Speciated VOC Emission Inventory and Spatial Patterns of Ozone Formation Potential in the Pearl River Delta, China

2009 ◽  
Vol 43 (22) ◽  
pp. 8580-8586 ◽  
Author(s):  
Junyu Zheng ◽  
Min Shao ◽  
Wenwei Che ◽  
Lijun Zhang ◽  
Liuju Zhong ◽  
...  
Keyword(s):  
Spatial Patterns ◽  
Pearl River Delta ◽  
Emission Inventory ◽  
Pearl River ◽  
Ozone Formation ◽  
Voc Emission ◽  
Formation Potential ◽  
The Pearl River Delta ◽  
Ozone Formation Potential ◽  
The Pearl River

scholarly journals Emission inventory of semi-volatile and intermediate-volatility organic compounds and their effects on secondary organic aerosol over the Pearl River Delta region

2019 ◽  
Vol 19 (12) ◽  
pp. 8141-8161 ◽  
Author(s):  
Liqing Wu ◽  
Xuemei Wang ◽  
Sihua Lu ◽  
Min Shao ◽  
Zhenhao Ling
Keyword(s):  
Organic Compounds ◽  
Pearl River Delta ◽  
Emission Inventory ◽  
Secondary Organic Aerosol ◽  
Organic Aerosol ◽  
Pearl River ◽  
The Pearl River Delta ◽  
Mobile Emissions ◽  
The Pearl River ◽  
Emission Ratios

Abstract. Semi-volatile and intermediate-volatility organic compounds (S–IVOCs) are considered critical precursors of secondary organic aerosol (SOA), which is an important component of fine particulate matter (PM2.5). However, knowledge of the contributions of S–IVOCs to SOA is still lacking in the Pearl River Delta (PRD) region, southern China. Therefore, in this study, an emission inventory of S–IVOCs in the PRD region was developed for the first time for the year 2010. The S–IVOC emissions were calculated based on a parameterization method involving the emission factors of POA (primary organic aerosol), emission ratios of S–IVOCs to POA, and domestic activity data. The total emissions of S–IVOCs were estimated to be 323.4 Gg, with major emissions from central cities in the PRD, i.e., Guangzhou, Foshan, and Shenzhen. On-road mobile sources and industries were the two major contributors of S–IVOC emissions, with contributions of ∼42 % and ∼35 %, respectively. Furthermore, uncertainties of the emission inventory were evaluated through Monte Carlo simulation. The uncertainties ranged from −79 % to 229 %, which could be mainly attributed to mass fractions of OC (organic carbon) to PM2.5 from on-road mobile emissions and emission ratios of IVOCs ∕ POA. The developed S–IVOC emission inventory was further incorporated into the Weather Research and Forecasting with Chemistry (WRF-Chem) model with a volatility basis-set (VBS) approach to improve the performance of SOA simulation and to evaluate the influence of S–IVOCs on SOA formation at a receptor site (Wan Qing Sha (WQS) site) in the PRD. The following results could be obtained. (1) The model could resolve about 34 % on average of observed SOA concentrations at WQS after considering the emissions of S–IVOCs, and 18 %–77 % with the uncertainties of the S–IVOC emission inventory considered. (2) The simulated SOA over the PRD region was increased by 161 % with the input of S–IVOC emissions, and it could be decreased to 126 % after the reaction coefficient of S–IVOCs with OH radical was improved. (3) Among all anthropogenic sources of S–IVOCs, industrial emission was the most significant contributor of S–IVOCs for SOA formation, followed by on-road mobile, dust, biomass burning, residential, and off-road mobile emissions. Overall, this study firstly quantified emissions of S–IVOCs and evaluated their roles in SOA formation over the PRD, which contributes towards significantly improving SOA simulation and better understanding of SOA formation mechanisms in the PRD and other regions in China.

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