Understanding long-term variability in storminess is essential for constraining future climate patterns in the eastern North Atlantic, a region shaped by complex ocean–atmosphere interactions. Here, we reconstruct storm, fire, and hydroclimate variability from grain size, inorganic geochemistry, plant macrofossil and molecular organic records since mid-Holocene at Glenties Bog, a coastal blanket bog in western Ireland. Comparing our results with existing palaeostorminess records, we provide new insights into a dynamic interplay between wind strength, fire incidence, and hydrological conditions throughout the Holocene. Long-term temperature evolution influenced the background wind and hydroclimatic state, while volcanic activity became a key forcing mechanism during parts of the late Holocene. The warm mid-Holocene coincided with lower wind strength and enhanced fire activity, while no assoication between storm periods and volcanic activity was inferred, indicating that the climate state at the time of the volcanic forcing affects the climatic response. These results indicate that future warming may lead to profound changes in wind strength, hydroclimate, and fire regimes. Notably, climate-change-induced lowering of peatland water tables may increase the susceptibility of blanket bogs to intense and deep peatland fires. This study provides new insight into Holocene wind and hydroclimate dynamics in the eastern North Atlantic, improving understanding of the mechanisms driving North Atlantic climate variability across contrasting climate states. • Multiproxy reconstruction of Holocene climate in the eastern North Atlantic • Holocene temperature trends influenced background wind and hydroclimate • Mid-Holocene warmth associated with dry conditions, fires, and low wind strengths • Late Holocene marked by wetter conditions and enhanced storminess • Volcanic activity associated with enhanced storminess during parts of the late Holocene

Abstract Peatlands play a critical role in the global carbon (C)‐climate cycle, acting as vast long‐term stores of disproportionately large quantities of C relative to their land area. In recent decades, climate‐driven shifts in fire regimes and peatland hydrophysical properties have occurred across Canada's boreal regions, increasing concerns about the vulnerability of peatland C to combustion losses. Understanding the magnitude and vulnerability of C lost during wildland fires in peatlands is therefore essential but remains highly uncertain. This study was conducted in the Athabasca Oil Sands Region of Alberta's Boreal Plains. C losses from peatlands during the 2016 Horse River Wildfire were estimated based on field‐collected soil C data and pre‐ and post‐fire airborne LiDAR data. C Losses were quantified across peatland types and ecotones and separated into above‐ and below‐ground combustion. Soil C losses were nearly an order of magnitude greater than vegetation C losses (2.11 ± 5.09 kg C m −2 vs. 0.38 ± 0.32 kg C m −2 , respectively). Bog ecotones were zones of significant soil C loss, with average losses of 16.5 kg C m −2 . LiDAR‐derived burned area and C losses were compared with the spectral burn severity index, dNBR. A binary burned/unburned classification showed strong agreement in bogs (88%) but poor agreement in swamps (48%). Vegetation C loss correlated moderately well with dNBR strength, whereas the relationship between soil C loss and dNBR was very weak. Comparisons between LiDAR‐derived soil C losses with estimates of C loss based on the fire disturbance module of the national C loss model, the Canadian Model for Peatlands (CaMP), indicated that C losses from bogs were greater than expected, particularly when ecotones were included, while fens and swamp C losses were on the low end of model expectations.

Background Smoldering underground fires in peatlands are among the most persistent and destructive wildfire types, but resisting conventional suppression methods. Aims This study experimentally evaluates a heat-pipe-based underground firebreak concept as a passive cooling strategy to mitigate smoldering propagation in stratified peat profiles. Methods Laboratory-scale experiments were conducted with an upper dry peat layer overlying a saturated layer. Heat pipes of varying lengths, geometries and quantities were installed with the condenser section positioned at the dry–saturated interface. Key results Under these conditions, heat pipes reduced peak smoldering temperatures and, in some configurations, quenched combustion. The intervention expanded the high-moisture, non-combustible region (‘safe zone’) more than fivefold compared with control tests. Longer pipes and configurations with larger condenser surface areas demonstrated greater thermal suppression effects. Conclusion The results provides a laboratory-scale proof of concept that heat pipes can function as passive thermal sinks and promote moisture redistribution at the dry–saturated interface in peat columns. Implications These findings are limited to controlled laboratory experiments. Substantial additional experimental, modeling and field research are required before assessing real-world deployment potential.

