Next-Generation Wastewater Treatment: Omics and AI-Driven Microbial Strategies for Xenobiotic Bioremediation and Circular Resource Recovery

Abstract

Wastewater treatment plants (WWTPs) function as engineered ecosystems in which microbial consortia mediate nutrient cycling, xenobiotic degradation, and heavy metal detoxification. This review discusses a forward-looking roadmap that integrates microbial ecology, multi-omics diagnostics, and artificial intelligence (AI) for next-generation treatments. Meta-analyses suggest that a globally conserved core microbiome indicates sludge functions, with high predictive value for treatment stability. Multi-omics approaches, including metagenomics, metatranscriptomics, and environmental DNA (eDNA) profiling, have integrated microbial composition with greenhouse gas (GHG) emissions, showing that WWTPs contribute 2–5% of anthropogenic nitrous oxide (N₂O) emissions. Emerging AI-enhanced eDNA models have achieved >90% predictive accuracy for effluent quality and antibiotic resistance gene (ARG) prevalence, facilitating near-real-time monitoring and adaptive control of effluent quality. Key advances include microbial strategies for degrading organic pollutants, pesticides, and heavy metals and monitoring industrial effluents. This review highlights both translational opportunities, including engineered microbial consortia, AI-driven digital twins and molecular indices, and persistent barriers, including ARG dissemination, resilience under environmental stress and regulatory integration. Future WWTPs are envisioned as adaptive, climate-conscious biorefineries that recover resources, mitigate ecological risks, and reduce their carbon footprint.

1. Introduction

Rapid population growth, urbanization, and increasing industrial activity continue to intensify global freshwater scarcity, positioning wastewater treatment plants (WWTPs) as frontline infrastructure for environmental protection and public health [1,2]. The global annual wastewater production exceeds 360 billion m³ annually. However, only half of this water undergoes treatment, and barely 40 billion m³ is safely reused, highlighting a major gap in circular water management [3]. WWTPs function as engineered ecosystems, where microbial processes remove solids, nutrients, micropollutants, and pathogens while mitigating the risks of toxic organics, heavy metals, pharmaceuticals, and antimicrobial residues [4,5,6]. Wastewater sludge, a semi-solid by-product of these processes, contains nutrients and organic matter useful in agriculture but may also harbor heavy metals, pathogens, and emerging contaminants [1,2]. Beyond sanitation, WWTPs are increasingly recognized as multifunctional biofactories that enable energy recovery, nutrient recycling, and greenhouse gas (GHG) mitigation, which are essential for a circular and climate-resilient economy.

Despite their central role in sanitation and environmental protection, WWTPs are not an ideal technology. Conventional systems often fail to achieve the complete removal of micropollutants, antibiotic resistance genes, and greenhouse gases, which constrains their sustainability and resilience. Xenobiotics are synthetic compounds, such as pesticides, pharmaceuticals, and industrial chemicals, that are often resistant to biodegradation and pose particular challenges for conventional treatment. These limitations underscore the pressing need for innovative approaches that integrate microbial ecology, multi-omics, and artificial intelligence [1,2]. Nearly 60% of the biologically treated wastewater worldwide depends on activated sludge systems because of their cost-effectiveness and operational flexibility [6]. Antibiotic resistance genes (ARGs) are genetic elements that allow microorganisms to survive antibiotic exposure, and their persistence in effluents is a major public health concern worldwide. Systematic reviews have reported that conventional treatments achieve only partial removal of antibiotic-resistant bacteria and antibiotic resistance genes (ARGs), which are genetic elements that enable microorganisms to withstand antibiotics and persist in effluents. Their removal efficiencies vary widely depending on the gene class and operational conditions [7,8]. The persistence of ARGs and pathogens in effluents makes WWTPs critical hotspots for the dissemination of resistance [9]. Moreover, microbial processes in WWTPs directly influence GHG emissions, with nitrous oxide (N₂O) release ranging from 0.005 to 1.1% of the influent nitrogen (N). Thus, WWTPs represent paradoxical systems that contribute to climate change but can also mitigate it if appropriately optimized.

Advanced biological and membrane-based processes, such as membrane bioreactors (MBRs), anaerobic digestion (AD), and aerobic granular sludge (AGS), can achieve over 95% nutrient removal and 99% pathogen reduction under optimized conditions, thereby facilitating the reuse of wastewater. Osmotic membrane desalination processes, including reverse osmosis (RO), forward osmosis (FO), pressure-retarded osmosis (PRO), and osmotically assisted reverse osmosis (OARO), have also shown significant potential for water recovery. The spacer geometry and flow dynamics in such systems can be optimized using computational fluid dynamics, 3D printing, and artificial intelligence, thereby enhancing fouling resistance and treatment efficiency [10]. When integrated with resource recovery modules, these include biogas production, phosphorus (P) crystallization, and water reuse. These processes increasingly position WWTPs as circular biorefineries capable of lowering net carbon (C) emissions by 20–35% [11]. However, persistent contaminants, such as polyfluoroalkyl substances, nanoplastics, and pharmaceuticals, remain resistant to conventional methods, necessitating innovations in treatment and monitoring [12]. Hybrid MBR–ozonation systems tested in Europe and East Asia remove >95% of pharmaceutical residues, but their high energy demand highlights the need for AI-optimized and sustainability-driven process control [2].

The efficiency of biological treatment depends on the structural and metabolic diversity of activated sludge microbiomes. Meta-analyses indicate that a globally conserved core community with strong functional redundancy highlights treatment stability [13,14,15], as discussed in Section 2. However, many dominant taxa remain uncultured, representing microbial “dark matter.” This uncultured fraction, which accounts for 60–65% of taxa, limits functional characterization and predictive modeling. Addressing this gap requires culture-independent omics approaches integrated with machine learning (ML) systems [9].

Community composition responds significantly to influent chemistry, retention time and oxygenation. Deterministic processes, such as homogeneous selection and pollutant removal stability, interact with stochastic events during disturbances, including sludge bulking and seasonal fluctuations [16]. High-throughput sequencing, metagenomics, and metagenome-assembled genomes (MAGs) have identified nearly 1500 species and 900 genera in WWTPs. Many of these taxa remain uncultured, reinforcing the significance of microbial “dark matter” in WWTPs [9,17]. Artificial intelligence (AI) and ML are emerging as transformative tools, achieving over 95% predictive accuracy for effluent quality and reducing aeration energy consumption in MBRs by 30–34% using deep reinforcement learning (DRL) [18,19,20]. These models also support the early detection of ARGs and pathogens, thereby strengthening biosurveillance capacity. The integration of omics datasets with AI-driven analytics has enabled the development of digital twins (DTs), AI-driven virtual replicas of WWTPs that simulate microbial, chemical, and operational dynamics in real time to support predictive management, which has been tested in Denmark, Singapore, and China [2].

This review discusses the current advances in microbial communities in WWTPs, emphasizing their diversity, ecological roles, and bioremediation potential. It critically examines molecular tools, such as 16S rRNA sequencing, metagenomics, and eDNA-based monitoring, while comparing case studies across municipal, industrial, and decentralized systems. Finally, the challenges of applying omics-derived insights in practice are discussed, and how omics–AI integration and engineered microbial consortia can guide the transition toward adaptive, resilient, and circular wastewater biorefineries is outlined.

