Search Results (385)

Suspended particulate matter (SPM) plays a pivotal role in marine source-to-sink sedimentary systems. Internal solitary waves (ISWs), a prevalent hydrodynamic phenomenon, significantly influence vertical mixing, cross-shelf material transport, and sediment resuspension. Acting as energetic nonlinear waves, ISWs can disrupt the settling trajectories of suspended particles, enhance lateral transport above the pycnocline, and generate nepheloid layers nearshore. Meanwhile, intense turbulent mixing induced by ISWs accumulates large quantities of SPM at both the leading surface and trailing bottom of the waves, thereby altering the structure and dynamics of the intermediate nepheloid layers. This review synthesizes recent advances in the in situ observational techniques for SPM under the influence of ISWs and highlights the key mechanisms governing their interactions. Particular attention is given to representative field cases in the SCS, where topographic complexity and strong stratification amplify ISWs–sediment coupling. Finally, current limitations in observational and modeling approaches are discussed, with suggestions for future interdisciplinary research directions that better integrate hydrodynamic and sediment transport processes. Full article

LEDs are readily controllable and demonstrate rapid switching capabilities. These attributes facilitate their efficient integration across a broad spectrum of applications. Indeed, their inherent versatility renders them ideally suited for diverse sectors, including consumer electronics, traffic signage, automotive technology, and architectural illumination. Furthermore, LEDs serve as effective light sources for applications in spectroscopy, agriculture, pest control, and wireless optical transmission. The capability to choice high-efficiency LED devices with a specified dominant wavelength renders them particularly well-suited for integration into underwater optical communication systems. In this paper, we present the state-of-the-art of Light-Emitting Diodes (LEDs) for use in underwater wireless optical communications (UOWC). In particular, we focus on the challenges posed by water turbidity and evaluate the optimal wavelengths for communication in coastal environments, especially in the presence of chlorophyll or suspended particulate matter. Given the growing development and applications of underwater optical communication, it is crucial that the topic becomes not only a subject of research but also part of the curricula in technical school and universities. To this end, we introduce a simple and cost-effective UOWC system designed for educational purposes. Some tests have been conducted to evaluate the system’s performance, and the results have been reported. Full article

Chlorophyll-a (Chla) and suspended particulate matter (SPM) are key indicators of water quality, playing critical roles in understanding marine biogeochemical processes and ecosystem health. Although satellite data from the Chinese Ocean Color and Temperature Scanner (COCTS) onboard the Haiyang-1C/D satellites is freely available, there has been limited validation of its standard Chla and SPM products. This study is a first step to address this gap by evaluating COCTS-derived Chla and SPM products against in situ measurements in French coastal waters. The matchup analysis showed robust performance for the Chla product, with a median symmetric accuracy (MSA) of 50.46% over a dynamic range of 0.13–4.31 mg·m⁻³ (n = 24, Bias = 41.11%, Slope = 0.93). In contrast, the SPM product showed significant limitations, particularly in turbid waters, despite a reasonable performance in the matchup exercise, with an MSA of 45.86% within a range of 0.18–10.52 g·m⁻³ (n = 23, Bias = −14.59%, Slope = 2.29). A comparison with another SPM model and Moderate Resolution Imaging Spectroradiometer (MODIS) products showed that the COCTS standard algorithm tends to overestimate SPM and suggests that the issue does not originate from the input radiometric data. This study provides the first regional assessment of COCTS Chla and SPM products in European coastal waters. The findings highlight the need for algorithm refinement to improve the reliability of COCTS SPM products, while the Chla product demonstrates suitability for water quality monitoring in low to moderate Chla concentrations. Future studies should focus on the validation of COCTS ocean color products in more diverse waters. Full article

