As is a prominent semi-metallic toxic compound categorized as a HM that presents substantial ecological and health hazards. Regarded as a worldwide health risk factor, As occurs naturally in the earth's crust in numerous forms including oxides, sulfides, and salts of iron, sodium, calcium, and copper from where it leaches into drinking water (Jaishankar et al., 2014; Abdul et al., 2015; Balali-Mood et al., 2021). Ranked as the 20th most prevalent element in the Earth's crust, it contaminates water by natural deposits, industrial activities, pesticides, and improper disposal (Maiti et al., 2019; Jaishankar et al., 2014). As acts as a protoplasmic toxin that targets sulfhydryl groups in cells, disrupting cellular respiration, enzyme activitjaishankary, and mitotic processes. Predominantly absorbed in the small intestine, it can also enter via inhalation, ingestion, and dermal contact. As is converted into monomethylarsonic acid (MMA) and dimethylarsinic acid (DMA), with MMA being the primary urinary excretion form (Patel et al., 2023; Siddique et al., 2020). As may cause acute and chronic toxicity by inhibiting sulfhydryl-dependent enzymes which disrupts pyruvate dehydrogenase and the Krebs cycle, reducing Adenosine Triphosphate (ATP) production, and damaging capillary endothelium, leading to vascular permeability, vasodilation, and circulatory collapse (Shen et al., 2013; Jaishankar et al., 2014; Balali-Mood et al., 2021).

Lead, a bright silver toxic element in nature is one of the causes of serious pollution and health problems all over the world. Depending on the environmental conditions, Pb starts to get dull and forms a complex mixture of compounds when exposed to the air (Madkour, 2020). Pb being a highly toxic element with no biological role disrupts plant physiology by inducing reactive oxygen species (ROS), damaging lipid membrane, chlorophyll, and inhibiting photosynthesis. Studies indicate that in tea plants, Pb slows down the plant's growth, reduces the biomass and its quality. In terms of source, Pb can encompass through industrial operations, diet, tobacco use, water, and residential origins (Gupta et al., 2024; Trentin et al., 2025; Wu et al., 2011). Pb can also leach into the soil and water bodies through various industrial plants resulting in human and animal exposure through food and drinking water (Ali et al., 2022; Generalova et al., 2025). Pb is mostly absorbed via the respiratory and digestive systems and can cause neurological, pulmonary, urinary, and cardiovascular diseases due to immunological regulation, oxidative stress, and inflammatory processes. The global threshold for Pb poisoning is 10 μg/dl in blood (Balali-Mood et al., 2021; Siddiqi et al., 2023). Environmental Pb exposure continues to pose a public health risk, even at minimal concentrations and yet it is employed in more than 900 diverse sectors like mining, smelting, and battery manufacturing (Siddiqi et al., 2023).

Cadmium is a toxic non-biodegradable trace element from natural and human-induced sources that contaminates soil and groundwater. With a half-life of 20 years, Cd is among the most hazardous HMs and mobile elements in the environment, substituting calcium in minerals because of its analogous ionic radius, same charge, and chemical properties. The WHO suggest a recommendation value of 3 μg/L in terms of drinking water quality but higher concentrations of Cd may disrupt essential bodily functions, leading to acute or chronic illnesses (Khan et al., 2022; Kubier et al., 2019). Upon absorption by humans, Cd accumulates within the body during a lifetime (Jaishankar et al., 2014). Cd occurs naturally in soil and minerals such as sulfides, sulfates, carbonates, chlorides, and hydroxides. Similarly, Cd is also present in water and can enter the human diet via vegetables. Elevated Cd levels may stem from industrial operations, consumption of contaminated food, and tobacco use (Kubier et al., 2019; Balali-Mood et al., 2021). The presence of hexavalent Cd has been classified as a group I occupational carcinogen by the International Agency for Research on Cancer (IARC) (Balali-Mood et al., 2021; Khoshakhlagh et al., 2024). Occupational Cd exposure may also stem from sources like in the alloy, battery, glass manufacturing, and electroplating sectors. This is why Cd levels in the atmosphere are regularly monitored in certain nations due of their utmost importance (Lombaert et al., 2023; Mehrifar et al., 2025).

Mercury is a naturally occurring, lustrous silver-white, odorless liquid metal present in air, water, and soil. It exists as metallic elements, inorganic salts and organic molecules with varying toxicity and bioavailability (Sattar et al., 2025). Since elemental Hg exists as a liquid at ambient temperature it can be vaporized, whereas organic Hg compounds exhibit more toxicity than their inorganic counterparts due to higher bioavailability (Park and Zheng, 2012; Wu et al., 2024). Mercury is mostly utilized in electrical equipment, industry, and scientific devices (Jang et al., 2005; Park and Zheng, 2012; Shukla, 2025). Once Hg is heated, it can change into a colorless and odorless gas which is very toxic and bio-accumulative, eventually impacting the marine ecosystem adversely (Jaishankar et al., 2014). Sources of Hg pollution includes agriculture, municipal wastewater effluents, mining, incineration, and industrial wastewater discharges (Veeraswamy et al., 2023). Being highly toxic and pervasive contaminant, Hg poses risks to human health and ecosystem integrity. Besides, anthropogenic Hg emissions exceed natural geogenic sources, creating reservoirs that drives global dispersion. Ultimately, Hg accumulates in persistent soil reservoirs and deep oceanic waters and sediments (Brocza et al., 2024; Driscoll et al., 2013).

Chromium, a naturally occurring HM in the earth's crust and saltwater, ranks seventh in abundances and is released by oil, coal, and petroleum combustion (Jaishankar et al., 2014; Balali-Mood et al., 2021). Cr occurs as ores in natural deposits and includes other elements, such as ferric chromite (FeCr₂O₄), crocoite (PbCrO₄), and chrome ochre (Cr₂O₃) (Ihsanullah et al., 2016). Expected to be the sixth most abundant transition metal, makes it very dangerous in potable water, with trivalent Cr (Cr(III)) and hexavalent Cr (Cr(VI)) being the most stable forms. Among the two, Cr (III) is relatively less harmful than Cr (VI) and is a necessary component for human physiology (Ayele and Godeto, 2021; Balali-Mood et al., 2021). Conversely, Cr(VI) is very toxic and present in industrial effluents, resulting in severe diarrhea, vomiting, lung congestion, and damage to the liver and kidneys (Yan et al., 2023; Yatera et al., 2018). Cr is utilized in the textile industry, electroplating, leather tanning, metal finishing, and chromate production (Jaishankar et al., 2014; Ihsanullah et al., 2016). Highlighting the toxicity, a meta-analysis of 973,697 workers revealed that 11,564 developed cancer as a result of exposure to Cr in food and water or through skin contact with Cr-laden items (Balali-Mood et al., 2021).

