Hasil untuk "Environmental pollution"

C. Alimba, C. Faggio

The global plastics production has increased from 1.5 million tons in the 1950s to 335 million tons in 2016, with plastics discharged into virtually all components of the environment. Plastics rarely biodegrade but through different processes they fragment into microplastics and nanoplastics, which have been reported as ubiquitous pollutants in all marine environments worldwide. This study is a review of trend in marine plastic pollution with focus on the current toxicological consequences. Microplastics are capable of absorbing organic contaminants, metals and pathogens from the environment into organisms. This exacerbates its toxicological profile as they interact to induced greater toxic effects. Early studies focused on the accumulation of plastics in the marine environment, entanglement of and ingestions by marine vertebrates, with seabirds used as bioindicators. Entanglement in plastic debris increases asphyxiation through drowning, restrict feeding but increases starvation, skin abrasions and skeletal injuries. Plastic ingestion causes blockage of the guts which may cause injury of the gut lining, morbidity and mortality. Small sizes of the microplastics enhance their translocation across the gastro-intestinal membranes via endocytosis-like mechanisms and distribution into tissues and organs. While in biological systems, microplastics increase dysregulation of gene expression required for the control of oxidative stress and activating the expression of nuclear factor E2-related factor (Nrf) signaling pathway in marine vertebrates and invertebrates. These alterations are responsible for microplastics induction of oxidative stress, immunological responses, genomic instability, disruption of endocrine system, neurotoxicity, reproductive abnormities, embryotoxicity and trans-generational toxicity. It is possible that the toxicological effects of microplastics will continue beyond 2020 the timeline for its ending by world environmental groups. Considering that most countries in African and Asia (major contributors of global plastic pollutions) are yet to come to terms with the enormity of microplastic pollution. Hence, majority of countries from these regions are yet to reduce, re-use or re-circle plastic materials to enhance its abatement.

Prince O Ukaogo, Ugochukwu Ewuzie, C. Onwuka

Abstract Environmental pollution is not a new phenomenon, yet it remains the world’s greatest problem facing humanity, and the leading environmental causes of morbidity and mortality. Man’s activities through urbanization, industrialization, mining, and exploration are at the forefront of global environmental pollution. Both developed and developing nations share this burden together, though awareness and stricter laws in developed countries have contributed to a larger extent in protecting their environment. Despite the global attention towards pollution, the impact is still being felt due to its severe long-term consequences. This chapter examines the types of pollution—air, water, and soil; the causes and effects of pollution; and proffers solutions in combating pollution for sustainable environment and health.

G. Izydorczyk, K. Mikula, D. Skrzypczak et al.

This paper presents the latest overview of the environmental impact of wastes from the non-ferrous metallurgical industry. Ashes, slags and dusts - by-products from mining and metal processing - are sources of toxic metals, such as Pb, Cd, Hg, As, Al, as well as particulate matter. Physical, chemical and biological processes transform industrial wastes and cause water, soil and air pollution. Improperly protected heaps are subject to wind erosion and rain water leaching. Heavy metals and particulate matter are transported over long distances, contaminating the soil, living areas, watercourses, while in combination with mist they create smog. Water erosion releases heavy metals, which are leached into groundwater or surface runoff. This paper focuses on the range of pollution emissions from non-ferrous metallurgy wastes, hazards, mechanisms of their formation and fallouts, on the current state of technology and technological risk reduction solutions. The impact of pollution on human health and the biosphere, and methods of waste reduction in this industry sector are also presented. A sustainable and modern mining industry is the first step to cleaner production.

