Baker Kirk R, Simon Heather, Henderson Barron, Tucker Colby, Cooley David, Zinsmeister Emma
U.S. Environmental Protection Agency, Research Triangle Park, North Carolina 27709, United States.
U.S. Environmental Protection Agency, Washington, D.C. 20460, United States.
Environ Sci Technol. 2023 Oct 3;57(39):14626-14637. doi: 10.1021/acs.est.3c03317. Epub 2023 Sep 18.
Reduced complexity tools that provide a representation of both primarily emitted particulate matter with an aerodynamic diameter less than 2.5 μm (PM), secondarily formed PM, and ozone (O) allow for a quick assessment of many iterations of pollution control scenarios. Here, a new reduced complexity tool, Pattern Constructed Air Pollution Surfaces (PCAPS), that estimates annual average PM and seasonal average maximum daily average 8 h (MDA8) O for any source location in the United States is described and evaluated. Typically, reduced complexity tools are not evaluated for skill in predicting change in air pollution by comparison with more sophisticated modeling systems. Here, PCAPS was compared against multiple types of emission control scenarios predicted with state-of-the-science photochemical grid models to provide confidence that the model is realistically capturing the change in air pollution due to changing emissions. PCAPS was also applied with all anthropogenic emissions sources for multiple retrospective years to predict PM chemical components for comparison against routine surface measurements. PCAPS predicted similar magnitudes and regional variations in spatial gradients of measured chemical components of PM. Model performance for capturing ambient measurements was consistent with other reduced complexity tools. PCAPS also did well at capturing the magnitude and spatial features of changes predicted by photochemical transport models for multiple emissions scenarios for both O and PM. PCAPS is a flexible tool that provides source-receptor relationships using patterns of air quality gradients from a training data set of generic modeled sources to create interpolated air pollution gradients for new locations not part of the training database. The flexibility provided for both sources and receptors makes this tool ideal for integration into larger frameworks that provide emissions changes and need estimates of air quality to inform downstream analytics, which often includes an estimate of monetized health effects.
降低复杂度的工具能够呈现主要排放的空气动力学直径小于2.5微米的颗粒物（PM）、二次形成的PM以及臭氧（O），从而可以快速评估污染控制情景的许多迭代情况。在此，描述并评估了一种新的降低复杂度工具——模式构建空气污染表面（PCAPS），它可估算美国任何源位置的年平均PM和季节性平均最大日平均8小时（MDA8）O。通常，降低复杂度的工具不会通过与更复杂的建模系统比较来评估其预测空气污染变化的技能。在此，将PCAPS与用先进的光化学网格模型预测的多种排放控制情景进行了比较，以确保该模型能切实捕捉到因排放变化而导致的空气污染变化。PCAPS还应用于多个回顾年份的所有人为排放源，以预测PM化学成分，以便与常规地面测量结果进行比较。PCAPS预测的PM测量化学成分的空间梯度在量级和区域变化方面相似。捕捉环境测量值的模型性能与其他降低复杂度的工具一致。PCAPS在捕捉光化学传输模型针对O和PM的多种排放情景预测的变化的量级和空间特征方面也表现出色。PCAPS是一种灵活的工具，它利用来自通用建模源训练数据集的空气质量梯度模式提供源 - 受体关系，为不属于训练数据库一部分的新位置创建插值空气污染梯度。该工具为源和受体提供的灵活性使其非常适合集成到更大的框架中，这些框架提供排放变化并需要空气质量估计以支持下游分析，下游分析通常包括货币化健康影响的估计。
