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Article

A Cyclic Graywater Treatment Model for Sustainable Wastewater Management Applied in a Small Scale

by
Hanen Filali
1,*,
Malak Moussa
1,
Narcis Barsan
2,*,
Valentin Nedeff
2,
Oana Irimia
2 and
Mohamed Hachicha
1
1
Laboratory of Non-Conventional Water Valorization LR16INRGREF02, National Research Institute of Rural Engineering, Water, and Forestry, University of Carthage, 10 Rue Hédi Karray, Manzeh 4, Ariana 2080, Tunisia
2
Faculty of Engineering, “Vasile Alecsandri” University of Bacau, Calea Marasesti 156, 600115 Bacau, Romania
*
Authors to whom correspondence should be addressed.
Appl. Sci. 2025, 15(5), 2836; https://doi.org/10.3390/app15052836
Submission received: 3 February 2025 / Revised: 27 February 2025 / Accepted: 28 February 2025 / Published: 6 March 2025
(This article belongs to the Special Issue Sustainable Environmental Engineering)
Browse Figures
Versions Notes

Abstract

:
Water scarcity presents a critical challenge to global sustainability, exacerbated by population growth, climate change, and environmental pollution. In this context, graywater reuse has emerged as a promising solution, offering substantial water savings with significant potential for agricultural applications. However, efficient treatment methods are essential to ensure safe reuse, as contaminants vary depending on the source. This study introduces a cyclic graywater treatment system that integrates both mechanical and biological filtration processes. A key feature of this system is the inclusion of Chenopodium quinoa, a resilient plant known for its phytoremediation potential, which enhances filtration efficiency and facilitates contaminant removal. The study examines the impact of treated graywater on soil and quinoa properties, focusing on its suitability for irrigation. The results show that the cyclic treatment system significantly improves graywater quality, enhancing the removal of biological and microbiological contaminants, such as BOD, with a significant decrease ranging from 31.33 mg O2/L to 15.74 mg O2/L is observed after treatment. For COD, the average values decreased from 102.64 mg O2/L to 54.19 mg O2/L after treatment, making the treated graywater compliant with Tunisian regulation NT 106.03 and WHO guidelines. Cyclic treatment significantly reduced the microbial load of graywater. For example, for E. coli, the average decreased from 0.87 log 10/100 mL in RGW to 0.58 log 10/100 mL in GWT3. The results demonstrate that the cyclic treatment process can predict the graywater quality beyond the three tested stages. This study highlights the potential of plant-based cyclic graywater treatment systems as an eco-friendly and scalable approach for sustainable water management in agriculture.

