Environmental Monitoring Approaches Research Articles

Validation of Citizen Science Meteorological Data: Can They Be Considered a Valid Help in Weather Understanding and Community Engagement?

Citizen science has emerged as a potent approach for environmental monitoring, leveraging the collective efforts of volunteers to gather data at unprecedented scales. Within the framework of the I-CHANGE project, MeteoTracker, a citizen science initiative, was employed to collect meteorological measurements. Through MeteoTracker, volunteers contributed to a comprehensive dataset, enabling insights into local weather patterns and trends. This paper presents the analysis and the results of the validation of such observations against the official Italian civil protection in situ weather network, demonstrating the effectiveness of citizen science in generating valuable environmental data. The work discusses the methodology employed, including data collection and statistical analysis techniques, i.e., time-series analysis, spatial and temporal interpolation, and correlation analysis. The overall analysis highlights the high quality and reliability of citizen-generated data as well as the strengths of the MeteoTracker platform. Furthermore, our findings underscore the potential of citizen science to augment traditional monitoring efforts, inform decision-making processes in environmental research and management, and improve the social awareness about environmental and climate issues.

Investigation of the contamination behavior in water and soil of an inactive dump from chemical analysis and geophysical method

In rapidly growing urban areas of developing countries, inappropriate urbanization practices near water bodies lead to significant environmental degradation, including deforestation, soil impermeabilization, and direct waste discharge into rivers and streams. This not only compromises water quality and ecosystem health but also exacerbates flood risks for nearby communities. Dumpsites, common in these regions due to inadequate waste management infrastructure, pose significant environmental threats, including soil and groundwater contamination. Our study employed a frequency-domain electromagnetic survey on an inactive dumpsite adjacent to one of Brazil’s major rivers. This geophysical method provided critical insights for geoenvironmental investigations, facilitating the collection of soil and groundwater samples to understand leachate behavior. We found that the shallower electrical conductivity map aligned with the waste disposal area's boundaries, verified by on-site surveys and excavations. Historical aerial images supported this interpretation and helped explain conductive electrical anomalies in the deeper conductivity map, linked to old irregular housing and waste disposal around the dumpsite. Moreover, the electrical conductivity maps revealed NE-oriented percolation at depth in the study area. Groundwater monitoring wells and soil analyses identified chemical elements exceeding standards set by Brazilian and international environmental agencies in this flow direction. These findings highlight the importance of using investigation techniques with superior spatial resolution compared to traditional environmental monitoring approaches. Electromagnetic methods prove effective in enhancing understanding and management of contamination in dumpsite environments.

Quantitative screening of organophosphorus and organochlorine pesticides in water and soil using comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry

Organophosphorus and organochlorine pesticides, historically or currently widely used in agriculture, are prevalent in the environment and pose significant risks to public health and ecosystems. This study aimed to develop a robust analytical method for quantifying these pesticides in soil and water. By optimizing solid-phase extraction (SPE) and Quick, Easy, Cheap, Effective, Rugged, and Safe (QuEChERS) procedures, and utilizing comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC × GC-TOF-MS), we achieved sensitive detection levels of all organophosphorus and organochlorine pesticides at 10 μg/kg for soil and 0.1 μg/kg for water samples. The mean recoveries were in the range of 74.2–118.3 % and 72.3–106.9 % at fortified concentration levels in soil and water, respectively. The method exhibited good linearity (R² ≥ 0.9874) and precision (relative standard deviations from 0.1 % to 29.9 %), providing a reliable approach for environmental monitoring and advancing the capabilities in pesticide residue analysis.

A survey on autonomous environmental monitoring approaches: towards unifying active sensing and reinforcement learning.

