论文标题
使用数值模型输出对野生火灾约束的PM2.5的空间因果分析
A spatial causal analysis of wildland fire-contributed PM2.5 using numerical model output
论文作者
论文摘要
Wildland Fire烟雾含有危险水平的细颗粒物PM2.5，这是一种污染物，对健康产生不利影响。估计归因于PM2.5浓度是量化对空气质量和随后的健康负担的影响的关键。这是一个具有挑战性的问题，因为在监视站中仅测量了总PM2.5，并且所有其他来源的Fire-Attriput-abtriputtibut pm2.5和PM2.5都与时空相关。我们提出了一个框架，用于使用新的因果推理框架和PM2.5偏置调整的化学模型表示，以估算所有其他来源的fire fire摄取的PM2.5和PM2.5。使用社区多尺度空气质量建模系统（CMAQ）模拟PM2.5的化学模型表示，在2008-2012野火季节中，在连续的美国进行有和没有火灾排放。通过监视位点的观察值对同一空间域和时间段的观察进行校准。我们使用一个解释空间变化的贝叶斯模型来估计野生火灾对PM2.5的影响以及该估计值具有有效因果解释的状态假设。我们的结果包括对美国连续的野火烟雾对PM2.5的绝对，相对和累积贡献的估计，我们还计算了与野火烟雾归因于PM2.5相关的健康负担。
Wildland fire smoke contains hazardous levels of fine particulate matter PM2.5, a pollutant shown to adversely effect health. Estimating fire attributable PM2.5 concentrations is key to quantifying the impact on air quality and subsequent health burden. This is a challenging problem since only total PM2.5 is measured at monitoring stations and both fire-attributable PM2.5 and PM2.5 from all other sources are correlated in space and time. We propose a framework for estimating fire-contributed PM2.5 and PM2.5 from all other sources using a novel causal inference framework and bias-adjusted chemical model representations of PM2.5 under counterfactual scenarios. The chemical model representation of PM2.5 for this analysis is simulated using Community Multi-Scale Air Quality Modeling System (CMAQ), run with and without fire emissions across the contiguous U.S. for the 2008-2012 wildfire seasons. The CMAQ output is calibrated with observations from monitoring sites for the same spatial domain and time period. We use a Bayesian model that accounts for spatial variation to estimate the effect of wildland fires on PM2.5 and state assumptions under which the estimate has a valid causal interpretation. Our results include estimates of absolute, relative and cumulative contributions of wildfire smoke to PM2.5 for the contiguous U.S. Additionally, we compute the health burden associated with the PM2.5 attributable to wildfire smoke.
