Chemical Oxygen Demand Measurements Research Articles

Enhanced degradation performance toward para-nitrophenol of adapted immobilized microbial community on coconut coir

Nitrophenol pollutants, including para-nitrophenol (p-NP), are known for their harmful environmental impact due to their persistence, toxicity, and widespread distribution in water sources. While biodegradation generally offers a more effective removal of organic pollutants compared to chemical or physical methods, degrading persistent and toxic compounds like p-NP remains challenging. In this study, a microbial community derived from food processing wastewater was immobilized on coconut coir and adapted to p-NP before being employed for p-NP biodegradation. The spectroscopic analysis demonstrates the effective biodegradation performance of the adapted microbial community, achieving 99% degradation of 50 mg L⁻1 p-NP in 38 min and 250 mg L⁻1 p-NP in 4.65 h. The degradation ability of immobilized cells was determined across a broad range of stirring speeds, temperatures, pH levels, and p-NP solution volumes. Complete mineralization of p-NP was confirmed by chemical oxygen demand (COD) measurements of the treated solution and in-situ CO2 generation. Notably, the p-NP degradation performance of the adapted immobilized microbial community remained stable for the first 40 days, with only a slight decrease observed after 47 days of cold preservation at 4 °C. An average p-NP degradation rate of 0.75 mg L⁻1 min⁻1 was maintained over 54 consecutive runs. Significant alterations in microbial diversity were identified through 16S metabarcoding analysis. The unadapted microbial community comprised a diverse range of genera, while the adapted community showed reduced diversity with an enrichment of specific genera known for p-NP degradation, such as unidentified members of the Micrococcaceae family, Paenarthrobacter spp., and Zoogloea spp.

The influence of operational factors on the photocatalytic degradation of methylene blue dye in aqueous Sr-Au-ZnO suspensions under UV-A light

Any of several processes that break down dyes, ideally into harmless chemicals, is referred to as industrial dye degradation. Water waste discharges various colors, particularly those used in the textile industry like methyl red and methylene blue, into ecosystems, leading to significant pollution of the water supply. Under UV-A irradiation, the photocatalytic degradation of a commercial heterocyclic aromatic chemical molecule called methylene blue (MB) has been investigated using an aqueous solution of Sr-Au-ZnO as a photocatalyst. Research has been done on how different process characteristics affect the degradation process. For the mineralization of MB dye under UV-A light, it was found that the optimized Sr-Au-ZnO was more effective than commercial catalysts (ZnO and benchmark photocatalyst Degussa P25), single metal dopants (Sr-ZnO, Au-ZnO), and prepared ZnO. The effects of operational parameters, such as the quantity of photocatalyst, dye concentration, and starting pH, on the photo-mineralization of MB are analyzed before optimal values are given. Chemical oxygen demand (COD) measurements have confirmed that MB is mineralized. Using GC–MS analysis, the intermediates produced during photodegradation were predicted, and an appropriate degradation pathway was suggested. This procedure can be used for treating wastewater from sewage since optimized Sr-Au-ZnO is reusable.

Ultraviolet visible spectral water chemical oxygen demand detection method based on two deep neural network model

Water resources are precious resources around the world, but pollution is becoming increasingly severe. While the degree of damage to water caused by organic pollutants is reflected by chemical oxygen demand, which is also an important indicator for hydrological monitoring. Therefore, accurate measurement of chemical oxygen demand is necessary. The rapid development of water quality detection by ultraviolet visible spectrum analysis as an efficient, convenient, and pollution-free new water quality detection method has been achieved. When we were processing ultraviolet visible spectral absorbance data, various environmental factors can affect the chemical oxygen demand light absorption value, thus affecting the accuracy of detection. So there are some goals, such as more comprehensively extracting the feature wavelength and reducing the interference to achieve better detection results. It is expected that the model can be applied to a portable water quality detection device. It is necessary to consider detection time, reduce model training parameters, and improve detection speed. Therefore, this article proposes Competitive Adaptive Weighted Sampling-Convolutional Neural Network-Long Short-Term Memory and Competitive Adaptive Weighted Sampling-Convolutional Long Short-Term Memory models for detecting chemical oxygen demand in water. To verify the effectiveness of the proposed model, the model was trained on the experimental dataset and the detection results of all models involved in the article were compared. It can be confirmed that the application of these two proposed algorithms results in less detection time and higher accuracy in concentration detection.

