeds, and pollutants.
The experiment was performed in a 7-L glass container with dimensions of 12 × 8 × 8 inches and aerated to maintain the DO at the saturated level of 8 mg/L. The experiment was conducted at a room temperature of 25 °C, and 10 fish were used for every trial. The treated wastewater sample obtained after every week was tested for fish toxicity, with tap water being used as a control. The total number of dead fish was recorded every 24 h for every trial.

2.7. HPLC for Sucrose Quantification

As sugar industry wastewater contains high levels of sucrose, which contributes significantly to the COD, sucrose was selected as a parameter for HPLC detection. The HPLC system was a Scion instrument series 6000 (Goes, The Netherlands) equipped with a C18 column (4.6 mm × 250 mm) and a UV detector set at a wave length of 190 nm to quantify sucrose after degradation. The system was operated in isocratic mode with a mobile phase of acetonitrile, with water (75:25, v/v) at a flow rate of 1.0 mL/min for 15 min. The sample injection volume was 20 µL and the column temperature was 30 °C. The peak area was measured at 190 nm. Residual sucrose was estimated using the following formula.
R e s i d u a l   s u c r o s e = A r e a   o f   s a m p l e A r e a   o f   s t a n d a r d × c o n c e n t r a t i o n   o f   s t a n d a r d

2.8. Statistical Analysis

Data were processed through Microsoft Excel for Mac, version 16.6.1 to obtain standard deviation and standard error results. Statistical analysis of the experimental data was performed using GraphPad Prism (version 10). Various statistical tests (Kolmogorov–Smirnov and Shapiro–Wilk normality tests) were performed to check data distribution and normality (data was log-transformed if necessary). The results (with data approximated to normality) were accepted when α = 0.05.
Statistical tests, including paired (t-test, unpaired t-test, Mann–Whitney test, one sample t-test, and Wilcoxon test) and non-parametric tests (One-way ANOVA, Kruskal–Wallis, Brown-Forsythe, Welch ANOVA, and a Friedman test), were performed. The values less than 0.05 (p = 0.05) were recorded and considered statistically significant.

3. Results

3.1. Overall Performance of the Wetland Reactors

Table 1 shows the pH values of treated and untreated samples. As the biofilm and macrophytes develop, the pH of the samples gradually increases towards neutrality. The untreated sample had a highly acidic pH, but after two months of operation, it became fully neutralized. This was due to the development of sustained microbial communities in CWs, which successfully treat acidic industrial effluents [26]. Normally, chemical agents are added to adjust the pH, which increases operating costs. However, CWs have significant buffering capacities [27]. As shown in Table 2, the pH becomes slightly acidic again during winter (November and December), but the process remains effective.
As the wastewater passed through the wetland, the presence of ions (sulfides, chlorides, etc.) were reduced and EC also decreased.
The optimum healthy level of DO for water should be between 6 to 8 mg/L. The DO level in the untreated sample was notably low. Following treatment, the DO value in the treated sample initially rises, with a significant increase observed after the second cycle of treatment. Initially, the amount of organic content was higher and the available oxygen was lower. After treatment, the DO level increased in the first cycle and significantly in the second cycle, attributed to a longer retention time of 14 days compared to 7 days in the first cycle. Seasonal variation affected the DO level, which again decreased in November due to lower microbial activity, plant uptake, reduced vegetation and anaerobic conditions due to frost formation and increased water density in winter. Table 2 shows that the DO value increased from untreated to treated samples and from the 1st cycle to 2nd cycle.
Total CFUs were highest in the untreated samples and reduced gradually across the wetland reactors, with the highest reductions observed in September.
The total sulfate and total nitrogen values of the untreated samples were relatively moderate and already within the range specified by national environmental quality standards (https://environment.gov.pk), as shown in Table 2. The reduction efficiency was 95% and 96% for total sulfate and total nitrogen, respectively, across the wetland reactor. Initially, the total nitrogen and total sulfate values in the untreated sample were 97 mg/L and 98 mg/L, respectively, which were not excessively high. Consequently, the reduction efficiency was notably high.
Figure 2A indicates that the COD of wastewater across the wetland was reduced significantly, with a removal efficiency of 88% across the whole wetland cycle. The removal percentage was 80% after the first cycle (after the 1st week) and increased to almost 90% after the 2nd cycle (after the 2nd week). The highest removal was observed in September, which was 280 mg/L, while the minimum removal was observed at the end of November.
In Figure 2B, it is evident that the BOD removal efficiency remained constant at 97% throughout the entire wetland cycle. Following the first cycle (after the 1st week), the removal percentage was 90%, which increased to nearly 97% after 2nd cycle (after the 2nd week). The highest removal, reaching 70 mg/L, was observed in September.
The total solids in the treated samples were reduced significantly, almost to a negligible range, as the samples used were already diluted with tap and domestic wastewater as shown in Figure 2C.
Figure 2D indicates the measurements of the absorption coefficient (decolorizing indicators) in the raw and treated samples, at different wavelengths. The absorption coefficient indicates that at all wavelengths (350 nm, 500 nm, and 600 nm), the values were decreasing, indicating gradual decolorizing.

