Qian Wu1,2, Xiao Tang This email address is being protected from spambots. You need JavaScript enabled to view it.1,5, Lei Kong1,2, Zirui Liu1, Duohong Chen4, Miaomiao Lu3, Huangjian Wu1, Jin Shen4, Lin Wu1, Xiaole Pan1,5, Jie Li1,5, Jiang Zhu1,2, Zifa Wang1,2,5

 

1 LAPC&ICCES, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
2 University of Chinese Academy of Sciences, Beijing 100049, China
3 State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
4 State Environmental Protection Key Laboratory of Regional Air Quality Monitoring, Guangdong Environmental Monitoring Center, Guangzhou 510308, China
5 Center for Excellence in Regional Atmospheric Environment, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China


Received: February 24, 2020
Revised: May 25, 2020
Accepted: July 21, 2020

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.


Download Citation: ||https://doi.org/10.4209/aaqr.2020.02.0075 


Cite this article:

Wu, Q., Tang, X., Kong, L., Liu, Z., Chen, D., Lu, M., Wu, H., Shen, J., Wu, L., Pan, X., Li, J., Zhu, J. and Wang, Z. (2020). Model Evaluation and Uncertainty Analysis of PM2.5 Components over Pearl River Delta Region Using Monte Carlo Simulations. Aerosol Air Qual. Res. https://doi.org/10.4209/aaqr.2020.02.0075


HIGHLIGHTS

  • Model performance of PM2.5 components over PRD region has been evaluated.
  • Monte Carlo simulations were conducted to investigate the model uncertainty.
  • Uncertainty in emissions can explain most of the modeling errors in OC and BC.
  • The biases in sulfate and ammonium modeling were identified.
 

ABSTRACT


Sulfate, nitrate, ammonium, organic carbon (OC) and black carbon (BC) are the key components of PM2.5, but their simulations are still facing high uncertainty. This study aims to evaluate and investigate the modeling uncertainty of these aerosols over the Pearl River Delta (PRD) region based on Monte Carlo simulations of a Nested Air Quality Prediction Modeling System (NAQPMS) during 2015. Emission inventory as one of the most important uncertainty sources was perturbed according to their uncertainties to derive 50 ensemble simulations with a 15 km horizontal resolution. The surface observations of sulfate, nitrate, ammonium, OC and BC from 10 sites in PRD region for one year were used to evaluate the performance of the ensemble simulations. The results suggested that the ensemble mean could well reproduce the spatial and temporal variations of nitrate, ammonium, OC and BC with the correlation coefficients above 0.74 and their mean biases less than 2 μg m-3. However, the model has poor skills in the sulfate modeling with the correlation coefficients 0.26 and remarkable underestimation in winter. Further analysis for such modeling uncertainties suggested that the uncertainties in emissions can explain most of the modeling uncertainties in OC and BC, but the mean biases in sulfate and ammonium modeling especially during the wintertime are probably caused by the uncertainty in heterogeneous reaction modeling.

 


Keywords: PM2.5 components; PRD region; Monte Carlo simulations; Uncertainty analysis.



Aerosol Air Qual. Res. 20:-. https://doi.org/10.4209/aaqr.2020.02.0075 


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