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

Abstract

Accurate quantitative description of the atmospheric fine particulate matter (PM_2.5) burden requires an understanding of aerosol amounts and properties that transcends measurement platforms. For example, air quality studies often seek to describe ambient PM_2.5 with columnar aerosol optical depth (AOD), point measurements of mass, or some combination. PM_2.5 chemical constituents affect such measurements differently. We investigate the ratio of PM_2.5-to-AOD (η) from 2005 to 2016 at multiple surface locations across the contiguous U.S. using observations and models, and quantitatively account for PM_2.5 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 PM_2.5 mass and AOD. Accounting for loss of nitrate and ALW from PM_2.5 monitors improves consistency among η calculations in space and time. Co-occurrence of extreme PM_2.5 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 PM_2.5 chemical composition during average conditions. This suggests an increased fraction of ambient PM_2.5 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 PM_2.5 mass concentrations. The poor understanding of organic compounds and their PM_2.5 sampling artifacts remains a critical open question.