Heavy metal (Cu2+) removal from wastewater by metal-organic framework composite adsorbent: Simulation-based- artificial neural network and response surface methodology.

Metal-organic framework (MOF)--based composites have received significant attention in a variety of applications, including pollutant adsorption processes. The current investigation was designed to model, forecast, and optimize heavy metal (Cu2+) removal from wastewater using a MOF nanocomposite. This work has been modeled by response surface methodology (RSM) and artificial neural network (ANN) algorithms. In addition, the optimization of the mentioned factors has been performed through the RSM method to find the optimal conditions. The findings show that RSM and ANN can accurately forecast the adsorption process's the Cu2+ removal efficiency (RE). The maximum values of RE are achieved at the highest value of time (150 min), the highest value of adsorbent dosage (0.008 g), and the highest value of pH (=6). The R2 values obtained were 0.9995, 0.9992, and 0.9996 for ANN modeling of adsorption capacity based on different adsorbent dosages, Cu2+ solution pHs, and different ion concentrations, respectively. The ANN demonstrated a high level of accuracy in predicting the local minima of the graph. In addition, the RSM optimization results showed that the optimum mode for RE occurred at an adsorbent dosage value of 0.007 g and a time value of 144.229 min.

Hydrogeochemical assessment of aquifer salinization in north-eastern Morocco's Gueroaou coastal plain using statistical methods.

The Guerouaou aquifer investigation spanning 280 km2 in Ain Zohra yields promising outcomes, instilling optimism for regional water quality. These analyses were applied to 45 sampling instances from 43 wells, enabling a comprehensive water quality assessment. Groundwater conductivity ranged from medium to high, peaking at 18360 ms/cm2. The conductivity reveals insights about the groundwater's mineralization. Key physiochemical parameters fell within desirable thresholds, bolstering the positive perspective. HCO3- levels spanned 82-420 mg/L, while chloride content ranged from 38 to 5316 mg/L, benefiting water quality. NO3- ions, vital for gauging pollution, ranged from 0 to 260 mg/L, indicating favorable results. Cation concentrations exhibited encouraging variations: Ca2+- 24 to 647 mg/L, Mg2+- 12 to 440 mg/L, Na+- 18 to 2722 mg/L, K+- 1.75 to 28.65 mg/L. These collectively favor water quality. Halite breakdown dominated mineralization, as evidenced by the prevalence of Na-Cl-Na-SO4 facies. Water resource management and local communities need effective management and mitigation strategies to prevent saltwater intrusion.

Effects of CO2 concentration and time on algal biomass film, NO3-N concentration, and pH in the membrane bioreactor: Simulation-based ANN, RSM and NSGA-II.

The practice of aquaculture is associated with the generation of a substantial quantity of effluent. Microalgae must effectively assimilate nitrogen and phosphorus from their surrounding environment for growth. This study modeled the algal biomass film, NO3-N concentration, and pH in the membrane bioreactor using the response surface methodology (RSM) and an artificial neural network (ANN). Furthermore, it was suggested that the optimal condition for each parameter be determined. The results of ANN modeling showed that ANN with a structure of 5-3 and employing the transfer functions tansig-logsig demonstrated the highest level of accuracy. This was evidenced by the obtained values of coefficient (R2) = 0.998, R = 0.999, mean squared error (MAE) = 0.0856, and mean square error (MSE) = 0.143. The ANN model, characterized by a 5-5 structure and employing the tansig-logsig transfer function, demonstrates superior accuracy when predicting the concentration of NO3-N and pH. This is evidenced by the high values of R2 (0.996), R (0.998), MAE (0.00162), and MSE (0.0262). The RSM was afterward employed to maximize the performance of algal film biomass, pH levels, and NO3-N concentrations. The optimal conditions for the algal biomass film were a concentration of 2.884 mg/L and a duration of 6.589 days. Similarly, the most favorable conditions for the NO3-N concentration and pH were 2.984 mg/L and 6.787 days, respectively. Therefore, this research uses non-dominated sorting genetic algorithm II (NSGA II) to find the optimal NO3-N concentration, algal biomass film, and pH for product or process quality. The region has the greatest alkaline pH and lowest NO3-N content.

Hazardous waste management (Buxus papillosa) investment for the prosperity of environment and circular economy: Response surface methodology-based simulation.

