Abstract The spatiotemporal pattern of particulate matter (PM) concentrations is an important factor in predicting health issues in inhabitants of urban areas. The integration of satellite-derived aerosol optical depth (AOD) data with ground-level PM concentration data, obtained from monitoring networks, has contributed to better characterization of the spatiotemporal variability of aerosols worldwide. However, before using satellite AOD data as a proxy for PM in epidemiological and air quality studies in specific regions, the applicability of that strategy must be evaluated. In this study, we evaluate the use of the high-resolution AOD, derived from Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm, as a predictor of surface PM concentrations in the Metropolitan Area of Sao Paulo (MASP). We found relatively weak or negative correlations between PM concentrations and MAIAC AOD, even after vertical correction by planetary boundary layer height and the hygroscopic growth factor. The weak correlations reported in this study are mainly due to the mismatch between the current MAIAC aerosol model and the properties of local aerosols in the MASP. Our results suggest that sources of aerosol particles in the MASP are quite diverse and that there is therefore no single optical model suitable for use with satellite-derived AOD.

中文翻译：

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探索圣保罗大都市区高分辨率气溶胶光学深度值与地面细颗粒物浓度之间的关系

摘要 颗粒物 (PM) 浓度的时空模式是预测城市地区居民健康问题的重要因素。卫星衍生的气溶胶光学深度 (AOD) 数据与从监测网络获得的地面 PM 浓度数据的整合有助于更好地表征全球气溶胶的时空变异性。然而，在特定地区的流行病学和空气质量研究中使用卫星 AOD 数据作为 PM 的代理之前，必须评估该策略的适用性。在本研究中，我们评估了从大气校正多角度实施 (MAIAC) 算法派生的高分辨率 AOD 作为圣保罗都会区 (MASP) 地表 PM 浓度预测指标的使用。我们发现 PM 浓度与 MAIAC AOD 之间的相关性相对较弱或呈负相关，即使在通过行星边界层高度和吸湿性生长因子进行垂直校正后也是如此。本研究报告的弱相关性主要是由于当前的 MAIAC 气溶胶模型与 MASP 中局部气溶胶的特性不匹配。我们的结果表明，MASP 中气溶胶颗粒的来源非常多样化，因此没有适用于卫星衍生 AOD 的单一光学模型。