3.2. Organics Degradation, Pre-Treatment, Dilution, the Shading Effect, and Seasonal Variation

Pre-treatment is typically essential before using CWs due to the high amounts of organics, suspended particles, ammonia, and other contaminants found in many industrial wastewaters. The BOD/COD ratio is a parameter that infers biological degradability. Wastewater is readily biodegradable if this ratio is larger than 0.5 [32]. Conversely, wastewater from industries such as those of pulp and paper typically exhibits low BOD5/COD ratios, resulting in poor biodegradability [33]. The sugar industry effluent sample provided for the current study has a high COD/BOD ratio, required for the efficient performance of the wetland. It was observed that a total of 90% and 97% removal efficiency was observed for COD and BOD, respectively, as shown in Figure 2A,B.
Generally, wetlands play a crucial role in effectively removing COD and BOD, as well as reducing bacterial contaminants [34,35]. HSSF-CWs exhibited a greater COD removal rate compared to VSSF (Vertical Subsurface Flow). In a recent study, an HSSF-CW coupled with tube settler was used for hospital wastewater treatment at lab scale and achieved an overall removal efficiency of 94% for COD, (98% for TSS, 96% for BOD5, and 79% for Phosphate [36]. In wetlands, the high reduction of COD and BOD levels is likely attributable to the sedimentation/filtration of suspended solids and microbial degradation. VSSF-CWs, in particular, are more efficient in removing BOD compared to HSSF-CWs. This is attributed to the irregular feeding intervals, and unsaturated flow conditions in VSSF-CWs, facilitating increased oxygen transfer from the atmosphere to the filter bed [37].
The HSSF channel is situated below the ground and loaded with sand and gravel of particular sizes and types. When designing HSSF channels, numerous considerations must be taken into account. HSSF-CWs using three different treatment media (gravel, pieces of plastic pipes, and shredded tire rubber chips) were investigated for wastewater treatment. It was observed that after 180 days of operation, the wetland cells reached steady porosity and had started stable treatment. Additionally, rubber and gravel beds performed worse in terms of reducing pollutants than plastic media beds, whereas gravel media was more advanced than rubber media [38]. In HSSF-CWs, it is crucial to ensure that the flow of wastewater remains within the specified limit to keep the water underground. Additionally, prior to entering the channel, the water should be cleaned of solid particles to avoid clogging. If ABR (anaerobic baffled reactor) seeding with cattle manure is established before the wetland, the removal efficiency could increase to a higher acceptable range [39]. The ABR was established for the effluent from the sugar industry and seeded with flocculant sludge. A four-compartment system to treat wastewater with a high concentration of sugar and an effluent COD of 808 mg/L is still unable to fulfill the secondary emission standard [40]. Therefore, an HSSF-CW could be established as a secondary treatment method to reduce contaminant levels to be within an acceptable range.
In the current study, wastewater was diluted with tap water and domestic wastewater before being incorporated into the wetland. After dilution, it was found that COD removal with a 50% domestic water dilution was high as compared to a 50% tap water dilution, as shown in Table 1 and Figure 2A.
The drop in water temperature during transit through the CWs further demonstrated the energy advection and shading impacts of wetland macrophytes. Moreover, the algal growth is inhibited by the overgrowth of macrophytes, leading to a decrease in the observed growth [41]. As the current research was conducted outdoors, under a shed located outside the department, the efficiency of the wetland may not have reached 100%.
Evapotranspiration (ET) significantly influences the functioning of a wetland ecosystem [21]. In warm, tropical climates, ET from a CW can be substantial, potentially affecting the water balance and consequently impacting outflow nutrient concentrations as well as treatment performance. Therefore, it serves as an indicator of the treatment efficiency of a tropical CW. During the lab-scale wetland experiment, it was noted that the maximum removal efficiency (90%) (Table 1) was achieved during the summer interval, whereas efficiency was reduced during the winter interval. However, the maximum efficiency was observed during September.