Preliminary treatment of landfill leachate by hydrodynamic cavitation supported by Fenton process

Hydrodynamic cavitation as an effective and environmentally friendly method of treating wastewater. Massive amounts of energy may be released into the surrounding liquid during hydrodynamic cavitation, resulting in mechanical , chemical and thermal impacts. Bacteria and organic materials in sewage can be broken down by these circumstances. Furthermore, a coupling effect may be created by combining hydrodynamic cavitation with other water treatment techniques. In this study it is aim to investigate and improve the hydrodynamic cavitation (HDC) process supported by Fenton process for the pretreatment of landfill leachate. In the second phase of the study, the effectiveness of the hydrodynamic cavitation process was investigated in conjunction with the Fenton process. The parameters such as the number of cavitation events, pH, and temperature were evaluated. The effluent was characterized and monitored for COD measurements. The consequences of operational variables such H2O2, Fe+2 , and pH values were investigated to determine the optimal Fenton oxidation process parameters. The findings of the experiment showed that pH values were ideal for Fenton oxidation of 3.5-4.5, 30 mM H2O2, and 5 mM Fe+2. A combined treatment process of Fe+2+H2O2, HDC + Fenton, and Cavitation alone were conducted for the treatment of landfill leachate. The results showed that the removal rates of chemical oxygen demand (COD) for the combined processes were 32.85%, 44.28%, and 7%, respectively. Temperature, pH, and the number of cavitation events were among the parameters that were assessed. The effluent was measured for COD and was characterized.

On-line measurement of COD and nitrate in water against stochastic background interference based on ultraviolet–visible spectroscopy and physics-informed multi-task learning

Traditional ultraviolet–visible spectroscopic quantitative analytical methods face challenges in simultaneous and long-term accurate measurement of chemical oxygen demand (COD) and nitrate due to spectral overlap and the interference from stochastic background caused by turbidity and chromaticity in water. Addressing these limitations, a compact dual optical path spectrum detection sensor is introduced, and a novel ultraviolet–visible spectroscopic quantitative analysis model based on physics-informed multi-task learning (PI-MTL) is designed. Incorporating a physics-informed block, the PI-MTL model integrates pre-existing physical knowledge for enhanced feature extraction specific to each task. A multi-task loss wrapper strategy is also employed, facilitating comprehensive loss evaluation and adaptation to stochastic backgrounds. This novel approach significantly outperforms conventional models in COD and nitrate measurement under stochastic background interference, achieving impressive prediction R2 values of 0.941 for COD and 0.9575 for nitrate, while reducing root mean squared error (RMSE) by 60.89 % for COD and 77.3 % for nitrate in comparison to the conventional chemometric model partial least squares regression (PLSR), and by 30.59 % and 65.96 %, respectively, in comparison to a benchmark convolutional neural network (CNN) model. The promising results emphasize its potential as a spectroscopic instrument designed for online multi-parameter water quality monitoring against stochastic background interference, enabling long-term accurate measurement of COD and nitrate levels.

Green synthesis of N, B co-doped TiO2 nanoparticles with enhanced photocatalytic and antioxidant activities

BackgroundReactive dyes are widely used in the fabric production, and because they are poisonous, and hazardous, their release into the environment today indicates a rise in pollution in the environment. Biosynthetic approach that is also environmentally benign has gained an abundance of recognition for its eco-friendly products, biocompatibility, and long-term economic viability. MethodsIn the present investigation, TiO2 (T) and N, B co-doped-TiO2 (NB-T) nanoparticles (NPs) were prepared biologically using mangrove aqueous extract by the sol-gel method to compare their photocatalytic degradation activity of an aqueous solution of reactive red 250 dye (RR250) under visible light irradiation at different times. The antioxidant activities of T and NB-T NPs samples were evaluated. The synthesized samples were characterized using different techniques. Main findingsThe findings showed that photocatalytic degradation activity of NB-T NPs reaches 100 % after 60 min of the degradation process, where it reaches maximum (38.1 %) for T NPs at the highest catalyst dose. The results showed that NB-T NPs have a conformal surface structure and stronger photocatalytic properties than Pure T. Measurements of Chemical Oxygen Demand (COD) were made, and a significant reduction in values was seen, showing the technique's high potential for removing RR250 dye from aqueous solutions. The NB-T sample showed the maximum activity, as indicated by the estimated IC50 value (34.98 μg/mL). Both T and NB-T NPs showed significant DPPH free radical scavenging activity.

