Hasil untuk "Environmental pollution"

S. Sarkodie, S. Adams

This study examined the impact of disaggregate and aggregate energy, economic development, urbanization and political institutional quality on environmental pollution using a time series data spanning from 1971 to 2017. The study employed response surface regressions, structural break cumulative sum (CUSUM) test based on recursive residuals and ordinary least squares (OLS) residuals for parameter stability en route to estimating the autoregressive distributed lag (ARDL) regression. The environmental Kuznets curve (EKC) hypothesis is valid in South Africa with an extreme point of ZAR 56,114 which occurred in 2011. Evidence from the study reveals that political institutional quality plays a huge role in the social, governance and economic readiness to mitigate climate change and its impact. Structural adjustment in disaggregate and aggregate energy consumption, economic growth, and political institutional quality play a critical role in environmental quality. Fossil-fuel rich countries require diversification of the energy portfolio by incorporating renewable energy sources which will promote environmental sustainability and improve air quality while reducing their economy's vulnerability to price volatility. A paradigm shift from energy and carbon-intensive industries to a service-oriented economy will cause a structural economic change thus, aiding in the mitigation of climate change and its impacts.

Xurui Mai, Jing Tang, Juexuan Tang et al.

Controlling heavy metal pollution in agricultural soil has been a significant challenge. These heavy metals seriously threaten the surrounding ecological environment and human health. The effective assessment and remediation of heavy metals in agricultural soils are crucial. These two aspects support each other, forming a close and complete decision-making chain. Therefore, this review systematically summarizes the distribution characteristics of soil heavy metal pollution, the correlation between soil and crop heavy metal contents, the presence pattern and migration and transformation mode of heavy metals in the soil-crop system. The advantages and disadvantages of the risk evaluation tools and models of heavy metal pollution in farmland are further outlined, which provides important guidance for an in-depth understanding of the characteristics of heavy metal pollution in farmland soils and the assessment of the environmental risk. Soil remediation strategies involve multiple physical, chemical, biological and even combined technologies, and this paper compares the potential and effect of the above current remediation technologies in heavy metal polluted farmland soils. Finally, the main problems and possible research directions of future heavy metal risk assessment and remediation technologies in agricultural soils are prospected. This review provides new ideas for effective assessment and selection of remediation technologies based on the characterization of soil heavy metals.

Kui Liu, Boqiang Lin

Abstract As the problem of China's environmental pollution is becoming more serious, people have paid more attention to the environmental pollution control. In this paper, we first built a comprehensive environmental pollution index including sulfur dioxide, smoke and dust, wastewater and solid waste emissions to measure the environmental pollution of each province synthetically. The method of space econometrics was then applied to analyze the main influencing factors of environmental pollution. Three conclusions were drawn from the empirical results. First, there is a significant spatial correlation between environmental pollution of different provinces. Second, there exists an inverted N relationship between environmental pollution and the economic development, moreover, most provinces have not arrived the second turning point of the inverted N curve except for several economically developed provinces. Third, both industrial structure and R&D investment have a significant influence on environmental pollution, while the influence of FDI is not significant. All these imply the necessity to establish a regional joint prevention and control mechanism to cope with the increasing environmental pollution.

Danish, Zhaohua Wang

The dilemma of rising environmental pollution in recent decades has raised the demand for clean energy sources. However, some newly developed energy sources, such as biomass energy, may or may not contribute to reducing environmental stress. This study investigates biomass energy consumption and environmental pollution in BRICS countries by applying the generalized system method of moment (GMM) model for empirical estimation from 1992 to 2013. The results show that biomass energy consumption behaves as a clean energy source in reducing environmental pollution. The study also found support for the presence of an N-shaped relationship between income and pollution and found that trade openness is the only factor that contributes to pollution in BRICS countries. These results can assist policymakers in considering biomass energy as a clean source of energy in the effort to achieve both energy security and environmental sustainability.

