• Urban wetlands act as both sinks and secondary sources of airborne microplastics. • FTIR detected PE, PP, PET, PS, and PVC across air, water, soil, and plant samples. • GIS and HYSPLIT showed MPs can disperse 350 km and rise up to 1500 m altitude. • PET dominated (55%), with strong seasonal links to asthma, COPD, and bronchitis. • Positive correlation (r = 0.68, p < 0.05) between MPs and respiratory illness cases. • Findings stress urgent need for wetland restoration and integrated pollution control. This study investigated the role of Ukkadam Lake, an urban wetland in Coimbatore, India, as both a sink and secondary source of airborne microplastics, and explored their implications for respiratory health. The selection of this urban wetland was motivated by its ecological significance and increasing vulnerability to anthropogenic pollution, particularly from urban runoff. The deterioration of the site has been progressive in recent years, owing to the discharge of untreated effluents and sewage, as well as the invasive proliferation of water hyacinth. This study investigates the role of urban wetlands as both sinks and potential secondary sources of microplastics during the period 2020−2024. Special emphasis was placed on the combined impacts of microplastic pollution and fine particulate matter on respiratory health issues. The FTIR analysis detected five main types of polymers in the samples: polyethylene (38.5%), polypropylene (27.3%), PET (19.6%), polystyrene (9.4%), and PVC (5.2%). Among these, PET was the most common in aquatic plants, revealing the bioaccumulation of microplastics in the vegetation. GIS mapping showed that pollution hotspots were mainly found near urban–wetland boundaries. Dispersion modeling (HYSPLIT) indicated that airborne microplastics can rise up to 1500 m and travel more than 350 km. Seasonal weather patterns and prevailing winds strongly influence their movement. In summary, the results revealed a significant association between airborne microplastic exposure and respiratory health risks in the local population. These suggested the urgent need for targeted mitigation strategies, including the restoration and active management of urban wetlands, enhanced regulation of point and non-point pollution sources, and the development of integrated monitoring frameworks to support sustainable urban ecological management and public health protection.

Abstract Artificial intelligence (AI) models were developed to determine the center of tropical cyclones (TCs) in the western North Pacific. These models integrated information from six channels of geostationary satellite imagery: the brightness temperature of four infrared (IR) and one shortwave IR channels, as well as the reflectivity of one visible channel. The first model is a convolutional neural network designed for spatial data processing, and the second is a convolutional long short-term memory model that effectively captures spatiotemporal information. For training, verification, and testing purposes, spatial images from six channels were obtained from the Japanese Himawari-8 satellite from 2016 to 2021. The position of the European Centre for Medium-Range Weather Forecasts 6- or 12-h prediction was assigned as an initial value to the AI models. Errors in the initial value were 20–50 km compared to the Joint Typhoon Warning Center best track, depending on TC intensity. Weak (strong) TCs exhibited large (small) errors. This error dependency was found in Automated Rotational Center Hurricane Eye Retrieval (ARCHER) product, which is currently used by several operational organizations. ARCHER errors were typically small when observations from both geostationary and polar-orbiting satellites were included. Significant errors remained in the absence of microwave channel information from polar-orbiting satellites. This study successfully developed two AI models that consistently determined the location of the TC center using only six-channel images from geostationary satellites. These models exhibited comparable or better performance than the ARCHER products. The newly developed AI models can potentially be implemented for operational use. Signicance Statement This study has developed AI models that can determine the center of TCs in the western North Pacific, which can be applied to meteorological organizations that have limited access to real-time polar-orbiting satellite data. The newly developed AI models derived positions comparable to or better than ARCHER, which is currently one of the most precise systems for determining the position of the TC center. The successful development of these AI models is very opportune as it was only possible thanks to the recent availability of high-frequency data observed from geostationary satellite, coupled with the rapid technological advances in AI algorithms.

