Search Results (2,766)

The agricultural sector is a cornerstone of Serbia’s economy, ensuring national food security and contributing significantly to GDP, but it also generates notable environmental pressures, particularly through air and water pollution. This paper investigates the impact of agricultural enterprises’ environmental pressures on their financial performance between 2011 and 2021. The sample comprises 52 of the 63 agricultural enterprises listed in the national PRTR register as major air polluters in Serbia. Using enterprise-level data, environmental performance is measured through air emissions relative to revenues, while profitability is captured by return on assets (ROA). Panel regression analysis is conducted with Dynamic Ordinary Least Squares (DOLS) and Fully Modified Ordinary Least Squares (FMOLS) estimators to assess the long-run relationship between eco-efficiency and financial outcomes. The results show that reductions in environmental pressure are associated with improved profitability, highlighting the trade-offs and synergies between ecological responsibility and economic performance. These findings underscore the importance of promoting eco-efficiency as both a managerial strategy and a public policy priority, offering evidence to support Serbia’s alignment with EU environmental and agricultural sustainability goals. Full article

As industrial waste gas, nitrogen dioxide (NO₂) is a serious hazard to air pollution and human health, and there is a pressing demand for developing high-performance NO₂ gas sensors. Tin disulfide (SnS₂), a representative two-dimensional metal sulfide characterized by a significant specific surface area, a suitable electron band gap, and an easily tunable layered structure, shows a broad application prospect in gas sensing applications. Nevertheless, SnS₂-based gas sensors suffer from poor sensitivity, which seriously hinders their application in room temperature gas sensing. In this study, Ag/SnS₂ heterojunction nanomaterials were synthesized by an in situ reduction approach. The findings reveals that the gas-sensitive response of the Ag/SnS₂ nanocomposites at room temperature under visible light irradiation can achieve 10.5 to 1 ppm NO₂, with a detection limit as low as 200 ppb, which realizes the room-temperature detection of Sub-ppm NO₂. Meanwhile, the sensor exhibits good selectivity, reproducibility (cyclic stability > 95%). The improved gas sensitivity of the Ag/SnS₂ sensor can be due to the synergistic effect of the carrier separation at the Ag/SnS₂ Schottky junction and the localized surface plasmon resonance (LSPR) of Ag nanoparticles. The LSPR effect significantly enhances light absorption and surface-active site density, facilitating trace NO₂ detection at room temperature. This study provides the foundation for the subsequent development of room temperature layered metal sulfide gas sensors. Full article

Neutrophilic asthma (NA) is an inflammatory phenotype of asthma, characterized by predominantly neutrophilic infiltrations in bronchial mucosa. It is usually diagnosed on the basis of high neutrophil count in induced sputum (from >40% to >76%) with low eosinophils (<2%). The prevalence of NA ranges from 16% to 28% of the adult asthma population depending on the definitions and study methods applied. A clinical picture of NA is characterized by late onset of symptoms, higher exacerbation rate, lower level of symptoms control, and poorer response to steroids compared to eosinophilic phenotype. Comorbidities such as obesity and GERD as well as the influence of environmental factors (air pollution, smoking, bacterial infections) contribute to the development and severe course of the disease. NA is T2-low disease with predominantly Th1/Th17-type inflammation. Neutrophils are key cells responsible for initiating and sustaining inflammation. In addition to their primary functions like phagocytosis, degranulation, and NETosis, neutrophils release several pro-inflammatory cytokines (IL-1α, IL-1β, IL-6, TNF) and chemokines (CXCL-1, -2, -8, -9, -10) responsible for the recruitment of other neutrophils or T cells. Increasing knowledge about the biology of neutrophiles and their role in asthma results in new potential therapies that could improve control of NA, particularly new biologicals targeting Th1/Th17-related cytokines. In this review, we discuss the prevalence, mechanisms, and clinical features of neutrophilic asthma. Furthermore, current therapeutic options and some promising perspectives for the near future are presented. Full article