Dealing with the current defaults of environmental toxicity, heating, waste management, and economic crises, exploration of novel non-edible, toxic, and waste feedstock for renewable biodiesel synthesis is the need of the hour. The present study is concerned with Buxus papillosa with seeds oil concentration (45% w/w), a promising biodiesel feedstock encountering environmental defaults and waste management; in addition, this research performed simulation based-response surface methodology (RSM) for Buxus papillosa bio-diesel. Synthesis and application of novel Phyto-nanocatalyst bimetallic oxide with Buxus papillosa fruit capsule aqueous extract was advantageous during transesterification. Characterization of sodium/potassium oxide Phyto-nanocatalyst confirmed 23.5 nm nano-size and enhanced catalytic activity. Other characterizing tools are FTIR, DRS, XRD, Zeta potential, SEM, and EDX. Methyl ester formation was authenticated by FTIR, GC-MS, and NMR. A maximum 97% yield was obtained at optimized conditions i.e., methanol ratio to oil (8:1), catalyst amount (0.37 wt%), reaction duration (180 min), and temperature of 80 °C. The reusability of novel sodium/potassium oxide was checked for six reactions. Buxus papillosa fuel properties were within the international restrictions of fuel. The sulphur content of 0.00090% signified the environmental remedial nature of Buxus papillosa methyl esters and it is a highly recommendable species for biodiesel production at large scale due to a t huge number of seeds production and vast distribution.

Wastewater treatment with nanomaterials for the future: A state-of-the-art review.

Aquatic and terrestrial ecosystems are both threatened by toxic wastewater. The unique properties of nanomaterials are currently being studied thoroughly for treating sewage. Nanomaterials also have the advantage of being capable of removing organic matter, fungi, and viruses from wastewater. Advanced oxidation processes are used in nanomaterials to treat wastewater. Additionally, nanomaterials have a large effective area of contact due to their tiny dimensions. The adsorption and reactivity of nanomaterials are strong. Wastewater treatment would benefit from the development of nanomaterial technology. Second, the paper provides a comprehensive analysis of the unique characteristics of nanomaterials in wastewater treatment, their proper use, and their prospects. In addition to focusing on their economic feasibility, since limited forms of nanomaterials have been manufactured, it is also necessary to consider their feasibility in terms of their technical results. According to this study, the significant adsorption area, excellent chemical reaction, and electrical conductivity of nanoparticles (NPs) contribute to the successful treatment of wastewater.

Experimental investigation of H3PO4 activated papaya peels for methylene blue dye removal from aqueous solution: Evaluation on optimization, kinetics, isotherm, thermodynamics, and reusability studies.

This investigation is centered on the effectiveness of methylene blue (MB), a cationic dye, adsorbed from an aqueous media by H3PO4 activated papaya skin/peels (PSPAC), with initial pH (2-10), contact time (30-180 min), MB dye concentration (varying from 10 to 50 mg/L), and MB dose (0.1-0.5 gm). The findings show that the best optimal conditions for MB dye removal occur at a 6 pH, 0.3 gm dose of PSPAC adsorbent for 10 mg/L MB dye concentration, with 90 min of contact time. To optimize and validate the extraction efficiency of MB dye, a response surface methodology (RSM) study was conducted using a central composite design (CCD) with a regression model showing R2 = 0.9940. FT-IR spectroscopy shows, CO, and O-H stretching functional groups while FE-SEM is assessed to supervise morphological features of the PSPAC adsorbent. The peak adsorption capacity with 46.95 mg/g for the Langmuir isotherm model conveniently satisfies the adsorption process with R2 = 0.9984 while with R2 = 0.999, a kinetic model, pseudo-second-order, confirms MB dye adsorption by PSPAC adsorbent. Moreover, thermodynamic parameters including ΔGᵒ, ΔH°, and ΔS° were computed and found to be spontaneous and exothermic. Furthermore, regeneration studies employed with NaOH (0.1 M) and HCl (0.1 M) solution media show an acceptable MB removal efficiency consecutive up to three cycles. The study highlights that H3PO4 papaya skin/peel (PSPAC) is an effectual, sustainable, reasonably available biosorbent to remove industrial cationic dyes disposal.

Evaluation of the impact of alkyl silane entity (APTES) on photocatalyst (TiO2) and their photocatalytic degradation of organic compounds for environmental remediation.