• Urban wetlands act as both sinks and secondary sources of airborne microplastics. • FTIR detected PE, PP, PET, PS, and PVC across air, water, soil, and plant samples. • GIS and HYSPLIT showed MPs can disperse 350 km and rise up to 1500 m altitude. • PET dominated (55%), with strong seasonal links to asthma, COPD, and bronchitis. • Positive correlation (r = 0.68, p < 0.05) between MPs and respiratory illness cases. • Findings stress urgent need for wetland restoration and integrated pollution control. This study investigated the role of Ukkadam Lake, an urban wetland in Coimbatore, India, as both a sink and secondary source of airborne microplastics, and explored their implications for respiratory health. The selection of this urban wetland was motivated by its ecological significance and increasing vulnerability to anthropogenic pollution, particularly from urban runoff. The deterioration of the site has been progressive in recent years, owing to the discharge of untreated effluents and sewage, as well as the invasive proliferation of water hyacinth. This study investigates the role of urban wetlands as both sinks and potential secondary sources of microplastics during the period 2020−2024. Special emphasis was placed on the combined impacts of microplastic pollution and fine particulate matter on respiratory health issues. The FTIR analysis detected five main types of polymers in the samples: polyethylene (38.5%), polypropylene (27.3%), PET (19.6%), polystyrene (9.4%), and PVC (5.2%). Among these, PET was the most common in aquatic plants, revealing the bioaccumulation of microplastics in the vegetation. GIS mapping showed that pollution hotspots were mainly found near urban–wetland boundaries. Dispersion modeling (HYSPLIT) indicated that airborne microplastics can rise up to 1500 m and travel more than 350 km. Seasonal weather patterns and prevailing winds strongly influence their movement. In summary, the results revealed a significant association between airborne microplastic exposure and respiratory health risks in the local population. These suggested the urgent need for targeted mitigation strategies, including the restoration and active management of urban wetlands, enhanced regulation of point and non-point pollution sources, and the development of integrated monitoring frameworks to support sustainable urban ecological management and public health protection.

Abstract Artificial intelligence (AI) models were developed to determine the center of tropical cyclones (TCs) in the western North Pacific. These models integrated information from six channels of geostationary satellite imagery: the brightness temperature of four infrared (IR) and one shortwave IR channels, as well as the reflectivity of one visible channel. The first model is a convolutional neural network designed for spatial data processing, and the second is a convolutional long short-term memory model that effectively captures spatiotemporal information. For training, verification, and testing purposes, spatial images from six channels were obtained from the Japanese Himawari-8 satellite from 2016 to 2021. The position of the European Centre for Medium-Range Weather Forecasts 6- or 12-h prediction was assigned as an initial value to the AI models. Errors in the initial value were 20–50 km compared to the Joint Typhoon Warning Center best track, depending on TC intensity. Weak (strong) TCs exhibited large (small) errors. This error dependency was found in Automated Rotational Center Hurricane Eye Retrieval (ARCHER) product, which is currently used by several operational organizations. ARCHER errors were typically small when observations from both geostationary and polar-orbiting satellites were included. Significant errors remained in the absence of microwave channel information from polar-orbiting satellites. This study successfully developed two AI models that consistently determined the location of the TC center using only six-channel images from geostationary satellites. These models exhibited comparable or better performance than the ARCHER products. The newly developed AI models can potentially be implemented for operational use. Signicance Statement This study has developed AI models that can determine the center of TCs in the western North Pacific, which can be applied to meteorological organizations that have limited access to real-time polar-orbiting satellite data. The newly developed AI models derived positions comparable to or better than ARCHER, which is currently one of the most precise systems for determining the position of the TC center. The successful development of these AI models is very opportune as it was only possible thanks to the recent availability of high-frequency data observed from geostationary satellite, coupled with the rapid technological advances in AI algorithms.

The enhanced seasonal amplitude of atmospheric CO₂ has been viewed so far primarily as a Northern Hemisphere phenomenon. Yet, analyses of atmospheric CO₂ records from 49 stations between 1980 and 2018 reveal substantial trends and variations in this amplitude globally. While no significant trends can be discerned before 2000 in most places, strong positive trends emerge after 2000 in the southern high latitudes. Using factorial simulations with an atmospheric transport model and analyses of surface ocean <i>P</i>co₂ observations, we show that the increase is best explained by the onset of increasing seasonality of air-sea CO₂ exchange over the Southern Ocean around 2000. Underlying these changes is the long-term ocean acidification trend that tends to enhance the seasonality of the air-sea fluxes, but this trend is modified by the decadal variability of the Southern Ocean carbon sink. The seasonal variations of atmospheric CO₂ thus emerge as a sensitive recorder of the variations of the Southern Ocean carbon sink.

