Real Environmental Conditions Research Articles

Clean water production from plastic and heavy metal contaminated waters using redox-sensitive iron nanoparticle-loaded biochar

The unceasing release of tiny plastics (microplastics and nanoplastics) and their additives, like metal ions, into the aquatic systems from industries and other sources is a globally escalating problem. Their combined toxic effects and human health hazard are already proven; hence, their remediation is requisite. This study utilised the nano-zerovalent iron-loaded sugarcane bagasse-derived biochar (nZVI-SBC) for simultaneous removal of Nanoplastics (NPs) of different functionality and size along with metal ions (Ni2+, Cd2+, AsO43−, and CrO42−). Batch and column experiments were conducted, and the results showed an efficient removal of contaminants with maximum sorption of carboxylate-modified NPs of size 500 nm (qmax = 90.3 mg/g) among all three NPs types. Significant removal was observed in Cd2+ in case of cations and CrO42− in case of anions with qmax = 44.0 and 87.8 mg/g, respectively. Kinetics and the isotherm modelling better fitted the pseudo-second-order kinetic model and Sips isotherm model, respectively for both NPs and metal ions. The designed material worked well in pH range of 4–8, ionic strength 1–20 mM and in complex aqueous matrices, with >90% removal. FTIR, zeta potential and the imaging analysis of the reaction precipitates confirmed the electrostatic attraction, pore retention and complexation as the potential mechanisms for removing NPs, whereas, XPS studies confirmed the reduction co-precipitation and surface complexation as the possible mechanism for removing metal ions. High values of attachment efficiency factor calculated from colloidal filtration theory (CFT) validated the experimental results and justified the high sorption of carboxylate modified 500 nm NPs particles. The synthesized material successfully removed both NPs of varying size and functionality and metal ions simultaneously with significant efficacy in complex environmental samples proving the broad applicability of material in realistic environmental conditions and different types of water treatment processes.

On predicting annual output energy of 4-terminal perovskite/silicon tandem PV cells for building integrated photovoltaic application using machine learning

Building integrated photovoltaic (BIPV), based on tandem PV cells, is considered a new alternative for combining solar energy with buildings. Accurately predicting the BIPV-harvested annual output energy (Eout,annual) is crucial for evaluating the BIPV performance. Machine learning (ML) is a potential candidate for solving such a problem without the time-consuming process of experimental investigations. This contribution proposes an artificial neural network (ANN) to predict the Eout,annual of 4-terminal perovskite/silicon (psk/Si) PV cells under realistic environmental conditions. The input variables of the proposed model consist of the input solar irradiance (Pin), incident light's angle (Ain), the PV module's temperature (Tmod), the psk absorber's thickness (Thpsk), and the psk absorber's bandgap (Bpsk). The input data were received from the simulated results. This work also evaluates the degree of importance of each input variable and optimizes the architecture of the ANN using the surrogate algorithm before predictions. The optimized ANN-3 (three hidden layers) model shows superior performance indicators, including a mean squared error of MSE = 0.02283, correlation coefficient R = 0.99999, and Willmott's index of agreement Iw = 0.99999. Consequently, the predicted highest Eout,annual at Bpsk of 1.71 eV is 297.73, 115.01, 193.98, and 97.6 kWh/m2 for the rooftop, east, south, and west facades, respectively.

In situ field experiment shows the potential of methanogenic archaea for biomethane production from underground gas storage in natural rock environment

Biological methanation occurs naturally in the specific rock environment of some underground gas reservoirs. To use the natural potential of hydrogenotrophic methanogenic archaea in the underground gas reservoirs environment in the future for any type of energy conversion, better knowledge of these systems is required. Most studies investigating the potential for biological methane production from hydrogen and carbon dioxide have been conducted in laboratory-scale systems, making it impossible to evaluate the process under realistic environmental conditions. A unique field experiment of biomethanation in an underground gas reservoir confirmed the feasibility of these facilities for targeted production of biologically produced methane. The experiment followed data previously obtained from long-term experiments carried out in the laboratory, including 300 days lasting operation of reactor and cultivation experiments, which led to repeated isolation of Methanothermobacter sp. This species was the dominant methanogen, which raised from 1% to 43% of all microbial population after 22 days of field experiment, while the complete conversion of hydrogen took place. In addition, the study is supported by metagenomic analysis to gain deep insight into the microbiome of the underground gas reservoir.

