Hasil untuk "Environmental pollution"

W. Mrozik, M. Rajaeifar, O. Heidrich et al.

There is a growing demand for lithium-ion batteries (LIBs) for electric transportation and to support the application of renewable energies by auxiliary energy storage systems. This surge in demand requires...

T. Parmar, Dr. Deepak Rawtani, Y. K. Agrawal

K. L. Law, R. Narayan

Venkatesan Selvaraj, Saradhambal Ramachandran Singarasubramania, Parthasarathy Pandu et al.

Abstract This study examines the presence of heavy metals (HMs) and their environmental impacts in samples collected from the surface sediments of Karaikkal Beach, located on the southeastern coast of India. To assess the textural attributes and heavy metal content in the area, 26 sediment samples were collected and analyzed using atomic absorption spectroscopy (AAS). The sediments are composed primarily of sand (98.56%), followed by silt (1.2%), clay (0.41%), and calcium carbonate, which ranges from 3.19% to 6.71%, with a mean value of 4.77% present at a significant level. Organic inputs from riverine sources were also observed to influence sediment composition, and the average organic matter concentration is 0.52%, with values ranging from 0.26% to 0.75%. The HM concentrations followed the descending order: Fe (22,434.42–36,525.69 µg/g) > Mn (230.15–395.49 µg/g) > Cr (114.33–244.63 µg/g) > Ni (13.60–24.22 µg/g) > Pb (29.89–55.96 µg/g) > Cu (22.47–36.52 µg/g) > Zn (24.14–40.69 µg/g) > Co (12.00–20.32 µg/g). Fe and Mn concentrations were primarily controlled by fluvial inputs and terrestrial influences. The derived indices such as enrichment factor (EF), geo-accumulation index (Igeo), contamination factor (CF), pollution load index (PLI), sediment pollution index (SPI), and potential ecological risk index (PERI) reveal that the coastal sediments mostly fall within the unpolluted to slightly polluted categories, indicating a low ecological threat. The origin of metal enrichment in the sediment fractions is attributed to natural geogenic sources. The sources of HMs and their inter-element associations were interpreted using principal component analysis (PCA) and a correlation matrix. These baseline data underscore the importance of continuous environmental monitoring to identify emerging pollution patterns and guide sustainable coastal management.

Zunrong Zhou, Xiang Li

This study examines how green finance influences high-quality economic development, with a particular focus on its spatial spillover mechanisms. Specifically, we investigate the competing roles of technology spillover and the pollution haven effect. Using provincial panel data from China (2010–2021) and applying a Spatial Durbin Model (SDM), we deconstruct the total effect of green finance into three distinct components: the local technological progress effect, the positive technology spillover effect, and the negative pollution haven effect. While acknowledging limitations related to the macro-level data granularity and the indirect nature of the mechanism tests, our analysis yields three main findings. First, green finance development shows significant regional disparities. It has progressed most rapidly in the eastern region, remained relatively stable in the central region, and declined in the western region. Second, green finance exerts a strong positive direct effect on local high-quality economic development. This promoting effect becomes even stronger in more developed regions. Third, green finance generates significant negative spatial spillovers on neighboring regions. These are primarily driven by the pollution haven effect, which involves the cross-regional relocation of polluting industries. However, local technological progress partially mitigates these adverse externalities. Overall, our findings reveal the dual nature of the spatial externalities associated with green finance. They also highlight the urgency of coordinated regional environmental governance to prevent “green leakage” and to promote balanced, high-quality economic development.

Zhi-Nan Lu, Heyin Chen, Y. Hao et al.

Pritisha Sarkar, Kushalava reddy Jala, Mousumi Saha

Acknowledging the effects of outdoor air pollution, the literature inadequately addresses indoor air pollution's impacts. Despite daily health risks, existing research primarily focused on monitoring, lacking accuracy in pinpointing indoor pollution sources. In our research work, we thoroughly investigated the influence of indoor activities on pollution levels. A survey of 143 participants revealed limited awareness of indoor air pollution. Leveraging 65 days of diverse data encompassing activities like incense stick usage, indoor smoking, inadequately ventilated cooking, excessive AC usage, and accidental paper burning, we developed a comprehensive monitoring system. We identify pollutant sources and effects with high precision through clustering analysis and interpretability models (LIME and SHAP). Our method integrates Decision Trees, Random Forest, Naive Bayes, and SVM models, excelling at 99.8% accuracy with Decision Trees. Continuous 24-hour data allows personalized assessments for targeted pollution reduction strategies, achieving 91% accuracy in predicting activities and pollution exposure.

