Faulstich Sam D, Strickland Matthew J, Holmes Heather A
University of Utah; Department of Chemical Engineering; Salt Lake City, UT.
University of Nevada, Reno; School of Public Health; Reno, NV.
Int J Wildland Fire. 2025 Feb;34(2). doi: 10.1071/wf24040. Epub 2025 Feb 20.
Daily fire progression information is crucial for public health studies that examine the relationship between population-level smoke exposures and subsequent health events. Issues with remote sensing used in fire emissions inventories (FEI) lead to the possibility of missed exposures that impact the results of acute health effects studies.
This paper provides a method for improving an FEI dataset with readily available information to create a more robust dataset with daily fire progression.
High temporal and spatial resolution burned area information from two FEI products are combined into a single dataset, and a linear regression model fills gaps in daily fire progression.
The combined dataset provides up to 71% more PM emissions, 69% more burned area, and 367% more fire days per year than using a single source of burned area information.
The FEI combination method results in improved FEI information with no gaps in daily fire emissions estimates.
The combined dataset provides a functional improvement to FEI data that can be achieved with currently available data.
每日火灾蔓延信息对于研究人群层面烟雾暴露与后续健康事件之间关系的公共卫生研究至关重要。火灾排放清单（FEI）中使用的遥感技术存在问题，可能导致遗漏暴露情况，从而影响急性健康影响研究的结果。
本文提供一种利用现有信息改进FEI数据集的方法，以创建一个更强大的包含每日火灾蔓延情况的数据集。
将来自两种FEI产品的高时空分辨率燃烧面积信息合并到一个数据集中，并使用线性回归模型填补每日火灾蔓延的空白。
与使用单一燃烧面积信息来源相比，合并后的数据集每年提供的颗粒物排放量多71%、燃烧面积多69%、火灾天数多367%。
FEI合并方法可改善FEI信息，且每日火灾排放估计无空白。
合并后的数据集对FEI数据进行了功能改进，利用现有数据即可实现。
