Atmospheric and Oceanic Sciences

As the world’s largest archipelagic state, Indonesia is increasingly exposed to microplastic contamination across its aquatic environments. This review synthesizes evidence from 497 publications reporting microplastic occurrence in environmental matrices across the Indonesian archipelago, including freshwater and marine waters, sediments, and aquatic biota. The analysis examines reported abundance levels, dominant polymer types, spatial patterns of contamination, and methodological approaches used in microplastic research conducted in Indonesia. The literature reveals a pronounced geographic imbalance in research coverage. More than 60% of studies were conducted in western Indonesia, with a strong concentration on Java Island, whereas large parts of eastern Indonesia remain poorly documented. Reported concentrations vary widely among environmental matrices and sampling designs, reflecting differences in both environmental conditions and analytical practices. Notably, studies that did not apply rigorous quality assurance procedures or confirm polymer composition using spectroscopic techniques tended to report markedly higher concentrations, suggesting that methodological limitations may contribute to inflated contamination estimates. To place these observations into a comparable framework, risk band thresholds were derived from the abundance dataset using a quantile-based statistical approach. Overall, this review provides an integrated baseline of microplastic contamination across Indonesian environments and identifies critical geographic and methodological gaps that should guide future monitoring strategies and environmental risk assessments.

Abstract This piece concludes the Focused Issue on Just Transitions in the Global South intended to make up for the limited studies of dynamics of just transitions in the Global South with its unique colonial history, structural constraints, development challenges, and weak governance systems. A careful reading of the contributions from this collection reveals three interrelated themes: (a) just transition as contestation of development visions; (b) the on-set of green extractivism; and (c) a gradual movement towards shifted responsibility where developing countries are subtly shifting the burden of global clean transition to countries and communities that are least responsible for climate change and least equipped to bear the cost. The findings suggest that a broader and more nuanced understanding of just transitions is required, one that accounts for national contexts, realities, and priorities, global connectivity, and the need of the most vulnerable and affected communities. The most important question for the next decade for climate politics may not be how quickly the world transitions but whether the transition itself becomes another mechanisms through which historical injustices are reproduced.

This paper proposes a new grid-tied transformer-less boost switched capacitor topology (TLBSCT) that employs three capacitors and twelve switches to generate seven levels with a gain of three times. The salient features of the TLBSCT are its boosting capacity, zero leakage current, minimum switching devices and lower voltage stress. The capacitors of the proposed TLBSCT have self-balancing characteristics. The proposed TLBSCT offers a brief discussion of the configuration, principle of working and the design of the parameter, as well as its control scheme. In addition, a comparative study of the proposal against the current transformerless inverter (TLI) shows the better performance of the proposed approach(PA). Also, the theoretical concept and viability of the suggested design have been demonstrated by simulations and experiments. This work contributes to SDG 7: Affordable and Clean Energy by improving efficient and reliable grid-connected solar power conversion systems.

This study presents U-Pb ages together with newly obtained systematic Hf isotopic data from detrital zircons of the Middle Ordovician Miboshan Formation in the eastern Hexi Corridor (northwestern China), aiming to constrain its provenance characteristics and the relationship of the Alxa Block with the North China Craton. Four major age groups are identified: 500–650 Ma, 750–1200 Ma, 1500–2000 Ma, and 2400–3000 Ma. Comparison with the ages of basement rocks from adjacent tectonic units and their Hf isotopic features indicates that the Miboshan Formation has multiple provenances: the central-northern Qilian Orogenic Belt contributed the Late Neoproterozoic to Early Paleozoic zircons, while the 750–1200 Ma zircons were primarily sourced from both the central-northern QOB and the Alxa Block. The 1500–2000 Ma zircons mainly originated from the Alxa Block, and the 2400–3000 Ma zircons are related to recycled materials from the Alxa Block and the central-northern QOB. In conjunction with detrital zircon data from regional Paleozoic strata, this study proposes that the Alxa Block was independent of the North China Craton before the Middle Ordovician, and that the two blocks began to approach each other in the Late Ordovician, with their final amalgamation possibly occurring no earlier than the Late Devonian.