Zinc, the 23rd most prevalent element in the Earth's crust is an essential metal, vital for numerous biochemical processes and physiological activities. Zn acts as a cofactor for major proteins and is a key factor in the control of immunomodulatory activities such as the regulation of enzymes, proteins, DNA and DNA binding proteins, the immune system, and cell metabolism (Hussain et al., 2022; Kiouri et al., 2023). Excessive anthropogenic Zn can be toxic and cause vomiting, diarrhea, abdominal pain, nausea, and cramps (Hussain et al., 2022; Schoofs et al., 2024). Industrial sources of Zn contamination include galvanization, production of brass, bronze, Zn-based alloys, metal-coating, paint, dyes, and pigments, whereas soil contamination arises from smelting activities and mining (Hussain et al., 2022; Van et al., 2024). Zn concentrations in wastewater vary from under 1 ppm to over 48,000 ppm according to many studies (Rahman et al., 2021). Zn toxicity may also result from excessive supplements, contaminated environments, or natural exposure, with its bioavailability mainly coming from vegetables, beef, and dairy. Additionally, environmental Zn arises from soil and water via weathering and industrial discharges, with major inputs from mining, combustion, and steel production (Hussain et al., 2022; Khalef et al., 2022; Parmar and Thakur, 2013).

Chemical precipitation is one of the widely used traditional methods applied in the process of water treatment for the removal of HMs from the wastewaters. Being simple makes it the best method for removal of suspended solids, oils, metals, etc. from wastewater (Gahrouei et al., 2024; United States Environmental Protection Agency Office of Water Washington, 2000). The process of chemical precipitation includes the reactions of reagents known as precipitants with the HMs, forming compounds that is insoluble. This solid precipitate then can be filtered out by various methods. Another process involved in this method is the removal of ionic constituents where the pH of a solution is adjusted to a point that helps in reaching the maximum lowest point of its solubility. With increase in pH, dissolved metal ions convert into metal hydroxide. Along with the ion implantations and high temperature it leads to formation of precipitates and is then followed by solid separation operations like filtration and coagulation (Ayach et al., 2024; Zueva, 2018). This method proves to be very effective in removal of HMs with removal efficiencies reaching to 99.99% in carbonate, hydroxide and sulfide precipitation processes at pH ranging from 2–12, but it produces significant amount of sludge containing toxic elements. During the precipitation method, the use of sulfide containing reagents is done and this can cause release of hydrogen sulfide gas that is very toxic and can impact the wellbeing of humans and the environment (Pohl, 2020; Gahrouei et al., 2024).

Ultrafiltration (UF) is one of the best proven technologies for the removal of HMs from wastewater in different industrial processes and water treatment processes. This method of HMs recovery includes the utilization of a semi permeable membrane with different pore sizes to allow selective small particles or molecules to pass through and retain large ones (Barakat and Schmidt, 2010; Aziz et al., 2024). Ultrafiltration technique is operational at certain low operating pressure known as transmembrane operating pressure that aids in the passing of small molecules through the membrane (Qasem et al., 2021). Water soluble polymer is another essential component of this method that binds itself to the cations in order to increase the molecular weight and size of the molecules. This allows for easy filtration of heavy molecules and recovers the HMs. However, the choice of water-soluble polymer is of utmost importance as the concept of molecular weight and the ability to complex HMs ions is based on this component only.

The UF technique has proven to be very useful along with the incorporation of the water-soluble polymer. This is evident from the study by Alfalahy and Al-Jubouri (2022) where they investigated the efficiency of zeolite incorporated polyethersulfone (PES) membranes for the removal of Pb(II) ions from aqueous solutions. The performance was evaluated under varying operational parameters, including pH (2–7), initial lead concentration (50–200 ppm), feed temperature (25 °C, 36 °C, and 46 °C), and a constant transmembrane pressure (TMP) of 1.6 bar. The optimum removal efficiency of 97% was achieved at pH 6, a temperature of 25 °C, TMP of 1.6 bar, and an initial lead concentration of 50 ppm. Other studies related to the development of UF membranes for the HMs removal also achieved removal efficiencies of 94.8% with Extracellular polymer substances-enhanced UF (EPS-UF), 94% with Polyelectrolyte-enhanced UF followed by dithionite-based chemical reduction, 90%−96% with a mixed matrix membrane of UF impregnated with Graphene oxide, 99% with Polyethersulfone/hydrous manganese dioxide UF mixed matrix membrane, 99.7% with Polysulfone amine-functionalized nanocomposite membrane. But it also has few drawbacks including inefficient UF membranes and time consuming as higher volumes will take longer time to process and might cause clogged filters after a long use (Barakat and Schmidt, 2010; Shoshaa et al., 2023; Alfalahy and Al-Jubouri, 2022).

Phytoremediation is a plant based green strategy or technique that utilizes the plants to extract, remove or remediate the contaminated water bodies, soil or other media. Several Plants have the ability to uptake the HMs from water or soil and accumulate them in their tissues to prevent from leaching or sometimes they degrade the pollutant. However, due to different mechanisms of ion uptake, each species of plants has varied ability to accumulate HMs based on their genetics, morphology, or anatomical characters (Kafle et al., 2022; Lone et al., 2008). Plants absorb ionic compound like HMs with their root systems from the soil or contaminated water even at very low concentrations. Due to presence of good root systems, plants set up rhizosphere ecosystem and accumulate the HMs and regulate the absorption rate of HMs, thereby, recovering the contaminated soil and regaining the soil fertility (Yan et al., 2020). Phytoremediation incorporates the use different mechanisms depending on the contaminants present, environmental conditions, and the plant species. These includes (1) Phyto stabilization, (2) Phytoextraction, (3) Phytovolatization, (4) Phyto filtration, (5) Immobilization (Ghosh and Singh, 2005; Nedjimi, 2021).

Table 2 presents various types of mechanisms in phytoremediation. Phytoremediation offers a range of compelling advantages that make it an attractive strategy for environmental cleanup. Since it is an autotrophic process that is powered by sunlight, it is naturally very cheap, only requiring a little maintenance and low installation costs. Being environmentally friendly, it guarantees less release of pollutants to the surrounding ecosystems, thus being a sustainable alternative to the other traditional methods of remediation. Phytoremediation is also very suitable for large scale bioremediation and the biomass can be disposed of easily. Besides that, it is very important for the metal ions to be immobilized, hence phytoremediation prevents the metal ions from being eroded and reduce the possibility of contaminants leaking into the water bodies or soil that are close by. On top of that, by providing the soil with organic matter, phytoremediation helps to make soil more fertile and is a way of renewing land for the future (Yan et al., 2020; Nedjimi, 2021; Kafle et al., 2022).

One of the studies by Balamoorthy et al. (2022) demonstrated the efficiency of phytoremediation for the removal of HMs in wastewater. The study was performed in a laboratory with pH adjusted to 6, temperature at 25° ± 5 °C and the relative humidity at 35. Synthetic wastewater containing the HMs like Cd (0.25 mg/L), Pb (0.5 mg/L), and Cu (2 mg/L) was treated for 16 days using Mimosa pudica. Lab results showed reduction in concentrations to 0.02 mg/L Cd, 0.21 mg/L Pb, and 0.4 mg/L Cu, indicating accumulation efficiencies of 92%, 58%, and 80%, respectively (Balamoorthy et al., 2022).

