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Article

Performance Assessment of Natural Wastewater Treatment Plants by Multivariate Statistical Models: A Case Study

by
Mahmoud Gad
1,*,
Sayeda M. Abdo
2,
Anyi Hu
3,
Mohamed Azab El-Liethy
4,
Mohamed S. Hellal
5,
Hala S. Doma
5 and
Gamila H. Ali
2
1
Environmental Parasitology Laboratory, Water Pollution Research Department, National Research Centre, Giza 12622, Egypt
2
Hydrobiology Laboratory, Water Pollution Research Department, National Research Centre, Giza 12622, Egypt
3
CAS Key Laboratory of Urban Pollutant Conversion, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China
4
Environmental Microbiology Laboratory, Water Pollution Research Department, National Research Centre, Giza 12622, Egypt
5
Water Pollution Research Department, National Research Centre, Giza 12622, Egypt
*
Author to whom correspondence should be addressed.
Sustainability 2022, 14(13), 7658; https://0-doi-org.brum.beds.ac.uk/10.3390/su14137658
Submission received: 24 May 2022 / Revised: 14 June 2022 / Accepted: 16 June 2022 / Published: 23 June 2022
(This article belongs to the Special Issue Sustainability and Circular Economy: Frontier Research on Water Treatment and Purification)
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Abstract

:
Waste stabilization ponds (WSPs) as natural wastewater treatment plants are commonly utilized for wastewater treatment due to their simple design, low cost, and low-skilled operator requirements. Large-scale studies assessing the performance of WSPs using multivariate statistical models are scarce. Therefore, this study was conducted to assess the performance of 16 full-scale WSPs regarding physicochemical parameters, algae, bacterial indicators, and pathogens (e.g., Cryptosporidium, Entamoeba histolytica) by using multivariate statistical models. The principal component analysis revealed that the chemical pollutants were removed significantly (p < 0.001) through the treatment stages of 16 WSPs, indicating that the treatment stages made a substantial change in the environmental parameters. The non-multidimensional scale analysis revealed that the treatment stages restructured the bacterial indicators significantly (p < 0.001) in the WSPs, implying that the bacterial indicators were removed with the progress of the treatment processes. The algal community exhibited a distinct pattern between the geographical location (i.e., upper WSPs versus lower WSPs) and different treatment stages (p < 0.001). Four out of the sixteen WSPs did not comply with the Egyptian ministerial decree 48/1982 for discharge in agriculture drainage; three of these stations are in lower Egypt (M.K., Al-Adlia, and Ezbet El-Borg), and one is in upper Egypt (Armant). The continuous monitoring of WSPs for compliance with regulatory guidelines with the aid of multivariate statistical models should be routinely performed.