Environmental pollution by diverse organic pollutants is a serious issue facing humanity, and the scientific community is working hard to find a solution to climatic change due to pollution. Along the same lines, we have tried to find a material/method which is economical and less laborious for achieving the same desired objectives. In this work, the surface modification of titanium dioxide to be used as a photocatalyst was carried out with different concentrations of alkyl silane agent APTES (3-aminopropyltriethoxysilane) and studied their impact on the degradation of representative compound, i.e., methylene blue. The surface-modified TiO2-APTES nanoparticles were obtained via the solvothermal process. The APTES in different molar (0.21-0.41 M) concentrations was obtained by dissolving APTES in ethanol. The obtained samples were characterized through Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), thermogravimetric analysis (TGA), scanning electron microscopy, and UV-visible spectroscopy. The photocatalytic activity was inferred from the degradation ability of functionalized nanoparticles for methylene blue and evaluated by UV-visible spectroscopy. Our results demonstrated a significant 70% degradation rate of methylene blue.

Zinc oxide nanoparticles adsorb emerging pollutants (glyphosate pesticide) from aqueous solutions.

The present study captures the precipitation synthesis of zinc nanoparticles and modification with alumina and oleic acid. The crystalline size evaluated from the XRD profile of the zinc oxide nanoparticles was 18.05 nm but was reduced to 14.20 and 14.50 nm upon modification with oleic acid and alumina. The XRD spectra also showed evidence of the amorphous nature of zinc oxide nanoparticles and subsequent enhancement upon modification. A porous appearance was observed in the SEM instrumentation but seems to be enhanced by modification. The FTIR absorption spectra of the nanoparticles showed a peak associated with ZnO vibration around 449 cm, but the enhanced intensity was observed due to modification. The prepared ZnO-NPs and the modified samples were good materials for the adsorption removal of glyphosate from water, recording efficiencies above 94% at neutral pH and showing a possible incremental trend with an enhanced period of contact and adsorbent dosage. The adsorbents showed maximum capacity that ranged from 82.85 to 82. 97 mg/g. The adsorption models of Freundlich, Temkin, Dubinin-Radushkevich and BET showed excellent fitness. Results from computational results complemented experimental data and were used to identify the sites for adsorption and characteristics of molecular descriptors for the systems.

Ultrasound-enhanced catalytic degradation of simulated dye wastewater using waste printed circuit boards: catalytic performance and artificial neuron network-based simulation.

Recent developments of heterogeneous advanced oxidation for refractory organic contaminants and catalysts made of solid waste have attracted much attention. In this work, waste printed circuit board (wPCB) was used for catalytic degradation of simulated textile wastewater enhanced by ultrasound. Catalytic degradation of rhodamine B (RhB) and methylene blue (MB) was conducted in the presence of H2O2. Effect of ultrasound, wPCB, H2O2, pH, and dye concentration was investigated by single factor experiments. The growing catalytic efficiency was determined by ultrasound. The removal efficiency of MB and RhB are influenced by wPCB, H2O2, pH, and dye concentration. Degradation efficiency is accelerated with increasing wPCB dosage and H2O2 and decreasing dye concentration. Effective degradation of MB and RhB is obtained under broader pH region, attractively at neutral pH. Under optimal conditions, MB removal reaches 98.83% at 90 min while RhB removal reaches 99.57% at 80 min. Hydroxyl radicals play an important role in catalytic process. Tentative mechanism for catalytic degradation of MB and RhB are discussed based on multiple characterizations. Superior reusability of wPCB proves that wPCB is highly durable catalyst. Due to low cost and high efficiency, wPCB is attractive as effective catalyst for treatment of organic wastewater. Artificial neuron network-based (ANN) simulation, as a widely used artificial intelligence algorithm, was one of preferred methods for the wastewater treatment due to its unique properties in solving complex processes. An ANN model was designed for the prediction of the performance of ultrasound-enhanced catalytic degradation with a high R value (0.99).

A comprehensive review of various approaches for treatment of tertiary wastewater with emerging contaminants: what do we know?