The environmental pollution caused by various sources has escalated the climate crisis making the need to establish reliable, intelligent, and persistent environmental monitoring solutions more crucial than ever. Mobile sensing systems are a popular platform due to their cost-effectiveness and adaptability. However, in practice, operation environments demand highly intelligent and robust systems that can cope with an environment's changing dynamics. To achieve this reinforcement learning has become a popular tool as it facilitates the training of intelligent and robust sensing agents that can handle unknown and extreme conditions. In this paper, a framework that formulates active sensing as a reinforcement learning problem is proposed. This framework allows unification with multiple essential environmental monitoring tasks and algorithms such as coverage, patrolling, source seeking, exploration and search and rescue. The unified framework represents a step towards bridging the divide between theoretical advancements in reinforcement learning and real-world applications in environmental monitoring. A critical review of the literature in this field is carried out and it is found that despite the potential of reinforcement learning for environmental active sensing applications there is still a lack of practical implementation and most work remains in the simulation phase. It is also noted that despite the consensus that, multi-agent systems are crucial to fully realize the potential of active sensing there is a lack of research in this area.

Polypyrrole-carbon black/BiPO4 composites as efficient electrocatalysts for sensitive detection of furazolidone: A cost-effective approach for environmental monitoring

Designing a more efficient electrocatalyst remains essential for developing a new sensor probe to detect environmental toxicity efficiently. This study employs the polypyrrole‑carbon black/bismuth phosphate (PPy-CB/BiPO4) composites approach to tune the electrocatalytic performance for sensitive and selective monitoring of hazardous furazolidone (FZD). The prepared PPy-CB/BiPO4 composites have been well-characterized using various material analysis and electro-analytical techniques. The prepared PPy-CB/BiPO4 composites were coated on a flat, polished glassy carbon electrode (GCE) as an active sensing substrate to achieve a dynamic detection range. As a result, the fabricated working electrode for electrochemical monitoring of FZD has reliable detection parameters, including a wide linear range (0.01–1010.6 µM), excellent selectivity and sensitivity (0.245μA μM−1 cm−2), and low detection limit (LOD, 6.0 *10−9 M). This novel sensor was applied to FZD-contaminated real-samples as a proof-efficient electrode platform and realized a satisfactory outcome. This study provides insights and a new pathway for exploring 5% PPy-CB/BiPO4 composites to produce a simple and cost-effective FZD monitoring.

Colorimetric Detection of Cu2+ and Ag+ Ions Using Multi-Responsive Schiff Base Chemosensor: A Versatile Approach for Environmental Monitoring.

In this study, we have synthesized a novel Schiff base-centered chemosensor, designated as SB, with the chemical name ((E)-1-(((6-methylbenzo[d]thiazol-2-yl) imino)methyl)naphthalen-2-ol). This chemosensor was structurally characterized by FT-IR, 1H NMR, UV-Vis and fluorescence spectroscopy. After structural characterization the chemosensor SB was subsequently employed for the detection of Cu2+ and Ag+, using fluorescence spectroscopy. The chemosensor SB showed excellent ability to recognize the target metal ions, leading to fluorescence enhancement and color change from yellow to yellowish orange for Cu2+ and yellow to radish for Ag+ ions. The detection capabilities of this chemosensor were impressive, showing excellent selectivity and an exceptionally low detection limit of 0.0016 µM for Cu2+ and 0.00389 µM for Ag+. Most notably, our approach enables the quantitative detection both metal ions in different water and soil samples at trace level. This achievement holds great promise for analytical applications and offers significant contributions to the field of chemical sensing and environmental protection.

Advancing environmental monitoring: A comparative analysis of sequential radiochemical methods for precise alpha-particle spectrometry analysis of 241Am in soil and marine sediment samples