1. Introduction

There is a global lack of freshwater resources, which is aggravated in dry and semi-arid regions. This is attributable in part to population development, the increased pollution of water from human activities, and climate and weather variation [1,2]. Most rural areas of developing countries do not have reliable access to clean and safe water, so people often drink, wash their clothes, swim, and water their crops with polluted water [3,4]. Around 785 million people still lack primary drinking water infrastructure according to the World Health Organization (WHO), with 144 million people relying on surface water for their basic water needs. Consequently, surface water will continue to be exploited for household reasons [5], while others rely on the delivery of groundwater sources through water tankers. This unprotected water supply has been linked to the deaths of millions of children [6]. The microbiological and physicochemical quality of surface water, which is frequently compromised by various contaminants due to several human activities, is a major public health concern [7]. The release of untreated or inadequately treated wastewater is one of the major sources of surface water contamination [8].
Wastewater management is a significant problem in developing nations due to population increase, low levels of knowledge, urbanization, industry, low income, and insufficient expertise [9,10]. Unfortunately, an increase in the volume of wastewater generated is not always accompanied by an increase in the capacity of the infrastructure for treating wastewater [10,11]. Inadequate wastewater treatment frequently results in environmental degradation and human sickness. It is essential to treat wastewater adequately to protect surface and groundwater sources [12,13].
The presence of harmful microorganisms and high loads of nutrients are the primary problems of improperly treated wastewater [14]. Although phosphates and nitrates are beneficial to plants as macronutrients, they can contribute to eutrophication when present in excessive concentrations, leading to the overgrowth of algae, which depletes dissolved oxygen in surface water [15,16]. This, in turn, contributes an undesirable stench to the water, diminishes its aesthetic value, and can result in the mortality of fish and other benthic species, leading to a loss of biodiversity [15,17]. Similarly, high concentrations of wastewater pathogens in surface water have been associated with several diseases, including giardiasis (Giardia intestinalis), cholera (Vibrio cholerae), schistosomiasis (Schistosoma), typhoid fever (Salmonella typhi), poliomyelitis (poliovirus), stomach ulcers (Helicobacter pylori), dysentery (Entamoeba histolytica), and parasitic nematode infections [5,18,19].
Managing wastewater is essential for preventing environmental deterioration and public health issues. However, the cost of constructing and operating a conventional wastewater treatment plant (WWTP) is extremely high, and its management requires well-trained staff. The majority of developing countries cannot pay the associated costs. WSPs are a suitable alternative to conventional WWTPs for domestic, animal, and industrial wastewater treatment. These ponds provide a functional and cost-effective method for treating these types of wastewater [20,21]. When compared to other types of wastewater treatment systems, WSP systems have a lower initial cost; require less technical manpower; are simpler to build, manage, and maintain; and do not require as much expensive mechanized equipment [22]. Another advantage of WSPs is that greenhouse gas emissions in WSPs are lower than in other conventional treatment methods [23]. However, some disadvantages have been associated with WSPs, including the large land area and certain type of soil required, their potential as breeding sites for mosquitoes, and the limited effluent quality control [24].
Microalgal–bacterial WSP systems have gained the attention of researchers in the last two decades. In such systems, low water turbidity and a high volume of sunlight enhance the interaction of bacterial species and algae. The pond upper zone is supersaturated with molecular oxygen due to the growth of algae through the photosynthesis process. These high natural amounts of oxygen enhance the active aerobic bacteria to oxidize organic matter, resulting in the production of the inorganic nutrients NH3, PO43−, and CO2, which are essential for algal growth. Moreover, WSPs can be operated using a very low bacterial population, within the range of 106 to 107 CFU/mL [25,26,27]. Furthermore, the treatment efficiency and microalgal–bacterial function rely on several factors, such as dissolved oxygen, pH, sunlight, and pond design [21].
Several studies have demonstrated that the effluent quality from WSP systems in most developing countries barely meets the permissible discharge limits [16,28]. The limited treatment efficiency could be attributed to inadequate pond design, the inappropriate operation and management of the pond system, and the overloading of the capacity [29]. The implementation of well-designed and -operated WSP systems can yield a high-quality effluent amenable for reuse in irrigation. In addition, WSPs could be designed for the treatment of different types of wastewater, including industrial and municipal wastewater [30].
In Egypt, about 38 WSPs have been constructed in different cities and villages across all the country’s regions [31]. However, most of these ponds produce low-quality treated effluent that does not conform to the regulations for discharge or reuse. The low efficiency of these ponds results from the over- or underestimation of the water capacity [32]. Some small-scale studies on WSPs in Egypt can be found [33,34,35,36]. However, no complete evaluation study of different Egyptian WSPs has been completed so far. The management and operation of WSPs according to different performance parameters are very important for controlling pond efficiency. One of the recent methods used for the evaluation of wastewater treatment plants is the application of multivariate statistical techniques. This method supplies a mechanism for effectively quantifying the quality of treated wastewater and the plant efficiency by analyzing interrelated data [37]. Recently, this evaluation technique has been widely applied in the evaluation of different treatment plants [37,38,39]. Thus, this study was carried out to evaluate the removal efficiency of chemical and microbiological contaminants in 16 WSPs in Egypt. Multivariate and univariate statistical methods were employed to assess the performance of these WSPs for restructuring the algal community; bacterial indicators (i.e., total coliform, fecal coliform, and E. coli); and pathogens (i.e., Cryptosporidium spp., Giardia intestinalis, and Entamoeba histolytica), as well as to test the change in the physicochemical profile across the treatment stages.

2. Materials and Methods

2.1. WSP Locations and Sample Collection

Wastewater samples were collected from 16 waste stabilization ponds (El-Gobel, Armant, El-Rawageh, Farshoot, Qus, Ezbet El-Borg, El-Adlia, Abo-Dihoom, Attfih, El-Zarabi, M.K., Attaka, Sharm El-Shikhh, El Koliaa, El-Monira, and El-Kharga) from 11 Egyptian governorates. The 11 Egyptian governorates were classified according to demographic and health surveys (DHS) into 6 in upper Egypt (i.e., Luxor, Qena, Asyut, Giza, Fayoum, and New Valley) and 5 in lower Egypt (i.e., Kafr El-Sheikh, Damietta, Port Said, Seuz, and South Sinai) [40]. The frontier governorates New Valley and South Sinai were considered to be affiliated to upper and lower Egypt governorates, respectively (Table 1). The location of the governorates and sampling points are presented in Figure S1. The design, actual capacity, and detention time for each plant are summarized in Table 1. Triplicate samples were collected from each stage (influent, anaerobic, facultative, and effluent (after maturation pond)) of each WSP (Figure S2); transported at 4 °C; and analyzed in the National Research Centre laboratories. The sample volume for parasitic helminths and protozoa analysis was 5 L [41]. For bacterial indicators examination, each sample was collected in a 1 L sterile bottle, and the samples were transferred to an icebox within 2–8 h. The sample volume for physicochemical parameters and algal analysis was 3 L [42]. The algal samples were collected in brown bottles and fixed with Lugol’s iodine solution.

