Applications of Online UV-Vis Spectrophotometer for Drinking Water Quality Monitoring and Process Control: A Review

Spectral fusion-based machine learning classifiers for discriminating membrane breakage in multiple scenarios

Membrane breakage can lead to filtration failure, which allows harmful substances to enter the effluent, posing potential hazards to human health and the environment. This study is an innovative combination of fluorescence and ultraviolet-visible (UV-Vis) spectroscopy to identify membrane breakage. It aims to unravel more comprehensive information, improve detection sensitivity and selectivity, and enable real-time monitoring capabilities. Fluorescence and UV-Vis data are extracted through variance partitioning analysis (VPA) and integrated through a decision tree algorithm to form a superior system with enhanced discrimination capabilities. VPA improves discrimination efficiency by extracting key information from spectral data and eliminating redundancy. The decision tree algorithm, on the other hand, can process large amounts of data simultaneously. In addition, the method has a wide range of applications and can be used in various scenarios accurately. The scenarios include domestic sewage, micropollutant water, aquaculture wastewater, and secondary treated sewage. The experimental results validate the application of machine learning classifiers in membrane breakage detection with an accuracy rate of 96.8 % to 97.4 %.

Assessment of Mononuclear/Dinuclear copper acylhydrazone complexes for lung cancer treatment

Non-platinum metal-based complexes have good potential for cancer treatment. Here, we designed and synthesized five hydrazone copper(II) complexes, [Cu2(HL)2Cl2] 1A, [Cu2(HL)2(NO3)H2O]·NO3 2A, [Cu2(HL)2Br2] 3A, [Cu(L)pyridine] 1B and [Cu(HL)(pyridine)Br] 3B, and evaluated their anti-lung cancer activities. MTT experiments revealed that these copper(II) complexes exhibit higher anticancer activity than cisplatin. Mechanism studies revealed that complex 3A induced G1 phase cell cycle arrest, and induced cell apoptosis via reactive oxygen species (ROS)-mediated mitochondrial dysfunction. Scratch wound healing assay was also performed, revealing that complex 3A have good anti-cell migration activity. Hemolysis assays showed good blood biocompatibility of complex 3A. Furthermore, complex 3A can significantly inhibit the proliferation of A549 3D tumor spheroid. An in vivo anticancer study showed that complex 3A could delays the growth of A549 tumor xenografts with lower systemic toxicity. These results highlight the great possibility of developing highly active copper complexes as anti-lung cancer agents.

Data fusion strategy based on ultraviolet-visible spectra and near-infrared spectra for simultaneous and accurate determination of key parameters in surface water

Chemical oxygen demand (COD), ammonia nitrogen (AN) and total nitrogen (TN) are the key parameters to reflect the degree of surface water pollution. Ultraviolet - visible (UV-Vis) spectroscopy and near - infrared (NIR) spectroscopy are ideal techniques for rapid monitoring of these indicators. In this study, a strategy based on the fusion of UV-Vis and NIR spectral data (UV-Vis-NIR) for water quality detection was proposed to further improve the quantitative analysis accuracy of spectroscopic methods. Seventy river samples with different levels of pollution were used for spectroscopic analysis. The UV-Vis-NIR fusion spectrum of each water sample was obtained by directly splicing sample's UV-Vis spectrum and NIR diffuse transmission spectrum. The UV-Vis-NIR fusion models were optimized through using different variable selection algorithms. The results show that the UV-Vis-NIR fusion models for surface water COD, AN and TN achieves better prediction results (the root mean square errors of prediction are 6.95, 0.195, and 0.466, respectively) than single-spectroscopic based models. Since better prediction performances were shown under different optimization conditions, the robustness of fusion models were also better than the single-spectroscopic based models. Therefore, the data fusion strategy proposed in this study has a promising application prospect for further accurate and rapid monitoring of surface water quality.

