Environmental Monitoring Methods Research Articles

New methodological approach for deep penetrating geochemistry and environmental studies, Part 1: on-site soil extraction of trace and rare earth elements

The use of on-site soil extractions for direct quantification provides a fast and efficient method for environmental monitoring and mineral prospecting. In this study, an on-site extraction protocol was developed and tested on northern boreal soils using partial extractants: 0.01 M calcium chloride (CaCl 2 ) and 0.43 M nitric acid (HNO 3 ). CaCl 2 is commonly used as an extractant for mobile and directly available elements, while 0.43 M HNO 3 is used to extract also the reactive pool with the weakly bound elements from soil particle surfaces. These two extractants were chosen because (i) they have no specific constraints for electrochemical or spectrometric analysis for element quantification and (ii) they are more environmentally friendly than the ones often used for mineral exploration, e.g. the aqua regia (AR) or mobile metal ions such as Ionic Leach™ (IL, ALS). Extractions with HNO 3 offered the same patterns as those obtained with AR but with better contrast for anomalies, while the CaCl 2 results were very similar to those obtained with IL. Nevertheless, neither HNO 3 nor CaCl 2 was effective in mobilizing Au, W or Sn due to their low concentrations. Our study demonstrated that on-site soil extractions with HNO 3 and CaCl 2 were potentially good candidates for the detection of REEs, such as Ce and La, and some trace elements, especially Cd, for both extractions, Co, Pb and U with HNO 3 , and Mn and Ni with CaCl 2 . This work highlights the potential of on-site extraction using partial extractants. This is a key step prior to the quantification of elements by on-site voltametric techniques.

Development of a chitosan-gold nanocomposite based disposable pencil graphite electrode for the selective detection of thiram in peach juice

ABSTRACT Hazardous chemicals that are mostly employed in modern agriculture for pest management have caused long-term environmental accumulation as well as major issues with food safety and human health. So, there is an urgent demand for efficient detection methods for environmental monitoring. To do this, the most contemporary electrochemical sensor technology is suggested. In this study, a new electrochemical sensor has been developed to detect thiram (Th) fungicide, utilising a modified activated pencil graphite electrode (a-PGE) made from chitosan-gold nanoparticle polymer (Ch-AuNPs) for the first time. The Ch-AuNPs/a-PGE surface has been characterised using cyclic voltammetry (CV), scanning electron microscopy (SEM), and energy dispersive X-ray spectroscopy (EDS). Following adjustments to experimental parameters, the proposed electrochemical sensor demonstrated a linear calibration curve within the range of 4.90–140 μM, displaying a rapid response to thiram with a detection limit of 1.15 μM. The sensor exhibited notable selectivity, stability, and reproducibility. To assess its practical utility, the sensor was applied to analyse thiram in peach juice, and the recovery process indicated its effectiveness as a practical, swift, and efficient tool in pesticide residue monitoring.

Answering the key stakeholder questions about the impact of offshore wind farms on marine life using hypothesis testing to inform targeted monitoring

Abstract Stakeholders need scientific advice on the environmental impacts of offshore wind (OW) before the facilities are installed. The utility of conventional environmental monitoring methods as a basis for forecasting OW impacts is limited because they do not explain the causes of the observed effects. We propose a multistep approach, based on process-oriented hypothesis testing, targeted monitoring and numerical modeling, to answer key stakeholder questions about planning an OW facility: Q1—Where do we place future OW farms so that impacts on the ecosystem are minimized? Q2—Which species and ecosystem processes will be impacted and to what degree? Q3—Can we mitigate impacts and, if so, how? and Q4—What are the risks of placing an OW facility in one location vs. another? Hypothesis testing can be used to assess impacts of OW facilities on target species-ecological process. This knowledge is transferable and is broadly applicable, a priori, to assess suitable locations for OW (Q1). Hypothesis testing can be combined with monitoring methods to guide targeted monitoring. The knowledge generated can identify the species/habitats at risk (Q2), help selecting/developing mitigation measures (Q3), and be used as input parameters for models to forecast OW impacts at a large spatial scale (Q1; Q4).

