Department of Public Health Sciences, University of Rochester School of Medicine and Dentistry, Rochester, NY, 14642, USA.
Department of Environmental Sciences, Informatics and Statistics, Ca' Foscari University of Venice, Venice, Italy.
Environ Pollut. 2022 Oct 1;310:119797. doi: 10.1016/j.envpol.2022.119797. Epub 2022 Jul 18.
In the past several decades, a variety of efforts have been made in the United States to improve air quality, and ambient particulate matter (PM) concentrations have been used as a metric to evaluate the efficacy of environmental policies. However, ambient PM concentrations result from a combination of source emission rates and meteorological conditions, which also change over time. Dispersion normalization was recently developed to reduce the influence of atmospheric dispersion and proved an effective approach that enhanced diel/seasonal patterns and thus provides improved source apportionment results for speciated PM mass and particle number concentration (PNC) measurements. In this work, dispersion normalization was incorporated in long-term trend analysis of 11-500 nm PNCs derived from particle number size distributions (PNSDs) measured in Rochester, NY from 2005 to 2019. Before dispersion normalization, a consistent reduction was observed across the measured size range during 2005-2012, while after 2012, the decreasing trends slowed down for accumulation mode PNCs (100-500 nm) and reversed for ultrafine particles (UFPs, 11-100 nm). Through dispersion normalization, we showed that these changes were driven by both emission rates and dispersion. Thus, it is important for future studies to assess the effects of the changing meteorological conditions when evaluating policy effectiveness on controlling PM concentrations. Before and after dispersion normalization, an evident increase in nucleation mode particles was observed during 2015-2019. This increase was possibly enabled by a cleaner atmosphere and will pose new challenges for future source apportionment and accountability studies.
在过去几十年中,美国采取了多种措施来改善空气质量,环境中颗粒物(PM)浓度被用作评估环境政策效果的指标。然而,环境 PM 浓度是源排放率和气象条件共同作用的结果,而这些因素也会随时间发生变化。最近开发的分散归一化方法用于减少大气扩散的影响,事实证明这是一种有效的方法,增强了日/季节性变化模式,从而为基于粒子数大小分布(PNSD)测量的特定 PM 质量和粒子数浓度(PNC)的源分配结果提供了改进。在这项工作中,分散归一化方法被应用于对罗切斯特,NY 从 2005 年到 2019 年期间测量的 11-500nm PNC 的长期趋势分析。在进行分散归一化之前,在 2005-2012 年期间,在所测量的尺寸范围内观察到一致的减少,而在 2012 年之后,积聚模态 PNC(100-500nm)的下降趋势减缓,超细颗粒(UFPs,11-100nm)的下降趋势则相反。通过分散归一化,我们表明这些变化是由排放率和扩散共同驱动的。因此,在评估控制 PM 浓度的政策效果时,评估气象条件变化的影响对于未来的研究非常重要。在进行分散归一化之前和之后,在 2015-2019 年期间观察到了成核模态颗粒的明显增加。这种增加可能是由于大气更加清洁,这将给未来的源分配和责任研究带来新的挑战。