1. Introduction

Water is a vital resource, indispensable for human survival, ecosystem function, and global food security. Its sustainable management is, therefore, a critical challenge for ensuring the resilience of both human societies and the natural environment. The global population is projected to reach nearly 9.7 billion by 2050 [1], with direct implication in the increasing of domestic and agricultural water needs. The global demand for water is increasing by approximately 1% annually, with projections indicating a 55% rise from 2000 to 2050 [2]. Clean water scarcity has become a major problem for the entire world’s population. Water quantity and quality problems must be intertwined, since areas with acute water scarcity frequently struggle with serious pollution [3,4]. Climate change exacerbates water scarcity and quality risks in urban areas, with 88.8–99.7% of large cities expected to face increased water challenges by 2050, primarily due to rising demand, population growth, and agricultural runoff [5].
Graywater, which can be defined as domestic wastewater, excluding toilet waste, presents a viable solution to global water scarcity, constitutes 60–75% of household wastewater, and can reach 90% with the use of vacuum toilets [6]. The implementation of graywater reuse systems can lead to substantial water savings across different building typologies, with reductions of 46% for hotels, 44% for residential buildings, and 29% for industrial facilities [7]. Graywater is generally considered safer to handle than black water due to its lower contamination levels, making it suitable for non-potable applications, such as toilet flushing and landscaping [8]. Agriculture, which accounts for over 76% of total water consumption in Tunisia, relies heavily on water resources. In response to increasing irrigation demands and water scarcity, treated wastewater—more specifically, graywater—has emerged as a crucial alternative resource [9,10]. Studies indicate that irrigation with graywater can improve soil properties by increasing organic carbon content and enhancing nutrient availability, thereby supporting crop growth [11]. Indeed, treated graywater has been linked to significant increases in crop yield, such as olive trees, which thrived under irrigation with treated graywater [12].
Depending on domestic activities, graywater’s composition varies. For example, kitchen wastewater has a high biochemical oxygen demand (BOD), indicating a significant amount of organic matter, whereas laundry effluent usually has a higher pH and chemical oxygen demand (COD) due to the use of certain soaps and detergents [6]. Given the varying composition of graywater, effective treatment methods are essential to ensure its safe and efficient reuse. These methods aim to address specific contaminants depending on the source of the graywater, ultimately enabling its application in recycling applications while maintaining environmental and health standards. Treatment methods, including the use of polymers such as Poly Aluminum Chloride (PAC), have been demonstrated to effectively reduce contaminants, enhance water quality, and minimize sludge production [13]. In particular, PAC excels in coagulating and removing turbidity, suspended solids, and organic contaminants across a broad pH range, requiring lower dosages and producing less waste, making it a cost-efficient and operationally advantageous option for graywater treatment. Moreover, Vertical Treatment Systems have shown high efficiency in contaminant removal, with studies reporting a 90% reduction in BOD5 and an 83% reduction in COD, making treated graywater suitable for irrigation [12]. Similarly, constructed wetlands (CWs) employing a mixed filter bed of seashells, ceramic brick fragments, and sand have demonstrated remarkable efficacy in removing organic matter, anionic surfactants, and total phosphorus, highlighting their potential as an effective and sustainable graywater treatment method [14]. Ref. [15] investigated the application of a non-aerated hybrid constructed wetland, integrating both horizontal and vertical flow configurations for graywater treatment. The study demonstrated the system’s efficacy in removing heavy metals and microorganisms, achieving significant reductions in contaminant concentrations, thereby presenting it as a promising and sustainable treatment option. Ref. [16] investigated four graywater treatment methods: Type A (geotextile-fitted trickling filter and sand filter), Type B (fibrous mineral wool packs), Type C (fine-meshed plastic filter), and Type D (conventional sand filter), each with varying removal efficiencies for contaminants. Ref. [16] concluded that, while these systems effectively reduce certain pollutants, they may encounter difficulties in effectively eliminating pathogens, necessitating the incorporation of additional treatment steps for a more comprehensive and reliable graywater treatment process. Refs. [17,18] presented a comprehensive overview of both existing and emerging graywater treatment technologies, emphasizing that current systems encounter considerable challenges in terms of efficiency, scalability, and resource recovery.
Considering the complex processes of wastewater management, this study introduces a novel graywater treatment approach, namely “cyclic treatment”, which draws inspiration from conventional wastewater treatment systems, such as macrophyte-planted filters. The process involves subjecting raw graywater to multiple treatment stages, each consisting of sand-filled containers with a gravel layer. The effluent from each stage represents progressively treated graywater, with a gradual reduction in contaminant levels. The use of a cyclic treatment system, rather than conventional continuous flow systems, is justified by its ability to better manage variable contaminant loads. The intermittent flow allows for enhanced pollutant contact time, improving the removal efficiency of contaminants in graywater. This approach provides more effective treatment by adapting to fluctuations in influent quality and preventing issues such as clogging, which can be common in continuous flow systems. The experimental setup is divided into two distinct conditions: one with the presence of quinoa plants, such as Chenopodium Quinoa Willd, and one without, facilitating a comparative evaluation of the role of plant-based filtration in enhancing treatment efficiency and overall performance.
Notably, this study incorporates Chenopodium quinoa as a filtering medium due to its unique properties that enhance filtration performance, promote natural bio-adsorption, and provide a sustainable, eco-friendly alternative to conventional materials in graywater treatment. Numerous studies have demonstrated the plant’s high agronomic adaptability to adverse climatic conditions, such as elevated soil salinity, extreme temperatures, and drought, all of which are exacerbated by climate change [19,20]. Furthermore, quinoa has been shown to tolerate salinity levels exceeding those of seawater [21], which enhances its resilience to various abiotic stresses [22]. In a greenhouse experiment, Ref. [23] compared the salinity tolerance of Chenopodium quinoa with that of the model halophyte Thellungiella halophila, finding that quinoa exhibited superior tolerance to salt stress under the tested conditions.
In addition to its resilience to salinity, quinoa’s phytoremediation potential makes it particularly suitable for graywater treatment. Studies have demonstrated quinoa’s ability to accumulate heavy metals, such as cadmium (Cd) and lead (Pb), when exposed to varying concentrations of these contaminants [24]. As soil concentrations of Cd and Pb increased, so did their accumulation in quinoa tissues, while the dry weight of stems, roots, and seeds decreased under elevated metal levels. Similarly, Ref. [25] reported that quinoa can accumulate Cd in both its root and aerial tissues, with the accumulation increasing proportionally to soil Cd concentrations. Other research has indicated that quinoa, like many plants, has the capacity to accumulate substantial amounts of heavy metals, particularly Ni, Cr, and Cd, in its leaves [26]. Notably, quinoa has been classified as a hyperaccumulator for six trace metals, with a bioconcentration factor exceeding 1 [27]. When compared to other plants such as Brassica napus and Eichhornia crassipes, quinoa’s adaptability to a wide range of contaminants, combined with its superior salinity tolerance, positions it as a more versatile candidate for phytoremediation [28]. While aquatic species like Eichhornia crassipes and Centella asiatica exhibit notable removal efficiencies for specific pollutants, such as E. crassipes, removing up to 98% of phosphates in aquaculture wastewater [29]. Quinoa’s ability to stabilize and accumulate heavy metals makes it a more comprehensive solution. Additionally, plants like Lemna minor and Salvinia minima have demonstrated high removal efficiencies for total suspended solids and ammonia nitrogen in wastewater [30], but quinoa’s enhanced ability to sequester heavy metals positions it as a superior option for treating contaminated soils. Considering these factors, particularly its tolerance to salinity and its phytoremediation capabilities, quinoa was selected as the ideal plant for our nature-based cyclic graywater treatment system, offering both environmental sustainability and enhanced treatment performance.
However, there are practically no reported studies with valid scientific experiments examining the effects of cyclic treatment on quinoa, soil, and drainage water characteristics. Thus, the aim of the present study is to evaluate the efficiency of cyclic graywater treatment in improving water quality at the farm level. Specifically, the study will assess the impact of irrigation with raw versus treated graywater from each treatment level on soil characteristics (pH, ECe, BOD5, COD, and contaminants) under various conditions (with and without plants). Additionally, it will investigate the changes in drainage water characteristics (volume, pH, EC, soluble salts, BOD5, COD, and microbiological parameters). Finally, the study will analyze quinoa growth parameters for each treatment level. It is hypothesized that cyclic graywater treatment will improve soil and drainage water quality, while influencing quinoa growth positively under varying conditions of irrigation.

2. Materials and Methods

The experimental phase took place from 29 March to 25 August 2019, at the National Institute for Research in Rural Engineering, Water and Forests (INRGREF, LR VENC, Ariana, Tunisia) (Latitude: 36.8496053, longitude 10.1951971) on two trials (without plants from 29 March to 25 May 2019 and with plants from 29 May to 25 August 2019) in plastic pots (diameter: 32 cm, height: 30 cm) in natural greenhouse conditions. Each pot was equipped with holes in the bottom to facilitate the evacuation of drainage water. Each pot was equipped with holes in the bottom to facilitate the evacuation of drainage water. The dates of the experiment were chosen according to the quinoa growth cycle, which usually begins in spring.
Irrigation water quality: The graywater used for irrigation was supplied from a household in the Soukra area Tunisia, where the treatment unit of graywater was initially set up in 2007 as part of a project called “Pure valorization of rainwaters and graywater’s in Soukra” (brought back from the raw graywater storage basin of a farmer’s household). The purification treatment was biological and the vertical treatment included the use of macrophyte plants. Before irrigation and before cyclic treatment, the raw graywater was analyzed; its characteristics are given in Table 1. The irrigation water quality met most of the NT 106.03 standards. The pH value of 7.69 was within the acceptable range (6.5 to 8.5), and the electrical conductivity (EC) of 2.41 dS/m was below the 7 dS/m threshold, indicating low salinity, which is suitable for agriculture. While there are no specific NT 106.03 limits for anions like bicarbonates (8.45 meq/L), chlorides (11.79 meq/L), and sulfates (4.65 meq/L), their concentrations could lead to soil salinity buildup, requiring monitoring. The sodium concentration (13.18 meq/L) was relatively high and could impact soil structure, but the Tunisian standard (NT 106.03) and the EU standard do not set a direct sodium limit. Other cations, such as potassium (0.67 meq/L), calcium (5.96 meq/L), and magnesium (5.42 meq/L), were within normal ranges. Heavy metals like copper (Cu), manganese (Mn), iron (Fe), and zinc (Zn) were within limits, but nickel (Ni), cobalt (Co), chromium (Cr), and cadmium (Cd) exceeded the NT 106.03 thresholds and could pose risks of soil accumulation and plant growth issues. In summary, the water was generally acceptable, but the elevated levels of sodium and heavy metals required careful monitoring or treatment.

