Scientific uncertainties in atmospheric mercury models I: Model science evaluation

被引:183
作者
Lin, Che-Jen [1 ]
Pongprueksa, Pruek
Lindberg, Steve E.
Pehkonen, Simo O.
Byun, Daewon
Jang, Carey
机构
[1] Lamar Univ, Dept Civil Engn, Beaumont, TX 77710 USA
[2] Oak Ridge Natl Lab, Div Environm Sci, Oak Ridge, TN 37831 USA
[3] Univ Nevada, Dept Nat Resources & Environm Sci, Reno, NV 89557 USA
[4] Natl Univ Singapore, Div Environm Sci & Engn, Singapore 117548, Singapore
[5] Univ Houston, Dept Geosci, Houston, TX 77204 USA
[6] US EPA, Off Air Qual Planning & Stand, Res Triangle Pk, NC 27711 USA
关键词
atmospheric mercury; modeling; chemical mechanism; deposition; mercury speciation; aqueous sorption; cloud water; emission inventory; initial and boundary conditions;
D O I
10.1016/j.atmosenv.2006.01.009
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Eulerian-based, first-principle atmospheric mercury models are a useful tool to assess the transport and deposition of mercury. However, there exist uncertainty issues caused by model assumptions/simplifications and incomplete understanding of mercury science. In this paper, we evaluate the model science commonly implemented in atmospheric mercury models. The causes of the uncertainties are assessed in terms of gas phase chemistry, aqueous phase chemistry. aqueous phase speciation, aqueous phase sorption, dry deposition, wet deposition, initial and boundary conditions, emission inventory preparation, and domain grid resolution. We also present a new dry deposition scheme for estimating the deposition velocities of GEM and RGM based on RADM formulation. From our evaluation, mercury chemistry introduces the greatest uncertainty to models due to the inconsistent kinetic data and lack of deterministic product identification in the atmosphere. Model treatments of deposition velocities and aqueous Hg(II) sorption can also lead to distinct simulation results in mercury dry and wet depositions. Although model results may agree well with limited field data of GEM concentrations and Hg(II) wet deposition, it should be recognized that model uncertainties may compensate with each other to yield favorable model performance. Future research needs to reduce model uncertainties are projected. (c) 2006 Elsevier Ltd. All rights reserved.
引用
收藏
页码:2911 / 2928
页数:18
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