2. Microbial Diversity and Ecology in WWTPs

WWTPs are complex engineered ecosystems sustained by diverse microbial communities, including bacteria, archaea, fungi, algae, protozoa, and metazoans that facilitate pollutant degradation and nutrient transformation. Bacteria dominate numerically and functionally, representing more than 80% of cellular abundance [21,22]. Meta-analyses (MiDAS 4 database analyses) have shown a conserved core of about 150 genera that account for most of the abundance across >700 systems, highlighting the efficiency, redundancy, and stability of global WWTP microbiomes [15,23]. Despite the broad taxonomic diversity, functional performance is largely concentrated within this microbial core, which has important implications for predictive modeling and bioaugmentation.

Proteobacteria, Bacteroidetes, Chloroflexi, and Firmicutes consistently dominate WWTP microbiomes, whereas functional groups such as Zoogloea, Dechloromonas, and Nitrospira support nitrification, denitrification, and P accumulation [21,24]. Community assembly is primarily shaped by operational conditions, such as pH, oxygen availability, and influent composition, rather than by inoculum origin [22,25]. Recent studies have highlighted the disproportionate ecological roles of low-abundance taxa, including candidate phyla radiation (CPR) bacteria and the Patescibacteria superphylum, which significantly contribute to nutrient cycling and horizontal gene transfer (HGT), an underexplored frontier in wastewater microbiology.

Archaea such as Methanosaeta and Methanosarcina sustain acetoclastic methanogenesis under mesophilic conditions [21,26]. Protozoa and metazoans, although representing only 5–10% of the biomass, support sludge clarification by grazing on bacteria and reducing pathogens through predation [27,28].

Community assembly reflects the interplay between deterministic factors (e.g., temperature and reactor design) and stochastic processes, including dispersal and ecological drift. Deterministic selection supports stability, but stochastic events dominate during disturbances such as bulking [16,23]. Comparative surveys have shown that municipal WWTPs maintain globally conserved core taxa, whereas industrial WWTPs select microbiomes adapted to xenobiotics, high salinity, and extreme pH conditions [29,30]. For instance, textile WWTPs are enriched in Planctomycetes, Thaumarchaeota, and Acidobacteria, indicating their adaptation to dye-laden and hypersaline influents [31]. Functional gene profiling has further indicated that industrial sludge contains 35–55% fewer nitrification and denitrification genes than municipal sludge [32], highlighting the performance constraints under toxic stress.

Beyond 16S surveys, shotgun metagenomics directly connects taxonomy and function. The MiDAS 4 catalog provides >9000 amplicon sequence variants and a standardized taxonomy for integrating microbial diversity and treatment outcomes [15]. Shotgun metagenomics has uncovered >500 ARGs and >100 mobile genetic elements in activated sludge [33], confirming that WWTPs are major ARG reservoirs. Genome-resolved MAGs and single-cell genomics further enable the mapping of ARGs and nutrient-cycling genes to specific hosts [9], thereby improving the resolution of HGT pathways.

Although 16S rRNA sequencing remains indispensable for taxonomic surveys, shotgun metagenomics provides a higher functional resolution, particularly for uncultured taxa [34]. The integration of these approaches has enhanced pathogen and ARG detection and supported the predictive modeling of treatment efficiency [35]. AI-driven ecological network analyses further help identify the thresholds at which microbial populations are at risk of collapse, providing an early warning tool for operators.

Despite these advances, approximately 60–65% of WWTP taxa remain uncultured, representing microbial “dark matter” [9,17]. This limits functional annotation and hinders the design of reliable bioaugmentation and synthetic consortia. Therefore, future research should prioritize culturomics, single-cell sequencing, and omics–AI integration to bridge this gap. Moreover, although core groups are globally conserved, their resilience to stressors such as salinity, nanoplastics, and antibiotic mixtures remains poorly quantified, reducing predictive accuracy.

3. Xenobiotic Bioremediation in WWTPs

Bioremediation in WWTPs utilizes the metabolic capacity of microorganisms, either naturally present in activated sludge or introduced through bioaugmentation, to degrade, detoxify, or transform pollutants into less harmful forms [36]. Beneficial microbes, including bacteria, fungi, archaea, and algae, have been shown to degrade hydrocarbons, pesticides, dyes, heavy metals, plastics, and pharmaceuticals, with removal efficiencies of 80–95% under optimized conditions [37,38].

Table 1. Representative microbial taxa, enzymatic pathways, potential removal efficiencies, and key limitations of xenobiotic degradation in WWTPs.

Pollutants	Representative Taxa	Enzymes/Mechanisms	Reported Efficiency/Conditions	Limitations/Challenges	Reference
Hydrocarbons/Polycyclic Aromatic Hydrocarbons (PAHs)	Pseudomonas aeruginosa, P. putida, Bacillus subtilis, Rhodococcus spp.	Oxygenases, dehydrogenases, ring-cleavage enzymes	90–95% total petroleum hydrocarbon removal within 7–10 d (aerobic); consortia 20–40% faster mineralization; immobilized cells show 30% higher kinetics	Efficiency decreases under fluctuating influent load; immobilized consortia stability remains uncertain	[39,40,41,42,43,44]
Textile dyes	Bacillus firmus, Klebsiella oxytoca, Aspergillus spp., Thermoalkaliphilic consortia	Azoreductases, peroxidases	90% decolorization in 24–72 h (anaerobic–aerobic); thermo-alkaliphiles: 98% under salinity/pH stress; biofilm consortia: 78.6% in raw textile effluent	Real effluents with dye–metal–salt mixtures inhibit microbial activity; scalability issues persist	[45,46,47,48,49,50,51,52]
Agrochemicals	Pseudomonas nitroreducens AR-3, Bacillus spp., Serratia marcescens, Alcaligenes aquatilis	Hydrolases, dehalogenases, oxidoreductases	AR-3: 97% chlorpyrifos in 8 h (100 mg L−1); mixed consortia: 75–87% chlorpyrifos in 20 d; Bacillus-based consortia: 99.3% chlorantraniliprole in 20 d; biobed consortia: >90% atrazine/carbofuran/glyphosate removal	Persistence varies among pesticide classes; mixed pesticide loads reduce efficiency	[53,54,55,56,57,58,59]
Heavy metals	Aspergillus niger, Penicillium simplicissimum, Saccharomyces cerevisiae, Candida albicans, Geobacter metallireducens, Shewanella oneidensis	Biosorption proteins, reductases, metal transporters	A. niger: 98% Cd, 43% Cr uptake; dead biomass: 100% Cr(VI)/Zn(II); S. cerevisiae: 40–80 mg g−1 Pb uptake; G. metallireducens: V reduced from 50 µM to 6 µM in aquifer trial; CRISPR strains: improved Cd, Cu, Hg, Ni, Fe uptake	Mixed-metal stress and sulfide toxicity constrain scalability; GMOs raise biosafety concerns	[60,61,62,63,64,65,66]
Plastics/Microplastics	Pseudomonas, Rhodococcus, Aspergillus, Ideonella sakaiensis	Esterases, hydrolases, laccases	Degrades polyethylene terephthalate, polyethylene, polystyrene, polyurethane; efficiencies under WWTP conditions remain poorly established	Efficiencies poorly established under WWTP conditions; slow kinetics	[67,68]
Emerging pathogens	Mixed sludge microbiomes, beneficial consortia	Competitive exclusion, predation, ARG suppression	Opportunistic pathogens (Legionella, Leptospira) persist at 102–107 CFU L−1; AI models achieve >90% accuracy in outbreak prediction	Opportunistic pathogens persist; ARG suppression remains incomplete	[23,28,69,70,71,72]

consolidates representative findings from several studies to provide an integrated overview of microbial xenobiotic degradation, summarizing pollutant categories, microbial taxa, enzymatic pathways, quantitative removal efficiencies, and associated limitations.