As the core space for students’ daily living and learning, the quality of the indoor wind environment and air quality in dormitory buildings is particularly critical. However, existing studies often neglect natural ventilation optimization under local climatic conditions and the multidimensional evaluation of health benefits, leaving notable gaps in dormitory design. Under the Healthy China Initiative, the indoor wind environment in university dormitories directly impacts students’ health and learning efficiency. This study selects dormitory buildings in Xuzhou as the research object and employs ANSYS FLUENT 2020 software for computational fluid dynamics (CFD) simulations, combined with orthogonal experimental design methods, to systematically investigate and optimize the indoor wind environment with a focus on healthy ventilation standards. The evaluation focused on three key metrics—comfortable wind speed ratio, air age, and CO₂ concentration—considering the effects of building orientation, corridor width, and window geometry, and identifying the optimal parameter combination. After optimization based on the orthogonal experimental design, the proportion of comfortable wind speed zones increased to 44.6%, the mean air age decreased to 258 s, and CO₂ concentration stabilized at 613 ppm. These results demonstrate that the proposed optimization framework can effectively enhance indoor air renewal and pollutant removal, thereby improving both air quality and the health-related performance of dormitory spaces. The novelty of this study lies in integrating regional climate conditions with a coordinated CFD–orthogonal design approach. This enables precise optimization of dormitory ventilation performance and provides locally tailored, actionable evidence for advancing healthy campus design. Full article

Reducing food loss and waste (FLW) is a global priority under UN SDG 12.3, yet Taiwan has lacked stage-specific FLW data and systematic valuation of its environmental and economic implications. This study addresses these gaps by integrating localized FLW estimates from the APEC-FLOWS database with an enhanced analytical framework—the Environmentally Extended Input–Output Valuation (EEIO-V) model. The EEIO-V extends conventional input–output analysis by monetizing multiple environmental burdens, including greenhouse gases, air pollutants, wastewater, and solid waste, thereby linking FLW reduction to tangible economic benefits and policy design. The simulations reveal substantial differences in environmental cost reductions across supply chain stages, with downstream interventions delivering the largest benefits, particularly in reducing air pollution and greenhouse gases. By contrast, upstream measures contribute relatively smaller improvements. These findings highlight the novelty of EEIO-V in bridging environmental valuation with system-level FLW analysis, and they provide actionable insights for designing cost-effective, stage-specific strategies that prioritize downstream interventions to advance Taiwan’s sustainability and policy goals. Full article

The concentration characteristics of SO₂, NO₂, O₃, and CO in the marine atmosphere are of great significance for understanding air–sea interactions and regional atmospheric chemical processes. However, due to the challenging conditions of marine monitoring, long-term continuous observational data remain scarce. To address this gap, this study proposes a Transfer Learning–Convolutional Neural Network (TL-CNN) model that integrates ERA5 meteorological data, EAC4 atmospheric composition reanalysis data, and ground-based observations through multi-source data fusion. During data preprocessing, the Data Interpolating Empirical Orthogonal Function (DINEOF), inverse distance weighting (IDW) spatial interpolation, and Gaussian filtering methods were employed to improve data continuity and consistency. Using ERA5 meteorological variables as inputs and EAC4 pollutant concentrations as training targets, a CNN-based inversion framework was constructed. Results show that the CNN model achieved an average coefficient of determination (R²) exceeding 0.80 on the pretraining test set, significantly outperforming random forest and deep neural networks, particularly in reproducing nearshore gradients and regional spatial distributions. After incorporating transfer learning and fine-tuning with station observations, the model inversion results reached an average R² of 0.72 against site measurements, effectively correcting systematic biases in the reanalysis data. Among the pollutants, the inversion of SO₂ performed relatively poorly, mainly because emission reduction trends from anthropogenic sources were not sufficiently represented in the reanalysis dataset. Overall, the TL-CNN model provides more accurate pollutant concentration fields for offshore regions with limited observations, offering strong support for marine atmospheric environment studies and assessments of marine ecological effects. It also demonstrates the potential of combining deep learning and transfer learning in atmospheric chemistry research. Full article

