Search Results (126)

Air pollution monitoring in ungauged zones presents unique challenges yet remains critical for understanding environmental health impacts and socioeconomic dynamics in the Eastern Mediterranean region. This study investigates air pollution patterns in northwestern Syria during 2019–2024, analyzing NO₂ and CO concentrations using Sentinel-5P TROPOMI satellite data processed through Google Earth Engine. Monthly concentration averages were examined across eight key locations using linear regression analysis to determine temporal trends, with Spearman’s rank correlation coefficients calculated between pollutant levels and five meteorological parameters (temperature, humidity, wind speed, atmospheric pressure, and precipitation) to determine the influence of political governance, economic conditions, and environmental sustainability factors on pollution dynamics. Quality assurance filtering retained only measurements with values ≥ 0.75, and statistical significance was assessed at a p < 0.05 level. The findings reveal distinctive spatiotemporal patterns that reflect the region’s complex political-economic landscape. NO₂ concentrations exhibited clear political signatures, with opposition-controlled territories showing upward trends (Al-Rai: 6.18 × 10⁻⁸ mol/m²) and weak correlations with climatic variables (<0.20), indicating consistent industrial operations. In contrast, government-controlled areas demonstrated significant downward trends (Hessia: −2.6 × 10⁻⁷ mol/m²) with stronger climate–pollutant correlations (0.30–0.45), reflecting the impact of economic sanctions on industrial activities. CO concentrations showed uniform downward trends across all locations regardless of political control. This study contributes significantly to multiple Sustainable Development Goals (SDGs), providing critical baseline data for SDG 3 (Health and Well-being), mapping urban pollution hotspots for SDG 11 (Sustainable Cities), demonstrating climate–pollution correlations for SDG 13 (Climate Action), revealing governance impacts on environmental patterns for SDG 16 (Peace and Justice), and developing transferable methodologies for SDG 17 (Partnerships). These findings underscore the importance of incorporating environmental safeguards into post-conflict reconstruction planning to ensure sustainable development. Full article

Air pollution poses a growing threat to public health and the environment, highlighting the need for continuous and precise urban air quality monitoring. The aim of this study was to implement and evaluate an autonomous air quality monitoring platform developed by the University of Ruse, “Angel Kanchev”, under Bulgaria’s National Recovery and Resilience Plan (project BG-RRP-2.013-0001), co-financed by the European Union through the NextGenerationEU initiative. The system, based on Libelium’s mobile sensor technology, was installed at a height of two meters on the university campus near Rodina Boulevard and operated continuously from 1 March 2024 to 30 March 2025. Every 15 min, it recorded concentrations of CO, CO₂, NO₂, SO₂, PM₁, PM_2.5, and PM₁₀, along with meteorological parameters (temperature, humidity, and pressure), transmitting the data via GSM to a cloud-based database. Analyses included a distributional assessment, Spearman rank correlations, Kruskal–Wallis tests with Dunn–Sidak post hoc comparisons, and k-means clustering to identify temporal and meteorological patterns in pollutant levels. The results indicate the high operational stability of the system and reveal characteristic pollution profiles associated with time of day, weather conditions, and seasonal variation. The findings confirm the value of combining calibrated IoT systems with advanced statistical methods to support data-driven air quality management and the development of predictive environmental models. Full article

Air quality and environmental issues have gained attention from countries and organizations worldwide over the past several decades. In recent years, carbon peak and carbon neutrality have been mentioned at many international conferences and meetings aimed at reducing and controlling environmental challenges. This study focuses on trend analysis and expectations for the duration of control for environmental air quality (EAQ) indicators, assesses the current EAQ conditions across global countries, and presents reasonable suggestions for environmental control. The study begins by examining the annual, per capita, and per square meter (${\mathrm{m}}^2$) carbon dioxide (CO₂) emission peak and standardizations, where carbon standardization is a replacement for carbon neutrality. A similar quantitative methodology was employed to assess classical air quality factors such as sulfur dioxide (SO₂) and nitrogen oxides (NO_x). The findings suggest that the average control year length (ACYL) of NO_x is longer than that of SO₂, and the ACYL of SO₂ is, in turn, longer than that of CO₂. From an energy structure perspective, regressions results indicate that biofuel and wind power contribute to improvements in EAQ, while coal, oil, and gas power exert negative impacts. Moreover, a long-term EAQ model utilizing an adjusted max–min normalization method is proposed to integrate various EAQ indicators. This study also presents an EAQ ranking for global countries and recommends countries with critical EAQ challenges. The results demonstrate that it is plausible to control EAQ factors at an excellent level with advances in control technologies and effective measures by government, industries, and individuals. Full article

