Distance-decay equations of antibiotic resistance genes across freshwater reservoirs.

Distance-decay (DD) equations can discern the biogeographical pattern of organisms and genes in a better way with advanced statistical methods. Here, we developed a data Compilation, Arrangement, and Statistics framework to advance quantile regression (QR) into the generation of DD equations for antibiotic resistance genes (ARGs) across various spatial scales using freshwater reservoirs as an illustration. We found that QR is superior at explaining dissemination potential of ARGs to the traditionally used least squares regression (LSR). This is because our model is based on the 'law of limiting factors', which reduces influence of unmeasured factors that reduce the efficacy of the LSR method. DD equations generated from the 99th QR model for ARGs were 'Sall = 90.03e-0.01Dall' in water and 'Sall = 92.31e-0.011Dall' in sediment. The 99th QR model was less impacted by uneven sample sizes, resulting in a better quantification of ARGs dissemination. Within an individual reservoir, the 99th QR model demonstrated that there is no dispersal limitation of ARGs at this smaller spatial scale. The QR method not only allows for construction of robust DD equations that better display dissemination of organisms and genes across ecosystems, but also provides new insights into the biogeography exhibited by key parameters, as well as the interactions between organisms and environment.

Microplastic accumulation in groundwater: Data-scaled insights and future research.

Given that microplastics (MPs) in groundwater have been concerned for risks to humans and ecosystems with increased publications, a Contrasting Analysis of Scales (CAS) approach is developed by this study to synthesize all existing data into a hierarchical understanding of MP accumulation in groundwater. Within the full data of 386 compiled samples, the median abundance of MPs in Open Groundwater (OG) and Closed Groundwater (CG) were 4.4 and 2.5 items/L respectively, with OG exhibiting a greater diversity of MP colors and larger particle sizes. The different pathways of MP entry (i.e., surface runoff and rock interstices) into OG and CG led to this difference. At the regional scale, median MP abundance in nature reserves and landfills were 17.5 and 13.4 items/L, respectively, all the sampling points showed high pollution load risk. MPs in agricultural areas exhibited a high coefficient of variation (716.7%), and a median abundance of 1.0 items/L. Anthropogenic activities at the regional scale are the drivers behind the differentiation in the morphological characteristics of MPs, where groundwater in residential areas with highly toxic polymers (e.g., polyvinylchloride) deserves prolonged attention. At the local scale, the transport of MPs is controlled by groundwater flow paths, with a higher abundance of MP particles downstream than upstream, and MPs with regular surfaces and lower resistance (e.g., pellets) are more likely to be transported over long distances. From the data-scaled insight this study provides on the accumulation of MPs, future research should be directed towards network-based observation for groundwater-rich regions covered with landfills, residences, and agricultural land.

Significant regional disparities in riverine microplastics.

Research on riverine microplastics has gradually increased, highlighting an area for further exploration: the lack of extensive, large-scale regional variations analysis due to methodological and spatiotemporal limitations. Herein, we constructed and applied a comprehensive framework for synthesizing and analyzing literature data on riverine microplastics to enable comparative research on the regional variations on a large scale. Research results showed that in 76 rivers primarily located in Asia, Europe, and North America, the microplastic abundance of surface water in Asian rivers was three times higher than that in Euro-America rivers, while sediment in Euro-American rivers was five times more microplastics than Asia rivers, indicating significant regional variations (p < 0.001). Additionally, based on the income levels of countries, rivers in lower-middle and upper-middle income countries had significantly (p < 0.001) higher abundance of microplastics in surface water compared to high-income countries, while the opposite was true for sediment. This phenomenon was preliminarily attributed to varying levels of urbanization across countries. Our proposed framework for synthesizing and analyzing microplastic literature data provides a holistic understanding of microplastic disparities in the environment, and can facilitate broader discussions on management and mitigation strategies.

Data-driven discoveries on widespread contamination of freshwater reservoirs by dominant antibiotic resistance genes.

The propagation of antibiotic resistance genes (ARGs) in freshwater reservoirs threatens ecosystem security and human health, and has attracted increasing attention. A series of recent research articles on ARGs provides a unique opportunity for data-driven discoveries in this emerging field. Here, we mined data from a total of 290 samples from 60 reservoirs worldwide with a data-driven framework (DD) developed to discover geographical distribution, influencing factors and pollution hotspots of ARGs in freshwater reservoirs. Most data came from Asia and Europe where nine classes of ARGs were most frequently detected in reservoirs with multi-drug resistance and sulfonamide resistance genes prevailing. Factors driving distribution of reservoir ARGs differed between reservoir waters and sediments, and interactions among these factors had linear or nonlinear enhancement effects on the explanatory power of ARG distribution. During the cold season, small-sized reservoir waters rich in organic carbon, mobile genetic elements (MGEs) and antibiotics had a higher pollution potential of ARGs; during the spring drought, sediments in large reservoirs located in densely populated areas were more conducive to dissemination of ARGs due to their richness in antibiotics and MGEs. Thus, distribution pattern of ARG pollution hotspots in reservoir waters and sediments varies greatly depending on the differences of internal and external factors. From the "One Health" perspective, this widespread contamination of freshwater reservoirs by ARGs we discovered through the DD framework should be a push to promote integrated research across regions and disciplines. Especially the human - food-chain - ecosystem interface needs an improved understanding of ARG contamination mechanisms and targeted monitoring and evaluation systems should be developed to maintain all ecosystem services in freshwater reservoirs as well as to safeguard human health.