2.1. Description of the Experimental System for Cyclic Treatment of Gray Water

The cyclic graywater treatment system (CGTS) is an ecological treatment system designed and constructed to use natural processes with gravel filters, soil, and vegetation (quinoa) for graywater treatment [12]. This system is appropriate for treating and reusing on-site graywater (Figure 1).
The process consists of seven pots that have been arranged in a cascade (staircase) to ensure 3 levels of graywater treatment, allowing the irrigation of quinoa with 3 qualities of water (GWT1: graywater drained from the first treatment; GWT2: graywater drained from the second treatment; and GWT3: graywater drained from the third treatment). The plastic pots have a diameter of 32 cm and a height of 30 cm. Every pot features an aperture to let drainage water escape and a gravel bed at the bottom. A geotextile filter is placed on top of a 3 cm thick layer of gravel in each pot to filter the drained water. This thickness and the usage of geotextile to enable proper water drainage have been made possible by preliminary testing. A 25 cm thick layer of soil weighing 20 kg sits on top of this gravel layer.
The first-stage treatment (T1) consists of four pots that received untreated graywater for preliminary filtration and partial treatment. Two more pots, representing the second treatment level (T2), are irrigated with the effluent from these pots. This is a cascade system, in which the second step benefits from the water that has already been processed in the first stage. Water from the second level is used to irrigate the single pot in the final stage, which is treated at the third level (T3).

2.2. Sampling and Analyses

To evaluate the phytoremediating potential of quinoa, water samples were taken during two trials: one with no plants, from 29 March to 25 May 2019, and another with a quinoa plant, from 29 May to 25 August 2019. These dates were selected based on quinoa’s development cycle, which typically starts in the spring. Graywater collected from the Soukra household on the day of irrigation and drained water from each treatment level (GWT1, GWT2, and GWT3) made up the irrigation water. Graywater from the system’s several cycle treatment levels (T1, T2, and T3) was subjected to quality checks. To ensure the reliability and representativeness of the results, a comprehensive sampling strategy was employed throughout the trial period. Sampling was conducted across two distinct experimental conditions (with and without plants), three treatment levels, and four different graywater qualities (one raw graywater and three water qualities from each treatment level). This led to a total of 112 samples collected over 14 irrigation events. Following sample collection, the samples were placed in a refrigerator for chemical, biological, and microbiological analysis.
At each treatment level, the samples were gathered in sterile glass bottles with a capacity of one liter. pH, soluble salts (Ca2+, Mg2+, Na+, K+, HCO3−, SO42−, Cl−), heavy metals (Cd, Cu, Co, Cr, Fe, Mn, Ni, Pb, Zn), two biochemical parameters (chemical oxygen demand (COD), biological oxygen demand (BOD), electrical conductivity (ECw), and microbiological characterization, (fecal coliforms (FC)), total coliforms (TC), Escherichia coli (E. coli), and fecal streptococci (FS)) were among the analyses. Using a direct reading pH meter and the electrometric method, the pH was measured (NF ISO 10.390 2005). A pen-type conductivity meter (model 8361, Cond. & TDS) was used to measure ECw. Complexometry was used to detect calcium (Ca2+) and magnesium (Mg2+) while ethylenediaminetetraacetic acid (EDTA) was present. To measure sodium (Na+) and potassium (K+), a JENWAY PFP7 flame photometer (NF-A20-603) was used. Titration with sulfuric acid (H2SO4), in the presence of phenolphthalein and methyl orange, was used to detect bicarbonate (HCO3−). In the presence of silver nitrate (AgNO3), precipitation titration was used to quantify chloride (Cl−). Using the UV-VIS spectrometer set to 650 nm, sulfate (SO42−) was measured by nephelometry in the presence of 0.1 N hydrochloric acid. Atomic absorption spectrometry (Perkin Elmer) and flame emission spectrophotometry (Jenway, PFP7) were used to measure the levels of heavy metals. Using an OxiTop (Inductive Stirring System), BOD5 and COD were measured in accordance with the May 1998 version of the NF EN 1899-1 standard. Bacteriological analyses were performed at the Tunisian private laboratory MULTILAB. The Multiple Tube Fermentation method (Most Probable Number, MPN) was employed to estimate microorganism concentrations based on the number of positive tubes showing turbidity. This method was used to quantify TC, FC, FS, and E. coli, which are critical indicators of water quality. The number of positive tubes observed was used to determine the MPN values according to McGrady’s table [31]. The specific culture media and incubation conditions used for each microorganism were as follows. For Coliforms, the presumptive test was performed using lactose broth, and the confirmatory test was performed using Brilliant green bile lactose broth (BLBVB), incubated at 37 °C for 48 h, followed by 44 °C for 48 h. For Streptococci, the presumptive test was performed with Rothe medium and the confirmatory test with Litsky medium, incubated at 37 °C for 48 h, followed by 37 °C for 24 to 48 h. The same method was applied to the soil samples, using the extraction from the saturated paste to quantify the pathogenic bacteria in soil.
The soil samples were taken near the end of the quinoa cycle in late August 2019 to describe the effects of irrigation with treated graywater on the soil. The samples were taken at a pace of two samples per pot, or roughly 500 g per sample. Fifteen samples were gathered in total. Every sample was gathered in plastic bags with numbers on them. A number of indicators, including pH, ECe, soluble salts, total nitrogen, and TMEs, were examined in order to ascertain the impact of graywater irrigation on the soil. A sample suspended in distilled water (1:2.5; w:w) was used to measure the pH values of the soil at 25 °C. The Riverside Laboratory’s suggested approach (US Salinity Laboratory Staff; Richards, 1954) was used to test the electrical conductivity of saturated paste extract (ECe). The different anions and cations were then chemically analyzed, and ECe measurements were performed using the extract from the saturated paste. Following the same guidelines as for the water samples, the main components (soil soluble salts) found in the soil solutions—chlorides, calcium, magnesium, sodium, bicarbonates, potassium, and sulfates—were identified. Atomic absorption was used to assess TMEs in accordance with ISO 14869-1 (2001).
The quinoa plants were at their largest growth at the conclusion of the experiment (Figure 1). After that, they were harvested in order to assess the impact of watering using various treated graywater qualities. After carefully removing the roots from the ground, distilled water was used to wash them (Figure 2). The samples were divided into four categories: fruits, stems, leaves, and roots. Weighing was used to determine the plants’ fresh biomass (FB) and dry biomass (DB) after 48 h of the fresh matter being baked at 60 °C. For the various chemical studies, the various dried plant pieces were prepared by finely grinding them. After meticulous cleaning with distilled water, various plant parts—leaves, stems, and roots—were retrieved. Flame emission spectrometry was used to identify the main components. The MOHR approach, which is based on the idea of identifying chlorides using silver nitrate in the presence of potassium chromate, was used to determine the chloride ions. Atomic absorption spectrometry was used to measure calcium and magnesium ions in accordance with ISO 14869-1 (2001). Using vanado-molybdate colorimetry, the extraction product’s total phosphorus content was ascertained. Flame atomic absorption spectrometry was used to identify the metals.