Shuhong Wang, Yuqing He, M. Song

The production and trade activities of global value chains (GVC) increasingly contribute to the gross domestic product (GDP) of developing countries. However, while the development of domestic economies brings about technological progress, it also may have negative consequences such as environmental pollution. Technological progress can be conducive to reducing pollution emissions. Our motive is to realize the dual effects of technological progress and energy saving and emission reduction in developing countries' participation in the global value chain through research on GVC, technological progress and environmental pollution. Improving environmental quality through technological advances and reducing the adverse effects of inequality in trade status, enabling developing countries to obtain dividends from participating in the value chain as much as possible, and mitigating the adverse effects of environmental pollution during development. And lay the policy foundation for developing countries to go green. However, most of the current researches focus on the relationship between GVC and technological progress or GVC and environmental pollution, and the research on the relationship between the three is scarce. We use the WIOD database to try to find the relationship between developing country global value chain participation, technological progress and environmental pollution. Wang's method for GVC embedding is adopted for empirical research on input and output, energy utilization, high-skilled labor ratio, and pollution emissions in five typical developing countries, namely, Brazil, Russia, India, China, and Mexico. The relationships among the degree of participation in GVC, technological progress, and environmental pollution is studied from the perspective of industries in each country. The outcomes indicate that there is a value chain threshold in developing countries. When the degree of participation in a value chain is lower than the threshold, technological progress can result in an increase in pollution; otherwise, technological progress can reduce emissions. These results provide a theoretical basis and practical suggestions for developing countries to realize their own energy conservation, emission reductions, and green development while participating in globalized value chains. However, because the data related to the WIOD database is limited to 1995-2009, there are a few flaws in data integrity, which is also a challenge we face.

Y. Hao, Shaoqing Zheng, Mingyuan Zhao et al.

Abstract Since China’s reform and opening up, the urban economy has substantially developed, and the nation’s urbanization process has advanced to gradually enter a “new normal” phase. In this process, China’s population and industries continue to inundate its cities, and environmental pollution has become a critical problem. This study uses panel data spanning 1998 to 2015 from 29 Chinese provinces and a comprehensive framework of well-designed generalized method of moments (GMM) models to accurately examine the relationships between urbanization and per capita sulfur dioxide (PSO2), per capita industrial soot (PSOOT), and per capita industrial waste gas (PIWG). A newly developed dynamic threshold panel model that incorporates GMM characteristics is utilized to explore how urbanization affects environmental pollution under different industrial structures. This study finds evidence of a comprehensive relationship among urbanization development, the industrial structure, and environmental pollution. Urbanization has contributed to increased environmental pollution. A nonlinear relationship exists between urbanization and environmental pollution in China. Further, urbanization increasingly promotes environmental pollution with an increase in the proportion of secondary industries; however, such promotion decreases with an increase in the proportion of tertiary industries.

Ling LE, Linhai LU, Xiaojun LI et al.

ObjectiveCompared with traditional concrete construction, the application of prefabricated assembly construction based on digital twin technology in urban rail transit station construction can effectively ensure component production quality, reduce environmental pollution and lower resource consumption. Therefore, an in-depth research on digital twin technology suitable for prefabricated assembly station construction should be conducted. MethodFirst, in station construction, the overall architecture featuring "4 horizontal + 4 vertical + N platforms" for the application of digital twin technologies, such as BIM (building information modeling) and IoT (Internet of things) is proposed. Second, the modeling process and methodology of BIM are presented. By adopting methods such as mathematical model separation, lightweight processing, and mathematical model association, the established BIM data are imported into the platform, and a technical workflow for uploading IoT monitoring data to the BIM platform is established. Finally, taking a certain underground prefabricated assembly superimposed station in the Phase I project of Jinan Urban Rail Transit Line 8 as a case study, the application effect of the digital twin technology for prefabricated assembly superimposed stations based on BIM+IoT integration is analyzed. Result & Conclusion The proposed digital twin technology shows good application effects in the case station, achieving design goals such as construction progress query, structural safety monitoring, quality management control, and process auxiliary design, and realizing data management interaction and sharing throughout the components full life cycle.

Reema H. Alasfar, R. Isaifan

The concern about aluminum (Al) toxicity has been proven in various cases. Some cases are associated with the fact that Al is a neurotoxic substance that has been found in high levels in the brain tissues of Alzheimer’s disease (AD), epilepsy, and autism patients. Other cases are related to infants, especially premature infants and ones with renal failure, who are at the risk of developing the central nervous system (CNS) and bone toxicity. This risk is a result of infants’ exposure to Al from milk formulas, intravenous-feeding solutions, and possibly from aluminum-containing vaccinations. Furthermore, most antiperspirants contain aluminum compounds that raise human exposure to toxic Al. This review paper is intended to discuss in detail the above concerns associated with aluminum, and hence urges the need for more studies exploring the effects of overexposure to Al and recommending mitigation actions.