Airborne microorganisms (bioaerosols) represent hazardous environmental contaminants due to their capacity to replicate, survive in diverse matrices, and disseminate antimicrobial resistance. The sites were selected to represent four distinct rural microenvironments (agricultural, tribal, domestic, and commercial) differing in land use, human activity, and ecological characteristics. Despite increasing evidence from urban and hospital environments, rural bioaerosols remain underexplored, particularly under environmentally relevant conditions where exposure is continuous. Using a MicroBio MB1 air sampler, bacterial concentrations were consistently higher than fungal loads, with winter peaks of 880 colony-forming units (CFU) m −3 in commercial zones and 635 CFU m −3 in tribal sites. In summer, bacterial levels declined overall except in domestic environments (720 CFU m −3 ), while fungal loads increased, reaching 120 CFU m −3 in domestic sites. Gram staining confirmed the predominance of Gram-positive bacilli and cocci ( Bacillus cereus , Staphylococcus ), while Lactophenol Cotton Blue staining revealed filamentous fungi, predominantly Aspergillus and Penicillium . Matrix-assisted laser Desorption/Ionization Time-of-flight Mass Spectrometry validated species-level identities, and antimicrobial susceptibility tests showed resistance to erythromycin and amphotericin B, while ciprofloxacin and chloramphenicol remained effective against bacteria, and clotrimazole and fluconazole demonstrated seasonal efficacy against fungi. These results identify rural bioaerosols as critical yet overlooked reservoirs of hazardous microbes and antimicrobial resistance and highlight an urgent need for rural antimicrobial resistance surveillance to support integrated monitoring and public health interventions.

Microplastic (MP) pollution is a serious environmental and public health concern. However, the mechanisms governing MP distribution and transport in rural household environments remain poorly understood. This study presents the first integrated compartmental analysis of MP transfer between outdoor and indoor matrices, including soil, indoor air (inlet and outlet), and reused cooking oil in households located in Karunya Nagar (10.938523°N, 76.743782°E), a rural area in India. Samples were selected based on waste mismanagement profiles and analyzed using Fourier transform infrared spectroscopy. High plastic loads detected in the soil samples consisted of polypropylene (PP, 38.21%), polyethylene terephthalate (PET, 32.85%), polyethylene (22.78%), and polystyrene (PS, 6.16%), likely originating from agricultural films, packaging, and burned household waste. Samples from the air inlet predominantly contained PP (41.72%) and PET (33.94%), suggesting resuspension from contaminated soil due to wind action. Substantial concentrations of PET (42.4%) and PS (25.4%) were detected in reused indoor cooking oil, representing increases of 29.1% and 14.2%, respectively, relative to the air inlet samples. This suggests sorption and thermal transfer during cooking processes in open containers. Increases of 9.4% PP and 0.27% PET in air outlet profiles provide evidence of re-aerosolization during heating and inefficient ventilation. Polymer aging and oxidative degradation were confirmed by spectral intensities at 1783.1 cm<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−1</sup>, 1161.15 cm<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−1</sup>, and 509.21 cm<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−1</sup>. These results reveal a continuous, cyclic pathway of MP entry, accumulation, and secondary emission within rural households, demonstrating an overlooked but critical exposure route that necessitates targeted waste management and health interventions.

Abstract This study investigates the statistical and dynamical relationship between the El Niño-Southern Oscillation (ENSO) and winter surface air temperature (SAT) in Korea, using station observations and reanalysis data from 1920 to 2023. Historical SAT records are compiled from 7, 14, and 60 stations for 1920–1959, 1960–1972, and 1973–2023, respectively. Despite the statistically significant correlation ( r = 0.28) between the Niño 3.4 index and winter SAT in Korea, ENSO alone explains only a limited amount of interannual variability. Classifying the SAT anomalies according to the ENSO phase (i.e., warm for El Niño and cold for La Niña), the Niño 3.4 index yields binary-classification accuracy of 0.68; however, about half of the correctly classified anomalies fall within ±0.5 standard deviations from the climatological mean. Also, composite circulation patterns based on ENSO phases differ structurally from those associated with actual SAT anomalies. A multiple linear regression analysis reveals that mid- to high-latitude climate variables, such as the East Asian winter monsoon, western North Pacific (WNP) sea surface temperatures (SSTs), and the Arctic Oscillation, exhibit stronger and more stable associations with Korean winter SAT than ENSO. Especially WNP SSTs show the largest standardized regression coefficients (&gt; 5.0) to indicate their dominant role. This study suggests the need for integrated forecasting approaches that consider both the tropical and extratropical influences, rather than relying solely on ENSO signals for improving the accuracy of seasonal climate predictions and supporting adaptive risk management strategies for wintertime extremes in the Korean Peninsula.