Clean and safe air quality is essential for public health, yet particulate matter (PM) significantly degrades air quality and poses serious health risks. The Gulf Cooperation Council (GCC) countries are particularly vulnerable to frequent and intense dust storms due to their vast desert landscapes. This study presents the first health risk assessment of carcinogenic and non-carcinogenic risks associated with exposure to PM2.5 and PM10 bound heavy metals and polycyclic aromatic hydrocarbons (PAHs) based on air quality data collected during the years of 2016–2018 near Dubai International Airport and Abu Dhabi International Airport. The results reveal no significant carcinogenic risks for lead (Pb), cobalt (Co), nickel (Ni), and chromium (Cr). Additionally, AI-based regression analysis was applied to time-series dust monitoring data to enhance predictive capabilities in environmental monitoring systems. The estimated incremental lifetime cancer risk (ILCR) from PAH exposure exceeded the acceptable threshold (10⁻⁶) in several samples at both locations. The relationship between visibility and key environmental variables—PM1, PM2.5, PM10, total suspended particles (TSPs), wind speed, air pressure, and air temperature—was modeled using three machine learning algorithms: linear regression, support vector machine (SVM) with a radial basis function (RBF) kernel, and artificial neural networks (ANNs). Among these, SVM with an RBF kernel showed the highest accuracy in predicting visibility, effectively integrating meteorological data and particulate matter variables. These findings highlight the potential of machine learning models for environmental monitoring and the need for continued assessments of air quality and its health implications in the region. Full article

Salient object detection (SOD) plays a critical role in underwater exploration systems. Traditional SOD approaches encounter notable constraints in underwater image analysis, primarily stemming from light scattering and absorption effects induced by suspended particulate matter in complex underwater environments. In this work, we propose a deep learning-based multimodal method guided by multi-polarization parameters that integrates polarization de-scattering mechanisms with the powerful feature learning capability of neural networks to achieve adaptive multi-target SOD in an underwater turbid scattering environment. The proposed polarization-enhanced salient object detection network (PESODNet) employs a multi-polarization-parameter-guided, material-aware attention mechanism and a contrastive feature calibration unit, significantly enhancing its multi-material, multi-target detection capabilities in underwater scattering environments. The experimental results confirm that the proposed method achieves substantial performance improvements in multi-target underwater SOD tasks, outperforming state-of-the-art models of salient object detection in detection accuracy. Full article

Antimicrobial use in food animals selects for antimicrobial-resistant (AMR) bacteria, which most commonly reach humans via the food chain. However, AMR bacteria can also escape the feedyard via agricultural runoff, manure used as crop fertilizer, and even dust. A study published in 2015 reported AMR genes in dust from cattle feedyards; however, one of the study’s major limitations was the failure to investigate gene presence in viable bacteria, or more importantly, viable bacteria of importance to human health. Our main objective was to investigate the presence and quantity of viable bacteria and antimicrobial-resistant (AMR) determinants in fugitive bioaerosols from cattle feedyards in the downwind environment. Six bioaerosol sampling campaigns were conducted at three commercial beef cattle feedyards to assess variability in viable bacteria and AMR determinants associated with geographic location, meteorological conditions, and season. Dust samples were collected using four different sampling methods, and spiral plated in triplicate on both non-selective and antibiotic-selective media. Colonies of total aerobic bacteria, Enterococcus spp., Salmonella enterica, and Escherichia coli were enumerated. Viable bacteria, including AMR bacteria, were identified in dust from cattle feedyards. Bacteria and antimicrobial resistance genes (ARGs via qPCR) were mainly found in downwind samples. Total suspended particles (TSPs) and impinger samples yielded the highest bacterial counts. Genes encoding beta-lactam resistance (bla_CMY-2 and bla_CTX-M) were detected while the most common ARG was tet(M). The predominant Salmonella serovar identified was Lubbock. Further research is needed to assess how far viable AMR bacteria can travel in the ambient environment downwind from cattle feedyards, to model potential public health risks. Full article

