Abstract: Urban air pollution remains one of the most pressing environmental challenges of the twenty-first century, particularly in rapidly expanding metropolitan regions. Accurate monitoring of atmospheric pollutants is essential for understanding pollution dynamics and designing effective mitigation strategies. This study presents an integrated framework for monitoring urban air quality by combining satellite observations, ground-based monitoring networks, and artificial intelligence techniques. The approach utilizes multi-sensor satellite datasets, including aerosol optical depth retrievals, nitrogen dioxide column measurements, and land surface temperature observations, to capture spatial patterns of atmospheric pollution. Machine learning algorithms were developed to integrate heterogeneous datasets and estimate near-surface concentrations of key pollutants such as particulate matter (PM₂.₅), nitrogen dioxide (NO₂), and ozone (O₃). The proposed system was evaluated in several major urban regions characterized by different climatic and emission conditions. Results indicate that the AI-assisted framework significantly improves the accuracy of pollution estimates compared with traditional interpolation methods. The integration of satellite data with machine learning models allows better detection of pollution hotspots and temporal variability associated with traffic activity, industrial emissions, and meteorological conditions. The study also demonstrates the potential of remote sensing technologies to complement ground-based monitoring networks in regions where observational infrastructure is limited. Sensitivity analyses show that incorporating high-resolution satellite imagery improves spatial representation of urban pollution gradients. Furthermore, the framework can support near-real-time environmental monitoring and policy decision-making. ¬Overall, the results highlight the growing importance of artificial intelligence in environmental monitoring and atmospheric research. The integration of multi-sensor satellite observations with advanced data analytics provides a promising pathway toward more accurate and comprehensive urban air quality assessments.

Abstract: The development of environmentally benign catalytic systems is a major priority in contemporary synthetic chemistry, particularly for industrial reactions traditionally dependent on mineral acids and volatile organic solvents. This study reports the synthesis and catalytic performance of a series of biomass-derived ionic liquids prepared from renewable carbohydrate precursors and evaluated as recyclable catalysts for esterification reactions. The ionic liquids were characterized using nuclear magnetic resonance spectroscopy, Fourier-transform infrared spectroscopy, and thermogravimetric analysis to determine structural stability and thermal properties. Catalytic activity was investigated using model esterification reactions involving acetic acid and several aliphatic alcohols under solvent-free conditions. Reaction yields, selectivity, and catalyst stability were systematically examined across different temperatures and substrate ratios. Results demonstrate that the ionic liquid catalysts significantly enhance reaction rates and achieve product yields exceeding 92% within relatively short reaction times. Recyclability tests indicate that the catalysts maintain consistent performance over five consecutive reaction cycles without significant structural degradation. Kinetic analysis suggests that hydrogen-bonding interactions within the ionic liquid environment facilitate substrate activation and improve reaction efficiency. Green chemistry metrics including atom economy and E-factor analysis further confirm the environmental advantages of the developed system compared with conventional acid-catalyzed processes. The findings highlight the potential of biomass-derived ionic liquids as sustainable catalytic media capable of reducing hazardous waste generation while maintaining high catalytic performance in organic synthesis.

Abstract: Urban expansion is rapidly transforming land surfaces worldwide, altering atmospheric processes and influencing air quality in densely populated regions. Changes in vegetation cover, impervious surfaces, and built-up infrastructure modify surface-atmosphere interactions that regulate pollutant dispersion, chemical reactions, and boundary-layer dynamics. This study investigates the relationship between land surface transformation and urban atmospheric composition using satellite-derived land-use data and long-term air quality observations. High-resolution land use and land cover datasets were combined with atmospheric pollutant measurements to examine spatial patterns of nitrogen dioxide (NO₂), particulate matter (PM₂.₅), and aerosol optical depth. The analysis reveals significant differences in pollutant concentrations between vegetated and highly urbanized zones. Areas characterized by dense built-up infrastructure consistently show elevated pollutant levels compared with regions containing greater vegetation coverage. This pattern is attributed to reduced dry deposition capacity, increased emissions from transportation and industrial sources, and altered atmospheric mixing processes. Temporal analysis indicates that expanding urban infrastructure contributes to increasing pollution exposure in rapidly growing cities. Vegetated areas, including urban parks and green corridors, appear to mitigate pollutant concentrations through enhanced deposition and improved microclimatic conditions. Numerical simulations suggest that increasing urban green spaces could reduce particulate matter concentrations by up to 10% in certain urban environments. These findings highlight the importance of integrating environmental considerations into urban planning strategies. Sustainable land management practices and green infrastructure development may play an important role in improving urban air quality. The study provides valuable insights into how land surface transformation shapes atmospheric conditions in urban ecosystems and offers guidance for future urban environmental management policies.