Physicochemical properties of water in the Nida valley, Poland

The investigation of Nida Valley water aimed to assess fluctuations in physicochemical properties. In this study, environmental monitoring method was utilized to evaluate the changes in physicochemical properties of water. Over a 24-month period, from June 2021 to May 2023, a total of 228 water samples were collected from 10 sampling sites, with a monthly sampling frequency. Statistical analyses were utilized including the Shapiro–Wilk test (α = 0.05), Kruskal–Wallis test and Wilcoxon (Mann–Whitney) rank sum test (α = 0.05), Pearson correlation analysis (α = 0.001) and principal component analysis (PCA). Statistical analyses revealed significant differences between months in GW samples for for temperature, dissolved oxygen, pH, total nitrogen, total phosphorus, chloride, manganese, and zinc in GW samples and for T and DO in SW samples. Pearson correlation coefficient analysis (α = 0.001) identified strong positive correlations within the SW dataset. Similarly, significant positive correlations were observed among the GW dataset. Noteworthy positive correlations were also detected between the GW and SW datasets. Principal component analysis (PCA) revealed a substantial dissimilarity between GW2 samples compared to others, characterized by elevated manganese, iron, and Sulfate content. Two distinct groups emerged: Group 1 included samples at GW1, GW2, GW3, GW5, and SW2, while Group 2 comprised all other samples. This study demonstrated the stability in the physicochemical properties of SW and underscore a discernible correlation between the hydrochemical compositions of both SW and GW in the riparian area. Outstanding characteristics in hydrochemical component of sample waters have been indicated.

Artificial Neural Networks for Mapping Coastal Lagoon of Chilika Lake, India, Using Earth Observation Data

This study presents the environmental mapping of the Chilika Lake coastal lagoon, India, using satellite images Landsat 8-9 OLI/TIRS processed using machine learning (ML) methods. The largest brackish water coastal lagoon in Asia, Chilika Lake, is a wetland of international importance included in the Ramsar site due to its rich biodiversity, productivity, and precious habitat for migrating birds and rare species. The vulnerable ecosystems of the Chilika Lagoon are subject to climate effects (monsoon effects) and anthropogenic activities (overexploitation through fishing and pollution by microplastics). Such environmental pressure results in the eutrophication of the lake, coastal erosion, fluctuations in size, and changes in land cover types in the surrounding landscapes. The habitat monitoring of the coastal lagoons is complex and difficult to implement with conventional Geographic Information System (GIS) methods. In particular, landscape variability, patch fragmentation, and landscape dynamics play a crucial role in environmental dynamics along the eastern coasts of the Bay of Bengal, which is strongly affected by the Indian monsoon system, which controls the precipitation pattern and ecosystem structure. To improve methods of environmental monitoring of coastal areas, this study employs the methods of ML and Artificial Neural Networks (ANNs), which present a powerful tool for computer vision, image classification, and analysis of Earth Observation (EO) data. Multispectral satellite data were processed by several ML image classification methods, including Random Forest (RF), Support Vector Machine (SVM), and the ANN-based MultiLayer Perceptron (MLP) Classifier. The results are compared and discussed. The ANN-based approach outperformed the other methods in terms of accuracy and precision of mapping. Ten land cover classes around the Chilika coastal lagoon were identified via spatio-temporal variations in land cover types from 2019 until 2024. This study provides ML-based maps implemented using Geographic Resources Analysis Support System (GRASS) GIS image analysis software and aims to support ML-based mapping approach of environmental processes over the Chilika Lake coastal lagoon, India.