3.1. Organic Pollutants Degradation

Bacteria such as Pseudomonas aeruginosa, Pseudomonas putida, Bacillus subtilis, and Rhodococcus spp. are highly efficient in degrading petroleum hydrocarbons and polycyclic aromatic hydrocarbons (PAHs) through oxygenase-mediated pathways, achieving 90–95% removal of total petroleum hydrocarbons within 7–10 d under optimized aerobic conditions [39,40]. These pathways primarily involve monooxygenase and dioxygenase enzymes that introduce molecular oxygen into aromatic rings, facilitating their further breakdown and mineralization.

Microbial consortia further enhance the degradation of mixed hydrocarbon wastes, where synergistic metabolic interactions among bacteria accelerate mineralization rates by 20–40% compared with monocultures [41]. Similarly, fungal genera such as Aspergillus, Fusarium, Penicillium, and Phanerochaete contribute significantly through ligninolytic enzymes, including laccases and peroxidases, which oxidize phenolic compounds, PAHs, and chlorinated hydrocarbons. For instance, Phanerochaete chrysosporium achieved 85% phenanthrene and 92% bisphenol A removal within 3 h under laccase-optimized conditions [42].

The immobilization of microbial cells on carriers, such as alginate, biochar, or polyurethane foam, further improves degradation performance. Immobilized systems exhibit approximately 30% higher degradation kinetics than planktonic cells due to enhanced cell stability, biofilm formation, and resistance to toxic shock. This immobilization strategy enhances both the degradation kinetics and stress tolerance of microbial consortia compared to freely suspended cells [43,44]. Although these approaches have demonstrated high degradation efficiencies under laboratory or batch conditions, their scalability in continuous-flow wastewater treatment plants (WWTPs) is limited. Fluctuating influent compositions, hydraulic loads, and long-term stability issues of immobilized consortia present major challenges for sustained field-level applications.

3.2. Textile Dyes and Industrial Effluents

Textile effluents contain complex mixtures of recalcitrant dyes, salts, and heavy metals that inhibit conventional physicochemical and biological treatment processes. These pollutants, particularly azo and anthraquinone dyes, pose significant environmental challenges owing to their chemical stability and toxicity.

Bacterial strains such as Bacillus firmus, Klebsiella oxytoca, and Staphylococcus aureus, along with fungi including Aspergillus and Myrothecium, can degrade these dyes via azoreductase- and peroxidase-mediated pathways. Under sequential anaerobic–aerobic treatments, these microorganisms achieve up to 90% decolorization within 24–72 h [45,47]. Mixed and biofilm-forming microbial consortia have consistently demonstrated superior performance compared to monocultures. For example, thermoalkaliphilic consortia achieved >98% color removal within 24–72 h under high salinity and pH stress [50,51], whereas biofilm-supported mixed cultures achieved 78.6% decolorization within 48 h in raw textile effluents [52]. These findings highlight the enhanced metabolic versatility and stress tolerance of microbial communities under conditions relevant to industrial applications.

Further improvements in textile wastewater treatment have been achieved through hybrid systems that integrate microbial degradation with phytoremediation and advanced oxidation processes (AOPs). Such integrated configurations achieved COD removal efficiencies of 86–99.5% and up to 95% dye removal in pilot-scale studies [48,49]. In particular, thermoalkaliphilic and biofilm-forming consortia exhibit high operational resilience and maintain their activity under fluctuating salinity and pH stress [50,51,52].

Despite these advances, large-scale implementation remains a challenge. The complex and variable composition of textile effluents, particularly the interactions among dyes, salts, and heavy metals, can inhibit microbial metabolism and enzyme activity. Consequently, future developments should focus on adaptive hybrid treatment strategies that couple microbial consortia with chemical oxidation or electrochemical processes, supported by real-time process control for stable, long-term operation.

3.3. Pesticide Biodegradation

Pesticides such as chlorpyrifos, atrazine, endosulfan, and chlorantraniliprole are persistent organic contaminants frequently detected in agricultural effluents and wastewater treatment plant (WWTP) influents. Their biodegradation is primarily mediated by bacterial genera such as Bacillus, Pseudomonas, and Enterobacter, which employ hydrolases, dehalogenases, and oxidoreductases to cleave ester, halogen, and nitro functional groups, respectively [53,54].

Among the individual strains, Pseudomonas nitroreducens AR-3 demonstrated exceptionally high enzymatic activity, achieving 97% removal of chlorpyrifos (100 mg L⁻¹) within 8 h, with only trace levels of the intermediate 3,5,6-trichloro-2-pyridinol (TCP), confirming efficient hydrolytic cleavage of organophosphate linkages [55,59]. However, mixed microbial consortia consistently outperform single isolates because of their synergistic metabolic complementarity and enhanced substrate range. For instance, consortia comprising Pseudomonas fluorescens, B. subtilis, and Serratia marcescens achieved 75–87% chlorpyrifos degradation within 20 d, while a consortium of four Bacillus strains with Alcaligenes aquatilis and P. aeruginosa removed up to 99.3% of chlorantraniliprole (20 mg kg⁻¹) within 20 d [56,57].

Biobed-derived microbial consortia have shown remarkable potential for degrading complex pesticide mixtures. These systems achieved >90% removal of atrazine, carbofuran, and glyphosate within a few days, while the half-lives (DT₅₀) of more recalcitrant compounds, such as diazinon and 2,4-D, ranged from 6.6 to 8.6 d, reflecting structural variability in persistence among pesticide classes [58,59]. In addition to rapid detoxification, biobed systems produce irrigation-quality effluents and support circular agricultural frameworks through resource reuse.

Despite these promising results, the biodegradation of pesticides in full-scale WWTPs remains constrained by fluctuating contaminant loads, co-contaminant toxicity, and variable microbial consortia composition. Future advancements should focus on engineering biosurfactant-producing and enzyme-overexpressing consortia, coupled with AI-driven predictive modeling, to dynamically optimize degradation pathways and enhance removal efficiency under field conditions [55,56,57].

3.4. Heavy Metal Bioremediation

Microbial systems remediate heavy metals through three primary mechanisms: biosorption, bioaccumulation, and enzymatic transformation. These processes collectively reduce metal toxicity, mobility, and bioavailability in contaminated environments, offering sustainable alternatives to physicochemical treatments [60,61,62,63].

Fungal systems demonstrate outstanding biosorptive and bioaccumulative potential. Filamentous fungi, such as Aspergillus niger and Penicillium simplicissimum, have achieved biosorption efficiencies of up to 98.4% for Pb and 100% for Cr in aqueous media, completely removing Cd and Pb from sewage within 6 d [60]. An indigenous strain, A. niger M1DGR, bioaccumulated 98% Cd and 43% Cr, corresponding to adsorption capacities of 0.580 mg Cd g⁻¹ and 0.152 mg Cr g⁻¹ biomass under optimized conditions [61]. Even non-viable fungal biomass is effective; for instance, dead fungal cells removed 100% of Cr(VI) and Zn(II) and over 83% of Hg(II) and fluoride ions at 28 °C and controlled pH [62].

Yeast-based systems also exhibit robust biosorption capacity. Saccharomyces cerevisiae absorbs 40–80 mg g⁻¹ Pb, 10–60 mg g⁻¹ Cd, and 5.5 mg g⁻¹ Cr, while Candida albicans removes 76% Cr, 57% Pb, and 46% Cd, benefiting from high biomass productivity and genetic tractability [46]. These attributes make yeast a promising candidate for large-scale metal recovery and effluent polishing.

