Unraveling the Phosphorus Adsorption Mechanisms in Three-dimensional Reduced Graphene Oxide Materials

To prevent eutrophication, controlling the phosphate concentration levels is one of the most important issues in surface water management. One of the most utilized methods is phosphate adsorption. However, its application faces a bottleneck due to the unclear understanding of adsorption and interaction mechanisms. The present work unlocks the phosphorus adsorption mechanisms in three-dimensional reduced graphene oxide with different reduction levels and pore sizes to remove phosphate from water using experiments and multiscale simulations. Experiments were performed to evaluate the influence of pH, ionic strength, and temperature on the adsorption. Molecular Dynamics and Ab Initio simulations evaluated the influence of the pore size and oxidation degrees of the materials. We show that the adsorption capacity of the materials increases with increasing pH and ionic strength and decreasing temperature. It is observed that the more oxidized the material and the less compact the structure, the better the adsorption. These results are theoretically explained in terms of the interaction of functional groups and the clustering of phosphate ions, which results in better adsorption in materials with larger pores. The underlying mechanisms for the 3D-reduced graphene oxide performance were confirmed by spectroscopy analysis. All the results show that 3D-reduced graphene oxide can sorb phosphate in different complex water remediation systems with characteristics that can be modulated by changing the material synthesis method.

HUM: A review of hydrochemical analysis using ultraviolet-visible absorption spectroscopy and machine learning

There is a need to develop improved methods for water quality analysis. Traditionally, water quality analysis is performed in a laboratory on discrete samples or in the field with simple sensors, but these methods have inherent limitations. Ultraviolet-visible absorption spectroscopy (UVAS) is a commonly used laboratory technique for water quality analysis and is being applied more broadly in combination with machine learning (ML) to allow for the detection of multiple analytes without sample pretreatments. This methodology (referred to here as Hydrochemical analysis using Ultraviolet-visible absorption spectroscopy and Machine learning; 'HUM') can be applied in the laboratory or in situ while requiring less time, labor, and materials compared to traditional laboratory analysis. HUM has been used for the quantification of a variety of chemicals in a variety of settings, but information is lacking related to instrumental setup, sample requirements, and data analysis procedures. For instance, there is a need to investigate the influence of spectral parameters (e.g., sensitivity, signal-to-noise ratio, and spectral resolution) on measurement error. There is also a lack of research aimed at developing ML algorithms specifically for HUM. Finally, there are emerging concepts such as sensor fusion and model-sensor fusion which have been applied to similar fields but are not common in studies involving HUM. This review suggests the need for further studies to better understand the factors that influence HUM measurement accuracy along with the need for hardware and software developments so that the methodology can ultimately become more robust and standardized. This, in turn, could increase its adoption in both academic and non-academic settings. Once the HUM methodology has matured, it could help to reduce the environmental impacts of society by improving our understanding and management of environmental systems through high-frequency data collection and automated control of water quality in environmentally relevant systems.

A rapid and simplified method for evaluating the performance of fungi-algae pellets: A hierarchical analysis model

Microbial pellets technology has undergone extensive research recently and has increasingly matured, showing significant promise. However, the performance of microbial pellets cannot be predicted quickly by the current evaluating methods because they are complicated to operate, take a long time, and pose a risk to the environment. In this study, a representative microbial pellet, fungi-algae pellet, was selected as the research object. Eight evaluation parameters and four evaluation indices were chosen to construct the performance evaluation system of the fungal-algal pellets using the analytic hierarchy process (AHP) and weighting method. Combining the correlation analysis and expert opinion, we found that among the eight parameters selected, the adsorption saturation rate of mycelial pellets on algae had the most significant influence weight on the performance of fungi-algae pellet, followed by algal culture time and fungal incubation time. This research proposes and validates the Performance Evaluation Value (PEV) of fungi-algae pellet and its calculation method. We also discuss the effectiveness of this new evaluation system in saving time, cost, and emission reductions. The results of this paper enable the rapid evaluation of fungi-algae pellets and promote the better development of fungi-algae pellets technology and even other multi-microbial symbiotic pellet technologies.

