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Bayesian Inference Approach to Quantify Primary and Secondary Organic Carbon in Fine Particulate Matter Using Major Species Measurements by Kezheng Liao; Qiongqiong Wang; Shan Wang; Jian Zhen Yu is a Environmental Science article available to read on EtoBox.

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The determination of primary organic carbon (POC) and secondary organic carbon (SOC) in fine particulate matter using ambient measurements is essential in atmospheric chemistry. A novel Bayesian inference (BI) approach is proposed to achieve such quantification using only major component measurement data and tested in two case studies. One case study composes of filter-based daily compositional data made in the Pearl River Delta region, China, during 2012, while the other uses online measurement data recorded at the Dianshan Lake monitoring site in Shanghai in wintertime 2019. Source-specific organic trace measurement data are available in both the cases so that positive matrix factorization (PMF) analysis is performed, where PMFresolved POC and SOC are used as the best available reference values for model evaluation. Meanwhile, traditional techniques, i.e., minimum ratio value, minimum R squared, and multiple linear regression, are also employed and evaluated. For both the cases, the BI models have shown significant advantages in accurately estimating POC and SOC amounts over conventional methods. Further analysis suggests that using sulfate as the SOC tracer in BI model gives the

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Author

Kezheng Liao; Qiongqiong Wang; Shan Wang; Jian Zhen Yu

Publisher

American Chemical Society (ACS)

Published

2023

Language

EN

Field

Environmental Science (Physical Sciences)