3.3. Toxicity Testing Results

3.3.1. Phytotoxicity against Wheat Seed Germination

The phytotoxicity of wheat seed germination was observed against treated and untreated wastewater in both clay pots and Petri dishes Table 3. No seed germination was observed using untreated wastewater for the whole month in Petri dishes. In clay pots, it took almost 10 days for a wheat seed to germinate using untreated wastewater. The seeds germinated normally within 4 to 7 days using treated wastewater through the CWs (Table 2). According to a study [42], the phytotoxicity of winery wastewater against garden cress was highly toxic with no seed germination in CWs. This severe toxicity was caused by a low pH and high COD (16,800 mg/L). Mostly, a decline in plant growth is observed due to the COD and phenolic compounds. Heavy metals and organic pollutants may also be significant hazardous agents in wastewater. The toxic effects of textile wastewater on common bread wheat seed germination and seedling growth were examined in a study. A total of sixteen samples were collected, and their physicochemical characteristics were examined. The effluent had a pH range of 2.0 to 8.12 and was high in hardness and chlorine. With the relative germination percentage falling to 36.66% from the control’s 100% germination percentage, there was a noticeable decline in both the germination percentage and seedling growth compared to the control [43]. Likewise, the wastewater inhibited the growth of the seedlings. In comparison to the control, seedling growth was higher in wastewater with a lower concentration. The results show that the high concentration of dissolved particles in textile wastewater lowers the energy supply through anaerobic respiration, which slows seedling growth and development.
Wheat germination, seedling fresh weight, seedling dry weight, the vigor index, the tolerance index, plant height, the number of leaves, root fresh weight, shoot fresh weight, root dry weight, shoot dry weight, and root length are all significantly reduced when polluted water containing lead is used for wheat irrigation. Physiological measures, such as the photosynthetic rate, transpiration rate, and stomatal conductance, also exhibit negative responses. Biochemical markers such as carotenoids, total chlorophyll, chlorophyll a and chlorophyll b also demonstrated detrimental effects [44].

3.3.2. Fish Toxicity

The toxicity of wastewater was tested against Tilapia fish. A total of 10 fish were used for the toxicity assay of water from each stage. None of the fish survived for 5 h in untreated water samples (0/10), while 5 fish (5/10) survived in the samples after one week of treatment and 8 fish (8/10) survived for 5 days in water samples collected after the 2nd week of treatment. Tap water was used as the control and the observed percentage of fish mortality in tap water was zero (10/10 survived).
In a previous study, a bioassay was conducted using common grass carp (Ctenopharyngodon idella) by evaluating the toxic effects of tannery effluents for 96 h in laboratory conditions. The effluent used for the assay was treated through a CW, and 96 h-LC50 (lethal concentration inducing 50% mortality) and acute toxic concentration were observed. The toxicity assay demonstrated that the presence of significant quantities of salts and the metal chromium (Cr) in the raw tannery effluent altered the morphology, physiology, and behavioral response of fish, resulting in high acute toxicity and a 100% death rate. Additionally, the toxicity and fish mortality of the tannery effluent treated using CWs with different plant species (B. mutica, L. fusca, and T. domingensis) were greatly reduced [8].
In another study, the effluent from municipal wastewater containing heavy metals was evaluated for toxicity against Chlorella pyrenoidosa and Daphnia magna. Wastewater was treated using a CW and it was observed that cadmium removal through the CW allowed plankton growth, which clearly benefited the growth of C. pyrenoidosa and D. magna [45].

3.4. HPLC Results

Sucrose was quantified in the untreated and treated samples by HPLC, and it was found that the sucrose significantly reduced from the untreated to the treated samples. The maximum sucrose reduction, up to 95%, was observed during August and September, providing the maximum degradation efficiency (COD 88%, BOD 97%) and sucrose contribution towards enhancing these parameters. Degradation efficiency was reduced beyond October during winter due to the dilution level and seasonal variation. As indicated in Table 2, the sugar wastewater was undiluted (100%) in November and December, coinciding with a temperature drop during these months. It is known that dilution with domestic wastewater and higher temperatures enhance degradation efficiency in constructed wetlands (CWs). Figure 3’s HPLC graph illustrates a reduction in sucrose degradation efficiency during these months (73%), attributable to these factors.

3.5. Statistical Analysis

A comprehensive table showing the name of the statistical test along with the level of significance is given below. Tick marks show a significant difference was present between the treated and untreated samples for a particular parameter Table 4.