Microplastic (MP) pollution has emerged as a critical global environmental concern, impacting soil, water, and insect ecosystems. This study quantified MP prevalence in soil, water, and insect samples collected from specified rural and semi-urban study areas in the southern India, using Fourier-transform infrared (FTIR) spectroscopy for contamination assessment. The results revealed a predominance of polypropylene/polystyrene (PP/PS; 91.3%), followed by polyethylene (PE; 15.1%), polyethylene terephthalate (PET; 9.2%), and polyamide (PA; 6.2%). Insect samples showed high MP adherence, particularly in blister beetles, click beetles, and carpenter bees, suggesting their role as vectors for MP dissemination, mainly through adherence pathways. FTIR analysis confirmed characteristic MP absorption peaks at 1637.6 cm^-1 (PP/PS), 1031.9 cm^-1 (PE), 582.5 cm^-1 (PET), and 3448.7 cm^-1 (-OH groups), indicating interactions between MP and organic matter. FTIR analysis of soil samples showed PE as the dominant MP, with higher quantities in garbage sites (36.0%) and residential areas (34.9%) compared to agricultural farms (18.9%). Soil samples varied significantly, with bulk density (1.1-1.4 g cc⁻¹), porosity (36.1-58.0%), and organic carbon content (0.7-1.9%), indicating potential impacts on fertility and microbial activity. Water samples from irrigation sources showed detectable PET (1.2%) and PA (0.7%) concentrations, with a distinct peak at 2316.5 cm⁻¹, raising concerns about agricultural sustainability and food safety. These findings highlight the urgent need for stricter waste management regulations and further studies into the long-term environmental and human health risks of MP pollution.

On 8 August 2022, a quasi-stationary mesoscale convective system (MCS) produced prolonged extreme rainfall (> 100 mm h −1 ) over the Seoul metropolitan area, Republic of Korea. These organized convective systems, primarily composed of convective components with interspersed stratiform elements, are characterized by the successive local generations of convective cells. This study analyzes data from multiple observational platforms to understand the formation of the MCS and its microphysical evolution. The convective cells were initiated just off the west coast near Seoul, along the boundary between warm and cold air masses. Low-level convergence and favorable environmental conditions, including low lifting condensation level, level of free convection, and large convective available potential energy, facilitated the triggering of these cells. As the cells moved inland, they intensified rapidly due to the land-sea surface-friction contrast. Radar data show that as the MCS matured, reflectivity increased and the differential reflectivity decreased downward above the melting level, indicating the growth of ice crystals and graupels. The evolution of microphysical structure led to increases in both the mean diameter ( D m ) and the number concentration ( N T ) of raindrops. This observation also suggests that the rainfall intensity was more closely related to the increase in N T than D m during the period of heavy downpours. This study improves our understanding of the mesoscale processes related to extreme rainfall in Korea, critical for forecasting local heavy rainfall. • We investigated an MCS with unique back-building features causing record rainfall. • Low-level convergence and instability triggered convective cell initiation. • Surface friction contrasts near the Yellow Sea intensified convective activity. • Raindrop number increase contributed more to rainfall than size changes.

Persistent organic pollutants (POPs) are highly toxic and long-lived environmental contaminants that easily adsorb onto the surfaces of microplastics (MPs). While urban and industrial environments have been extensively studied, rural areas, especially in developing countries, have received limited attention. In such regions, uncontrolled waste dumping exacerbates the contamination of water and soil systems by MPs and associated POPs, causing significant environmental and health concerns. This study quantified MP pollution in soil and water near unregulated waste‐dumping sites in Tamil Nadu, India. A total of 20 environmental samples (10 soil and 10 water) were collected from two active rural dump sites. MPs were extracted using density separation and characterized by stereomicroscopy and Fourier transform infrared spectroscopy. MPs were detected in all samples, with polypropylene (PP) and polyethylene (PE) identified as the dominant polymer types. Soil samples contained 49.87% PP and 21.62% polyethylene terephthalate, while water samples comprised 57.14% PP and 28.57% PE. These polymers were particularly effective at adsorbing and transporting POPs through environmental media. The presence of MPs and POPs in drinking water sources and agricultural soils poses a significant threat to the ecological integrity of these rural areas and the health of their communities. The present results underscore the urgent need for enhanced waste management practices and robust water protection policies to mitigate the long-term health impacts and environmental degradation in these regions.