Microbial biosorption is an efficient method employed for the removal of HMs and xenobiotic compounds from contaminated soil and water. It generally relies on the passive, metabolically independent interaction between microbial biomass and pollutants, mainly through the processes like ion exchange, complexation, and physical adsorption. Living biomass has continuously depicted greater biosorption capacity than the non-living biomass due to its active metabolization and interaction with functional groups like amine, carboxyl, phosphonate, and hydroxyl present in the cell walls of microbes (Priya et al., 2022; Tripathi et al., 2023). Gram-positive bacteria that have thicker peptidoglycan layers are especially sorptive (Rohde, 2019; Yao et al., 2025). Besides, bacteria, fungi, algae, and yeasts, all can be used as biosorbents. The efficiency of their biosorption depends on the environmental conditions, sorbing materials, and the type of metal ions (Priya et al., 2022; Tripathi et al., 2023). Biosorption is carried out at temperatures between 20 and 35 °C for being the optimal range and for this reason, it is recommended for toxic metals such as Cd, Cr, Pb, Hg, and As (Pham et al., 2022; Priya et al., 2022).

Evidence from the study by El-Naggar et al. (2020), confirms that microbial biosorption using Pseudomonas alcaliphila strain NEWG-2 for the bio-removal of Cr⁶⁺ can achieve a great removal efficiency of 96.6% of the total 200 mg/L Cr⁶⁺ under optimized conditions and by using yeast extract 5.6 g/L, glucose 4.9 g/L, with pH 7 for 48 h (El-Naggar et al., 2020; Pham et al., 2022). Another study reported the efficient removal of Uranium (VI) by 50% from contaminated water with the employing of Acidithiobacillus ferrooxidans strain 8,455 in ex-situ conditions (Romero-González et al., 2016).

To distinguish between bioaccumulation and biosorption, the latter one involves physicochemical adsorption of contaminants on microbial surfaces without metabolic transformation. While both are used for the removal of metal cations from the solutions. The use of immobilized microbial consortia provides more efficiency and can be used for certain pollutants without the need for transport and disposal, which is costly. This technology, however, still faces problems such as the survival of microbial cultures and scalability before it can be commercialized widely. Still, biosorption complies with the principles of green chemistry and is in line with regulatory standards, hence it can be considered as a positive alternative to wastewater treatment (Cao et al., 2022; Chojnacka, 2010; Derco and Vrana, 2018; Najim et al., 2024; Qattan, 2025).

Algae-based methods for HMs removal from water are a green, and cheap biotechnological way to solve the problem of water pollution. This approach harmoniously combines biological and physicochemical reactions of microalgae with pollutants. Some HMs like molybdenum, cobalt, copper, Zn, Ni, manganese, iron, and boron are essential micronutrients that support metabolism of microalgae, while others metals like Hg, Cd, Pb, As, and aluminum are toxic and delay the growth of microalgae. Among all bio-remediators, microalgae are the most powerful which is mainly due to their extremely high tolerance capacity, large surface area, very rapid growth, and ability to perform with both living and dead biomass (Chugh et al., 2022; El-Sheekh et al., 2025). The clean-up process is mainly comprised of two stages—biosorption and bioaccumulation. The biosorption stage is a very fast one and it is extracellular and passive. Biosorption is the process by which HMs are adsorbed on the algal cell surface. Sometimes it may involve metabolism while most of the time it is a non-metabolic process. It is highly dependent on the cell wall functional groups like hydroxyl, carboxyl, sulfate, and amino groups which bind metal ions via different metallurgical mechanisms including electrostatic attraction, complexation, chelation, ion exchange, and surface precipitation. In addition, both live and dead algal biomass are able to perform biosorption. Thus, it is considered an energy-saving process for the elimination of pollutants.

Several species such as Chlorella vulgaris, Scenedesmus sp., and Nostoc sp. have demonstrated that they can be highly effective in removing HMs and organic pollutants from wastewater (Chugh et al., 2022; Chen et al., 2023; Machado et al., 2024). The bioaccumulation stage is an energy-demanding active intracellular one in which the metals are carried via cell membranes and eventually get stored in cytoplasmic or organelle compartments. Thus, it leads to the capture and detoxification of HMs by stabilizing intracellular complexes or converting them into less toxic forms (Chen et al., 2023). On top of that, algae apply few strategies such as gene regulation, chelation, antioxidation, and compartmentalization, which help to overcome metal toxicity and ensure the vital cellular functions are protected (Chugh et al., 2022).

Moreover, immobilization methods have been used to upgrade the efficacy of remediation by making algal biomass more stable and reusable (El-Sheekh et al., 2025). Besides that, the EPS released by algae create a negatively charged shield that grabs the metal ions and thereby, further diminishing the toxic interactions (Liu et al., 2025). The biopolymers in the algal matrices—consisting of polysaccharides, proteins, lipids, and nucleic acids serve as strong metal-binding ligands and open the door to the formation of flocs that facilitate pollutant separation (Pal and Paul, 2008). Many studies conducted have shown that microalgae like Chlorella minutissima removed 62% Zn, 84% manganese, 74% Cd, and 84% copper. Cladophora sp. removed 99% copper and 85% Zn and are highly efficient in the removal of As from drinking water as well (Hasnain et al., 2023). Due to their eco-friendly properties, which include CO₂ capture, nutrient recycling, and tolerance to industrial effluents, microalgae-based technologies such as high-rate algal ponds (HRAPs) can achieve removal efficiencies of over 90% for certain pollutants. As a result, phycoremediation via algae represents a green, efficient, and renewable way of reclaiming water quality, which at the same time, is beneficial for the environment and leads to economic sustainability (Delrue et al., 2016; Calijuri et al., 2025).

CWs treatment systems are biologically engineered ecosystems which emulate the natural processes of wetlands for the removal of pollutants such as HMs from wastewater. While traditional wastewater treatment systems are energy-intensive, CWs use energy that is renewable and naturally available such as solar radiation, wind, and precipitation. Though these systems occupy a lot of space, they are energy-efficient and therefore, can provide long hydraulic retention times, up to 14 days, which increases their performance in stabilizing variable inflows and pollutant loads (Hassan et al., 2021; Knox et al., 2021). CWs operate through the interaction of components such as the wetland vegetation, soils, and the diverse microbial communities, thus forming a strong biogeochemical filter that is capable of the removal of pollutants through various mechanisms like physical settling, sedimentation, adsorption, chemical precipitation, and biological uptake processes (Biswal and Balasubramanian, 2022).

As far as HMs removal is concerned, these systems can be a viable solution to those that are energy and cost-intensive such as chemical precipitation, ion exchange, and reverse osmosis (Knox et al., 2021; Yu et al., 2022). This has resulted in the high removal rates of aggregate organics (91%) and HMs (Cd: 32%, Cr: 86%, Cu: 92%, Fe: 83%, Ni: 91%, Pb: 43%, and Zn: 96%) in modified CWs that contain biochar, zeolite, and granular activated carbon (GAC). Additionally, the hydraulic retention time (HRT) has been significantly reduced (Abdel-Fattah et al., 2015; Ofiera et al., 2025). In many of the CW systems, these unique properties have been realized by the optimization of system design, substrate selection, and the integration of plants. A study by Singh and Chakraborty (2023) found that CWs have the potential to treat acid mine drainage (AMD). In particular, metal removal efficiencies achieved by the system were Fe (100%), Al (87%), Zn (98%), Ni (98%), Cr (99%) (Singh and Chakraborty, 2023). In one of the other studies conducted by Saeed et al. (2021), two hybrids subsurface flow CW systems were developed, with each one of them comprising a vertical flow (VF) unit followed by a horizontal flow (HF) unit. These systems were filled with either organic media (coco peat) or construction materials (brick and sand) and planted with Phragmites australis or Chrysopogon zizanioides (Vetiver). The total removal efficiencies achieved for Zn, Cr, Ni, and Pb in the hybrid systems were between 20%−97%, 95%−99%, 55%−73%, and 69%−83%, respectively (Saeed et al., 2021).