J. Chatkin, L. Corrêa, U. Santos

Air pollution is a worrisome risk factor for global morbidity and mortality and plays a special role in many respiratory conditions. It contributes to around 8 million deaths/year, with outdoor exposure being responsible for more than 4.2 million deaths throughout the world, while more than 3.8 million die from situations related to indoor pollution. Pollutant agents induce several respiratory symptoms. In addition, there is a clear interference in numerous asthma outcomes, such as incidence, prevalence, hospital admission, visits to emergency departments, mortality, and asthma attacks, among others. The particulate matter group of pollutants includes coarse particles/PM 10 , fine particles/PM 2.5 , and ultrafine particles/PM 0.1 . The gaseous components include ground-level ozone, nitrogen dioxide, sulfur dioxide, and carbon monoxide. The timing, load, and route of allergen exposure are other items affecting allergic disease phenotypes. The complex interaction between pollutant exposures and human host factors has an implication in the development and rise of asthma as a public health problem. However, there are hiatuses in the understanding of the pathways in this disease. The routes through which pollutants induce asthma are multiple, and include the epigenetic changes that occur in the respiratory tract microbiome, oxidative stress, and immune dysregulation. In addition, the expansion of the modern Westernized lifestyle, which is characterized by intense urbanization and more time spent indoors, resulted in greater exposure to polluted air. Another point to consider is the different role of the environment according to age groups. Children growing up in economically disadvantaged neighborhoods suffer more important negative health impacts. This narrative review highlights the principal polluting agents, their sources of emission, epidemiological findings, and mechanistic evidence that links environmental exposures to asthma.

Mir Mohammad Ali, Delower Hossain, Al-Imran et al.

Heavy metals (HMs) are natural environmental constituents, but their geochemical processes and biochemical equilibrium have been altered by indiscriminate use for human purposes. Due to their toxicity, persistence in the environment and bioaccumulative nature; HMs are well-known environmental contaminants. As result, there is excess release into natural resources such as soil and marine habitats of heavy metals such as cadmium, chromium, arsenic, mercury, lead, nickel, copper, zinc, etc. Their natural sources include the weathering of metal-bearing rocks and volcanic eruptions, while mining and other industrial and agricultural practices include anthropogenic sources. Prolonged exposure and increased accumulation of such heavy metals may have detrimental effects on human life and aquatic biota in terms of health. Finally, the environmental issue of public health concern is the pollution of marine and terrestrial environments with toxic heavy metals. Therefore, because of the rising degree of waste disposal from factories day by day, it is a great concern. Pollution of HMs is therefore a problem and the danger of this environment needs to be recognized.

Torsten Tiltack

This paper introduces AIJIM, the Artificial Intelligence Journalism Integration Model -- a novel framework for integrating real-time AI into environmental journalism. AIJIM combines Vision Transformer-based hazard detection, crowdsourced validation with 252 validators, and automated reporting within a scalable, modular architecture. A dual-layer explainability approach ensures ethical transparency through fast CAM-based visual overlays and optional LIME-based box-level interpretations. Validated in a 2024 pilot on the island of Mallorca using the NamicGreen platform, AIJIM achieved 85.4\% detection accuracy and 89.7\% agreement with expert annotations, while reducing reporting latency by 40\%. Unlike conventional approaches such as Data-Driven Journalism or AI Fact-Checking, AIJIM provides a transferable model for participatory, community-driven environmental reporting, advancing journalism, artificial intelligence, and sustainability in alignment with the UN Sustainable Development Goals and the EU AI Act.

Xu Fan, Yuetan Lin, Bing Gong et al.

Air pollution has become a major threat to human health, making accurate forecasting crucial for pollution control. Traditional physics-based models forecast global air pollution by coupling meteorology and pollution processes, using either online or offline methods depending on whether fully integrated with meteorological models and run simultaneously. However, the high computational demands of both methods severely limit real-time prediction efficiency. Existing deep learning (DL) solutions employ online coupling strategies for global air pollution forecasting, which finetune pollution forecasting based on pretrained atmospheric models, requiring substantial training resources. This study pioneers a DL-based offline coupling framework that utilizes bilinear pooling to achieve offline coupling between meteorological fields and pollutants. The proposed model requires only 13% of the parameters of DL-based online coupling models while achieving competitive performance. Compared with the state-of-the-art global air pollution forecasting model CAMS, our approach demonstrates superiority in 63% variables across all forecast time steps and 85% variables in predictions exceeding 48 hours. This work pioneers experimental validation of the effectiveness of meteorological fields in DL-based global air pollution forecasting, demonstrating that offline coupling meteorological fields with pollutants can achieve a 15% relative reduction in RMSE across all pollution variables. The research establishes a new paradigm for real-time global air pollution warning systems and delivers critical technical support for developing more efficient and comprehensive AI-powered global atmospheric forecasting frameworks.

Nazanin Zounemat-Kermani, Sadjad Naderi, Claire H. Dilliway et al.