In the last few decades, environmental contaminants (ECs) have been introduced into the environment at an alarming rate. There is a risk to human health and aquatic ecosystems from trace levels of emerging contaminants, including hospital wastewater (HPWW), cosmetics, personal care products, endocrine system disruptors, and their transformation products. Despite the fact that these pollutants have been introduced or detected relatively recently, information about their characteristics, actions, and impacts is limited, as are the technologies to eliminate them efficiently. A wastewater recycling system is capable of providing irrigation water for crops and municipal sewage treatment, so removing ECs before wastewater reuse is essential. Water treatment processes containing advanced ions of biotic origin and ECs of biotic origin are highly recommended for contaminants. This study introduces the fundamentals of the treatment of tertiary wastewater, including membranes, filtration, UV (ultraviolet) irradiation, ozonation, chlorination, advanced oxidation processes, activated carbon (AC), and algae. Next, a detailed description of recent developments and innovations in each component of the emerging contaminant removal process is provided.

Optimizing removal of antiretroviral drugs from tertiary wastewater using chlorination and AI-based prediction with response surface methodology.

Chemical and pharmaceutical chemicals found in water sources create substantial risks to human health and the environment. The presence of pharmaceutical contaminants in water can cause antibiotic resistance development, toxicity to aquatic organisms, and endocrine disruption. Hence, the elimination of chemicals and other contaminants from wastewater prior to its release is a burgeoning concern in the domains of engineering and science. The use of treatment technologies in wastewater treatment plants can remove pharmaceutical contaminants through the oxidation process. However, many traditional wastewater treatment plants lack the advanced monitoring tools required to detect low concentrations of pharmaceuticals. Without the ability to detect these compounds, it's challenging to treat them effectively. The goal of this study was to use Response Surface Methodology (RSM) and Artificial Neural Networks (ANN) algorithms to model and improve how Nevirapine and Efavirenz break down in different chlorination conditions. The RSM analysis revealed statistically significant models (F-values: Nevirapine, pH-t: 108.15, T-t: 76.55, ICC-t: 110.84), indicating a strong correlation between operational parameters (pH, temperature, and initial chlorine concentration) and degradation behavior. The ANN model accurately predicted the degradation of both Nevirapine and Efavirenz under various chlorination conditions, as confirmed by analyzing actual-predicted graphs, residual plots, and Mean Squared Error (MSE) values. The ANN model using ICC-t achieved the highest MOD value of 31.31 % for Nevirapine. The ANN model based on ICC-t yielded a maximum MOD value of 16.06 % for Efavirenz. These findings provide valuable insights into optimizing chlorination processes for better removal of these pharmaceutical contaminants from water.

Removing polyfluoroalkyl substances (PFAS) from wastewater with mixed matrix membranes.

Polyfluoroalkyl and perfluoroalkyl (PFAS) chemicals are fluorinated and exhibit complicated behavior. They are determined and highly resistant to ecological modifications that render plants ecologically robust. Thermal stability and water and oil resistance are examples of material qualities. Their adverse consequences are causing increasing worry due to their bioaccumulative nature in humans and other creatures. Direct data indicates that PFAS exposure in humans causes endocrine system disruption, immune system suppression, obesity, increased cholesterol, and cancer. Several PFASs are present in drinking water at low doses and may harm people. These cancer-causing PFAS have caused concern for water bodies all around the globe. Analytical techniques are used to identify and measure PFAS in an aqueous medium (membrane). Furthermore, a deeper explanation is provided for PFAS removal methods, including mixed matrix membrane (MMM) technology. By removing over 99 % of the PFAS from wastewater, MMMs may effectively remove PFAS from sewage when the support matrix contains adsorbing components. Furthermore, we consider several factors affecting the removal of PFAS and practical sorption methods for PFAS onto various adsorbents.

WITHDRAWN: Effects of hydraulic retention time and cultivation on nutrients removal and biomass production in wastewater by membrane photobioreactor: Modeling and optimization by machine learning and response surface methodology.

This article has been withdrawn at the request of the Editor-in-Chief.

Valorization of Waste Watermelon Rinds as a Bio-adsorbent for Efficient Removal of Methylene Blue Dye from Aqueous Solutions.

