Abstract Black carbon (BC) is a major component in atmospheric particulate matter (PM), which causes adverse health impacts and contributes significantly to climate change. Without widespread and accurate BC measurements, it remains difficult to track incomplete combustion sources and reduce BC emissions. Currently commercial BC sensors remain too costly to be deployed widely. In this work, a fast, cost-effective, and easily accessible method based on a smartphone camera was used to quantify color information of PM collected on filters to estimate BC and elemental carbon (EC) loadings. A robust RGB (red, green, blue)-based linear interaction model was built and validated using 1878 PM samples collected in three different regions with collocated BC and EC measurements. After applying image correction methods, this model shows a good predictability with an R-squared (R2) of 0.904 with state-of-the-art BC measurement techniques, and a coefficient of variation of the root mean square error (CV(RMSE)) of 25.3% despite the complex sources and different reference measurement techniques. This work validates the viabilities of using smartphones to quantify BC or EC loading on PM filters with a unified model and track incomplete combustion sources.

中文翻译：

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使用智能手机对微粒黑碳 (BC) 进行光学分析的改进方法

摘要 黑碳 (BC) 是大气颗粒物 (PM) 的主要成分，会对健康造成不利影响并显着导致气候变化。如果没有广泛和准确的 BC 测量，跟踪不完全燃烧源和减少 BC 排放仍然很困难。目前，商用 BC 传感器仍然过于昂贵，无法广泛部署。在这项工作中，一种基于智能手机相机的快速、经济高效且易于访问的方法用于量化过滤器上收集的 PM 的颜色信息，以估计 BC 和元素碳 (EC) 负载。使用在三个不同区域收集的 1878 个 PM 样本构建并验证了基于 RGB（红色、绿色、蓝色）的稳健线性交互模型，并同时进行 BC 和 EC 测量。应用图像校正方法后，该模型显示出良好的可预测性，使用最先进的 BC 测量技术的 R 平方 (R2) 为 0.904，并且均方根误差 (CV(RMSE)) 的变异系数为 25.3%，尽管复杂的源和不同的参考测量技术。这项工作验证了使用智能手机通过统一模型量化 PM 过滤器上的 BC 或 EC 负载并跟踪不完全燃烧源的可行性。