Hyperspectral Image Classification Based on Dense Pyramidal Convolution and Multi-Feature Fusion

In recent years, hyperspectral image classification techniques have attracted a lot of attention from many scholars because they can be used to model the development of different cities and provide a reference for urban planning and construction. However, due to the difficulty in obtaining hyperspectral images, only a limited number of pixels can be used as training samples. Therefore, how to adequately extract and utilize the spatial and spectral information of hyperspectral images with limited training samples has become a difficult problem. To address this issue, we propose a hyperspectral image classification method based on dense pyramidal convolution and multi-feature fusion (DPCMF). In this approach, two branches are designed to extract spatial and spectral features, respectively. In the spatial branch, dense pyramid convolutions and non-local blocks are used to extract multi-scale local and global spatial features in image samples, which are then fused to obtain spatial features. In the spectral branch, dense pyramidal convolution layers are used to extract spectral features in image samples. Finally, the spatial and spectral features are fused and fed into fully connected layers to obtain classification results. The experimental results show that the overall accuracy (OA) of the method proposed in this paper is 96.74%, 98.10%, 98.92% and 96.67% on the four hyperspectral datasets, respectively. Significant improvements are achieved compared to the five methods of SVM, SSRN, FDSSC, DBMA and DBDA for hyperspectral classification. Therefore, the proposed method can better extract and exploit the spatial and spectral information in image samples when the number of training samples is limited. Provide more realistic and intuitive terrain and environmental conditions for urban planning, design, construction and management.

Chemical Weathering Patterns of Diluted Bitumen Spilled into Freshwater Limnocorrals.

Due to the sudden nature of oil spills, few controlled studies have documented how oil weathers immediately following accidental release into a natural lake environment. Here, we evaluated the weathering patterns of Cold Lake Winter Blend, a diluted bitumen (dilbit) product, by performing a series of controlled spills into limnocorrals installed in a freshwater lake in Northern Ontario, Canada. Using a regression-based design, we added seven different dilbit volumes, ranging from 1.5 to 180 L, resulting in oil-to-water ratios between 1:71,000 (v/v) and 1:500 (v/v). We monitored changes in the composition of various petroleum hydrocarbons (PHCs), including n-alkanes, polycyclic aromatic hydrocarbons (PAHs), and oil biomarkers in dilbit over time, as it naturally weathered for 70 days. Depletion rate constants (kD) of n-alkanes and PAHs ranged from 0.0009 to 0.41 d-1 and 0.0008 to 0.38 d-1, respectively. There was no significant relationship between kD and spill volume, suggesting that spill size did not influence the depletion of petroleum hydrocarbons from the slick. Diagnostic ratios calculated from concentrations of n-alkanes, isoprenoids, and PAHs indicated that evaporation and photooxidation were major processes contributing to dilbit weathering, whereas dissolution and biodegradation were less important. These results demonstrate the usefulness of large scale field studies carried out under realistic environmental conditions to elucidate the role of different weathering processes following a dilbit spill.

Snow Survival Training of Astronauts: Through the eyes of a Flight Surgeon

Manned space flight missions always harbor within themselves the possibility of an off-nominal re-entry and landing. The crew has to be prepared to deal with such an eventuality. Hence, survival training post-landing forms an integral part of the pre-flight preparation of an astronaut. The survival training is aimed at enhancing the ability of the crew to take rational decisions and actions during extreme situations after an emergency escape from a spacecraft, till the arrival of the search and rescue team. The training enables the crew to acquire the skills required in the event of an off-nominal landing (or splashdown). This paper deals with the snow survival training for astronaut candidates conducted by a foreign Astronaut Training Centre. The Snow Survival Training program consists of theoretical lectures, practical demonstrations in the field, and 48 h of snow survival in real environmental conditions. With the advent of the Human Spaceflight Programme, the lessons learnt in the snow survival training would be helpful in conducting extreme weather training for Indian Astronauts as well as serve as a welcome addition to the existing training pattern of Jungle and Snow Survival courses for aircrew in India.