In Situ Water Quality Monitoring Using an Optical Multiparameter Sensor Probe

Optical methods such as ultraviolet/visible (UV/Vis) and fluorescence spectroscopy are well-established analytical techniques for in situ water quality monitoring. A broad range of bio-logical and chemical contaminants in different concentration ranges can be detected using these methods. The availability of results in real time allows a quick response to water quality changes. The measuring devices are configured as portable multi-parameter probes. However, their specification and data processing typically cannot be changed by users, or only with difficulties. Therefore, we developed a submersible sensor probe, which combines UV/Vis and fluorescence spectroscopy together with a flexible data processing platform. Due to its modular design in the hardware and software, the sensing system can be modified to the specific application. The dimension of the waterproof enclosure with a diameter of 100 mm permits also its application in groundwater monitoring wells. As a light source for fluorescence spectroscopy, we constructed an LED array that can be equipped with four different LEDs. A miniaturized deuterium-tungsten light source (200-1100 nm) was used for UV/Vis spectroscopy. A miniaturized spectrometer with a spectral range between 225 and 1000 nm permits the detection of complete spectra for both methods.

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.

Contactless Optical Liquid Identifier

This paper presents the development of a portable device that can be used for liquid identification using the concept of Beer-Lambert law. The final device can recognize up to 30 different materials in their liquid state and provides some other useful features that can enhance its operation. The user can set a group of 30 liquids according to specific needs and applications. The device can provide the user with some specific settings to start the measurements. The volume of a fluid is identified by a 4 mL sample inserted in the cuvette, then the user can start measuring the concentration of some common solvents like methanol, ethanol, etc. by passing a light of different wavelengths through the sample. The amount of the transferred light is measured using photodetectors and the readings depend on the amount of absorption. The amount of absorption depends on the sample types, colors, contents, and concentrations, so it is used as the main indicator to identify a specific sample.

Detecting Contaminants in Water Based on Full Scattering Profiles within the Single Scattering Regime

Clean water is essential for maintaining human health. To ensure clean water, it is important to use sensitive detection methods that can identify contaminants in real time. Most techniques do not rely on optical properties and require calibrating the system for each level of contamination. Therefore, we suggest a new technique to measure water contamination using the full scattering profile, which is the angular intensity distribution. From this, we extracted the iso-pathlength (IPL) point which minimizes the effects of scattering. The IPL point is an angle where the intensity values remain constant for different scattering coefficients while the absorption coefficient is set. The absorption coefficient does not affect the IPL point but only attenuates its intensity. In this paper, we show the appearance of the IPL in single scattering regimes for small concentrations of Intralipid. We extracted a unique point for each sample diameter wherein light intensity remained constant. The results describe a linear dependency between the angular position of the IPL point and the sample diameter. In addition, we show that the IPL point separates the absorption from the scattering, which allows the absorption coefficient to be extracted. Eventually, we present how we used the IPL point to detect the contamination levels of Intralipid and India ink in concentrations of 30-46 and 0-4 ppm, respectively. These findings suggest that the IPL point is an intrinsic parameter of a system that can be used as an absolute calibration point. This method provides a new and efficient way of measuring and differentiating between various types of contaminants in water.

Water Quality Index and Human Health Risk Assessment of Drinking Water in Selected Urban Areas of a Mega City

