Microbiome analysis in Asia's largest watershed reveals inconsistent biogeographic pattern and microbial assembly mechanisms in river and lake systems.

Microorganisms are critical to the stability of aquatic environments, and understanding the ecological mechanisms of microbial community is essential. However, the distinctions and linkages across biogeographic patterns, ecological processes, and formation mechanisms of microbes in rivers and lakes remain unknown. Accordingly, microbiome-centric analysis was conducted in rivers and lakes in the Yangtze River watershed. Results revealed significant differences in the structure and diversity of microbial communities between rivers and lakes, with rivers showing higher diversity. Lakes exhibited lower community stability, despite higher species interactions. Although deterministic processes dominated microbial community assembly both in rivers and lakes, higher stochastic processes of rare and abundant taxa exhibited in rivers. Spatial factors influenced river microbial community, while environmental factors drove differences in the lake bacterial community. This study deepened the understanding of microbial biogeography and formation mechanisms in large watershed rivers and lakes, highlighting distinct community aggregation patterns between river and lake microorganisms.

Evaluation of an Automated Ultrafiltration System for Concentrating a Range of Viruses from Saline Waters.

Pathogenic viruses in environmental water are usually present in levels too low for direct detection and thus, a concentration step is often required to increase the analytical sensitivity. The objective of this study was to evaluate an automated filtration device, the Innovaprep Concentrating Pipette Select (CP Select) for the rapid concentration of viruses in saline water samples, while considering duration of process and ease of use. Four bacteriophages (MS2, P22, Phi6, and PhiX174) and three animal viruses (adenovirus, coronavirus OC43, and canine distemper virus) were seeded in artificial seawater, aquarium water, and bay water samples, and processed using the CP Select. The recovery efficiencies of viruses were determined either using a plaque assay or droplet digital PCR (ddPCR). Using plaque assays, the average recovery efficiencies for bacteriophages ranged from 4.84 ± 3.8% to 82.73 ± 27.3%, with highest recovery for P22 phage. The average recovery efficiencies for the CP Select were 39.31 ± 26.6% for adenovirus, 19.04 ± 11.6% for coronavirus OC43, and 19.84 ± 13.6% for canine distemper virus, as determined by ddPCR. Overall, viral genome composition, not the size of the virus, affected the recovery efficiencies for the CP Select. The small sample volume size used for the ultrafilter pipette of the system hinders the use of this method as a primary concentration step for viruses in marine waters. However, the ease of use and rapid processing time of the CP Select are especially beneficial when rapid detection of viruses in highly contaminated water, such as wastewater or sewage-polluted surface water, is needed.

Application of environmental monitoring programs and root cause analysis to identify and implement interventions to reduce or eliminate Listeria populations in apple packinghouses.

Controlling Listeria in produce packinghouses can be challenging due to the large number of potential contamination routes. For example, repeated isolation of the same Listeria subtype in a packinghouse could indicate persistence in the packinghouse or reintroduction of the same Listeria from an upstream source. To improve understanding of Listeria transmission patterns in packinghouses, we performed a longitudinal study in four apple packinghouses, including testing of 1,339 environmental sponges and whole genome sequencing (WGS)-based characterization of 280 isolates. Root cause analysis and subsequent intervention implementation was also performed and assessed for effectiveness. Listeria prevalence among environmental sponges collected from the four packinghouses was 20% (range of 5 to 31% for individual packinghouses). Sites that showed high Listeria prevalence included drains, forklift tires and forks, forklift stops, and waxing area equipment frames. A total of 240/280 WGS-characterized isolates were represented in 41 clusters, each containing two or more isolates that differed by ≤50 high-quality single nucleotide polymorphisms (hqSNPs); 21 clusters were isolated from one packinghouse over ≥2 samplings (suggesting persistence or possibly reintroduction), while 11 clusters included isolates from >2 packinghouses, suggesting common upstream sources. Some interventions successfully (i) reduced Listeria detection on forklift tires and forks (across packinghouses) and (ii) mitigated packinghouse-specific Listeria issues (e.g., in catch pans). However, interventions that lacked enhanced equipment disassembly when persistence was suspected typically appeared to be unsuccessful. Overall, while our data suggest a combination of intensive environmental sampling with subtyping and root cause analysis can help identify effective interventions, implementation of effective interventions continues to be a challenge in packinghouses.