Particulate matter (PM) is a major health risk, particularly in indoor environments where air quality should be optimized and pollution reduced efficiently. While technical air purification systems can be costly and impractical, indoor plants offer a sustainable alternative. Using a novel methodology, four common indoor plants were evaluated for their potential to reduce PM_2.5. PM_2.5 was introduced via incense in a custom-designed test chamber with air circulating at 0.3 m/s. Air quality was continuously monitored with an AirGradient Open Air device (Model O-1PST), an optical particle counter. Statistical significance was confirmed by independent t-tests and ANOVA. Calcium chloride regulated relative humidity in the chamber. The plants Epipremnum aureum, Chlorophytum comosum, Nephrolepis exaltata, and Maranta leuconeura were assessed for their PM_2.5-binding capacity. Nephrolepis exaltata showed the highest reduction efficiency. Maranta leuconeura with its hemispherical leaf cells was tested for the first time and proved to trap particles within its leaf structure. It is ranked second and showed a stronger dependence on ambient PM_2.5 concentrations for reduction efficiency. Full article

Seaports, as major transportation hubs, generate significant air pollution due to intensive ship traffic, directly affecting local air quality. While emission inventories are commonly used to manage ship-based air pollution, they reflect only the emission-related aspect of a specified period and area, limiting the broader interpretability and comparability of the results. To overcome the mentioned challenges, this research presents the PrE-PARE model, which enables the prediction, analysis, and risk evaluation of ship-sourced air pollution in port areas. The model comprises three interconnected modules. The first is applied for quantifying emissions using detailed technical and movement datasets, which are combined into individual voyage trajectories to enable a high-resolution analysis of ship-based air pollutants. In the second module, the Multivariate Adaptive Regression Splines (MARS) machine learning method is adapted to predict emissions in varying operational scenarios. In the third module, novel metric methods are introduced, enabling a standardised efficiency comparison between ships. These methods are supported by a unique classification system to determine the emission risk in different periods, evaluate the intensity of various ship types, and rank individual ships based on their operational efficiency and emission optimisation potential. By combining new methods with technical and operational shipping data, the model provides a transparent, comparable, and adaptable system for emissions monitoring. The results demonstrate that the PrE-PARE model has the potential to improve strategic planning and air quality management in ports while remaining flexible enough to be applied in different contexts and future scenarios. Full article

One of the emerging services provided by roadside green infrastructure is its contribution to the quality of landscape phenology, which is measured through the succession of colours and forms throughout the seasons. In the seasonal dynamics of space, flowering phenological patterns play a key role, particularly in early blooming species such as Cornus mas L. Therefore, this paper aims to highlight the significance of the Cornelian cherry as a component of roadside green infrastructure in the southwestern suburban zone of Belgrade. Through an integrative approach to phenological and climatic elements, and by means of a specific case study covering the period from 2007 to 2025, under climate change conditions, the influence of air temperature and precipitation on local flowering patterns of the Cornelian cherry has been assessed. Based on 1140 phenological observations conducted over 19 consecutive years, from January to April, key flowering elements were identified—those that influence pollination, fruiting, and the species’ practical potential. The Mann–Kendall, Sen’s slope, Rayleigh, and Watson–Williams tests were used to examine spatio-temporal changes in flowering patterns, while the Spearman Rank test and circular statistics were applied to quantify correlations among the analysed parameters. The results confirm that Cornelian cherry is an adaptive and sustainable species that continuously provides visual identity during its flowering period, while simultaneously reflecting climate change through phenological responses. These phenological responses are closely linked to local climatic conditions. In addition to enriching landscape phenology with vibrant visual features during the colder months, Cornelian cherry also enhances biodiversity by providing ecosystem services as a nectar-producing species, with its pollen serving as an early and valuable food source for bees. The study also confirms that the seasonal dynamics of landscape phenology can be used as a scientifically valid criterion for assessing the ecological quality of roadside green infrastructure. Full article