The present study was conducted to evaluate the quality of drinking water and assess the potential health hazards due to water contaminants in selected urban areas of Lahore, Pakistan. Water samples were collected from ten sites and analyzed for different physico-chemical parameters including turbidity, color, pH, total dissolved solids (TDS), nitrates, fluoride, residual chlorine, and total hardness. Additionally, heavy metal (arsenic) and microbial parameters (E. coli) were also determined in the water samples. Drinking water quality evaluation indices, including the water quality index (WQI) for physico-chemical and biological parameters and human health risk assessment (HHRA) for heavy metal were estimated using the analytical results of the target parameters. It was found in most of the areas that the levels of arsenic, fluoride, TDS, and residual chlorine were higher than those recommended by the National Environmental Quality Standard (NEQS) and World Health Organization (WHO) guidelines. In addition to the physico-chemical parameters, microbial content (E. coli) was also found in the drinking water samples of the selected areas. Statistical analysis of the results indicated that levels of target parameters in drinking water samples are significantly different between sampling sites. The WQI for all physico-chemical and microbial parameters indicated that drinking water in most of the areas was unfit and unsuitable (WQI > 100) for drinking purposes except for the water of Bhatti Gate and Chota Gaon Shahdara with a WQI of 87 and 91, respectively. Drinking water in these areas had a very poor WQI rating. According to HHRA, drinking water from the selected sites was found to be of high risk to children and adults. The carcinogenic risk of arsenic indicated that all samples were of high risk to both adults and children (4.60 and 4.37 × 10^-3, respectively). Regular monitoring of drinking water quality is essential, and proactive measures must be implemented to ensure the treatment and availability of safe drinking water in urban areas.

Modelling coagulant dosage in drinking water treatment plant using advance machine learning model: Hybrid extreme learning machine optimized by Bat algorithm

Despite the high importance of coagulation process in drinking water treatment plant (DWTP), challenge remains in effectively linking raw water quality measured at the inlet of the DWTP with coagulant dosage rate. This study proposes an integral modelling framework using hybrid extreme learning machine and Bat metaheuristic algorithm (ELM-Bat) for modelling coagulant dosage rate using water temperature, pH, specific conductance, dissolved oxygen, and water turbidity. The aluminum sulphate (Al₂ (SO₄)₃.18H₂O) coagulant is determined using conventional Jar-Test procedure. Results obtained using the hybrid ELM-Bat were compared to those obtained using standalone ELM, outlier robust extreme learning machine (ORELM), online sequential extreme learning machine (OSELM), optimally pruned extreme learning machine (OPELM), and kernel extreme learning machine (KELM). First, the models have been calibrated during the training stage and in a second stage; they are validated using various statistical metrics, i.e., RMSE, MAE, the correlation coefficient (R), and the Nash-Sutcliffe model efficiency (NSE). We found that the hybrid ELM-Bat was significantly more accurate and it has yielded accuracy higher than all other models. During the validation stage, the R and NSE values calculated using the ELM-Bat were ≈0.959 and ≈0.918 exhibiting an improvement rates of approximately (≈15.26% and ≈33.82%), (≈10.35% and ≈21.92%), (≈14.98% and ≈31.89%), (≈7.63% and ≈16.35%), (≈10.99% and ≈23.05%), compared to the values obtained using the ELM, OPELM, OSELM, KELM and ORELM, respectively. Besides, the new ELM-Bat model has shown to have high predictive capabilities, which can be used optimally for calculating the optimal coagulant dosage with high accuracy.

Advances in Catchment Science, Hydrochemistry, and Aquatic Ecology Enabled by High-Frequency Water Quality Measurements

High-frequency water quality measurements in streams and rivers have expanded in scope and sophistication during the last two decades. Existing technology allows in situ automated measurements of water quality constituents, including both solutes and particulates, at unprecedented frequencies from seconds to subdaily sampling intervals. This detailed chemical information can be combined with measurements of hydrological and biogeochemical processes, bringing new insights into the sources, transport pathways, and transformation processes of solutes and particulates in complex catchments and along the aquatic continuum. Here, we summarize established and emerging high-frequency water quality technologies, outline key high-frequency hydrochemical data sets, and review scientific advances in key focus areas enabled by the rapid development of high-frequency water quality measurements in streams and rivers. Finally, we discuss future directions and challenges for using high-frequency water quality measurements to bridge scientific and management gaps by promoting a holistic understanding of freshwater systems and catchment status, health, and function.