Metal-reducing bacteria, such as Geobacter metallireducens and Shewanella oneidensis, detoxify metals and radionuclides through enzymatic reduction and precipitation pathways. G. metallireducens has been applied successfully in field-scale remediation, reducing vanadium concentrations from 50 µM to 6 µM and facilitating uranium reduction in aquifers [64]. Similarly, S. oneidensis enzymatically converts ionic mercury (Hg²⁺) to elemental mercury (Hg⁰) under anaerobic conditions, significantly lowering its environmental toxicity [65].

Genetic and synthetic biological advancements have further strengthened the microbial metal remediation process. Recombinant strains expressing metal-binding peptides, such as metallothioneins and phytochelatins, exhibit enhanced uptake and tolerance capacities. Recent CRISPR–Cas9-engineered microbes outperform wild-type strains in removing Cd, Cu, Hg, Ni, and Fe by optimizing metal transporter systems and detoxification pathways [66].

Despite these advances, large-scale implementation remains constrained by sulfide toxicity, mixed metal stress, and ecological concerns regarding the release of genetically modified microorganisms. Future strategies should emphasize consortium-based biosystems, biofilm reactors, and contained bioreactor configurations that integrate genetically enhanced microbes while ensuring environmental safety and regulatory compliance [64,65].

3.5. Urban Wastewater and Emerging Pathogens

Urban wastewater treatment plants (WWTPs) harbor highly complex and dynamic microbiomes shaped by influent composition, industrial discharge, and chemical pollutants. These microbial assemblages underpin biological treatment efficiency but can also serve as reservoirs for opportunistic pathogens and antibiotic-resistant genes (ARGs). Although activated sludge systems effectively reduce pathogen loads, opportunistic taxa such as Legionella and Leptospira frequently persist in treated effluents, with concentrations ranging from 10² to 10⁷ CFU L⁻¹, thereby posing aerosol-based transmission risks during water reuse and sludge handling operations [69,70].

Beneficial microbial populations within the sludge community contribute to effluent stability through competitive exclusion, predation, and pathogen suppression mediated by biofilms [23,28]. Long-term and seasonal studies have demonstrated that deterministic selection processes preserve functional stability despite influent variability, although ARG prevalence can transiently increase during storm events or under high organic loading conditions [29]. The integration of advanced biosurveillance tools, including environmental DNA (eDNA) metabarcoding, quantitative PCR (qPCR), and digital PCR, has enabled near-real-time pathogen and ARG monitoring in WWTPs. Furthermore, AI-enhanced predictive models have achieved >90% accuracy in forecasting potential pathogen outbreaks and treatment inefficiencies, based on wastewater microbiome signatures [71]. The incorporation of data-driven biosurveillance into operational controls is crucial for enhancing public health resilience and ensuring microbiological safety in water reuse systems.

Beyond conventional microbial consortia, enzymatic biocatalysis has emerged as a promising approach for xenobiotic degradation in wastewater. Isolated enzymes, such as laccases, peroxidases, oxygenases, and hydrolases, can selectively catalyze the breakdown of persistent organic pollutants under mild physicochemical conditions, thereby reducing toxicity without the need to maintain viable microbial populations. Recent advances in enzyme immobilization on nanomaterials, biochar, and polymeric matrices have substantially improved catalytic stability, reusability, and efficiency. These immobilized enzymatic systems have achieved >85–95% removal of pesticides, dyes, and phenolic compounds in optimized bioreactor configurations [72].

Despite their potential, enzyme-based technologies face practical constraints, including denaturation under mixed-pollutant stress, high production costs, and limited long-term stability. Consequently, future research should prioritize the integration of enzymatic systems with microbial consortia and AI-driven process optimization to create hybrid biocatalytic frameworks capable of dynamically adapting to fluctuating wastewater compositions. Such synergistic systems represent a next-generation approach for resilient and energy-efficient xenobiotic removal in urban WWTPs.

4. Molecular Tools for Characterization of Microbial Communities

Molecular tools have transformed our understanding of WWTP microbiomes, extending beyond the limitations of culture-based methods. High-throughput approaches, such as 16S rRNA sequencing, metagenomics, metatranscriptomics, and environmental DNA (eDNA) profiling, have revealed microbial diversity, functional potential, and community dynamics with unprecedented resolution [15,27]. Beyond taxonomy, these tools enable the detection of pathogens, ARGs, and metabolic pathways that are directly related to treatment outcomes. Figure 1 illustrates the integration of microbial diversity, ecological functions, and molecular innovations, whereas

Table 2. Molecular and biochemical tools for microbial community characterization in WWTPs.

Technique	Target Biomarker	Strengths	Limitations	Applications	Performance	References
16S rRNA gene sequencing	16S rRNA gene (bacteria/archaea)	High taxonomic resolution; detects uncultured taxa	Limited functional insight; primer bias; rare taxa may be overlooked	Community profiling; diversity analysis; microbiome detection	Detects ≥1% relative abundance taxa; ~104 cells mL−1 detection limit	[15,27,73,74,75]
Full-length 16S rRNA sequencing	Complete 16S rRNA gene	Species-level classification; enhanced taxonomic accuracy	Higher cost; requires long-read platforms and advanced analysis	Pathogen detection, species-level taxonomic profiling	Error rates as low as 0.007%; species resolution >60% vs. <5% for short reads	[15,27]
Metagenomics (Shotgun)	Total DNA (coding and non-coding genes)	Functional and taxonomic insights; ARG/metabolic pathway detection	Computationally intensive; higher sequencing cost	Functional profiling; ARG monitoring; novel gene discovery	~107 reads/sample; detects 100s of ARG subtypes; sequencing depth >20 Gb	[76,77]
Digital PCR (dPCR)	Specific genes (e.g., ARGs, 16S, pathogens)	Ultra-sensitive absolute quantification; high precision	Target-specific; requires prior sequence knowledge	ARG quantification; rare gene detection	Detects down to 0.1% rare alleles; CV <5%; LoD ~10–100 copies µL−1	[75,77,78]
Quantitative PCR (qPCR)	Targeted genes (ARGs, pathogens, PAOs)	Rapid, sensitive, quantitative	Limited multiplexing; primer design critical	Monitoring of target pathogens or ARGs; process optimization	LoD ~102–103 copies mL−1; efficiency 90–105%	[73,77,78]
Fluorescence in situ hybridization (FISH)	rRNA sequences, functional genes	Visualizes morphology and spatial organization; detects active cells	Probe design required; limited multiplexing; labor-intensive	In situ localization; biofilm/community structure analysis	Resolution ~0.5–1 µm; requires ≥103–104 cells for detection	[74,75,79]
Functional gene microarrays (GeoChip)	Known functional gene (N, P, S cycling)	High-throughput; sensitive to metabolic shifts	Limited to predefined genes; less sensitive than metagenomics	Functional diversity; nutrient cycling assessment	>365,000 genes; detects genes down to 10 pg DNA	[79,80]
High-throughput qPCR arrays	Multiple ARGs, mobile elements, integrons	Parallel quantification of many genes, seasonal/spatial analysis	Limited to known targets, primer intensive	ARG hotspot mapping; risk assessment	Detects up to 300–400 ARGs in parallel; sensitivity 102–103 copies/mL; quantification range 6–8 log units	[77,78]
Dehydrogenase activity (DHA) assay	Enzymatic activity (dehydrogenases)	Simple, cost-effective, proxy for total microbial activity	No taxonomic specificity; affected by stressors	Process monitoring; toxicity assessment	LoD ~0.05 µg formazan/g sludge; activity correlates with microbial biomass	[81]

compares the major tools in terms of biomarkers, strengths, and limitations. Collectively, these advances highlight opportunities to connect molecular signals with predictive and adaptive process control in wastewater biotreatment.