Context This conceptual study explores the role and application of landscape polarisation principles in addressing applied issues of agriculture and soil health maintenance. The Nature Frame concept reflects universal principles of sustainable landscape management and land use; promoting these principles in agriculture can establish a robust system of sustainable soil use and contribute to policymaking aimed at achieving the objectives of the Soil Monitoring and Resilience Directive. Objectives To present the concept of a Nature Frame as a tool for modeling and optimizing the intensity of agroecosystem use and prioritizing agricultural measures in order to ensure their sustainable functioning, biodiversity dispersal, and the preservation of Soil Organic Carbon (SOC). Methodology An analysis of the pilot area (Kelmė distr. mun., Lithuania) was performed, incorporating the 2025 crop declaration dataset of the National Paying Agency, and soil erosion and SOC distribution data from the Lithuanian Soil Information System database Dirv_DR10LT. A Nature Frame model for the pilot area was developed to identify soil problems related to SOC migration and accumulation. The importance of the areas for the conservation and restoration of biodiversity was also identified. Based on this analysis, a landscape management model for the pilot area’s agroecosystems was developed, incorporating measures to address these issues. Results The pilot area, as defined by the measures for managing the quality of agroecosystems and soils, is divided into the following categories: Priority 1 (areas of the Nature Frame), where intensive agriculture is practiced and where problems related to soil degradation are encountered. In these areas, the landscape structure must first be restored and soil degradation issues addressed. Within this priority, the following are distinguished: geoecological divides (agrarian forested hilly landscape) – measures for biogen accumulation and stabilization, anti-erosion measures; geoecological divides (agricultural hilly landscape) – measures for biogenic and carbon accumulation and stabilization, anti-erosion and biodiversity enhancement measures; Internal areas of geoecological stabilization – measures to support biodiversity and the ecological functions of the landscape; migration corridors – control of biogenic accumulation and biodiversity enhancement measures. Priority 2 (hilly areas with eroded soils outside the Nature Frame), where intensive agricultural activity is carried out. Measures for biogens and carbon accumulation and stabilization, as well as anti-erosion measures, are planned for these areas. Conclusions The study revealed that the Natural Framework covers 75.16% of the territory of Kelmė distr. mun., while as much as 60.73% of all agricultural land within the municipality falls within the Natural Framework. Of this area, 61.24% of agricultural land is intensively used, being cultivated as arable land under crop rotation systems. Furthermore, 78.42% of all eroded soils are concentrated within Natural Framework territories, particularly in geoecological divides and migration corridors. These findings indicate that the agroecosystems of the study area are being managed unsustainably and that landscape planning measures are required. The Natural Framework concept, operating through the identification of landscape geoecological potential as well as potential risks and environmental problems, provides a valuable basis for the targeted differentiation of areas where restrictions on agricultural intensification are environmentally justified. Priority should be given to measures aimed at soil conservation, biodiversity enhancement, and the restoration of ecosystem functions. Its application may contribute to more effective agroecosystem management, SOC preservation, soil degradation reduction, and the achievement of European Green Deal objectives. Implications Applying the Nature Frame concept allows support and environmental protection requirements to be specifically directed to areas where soil degradation is most severe, thereby increasing the cost-effectiveness of agricultural investments. This approach integrates ecosystem services into intensive agriculture, rather than simply setting aside protected areas. This enhances the effectiveness of environmental protection. Linking agricultural policy to the Nature Frame becomes a practical tool for restoring soil health. It not only halts erosion but also actively contributes to restoring soil properties and increasing the resilience of agroecosystems.