Air pollution is a growing global health threat, exacerbated by climate change and linked to cardiovascular and respiratory diseases. While personal sensing devices enable real-time physiological monitoring, their integration with environmental data for individualised health prediction remains underdeveloped. Here, we present a modular, cloud-based framework that predicts personalised physiological responses to pollution by combining wearable-derived data with real-time environmental exposures. At its core is an Adversarial Autoencoder (AAE), initially trained on high-resolution pollution-health data from the INHALE study and fine-tuned using smartwatch data via transfer learning to capture individual-specific patterns. Consistent with changes in pollution levels commonly observed in the real-world, simulated pollution spikes (+100%) revealed modest but measurable increases in vital signs (e.g., +2.5% heart rate, +3.5% breathing rate). To assess clinical relevance, we analysed U-BIOPRED data and found that individuals with such subclinical vital sign elevations had higher asthma burden scores or elevated Fractional Exhaled Nitric Oxide (FeNO), supporting the physiological validity of these AI-predicted responses. This integrative approach demonstrates the feasibility of anticipatory, personalised health modelling in response to environmental challenges, offering a scalable and secure infrastructure for AI-driven environmental health monitoring.

Dennis Teutscher, Fedor Bukreev, Adrian Kummerlaender et al.

Digital twin (DT) technology is increasingly used in urban planning, leveraging real-time data integration for environmental monitoring. This paper presents an urban-focused DT that combines computational fluid dynamics simulations with live meteorological data to analyze pollution dispersion. Addressing the health impacts of pollutants like particulate matter and nitrogen dioxide, the DT provides real-time updates on air quality, wind speed, and direction. Using OpenStreetMaps XML-based data, the model distinguishes between porous elements like trees and solid structures, enhancing urban flow analysis. The framework employs the lattice Boltzmann method (LBM) within the open-source software OpenLB to simulate pollution transport. Nitrogen dioxide and particulate matter concentrations are estimated based on traffic and building emissions, enabling hot-spot identification. The DT was used from November 7 to 23, 2024, with hourly updates, capturing pollution trends influenced by wind patterns. Results show that alternating east-west winds during this period create a dynamic pollution distribution, identifying critical residential exposure areas. This work contributes a novel DT framework that integrates real-time meteorological data, OpenStreetMap-based geometry, and high-fidelity LBM simulations for urban wind and pollution modeling. Unlike existing DTs, which focus on structural monitoring or large-scale environmental factors, this approach enables fine-grained, dynamic analyses of urban airflow and pollution dispersion. By allowing interactive modifications to urban geometry and continuous data updates, the DT serves as a powerful tool for adaptive urban planning, supporting evidence-based policy making to improve air quality and public health.

Luca Aiello, Raffaele Argiento, Sirio Legramanti et al.

Air pollution is a major global health hazard, with fine particulate matter (PM10) linked to severe respiratory and cardiovascular diseases. Hence, analyzing and clustering spatio-temporal air quality data is crucial for understanding pollution dynamics and guiding policy interventions. This work provides a review of Bayesian nonparametric clustering methods, with a particular focus on their application to spatio-temporal data, which are ubiquitous in environmental sciences. We first introduce key modeling approaches for point-referenced spatio-temporal data, highlighting their flexibility in capturing complex spatial and temporal dependencies. We then review recent advancements in Bayesian clustering, focusing on spatial product partition models, which incorporate spatial structure into the clustering process. We illustrate the proposed methods on PM10 monitoring data from Northern Italy, demonstrating their ability to identify meaningful pollution patterns. This review highlights the potential of Bayesian nonparametric methods for environmental risk assessment and offers insights into future research directions in spatio-temporal clustering for public health and environmental science.

F. O. Ajibade, F. O. Ajibade, B. Adelodun et al.

Abstract Environmental pollution is increasingly becoming an issue of significant public interest in many developing countries and the international community at large. The growing socioeconomic developments as witnessed globally in recent times due to rapid urbanization and industrialization have led to the overexploitation of natural resources, while inadvertently resulted in severe environmental problems. In this chapter, we attempted to present the effect of environmental pollution to our physical environment as well as its socioeconomic impacts. Specifically, we focused more on the major types of pollution, which are widely recognized as international public health problems, namely, land/soil, water, air, noise, and plastic/microplastic pollution, enumerating their effects and how they have acted as an obstruction to the social and economic progress by placing tremendous pressure on resources and environment. Possible ways of ensuring environmental quality and sustainability leading to improved public health and well-being alongside with different kinds of intervention were proposed.