Nature-based solutions (NBSs) for remediation of various emerging contaminants have gained impetus during the last few decades. In the current study, watermelon (citrullus lanatus), a highly consumed seasonal fruit, was used as a feedstock waste biomass for biochar synthesis through valorization of watermelon rinds. The watermelon biochar (WM-BC) was synthesized through slow pyrolysis at 550°C under anoxic conditions. Langmuir model with R2>99, was found to best fit the adsorption isotherm, and the adsorption kinetics was best described by the pseudo-second-order model. Various characterization tools including FTIR, SEM, BET, XRD, and TEM were used to evaluate the surface morphology of the biochar. The removal efficiency increased from 35% (dosage = 0.4 g), to 81% at WM-BC dosage of 2 g. A maximum adsorption capacity of 115.61 mg/g was found. The results from kinetic and isotherm model model suggested that the adsorption was favorable and multilayer adsorption can be considered. The adsorption mechanism was found to be governed by the co-existing factors such as hydrogen bonding, electrostatic interactions, and aromatic interactions. Results suggest that WM-BC has high potential to be employed as an adsorbent for efficient remediation of methylene blue dyes from aqueous solutions.

Curve fitting model of Polycarbonate Al2O3-nanoparticle membranes for removing emerging contaminants from wastewater: Effect of temperature and nanoparticles.

The purpose of this paper is to demonstrate the use of the phase separation procedure in order to synthesize ultrafiltration polycarbonate containing aluminum oxide (Al2O3) nanoparticles (NPs) to remove emerging contaminants from wastewater at varying temperatures and nanoparticle contents. In the membrane structure, Al2O3-NPs are loaded at rates of 0≤φ≤1% volume. Fourier transform infrared (FTIR), atomic force microscopy (AFM), and scanning electron microscopy (SEM) were used to characterize the fabricated membrane containing Al2O3-NPs. Nevertheless, volume fractions ranged from 0 to 1% during the experiment, which was conducted between 15 and 55 °C. An analysis of the ultrafiltration results was conducted by using a curve-fitting model to determine the interaction between these parameters and the effect of all independent factors on the emerging containment removal. Shear stress and shear rate for this nanofluid are nonlinear at different temperatures and volume fractions. Viscosity decreases with increasing temperature at a specific volume fraction. In order to remove emerging contaminants, a decrease in viscosity at a relative level fluctuates, resulting in more porosity in the membrane. NPs become more viscous with an increasing volume fraction at any given temperature on the membrane. For example, a maximum relative viscosity increases of 34.97% is observed for a 1% volume fraction at 55 °C. A novel model is then used to measure the viscosity of nanofluid. This indicates that the results and experimental data are in very close agreement, as the maximum deviation is 2.6%.

A novel investigations on medical and non-medical mask performance with influence of marine waste microplastics (polypropylene).

The entire human race is struggling with the spread of COVID-19. Worldwide, the wearing of face masks is indispensable to prevent such spread. Despite numerous studies reporting on the fabrication of face masks and surgical masks to reduce spread and thus human deaths, this novel work is considered the marine waste of microplastics, namely Polypropylene (PP) polymer, used to fabricate non-woven fabric masks through the melt-blown process. This experimental work aims to maximize the mask's quality and minimize its fabrication cost by optimizing the melt-blown process parameters and using microplastics. The melt-blown process was used to make masks. Parameters such as extruder temperature, hot air temperature, melt flow rate, and die-to-collector distance (DCD) were investigated as independent variables. The quality of the mask was investigated in terms of bacterial filtration efficiency (BFE), particle filtration efficiency (PFE), and differential pressure. The Taguchi L16 orthogonal array and Taguchi analysis were employed for experimental design and statistical optimization, respectively. The results reveal that the higher BFE and PFE are recorded at 96.7 % and 98.6 %, respectively. The surface morphological investigation on different layers ensured the fine and uniform porosity of the layers and exhibited minimum breath resistance (a low differential pressure of 0.00152 kPa/cm2). Hence the chemically treated marine waste microplastics improved the masks' performance.

Study comparing the tribological behavior of propylene glycol and water dispersed with graphene nanopowder.

Nanofluids made up of propylene glycol, and water and graphene nanopowder dispersed throughout them are the primary focus of our study. Nanofluids were created by mixing propylene glycol and water in quantities of 100:0, 75:25, and 50:50. The essential fluids used in this experiment were propylene glycol and water. Graphene was dispersed in these three different base fluids at percentages of 0.25 and 0.5, respectively. This body of work's fundamental objective is to explore nanofluids' tribological behavior. This behavior was observed with a pin-on-disc device, and the impact for load on wear, coefficient of friction, and frictional force was investigated. The tests were conducted with weights ranging from 1 to 3 kg. It was revealed that as the load ascended, there was a reduction in the amount of wear, the coefficient of friction, and the frictional force for the most of the samples tested. Still, there was an increase in the amount of wear and friction coefficient, including the frictional force for some of the samples.

