Chemically specific sampling bias: the ratio of PM2.5 to surface AOD on average and peak days in the U.S.†

IF 2.8 Q3 ENVIRONMENTAL SCIENCES
Simon Rosanka, Madison M. Flesch, Yin Ting T. Chiu and Annmarie G. Carlton
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引用次数: 0

Abstract

Accurate quantitative description of the atmospheric fine particulate matter (PM2.5) burden requires an understanding of aerosol amounts and properties that transcends measurement platforms. For example, air quality studies often seek to describe ambient PM2.5 with columnar aerosol optical depth (AOD), point measurements of mass, or some combination. PM2.5 chemical constituents affect such measurements differently. We investigate the ratio of PM2.5-to-AOD (η) from 2005 to 2016 at multiple surface locations across the contiguous U.S. using observations and models, and quantitatively account for PM2.5 sampling bias of nitrate and aerosol liquid water (ALW). We find η peaks during winter and is lowest in summer at all locations, despite contrasting seasonality in PM2.5 mass and AOD. Accounting for loss of nitrate and ALW from PM2.5 monitors improves consistency among η calculations in space and time. Co-occurrence of extreme PM2.5 mass concentrations and AOD events declined in the eastern U.S. but not in the west. On peak days, in all locations, ALW mass concentrations are higher and fractional contributions are larger relative to PM2.5 chemical composition during average conditions. This suggests an increased fraction of ambient PM2.5 is detectable via optical methods but not well described by surface mass networks on peak days. The Community Multiscale Air Quality (CMAQ) model reproduces similar spatial and temporal variability in η to surface observations in winter and summer simulations at the beginning and end of the analysis period. Accounting for sampling artifacts in surface monitors may improve agreement with model predictions and remote sensing of PM2.5 mass concentrations. The poor understanding of organic compounds and their PM2.5 sampling artifacts remains a critical open question.

Abstract Image

化学特异性采样偏差:美国平均日和高峰日 PM2.5 与地表 AOD 之比†。
要对大气中的细颗粒物(PM2.5)进行准确的定量描述,就需要了解气溶胶的数量和特性,这一点超越了测量平台。例如,空气质量研究通常寻求通过柱状气溶胶光学深度(AOD)、质量点测量或某些组合来描述环境 PM2.5。PM2.5 的化学成分会对此类测量产生不同的影响。我们利用观测数据和模型研究了 2005 年至 2016 年美国毗连地区多个地表位置的 PM2.5 与 AOD 之比(η),并定量考虑了硝酸盐和气溶胶液态水(ALW)对 PM2.5 的采样偏差。我们发现,在所有地点,尽管 PM2.5 质量和 AOD 的季节性不同,η 在冬季达到峰值,而在夏季最低。考虑到PM2.5监测仪中硝酸盐和ALW的损失,可提高η计算在空间和时间上的一致性。在美国东部,PM2.5质量浓度和AOD极端事件的同时发生率有所下降,而在西部则没有。在所有地点的高峰日,相对于平均条件下的 PM2.5 化学成分,ALW 质量浓度更高,分数贡献更大。这表明,通过光学方法可检测到的环境 PM2.5 分数增加了,但在高峰日,地表质量网络并不能很好地描述这一点。社区多尺度空气质量(CMAQ)模型在分析期开始和结束时的冬季和夏季模拟中,再现了与η表面观测相似的空间和时间变化。考虑地表监测仪的采样误差可提高模型预测与遥感 PM2.5 质量浓度的一致性。对有机化合物及其 PM2.5 取样伪影的不甚了解仍然是一个关键的未决问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
2.90
自引率
0.00%
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