In this era of perpetual advancement and innovation, the term “smart” is frequently misused. Linking smartness to a city should reflect and solve multiple problems with a single solution. A city, district, or area can only be smart when it contemplates different development axes rather than having just a single strength. This work is an effort to make an area of Varanasi in Uttar Pradesh, India, smart by concentrating the actions on five principal axes—Environment, Energy, Mobility, Community, and Economy. Practical indicators have been selected and well formalised to obtain an output value that can support the methodology to rank each action in its executable manner. Software like ENVI-met (to simulate greening and pollution) and PVSyst (to simulate rooftop solar PV) have been used to simulate the actions proposed, and a detailed discussion for each result has been presented. The methodology involves the creation of a model based on morphological, structural, and environmental data, as well as using SWOT analysis and community feedback to identify key areas for intervention. The results demonstrate the effectiveness of the proposed interventions, with notable reductions in CO₂ emissions, improved air quality, and significant energy savings through the implementation of Nature-Based Solutions, solar PV systems, and electric mobility. Full article

The operation of Heating Ventilation and Air Conditioning (HVAC) systems in densely occupied spaces results in considerable energy consumption. In the post-pandemic context, stricter indoor air quality standards and higher ventilation rates further increase energy demand. In this paper, the energy retrofit of a partial recirculation all-air HVAC system serving a university lecture room located in Southern Italy is analyzed. Multi-Objective Optimization (MOO) and Multi-Criteria Decision-Making (MCDM) approaches are used to find optimal design alternatives and rank these considering two different decision-makers, i.e., public and private stakeholders. Among the Pareto solutions obtained from optimization, the optimal alternative is identified, encompassing three Key Performance Indicators and using a new robust MCDM approach based on four methods, i.e., TOPSIS, VIKOR, WASPAS, and MULTIMOORA. The results show that, in the post-pandemic era, baseline retrofit scenarios for infection reduction that do not involve the introduction of demand control ventilation strategies cause energy consumption to increase from negligible values up to 59%. On the contrary, baseline retrofit scenarios involving demand control ventilation strategies cause energy consumption to decrease between 5% and 38%. The findings offer valuable guidance for HVAC system retrofits in higher education and similar buildings, emphasizing the potential to balance occupant health, energy efficiency, and cost reduction. The results also highlight significant CO₂ reductions and minimal impacts on thermal comfort, showcasing the potential for substantial energy savings through targeted retrofits. Full article

This study investigated the impact of four drying techniques—hot air drying (HAD), vacuum hot air drying (VFAD), microwave drying (MWD), and vacuum freeze-drying (VFD)—on the structural, physicochemical, and functional properties of scallop adductor muscles, a critical marine resource in the food industry. The results demonstrated that VFD optimally preserved the ultrastructural integrity of the tissue, maintaining its surface fibrous architecture and achieving a superior recovery ration (78%) and rehydration ration (186.5%) compared to HAD, VFAD, and MWD. While the zeta potential remained statistically invariant across methods, HAD induced the largest particle agglomeration, followed by MWD. Notably, VFD enhanced protein stability, increasing the sulfhydryl content by 163.2% and reducing carbonyl formation by 48.1% relative to HAD, whereas MWD had the opposite effect. Multispectral analyses revealed the severe disruption of protein secondary and tertiary structures after MWD, while VFD minimized conformational denaturation. Statistical modeling ranked the drying sensitivity parameters as follows: surface hydrophobicity > hardness> β-turn content > dityrosine crosslinking > transverse relaxation time T23. These findings underscore VFD as the optimal method for mitigating structural degradation and oxidative damage in scallop processing, providing actionable insights to enhance the technofunctional quality of shelf-stable scallop products. Full article