The incidence of haze from Indonesian forest and peatland fires is a major concern due to its adverse health effects, particularly due to fine particulate matter (PM2.5) exposure, leading to adverse effects on the lungs. This study designed a whole-body PM2.5 exposure chamber to develop a PM2.5 exposure model with varying concentrations and durations, and then evaluated its impact on lung tissue. Thirty-six male Wistar rats were randomly divided into four groups: a control group and three treatment groups with PM2.5 exposure at concentrations of 300, 500, and 700 µg/m³ for 10, 20, and 30 days. The morphology of PM2.5 was characterized using a scanning electron microscope (SEM) equipped with an energy-dispersive X-ray spectroscopy (EDS) detector. Lung tissue histology was assessed using hematoxylin and eosin for lung injury scoring and Masson's trichrome for the percentage of fibrotic areas. SEM-EDS results showed particles deposited within the alveolar cavity. The highest PM2.5 exposure group (700 µg/m³) had significantly higher lung tissue injury scores and fibrotic areas than the control group across all durations (p < 0.05). Exposure to PM2.5 from peat land-burning smokecauses significant lung tissue injury and fibrosis in a dose-dependent manner in Wistar rats.

Mercury (Hg) emissions from open biomass burning (OBB) represent a significant component of global atmospheric Hg cycling. Previous estimates have relied on the emission factor (EF) approach, which carries substantial uncertainties due to its inherent limitations. Here, we developed a mass-balance model to re-estimate global Hg emissions from OBB during 2010-2019, enabling quantification of emissions from individual vegetation tissues. Our results indicate that global annual Hg emissions average 280 Mg yr-1 (ranging 93-803 Mg yr-1), including 129 Mg yr-1 from vegetation-derived combustion, 81 Mg yr-1 from litterfall combustion, and 70 Mg yr-1 from peatland fires. Emissions exhibit strong latitudinal and longitudinal variability, with hotspots in central Africa, the Indo-China Peninsula, and boreal Asia. By category, the largest contributors are Africa (49%) among continents, savannas (29%) among biomes, and leaves (58%) among vegetation-derived emissions. Seasonal peaks occur in January, March, and August, while total annual emissions remain relatively stable over the decade, despite extreme anomalies such as the 2015 Indonesian fires. This approach reveals distinct emission sources and high spatial heterogeneity, providing a more accurate and nuanced assessment of Hg emissions from global OBB.

Abstract This study investigates the composition of carbonaceous aerosols and identifies their sources in three sites across southern Thailand: Surat-Thani, Hat-Yai, and Phuket, from June 2023 to May 2024. The findings highlight that PM 0.1 characteristics vary across different locations, primarily influenced by seasonal monsoons. In the upper south, including Surat-Thani and Phuket, PM 0.1 is significantly affected by air masses transporting pollutants from central Thailand and Cambodia during the northeast monsoon. The average PM 0.1 concentrations recorded were 1.43 ± 0.93 µg/m 3 in Surat-Thani and 0.71 ± 0.55 µg/m 3 in Phuket during this period. In contrast, in the lower south, particularly Hatyai, PM 0.1 is dominantly influenced by transboundary haze originating from Indonesian peatland fires, which is most pronounced during the southwest monsoon, with PM 0.1 concentrations reaching 1.43 ± 0.71 µg/m 3 during haze episodes. Organic carbon and elemental carbon were identified as the key contributors to PM 0.1 , with OC/EC ratios indicating distinct source contributions. The OC/EC ratio at Surat-Thani and Phuket was 2.68–3.52, suggesting contributions from both biomass burning and vehicle emissions, while at Hat Yai, the ratio was 3.77–4.69, indicating a dominant influence from biomass combustion. Principal Component Analysis further confirmed that biomass burning, engine exhaust, and secondary organic aerosols are the primary sources of PM 0.1 in the region. These findings provide crucial insights into how seasonal monsoons shape air pollution patterns in southern Thailand, emphasizing the need for targeted air quality management and mitigation strategies. Graphical Abstract

