AAAR 37th Annual Conference October 14 - October 18, 2019 Oregon Convention Center Portland, Oregon, USA
Abstract View
Impacts of Spatial Distribution and Spatial Resolution of Emissions on Air Quality Model
YITING LI, Michael Kleeman, University of California, Davis
Abstract Number: 565 Working Group: Urban Aerosols
Abstract Air quality models are widely used to estimate human exposure to air pollution and to predict the health benefits of proposed emissions control programs. Air quality models predict pollutant spatial gradients that influence their overlap with human populations and therefore determine their public health impact. The accuracy of pollutant spatial fields is linked to: 1) the accuracy of the underlying emissions inventory, which often relies on spatial surrogates to determine where emissions occur within each county; and 2) the spatial resolution of the emissions inventory and air quality model, which affect the ability to capture concentration gradients around populated regions.
Here we study the impacts of emissions spatial distribution and spatial resolution on predicted pollutant concentration fields and the resulting air quality model performance. Several major surrogates are updated to improve the spatial distribution of emissions originally at 4km resolution. Surrogates including population, total housing, single-family housing, total employment, industrial employment, agricultural employment and service & commercial employment are created using high spatial resolution socio-economic data. The surrogate for construction equipment is updated using information from the California Water Resource Board NOI records and Caltrans’ on-road construction database. The Longitudinal Employer-Household Dynamics database is used to update the surrogate for industrial equipment. These major surrogates are used to allocate area emissions at 1km resolution.
Simulations were conducted to evaluate if the updated spatial surrogates and downscaled emissions improved the accuracy of predicted particulate matter concentrations during past episodes. Model predictions for future episodes were also evaluated to determine if the two changes significantly affected population exposure to pollutants under various future energy scenarios. The goal of this research is to improve the accuracy of exposure calculations allowing for more detailed analysis of historical air pollution health impacts and more realistic analysis of changes to public health associated with future emissions reductions.