Biocomposite of hydrochar and Lindnera jadinii with magnetic properties for adsorptive removal of cadmium ions

The aim of the work was to check the possibility of obtaining a biocomposite with magnetic properties capable of removing cadmium ions from aqueous solutions. The biocomposite consists of hydrochar obtained by hydrothermal carbonization of raffia fibers, Lindnera jadinii yeast and Fe3O4 nanoparticles. The obtained biocomposite was characterized by SEM, FTIR, VSM, XRD, TGA and BET analysis. The study examined the effect of sorption time, initial concentration of cadmium ions, temperature, the pHZPC and the reusability of the biocomposite. Based on the obtained results, non-linear equilibrium and kinetic models were selected. The equilibrium model with the highest matching factor describing the sorption of Cd(II) by the bionanocomposite was the Temkin isotherm model R2 = 0.9960 and ARE = 2.06%, while the kinetic model with the best fit was the Elovich model R2 = 0.9928 and ARE = 5.44%. The results showed that the maximum Cd(II) adsorption capacity of the biocomposite was equal to 16.34 mg/g at 25 °C and increased with increasing temperature up to 26.22 mg/g at 40 °C. The performed Cd desorption tests showed a significant efficiency of the biocomposite for five successive cycles of the sorption-desorption process with the use of HNO3, obtaining a leaching degree of 96–98.8%. The conducted research provides new data on the use of composites obtained from biomass, microorganisms and Fe3O4 in the bioremediation of heavy metals from water media and proves the effectiveness of removal and potential use in real environmental conditions of the biocomposite due to its easy recovery from contaminated aqueous solutions.

The Exothermic Effects of Textile Fibers during Changes in Environmental Humidity: A Comparison between ISO:16533 and Dynamic Hot Plate Test Method

The exothermic effects of high regain fiber types have been described before; yet, there have not been reliable tests to demonstrate these effects on the human body. Most test methods focus on steady-state measurements; therefore, these exothermic effects during changes in environmental humidity are typically not analyzed or quantified. We have conducted a set of fabric tests that shows the connection between the exothermic effect of water vapor uptake and its consequence for heat loss through the fabric in transient conditions. We have performed the ISO:16533 standard test, a dynamic hot plate test developed by Naylor to measure the exothermic property of the fabric, and dynamic regain tests to connect the dots between these tests and the water vapor uptake phenomenon. Although the ISO:16533 test method tends to show the temperature increase in fibers, it cannot differentiate between the hygroscopic fiber (wool, viscose, cotton) types (p > 0.001). In addition, sensor size and sample folding techniques could impact the temperature increase. On the other hand, the Naylor hot plate test showed a greater difference in heat release among the fiber types (wool showed 20% higher heat release than viscose, 50% more than cotton), although the relative humidity changes in the chamber take time, which might not reflect a step-wise change in humidity. So far, these test methods have proven to be the most reliable for determining the exothermic behavior of textile fiber. However, these test methods still have limitations and cannot simulate realistic environmental conditions considering an instantaneous change in the environment. This paper reflects the comparison between the two test methods and recommends directions to accurately address the theory of water vapor uptake under dynamic conditions.

Air-Processable Perovskite Solar Cells by Hexamine Molecule Phase Stabilization.