Environmental relevance monitoring and assessment of ochreous precipitates, hydrochemistry and water sources from abandoned coal mine drainage.

This study investigated the mineralogical and chemical characteristics of ochreous precipitates and mine water samples from abandoned Upper Carboniferous hard coal mines in an extensive former mining area in western Germany. Mine water characteristics have been monitored and assessed using a multi-methodological approach. Thirteen mine water discharge locations were sampled for hydrochemical analysis, with a total of 46 water samples seasonally collected in the whole study area for stable isotopic analyses. Mineralogical composition of 13 ochreous precipitates was identified by a combination of powder X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), and field emission scanning electron microscopy (FE-SEM/EDS). Results showed that abandoned mine drainage was characterized by circumneutral pH, Eh values ranging from 163 to 269 mV, relatively low concentrations of Fe and Mn, and was dominated by HCO3- > SO42- > Cl- > NO3- and Na+ > Ca2+ > Mg2+ > K+. Goethite and ferrihydrite were the dominant precipitated Fe minerals, with traces of quartz, dolomite, and clay minerals. Some metal and metalloid elements (Mn, Al, Si, and Ti) were found in the ochreous sediments. The role of bacteria in the formation of secondary minerals was assessed with the detection of Leptothrix ochracea. The δ18O and δ2H values of mine water plotted on and close to the GMWL and LMWLs indicated local derivation from meteoric water and represented the annual mean precipitation isotopic composition. Results might help to develop strategies for the management of water resources, contaminated mine water, and public health.

Chronic exposure to drinking water nitrate and trihalomethanes in the French CONSTANCES cohort.

Trihalomethanes (THMs) and nitrate are widespread chemicals in drinking water. Chronic exposure has been associated with increased cancer risk despite inconclusive evidence, partly due to the challenges in long-term exposure assessment and potential exposure misclassification. We estimated concentrations of nitrate and THMs in drinking water using a public regulatory monitoring database (SISE-Eaux) for CONSTANCES, a French population-based prospective cohort. We obtained 26,322,366 measurements of drinking water parameters from 2000 to 2020. We excluded missing, implausible and duplicated measurements; we corrected or imputed missing geocodes of sampling locations; we calculated the annual median concentration of nitrate and THMs by surveillance area. To predict missing annual median concentrations, linear mixed models with random intercept using surveillance area as a clustering variable were developed for each region for nitrate and the four THM components (chloroform, chlorodibromomethane, bromodichloromethane and bromoform) separately. Concentrations in the nearest surveillance area from the household were merged per year among 75,462 participants with residential history geocoded for 2000-2020. Estimated concentrations resulting from this approach were compared with measured concentrations in 100 samples collected in Paris, Rennes and Saint-Brieuc in 2021. Median annual concentrations of total THMs and nitrate at study participants' homes for 2000-2020 were, respectively, 15.7 µg/l (IQR: 15.2) and 15.2 mg/l (IQR: 20.8). Among these, 35% were based on measurements for nitrate (16% for THMs), 44% (46%) were predicted using on linear mixed models, and 21% (38%) were based on distribution unit median values. Conditional R2 predictive models ranged from 0.71 to 0.91 (median: 0.85) for nitrate, and from 0.48 to 0.80 for THMs (median: 0.68). These concentrations will allow future association analyses with risk of breast and colorectal cancer. Our cleaning process introduced here could be adapted to other large drinking water monitoring data.

ICP-MS As a Contributing Tool to Nontarget Screening (NTS) Analysis for Environmental Monitoring.

Due to the increasing number of chemicals released into the environment, nontarget screening (NTS) analysis is a necessary tool for providing comprehensive chemical analysis of environmental pollutants. However, NTS workflows encounter challenges in detecting both known and unknown pollutants with common chromatography high-resolution mass spectrometry (HRMS) methods. Identification of unknowns is hindered by limited elemental composition information, and quantification without identical reference standards is prone to errors. To address these issues, we propose the use of inductively coupled plasma mass spectrometry (ICP-MS) as an element-specific detector. ICP-MS can enhance the confidence of compound identification and improve quantification in NTS due to its element-specific response and unambiguous chemical composition information. Additionally, mass balance calculations for individual elements (F, Br, Cl, etc.) enable assessment of total recovery of those elements and evaluation of NTS workflows. Despite its benefits, implementing ICP-MS in NTS analysis and environmental regulation requires overcoming certain shortcomings and challenges, which are discussed herein.