4. Conclusions

In conclusion, the study highlights the potential of horizontal subsurface flow-constructed wetlands (HSSF-CWs) as efficient and viable methods for minimizing the adverse ecological effects associated with sugar industry wastewater. The study revealed that the wetland system had a significant removal efficiency, with remarkable achieving rates of 88% for COD, 97% for BOD, 96% for total nitrogen, and 95% for sulfate. Furthermore, the system demonstrated improved removal efficiency when subjected to domestic wastewater relative to tap water, owing to the abundance of microbial communities required for efficient treatment. The study additionally highlighted the significance of pre-treatment, dilution, shading effects, and seasonal fluctuations in affecting the proper functioning of wetland systems. The research showed that pre-treatment is crucial for eliminating suspended matter and pollutants, and dilution with domestic wastewater enhanced COD removal compared to tap water dilution. Seasonal fluctuations, specifically temperature shifts, influenced the productivity of the wetland reactor, with the summer season exhibiting the highest removal efficiency. The results of the toxicity test further confirmed the effectiveness of the wetland reactor, as treated wastewater exhibited no adverse effects on the germination of wheat seeds and the survival of fish. However, untreated wastewater showed significant phytotoxicity which rapidly induced fish death, highlighting the significance of suitable wastewater treatment.
This approach also has some limitations. In comparison to other treatment approaches, it requires a substantial amount of land, especially for large-scale applications. The effectiveness of the treatment is significantly influenced by precipitation and temperature. Regular maintenance of vegetation and sediment removal is necessary to achieve optimal performance. The establishment of microbial and vegetation components may result in a longer start-up time. Moreover, since only denitrification occurs, unlike in hybrid constructed wetlands (HCWs) where both nitrification and denitrification processes are present, its ability to remove nutrients, particularly nitrogen, may be limited. Additionally, changes in the hydraulic loading rate can affect performance and there is a risk of potential clogging in the case of HF-CW.

Author Contributions

Conceptualization, M.A. and S.A.; methodology, T.U.R.; software, B.A.; validation, H.W., K.A.G. and M.A.; formal analysis, A.H.; investigation, T.U.R. and H.W.; resources, M.A.; data curation, R.A.; writing—original draft preparation, T.U.R.; writing—review and editing, H.W., K.A.G. and M.A.; visualization, T.U.R.; supervision, M.A.; project administration, M.A.; funding acquisition, M.A. All authors have read and agreed to the published version of the manuscript.

Funding

This research received funding from the Higher education Commission (HEC) Pakistan under technology transfer support fund (TTSF-131) project entitled “Development of efficient biological water purification systems for industrial wastewater”.

Data Availability Statement

All the data are present in the research paper.