4.1. eDNA and Metagenomics

High-throughput sequencing has consistently identified Proteobacteria as the dominant bacterial phylum in urban wastewater treatment plants (WWTPs), contributing approximately 30–60% of the total microbial communities. Other prevalent phyla include Bacteroidetes, Chloroflexi, and Firmicutes, which together sustain key metabolic functions in nutrient removal and organic matter degradation [82,83]. However, the composition of these communities is highly dynamic, with influent chemistry, hydraulic conditions, and environmental stressors, such as salinity fluctuations, heavy metal concentrations, and toxic organic loadings, driving site-specific ecological shifts.

Shotgun metagenomic analyses have revealed extensive antibiotic resistance gene (ARG) diversity within activated sludge systems. In one comprehensive dataset, 181 ARG subtypes representing 22 resistance classes were detected, with abundances 1.3–2.0 orders of magnitude higher than those in anaerobic digestion sludge, establishing WWTPs as major reservoirs of antibiotic resistance [84]. Metatranscriptomic profiling has revealed that approximately 66% of ARGs are transcriptionally active, with plasmid-associated ARGs exhibiting nearly double the expression frequency of their chromosomal counterparts, underscoring the high potential for horizontal gene transfer (HGT) and ARG dissemination within treatment ecosystems [85].

Environmental stressors exacerbate these risks by modulating microbial dynamics and gene mobility. For example, exposure to polystyrene nanoplastics (0.5–50 mg L⁻¹) increased ARG abundance in activated sludge by up to 59%, while accelerating HGT through mobile genetic elements and interspecies host shifts [86]. Similarly, network-based ecological analyses have demonstrated that heavy metals and elevated salinity reduce microbial redundancy, disrupt core trophic interactions, and destabilize treatment performance, thereby increasing the likelihood of operational failure [87].

Beyond resistance ecology, metagenomic and functional gene analyses have revealed strong linkages between microbial community structure and greenhouse gas (GHG) fluxes. Nitrifying and denitrifying bacteria are the principal contributors to nitrous oxide (N₂O) emissions, which are important climate-forcing gases. Although earlier case studies reported emission factors ranging from 0.005% to 0.008% of influent nitrogen (N), revised global estimates suggest that approximately 1.1% of the total nitrogen entering WWTPs is emitted as N₂O, signifying that these systems account for nearly 3–5% of global anthropogenic N₂O emissions [88,89]. These findings underscore the climatic significance of microbial metabolism in wastewater systems.

Future research should prioritize the integration of metagenomic, metatranscriptomic, and metabolomic datasets into AI-driven ecological modeling frameworks. These integrative platforms can enhance the prediction of ARG dissemination dynamics, identify emission hotspots, and enable adaptive operational controls to mitigate both public health and climate impacts under fluctuating influents and environmental conditions.

4.2. 16S rRNA Gene Sequencing

The 16S rRNA gene remains the cornerstone of microbial taxonomic profiling because of its universal presence and highly conserved regions interspersed with variable domains that enable phylogenetic discrimination. Full-length 16S sequencing (FL-16S) using long-read platforms, such as PacBio SMRT (HiFi reads), provides species-level resolution with exceptionally low error rates (≈0.007%), comparable to or exceeding Illumina MiSeq performance [90]. In a comparative study, PacBio-based FL-16S sequencing resolved 61.8% of amplicon sequence variants (ASVs) at the species level, whereas conventional V4–V5 hypervariable regions achieved only 3.5% resolution [91]. Similarly, Nanopore sequencing demonstrated strong concordance with Illumina datasets, with 945 of 999 taxa showing agreement with a mean abundance difference of merely 0.56%, while enabling real-time taxonomic identification [92].

Applications in wastewater microbiome analysis have substantially benefited from these technological advances. Quantitative 16S sequencing (qSeq), when coupled with curated pathogen reference databases, enables the accurate quantification of pathogenic and functional taxa across treatment stages. In one study, pathogen loads decreased from 6.8 × 10⁷ copies mL⁻¹ in influent to 1.6 × 10⁵ copies mL⁻¹ in effluent; however, clinically significant taxa such as Aeromonas, Mycobacterium, Klebsiella, and pathogenic Escherichia persisted at approximately 4 × 10³ CFU mL⁻¹, underscoring incomplete pathogen removal [93].

Functionally, taxa including Nitrosomonas, Nitrospira, Paracoccus, and Thauera have been consistently identified as reliable bioindicators of nitrogen removal performance. Among these, Nitrospira, a key nitrite oxidizer, exhibits global ubiquity in activated sludge but remains challenging to culture and is often underrepresented when conventional PCR primers are employed [94]. Large-scale meta-analyses have further revealed a globally conserved “core microbiome” in WWTPs dominated by Comamonas, Pseudomonas, Acidovorax, Arcobacter, and Acinetobacter. These genera form ecologically stable consortia that underpin carbon oxidation, nitrification–denitrification, and overall resilience [95].

The integration of 16S-based taxonomic profiling with shotgun metagenomics, metatranscriptomics, and machine learning (ML) models offers a promising approach. Such integration will enable a transition from descriptive microbial inventories to predictive, process-linked diagnostics for monitoring treatment stability, identifying functional shifts, and mitigating pathogen-related risks in wastewater systems.

4.3. eDNA-Based Performance Monitoring

Environmental DNA (eDNA) metabarcoding has emerged as a highly sensitive, cost-efficient, and scalable approach for monitoring microbial community succession, resilience, and stress responses in wastewater treatment plants (WWTPs) and other environments. Unlike traditional sludge biotic indices, eDNA-based community profiling captures both viable and non-viable microbial signatures, offering a more comprehensive representation of ecosystem dynamics.

Comparative analyses have demonstrated that eDNA-derived community indices correlate strongly with treatment performance metrics, exhibiting correlation coefficients of R = 0.82–0.87 for chemical oxygen demand (COD) and nutrient removal, significantly outperforming traditional sludge biotic indices (R = 0.60–0.65) [96,97]. These findings underscore the potential of eDNA metabarcoding as a reliable molecular proxy for the real-time assessment of treatment efficiency and microbial functional stability.

Recent advancements have integrated artificial intelligence (AI) with eDNA-based biosurveillance to enhance its predictive capability. Deep learning models trained on longitudinal eDNA and operational datasets have achieved R² values of 0.93 for COD and total nitrogen (TN) prediction, with mean absolute prediction errors (MAPE) of only 4–6%. Beyond performance forecasting, these AI–eDNA systems provide early warning functionality, accurately predicting antibiotic resistance gene (ARG) surges—ranging between 10² and 10³ copies mL⁻¹ up to 48 h in advance. Such predictive precision facilitates adaptive process control, enabling the dynamic optimization of aeration, sludge retention time, and chemical dosing [72].

Future research should focus on scaling AI–eDNA platforms into multi-site, real-time monitoring networks that integrate molecular data with operational analytics. Such integration will allow WWTP operators to directly link microbial ecological signals with process optimization, public health risk mitigation, and climate-resilient infrastructure management, ultimately transforming wastewater systems into intelligent and self-adaptive ecosystems.

4.4. Functional Gene Analysis

Functional gene arrays (FGAs), particularly GeoChip, have revolutionized high-throughput functional profiling of microbial communities by simultaneously detecting thousands of genes associated with nutrient cycling, xenobiotic degradation, antibiotic and metal resistance, and environmental stress responses. These arrays enable comprehensive functional diagnostics that bridge the gap between microbial diversity and ecosystem processes in wastewater treatment plants (WWTPs).

