Constraining ozone-precursor responsiveness using ambient measurements
Author
Digar, Antara
Cohan, Daniel S.
Xiao, Xue
Foley, Kristen M.
Koo, Bonyoung
Yarwood, Greg
Date
2013Citation
Published Version
Abstract
This study develops probabilistic estimates of ozone (O3) sensitivities to precursor
emissions by incorporating uncertainties in photochemical modeling and evaluating model
performance based on ground-level observations of O3 and oxides of nitrogen (NOx).
Uncertainties in model formulations and input parameters are jointly considered to identify
factors that strongly influence O3 concentrations and sensitivities in the Dallas-Fort
Worth region in Texas. Weightings based on a Bayesian inference technique and
screenings based on model performance and statistical tests of significance are used to
generate probabilistic representation of O3 response to emissions and model input
parameters. Adjusted (observation-constrained) results favor simulations using the sixth
version of the carbon bond chemical mechanism (CB6) and scaled-up emissions of NOx,
dampening the overall sensitivity of O3 to NOx and increasing the sensitivity of O3 to
volatile organic compounds in the study region. This approach of using observations to
adjust and constrain model simulations can provide probabilistic representations of
pollutant responsiveness to emission controls that complement the results obtained from
deterministic air-quality modeling.
Type
Journal article