Anika Rahman, Mst. Taskia Khatun

This study analyzes and predicts air pollution in Asia, focusing on PM 2.5 levels from 2018 to 2023 across five regions: Central, East, South, Southeast, and West Asia. South Asia emerged as the most polluted region, with Bangladesh, India, and Pakistan consistently having the highest PM 2.5 levels and death rates, especially in Nepal, Pakistan, and India. East Asia showed the lowest pollution levels. K-means clustering categorized countries into high, moderate, and low pollution groups. The ARIMA model effectively predicted 2023 PM 2.5 levels (MAE: 3.99, MSE: 33.80, RMSE: 5.81, R: 0.86). The findings emphasize the need for targeted interventions to address severe pollution and health risks in South Asia.

Vishal Nedungadi, Muhammad Akhtar Munir, Marc Rußwurm et al.

Air pollution remains a leading global health risk, exacerbated by rapid industrialization and urbanization, contributing significantly to morbidity and mortality rates. In this paper, we introduce AirCast, a novel multi-variable air pollution forecasting model, by combining weather and air quality variables. AirCast employs a multi-task head architecture that simultaneously forecasts atmospheric conditions and pollutant concentrations, improving its understanding of how weather patterns affect air quality. Predicting extreme pollution events is challenging due to their rare occurrence in historic data, resulting in a heavy-tailed distribution of pollution levels. To address this, we propose a novel Frequency-weighted Mean Absolute Error (fMAE) loss, adapted from the class-balanced loss for regression tasks. Informed from domain knowledge, we investigate the selection of key variables known to influence pollution levels. Additionally, we align existing weather and chemical datasets across spatial and temporal dimensions. AirCast's integrated approach, combining multi-task learning, frequency weighted loss and domain informed variable selection, enables more accurate pollution forecasts. Our source code and models are made public here (https://github.com/vishalned/AirCast.git)

Brij Bidhin Desai, Yukta Arvind Rajapur, Aswathi Mundayatt et al.

Urban air pollution poses significant risks to public health, environmental sustainability, and policy planning. Effective air quality management requires predictive tools that can integrate diverse datasets and communicate complex spatial and temporal pollution patterns. There is a gap in interactive tools with seamless integration of forecasting and visualization of spatial distributions of air pollutant concentrations. We present CityAQVis, an interactive machine learning ML sandbox tool designed to predict and visualize pollutant concentrations at the ground level using multi-source data, which includes satellite observations, meteorological parameters, population density, elevation, and nighttime lights. While traditional air quality visualization tools often lack forecasting capabilities, CityAQVis enables users to build and compare predictive models, visualizing the model outputs and offering insights into pollution dynamics at the ground level. The pilot implementation of the tool is tested through case studies predicting nitrogen dioxide (NO2) concentrations in metropolitan regions, highlighting its adaptability to various pollutants. Through an intuitive graphical user interface (GUI), the user can perform comparative visualizations of the spatial distribution of surface-level pollutant concentration in two different urban scenarios. Our results highlight the potential of ML-driven visual analytics to improve situational awareness and support data-driven decision-making in air quality management.

Chao Zhang, Yulin Lu

Since the Industrial Revolution, the world economy has experienced rapid development, and China's economy has also achieved an unprecedented takeoff in the past. Behind the economic growth, population surge, and continuous improvement of people's living standards lies the enormous consumption of fossil energy and environmental pollution. This kind of pollution has caused irreparable damage to the world. The most concerned environmental issue globally at present is the global warming caused by carbon dioxide emissions. China is in a stage of rapid development, and as the largest developing country, China's development path has a significant impact on global climate change. At the same time, the global community also puts pressure on China to limit carbon dioxide emissions. To address energy shortages and environmental issues, countries around the world have introduced corresponding energy and environmental regulations. Due to different culture and government systems, the effects of energy and environmental regulations in various countries are also different. Therefore, it is still necessary to discuss China's energy and environmental regulations.This paper uses data from prefecture-level cities between 2003 and 2008 to discuss the impact of the "Eleventh Five-Year Plan" environmental regulations on urbanization rates. It first provides a theoretical analysis of the relationship between environmental regulation and urbanization, finding that environmental regulation can influence urban population mobility through both crowding-in and crowding-out effects.