Sound is crucial in marine ecosystems, serving as the primary conduit for information transfer for many organisms. The effects of anthropogenic noise on marine organisms vary from behavioral changes to physical harm. The Gulf of Tribugá has diverse ecosystems and species of high ecological and economic value. This research aimed to characterize three sites in the Gulf of Tribugá using acoustic indices, distinguishing between conditions with and without boat noise across different years. Since 2018, 1518 recordings from passive acoustic monitoring at three sites in the Gulf have been analyzed, categorized as having boat noise present or not. A descriptive analysis of acoustic indices was conducted (1) across space and time, (2) between times with and without boat noise, and (3) between two frequency ranges. Passive acoustic monitoring at three Colombian sites (2018 -2022) used EAR and SNAP recorders deployed at ~16 -25 m, recording 10 min every 20 min at 15,625 or 48,000 Hz. Eight 10_min files daily were manually annotated for sound sources and boat presence. Acoustic indices (ACI, ADI, AEI) and SPL were computed in R and MANTA. Statistical comparisons assessed temporal/spatial differences with/without boat noise. All sites had significantly different Acoustic Complexity Index values based on boat noise presence, which were dependent on the frequency range examined. The absolute values of the acoustic indices varied over time, as significant differences were found across an annual time span for the Acoustic Evenness Index and Acoustic Diversity Index when comparing recordings with and without boat noise. Furthermore, a difference in sound pressure levels of 10 dB re 1 µPa over the full bandwidth was found before compared to after COVID-19. These results could serve as a guide for other studies and continued monitoring in the area, but they are not comparable with other sites, as changes may depend on different factors, or the indices may not be suitable for other cases.

Temperature variations significantly degrade the measurement accuracy of fiber optic current sensors (FOCS) in critical power systems applications such as high-voltage transmission and renewable energy integration. To address this, we propose an intelligent error compensation method based on an improved Quantum-behaved Particle Swarm Optimization-Neural Network (Levy-Weighted-QPSO-NN) algorithm. The approach leverages easily measurable state parameters-sensing ring temperature, received optical power, half-wave voltage, SLD temperature, and SLD current-as inputs to predict temperature-induced current ratio difference. Experimental validation involved three sensing rings subjected to temperature cycling (-45 °C to 70 °C), emulating harsh substation environments. The Levy-Weighted-QPSO-NN model achieved 91.11% average prediction accuracy for ratio difference with a correlation coefficient (R²) of 0.9223, outperforming QPSO-NN (85.69%) and Weighted-QPSO-NN (88.31%). Key metrics (MAE: 0.0784; RMSE: 0.0819) confirmed superior stability and accuracy. Robustness testing demonstrated consistent performance across varying population sizes (25-70) and iterations (90-150). Using predicted ratio differences for real-time compensation reduced measurement errors from 0.82% to 0.13%, meeting IEC 61869-6/8 and GB/T standards for Class 0.2S accuracy. This method eliminates reliance on complex hardware modifications, offering a generic, algorithm-driven solution for temperature-dependent FOCS errors.

BACKGROUND: C-sections (CS) can be lifesaving in certain medical situations, but their prevalence has surged beyond recommended levels globally, raising concerns about inappropriate medical interventions and healthcare delivery quality. OBJECTIVES: The study explores whether medical conditions alone determine C-sections or if socio-economic and institutional factors (i.e., Private/Public) also play a significant role. METHOD: Using WHO and World Bank data, the relationship between C-section rates and income was analysed at the global level. Furthermore, at the national and state levels in India, the same analysis was conducted using data from the Ministry of Statistics and Programme Implementation and NFHS-5. Additionally, utilising the Birth recode datasets of NFHS-4 and 5, multivariate logistic regression was performed with C-section as the outcome variable and socio-economic and institutional variables, such as place of residence, levels of education, wealth index, and place of delivery, as predictor variables. RESULTS: Throughout the analysis, we found the institutional setting as the most significant influencing factor of CS rates, at both the national (OR: 4.11, 95% CI 3.98-4.24; NFHS-5) and state level for Bihar (OR: 16.19, 95% CI 13.76-19.05; NFHS-5), Uttar Pradesh (OR: 8.62, 95% CI 7.84-9.47; NFHS-5), Tamil Nadu (OR: 3.08, 95% CI 2.70-3.52; NFHS-5) and Andhra Pradesh (OR: 4.16, 95% CI 3.40-5.09, NFHS-5). On the contrary, we found that socioeconomic factors influenced the likelihood of CS only in states where medical infrastructure was lacking, indicating that socioeconomic factors are not directly responsible for CS rates; they are complicit only in determining institutional access. Additionally, an inverted U-shaped relationship was found between national per capita income and CS rates, indicating global inequality in the quality of healthcare. Within India, this relationship increasingly mirrors the global trend, possibly due to disparities in healthcare access and quality. CONCLUSION: Our analysis confirms that the increase in CS rates is not solely caused by medical conditions, but is also significantly influenced by non-medical factors, particularly institutional factors. Economic incentives strongly drive private healthcare providers to prefer CS deliveries. The results suggest the need for targeted policy interventions to mitigate perverse incentives for private facilities and enhance public medical infrastructure, particularly in underserved regions.

