State of Wildfires 2023–2024

Earth system science data, Journal Year: 2024, Volume and Issue: 16(8), P. 3601 - 3685

Published: Aug. 13, 2024

Abstract. Climate change contributes to the increased frequency and intensity of wildfires globally, with significant impacts on society environment. However, our understanding global distribution extreme fires remains skewed, primarily influenced by media coverage regionalised research efforts. This inaugural State Wildfires report systematically analyses fire activity worldwide, identifying events from March 2023–February 2024 season. We assess causes, predictability, attribution these climate land use forecast future risks under different scenarios. During 2023–2024 season, 3.9×106 km2 burned slightly below average previous seasons, but carbon (C) emissions were 16 % above average, totalling 2.4 Pg C. Global C record in Canadian boreal forests (over 9 times average) reduced low African savannahs. Notable included record-breaking extent Canada, largest recorded wildfire European Union (Greece), drought-driven western Amazonia northern parts South America, deadly Hawaii (100 deaths) Chile (131 deaths). Over 232 000 people evacuated Canada alone, highlighting severity human impact. Our revealed that multiple drivers needed cause areas activity. In Greece, a combination high weather an abundance dry fuels probability fires, whereas area anomalies weaker regions lower fuel loads higher direct suppression, particularly Canada. Fire prediction showed mild anomalous signal 1 2 months advance, Greece had shorter predictability horizons. Attribution indicated modelled up 40 %, 18 50 due during respectively. Meanwhile, seasons magnitudes has significantly anthropogenic change, 2.9–3.6-fold increase likelihood 20.0–28.5-fold Amazonia. By end century, similar magnitude 2023 are projected occur 6.3–10.8 more frequently medium–high emission scenario (SSP370). represents first annual effort catalogue events, explain their occurrence, predict risks. consolidating state-of-the-art science delivering key insights relevant policymakers, disaster management services, firefighting agencies, managers, we aim enhance society's resilience promote advances preparedness, mitigation, adaptation. New datasets presented this work available https://doi.org/10.5281/zenodo.11400539 (Jones et al., 2024) https://doi.org/10.5281/zenodo.11420742 (Kelley 2024a).

Language: Английский

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Data Augmentation Strategies for Improved PM2.5 Forecasting Using Transformer Architectures

Atmosphere, Journal Year: 2025, Volume and Issue: 16(2), P. 127 - 127

Published: Jan. 24, 2025

Breathing in fine particulate matter of diameter less than 2.5 µm (PM2.5) greatly increases an individual’s risk cardiovascular and respiratory diseases. As climate change progresses, extreme weather events, including wildfires, are expected to increase, exacerbating air pollution. However, models often struggle capture pollution events due the rarity high PM2.5 levels training datasets. To address this, we implemented cluster-based undersampling trained Transformer improve event prediction using various cutoff thresholds (12.1 µg/m3 35.5 µg/m3) partial sampling ratios (10/90, 20/80, 30/70, 40/60, 50/50). Our results demonstrate that threshold, paired with a 20/80 ratio, achieved best performance, RMSE 2.080, MAE 1.386, R2 0.914, particularly excelling forecasting events. Overall, on augmented data significantly outperformed those original data, highlighting importance resampling techniques improving quality accuracy, especially for high-pollution scenarios. These findings provide critical insights into optimizing models, enabling more reliable predictions By advancing ability forecast levels, this study contributes development informed public health environmental policies mitigate impacts pollution, advanced technology building better digital twins.

Language: Английский

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Wildfire combustion emission inventory in Southwest China (2001–2020) based on MODIS fire radiative energy data

Atmospheric Pollution Research, Journal Year: 2024, Volume and Issue: 15(11), P. 102279 - 102279

Published: Aug. 5, 2024

Language: Английский

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Assessing bias in personal exposure estimates when indoor air quality is ignored: A comparison between GPS-enabled mobile air sensor data and stationary outdoor sensor data

The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 950, P. 175249 - 175249

Published: Aug. 3, 2024

Language: Английский

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Resilience Patterns of Multiscale Human Mobility Under Extreme Rainfall Events Using Massive Individual Trajectory Data

Annals of the American Association of Geographers, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 25

Published: Jan. 2, 2025

Understanding human mobility's resilience during extreme rainfall is paramount for enhancing disaster response and urban resilience. Most studies, however, have overlooked the complexity of patterns across scales, missing out on varied spatial anomalies their underlying causes. To bridge this gap, we propose a framework using massive individual trajectory data to dissect mobility scales. By leveraging dynamic network model, quantify flows employ curves determine at urban-agglomeration regional Our study, centering from Typhoon Mawar, covers Osaka Nagoya in Japan. The findings reveal marked reduction movement, although structure networks remains relatively unchanged. Based quadrant distribution inflows outflows, that ratio abnormal normal stands approximately 3:2, consistency maintained both Interestingly, are intricately linked local geographical settings built environment, revealing disparities based income, gender, age. These insights invaluable policymakers improve postdisaster recovery efforts guide future infrastructure development toward greater