Environmental monitoring of alpha-emitting radionuclides is crucial for assessing the long-term impacts of nuclear activities and understanding ecosystem dynamics. Thus, long-term marine environmental monitoring needs a precise analytical method for determining isotopes in sediment and soil samples. Because 241Am is the first decay product of 241Pu, its activity in the environment will grow during the next 50 years. This study aims to compare and evaluate different sequential radiochemical methods for 241Am analysis, focusing on their precision and accuracy, to establish a more rapid, refined and reliable approach for environmental monitoring. Four radiochemical methods for 241Am determination in soil and marine sediment samples were compared using alpha-particle spectrometry. This sensitive technique plays a key role in quantifying low-level radioactive contaminants in sediment and soil samples. The procedure was optimised using reference materials prepared by IAEA: sediment (IAEA-300, IAEA-384 and IAEA-385) and soil (IAEA-327 and IAEA-375), and digestion, radiochemical separation, and purification on anion exchange resin (AG 1X8) and chromatographic resins (UTEVA, TRU) were used. The effectiveness of the Ca-oxalate precipitation method for iron removal was also evaluated. We used microcoprecipitation on neodymium fluoride (NdF3) to prepare measurement sources. As a significant outcome, we suggested the potential exclusion of co-precipitation (method D) in the determination of the 241Am isotope in sediment and soil samples. This recommendation aligns with our goal of developing a precise sequential radiochemical method for alpha-particle spectrometry. Our approach offers a valuable and optimised tool for monitoring the environmental impact of nuclear activities and accidents, particularly in marine ecosystems.

Automated detection of wildlife in proximity to marine renewable energy infrastructure using machine learning of underwater imagery

Environmental interactions of marine renewable energy (MRE) projects are challenging to monitor, and key questions remain about their potential impacts. Processing large volumes of environmental data acquired from submarine monitoring and the use of machine learning to identify presence and interactions of marine wildlife with MRE infrastructure are powerful tools for assessing the environmental response to MRE infrastructure. The use of automated image analysis for species identification and enumeration using algorithms like convolutional neural networks can vastly reduce the time required to extract usable data from submarine imagery compared to manual expert processing. We present a novel industry-ready image processing workflow for automated wildlife detection developed using 1000+ hours of underwater video footage obtained by Nova Innovation Ltd. from their operational tidal stream turbine array at Bluemull Sound in Shetland, Scotland. The objective of this work was to develop a workflow and associated algorithms to automatically filter many hours of underwater video, remove unwanted footage, and extract only video containing marine mammals, diving birds or fish. The workflow includes object detection through advanced image analysis, image classification using machine learning, statistical analyses such as quantification of data storage reduction and number of detections, and automated production of a summary report. Blind tests were undertaken on a subset of videos to quantify and iteratively improve the accuracy of the results. The final iteration of the workflow delivered an accuracy of 80% for the identification of marine mammals, diving birds and fish when a three-category (wildlife, algae, and background) classification system was used. The accuracy rose to 95% when a two-category system was used, and objects were classified simply as ‘target’ or ‘non-target’. The entire workflow can be run from video inception to production of an automated results report in approximately 30 minutes, dependent on size of input data, when environmental conditions such as water clarity and key species of interest are familiar to the algorithm. The accuracy and runtime speed of the workflow can be improved through expanding the training dataset of images used in the development of this initial tool by including additional species and water conditions. Application of this workflow significantly reduces manual processing and interpretation time, which can be a significant burden on project developers. Automated processing provides a subset for more focused manual scrutiny and analysis, while reducing the overall size of dataset requiring storage. Auto-reporting can be used to provide outputs for marine regulators to meet monitoring reporting conditions of project licences. Integration of this workflow with automated passive acoustic monitoring systems can provide a holistic environmental monitoring approach using both underwater imagery and acoustics.

Fluorescent Mg(DBM)2-doped alginate nanofibrous membranes for sensitive, quantitative and visible detection of Cd2+

It was essential to effectively track heavy metal ions in water systems. A novel Mg(DBM)2@Calcium alginate (Mg(DBM)2@CaAlG) nanofibrous membrane sensor was fabricated by electrospinning for visual and quantitative detection of Cd2+ in water. The sensor possessed high selectivity, portability and anti-interference features in detection of Cd2+. Quick monitoring within 8 min was realized with the limit of detection of 0.14 nM, which was much lower than the World Health Organization’s permission of [Cd2+] in potable water. The fluorescence enhancement effect of the membrane achieved by ions exchange and complexation. Moreover, combined with a smartphone, the membrane enables realize real-time, on-site and quantitative detection by capturing the fluorescence color change. The recovery rate of membrane on tap water and seawater samples was close to 100 %, verifying excellent applicability. This detection method showed a broad application prospect in real-time and on-site detection of Cd2+ in drinking water and provided a feasible approach for environmental monitoring.

