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., Wang, Z. (2021). Model Evaluation and Uncertainty Analysis of PM2.5 Components over Pearl River Delta Region Using Monte Carlo Simulations. Aerosol Air Qual. Res. 21, 200075. 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 predicting their concentrations remains a challenge because of high uncertainties in the modeling. Employing the Nested Air Quality Prediction Modeling System (NAQPMS) developed by the Institute of Atmospheric Physics, Chinese Academy of Sciences, this study investigated the uncertainties in Monte Carlo simulations of these aerosols in the Pearl River Delta (PRD) region during 2015. 50 ensemble simulations with a 15 km horizontal resolution were derived by perturbing the emission data for sulfate, nitrate, ammonium, OC and BC from an emission inventory, which is one of the largest sources of uncertainty. Then, surface observations of these species collected from 10 sites across the region for 1 year were used to evaluate the performance of the ensemble simulations. The high correlation coefficients (> 0.74) and low mean biases (< 2 µg m–3) between the mean values of the ensemble and the observation data suggested that the model fairly accurately reproduced spatial and temporal variations in the nitrate, ammonium, OC and BC. However, the predicted sulfate concentrations, which displayed a correlation coefficient of 0.26, were far less reliable, particularly owing to the significant underestimation during winter. Further analysis revealed that uncertainties in the emission data explained most of the discrepancies for the OC and BC, but the mean biases for the sulfate and ammonium, especially during winter, probably stemmed from uncertainties in the heterogeneous reaction modeling.


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



Aerosol Air Qual. Res. 21 :200075 -. https://doi.org/10.4209/aaqr.2020.02.0075  


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