Aquatic Environmental Monitoring Research Articles

Metabarcoding of Fish Larvae in the Merbok River Reveals Species Diversity and Distribution Along its Mangrove Environment.

The Merbok River (north-west of Peninsular Malaysia) is a mangrove estuary that provides habitat for over 100 species of fish, which are economically and ecologically important. Threats such as habitat loss and overfishing are becoming a great concern for fisheries conservation and management. The identification of larval fish in this estuarine system is important to complement information on the adults. This is because the data could inform the spawning behaviour, reproductive biology, selection of nursery grounds and migration route of fish. Such information is invaluable for fisheries and aquatic environmental monitoring, and thus for their conservation and management. However, identifying fish larvae is a challenging task based only on morphology and even traditional DNA barcoding. To address this, DNA metabarcoding was utilised to detect the diversity of fish in the Merbok River. To complete the study, the fish larvae were collected at six sampling sites of the river. The extracted larval DNA was amplified for the Cytochrome Oxidase subunit 1 (COI) and 12S ribosomal RNA (12S rRNA) genes based on the metabarcoding approach using shotgun sequencing on the next-generation sequencing (NGS) Illumina MiSeq platform. Eighty-nine species from 65 genera and 41 families were detected, with Oryzias javanicus, Oryzias dancena, Lutjanus argentimaculatus and Lutjanus malabaricus among the most common species. The lower diversity observed from previous morphological studies is suggested to be mainly due to seasonal variation over the sampling period between the two methods and limited 12S rRNA sequences in current databases. The metabarcode data and a validation Sanger sequencing step using 15 species-specific primer pairs detected three species in common: Oryzias javanicus, Decapterus maruadsi and Pennahia macrocephalus. Several discrepancies observed between the two molecular approaches could be attributed to contaminants during sampling and DNA extraction, which could mask the presence of target species, especially when DNA from the contaminants is more abundant than the target organisms. In conclusion, this rapid and cost-effective identification method using DNA metabarcoding allowed the detection of numerous fish species from bulk larval samples in the Merbok River. This method can be applied to other sites and other organisms of interest.

Materials, Methods, and Experiments in the Study of Black Sea Algal Viruses

Major studies of the Black Sea algal viruses in Russia have been conducted at scientific institutes located in the city of Sevastopol. A review of the methodological approaches used during 2002–2019 shows that both classical virological methods and methods that were recently developed and patented (among them those by the authors) are applicable to the search for, isolation, and study of algal viruses. The study of samples of clinical material and material of aquatic organisms, as well as model experiments, including those using a unique laboratory apparatus and a test stand, revealed new data on the biology and ecology of marine viruses. Based on the results from these studies, it was proposed to use algal viruses monitoring as a component in aquatic environment monitoring, in which algal viruses are considered as biological indicators and/or markers.

Разработка метода лазерной искровой спектроскопии для задач геологоразведки и экологического мониторинга водных сред в режиме реального времени

Разработка метода лазерной искровой спектроскопии для задач геологоразведки и экологического мониторинга водных сред в режиме реального времени

Prediction of dissolved oxygen concentration in aquatic systems based on transfer learning

Prediction of dissolved oxygen (DO) concentration pattern is important for aquatic system management and environmental monitoring. The large amounts of experimental data needed often limit the ability to develop a reliable DO prediction model for a given aquatic system. In this research, deep learning and transfer learning techniques were applied to take advantage of a large available dataset for one aquatic system in predicting DO concentration trend in another (target) system for the first time. A pre-training DO prediction model incorporating deep learning algorithms of ResNets, BiLSTM, and Attention was established based on the large dataset. The knowledge obtained and retained by the pre-training model was then transferred to develop a DO prediction model for the target system with a much smaller amount of available data. To show the benefits of transfer learning, a DO prediction model of the same structure was developed for the target system with its own data without transfer learning from the first system. The root mean square error (RMSE), mean absolute percentage error (MAPE), coefficient of determination (R2), index of agreement (d), and Nash-Sutcliffe efficiency coefficient (NSE) were used to measure the performance of the models. The results showed that the model structure used was useful in learning and retaining knowledge from the first system. In terms of all performance measures, transfer learning improved DO time series prediction for the target aquatic system and allowed development of a prediction model for the target system without a large set of measured data.

