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Application of data fusion methods for urban-scale air quality characterization
Citation:
Isakov, V., A. Valencia, S. Arunachalam, B. Brian Naess, M. Breen, AND M. Serre. Application of data fusion methods for urban-scale air quality characterization. 2020 International Conference on Air Quality – Science and Application, Thessaloniki, GREECE, March 09 - 13, 2020.
Impact/Purpose:
The presentation provided an overview of EPA research efforts to develop modelling techniques to characterize urban-scale air quality and estimate the relative contribution of air pollution sources in the community
Description:
Urban-scale air quality characterization is critical for developing future air pollution mitigation strategies at the community scale. While monitoring-based assessments can characterize local-scale air quality when a sufficient number of monitors are deployed, modelling is also required to fully understand the relative emission source contribution. In this study, we used a combination of dispersion modeling and measurements from the Kansas City TRansportation local-scale Air Quality Study (KC-TRAQS), as well as data fusion methods, to characterize air quality. The application of the Bayesian Maximum Entropy (BME) method of modern spatiotemporal geostatistics helped us identify hot spots in the study area, improve emissions inputs for dispersion modeling applications, and identify contributions from local and regional air pollution sources
URLs/Downloads:
https://www.herts.ac.uk/airqualityconference/international-conference-on-air-quality-science-and-application