Project No: 16218926

Title: Long-term trends and spatial characteristics of primary and secondary organic carbon levels in airborne fine particulate matter in three East Asian regions

Principal Investigator: Dr. Kezheng LIAO

Co-Investigator: Prof. Jiyi LEE, Prof. Qi YING, Prof. Jianzhen YU, Dr. Shuhui ZHU

Abstract:

Organic aerosols constitute a significant portion of airborne fine particulate matter (PM2.5) and are of high scientific interest due to their complex composition and diverse properties. Numerous studies have discussed their adverse health effects and impacts on the atmospheric environment. Organic carbon (OC) serves as a convenient surrogate for total organics, as it can be easily measured and is routinely monitored. OC is categorized into primary organic carbon (POC), which does not undergo chemical changes after emission, and secondary organic carbon (SOC), which is formed through secondary reactions in the atmosphere. Therefore, POC and SOC components have different chemical structures and distinct physiochemical properties, such as light absorption and hygroscopicity. Quantifying POC and SOC in ambient aerosols is essential for understanding pollution patterns and formulating effective control measures. Long-term data of POC and SOC are valuable for evaluating the efficacy of historical environmental regulations and guiding improvements in numerical air quality models. Nonetheless, reliable long-term observations of these two components are scarce to absent in many regions. To address this gap, we propose the following research project. First, we will determine the concentrations of POC and SOC using series of long-term measurement data of major PM2.5 components collected at monitoring stations in three East Asian regions, namely the Pearl River Delta region, the Yangtze River Delta region, and Korean Peninsula. A novel Bayesian inference (BI) approach has been proposed and proven to be robust and credible in previous publications. Our goal is to modify the BI method to enhance its versatility, which will enable us to obtain the long-term estimates of POC and SOC in these regions for the first time. Second, spatiotemporal variations and determining factors for POC and SOC concentrations shall be investigated. We will use time series decomposition techniques and non-parametric estimators to quantify the changing rates of ambient concentrations of POC and SOC in aforementioned regions throughout the monitoring periods. Information of source indicators, control measures, and meteorological events will be collected to elucidate the driving forces behind these variations. Third, we will conduct numerical simulations and source apportionment for POC and SOC using a regional air quality model and compare modeling results to measurements. A hybrid model that integrates measurement data into numerical modeling is developed and will help refine the emission inventories for POC, while sensitivity tests on secondary reaction mechanisms will provide insight for improving SOC modeling.