Abstract：
Aerosol Optical Depth (AOD) data derived from satellites is crucial for estimating spatially-resolved PM concentrations, but existing AOD data over land remain affected by several limitations (e.g., data gaps, coarser resolution, higher uncertainty or lack of size fraction data), which weakens the AOD-PM relationship. We developed a 0.1° resolution daily AOD data set over Europe over the period 2003-2020, based on two-stage Quantile Machine Learning (QML) frameworks. Our approach first fills gaps in satellite AOD data and then constructs three components\' models to obtain reliable full-coverage AOD along with Fine-mode AOD (fAOD) and Coarse-mode AOD (cAOD). These models are based on AERONET (AErosol RObotic NETwork) observations, Gap-filled satellite AOD, climate and atmospheric composition reanalyses. Our QML AOD products exhibit better quality with an out-of-sample R2 equal to 0.68 for AOD, 0.66 for fAOD and 0.65 for cAOD, which is 23-92 %, 11-13 % and 115-132 % higher than the corresponding satellite or reanalysis products, respectively. Over 91.6 %, 81.6 %, and 88.9 % of QML AOD, fAOD and cAOD predictions fall within ±20 % Expected Error (EE) envelopes, respectively. Previous studies reported that a weak satellite AOD-PM correlation across Europe (Pearson correlation coefficient (PCC) around 0.1). Our QML products exhibit higher correlations with ground-level PMs, particularly when broadly matched by size: AOD with PM10, fAOD with PM2.5, cAOD with PM coarse (R = 0.41, 0.45 and 0.26, respectively). Different AOD fractions more effectively distinct PM size fractions, than total AOD. Our QML aerosol dataset and models pioneer full-coverage, daily high-resolution monitoring of fine-mode and coarse-mode aerosols, effectively addressing existing AOD challenges for further PMs exposures\' estimations. This dataset opens avenues for more in-depth exploration of the impacts of aerosols on human health, climate, visibility, and biogeochemical processes, offering valuable insights for air quality management and environmental health risk assessment.
摘要：
来自卫星的气溶胶光学深度（AOD）数据对于估计空间分辨的PM浓度至关重要。但现有的土地AOD数据仍然受到几个限制的影响(例如，数据缺口，更粗糙的分辨率，更高的不确定性或缺乏尺寸分数数据)，这削弱了AOD-PM关系。我们基于两阶段分位数机器学习（QML）框架，在2003-2020年期间在欧洲开发了0.1°分辨率的每日AOD数据集。我们的方法首先填补了卫星AOD数据中的空白，然后构建了三个组件模型，以获得可靠的全覆盖AOD以及精细模式AOD（fAOD）和粗模式AOD（cAOD）。这些模型基于AERONET（AErosol机器人网络）的观察，间隙填充卫星AOD,气候和大气成分重新分析。我们的QMLAOD产品具有更好的质量，AOD的样品外R2等于0.68，fAOD为0.66，cAOD为0.65，这是23-92%，比相应的卫星或再分析产品高出11-13%和115-132%，分别。91.6%以上,81.6%,和88.9%的QMLAOD，fAOD和cAOD预测落在±20%预期误差(EE)包络线内，分别。先前的研究报道，整个欧洲的弱卫星AOD-PM相关性（皮尔逊相关系数（PCC）约为0.1）。我们的QML产品与地面PM表现出更高的相关性，特别是当尺寸大致匹配时：AOD与PM10，fAOD与PM2.5，cAOD与PM粗（R分别为0.41、0.45和0.26）。不同的AOD分数更有效地区分PM尺寸分数，总AOD。我们的QML气溶胶数据集和模型率先全面覆盖，每日高分辨率监测细模式和粗模式气溶胶，有效解决现有的AOD挑战，以进一步估算PM暴露。该数据集为更深入地探索气溶胶对人类健康的影响开辟了途径，气候,可见性,和生物地球化学过程，为空气质量管理和环境健康风险评估提供有价值的见解。