Towards a Long-Term Unmanned Aerial Vehicle (UAV) Monitoring Framework for Post-Mining Effects: Prosper-Haniel Case

Direct and indirect effects after mine operations cease operating must ideally be subjected to perpetual monitoring routines in order to detect possible risks or avoid adverse effects on the surrounding ecosystems at an early stage. In this contribution, mining subsidence lakes created inside the nature reserve Kirchheller Heide and Hilsfeld Forest are subjected to analysis for a long-term monitoring scheme. For this purpose, we employ high-resolution unmanned aerial system (UAS)-based multispectral and thermal mapping tools to provide a fast, non-invasive and multitemporal environmental monitoring method. Specifically, we propose to monitor vegetation evolution through multispectral analysis, biotypes identification using machine learning algorithms, and water surface extent detection, together with their thermal behavior. The aim of this contribution is to present the proposed workflow and first results to establish a baseline for future analyses and subsequent surveys for long-term multi-temporal monitoring.

En-WBF: A Novel Ensemble Learning Approach to Wastewater Quality Prediction Based on Weighted BoostForest

With the development of urbanization, the accurate prediction of effluent quality has become increasingly critical for the real-time control of wastewater treatment processes. The conventional method for measuring effluent biochemical oxygen demand (BOD) suffers from significant time delays and high equipment costs, making it less feasible for timely effluent quality assessment. To tackle this problem, we propose a novel approach called En-WBF (ensemble learning based on weighted BoostForest) to predict effluent BOD in a soft-sensing manner. Specifically, we sampled several independent subsets from the original training set by weighted bootstrap aggregation to train a series of gradient BoostTrees as the base models. Then, the predicted effluent BOD was derived by weighting the base models to produce the final prediction. Experiments on real datasets demonstrated that on the UCI dataset, the proposed En-WBF approach achieved a series of improvements, including by 28.4% in the MAE, 40.9% in the MAPE, 29.8% in the MSE, 18.2% in the RMSE, and 2.3% in the R2. On the Fangzhuang dataset, the proposed En-WBF approach achieved a series of improvements, including by 8.8% in the MAE, 9.0% in the MAPE, 12.8% in the MSE, 6.6% in the RMSE, and 1.5% in the R2. This paper contributes a cost-effective and timely solution for wastewater treatment management in real practice with a more accurate effluent BOD prediction, validating the research in the application of ensemble learning methods for environmental monitoring and management.

An Update in Computational Methods for Environmental Monitoring: Theoretical Evaluation of the Molecular and Electronic Structures of Natural Pigment-Metal Complexes.

Metals are beneficial to life, but the presence of these elements in excessive amounts can harm both organisms and the environment; therefore, detecting the presence of metals is essential. Currently, metal detection methods employ powerful instrumental techniques that require a lot of time and money. Hence, the development of efficient and effective metal indicators is essential. Several synthetic metal detectors have been made, but due to their risk of harm, the use of natural pigments is considered a potential alternative. Experiments are needed for their development, but they are expensive and time-consuming. This review explores various computational methods and approaches that can be used to investigate metal-pigment interactions because choosing the right methods and approaches will affect the reliability of the results. The results show that quantum mechanical methods (ab initio, density functional theory, and semiempirical approaches) and molecular dynamics simulations have been used. Among the available methods, the density functional theory approach with the B3LYP functional and the LANL2DZ ECP and basis set is the most promising combination due to its good accuracy and cost-effectiveness. Various experimental studies were also in good agreement with the results of computational methods. However, deeper analysis still needs to be carried out to find the best combination of functions and basis sets.

Advancing microplastic surveillance through photoacoustic imaging and deep learning techniques

Microplastic contamination presents a significant global environmental threat, yet scientific understanding of its morphological distribution within ecosystems remains limited. This study introduces a pioneering method for comprehensive microplastic assessment and environmental monitoring, integrating photoacoustic imaging and advanced deep learning techniques. Rigorous curation of diverse microplastic datasets enhances model training, yielding a high-resolution imaging dataset focused on shape-based discrimination. The introduction of the Vector-Quantized Variational Auto Encoder (VQVAE2) deep learning model signifies a substantial advancement, demonstrating exceptional proficiency in image dimensionality reduction and clustering. Furthermore, the utilization of Vector Quantization Microplastic Photoacoustic imaging (VQMPA) with a proxy task before decoding enhances feature extraction, enabling simultaneous microplastic analysis and discrimination. Despite inherent limitations, this study lays a robust foundation for future research, suggesting avenues for enhancing microplastic identification precision through expanded sample sizes and complementary methodologies like spectroscopy. In conclusion, this innovative approach not only advances microplastic monitoring but also provides valuable insights for future environmental investigations, highlighting the potential of photoacoustic imaging and deep learning in bolstering sustainable environmental monitoring efforts.

