Zhao Tianlang, Mao Jingqiu, Gupta Pawan, Zhang Huanxin, Wang Jun
Geophysical Institute and Department of Chemistry and Biochemistry, University of Alaska Fairbanks, Fairbanks, Alaska 99775, United States.
Goddard Space Flight Center, NASA, Greenbelt, Maryland 20771, United States.
ACS EST Air. 2024 Aug 26;1(9):1164-1176. doi: 10.1021/acsestair.4c00120. eCollection 2024 Sep 13.
Wildfire is one of the main sources of PM (particulate matter with aerodynamic diameter < 2.5 μm) in the Alaskan summer. The complexity in wildfire smokes, as well as limited coverage of ground measurements, poses a big challenge to estimate surface PM during wildfire season in Alaska. Here we aim at proposing a quick and direct method to estimate surface PM over Alaska, especially in places exposed to strong wildfire events with limited measurements. We compare the AOD-surface PM conversion factor (η = PM/AOD; AOD, aerosol optical depth) from the chemical transport model GEOS-Chem (η) and from observations (η). We show that η is biased high compared to η under smoky conditions, largely because GEOS-Chem assigns the majority of AOD (67%) within the planetary boundary layer (PBL) when AOD > 1, inconsistent with satellite retrievals from CALIOP. The overestimation in η can be to some extent improved by increasing the injection height of wildfire emissions. We constructed a piecewise function for η across different AOD ranges based on VIIRS-SNPP AOD and PurpleAir surface PM measurements over Alaska in the 2019 summer and then applied it on VIIRS AOD to derive daily surface PM over continental Alaska in the 2021 and 2022 summers. The derived satellite PM shows a good agreement with corrected PurpleAir PM in Alaska during the 2021 and 2022 summers, suggesting that aerosol vertical distribution likely represents the largest uncertainty in converting AOD to surface PM concentrations. This piecewise function, η', shows the capability of providing an observation-based, quick and direct estimation of daily surface PM over the whole of Alaska during wildfires, without running a 3-D model in real time.
野火是阿拉斯加夏季细颗粒物（空气动力学直径小于2.5微米的颗粒物）的主要来源之一。野火烟雾的复杂性以及地面测量覆盖范围有限，给估算阿拉斯加野火季节的地表细颗粒物带来了巨大挑战。在此，我们旨在提出一种快速直接的方法来估算阿拉斯加的地表细颗粒物，特别是在受强烈野火事件影响且测量数据有限的地区。我们比较了化学传输模型GEOS-Chem的气溶胶光学厚度-地表细颗粒物转换因子（η = 细颗粒物/气溶胶光学厚度；气溶胶光学厚度，AOD）和观测得到的转换因子（η）。我们发现，在烟雾条件下，与η相比，η存在偏高偏差，这主要是因为当气溶胶光学厚度>1时，GEOS-Chem将大部分气溶胶光学厚度（67%）分配在行星边界层（PBL）内，这与CALIOP卫星反演结果不一致。通过提高野火排放的注入高度，η的高估在一定程度上可以得到改善。我们基于2019年夏季阿拉斯加的VIIRS-SNPP气溶胶光学厚度和PurpleAir地表细颗粒物测量数据，构建了不同气溶胶光学厚度范围内η的分段函数，然后将其应用于VIIRS气溶胶光学厚度，以推导2021年和2022年夏季阿拉斯加大陆的每日地表细颗粒物。推导得到的卫星细颗粒物与2021年和2022年夏季阿拉斯加经校正的PurpleAir细颗粒物显示出良好的一致性，这表明气溶胶垂直分布可能是将气溶胶光学厚度转换为地表细颗粒物浓度时最大的不确定性来源。这个分段函数η'显示了在野火期间无需实时运行三维模型就能基于观测对阿拉斯加全境每日地表细颗粒物进行快速直接估算的能力。