Widespread occurrence of microplastics in marine bays with diverse drivers and environmental risk.

Microplastic contamination in the sediment of marine bays has attracted widespread attention, whereas the distribution, sedimentation, morphology and risk of microplastics at regional scale remain poorly understood. By introducing a data mining framework into microplastic research, we compiled a microplastic dataset of 649 samples from 24 bays to enhance the understanding of geographical difference and drivers, transfer, composition profile and environmental risk of sedimental microplastics. Microplastic abundance varied from 0.72 to 1963.96 items/kg dry weight, with higher concentrations mainly occurring in East Asian bays. The spatial pattern in abundance was driven by the river plastic emissions, aquaculture production and hydrodynamic condition. A significantly positive correlation between microplastic abundance in water and sediment was found, and microplastic sedimentation was related to polymer density, hydrodynamic conditions and sediment properties. The dominant shape and polymer of sedimental microplastics were fiber and polypropylene, respectively, and the similarity of microplastic composition decreased with increasing geographical distance. The environmental risks of microplastics were partitioned into three classes (Rank II-Rank IV) with a two-dimensional assessment system considering the bioavailability and toxicity of microplastics, and Asian bays were identified as potential high-risk areas. To reduce the environmental risk of sedimental microplastics in bays, priority should be given to the removal of microfibers, and control measures depend on the risk classes and dominant polymers. Microplastic abundance and composition were significantly affected by methodological choices regarding sampling, pretreatment and identification, suggesting a unified methodology is essential to further enhance our knowledge on the distribution and risk of microplastics in marine bays.

A Study on the Application of Distributed System Technology-Guided Machine Learning in Malware Detection.

In recent years, with the development of information technology, the Internet has become an essential tool for human daily life. However, as the popularity and scale of the Internet continue to expand, malware has also emerged as an increasingly widespread trend, and its development has brought many negative impacts to the society. As the number of types of malware is getting enormous, the attacks are constantly updated, and at the same time, the spread is very fast, causing more and more damage to the network, the requirements and standards for malware detection are constantly rising. How to effectively detect malware is a research trend; in order to tackle the new needs and problems arising from the development of malware, this paper proposes to guide machine learning algorithms to implement malware detection in a distributed environment: firstly, each detection node in the distributed network performs anomaly detection on the captured software information and data, then performs feature analysis to discover unknown malware and obtain its samples, updates the new malware features to all feature detection nodes in the whole distributed network, and trains the random forest-based machine learning algorithm for malware classification and detection, thus completing the global response processing capability for malware. By building a distributed system framework, the global capture capability of malware detection is enhanced to robustly respond to the increasing and rapid spread of malware, and machine learning algorithms are integrated into it to achieve effective detection of malware. Extended experiments on the Ember 2017 and Ember 2018 databases show that our proposed approach achieves advanced performance and effectively addresses the problem of malware detection.

Global meta-analysis of microplastic contamination in reservoirs with a novel framework.

Microplastic contamination in reservoirs is receiving increasing attention worldwide. However, a holistic understanding of the occurrence, drivers, and potential risks of microplastics in reservoirs is lacking. Building on a systematic review and meta-analysis of 30 existing publications, we construct a global microplastic dataset consisting of 440 collected samples from 43 reservoirs worldwide which we analyze through a framework of Data processing and Multivariate statistics (DM). The purpose is to provide comprehensive understanding of the drivers and mechanisms of microplastic pollution in reservoirs considering three different aspects: geographical distribution, driving forces, and ecological risks. We found that microplastic abundance varied greatly in reservoirs ranging over 2-6 orders of magnitude. Small-sized microplastics (< 1 mm) accounted for more than 60% of the total microplastics found in reservoirs worldwide. The most frequently detected colors, shapes, and polymer types were transparent, fibers, and polypropylene (polyester within aquatic organisms), respectively. Geographic location, seasonal variation and land-use type were main factors influencing microplastic abundance. Detection was also dependent on analytical methods, demonstrating the need for reliable and standardized methods. Interaction of these factors enhanced effects on microplastic distribution. Microplastics morphological characteristics and their main drivers differed between environmental media (water and sediment) and were more diverse in waters compared to sediments. Similarity in microplastic morphologies decreased with increasing geographic distance within the same media. In terms of risks, microplastic pollution and potential ecological risk levels are high in reservoirs and current policies to mitigate microplastic pollution are insufficient. Based on the DM framework, we identified temperate/subtropical reservoirs in Asia as potential high-risk areas and offer recommendations for analytical methods to detect microplastics in waters and sediments. This framework can be extended and applied to other multi-scale and multi-attribute contaminants, providing effective theoretical guidance for reservoir ecosystems pollution control and management.