2.2. Physicochemical Characterization

The physicochemical analysis included pH, total suspended solids (TSS), chemical oxygen demands (COD), biological oxygen demand (BOD), total Kjeldahl nitrogen (TKN), and total phosphorus (TP). COD was measured according to the close reflux dichromate method no. 5210-D, using a HANNA COD thermos reactor model HI 839800 and a HANNA Water Test Spectrophotometer Model HI 83300. BOD was measured according to the 5-day BOD test method no. 5210-B, while pH was measured using a bench pH meter model Jenway 3510. TSS was measured gravimetrically after sample filtration using GF/C paper by method no. 2540-D. TKN was determined using Gerhardt digestion and distillation apparatus, Vapodest 20sn, while TP was measured using the persulfate digestion method and a UV spectrophotometer Carry 100. All the analyses were carried out according to standard methods for examination of water and wastewater [43].

2.3. Bacteriological and Parasitological Analyses

Bacterial indicators including total coliform (TC), fecal coliform (FC), and E. coli were determined using the most probable number (MPN) method [43]. Parasitic nematode ova were detected microscopically after floatation using zinc sulphate [41,44]. The protozoa pathogens investigated in this study were Cryptosporidium spp. [45], Entamoeba histolytica [46], and Giardia intestinalis [47]. To detect the target protozoa in the samples, PCR was performed in a 25 µL reaction volume using a Cosmo PCR red master mix (Willowfort company, Birmingham, UK). The reaction mixture was composed of 5 µL of the DNA template, 12.5 µL of the master mix, 0.5 µL from each primer (forward and reverse) (Table S1), and 6.5 µL of nuclease-free water. The PCR temperature conditions were 95 °C for 5 min for the predenaturation step and 35 cycles of 30 s at 95 °C, 30 s at 55 °C, and 45 s at 60 °C. These cycles were followed by the final extension step at 72 °C for 10 min. Nuclease-free water was also included in each run as a negative control.

2.4. Algal Community Analysis

The algal analysis was carried out by collecting different subsamples from inside the maturation and facultative ponds at different depths; these subsamples were mixed to form one sample, left to settle overnight, and examined using Sedgewick Rafter cells. The identification of the algal community at the species level was performed microscopically (Olympus X3 microscope, Olympus Corporation, Tokyo, Japan) according to the key of freshwater algae [43,48]. The growth rate of algal biomass was assessed by determining the chlorophyll a (CHLA) content [43]. The proportion of algal biomass in the WSPs was estimated from the chlorophyll a concentration using the equation described by Raschke (1993) [49].

2.5. Statistical Analysis

Principal component analysis (PCA) based on the Euclidean distance was employed to characterize the patterns of physicochemical parameters in the WSPs. Non-metric multidimensional scaling (nMDS) analysis based on the Bray–Curtis distance index was used to map bacterial indicators in WSPs across the treatment stages. Distance-based redundancy analysis (db-RDA) was used to show the relation between algal community (response group) and physicochemical parameters (explanatory group) in the WSPs. The permutational multivariate analysis of variance (PERMANOVA) and the analysis of similarity (ANOSIM) were used to test the significance of differences for physicochemical parameters or microbiological parameters between the treatment stages and governorates [50]. The Wilcoxon statistical test was used to investigate the significance between the treatment stages in terms of the physicochemical and biological parameters. Statistical analyses and visualization were performed using Origin (Pro) 2021 (OriginLab Corporation, Northampton, MA, USA) and PRIMER v.7.0.21 (Quest Research Limited, Auckland, New Zealand).

