Direct DOC and nitrate determination in water using dual pathlength and second derivative UV spectrophotometry

Fluorescence spectrometric analysis for diagnosing compositional variations in effluent organic matter by chlorination and ozonation

Analyzing the reactivity of organic matter to oxidants such as chlorination and ozonation is crucial for evaluating the effectiveness of water treatment systems and their potential impacts on environmental safety and human health. This study explored the changes in organic substances, specifically bovine serum albumin (BSA), humic acid sodium salt (HA), and effluent organic matter (EfOM) from a wastewater treatment facility during chlorination and ozonation. Four spectrometric techniques were employed: ultraviolet absorbance at 254 nm (UVA254), fluorescent excitation-emission matrix (EEM), synchronous fluorescence two-dimensional correlation spectroscopy (SF-2DCOS), and EEM-parallel factor integrated 2DCOS (EEM-PARAFAC-2DCOS). The findings revealed that ozone possesses superior oxidizing properties compared to chlorine, as evidenced by UVA254 and EEM analyses, resulting in more diverse structural modifications in EfOM. SF-2DCOS and EEM-PARAFAC-2DCOS provided comprehensive details on the direction and sequence of these changes, with EEM-PARAFAC-2DCOS delivering clear and intuitive insights. Protein-like and fulvic-like substances were susceptible to chlorination and ozonation, exhibiting different reaction sequences with each oxidant. Furthermore, variations in protein-like and humic-like components in actual EfOM samples may not align precisely with those in model substances, emphasizing the importance of considering specific organic matter variations in real EfOM samples compared to model substances. This research offered a deeper understanding of the reactivity and transformation of organic matter in wastewater treatment processes through simple and rapid spectroscopic methods, potentially improving the management and mitigation of undesired byproducts.

Transformation and drive mechanism of nitrogen functional genes at estuaries in dry and wet seasons

The nitrogen cycle plays a vital role in maintaining ecological health and biodiversity. In aquatic systems, nitrogen transformation genes significantly contribute to biological nitrogen cycling. Although the function of these genes is known to be influenced by environmental factors, there is limited research exploring the relationship between nitrogen transformation genes and environmental factors. Therefore, the correlations, between nitrogen transformation genes and environmental factors, were investigated at the estuaries of Chaohu lake (China) in different seasons. The results showed that the values of temperature, pH, organic compounds, nitrogen, and dissolved oxygen were higher in dry season, whereas the abundance of the genes was lower in dry season. In addition, the abundance of the anaerobic ammoxidation gene was much lower than the nitrification gene and denitrification gene. The results indicated that biological nitrification and denitrification were the primary mechanisms for nitrogen removal at estuaries in different seasons, and the reduction of nitric oxide may be a limiting step in the denitrification process. The Co-occurrence Network and Mantel test indicated that, during the dry season, the temperature was the primary driver of ammonification and nitrification functions, the NO3- and NO2- were the primary drivers of denitrification, and the total nitrogen (TN) and NH4+ were the main drivers of anaerobic ammonia oxidation. During the wet season, the dissolved oxygen was the primary driver of ammonification and nitrification functions, the chemical oxygen demand was the primary driver of denitrification, and the TN was the main driver of anaerobic ammonia oxidation. This study provides valuable insights into nitrogen cycling in surface water, contributing to a better understanding of this important process.

The Development of a Novel Nitrate Portable Measurement System Based on a UV Paired Diode-Photodiode

Nitrates can cause severe ecological imbalances in aquatic ecosystems, with considerable consequences for human health. Therefore, monitoring this inorganic form of nitrogen is essential for any water quality management structure. This research was conducted to develop a novel Nitrate Portable Measurement System (NPMS) to monitor nitrate concentrations in water samples. NPMS is a reagent-free ultraviolet system developed using low-cost electronic components. Its operation principle is based on the Beer-Lambert law for measuring nitrate concentrations in water samples through light absorption in the spectral range of 295-315 nm. The system is equipped with a ready-to-use ultraviolet sensor, light emission diode (LED), op-amp, microcontroller, liquid crystal display, quartz cuvette, temperature sensor, and battery. All the components are assembled in a 3D-printed enclosure box, which allows a very compact self-contained equipment with high portability, enabling field and near-real-time measurements. The proposed methodology and the developed instrument were used to analyze multiple nitrate standard solutions. The performance was evaluated in comparison to the Nicolet Evolution 300, a classical UV-Vis spectrophotometer. The results demonstrate a strong correlation between the retrieved measurements by both instruments within the investigated spectral band and for concentrations above 5 mg NO3-/L.