The concentrations and behavior of classic phthalates and emerging phthalate alternatives in different environmental matrices and their biological health risks.

Because of their excellent plasticity, phthalates or phthalic acid esters (PAEs) are widely used in plastic products. However, due to the recognized toxicity of PAEs and legislative requirements, the production and use of emerging PAE alternatives have rapidly grown, such as di-isononyl cyclohexane-1,2-dicarboxylate (DINCH) and di(2-ethylhexyl) terephthalate (DEHTP) which are the primary replacements for classic PAEs. Nowadays, PAEs and emerging PAE alternatives are frequently found in a variety of environmental media, including the atmosphere, sludge, rivers, and seawater/sediment. PAEs and emerging PAE alternatives are involved in endocrine-disrupting effects, and they affect the reproductive physiology of different species of fish and mammals. Therefore, their presence in the environment is of considerable concern due to their potential effects on ecosystem function and public health. Nevertheless, current research on the prevalence, destiny, and conduct of PAEs in the environment has primarily focused on classic PAEs, with little attention given to emerging PAE alternatives. The present article furnishes a synopsis of the physicochemical characteristics, occurrence, transport, fate, and adverse effects of both classic PAEs and emerging PAE alternatives on organisms in the ecosystem. Our analysis reveals that both classic PAEs and emerging PAE alternatives are widely distributed in all environmental media, with emerging PAE alternatives increasingly replacing classic PAEs. Various pathways can transform and degrade both classic PAEs and emerging PAE alternatives, and their own and related metabolites can have toxic effects on organisms. This research offers a more extensive comprehension of the health hazards associated with classic PAEs and emerging PAE alternatives.

Can remotely sensed catchment to lake area ratios predict mercury levels in subarctic fishes?

Mercury concentrations ([Hg]) in fish reflect a complex array of interacting biogeochemical and ecological variables. In northern regions where fish are a critical subsistence food, understanding and predicting changes of fish [Hg] can be particularly difficult due to paucity of comprehensive data associated with logistical challenges of field sampling. Building on previous work where we elucidated causal relationships between fish [Hg] and a variety of catchment, water quality, and ecological variables in subarctic lakes, we investigated whether using only ratios of catchment area to lake area (CA:LA) can predict [Hg] in northern freshwater fish species. As CA:LA can be sensed remotely, they may be more feasible and practical to obtain than field data in far northern regions. Our study included thirteen remote lakes that represent a CA:LA gradient of 6.2-423.5 within an ∼66,000 km2 subarctic region of Northwest Territories, Canada. We found that size-standardized [Hg] in three widespread fish species, including Lake Whitefish (Coregonus clupeaformis), Walleye (Sander vitreus), and Northern Pike (Esox lucius), were significantly and positively related to CA:LA (p < 0.007, r2 = 67-80%), indicating higher fish [Hg] in smaller lakes surrounded by relatively larger catchments. Our findings provide compelling evidence that remotely sensed CA:LA can be used to predict [Hg] in northern fishes and aid in prioritizing understudied and subsistence fishing lakes of the Canadian subarctic for development of comprehensive [Hg] monitoring programs.

5G-enabled UAVs for energy-efficient opportunistic networking.

The article explores the potential of 5G-enabled Unmanned Aerial Vehicles (UAVs) in establishing opportunistic networks to improve network resource management, reduce energy use, and boost operational efficiency. The proposed framework utilizes 5G-enabled drones and edge command and control software to provide energy-efficient network topologies. As a result, UAVs operate edge computing for efficient data collecting and processing. This invention enhances network performance using modern Artificial Intelligence (AI) algorithms to improve UAV networking capabilities while conserving energy. An empirical investigation shows a significant improvement in network performance measures when using 5G technology compared to older 2.4 GHz systems. The communication failure rate decreased by 50 %, from 12 % to 6 %. The round-trip time was lowered by 58.3 %, from 120 Ms to 50 Ms. The payload efficiency improved by 13.3 %, dropping from 15 % to 13 %. The data transmission rate increased significantly from 1 Gbps to 5 Gbps, representing a 400 % boost. The numerical findings highlight the significant impact that 5G technology may have on UAV operations. Testing on a 5G-enabled UAV confirms the effectiveness of our technique in several domains, including precision agriculture, disaster response, and environmental monitoring. The solution seriously improves UAV network performance by reducing energy consumption and using peripheral network command-and-control software. Our results emphasize the versatile networking capacities of 5G-enabled drones, which provide new opportunities for UAV applications.