A novel molecularly imprinted photoelectrochemical sensor utilizing Ag/SnO2 for the highly sensitive and selective detection of 2,4-dichlorophenoxyacetic acid

The pervasive use of herbicides in agriculture has necessitated the development of sensitive and selective detection methods for environmental monitoring. In this work, a novel photoelectrochemical sensor based on molecular imprinting technology was engineered to specifically detect 2,4-dichlorophenoxyacetic acid (2,4-D) using an Ag/SnO2 composite. The SnO2 was chosen as the photoelectric material due to its abundant surface defects, excellent thermal stability, and high electron carrier density, which are essential for enhanced photoelectrochemical reactions. The introduction of Ag to the SnO2 matrix exploited the surface plasmon resonance effect to bridge its wide band gap, thereby improving the photoelectric conversion efficiency. This Ag/SnO2 composite was then deposited onto an indium tin oxide (ITO) substrate to utilize its high conductivity and optical transparency, followed by the strategic layering of molecularly imprinted polymers (MIPs) that offer selective recognition sites for 2,4-D. To ensure the robustness of the sensor, a Nafion solution was meticulously applied dropwise, which served to immobilize the MIPs/Ag/SnO2 complex on the ITO surface and enhance the sensor's durability. Under optimized conditions, the sensor exhibited a broad linear detection range from 3 × 10-12 to 3 × 10-5 mol/L and a low detection limit of 1.13 × 10-12 mol/L (S/N = 3). The sensor also demonstrated exceptional performance in the analysis of 2,4-D in complex matrices, such as milk, tap water, and beef, highlighting its potential for practical applications in the accurate monitoring of herbicide contamination. These findings affirm the sensor's superior photoelectrochemical properties and introduce an innovative method for the sensitive detection of 2,4-D in environmental and food samples.

Fuel Burn Method Assessment Using Automatic Dependent Surveillance–Broadcast and European Reanalysis Data: Limited Flight Sample Analysis

Fuel burn during the actual route flown is an important indicator of aircraft operational efficiency. This study aims to assess and systematically evaluate the method for fuel consumed during flights using data from the automatic dependent surveillance–broadcast (ADS-B), European reanalysis (ERA5) meteorological dataset, and BADA 3 performance. A literature background and comprehensive methodology are provided for fuel estimation using track data. The airborne part of the trajectory was used to estimate the total trip fuel consumed during several flights of a commercial airliner. The calculated fuel burn is compared with measured fuel consumption from the flight data recorder (FDR). The results show that fuel consumption for the entire airborne part of the trajectory can be estimated with an average error of 1.2% and with a standard deviation of 1.3%. Detailed results of fuel burn for individual flight phases, from the initial climb to the approach, are also presented. In addition, this paper also discusses the sources of errors and the potential applications of the method for network operations and environmental monitoring.

Hemin/G-quadruplex functionalized porphyrin-based MOF nanozymes and their application in mediating long-lasting chemiluminescence optical fiber aptasensor for sensitive and accurate detection of ricin