Acknowledgments

The authors would like to acknowledge Sanghar Sugar Mills Limited for providing sugar wastewater samples.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. US Department of Agriculture. Pakistan: Sugar Annual. Available online: https://fas.usda.gov/data/pakistan-sugar-annual-6 (accessed on 24 April 2024).
  2. Saranraj, P.; Stella, D. Impact of Sugar Mill Effluent to Environment and Bioremediation: A Review. World Appl. Sci. J. 2014, 30, 299–316. [Google Scholar] [CrossRef]
  3. Profit by Pakistan Today. Right Bank Outfall Drain-II Delays Increase Cost of Project to Rs62b. Available online: https://profit.pakistantoday.com.pk/2017/06/16/right-bank-outfall-drain-ii-delays-increase-cost-of-project-to-rs62b/ (accessed on 24 May 2024).
  4. Azizullah, A.; Khattak, M.N.; Richter, P.; Häder, D.P. Water Pollution in Pakistan and Its Impact on Public Health—A Review. Environ. Int. 2011, 37, 479–497. [Google Scholar] [CrossRef] [PubMed]
  5. Yang, X.; Chen, Z.; Zhao, W.; Liu, C.; Qian, X.; Zhang, M.; Wei, G.; Khan, E.; Ng, Y.H.; Ok, Y.S. Recent Advances in Photodegradation of Antibiotic Residues in Water. Chem. Eng. J. 2021, 405, 126806. [Google Scholar] [CrossRef] [PubMed]
  6. Zafar, U.; Hare, C.; Hassanpour, A.; Ghadiri, M. Ball Indentation on Powder Beds for Assessing Powder Flowability: Analysis of Operation Window. Powder Technol. 2017, 310, 300–306. [Google Scholar] [CrossRef]
  7. Jia, S.; Yang, Z.; Ren, K.; Tian, Z.; Dong, C.; Ma, R.; Yu, G.; Yang, W. Removal of Antibiotics from Water in the Coexistence of Suspended Particles and Natural Organic Matters Using Amino-Acid-Modified-Chitosan Flocculants: A Combined. J. Hazard. Mater. 2016, 317, 593–601. [Google Scholar] [CrossRef] [PubMed]
  8. Ashraf, S.; Naveed, M.; Afzal, M.; Ashraf, S.; Ahmad, S.R.; Rehman, K.; Zahir, Z.A.; Núñez-Delgado, A. Evaluation of Toxicity on Ctenopharyngodon Idella Due to Tannery Effluent Remediated by Constructed Wetland Technology. Processes 2020, 8, 612. [Google Scholar] [CrossRef]
  9. Jaramillo, M.F.; Restrepo, I. Wastewater Reuse in Agriculture: A Review about Its Limitations and Benefits. Sustainability 2017, 9, 1734. [Google Scholar] [CrossRef]
  10. Ayyasamy, P.M.; Yasodha, R.; Rajakumar, S.; Lakshmanaperumalsamy, P.; Rahman, P.K.S.M.; Lee, S. Impact of Sugar Factory Effluent on the Growth and Biochemical Characteristics of Terrestrial and Aquatic Plants. Bull. Environ. Contam. Toxicol. 2008, 81, 449–454. [Google Scholar] [CrossRef]
  11. Dalzell, B.J.; Mulla, D.J.; Gowda, P.H. undefined Modeling and Evaluation of Alternative Agricultural Management Practices in Sand Creek Watershed. In Soil Erosion; American Society of Agricultural and Biological Engineers: St. Joseph, MI, USA, 2001. [Google Scholar]
  12. Sotero-Santos, R.B.; Rocha, O.; Povinelli, J. Evaluation of Water Treatment Sludges Toxicity Using the Daphnia Bioassay. Water Res. 2005, 39, 3909–3917. [Google Scholar] [CrossRef]
  13. Wang, W.C.; Freemark, K. The Use of Plants for Environmental Monitoring and Assessment. Ecotoxicol. Environ. Saf. 1995, 30, 289–301. [Google Scholar] [CrossRef]
  14. Gibson, M.A.; Bruck, J. Efficient Exact Stochastic Simulation of Chemical Systems with Many Species and Many Channels. J. Phys. Chem. A 2000, 104, 1876–1889. [Google Scholar] [CrossRef]
  15. Quan, Q.; Shen, B.; Zhang, Q.; Ashraf, M.A. Research on Phosphorus Removal in Artificial Wetlands by Plants and Their Photosynthesis. Braz. Arch. Biol. Technol. 2016, 59, e16160506. [Google Scholar] [CrossRef]
  16. Wang, Y.; Cai, Z.; Sheng, S.; Pan, F.; Chen, F.; Fu, J. Comprehensive Evaluation of Substrate Materials for Contaminants Removal in Constructed Wetlands. Sci. Total Environ. 2020, 701, 134736. [Google Scholar] [CrossRef] [PubMed]
  17. Wu, H.; Wang, R.; Yan, P.; Wu, S.; Chen, Z.; Zhao, Y.; Cheng, C.; Hu, Z.; Zhuang, L.; Guo, Z.; et al. Constructed Wetlands for Pollution Control. Nat. Rev. Earth Environ. 2023, 4, 218–234. [Google Scholar] [CrossRef]
  18. Qadir, M.; Sharma, B.R.; Bruggeman, A.; Choukr-Allah, R.; Karajeh, F. Non-Conventional Water Resources and Opportunities for Water Augmentation to Achieve Food Security in Water Scarce Countries. Agric. Water Manag. 2007, 87, 2–22. [Google Scholar] [CrossRef]