H. Tsai, Pei-Yu Wu, Jiun-Chi Huang et al.

Chronic kidney disease (CKD) is a global public health problem associated with high rates of morbidity and mortality due to end-stage renal disease and cardiovascular disease. Safe and effective medications to reverse or stabilize renal function in patients with CKD are lacking, and hence it is important to identify modifiable risk factors associated with worsening kidney function. Environmental pollutants, including metals, air pollutant, phthalate and melamine can potentially increase the risk of CKD or accelerate its progression. In this review, we discuss the epidemiological evidence for the association between environmental pollution and kidney disease, including heavy metals, air pollution and other environmental nephrotoxicants in the general population.

Keshav Goyal, Sooraj Sathish, Shrisha Rao

In the context of increasingly complex environmental challenges, effective pollution control mechanisms are crucial. By extending the state of the art auction mechanisms, we aim to develop an efficient approach for allocating pollution abatement resources in a multi-pollutant setting with pollutants affecting each other's reduction costs. We modify the Combinatorial Multi-Round Ascending Auction for the auction of escape permits of pollutants with co-dependent reduction processes, specifically, greenhouse gas emissions and nutrient runoff in Finnish agriculture. We show the significant advantages of this mechanism in pollution control through experiments on the bid prices and amount of escape permits sold in multiple auction simulations.

Fan Zhao, Yijia Chen, Dianhan Xi et al.

Hermit crabs play a crucial role in coastal ecosystems by dispersing seeds, cleaning up debris, and disturbing soil. They serve as vital indicators of marine environmental health, responding to climate change and pollution. Traditional survey methods, like quadrat sampling, are labor-intensive, time-consuming, and environmentally dependent. This study presents an innovative approach combining UAV-based remote sensing with Super-Resolution Reconstruction (SRR) and the CRAB-YOLO detection network, a modification of YOLOv8s, to monitor hermit crabs. SRR enhances image quality by addressing issues such as motion blur and insufficient resolution, significantly improving detection accuracy over conventional low-resolution fuzzy images. The CRAB-YOLO network integrates three improvements for detection accuracy, hermit crab characteristics, and computational efficiency, achieving state-of-the-art (SOTA) performance compared to other mainstream detection models. The RDN networks demonstrated the best image reconstruction performance, and CRAB-YOLO achieved a mean average precision (mAP) of 69.5% on the SRR test set, a 40% improvement over the conventional Bicubic method with a magnification factor of 4. These results indicate that the proposed method is effective in detecting hermit crabs, offering a cost-effective and automated solution for extensive hermit crab monitoring, thereby aiding coastal benthos conservation.

I. Luviano Soto, Y. Concha Sánchez, A. Raya

This study investigates the application of an artificial neural network framework for analysing water pollution caused by solids. Water pollution by suspended solids poses significant environmental and health risks. Traditional methods for assessing and predicting pollution levels are often time-consuming and resource-intensive. To address these challenges, we developed a model that leverages a comprehensive dataset of water quality from total suspended solids. A convolutional neural network was trained under a transfer learning approach using data corresponding to different total suspended solids concentrations, with the goal of accurately predicting low, medium and high pollution levels based on various input variables. Our model demonstrated high predictive accuracy, outperforming conventional statistical methods in terms of both speed and reliability. The results suggest that the artificial neural network framework can serve as an effective tool for real-time monitoring and management of water pollution, facilitating proactive decision-making and policy formulation. This approach not only enhances our understanding of pollution dynamics but also underscores the potential of machine learning techniques in environmental science.

Alisa Luangrath, Joorim Na, Pandi Kalimuthu et al.