Incorporation of water quality index models with machine learning-based techniques for real-time assessment of aquatic ecosystems

Water quality index (WQI) is a well-established tool for assessing the overall quality of fresh inland-waters. However, the effectiveness of real-time assessment of aquatic ecosystems using the WQI is usually impacted by the absence of some water quality parameters in which their accurately in-situ measurements are impossible and face difficulties. Using a rich water quality dataset spanned from 1980 to 2023, we employed four machine learning-based models to estimate the British Colombia WQI (BCWQI) in the Lake Päijänne, Finland, without parameters like chemical oxygen demand (COD) and total phosphorus (TP). Measurement of both COD and TP is time-consuming, needs laboratory equipment and labor costs, and faces sampling-related difficulties. Our results suggest the machine learning-based models successfully estimate the BCWQI in Lake Päijänne when TP and COD are omitted from the dataset. The long-short term memory model is the least sensitive model to exclusion of COD and TP from inputs. This model with the coefficient of determination and root-mean squared error of 0.91 and 0.11, respectively, outperforms the support vector regression, random forest, and neural network models in real-time estimation of the BCWQI in Lake Päijänne. Incorporation of BCWQI with the machine learning-based models could enhance assessment of overall quality of inland-waters with a limited database in a more economical and time-saving way. Our proposed method is an effort to replace the traditional offline water quality assessment tools with a real-time model and improve understanding of decision-makers on the effectiveness of management practices on the changes in lake water quality.

Strategies for the quantification and characterization of nanoplastics in AOPs research

There is a growing interest in developing new targeted degradation technologies for the removal of micro- and nanoplastics (NPs) in water, corresponding to increased public concerns regarding their potential negative impacts on urban water systems, and consequently on human life quality. Recently, Advanced Oxidation Processes (AOPs) have been proposed as promising treatment alternatives for effective degradation of NPs in water. However, the selection of appropriate analytical methods for monitoring these oxidation tests remains a challenge. Herein, the feasibility of different characterization strategies for monitoring the evolution of NPs in water upon oxidation tests was systematically studied using polystyrene (PS) NPs of different particle sizes (D0 = 140, 252, 460, and 909 nm) as model plastic pollutants. To quantify NPs in water, Total Organic Carbon (TOC), Chemical Oxygen Demand (COD) and turbidity measurements were assessed. Moreover, turbidity was correlated to the particle size and PS NPs concentration by developing a response surface. Among the analytical techniques employed to characterize the solid particles, transmission electronic microscopy (TEM) was used to evaluate morphology and particle size. Alternatively, the viability of Dynamic Light Scattering (DLS), Nanoparticle Tracking Analysis (NTA) and Atomic Force Microscopy (AFM) to determine particle size is discussed. Chemical surface modifications were explored by Fourier-Transform Infrared Spectroscopy (FTIR). As a proof of concept, the degradation of PS NPs in water upon photo-Fenton oxidation was investigated at ambient conditions and fully characterized using the mentioned techniques.