Wildland fires are significant sources of organic compounds, but traditional global fire emission inventories only include primary organic aerosols (POA) and volatile organic compounds (VOCs) and lack intermediate-volatility and semivolatile organic compounds (IVOCs and SVOCs), which could underestimate the environmental impact of wildland fires. We developed a global wildland fire organic emission inventory (1997-2023) with full-volatility coverage using volatility-binned and chemically specific emission factors by vegetation type. Compared to the traditional POA + VOC framework, full-volatility organic emission inventories filled a gap of 25.1 Mt/year of I/SVOCs; grassland, tropical forest, boreal forest, peatland, and temperate forest fires contributed 66%, 13%, 11%, 6%, and 4%, respectively, to full-volatility emissions (averaged over 1997-2023). Southern Hemisphere Africa was the top emission hotspot, with full-volatility organic emissions of 4.4 t/km2/year, 1.3-6.9 times greater than the next highest-emitting emission hotspots: Northern Hemisphere Africa, Southern Hemisphere South America, and Equatorial Asia. On a global scale, wildland fire organic emissions are 79% of anthropogenic organic emissions, but their I/SVOC emissions are comparable. With a more comprehensive consideration of the mass and chemical speciation of full-volatility organics, this emission inventory could enhance our understanding of the impact of wildland fires on air quality and human health.

This study analyzes land cover change in Riau Province from 2015 to 2024, focusingon deforestation and degradation as indicators of ecosystem sustainability. Landsat 8 OLI/TIRSand Landsat 9 OLI-2 imagery processed in Google Earth Engine (GEE), combined with MODIShotspot data (MOD14A1) and socioeconomic indicators—Gross Regional Domestic Product(GRDP) and Open Unemployment Rate (OUR) from Statistics Indonesia (BPS)—were used toassess spatiotemporal patterns. The Normalized Difference Vegetation Index (NDVI) wasapplied with thresholds for deforestation (NDVI &lt; –0.3) and degradation (–0.3 ? NDVI ? –0.1).Results show that 2015 was the most severe period, dominated by peatland fires, while 2019recorded forest loss at a lower intensity and 2020–2024 indicated partial vegetation recoverylinked to restoration efforts. Pelalawan, Indragiri Hilir, and Kampar were the most affecteddistricts. Correlation analysis revealed that fire hotspots had the strongest association with landcover change, while economic and social indicators showed weaker relationships. Peatland firesremain the main driver of land degradation, emphasizing the need to strengthen fire management,peatland protection, and sustainable plantation governance to support Sustainable DevelopmentGoal (SDG) 15 on Life on Land, particularly the target of Land Degradation Neutrality (15.3.1)by 2030.

Prediction of groundwater level in Indonesian tropical peatland forest plantations using machine learning