Marco Mingione, Pierfrancesco Alaimo Di Loro, Francesco Lagona et al.

Motivated by the study of pollution trends in the city of Bergen, we introduce a flexible statistical framework for modeling multivariate air pollution data via a nonhomogeneous Hidden Semi-Markov Vector Auto-Regression. The hidden process captures unobserved environmental conditions, while the vector autoregressive structure accounts for temporal autocorrelation and cross-pollutant dependencies. The model further allows time-varying environmental conditions to influence both the average levels of pollutant concentrations and the duration of different transient states. Parameters are estimated via maximum likelihood using a tailored Expectation-Maximization (EM) algorithm, integrated with state-specific $\ell_1$ regularization to control overfitting and automatically select relevant temporal lags. The proposal is tested on simulated data under different scenarios and then applied to daily concentrations of nitrogens and particulate matter recorded in a urban area. Environmental risk is assessed by a Shapley value-based decomposition that attribute marginal risk contributions. This approach offers a comprehensive framework for multivariate environmental risk modeling, enabling better identification of high-pollution episodes and informing policy interventions.

Xiaofeng Xu, Weipeng Yu, Xingduo Fan et al.

Abstract Currently, bamboo fibers (BFs) are commonly processed through alkali boiling softening pretreatment, which generates wastewater that poses environmental pollution risks. This process is also complex and requires significant human and material resources. In contrast, the saturated steam softening pretreatment method studied in this study is environmentally friendly and significantly simplifies the post-processing of bamboo fiber preparation. Additionally, it provides methods and parameters for the hygrothermal-mechanical extraction of bamboo fibers. In this study, three-year-old bitter bamboo (Pleioblastus amarus) growing in Zhongtai town, Yuhang district, Hangzhou city, China was selected as the raw material. Firstly, bamboo fibers were prepared by crushing and mechanical extraction after softening through alkaline boiling and saturated steam pretreatment, respectively. The yield, mechanical properties, and other indicators of the fibers were then tested and compared. Subsequently, Scanning electron microscopy (SEM) was employed to observe and comparatively analyze the microstructural morphology of the two types of fibers. Infrared spectroscopy (IR) was performed on the functional groups of bamboo after alkali boiling and saturated steam softening to investigate changes in cellulose, hemicellulose, and lignin. Finally, the mechanism of mechanical extraction of bitter bamboo (Pleioblastus amarus) fibers by saturated steam pretreatment was further analyzed.

Jorge Alonso Alcalá Jáuregui, María Fernanda Ramírez Cubos, Ángel Natanael Rojas Velázquez et al.

Biomass burning (BB) serves as both an energy source and an environmental indicator. This study examined how CO₂ and fine particle emissions vary during the combustion of biomass from three tree species to determine their contribution to environmental pollution. Leave and stem samples were taken from A. farnesiana (huizache) tree, S. molle (pirul), and P. laevigata (mesquite). The dry biomass was thermally processed in a muffle furnace at temperatures ranging from 50°C to 450°C. Emissions of CO₂, particles smaller than 2.5 microns (PM2.5), particles smaller than 10 microns (PM10), and total volatile organic compounds (TVOC) were measured. The highest emission levels occurred during the pyrolysis process between 250°C and 450°C in both leaves and stems. Among the leaves, the highest emissions of PM2.5 and PM10 were found in huizache, while the highest values were found in mesquite stems. In terms of leaves, mesquite had the highest CO₂ emissions, followed by huizache and pirul. Regarding the stems, pirul had the highest atmospheric emissions of CO₂, followed by huizache and mesquite. In all cases, emission levels exceeded the limits established by Mexican and international environmental regulations, indicating a significant risk to the environment and public health. Highlights: • Biomass burning (BB) is the combustion of plant materials, which are widely used for energy production • This study experimentally verified the environmental impacts of biomass burning for three tree species under a laboratory pyrolysis process. • The highest PM5 and PM10 emissions occurred in A. farnesiana leaves and in P. laevigata stems. • The order of highest CO₂ emissions in leaves was laevigata > A. farnesiana > S. molle; in stems, it was S. molle > A. farnesiana > P. laevigata. • Further comparisons across biomass burning sources and processes should strengthen evaluations of environmental impact considering air pollution.