Compost amendment in urban gardens: elemental and isotopic analysis of soils and vegetable tissues.

Urban horticulture poses a sustainable form of food production, fosters community engagement and mitigates the impacts of climate change on cities. Yet, it can also be tied to health challenges related to soil contamination. This work builds on a previous study conducted on eleven urban gardens in the city of Vienna, Austria. Following the findings of elevated Pb levels in some soil and plant samples within that project, the present study investigates the elemental composition of soil and plants from two affected gardens 1 year after compost amendment. Inductively coupled plasma mass spectrometry (ICP-MS) analysis of skin, pulp and seeds of tomato fruits revealed minor variations in elemental composition which are unlikely to have an impact on food safety. In turn, a tendency of contaminant accumulation in root tips and leaves of radishes was found. Washing of lettuce led to a significant reduction in the contents of potentially toxic elements such as Be, Al, V, Ni, Ga and Tl, underscoring the significance of washing garden products before consumption. Furthermore, compost amendments led to promising results, with reduced Zn, Cd and Pb levels in radish bulbs. Pb isotope ratios in soil and spinach leaf samples taken in the previous study were assessed by multi-collector (MC-) ICP-MS to trace Pb uptake from soils into food. A direct linkage between the Pb isotopic signatures in soil and those in spinach leaves was observed, underscoring their effectiveness as tracers of Pb sources in the environment.

Assessing soil pollution concerns in proximity to Fence-type solar photovoltaic system installations.

This study conducted in the Kyungpook National University Eco-friendly Agriculture Research Centre between 2022 and 2023 investigates the environmental implications of fence-type solar photovoltaic (PV) systems in diverse agricultural settings. Despite the increasing adoption of solar energy for climate change mitigation, there is a noticeable gap in research regarding the potential environmental impact of these specific PV systems. Focusing on heavy metal concentrations, including Cadmium (Cd), Copper (Cu), Arsenic (As), Mercury (Hg), Lead (Pb), Hexavalent Chromium (Cr+6), Zinc (Zn), and Nickel (Ni), across distinct fields, the study reveals significant fluctuations. Notably, the Rice Field experienced a substantial increase in Cd levels from 0.47 mg/kg in 2022 to 1.55 mg/kg in 2023, while Cu and Pb concentrations decreased to acceptable levels in 2023. The findings underscore the dynamic nature of heavy metal concentrations, emphasizing the importance of continuous soil quality monitoring to prevent contamination. This research provides valuable insights into the impact of fence-type solar PV system installations on agricultural soil quality, emphasizing the urgent need to secure these ecosystems through vigilant monitoring and environmental management practices.

Supramolecular Materials as Solid-Phase Microextraction Coatings in Environmental Analysis.

Solid-phase microextraction (SPME) has been widely proposed for the extraction, clean-up, and preconcentration of analytes of environmental concern. Enrichment capabilities, preconcentration efficiency, sample throughput, and selectivity in extracting target compounds greatly depend on the materials used as SPME coatings. Supramolecular materials have emerged as promising porous coatings to be used for the extraction of target compounds due to their unique selectivity, three-dimensional framework, flexible design, and possibility to promote the interaction between the analytes and the coating by means of multiple oriented functional groups. The present review will cover the state of the art of the last 5 years related to SPME coatings based on metal organic frameworks (MOFs), covalent organic frameworks (COFs), and supramolecular macrocycles used for environmental applications.

Optimizing Antimony Speciation Analysis via Frontal Chromatography-ICP-MS to Explore the Release of PET Additives.