Effective correction of dissolved organic carbon interference in nitrate detection using ultraviolet spectroscopy combined with the equivalent concentration offset method

Nitrate contamination in water sources poses a substantial environmental and health risk. However, accurate detection of nitrate in water, particularly in the presence of dissolved organic carbon (DOC) interference, remains a significant analytical challenge. This study investigates a novel approach for the reliable detection of nitrate in water samples with varying levels of DOC interference based on the equivalent concentration offset method. The characteristic wavelengths of DOC were determined based on the first-order derivatives, and a nitrate concentration prediction model based on partial least squares (PLS) was established using the absorption spectra of nitrate solutions. Subsequently, the absorption spectra of the nitrate solutions were subtracted from that of the nitrate-DOC mixed solutions to obtain the difference spectra. These difference spectra were introduced into the nitrate prediction model to calculate the equivalent concentration offset values caused by DOC. Finally, a DOC interference correction model was established based on a binary linear regression between the absorbances at the DOC characteristic wavelengths and the DOC-induced equivalent concentration offset values of nitrate. Additionally, a modeling wavelength selection algorithm based on a sliding window was proposed to ensure the accuracy of the nitrate concentration prediction model and the equivalent concentration offset model. The experimental results demonstrated that by correcting the DOC-induced offsets, the relative error of nitrate prediction was reduced from 94.44% to 3.36%, and the root mean square error of prediction was reduced from 1.6108 mg L-1 to 0.1037 mg L-1, which is a significant correction effect. The proposed method applied to predict nitrate concentrations in samples from two different water sources shows a certain degree of comparability with the standard method. It proves that this method can effectively correct the deviations in nitrate measurements caused by DOC and improve the accuracy of nitrate measurement.

Characterizing dissolved organic matter and bacterial community interactions in a river network under anthropogenic landcover

Anthropogenic landcover could rise nutrient concentrations and impact the characteristics and bioavailability of dissolved organic matter (DOM) in a river network. Exploring the interactions between DOM and microbials might be conducive to revealing biogeochemistry behaviors of organic matter. In this study, synchronous fluorescence spectra (SFS) with Gaussian band fitting and two-dimensional correlation spectroscopy (2D-COS) were employed to identify DOM fractions and reveal their interactions with bacterial communities. DOM was extracted from a river network under eco-agricultural rural (RUR), eco-residential urban (URB), eco-economical town (TOW), and eco-industrial park (IND) regions in Jiashan Plain of eastern China. The overlapping peaks observed in the SFS were successfully separated into four fractions using Gaussian band fitting, i.e., tyrosine-like fluorescence (TYLF), tryptophan-like fluorescence (TRLF), microbial humic-like fluorescence (MHLF), and fulvic-like fluorescence (FLF) materials. Across all four regions, TRLF (44.79% ± 7.74%) and TYLF (48.09% ± 8.85%) were the dominant components. Based on 2D-COS, variations of TYLF and TRLF were extremely larger than those of FLF in RUR-TOW. However, in URB-IND, the former exhibited lower variations compared to the latter. These suggested that FLF be likely derived continuously from lignin and other residue of terrestrial plant origin along the river network, and TYLF and TRLF be originated discontinuously from domestic wastewater in RUR-TOW. By high-throughput sequenced OTUs, the number of organisms in RUR-TOW could be higher than those in URB-IND, while genes associated with carbohydrate metabolism were lower in former than those in the latter. According to co-occurrence networks, microbes could promote the production of TYLF and TRLF in RUR-TOW. In contrast, microbial communities in URB-IND might contribute to decompose FLF. The obtained results could not only reveal interactions between DOM fractions and bacterial communities in the river network, but this methodology may be applied to other water bodies from different landscapes.