BACKGROUND: The issue of self-medication with anti-malarial drugs is a critical public health challenge. Despite national guidelines promoting diagnostic-confirmed treatment, evidence addressing the prevalence of self-medication with anti-malarial drugs and drivers in Ethiopia is scarce. Therefore, this study aimed to bridge this gap and to provide meaningful insights into ongoing efforts to curb the misuse of anti-malarial drugs and helping the strategy of malaria eliminationby investigating the prevalence of self-medication with anti-malaria drugs and its associated factors. METHODS: An institution-based cross-sectional study was conducted in Dera District, Northwest Ethiopia, from June 1-30, 2025. A total of 591 febrile patients were selected using a stratified multistage sampling technique. Data were collected through interviewer-administered structured questionnaires developed by reviewing different related literatures. Data entry and analysis were done using Epi-Data version 4.6 and STATA 17. Multivariable binary logistic regression analysis was performed to identify factors associated with self-medication. Statistical significance was declared at a p-value less than 0.05. RESULT: The prevalence of self-medication with anti-malarial drugs was 42.8% (95% CI: 38.9%-46.8%). Factors positively associated with self-medication included: monthly income of ≥5000 ETB (AOR = 1.64, 95% CI: 1.04-2.59), poor knowledge about malaria (AOR = 1.68, 95% CI: 1.12-2.55), poor risk perception towards self-medication (AOR = 2.10, 95% CI: 1.40-3.14), and distance ≤5 km from private drug sellers (AOR = 1.65, 95% CI: 1.10-2.47). Community-based health insurance membership was negatively associated with self-medication (AOR = 0.60, 95% CI: 0.41-0.88). CONCLUSION: Self-medication with anti-malarial drugs was moderate (33.4%_66.6%) in the study area based on percentile classification. The findings highlighted the need for designing different strategies to reduce inappropriate drug use and promoting community-based health insurance enrollment.

Land-use change and habitat fragmentation in southern Chile have favored synanthropic bat species, promoting their contact with domestic animals and humans, representing a potential risk of exposure. This study aimed to characterize dwellings with bat colonies in rural zones of the Los Ríos and Los Lagos regions, estimate the frequency of human-bat contact, and determine the health risk perception from residents. A cross-sectional descriptive study was conducted in 69 rural dwellings. A structured survey was used to gather information on demographics, household characteristics, frequency of contacts, and risk perception. Data regarding contact levels and household attributes were evaluated using descriptive statistics and non-parametric tests. Surveyed dwellings were predominantly wooden construction, whit 67% presenting unsealed openings, and bat presence was reported in 46% of households. Higher contact levels were significantly associated with the presence of pets (dogs p = 0.0014, V = 0.69; cats p = 0.021, V = 0.5) and vulnerable residents (minors or seniors, p = 0.0014, V = 0.67). Conversely, dwelling structure did not present a clear pattern differentiating the contact level. Although 84% of respondents acknowledged disease transmission risk, primarily rabies, there was a significant gap in risk perception regarding livestock, in addition to a lack of consistent preventive actions regardless of health risk knowledge. This perception paradox requires a One Health educational approach, beyond traditional rabies management. These results highlight a high frequency of cohabitation creating significant potential for exposure and representing a potential public health concern in southern Chile.