Integrated Assessment of CO2-Induced Acidification Lethal and Sub-Lethal Effects on Tropical Mussels Perna perna

Leakages of CO2 capture and storage systems from the seabed are able to cause significant adverse biological effects in marine species. Adult mussels were exposed to different CO2 enrichment scenarios (pH from 8.3 to 6.0) for 96 h, and endpoints (lysosomal membrane deterioration, lipid peroxidation and primary damages in DNA) were assessed. Mortality and reduced health status can occur after short exposure of the tropical mussel Perna perna to pH levels lower than 7.5. Results pointed out cytogenotoxic effects in the hemolymph and gills after 48 and 96 h of exposure, respectively. These findings should be considered when environmental monitoring approaches are performed in tropical marine areas employing CCS strategies.

Hybrid nanomaterial-based indirect electrochemical sensing of glyphosate in surface water: a promising approach for environmental monitoring.

Glyphosate (GLY), a widely utilized pesticide, poses a significant threat to human health even at minute concentrations. In this study, we propose an innovative electrochemical sensor for the indirect detection of GLY in surface water samples. The sensor incorporates a nanohybrid material composed of multi-layer graphene decorated with gold nanoparticles (AuNPs), synthesized in a single-step electrochemical process. To ensure portability and on-site measurements, the sensor is developed on a screen-printed electrode, chosen for its integration and miniaturization capabilities. The proposed sensor demonstrates remarkable sensitivity and selectivity for GLY detection in surface water samples, with an exceptional limit of detection (LOD) of 0.03 parts per billion (ppb) in both buffer and surface water matrices. Moreover, it exhibits a remarkably high sensitivity of 0.15 μA ppb-1. This electrochemical sensor offers a promising approach for accurate GLY monitoring, addressing the urgent need for reliable pesticide detection in environmental samples. The proposed sensor showed high selectivity towards GLY, when analysed in the presence of other pesticides such as phosmet, chlorpyrifos and glufosinate-ammonium. The recovery percentages of GLY from spiked surface water samples were between 93.8 and 98.9%. The study's broader implications extend to revolutionizing the way environmental chemistry addresses pesticide contamination, water quality assessment, and sustainable management of environmental pollutants. By pushing the boundaries of detection capabilities and offering practical solutions, this research contributes to the advancement of knowledge and practices that are essential for preserving and protecting our environment.

A protocol for longitudinal monitoring of individual building occupants and their environments.

Buildings account for approximately 40% of the energy consumption across the European Union, so there is a requirement to strive for better energy performance to reduce the global impact of urbanised societies. However, energy performant buildings can negatively impact building occupants (e.g., comfort, health and/or wellbeing) due to a trade-off between airtightness and air circulation. Thus, there is a need to monitor Indoor Environmental Quality (IEQ) to inform how it impacts occupants and hence redefine value within building performance metrics. An individualised study design would enable researchers to gain new insights into the effects of environmental changes on individuals for more targeted e.g., health interventions or nuanced and improved building design(s). This paper presents a protocol to conduct longitudinal monitoring of an individual and their immediate environment. Additionally, a novel approach to environmental perception gathering is proposed that will monitor environmental factors at an individual level to investigate subjective survey data pertaining to the participant’s perceptions of IEQ (e.g., perceived air quality, thermal conditions, light, and noise). This protocol has the potential to expose time-differential phenomena between environmental changes and an individual’s behavioural and physiological responses. This could be used to support building performance monitoring by providing an interventional assessment of building performance renovations. In the future it could also provide building scientists with a scalable approach for environmental monitoring that focuses specifically on individual health and wellbeing.

Listeria monocytogenes post-outbreak management - When could a food production be considered under control again?