Pilar Ortíz Sandoval, Margarita Aguilera‐Gómez, Anna Kostka et al.

Abstract Anthropogenic activities, such as industrial processes, urban development, intensive agriculture and waste disposal, have significantly contributed to the continuous introduction and accumulation of a wide array of xenobiotic compounds into natural ecosystems. Among them, emerging contaminants (ECs) such as pharmaceuticals, endocrine‐disrupting chemicals (EDCs), and per‐ and polyfluoroalkyl substances (PFAS) are of increasing concern due to their persistence, bioactivity and limited regulation. ECs enter ecosystems through diverse pathways including wastewater discharge, agricultural runoff and atmospheric deposition. Once released, many of these xenobiotics can bioaccumulate in organisms and enter the food chain, posing serious risks to food safety and public health. Traditional physico‐chemical remediation methods are often insufficient or environmentally taxing, prompting a shift toward bio‐based alternatives like bioremediation. These approaches, which rely on the activity of microbial communities to degrade pollutants, offer more sustainable solutions but require further interdisciplinary research to optimise their use. The One Health framework provides an effective model for addressing the complex risks posed by xenobiotics. This research programme aims to harmonise methodologies for cumulative dietary risk assessment across Europe and explore microbial strategies for xenobiotic degradation. By integrating microbiomics, toxicology, environmental science and food safety, this approach supports the development of safer food systems and more effective pollution management in line with the ‘farm to fork’ and One Health principles.

M. Marín-Martín, E. Tejedor, G. Benito et al.

<p>The Mediterranean basin, a recognized climate change hotspot, faces increasing hydroclimatic pressures, particularly from severe drought and precipitation events. To assess contemporary changes and potentially manage future impacts, it is crucial to understand the long-term context of this variability beyond the relatively short instrumental record. This study utilizes tree-ring records to reconstruct past hydroclimate in the Iberian Range of eastern Spain, a water-sensitive Mediterranean environment. We present a well-replicated tree-ring width chronology from <i>Pinus sylvestris</i> and <i>Pinus nigra</i> trees that calibrates and verifies significantly against cumulative instrumental precipitation over a 320 d period ending in June (<span class="inline-formula"><i>r</i></span> <span class="inline-formula">=</span> 0.749; <span class="inline-formula"><i>p</i></span> <span class="inline-formula">&lt;</span> 0.01). The resulting 520-year reconstruction reveals substantial multi-centennial variability in precipitation and reveals an increase in the frequency and intensity of hydroclimatic extremes (both wet and dry) during the late 20th and early 21st centuries compared to the longer-term baseline. The reconstruction has a spatial representativeness centred over eastern and central Iberia and covaries with independent historical drought indices derived from rogation ceremony records during the late 18th and early 19th centuries. The documented intensification of hydroclimatic extremes is consistent with climate change projections and provides a baseline for evaluating ecosystem resilience and water resource vulnerability.</p>

Jie Yang, Yaozhong Wang, Chang Tang et al.

Industrialized nations have witnessed a decline in environmental quality over the years. The potential of digitalization in mitigating environmental pollution is of significant interest. Drawing on firm-level data from listed Chinese companies between 2010 and 2020, including pollutant and financial metrics, this study investigates the influence of digitalization on industrial environmental pollution. We found that digitalization substantially diminishes the intensity of industrial pollution emissions. These findings hold even after employing instrumental variable tests, substituting the dependent variable with carbon dioxide emissions, and conducting a quasi-natural experiment in intelligent manufacturing. Moreover, our exploration of the underlying mechanisms reveals that the decline in pollution emission intensity attributable to digitalization stems from both structural and technological factors; specifically, it enhances environmental investment and fosters green innovation. The benefits of digitalization in curbing emission intensity are pronounced for firms characterized by lower pollution levels, executive leadership with environmental work backgrounds, heightened capital intensity, and elevated media coverage.