Biodegradable plastics (BPs) are widely used as alternatives to non-BPs due to their inherent ability to undergo facile degradation. However, the ecotoxicological impact of biodegradable microplastics (MPs) rarely remains scientific documented especially to aquatic ecosystem and organisms compared to conventional microplastics. Therefore, this study aimed to investigate the ecotoxicity of biodegradable polylactic acid (PLA) MPs to Daphnia magna with that of conventional polyethylene (PE) MPs with and without ultraviolet (UV) treatment (4 weeks). The acute toxicity (48 h) of PLA MPs was significantly higher than that of PE MPs, potentially attributable to their elevated bioconcentration resulting from their higher density. UV treatment notably reduced the particle size of PLA MPs and induced new hydrophilic functional groups containing oxygen. Thus, the acute lethal toxicity of PLA MPs exhibited noteworthy increase, compared to before UV treatment after UV treatment, which was greater than that of UV-PE MPs. In addition, UV-PLA MPs showed markedly elevated reactive oxygen species concentration in D. magna compared to positive control. However, there was no significant increase in the level of lipid peroxidation, possibly due to successful defense by antioxidant enzymes (superoxide dismutase and catalase). These findings highlight the ecotoxicological risks of biodegradable MPs to aquatic organisms, which require comprehensive long-term studies.

Pavlos Tafidis, Mehdi Gholamnia, Payam Sajadi et al.

Abstract Air pollution is a significant and pressing environmental and public health concern in urban areas, primarily driven by road transport. By gaining a deeper understanding of how traffic dynamics influence air pollution, policymakers and experts can design targeted interventions to tackle these critical issues. In order to analyse this relationship, a series of regression algorithms were developed utilizing the Google Project Air View (GPAV) and Dublin City’s SCATS data, taking into account various spatiotemporal characteristics such as distance and weather. The analysis showed that Gaussian Process Regression (GPR) mostly outperformed Support Vector Regression (SVR) for air quality prediction, emphasizing its suitability and the importance of considering spatial variability in modelling. The model describes the data best for particulate matter (PM2.5) emissions, with R-squared (R2) values ranging from 0.40 to 0.55 at specific distances from the centre of the study area based on the GPR model. The visualization of pollutant concentrations in the study area also revealed an association with the distance between intersections. While the anticipated direct correlation between vehicular traffic and air pollution was not as pronounced, it underscores the complexity of urban emissions and the multitude of factors influencing air quality. This revelation highlights the need for a multifaceted approach to policymaking, ensuring that interventions address a broader spectrum of emission sources beyond just traffic. This study advances the current knowledge on the dynamic relationship between urban traffic and air pollution, and its findings could provide theoretical support for traffic planning and traffic control applicable to urban centres globally.

Luis D. Loor-Urgilés, Tabata N. Feijoó, Carlos A. Martínez-Huitle et al.

Abstract Chemical oxygen demand-COD is essential for water pollution control and monitoring and is also used to validate wastewater treatment technologies. Conventional COD determination use of costly toxic inputs that do not align with Sustainable Development Goals 6. To address these environmental challenges, photocatalytic (PC)- and photoelectrocatalytic (PEC)-COD sensors have emerged as a solution. This comprehensive review examines PC-COD and PEC-COD sensors in terms of nanomaterials used and their properties, focusing on how multiple variables influence PC activity and sensor performance. Analytical principles and operational variables affecting performance in COD determination are discussed. Finally, a series of materials and conditions are proposed to improve the viability of PEC-COD sensors currently and in the future.

Mohammad Erfatpour, Dustin MacLean, Rachid Lahlali et al.

The ovule is a plant structure that upon fertilization, transforms into a seed. Successful fertilization is required for optimum crop productivity and is strongly affected by environmental conditions including temperature and precipitation. Climate change refers to sustained changes in global or regional climate patterns over an extended period, typically decades to millions of years. These shifts can result from natural processes like volcanic eruptions and solar radiation fluctuations, but in recent times, human activities—especially the burning of fossil fuels, deforestation, and industrial emissions—have accelerated the pace and scale of climate change. Human-induced climate change impacts the agricultural sector mainly through global warming and altering weather patterns, both of which create conditions that challenge agricultural production and food security. With food demand projected to sharply increase by 2050, urgent action is needed to prevent the worst impacts of climate change on food security and allow time for agricultural production systems to adapt and become more resilient. Gaining insights into the female reproductive part of the flower and seed development under extreme environmental conditions is important to oversee plant evolution, agricultural productivity, and food security in the face of climate change. This review summarizes the current knowledge on plant reproductive development and the effects of temperature and water stress, soil salinity, elevated carbon dioxide, and ozone pollution on the female reproductive structure and development across grain legumes, cereal, oilseed, and horticultural crops. It identifies gaps in existing studies for potential future research and suggests suitable mitigation strategies for sustaining crop productivity in a changing climate.

Kashif Abbass, Huaming Song, Farina Khan et al.