In cases of outbreaks, food business operators face inspections, recall actions and delisting by retailers. This could have happened to an Austrian meat processor whose products have been associated with a cluster of seven cases of listeriosis spread over the years 2015–2017. Sequencing of clinical and foodborne isolates by public health specialists raised the suspect of a single source outbreak since all strains were of MLST 155, cgMLST 1234.Since the family-driven business was highly motivated to save their business, a crisis management scheme was applied that was agreed upon with national authorities. An end-product–based approach testing every single lot for L. monocytogenes was set into power and only negative lots were released for delivery. We combined the active food lot controls of food authorities with a Listeria environmental transmission mapping procedure. The environmental monitoring approach included 19 sampling activities during 3.5 years resulting in 1632 samples. This scheme allowed to trace and mitigate the Listeria contamination but did not jeopardize the processing of meat products. In total, 14 measures were set into power that reduced the overall Listeria occurrence after sanitation of 50–75 % (sampling event I, II) to 0.0–3.8 % (sampling events XIII to XIX). The outbreak-associated ST155/CT1234 clone was not detected in the third sampling event onwards but popped up during the sampling event VIII again. From then on, the outbreak clone ST155/CT1234 was no longer detected in the food business operator (FBO). We conclude that an intense combined investigation of food lots and environmental samples is needed to identify the source and verify that contamination levels are under control.Initially public health authorities suspected contamination of the slicer, but the monitoring approach has localized the source of ST155/CT1234 in a Schnitzel sorting machine.Other factors leading to the contamination scenario were inadequate conveyor belt hygiene. An inadequate crate washing system and an inadequate hygiene lock led to Listeria spreading between compartments. All transmission routes could be effectively interrupted. A root cause analysis and preventive maintenance program implemented in the FPE is mandatory for food processing facilities.

Rapid Detection of the Change in Surface Flow Patterns Near Fish Passages at Hydropower Dams With the Use of UAS Based Videos Under Controlled Discharge Conditions

The importance of keeping river environments healthy drives the scientific community towards the improvement of sustainable and validated environmental monitoring approaches. Accurate data on the state of the ecosystems provided rapidly are key in order to correctly assess, which interventions and management decisions are suitable, and which must be avoided. This paper analyses a rapid non-intrusive approach to change detection in surface flow patterns near fish passages at hydropower dams with the goal to improve the understanding of factors influencing fish passage discoverability. This, in turn, is of great relevance to the sustainability of migrating riverine fish populations from both ecological and economical perspectives. The present study includes three unique experiments performed at a large-scale hydropower dam site with an integrated fish passage under controlled discharge conditions. The analysis is performed with the use of the freely available KLT-IV software. The use of an Unmanned Aerial System (UAS) as a camera carrier platform provides the key flexibility in terms of any study site selection. The use of KLT-IV speeds up and simplifies flow pattern analysis, especially when compared to labour-intensive modelling relying on point-based ground truth data. In this paper, we demonstrate that the selected approach can be effectively applied to identify changes in surface flow patterns both in terms of flow velocity magnitudes and in terms of flow directions. It shows that the identification of actual flow patterns near the fish passage entrance provides more information on the potential discoverability of the fish passage than traditionally measured bulk discharge values alone.

Automatic Quality Control of Crowdsourced Rainfall Data With Multiple Noises: A Machine Learning Approach

AbstractIn geophysics, crowdsourcing is an emerging nontraditional environmental monitoring approach that supports data acquisition from individual citizens. However, because of the involvement of undertrained citizens and imprecise low‐cost sensors, crowdsourced data applications suffer from different types of noises that can deteriorate the overall monitoring accuracy. In this study, we propose a machine learning approach for automatic crowdsourced data quality control (CSQC) that detects and removes noisy data inputs in spatially and temporally discrete crowdsourced observations coming from both fixed‐point sensors (e.g., surveillance cameras) and moving sensors (e.g., moving cars/pedestrians). We design a set of features from original and interpolated rainfall data and use them to train and test the CSQC models using both supervised and unsupervised machine learning algorithms. The performances of the CSQC models under various scenarios assuming no retraining are also tested (hereafter referred to as transferability). The results based on synthetic but realistic data show that the CSQC models can significantly reduce the overall rainfall estimate errors. Under the stationary assumption, the CSQC models based on both supervised and unsupervised algorithms perform well in noisy data identification and overall rainfall estimation error reduction; however, if the model is transferred to other cities with different rainfall patterns or noise compositions (without retraining), supervised multilayer perceptrons (MLPs) show the best performance.