Triboelectric Nanogenerator Powered Electrowetting-on-Dielectric Actuator for Concealed Aquatic Microbots.

Aquatic microbots have drawn great research interest due to the demands in aquatic environmental monitoring, inspection, and confined space exploration. Current actuation methods heavily rely on mechanical motion powered by large-amplitude and high-frequency sources, which limit the applications with portability and concealment requirements. Herein we propose a triboelectric nanogenerator (TENG)-enabled electrowetting-on-dielectric (EWOD) actuator (TENG-EWA) for aquatic microbots. The transferred tribo-charges of a disc TENG alternatively modify the surface energy of the EWOD actuator, yielding a capillary wave propagation. The reaction force of the capillary wave actuates the microbot on the water surface. The characteristics of the TENG induced capillary wave are analyzed experimentally and modeled theoretically. An optical transparent microbot (weight of 0.07 g, body length of 1 cm) was actuated forward at a maximum locomotion velocity of 1 cm/s. Diverse locomotion functions are demonstrated: with a load of 3 times to the robot net weight, in seawater, at a silicone-oil/deionized water interface. Besides, the locomotion of the microbot was demonstrated by a wind-driven TENG, and a good concealment performance was achieved under infrared camera and decibel meter. The proposed aquatic TENG-Bot not only shows the potential of converting environmental energy into actuation force for microbots but also reveals advantages in optical, sonic, and infrared concealment.

Experimental evidence on the effects of temperature and salinity in morphological traits of the Microcystis aeruginosa complex

Microcystis aeruginosa complex (MAC) encompasses noxious colonial bloom forming cyanobacteria. MAC representatives bloom in eutrophic freshwater and brackish ecosystems with stagnant water, were temperature and salinity are the main variables modulating their distribution, biomass and toxicity. Cell abundance and biovolume of MAC colonies define regulatory standards for public health. These variables depend upon colony size that in turn changes with environmental conditions. Here, we conducted two series of experiments to evaluate the response of MAC colonies morphological traits (length, volume, mucilage and number of cells) to temperature and salinity. In two series of experiments in the laboratory, we exposed natural MAC communities to three different temperatures (10, 21 and 30 °C) and four salinity levels (0, 5, 10 and 25 ppt) typically found in estuaries. We found that average colony length, volume and mucilage thickness did not change with temperature, but the cell-free space inside the colonies was smaller at the highest evaluated temperature (30 °C). Salinity fostered an increase in colony length, volume and mucilage thickness, while cell-free space diminished, resulting in higher cell density. The number of cells per colony was significantly related to colony size (length and volume) and both, temperature and salinity, affected the parameters of the relationships. Based on present results we propose statistical models to predict cell number per colony based on length and volume and accounting for the effect of salinity and temperature on these traits. This is applicable to ecological studies and to the monitoring of estuarine aquatic environments, by means of a fast and more accurate estimation of cell numbers to define MAC toxic populations early warning systems. A protocol is suggested for its application while the analysis of the interaction of temperature and salinity, as well as the variability in natural environments are objectives for future researches.

In Situ Investigation of Performance Reference Compound-Based Estimates of PCB Equilibrated Passive Sampler Concentrations and Cfree in the Marine Water Column.