Improving algal bloom detection using spectroscopic analysis and machine learning: A case study in a large artificial reservoir, South Korea

The prediction of algal blooms using traditional water quality indicators is expensive, labor-intensive, and time-consuming, making it challenging to meet the critical requirement of timely monitoring for prompt management. Using optical measures for forecasting algal blooms is a feasible and useful method to overcome these problems. This study explores the potential application of optical measures to enhance algal bloom prediction in terms of prediction accuracy and workload reduction, aided by machine learning (ML) models. Compared to absorption-derived parameters, commonly used fluorescence indices such as the fluorescence index (FI), humification index (HIX), biological index (BIX), and protein-like component improved the prediction accuracy. However, the prediction accuracy was decreased when all optical indices were considered for computation due to increased noise and uncertainty in the models. With the exception of chemical oxygen demand (COD), this study successfully replaced biochemical oxygen demand (BOD), dissolved organic carbon (DOC), and nutrients with selected fluorescence indices, demonstrating relatively analogous performance in either training or testing data, with consistent and good coefficient of determination (R2) values of approximately 0.85 and 0.74, respectively. Among all models considered, ensemble learning models consistently outperformed conventional regression models and artificial neural networks (ANNs). However, there was a trade-off between accuracy and computation efficiency among the ensemble learning models (i.e., Stacking and XGBoost) for algal bloom prediction. Our study offers a glimpse of the potential application of spectroscopic measures to improve accuracy and efficiency in algal bloom prediction, but further work should be carried out in other water bodies to further validate our proposed hypothesis.

Nitrite Quantification by Second Derivative Chemometric Models Mitigates Natural Organic Matter Interferences under Chloraminated Drinking Water Distribution System Conditions

Nitrite (NO2-) production in chloraminated drinking water distribution systems (CDWDSs) is among the first bulk water indicators of a nitrification event and is typically quantified using ion chromatography (IC) or colorimetric techniques. NO2- can also be quantified using chemometric models (CMs) formulated using molar absorptivity (Ɛ) and/or ultraviolet absorbance (UVA) spectra, but concerns exist regarding their accuracy and generalizability because of varying source water natural organic matter (NOM), monochloramine (NH2Cl), bromide (Br-), and other species in CDWDSs. We demonstrate that the impact of NOM was mitigated in the second derivative molar absorptivity (Ɛ″) and UVA spectra (UVA″) between 200-300 nm and developed a generalizable CM for NO2- quantification. The Ɛ″+UVA″ CM was calibrated with daily NO2- measurements by IC from five biofilm annular reactor (BAR) tests with feedwater from Fayetteville, Arkansas, USA (FAY1, n = 275) and validated with eight BAR tests (n = 376) with another Fayetteville water (FAY2) and two waters from Dallas, Texas, USA (DAL1 and DAL2). The Ɛ″+UVA″ CM used Ɛ″ for NO2-, nitrate (NO3-), Br-, and NH2Cl at wavelengths of 213-, 225-, 229- and 253 nm, had an adjusted R2 of 0.992 for FAY1 and 0.987 for the other waters, and had a method detection limit (MDL) of 0.050 mg·L-1-N. NO2- challenge samples with three reconstituted NOM types and Br- indicated the Ɛ″+UVA″ CM was generalizable at NOM concentrations like those in the BAR tests (≤ 2.5 mg·L-1-C). The Ɛ″+UVA″ CM accurately simulated NO2- in field tests from two CDWDSs undergoing nitrification, including one with NOM at 3.5 mg·L-1-C, illustrating a practical application of the CM for identifying biological ammonia oxidation.