Antimony (Sb) contamination poses significant environmental and health concerns due to its toxic nature and widespread presence, largely from anthropogenic activities. This study addresses the urgent need for an accurate speciation analysis of Sb, particularly in water sources, emphasizing its migration from polyethylene terephthalate (PET) plastic materials. Current methodologies primarily focus on total Sb content, leaving a critical knowledge gap for its speciation. Here, we present a novel analytical approach utilizing frontal chromatography coupled with inductively coupled plasma mass spectrometry (FC-ICP-MS) for the rapid speciation analysis of Sb(III) and Sb(V) in water. Systematic optimization of the FC-ICP-MS method was achieved through multivariate data analysis, resulting in a remarkably short analysis time of 150 s with a limit of detection below 1 ng kg-1. The optimized method was then applied to characterize PET leaching, revealing a marked effect of the plastic aging and manufacturing process not only on the total amount of Sb released but also on the nature of leached Sb species. This evidence demonstrates the effectiveness of the FC-ICP-MS approach in addressing such an environmental concern, benchmarking a new standard for Sb speciation analysis in consideration of its simplicity, cost effectiveness, greenness, and broad applicability in environmental and health monitoring.

Elastic Self-Recovering Hybrid Nanogenerator for Water Wave Energy Harvesting and Marine Environmental Monitoring.

To achieve large-scale development of triboelectric nanogenerators (TENGs) for water wave energy harvesting and powering the colossal sensors widely distributed in the ocean, facile and scalable TENGs with high output are urgently required. Here, an elastic self-recovering hybrid nanogenerator (ES-HNG) is proposed for water wave energy harvesting and marine environmental monitoring. The elastic skeletal support of the ES-HNG is manufactured using three-dimensional (3D) printing technology, which is more conducive to the large-scale integration of the ES-HNG. Moreover, the combination of a TENG and an electromagnetic generator (EMG) optimizes the utilization of device space, leading to enhanced energy harvesting efficiency. Experimental results demonstrate that the TENG achieves a peak power output of 42.68 mW, and the EMG reaches a peak power output of 4.40 mW. Furthermore, various marine environment monitoring sensors, such as a self-powered wireless meteorological monitoring system, a wireless alarm system, and a water quality monitoring pen, have been successfully powered by the sophisticated ES-HNG. This work introduces an ES-HNG for water wave energy harvesting, which demonstrates potential in marine environment monitoring and offers a new solution for the sustainable development of the marine internet of things.

Sustainable Sensing with Paper Microfluidics: Applications in Health, Environment, and Food Safety.

This manuscript offers a concise overview of paper microfluidics, emphasizing its sustainable sensing applications in healthcare, environmental monitoring, and food safety. Researchers have developed innovative sensing platforms for detecting pathogens, pollutants, and contaminants by leveraging the paper's unique properties, such as biodegradability and affordability. These portable, low-cost sensors facilitate rapid diagnostics and on-site analysis, making them invaluable tools for resource-limited settings. This review discusses the fabrication techniques, principles, and applications of paper microfluidics, showcasing its potential to address pressing challenges and enhance human health and environmental sustainability.

Mercury Dynamics in the Sea of Azov: Insights from a Mass Balance Model.

The Sea of Azov, an inland shelf sea bounding Ukraine and Russia, experiences the effects of ongoing and legacy pollution. One of the main contaminants of concern is the heavy metal mercury (Hg), which is emitted from the regional coal industry, former Hg refineries, and the historic use of mercury-containing pesticides. The aquatic biome acts both as a major sink and source in this cycle, thus meriting an examination of its environmental fate. This study collated existing Hg data for the SoA and the adjacent region to estimate current Hg influxes and cycling in the ecosystem. The mercury-specific model "Hg Environmental Ratios Multimedia Ecosystem Sources" (HERMES), originally developed for Canadian freshwater lakes, was used to estimate anthropogenic emissions to the sea and regional atmospheric Hg concentrations. The computed water and sediment concentrations (6.8 ng/L and 55.7 ng/g dw, respectively) approximate the reported literature values. The ongoing military conflict will increase environmental pollution in the region, thus further intensifying the existing (legacy) anthropogenic pressures. The results of this study provide a first insight into the environmental Hg cycle of the Sea of Azov ecosystem and underline the need for further emission control and remediation efforts to safeguard environmental quality.

The Development of a Rapid, Cost-Effective, and Green Analytical Method for Mercury Speciation.