Development of ultrathin 2D layered titanate Pt/K2Ti4O9/rGO nanocomposites for enhanced visible active photocatalytic detoxification of harmful organic pollutants

In this work, different wt% of rGO-loaded Pt-K2Ti4O9 (x = 2.5, 5, 7.5, 10, and 12.5 wt%) nanocomposites were prepared by a facile hydrothermal process and used for the degradation of two different dyes such as congo red (CR) methylene blue (MB) and tetracycline (TC) antibiotic solution under visible light illumination. The bare K2Ti4O9 (labeled as KTO) and the most active Pt-K2Ti4O9/5% rGO (labeled as Pt-KTO/5% rGO) composite catalysts were thoroughly characterized by XRD, FT-IR, Raman, XPS, FE-SEM with EDS, HR-TEM, UV-DRS, and PL analyses. The photocatalytic activity of Pt-KTO/5% rGO was remarkable compared to bare KTO and other rGO-supported catalysts. Pt-KTO/5% rGO composite photocatalyst showed enhanced photocatalytic degradation of 97%, 99%, & 99% for CR, MB & TC at 180, 120 and 180 min, respectively. Mineralization of dyes (TOC of 85%, 87%, and COD of 76%, 79% for CR, MB) and antibiotics (TOC of 78% and COD of 69% for TC) was confirmed by chemical oxygen demand (COD) and total organic carbon (TOC) measurements. The Pt-KTO/5% rGO catalyst showing the highest activity also demonstrates recyclability. A suitable photocatalytic reaction mechanism has been proposed for the higher activity of Pt-KTO/5% rGO composite catalyst under visible light irradiation.

Steady-State and Dynamic Simulation for Wastewater Treatment Plant Management: Case Study of Maghnia City, North-West Algeria

Given the critical importance of addressing effluent quality concerns, the present study was dedicated to developing a dynamic simulation model based on the Activated Sludge Model 1 (ASM1) of a wastewater treatment plant located in Maghnia City, Algeria. The model calibration process involved collecting and analyzing 56 samples from the plant over a period of 18 months (from July 2021 to January 2023). Thirteen physicochemical parameters were analyzed to identify the variations in their water quality over time. Stoichiometric and kinetic parameters were adjusted during the plant calibration process. These modifications resulted in a reasonable alignment with the investigated variables, enabling the accurate prediction of the wastewater treatment plants (WWTPs)’ steady-state behavior regarding the removal measurements of chemical oxygen demand (COD), total suspended solids (TSS), and ammonium (NH4-N). The model was validated using 14-day measurements spanning a 4-month duration, and the results indicated good agreement between the observed and simulated effluent variable of chemical oxygen demand (COD) with a root mean square error (RMSE) of 23%. These findings highlight the utility of the ASM1 Model in comprehending and managing the intricate dynamics of the activated sludge process in wastewater treatment plants.

Effectiveness of Chemical Oxygen Demand as an Indicator of Organic Pollution in Aquatic Environments

The chemical oxygen demand (COD) is an essential indicator of organic pollution that represents the amount of bulk carbon in water. COD is strongly correlated with nutrient cycles and other pollutants in the environment, but it has a limited ability to quantify the amount of organic carbon (OC), of which a large proportion is made up of refractory dissolved organic carbon (RDOC) and is a potential carbon sink. Moreover, the biodegradability of OC in terms of its fate and destination should be explored, as well as how this is reflected by COD. Methods based on particle size, spectroscopy, and isotopic tracing are expected to help with deciphering the bioavailability of COD-responsive OC and explore the processes of biogeochemical cycles. As the pressure on the environment from anthropogenic inputs increases, understanding the bioavailability of OC associated with COD will help with developing more precise scientific indicators for environmental monitoring and identifying how new tools will increase knowledge of the carbon cycle. In this review, we discuss the application, scope, means, and advances of COD measurement. Based on data in the literature, we estimate the global RDOC stock and assess the impact of anthropogenic RDOC on the carbon cycle in offshore bays. This review presents new insights into the behavior of OC in aquatic environments and a potential pathway for ocean negative carbon emissions by expanding the role of RDOC as a carbon sink to offset the effect of anthropogenic carbon emissions.

Improved boosting and self-attention RBF networks for COD prediction based on UV-vis.

Chemical Oxygen Demand (COD) is crucial for assessing water quality. Compared to traditional chemical detection methods, UV-vis spectroscopy for measuring COD offers advantages such as speed, reduced consumption of materials, and no secondary pollution. Considering the impact of suspended particles in water, this paper proposes an optimized boosting model based on a combination strategy for turbidity compensation, using absorption spectra obtained from reservoir water samples via UV-vis. A self-attention mechanism is introduced into the radial basis function (RBF) network, resulting in a COD detection model based on the saRBF framework. This model facilitates comprehensive optimization of the entire process, from turbidity compensation of the original absorption spectrum to the subsequent COD prediction. Experimental results show that the proposed COD measurement model achieves a coefficient of determination of 0.9267, a root mean square error of 1.2669, and a mean absolute error of 1.0097, outperforming other COD measurement models. This work provides a new approach for turbidity compensation and COD detection research.