Pharmaceutical residues in the ecosystem: Antibiotic resistance, health impacts, and removal techniques.

Hospital wastewater has emerged as a major category of environmental pollutants over the past two decades, but its prevalence in freshwater is less well documented than other types of contaminants. Due to compound complexity and improper operations, conventional treatment is unable to remove pharmaceuticals from hospital wastewater. Advanced treatment technologies may eliminate pharmaceuticals, but there are still concerns about cost and energy use. There should be a legal and regulatory framework in place to control the flow of hospital wastewater. Here, we review the latest scientific knowledge regarding effective pharmaceutical cleanup strategies and treatment procedures to achieve that goal. Successful treatment techniques are also highlighted, such as pre-treatment or on-site facilities that control hospital wastewater where it is used in hospitals. Due to the prioritization, the regulatory agencies will be able to assess and monitor the concentration of pharmaceutical residues in groundwater, surface water, and drinking water. Based on the data obtained, the conventional WWTPs remove 10-60% of pharmaceutical residues. However, most PhACs are eliminated during the secondary or advanced therapy stages, and an overall elimination rate higher than 90% can be achieved. This review also highlights and compares the suitability of currently used treatment technologies and identifies the merits and demerits of each technology to upgrade the system to tackle future challenges. For this reason, pharmaceutical compound rankings in regulatory agencies should be the subject of prospective studies.

Wastewater treatment with algal based membrane bioreactor for the future: Removing emerging containments.

The difficulty of developing pollutants in aquatic ecosystems and their potential effects on animals and plants have been raised. Sewage effluent can seriously harm a river's plant and animal life by reducing the water's oxygen content. Due to their increasing use and poor elimination in traditional municipal wastewater treatment plants (WWTPs), pharmaceuticals are one of the developing pollutants that have the potential to penetrate aquatic ecosystems. Due to undigested pharmaceuticals and their metabolites, which constitute a significant class of potentially hazardous aquatic pollutants. Using an algae-based membrane bioreactor (AMBR), the primary objective of this research was to eliminate emerging contaminants (ECs) identified in municipal wastewater. The first part of this research covers the basics of growing algae, an explanation of how they work, and how they remove ECs. Second, it develops the membrane in the wastewater, explains its workings, and uses the membrane to remove ECs. Finally, an algae-based membrane bioreactor for removing ECs is examined. As a result, daily algal production using AMBR technology might range from 50 to 100 mg/Liter. These kinds of machines are capable of nitrogen and phosphorus removal efficiencies of 30-97% and 46-93%, respectively.

SARS-CoV-2 removal by mix matrix membrane: A novel application of artificial neural network based simulation in MATLAB for evaluating wastewater reuse risks.

The COVID-19 outbreak led to the discovery of SARS-CoV-2 in sewage; thus, wastewater treatment plants (WWTPs) could have the virus in their effluent. However, whether SARS-CoV-2 is eradicated by sewage treatment is virtually unknown. Specifically, the objectives of this study include (i) determining whether a mixed matrixed membrane (MMM) is able to remove SARS-CoV-2 (polycarbonate (PC)-hydrous manganese oxide (HMO) and PC-silver nanoparticles (Ag-NP)), (ii) comparing filtration performance among different secondary treatment processes, and (iii) evaluating whether artificial neural networks (ANNs) can be employed as performance indicators to reduce SARS-CoV-2 in the treatment of sewage. At Shariati Hospital in Mashhad, Iran, secondary treatment effluent during the outbreak of COVID-19 was collected from a WWTP. There were two PC-Ag-NP and PC-HMO processes at the WWTP targeted. RT-qPCR was employed to detect the presence of SARS-CoV-2 in sewage fractions. For the purposes of determining SARS-CoV-2 prevalence rates in the treated effluent, 10 L of effluent specimens were collected in middle-risk and low-risk treatment MMMs. For PC-HMO, the log reduction value (LRV) for SARS-CoV-2 was 1.3-1 log10 for moderate risk and 0.96-1 log10 for low risk, whereas for PC-Ag-NP, the LRV was 0.99-1.3 log10 for moderate risk and 0.94-0.98 log10 for low risk. MMMs demonstrated the most robust absorption performance during the sampling period, with the least significant LRV recorded in PC-Ag-NP and PC-HMO at 0.94 log10 and 0.96 log10, respectively.