Low-density polyethylene sheets are used as passive samplers for aquatic environmental monitoring to measure the freely dissolved concentration (Cfree ) of hydrophobic organic contaminants (HOCs). Freely dissolved HOCs in water will partition into the polyethylene until a thermodynamic equilibrium is achieved; that is, the HOC's activity in the passive sampler is the same as its activity in the surrounding environment. One way to evaluate the equilibrium status or estimate the uptake kinetics is by using performance reference compounds (PRCs). A fractional equilibrium (feq ) can be determined for target HOCs, under the assumption that PRC desorption from the passive sampler occurs at the same rate as for the unlabeled target HOCs. However, few investigations have evaluated how effectively and accurately PRCs estimate target contaminant Cfree under in situ conditions. In the present study, polyethylene passive samplers were preloaded with 6 13 C-labeled polychlorinated biphenyls (PCBs) as PRCs; deployed in New Bedford Harbor, Massachusetts, USA; and collected after 30-, 56-, 99-, and 129-d deployments. Using this unique temporal sampling design, PRC results from each deployment were fit to a diffusion model to estimate the Cfree of 27 PCB congeners and compare the results between the different deployment times. Smaller PCBs had variable concentrations over the 4 deployments, whereas mid-molecular weight PCBs had consistent Cfree measurements for all deployments (relative standard deviation <20%). High-molecular weight PCBs had the largest Cfree estimates after 30 d; these estimates and their standard deviations decreased with longer deployment times. These findings suggest that when targeting PCBs with more than 6 chlorines or contaminants with a log octanol-water partition coefficient ≥6.5, a deployment time longer than 30 d may be prudent. Environ Toxicol Chem 2020;39:1165-1173. © 2020 SETAC.

Towards the Development of an Affordable and Practical Light Attenuation Turbidity Sensor for Remote Near Real-Time Aquatic Monitoring.

Turbidity is a key environmental parameter that is used in the determination of water quality. The turbidity of a water body gives an indication of how much suspended sediment is present, which directly impacts the clarity of the water (i.e., whether it is cloudy or clear). Various commercial nephelometric and optical approaches and products exist for electronically measuring turbidity. However, most of these approaches are unsuitable or not viable for collecting data remotely. This paper investigates ways for incorporating a turbidity sensor into an existing remote aquatic environmental monitoring platform that delivers data in near real-time (i.e., 15-min intervals). First, we examine whether an off-the-shelf turbidity sensor can be modified to provide remote and accurate turbidity measurements. Next, we present an inexpensive design for a practical light attenuation turbidity sensor. We outline the sensor’s design rationale and how various technical and physical constraints were overcome. The turbidity sensor is calibrated against a commercial turbidimeter using a Formazin standard. Results indicate that the sensor readings are indicative of actual changes in turbidity, and a calibration curve for the sensor could be attained. The turbidity sensor was trialled in different types of water bodies over nine months to determine the system’s robustness and responsiveness to the environment.

Electrostatic Self‐Assembled Bracelet‐Like Au@Pt Nanoparticles: An Efficient Electrocatalyst for Highly Sensitive Non‐Enzymatic Hydrogen Peroxide Sensing

AbstractIn this study, a non‐enzymatic electrochemical sensor was developed based on bracelet‐like Au@Pt nanoparticles (B−Au@Pt NPs) and used for extremely sensitive detection of hydrogen peroxide (H2O2). The uniquely shaped B−Au@Pt NPs with a well‐defined core–shell structure were synthesized by a combination of seed‐mediated growth and electrostatic self‐assembly, especially using both sodium citrate (SC) and cetyltrimethylammonium chloride (CTAC). Through characterization by dynamic light scattering (DLS), high‐resolution transmission electron microscopy (HRTEM) and UV/Vis absorption spectra, it was evident that each Au NP was surrounded by several ultrasmall Pt NPs, all of which were detached from and lose contact with Au cores. Compared with sea urchin‐like Au@Pt NPs (S−Au@Pt NPs) synthesized only by a seed‐mediated growth, B−Au@Pt NPs presented obvious electrochemical response and exhibited substantially enhanced electrocatalytic activity towards H2O2 reduction. This H2O2 sensor, denoted as B−Au@Pt NPs/GCE (GCE=glassy carbon electrode), with enhanced sensitivity, has shown much better sensing performance than other sensors based on Pt reported earlier, particularly the high sensitivity of 882.2 μA mM−1 cm−2. Moreover, this non‐enzymatic electrochemical sensor based on B−Au@Pt NPs may offer the potential for sensitive detection of H2O2 in aquatic environmental monitoring.