M. Destek, A. Aslan

Although it is widely accepted that renewable energy consumption is vital for environmental sustainability, the environmental effectiveness of individual renewable energy types is often overlooked. Therefore, this paper examined the multivariate relationship between disaggregated renewable energy (hydroelectricity, wind, solar and biomass) consumption, economic growth and environmental pollution for the period from 1991 to 2014 in G-7 (The Group of Seven) countries. The study used both augmented mean group estimator and panel bootstrap causality method to consider the cross-sectional dependence and country specific heterogeneity across G-7 countries. Empirical findings indicate that increasing biomass energy consumption was efficient to reduce carbon emission in France, Germany, Japan and the United States; increasing hydroelectricity usage was efficient to reduce carbon emission in Italy and the United Kingdom; wind energy consumption reduced emission in Canada and solar energy usage was efficient on reducing emission in France and Italy for observed period. Moreover, in case of panel, it is found that increasing hydroelectricity, biomass and wind energy consumption reduced carbon emissions while the impact of solar energy consumption is statistically insignificant in G-7 countries. In addition, the hydroelectricity consumption was found the most efficient renewable energy source to reduce environmental pollution for the panel of G-7 countries.

Xiu Wang, Yan Luo

Abstract With China’s economy gradually entering a new stage of high-quality development, how to effectively improve the level of technological innovation, rational use of foreign investment, and reduce the level of environmental pollution have become important tasks of China’s ecological civilization construction. This study used panel data from 30 provinces in China from 2006 to 2016, using FDI quantity and FDI quality as threshold variables, and using the Hansen nonlinear panel threshold regression model to determine whether there is an EKC curve relationship between technological innovation capabilities and environmental pollution test. The results show that there is a complex nonlinear relationship between technological innovation capability and environmental pollution when taking FDI quantity and quality as dual thresholds. When the level of FDI is low, the capacity for scientific and technological innovation aggravates environmental pollution levels, while when the level of FDI crosses a higher threshold, the capacity for scientific and technological innovation improves environmental quality. However, with a further increase of the level of FDI, the positive effect diminishes to some extent. When FDI quality is low, technological innovation capability aggravates environmental pollution levels, but the impact is not significant. With an improvement in FDI quality, the positive effects of scientific and technological innovation ability on environmental pollution are strengthened. Our results can serve as a reference for effectively promoting scientific and technological innovation and for achieving green sustainable development.

T. Lomonaco, Enrico Manco, A. Corti et al.

Environmental pollution associated to plastic debris is gaining increasing relevance not only as a threat to ecosystems but also for its possible harmful effects on biota and human health. The release of toxic volatile organic compounds (VOCs) is a potential hazard associated with the environmental weathering of plastic debris. Artificial aging of reference polymers (polystyrene, polypropylene, polyethylene terephthalate, high and low density polyethylene) was performed in a Solar Box at 40 °C and 750 W/m2. The volatile degradation products were determined before and after 1, 2, 3 and 4 weeks of aging using a validated analytical procedure combining headspace (HS) with needle trap microextraction (NTME) and gas chromatography/mass spectrometry (GC-MS). A progressive increase in VOCs was observed during artificial photo-degradation, whose chemical profile resulted polymer-dependent and included carbonyls, lactones, esters, acids, alcohols, ethers, aromatics. The amount of extractable fraction in polar solvents generally showed a similar trend. The same analytical procedure was used to determine VOCs released from plastic debris collected at a marine beach. All samples released harmful compounds (e.g. acrolein, benzene, propanal, methyl vinyl ketone, and methyl propenyl ketone), supporting the initial hypothesis that microplastics represent an unrecognized source of environmental pollution.

Yuan Hong, X. Lyu, Y. Chen et al.

Abstract Given the important economic role played by local governments in the development of industries in China, this paper incorporates the competition behaviors of local governments into the analytical framework of environmental externalities of industrial agglomeration; accordingly, it theoretically explores the environmental externalities that may arise from industrial agglomeration induced by local governments' competition. On the basis of panel data covering 282 Chinese prefecture-level cities from 2007 to 2016, this paper uses spatial econometric methods to test the impact of the interaction between industrial agglomeration and local governments' competition on environmental pollution and spatial spillover effects. The empirical test results show that, compared with simple industrial agglomeration, the combination of industrial agglomeration and local governments' competition is the key factor explaining the current changes in the environmental pollution level and that the interaction between industrial agglomeration mode and local government competitive behaviors aggravates environmental pollution. Among them, the interaction between specialized agglomeration and local governments' tax competition exerts negative externalities on the environment through the overcrowding effect, while the interaction between diversified agglomeration and local governments' investment competition has negative externalities on the environment through the centralized emission effect. Both have significant spatial spillover effects. Adjacent regions will form a larger-scale overcrowding effect and concentrated emission effect under the joint influence of the “race to the bottom” competitive strategy interaction and related industries. Lastly, based on our research conclusion, this paper proposes some policy suggestions to optimize local governments’ competitive behaviors and industrial agglomeration and other market forces to jointly control environmental pollution.