With the increasing emphasis on environmental protection and sustainable development, improving air pollution control technology has become imperative. In this study, Ce-based catalysts are used as research objects to explore the effects of hydrothermal aging on their performance in oxidizing PM. Different Mn, Na, Pt and Zr-doped Ce-based catalysts were prepared based on the impregnation method and the PM oxidation performance of Ce-based catalysts before and after hydrothermal aging was investigated using thermogravimetric experiments, and the catalytic activity change pattern of fresh/hydrothermal aging Ce-based catalysts was analyzed by comparing the comprehensive combustion index S and combustion stability index Rw, revealing the PM oxidation process. The conclusion showed that the cerium-based catalyst significantly enhanced the oxidation efficiency of PM compared with PU. By comparing the performance of different metal-modified catalysts, it was found that the order of activity was: Pt > Na > Mn > Zr. With the metal doping increased, only the comprehensive combustion index S and combustion stability index Rw of Na/CeO₂ catalysts decreased. After hydrothermal aging treatment, the Zr/CeO₂ catalysts showed the best hydrothermal aging resistance, and the comprehensive combustion index S and combustion stability index Rw remained stable (<5%). Ce-based catalysts have the strongest to weakest hydrothermal aging resistance in the following order: Zr > Mn > Pt > Na. This study not only provides an important scientific reference for the application of Ce-based catalysts in the field of environmental purification but also contributes new ideas and methods to promote the green and sustainable development of air pollution control technology. Full article

Particulate matter (PM) concentration, especially PM_2.5, is a major culprit of environmental pollution from unreasonable energy system emissions that significantly affects visibility, climate, and public health. The prediction of PM_2.5 concentration holds significant importance in the early warning and management of severe air pollution, since it enables the provision of guidance for scientific decision-making through the estimation of impending PM_2.5 concentration. However, due to diversified human activities, seasonal factors and industrial emissions, the air quality data not only show local anomalous mutability, but also global dynamic change characteristics. This hinders existing PM_2.5 prediction models from fully capturing the aforementioned characteristics, thereby deteriorating the model performance. To address these issues, this study proposes a framework integrating multi-scale temporal convolutional networks (TCNs) and a transformer network (called MSTTNet) for PM_2.5 concentration prediction. Specifically, MSTTNet uses multi-scale TCNs to capture the local correlations of meteorological and pollutant data in a fine-grained manner, while using transformers to capture the global temporal relationships. The proposed MSTTNet’s performance has been validated on various air quality benchmark datasets in the cities of China, including Beijing, Shanghai, Chengdu, and Guangzhou, by comparing to its eight compared models. Comprehensive experiments confirm that the MSTTNet model can improve the prediction performance of 2.42%, 2.17%, 2.87%, and 0.34%, respectively, with respect to four evaluation indicators (i.e., Mean Absolute Error, Root Mean Square Error, Mean Absolute Percentage Error, and R-square), relative to the optimal baseline model. These results confirm MSTTNet’s effectiveness in improving the accuracy of PM_2.5 concentration prediction. Full article

Indoor air pollution is a major cause of noncommunicable diseases, and increasing people’s knowledge about the related risks is a key action for prevention. Many studies describe questionnaires for evaluating knowledge on indoor air quality that often involve selected population groups and take time to fill out. This study describes the validation of a questionnaire built “ad hoc” that aims to be easy to fill out, reliable, and valid. The validation process integrated two psychometric approaches: the Classical Test Theory (CTT), which uses the Kuder–Richardson 20 (KR-20) formula to measure the internal consistency and reliability of the questionnaire as a whole, and the Item Response Theory (IRT), which evaluates each statement (item)’s validity. The questionnaire, distributed using social media to a self-selected sample of people, reached a sample of 621 subjects. In terms of internal consistency, the questionnaire was found to be satisfactory, with a KR-20 value of 0.74 (CI 0.71–0.77). The IRT analysis showed that the statements included in the questionnaire can distinguish between high-performing and low-performing interviewees, since 100% of the items reached a value of the “discrimination parameter a_j” that was within or above the recommended range. In terms of difficulty, many statements (53.3%) showed a low level of difficulty, obtaining a low “difficulty parameter b_j” value, while another 20% of the items showed a high level of difficulty. Regarding the pseudo-guessing parameter, known as the c-parameter, the probability of answering correctly for a low-performing interviewee was observed in three items (1, 6, and 9), and the same statements fell outside the range for all three parameters evaluated in the IRT. The application of the IRT highlights the criticality of some questions that would not have emerged using the CTT approach alone. Although the questionnaire is acceptable overall, it will be appropriate to evaluate whether to revise or exclude the critical questions in order to improve the instrument’s performance. Full article

