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Photochemical grid model implementation and application of VOC, NOx, and O3 source apportionment
Citation:
Kwok, R., K. Baker, S. Napelenok, AND G. Tonnesen. Photochemical grid model implementation and application of VOC, NOx, and O3 source apportionment. Geoscientific Model Development . Copernicus Publications, Katlenburg-Lindau, Germany, 8:99-114, (2015).
Impact/Purpose:
Published in the journal, Geoscientific Model Development.
Description:
For the purposes of developing optimal emissions control strategies, efficient approaches are needed to identify the major sources or groups of sources that contribute to elevated ozone (O3) concentrations. Source-based apportionment techniques implemented in photochemical grid models track sources through the physical and chemical processes important to the formation and transport of air pollutants. Photochemical model source apportionment has been used to track source impacts of specific sources, groups of sources (sectors), sources in specific geographic areas, and stratospheric and lateral boundary inflow on O3. The implementation and application of a source apportionment technique for O3 and its precursors, nitrogen oxides (NOx) and volatile organic compounds (VOCs), for the Community Multiscale Air Quality (CMAQ) model are described here. The Integrated Source Apportionment Method (ISAM) O3 approach is a hybrid of source apportionment and source sensitivity in that O3 production is attributed to precursor sources based on O3 formation regime (e.g., for a NOx-sensitive regime, O3 is apportioned to participating NOx emissions). This implementation is illustrated by tracking multiple emissions source sectors and lateral boundary inflow. NOx, VOC, and O3 attribution to tracked sectors in the application are consistent with spatial and temporal patterns of precursor emissions. The O3 ISAM implementation is further evaluated through comparisons of apportioned ambient concentrations and deposition amounts with those derived from brute force zero-out scenarios, with correlation coefficients ranging between 0.58 and 0.99 depending on specific combination of target species and tracked precursor emissions. Low correlation coefficients occur for chemical regimes that have strong nonlinearity in O3 sensitivity, which demonstrates different functionalities between source apportionment and zero-out approaches, where appropriate use depends on whether source attribution or source sensitivity is desired.