Machine Learning Predicts the Presence of 2,4,6-Trinitrotoluene in Sediments of a Baltic Sea Munitions Dumpsite Using Microbial Community Compositions.

Bacteria are ubiquitous and live in complex microbial communities. Due to differences in physiological properties and niche preferences among community members, microbial communities respond in specific ways to environmental drivers, potentially resulting in distinct microbial fingerprints for a given environmental state. As proof of the principle, our goal was to assess the opportunities and limitations of machine learning to detect microbial fingerprints indicating the presence of the munition compound 2,4,6-trinitrotoluene (TNT) in southwestern Baltic Sea sediments. Over 40 environmental variables including grain size distribution, elemental composition, and concentration of munition compounds (mostly at pmol⋅g–1 levels) from 150 sediments collected at the near-to-shore munition dumpsite Kolberger Heide by the German city of Kiel were combined with 16S rRNA gene amplicon sequencing libraries. Prediction was achieved using Random Forests (RFs); the robustness of predictions was validated using Artificial Neural Networks (ANN). To facilitate machine learning with microbiome data we developed the R package phyloseq2ML. Using the most classification-relevant 25 bacterial genera exclusively, potentially representing a TNT-indicative fingerprint, TNT was predicted correctly with up to 81.5% balanced accuracy. False positive classifications indicated that this approach also has the potential to identify samples where the original TNT contamination was no longer detectable. The fact that TNT presence was not among the main drivers of the microbial community composition demonstrates the sensitivity of the approach. Moreover, environmental variables resulted in poorer prediction rates than using microbial fingerprints. Our results suggest that microbial communities can predict even minor influencing factors in complex environments, demonstrating the potential of this approach for the discovery of contamination events over an integrated period of time. Proven for a distinct environment future studies should assess the ability of this approach for environmental monitoring in general.

Quantifying and predicting antimicrobials and antimicrobial resistance genes in waterbodies through a holistic approach: a study in Minnesota, United States

The environment plays a key role in the spread and persistence of antimicrobial resistance (AMR). Antimicrobials and antimicrobial resistance genes (ARG) are released into the environment from sources such as wastewater treatment plants, and animal farms. This study describes an approach guided by spatial mapping to quantify and predict antimicrobials and ARG in Minnesota’s waterbodies in water and sediment at two spatial scales: macro, throughout the state, and micro, in specific waterbodies. At the macroscale, the highest concentrations across all antimicrobial classes were found near populated areas. Kernel interpolation provided an approximation of antimicrobial concentrations and ARG abundance at unsampled locations. However, there was high uncertainty in these predictions, due in part to low study power and large distances between sites. At the microscale, wastewater treatment plants had an effect on ARG abundance (sul1 and sul2 in water; blaSHV, intl1, mexB, and sul2 in sediment), but not on antimicrobial concentrations. Results from sediment reflected a long-term history, while water reflected a more transient record of antimicrobials and ARG. This study highlights the value of using spatial analyses, different spatial scales, and sampling matrices, to design an environmental monitoring approach to advance our understanding of AMR persistence and dissemination.

Optimizing Human Intestinal Enteroids for Environmental Monitoring of Human Norovirus.