Abstract: The development of environmentally benign catalytic systems has become a major priority in modern chemical research. Conventional organic synthesis often relies on toxic solvents and metal catalysts that generate hazardous waste and limit process sustainability. In this study, a series of bio-derived ionic liquids were synthesized and evaluated as green catalytic media for common organic transformations. The ionic liquids were prepared from renewable biomass-derived precursors and characterized using spectroscopic and thermal analysis techniques. Their catalytic performance was investigated in several model reactions including esterification, aldol condensation, and selective oxidation. Experimental results demonstrate that the ionic liquid systems significantly enhance reaction efficiency while reducing solvent consumption and reaction time. In several cases, product yields exceeded those obtained using conventional catalytic systems. The ionic liquids also showed excellent thermal stability and could be recycled multiple times without noticeable loss of catalytic activity. Mechanistic investigations suggest that the unique hydrogen bonding networks within the ionic liquid structure facilitate enhanced substrate activation and improved reaction kinetics. Beyond catalytic efficiency, the environmental advantages of these systems were evaluated through green chemistry metrics, including atom economy and E-factor analysis. The results indicate that bio-derived ionic liquids can substantially reduce waste generation and improve overall process sustainability. Furthermore, the catalysts demonstrated compatibility with a range of organic substrates, suggesting broad applicability in synthetic chemistry. The findings highlight the potential of bio-derived ionic liquids as versatile catalytic platforms for sustainable chemical manufacturing. As the demand for environmentally responsible industrial processes continues to grow, such green catalytic systems may play an increasingly important role in the development of next-generation synthetic methodologies.

Abstract: Marine mammals play a critical ecological role in marine ecosystems and serve as important indicators of ocean health. This study investigates the population dynamics of coastal dolphin communities in relation to changing oceanographic conditions and increasing human activity in coastal environments. Long-term observational surveys were conducted to estimate population size, seasonal migration patterns, and reproductive success across multiple coastal habitats. Environmental variables including sea surface temperature, prey availability, and ocean productivity were analysed to evaluate their influence on dolphin population distribution. Statistical analysis reveals significant correlations between prey abundance and dolphin population density across the study region. Observational data also indicate that rising ocean temperatures influence migration timing and habitat selection patterns in several dolphin groups. Additional analyses were conducted to assess the impacts of human activities such as maritime traffic and fishing operations on dolphin behaviour. Increased vessel activity was found to disrupt feeding behaviour and acoustic communication in certain habitats. Despite these pressures, some dolphin populations demonstrate adaptive responses through changes in movement patterns and group structure. The results highlight the complex interactions between marine mammals and rapidly changing ocean environments and emphasize the importance of continuous ecological monitoring to support effective conservation strategies and marine ecosystem management.

Abstract: Urban ecosystems expose wildlife to novel environmental pressures, among which anthropogenic noise represents one of the most pervasive stressors affecting animal communication systems. This study examines acoustic adaptations in urban bird communities inhabiting environments characterized by persistent anthropogenic noise generated by traffic and urban infrastructure. Field observations were conducted across multiple urban habitats with varying noise intensities, and acoustic recordings of bird vocalizations were collected during peak breeding seasons. Vocalization frequency, amplitude, and timing patterns were analysed using digital signal processing techniques to determine behavioural adjustments associated with noise exposure. Results indicate that several bird species exhibit significant shifts in vocal frequency, producing songs at higher frequencies to avoid masking by low-frequency urban noise. Temporal adjustments in singing behaviour were also observed, with birds increasing vocal activity during early morning hours when background noise levels are relatively lower. Comparative analyses reveal that species inhabiting high-noise environments display narrower frequency bandwidths and increased song repetition rates compared with populations in quieter habitats. Additional behavioural observations suggest changes in territory defence strategies and altered mating displays associated with modified acoustic communication. These findings demonstrate that urban bird species exhibit notable behavioural plasticity in response to anthropogenic noise, although such adaptations may also involve energetic costs and ecological trade-offs. Understanding how wildlife responds to rapidly changing acoustic environments provides important insights for urban biodiversity conservation and highlights the need for ecological considerations in urban planning and infrastructure development.