Method for measuring the extinction coefficient of fluorescing media within the emission band

We propose and test a method for determining a fluorescent medium's absorption or extinction index while it is fluorescing. The method uses an optical arrangement that records changes in fluorescence intensity at a fixed viewing angle as a function of the angle of incidence of an excitation light beam. We tested the proposed method on polymeric films doped with Rhodamine 6G (R6G). We found a strong anisotropy in the fluorescence emission and, thus, limited the method to TE-polarized excitation light. The method proposed is model dependent, and we provide a simplified model for its use in this work. We report the extinction index of the fluorescing samples at a selected wavelength within the emission band of the fluorophore R6G. We found that the extinction index at the emission wavelengths in our samples is appreciably larger than the extinction index at the excitation wavelength, which is the opposite of what one might expect from measuring the absorption spectrum of the medium with a spectrofluorometer. The proposed method could be applied to fluorescent media with additional absorption other than by the fluorophore.

Research on Small-Scale Detection Instrument for Drinking Water Combined Laser Spectroscopy and Conductivity Technology

In order to realize rapid and accurate evaluation of drinking water quality, a small-scale water quality detection instrument is designed in this paper that can detect two representative water quality parameters: the permanganate index and total dissolved solids (TDS). The permanganate index measured by the laser spectroscopy method can achieve the approximate value of the organic matter in water, and the TDS measured by the conductivity method can obtain the approximate value of the inorganic matter in water. In addition, to facilitate the popularization of civilian applications, the evaluation method of water quality based on the percent-scores proposed by us is presented in this paper. The water quality results can be displayed on the instrument screen. In the experiment, we measured the water quality parameters of the tap water as well as those after the primary and secondary filtration in Weihai City, Shandong Province, China. The testing results show that the instrument can quickly detect dissolved inorganic and organic matter, and intuitively display the water quality evaluation score on the screen. The instrument designed in this paper has the advantages of high sensitivity, high integration, and small volume, which lays the foundation for the popularity of the detection instrument.

A method based on a one-dimensional convolutional neural network for UV-vis spectrometric quantification of nitrate and COD in water under random turbidity disturbance scenario

This paper proposed a novel spectrometric quantification method for nitrate and COD concentration in water using a double-channel 1-D convolution neural network for relatively long UV-vis absorption spectra data (2600 points). To improve the model's ability to resist turbidity disturbance, a new dataset augmentation method was applied and the absorption spectra of nitrate and COD under different turbidity disturbances were successfully simulated. Compared to the PLSR model, the value of RRMSEP for the CNN model was reduced from 6.1% to 1.4% in nitrate solution and 4.5% to 1.3% in COD solution. Compared to the PLSR model, the regression accuracy of the CNN model was increased from 56% to 93% in nitrate solution and 68% to 91% in COD solution. The test on the actual solution under different turbidity disturbances shows that the 1D-CNN model had a bias rate of less than 2% in both nitrate and COD solutions, while the worst bias rate in the PLSR method was 15%.

Advancements in Monitoring Water Quality Based on Various Sensing Methods: A Systematic Review

Nowadays, water pollution has become a global issue affecting most countries in the world. Water quality should be monitored to alert authorities on water pollution, so that action can be taken quickly. The objective of the review is to study various conventional and modern methods of monitoring water quality to identify the strengths and weaknesses of the methods. The methods include the Internet of Things (IoT), virtual sensing, cyber-physical system (CPS), and optical techniques. In this review, water quality monitoring systems and process control in several countries, such as New Zealand, China, Serbia, Bangladesh, Malaysia, and India, are discussed. Conventional and modern methods are compared in terms of parameters, complexity, and reliability. Recent methods of water quality monitoring techniques are also reviewed to study any loopholes in modern methods. We found that CPS is suitable for monitoring water quality due to a good combination of physical and computational algorithms. Its embedded sensors, processors, and actuators can be designed to detect and interact with environments. We believe that conventional methods are costly and complex, whereas modern methods are also expensive but simpler with real-time detection. Traditional approaches are more time-consuming and expensive due to the high maintenance of laboratory facilities, involve chemical materials, and are inefficient for on-site monitoring applications. Apart from that, previous monitoring methods have issues in achieving a reliable measurement of water quality parameters in real time. There are still limitations in instruments for detecting pollutants and producing valuable information on water quality. Thus, the review is important in order to compare previous methods and to improve current water quality assessments in terms of reliability and cost-effectiveness.