M. Bonato, F. Corrà, M. Bellio et al.

Due to their unique properties, perfluorinated substances (PFAS) are widely used in multiple industrial and commercial applications, but they are toxic for animals, humans included. This review presents some available data on the PFAS environmental distribution in the world, and in particular in Europe and in the Veneto region of Italy, where it has become a serious problem for human health. The consumption of contaminated food and drinking water is considered one of the major source of exposure for humans. Worldwide epidemiological studies report the negative effects that PFAS have on human health, due to environmental pollution, including infertility, steroid hormone perturbation, thyroid, liver and kidney disorders, and metabolic disfunctions. In vitro and in vivo researches correlated PFAS exposure to oxidative stress effects (in mammals as well as in other vertebrates of human interest), produced by a PFAS-induced increase of reactive oxygen species formation. The cellular antioxidant defense system is activated by PFAS, but it is only partially able to avoid the oxidative damage to biomolecules.

Adel Beig Babaei, Arash Dara, Hanieh Sadat Taheri

This study examines the effectiveness of a composite material made from reduced graphene oxide (rGO) and magnetic nanoparticles for the adsorption of heavy metals from wastewater, addressing a critical environmental issue as heavy metal pollution poses significant risks to human health. Traditional water treatment methods often fail to adequately remove these persistent contaminants. The composite takes advantage of graphene's high surface area and adsorption capacity, while the magnetic nanoparticles facilitate easy separation and reusability of the adsorbent. Characterization techniques such as X-ray diffraction (XRD) and Raman spectroscopy were employed to confirm the composite's structural integrity and the presence of rGO, highlighting its functional properties. The study's focus lay on investigating mercury removal efficiency across varying pH levels (1–6), temperatures (25 °C), mercury concentrations (10 g/L), adsorbent amounts (0.01–0.05 g/L), and contact times (120–360 s). The findings indicated that optimal mercury adsorption occurred at pH 6, with a 100 s contact time, 25 °C, and 0.05 g of adsorbent. The maximum mercury removal achieved was quantified at 9.15 µg/L, demonstrating the potential of iron nanoparticle-magnetized nano graphene oxide as an efficient and sustainable solution for heavy metal remediation in wastewater treatment applications. The results obtained showed that graphene oxide magnetized with iron nanoparticles can be effectively used to remove mercury from water and wastewater samples.Overall, this research highlights a promising pathway towards addressing the pressing challenge of water pollution with heavy metals.

Akram Aqil Syahru, Nasrullah, Aven Ghina Salsabila et al.

Environmental degradation driven by negative externalities and fiscal inequality demands a reconfiguration of taxation grounded in the Polluter Pays Principle (PPP). This study aims to develop a normative–comparative framework for a green tax system that internalizes pollution costs while promoting fiscal justice. Using a normative legal research method, the analysis explores the theoretical and institutional foundations of green taxation, drawing from Indonesia’s environmental legislation, the Rio Declaration, and European Union guidelines, while examining fiscal equity and progressive redistribution. A comparative perspective highlights the implementation of PPP across jurisdictions: South Africa’s carbon tax, Portugal’s corporate and VAT-based green tax, and Indonesia’s emerging carbon pricing scheme. The study focuses on legal mechanisms of redistribution, including targeted cash transfers, tax credits, and tax-shift models, as well as the role of fiscal transparency and administrative oversight in mitigating regressive impacts. The findings indicate that a green tax framework rooted in PPP and supported by progressive redistribution and legal transparency enhances ecological accountability, social equity, and policy legitimacy. This paper contributes to environmental fiscal reform discourse by proposing a legally grounded and equitable model for sustainable green tax implementation.

I. A. Zhirnov, K. A. Nazmieva, A. I. Khabibullina et al.

Introduction. The environment undoubtedly affects the physiological processes in human body, which may be both beneficial and harmful. From 2011 to 2021, the incidence of female infertility in Russia increased by 30 % reaching 789.1 cases per 100,000 women in 2021.Aim: to assess an impact of environmental factors including endocrine disrupting chemicals (EDC) on women's reproductive health by assessing available publications.Materials and Methods. Available studies were searched through the scientific literature databases until April 2024 by revealing 5,732 articles in the PubMed/MEDLINE database, 6,587 in Google Scholar and 2,350 in eLibrary. The publications were selected in accordance with PRISMA recommendations. The current review included 90 publications.Results. Fertility experimental and epidemiological studies showed that environmental factors such as climate, temperature, seasonality, radiation, air pollution, diet and energy balance, working environment, bad habits (e.g. smoking), EDC (plasticizers, heavy metals, parabens, pesticides, industrial chemicals and their by-products, medicines, perfluorochemicals, antibacterial agents) may be associated with impaired female reproductive function.Conclusion. Environmental factors, including EDC, have a significant impact on women's reproductive health and can negatively affect fertility. The main findings of current research confirm a need to raise awareness of the risks associated with exposure to chemicals on women's body.