Characterizing the spatiotemporal distribution of dissolved organic matter (DOM) in the Yongding River Basin: Insights from flow regulation

Artificial flow regulation is an important measure to alleviate water shortages and improve the ecological quality of river basins. Dissolved organic matter (DOM) plays a crucial role in the carbon cycle and regulates biogeochemical and ecological processes in aquatic systems. Among the numerous studies on the effects of anthropogenic activities on the quality and quantity of river DOM, few studies have focused on the influence of different artificially regulated flow on the composition, source, and fate of fluvial DOM. This study aims to elucidate the impact of different artificial regulation modes of river flows on the source, migration, and transformation of DOM. The optical properties of DOM were used to explore the temporal and spatial distribution characteristics of DOM in the Yongding River Basin, where artificial regulation of river flows by cross-basin and inner-basin water transfers were implemented. Excitation-emission matrix fluorescence spectroscopy coupled with parallel factor analysis revealed four fluorescent substances of DOM in the water: one microbial humic-like (C1), one terrestrial humic-like (C2), one non-point source pollution humic-like (C4), and one tryptophan-like (C3) substance. Due to cross-basin water transfer from the Yellow River, the flow is the highest (21.79 m³/s) during spring, which was the reason that the signal of C2 was stronger during spring (71.45 QSU) compared to summer (57.12 QSU) and autumn (51.78 QSU). Due to inner-basin water transfer from upstream reservoirs, C3 derived from autochthonous sources were higher during autumn (130.81 QSU) than during spring (77.17 QSU) and summer (93.16 QSU). With no water transfer, more C1 were present at higher temperatures during summer (141.51 QSU) than during spring (126.73 QSU) and autumn (128.8 QSU). Moreover, C4 originating from urban and/or agricultural non-point source runoff increased during summer (57.07 QSU) than during spring (33.29 QSU) and autumn (52.27 QSU) because of increased rainfall. The different modes of artificial regulation of river flows changed the hydrological characteristics of the basin, which in turn altered the temporal and spatial distribution characteristics of the quantity and quality of DOM. The finding of this study can help promote the development of appropriate management strategies for artificial regulation of river flows in the basin. Furthermore, this study provides a basis for investigating the effects of different artificial flow regulations on the carbon cycles and ecological risks of rivers in the basin.

Direct-determination of high-concentration sulfate by serial differential spectrophotometry with multiple optical pathlengths

Direct-determination is a fast and free of contamination method for analysis of substances in water samples. However, direct-determination of SO₄^2- with high concentration in environmental systems is still challenging due to deviation from Beer-Lambert law is generally observed when a substance with high concentration is directly determined, resulting in poor accuracy, sensitivity, and narrow linear range. In this study, a simple and rapid method for the direct-determination of SO₄^2- with high concentration was proposed. Serial high-absorbance differential spectrophotometry was applied to incrementally widen the determination range under different optical pathlengths. In this process, the effects of optical pathlength and reference concentration on sensitivity were further investigated. The results showed that SO₄^2- could be accurately quantified within a concentration range of 0-4.10 g/L, and the determination range by this method was 10-fold and 19.5-fold wider than those by conventional differential spectrophotometry and conventional spectrophotometry, respectively. And the applicable ranges of sensitivity were obtained at various optical pathlengths by adjusting the reference solution concentration. This approach exhibited several advantages over conventional methods, including high accuracy, excellent precision, low cost, less time consumption, and easy operation. This method is promising and can provide accurate and reliable data support for environmental monitoring and pollution control.