Effects of Floods on Zooplankton Community Structure in the Huayanghe Lake

Floods can change the physicochemical factors of the water body and the zooplankton community. In the summer of 2020, Huayanghe Lake experienced floods. Here, eight cruises were conducted in Huayanghe Lake from 2020 to 2022 to study the response of environmental factors and the zooplankton community to the floods. The results demonstrated that floods increased the concentrations of total nitrogen, total phosphorus and chlorophyll a. In addition, during the floods, the number of rotifer species increased, while the number of cladoceran and copepod species decreased. Floods also reduced the average density and biomass of zooplankton. The results of Pearson correlation analysis and redundancy analysis showed that environmental factors, such as water depth, water temperature, transparency, nitrogen and phosphorus concentration, conductivity, coverage of aquatic vegetation and chlorophyll a, were closely related to the seasonal dynamics of zooplankton in Huayanghe Lake. Our research emphasizes that zooplankton can quickly respond to floods, providing data support for the ecological relationship between flooding and the zooplankton community, which is crucial for the preservation and restoration of the lake water ecosystem.

Optofluidic Platform for Rapid On-Chip Analysis of Total Phosphorus in Surface Water Using Absorption Spectrometry

Optofluidic devices are of high interest for online monitoring and analyzing biochemical targets in water by integrating the complex on-chip pretreatment of target analytes and spectral analysis. Compared with the traditional bulk equipment, versatile optical detection and biochemical analysis are more easily integrated on an optofluidic chip, which promotes the development of on-chip real-time rapid detection and monitoring. Here, we report an optofluidic platform for online monitoring total phosphorous in water by absorption spectrometry, which naturally combines the merits of both the photo-Fenton effect and microfluidics to realize the rapid on-chip digestion of phosphate at room temperature and normal pressure. The functional cells for chromogenic reaction and optical absorption detection are, respectively, fabricated on the platform to analyze the content of total phosphorus in surface water. In the experiment, the on-chip digestion time of phosphate is dramatically declined to 8.6 sec, and thus, the detection time is greatly shortened to a few minutes. The detection range of total phosphorus is demonstrated as 0.005-1.00 mg L^-1, which satisfies the detection requirements of most environmental water samples. Its availability for measuring the total phosphorous in real water samples is also verified. Predictably, this platform is adapted to on-chip analysis of many other biochemical targets in water.

Advances in Technological Research for Online and In Situ Water Quality Monitoring—A Review

Monitoring water quality is an essential tool for the control of pollutants and pathogens that can cause damage to the environment and human health. However, water quality analysis is usually performed in laboratory environments, often with the use of high-cost equipment and qualified professionals. With the progress of nanotechnology and the advance in engineering materials, several studies have shown, in recent years, the development of technologies aimed at monitoring water quality, with the ability to reduce the costs of analysis and accelerate the achievement of results for management and decision-making. In this work, a review was carried out on several low-cost developed technologies and applied in situ for water quality monitoring. Thus, new alternative technologies for the main physical (color, temperature, and turbidity), chemical (chlorine, fluorine, phosphorus, metals, nitrogen, dissolved oxygen, pH, and oxidation–reduction potential), and biological (total coliforms, Escherichia coli, algae, and cyanobacteria) water quality parameters were described. It was observed that there has been an increase in the number of publications related to the topic in recent years, mainly since 2012, with 641 studies being published in 2021. The main new technologies developed are based on optical or electrochemical sensors, however, due to the recent development of these technologies, more robust analyses and evaluations in real conditions are essential to guarantee the precision and repeatability of the methods, especially when it is desirable to compare the values with government regulatory standards.

Cost-effective phosphorus removal from aqueous solution by a chitosan/lanthanum hydrogel bead: Material development, characterization of uptake process and investigation of mechanisms

Excessive phosphorus is one of the main reasons leading to eutrophication that causes severe ecosystem imbalance and negative human health impacts. In this study, several chitosan (CS)/lanthanum (La) hydrogel beads were first synthesized and tested for phosphorus removal. The stable cross-linked CS/La hydrogel bead prepared with the optimized conditions of 10 wt% La/CS and 1.5 mL of 5% glutaraldehyde demonstrated exceptional performance in the removal. It removed phosphate effectively from an aqueous solution in the pH range from 2 to 7. The complete phosphate uptake was achieved at contact time of 6 h under the completely mixing batch condition. The experimental maximum adsorption capacity of 107.7 mg g^-1 was observed at solution pH 4. The phosphate adsorption was well described by the Freundlich isotherm and the intraparticle surface diffusion model. Furthermore, the adsorbent was effectively regenerated and reused in a five-cycle adsorption-desorption operation. The removal of phosphate can be attributed to electrostatic attraction and ion exchange. Moreover, the bead was capable of removing heavy metals: copper, zinc and lead. This adsorbent may be served as a cost-effective material for the treatment of phosphorus-contaminated water so as to minimize the occurrence of eutrophication.