Abstract Peatlands in Southeast Asia regularly experience fire due to clearance of forests and drainage for agriculture and plantation development. Fire represents a mainstay for rural communities managing tropical landscapes, but these can lead to uncontrolled ‘wild’ fires that pose a major threat to people and the ecosystem, leading to a cycle of increased susceptibility to fire and increased vulnerability of people and peat ecosystem to future fires. Using an exposure-sensitivity-adaptive capacity framework, we constructed indicators of exposure, sensitivity, and adaptive capacity of peatlands and communities to fires in Sumatra and Kalimantan (Indonesia) and used these indicators to calculate the social and ecological vulnerability of peatlands and communities to fires. We operationalized this framework and defined spatial indicators which we used to construct three indices of vulnerability (ecological vulnerability, social vulnerability of burning, and social vulnerability smoke-haze). Our assessment found peatlands with high ecological vulnerability on eastern Sumatra (Riau, Jambi, South Sumatra), southern Kalimantan (Central, South Kalimantan), and East Kalimantan. Majority of these provinces overlapped with sites of high social vulnerability for burning (North Sumatra, Riau, South Sumatra, Central Kalimantan, South Kalimantan) and high social vulnerability for smoke-haze (Riau, Jambi, South Sumatra, Central Kalimantan, South Kalimantan). As districts play an important role in land use decisions and fire mitigation efforts, we identified the top six districts that had high numbers of villages with high ecological and social vulnerability scores. Hotspot analyses showed that ecological vulnerability hotspots were co-located with social vulnerability hotspots but clusters of social vulnerability hotspots for burning did not completely overlap with social vulnerability hotspots for smoke-haze. Our vulnerability assessment of peatlands and villages is the foundation for an important tool for policymakers at multiple governance levels to identify high ecological and social vulnerability to peatland fires and channel aid and mitigation efforts where they are most needed.

This study contributes to the understanding of forest fire susceptibility by applying a binary logistic regression model combined with a Geographic Information Systems (GIS) to map hotspot vulnerability in West Kalimantan, Indonesia, an approach not extensively explored in previous research. Forest fire is one of the environmental problems. In West Kalimantan, land fires are a routine disaster that is experienced almost every year. In this paper, a binary logistic regression model was used to identify land fire in west Kalimantan. In addition, mapping of confidence of hotspot susceptibility was carried out in West Kalimantan. The data used were 72 hotspots spread across in seven districts of West Kalimantan in 2020. The independent variables used were land cover, slope, topography, distance of hotspots to rivers, distance of hotspots to roads and distance of hotspots to settlements. While the dependent variable was the point which was classified into hotspots and non-hotspots. Results showed that the method identified that the variables significantly influencing land fires include the distance of the points to the river and the distance of the points to the road. The Binary Logistic Regression model of the land fire in West Kalimantan has a classification accuracy rate is 84.03%. From the results of weighting and visualization using GIS shown that the area that has a very high level of vulnerability is the city of Pontianak (42.97%). Meanwhile, areas that have a moderate level of vulnerability include Kayong Utara, Kubu Raya, Mempawah, Sambas, Sanggau, Sekadau and Sintang districs. Kubu Raya and Kayong Utara districts in the medium vulnerability level have the largest forest fire districts (43.70% and 41.25%). Meanwhile, districts that are in the very low vulnerability level are Bengkayang, Singkawang, Landak and Melawi districts.

This study aims to analyze the influence of sea surface temperature (SST), outgoing longwave radiation (OLR), and the Indian Ocean Dipole (IOD) on rainfall dynamics in the peatlands of East Sumatra’s coast. Data from ERA5 (SST, OLR), BMKG (rainfall), and a calculated IOD index (1982–2024) were analyzed quantitatively using Python and Pearson correlation. Results show SST in the Indian and Pacific Oceans correlates significantly and positively with rainfall (r &gt; 0.6; p &lt; 0.05), confirming the dominant roles of IOD and ENSO. OLR exhibits a strong negative correlation (r &lt; –0.7; p &lt; 0.05), reflecting its inverse relationship with convective activity and rainfall. The IOD index is significantly correlated with rainfall variation, particularly during positive IOD events, which are linked to rainfall deficits and increased peat fire risk. This quantitative analysis provides a basis for predicting rainfall and mitigating peatland fires.