3. Results

The PCA revealed that the physicochemical (ph-ch) characteristics changed significantly through the treatment stages of the 16 WSPs (Table 2). These results were supported by PERMANOVA (p < 0.001) and ANOSIM (p < 0.001), indicating that the treatment stages had a substantial impact on the environmental parameters (Table 2). Moreover, the chemical profile exhibited spatial variation (p < 0.05) between the geographical locations (upper and lower Egypt) as well as WSPs (Table S2, Figure 1). The physicochemical parameters are summarized in Figures S3–S10. The physicochemical parameters exhibited a significant variation between different treatment stages, especially between inlets and outlets (Wilcoxon test; p < 0.05) in the majority of upper and lower Egypt WSPs. The overall removal of COD, BOD, TSS, TKN, and TP ranged between 17.9 and 91.8%, 26.1 and 91.6%, 55.1 and 93.6%, 19.6 and 86.8%, and 21.6 and 59.5% in upper Egypt WSPs, respectively (Figure 2A). In lower Egypt, the removal rates of the same ph-ch parameters ranged between 39.6 and 82.7%, 44.0 and 84.9%, 45.0 and 97.7%, 4.3 and 10.3%, and 4.3 and 28.7% (Figure 2B). In some cases, WSPs in upper (e.g., Farshoot and El-Gobel) and lower Egypt (e.g., Attaka) failed to remove nutrients or organic pollutants (Figure 2). However, the effluents produced by these WSPs (Figure 2) still complied with the Egyptian legislation (the Egyptian Code 501/2015 category (D) for reuse) for the irrigation of trees (Table S3): for example, El-Gobel and Al-Rawageh (Luxor governorate, upper Egypt); Farshoot and Qus (Qena governorate, upper Egypt); Abo-Dihoom (Fayoum governorate, upper Egypt); and Atfih (Giza governorate, upper Egypt). Other WSPs produced a final effluent that did not comply with the Egyptian ministerial decree 48/1982 for discharge in agriculture drainage, such as M.K. (Port Said governorate, lower Egypt); Armant (Luxor, upper Egypt); and Al-Adlia and Ezbet El-Borg (Damitta governorate, lower Egypt) (Table S4).
Overall, the nMDS revealed that the bacterial indicators changed significantly through the treatment stages in the 16 WSPs (Figure S11). These results were confirmed by PERMANOVA (p < 0.001) and ANOSIM (p < 0.001) (Table 3 and Table S5). Although the WSPs removed the bacterial indicators significantly, several WSPs still did not comply with Egyptian ministerial decree 48/1982. The TC ranged between 2.8 × 107 and 6.4 × 109, and 2.4 × 102 and 1.4 × 106 (Wilcoxon test; p < 0.05) in the inlets and outlets of upper Egypt WSPs, respectively. The FC ranged between 6.4 × 106 and 7.5 × 108, and 2.1 × 102 and 2.1 × 104 (Wilcoxon test; p < 0.05) in the inlets and outlets of upper Egypt WSPs, respectively. The E. coli ranged between 2.0 × 106 and 4.8 × 108, and 1.2 × 102 and 1.0 × 104 (Wilcoxon test; p < 0.05) in the inlets and outlets of upper Egypt WSPs, respectively (Figure 3). The TC ranged between 2.1 × 108 and 9.3 × 1010 in the inlets and 1.4 × 103 and 9.3 × 105 in the outlets of lower Egypt WSPs (Wilcoxon test; p < 0.05). The FC ranged between 1.5 × 108 and 7.5 × 1010 in the inlets and 7.5 × 102 and 1.2 × 104 in the outlets of lower Egypt WSPs (Wilcoxon test; p < 0.05). The E. coli ranged between 1.2 × 108 and 4.8 × 1010 in the inlets and 2.8 × 102 and 1.1 × 104 in the outlets of lower Egypt WSPs (Wilcoxon test; p < 0.05) (Figure 4). The removal of bacterial indicators ranged between 97.5 and 100%, and 99.4 and 100% in the upper (Figure S11A) and lower Egypt (Figure S11B) WSPs, respectively.
Nematode ova were detected in the inlets and all treatment stages of El-Gobel, El-Rawageh, Farshoot, Qus, Ezbet El-Borg, El-Adlia, Abo-Dihoom, Attfih, El-Zarabi, M.K., Attaka, Sharm El-Shikh, El Koliaa, El-Monira, and El-Kharga; their counts decreased with the progression of the treatment (Figure S13). The removal of parasitic nematode ova ranged between 44 and 100% and 66.7 and 81.1% in upper and lower Egypt WSPs, respectively. The removal of Cryptosporidium spp. ranged from 33.3% to 66.7% in upper and lower Egypt WSPs. A higher removal rate for Giardia intestinalis (100%) and Entamoeba histolytica (66.66–100%) were recorded in upper and lower Egypt WSPs (Figure 5). Although the outlet of the Armant WSP was free from nematodes, the station was not in compliance with the Egyptian decree from the physicochemical point of view. The parasite analysis also showed that the M.K. WSP was not in compliance with Egyptian decree 48/1982 for discharge in agriculture drainage.
The db-RDA revealed that pH was a predominant factor influencing the compositions of the algal community, whereas TKN was identified as the second most important physicochemical factor affecting the algal community in WSPs (Figure 6). Microscopical observations enabled the identification of 18 algae species belonging to 4 divisions. The algal community exhibited a distinct pattern between geographical locations (i.e., upper WSPs versus lower WSPs) and different treatment stages (ANOSIM; p < 0.001) (Table S6). For example, the most dominant species in the El-Koliaa WSP (Kafr El-Sheikh, lower Egypt) were Euglena sp. (Euglenophyta), followed by Spondylomorum quarternarium. In contrast, the most dominant algal species in the El-Monira WSP (New Valley; upper Egypt) was Microcystis sp. (Cyanophyta). Another species (Chlamydomonas sp. (Chlorophyta)) was dominant in another governorate in Egypt (i.e., Suez). Actinastrum sp. (Chlorophyta) and Nitzschia sp. (Bacillariophyta) were the most dominant species in the El-Zarabi WSP (Asyut governorate, upper Egypt) (Figure 7 and Figure 8).