The statistical data presented here clearly show that plants not only facilitated the capture of metals via phytoremediation but also controlled pH and thus made it possible for microbial metal transformation processes to take place. These results emphasize the ecological and technological aspects of using vegetated wetlands for HMs pollution abatement. Moreover, the economic practicability of these systems makes them a great option for the developing areas which are characterized by inadequate wastewater treatment infrastructure and where there is an increasing demand for freshwater due to population growth and climate variability. Apart from water purification, CWs also generate some other secondary benefits such as the creation of habitats, groundwater recharge, and public environmental education. Therefore, CWs done with proper design and monitoring are an environmentally sustainable, cheap, and scalable method of HMs bioremediation, which is in harmony with current resource-saving and water reuse goals (Stefanakis, 2019; Sharma et al., 2022; Agaton and Guila, 2023). Figure 2 depicts various mechanism of HMs removal in CWs (Malyan et al., 2021; Yu et al., 2021; Zhang et al., 2024).

While CWs have proven to be efficient in HMs removal from contaminated water, they demonstrate seasonal variability which negatively affects the treatment efficiency. This is mainly due to the factors like temperature fluctuations, plant phenology, and hydrological shifts (Dykes et al., 2025). Low temperature and vegetation dormancy in the autumn and winter season reduces the microbial activities and uptake of nutrient which results in poor nitrogen and phosphorus retention (Zhai et al., 2016). Variable influent discharge, fluctuating hydrological condition further affects the pollutant removal efficiency. Various temperature dependent process like nitrification, denitrification, and plant assimilation also gets hindered and shows climate seasonality, leading to inconsistent treatment outcomes. These fluctuations emphasize the necessity for regular monitoring and adaptive management to address the uncertainties and optimize the systems for year-round performance (Dykes et al., 2025; Mesquita et al., 2017).

Dykes et al. (2025) conducted a study over 1 year to examine the effects of seasonality on the integrated CWs. He found out notable seasonal fluctuations in the concentrations removal rates (CRR) and Mass Removal Rates (MRR) for all nutrients. Nitrate CRRs ranged from −39.1% to +51.64%, corresponding to decreases of up to 14.57 mg/L and increases of 26.71 mg/L in effluent concentrations, while MMRs varied between −77.13% and +84.25%, indicating changes from −38.93 kg/day to +26.69 kg/day. For phosphate, CRRs ranged from −22.79% to +2.57%, and MMRs ranged from −71% to +93.22%, corresponding to −0.57 kg/day to +0.26 kilograms per day. Additionally, the Nitrogen removal declined in late autumn and winter due to low temperature and low vegetation activity. Overall, the winter and high flow periods posed increased nutrient risks to the receiving waters during the storm and high wastewater treatment plants discharge events (Dykes et al., 2025). In another study by Chen and Shiau (2025), higher alpha diversity in CWs during the winter season and in subsurface soils was observed and was referred to be the result of elevated summer water temperature heating the surface soils, which could inhibit the microbial communities (Chen and Shiau, 2025). As reported by Zhai et al. (2016), the artificially aerated CWs when applied on a full-scale hybrid CW for 1 year achieved good efficiency in the pollutant removal including the 65% total nitrogen and total phosphorus approximately. However, the nutrient removal was lower in the winter season, most likely due to the reduced microbial activity and plant uptake temperature (Zhai et al., 2016).

Molecular and genetic engineering have significantly improved bioremediation by increasing the efficacy and functionality of microorganisms and plants employed in remediation efforts and has proven to be an effective remedy for the inefficient and slow breakdown of waste and other organic materials. The efficiency of microbes depends upon the fact that whether it has genes that can assist in the absorption of metals and can survive the prevailing environmental conditions where it is employed (Rafeeq et al., 2023). Genetically engineered microorganisms (GEMs) are microorganisms (such as bacteria, fungi, and yeast) that have been modified with the help of genetic engineering. This technology can be used to insert some specific genes in a microorganism or a plant to improve its capacity to absorb, ingest, or alleviate the HMs' effects. GEMs are bacteria wherein a stronger protein has been introduced via biotechnological or genetic engineering means to enhance the desired trait.

The use of engineered microorganisms is safer and economically more sound than the alternative methods (Hanlon and Sewalt, 2021; Rafeeq et al., 2023). The genetically engineered microbes are efficient and capable of breaking down most of the contaminants that normal indigenous microbes cannot degrade (Rebello et al., 2021). Genetically modified bacteria with improved biosorption and bioaccumulation capacities can be created to specifically target HMs like As, Hg, Zn, Cd, Cr (Fatima et al., 2024). Transgenic plants can be developed to enhance tolerance and absorption of HMs, hence improving phytoremediation efficacy. Among the molecular approaches, CRISPR-Cas9 is the most precise technology for gene editing linked to HMs metabolism and thus supporting the production of tailored bioremediation agents. The innovations in molecular and genetic engineering have a major impact on the effectiveness of bioremediation techniques (Farias et al., 2011; Chen et al., 2024).

Molecular and genetically modified microbes also raise concerns about the biosafety and regulatory issues spanning human health, the environment and dual use risk like bioterrorism or bioweapons development (Mahdizade Ari et al., 2024; Zeng et al., 2022). Although, most microbial strains are engineered for the diverse advantages like therapeutics delivery, nitrogen fixation, and industrial bioproduction, genetic modifications might unpredictably affect the host range, virulence, immunogenicity, and metabolic interactions, complicating evidence-based risk assessment (Mahdizade Ari et al., 2024; Rubinstein et al., 2025). Besides, the Horizontal Gene Transfer (HGT) and rapid microbial evolution raise critical concerns about various engineered traits like antibiotic resistance markers or novel metabolic pathways may spread into the natural microbiomes and non-target organisms. Field deployment of the engineered organisms for environmental applications like bioremediation, and agriculture faces uncertainties related to persistence, ecosystem disruption and living pollution. And that too the long-term empirical data for these remains very limited (Lensch et al., 2024; Rubinstein et al., 2025). Regulatory frameworks derived from the Genetically modified organisms (GMO) and contained use frameworks are increasingly challenged by the modularity of synthetic biology, affordable DNA synthesis, and Do It Yourself (DIY) platforms. This necessitates for the adaptive multi-tiered governance that is safe by design safeguard technologies and enhanced biosecurity oversight (Asin-Garcia et al., 2023; Bouchaut and Asveld, 2020; Zeng et al., 2022).

