Study on an Online Detection Method for Ground Water Quality and Instrument Design

Distribution and Biological Response of Nanoplastics in Constructed Wetland Microcosms: Mechanistic Insights into the Role of Photoaging

Concern over nanoplastic contamination of wetland ecosystems has been increasing. However, little is known about the effect of photoaging on the distribution and biological response of the nanoplastics. Here, palladium-labeled polystyrene nanoplastics (PS-Pd NPs) at 0.05-50 mg/L were exposed to constructed wetland microcosms containing floating (Eichhornia crassipes) and submerged (Vallisneria natans) macrophytes. Results demonstrate that PS-Pd NPs' concentration in surface water after 2-4 weeks of exposure was decreased by over 98.4% as compared with that in the 1st week. Photoaging enhanced the surface charge and colloidal stability of PS-Pd NPs, with a subsequent increase of the content of PS-Pd NPs in surface and middle layer water by 264.6 and 207.4%, respectively. Additionally, photoaging significantly enhanced the accumulation of PS-Pd NPs in E. crassipes roots by 6.9-65.0% and significantly decreased it in V. natans shoots by 59.7-123.0%. PS-Pd NPs inhibited the growth of V. natans by 43.8% at 50 mg/L. Mechanistically, PS-Pd NPs induced oxidative stress in V. natans, leading to the disruption of the metabolic pathway. Interestingly, PS-Pd NP exposure inhibited nitrification in wetland ecosystems due to the alteration of the related bacterial community (Ellin6067 decreased by 13.19%). These findings deepen our understanding of the environmental fate and risk of plastic particles in wetland ecosystems.

A spectral learning path for simultaneous multi-parameter detection of water quality

Water quality parameters (WQP) are the most intuitive indicators of the environmental quality of water body. Due to the complexity and variability of the chemical environment of water body, simple and rapid detection of multiple parameters of water quality becomes a difficult task. In this paper, spectral images (named SPIs) and deep learning (DL) techniques were combined to construct an intelligent method for WQP detection. A novel spectroscopic instrument was used to obtain SPIs, which were converted into feature images of water chemistry and then combined with deep convolutional neural networks (CNNs) to train models and predict WQP. The results showed that the method of combining SPIs and DL has high accuracy and stability, and good prediction results with average relative error of each parameter (anions and cations, TOC, TP, TN, NO₃^--N, NH₃-N) at 1.3%, coefficient of determination (R²) of 0.996, root mean square error (RMSE) of 0.1, residual prediction deviation (RPD) of 16.2, and mean absolute error (MAE) of 0.067. The method can achieve rapid and accurate detection of high-dimensional water quality multi-parameters, and has the advantages of simple pre-processing and low cost. It can be applied not only to the intelligent detection of environmental waters, but also has the potential to be applied in chemical, biological and medical fields.

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%.

Online water quality monitoring based on UV-Vis spectrometry and artificial neural networks in a river confluence near Sherfield-on-Loddon

Water quality monitoring is very important in agricultural catchments. UV-Vis spectrometry is widely used in place of traditional analytical methods because it is cost effective and fast and there is no chemical waste. In recent years, artificial neural networks have been extensively studied and used in various areas. In this study, we plan to simplify water quality monitoring with UV-Vis spectrometry and artificial neural networks. Samples were collected and immediately taken back to a laboratory for analysis. The absorption spectra of the water sample were acquired within a wavelength range from 200 to 800 nm. Convolutional neural network (CNN) and partial least squares (PLS) methods are used to calculate water parameters and obtain accurate results. The experimental results of this study show that both PLS and CNN methods may obtain an accurate result: linear correlation coefficient (R²) between predicted value and true values of TOC concentrations is 0.927 with PLS model and 0.953 with CNN model, R² between predicted value and true values of TSS concentrations is 0.827 with PLS model and 0.915 with CNN model. CNN method may obtain a better linear correlation coefficient (R²) even with small number of samples and can be used for online water quality monitoring combined with UV-Vis spectrometry in agricultural catchment.