Emilia Vlahos, Yayaati Chachan, Vincent Savignac et al.

Atmospheric composition of exoplanets is often considered as a probe of the planet's formation condition. How exactly the initial chemical memory may be altered from the birth to the final state of the planet, however, remains unknown. Here, we develop a simple model of pollution of planetary atmosphere by the vaporization of infalling planetesimal of varying sizes and composition (SiO$_2$ inside 1 au and H$_2$O outside 1 au), following their trajectory and thermal evolution through the upper advective and radiative layers of a sub-Neptune class planet during the late stage of disk evolution. We vary the rate of pollution by changing the solid content of the disk and by dialing the level of disk gas depletion which in turn determines the rate of planetary migration. We find that pollution by silicate grains will always be limited by the saturation limit set by the thermal state of the atmosphere. By contrast, pollution by water ice can lead to $\sim$2--4 orders of magnitude variation in the atmospheric water mass fraction depending on the solid and gas content of the disk. Both cases suggest that post-formation pollution can erase the initial compositional memory of formation. Post-formation pollution can potentially transform sub-Neptunes with H/He-dominated envelope that initially formed beyond the iceline to waterworlds (water-enriched envelope) when the disk gas is depleted by $\gtrsim$2 orders of magnitude, allowing gentle migration. We additionally discuss the expected C/O ratio profile under pollution by water and refractory carbon species.

J. A. Martínez-Cadena, J. M. Sánchez-Cerritos, A. Marin-Lopez et al.

In the recent two decades, the Mexico City Metropolitan Area (MCMA) has been plagued by high concentrations of air pollutants, risking the health integrity of its inhabitants. Although some policies have been undertaken, they have been insufficient to deplete high air pollutants. Environmental contingencies are commonly imposed when the ozone concentration overpasses a certain threshold, which is well above the recommended maximum by the WHO. This work used a causal version of a generalized Morlet wavelet to characterize the dynamics of daily ozone concentration in the MCMA. The results indicated that the formation of dangerous ozone concentration levels is a consequence of accumulation and incomplete dissipation effects acting over a wide range of time scales. Ozone contingencies occurred when the wavelet coefficient power is increasing, which was linked to an inti-persistence behavior. It was proposed that the wavelet methodology could be used as a further tool for signaling the potential formation of adverse ozone pollution scenarios.

Fatai A. Olabemiwo, Claudia Kunney, Rachel Hsu et al.

IntroductionPlastic pollution has surged due to increased human consumption and disposal of plastic products. Microbial communities capable of utilizing plastic as a carbon source may play a crucial role in degrading and consuming environmental plastic. In this study, we investigated the potential of a modified Winogradsky column (WC) to enrich Connecticut landfill soil for plastic-degrading bacteria and genes.MethodsBy filling WCs with landfill soil and inorganic Bushnell Haas medium, and incorporating polyethylene (PE) strips at different soil layers, we aimed to identify bacterial taxa capable of degrading PE. We employed high-throughput 16S rRNA sequencing to identify the microbes cultivated on the plastic strips and the intervening landfill soil. We used PICRUSt2 to estimate the functional attributes of each community from 16S rRNA sequences.Results and discussionAfter 12 months of incubation, distinct colors were observed along the WC layers, indicating successful cultivation. Sequencing revealed significant differences in bacterial communities between the plastic strips and the intervening landfill-soil habitats, including increased abundance of the phyla Verrucomicrobiota and Pseudomonadota (néé Proteobacteria) on the strips. Based on inferred genomic content, the most highly abundant proteins in PE strip communities tended to be associated with plastic degradation pathways. Phylogenetic analysis of 16S rRNA sequences showed novel unclassified phyla and genera enriched on the plastic strips. Our findings suggest PE-supplemented Winogradsky columns can enrich for plastic-degrading microbes, offering insights into bioremediation strategies.