Stormwater monitoring using on-line UV-Vis spectroscopy

Stormwater runoff contains a myriad of pollutants, including faecal microbes, and can pose a threat to urban water supplies, impacting both economic development and public health. Therefore, it is a necessity to implement a real-time hazard detection system that can collect a substantial amount of data, assisting water authorities to develop preventive strategies to ensure the control of hazards entering drinking water sources. An on-line UV-Vis spectrophotometer was applied in the field to collect real-time continuous data for various water quality parameters (nitrate, DOC, turbidity and total suspended solids) during three storm events in Mannum, Adelaide, Australia. This study demonstrated that the trends for on-line and comparative laboratory-analysed samples were complimentary through the events. Nitrate and DOC showed a negative correlation with water level, while turbidity and total suspended solids indicated a positive correlation with water level during the high rainfall intensity. The correlations among nitrate, DOC, turbidity, total suspended solids and water level are the opposite during low rainfall intensity. Nitrate, one of the main pollutants in stormwater, was investigated and used as a surrogate parameter for microbial detection. However, the microbiological data (Escherichia coli) from captured storm events showed poor correlations to nitrate and other typical on-line parameters in this study. This is possibly explained by the nature of the stormwater catchment outside of rain events, where the sources of bacteria and nutrients may be physically separated until mixed during surface runoff as a result of rainfall. In addition, the poor correlations among the microbiological data and on-line parameters could be due to the different sources of bacteria and nutrients that were transported to the stormwater drain where sampling and measurement were conducted.

Development of total suspended matter prediction in waters using fractional-order derivative spectra

More and more hyper-spectral satellites will be used to estimate total suspended matter (TSM) in waters instead of multi-spectral satellites, such as China's Gaofen-5 and Zhuhai-1. Although they have not been widely used because of the consistency of sampling and image time. Hence, the study based on measured hyper-spectroscopy is important for applying to hyper-spectral satellites. Fractional-order derivatives (FODs) considers more detailed spectral information, and it is a better spectral preprocessing method than conventional integer-order derivatives. The application and analysis of FODs for spectra in waters is rare. If FOD is successfully applied to estimate TSM, the TSM mapping with FOD using hyper-spectral satellites will be meaningful. Based on these points, this study aimed to apply FOD to predict TSM and to prove the prediction feasibility of FOD in waters. Different prediction models and eight FOD transformation processes with increment of 0.25 per step for 392 spectral reflectance data from China were used and compared. The prediction models include the optimum models of the single wavelength, ratio index, difference index and TSM index at each FOD order, and the random forest (RF) model with all wavelengths was also used. Discrete wavelet transform (DWT) was used to reduce noise and improve the model accuracy after using FOD. Our results achieved the followings First, FOD enhanced spectral characteristics at 500-600 nm and 800 nm that were affected by TSM. Second, the correlation between TSM and FOD spectra was enhanced (e.g., the correlation coefficients of 19 wavelengths (789-807 nm) of 0.75-order were higher than 0.8 but the original spectra were not). Third, FOD improved the performance of different prediction models, and the RF model from 0.5-order to 1.25-order derivative spectra all led good results (). Fourth, DWT can reduce the noise and improve the performance, and FOD-DWT model of 1.25-order led the R² of 0.84, RMSE of 16.30 and MAPE of 78.62 in validation. Overall, our results suggest that FOD can improve the prediction performance for most models, and the optimum order of some models is not integer. Our results also provide a reference for predicting other water quality parameters and mapping these parameters using hyper-spectral satellites. The accurate estimation of TSM is helpful for protecting ecological and social environments.

Development of an Optical Method to Monitor Nitrification in Drinking Water

Nitrification is a common issue observed in chloraminated drinking water distribution systems, resulting in the undesirable loss of monochloramine (NH₂Cl) residual. The decay of monochloramine releases ammonia (NH₃), which is converted to nitrite (NO₂^-) and nitrate (NO₃^-) through a biological oxidation process. During the course of monochloramine decay and the production of nitrite and nitrate, the spectral fingerprint is observed to change within the wavelength region sensitive to these species. In addition, chloraminated drinking water will contain natural organic matter (NOM), which also has a spectral fingerprint. To assess the nitrification status, the combined nitrate and nitrite absorbance fingerprint was isolated from the total spectra. A novel method is proposed here to isolate their spectra and estimate their combined concentration. The spectral fingerprint of pure monochloramine solution at different concentrations indicated that the absorbance difference between two concentrations at a specific wavelength can be related to other wavelengths by a linear function. It is assumed that the absorbance reduction in drinking water spectra due to monochloramine decay will follow a similar pattern as in ultrapure water. Based on this criteria, combined nitrate and nitrite spectra were isolated from the total spectrum. A machine learning model was developed using the support vector regression (SVR) algorithm to relate the spectral features of pure nitrate and nitrite with their concentrations. The model was used to predict the combined nitrate and nitrite concentration for a number of test samples. Out of these samples, the nitrified sample showed an increasing trend of combined nitrate and nitrite productions. The predicted values were matched with the observed concentrations, and the level of precision by the method was ± 0.01 mg-N L^-1. This method can be implemented in chloraminated distribution systems to monitor and manage nitrification.