PURPOSE: To evaluate the diagnostic value of machine learning models based on dual-phase 99mTc-MIBI SPECT/CT semiquantitative parameters for differentiating benign and malignant pulmonary nodules. METHODS: This retrospective study included 132 patients with pulmonary nodules, including 30 benign and 102 malignant lesions. All patients underwent dual-phase 99mTc-MIBI SPECT/CT at approximately 20 minutes and 2 hours after tracer injection. Semiquantitative parameters, including early and delayed tumor-to-normal ratios (T/N) and retention indices (RI), were calculated. Clinical variables and imaging parameters were analyzed using univariable and multivariable logistic regression, and selected variables were further used to develop machine learning models. RESULTS: Malignant nodules showed significantly higher early-phase uptake and lower retention index values than benign nodules. Multivariable analysis identified elevated CEA and RImax as independent predictors of malignancy. Machine learning models built on these simple semiquantitative parameters showed promising diagnostic performance, with an AUC of 0.944 (95% CI: 0.883-0.990) for SVM on the training set, 0.805 (95% CI: 0.678-0.912) for Logistic Regression (LR), 0.881 (95% CI: 0.800-0.949) for Artificial Neural Network (ANN), and 0.979 (95% CI: 0.951-0.995) for Random Forest (RF), demonstrating their effectiveness in classifying pulmonary nodules. CONCLUSION: Dual-phase 99mTc-MIBI SPECT/CT semiquantitative parameters provide useful information for distinguishing benign from malignant pulmonary nodules. A machine learning strategy based on simple and interpretable parameters may offer a practical tool for pulmonary nodule assessment, especially in settings where complex imaging analysis is not feasible.

INTRODUCTION: Cardiovascular disease (CVD) is a leading cause of mortality worldwide, with an especially high burden in developing countries such as Iran. Understanding spatial disparities and temporal trends in CVD hospitalizations can guide targeted public health interventions in high-risk regions. METHODS: Data was sourced from Mashhad University of Medical Sciences for five years (2016-2020) and included CVD cases classified by ICD-10 codes, excluding records with non-resident status or incomplete addresses. Temporal trends were assessed through a monthly classical decomposition, ARIMA modeling for forecasting, and Joinpoint regression (JR) to detect shifts over time. Spatial analyses included calculating relative risks, examining spatial autocorrelation, identifying hotspots, and applying flexible spatial scan statistics (FSSS) to detect clusters with irregular shapes. RESULTS: The study included 52,132 CVD cases, with a median age of 64 years (IQR: 53_76) and a male predominance (54.44%). Temporal analysis showed significant fluctuations, with the highest hospitalization rate in 2019 and the seasonal pattern was repeated annually. ARIMA(0,0,0)(0,0,1)12 modeling revealed a seasonal moving average of -0.722 and a mean of 844.95 hospitalizations/month, capturing seasonal fluctuations with a stable trend reflecting cardiovascular health risks. The JR analysis showed a rising hospitalization trend with a monthly percent change of 1.69 [95% CI: 1.44_2.29; p = 0.001] for males and 1.99 [95% CI: 1.73_2.68; p = 0.001] for females, followed by a sharp decline after November 2019. After a positive spatial autocorrelation (Global Moran's I of 0.176 (p < 0.001)), hot spot classification revealed central Mashhad as a high-risk zone with concentrated clusters of CVD hospitalizations. The Flexible Spatial Scan Statistics (FSSS) analysis indicated that the Most Likely Cluster (MLC) was situated in the city center, presenting an approximate 10-fold relative risk [RR: 9.804, LLR: 2328.3, p < 0.001]. CONCLUSIONS: This study provides the first extensive spatial and temporal analysis of CVD hospitalizations in Mashhad, underscoring critical high-risk areas in need of focused public health interventions. Addressing these spatial disparities can significantly improve cardiovascular health outcomes and bolster resilience within the local populations.