The evolution of GeoChip technology has markedly expanded its functional scope. GeoChip 4.0 contains approximately 82,000 probes representing 142,000 coding sequences across 410 functional gene families, whereas the latest iteration, GeoChip 5.0 (M), encompasses 162,000 probes targeting over 365,000 genes from 1447 functional gene families, including those of bacteria, archaea, fungi, protists, and viruses [98,99]. This expansion has greatly enhanced the ability to detect low-abundance genes and cross-domain interactions relevant to wastewater ecosystems.

The application of GeoChip in WWTPs has revealed distinct functional adaptations to pollutant stress. For example, studies on coking wastewater treatment plants detected an average of 61,940 functional genes, including those encoding ring-cleavage dioxygenases for aromatic hydrocarbon degradation and nitrogenous pollutant pathways, such as nbzA, tdnB, and scnABC [100]. Similarly, in metal-contaminated sediments, GeoChip analyses identified approximately 1793–1831 metal homeostasis genes per site. A significant positive correlation between arsenic resistance gene abundance and arsenic concentration (r_m = 0.3243; p = 0.018) demonstrated the sensitivity of GeoChip in detecting microbial stress responses under varying contaminant loads [101].

Future studies should focus on integrating functional gene arrays with metagenomic and metatranscriptomic frameworks to capture both gene presence and transcriptional activity. Such integrative multi-omics approaches will enable real-time tracking of stress responses, functional redundancy, and adaptive shifts, thereby translating gene-level signals into predictive control strategies for optimizing bioreactor operation and pollutant removal efficiency at WWTPs.

4.5. Molecular and Enzymatic Surveillance Tools

Quantitative PCR (qPCR) and digital PCR (dPCR) are cornerstone molecular tools for quantifying pathogens and antibiotic resistance genes (ARGs) in wastewater systems. While qPCR remains widely used for relative quantification, dPCR provides absolute quantification by partitioning samples into thousands of nanoliter-scale reactions, thereby eliminating the need for standard curves and minimizing the influence of amplification inhibitors that are commonly present in wastewater matrices. This approach enables the detection of rare genetic targets at frequencies as low as 0.1%, offering superior sensitivity and precision compared to conventional qPCR [102].

High-throughput qPCR (HT-qPCR) studies have revealed that the total ARG abundance in influents is typically 2–3 log units higher than that in treated effluents. Nonetheless, a residual ARG burden persists in downstream environments, highlighting the ecological and public health risks associated with incomplete attenuation [103]. The ability to couple dPCR’s quantitative precision of dPCR with HT-qPCR’s broad target coverage of HT-qPCR now allows for high-resolution temporal tracking of resistance gene fluxes across treatment stages and discharge points.

Fluorescence in situ hybridization (FISH) is indispensable for spatially resolving functional microbial guilds, such as nitrifiers, denitrifiers, and polyphosphate-accumulating organisms (PAOs), within biofilms and activated sludge flocs. FISH offers micron-scale localization and enables the visualization of community architecture, co-localization patterns, and niche partitioning that underlie treatment performance [79,81]. Complementary dehydrogenase activity (DHA) assays are rapid, low-cost biochemical proxies for assessing overall microbial metabolic activity and toxicity. Although less specific than molecular methods, DHA assays provide valuable real-time functional insights into the physiological state of treatment consortia, supporting onsite process monitoring.

Future monitoring frameworks should integrate PCR-based quantification, FISH spatial mapping, and enzymatic activity assays within a unified surveillance platform. Coupling these molecular and biochemical tools with AI-driven anomaly detection and process control systems can transform WWTP monitoring from reactive assessment to predictive, adaptive, and real-time bioprocess management, enhancing both treatment stability and biosafety resilience.

5. Case Studies of Microbial Communities in WWTPs

5.1. Municipal WWTPs

Municipal wastewater primarily consists of domestic sewage that is enriched with organic matter and nutrients. The activated sludge process remains the predominant treatment configuration, in which diverse microbial consortia drive organic degradation, nutrient cycling, and pathogen attenuation. High-throughput sequencing and metagenomic analyses have consistently revealed that Bacteroidetes (≈32.2%) and Proteobacteria/Pseudomonadota (≈32%) dominate the microbial assemblages, followed by Firmicutes (≈20.2%), Chloroflexi (≈6.1%), Actinobacteria (≈3.4%), and Acidobacteria (≈1.3%), with other phyla comprising the remaining minor fractions [104]. These groups collectively sustain carbon oxidation, nitrification–denitrification, and sludge floc formation.

Regional and seasonal variations exert a significant influence on WWTP community composition. In Finland, sludge from domestic, hospital, and agroindustrial sources was dominated by Bacteroidetes (35–44%), Firmicutes (30–37%), and Proteobacteria (15–20%); however, during winter, Proteobacteria abundance rose sharply to ~84%, whereas Bacteroidetes abundance declined to 9%, demonstrating a clear temperature-driven selection effect [105]. Similarly, 16S metagenomic surveys of Polish sewage treatment plants revealed highly variable phylum-level profiles, with Proteobacteria ranging from 5.5 to 56.9%, Actinobacteria from 4.5 to 41.0%, and Cyanobacteria occasionally dominating up to 60.9%, reflecting influent source heterogeneity [106].

Distinct community structures are also observed in high-altitude WWTPs, where environmental stressors such as low temperature, hypoxia, and nutrient limitation drive the formation of unique microbial assemblages. Proteobacteria (41.2–57.0%) and Bacteroidetes (16.8–41.4%) dominate, with notable contributions from Planctomycetes (2.9–7.5%) and Chloroflexi (1.8–6.1%) [107]. Class-level analyses showed a predominance of Gammaproteobacteria (30.1–45.2%) and Betaproteobacteria (up to 84.8%), whereas Alphaproteobacteria remained less abundant (2.6–7.7%). Influent-to-effluent profiling revealed a reduction in Proteobacteria from 85.5% to 55.4%, suggesting selective removal pressures and functional reorganization during the treatment [108].

Seasonal dynamics play a critical ecological role. In northern China, winter surveys reported Proteobacteria (26.7–48.9%) and Bacteroidetes (19.3–37.3%) as the dominant phyla, while filamentous taxa such as Saprospiraceae, Flavobacterium, and Tetrasphaera accounted for 60–83% of the total reads and were strongly associated with sludge bulking events [109]. Despite this spatial and temporal variability, cross-regional comparisons have revealed a conserved core microbiome in municipal WWTPs. For example, Begmatov et al. [29] identified 63 genus-level OTUs consistently present across 13 Danish WWTPs, highlighting microbial functional convergence despite taxonomic variability in the OTUs.

Functional metagenomic investigations further support this observation, revealing redundancy in critical metabolic pathways, such as denitrification and phosphorus removal, which ensure process-level stability even under taxonomic turnover [110]. Collectively, these findings indicate that municipal WWTP microbiomes exhibit regional taxonomic variability but have global functional resilience.

Future research should aim to integrate multi-omics datasets (metagenomics, metatranscriptomics, and metabolomics) with machine learning (ML) modeling to link localized perturbations, such as seasonal bulking or influent toxicity, to conserve global functions. Such approaches will advance the development of predictive and adaptive management frameworks to ensure the long-term stability and sustainability of WWTP operations across diverse environmental contexts.

5.2. Industrial WWTPs

Industrial effluents, particularly those originating from the textile sector, present extreme physicochemical conditions, characterized by high concentrations of dyes, surfactants, heavy metals, and organic solvents, as well as elevated COD, low BOD, and variable pH. These conditions impose severe stress on microbial communities and hinder the efficiency of biological treatment.