Perovskite solar cells have emerged as a potential energy alternative due to their low cost of fabrication and high power conversion efficiency. Unfortunately, their poor ambient stability has critically limited their industrialization and application in real environmental conditions. Here, we show that by introducing hexamine molecules into the perovskite lattice, we can enhance the photoactive phase stability, enabling high-performance and air-processable perovskite solar cells. The unencapsulated and freshly prepared perovskite solar cells produce a power conversion efficiency of 16.83% under a 100 mW cm-2 1.5G solar light simulator and demonstrate high stability properties when being stored for more than 1500 h in humid air with relative humidity ranging from 65 to 90%. We envisage that our findings may revolutionize perovskite solar cell research, pushing the performance and stability to the limit and bringing the perovskite solar cells toward industrialization.

Monitoring weed mechanical and chemical damage stress based on chlorophyll fluorescence imaging.

Currently, mechanical and chemical damage is the main way to carry out weed control. The use of chlorophyll fluorescence (CF) technology to nondestructively monitor the stress physiological state of weeds is significant to reveal the damage mechanism of mechanical and chemical stresses as well as complex stresses. Under simulated real field environmental conditions, different species and leaf age weeds (Digitaria sanguinalis 2-5 leaf age, and Erigeron canadensis 5-10 leaf age) were subjected to experimental treatments for 1-7 days, and fluorescence parameters were measured every 24h using a chlorophyll fluorometer. The aim of this study was to investigate the changes in CF parameters of different species of weeds (Digitaria sanguinalis, Erigeron canadensis) at their different stress sites under chemical, mechanical and their combined stresses. The results showed that when weeds (Digitaria sanguinalis and Erigeron canadensis) were chemically stressed in different parts, their leaf back parts were the most severely stressed after 7 days, with photosynthetic inhibition reaching R=75%. In contrast, mechanical stress differs from its changes, and after a period of its stress, each parameter recovers somewhat after 1 to 2 days of stress, with heavy mechanical stress R=11%. Complex stress had the most significant effect on CF parameters, mainly in the timing and efficiency of changes in Fv/Fm, Fq'/Fm', ETR, Rfd, NPQ and Y(NO), with R reaching 71%-73% after only 3-4 days of complex stress, and its changes in complex stress were basically consistent with the pattern of changes in its chemical stress. The results of the study will help to understand the effects of mechanical and chemical stresses and combined stresses on CF parameters of weeds and serve as a guide for efficient weed control operations and conducting weed control in the future.

First insight in element localisation in different body parts of the acanthocephalan Dentitruncus truttae using TEM and NanoSIMS

Acanthocephalans, intestinal parasites of vertebrates, are characterised by orders of magnitude higher metal accumulation than free-living organisms, but the mechanism of such effective metal accumulation is still unknown. The aim of our study was to gain new insights into the high-resolution localization of elements in the bodies of acanthocephalans, thus taking an initial step towards elucidating metal uptake and accumulation in organisms under real environmental conditions. For the first time, nanoscale secondary ion mass spectrometry (NanoSIMS) was used for high-resolution mapping of 12 elements (C, Ca, Cu, Fe, N, Na, O, P, Pb, S, Se, and Tl) in three selected body parts (trunk spines, inner part of the proboscis receptacle and inner surface of the tegument) of Dentitruncus truttae, a parasite of brown trout (Salmo trutta) from the Krka River in Croatia. In addition, the same body parts were examined using transmission electron microscopy (TEM) and correlated with NanoSIMS images. Metal concentrations determined using HR ICP-MS confirmed higher accumulation in D. truttae than in the fish intestine. The chemical composition of the acanthocephalan body showed the highest density of C, Ca, N, Na, O, S, as important and constitutive elements in living cells in all studied structures, while Fe was predominant among trace elements. In general, higher element density was found in trunk spines and tegument, as body structures responsible for substance absorption in parasites. The results obtained with NanoSIMS and TEM-NanoSIMS correlative imaging represent pilot data for mapping of elements at nanoscale resolution in the ultrastructure of various body parts of acanthocephalans and generally provide a contribution for further application of this technique in all parasite species.