Tracing the source of air pollution presents a significant challenge, especially in densely populated urban areas, because of the unpredictable and complex nature of aerodynamics. To address this issue, intelligent lamp posts have been developed with smart sensors and edge computing capabilities. These lamp posts serve as nodes in the EIPA (Edge Intelligent Perception Agent) network within urban campuses. These lamp posts aim to track air pollutants by employing a tracking algorithm that utilizes big data learning and Gaussian diffusion models. This approach focuses on monitoring the quality of urban air and identifying pollution sources, rather than relying solely on traditional CFD simulations for air pollution dispersion. The algorithm comprises three primary components: (1) the Federated Learning framework built on the EIPA system; (2) the LSTM model implemented on the edge nodes of the EIPA system; and (3) a genetic algorithm utilized for optimizing the model parameters. By using CFD simulations in a simulated city park, training data on air dynamic movements is gathered. The usefulness of the method for tracing air pollutants based on federated learning of edge intelligent perception agents is demonstrated by the outcomes of algorithm training. Experimental results show that, compared to the traditional genetic algorithm (GA) and LSTM + genetic algorithm, the proposed FL + LSTM + GA method significantly improves the pollution source positioning accuracy to 99.5% and reduces the average absolute error (MAE) of Gaussian model parameter estimation to 0.20. Full article

This study presents an integrated assessment of anchorage-related emissions and air quality impacts in the Panama Canal region through Automatic Identification System (AIS) data, bottom-up emission estimation, and atmospheric dispersion modeling. One year of terrestrial AIS observations (July 2024–June 2025) captured 4641 vessels with highly variable waiting times: mean 15.0 h, median 4.9 h, with maximum episodes exceeding 1000 h. Annual emissions totaled 1,390,000 tons of CO₂, 20,500 tons of NO_x, 4250 tons of SO₂, 656 tons of PM₁₀, and 603 tons of PM_2.5, with anchorage activities contributing 497,000 tons of CO₂, 7010 tons of NO_x, 1520 tons of SO₂, 232 tons of PM₁₀, and 214 tons of PM_2.5. Despite the main engines being shut down during anchorage, these activities consistently accounted for 34–36% of the total emissions across all pollutants. High-resolution emission mapping revealed hotspots concentrated in anchorage zones, port berths, and canal approaches. Dispersion simulations revealed strong meteorological control: northwesterly flows transported emissions offshore, sea–land breezes produced afternoon fumigation peaks affecting Panama City, and southerly winds generated widespread onshore impacts. These findings demonstrate that anchorage operations constitute a major source of shipping-related pollution, highlighting the need for operational efficiency improvements and meteorologically informed mitigation strategies. Full article

Bulgaria continues to face serious challenges related to air quality. To mitigate traffic-related air pollution and in line with the European regulations, the Metropolitan Municipal Council of Sofia has adopted and introduced low-emission zones (LEZs) in the city centre. The goal of this study is to address the specific needs of urban planning in the city in support of local decision-making. A bespoke emission inventory was developed for the LEZs in Sofia, and high-resolution numerical simulations (100 m resolution) were carried out to assess the effect of the measures implemented to reduce emissions in the central part of the city. The results show a decrease in nitrogen dioxide concentrations along major roads and intersections, but projected concentrations will still be high. No significant improvement is expected for particulate matter pollution due to the limitations of this study. High-resolution (100 m) emission inventories of domestic heating, minor roads, and bare soil surfaces, the major sources of particulate matter pollution, are not included in this study. An integrated model is needed to analyse and compare different scenarios for the development of the transport system, and the gradual introduction of LEZs must be accompanied by a number of other additional measures and actions. Full article