Confidence Levels, Sensitivity, and the Role of Bathymetry in Coral Reef Remote Sensing

Remote sensing is playing an increasingly important role in the monitoring and management of coastal regions, coral reefs, inland lakes, waterways, and other shallow aquatic environments. Ongoing advances in algorithm development, sensor technology, computing capabilities, and data availability are continuing to improve our ability to accurately derive information on water properties, water depth, benthic habitat composition, and ecosystem health. However, given the physical complexity and inherent variability of the aquatic environment, most of the remote sensing models used to address these challenges require localized input parameters to be effective and are thereby limited in geographic scope. Additionally, since the parameters in these models are interconnected, particularly with respect to bathymetry, errors in deriving one parameter can significantly impact the accuracy of other derived parameters and products. This study utilizes hyperspectral data acquired in Hawaii in 2000–2001 and 2017–2018 using NASA’s Classic Airborne Visible/Infrared Imaging Spectrometer to evaluate performance and sensitivity of a well-established semi-analytical inversion model used in the assessment of coral reefs. Analysis is performed at several modeled spatial resolutions to emulate characteristics of different feasible moderate resolution hyperspectral satellites, and data processing is approached with the objective of developing a generalized, scalable, automated workflow. Accuracy of derived water depth is evaluated using bathymetric lidar data, which serves to both validate model performance and underscore the importance of image quality on achieving optimal model output. Data are then used to perform a sensitivity analysis and develop confidence levels for model validity and accuracy. Analysis indicates that derived benthic reflectance is most sensitive to errors in bathymetry at shallower depths, yet remains significant at all depths. The confidence levels provide a first-order method for internal quality assessment to determine the physical extent of where and to what degree model output is considered valid. Consistent results were found across different study sites and different spatial resolutions, confirming the suitability of the model for deriving water depth in complex coral reef environments, and expanding our ability to achieve automated widespread mapping and monitoring of global coral reefs.

Impact of rapid urbanization on the surface water's quality: a long-term environmental and physicochemical investigation of Tajan river, Iran (2007-2017).

Sustainable development of our society requires protection and monitoring of aquatic environments, as they are the pivotal water resources and niche for wildlife animals. In this research, physicochemical parameters of Tajan river's water were determined and compared with standards to assess the rural and human activity impact on water quality. First, monitoring and analyzing the water quality of the river were performed for 11years (from 2007 to 2017). Based on calculated water quality index (WQI) values, Tajan river's water quality falls into four categories: good water with WQI less than 30 at upstream and middle of the river, poor water, and very poor to even unsuitable water (WQI ranges from 86 to 134) at the more urbanized locations. The high values of WQI are due to an elevated concentration of sulfate, nitrate, total dissolved solids (TDS), and chloride substances. The source of contaminations, according to the geochemistry of the area and having more than 90% growth rate in the population near to the most polluted location, is probably anthropogenic activities. Then, the long-term experimental data set has been utilized for developing a statistical model to be used for prediction of water quality with a few chemical analyses, generating a rapid and low-cost water quality evaluation for this river. The model was developed based on the stepwise multiple linear regression (MLR) approach and confirmed the experimental observation data of the most defective elements on WQI were respectively SO4, NO3, TDS, Cl, pH, and EC. This study presents a long-term evaluation of changes in surface water quality at a region with a recent rapid urban and civil development providing a suitable case to understand better human-water relations.

A Long-Range 2.4G Network System and Scheduling Scheme for Aquatic Environmental Monitoring

Wireless communications for applications of inshore fishery and large area aquatic environmental monitoring are really challenging, due to the characteristics of a long monitoring period, large coverage area, and adverse transmission conditions. Recently, LPWAN (low-power wide-area network) became the new solution to address these challenges, due to its long transmission distance and low power consumption of end-nodes. In this paper, we designed a novel network system for aquatic environmental monitoring, based on long-range 2.4G technology, which consisted of a low cost dual-channel gateway and end-nodes. A DMSF (dual-channel multiple spreading factors)–TDMA (time division multiple access) MAC (medium access control) scheme for this system was proposed, which largely reduces the channel collision probability, and improves the real-time for urgent data and the average lifetime of end-nodes. We verified the applicability of the long-range 2.4G technology in an aquatic environment, by point-to-point communication experiments over lake water. The performance evaluation and analysis of DMSF–TDMA is presented through simulations, and comparison with other existing schemes. The results demonstrated the benefit of our proposed scheme, in terms of the packet delivery rate, delay, and energy consumption.