During recent summers, warm and dry conditions have increased the occurrence of wildfires and potentially peat-fires across Alaska. Limitations in resolving the fine-scale distribution of peatlands and climate observations have constrained our ability to accurately predict peat-fire dynamics. Using a new high-resolution peatland map of Alaska, we evaluated the climate and environmental controls of past and future peat-fire activity. Ensemble machine learning models identified reduced soil moisture, higher temperatures, and evapotranspiration as key predictors of annual total burned peatland area (tenfold CV R2 = 0.62, RMSE = 221.1 km2). By the end of the twenty-first century, models forced with climate datasets from representative concentration pathways (RCPs) 4.5, 6.0, and 8.5 emission scenarios project a statewide doubling of burned peatlands (increasing 61–121%), with regional increases ranging from 25–165% in polar, 61–95% in boreal, and 102–106% in maritime ecoregions. These projections indicate that wildfires will progressively encroach further into organic-rich moist and wet peaty soils, potentially amplifying soil carbon release across Alaska.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-19682-4.

Peatlands are characterized by the accumulation of decomposed plant remains, which result in an organic carbon content of approximately 16 percent and form a layer at least 40 cm thick. Peat ecosystems play a crucial role in supporting biodiversity conservation, maintaining water availability, and regulating the climate. However, human activities threaten these functions, especially during the dry season, which often leads to extensive fires. Post-fire succession is a natural process through which the land attempts to restore its original state. Monitoring succession after peatland fires can be conducted using satellite-based remote sensing technology, which provides spatiotemporal information. This study utilized a time series of three Landsat satellites, namely Landsat 5 (TM), Landsat 8 (OLI), and Landsat 9 (OLI2), to monitor succession in burnt peat areas on Bengkalis Island from 2000 onward. Additionally, hotspot data from FIRMS NASA and MODIS were incorporated. The results showed a total of 3,689 hotspots recorded between 2005 and 2023. The confirmed land cover types in the succession area include swamps, water bodies, and oil palm plantations. The information from this research is expected to inform policymaking by the government or peatland area managers, and serve as a reference for further studies.

Peatland fires contribute to overall ecosystem degradation, affecting physical, chemical, and biological components, including the loss of carbon stocks, alterations in soil structure, and a decline in land quality. This study aims to analyze changes in morphological characteristics, physical and chemical properties, and vegetation cover in burned and unburned peatlands in Pelalawan Regency. The research was conducted in the peatlands of Pangkalan Kerinci District using a descriptive approach with purposive soil sampling. The results indicated that across all three peatland conditions—peat swamp land, burned peat, and unburned peat—the peat maturity level was classified as sapric, with relatively similar physical characteristics. Peat swamp land exhibited higher water content, bulk density, particle density, and porosity compared to the other two conditions, while burned land showed the lowest water content and porosity. Variations in soil color did not correspond to significant changes in soil physical properties, suggesting that peatland fires in Pelalawan Regency did not substantially affect the morphological, physical, or chemical characteristics of the soil, which remained within the same classification range. Additionally, on the peatland burned in 2019, a natural succession process was observed, with the dominant vegetation being the grass Leersia oryzoides.

Indonesian peat is the largest tropical peat in the world, most of which is found on the island of Sumatra. The occurrence of forest and peatland fires in Indonesia has caused a reduction in the area of forests and peatlands, making rehabilitation activities necessary considering the crucial role of peat in the ecosystem. Rehabilitation activities can be carried out by utilizing the presence of soil microbes in the form of Arbuscular Mycorrhizal Fungi (AMF). The occurrence of fires in forests and peatlands impacts the physical and chemical properties of peat soil, biodiversity, and the presence of Arbuscular Mycorrhizal Fungi (AMF). The impact of peatland fires on the presence of FMA is that peatland fires will destroy the natural habitat of FMA, significantly reducing the FMA population; as a result of peatland fires, the condition of the peat soil will change, with an increase in soil temperature and changes in soil pH, which can affect the condition of FMA; the loss of organic soil matter due to the fire can reduce the availability of carbon needed by FMA for its development.

