Global health burden from acute exposure to fine particles emitted by fires

Research Square (Research Square), Год журнала: 2025, Номер unknown

Опубликована: Апрель 24, 2025

Acute exposure to emissions from fires presents a significant and immediate threat human health. Inhalation of wildfire smoke other pollutants can lead various health issues, including respiratory cardiovascular problems. Our study uses the SILAM chemical transport model, integrated with IS4FIRES fire information system, assess population fire-related PM2.5, along burden all-cause, respiratory, deaths. results show that while population-weighted all-source PM2.5 has declined in Europe high-income North America, fire-PM2.5 increased significantly Eastern Central Europe, Tropical Latin sub-Saharan Africa. Extreme events have tripled globally since 1990s, more than half global experiencing minimum perpetual occurrence (least 1% fire-PM_2.5 for 50 instances 3 consecutive days calendar year) 2010–2018. contributed 99,000 (95% CI − 55,000–149,000) all-cause deaths annually 2010-18, disease burdens, particularly findings highlight escalating risks emissions, emphasizing urgent need mitigation strategies as becomes growing contributor air pollution-related mortality.

Язык: Английский

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Simulated fire observables as indicators for optimizing wireless sensor networks in wildfire risk monitoring

Ecological Indicators, Год журнала: 2025, Номер 175, С. 113509 - 113509

Опубликована: Май 17, 2025

Язык: Английский

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Wildland fire smoke exposure disparities by wildland urban interface category and land ownership

Landscape and Urban Planning, Год журнала: 2025, Номер 263, С. 105423 - 105423

Опубликована: Июнь 2, 2025

Язык: Английский

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Evaluating estimation methods for wildfire smoke and their implications for assessing health effects

EarthArXiv (California Digital Library), Год журнала: 2024, Номер unknown

Опубликована: Июнь 13, 2024

Growing wildfire smoke represents a substantial threat to air quality and human health in the US across much of globe. However, impact on remains imprecisely understood, due uncertainties both measurement population exposure dose-response functions linking health. Here, we compare daily smoke-related surface fine particulate matter (PM2.5) concentrations estimated using three approaches, including two chemical transport models (CTMs): GEOS-Chem Community Multiscale Air Quality (CMAQ), one machine learning (ML) model over contiguous 2020, historically active fire year. We study consequences these different approaches for estimating PM2.5 effects mortality. In western US, compared against measurements from Environmental Protection Agency (EPA) PurpleAir sensors, find that CTMs overestimate during extreme episodes by up 3-5 fold, while ML estimates are largely consistent with measurements. eastern where levels were lower show modestly better agreement develop calibration framework integrates CTM- ML-based yields outperform each individual approach. When combining county-level mortality rates, low-level but large discrepancies high-level methods. Our research highlights benefits costs estimation methods understanding impacts smoke, demonstrates importance bench-marking available

Язык: Английский

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A Novel Flexible Geographically Weighted Neural Network for High-Precision PM2.5 Mapping across the Contiguous United States

ISPRS International Journal of Geo-Information, Год журнала: 2024, Номер 13(7), С. 217 - 217

Опубликована: Июнь 22, 2024

Air quality degradation has triggered a large-scale public health crisis globally. Existing machine learning techniques have been used to attempt the remote sensing estimates of PM2.5. However, many models ignore spatial non-stationarity predictive variables. To address this issue, study introduces Flexible Geographically Weighted Neural Network (FGWNN) estimate PM2.5 based on multi-source data. FGWNN incorporates Geographical Neuron (FGN) and Activation Function (GWAF) within framework Artificial (ANN) capture intricate non-stationary relationships among A robust air estimation model was constructed using data Aerosol Optical Depth (AOD), Normalized Difference Vegetation Index (NDVI), Temperature (TMP), Specific Humidity (SPFH), Wind Speed (WIND), Terrain Elevation (HGT) as inputs, Ground-Based observation. The results indicated that successfully generates with 2.5 km resolution for contiguous United States (CONUS) in 2022. It exhibits higher regression accuracy compared traditional ANN Regression (GWR) models. holds potential applications high-precision high-resolution scenarios.

Язык: Английский

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Prescribed burn related increases of population exposure to PM2.5 and O3 pollution in the southeastern US over 2013–2020

Environment International, Год журнала: 2024, Номер 193, С. 109101 - 109101

Опубликована: Окт. 28, 2024

Язык: Английский

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Evaluating Chemical Transport and Machine Learning Models for Wildfire Smoke PM_2.5: Implications for Assessment of Health Impacts

Environmental Science & Technology, Год журнала: 2024, Номер unknown

Опубликована: Дек. 18, 2024

Growing wildfire smoke represents a substantial threat to air quality and human health. However, the impact of on health remains imprecisely understood due uncertainties in both measurement exposure population dose-response functions linking Here, we compare daily smoke-related surface fine particulate matter (PM

Язык: Английский

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The Impact of Wildfire Smoke Exposure on Excess Mortality and Later-Life Socioeconomic Outcomes: The Great Fire of 1910

Опубликована: Янв. 1, 2024

Язык: Английский

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Seasonal persistence of the nitrogen oxides production in Mexico City

International Journal of Environmental Science and Technology, Год журнала: 2024, Номер unknown

Опубликована: Ноя. 8, 2024

Abstract This study investigates the seasonal influence on nitrogen oxide NO x pollution records at four monitoring sites in Mexico City from 2010 to 2018. The analysis employs second-order structure function examine trends concentration fluctuations. findings reveal that fluctuations follow a power law pattern characterized by Hurst exponents, predominantly statistical persistence regime, with scaling range spanning three orders of magnitude. Specifically, autumn period exhibits an exponent $$\overline{H}=0.72$$ H ¯ = 0.72 , indicating relatively smaller compared other seasons, but potential for concentrations surpass those periods. In contrast, spring, summer, and winter, are exponents $$\overline{H}=0.59$$ 0.59 $$\overline{H}=0.61$$ 0.61 $$\overline{H}=0.62$$ 0.62 respectively, demonstrating greater lower autumn. These results consistent various studies conducted worldwide. Additionally, negative correlation between ozone ( O 3 ) has been established during winter season, as display persistent anti-persistent behavior, respectively.

Язык: Английский

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Drivers of environmental externality reduction in China's electric power industry: A spatial-temporal analysis

Journal of Environmental Management, Год журнала: 2024, Номер 373, С. 123612 - 123612

Опубликована: Дек. 5, 2024

Язык: Английский

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