First comparative assessment of contamination by plastics and non-synthetic particles in three bivalve species from an Italian sub-alpine lake

This study aimed to compare the contamination from plastics and non-synthetic particles in the three freshwater bivalve mollusks Unio elongatulus, (native) and Corbicula fluminea and Dreissena polymorpha (invasive), collected in Lake Maggiore, the second greatest Italian lake. Organisms were collected from eight sites located throughout the lake, during three years (2019–2021). The quali-quantitative characterization of particles has been carried out using a Fourier Transform Infrared Microscope System (μFT-IR). Results showed that both plastics and non-synthetic particles released in the water are taken up by bivalves, even though low intake-up to 6 particles/individuals-were measured for all the three species. Microfibers of both synthetic (polyester, polyamide) and natural (cellulose) origin represented the particles mostly ingested by bivalves. A significant decrease of particle loads was observed in 2020 with respect to 2019 and 2021, significantly different for D. polymorpha and U. elongatulus, suggesting a transient reduction of the particle release in the lake in this year. Our findings highlight the need to improve the understanding of the mechanisms of uptake and clearance of these contaminants by filter feeding organisms, and their adverse consequences in realistic environmental conditions.

A Review of Surrogate Safety Measures Uses in Historical Crash Investigations

Historical road crash data are the main indicator for measuring road safety outcomes. Over the past few decades, significant efforts have been made in obtaining and exploiting Surrogate Safety Measures (SSMs). SSMs have the potential to provide excellent sustainable road safety indicators and proxy measurements which can complement traditional historical crash analyses or even substitute them. By using SSMs, crash data collection demands can be bypassed and areas can be investigated before crashes occur. Due to such advantages, the objective of the present research is to provide a review of the scientific literature regarding studies exploiting SSMs for historical crash record investigations. Specifically, 34 studies were examined, providing insights on the different types of SSMs collected under real road environment conditions, the way they are collected, their connection with specific road crash types, and the type of the developed statistical models are examined and discussed. Particular focus is also placed on the temporal dimension of the collection period of both SSMs and road crashes. Finally, the overall trends deriving from the reviewed studies are summarized and future research directions are provided.

Sea2Cloud: From Biogenic Emission Fluxes to Cloud Properties in the Southwest Pacific

Abstract The goal of the Sea2Cloud project is to study the interplay between surface ocean biogeochemical and physical properties, fluxes to the atmosphere, and ultimately their impact on cloud formation under minimal direct anthropogenic influence. Here we present an interdisciplinary approach, combining atmospheric physics and chemistry with marine biogeochemistry, during a voyage between 41° and 47°S in March 2020. In parallel to ambient measurements of atmospheric composition and seawater biogeochemical properties, we describe semicontrolled experiments to characterize nascent sea spray properties and nucleation from gas-phase biogenic emissions. The experimental framework for studying the impact of the predicted evolution of ozone concentration in the Southern Hemisphere is also detailed. After describing the experimental strategy, we present the oceanic and meteorological context including provisional results on atmospheric thermodynamics, composition, and flux measurements. In situ measurements and flux studies were carried out on different biological communities by sampling surface seawater from subantarctic, subtropical, and frontal water masses. Air–Sea-Interface Tanks (ASIT) were used to quantify biogenic emissions of trace gases under realistic environmental conditions, with nucleation observed in association with biogenic seawater emissions. Sea spray continuously generated produced sea spray fluxes of 34% of organic matter by mass, of which 4% particles had fluorescent properties, and which size distribution resembled the one found in clean sectors of the Southern Ocean. The goal of Sea2Cloud is to generate realistic parameterizations of emission flux dependences of trace gases and nucleation precursors, sea spray, cloud condensation nuclei, and ice nuclei using seawater biogeochemistry, for implementation in regional atmospheric models.

Designing, Manufacturing, and Testing of 50 Wp Solar Panel Monitor Equipment Using Arduino and Internet of Things

Monitoring the performance of solar panels is very necessary to assess the performance of a solar panel in real environmental conditions. The aim of the research is to study the real-time monitoring of solar panel with an Internet of Things (IoT) System. The designed solar panel performance monitoring system is equipped with a wifi module, and voltage, temperature, and humidity sensors. The design of this solar panel monitoring system uses Arduino and the IoT system a function of by reading, sending, and processing data obtained by sensors. The performance monitoring system on this solar panel utilizes the concept of sending solar panel data to other components via the Internet. By using the internet which is integrated into applications and databases as data storage. The solar panels used in the test are Polycrystalline type with a capacity of 50 Wp. The result is that this tool has successfully tested four solar panels with accurate results under optimal condition.