Mercury is a naturally occurring metal found in various inorganic and organic forms within the environment. Due to its high toxicity, there is global concern regarding human exposure to this element. The combination of high-performance liquid chromatography and inductively coupled plasma mass spectrometry (HPLC-ICP-MS) is commonly used to analyze the different forms of mercury in a sample due to its high sensitivity and ability to selectively detect mercury. However, the traditional HPLC-ICP-MS methods are often criticized for their lengthy analysis times. In this study, we have refined the conventional approach by transitioning to ultra-high performance liquid chromatography coupled with inductively coupled plasma mass spectrometry (UHPLC-ICP-MS). This modification has resulted in significant reductions in runtime as well as reagent and argon usage, thereby offering a more rapid, environmentally friendly, and cost-effective method. We successfully adapted an HPLC-ICP-MS method to UHPLC-ICP-MS, achieving the analysis of Hg2+ and MeHg+ within 1 min with a mobile phase consumption of only 0.5 mL and a sample volume of 5.0 µL; this is a major advance compared to HPLC analysis with run times generally between 5 and 10 min. The method's performance was assessed by analyzing muscle and liver tissue samples (serving as reference material) from fish, demonstrating the versatility of the method in relation to different complex matrices.

Multi-Scenario Validation and Assessment of a Particulate Matter Sensor Monitor Optimized by Machine Learning Methods.

OBJECTIVE: The aim was to evaluate and optimize the performance of sensor monitors in measuring PM2.5 and PM10 under typical emission scenarios both indoors and outdoors. METHOD: Parallel measurements and comparisons of PM2.5 and PM10 were carried out between sensor monitors and standard instruments in typical indoor (2 months) and outdoor environments (1 year) in Shanghai, respectively. The optimized validation model was determined by comparing six machining learning models, adjusting for meteorological and related factors. The intra- and inter-device variation, measurement accuracy, and stability of sensor monitors were calculated and compared before and after validation. RESULTS: Indoor particles were measured in a range of 0.8-370.7 µg/m3 and 1.9-465.2 µg/m3 for PM2.5 and PM10, respectively, while the outdoor ones were in the ranges of 1.0-211.0 µg/m3 and 0.0-493.0 µg/m3, correspondingly. Compared to machine learning models including multivariate linear model (ML), K-nearest neighbor model (KNN), support vector machine model (SVM), decision tree model (DT), and neural network model (MLP), the random forest (RF) model showed the best validation after adjusting for temperature, relative humidity (RH), PM2.5/PM10 ratios, and measurement time lengths (months) for both PM2.5 and PM10, in indoor (R2: 0.97 and 0.91, root-mean-square error (RMSE) of 1.91 µg/m3 and 4.56 µg/m3, respectively) and outdoor environments (R2: 0.90 and 0.80, RMSE of 5.61 µg/m3 and 17.54 µg/m3, respectively), respectively. CONCLUSIONS: Sensor monitors could provide reliable measurements of PM2.5 and PM10 with high accuracy and acceptable inter and intra-device consistency under typical indoor and outdoor scenarios after validation by RF model. Adjusting for both climate factors and the ratio of PM2.5/PM10 could improve the validation performance.

Non-Invasive Multi-Gas Detection Enabled by Cu-CuO/PEDOT Microneedle Sensor.

Metal-oxide-based gas sensors are extensively utilized across various domains due to their cost-effectiveness, facile fabrication, and compatibility with microelectronic technologies. The copper (Cu)-based multifunctional polymer-enhanced sensor (CuMPES) represents a notably tailored design for non-invasive environmental monitoring, particularly for detecting diverse gases with a low concentration. In this investigation, the Cu-CuO/PEDOT nanocomposite was synthesized via a straightforward chemical oxidation and vapor-phase polymerization. Comprehensive characterizations employing X-ray photoelectron spectroscopy (XPS), scanning electron microscopy (SEM), X-ray diffraction (XRD), and micro Raman elucidated the composition, morphology, and crystal structure of this nanocomposite. Gas-sensing assessments of this CuMPES based on Cu-CuO/PEDOT revealed that the response current of the microneedle-type CuMPES surpassed that of the pure Cu microsensor by nearly threefold. The electrical conductivity and surface reactivity are enhanced by poly (3,4-ethylenedioxythiophene) (PEDOT) polymerized on the CuO-coated surface, resulting in an enhanced sensor performance with an ultra-fast response/recovery of 0.3/0.5 s.