Human noroviruses (HuNoV) are the leading cause of gastrointestinal illness and environmental monitoring is crucial to prevent HuNoV outbreaks. The recent development of a HuNoV cell culture assay in human intestinal enteroids (HIEs) has enabled detection of infectious HuNoV. However, this complex approach requires adaptation of HIEs to facilitate HuNoV replication from environmental matrixes. Integrating data from 200 experiments, we examined six variables: HIE age, HIE basement membrane compounds (BMC), HuNoV inoculum processing, HuNoV inoculum volume, treatment of data below limit of detection (LOD), and cutoff criteria for determining positive HuNoV growth. We infected HIEs with HuNoV GII.4 Sydney positive stool and determined 1.4 × 103 genome equivalents per HIE well were required for HuNoV replication. HIE age had minimal effect on assay outcomes. LOD replacement and cutoff affected data interpretation, with lower values resulting in higher estimated HuNoV detection. Higher inoculum volumes lead to minimal decreases in HuNoV growth, with an optimal volume of 250uL facilitating capture of low concentrations of HuNoVs present in environmental isolates. Processing of HuNoV inoculum is valuable for disinfection studies and concentrating samples but is not necessary for all HIE applications. This work enhances the HuNoV HIE cell culture approach for environmental monitoring. Future HIE research should report cell age as days of growth and should clearly describe BMC choice, LOD handling, and positive cutoff.

Global Trends of Benthic Bacterial Diversity and Community Composition Along Organic Enrichment Gradients of Salmon Farms.

The analysis of benthic bacterial community structure has emerged as a powerful alternative to traditional microscopy-based taxonomic approaches to monitor aquaculture disturbance in coastal environments. However, local bacterial diversity and community composition vary with season, biogeographic region, hydrology, sediment texture, and aquafarm-specific parameters. Therefore, without an understanding of the inherent variation contained within community complexes, bacterial diversity surveys conducted at individual farms, countries, or specific seasons may not be able to infer global universal pictures of bacterial community diversity and composition at different degrees of aquaculture disturbance. We have analyzed environmental DNA (eDNA) metabarcodes (V3–V4 region of the hypervariable SSU rRNA gene) of 138 samples of different farms located in different major salmon-producing countries. For these samples, we identified universal bacterial core taxa that indicate high, moderate, and low aquaculture impact, regardless of sampling season, sampled country, seafloor substrate type, or local farming and environmental conditions. We also discuss bacterial taxon groups that are specific for individual local conditions. We then link the metabolic properties of the identified bacterial taxon groups to benthic processes, which provides a better understanding of universal benthic ecosystem function(ing) of coastal aquaculture sites. Our results may further guide the continuing development of a practical and generic bacterial eDNA-based environmental monitoring approach.

Assessment of Transcriptomic and Apical Responses of Daphnia magna Exposed to a Polyethylene Microplastic in a 21-d Chronic Study.

There is global concern regarding the fate and effects of microplastics in the environment, particularly in aquatic systems. In the present study, ethylene acrylic acid copolymer particles were evaluated in a chronic toxicity study with the aquatic invertebrate Daphnia magna. The study design included a natural particle control treatment (silica) to differentiate any potential physical effects of a particle from the intrinsic toxicity of the test material. In addition to the standard endpoints of survival, growth, and reproduction, the transcriptomic profiles of control and ethylene acrylic acid copolymer-exposed D. magna were evaluated at the termination of the 21-d toxicity study. No significant effects on D. magna growth, survival, or reproduction were observed in comparison with both particle and untreated control groups. Significant transcriptomic alterations were induced at the highest treatment level of 2.3 × 1012 particles of the ethylene acrylic acid copolymer/L in key pathways linked to central metabolism and energy reserves, oxidative stress, and ovulation and molting, indicating a global transcriptomic response pattern. To put the results in perspective is challenging at this time, because, to date, microplastic environmental monitoring approaches have not been equipped to detect particles in the nanosize range. However, our results indicate that ethylene acrylic acid copolymer microplastics in the upper nanosize range are not expected to adversely affect D. magna growth, survival, or reproductive outcomes at concentrations of up to 1012 particles/L. Environ Toxicol Chem 2020;39:1578-1589. © 2020 SETAC.