Calibration of high-fidelity hydrodynamic models utilizing on-site vessel response measurements

With a focus on weather-restricted marine operations, a comprehensive work on numerical model calibration is conducted to improve the vessel response prediction accuracy in real environmental conditions. This framework involves using existing physics-based models in which the metocean and system parameters are entered as inputs to derive vessel responses. The numerical models based on linear potential flow theory resolve only a part of the physics, i.e., it does not account for viscous damping effects. Moreover, significant uncertainties are associated with the system parameters related to the vessel operational conditions that are not known or measured directly. Therefore, the present paper integrates the vessel model with a derivative-free Lipschitz optimization technique to calibrate the vessel system parameters. For efficient optimization, the most influential variables are identified by conducting a sensitivity study for relevant quantities of interest using a cost-effective Polynomial Chaos model. The sensitivity analysis assumes independent uniform distributions for the system parameters with proper lower and upper limits. Subsequently, estimates of the highly influential variables are optimized using on-site vessel measurements, and in turn, utilized for superior response estimation. The response computations based on the numerical wave spectrum produced superior results in comparison with the results based on the parametric wave spectrum. The calibration algorithm is validated with full-scale measurements using frequency and time domain approaches for simulating vessel motions. It has been quantitatively revealed that the Center of Gravity parameters associated with the vessel operational characteristics and sea state-dependent additional damping coefficients govern the Roll response variation to a great extent. The calibration of these parameters resulted in improved Roll spectra estimations that match the measured spectra closely.

The importance of superoxide anion for Escherichia coli biofilm removal using plasma-activated water

Microbial biofilms cause contaminations in different environmental settings, including pipelines, filters, membranes, food and processing infrastructure. They ultimately pose a major risk to human health and necessitate costly cleaning and repair. Cold plasma, a partially ionised gas, and plasma-activated water (PAW) exhibit powerful disinfectant activity. However, the optimal generating conditions, such as the choice of gas used to produce PAW, remain unclear. Here, a range of different PAWs were generated from argon, nitrogen, air, and oxygen in a plasma bubble spark discharge (BSD) reactor capable of directly treating Escherichia coli (ATCC 25922) biofilms in situ. We measured the reactive oxygen and nitrogen species (RONS) (H2O2, NO3-, NO2-) in PAW and the excited species via optical emission spectroscopy (OES). PAW generated using oxygen (PAW-O2) was the most effective and completely removed E. coli biofilms on stainless steel surfaces. Confocal microscopy demonstrated that PAW treatment removed most biofilm cells from the surface with only a few dead cells remaining. We demonstrated that intracellular ROS level increases significantly in the PAW-O2-treated biofilms. Using molecular scavengers, we showed that superoxide anion radical (•O2-) played a critical role in the inactivation of E. coli biofilms. We also confirmed the generation of •O2- in the PAW-O2 via electron paramagnetic resonance (EPR) spectrometry. The potential chemical reactions that occurred in PAW were hypothesized via optical emission spectra (OES). Our results demonstrate the importance of input gas and plasma operating conditions to maximise effective RONS production for optimal biofilm removal under real environmental and industry-relevant conditions.