A novel hemin/G-quadruplex functionalized porphyrin-based MOF nanozyme (HGP-PCN) was firstly synthesized and employed to fabricate a chemiluminescence (CL) assay strategy for ricin detection. Porphyrin-based MOFs with large specific area could fully expose their catalytic centers, provide functional interface to load hemin/G-quadruplex, and then generate “double catalytic activity amplification”. The catalytic mechanism of HGP-PCN was thoroughly illustrated by chemical characterizations and theoretical calculations. The continuous generation of •OH, O2•−, and 1O2 from H2O2 catalyzed by Fe-N4 active sites on the HGP-PCN nanozyme could induce long-lasting and stable CL signals, which is helpful to fabricate sensitive and accurate CL sensing platform. The HGP-PCN nanozymes mediated long-lasting CL optical fiber aptasensor (CN-LCOFA) was fabricated to quantify ricin. The ricin toxic B (RTB) chain aptamers labelled HGP-PCN (Apt-HGP-PCN) was used for signaling while functionalized optical fiber probe was used as biorecognition interface. The CN-LCOFA could ultra-sensitively detect ricin within 23 min, the dynamic linear range was 4.1–185.0 ng/mL, and the LOD was 1.28 ng/mL. The analytical performance of CN-LCOFA including detection time, detection range and LOD was significantly superior to the ricin detection methods that reported in currently literatures. The detection accuracy of CN-LCOFA for ricin spiked in human serum was comparable with that of HPLC-MS. This novel nanozyme-catalyzed long-lasting CL system and ultrasensitive assay strategy could provide effective methods for food safety and environmental monitoring.

Innovative Environmental Monitoring Methods Using Multispectral UAV and Satellite Data

Innovative Environmental Monitoring Methods Using Multispectral UAV and Satellite Data

Spatiotemporal patterns and evolutionary trends of eco-environmental quality in arid regions of Northwest China.

The arid regions of northwest China suffer from water shortages, low land quality, and a fragile ecological environment, while social and economic development has increased the ecological and environmental load. The spatiotemporal pattern and evolutionary trend of ecological environmental quality were investigated by constructing a remote sensing-based ecological environmental index (EQI) evaluation model incorporating four indicators: drought index (DI), soil erosion index (SEI), greenness index (GI), and carbon exchange index (CEI). The study found that between 2001 and 2020, the DI, the SEI, and the CEI in the northwest arid region exhibited a downward trend with reduction rates of - 3e-05, -0.0006, and -0.0018, respectively. However, the GI demonstrated an upward trend, with a growth rate of 0.002. The average EQI in 2020 was 0.315, indicating a fair grade, with only 11.56% falling above the medium level. A general increasing trend was observed throughout the study period in EQI, with an incremental rate of 0.0002. Areas with future improvements in EQI accounted for 57.547% and were principally located in the eastern part of Inner Mongolia, Qinghai, and the northern and southern portions of Xinjiang. Notably, land use was significantly correlated with EQI (p < 0.01), with a hierarchy of effects that ran: forest land (0.678) > cultivated land (0.422) > grassland (0.382) > wasteland (0.138). The highly robust findings presented here offer innovative methods for ecological and environmental monitoring in the arid region of the northwest, with potential implications at an international scale.

Analysis of Alternative Methods of Environmental Monitoring for Listeria in Food Production Facilities

Validated alternative test methodologies may be used in place of culture-based methods recommended for environmental monitoring programs (EMPs) for Listeria in food production facilities. In order to help guide decisions on which testing method to use to simplify Listeria EMP implementation in food production facilities, alternative methods were compared to the culture-based method in actual EMPs for Listeria. Seventy-two samples collected from two facilities of souzai production businesses that use meat and meat products as ingredients, one facility of processed meat product production business, and one facility of processed meat product and souzai production business were applied to EMPs for Listeria using the culture-based method, 3MTM Molecular Detection System (MDS), and InSite L. mono Glo (InSite). The kappa coefficient in MDS was 0.65 for Listeria monocytogenes and 0.74 for Listeria spp., both of which were deemed substantial compared with the culture-based method. The kappa coefficient in InSite was −0.01 for L. monocytogenes and 0.50 for Listeria spp., which indicated poor and moderate reproducibility, respectively. When the medium of InSite was smeared on agar medium, 7 of the 19 samples tested positive only for Listeria spp. (negative for L. monocytogenes) but L. monocytogenes was cultured, indicating that the sensitivity of detecting L. monocytogenes via fluorescence may be low. MDS was considered a useful alternative for both L. monocytogenes and Listeria spp. as targets, and InSite was not possible as a substitute for detecting L. monocytogenes; however, it is considered a helpful alternative method for detecting Listeria spp. EMPs for Listeria often target Listeria spp. as an indicator of L. monocytogenes. The alternative methods studied in this study are rapid, simple, and useful in EMPs for Listeria. However, the data in this study were a comparatively small sample set and impacted by variability, so more robust comparisons are desirable in the future.