Students in South Sumatra are familiar with the direct impacts of forest and peatland fires, yet understanding their connection to global warming remains limited and requires targeted educational interventions. This study investigates conceptual change among 16 students in Palembang after participating in modeling-based learning activities about forest and peatland fires. Although Palembang does not experience such fires directly, the city is heavily affected by the resulting haze, which impacts health, visibility, and daily activities, including school attendance. A qualitative approach was used, and data were collected through multiple-choice questions and student-generated drawings to capture shifts in understanding. The results revealed varied levels of conceptual understanding, categorized as scientific conception from the beginning (37%), static understanding (20%), disorientation (17%), revision or reconstruction (8%), and construction of new understanding (18%). A total of 26% of students demonstrated positive conceptual change after the intervention. These findings suggest that modeling serves as an effective tool to help students visualize complex environmental processes and make connections between local phenomena and global issues. The study highlights the importance of incorporating interactive, visual learning strategies in environmental education to foster deeper, more accurate understanding among students, particularly in areas indirectly affected by ecological disasters. Such approaches can empower students to become more informed and responsible citizens in the face of climate change.

Concurrent study of long-range transport of fine and ultrafine particles from peatland fires in lower Southeast Asia

Peatland fires continue to recur in the Banjarbaru area, South Kalimantan, especially in the dry season. Community empowerment is the key to overcoming these fires and realizing resilient villages from land fire disasters. This study analyzes community empowerment to form a peatland fire disaster-resilient village. Data collection uses questionnaires on fire care communities and people living in disaster-prone lands. There were 37 questions asked to formulate a community empowerment model, with around 382 respondents involved in community empowerment activities to reduce the risk of land fires and the fire care community. Data analysis uses SEM (Structural Equation Model) to formulate a community empowerment model to realize disaster-resilient villages in peatland-prone areas prone to fire. The study results show that physical capital, human resource capital and social capital significantly influence community empowerment capabilities and the empowerment process. The capital can still not empower the community optimally to form a disaster-resilient village. The community is still not able to independently make decisions to overcome peatland fires and form a peatland fire disaster-resilient village. There is a need for the role of the Government through various empowerment programs to improve the ability and capacity of the community to overcome peatland fires.

ABSTRACT A detailed, spatially explicit fire inventory is essential for improving our understanding of biomass burning and for supporting the formulation of targeted fire mitigation policies. However, such fire inventories remain limited, especially in tropical regions. Existing active fire (AF) products derived from low-resolution sensors (e.g. MODIS and VIIRS) generally have high omission errors (OE), especially when detecting small and relatively colder temperatures fires. While moderate-resolution sensors offer unprecedented opportunities for detecting small and subtle fires, they face the dilemma of high commission errors (CE). To address this problem, we propose an object-oriented method to effectively detect AFs from Sentinel-2 MSI images, which focuses on suppressing the interference of various CEs through object-level inter-spectral criteria cloud filtering, seamline exclusion based on granule footprints, and false positive refinement based on random forest classification model. Using more than 55,000 Sentinel-2 MSI images acquired during 2016–2021, we have compiled a novel 20 m fire inventory covering forests and peatlands in Borneo. Initial assessment of the fire inventory suggests a CE of approximately 7.2% and an OE of 11.5%. Analysis of the Borneo fire inventory revealed the following: (i) A significant concentration of AFs was observed in Kalimantan, with Central Kalimantan accounting for approximately 55.9% of all detected peatland fires in Borneo, and West Kalimantan contributing 33.7% of forest fires. (ii) Peatland fires dominated widespread fires in Borneo in 2019, with 1.4 to 2.6 times the size and 3.1 to 16 times the number compared to other years in 2016–2021. (iii) The MSI AF detections show slight differences in spatiotemporal patterns compared to MODIS and VIIRS AF products, which is attributed to variations in sensitivity to small fires. Our study clarifies the spatial dynamic distribution of AFs in Borneo, providing fundamental support for local fire monitoring, fire regime, and carbon emission research.