The impacts of different high-throughput profiling approaches on the understanding of bacterial antibiotic resistance genes in a freshwater reservoir

Bacterial antibiotic resistance genes (ARGs), a kind of emerging environmental pollutants, greatly threat human health through pathogenic bacteria. High-throughput quantitative PCR (HT-qPCR) and metagenomic approaches are two popular tools applied in aquatic environmental ARGs monitoring. However, current poor knowledge of different ARG profiling approaches' impacts on the understanding of the ARGs in aquatic environments greatly limit the further field application of these two approaches. For filling such knowledge gap, this study simultaneously employed these two approaches to examine and compare the ARGs in a freshwater reservoir across space and time. We found metagenomic approach detected more ARG subtypes and much higher bacitracin resistance genes' abundances than HT-qPCR. In general, HT-qPCR and metagenomics analyses both revealed similar ARG dynamic patterns and co-occurrence patterns between ARGs and bacterial taxa as well as the relationships between ARGs and environmental factors. Our results indicated the impacts of different ARG profiling approaches on the understanding of bacterial ARGs might be minor or negligible. HT-qPCR approach has the superiorities of time-saving, absolute quantification, low requirement for bioinformatics skills but also has some drawbacks including higher PCR amplification & primer bias, higher primer dependency and relative lower ARG subtype quantification capability compared to metagenomic approach. We suggest HT-qPCR approach can be employed for routine aquatic environmental monitoring, and metagenomic approach could be applied in comprehensive surveys for getting more ARG subtype information. Our data can be a useful reference for choosing right ARG profiling approaches for bacterial ARGs monitoring and risk assessment in aquatic environments.

Analysis of cloud condition on Sentinel-2 MSI and Landsat-8 OLI images of a public supply lake in Belém-Pará-Brazil

The eutrophication process leads to reduced water quality and economic losses worldwide. Furthermore, it is possible to apply remote sensing techniques for monitoring of aquatic environments. In this paper, we analysed the combined use of Sentinel-2 Multispectral Instrument and Landsat-8 Operational Land Imager data to monitor a eutrophic aquatic environment under adverse cloudy conditions, from July 2016 to July 2018. Data pre-selection was performed, and then the images were acquired for further investigation. After that, we created a key to the interpretation of cloud conditions for the study area and grouped each of 125 scenes in a Principal Component Analysis (PCA). The PCA grouped months with similarities in cloud conditions, highlighting their patterns in terms of the rainy and dry seasons for the study area. Another interesting result was that, even under the inherent adverse cloud regime of the Amazon, the combined use of both free satellite imagery data could be useful for further analyses, such as measuring of chlorophyll a, coloured dissolved organic matters, total suspended solids and turbidity. However, we highlight that, firstly, studies must be made to validate the data in situ, so that monitoring programs can be built through remote sensing applications.

Contamination, ecological risk and source identification of metals by multivariate analysis in surface sediments of the khouran Straits, the Persian Gulf

Sediments are important hosts for metals and such should be incorporated in aquatic environment monitoring programs. In the present study, the concentrations of metals, multiple geochemical indices and guidelines along with multivariate statistical analysis were applied to identify the accumulation, sources and pollution levels of metals in the surface sediment. The mean metal concentrations in the sediments were ranked as follows: Fe > Cr > Ni > V > Zn > Cu > Pb > Cd. Based on the geo-accumulation index and multiple pollution indices the overall pattern of environmental quality status of the khouran Straits were uncontaminated to slightly polluted. Pearson correlation matrix combined with multivariate principal component analysis and cluster analysis suggest that Cd and Pb originated from anthropogenic sources, whereas Fe, Cu, Zn, Ni, V and Cr mostly originated both from natural processes and human activities with predominant anthropogenic influence in the investigated ecosystem.

Aquatic environment monitoring using a drone-based fluorosensor

A drone-based system for monitoring of laser-induced fluorescence from the aquatic environment was constructed. Fixed-range remote-sensing demonstration measurements were performed, and field recordings of natural river water fluorescence, oil-slicks as well as dye-marked natural water volumes were taken at drone flying heights of about 10 m. Our fluorosensor, weighing only 1.5 kg, and carried by a commercial drone, illustrates how airborne remote sensing based on fluorescence can be made cost-effective and readily applicable, while presently only in ambient low-light-level conditions.

