Forest fire smoke is a growing public health concern as more intense and frequent fires are expected under climate change. Remote sensing is a promising tool for exposure assessment, but its utility for health studies is limited because most products measure pollutants in the total column of the atmosphere, and not the surface concentrations most relevant to population health. Information about the vertical distribution of smoke is vital for addressing this limitation. The CALIPSO satellite can provide such information but it cannot cover all smoke events due to its narrow ground track. In this study, we developed a random forests model to predict the minimum height of the smoke layer observed by CALIPSO at high temporal and spatial resolution, using information about fire activity in the vicinity, geographic location, and meteorological conditions. These pieces of information are typically available in near-real-time, ensuring that the resulting model can be easily operationalized. A total of 15,617 CALIPSO data blocks were identified as impacted by smoke within the province of British Columbia, Canada from 2006 to 2015, and 52.1% had smoke within the boundary layer, where the population might be exposed. The final model explained 82.1% of the variance in the observations with a root mean squared error of 560 m. The most important variables in the model were wind patterns, the month of smoke observation, and fire intensity within 500 km. Predictions from this model can be 1) directly applied to smoke detection from the existing remote sensing products to provide another dimension of information; 2) incorporated into statistical smoke models with inputs from remote sensing products; or 3) used to inform estimates of vertical dispersion in deterministic smoke models. These potential applications are expected to improve the assessment of ground-level population exposure to forest fire smoke.

中文翻译：

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使用机器学习和来自 CALIPSO 卫星的数据预测大气中森林火灾烟雾的最小高度

森林火灾烟雾是一个日益严重的公共卫生问题，因为在气候变化下预计火灾会更加激烈和频繁。遥感是一种很有前途的暴露评估工具，但它在健康研究中的效用有限，因为大多数产品测量的是大气总柱中的污染物，而不是与人口健康最相关的表面浓度。有关烟雾垂直分布的信息对于解决此限制至关重要。CALIPSO 卫星可以提供此类信息，但由于其地面轨迹较窄，因此无法涵盖所有​​烟雾事件。在这项研究中，我们开发了一个随机森林模型，利用附近火灾活动、地理位置和气象条件的信息，预测 CALIPSO 在高时空分辨率下观测到的烟雾层的最小高度。这些信息通常近乎实时地可用，确保生成的模型可以轻松操作。从 2006 年到 2015 年，加拿大不列颠哥伦比亚省内共有 15,617 个 CALIPSO 数据块被确定为受到烟雾的影响，其中 52.1% 的边界层内有烟雾，人口可能会暴露在那里。最终模型解释了观测值中 82.1% 的方差，均方根误差为 560 m。模型中最重要的变量是风模式、烟雾观测月份和 500 公里内的火灾强度。该模型的预测可以： 1）直接应用于现有遥感产品的烟雾检测，提供另一个维度的信息；2) 将遥感产品的输入纳入统计烟雾模型；或 3) 用于通知确定性烟雾模型中垂直分散的估计。这些潜在的应用有望改进对地面人口暴露于森林火灾烟雾的评估。