Chemical Oxygen Demand Measurements Research Articles

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 12.5wt.%) 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 180min, 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/rGO at neutral pH 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.

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.

Capability evaluation of real-time inline COD detection technique for dynamic water footprint management in the beverage manufacturing industry

This paper reports the development of a real-time inline Chemical Oxygen Demand (COD) detection technique in a beverage manufacturing plant in England and the evaluation of its capability for dynamic Water Footprint (WF) management. The inline technique employed Ultraviolet–Visible (UV-VIS) spectroscopy and Moving Window Partial Least Squares (mwPLS), which was then applied to calculating Grey WF for the production activities in the plant, referred to here as WFrt. A traditional offline COD measurement method was also utilised for the Grey WF calculation, to act as the reference method, referred to here as WFtrad. In a method-comparison study (Bland-Altman Plot), the results showed that WFrt detected the order of magnitude variation of WFtrad, and WFtrad was on average between 0.897 and 1.243 times WFrt with no systematic bias. This indicates that WFrt may be used for both short-time frame (minutes to hours) WF monitoring and long-term (weeks to months) analysis of trends and the effect of WF optimisation strategies.

What is the best procedure for determining removal rate coefficients in horizontal flow treatment wetlands: influent and effluent concentrations or longitudinal concentration profiles?

First-order removal rate coefficients (k) are used in predictive equations for estimating effluent concentrations from horizontal flow (HF) wetlands. Due to limited resources, influent and effluent concentration data from existing systems are frequently used in the estimation of k values from operating systems, but another choice is to use concentration data along the longitudinal profile of the HF wetland. Based on a dataset with 41 HF wetlands/studies obtained from a literature survey, with chemical oxygen demand (COD) measurements at different sampling points, volumetric (kV) and areal (kA) removal rate coefficients for the Tanks-In-Series (TIS) model have been obtained using the two estimation methods. In general, removal rate coefficients derived from longitudinal profiles of concentrations were higher than those obtained by using data from influent and effluent concentrations, reflecting the fact that constituent removal is mostly accomplished before the wastewater reaches the outlet zone. Deriving coefficients from longitudinal profiles is more comprehensive, providing a better explanation of the internal removal taking place in the treatment wetland. However, the more widely used approach of calculating kV and kA from influent/effluent concentrations may lead to a safer design of horizontal flow wetlands, because of underestimation of the actual removal rate coefficients.

Simultaneous measurement of chemical oxygen demand and turbidity in water based on broad optical spectra using backpropagation neural network

How to overcome the measurement interference between chemical oxygen demand (COD) and turbidity in absorption spectra of water has attracted lots of attention recently. Previous studies focused on eliminating the influence of turbidity, which could not measure COD and turbidity simultaneously. In this paper, we proposed a new method based on ultraviolet-visible-near infrared (UV-Vis-NIR) absorption measurements using a backpropagation neural network (BPNN). The BPNN model was established within the Tensorflow framework. And the absorbance of the wavelength ranges of 240–1040 nm and the actual values of COD and turbidity in the training set were defined as input and output, respectively. Then the BPNN model was trained with training sets of different sizes. And the original absorbance in the test set without any compensation was used to test the performance of the trained BPNN, which indicated that the larger the sample size, the better the adjustment effect of the model. The optimal training sample size was 2400. The correlation coefficient of the model is greater than 0.9998, and the RMSE between the predicted value and the actual value of COD and turbidity is 0.19 mg/L and 0.34 NTU, respectively. Our results show that the method based on BPNN not only can eliminate the influence of turbidity on the accuracy of COD but also realize the accurate measurement of turbidity itself. Furthermore, this method provides a potential strategy for the simultaneous and rapid measurement of other water quality parameters by using the absorption spectrum.

Research on COD measurement method based on UV-Vis absorption spectra of transmissive and reflective detection systems

The world’s rapid industrialisation and population expansion have led to water pollution, causing significant disruption to the activities of humans, animals, and plants. Organic contamination content in water is commonly evaluated by measuring the chemical oxygen demand (COD). However, traditional COD detection methods often require additional reagents, resulting in secondary contamination and extended detection time. In this study, we propose and implement a reflective detection system that measures the UV-Vis absorption spectra of COD in water without contact measurement. We compared the modeling results of the transmissive and reflective detection systems using three regression analysis algorithms. We also assessed the modeling results using various spectral preprocessing and different feature selection bands. The results of the standard samples confirmed the viability of the reflective detection system for detecting COD, with the impressive coefficient of determination (R2) of 0.98892, the root mean square error (RMSE) of 2.86776, and the detection time of only 47.6 s. For the transmissive detection system, the R2 was 0.99976, the RMSE was 0.41979, and the detection time was 162.4 s. Overall, this study proposes two referenceable detection methods for measuring COD concentrations, which can be adapted to suit various job demands.