Nano-bioremediation, the use nanotechnology in bioremediation, is one of the most recent developments to a green and almost energy independent removal technology for the abatement of HMs in polluted media. The incorporation of nanotechnology to bioremediation has made it possible to exploit the combined effect of nanotechnology and biological systems. The principal means of the nano-bioremediation are adsorption and reduction where the biogenic nanoparticles bind to the pollutants either by physical or chemical adsorption or through direct sequential reduction pathways. Chemical adsorption facilitated by functional groups on the surface of nanoparticles is usually preferred mostly because of its excellent binding efficiency (Milano et al., 2024; Thangavelu and Veeraragavan, 2022). Figure 3 presents various mechanism of HMs removal with the integration of nanotechnology (Akpomie et al., 2022; Baby et al., 2022; Bakhtiari et al., 2024; de Silva et al., 2025; Karnwal and Malik, 2024; Le et al., 2019; Olawade et al., 2024; Solanki et al., 2024; Thekkudan et al., 2017; Yang et al., 2019).

Nanostructured materials exhibit enhanced reactivity and lower activation energy owing to their high surface area to mass ratio and quantum effects, enabling them to reach and remediate contaminated environments that are inaccessible to bulk materials. Carbon based nanomaterials like graphene oxide, fullerenes, and carbon nanotubes have signified excellent adsorption capacities for metal ions due to their modifiable surface chemistry and extraordinary electronic characteristics (Mandeep and Shukla, 2020). Metal and metal oxide nano adsorbents entailing iron, silver and gold nanoparticles have also depicted high selectivity and efficiency in the removal of HMs like As, Cd, Hg, and Cr (Onotu et al., 2025; Safavi et al., 2025). Demonstrating the capability of nano-technology, iron oxide nanocomposites enclosed in the microporous silica exhibited high As adsorption capacities 47 times higher than the conventional adsorbents, while silver nanoparticles synthesized using the leaf extract of Ficus benjamina effectively removed Cd ions (Thangavelu and Veeraragavan, 2022). Biogenic synthesis methods with the integration of fungi (e.g., Aspergillus niger), algae (e.g., Chlorococcum sp.), and plants (e.g., Aloe vera) have made easy the production of eco-friendly nanoparticle production, lowering chemical usage, and aggregation issues (Sharma and Sharma, 2022).

Further, the functionalization with biomolecules like enzymes increases the environmental compatibility by lowering the surface energy and cellular interactions (Abdel-Mageed, 2025). Moreover, the magnetic nanoparticles coated with activated carbon or silver have already achieved notable removal efficiencies of 36.56% chemical oxygen demand (COD) and 97.4% fluoride ions from wastewater. The integration of microbial fuel cells and nano catalysts also has opened a way for bioelectricity generation alongside the remediation processes. Overall, the incorporation of nano technology in the field of bioremediation offers scalable, cost effective, viable and environmentally excellent approach to mitigate HMs with the potential to revolutionize wastewater treatment and environmental restoration through tailored nanomaterial configuration/design and assisted synthesis (Douvris et al., 2023; Milano et al., 2024).

While the beneficial effects of the nano materials are widely known, several studies have suggested toward the negative effects like nano-particle toxicity and aggregation (Islam, 2025). Nano-particles toxicity arises due to their unique nanoscale properties. Toxicity varies with parameters like charge, size, shape, surface properties, and zeta potential which potentially influence the uptake and persistence in biological cells (Akintola and Yahaya, 2024; Karakoti et al., 2006). While the nanomaterials can easily interact with microbes, plants, and animals by entering various aquatic systems via runoffs and other sources, toxicity through inhalation can also affect animals and humans. Inhalation being the primary exposure route, as even < 50 μg/m3 of the carbon nanotubes or fullerenes in air can enter the body. Dermal exposure is one other pathway for nano particle to enter human body through sunscreens and cosmetics made of TiO₂ and ZnO (Akintola and Yahaya, 2024; Karakoti et al., 2006). Once released, these small sized particles can disperse widely, accumulate in sediments, bioaccumulate through food chains, potentially causing oxidative stress in algae and invertebrates (Akintola and Yahaya, 2024; Islam, 2025).

Besides, nano particles can penetrate plant roots and move through xylem where the silver nanoparticles (AgNPs) accumulate in the tissues and entering food chain. While the mobility and toxicity of the nano particles are generally influenced by environmental factors like pH, temperature, and pollutants, they can also be dependent on soil conditions, nutrient availability, and plant species like in case of AgNPs. AgNPs can easily dissolve and oxidized to Ag⁺, which is more toxic than the particulate form, potentially harming biodiversity, soil health, and microbial communities. One another harmful effects of nanoparticle sis the aggregation, where the nano particles cluster together causing alteration in their stability, reactivity, and behavior in diverse environments. Driven by surface charge, ionic strength, pH and multivalent cations, nanoparticle aggregation along with dissolution of metal based engineered nano particles (ENPs) releases ions with distinct mobility and toxicity. Soil constituents like clays and oxides strongly adsorb nanoparticles, influencing the transport and hetero-aggregation. Widely used ZnO, iron oxide, Fe₂O₃ and TiO₂ may help enhance growth of the plants and other processes at low concentrations but may also cause oxidative stress, reduced germination, root changes, microbial disruption, and soil accumulation at higher levels (Ihtisham et al., 2021; Islam, 2025; Singh and Saxena, 2022).

AI is one of the increasingly recognized transformative tools that is being widely used in the bioremediation of HMs, providing data drive solutions for the monitoring, optimization, and predictive modeling (Blessing and Olateru, 2025). AI systems facilitate environmental data acquisition from the diverse contaminated sites of soil and water, and through the analysis of variables such as microbial activity, pollutant concentration, pH, temperature, and nutrient levels. Thus, bioremediation conditions can be dynamically adjusted to enhance microbial efficiency and pollutant degradation (Akintola, 2024). Predictive modeling is instrumental in forecasting remediation outcomes, enabling the timely interventions that both quicken and ensure the success of the process. Several AI algorithms that include machine learning (ML), neural networks (ANN), genetic algorithms (GA), particle swarm optimization (PSO), and boosted regression trees (BRTs) are utilized to promote the simulation of complex environmental systems and the improvement of treatment strategies by optimization. These models can work diligently with the historical data on pollutant dispersion to efficiently solve the issues of identifying the high-risk zones, and support the targeted interventions. ANN, inspired from the structure of biological neurons, is quite powerful when it comes to handling multivariate non-linear complex problems and therefore widely adopted in water treatment processes modeling.

Several studies have reported that ANN based models significantly improves and enhances the prediction accuracy (R²) and reduces the common error metrics in comparison with the conventional methods of waste water treatments (Alsulaili and Refaie, 2021; Minh et al., 2024; Sabour and Amiri, 2017). Alsulaili and Refaie (2021) in a study achieved the R² values of 0.752 for BOD, 0.612 for COD, and 0.631 for TSS in WWTP effluent, with an influent BOD5 soft sensor reaching R² = 0.754 for real-time control (Alsulaili and Refaie, 2021). Ibrahim et al. (2024) reported R² up to about 0.89 for COD/BOD prediction, outperforming linear models (Sabour and Amiri, 2017; Ibrahim et al., 2024). While Sabour and Amiri (2017) showed ANN superior to RSM in modeling Fenton treatment of landfill leachate (Sabour and Amiri, 2017). Minh et al. (2024) further confirmed ANN performance with R² = 0.9604 vs. RSM's 0.9528, along with lower MAE, MSE, and RMSE, and greywater BOD prediction studies reported R² = 0.88 for ANN models (Minh et al., 2024). Zhao et al. (2020) performed a bibliometric analysis between the period of 1995–2019 to determine the trends in the application of A.I to waste water treatment. Their findings indicated rapid growing acceptance and reliance on the ANN modeling for simulation and prediction of performance of biological waste water treatment plant (Dantas et al., 2023; Zhao et al., 2020).