Education is crucial for leading a productive life and obtaining necessary resources. Higher education institutions are progressively incorporating artificial intelligence into conventional teaching methods as a result of innovations in technology. As a high academic record raises a university’s ranking and increases student career chances, predicting learning success has been a central focus in education. Both performance analysis and providing high-quality instruction are challenges faced by modern schools. Maintaining high academic standards, juggling life and academics, and adjusting to technology are problems that students must overcome. In this study, we present a comprehensive dataset, SAPEx-D (Student Academic Performance Exploration), designed to predict student performance, encompassing a wide array of personal, familial, academic, and behavioral factors. Our data collection effort at Air University, Islamabad, Pakistan, involved both online and paper questionnaires completed by students across multiple departments, ensuring diverse representation. After meticulous preprocessing to remove duplicates and entries with significant missing values, we retained 494 valid responses. The dataset includes detailed attributes such as demographic information, parental education and occupation, study habits, reading frequencies, and transportation modes. To facilitate robust analysis, we encoded ordinal attributes using label encoding and nominal attributes using one-hot encoding, expanding our dataset from 38 to 88 attributes. Feature scaling was performed to standardize the range and distribution of data, using a normalization technique. Our analysis revealed that factors such as degree major, parental education, reading frequency, and scholarship type significantly influence student performance. The machine learning models applied to this dataset, including Gradient Boosting and Random Forest, demonstrated high accuracy and robustness, underscoring the dataset’s potential for insightful academic performance prediction. In terms of model performance, Gradient Boosting achieved an accuracy of 68.7% and an F1-score of 68% for the eight-class classification task. For the three-class classification, Random Forest outperformed other models, reaching an accuracy of 80.8% and an F1-score of 78%. These findings highlight the importance of comprehensive data in understanding and predicting academic outcomes, paving the way for more personalized and effective educational strategies. Full article

Sustainable Cities and Communities within the European Union (EU) are crucial for achieving Sustainable Development Goal (SDG) 11, which aims to make cities and human settlements inclusive, safe, resilient, and sustainable. This goal is particularly pertinent to the EU due to challenges such as urbanization, climate change, infrastructure demands, transport issues, and natural resource management. The implementation of SDG 11 across Europe shows varying levels of success among countries and regions, highlighting the need for tailored, local strategies for sustainable urban development. The primary goal of this paper is to employ the Multi-Criteria Method Integrating Distances to Ideal and Anti-Ideal Points to determine the Sustainable Cities and Communities Index (SCCI). Using Eurostat data, this method provides a comprehensive evaluation and ranking of EU countries based on their performance in achieving SDG 11 in EU countries in 2015 and 2020. By integrating various indicators related to urban sustainability—such as access to public transport, air quality, land use, and housing conditions—the SCCI offers a nuanced understanding of how different countries perform relative to one another. The SCCI facilitates the identification of best practices and areas requiring improvement by comparing each country’s performance to ideal and anti-ideal points. This comparison allows policymakers to develop more targeted and effective strategies. Additionally, it highlights disparities between countries and regions, which is essential for fostering regional cooperation and ensuring equitable progress towards sustainable urban development across the EU. This study confirmed significant disparities among EU countries in the realization of SDG 11 in 2015 and 2020, revealing that Italy achieved the most substantial progress, while Spain experienced the greatest regress during the analyzed period. Full article

This study investigates Miscanthus biochar’s potential to reduce ammonia (NH₃) emissions in poultry production. Biochar from lignocellulosic biomass has proven a versatile tool in environmental remediation for water, soil, and air quality applications with ample opportunity for inclusion in agricultural systems. Ammonia emissions present a concern for animal/human health and the environment. The impacts of biochar production temperature (400 and 700 °C), organic acid activation (acetic acid, citric acid), and application rate (0.24 and 0.49 kg m⁻²) on broiler litter NH₃ emissions were evaluated. Biochar production parameters, i.e., temperature, and acid type were found to significantly impact its performance as an NH₃ control measure. The following factors, ranked by magnitude of impact, were found to statistically impact the NH₃ emission rate: biochar application rate (p < 0.001), biochar production temperature (p = 0.003), and lastly acid type (p = 0.007). The best performing biochar was produced at 400 °C, activated with acetic acid, and applied at a high addition rate (0.49 kg m⁻²). This treatment reduced cumulative NH₃ volatilization after 2 weeks by 19.7%. Full article