Nghiem Thanh Pham, Tung Kieu, Duc-Manh Nguyen et al.

Small Language Models (SLMs) offer computational efficiency and accessibility, yet a systematic evaluation of their performance and environmental impact remains lacking. We introduce SLM-Bench, the first benchmark specifically designed to assess SLMs across multiple dimensions, including accuracy, computational efficiency, and sustainability metrics. SLM-Bench evaluates 15 SLMs on 9 NLP tasks using 23 datasets spanning 14 domains. The evaluation is conducted on 4 hardware configurations, providing a rigorous comparison of their effectiveness. Unlike prior benchmarks, SLM-Bench quantifies 11 metrics across correctness, computation, and consumption, enabling a holistic assessment of efficiency trade-offs. Our evaluation considers controlled hardware conditions, ensuring fair comparisons across models. We develop an open-source benchmarking pipeline with standardized evaluation protocols to facilitate reproducibility and further research. Our findings highlight the diverse trade-offs among SLMs, where some models excel in accuracy while others achieve superior energy efficiency. SLM-Bench sets a new standard for SLM evaluation, bridging the gap between resource efficiency and real-world applicability.

Nacira Agram, Bernt Øksendal, Frank Proske et al.

This paper studies a nonzero-sum stochastic differential game in the context of shared spatial-domain pollution control. The pollution dynamics are governed by a stochastic partial differential equation (SPDE) driven by a Brownian sheet, capturing the stochastic nature of environmental fluctuations. Two players, representing different regions, aim to minimize their respective cost functionals, which balance pollution penalties with the cost of implementing control strategies. The nonzero-sum framework reflects the interdependent yet conflicting objectives of the players, where both cooperation and competition influence the outcomes. We derive necessary and sufficient conditions for Nash equilibrium strategies, using a maximum principle approach. This approach involves the introduction of a new pair of adjoint variables, (L_1, L_2), which do not appear in a corresponding formulation with the classical (1-parameter) Brownian motion. Finally, we apply our results to two case studies in pollution control, demonstrating how spatial and stochastic dynamics shape the equilibrium strategies.

Song Ma, Yanchao Wang, Richard Bucknall et al.

This paper proposes an uncertainty-aware marine pollution source tracking framework for unmanned surface vehicles (USVs). By integrating high-fidelity marine pollution dispersion simulation with informative path planning techniques, we demonstrate effective identification of pollution sources in marine environments. The proposed approach is implemented based on Robot Operating System (ROS), processing real-time sensor data to update probabilistic source location estimates. The system progressively refines the estimation of source location while quantifying uncertainty levels in its predictions. Experiments conducted in simulated environments with varying source locations, wave conditions, and starting positions demonstrate the framework's ability to localise pollution sources with high accuracy. Results show that the proposed approach achieves reliable source localisation efficiently and outperforms the existing baseline. This work contributes to the development of full autonomous environmental monitoring capabilities essential for rapid response to marine pollution incidents.

Elie Azeraf, Audrey Wagner, Emilie Bialic et al.

Air pollution remains one of the most pressing environmental challenges of the modern era, significantly impacting human health, ecosystems, and climate. While traditional air quality monitoring systems provide critical data, their high costs and limited spatial coverage hinder effective real-time pollutant identification. Recent advancements in micro-sensor technology have improved data collection but still lack efficient methods for source identification. This paper explores the innovative application of machine learning (ML) models to classify pollutants in real-time using only data from optical micro-sensors. We propose a novel classification framework capable of distinguishing between four pollutant scenarios: Background Pollution, Ash, Sand, and Candle. Three Machine Learning (ML) approaches - XGBoost, Long Short-Term Memory networks, and Hidden Markov Chains - are evaluated for their effectiveness in sequence modeling and pollutant identification. Our results demonstrate the potential of leveraging micro-sensors and ML techniques to enhance air quality monitoring, offering actionable insights for urban planning and environmental protection.