The global rapid urbanization intensifies environmental challenges related to climate change, such as air pollution and the urban heat island (UHI) effect in built environments. The need to optimize nature-based solutions (NBSs) is imperative to mitigate climate change and adapt to extreme weather phenomena. Against this background, this review offers an analysis regarding the integration of vertical greenery systems (VGSs) into urban environments so as to capitalize on their environmental, social, and economic benefits. Key aspects of the review include the positive role of VGSs in UHI mitigation, air quality improvement, stormwater management, and biodiversity enhancement, while examining social aspects (i.e., improved well-being and mental health, noise reduction, and urban built aesthetics). Finally, parameters related to economic benefits and energy efficiency are assessed. The submission further analyses the significant challenges that VGSs face, such as high maintenance costs, structural risks, plant health issues, fire hazards, and other limitations (legislative and technical). The crucial need for interdisciplinary collaborations among urban planners, architects, environmental engineers, and stakeholders is highlighted, in order to successfully integrate VGSs into urban buildings. Thus, this paper aims to identify key strategies for optimizing VGSs’ implementation and provide valuable insights for policymakers and researchers aiming to enhance urban sustainability through vertical greening. Full article

In many developing countries, including South Africa, electricity providers have consistently faced challenges in meeting rising energy demands. Since 2008, South Africa has implemented widespread electricity rationing—commonly referred to as “loadshedding”—due to a combination of operational inefficiencies and structural constraints. Loadshedding continues to be a critical challenge in South Africa, significantly affecting the economy, livelihoods, public health, and broader socio-economic conditions. This study explores the link between loadshedding and air quality by analyzing atmospheric emissions during two contrasting periods: 2019, a year with minimal loadshedding; and 2023, which experienced severe and prolonged outages. The analysis reveals a decline in nitrogen dioxide (NO₂) and sulfur dioxide (SO₂) levels during the intense loadshedding period of 2023. The results indicate that, beyond the influence of weather patterns, reductions in emissions—such as those caused by decreased electricity generation—contribute meaningfully to improved air quality. Overall, the data suggest that reduced power production during high levels of loadshedding links with lower emissions and enhanced air quality. These findings reinforce the potential benefits of transitioning to cleaner, alternative energy sources for achieving long-term reductions in air pollution and fostering a healthier environment. Remote sensing is a critical tool for environmental monitoring in developing countries, offering cost-effective, wide-area data collection to address issues like air pollution, and climate impact. It supports policy-making by providing timely, objective insights for sustainable development, resource management, and disaster response, aligning with SDGs. Full article

Urban expansion intensifies population exposures to fine particulate matter (PM_2.5). Trees mitigate pollution by dry deposition, in which particles settle on plants. However, city-scale models frequently overlook differences in tree species and structure. This study assesses PM_2.5 removal by individual city-owned street trees in Mississauga, Canada, throughout the 2019 leaf-growing season (May to September). Using a modified i-Tree Eco framework, we evaluated the removal of PM_2.5 by 200,560 city-owned street trees (245 species) in Mississauga from May to September 2019. The model used species-specific deposition velocities (V_d) from the literature or leaf morphology estimates, adjusted for local winds, a 3 m-resolution satellite-derived Leaf Area Index (LAI), field-validated, crown area modelled from diameter at breast height, and 1 km² resolution PM_2.5 data geolocated to individual trees. About twenty-eight tons of PM_2.5 were removed from 200,560 city-owned trees (245 species). Coniferous species (14.37% of trees) removed 25.62 tons (92% of total), much higher than deciduous species (85.63%, 2.18 tons). Picea pungens (18.33 tons, 66%), Pinus nigra (3.29 tons, 12%), and Picea abies (1.50 tons, 5%) are three key species. Conifers’ removal efficiency originates from the faster deposition velocities, larger tree size, and dense foliage, all of which enhance particle deposition. This study emphasizes species-specific approaches for improving urban air quality through targeted tree planting. Prioritizing coniferous species such as spruce and pine can improve pollution mitigation, providing actionable strategies for Mississauga and other cities worldwide to develop green infrastructure planning for air pollution. Full article