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Frontiers of Environmental Science & Engineering

ISSN 2095-2201

ISSN 2095-221X(Online)

CN 10-1013/X

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Front. Environ. Sci. Eng.    2018, Vol. 12 Issue (1) : 13    https://doi.org/10.1007/s11783-018-1010-6
RESEARCH ARTICLE
Development and case study of a new-generation model-VAT for analyzing the boundary conditions influence on atmospheric mercury simulation
Wenwei Yang1, Yun Zhu1,2(), Carey Jang3, Shicheng Long4, Che-Jen Lin5, Bin Yu6, Zachariah Adelman7, Shuxiao Wang2, Jia Xing2, Long Wang1,2, Jiabin Li1
1. Guangdong Provincial Key Laboratory of Atmospheric Environment and Pollution Control, College of Environment and Energy, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China
2. State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
3. USEPA/Office of Air Quality Planning & Standards, RTP, NC 27711, USA
4. Guangzhou Urban Environmental Cloud Information Technology R&D Co. Ltd, Guangzhou 510006, China
5. Department of Civil Engineering, Lamar University, Beaumont, TX 77710-0024, USA
6. Guangzhou Environmental Monitoring Center Station, Guangzhou 510030, China
7. Institute for the Environment, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27517, USA
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Abstract

Performance of CMAQ-Hg is better using Model-driven BCs than default BC.

Model-VAT provides a better user experience to convert Model-driven BCs.

Model-VAT is designed to efficiently access and analyze the results of multi-models.

Atmospheric models are essential tools to study the behavior of air pollutants. To interpret the complicated atmospheric model simulations, a new-generation Model Visualization and Analysis Tool (Model-VAT) has been developed for scientists to analyze the model data and visualize the simulation results. The Model-VAT incorporates analytic functions of conventional tools and enhanced capabilities in flexibly accessing, analyzing, and comparing simulated results from multi-scale models with different map projections and grid resolutions. The performance of the Model-VAT is demonstrated by a case study of investigating the influence of boundary conditions (BCs) on the ambient Hg formation and transport simulated by the CMAQ model over the Pearl River Delta (PRD) region. The alternative BC options are taken from (1) default time-independent profiles, (2) outputs from a CMAQ simulation of a larger nesting domain, and (3) concentration files from GEOS-Chem (re-gridded and re-projected using the Model-VAT). The three BC inputs and simulated ambient concentrations and deposition were compared using the Model-VAT. The results show that the model simulations based on the static BCs (default profile) underestimates the Hg concentrations by ~6.5%, dry depositions by ~9.4%, and wet depositions by ~43.2% compared to those of the model-derived (e.g. GEOS-Chem or nesting CMAQ) BCs. This study highlights the importance of model nesting approach and demonstrates that the innovative functions of Model-VAT enhances the efficiency of analyzing and comparing the model results from various atmospheric model simulations.
Keywords Model and data visualization      Model and data analysis      CMAQ      Boundary conditions      Mercury     
Corresponding Author(s): Yun Zhu   
Issue Date: 18 December 2017
 Cite this article:   
Wenwei Yang,Yun Zhu,Carey Jang, et al. Development and case study of a new-generation model-VAT for analyzing the boundary conditions influence on atmospheric mercury simulation[J]. Front. Environ. Sci. Eng., 2018, 12(1): 13.
 URL:  
https://academic.hep.com.cn/fese/EN/10.1007/s11783-018-1010-6
https://academic.hep.com.cn/fese/EN/Y2018/V12/I1/13
Fig.1  Process of analyzing theboundary conditions influence on CMAQ-Hg simulation using new functionsof Model-VAT
Fig.2  Functional design of Model-VATincludes the previous functions of VERDI (left part) and new innovativefuncions (right part)
Fig.3  User-friendly GUI designsof Model-VAT (right part) comparing to VERDI (left part)
Method GEM (ng·m–3) RGM (pg·m–3) PHg (pg·m–3)
Min Max Avg Min Max Avg Min Max Avg
North GEOS-Chem 1.89 1.67 1.87 65.8 118.6 115.9 60.6 95.5 88.5
CMAQ-Hg China 5.72 1.20 2.46 15.8 572.5 97.2 16.2 255.3 61.6
Profile 1.46 1.46 1.46 16.4 16.4 16.4 10.8 10.8 10.8
West GEOS-Chem 1.57 1.89 1.69 60.0 118.6 79.7 31.8 95.5 58.0
CMAQ-Hg China 1.20 1.95 1.65 15.8 50.3 36.8 16.2 41.1 31.7
Profile 1.46 1.46 1.46 16.4 16.4 16.4 10.8 10.8 10.8
South GEOS-Chem 1.57 1.51 1.55 44.1 60.0 53.3 14.4 31.8 24.5
CMAQ-Hg China 1.27 1.20 1.24 13.6 16.2 15.3 13.3 16.2 14.8
Profile 1.46 1.46 1.46 16.4 16.4 16.4 10.8 10.8 10.8
East GEOS-Chem 1.85 1.51 1.68 44.1 113.2 78.6 14.4 79.9 47.1
CMAQ-Hg China 2.11 1.27 1.49 15.2 54.6 28.7 13.5 42.9 24.3
Profile 1.46 1.46 1.46 16.4 16.4 16.4 10.8 10.8 10.8
All GEOS-Chem 1.57 1.89 1.7 44.1 118.6 82.4 14.4 95.5 54.8
CMAQ-Hg China 1.20 5.72 1.73 13.6 572.5 46.3 13.3 255.3 33.9
Profile 1.46 1.46 1.46 16.4 16.4 16.4 10.8 10.8 10.8
Tab.1  Average valuesin the BC at ground surface
Fig.4  Box plots of all grid valuesin July 2014: (a) GEM period average concentrations (ng·m–3), (b) RGM periodaverage concentrations (pg·m–3), (c) PHg period average concentrations(pg·m–3), (d) THg period average concentrations (ng·m–3), (e) THg accumulateddry deposition (ng·m–2·mon–1) and (f) THg accumulated wet deposition (ng·m–2·mon–1)
Fig.5  Tile plots of simulated concentrationsand depositions using GEOS-Chem, CMAQ-China and default profile asBCs, respectively: (a–c) period average GEM concentration (ng·m–3) in July 2014;(d–f) period average RGM concentration (pg·m–3) in July 2014;(g–i) period average PHg concentration (pg·m–3) in July 2014;(j–l) period dry total mercury deposition (ng·m–2·mon–1) in July 2014;(m–o) period wet total mercury deposition (ng·m–2·mon–1) in July 2014
Station Location Period Observation value Model results using different BCs Reference
GEOS-Chem CMAQ-Hg the mainland of China Profile
Wang Qinsha 22.7N, 113.55E 11/2009 – 12/2009 2.9 1.51 1.51 1.43 Li et al. [28]
Guangzhou 23.124N, 113.355E 9/2009 – 4/2010 4.6±1.6 4.48 4.60 4.21 Chen et al. [29]
Mt. Dinghu 23.164N, 112.549E 11/2010 – 10/2011 5.07±2.89 2.98 3.01 2.79 Chen et al. [29]
Mt. Dinghu 23.164N, 112.549E 10/2009 – 5/2010 5.54±2.89 2.98 3.01 2.79 Liu et al. [30]
Guangzhou 23.124N, 113.355E 10/2010 – 11/2011 4.86±1.36 4.48 4.60 4.21 Liu et al. [31]
Tab.2  Comparisons ofmodel results in GEM concentrations (ng·m–3) with observations
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