Miniaturized Portable Total Phosphorus Analysis Device Based on Photocatalytic Reaction for the Prevention of Eutrophication

Phosphorus (P) is one of the most important elements in the aquatic ecosystem, but its overuse causes eutrophication, which is a serious issue worldwide. In this study, we developed a miniaturized portable total phosphorus (TP) analysis device by integrating a TP sensor with a photocatalyst to pretreat analyte and optical components (LED and photodetector) to measure the absorbance of the blue-colored analyte for real-time TP monitoring and prevention of eutrophication. The size of the miniaturized portable TP analysis device is about 10.5 cm × 9.5 cm × 8 cm. Analyte-containing phosphorus was pretreated and colored blue by colorizing agent as a function of the phosphorus concentration. Absorbance of the blue-colored analyte was estimated by the LED and the photodetector such that the phosphorus concentration was quantitatively measured. This device can obtain a wide linear response range from 0.5 mg/L to 2.0 mg/L (R2 = 0.97381), and its performance can be improved by increasing the intensity of the UV light emitted from the LED array. Consequently, the performance of this miniaturized portable TP analysis device was found to be similar to that of a conventional TP analysis system; thus, it can be used in automated in situ TP analysis.

Consistency of Suspended Particulate Matter Concentration in Turbid Water Retrieved from Sentinel-2 MSI and Landsat-8 OLI Sensors

Research on the consistency of suspended particulate matter (SPM) concentration retrieved from multisource satellite sensors can serve as long-time monitoring of water quality. To explore the influence of the atmospheric correction (AC) algorithm and the retrieval model on the consistency of the SPM concentration values, Landsat 8 Operational Land Imager (OLI) and Sentinel 2 MultiSpectral Imager (MSI) images acquired on the same day are used to compare the remote sensing reflectance (Rrs) SPM retrieval values in two high-turbidity lakes. An SPM retrieval model for Shengjin Lake is established based on field measurements and applied to OLI and MSI images: two SPM concentration products are highly consistent (R2 = 0.93, Root Mean Squared Error (RMSE) = 20.67 mg/L, Mean Absolute Percentage Error (MAPE) = 6.59%), and the desired results are also obtained in Chaohu Lake. Among the four AC algorithms (Management Unit of the North Seas Mathematical Models (MUMM), Atmospheric Correction for OLI'lite'(ACOLITE), Second Simulation of Satellite Signal in the Solar Spectrum (6S), Landsat 8 Surface Reflectance Code & Sen2cor (LaSRC & Sen2cor)), the two Rrs products, as well as the final SPM concentration products retrieved from OLI and MSI images, have the best consistency when using the MUMM algorithm in SeaWIFS Data Analyst System (SeaDAS) software. The consistency of SPM concentration values retrieved from OLI and MSI images using the same model or same form of models is significantly better than that retrieved by applying the optimal models with different forms.

Recent Advancement of the Sensors for Monitoring the Water Quality Parameters in Smart Fisheries Farming

Water quality is the most critical factor affecting fish health and performance in aquaculture production systems. Fish life is mostly dependent on the water fishes live in for all their needs. Therefore, it is essential to have a clear understanding of the water quality requirements of the fish. This research discusses the critical water parameters (temperature, pH, nitrate, phosphate, calcium, magnesium, and dissolved oxygen (DO)) for fisheries and reviews the existing sensors to detect those parameters. Moreover, this paper proposes a prospective solution for smart fisheries that will help to monitor water quality factors, make decisions based on the collected data, and adapt more quickly to changing conditions.

Turn-on detection of assorted phosphates by luminescent chemosensors

This review illustrates a variety of luminescent chemosensors for the selective detection of assorted phosphates via the “Turn-On” emission mechanism with focus on their design aspects, chemical structures and sensing mechanism.