4. Discussion

Sixteen WSPs from 11 governorates (upper and lower Egypt) were tested bacteriologically, parasitologically, and physiochemically in this study. Multivariate statistical analysis was employed to deal with the large dataset obtained from monitoring the WSPs. Four (i.e., M.K., Al-Adlia, Ezbet El-Borg, and Armant) out of sixteen WSPs did not comply with the Egyptian decree for wastewater discharge in surface water bodies. Three of these four WSPs stations (i.e., Al-Adlia, Ezbet El-Borg, and Armant) recorded a high level of COD and BOD, above the acceptable limit mentioned by the Egyptian decree. One WSP (i.e., M.K.) had a high count of parasitic nematode ova, above the acceptable limit of the Egyptian decree.
A number of challenges restrict WSPs in Egypt and many other developing countries from providing an effective service to their customers. Due to neglect, inadequate monitoring, or a lack of maintenance, the effluents of the majority of WSP systems in developing countries deteriorate over time and have a negative effect on the receiving water bodies, despite the fact that these systems were initially efficient [17,51]. Furthermore, a lack of separation of the algal biomass from the treated effluent can led to an increase in TSS, COD, and BOD concentration, which was observed in the current study. Adding a trickling filter (TF) and a laboratory-scale step-feed dual treatment (SFDT) to control algae in the oxidation ponds’ effluent has previously been recommended [52]. In this study, we found several signs of inefficient management, including but not limited to a smashed fence, damage to the pond side cement bank, the presence of randomly grown plants inside and outside the pond, wastewater overflow, and inlet clogging. Similar observations were presented in a previous study on WSPs [12]. Additionally, the overloading of the pond capacity is common in WSPs in developing countries and was observed in this study, e.g., at the El-Adlia WSP (Table 1). However, most of the WSPs did not carry out overloading due to budget and facility limitations, leading to the creation of dead zones within the pond, which may have impacted the pond’s effective volume and consequently reduced the intended HRT. It should be noted that the dead zones of the WSPs in the current study could not be measured. A pond’s estimated capacity should be well-designed and calculated by taking into consideration the change in HRT and pond volume within the operation period due to sludge accumulation. Neglecting such important factors could affect the treatment efficiency in the long term, resulting in poor effluent generation [21]. Accordingly, it is very important to ensure that WSPs work effectively in order to prevent the risk of inadequate wastewater treatment, which could have an effect on the environment and public health.
The removal of nutrients in the present study ranged from 4.3% to 86.8%. Lower removal rates (21 to 50%) from similar technologies have been recorded in previous studies [53], as well as higher removal rates (77–95%) [54]. Other types of wastewater treatment technologies, such as pilot-scale enhanced pond and wetland, removed 65 to 95% of nutrients [54]. The removal of nitrogen in WSPs is accomplished by the denitrification mechanism with the aid of ammonia-oxidizing bacteria, methanotrophs, and nitrite-oxidizing bacteria [55].
In this study, the M.K. WSP failed to remove the parasitic nematode ova to reach the acceptable limit for the Egyptian regulations. It was observed that the M.K. WSP suffered from improper maintenance and required renovations to almost all the equipment and repairs to the pond structure. Furthermore, the official personnel in most tested WSPs were not qualified to maintain and operate the facility. Pathogen elimination can be considerably reduced by malfunctions caused by inadequate maintenance [56]. The release of such poorly treated wastewater (i.e., not compliant with the Egyptian decree) into water bodies has negative impacts on humans and the environment in the short and long term. Several studies have reported a high level of human enteric parasites, bacteria, protozoa, and viruses in treated wastewater [14,17,41,57,58,59]. There have been reports of cholera and diarrhea outbreaks in developing countries due to the drinking of wastewater-contaminated water [7,14,60]. Gad et al. (2022) found that the Changle River watershed (China) was contaminated with Cryptosporidium species originating from wastewater (influent and effluent) and swine wastewater using machine learning methods (SourceTracker and FEAST). Therefore, it is vital to eliminate pathogens from wastewater in order to prevent waterborne infections.