  19. Ali, M.; Rousseau, D.P.L.; Ahmed, S. A Full-Scale Comparison of Two Hybrid Constructed Wetlands Treating Domestic Wastewater in Pakistan. J. Environ. Manag. 2018, 210, 349–358. [Google Scholar] [CrossRef] [PubMed]
  20. Waseem, H.; Jameel, S.; Ali, J.; Jamal, A.; Ali, M.I. Recent Advances in Treatment Technologies for Antibiotics and Antimicrobial Resistance Genes. In Antibiotics and Antimicrobial Resistance Genes. Emerging Contaminants and Associated Treatment Technologies; Springer: Cham, Switzerland, 2020; pp. 395–413. [Google Scholar] [CrossRef]
  21. Kadlec, R.; Knight, R.; Vymazal, J.; Brix, H.; Cooper, P.; Haberl, R. Constructed Wetlands for Pollution Control; IWA Publisher: London, UK, 2000. [Google Scholar]
  22. Pakistan Environmental Protection Agency. Available online: https://environment.gov.pk/ (accessed on 24 March 2024).
  23. Paul, M.; Shroti, G.K.; Mohapatra, S.; DasMohapatra, P.K.; Thatoi, H. A Comparative Study on Pretreatment of Rice Straw and Saccharification by Commercial and Isolated Cellulase–Xylanase Cocktails towards Enhanced Bioethanol Production. Syst. Microbiol. Biomanuf. 2024, 4, 731–749. [Google Scholar] [CrossRef]
  24. Saeed, T.; Sun, G. A Lab-Scale Study of Constructed Wetlands with Sugarcane Bagasse and Sand Media for the Treatment of Textile Wastewater. Bioresour. Technol. 2013, 128, 438–447. [Google Scholar] [CrossRef] [PubMed]
  25. Arienzo, M.; Christen, E.W.; Quayle, W.C. Phytotoxicity Testing of Winery Wastewater for Constructed Wetland Treatment. J. Hazard. Mater. 2009, 169, 94–99. [Google Scholar] [CrossRef]
  26. Wu, H.; Zhang, J.; Ngo, H.H.; Guo, W.; Hu, Z.; Liang, S.; Fan, J.; Liu, H. A Review on the Sustainability of Constructed Wetlands for Wastewater Treatment: Design and Operation. Bioresour. Technol. 2015, 175, 594–601. [Google Scholar] [CrossRef]
  27. Mbuligwe, S.E. Comparative Treatment of Dye-Rich Wastewater in Engineered Wetland Systems (EWSs) Vegetated with Different Plants. Water Res. 2005, 39, 271–280. [Google Scholar] [CrossRef] [PubMed]
  28. Soto, M.F.; Diaz, C.A.; Zapata, A.M.; Higuita, J.C. BOD and COD Removal in Vinasses from Sugarcane Alcoholic Distillation by Chlorella Vulgaris: Environmental Evaluation. Biochem. Eng. J. 2021, 176, 108191. [Google Scholar] [CrossRef]
  29. Salinity Tolerant Crops. A Technique Package for the Regional Training Course on. In Proceedings of the Mutation Breeding Approaches to Improving Tolerance to Salinity, Drought and Heat Stress in Crop Plants, RAS/5/045, Beijing, China, 13–22 October 2008. [Google Scholar]
  30. Marmorek, D.R.; Bernard, D.P.; Wedeles, C.H.R.; Sutherland, G.; Malanchuk, J.A.; Fallon, W.E. A Protocol for Determining Lake Acidification Pathways. Water Air Soil Pollut. 1989, 44, 235–257. [Google Scholar] [CrossRef]
  31. Blodgett, R.J. Serial Dilution with a Confirmation Step. Food Microbiol. 2005, 22, 547–552. [Google Scholar] [CrossRef]
  32. Al-Sulaiman, A.M.; Khudair, B.H. Correlation between BOD5 and COD for Al-Diwaniyah Wastewater Treatment Plants to Obtain the Biodigrability Indices. Pak. J. Biotechnol. 2018, 15, 423–427. [Google Scholar]
  33. Skrzypiecbcef, K.; Gajewskaad, M.H. The Use of Constructed Wetlands for the Treatment of Industrial Wastewater. J. Water Land Dev. 2017, 34, 233–240. [Google Scholar] [CrossRef]
  34. Zhang, D.Q.; Jinadasa, K.B.; Gersberg, R.M.; Liu, Y.; Tan, S.K.; Ng, W.J. Application of Constructed Wetlands for Wastewater Treatment in Tropical and Subtropical Regions (2000–2013). J. Environ. Sci. 2015, 30, 30–46. [Google Scholar] [CrossRef] [PubMed]
  35. Ali, J.; Ali, M.; Khan, I.; Khan, A.; Rafique, Z.; Waseem, H. Advances in Biodegradation and Bioremediation of Emerging Contaminants in the Environment. In Biological Approaches to Controlling Pollutants; Woodhead Publishing: Sawston, UK, 2022; pp. 121–138. [Google Scholar] [CrossRef]
  36. Khan, N.A.; El Morabet, R.; Khan, R.A.; Ahmed, S.; Dhingra, A.; Alsubih, M.; Khan, A.R. Horizontal Sub Surface Flow Constructed Wetlands Coupled with Tubesettler for Hospital Wastewater Treatment. J. Environ. Manag. 2020, 267, 110627. [Google Scholar] [CrossRef] [PubMed]