Obstacle Avoidance Based on Stereo Vision Navigation System for Omni-directional Robot

This paper addresses the problem of obstacle avoidance in mobile robot navigation systems. The navigation system is considered very important because the robot must be able to be controlled from its initial position to its destination without experiencing a collision. The robot must be able to avoid obstacles and arrive at its destination. Several previous studies have focused more on predetermined stationary obstacles. This has resulted in research results being difficult to apply in real environmental conditions, whereas in real conditions, obstacles can be stationary or moving caused by changes in the walking environment. The objective of this study is to address the robot’s navigation behaviors to avoid obstacles. In dealing with complex problems as previously described, a control system is designed using Neuro-Fuzzy so that the robot can avoid obstacles when the robot moves toward the destination. This paper uses ANFIS for obstacle avoidance control. The learning model used is offline learning. Mapping the input and output data is used in the initial step. Then the data is trained to produce a very small error. To support the movement of the robot so that it is more flexible and smoother in avoiding obstacles and can identify objects in real-time, a three wheels omnidirectional robot is used equipped with a stereo vision sensor. The contribution is to advance state of the art in obstacle avoidance for robot navigation systems by exploiting ANFIS with target-and-obstacles detection based on stereo vision sensors. This study tested the proposed control method by using 15 experiments with different obstacle setup positions. These scenarios were chosen to test the ability to avoid moving obstacles that may come from the front, the right, or the left of the robot. The robot moved to the left or right of the obstacles depending on the given Vy speed. After several tests with different obstacle positions, the robot managed to avoid the obstacle when the obstacle distance ranged from 173 – 150 cm with an average speed of Vy 274 mm/s. In the process of avoiding obstacles, the robot still calculates the direction in which the robot is facing the target until the target angle is 0.

Quality of Monitoring Optimization in Underwater Sensor Networks through a Multiagent Diversity-Based Gradient Approach.

Due to the complex underwater environment, conventional measurement and sensing methods used for land are difficult to apply directly in the underwater environment. Especially for seabed topography, it is impossible to perform long-distance and accurate detection by electromagnetic waves. Therefore, various types of acoustic and even optical sensing devices for underwater applications have been used. Equipped with submersibles, these underwater sensors can detect a wide underwater range accurately. In addition, the development of sensor technology will be modified and optimized according to the needs of ocean exploitation. In this paper, we propose a multiagent approach for optimizing the quality of monitoring (QoM) in underwater sensor networks. Our framework aspires to optimize the QoM by resorting to the machine learning concept of diversity. We devise a multiagent optimization procedure which is able to both reduce the redundancy among the sensor readings and maximize the diversity in a distributed and adaptive manner. The mobile sensor positions are adjusted iteratively using a gradient type of updates. The overall framework is tested through simulations based on realistic environment conditions. The proposed approach is compared to other placement approaches and is found to achieve a higher QoM with a smaller number of sensors.

Evaluating the Protectiveness of a Bioavailability-Based Environmental Quality Standard for the Protection of Aquatic Communities from Zinc Toxicity Based on Field Evidence.

Environmental quality standards (EQS) are typically derived from the results of laboratory studies on single species. There is always uncertainty surrounding the protectiveness of an EQS when applied to real ecosystems containing a multitude of chemical and physical stressors. Quantile regression was used with field biological data on invertebrates in United Kingdom waters to identify taxa that are responsive to bioavailable zinc exposures. A threshold based on the total abundance of eight responsive taxa is used as an indicator of the overall ecosystem sensitivity. The inclusion of some responsive but insensitive taxa in this ecological metric could bias the results toward a higher threshold. The least responsive species were progressively removed from the collective ecological metric, basing the analysis on a progressively smaller number of the more responsive species. Quantile regression analysis at the 95th quantile for the three most responsive taxa resulted in a 10% effect concentration of 14.8 µg L-1 bioavailable zinc, suggesting that the EQS of 10.9 µg L-1 bioavailable zinc is sufficiently protective of sensitive members of the invertebrate community. There is a compromise between the robustness of the analysis and the sensitivity of the subcommunity that it is based on. Analyses based on fewer taxa provide a more sensitive result. This approach assessed real ecosystem data and evaluated the uncertainty associated with the protectiveness of the EQS for zinc. The zinc EQS is sufficiently protective of sensitive members of benthic macroinvertebrate communities under real environmental conditions, including a mix of multiple substances. Environ Toxicol Chem 2023;42:1010-1021. © 2023 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.