Vannucci Pietro F, Foley Kristen, Murphy Benjamin N, Hogrefe Christian, Cohen Ronald C, Pye Havala O T
Oak Ridge Institute for Science and Engineering (ORISE) Fellow Program at the Office of Research and Development, US Environmental Protection Agency, 109 TW Alexander Dr, Research Triangle Park, North Carolina 27711, United States.
Department of Chemistry, University of California Berkeley, Berkeley, California 94720, United States.
ACS Earth Space Chem. 2024 Feb 5;8(2):381-392. doi: 10.1021/acsearthspacechem.3c00333.
Throughout the U.S., summertime fine particulate matter (PM) exhibits a strong temperature (T) dependence. Reducing the PM enhancement with T could reduce the public health burden of PM now and in a warmer future. Atmospheric models are a critical tool for probing the processes and components driving observed behaviors. In this work, we describe how observed and modeled aerosol abundance and composition varies with T in the present-day Eastern U.S. with specific attention to the two major PM components: sulfate (SO ) and organic carbon (OC). Observations in the Eastern U.S. show an average measured summertime PM-T sensitivity of 0.67 μg/m/K, with CMAQ v5.4 regional model predictions closely matching this value. Observed SO and OC also increase with T; however, the model has component-specific discrepancies with observations. Specifically, the model underestimates SO concentrations and their increase with T while overestimating OC concentrations and their increase with T. Here, we explore a series of model interventions aimed at correcting these deviations. We conclude that the PM-T relationship is driven by inorganic and organic systems that are highly coupled, and it is possible to design model interventions to simultaneously address biases in PM component concentrations as well as their response to T.
在美国各地，夏季细颗粒物（PM）呈现出强烈的温度（T）依赖性。降低PM随温度升高的增幅，可减轻当前及未来气候变暖情况下PM对公众健康造成的负担。大气模型是探究导致观测行为的过程和组成部分的关键工具。在这项研究中，我们描述了在美国东部地区，当前观测到的以及模型模拟的气溶胶丰度和成分如何随温度变化，特别关注两种主要的PM成分：硫酸盐（SO₄²⁻）和有机碳（OC）。美国东部地区的观测结果显示，夏季测得的PM对温度的平均敏感度为0.67 μg/m³/K，社区多尺度空气质量（CMAQ）v5.4区域模型的预测结果与该值非常吻合。观测到的SO₄²⁻和OC也随温度升高而增加；然而，该模型在各成分上与观测结果存在差异。具体而言，该模型低估了SO₄²⁻浓度及其随温度的升高幅度，同时高估了OC浓度及其随温度的升高幅度。在此，我们探索了一系列旨在纠正这些偏差的模型干预措施。我们得出结论，PM与温度的关系是由高度耦合的无机和有机系统驱动的，并且有可能设计模型干预措施，以同时解决PM成分浓度偏差及其对温度的响应问题。