Comparative 454-pyrosequencing analyses of municipal and textile WWTPs revealed pronounced differences in microbial richness and diversity. Municipal systems typically harbor approximately 1645 bacterial and 160 archaeal operational taxonomic units (OTUs), whereas textile WWTPs display significantly reduced richness, underscoring the selective pressure exerted by toxic dye intermediates and chemical stressors [32]. Taxonomically, textile sludge is enriched with Planctomycetes, Chloroflexi, Chlorobi, Acidobacteria, and archaeal Thaumarchaeota, in contrast to the Bacteroidetes–Proteobacteria dominance observed in municipal plants [32].

At the phylum level, metagenomic investigations have shown wide compositional variability in textile WWTPs: Proteobacteria constitute 24.4–94.8%, Bacteroidetes 0.5–44.8%, and Firmicutes 3.7–67.4%, whereas in municipal or natural environments, Proteobacteria account for 30.8–76.3%, Bacteroidetes 8.5–50%, and Actinobacteria 0.5–23.1% [111]. The reduced presence or complete absence of nitrifiers, denitrifiers, polyphosphate-accumulating organisms (PAOs), and sulfate-reducing bacteria (SRB) in textile systems indicates metabolic inhibition and ecological displacement, likely caused by dye toxicity, competitive exclusion, and oxidative stress [31,32].

Functional metagenomic analyses have further revealed the suppression of key metabolic pathways involved in carbon oxidation, nitrification–denitrification, and phosphorus removal, leading to decreased microbial activity and lower overall treatment performance [31]. Environmental parameters, such as salinity, organic load, and temperature fluctuations, exacerbate these inhibitory effects, resulting in the formation of distinct stress-adapted microbial assemblages [32].

To overcome these limitations, future research should emphasize the design of engineered microbial consortia, selective pretreatment strategies, and integrated physicochemical-biological hybrid systems that enhance resilience, functional diversity, and pollutant removal efficiency in industrial WWTPs [31,32,70]. Such adaptive frameworks are critical for restoring both ecological stability and process robustness under the challenging operational regimes characteristic of textile wastewater treatment.

5.3. Onsite WWTPS

Onsite wastewater treatment systems (OWTSs), including septic tanks, fixed-film reactors, anaerobic baffled reactors (ABRs), and constructed wetlands (CWs), are vital components of decentralized wastewater management, particularly in rural and peri-urban regions lacking centralized sewer infrastructure. These systems operate under variable inflow conditions, limited land availability, and fluctuating groundwater tables, leading to high temporal and spatial heterogeneity in microbial communities and their treatment performance [112,113].

5.3.1. Anaerobic Baffled Reactors (ABRs)

ABRs are widely recognized for their operational simplicity, low energy demand, and high resilience to variable organic loads. Laboratory-scale ABRs treating algae-laden influents (2000–2500 mg L⁻¹ COD) achieved COD removal efficiencies of 75–80%, reducing effluent COD to below 550 mg L⁻¹ within approximately 30 d. Under optimal conditions, removal stabilized at approximately 80% at organic loading rates (OLR) of 0.26–1.2 kg COD m⁻³·d⁻¹ [114]. Similarly, an ABR operated at a hydraulic retention time (HRT) of 24 h achieved 82% COD removal, with compartmental contributions of 62.9%, 29.4%, 13.9%, 12.0%, and 8.0% across compartments 1–5, respectively [115].

In a biocatalyzed electrolysis-assisted ABR for treating high-strength carbohydrate wastewater (6 g L⁻¹ COD), the removal efficiencies reached 95.4% (HRT = 24 h), 94.6% (HRT = 21 h), and 80.5% (HRT = 18 h), with compartment-specific removals of 50.6%, 13.9%, 7.6%, 7.4%, and 1.0%, respectively [116]. Collectively, these results demonstrate that ABRs can maintain robust COD removal (80–95%), depending on the reactor design, OLR, and phase separation efficiency.

5.3.2. Hybrid and Constructed Wetland Systems

Hybrid onsite systems that integrate ABRs with constructed wetlands (CWs) have demonstrated synergistic performance. An ABR–bio-rack wetland planted with Phragmites australis and Typha latifolia, treating domestic wastewater (influent COD = 751 mg L⁻¹; BOD₅ = 348 mg L⁻¹), achieved COD removal: 87% (21 h HRT) and 86% (27 h HRT), Soluble COD removal: 90% and 88%, BOD₅ removal: 93% and 92%, TSS removal: 88% and 86%, TN removal: 79% and 77%, PO₄–P removal: 21% and 14% [117]

Constructed wetlands alone are also cost-effective, nature-based treatment options. A full-scale CW treating secondary effluent over five years maintained effluent COD concentrations of 24.1 mg L⁻¹ (Year 4) and 36.0 mg L⁻¹ (Year 5) under influent COD >100 mg L⁻¹, meeting discharge standards of ≤40 mg L⁻¹ [118]. A multistage step-feeding tidal flow CW achieved 91–95% ammonium, 74–91% nitrate, and 66–81% total nitrogen (TN) removal, corresponding to N removal rates of 70–77 g N m^-·d⁻¹ over 420 d at an optimal step-feeding ratio of 80:20 (COD/N = 4–5) [119].

5.3.3. Plant–Microbe Interactions and Microbial Indicators

Plant–microbe interactions play a pivotal role in determining CW performance. In CWs planted with Phragmites australis, Heliconia littoralis, Canna indica, and Cyperus flabelliformis, both microbial biomass carbon (MBC) and Shannon’s Diversity Index (SDI) varied significantly by plant species, season, and substrate depth. Notably, higher MBC and SDI at 15–20 cm depth correlated positively with improved removal of BOD, NH₄–N, and NO₃–N, demonstrating their utility as bioindicators of treatment efficiency [120].

Onsite systems exhibit strong potential for decentralized wastewater treatment but remain vulnerable to fluctuations in influent quality, hydraulic loads, and temperature. Future research should prioritize the optimization of plant–microbe consortia, modular hybrid configurations, and digital monitoring tools (e.g., sensor-integrated AI systems) to enable adaptive and stable treatment in rural and peri-urban settings.

Table 3. Microbial community case studies in different WWTP types.

WWTP Type	Dominant Phyla/Genera (Range %)	Key Environmental Factors	Functional/Ecological Outcomes	Performance	References
Municipal	Bacteroidetes (30–44%), Proteobacteria (15–84%), Firmicutes (20–37%); core genera: Comamonas, Pseudomonas, Acidovorax, Acinetobacter	Seasonal shifts (e.g., winter increased Proteobacteria, and decreased Bacteroidetes); influent source variability	Conserved “core microbiome” ensures nutrient removal redundancy; sludge bulking connected to filamentous Saprospiraceae, Flavobacterium, Tetrasphaera	COD: 80–95%; TN: 65–85%; TP: 60–80%; Pathogen log-reduction: 2–3	[104,105,106,107,108,109,110]
Industrial	Proteobacteria (24–95%), Bacteroidetes (0.5–45%), Firmicutes (4–67%); enriched: Planctomycetes, Chloroflexi, Thaumarchaeota	High dye load, surfactants, heavy metals, extreme pH, salinity	Reduced richness; limited nitrifiers/denitrifiers; inhibited metabolic pathways for lower treatment efficiency	COD: 50–70%; TN: 30–50%; TP: <40%; Dye decolorization: 60–80% (lab scale)	[31,32,70,111]
Onsite (ABR, CW, hybrid systems)	Proteobacteria, Firmicutes, Actinobacteria; variable with plant species and depth	Variable inflows, shallow groundwater, plant–microbe interactions	COD removal 80–95%; nutrient removal enhanced in CWs with diverse vegetation; microbial biomass and Shannon diversity correlated with BOD/N removal	COD: 75–95%; BOD: 80–95%; TN: 50–70%; TP: 45–65%	[112,113,114,115,116,117,118,119,120]

summarizes representative case studies across municipal, industrial, and onsite WWTPs, emphasizing how environmental stressors, operational design, and ecological interactions jointly determine microbial structure and the treatment performance. These contrasts underscore the importance of tailored microbial management strategies suited to distinct treatment systems.