Investigation on integration of conducting polymers with Cu doped ZnO towards photocatalytic degradation of organic dye

Conducting polymers (CPs) and their nanocomposites with 4 wt% Cu doped ZnO (CZ) were synthesized through in situ polymerization process. X-ray diffraction analysis (XRD), Scanning electron microscopy (SEM) with energy dispersive X-ray (EDX), Attenuated total reflection -Fourier transform infrared spectroscopy (ATR-FTIR), UV–vis spectroscopy (UV–vis), and photoluminescence (PL) spectroscopy analysis were used to determine the structure, morphology, and optical features of synthesized nanocomposites. Prepared sample was investigated on photocatalytic dye degradation of Methylene Blue (MB) under natural sunlight in open atmosphere. The impact of CPs (polyaniline (PANI), polypyrrole (PPy) and polythiophene (PTh) integrated CZ was investigated towards photocatalytic dye degradation of MB. PPy nanocomposites exhibit the highest photocatalytic activity when compared to other CPs nanocomposites. The addition of PPy to the CZ lowered the band gap (2.33 eV), which is advantageous for absorbing visible light. The 5 wt% PPy with respect to weight of CZ (5PCZ) polymer nanocomposite exhibited best photocatalytic activity (92 % in 210 min) when compared to other synthesized samples. Catalyst showed good recyclability property even after three uses. The photocatalytic breakdown of MB was also verified by chemical oxygen demand (COD) measurement and MB degradation by products was investigated by using liquid chromatography-mass spectrometry (LC-MS). This research may leads to develop a sustainable environmental water purification technique.

Chitosan-modified cotton fiber: An efficient and reusable adsorbent in removal of harmful cyanobacteria, Microcystis aeruginosa from aqueous phases

In the present study, to remove harmful cyanobacterial species Microcystis aeruginosa from aqueous phases, adsorption-based strategy was utilized. For this strategy, the surface of cotton fiber was modified using chitosan molecules to develop a highly efficient and ecofriendly adsorbent in removal of Microcystis aeruginosa from aqueous solution. The pristine cotton fiber could not remove M. aeruginosa, while the chitosan-modified cotton (CS-m-Cotton) showed the 95% of cell removal efficiency within 12 h. The surface characteristics of chitosan-modified cotton compared to the pristine cotton fiber was examined by various surface analysis methods. In addition, the pre-treatment of pristine cotton using sodium hydroxide solution was an important factor for enhancement of chitosan modification efficiency on the cotton fiber. The developed chitosan-modified cotton fiber could be reusable for M. aeruginosa cell removal after the simple desorption treatment using ultrasonication in alkaline solution. During the repeated adsorbent regeneration and reuse, the chitosan-modified cotton maintained its M. aeruginosa removal efficiencies (>90%). From the acute toxicity assessment using the chitosan-modified cotton and, the measurements of chemical oxygen demand and microcystin level changes in the M. aeruginosa treatment process using the adsorbent, the environmental safety of the adsorption strategy using the developed adsorbent could be confirmed. Based on our results, the chitosan-modified cotton fiber could be proposed as an efficient and ecofriendly solution for remediation of harmful cyanobacterial species occurring water resources.