A High-Performance Optoelectronic Sensor Device for Nitrate Nitrogen in Recirculating Aquaculture Systems

The determination of nitrate nitrogen (NO₃-N) in recirculating aquaculture systems is of great significance for the health assessment of the living environment of aquatic animals. Unfortunately, the commonly used spectrophotometric methods often yield unstable results, especially when the ambient temperature varies greatly in the field measurement. Here, we have developed a novel handheld absorbance measurement sensor based on the thymol-NO₃-N chromogenic rearrangement reaction. In terms of hardware, the sensor adopts a dual channel/dual wavelength colorimeter structure that features a modulated light source transmitter and a synchronous detector receiver. The circuit measures the ratio of light absorbed by the sample and reference containers at two LEDs with peak wavelengths at 420 nm and 450 nm. Using the modulated source and synchronous detector rather than a constant (DC) source eliminates measurement errors due to ambient light and low frequency noise and provides higher accuracy. In terms of software, we design a new quantitative analysis algorithm for absorbance by studying colloid absorbing behavior. The application of a buffer operator embedded in the algorithm makes the sensor get the environmental correction function. The results have shown that the sensitivity, repeatability, precision and environmental stability are higher than that by ordinary spectrophotometry. Lastly, we have a brief overview of future work.

A review on spectroscopic methods for determination of nitrite and nitrate in environmental samples

Nitrate is an important pollutant found in environmental samples. Nitrate and nitrite pose various environmental as well as health hazards. Different methods of determining nitrate in various environmental samples developed during previous years include spectrophotometric, chemiluminescence, electrochemical detection, chromatographic, capillary electrophoretic, spectrofluorimetric methods. Out of these, methods based on spectroscopic detection of nitrate have been discussed in this review article due to their easy availability, high sensitivity, low detection limit, economical and facile nature. Methods based on spectrophotometry, Raman Spectroscopy, IR and FTIR Spectroscopy, atomic absorption spectroscopy (AAS), fluorescence spectroscopy, chemiluminescence, mass spectroscopy, molecular emission cavity analysis (MECA), electron paramagnetic resonance spectrometry (EPR) and nuclear magnetic resonance spectroscopy (NMR) have been reviewed. The basic principle, detection limits, detection range, RSD%, sample throughput/h, advantages and disadvantages have been discussed.

Quantifying tropical peatland dissolved organic carbon (DOC) using UV-visible spectroscopy

UV-visible spectroscopy has been shown to be a useful technique for determining dissolved organic carbon (DOC) concentrations. However, at present we are unaware of any studies in the literature that have investigated the suitability of this approach for tropical DOC water samples from any tropical peatlands, although some work has been performed in other tropical environments. We used water samples from two oil palm estates in Sarawak, Malaysia to: i) investigate the suitability of both single and two-wavelength proxies for tropical DOC determination; ii) develop a calibration dataset and set of parameters to calculate DOC concentrations indirectly; iii) provide tropical researchers with guidance on the best spectrophotometric approaches to use in future analyses of DOC. Both single and two-wavelength model approaches performed well with no one model significantly outperforming the other. The predictive ability of the models suggests that UV-visible spectroscopy is both a viable and low cost method for rapidly analyzing DOC in water samples immediately post-collection, which can be important when working at remote field sites with access to only basic laboratory facilities.