A Design of Real-Time Data Acquisition and Processing System for Nanosecond Ultraviolet-Visible Absorption Spectrum Detection

Ultraviolet-visible absorption spectroscopy is widely used to monitor water quality, and rapid optical signal detection is a key technology in the process of spectrum measurement. In this paper, an ultrafast spectrophotometer system that can achieve spectrum data acquisition in a single flash of the xenon lamp (within 200 ns) is introduced, and a real-time denoising method for the spectrum is implemented on a field programmable gate array (FPGA) to work cooperatively with the nanosecond spectrum acquisition system, in order to guarantee the quality of the spectrum signals without losing running speed. The hardware of the data acquisition and processing system are constructed on a Xilinx Spartan 6 FPGA chip and its peripheral circuit, including an analog to digital converter and a complementary metal-oxide-semiconductor transistor (CMOS) sensor’s diver circuit. An oversampling method that is suitable for the CMOS sensor’s output is proposed, which works on the CMOS sensor’s dark current noise and readout noise. Another moving-average filter method is designed adaptively, which works on the low-frequency component to filter out the residual spectrum noise of the spectrum signal. The implementation of the filter on the FPGA has been optimized by using a pipelined structure and dual high-speed random-access memory (RAM). As a result, the CMOS linear image sensor successfully captured the spectrum of xenon flash light at the readout clock frequency of 500 kHz and the processing manipulation to the full UV-Vis spectrum data was accomplished at a sub-microsecond speed performance. After the digital filter and oversampling technology were implemented, the coefficient of variation of the measurements reduced from 9.57% to 1.74%, while the signal noise ratio (SNR) of the absorption spectrum increased nine times, compared to the raw data of the CMOS sensor’s output. The tests towards different analyte samples were conducted, and the system shows good performance on distinguishing different concentrations of different analyte solutions on both ultra-violet and visible spectrum bands. The present work showcases the potential of the CMOS sensor’s technique for the fast detection of contaminated water containing nitrate and organic compounds.

Possibilities of Real Time Monitoring of Micropollutants in Wastewater Using Laser-Induced Raman & Fluorescence Spectroscopy (LIRFS) and Artificial Intelligence (AI)

The entire water cycle is contaminated with largely undetected micropollutants, thus jeopardizing wastewater treatment. Currently, monitoring methods that are used by wastewater treatment plants (WWTP) are not able to detect these micropollutants, causing negative effects on aquatic ecosystems and human health. In our case study, we took collective samples around different treatment stages (aeration tank, membrane bioreactor, ozonation) of a WWTP and analyzed them via Deep-UV laser-induced Raman and fluorescence spectroscopy (LIRFS) in combination with a CNN-based AI support. This process allowed us to perform the spectra recognition of selected micropollutants and thus analyze their reliability. The results indicated that the combination of sensitive fluorescence measurements with very specific Raman measurements, supplemented with an artificial intelligence, lead to a high information gain for utilizing it as a monitoring purpose. Laser-induced Raman spectroscopy reaches detections limits of alert pharmaceuticals (carbamazepine, naproxen, tryptophan) in the range of a few µg/L; naproxen is detectable down to 1 × 10−4 mg/g. Furthermore, the monitoring of nitrate after biological treatment using Raman measurements and AI support showed a reliable assignment rate of over 95%. Applying the fluorescence technique seems to be a promising method in observing DOC changes in wastewater, leading to a correlation coefficient of R2 = 0.74 for all samples throughout the purification processes. The results also showed the influence of different extraction points in a cleaning stage; therefore, it would not be sensible to investigate them separately. Nevertheless, the interpretation suffers when many substances interact with one another and influence their optical behavior. In conclusion, the results that are presented in our paper elucidate the use of LIRFS in combination with AI support for online monitoring.