The use of convolutional neural networks (CNNs), radial basis function (RBF) networks, and self-organizing maps (SOMs) as variants allows complex statistical modeling and real-time control systems to be performed (Bhagat et al., 2020; Blessing and Olateru, 2025; Kumari et al., 2025). Additionally, AI aids in the monitoring strategies and the designing of genetically engineered microbes that are tailored to the specific contaminants and are also equipped with the feedback mechanism for real-time effectiveness assessment (Akintola, 2024). When combined with AI, remote sensing technologies such as hyperspectral and LiDAR imaging become more efficient in carrying out and facilitating land cover mapping, object detection, and change analysis, with being further improved through data fusion and integration. These attributes facilitate the recognition of space and time patterns in pollutant concentrations and supply extensive understanding of environmental dynamics through AI supported pollution monitoring systems (Blessing and Olateru, 2025; Lovynska et al., 2024).

Despite being so useful and powerful, AI driven bioremediation faces challenges related to data quality, availability, training optimization, integrity, and ethical issues like environmental justice as well as equitable access. Nevertheless, future tech like AI, machine learning (ML), and deep learning (DL) pave the way for less expensive alternatives to conventional remediation processes through such means as cutting down human interventions and efforts, sampling requirements, and overall efficiency. The flexible capacity of AI reflects it's more general ecological applications linked to the quality of air, water, and soil, biodiversity monitoring, and resilience planning. As AI continue to evolve, its integration into bioremediation is a radical change to the intelligent, adaptive, and sustainable management of HMs contamination. Thus, it is considered one of the efficient technologies for environmental engineering (Akintola, 2024; Alotaibi and Nassif, 2024; Blessing and Olateru, 2025).

Biofilm mediated bioremediation put forward a promising, and a sustainable solution to deal with the environmental threat of HMs contamination that endangers almost all living organisms (Jasua et al., 2021). Biofilms are assemblages of microorganisms enveloped in a matrix of self-produced EPS, which contain water, proteins, carbohydrates, and extracellular DNA. This microbial community, which usually comprises bacteria, fungi, algae, and archaea, adheres to the surface and grows in a humid environment if there is enough nutrition. Their generation is a multistep process: irreversible attachment, quorum sensing, development of layered colony, and planktonic cell dispersion (Flemming et al., 2024; Jasu and Ray, 2021; Zhao et al., 2023). Biofilms protect from the various environmental stresses like pH changes, salinity, extreme temperatures, and toxic chemicals. Figure 4 presents various mechanism of HMs removal with the integration of biofilm technologies (Arora and Khosla, 2021; Mohapatra et al., 2020; Priyadarshanee and Das, 2021; Syed et al., 2022). Variable gene expression in biofilms controlled by nutrient and oxygen gradients allows metabolic specialization and division of labor, which is a part of the pollutant degradation and breakdown process. Chemotaxis, flagellar motility, and quorum sensing also help microbial coordination and movement toward contaminants (van Gestel et al., 2015; Yin et al., 2019). Biofilms are very effective in the sequestration and detoxification of HMs like Pb²⁺, Cd²⁺, and Zn²⁺, with EPS playing a significant role due to the anionic charge and enhanced capacity of binding metals (Mahto et al., 2022). Fungal-bacterial biofilms (FBBs) have depicted significant metal removal potential and reduced penetration into the tissues of plant during the remediation studies of soil. Superior metal uptake and enhanced resistance have been noticed in the strain related to biofilm formation, making them an ideal choice for in situ remediation.

Natural attenuation, a passive bioremediation strategy, depends on the indigenous biofilm forming microbes as to transform, immobilize, and detoxify pollutants without engineered intervention. Biofilms also act as a valuable indicator for the monitoring of environment, especially in the aquatic ecosystems (Jambon et al., 2018; Pandit et al., 2020; Henagamage et al., 2022). A change in biomass, species composition, pigment production, photosynthesis, and enzymatic activity can signal the rise or reduction of pollution levels (Rai, 2016; Yadav and Nikam, 2024). Microbial profiling is found to be a very efficient instrument for the determination of ecosystem health during seasonal changes and pollutant loads from metals such as Zn and Cd that affect the diversity of river biofilms. Besides, biofilms are the first biological interface with pollutants in water bodies and react rapidly when in contact with toxicants, thus providing a convenient early warning system. Their easy sampling method, rapid growth, and pollutant adsorption capacity make them even more valuable in environmental assessment. To sum up, the biofilm-based remediation system harnesses microbial adaptability, metabolic diversity, and ecological resilience, thereby providing an environmentally-friendly solution to the problem of HMs pollution while at the same time, functioning as a tool for environmental monitoring (Mitra and Mukhopadhyay, 2016; Griffero et al., 2024; Iqbal et al., 2025; Kollati et al., 2025).

The management and monitoring of water and soil HMs contamination have been exponentially improved through the integration of Internet of Things (IoT) technology into the bioremediation system. IoT devices equipped with low-cost sensors can detect the levels of HMs in the water that is used for irrigation, which is an essential aspect of precision agriculture. The Interconnected sensors to IoT networks share data in real-time to the platforms that can be updated immediately, thus making it possible to have instant insights and early warning to curb crop and soil damage. By integrating sophisticated components such as those based on NodeMcu8266 architecture, continuous monitoring and alarm mechanisms can be realized, thus allowing timely intervention (Anil Kumar and Mahabhaleshwara, 2024; Kumar et al., 2024; Kuppan et al., 2024). In the case of bioremediation and waste management, IoT-enabled sensors keep track of environmental variables like pH, temperature, and pollutant levels, thus ensuring and facilitating the automated control of nutrient supply to microbial communities and the optimization of the pollutant degradation process (Kuppan et al., 2024; Ngwenya et al., 2025).

Furthermore, systems of this nature can also help in the monitoring, and controlling of the bioremediation activities at many different remote locations, thus the need for human intervention is reduced and efficiency of operations is concurrently improved. IoT devices and networks have proved to be of great importance in the urban wastewater treatment as a result these are being embedded across the sewage treatment nodes and equipments, allowing for remote online control of pipe valves and real time management of treatment processes. The usage of such systems in the wastewater treatment leads to positive effects such as faster crisis response, standardized operations and increased energy and economic efficiency. The system's framework is oriented toward operations that are continuous for up to 24 h, with sensor deployment strategies designed to maintain integrity and prevent disturbances. Furthermore, smart soil remediation equipment utilizes IoT to collect samples and evaluate the status of the soil, which also involves the recognition of HMs and transmission of data for timely action and applications of remediation agents (Dhanaraju et al., 2022; Pamula et al., 2022; Mowla et al., 2023; Dada et al., 2024).