Could benthic biofilm analyses be used as a reliable proxy for freshwater environmental health?

The quality of freshwater undoubtedly reflects the health of our surrounding environment, society, and economy, as these are supported by various freshwater ecosystems. Monitoring efforts have therefore been considered a vital means of ensuring the ecological health of freshwater environments. Nevertheless, most aquatic environmental monitoring strategies largely focus on bulk water sampling for analysis of physicochemical and key biological indicators, which for the most part do not consider pollution events that occur at any time between sampling events. Because benthic biofilms are ubiquitous in aquatic environments, pollution released during sporadic events may be absorbed by these biofilms, which can act as repositories of pollutants. The aim of this study was to assess whether benthic biofilm monitoring could provide an efficient way of properly characterizing the extent of pollution in aquatic environments. Here, bulk water and benthic biofilms were sampled from three Hong Kong streams having various pollution profiles, and subsequently compared via high-resolution microscopy, metagenomic analysis, and analytical chemistry. The results indicated that biofilms were, indeed, reservoirs of environmental pollutants, having different profiles compared with that of the corresponding bulk water samples. Moreover, the results also suggested that biofilms sampled in polluted areas were characterized by a higher species richness. While the analytical testing of benthic biofilms still needs further development, the integration of chemical-pollutant profiles and biofilm sequencing data in future studies may provide unique perspectives for understanding and identifying pollution-related biofilm biomarkers.

Energy Consumption Analysis of DBR and VBF Protocols in Underwater Sensor Networks Using Aqua-Sim at Network Simulator 2

For decades, there have been significant interests in monitoring the aquatic environment for scientific exploration, commercial exploitation, coastline protection, and even disasters prevention and mitigation such as tsunami warnings. Highly precise, real-time, and temporal-spatial continuous aquatic environment monitoring system is extremely important for underwater life. To support and simulate such monitoring system, underwater sensor networks (UWSN) have emerged as a very powerful technique for many applications on underwater environment, including monitoring, measurement, surveillance, and control by using Aqua-Sim. Aqua-Sim is a simulator for UWSN developed on Network Simulator 2 platform which effectively simulates the attenuation of underwater acoustic channels and the collision behaviors in long delay acoustic networks. Currently, there are several routing protocols for UWSN which are implemented in Aqua-Sim. On this research, we did a simulation on Aqua-Sim by performing Vector-based Forwarding (VBF) protocol and Depth-based Routing (DBR) protocol performance analysis based on energy consumption parameter. Based on the result, it can be concluded that the VBF routing protocol requires more energy consumption than the DBR routing protocol.

Smart Environmental Monitoring and Assessment Technologies (SEMAT)-A New Paradigm for Low-Cost, Remote Aquatic Environmental Monitoring.

Expense and the logistical difficulties with deploying scientific monitoring equipment are the biggest limitations to undertaking large scale monitoring of aquatic environments. The Smart Environmental Monitoring and Assessment Technologies (SEMAT) project is aimed at addressing this problem by creating an open standard for low-cost, near real-time, remote aquatic environmental monitoring systems. This paper presents the latest refinement of the SEMAT system in-line with the evolution of existing technologies, inexpensive sensors and environmental monitoring expectations. We provide a systems analysis and design of the SEMAT remote monitoring units and the back-end data management system. The system’s value is augmented through a unique e-waste recycling and repurposing model which engages/educates the community in the production of the SEMAT units using social enterprise. SEMAT serves as an open standard for the community to innovate around to further the state of play with low-cost environmental monitoring. The latest SEMAT units have been trialled in a peri-urban lake setting and the results demonstrate the system’s capabilities to provide ongoing data in near real-time to validate an environmental model of the study site.

Development of 3D-Printed Embedded Temperature Sensor for Both Terrestrial and Aquatic Environmental Monitoring Robots

Abstract A linear resistive temperature sensor has been 3D printed through fused deposition modeling using a conductive filament based on polylactic acid (PLA) and graphene nano rods (GNR) composit...