2.3. Statistical Analyses

A SPSS 27 software was used to statistically analyze the data recovered from the various chemical analyses of the plant, soil, and graywater samples. The Tukey test was used to compare the means at the 0.05 confidence level in the statistical analysis of some of the collected data (BM SPSS statistics, v20).

3. Results

3.1. Characterization of Drained Graywater

3.1.1. Volume of Drained Graywater

The average volumes drained from the first treatment (GWT1), the second treatment (GWT2), and the third treatment (GWT3) were, respectively, 3.10 L, 2.41 L, and 1.96 L. With each treatment step, the average volume dropped significantly.
The drained volumes (Vn) were compared to the initial volume (V0). At the first treatment stage, the volume of GWT1 was approximately 78% of the initial volume (RGW), GWT2 was about 60%, and GWT3 was approximately 49%. A highly significant correlation was observed between the volumes (Figure 2). The volume drained at each stage, compared to the previous one (Vn-1). It ranged from 78% to 81%, with an average of 79%. The volumes followed a geometric sequence, with each term obtained by multiplying the previous one by a constant ratio. The geometric sequence is as follows:
Vn = qn·V0,
where q = 0.79, which is the calculated average value. The values derived from the previous equation closely matched the measured values.

3.1.2. Characterization of Drained Graywater

The results show that the average pH values were 7.69, 7.73, 7.92, and 7.94 for, respectively, RGW, GWT1, GWT2, and GWT3. These values comply with Tunisian regulation NT 106.03 and the World Health Organization’s guidelines (WHO, 2006), which set the pH range between 6.5 and 8.5. The pH increased slightly with each treatment, though not significantly (Table 2). For electrical conductivity (EC), the average values were 2.41, 2.85, 3.43, and 3.78 dS/m for RGW, GWT1, GWT2, and GWT3, respectively, showing a significant increase with each treatment and meeting the regulatory standards. The dry residue followed the same trend as the EC, with average values of 1.4, 2.36, 2.60, and 2.97 g/L for RGW, GWT1, GWT2, and GWT3, respectively.
The soluble salts showed significant variability in TGW quality due to the variability of raw graywater. The average values of anions and cations increased with each treatment. In addition, the ionic composition of the various graywater qualities reflected a sodium chloride facies, which remained unchanged after the different treatment stages. For SAR, the values were 5.6, 5.7, 6.4, and 7.1 for RGW, GWT1, GWT2, and GWT3, respectively, with slight, non-significant increases. SAR values for the treated graywater remained below 10. The measured values, calculated values, and the q factor for each parameter (U) are presented in Table 3. It can be observed that the different parameters also followed a geometric progression model:
Un = qn·U0.
Following the application of the geometric progression, it is observed that all the deduced values closely aligned with the measured values.
Significant correlations were observed between the different qualities of graywater for each parameter, as detailed in Table 3. The results indicate highly significant correlations across the various graywater qualities (RGW, GWT1, GWT2, and GWT3) for all the parameters analyzed.

3.1.3. Variation in the Biological and Microbiological Properties of Graywater

The results for the biological parameters (five-day biochemical oxygen demand (BOD) and chemical oxygen demand (COD)) and the four microorganisms (fecal coliforms (FC), total coliforms (TC), Escherichia coli (E. coli), and fecal streptococci (FS)) are presented in Figure 3. The average BOD values are 31.33, 24.82, 20.63, and 15.74 mg O2/L for RGW, GWT1, GWT2, and GWT3, respectively, complying with Tunisian regulation NT 106.03 and the WHO’s guidelines (30 mg O2/L). A significant decrease in BOD5 was observed with each treatment stage. For COD, the average values were 102.64, 88.85, 74.99, and 54.19 mg O2/L for RGW, GWT1, GWT2, and GWT3, respectively, also meeting the 90 mg O2/L limit set by both regulations. The microbial load of graywater, including FC, TC, E. coli, and FS, was higher in raw graywater compared to treated graywater. Cyclic treatment significantly reduced these microorganisms (Figure 3). For FC, the average decreased from 2.41 log10/100 mL in RGW to 0.57 log10/100 mL in GWT3. Similarly, TC dropped from 1.92 to 0.41 log10/100 mL, E. coli from 0.87 to 0.58 log10/100 mL, and FS from 1.96 to 0.56 log10/100 mL.

