Preprints
https://doi.org/10.5194/essd-2024-414
https://doi.org/10.5194/essd-2024-414
21 Oct 2024
 | 21 Oct 2024
Status: this preprint is currently under review for the journal ESSD.

Global high-resolution fire-sourced PM2.5 concentrations for 2000–2023

Yonghang Hu, Chenguang Tian, Xu Yue, Yadong Lei, Yang Cao, Rongbin Xu, and Yuming Guo

Abstract. Fires are a significant disturbance in Earth’s systems. Smoke aerosols emitted from fires can cause environmental degradation and climatic perturbations, leading to exacerbated air pollution and posing hazards to public health. However, research on the climatic and health impacts of fire emissions is severely limited by the scarcity of air pollution data directly attributed to these emissions. Here, we develop a global daily fire-sourced PM2.5 concentration ([PM2.5]) dataset at a spatial resolution of 0.25° for the period 2000–2023, using the GEOS-Chem chemical transport model driven with two fire emission inventories, the Global Fire Emissions Database version 4.1 with small fires (GFED4.1s) and the Quick Fire Emission Dataset version 2.5r1 (QFED2.5) . Simulated all-source [PM2.5] are bias-corrected using a machine learning algorithm, which incorporates ground observations from over 9000 monitoring sites worldwide. Then the simulated ratios between fire- and all-source [PM2.5] at individual grids are applied to derive fire-sourced [PM2.5]. Globally, the average fire-sourced [PM2.5] is estimated to be 1.94 μg m-3 with GFED4.1s and 3.74 μg m-3 with QFED2.5. Both datasets show consistent spatial distributions with regional hotspots in central Africa and widespread decreasing trends over most areas. While the mean levels of fire-sourced [PM2.5] are much larger at low latitudes, fire episodes at the boreal regions can cause comparable PM2.5 levels as in the tropics. This dataset serves as a valuable tool for exploring the impacts of fire-related air pollutants on climate, ecosystems, and public health, enabling accurate assessments and supports for decision-making in environmental management and policy.

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Yonghang Hu, Chenguang Tian, Xu Yue, Yadong Lei, Yang Cao, Rongbin Xu, and Yuming Guo

Status: open (until 07 Dec 2024)

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  • RC1: 'Comment on essd-2024-414', Anonymous Referee #1, 18 Nov 2024 reply
Yonghang Hu, Chenguang Tian, Xu Yue, Yadong Lei, Yang Cao, Rongbin Xu, and Yuming Guo

Data sets

GFED&QFED: Fire-sourced PM2.5 concentrations dataset (2018-2022) Yonghang Hu, Chenguang Tian, and Xu Yue https://doi.org/10.5281/zenodo.13380164

Yonghang Hu, Chenguang Tian, Xu Yue, Yadong Lei, Yang Cao, Rongbin Xu, and Yuming Guo

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Short summary
We develop a global dataset of daily fire-sourced PM2.5 concentration at a spatial resolution of 0.25° for 2000–2023, using a chemical transport model driven with two fire emission inventories and a machine learning approach trained with ground measurements from over 9000 sites. The dataset shows significant spatiotemporal variations of fire PM2.5 in the past decades, serving a useful tool for exploring the impacts of fire-related air pollutants on climate, ecosystems, and public health.
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