  37. Kadlec, R.H.; Wallace, S. Treatment Wetlands, 2nd ed.; CRC Press: Boca Raton, FL, USA, 2008. [Google Scholar]
  38. Zidan, A.R.A.; El-Gamal, M.M.; Rashed, A.A.; El-Hady Eid, M.A.A. Wastewater Treatment in Horizontal Subsurface Flow Constructed Wetlands Using Different Media (Setup Stage). Water Sci. 2015, 29, 26–35. [Google Scholar] [CrossRef]
  39. Saif, Y.; Ali, M.; Jones, I.M.; Ahmed, S. Performance Evaluation of a Field-Scale Anaerobic Baffled Reactor as an Economic and Sustainable Solution for Domestic Wastewater Treatment. Sustainability 2021, 13, 10461. [Google Scholar] [CrossRef]
  40. Zhang, D.; Gersberg, R.M.; Keat, T.S. Constructed Wetlands in China. Ecol. Eng. 2009, 35, 1367–1378. [Google Scholar] [CrossRef]
  41. Bojcevska, H.; Tonderski, K. Impact of Loads, Season, and Plant Species on the Performance of a Tropical Constructed Wetland Polishing Effluent from Sugar Factory Stabilization Ponds. Ecol. Eng. 2007, 29, 66–76. [Google Scholar] [CrossRef]
  42. Saviozzi, A.; Biasci, A.; Riffaldi, R.; Levi-Minzi, R. Long-Term Effects of Farmyard Manure and Sewage Sludge on Some Soil Biochemical Characteristics. Biol. Fertil. Soils 1999, 30, 100–106. [Google Scholar] [CrossRef]
  43. Rana, S.; Kumar, K. Study of Phytotoxic Effect of Textile Wastewater on Seed Germination and Seedling Growth of Triticum aestivum. Int. J. Biosci. Technol. 2017, 10, 58–66. [Google Scholar]
  44. Kanwal, A.; Farhan, M.; Sharif, F.; Hayyat, M.U.; Shahzad, L.; Ghafoor, G.Z. Effect of Industrial Wastewater on Wheat Germination, Growth, Yield, Nutrients and Bioaccumulation of Lead. Sci. Rep. 2020, 10, 11361. [Google Scholar] [CrossRef]
  45. Liu, M.; Li, X.; He, Y.; Li, H. Aquatic Toxicity of Heavy Metal-Containing Wastewater Effluent Treated Using Vertical Flow Constructed Wetlands. Sci. Total Environ. 2020, 727, 138616. [Google Scholar] [CrossRef]
Figure 1. The experimental arrangements showing the lab-scale wetland. The first three-month trial wetland pot was planted with 100% Typha latifolia and the second three-month trial wetland pot was planted with 50% Typha latifolia and 50% Phragmites australis.
Figure 1. The experimental arrangements showing the lab-scale wetland. The first three-month trial wetland pot was planted with 100% Typha latifolia and the second three-month trial wetland pot was planted with 50% Typha latifolia and 50% Phragmites australis.
Processes 12 01400 g001
Figure 2. The physicochemical parameters of raw and treated samples: (A) COD; (B) BOD; (C) Total suspended solids, total dissolved solids, and total volatile solids; and (D) Color removal. The error bars show the standard deviation values. TW = tap water, SWW = sugar wastewater, DWW = domestic wastewater. All the values are in mg/L.
Figure 2. The physicochemical parameters of raw and treated samples: (A) COD; (B) BOD; (C) Total suspended solids, total dissolved solids, and total volatile solids; and (D) Color removal. The error bars show the standard deviation values. TW = tap water, SWW = sugar wastewater, DWW = domestic wastewater. All the values are in mg/L.
Processes 12 01400 g002aProcesses 12 01400 g002b
Figure 3. The HPLC for sucrose quantification to determine the degradation and percent of removal efficiency.
Figure 3. The HPLC for sucrose quantification to determine the degradation and percent of removal efficiency.
Processes 12 01400 g003
Table 1. The water quality analysis parameters, method used, model of instruments, and references of the method.
Table 1. The water quality analysis parameters, method used, model of instruments, and references of the method.
ParameterMethod UsedModel of InstrumentsReference
Total SulfateSpecroquant cell test 1.14548.0001Spectroquant
Pharo 100
[19,28]
Total NitrogenSpecroquant cell test 1.14763.0001Spectroquant
Pharo 100
[19,28]
CODSpecroquant cell test 1.14541.0001Spectroquant Pharo 100[19,28]
BODSpecroquant cell test 1.00687.0001Spectroquant
Pharo 100
[19,28]
pHpH meterELE 970[19,28]
ECEC meterPCSTestr 35[29,30]
DODO meterCRISON OXI 45 +[29,30]
CFUSerial dilution_[31]
ColorAbsorption (spectrophotometer)Perkin Elmer Lambda 365[24]
Total solidsOven and furnance-[23]