On the other hand, several WSPs produced effluents that complied with the Egyptian codes (Table S4). For example, the Al-Rawageh and Farshoot WSPs achieved removal efficiencies of 44–83.3% and 33.3–100%, respectively, for parasitic nematodes and pathogenic protozoa (i.e., Cryptosporidium spp., Entamoeba histolytica, and Giardia intestinalis). These results were in line with those obtained in a previous study, where researchers recorded no parasitic stages in wastewater treated by centralized wastewater treatment facilities using aerated lagoon systems that released more than 25,000 m3/d of treated water [61]. Likewise, in Spain, researchers reported the 100% removal of helminth ova in a WSP [62]. However, the efficiency of helminth egg removal in full-scale WSP systems is highly variable and depends on important factors, including hydraulic retention time and sedimentation [56]. As such, systems with longer hydraulic retention times and a greater number of ponds in series are associated with the more effective removal of helminth ova and protozoa oo(cysts) [21,63].
Parasitic nematode ova were detected in different stages of the WSPs. Ascaris was dominant in different treatment stages of WSPs in upper and lower Egypt. Other parasitic nematode ova, for example, Trichostrongylus, were found in the inlet of the Farshoot WSP. South African wastewater effluents have been found to contain parasitic nematode egg counts that exceed regulatory limits [64]. Similar findings were observed in the effluents of the M.K. WSP (Table S4). The release of such effluents in the waterways not only causes aesthetic problems but also increases the risk of parasitic infections [64,65]. Helminth ova are the most resistant stage to physicochemical factors in WSPs; they can survive for years in WSP sludge [66] and are characterized by their high health risks for humans and animals [5]. It was observed that the noncompliant WSPs had a low detention time (Table 1). Accumulated sludge not only decreases the HRT, but it also decreases the removal efficiency of pathogens and helminths due to the reduction in the effective treatment volume [21]. The volume occupied by accumulated sludge in a WSP system often reduces the effective volume of the pond and consequently its treatment efficiency [67,68]. Hence, the cost of sludge removal should be incorporated into the operational cost of a WSP system, as failure to do this will amount to the failure of the entire treatment system [56,69].
Euglena sp., Oscillatoria sp., and Microcystis flosaqua were the most dominant species in the WSPs in the current study (Figure 7 and Figure 8) and in a previous study [70]. The presence of more algae in WSPs increases the concentration of dissolved oxygen produced by algae via photosynthesis, leading to the enrichment of bacteria for the degradation of organic compounds [70]. The biodegradation of organics releases CO2 in the water, which is used by algae for the reduction of nutrients (nitrogen and phosphorus) [23]. The separation of both algal and bacterial biomass to further improve the produced effluents can be achieved via filtration or chemical treatment followed by a settling process [52].
We recommend that waste stabilization pond operators upgrade the existing systems to prevent the overloading of the treatment plant. The plants grown randomly inside and outside the ponds should be removed on a regular basis. The monitoring of the inlet wastewater flow should be carried out using a flowmeter to determine the actual wastewater volume entering the pond and to prevent overflow. The effluent should be routinely tested for microbiological and physicochemical conformity with regulatory criteria.

5. Conclusions

Out of the sixteen studied WSPs, three WSPs from lower Egypt (i.e., M.K., Al-Adlia, and Ezbet El-Borg) and one station (Armant) from upper Egypt did not comply with the Egyptian decree for discharge in agriculture drainage. The removal of bacterial indicators and parasites reached 95–100% and 44–100% in the tested WSPs, respectively. The removal rate of the nutrients ranged from 19.6 to 86.8% in the upper Egypt WSPs and from 4.26 to 28.71% in the lower Egypt WSPs. The final effluent of the other WSPs (n = 12) complied with the Egyptian Code 501/2015 category (D) for the irrigation of trees. The multivariate statistical models revealed that different geographical locations (upper and lower Egypt) exhibited distinct physicochemical and algal community profiles. The differences in the physicochemical profiles between upper and lower Egypt might be due to the large input of industrial waste in lower Egypt WSPs compared to upper Egypt WSPs. The algal community, physicochemical and bacterial indicators, and parasites exhibited significant spatial variation and were restructured through the treatment stages of the WSPs. The main issues in the WSPs included in this study were a lack of maintenance and upgrading, overloading, and poorly trained staff. The continuous monitoring of WSPs using multivariate statistical methods could be useful for evaluating their performance. It would be more appropriate to use WSP effluents for tree irrigation rather than as discharge in surface water.