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

Water quality monitoring is an essential component of water quality management for water utilities for managing the drinking water supply. Online UV-Vis spectrophotometers are becoming popular choices for online water quality monitoring and process control, as they are reagent free, do not require sample pre-treatments and can provide continuous measurements. The advantages of the online UV-Vis sensors are that they can capture events and allow quicker responses to water quality changes compared to conventional water quality monitoring. This review summarizes the applications of online UV-Vis spectrophotometers for drinking water quality management in the last two decades. Water quality measurements can be performed directly using the built-in generic algorithms of the online UV-Vis instruments, including absorbance at 254 nm (UV254), colour, dissolved organic carbon (DOC), total organic carbon (TOC), turbidity and nitrate. To enhance the usability of this technique by providing a higher level of operations intelligence, the UV-Vis spectra combined with chemometrics approach offers simplicity, flexibility and applicability. The use of anomaly detection and an early warning was also discussed for drinking water quality monitoring at the source or in the distribution system. As most of the online UV-Vis instruments studies in the drinking water field were conducted at the laboratory- and pilot-scale, future work is needed for industrial-scale evaluation with ab appropriate validation methodology. Issues and potential solutions associated with online instruments for water quality monitoring have been provided. Current technique development outcomes indicate that future research and development work is needed for the integration of early warnings and real-time water treatment process control systems using the online UV-Vis spectrophotometers as part of the water quality management system.

Growth and Biochemical Composition Characteristics of Arthrospira platensis Induced by Simultaneous Nitrogen Deficiency and Seawater-Supplemented Medium in an Outdoor Raceway Pond in Winter

Arthrospira platensis, a well-known cyanobacterium, is widely applied not only in human and animal nutrition but also in cosmetics for its high amounts of active products. The biochemical composition plays a key role in the application performance of the Arthrospira biomass. The present study aimed to evaluate the growth and biochemical composition characteristics of A. platensis, cultured with a nitrogen-free and seawater-supplemented medium in an outdoor raceway pond in winter. The results showed that the biomass yield could achieve 222.42 g m⁻², and the carbohydrate content increased by 247% at the end of the culture period (26 d), compared with that of the starter culture. The daily and annual areal productivities were 3.96 g m⁻² d⁻¹ and 14.44 ton ha⁻¹ yr⁻¹ for biomass and 2.88 g m⁻² d⁻¹ and 10.53 ton ha⁻¹ yr⁻¹ for carbohydrates, respectively. On the contrary, a profound reduction was observed in protein, lipid, and pigment contents. Glucose, the main monosaccharide in the A. platensis biomass, increased from 77.81% to 93.75% of total monosaccharides. Based on these results, large-scale production of carbohydrate-rich A. platensis biomass was achieved via a low-cost culture, involving simultaneous nitrogen deficiency and supplementary seawater in winter.

Spectrophotometric Online Detection of Drinking Water Disinfectant: A Machine Learning Approach

The spectra fingerprint of drinking water from a water treatment plant (WTP) is characterised by a number of light-absorbing substances, including organic, nitrate, disinfectant, and particle or turbidity. Detection of disinfectant (monochloramine) can be better achieved by separating its spectra from the combined spectra. In this paper, two major focuses are (i) the separation of monochloramine spectra from the combined spectra and (ii) assessment of the application of the machine learning algorithm in real-time detection of monochloramine. The support vector regression (SVR) model was developed using multi-wavelength ultraviolet-visible (UV-Vis) absorbance spectra and online amperometric monochloramine residual measurement data. The performance of the SVR model was evaluated by using four different kernel functions. Results show that (i) particles or turbidity in water have a significant effect on UV-Vis spectral measurement and improved modelling accuracy is achieved by using particle compensated spectra; (ii) modelling performance is further improved by compensating the spectra for natural organic matter (NOM) and nitrate (NO₃) and (iii) the choice of kernel functions greatly affected the SVR performance, especially the radial basis function (RBF) appears to be the highest performing kernel function. The outcomes of this research suggest that disinfectant residual (monochloramine) can be measured in real time using the SVR algorithm with a precision level of ± 0.1 mg L⁻¹.

Synthesis of nickel nanoparticles using Citrullus colocynthis stem extract for remediation of Reactive Yellow 160 dye

In current years, pollution caused by synthetic dyes has become one of the most serious environmental issues. By rapidly developing industrial units, effluents having synthetic dyes are directly or indirectly being discharged into the environment. Bio-sorption is cost-effective way for the eradication of toxic dyes present in textile effluent. The present study involves the synthesis of nickel nanoparticles using Citrullus colocynthis stem extract. The characterization of synthesized nickel nanoparticles (Ni-NPs) was done by SEM. The synthesized Ni-NPs were used to degrade the Reactive Yellow 160 dye following the optimization of different experimental parameters. The maximum decolorization (91.4%) was obtained at 0.02% dye conc., 9 mg/L conc. of Ni-NPs, pH 7 at 40 °C. TOC and COD were used to assess the efficiency of this experiment. Percent reduction in COD and TOC was found to be 84.35% and 83.24% respectively. The degradation pathway of dye under study confirmed the formation of non-toxic end-products.