Bayesian Inference Approach to Quantify Primary and Secondary Organic Carbon in Fine Particulate Matter Using Major Species Measurements,Environmental Science & Technology

当前位置： X-MOL 学术 › Environ. Sci. Technol. › 论文详情

Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)

Bayesian Inference Approach to Quantify Primary and Secondary Organic Carbon in Fine Particulate Matter Using Major Species Measurements
Environmental Science & Technology ( IF 10.8 ) Pub Date : 2023-03-20 , DOI: 10.1021/acs.est.2c09412
Kezheng Liao ₁ , Qiongqiong Wang ₁ , Shan Wang ₂ , Jian Zhen Yu _{1,

2}

Affiliation

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 PMF-resolved 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 best model performance. This methodological advance provides an improved and practical tool to derive POC and SOC levels for addressing PM-related environmental impacts.

更新日期：2023-03-20

点击分享查看原文

点击收藏

阅读更多本刊最新论文本刊介绍/投稿指南11