Hollow CoZnSe@CN nanocage with enzymatic activity for determination of tetracycline using smartphone platforms and virtual reality revealing.

Antibiotic residues in the environment pose a serious threat to ecosystems and human health. Therefore, it is important to develop sensitive and rapid in situ detection methods. In this work, the designed nanozymes, with excellent four enzyme activities, were proved to be constituted ofunique hollow nanocage structures (CoZnSe@CN HCs). Based on the peroxidase-like enzymes, a portable colorimetric sensor was constructed for the on-site determinationof tetracycline (TC) in real samples. The linear range of TC detection was 0.1-100μM, and the detection limit was 0.02μM. At the same time, colorimetric detection and smartphones have also been combined for on-site colorimetric detection of TC. In-depth exploration of the detection mechanism showed that TC could be bound with the material, inhibiting the production of oxidized 3,3',5,5'-tetramethylbenzidine. The sensor was also used for the detection of TC in environmental soil and water samples. This study can provide an intelligent detection method for environmental monitoring.

An Environmental Monitoring Method of Ancient Buildings Based on the Micro Electro Mechanical System Accelerometers

In order to evaluate and compare the performance of MEMS accelerometers for the environmental monitoring of ancient buildings, four MEMS accelerometers of different types and a G1B-type force-balanced accelerometer are selected and installed on the same location in an ancient building for field testing. The ambient environmental monitoring of the building recorded by the MEMS accelerometers is compared with the G1B accelerometer in terms of time history, Fourier spectrum, and modal frequency identification. In addition, the earthquake-induced structural responses of the ancient building recorded by MEMS-I type accelerometer is compared with those recorded by the G1B accelerometer in both time and frequency domain. The test results show that the noise levels of the four MEMS accelerometers are all higher than that of the G1B accelerometer. Among them, the MEMS-I type accelerometer has a relatively lower level of sensor noise and achieves a good match with the G1B accelerometer in terms of modal frequency identification and earthquake-induced structural environmental measurement, which proves that the MEMS-I type accelerometer can record the strong structural vibration with acceptable accuracy and is feasible for daily environmental monitoring of the ancient buildings.

Application of X-ray Fluorescence and Radiation Methods for the Analysis of Medicinal Plant Raw Materials on the Example of &lt;i&gt;Achilleae millefolii herba&lt;/i&gt;

Introduction. Environmental monitoring is an important area of modern scientific research. This is due to the anthropogenic impact on the environment, which often negatively affects the diversity of the Russian medicinal flora. The use of modern instrumental analytical methods of medicinal plant raw materials is relevant due to the intensive development of science. The content of heavy metals, the elemental composition and the level of background radiation are important characteristics of ecological purity and safety of medicinal plant raw materials. These characteristics are of interest for assessing the possibility of harvesting and using medicinal plants at localization. The generally accepted instructions for harvesting were used.Aim. Investigation of the possibilities of using X-ray fluorescence and radiation methods for environmental monitoring of medicinal plant raw materials.Materials and methods. The research object was Achilleae millefolii herba. The harvestment of plant raw materials was carried out in the Bardymsky, Chernushinsky, Uinsky, Kishertsky, Oktyabrsky, Suksunsky, Ordinsky, Sivinsky districts of the Perm Region in the summer in 2022–2023. The X-ray fluorescence analyzer (XFA) ARL QUANT'X EDXRF was used to determine heavy metals and assess the elemental composition of plants. The analysis was carried out according to the methodology that was developed by scientists of the Pharmacognosy Department at the Perm State Pharmaceutical Academy. The radiation background in raw materials was assessed by using a special dosimeter RADEX RD1503.Results and discussion. As a result, it was found that harvesting of Achilleae millefolii herba is possible in the Perm Region studied areas. Bardymsky, Uinsky, Kishertsky, Ordinsky, Chernushinsky districts are the most recommended for harvesting plant raw materials. Samples from these areas are ecological purity and safe for use as medicinal raw materials and have a diverse elemental composition. The content of heavy metals does not exceed the maximum permissible concentrations according to the regulatory documentation requirements. The background radiation level does not exceed the threshold value compared to other areas in the Russian Federation.Conclusion. X-ray fluorescence analysis is a suitable and reliable analytical method for determining elements in plant raw materials. XFA is a multi-element express method that allows analyzing a sample with minimal sample preparation. The advantages of the method are good reproducibility, high sensitivity, and efficiency. Research continues.