Advanced hybrid systems utilized in the bioremediation of HMs in water is a novel technology that combines both biological and physicochemical methods to enhance the removal of HMs and microplastics. These hybrid systems synergistically combine microbial bioremediation with ultra-modern technologies such as nanotechnology, electrochemical methods, and membrane bioreactors, thereby diminishing the drawbacks of traditional bioremediation like slow kinetics and incomplete detoxification (Yadav et al., 2023; Khan et al., 2025; Vuković Domanovac et al., 2025). For instance, electrochemical treatment along with microbial bioreactors (MBRs) has shown enhanced performance by coupling biodegradation with physical retention and electrochemical transformation of HMs and other pollutants. Thus, resulting in the achievement of higher efficiency and longer operational lifespans than that of microbial methods alone. The enhanced efficiency of the integrated system of electro-membrane bioreactors (eMBRs) is evidenced from data collected by Ensano et al. (2016). For the municipal and industrial wastewaters, eMBRs achieved 85%−98% COD removal compared to 72%−95% for conventional MBRs. While the phosphorus removal ranged from 94%−99% compared with 59%−65% in conventional stems, demonstrating superior organic and nutrient removal. Besides, sludge retention times (SRTs) up to about 268 days and 81%−82% reductions in specific cake resistance and fouling index at 12A/m² current densities demonstrated superior long-term stability and reduced fouling (Ensano et al., 2016).

In these hybrid electrochemical MBR systems, pollutant breakdown is brought to the maximum level while membrane fouling, which is a common disadvantage in pure biological systems, is avoided (Ensano et al., 2016; Vasanthapalaniappan et al., 2020). Furthermore, the use of nanomaterials such as nanoscale zero-valent iron (nZVI) particles in conjunction with bioremediation brings about the enhancement of HMs adsorption and their reduction to less toxic forms. These nanoparticles are the most effective reductants and adsorbents as they can immobilize metals, whereas the microorganisms consume organic co-contaminants, thus forming a single remediation platform with greater sustainability (Verma et al., 2025; Wang et al., 2025). The recent emphasis on plant-microbe interactions mediated by plant growth promoting rhizobacteria (PGPR) is one of the main reasons for the advances in this field and it implies that plants together with metal-resistant microbes can significantly increase phytoextraction of HMs from water and soil. This hybrid phytoremediation technique utilizes microbial bioaugmentation and phytostimulation to get the remediation process faster and complete (Karnwal et al., 2024; Khoso et al., 2024). Besides that, hybrid systems that use biosorbents obtained from agricultural waste and lignocellulosic biomass not only attract metal ions but also offer numerous functional groups for metal binding, thereby lowering metal bioavailability in water (Lindholm-Lehto, 2019; Kainth et al., 2024). Table 3 presents the comparative analysis of various new technologies for the bioremediation of HMs. The integration of these biological components with nanotechnology, electrokinetic processes, and membrane filtration forms a multi-pronged, adaptable approach for HMs bioremediation. Hybrid bioremediation systems are, in essence, environmentally friendly, scalable, and economically feasible measures to reduce HMs pollution in water (Firincă et al., 2025).

AAS is a highly sensitive technique that finds its application most commonly in detecting minute traces of HMs in solutions or wastewater (Elkhatat, 2023). The method involves atomizing a sample, which may be done in a flame or graphite furnace, followed by measuring the absorption of light by the free atoms. Each metal has its unique wavelength at which it absorbs light, and the absorption is proportional to the metal concentration. The technique yields precise quantitative data and can detect metals such as Pb, Cd, copper, and As at a level as low as parts per billion or even parts per trillion. Samples are usually prepped through digestion to obtain atomic forms of metals. AAS is a method of choice because of its selectivity, sensitivity, relatively low cost, and the availability of standardized protocols. It especially finds application in routine monitoring of industrial and municipal wastewater in the environment (Slavin, 1994; Hina et al., 2023; Abdelmonem et al., 2025). Since AAS could detect only a single element at a time and could not establish simultaneous methods, these limitations paved the way for multi-element techniques such as Inductively Coupled Plasma Optical Emission Spectrometry (ICP-OES), which could detect several elements at a time and has several other benefits (Ferreira et al., 2018).

ICP-OES can detect several HMs at the same time by using a plasma source that excites atoms and they emit light at the characteristic wavelengths. The intensity of the emitted light is directly related to the amount of the metal. ICP-OES provides fast multi-element detection from trace levels to high concentrations, has high sensitivity and wide dynamic range. It is also very effective in handling complex wastewater matrices. The procedure involves the use of very advanced instruments, and operators must be highly skilled, thus making it expensive when compared to AAS. However, the capability of the instrument to perform a large number of metals determinations in one run shortens the total time of the analysis significantly. The technique is largely used by laboratories that monitor wastewater from metal industries (Dressler et al., 1998; Hou and Jones, 2000; Khan et al., 2021; Douvris et al., 2023).

To address the need of portable and cost-effective monitoring outside the lab, electrochemical methods emerged as practical alternatives of ICP-OES (Motshakeri et al., 2025). Electrochemical methods for HMs detection, such as anodic stripping voltammetry (ASV), make use of electrochemical cells wherein metals are pre-concentrated on electrodes and, upon the application of voltage, are stripped off. The electrical current thus generated is in direct proportion to the concentration of the metal (Ariño et al., 2022; Zhao et al., 2022). Such sensors offer rapid detection with high sensitivity of trace HMs like Pb, Hg, and Cd (Helim et al., 2024).

Most of the standard analytical methods like atomic absorption spectroscopy, atomic fluorescence spectroscopy, and x-ray fluorescence sensors needs complex equipment, expert personnel, and lengthy procedures. To overcome these issues, electrochemical sensors offer superior advantages via rapid, accurate, simple, and less costly analysis for real time multi parametric detection of HMs. Notable examples include dumbbell-like Au/Fe₃O₄ nanoparticles on screen-printed carbon electrodes (SPCE) for arsenic limit of detection (LOD 0.0215 ppb, sensitivity 9.43 μA/ppb), Fe₃O₄/Bi₂O₃/C₃N₄-modified glassy carbon electrode (GCE) for cadmium (LOD < 3 × 10⁻⁹ mol/L, range 0.01–3 μmol/L), DNA-based sensors with ethyl green/multi-walled carbon nanotubes (MWCNTs) (Cd LOD 2 nM, sensitivity 5 nA/nM), and comb-shaped Parafilm electrodes for Pb, Cu, Hg at 0.05 mg/L with signal enhancement ratio 1.42 during Anodic Stripping Voltammetry (ASV) (Madadelahi et al., 2025; Sulthana et al., 2024).

Additionally, the electrochemical sensors allow sensitive quantifications of a diverse range of HMs, with the detection limits generally spanning from nanomolar to sub ppb levels. Lead is detected by ASV at a copper electrode with (LOD) of 21 nM, while cadmium reaches 3.8 nM using Square Wave Voltammetry (SWV)/Differential Pulse Voltammetry (DPV) at a CNT–polyaniline-modified glassy carbon electrode. Mercury, arsenic, and copper are monitored by Cyclic Voltammetry (CV)/DPV with graphene or conducting-polymer composites, achieving LODs of 0.8 μM, 0.12 μM and about 1.2 × 10⁻⁶ M, respectively. Cr, Ni, and cobalt show μg/L limits (0.8, 2.5 and 2.4 μg/L), whereas manganese, Zn and iron are detected around 4.2, 1 μg/L and 0.05 mg/L. Advanced platforms, including Bi-film, ion-imprinted and nanocomposite electrodes, push LODs to 2.1 × 10⁻⁹ mol/L for Sn, 0.03 ppb for thallium (Tl) and 0.43 μg/L for uranium (U), highlighting the ultra-trace capabilities of modern electrochemical sensing techniques (Madadelahi et al., 2025; Sulthana et al., 2024; Tian et al., 2025).