The reduction of environmental pollutant emissions—including greenhouse gases, particulate matter, and other harmful substances—represents one of the foremost challenges in climate policy, economics, and industrial management today. Excessive emissions of CO₂, NO_X, and suspended particulates exert significant impacts on climate change as well as human health and welfare. Consequently, numerous studies and regulatory and technological initiatives are underway to mitigate these emissions. One critical area is intra-plant transport within manufacturing facilities, which, despite its localized scope, can substantially contribute to a company’s total emissions. This paper aims to assess the potential of computer simulation using FlexSim software as a decision-support tool for planning inter-operational transport, with a particular focus on environmental aspects. The study analyzes real operational data from a selected production plant (case study), concentrating on the optimization of the number of transport units, their routing, and the layout of workstations. It is hypothesized that reducing the number of trips, shortening transport routes, and efficiently utilizing transport resources can lead to lower emissions of carbon dioxide (CO₂) and nitrogen oxides (NO_X). The findings provide a basis for a broader adoption of digital tools in sustainable production planning, emphasizing the integration of environmental criteria into decision-making processes. Furthermore, the results offer a foundation for future analyses that consider the development of green transport technologies—such as electric and hydrogen-powered vehicles—in the context of their implementation in the internal logistics of manufacturing enterprises. Full article

Lakes are important sinks for antibiotics as suspended particulate matters (SPMs) in lakes have become significant carriers of antibiotic adsorption and migration. The light and heavy fractions of SPM are involved in the process of suspension and sedimentation in the aqueous environment. Combined with the adsorption behaviors of antibiotics onto SPM, a basis for the risk of antibiotic migration in lakes will be provided. In this study, SPM from Lake Taihu was collected and grouped according to density as light fraction (LF) and heavy fraction (HF), with heavy fraction including loosely bound humus (WLH) and tightly bound humus (TH). Adsorption studies were carried out with three typical antibiotics: tetracycline hydrochloride (TC), norfloxacin (NOR), and trimethoprim (TMP). The adsorption processes of all particulate fractions towards antibiotics were fast, which is consistent with pseudo-second-order kinetics. The adsorption in the TC and NOR groups was much higher than that in the TMP group, which was mainly related to the properties of the antibiotics. The LF group was the special component with the fastest adsorption rate, the largest adsorption amount, and the lowest desorption ratio, regardless of antibiotics, which is related to the organic matter content and the rich-carbon-containing functional groups in the LF group, such as -C=O. These findings highlight the need for further attention to the high adsorptive transport effect of LF on antibiotics in lake ecosystems. Full article

Air pollution is one of the most serious environmental threats, with particulate matter PM₁₀ and PM_2.5 representing its most harmful components, significantly affecting public health. These particles are primarily generated by transport, industry, residential heating, and agriculture, and are associated with increased incidence of respiratory and cardiovascular diseases, asthma attacks, and heart attacks, as well as chronic illnesses and premature mortality. The most vulnerable groups include children, the elderly, and individuals with pre-existing health conditions. This study focuses on the analysis of health risks associated with PM₁₀ and PM_2.5 air pollution in the city of Zvolen, which serves as a representative case due to its urban structure, traffic load, and industrial activity. The aim is to assess the current state of air quality, identify the main sources of pollution, and evaluate the health impacts of particulate matter on the local population. The results will be compared with selected Slovak cities—Banská Bystrica and Ružomberok—to understand regional differences in exposure and its health consequences. The results revealed consistently elevated concentrations of particulate matter (PM) across all analyzed cities, frequently exceeding the guideline values recommended by the World Health Organization (WHO), although remaining below the thresholds set by current national legislation. The lowest average concentrations were recorded in the city of Zvolen (PM₁₀: 20 μg/m³; PM_2.5: 15 μg/m³). These lower values may be attributed to the location of the reference monitoring station operated by the Slovak Hydrometeorological Institute (SHMÚ), situated on J. Alexy Street in the southern part of the city—south of Zvolen’s primary industrial emitter, Kronospan. Due to predominantly southerly wind patterns, PM particles are transported northward, potentially leading to higher pollution loads in the northern areas of the city, which are currently not being monitored. We analyzed trends in PM₁₀ and PM_2.5 concentrations and their relationship with hospitalization data for respiratory diseases. The results indicate a clear correlation between the concentration of suspended particulate matter and the number of hospital admissions due to respiratory illnesses. Our findings thus confirm the significant adverse effects of particulate air pollution on population health and highlight the urgent need for systematic monitoring and effective measures to reduce emissions, particularly in urban areas. Full article