3.2. Effect of Irrigation with Graywater on Quinoa

3.2.1. Effect on Soil Parameters

The soil samples were collected before and after quinoa planting to examine the changes in soil salinity under cyclic irrigation with treated graywater. The average electrical conductivity of the saturated paste extract (ECe) was calculated for each cyclic treatment level (Figure 4). Overall, soil salinity increased compared to the initial value (ECe = 0.99 dS/m), with significant increases in all trials. Figure 5 shows considerable variation in ECe based on the graywater quality and treatment level, following the same trend for both cultivated and bare soils. The average ECe increased from 2.58 to 4.10 dS/m in bare soils and from 2.80 to 4.36 dS/m in cultivated soils.
Similarly to the drained graywater section, the geometric sequence was applied to evaluate the effect of irrigation with treated graywater. Measured and calculated values for ECe followed a geometric model with low common ratios (q). Significant correlations were found between the same parameters at different treatment levels for both trials (bare and cultivated soil). This study compares the soil Ece as the function of cyclic treatment for two types of soil: bare soil and cultivated soil. For bare soil, the relationship is described by the equation y = 1.045x + 0.23, with a q value of 1.72 and a coefficient of determination R2 = 0.94, indicating a strong correlation. For cultivated soil, the equation y = 1.086x + 0.21 also shows a high correlation, with a q value of 1.77 and a coefficient of determination R2 = 0.94. These results suggest that both soil types respond similarly to cyclic treatment. This consistency, in response, highlights the reliability of the geometric model in predicting Ece under varying conditions.
Table 4 shows the average concentrations of contaminant microbes (FC, TC, E. coli, and FS) in the soil. Soil irrigated with treated graywater via cyclic treatment system showed a significant reduction in pathogenic bacteria: 91%, 73%, 69%, and 84% for FC, TC, E. coli, and FS, respectively. The levels at the third treatment stage met the WHO recommended thresholds for the reuse of treated wastewater in agriculture, which include a maximum of 1000 MPN/100 mL for TC, 100-1000 MPN/100 mL for E. coli, and 35 MPN/100 mL for FS.
Similarly to the previous section, a geometric sequence was applied to the microbial data to assess the impact of irrigation with treated graywater. A strong correlation was observed between the measured and calculated values for all microbes in the soils (Table 5), following a geometric model with low common ratios (q). Correlations were established for each microbe across the different treatment levels (Table 5). These correlations were negative and statistically significant.

3.2.2. Effect on Quinoa

The results show a significant decrease in the average diameter of quinoa plants irrigated with graywater (GWTP1 and GWTP2) compared to the control (Figure 5). The average diameters were 0.58, 0.50, and 0.36 for the control, GWTP1, and GWTP2, respectively. The average plant lengths were 48.73, 44.41, and 33.07, and root lengths were 15.73, 14.46, and 10.09 for the control, GWTP1, and GWTP2, respectively. Graywater irrigation caused a 26% and 30% decrease in aerial and root lengths, with a significant decrease in GWTP2.
Irrigation with graywater significantly impacted the fresh (FB) and dry biomass (DB) of quinoa organs. The control plants exhibited the highest fresh biomass compared to those irrigated with treated graywater (GWTP1 and GWTP2) (Figure 6). For fresh biomass, reductions of approximately 32% in roots, 55% in stems, 37% in leaves, and 27% in seeds were observed in quinoa plants irrigated with treated graywater. Comparable reductions of 33%, 45%,62%, and 32%, respectively, were recorded for the corresponding dry biomass when comparing irrigation with RGW to treated graywater.
The results (Figure 7) show that irrigation with TGW significantly increased Na+ levels: 37% in roots, 3% in stems, 16% in leaves, and 26% in seeds. Potassium was more abundant in leaves, with a notable increase in K+ concentrations, especially with GWTP2: 19% in roots, 66% in stems, 37% in leaves, and 50% in seeds. In addition, irrigation with treated graywater resulted in increased phosphorus (P) concentrations, with a 20% increase in roots, 32% in stems, 50% in leaves, and 64% in seeds at GWTP2. Regarding nitrogen (N), the highest concentrations were found in the roots and seeds. Irrigation with treated graywater led to a significant increase in N content across quinoa organs, with a 63% increase in roots, 53% in stems, 21% in leaves, and 32% in seeds. The results also show that irrigation with treated graywater significantly enhanced the accumulation of calcium (Ca2+) and magnesium (Mg2+) in quinoa. Calcium is primarily concentrated in roots, leaves, and stems, with a decrease in seeds. TGW treatment increased Ca2+ levels by 9% in roots, 38% in stems, 17% in leaves, and 18% in seeds. Magnesium content was also elevated, with increases of 11% in roots, 74% in stems, 41% in leaves, and 76% in seeds. These findings highlight the positive effect of irrigation with treated graywater on mineral uptake in quinoa.