Electrochemical methods are also portable and can be used for on-site testing, thus making them a suitable option for on-field wastewater monitoring. Nevertheless, they need calibration, clean electrodes, and can be affected by the interference of other substances. Despite the drawbacks, electrochemical methods provide a low-cost, real-time detection alternative which complements traditional lab-based techniques (Umapathi et al., 2022; Shabib et al., 2025). To overcome the limitations of lower sensitivity and limits of detection as in electrochemical methods of detection, biosensors can be used, which offers high sensitivity, selectivity, rapid, and accurate real time monitoring (Bansod et al., 2017; Hara and Singh, 2021; Wu et al., 2023).

Biosensors use biological recognition elements such as enzymes, antibodies, or microorganisms that selectively interact with HMs in wastewater and thus release an electrical or optical signal which can be used for detection (Fdez-Sanromán et al., 2025; Velusamy et al., 2022). They ensure fast, specific, and potentially real-time monitoring without the need for a complicated sample preparation step (Nath, 2024; Nnachi et al., 2022). Biosensors can specifically be designed for certain metals and are becoming increasingly popular as in situ environmental assessment tools. However, their sensitivity might be lower than that of instrumental techniques; also, they often require frequent calibration, and must be handled carefully in order to maintain the biological activity. Besides, they can be limited by stability and lifespan issues. The fact that these devices are easy to use and respond quickly makes them a valuable tool serving the functions of preliminary screening and continuous monitoring (Ding et al., 2015; Odobašić et al., 2019; Huang et al., 2023).

Since the core and most critical characteristics of biosensors are high sensitivity, short response time, specificity, and relatively less costly, they are best utilized for detection of toxic substances like pollutants, pesticides, and HMs. Biosensors may be tailored for the real time and continuous monitoring of certain pollutants in order to regulate and support the management of the water safety and quality. Biosensors can complement bioremediation of HMs and other pollutants by achieving on site and real time environmental data. Besides, biosensors can also be integrated into pollutant monitoring systems and employed as a predictive tool for assessing the chemical pollution in the environment (Huang et al., 2023; Odobašić et al., 2019).

Microbial fuel cell (MFCs) based configurations, employing carbon fiber or graphite felt electrodes in double or single chamber setups, achieved detection limits of 0.5–10 mg L⁻¹ for Cu, As, Cr, Hg in municipal and industrial wastewater with response times as fast as 5–18 min, complementing the in-situ adjustments during microbial biosorption or bioaccumulation. Whole-cell bacterial biosensors in sol-gel matrices detected Cd, Cu, Zn at 1.42 × 10⁻⁴−3.16 × 10⁻⁴ mg L⁻¹ in soil within 4–7 h, while DNAzyme and magnetoelastic systems offer ultrasensitive limits (e.g., 0.23–0.33 μM for Pb, 1 × 10⁻⁶ nM for Hg) in water samples, often in minutes. Additionally, whole cell biosensors, engineered with metal responsible promoters like arsR for arsenic, cadC for cadmium, and merR for mercury, detect ions such as Cd²⁺, Pb²⁺, and Hg²⁺ at ppb levels within minutes to hours, providing rapid feedback that traditional lab methods like ICP-MS cannot match. These portable cost-effective tools like MFCs, Surface-Enhanced Raman Spectroscopy (SERS) or conductometric biosensors ensures bioavailability assessment, prevent rebound pollution and optimize the strategies for bioremediation (Bereza-Malcolm et al., 2014; Huang et al., 2023).

Membrane technologies extend beyond detection by offering rapid removal, making them valuable in integrated monitoring–remediation systems. They complement biosensors and electrochemical methods by providing a physical barrier to the contaminants (Selivanovitch et al., 2024; Urtiaga, 2021; Kapepula and Luis, 2024). Membrane filtration technologies like nanofiltration and reverse osmosis are primarily aimed at HMs removal from wastewater (Castro and Abejón, 2024). Their main function is treatment, yet they can also be used as indirect supportive means for metal quantification by providing concentration in permeate and retentate streams. Such membranes are semipermeable and act as physical barriers that allow the separation of metal ions based on their size and charge. These methods provide removal efficiencies that are very high for a widely varied set of metals such as As, Cd, and Pb (Mukherjee, 2019; Azmi et al., 2025). While not being a direct detection tool, metal detection and approximate quantification can be obtained from system performance monitoring (Cavaliere et al., 2025; Phattaranawik et al., 2008). Membrane fouling, high operational costs, and the need for pre-treatment are the problems it faces. These approaches harmonize with detection in a way that they reduce metal loads and are thus necessary for wastewater management. Each of these methods brings different benefits and drawbacks with respect to their sensitivity, costs, complexity, and the context of application, thereby allowing for the most suitable method to be selected depending on the characteristics of the wastewater and the monitoring needs (Dawam et al., 2025; Ezugbe and Rathilal, 2020).

Detection methos for HMs vary widely in terms of suitability for various developing regions. In these regions, constrained infrastructure, high cost, and very limited technical expertise hinders the effective bioremediation monitoring. Laboratory based detection techniques like Inductively Coupled Plasma Optical Emission Spectrometry (ICP-OES) and AAS delivers high sensitivity with accuracy for multi-elemental analysis even at very low concentrations, making them one of the best detection tools for precise quantification. However, due to expensive large equipments, maintenance, expenses for inert gases and consumables plus stable electricity, clean laboratory environments, and need of highly skilled experts, resources are not generally available in remote or under-resourced regions of developing countries. These multiple barriers make these methods inherently limited to centralized labs, necessitating sample transport to over poor road networks which risks delay and contamination (Eskina et al., 2020; Filippidou and Chatzandroulis, 2023; Hu et al., 2023; Mukherjee et al., 2021). They do offer a more accessible alternative for pretreatment and industrial reuse with high sensitivity and low-cost setups plus the advantage of scalability and less space requirements. While highly portable for sampling or filtration, they do rely on the lab analysis for heavy metal quantification, complicating the logistic with inadequate transport and storage facilities in some developing regions (Aziz et al., 2024; Azmi et al., 2025; Filippidou and Chatzandroulis, 2023; Haider et al., 2024).

Table 4 presents a comparative overview highlighting sensitivity, cost, portability, and field applicability of different HMs' detection techniques for better understanding of these technologies. Other detection tools and techniques including the biosensors and electrochemical methods like anode stripping voltammetry are exceptionally well suited and provide high sensitivity with low-cost option for disposable/hand held units, high portability due to compact battery powered IoT ready designs and capability for in-situ or on-site real-time monitoring. These allows local technician with minimal training to perform rapid assessment at sites without being depended on advanced infrastructure (Eskina et al., 2020; Hara and Singh, 2021; Hu et al., 2023).