AAAR 31st Annual Conference
October 8-12, 2012
Hyatt Regency Minneapolis
Minneapolis, Minnesota, USA
Abstract View
Quantifying the Uncertainty of Particulate Matter in Regional Air Quality Models in the Presence of Uncertain Emission Inventories
WENXIAN ZHANG, Marcus Trail, Alexandra Tsimpidi, Yongtao Hu, Athanasios Nenes, Armistead Russell, Georgia Institute of Technology
Abstract Number: 464 Working Group: Urban Aerosols
Abstract Particulate matter (PM) is regulated as one of the criteria pollutants in NAAQS due to its adverse effects on human health and public welfare. As part of the air quality management process, regional air quality models are widely used to evaluate control strategy effectiveness. As such, the accuracy of the model simulations is of concern since a number of factors may introduce uncertainties in the simulation, such as uncertain emissions, chemical reaction rates, meteorological inputs, and initial/boundary conditions. This study focuses on quantifying how uncertain emission rates impact simulated PM concentrations in the Community Multi-scale Air Quality (CMAQ) model, and the assessment of pollutant responses to emission controls. The uncertainty is computed using a reduced-form model with Monte Carlo simulation. The reduced-form model is constructed using first- and second-order sensitivity coefficients obtained from a single CMAQ-HDDM/3D simulation. It represents the pollutant-precursor response and requires much less computational effort than applying the original model for uncertainty propagation. A case study has been conducted for an episode in Houston region. The uncertainties of NOX, SO2, NH3, and primary PM emissions, domain-wide and from specific sectors (e.g., point, area, on-road and non-road mobile), are considered. The uncertainties of simulated particulate matter species are quantified. The variation of the uncertainty due to different emission control strategies is also analyzed.