Supplementary Materials

The following supporting information can be downloaded at: https://0-www-mdpi-com.brum.beds.ac.uk/article/10.3390/su14137658/s1, Table S1: Primers used in this study, Table S2: ANOSIM to test the significance of the differences in physicochemical parameters between the treatment stages in all tested WSPs, Table S3: Egyptian regulations for treated wastewater reuse, Table S4: Compliant and noncompliant WSPs according to the Egyptian laws, Table S5: ANOSIM to test the significance of the differences in bacterial indicators between the treatment stages in all tested WSPs, Table S6: ANOSIM to test the significance of the differences in algal species between the treatment stages in all tested WSPs. The results with actual permutation less than 100 are excluded from the table, Figure S1: Map showing the location of the studied governorates, Figure S2: Typical design of WSP, Figure S3: Box and whiskers plot showing the concentration of physicochemical parameters in influent stage of upper Egypt WSPs, Figure S4: Box and whiskers plot showing the concentration of physicochemical parameters in anaerobic stage of upper Egypt WSPs, Figure S5: Box and whiskers plot showing the concentration of physicochemical parameters in facultative stage of upper Egypt WSPs, Figure S6: Box and whiskers plot showing the concentration of physicochemical parameters in effluent of upper Egypt WSPs, Figure S7: Box and whiskers plot showing the concentration of physicochemical parameters in influent of lower Egypt WSPs, Figure S8: Box and whiskers plot showing the concentration of physicochemical parameters in anaerobic stage of lower Egypt WSPs, Figure S9: Box and whiskers plot showing the concentration of physicochemical parameters in facultative stage of lower Egypt WSPs, Figure S10: Box and whiskers plot showing the concentration of physicochemical parameters in effluent of lower Egypt WSPs, Figure S11: Non-multidimensional scale analysis (nMDS) based on Bray–Curtis similarity index to map the bacterial indicators, Figure S12: Removal of bacterial indicators in (A) upper Egypt and (B) lower Egypt WSPs, Figure S13: Distribution of parasitic nematodes and protozoa pathogens in different treatment stages of WSPs. The parasitic nematodes are represented by ova counts, while the pathogenic protozoa are represented as present (1) or absent (0).

Author Contributions

M.G.: conceptualization, formal analysis, software, visualization, and writing (original draft, review, and editing). S.M.A.: methodology, data curation, validation, and writing (review and editing). A.H.: software and writing (review and editing). M.A.E.-L.: investigation, methodology, and writing (review). M.S.H.: methodology and writing (review and editing). H.S.D.: conceptualization, methodology, and writing (review and editing). G.H.A.: conceptualization, resources, project administration, supervision, funding acquisition, and writing. All authors have read and agreed to the published version of the manuscript.

Funding

The work was financially supported by the Academy of Scientific Research and Technology (ASRT), Egypt, grant number 4504.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The datasets used and analyzed in the current study are available from the corresponding author upon reasonable request.

Acknowledgments

The cooperation with Professor Anyi Hu was under an agreement between the National Research Centre (Egypt) and the Institute of Urban Environment, Chinese Academy of Sciences (China).

Conflicts of Interest

The authors declare no conflict of interest.