Revisiting seasonal dynamics of total nitrogen in reservoirs with a systematic framework for mining data from existing publications.

Investigation of seasonal variations of water quality parameters is essential for understanding the mechanisms of structural changes in aquatic ecosystems and their pollution control. Despite the ongoing rise in scientific production on spatiotemporal distribution characteristics of water quality parameters, such as total nitrogen (TN) in reservoirs, attempts to use published data and incorporate them into a large-scale comparison and trends analyses are lacking. Here, we propose a framework of Data extraction, Data grouping and Statistical analysis (DDS) and illustrate application of this DDS framework with the example of TN in reservoirs. Among 1722 publications related to TN in reservoirs, 58 TN time-series data from 19 reservoirs met the analysis requirements and were extracted using the DDS framework. We performed statistical analysis on these time-series data using Dynamic Time Warping (DTW) combined with agglomerative hierarchical clustering as well as Generalized Additive Models for Location, Scale, and Shape (GAMLSS). Three patterns of seasonal TN dynamics were identified. In Pattern V-Sum, TN concentrations change in a "V" shape, dropping to its lowest value in summer; in Pattern P-Sum, TN increases in late summer/early fall before decreasing again; and in Pattern P-Spr, TN peaks in spring. Identified patterns were driven by phytoplankton growth and precipitation (Pattern V-Sum), nitrate wet deposition and agricultural runoff (Pattern P-Sum), and anthropogenic discharges (Pattern P-Spr). Application of the DDS framework has identified a key bottleneck in assessing the dynamics of TN - low data accessibility and availability. Providing an easily accessible data sharing platform and increasing the accessibility and availability of raw data for research will facilitate improvements and expand the applicability of the DDS framework. Identification of additional spatiotemporal patterns of water quality parameters can provide new insights for more comprehensive pollution control and management of aquatic ecosystems.

Linking reservoir ecosystems research to the sustainable development goals.

Reservoirs account for about 10% of the freshwater stored in lakes worldwide. These reservoirs are home to 'reservoir ecosystems', that is, the aquatic and non-aquatic interactive ecosystems associated with artificial lakes where water is stored, typically behind a dam, for human purposes. While reservoir ecosystems provide various ecosystem services for sustainable development, their significance in research and policy has not been well understood and not well defined in the 2030 United Nation's (UN) Agenda for Sustainable Development. To advance understanding of reservoir ecosystems and their impact on policy, here we provide an overview of research on reservoir ecosystems and link it to UN SDGs and their Targets. Based on 5280 articles published in the last three decades, we applied network visualization to construct a framework for research addressing reservoir ecosystems. The framework covers four major themes: (1) ecosystem structure and function, (2) environmental pollution and stress effects, (3) climate impacts and ecological feedbacks, and (4) ecosystem services and management. We have found that sustainable reservoir ecosystems synergistically support 121 Targets of UN SDGs (71% of all). Reservoir ecosystems have both negative and positive implications for 15 targets (9%) and negative trade-offs for only 3 targets (2%). Thirty SDG Targets (18%) are unrelated to sustainable reservoir ecosystems. The synergies and trade-offs exist in three fields, securing basic material needs (SDGs 2, 6, 7, 14 and 15), pursuing common human well-being (SDGs 1, 3, 4, 5, 8 and 10), and coordinating sustainable governance policies (SDGs 9, 11, 12, 13, 16 and 17). Exploring these linkages allows better integration of reservoir ecosystems into the UN SDGs framework and guides sustainable management of reservoir ecosystems for sustainable development.

Quantitative identification of nitrate sources in a coastal peri-urban watershed using hydrogeochemical indicators and dual isotopes together with the statistical approaches.

Surplus nutrient load and complex migration and transformation processes are the challenges for water quality management in the peri-urban coastal watershed, leading to increasing concerns worldwide. We investigated the spatio-temporal variation of hydrogeochemical parameters in surface water of Jimei Lake watershed, and distinguished the sources and transformation of nitrate-N (NO3--N) using dual isotopes of nitrate (δ15N and δ18O in NO3-) with hydrogeochemical indicators. Principal component analysis (PCA) on hydrogeochemical parameters demonstrated that surface water was seriously polluted by nutrients, especially in the southeast of the downstream. There were signs of seawater intrusion and increased wastewater discharge in the mid-lower reaches with high ammonium concentrations. Nitrification occurred throughout the monitoring period with lower δ15N and δ18O values and NO3- derived from mixed pollution sources. Results of Bayesian model showed that dominant NO3- input originated from manure and sewage (M&S, 71% and 76% in the wet and dry season, respectively) and atmospheric deposition (22% and 16%, respectively). This result implied that the controls and treatment of M&S discharges are essential to alleviate of NO3- pollution. The proposed method is helpful to understand the origins of NO3- and may be suitable to develop measures for the reducing of nitrogen loadings in the peri-urban watershed.