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Development of the high-order decoupled direct method in three dimensions for particulate matter: enabling advanced sensitivity analysis in air quality models
Journal article   Open access   Peer reviewed

Development of the high-order decoupled direct method in three dimensions for particulate matter: enabling advanced sensitivity analysis in air quality models

W. Zhang, S. L. Capps, Y. Hu, A. Nenes, S. L. Napelenok and A. G. Russell
Geoscientific model development, v 5(2), pp 355-368
01 Jan 2012
url
https://doi.org/10.5194/gmd-5-355-2012View
Published, Version of Record (VoR)CC BY V4.0 Open

Abstract

Geology Geosciences, Multidisciplinary Physical Sciences Science & Technology
The high-order decoupled direct method in three dimensions for particulate matter (HDDM-3D/PM) has been implemented in the Community Multiscale Air Quality (CMAQ) model to enable advanced sensitivity analysis. The major effort of this work is to develop high-order DDM sensitivity analysis of ISORROPIA, the inorganic aerosol module of CMAQ. A case-specific approach has been applied, and the sensitivities of activity coefficients and water content are explicitly computed. Stand-alone tests are performed for ISORROPIA by comparing the sensitivities (first- and second-order) computed by HDDM and the brute force (BF) approximations. Similar comparison has also been carried out for CMAQ sensitivities simulated using a week-long winter episode for a continental US domain. Second-order sensitivities of aerosol species (e.g., sulfate, nitrate, and ammonium) with respect to domain-wide SO2, NOx, and NH3 emissions show agreement with BF results, yet exhibit less noise in locations where BF results are demonstrably inaccurate. Second-order sensitivity analysis elucidates poorly understood nonlinear responses of secondary inorganic aerosols to their precursors and competing species. Adding second-order sensitivity terms to the Taylor series projection of the nitrate concentrations with a 50% reduction in domain-wide NOx or SO2 emissions rates improves the prediction with statistical significance.

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Collaboration types
Domestic collaboration
Web of Science research areas
Geosciences, Multidisciplinary
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