Particulate matter is a mixture of fine dust and tiny droplets of liquid suspended in the air. PM₁₀ is a pollutant composed of particles smaller than 10 µm. These particles are harmful to the respiratory system. The air quality in the region and capital Lima in the Republic of Peru has been investigated in recent years. In this context, statistical analyses of PM₁₀ data with forecast models can contribute to planning actions that can improve air quality. The objective of this work is to perform a statistical analysis of the available PM₁₀ data and evaluate the quality of time series classical models and neural networks for short-term forecasting. This study demonstrates that classical time series models, particularly ARIMA and SSA, achieve lower average forecast errors than LSTM across stations SMP, CRB, and ATE. This finding suggests that for data with seasonal patterns and relatively short time series, traditional models may be more efficient and robust. Although neural networks have the potential to capture more complex relationships and long-term dependencies, their performance may be limited by hyperparameter settings and intrinsic data characteristics. Full article

Phytoplankton size classes (PSC), which categorize phytoplankton into pico- (<2 µm), nano- (2–20 µm), and microphytoplankton (>20 µm), have been widely used to describe functional group responses to environmental variability. Distribution of PSCs heavily influences marine ecosystems and biogeochemical processes. Despite the importance of PSC distributions, especially in the face of climate change, long-term studies on PSC variability and its driving factors are lacking. This study aimed to identify the key environmental drivers affecting summer PSC variability in the northern East China Sea (NECS) by analyzing 27 years (1998–2024) of satellite-derived data. Statistical analyses using random forest and multiple linear regression models revealed that euphotic depth (Z_eu) and suspended particulate matter (SPM) were the primary factors influencing PSC variation; deeper Z_eu values favored smaller picophytoplankton, whereas higher SPM concentrations supported larger PSCs. Long-term trend analysis showed a clear shift toward increasing picophytoplankton contributions (+2.4% per year), with corresponding declines in nano- and microphytoplankton levels (2.2% and 0.4% annually, respectively). These long-term changes are hypothesized to result from a persistent decline in SPM concentrations, which modulate light attenuation and nutrient dynamics in the euphotic zone. Marine heat waves intensify these shifts by promoting picophytoplankton dominance through enhanced stratification and reduced nutrient availability. These findings underscore the need for continuous monitoring to inform ecosystem management and predict the impacts of climate change in the NECS. Full article

This study, using Hyalella azteca and Chironomus riparius, evaluated the effects of exposure to heavy metal-contaminated sediments collected from the study area under three conditions: before remediation, after remediation, and suspended particulate matter (SPM). The selected toxicity tests allowed for the evaluation of biological responses across varying concentrations of heavy metals. Statistical analysis revealed no significant differences in survival or growth between sediment-exposed organisms and controls for either species. In addition, bioaccumulation of Cr, Ni, Cu, Zn, As, Cd, and Pb in both organisms was assessed and compared among the sediment conditions and the control. No statistically significant differences in tissue metal concentrations were found between organisms exposed to sediments from the study area and those in control conditions. Sequential extraction analysis indicated that a substantial proportion of metals in the sediments were bound in stable, non-bioavailable forms. These findings are consistent with the observed biological responses, as low levels of bioavailable metals corresponded with the absence of toxic effects. Together, the data confirm that the sediments, regardless of remediation stage or particle fraction, posed no significant biological risk under the conditions tested. Full article