Nanoferrites in photocatalytic wastewater treatment: Advancements, characterization, and environmental implications

Water is the fundamental and indispensable component of every organism on earth.Yet, as a consequence of urbanization and industrialization; sources of clean water have been polluted with dyes that are unsafe to aquatic creatures. Nanoferrites is a photocatalytic material having an incrediblenarrow band gap, physico-chemical equilibrium, a large porosity, excellent separation of carrier’s effectiveness, and paramagnetism, making it easily recoverable. As a result, ferrites act asarole of photocatalyst for wastewater treatment. In this review, the photocatalytic degradation of the dyes using nanoferrites as a host, bimetal, trimetal with doped and composite forms is covered. Characterizations including XRD, FT-IR, SEM, EDAX, TEM, BET, UV-DRS, TGA and Raman spectroscopy are used for investigation and confirm the development and design of nanoferrites. The synthesis of nanoferrites, implemented in photocatalytic research demonstrated their improved ability to degrade cancer-causing dyes and their ability to be recycled for additional cycles of examination has been summarized. Photocatalysis isasimple and ecologically beneficial processprovides the most recent and beneficial concept for cleaning wastewater using specifically manufactured nanoparticles. The fundamental objective of biological approaches, green nanoparticle manufacturing, multiple chemical andgreen synthesis strategies, the benefits of greener chemistry, and its usage in photocatalysis for the degradation of industrial dyes are all addressed in this review article. Overall, this study looks at the manner in which photocatalysis research may be advanced by employing ferritesynthesized using chemical andgreen methods for better environmental monitoring and zero energy waste.Approaches for modification have been emphasized, including elemental doping, composite synthesizing, and morphological modification. By using these modification techniques, ferrite's catalytic activity for the photocatalytic breakdown of organic pollutants in water has been enhanced. The manufacture scale of nanoferrites utilizing inexpensive, energy-efficient, and environmentally conscious methods are referred to as chemical synthesis and biological synthesis in which they are recognized as a potential synthesis approach that employs diverse chemical and biological ingredients respectively. Future study will focus significantly on the combination of various modification strategies to improve ferrites' photocatalytic activity.

Organisms as sensors in biohybrid entities as a novel tool for in-field aquatic monitoring

Rapidly intensifying global warming and water pollution calls for more efficient and continuous environmental monitoring methods. Biohybrid systems connect mechatronic components to living organisms and this approach can be used to extract data from the organisms. Compared to conventional monitoring methods, they allow for a broader data collection over long periods, minimizing the need for sampling processes and human labour. We aim to develop a methodology for creating various bioinspired entities, here referred to as ‘biohybrids’, designed for long-term aquatic monitoring. Here, we test several aspects of the development of the biohybrid entity: autonomous power source, lifeform integration and partial biodegradability. An autonomous power source was supplied by microbial fuel cells which exploit electron flows from microbial metabolic processes in the sediments. Here, we show that by stacking multiple cells, sufficient power can be supplied. We integrated lifeforms into the developed bioinspired entity which includes organisms such as the zebra mussel Dreissena polymorpha and water flea Daphnia spp. The setups developed allowed for observing their stress behaviours. Through this, we can monitor changes in the environment in a continuous manner. The further development of this approach will allow for extensive, long-term aquatic data collection and create an early-warning monitoring system.