4. Discussion

The experiment aimed to assess the quantity and quality of graywater treated by a cyclic treatment system (GWT1, GWT2, and GWT3), with a focus on its potential for reuse, particularly in terms of its effects on soil properties and quinoa growth. The results revealed several noteworthy trends that merit further scientific discussion.
The treatment process yielded graywater with a slightly alkaline pH, demonstrating minimal variation across treatment stages (7.7–7.9). This suggests that the cyclic treatment system effectively stabilizes the pH, keeping it within acceptable limits for agricultural reuse. This finding is consistent with the typical pH range observed in graywater treatment, which usually falls between 7.5 and 8, indicating a neutral to slightly alkaline quality suitable for various applications. This pH range is important for ensuring the safety and effectiveness of graywater reuse, particularly in agricultural contexts, as reported in previous studies [28,29]. According to [32], a neutral to slightly alkaline pH is beneficial for agricultural irrigation, as it minimizes the risk of soil acidification and promotes healthy plant growth. However, a marked increase in EC, especially in GWT3 compared to GWT1, indicates that the treatment leads to an accumulation of dissolved salts, particularly in the later stages of the process. These results are in line with previous research, which also documented increased salinity after graywater treatment [33,34], reinforcing the idea that salinity control is a key challenge in graywater reuse for irrigation. The increase in EC of treated graywater is influenced by various electrochemical processes, particularly through methods such as electrocoagulation and high-voltage electric fields. These treatments not only enhance the EC but also improve the overall quality of graywater for potential reuse. The reduction in BOD5 and COD across all treatment stages highlights the efficacy of cyclic treatment in reducing organic pollutants. In our experiment, the raw graywater initially exceeded the COD limits outlined in the NT 106.03 standard, but the treatment process progressively brought COD levels into compliance with these regulatory thresholds. This suggests that the cyclic treatment system is effective at removing organic contaminants, a critical factor for ensuring that treated graywater meets water quality standards for agricultural reuse. The significant reduction in BOD5 and COD also aligns with previous findings on the benefits of treatment systems in reducing organic load [35,36]. Similarly, Ref. [29] reported the efficiency of a graywater treatment system based on aeration and filtration in removing the BOD5 and COD parameters was 98–100 and 76–100%, respectively. Although the cyclic treatment system successfully removed a substantial proportion of targeted pathogens, the microbial load in the treated graywater still failed to meet the stringent standards set by the WHO. This underscores a critical limitation of the system, as effective pathogen removal is essential for ensuring safe reuse of graywater in agriculture. The gap between the treatment’s performance and the WHO’s guidelines suggests the need for further research to optimize microbial disinfection processes and enhance the overall safety of treated graywater [37]. In fact, various treatment methods have been explored, demonstrating significant effectiveness in pathogen reduction. According to [38], multi-stage filtration, including sand, activated charcoal, and coconut husk, achieved 100% removal of E. coli and coliforms in treated graywater. Moreover, Green roofs combined with chlorination effectively reduced total coliforms by up to 1.2 log units, with chlorination ensuring pathogen inactivation for up to three days [39]. Other studies have highlighted the efficiency of sand filtration and granulated blast furnace slag, achieving substantial reductions in both total and fecal coliforms, which significantly lowers health risks associated with graywater reuse [40]. Furthermore, staircase wetlands have shown impressive removal rates of 90–99% for total coliforms, while also contributing to improved soil microbial health, offering a dual benefit of graywater treatment and enhanced soil quality [41].
The influence of graywater irrigation on soil properties was assessed through a comparative analysis of soils irrigated with raw graywater and those irrigated with treated graywater. The results showed a significant increase in soil salinity (measured as EC of the extract, ECe) at all treatment levels, with a direct correlation to the increased EC of the treated graywater. This finding is consistent with previous studies, which have reported that graywater reuse tends to elevate soil salinity [42]. Research suggests that treated graywater can promote plant growth without causing significant changes in soil salinity, thereby maintaining a stable ionic balance [43]. Despite slight variations in pH, the increase in salinity poses a challenge for long-term soil health and agricultural productivity. Monitoring and managing soil salinity are, therefore, crucial for sustainable graywater irrigation.
The non-significant reduction in vegetative growth parameters (e.g., stem diameter, plant height, root length, fresh and dry weight) in quinoa irrigated with treated graywater suggests that increased salinity may have negatively impacted plant development. This observation is consistent with previous studies highlighting the adverse effects of salinity on plant growth [44]. In a similar context, Ref. [45] found that elevated salinity levels in irrigation water led to a noticeable reduction in plant height as well as in both fresh and dry weight across all three quinoa varieties studied. These findings emphasize the detrimental effects of salinity stress on quinoa’s growth performance. Although quinoa is recognized for its salinity tolerance [46,47]. Ref. [45] reported that despite the negative impact of salinity on growth parameters, quinoa yield remained unaffected by the increasing the EC of the irrigation water. This suggests that quinoa may possess a unique resilience to salinity, allowing it to sustain yield even under elevated salinity conditions. However, the observed decline in growth parameters in the present study suggests that excessive salinity could still limit quinoa’s overall performance. Further research is needed to evaluate the long-term effects of graywater irrigation on quinoa yield and quality.
The analysis of the mineral composition in quinoa irrigated with cyclically treated graywater reveals significant patterns of ion accumulation within its tissues. Notably, quinoa demonstrates a strong capacity to accumulate ions, which has been previously documented [48]. The preferential accumulation of Na+, Ca2+, and N in the roots suggests that quinoa may employ these tissues as reservoirs for essential macronutrients, potentially as a mechanism to minimize their toxic effects on other plant tissues. The accumulation of P and Mg2+ in the seeds reflects the plant’s strategy of channeling these nutrients into reproductive structures, which is consistent with their role in energy transfer, DNA synthesis, and cellular metabolism during seed development. The observed pattern of K+ accumulation, with initial concentration in the leaves followed by the roots, aligns with the typical physiological processes of potassium transport and storage in plants. Potassium is crucial for enzymatic activity and osmoregulation, particularly under saline conditions, and its movement into the roots may help mitigate the effects of salinity stress by maintaining cellular homeostasis.
The review of previous studies shows that the accumulation of ions in quinoa organs, particularly when irrigated with graywater, is significantly influenced by salinity levels and irrigation methods. Ref. [49] demonstrated that increasing salinity ECᵢ from 0.3 to 25 dS m⁻1 resulted in significant accumulation of sodium and chloride ions in quinoa shoots, with increases of 82.2% and 75.8%, respectively. In seeds, Na and Cl also showed notable increases (43.3% and 50%, respectively) under higher salinity, while calcium levels remained stable [49]. These findings highlight that salinity stress affects both the vegetative and reproductive stages of quinoa growth, as evidenced by the significant increases in Na+ and Cl+ concentrations in the seeds [50]. Additionally, Ref. [50] emphasized that seeds play a crucial role in nutrient content, with nitrogen and potassium being more concentrated in the shoots, which are essential for supporting overall plant growth. When comparing the findings of this study to previous research, such as [24], which reported significantly higher phosphorus content in quinoa compared to other crops like wheat and maize, it suggests that quinoa may possess a specialized nutrient uptake mechanism. This mechanism allows quinoa to efficiently manage and accumulate essential nutrients, particularly in saline environments. Additionally, Ref. [51] highlighted quinoa’s unique capacity for phosphorus acquisition, with genotypic variations showing phosphorus accumulation ranging from 1.2 to 11.8 mg. This ability to accumulate and compartmentalize ions may provide quinoa with a competitive advantage under conditions of nutrient stress or suboptimal soil quality, further reinforcing its reputation for salt tolerance and resilience.
Finally, the study identified a geometric progression in the quality of graywater through the cyclic treatment stages, which could be used to predict the quality of graywater beyond the three treatment levels tested. The observed significant correlations between the parameters analyzed provide a foundation for modeling the evolution of graywater quality. This predictive framework is valuable for optimizing treatment systems and ensuring that graywater can be safely reused in agricultural irrigation, particularly in regions facing water scarcity.
The cyclic treatment system demonstrated significant potential in improving the quality of graywater, challenges such as salinity control and pathogen removal remain. Future research should focus on refining the treatment process to address these issues and ensure the sustainable use of treated graywater in agriculture.

