InMAP: A model for air pollution interventions.
InMAP: A model for air pollution interventions.
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DOI：
10.1371/journal.pone.0176131
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发表时间：
2017
期刊：
影响因子：
3.7
通讯作者：
Marshall JD
中科院分区：
文献类型：
--
作者：
Tessum CW;Hill JD;Marshall JD
机械空气污染建模对于空气质量管理至关重要，但运行大多数模型所需的广泛专业知识和计算资源阻碍了它们在许多情况下的使用，而其结果本来是有用的。在这里，我们提出了 InMAP（空气污染干预模型），它提供了综合空气质量模型的替代方案，用于估计减排和其他潜在干预措施对空气污染健康的影响。 InMAP 估算了一次和二次细颗粒 (PM2.5) 浓度的年平均变化（空气污染结果通常会造成最大的货币化健康损害），归因于前体排放的年度变化。 InMAP 利用来自最先进的化学输运模型和可变空间分辨率计算网格输出的预处理物理和化学信息来执行模拟，其计算强度比综合模型模拟低几个数量级。在此处进行的比较中，InMAP 重新创建了 PM2.5 总浓度变化的综合模型预测，人口加权平均分数偏差 (MFB) 为 -17%，人口加权 R2 = 0.90。尽管 InMAP 并不是专门为重现观测到的总浓度而设计的，但它能够在已发布的总 PM2.5 空气质量模型性能标准内实现这一点。 InMAP 的潜在用途包括研究年平均 PM2.5 排放量潜在变化的暴露、健康和环境正义影响。如果可以从综合模型中获得适当的模拟输出，则可以训练 InMAP 在任何空间和时间域上运行。 InMAP 模型源代码和输入数据可根据开源许可证免费在线获取。
Mechanistic air pollution modeling is essential in air quality management, yet the extensive expertise and computational resources required to run most models prevent their use in many situations where their results would be useful. Here, we present InMAP (Intervention Model for Air Pollution), which offers an alternative to comprehensive air quality models for estimating the air pollution health impacts of emission reductions and other potential interventions. InMAP estimates annual-average changes in primary and secondary fine particle (PM2.5) concentrations—the air pollution outcome generally causing the largest monetized health damages–attributable to annual changes in precursor emissions. InMAP leverages pre-processed physical and chemical information from the output of a state-of-the-science chemical transport model and a variable spatial resolution computational grid to perform simulations that are several orders of magnitude less computationally intensive than comprehensive model simulations. In comparisons run here, InMAP recreates comprehensive model predictions of changes in total PM2.5 concentrations with population-weighted mean fractional bias (MFB) of −17% and population-weighted R2 = 0.90. Although InMAP is not specifically designed to reproduce total observed concentrations, it is able to do so within published air quality model performance criteria for total PM2.5. Potential uses of InMAP include studying exposure, health, and environmental justice impacts of potential shifts in emissions for annual-average PM2.5. InMAP can be trained to run for any spatial and temporal domain given the availability of appropriate simulation output from a comprehensive model. The InMAP model source code and input data are freely available online under an open-source license.