References

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Figure 1. Principal component analysis (PCA) plot for spatial variation of physicochemical parameters between upper and lower Egypt WSPs (principal components (PC1, 2, and 3) explained 82% of the total variation).
Figure 1. Principal component analysis (PCA) plot for spatial variation of physicochemical parameters between upper and lower Egypt WSPs (principal components (PC1, 2, and 3) explained 82% of the total variation).
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Figure 2. Circus plots showing the removal rate of physicochemical parameters in (A) upper Egypt WSPs and (B) lower Egypt WSPs.
Figure 2. Circus plots showing the removal rate of physicochemical parameters in (A) upper Egypt WSPs and (B) lower Egypt WSPs.
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Figure 3. Alluvial plot showing the distribution of bacterial indicators in upper Egypt WSPs.
Figure 3. Alluvial plot showing the distribution of bacterial indicators in upper Egypt WSPs.
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Figure 4. Alluvial plot showing the distribution of bacterial indicators in lower Egypt WSPs.
Figure 4. Alluvial plot showing the distribution of bacterial indicators in lower Egypt WSPs.
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Figure 5. Removal of parasitic nematodes and pathogenic protozoa.
Figure 5. Removal of parasitic nematodes and pathogenic protozoa.
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Figure 6. Distance-based redundancy analysis (db-RDA) showing the relation between algal community (response group) and physicochemical parameters (explanatory group).
Figure 6. Distance-based redundancy analysis (db-RDA) showing the relation between algal community (response group) and physicochemical parameters (explanatory group).
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Figure 7. Shade plot for algal communities based on presence/absence matrix in upper Egypt WSPs.
Figure 7. Shade plot for algal communities based on presence/absence matrix in upper Egypt WSPs.
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Figure 8. Shade plot for algal communities based on presence/absence matrix in lower Egypt WSPs.
Figure 8. Shade plot for algal communities based on presence/absence matrix in lower Egypt WSPs.
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Table
Table 1. The characteristics of the studied WSPs.
Table 1. The characteristics of the studied WSPs.
GovernoratePlant NameDesign
Capacity (m3/day)		Actual Capacity (m3/day)Detention Time (days)Discharging Point
GizaAtfih46,0004500–500020–46Irrigation of trees
DamiettaEzbet El-Borg20,00010,0003Agricultures drainage
El-Adlia100016003–5Farskour agriculture drainage
LuxorArmant18,000Not specifiedNot specifiedAgriculture drainage
El-Gobel20,00030,00032Irrigation of trees
El-Rawageh6000400030Not specified
QenaQus30,00018,00025–30Irrigation of trees
Farshoot30,000600030Irrigation of trees
Kafr El-SheikhEl Koliaa40030016Agricultural drain Kiro
New ValleyEl-Monira80035020Irrigation of trees
El-Kharga30,000Not specified33Irrigation of trees
SuezAttaka130,000200,0006Suez Gulf
Port-SaidM.K.190,000190,00011El-Manzalla
South SinaiSharm El-Shikh15,000940015–17Irrigation of trees
AsyutEl-Zarabi16,000800022–25Mountain creek
FayoumAbo Dihoom5000300015Irrigation of trees
Table
Table 2. PERMANOVA and ANOSIM to test the significance of the differences in physicochemical parameters between the treatment stages.
Table 2. PERMANOVA and ANOSIM to test the significance of the differences in physicochemical parameters between the treatment stages.
StagePERMANOVAANOSIM
tp-ValueRp-Value
Influent versus anaerobic2.95<0.0010.07<0.001
Influent versus facultative4.04<0.0010.15<0.001
Influent versus effluent4.80<0.0010.24<0.001
Anaerobic versus facultative1.800.0180.040.016
Anaerobic versus effluent2.66<0.0010.12<0.001
Facultative versus effluent1.510.0680.060.001
Table
Table 3. PERMANOVA and ANOSIM to test the significance of the differences in bacterial indicators between the treatment stages.
Table 3. PERMANOVA and ANOSIM to test the significance of the differences in bacterial indicators between the treatment stages.
Grouptp-ValueRp-Value
Influent versus anaerobic4.87<0.0010.25<0.001
Influent versus facultative8.77<0.0010.75<0.001
Influent versus effluent10.53<0.0010.95<0.001
Anaerobic versus facultative5.24<0.0010.30<0.001
Anaerobic versus effluent9.16<0.0010.81<0.001
Facultative versus effluent6.26<0.0010.43<0.001
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Gad, M.; Abdo, S.M.; Hu, A.; El-Liethy, M.A.; Hellal, M.S.; Doma, H.S.; Ali, G.H. Performance Assessment of Natural Wastewater Treatment Plants by Multivariate Statistical Models: A Case Study. Sustainability 2022, 14, 7658. https://0-doi-org.brum.beds.ac.uk/10.3390/su14137658

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Gad M, Abdo SM, Hu A, El-Liethy MA, Hellal MS, Doma HS, Ali GH. Performance Assessment of Natural Wastewater Treatment Plants by Multivariate Statistical Models: A Case Study. Sustainability. 2022; 14(13):7658. https://0-doi-org.brum.beds.ac.uk/10.3390/su14137658

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Gad, Mahmoud, Sayeda M. Abdo, Anyi Hu, Mohamed Azab El-Liethy, Mohamed S. Hellal, Hala S. Doma, and Gamila H. Ali. 2022. "Performance Assessment of Natural Wastewater Treatment Plants by Multivariate Statistical Models: A Case Study" Sustainability 14, no. 13: 7658. https://0-doi-org.brum.beds.ac.uk/10.3390/su14137658

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