Chemical Oxygen Demand as a Measure of Fluvial Organic Matter Oxidation State

AbstractThe oxidative ratio (OR) of the terrestrial biosphere is directly related to the size of the terrestrial biosphere carbon sink. In turn, OR of naturally occurring organic matter can be directly related to the oxidation state of the carbon in naturally occurring organic matter (Cox). Chemical oxygen demand (COD) is a widely measured water quality parameter that has been used as a short‐term substitute for the biochemical oxygen demand (BOD). Here, we propose that if the concentration of reduced species is known, then COD measurement can be used to assess the oxidation state (Cox) of fluvial organic C. Using a Bayesian hierarchical modeling approach, this study analyzed 21 years of water quality monitoring across England to calculate Cox of fluvial organic matter. The study showed that (a) COD could not be considered separately from the reduced species (e.g., NH4) commonly occurring in freshwater water samples, but it was still possible to calculate the Cox of dissolved organic carbon (DOC) and particulate organic carbon (POC). (b) The median Cox of DOC was 0.23 with a 95th percentile range of −0.1 to 0.4. (c) The median Cox of POC was 0.20 with a 95th percentile range of 0.03–0.37. (d) The estimated Cox in fluvial systems confirms that BOD is decoupled from the production of CO2. Including new Cox estimates in the global estimate of OR gives a new median value of 1.059 with a 95th percentile range of 1.047–1.071, giving the annual flux of CO2 to land (fland) of 1.45 ± 0.1 Gt C/year.

Fabrication of Ag/WO3/g-C3N4 composites for the photocatalytic degradation of harmful dyes

In the present work, it is noticed that the synthesis of photocatalytic bare and composite materials: i) bare graphitic carbonitride g-C3N4 (GCN), ii) modified with tungsten oxide WO3/g-C3N4 (WGCN), and iii) with both tungsten oxide and silver nanoparticles WO3/Ag/g-C3N4 (WAgGCN), by three different methods: calcination, precipitation, and ultrasonication. Also, their photocatalytic efficiencies were evaluated towards the photodegradation of different dyes under visible, UV, and sunlight irradiations with promising results. Here, the WAgGCN composite showed significant photocatalytic performance to decolorized Methylene Blue (98%) and Crystal Violet (95%), together with high chemical stability and excellent recyclability. The presence of Ag shows the influences of surface plasmon resonance and acts as an electron mediator in the formed heterostructures. The photogenerated holes performed vital roles in the catalytic degradation of dye solutions over the WAgGCN composite catalyst, confirmed by radical trapping experiments. Total Organic Carbon (TOC) and Chemical Oxygen Demand (COD) measurements confirmed the mineralization of the dyes.

An Expanded IrO2/Ti Durable Electrode for Electrochemical Degradation of Basic Red 46 Textile Dye in Both Single and Binary Mixture Solutions

The use of dimensionally stable anode (DSA) for degrading organic pollutants is of concern recently. The electrocatalytic technique can efficiently treat biodegradable organic matter like dye-containing effluent discharge. This study focuses on applying modified DSA meshed IrO2/Ti electrode for the electrochemical degradation of wastewater effluents of Basic Red 46 (BR46) textile dye using different supporting electrolytes; sodium sulfate (Na2SO4) or/and sodium chloride (NaCl). The parameters affecting the proposed electrochemical degradation process were evaluated to select the optimal operating conditions. The results revealed that using a mixture of supporting electrolyte Na2SO4 and NaCl enhanced the removal of dye color %, chemical oxygen demand (COD) %, and total organic carbon (TOC) % by 94.9%, 65.7%, and 76.0%, respectively. The UV-Vis and FT-IR spectroscopic techniques followed the degradation efficiency. FT-IR data were in good agreement with the COD, TOC, and UV-Vis measurements, confirming the high refractory performance of the BR46 dye. The redox process was performed on the surface of the modified electrode and explained via cyclic voltammetry (CV), and it showed a diffusion mass transport mechanism followed by product adsorption. The identification of the degradation products was assessed by gas chromatography–mass spectrometry (GC-MS) analysis. The electrochemical degradation and mineralization of a binary mixture of Basic Yellow (BY28) and BR46 textile dyes were also studied. It gave similar degradation and mineralization behavior to the same extent as the single dye in solutions.

