Uva Wellassa University

South Asia's marine and coastal environments face escalating pressures from industrialization, urbanisation, and climate variability, yet policy responses remain fragmented and underfunded. This review synthesises sources, pathways, and impacts of marine pollution and advances a framework that integrates scientific assessment with blue finance mechanisms tailored to the region's socio-ecological realities. It considers both legacy contaminants (heavy metals, hydrocarbons, nutrients) and emerging threats (microplastics, pharmaceuticals, nanomaterials), linking them to governance and financial readiness. The review highlights how monsoonal hydrology, inadequate infrastructure, and institutional fragmentation intensify pollutant accumulation and economic vulnerability. It further examines state-of-the-art monitoring technologies, nature-based solutions, and persistent policy gaps. The paper's central contribution is a finance-ready pathway coupling scientific indicators with governance reforms and instruments such as blue bonds, trust funds, and risk-sharing facilities. By embedding pollution management in a blue economy framework, this study informs strategies for coastal resilience and sustainable ocean-based livelihoods.

Ocean acidification (OA) and nutrient enrichment can separately or together threaten coral reefs by reducing calcification efficiency and increasing physiological stress, ultimately weakening reef resilience. Therefore, the study evaluates the prevailing OA level over the Sri Lankan coral reef areas using the aragonite saturation state (ΩAr) and assesses the nitrate (NO3−), and phosphate (PO43−) concentrations over the coral sites. The study was conducted on coral reefs on the eastern coast (EC), southern coast (SC), northern coast (NC), and west coast (WC) of Sri Lanka from April to June 2024. A total of 63 seawater samples were collected around each coastal site for analysis. The ΩAr were supersaturated (ΩAr > 1) and ranged from 2.98 ± 0.04 to 4.92 ± 0.12. Throughout the study period, the study sites had ΩAr values exceeding 2.92 ± 0.16, indicating that the nation's corals were resilient to deterioration, and the comparative analysis demonstrates that these sites were not vulnerable to OA. However, the NC exhibited significantly (P < 0.05) the lowest ΩAr values (3.2 ± 0.64), positioning the regions near the lower bound of optimal calcification conditions. While ΩAr values indicate low OA stress during sampling, elevated NO3− concentrations (2 – 5 μmol L−1) in SC (2.19 ± 1.28 µmol L−1) and WC (3.52 ± 1.48 µmol L−1) may exacerbate coral bleaching during thermal stress events, representing a co-stressor rather than OA effect. Coral bleaching HotSpot (HS) identification emphasizes how spatially distributed HS are from January to June. The OA risk assessment confirmed that climate change will bring high risk to the coral calcification, reproduction, and damage to the breeding ground, which impact on the ecology and economy of Sri Lanka.

Marine pollution threatens ocean ecosystems and human health through eutrophication, bioaccumulation, and habitat degradation. This article discusses chemical contaminants, nutrient pollution and eutrophication, marine debris, ocean noise pollution, global and regional hotspots of ocean pollution, the impact of pollution on marine biodiversity, mitigation strategies and global effects, restoration and cleanup efforts, and challenges and future outlooks in marine pollution. The excess nutrient levels disturb ecological balances, cause harmful algal blooms, and threaten biodiversity and coastal economies. While plastic pollution has received widespread attention, marine debris also includes non-plastic materials such as metal, glass, rubber, and textiles. Ocean noise pollution, primarily from shipping, seismic exploration, and military sonar, significantly disrupts marine ecosystems by interfering with the communication, navigation, and behavioral patterns of marine species. The buildup of plastic gyres such as the Great Pacific Garbage Patch is a key contributor to global ocean pollution. Remote polar regions can also show significant accumulation of pollutants due to long-distance marine and atmospheric transportation. Multi-faceted and integrated approaches, such as global regulatory frameworks, technological innovation, waste management improvement, and public engagement, are required to decrease ocean pollution. The growing awareness of marine pollution, especially for plastic debris, has fueled the pick-up trash before it disperses into the open ocean. Meanwhile, ecosystem restoration, ranging from mangrove replanting to coral reef rehabilitation, is crucial in rebuilding degraded marine habitats and promoting resilience to subsequent environmental and climatic pressures.

Realizing the full potential of incorporating mine tailings as supplementary cementitious materials (SCMs) to replace ordinary Portland cement (OPC) requires carefully balancing the benefits-such as cost reduction and emissions mitigation-while ensuring the mixtures achieve the required strength. Given the demonstrated effectiveness of combining machine learning (ML) with optimization algorithms in similar multi-objective optimization (MOO) problems, for the first time, this study employed a novel tabular prior data fitted network (TabPFN) model to forecast the uniaxial compressive strength (UCS) of those mix designs. The TabPFN model outperformed traditional boosting ML models, achieving an R2 of 0.973 and a low prediction error of 2.115 MPa. Notably, its pre-trained architecture reduced computational time by 1045 s. Building on this, a MOO case study was developed using the TabPFN model to predict UCS as the first objective, alongside separate equations used as objective functions to calculate cost and total emissions. This MOO problem was tackled using the non-dominated sorting genetic algorithm-II (NSGA-II). The optimized mixture designs achieved better balances between strength, cost, and emissions than those obtained through experimental methods, validating the use of this ML-based method for mixture design. Finally, a software tool-GreenMix AI-was developed to provide integrated access to the entire framework, translating advanced research into practical application. In essence, this research supports the reuse of mine tailings as SCMs and provides a practical pathway to developing more economical and sustainable cementitious mixtures.

This study presents a novel strategy to enhance the efficiency of dye-sensitized solar cells (DSSCs) by integrating a p-type La2O3 layer between the TiO2 photoanode and the electrolyte. The p-La2O3 layer suppresses charge recombination by inhibiting electron transfer from TiO2 to the electrolyte, improving charge separation and photovoltaic performance. Structural and compositional analyses (XRD, XRF, and SEM) confirm the presence of a uniform polycrystalline La2O3 layer on the 5-layered nanostructured TiO2 electrodes. Mott-Schottky analysis confirms that TiO2 is n-type and La2O3 is p-type, highlighting their complementary roles in efficient charge transfer. For the first time, DFT + U calculations were employed to investigate the band structure of La2O3. The electron diffusion length was found to be enhanced upon the integration of p-La2O3, as revealed by electrochemical impedance analysis. The optimized device exhibits a power conversion efficiency of 8.50 %, with a short-circuit current density of 17.70 mA cm-2 and an open-circuit voltage of 0.73 V, representing a 34.07 % enhancement compared to the reference six-layer photoanode DSSC (6.34 %). The results demonstrate the effectiveness of a p-type La2O3 layer in boosting device performance by reducing charge recombination. To our knowledge, this is the first report demonstrating the effectiveness of La2O3 as a blocking layer in quasi-solid-state DSSCs, presenting one of the highest efficiencies.