Improved estimates of smoke exposure during Australia fire seasons: importance of quantifying plume injection heights
Improved estimates of smoke exposure during Australia fire seasons: importance of quantifying plume injection heights
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DOI：
10.5194/acp-24-2985-2024
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发表时间：
2024-03
影响因子：
6.3
通讯作者：
Xu Feng;L. Mickley;M. Bell;Tianjia Liu;J. Fisher;M. Val Martin
中科院分区：
文献类型：
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作者：
Xu Feng;L. Mickley;M. Bell;Tianjia Liu;J. Fisher;M. Val Martin
摘要。在严重的燃烧季节，野火会对澳大利亚的空气质量产生重大影响，但对烟羽喷射高度的不完全了解对量化烟雾暴露构成了挑战。在这项研究中，我们使用了两种方法来量化注入到行星边界层（PBL）以上的火灾排放分数，并进一步研究了2009年至2020年期间羽流注入分数对澳大利亚北部和东南部主要城市野火烟雾中细颗粒物（PM2.5）日平均表面浓度的影响。对于第一种方法，我们依赖于来自野火综合监测和建模系统（IS4FIRES）的气候、月平均烟雾排放垂直剖面，以及来自NASA现代研究与应用回顾性分析（MERRA）版本2的同化PBL高度。对于第二种方法，我们开发了一种基于多角度成像光谱仪（MISR）观测和随机森林的新方法，机器学习模型使我们能够直接预测每个网格单元中PBL以上的每日羽流注入分数。我们将两种方法量化的烟羽喷射分数应用于目标城市的随机逆时拉格朗日输运（STILT）模型模拟的烟雾PM2.5浓度。我们发现羽流喷射高度的特征极大地影响了地表每日烟雾PM2.5的估计，特别是在严重的野火季节，当火灾产生的强烈热量可以使对流层中的烟雾升高时。然而，使用气候注入剖面不能很好地捕捉羽流注入分量的时空变异性，导致与MISR注入分量得出的通量相比，对PBL以上注入的日火排放通量低估了63%。我们的随机森林模型成功地再现了每日注入的火排放通量与MISR观测值（R2=0.88，归一化平均偏差= 10%），并预测从2009年到2020年，澳大利亚北部和东南部的火排放总量分别有27%和45%高于PBL。使用随机森林方法预测的羽流行为也使模型与野火源区附近几个主要城市的地表PM2.5观测结果有更好的一致性，在2009年至2020年的火灾季节，烟雾PM2.5占总PM2.5的5% - 52%。
Abstract. Wildfires can have a significant impact on air quality in Australia during severe burning seasons, but incomplete knowledge of the injection heights of smoke plumes poses a challenge for quantifying smoke exposure. In this study, we use two approaches to quantify the fractions of fire emissions injected above the planetary boundary layer (PBL), and we further investigate the impact of plume injection fractions on daily mean surface concentrations of fine particulate matter (PM2.5) from wildfire smoke in key cities over northern and southeastern Australia from 2009 to 2020. For the first method, we rely on climatological, monthly mean vertical profiles of smoke emissions from the Integrated Monitoring and Modelling System for wildland fires (IS4FIRES) together with assimilated PBL heights from NASA Modern-Era Retrospective Analysis for Research and Application (MERRA) version 2. For the second method, we develop a novel approach based on the Multi-angle Imaging SpectroRadiometer (MISR) observations and a random forest, machine learning model that allows us to directly predict the daily plume injection fractions above the PBL in each grid cell. We apply the resulting plume injection fractions quantified by the two methods to smoke PM2.5 concentrations simulated by the Stochastic Time-Inverted Lagrangian Transport (STILT) model in target cities. We find that characterization of the plume injection heights greatly affects estimates of surface daily smoke PM2.5, especially during severe wildfire seasons, when intense heat from fires can loft smoke high in the troposphere. However, using climatological injection profiles cannot capture well the spatiotemporal variability in plume injection fractions, resulting in a 63 % underestimation of daily fire emission fluxes injected above the PBL in comparison with those fluxes derived from MISR injection fractions. Our random forest model successfully reproduces the daily injected fire emission fluxes against MISR observations (R2=0.88, normalized mean bias = 10 %) and predicts that 27 % and 45 % of total fire emissions rise above the PBL in northern and southeastern Australia, respectively, from 2009 to 2020. Using the plume behavior predicted by the random forest method also leads to better model agreement with observed surface PM2.5 in several key cities near the wildfire source regions, with smoke PM2.5 accounting for 5 %–52 % of total PM2.5 during fire seasons from 2009 to 2020.