6. Challenges and Future Perspectives

6.1. Microbial Ecology of WWTPs

A major challenge is integrating taxonomic abundance and functional performance. Although 16S sequencing provides valuable taxonomic resolution, nearly 60% of the activated bacteria remain uncultured, representing microbial “dark matter” and only ~35% currently have reference genomes [9,121,122]. Functional redundancy, while supporting resilience, complicates predictions by obscuring the roles of individual taxa [123].

Spatiotemporal heterogeneity adds another layer of uncertainty. Seasonal temperature shifts, diurnal influent fluctuations, and sludge bulking events drive stochastic changes that reduce stability. Even in well-managed plants, N removal can fluctuate by up to 20% [16,124].

Multi-omics methods provide deeper insights into microbial ecologies. However, 30–40% of the detected genes remain unannotated [122]. Future research should focus on recovering genomes from uncultured lineages and integrating multi-omics with AI-based ecological modeling to identify taxa essential for resilience and those contributing to instability.

6.2. Need for Integrative Omics and AI Approaches

Multi-omics integration, which combines metagenomics, transcriptomics, proteomics, and metabolomics, represents a major opportunity for linking microbial presence and activity with metabolic flux. Recent studies have shown correlations between gene expression (nirK and amoA) and >90% N removal under optimized oxygen conditions [125,126]. Advanced platforms, such as bioBakery 3, have improved strain-level profiling and metabolic reconstruction [122].

AI and ML models are emerging as transformative tools. They achieved up to 95% accuracy in predicting effluent quality, microbial shifts, and ARG emergence [127,128]. AI-based anomaly detection can anticipate process failure up to 48 h in advance, reducing unplanned downtime by 30% in pilot plants [129]. Ensemble models have reported correlation coefficients of up to 0.96 for COD, BOD, and nutrient removal [130]. Transformers outperform recurrent models in dry conditions, whereas GRUs perform better under rainfall [131]. Hybrid architectures improve TN prediction accuracy by up to 33% [132], and dynamic neural networks achieve a COD RMSE as low as 2.9 mg·L⁻¹ [133]. Multi-attention RNNs can forecast effluent N with >98% accuracy [134], and deep models increasingly support integration with real-time control [135] (

Table 4. Machine learning and deep learning models used in wastewater parameter prediction.

Model Type	Target Parameters	Reported Accuracy	Reference
Random Forest, Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN)	COD, BOD5, nitrate, phosphate	Correlation coefficients up to 0.96; ensemble models outperform single learners	[130]
Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), Transformer	Effluent Quality Index (EQI)	Transformer achieved highest accuracy under dry conditions; GRU performed better under rainfall/storm conditions	[131]
Hybrid Deep Learning Model (Temporal Convolutional Network + LSTM)	Total nitrogen (TN)	Up to 33% higher accuracy vs. stand-alone DL; ~63% improvement vs. traditional ML	[132]
Dynamic Neural Networks (Nonlinear Autoregressive with Exogenous Inputs—NARX; Principal Component Analysis integrated NARX—PCA-NARX)	COD, TN	High accuracy: RMSE = 2.9 mg L−1 (COD), 0.8 mg L−1 (TN)	[133]
Multi-Attention RNN	Effluent total nitrogen (TNeff)	Forecast accuracy: 98.1% (1 h ahead), 96.3% (3 h ahead)	[134]
Random Forest, Deep Neural Network (DNN)	Total suspended solids (TSS), phosphate	Identified key operational variables; enabled integration with real-time control	[135]

).

Despite these advances, model generalizability and data integration across diverse WWTPs remain a bottleneck. A key frontier is the development of DTs that merge multi-omics datasets with advanced AI architectures for predictive and adaptive management purposes.

6.3. Engineering of Synthetic Microbial Consortia

Engineered synthetic consortia represent a promising opportunity for enhancing pollutant degradation, resilience, and resource recovery in WWTPs. Synthetic biology tools, including CRISPR-Cas editing, metabolic pathway reconstruction, and quorum-sensing modulation, facilitate the programming of microbial communities with defined ecological roles [136,137]. Engineered Pseudomonas–Bacillus consortia have been shown to degrade >90% of mixed PAHs within 5 d and exhibited higher resistance to salinity and toxic shocks than monocultures [138,139]. Integrating multi-omics data with AI supports the prediction of community dynamics and the optimization of consortium composition. Pilot studies suggest that engineered consortia can improve N removal by 25% and P uptake by 30% compared with natural sludge [140,141].

However, challenges remain in translating these findings into practice, including regulatory limitations, ecological risks, and scalability. Future efforts should prioritize biosafety, genetic stability, and stakeholder acceptance for responsible implementation. The key challenges and corresponding opportunities are summarized in

Table 5. Challenges vs. Opportunities in Next-Generation WWTPs.

Challenges	Opportunities	References
Microbial ecology uncertainty; “microbial dark matter”; functional redundancy	Integrative omics approaches for taxonomy-to-function linkage	[121,122,123,124,125,126]
Antibiotic resistance genes (ARGs) and pathogens persist despite treatment	High-throughput monitoring: qPCR, dPCR, HT-qPCR arrays, eDNA diagnostics	[73,75,77,78,103]
Greenhouse gas emissions (CH4, N2O) from microbial guilds	AI and digital twins for predictive GHG and energy optimization	[128,129,130,131,132,133]
Influent variability; bulking and foaming reduce stability	Synthetic biology and CRISPR-based engineered consortia	[104,107,134,135,136,137]
Scalability and cost barriers in enzymatic or advanced strategies	Hybrid strategies: immobilized enzymes, nanomaterials, AI-guided selection	[138,139,140,141]
Regulatory and biosafety issues (GMOs, nanomaterials)	Circular resource recovery: energy, nutrient, and material valorization	[10,11,14,15]

, which serves as a roadmap for guiding research and implementation in next-generation WWTPs.

7. Conclusions

This review highlights the essential role of microbial consortia in wastewater bioremediation, particularly in pesticide degradation and heavy metal detoxification. Advances in multi-omics and molecular diagnostics have transformed WWTP monitoring from descriptive assessments to predictive system-level diagnostics.

Omics-based studies have established strong links between microbial community dynamics, ARG dissemination, and greenhouse gas emissions, underscoring the dual role of WWTPs as protectors of environmental health and potential contributors to ecological risks.

AI-enhanced eDNA models, which achieved ~90% accuracy in predicting effluent quality and ARG prevalence, demonstrated the feasibility of real-time adaptive control of ARGs. When coupled with digital twin frameworks, these approaches can shift WWTP management from reactive to predictive and climate-conscious.

Future research should focus on (i) bioaugmentation strategies using engineered consortia for enhanced pollutant removal, (ii) development of standardized eDNA and functional gene indices to enable the global comparability of wastewater monitoring, (iii) integration of AI and DTs for predictive optimization of treatment efficiency and energy demand, and (iv) long-term evaluation of ecological stability and C footprints, with explicit quantification of microbial contributions to CO₂ and N₂O emissions.

In summary, although wastewater treatment plants are indispensable for sanitation and environmental protection, they remain imperfect technologies. Persistent limitations in micropollutant removal, greenhouse gas mitigation, and resistance control require continuous innovation. By integrating microbial ecology, multi-omics, and AI-driven modeling, future WWTPs can evolve into adaptive, climate-conscious, and sustainable biorefinery systems.