A model framework to reduce bias in ground-level PM_(2.5) concentrations inferred from satellite-retrieved AOD

摘要

We present a new method to infer ground-level fine particulate matter (PM2.5) from satellite remote sensing observations of aerosol optical depth (AOD). The conventional method generally uses a range of modelling approaches to determine PM2.5:AOD relationships that are subsequently used to infer ground-level PM2.5 concentrations from satellite-retrieved AOD. Here, we use a high-resolution atmospheric chemistry simulation to explore how changes in the vertical distribution of aerosol extinction coefficients affects the PM2.5:AOD relationship and how we can use that information to improve the robustness of inferred estimates of ground-level PM2.5 over eastern China. We define a metric, Gamma AODPBL, that describes the fraction of AOD that resides in the planetary boundary layer compared with the total columnar AOD. We determine physically-meaningful PM2.5: AOD relationships using data for which Gamma AODPBL = 50%, a criterion based on sensitivity analyses on data clusters that we identify using a hierarchical clustering method. We use statistical and machine learning methods to develop independent models that describe these PM2.5:AOD relationships, and use a Monte Carlo approach to quantify the improvement after our selection of more physically relevant data records. Benefiting from the improved representativeness of AOD for ground-level PM2.5, our method effectively reduces bias in inferred estimates of ground-level PM2.5 by 10-15% (9-12%) for space-borne sensors passing over in the morning (afternoon). It also captures more variations in ground-level PM2.5 by up to 8% (5%) for space-borne sensors passing over in the morning (afternoon), particularly over areas dominated by natural aerosols such as dust. Accordingly, our method improves the seasonal ground-level PM2.5 maps, e.g. the bias of the autumn (winter) mean of ground level PM2.5 estimates over Qinghai and Gansu (Shaaxi, Shanxi, and Henan) provinces reduces from-8% to-5% (11%-6%).