Table 2. The physicochemical and microbiological parameters of sugar industry wastewater before and after treatment at different dilution levels. TW = tap water, SWW = sugar wastewater, DWW = domestic wastewater, DO = dissolved oxygen, EC = electrical conductivity, TN = total nitrogen, TS = total sulfate, Inf = influent, Eff = effluent.
Table 2. The physicochemical and microbiological parameters of sugar industry wastewater before and after treatment at different dilution levels. TW = tap water, SWW = sugar wastewater, DWW = domestic wastewater, DO = dissolved oxygen, EC = electrical conductivity, TN = total nitrogen, TS = total sulfate, Inf = influent, Eff = effluent.
Sampling DayDilution LevelDOPhECCFU/mL (Log10)TNTS
Inf.Eff.Inf.Eff.Inf.Eff.Inf.Eff.Inf.Eff.Inf.Eff.
07 days
14 days
07 days
14 days
07 days
14 days
07 days
14 days
07 days
14 days
07 days
07 days
07 days
07 days
07 days
07 days
50%TW 50%SWW
50%TW 50%SWW
25%DWW 75%SWW
25%DWW 75%SWW
50%DWW 50%SWW
50%DWW 50%SWW
50%DWW 50%SWW
50%DWW 50%SWW
50%DWW 50%SWW
50%DWW 50%SWW
100%SWW
100%SWW
100%SWW
100%SWW
100%SWW
100%SWW
3.15
4.6
2.57
4.6
4.15
4.7
4.15
4.5
4.15
4.6
1.3
5.7
1.3
2.4
1.3
2.1
4.6
9.8
4.6
7.2
4.7
9.7
4.5
9.4
4.6
9.7
5.7
2.4
2.4
2
2.1
2
4.53
5.7
2.92
5.2
4.28
5.3
4.28
5.5
4.28
6.4
1.57
6.7
1.57
7.4
1.57
7.0
5.7
5.4
5.2
5.1
5.3
5.2
5.5
5.5
6.4
7.0
6.7
7.3
7.4
7.19
7.0
6.7
615
680
650
517
572
523
572
502
572
440
730
610
730
639
730
666
680
535
517
569
523
544
502
456
440
574
610
629
639
666
660
661
6.3
7.95
6.5
5.95
8.3
5.59
8.3
4.69
8.3
4.51
8.5
6.97
8.5
6.65
8.5
6.06
7.95
6.08
5.95
5.70
5.59
4.67
4.69
4.82
4.51
4.51
6.97
6.04
6.65
6.08
6.06
6.06
52.8
6
68.8
6.7
88.8
6.7
88.8
6.7
88.8
6.7
48.8
6.7
48.8
6.7
48.8
6.7
6
6.7
6.7
6.7
6.7
6.7
6.7
6.7
6.7
6.7
6.7
6.7
6.7
6.7
6.7
6.7
75
35
86.2
38
74
19
74
22
74
18
98
15
98
18
98
25
35
10
38
4
19
5
22
6
18
4
15
20
18
22
25
21
Table 3. The phytotoxicity of the untreated and treated wastewater samples against wheat seed germination.
Table 3. The phytotoxicity of the untreated and treated wastewater samples against wheat seed germination.
Sample TypeGermination Time in Petri Dishes (In Days)No. of Petri Dishes
(Out of 3)
Germination Time in Clay Pods (In Days)No. of Clay Pods
(Out of 3)
UntreatedNot germinated 102
Tap water (control)4343
Treated (after 1st week)5243
Treated (after 2nd week)4271
Treated (after 3rd week)5262
Table 4. The statistical significance of the physicochemical and microbiological parameters.
Table 4. The statistical significance of the physicochemical and microbiological parameters.
Statistical TestsParameters
Total SulfatepHDOECCFUTNCODBODTotal SolidsColor
One sample
t test
p value summary
Significant
Alpha (0.05)
YesYesYesYesYesYesYesYesYesYes
Wilcoxon test
p value summary
Significant
Alpha (0.05)
YesYesYesYesYesYesYesYesYesYes
One-way Anova
F valueF = 247F = 930F = 118F = 549F = 243F = 327F = 29F = 9.96F = 259F = 339
Unpaired
t-test
p value summary
Significant
p ≤ 0.05
YesYesYesYesYesYesYes
Paired t-test
p value summary
Significant
p ≤ 0.05
YesYesYesYesYesYesYes
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Ur Rehman, T.; Waseem, H.; Ali, B.; Haleem, A.; Abid, R.; Ahmed, S.; Gilbride, K.A.; Ali, M. Mitigation of Sugar Industry Wastewater Pollution: Efficiency of Lab-Scale Horizontal Subsurface Flow Wetlands. Processes 2024, 12, 1400. https://doi.org/10.3390/pr12071400

AMA Style

Ur Rehman T, Waseem H, Ali B, Haleem A, Abid R, Ahmed S, Gilbride KA, Ali M. Mitigation of Sugar Industry Wastewater Pollution: Efficiency of Lab-Scale Horizontal Subsurface Flow Wetlands. Processes. 2024; 12(7):1400. https://doi.org/10.3390/pr12071400

Chicago/Turabian Style

Ur Rehman, Talmeez, Hassan Waseem, Babar Ali, Abdul Haleem, Rameesha Abid, Safia Ahmed, Kimberley A. Gilbride, and Mahwish Ali. 2024. "Mitigation of Sugar Industry Wastewater Pollution: Efficiency of Lab-Scale Horizontal Subsurface Flow Wetlands" Processes 12, no. 7: 1400. https://doi.org/10.3390/pr12071400

APA Style

Ur Rehman, T., Waseem, H., Ali, B., Haleem, A., Abid, R., Ahmed, S., Gilbride, K. A., & Ali, M. (2024). Mitigation of Sugar Industry Wastewater Pollution: Efficiency of Lab-Scale Horizontal Subsurface Flow Wetlands. Processes, 12(7), 1400. https://doi.org/10.3390/pr12071400

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