Language: Английский

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Impact of gaseous smoke pollutants from modelled fires on air and soil quality

Carbon Research, Journal Year: 2025, Volume and Issue: 4(1)

Published: Feb. 1, 2025

Abstract Forest fires produce large volumes of pollutants in the atmospheric air. Fires contribute significantly to greenhouse gas emissions worldwide apart from industrial and traffic pollutants. The study reports results research on effect gaseous substances burning forest combustibles air quality deposition soil. It was determined a significant excess smoke such as carbon monoxide (3570 mg/m 3 ), nitrogen oxide dioxide (40 60 ) saturated hydrocarbons – methane, ethane, propane, butane, pentane, hexane, heptane, octane, nonane, decane, dodecane, tridecane, tetradecane, pentadecane, hexadecane, heptadecane, octadecane nonadecane. obtained evidence increased concentrations pollutants, including climate-active They can affect negatively both climate ecological state soils. A negative products combustion soil ( Haplic Chernozem by determined, which caused changes properties. reliably established that enzymatic activity decreased under influence fire during min. Catalase appeared be most sensitive indicator. catalase 25% compared control values. Peroxidase 15%, urease 20% phosphatase 16%. pH changed 7.8 6.3 after exposure smoke. Soil microbiota also adversely affected High sensitivity recorded for microscopic fungi. Their abundances 26%–87% 10–60 min exposure. Bacteria were found more resistant toxic (28%–33% decrease abundance). Therefore, considered one factors Graphical

Language: Английский

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Trends and Impacts of Climate-induced Extreme Weather Events in South Africa (1920-2023)

Environmental Development, Journal Year: 2025, Volume and Issue: unknown, P. 101183 - 101183

Published: Feb. 1, 2025

Language: Английский

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Unveiling the Impact of Wildfires on Nanoparticle Characteristics and Exposure Disparities through Mobile and Fixed-Site Monitoring in Toronto, Canada

Environmental Science & Technology, Journal Year: 2025, Volume and Issue: unknown

Published: March 12, 2025

This study investigates the impacts of wildfires on nanoparticle characteristics and exposure disparities in Toronto, integrating data from a large-scale mobile monitoring campaign fixed-site measurements during unprecedented 2023 wildfire season. Our results reveal changes particle days, with number concentrations decreasing by 60% diameter increasing 30% compared to nonwildfire days. Moreover, median lung deposited surface area (LDSA) levels rose 31% events. We employed gradient boosting models estimate near-road LDSA both The ratio (wildfire/nonwildfire) exceeded 2.0 certain areas along highways downtown Toronto. Furthermore, our findings show that marginalized communities faced greater increases than less ones. Under conditions, difference between most least groups was 16% for recent immigrants visible minorities 7% seniors children, statistically significant. delivers critical insights into spatiotemporal variations periods, demonstrating substantial health risks posed increased inequitable distribution these among Toronto's diverse population.

Language: Английский

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Trends and Decomposition of Changes in Mortality in Low‐ and Middle‐Income Countries, 1950–2019

Population and Development Review, Journal Year: 2025, Volume and Issue: unknown

Published: March 3, 2025

Abstract From 1950 to 2019, all countries experienced an increase in life expectancy at birth. However, the magnitude and pace of change varied. Lower income relatively larger increases, leading a convergence process. Nevertheless, disparities remained pronounced comparison wealthier countries. In accordance with health transition model, typically observe decline mortality rates by first achieving greater gains young adult ages. this study, we build on existing literature demonstrate that 55 percent 118 low‐ middle‐income analyzed had reached birth least 70 years. Notably, only two from sub‐Saharan Africa met threshold. Additionally, 54 transitioned into “cardiovascular revolution” stage, where improvements significantly increased Meanwhile, 49 advanced “slowing aging process” characterized ages 65 older than those aged 30–60. when applying more restrictive criterion focused above 80, proportion drops 19 percent. The results ongoing process divergence–convergence between high‐ low‐income within middle‐ groups.

Language: Английский

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Global data-driven prediction of fire activity

Nature Communications, Journal Year: 2025, Volume and Issue: 16(1)

Published: April 1, 2025

Abstract Recent advancements in machine learning (ML) have expanded the potential use across scientific applications, including weather and hazard forecasting. The ability of these methods to extract information from diverse novel data types enables transition forecasting fire weather, predicting actual activity. In this study we demonstrate that shift is feasible also within an operational context. Traditional forecasts tend over predict high danger, particularly fuel limited biomes, often resulting false alarms. By using on characteristics, ignitions observed activity, data-driven predictions reduce false-alarm rate high-danger forecasts, enhancing their accuracy. This made possible by quality global datasets evolution detection. We find input more important when improving than complexity ML architecture. While focus justified, our findings highlight importance investing high-quality and, where necessary create it through physical models. Neglecting aspect would undermine gains ML-based approaches, emphasizing essential achieve meaningful progress activity

Language: Английский

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