The success of educational institutions is fundamentally intertwined with the well-being and academic progress of their students. In this context, indoor air quality (IAQ) and thermal comfort play a critical role in creating conducive learning environments that support both health and academic performance. This work evaluates six ventilation systems and strategies for enhancing IAQ and thermal comfort, which prevail in educational buildings in the Spanish region of Catalonia. To do so, a multi-criteria analysis is performed based on the Analytic Hierarchy Process (AHP) method, considering economic, social, and environmental aspects. Ventilation systems are pairwise compared in terms of six criteria: initial and maintenance cost, classroom air quality, students’ thermal comfort in summer and winter, and energy consumption. Subsequently, weighted combinations of these criteria are established to rank the ventilation systems under five case scenarios. The results indicate that natural ventilation systems, particularly those with atriums and courtyards (N-AAC), offer a balanced solution, achieving satisfactory IAQ and thermal comfort while being more cost-effective and environmentally sustainable in certain contexts. The variation in the best solution across different scenarios demonstrates that the optimal choice is highly context-dependent, influenced by factors such as budget, climate, and infrastructure. This research provides a valuable foundation and methodology for decision-makers in educational institutions, supporting the selection of ventilation systems that maximize sustainability while enhancing students’ comfort and fostering learning environments. Full article

With rapid urbanization, maintaining a high quality of life (QoL) for city residents has become a critical challenge for policy-makers and urban planners. Smart cities, leveraging advanced technologies and data analytics, present a promising pathway to enhance urban services and promote sustainability. This paper introduces an innovative adaptation of the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method, integrating a Belief Structure (BTOPSIS) to improve the evaluation and interpretation of survey data. Our approach effectively addresses the distribution of responses across categories and the uncertainty often present in such data, including missing or ambiguous answers. Additionally, we perform a sensitivity analysis to assess the stability of the BTOPSIS rankings under varying utility function parameters, further validating the robustness of our method. We apply this framework to the 2023 ‘Quality of Life in European Cities’ survey, analyzing diverse urban factors such as public transport, healthcare, cultural facilities, green spaces, education, air quality, noise levels, and cleanliness. Additionally, our study offers a comparative analysis of BTOPSIS against other multi-criteria methods used for evaluation data from this report, showcasing its strengths and limitations in addressing the dataset’s complexity. Our findings reveal significant variations in residents’ perceived QoL across European cities, both between cities and within countries. Zurich and Groningen rank highest in satisfaction, while Tirana, Skopje, and Palermo are ranked lowest. Notably, residents of cities with populations under 500,000 report higher satisfaction levels than those in larger cities, and satisfaction levels are generally higher in EU and EFTA cities compared to those in the Western Balkans, with the highest satisfaction observed in northern and western Member States. To aid urban planners and policy-makers, we propose a ranking tool using the BTOPSIS method, capturing nuanced resident perceptions of living conditions across cities. These insights provide valuable guidance for strategic urban development and advancing the smart city agenda across Europe. Full article

With global environmental changes, lung cancer has become one of the most common types of cancer worldwide, posing a significant public health challenge. Jiangsu Province, located in the eastern part of China, is an economically and socially developed region. According to the latest cancer registration data in Jiangsu Province, lung cancer ranks first in both incidence and mortality of cancer in the province. Thus, studying the spatiotemporal distribution of lung cancer cases and analyzing the influence of various factors on this distribution are crucial for the effective prevention and control of the disease in Jiangsu Province. This study takes the statistical data of lung cancer patients in Jiangsu Province in 2020 as the research object, uses Geographic Information System (GIS) visualization and spatial analysis to study the spatial distribution characteristics of lung cancer patients in Jiangsu Province, and employs the geographical detector to numerically express the impact of various environmental factors on the distribution of lung cancer patients in Jiangsu Province. The results reveal a notable spatial clustering of lung cancer cases, with high-incidence areas concentrated in Suzhou, Nanjing, and Xuzhou cities. Among the seven environmental factors examined, PM_2.5, SO₂, and PM₁₀ concentration exert the most significant influence. This study employs multifactorial spatial analysis to elucidate the intricate relationships between people’s health and air quality, medical resource distribution, and lung cancer incidence in the process of pursuing sustainable development in cities and provides an important reference for the improvement in lung cancer prevention and control strategies. Full article