Benzo[a]pyrene is an important indicator of polycyclic aromatic hydrocarbons pollution that exhibits complex atmospheric dynamics influenced by meteorological factors and suspended particulate matter (SPM). Herein, the factors influencing B(a)P concentration were elucidated by analyzing the monthly environmental data for Kyoto, Japan, from 2001 to 2021 using an improved association rule algorithm. Results revealed that B(a)P concentrations were 1.3–3 times higher in cold seasons than in warm seasons and SPM concentrations were lower in cold seasons. The clustering performance was enhanced by optimizing the K-means method using the sum of squared error. The efficiency and reliability of the traditional Apriori algorithm were enhanced by restructuring its candidate itemset generation process, specifically by (1) generating C₂ exclusively from frequent itemset L₁ to avoid redundant database scans and (2) implementing the iterative pruning of nonfrequent subsets during L_k → C_k+1 transitions, adding the lift parameter, and eliminating invalid rules. Strong association rules revealed that B(a)P concentrations ≤ 0.185 ng/m³ were associated with specific meteorological conditions, including humidity ≤ 58%, wind speed ≥ 2 m/s, temperature ≥ 12.3 °C, and pressure ≤ 1009.2 hPa. Among these, changes in pressure had the most substantial impact on the confidence of the association rules, followed by humidity, wind speed, and temperature. Under the influence of high SPM concentrations, favorable meteorological conditions further accelerated pollutant dispersion. B(a)P concentration increased with increasing pressure, decreasing temperature, and decreasing wind speed. Principal component analysis confirmed the robustness and accuracy of our optimized association rule approach in quantifying complex, nonlinear relationships, while providing granular, interpretable insights beyond the traditional methods. Full article

Air pollution in cities is intensifying, inevitably affecting all living organisms, gincluding trees. Urban trees are vital for cities because they improve air quality and regulate the climate; however, like all living organisms, they are affected by the environment to which they are exposed. In cities, the primary atmospheric pollutants of inorganic origin include NO, SO_X, CO_X, O₃, and suspended particulate matter (PM_2.5 and PM₁₀). Each of these pollutants impacts population health, with urban trees undergoing a series of consequent alterations. In this study, we review the inorganic pollutants identified by the World Health Organization (WHO) as impacting air quality in cities in different regions of the world; discuss the regulations that govern NO₂, SO₂, CO, O₃, and PM_2.5 and PM₁₀ emissions and their impact they have on urban trees; analyze the processes involved in pollutant–tree interactions and the related tolerance and/or resistance mechanisms; and determine the tree species with the best tolerance, classified using an air pollution tolerance index (APTI). Full article

Metals in the Pearl River Estuary are potentially significant pollutants influenced by the region’s high population density and rapid industrial growth, but their distribution and impacts have not yet been thoroughly investigated. This study investigates the spatial distribution and environmental impacts of particulate and dissolved metals (including Cr, Mn, Fe, Co, Ni, Cu, Zn, Mo, Cd, Tl, and Pb) in the Pearl River Estuary using a combination of statistical methods and spatial analysis techniques. This is Part 2 of a series of papers. In Part 1, we mainly focused on the spatial characteristics of particulate and dissolved metals and the water environment factors influencing them. In Part 2, we mainly focus on the partitioning of metals between their particulate and dissolved forms and its influencing factors. The results show that the distribution of metals in the estuary is predominantly in the dissolved phase, except for Mn, which is more associated with particulates. Environmental factors such as temperature, oxygen content, and water depth exert a substantial impact on the metals’ partitioning behavior. In contrast, the pH value, salinity, and concentration of suspended matter have a minor influence. The results of this study highlight the importance of understanding metal partitioning in the estuary for effective water quality management and pollution control, which also provides valuable insights for pollution source tracking in this area. Full article