5. Conclusions

Treated graywater, when processed through simple and natural methods, such as the cyclic treatment system, offers a promising sustainable solution for addressing water stress in agriculture. To explore the effects of cyclic-treated graywater on soil and plant physicochemical properties, a series of measurements and analyses were conducted on samples from experimental pots exposed to different treatment levels (T1, T2, and T3). The data were correlated with the graywater irrigation treatments (GWT1, GWT2, GWT3) and the cultivation status (cultivated vs. bare soil).
Cyclic treatment of graywater demonstrated several benefits, including reductions in pH, biochemical indicators (COD, BOD), and pathogenic microorganisms, while EC increased. Notably, soil ECe also increased significantly at each treatment stage, signaling changes in salinity and alterations in the chemical composition of the soil solution. Irrigation with treated graywater led to a substantial reduction in soil pathogenic bacteria. Additionally, quinoa biomass was notably affected by the graywater treatments, with the control group exhibiting the highest fresh biomass compared to the plants irrigated with treated graywater. The graywater treatment further enhanced quinoa growth by significantly increasing the levels of minerals such as calcium and magnesium, with variations across different plant organs and treatment levels.
Overall, this study demonstrates that graywater irrigation through the cyclic treatment system represents a viable approach for sustainable agricultural practices. This method offers an effective strategy for utilizing non-conventional water sources in agriculture, contributing to the mitigation of global water limitations, particularly in arid regions. By reserving high-quality freshwater for human consumption, it plays a crucial role in conserving limited water resources and reducing pressure on freshwater supplies.
However, to scale this system for real-world applications, further research is necessary to evaluate long-term field trials in various agricultural settings and climatic conditions. Implementing this system on a larger scale will require careful consideration of site-specific factors, such as soil type, crop species, and local water availability. Economic feasibility also plays a critical role in scaling up, as the costs of installation, operation, and maintenance must be balanced with the benefits, including the potential reduction in reliance on potable water for irrigation. A thorough cost–benefit analysis would be essential to determine the long-term economic viability of widespread graywater use.
Future studies should explore advanced treatment technologies, such as UV disinfection or advanced filtration systems, to enhance pathogen removal efficiency and minimize any health risks associated with graywater reuse. Furthermore, research should focus on the long-term impacts of graywater irrigation on soil health, particularly the effects on soil microbial communities, nutrient cycling, and sustainability of crop yields over multiple growing seasons. Investigating the integration of graywater treatment systems with existing agricultural practices, such as organic farming, would also be an important avenue for future research.

Author Contributions

Conceptualization, H.F. and M.H.; methodology, H.F.; software, M.M.; validation, H.F, N.B. and M.H.; formal analysis, O.I.; investigation, H.F.; writing—original draft preparation, H.F.; writing—review and editing, N.B.; visualization, V.N.; supervision, M.H. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Data Availability Statement

All data used is in the article are available upon request.

Acknowledgments

The authors would like to express their gratitude to the technical staff of the Laboratory of Valorization of Non-Conventional Water (LR VENC), INRGREF (Tunisia), for their unwavering technical support. Our heartfelt thanks also go to the entire team of the Department of Engineering at Vasile Alecsandri University of Bacau (Romania) for their fruitful collaboration. Finally, we extend our sincere appreciation to the Agence universitaire de la Francophonie (AUF), whose support made this Tunisian–Romanian collaboration possible.

Conflicts of Interest

The authors declare no conflicts of interest.