Kinetic modeling of advanced starch oxidation with ozone in basic solutions

ABSTRACT The growing use of ozone (O3) in the last years to clean and enhance the physicochemical properties of starches, widely used as additives in food, beverage, and pharmaceutical industries, calls for a better understanding of their oxidation process. High pH, however, decomposes O3 rapidly, forming highly-oxidant radicals, thus increasing the complexity of the reaction system. In this study, a kinetic model for the ozonation reaction of starch in an alkaline medium was developed and experimentally validated. Firstly, the concentration of hydroxyl radicals (●OH) over the reaction time is evaluated through the degradation of pCBA (a probe compound) to later evaluate and model the oxidation process of starch based on Chemical Oxygen Demand (COD) measurements. By using a semi-batch reactor containing alkaline solutions at pH = 13, different O3 concentrations (0–42.30 g/Nm3) and temperatures (20–60 °C), a pseudo first-order kinetic model was proposed to model the pCBA-ozone reaction. This model estimates the ●OH formation, being [●OH] constant and proportional to the O3 continuous dose. Therefore, under the conditions studied, ●OH is a non-limiting reactant independent of temperature. The activation energy was estimated at 14.2 ± 2.1 and 14.5 ± 0.6 KJ/mol for 21.15 and 42.30 gO3/Nm3, respectively. Then, COD data were used to propose a kinetic model for ozone-starch oxidation in alkaline media, where besides O3 concentration and temperature, starch concentration (600–1700 mg/L) was also varied. According to the results, higher temperatures and longer reaction times resulted in a greater COD reduction, being it more pronounced at lower initial starch concentration. The activation energy of the ozone radicals-starch reaction was estimated at 8.1 ± 0.4 KJ/mol, being significantly lower than those reported for the reaction between pCBA and ●OH.

Electrochemical oxidation of the increasingly used disinfectant benzalkonium chloride

AbstractBenzalkonium chloride (BAC) is a key ingredient in many cleaning and disinfectant products due to it being an effective antiviral and biocidal agent. Because of its prolific use, especially following the recent global COVID pandemic, increased levels of BAC have been found in the environment, in particular, in wastewater, where it has negative impacts due to its toxicity. This necessitates an effective treatment for BAC in wastewater to reduce its toxicity. In this work, electrochemical oxidation of BAC on a boron‐doped diamond anode was studied to successfully remove BAC. The electrochemical measurements performed at different current densities confirmed that BAC was completely oxidized within 20 min of treatment at 50 mA/cm2. However, chemical oxygen demand (COD) measurements showed that around 50% of the initial BAC was completely mineralized after 1 h of degradation at 50 mA/cm2, while the remaining electrooxidation of BAC resulted in the production of transformation products.

Synergistic Effect of Iron and Copper Oxides in the Removal of Organic Dyes Through Thermal Induced Catalytic Degradation Process

This study proposes a new method for producing α-Fe2O3–CuO nanocatalyst that is both cost-effective and ecologically benign. The α-Fe2O3–CuO nanocomposite was prepared via moderate thermal oxidative decomposition of copper hexacyanoferrate. Its structure and surface morphology are affirmed via XRD, SEM, FTIR, EDX, TEM, XPS, and VSM. In the presence of H2O2, α-Fe2O3–CuO is employed as a heterogeneous catalyst to stimulate thermally induced degradation of dyes such as direct violet 4, rhodamine b, and methylene blue. The synergistic effect of Fe2O3 and CuO enhanced the catalytic activity of the nanocomposite compared to Fe2O3 and CuO separately. The effectiveness of DV4 degradation is optimized by evaluating multiple reaction parameters. The reaction rate increased substantially with the temperature, revealing its key role in the degradation process. Higher H2O2 levels and the inclusion of inorganic anions like chloride or nitrate also sped up the degradation process. While sulfate and humic acid, particularly at high doses, slowed it. The mechanism of H2O2 activation on α-Fe2O3–CuO is studied. The measurements of chemical oxygen demand and total organic carbon indicate that all dyes are highly mineralized. The remarkable performance and stability of this nanocomposite in removing diverse dyes render it a promising option for wastewater remedy.

Electrochemical Determination of Chemical Oxygen Demand (COD) in Surface Water Using a Microfabricated Boron-Doped Diamond (BDD) Electrode by Chronoamperometry

Chemical oxygen demand (COD) is an important indicator of the degree of organic pollution in water. However, the development of integrated and batch COD electrochemical sensors has always been challenging. In this study, a three-electrode integrated electrochemical sensor for the measurement of COD in surface water was evaluated. Using microfabrication with a microelectromechanical system (MEMS), the sensor was mass-produced and integrated with boron-doped diamond (BDD), Pt, and Ag/AgCl electrodes on the chip. The determination of glucose in optimal conditions provided a linear range from 5 to 200 mg L−1, a detection limit of 3.899 mg L−1, and satisfactory linearity (R2) of 0.998. As the sensor was fabricated by MEMS technology, good reproducibility was experimentally verified with relative standard deviations less than 4%, which suggests mass production of the sensor. The sensor was calibrated to be relatively stable in the presence of Cl− and NO2 −. A low-cost, miniature (6 mm2), and stable COD sensor was designed using microfabrication technology that may be mass-produced to build a water quality detection network in the Internet of Things era.

A COD measurement method with turbidity compensation based on a variable radial basis function neural network.

In recent years, ultraviolet-visible spectrometry has been widely used to measure sewage's chemical oxygen demand (COD). However, most methods that use UV-vis spectroscopy for COD measurement have not eliminated the influence of turbidity. Therefore, this article proposes a new COD measurement method using UV-vis spectroscopy. This method includes a new turbidity compensation algorithm and an algorithm for COD measurement using a variable radial basis function (VRBF) neural network. Our turbidity compensation algorithm first utilizes principal component analysis (PCA) to extract the characteristic wavelengths of the spectrum. Then, turbidity is used to fit the absorbance difference caused by turbidity at the characteristic wavelength, and a turbidity compensation model is obtained. The turbidity compensation model is used to remove the influence of turbidity from the absorbance spectrum, thereby compensating for its effect on the COD measurement. Secondly, the VRBF neural network model is used to measure the COD concentration. The results show that, compared with the traditional partial least squares regression model, the R2 coefficient of determination increases from 0.27 to 0.88, and the root-mean-square deviation decreases from 5.56 to 1.69. Compared with the improved bagging algorithm and MLP algorithm, this method can measure COD concentration from absorption spectra faster, more directly, and more accurately.