Cost-effective smartphone-based method for low range chemical oxygen demand analysis

Aiming the decentralization of monitoring policies and to facilitate the work of researchers, mainly in developing countries, the present method deals with the explanation of a simple and rapid protocol for chemical oxygen demand (COD) analysis through the use of digital smartphone devices coupled with a camera and a free app available for Android operating system that recognizes HSV (hue, saturation, value). The calibration of the method is done based on the theoretical values of potassium hydrogen phthalate for a proper and reliable build of the calibration curve by using the smartphone-based technique and the digested samples of COD. The coefficient of determination (R2) attained a value upper than 0.99, providing a high confidence levels, and the method achieved 97% of average accuracy in samples with COD values ranging from 0 to 150 mg L−1. Finally, the procedure here presented can be a great support for scientific laboratories and monitoring policies, once it can efficiently substitute expensive spectrophotometers and can improve and ensure the sustainable management of water sanitation, which is one of the sustainable goals proposed by the United Nations.•COD measurements, based on the use of a simple smartphone with a camera, can be a promising way for environmental analysis when spectrophotometers are not available, such as decentralized approaches.•The use of smartphone protocol is a novel initiative to fulfill sustainable development goal 6 on clean water and sanitation.•The smartphone is capable to read the difference of HSV values efficiently and can substitute the use of expensive spectrophotometers.

A straightforward approach utilizing an exponential model to compensate for turbidity in chemical oxygen demand measurements using UV-vis spectrometry.

Recently, ultraviolet-visible (UV-vis) absorption spectrometry has garnered considerable attention because it enables real-time and unpolluted detection of chemical oxygen demand (COD) and plays a crucial role in the early warning of emerging organic contaminants. However, the accuracy of detection is inevitably constrained by the co-absorption of organic pollutants and turbidity at UV wavelengths. To ensure accurate detection of COD, it is necessary to directly subtract the absorbance caused by turbidity from the overlaid spectrum using the principle of superposition. The absorbance of COD is confined to the UV range, whereas that of turbidity extends across the entire UV-vis spectrum. Therefore, based on its visible absorbance, the UV absorbance of turbidity can be predicted. In this way, the compensation for turbidity is achieved by subtracting the predicted absorbance from the overlaid spectrum. Herein, a straightforward yet robust exponential model was employed based on this principle to predict the corresponding absorbance of turbidity at UV wavelengths. The model was used to analyze the overlaid absorption spectra of synthetic water samples containing COD and turbidity. The partial least squares (PLS) method was employed to predict the COD concentrations in synthetic water samples based on the compensated spectra, and the corresponding root mean square error (RMSE) values were recorded. The results indicated that the processed spectra yielded a considerably lower RMSE value (9.51) than the unprocessed spectra (29.9). Furthermore, the exponential model outperformed existing turbidity compensation models, including the Lambert-Beer law-based model (RMSE = 12.53) and multiple-scattering cluster method (RMSE = 79.34). Several wastewater samples were also analyzed to evaluate the applicability of the exponential model to natural water. UV analysis yielded undesirable results owing to filtration procedures. However, the consistency between the compensated spectra and filtered wastewater samples in the visible range demonstrated that the model is applicable to natural water. Therefore, this proposed method is advantageous for improving the accuracy of COD measurement in turbid water.

Synthesis of Solar Light Active Reduced Graphene Oxide-ZnS Nanomaterial for Photocatalytic Degradation and Antibacterial Applications.

Good water quality is essential for life; therefore, decolorizing and detoxifying organic dye wastes (textile effluents) have gained immense environmental importance in recent years. Thus, the degradation of wastewater has become a potential need for our environment. This research aims to synthesize and investigate a ceramic-based nanomaterial catalyst for the degradation of dye solution under exposure to sunlight. A reduced graphene oxide-ZnS (rGO-ZnS) nanomaterial was qualitatively synthesized using a solvothermal method. The prepared nanomaterial was characterized using XRD, SEM, HR-TEM, EDX, XPS, and FT-IR techniques. The photocatalytic activity of the rGO-ZnS nanomaterial was checked using oxidative photocatalytic degradation of naphthol blue black dye (NBB) under direct sunlight irradiation. Here, the rGO/ZnS composite showed a significant photocatalytic performance to degraded NBB (93.7%) under direct solar light. Chemical Oxygen Demand (COD) measurements confirmed the mineralization of the dye. The influence of different radical scavengers on NBB degradation was studied. Optimum conditions for efficient degradation were determined. The antibacterial property of the prepared catalyst was studied.