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Applsci 15 02836 g001
Figure 1. Cyclic Graywater Treatment System.
Figure 1. Cyclic Graywater Treatment System.
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Figure 2. Correlation between graywater volumes and treatment. RGW: raw graywater; GWT1: graywater drained from the first treatment; GWT2: graywater drained from the second treatment; and GWT3: graywater drained from the third treatment.
Figure 2. Correlation between graywater volumes and treatment. RGW: raw graywater; GWT1: graywater drained from the first treatment; GWT2: graywater drained from the second treatment; and GWT3: graywater drained from the third treatment.
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Figure 3. Variation in biological parameters and microbiological water quality of graywater depending on the treatment. (a) BOD; (b) COD; (c) fecal coliforms (FC); (d) total coliforms (TC); (e) E. coli; (f) fecal streptococci (FS); RGW—raw graywater; GWT1: graywater drained from the first treatment; GWT2: graywater drained from the second treatment; and GWT3: graywater drained from the third treatment.
Figure 3. Variation in biological parameters and microbiological water quality of graywater depending on the treatment. (a) BOD; (b) COD; (c) fecal coliforms (FC); (d) total coliforms (TC); (e) E. coli; (f) fecal streptococci (FS); RGW—raw graywater; GWT1: graywater drained from the first treatment; GWT2: graywater drained from the second treatment; and GWT3: graywater drained from the third treatment.
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Figure 4. Variation in soil ECe according to the cyclic treatment.
Figure 4. Variation in soil ECe according to the cyclic treatment.
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Figure 5. Variation in quinoa growth parameters. (a) Aerial parts; (b) root part.
Figure 5. Variation in quinoa growth parameters. (a) Aerial parts; (b) root part.
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Figure 6. The variation in (a) fresh and (b) dry biomass of the different quinoa organs (roots, stems, leaves, and seeds).
Figure 6. The variation in (a) fresh and (b) dry biomass of the different quinoa organs (roots, stems, leaves, and seeds).
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Figure 7. Evaluated parameters concentrations variation in roots, stems, leaves, and seeds of quinoa. (a) Na+; (b) K+; (c) P; (d) N; (e) Ca2+; (f) (Mg2+).
Figure 7. Evaluated parameters concentrations variation in roots, stems, leaves, and seeds of quinoa. (a) Na+; (b) K+; (c) P; (d) N; (e) Ca2+; (f) (Mg2+).
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Table 1. Chemical characteristics of irrigation raw graywater (RGW). The values are the means ± standard deviation of 14 replications (n = 14).
Table 1. Chemical characteristics of irrigation raw graywater (RGW). The values are the means ± standard deviation of 14 replications (n = 14).
ParametersMeans ValuesNT106.03
pH7.69 ± 0.536.5–8.5
EC (dS/m)2.41 ± 0.457
Anions (meq/L)--
HCO3−8.45 ± 2.42
Cl−11.79 ± 2.46
SO42−4.65 ± 2.12
Cations (meq/L)--
Na+13.18 ± 3.97
K+0.67 ± 0.33
Ca2+5.96 ± 1.34-
Mg2+5.42 ± 1.93-
Heavy metals (mg/L)--
Cu0.010 ± 0.000.5
Mn0.015 ± 0.220.5
Fe0.066 ± 0.035
Pb0.047 ± 0.001
Ni0.042 ± 0.000.2
Co0.027 ± 0.010.1
Zn0.014 ± 0.005
Cr0.0240.1
Cd0.0080.01
Table 2. Variation in physicochemical parameters based on treatment. C was calculated and M was measured.
Table 2. Variation in physicochemical parameters based on treatment. C was calculated and M was measured.
UTreatment
RGWGWT1GWT2GWT3Ratio q
pHM7.69 ± 0.53 a7.73 ± 0.59 a7.92 ± 0.49 a7.94 ± 0.29 a1.01
C7.697.777.867.94
ECM2.41 ± 0.45 a2.85 ± 0.46 b3.43 ± 0.39 c3.78 ± 0.32 c1.16
C2.412.803.263.79
TDSM1.40 ± 0.45 a2.39 ± 0.46 b2.6 ± 0.39 c2.97 ± 0.32 c1.31
C1.41.842.413.16
Cl−M11.79 ± 2.46 a14.9 ± 2.65 b17.52 ± 2.90 bc19.8 ± 2.75 c1.19
C11.7914.0316.6919.86
SO42−M4.65 ± 2.12 a6.18 ± 2.24 ab7.8 ± 2.19 bc8.73 ± 2.03 c1.24
C4.655.757.118.8
HCO3−M5.93 ± 2.42 a6.89 ± 2.12 ab8.8 ± 1.87 bc10.25 ± 1.85 c1.20
C5.937.138.5610.29
Na+M13.18 ± 3.97 a15.23 ± 4.31 ab18.6 ± 3.98 bc21.88 ± 3.30 c1.18
C13.1815.6118.4921.9
Ca2+M5.96 ± 1.34 a7.45 ± 1.39 b9.14 ± 1.76 bc10.03 ± 1.43 c1.19
C5.967.098.4410.04
Mg2+M5.42 ± 1.93 a6.91 ± 2.12 ab7.75 ± 1.82 bc9.38 ± 2.25 c1.20
C5.426.527.839.42
K+M0.67 ± 0.33 a0.63 ± 0.26 a0.92 ± 0.39 ab1.04 ± 0.31 b1.18
C0.670.790.931.09
SARM5.6 ± 1.8 a5.7 ± 1.6 a6.4 ± 1.3 a7.1 ± 1.3 a1.08
C5.66.16.67.1
Table 3. Correlations between the different physicochemical parameters based on graywater treatment; y = ax + b.
Table 3. Correlations between the different physicochemical parameters based on graywater treatment; y = ax + b.
yabR2
pH0.0947.5850.89
EC0.4691.940.99
TDS0.4921.110.89
Cl−2.6659.340.99
SO42−1.3863.3750.98
HCO3−1.3874.250.93
Na+2.9479.8550.91
Ca2+1.3974.670.98
Mg2+1.2724.1850.98
K+0.140.4650.83
SAR0.4935.0150.92
Table 4. Variation in soil microbiological quality as a function of the treatment.
Table 4. Variation in soil microbiological quality as a function of the treatment.
Bare Soil
Control SoilT1T2T3Ratio q
FCM2.04 ± 0.04 b 1.19 ± 0.05 c0.31 ± 0.01 a0.19 ± 0.07 a0.49
C2.040.990.480.23
TCM1.10 ± 0.09 a0.52 ± 0.02 c0.36 ± 0.10 b0.30 ± 0.04 b0.67
C1.10.730.490.33
E. coliM0.61 ± 0.031 b0.42 ± 0.19 a0.27 ± 0.09 a0.19 ± 0.09 a0.68
C0.610.410.280.19
FSM1.31 ± 0.02 b1.03 ± 0.02 b0.42 ± 0.20 a0.21 ± 0.03 c0.56
C1.310.340.190.11
Table 5. Correlations between soil pathogenic microbes as a function of cyclic treatment. Y = ax + b.
Table 5. Correlations between soil pathogenic microbes as a function of cyclic treatment. Y = ax + b.
yabR2
FC−0.6432.540.93
TC−0.2561.210.82
E. coli−0.1410.7250.97
FS−0.2561.210.81
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Filali, H.; Moussa, M.; Barsan, N.; Nedeff, V.; Irimia, O.; Hachicha, M. A Cyclic Graywater Treatment Model for Sustainable Wastewater Management Applied in a Small Scale. Appl. Sci. 2025, 15, 2836. https://doi.org/10.3390/app15052836

AMA Style

Filali H, Moussa M, Barsan N, Nedeff V, Irimia O, Hachicha M. A Cyclic Graywater Treatment Model for Sustainable Wastewater Management Applied in a Small Scale. Applied Sciences. 2025; 15(5):2836. https://doi.org/10.3390/app15052836

Chicago/Turabian Style

Filali, Hanen, Malak Moussa, Narcis Barsan, Valentin Nedeff, Oana Irimia, and Mohamed Hachicha. 2025. "A Cyclic Graywater Treatment Model for Sustainable Wastewater Management Applied in a Small Scale" Applied Sciences 15, no. 5: 2836. https://doi.org/10.3390/app15052836

APA Style

Filali, H., Moussa, M., Barsan, N., Nedeff, V., Irimia, O., & Hachicha, M. (2025). A Cyclic Graywater Treatment Model for Sustainable Wastewater Management Applied in a Small Scale. Applied Sciences, 15(5), 2836. https://doi.org/10.3390/app15052836

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