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“Estimating Regional Background Air Quality using Space/Time Ordinary Kriging to Support Exposure Studies”
Citation:
Valencia, A., S. Arunachalam, Y. Akita, M. Serre, V. Garcia, AND V. Isakov. “Estimating Regional Background Air Quality using Space/Time Ordinary Kriging to Support Exposure Studies”. Presented at Annuual CMAS Conference, Chapel Hill, NC, October 28 - 30, 2013.
Impact/Purpose:
The National Exposure Research Laboratory (NERL) Atmospheric Modeling and Analysis Division (AMAD) conducts research in support of EPA mission to protect human health and the environment. AMAD research program is engaged in developing and evaluating predictive atmospheric models on all spatial and temporal scales for forecasting the air quality and for assessing changes in air quality and air pollutant exposures, as affected by changes in ecosystem management and regulatory decisions. AMAD is responsible for providing a sound scientific and technical basis for regulatory policies based on air quality models to improve ambient air quality. The models developed by AMAD are being used by EPA, NOAA, and the air pollution community in understanding and forecasting not only the magnitude of the air pollution problem, but also in developing emission control policies and regulations for air quality improvements.
Description:
Local-scale dispersion models are increasingly being used to perform exposure assessments. These types of models, while able to characterize local-scale air quality at increasing spatial scale, however, lack the ability to include background concentration in their overall estimation. These background concentrations generally include impacts from long-range transport of pollutants from distant sources, as well as non-inventoried anthropogenic, and other natural emissions in the local-scale study. Thus, incorporating these unaccounted concentrations in the total concentrations is necessary for a robust modeling analysis for use in exposure studies. We have developed a space/time ordinary kriging (STOK) method that takes advantage of the Bayesian Maximum Entropy (BME) library and allows us to estimate background concentrations at specific unmonitored locations in a region of interest. This approach includes measurements from limited AQS monitoring sites designated as background in a broad inter-state region, in addition to extensive probabilistic soft data that we developed. The soft data consist of AQS measurements from sites not designated as Background, combined with two CMAQ simulations (a baseline CMAQ simulation, and a CMAQ simulation where all local emissions have been zeroed out). We applied this methodology to support the Near-Road Exposures and Effects of Urban Air Pollutants Study (NEXUS) in Detroit, where a new dispersion model (RLINE) was used to characterize air quality from traffic exhaust in the near-road environment.