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SPATIAL PREDICTION OF FINE PARTICULATE MATTER
Citation:
Impact/Purpose:
Provide regional-scale, spatially explicit information on the extent and distribution of both stressors and sensitive resources.
Develop and evaluate techniques to integrate information on exposure and effects so that relative risk can be assessed and management actions can be prioritized.
Predict consequences of potential environmental changes under alternative future scenarios.
Effectively communicate economic and quality of life trade-offs associated with alternative environmental policies.
Develop techniques to prioritize areas for ecological restoration.
Identify information gaps and recommend actions to improve monitoring and focus research.
There are three task objectives that reflect the work done by the Landscape Characterization Branch (LCB) in support of the ReVA Program objectives:
Provide information management, spatial analysis support, and data and information accessibility for the ReVA Program
Provide program management support, technology transfer, and outreach.
Evaluate how much, where and what kind of land use changes based on socioeconomic factors are likely to affect ecological resources in the pilot study region over the next 25-50 years
Description:
A new national monitoring network for tbe measurement of fine particular matter (PM2.5) is currently under development. A primary goal of this network is to collect monitoring data in residential communities for the evaluation of compliance with particulate air quality standards, In this analysis, we apply an empirical Bayesian approach io predict PM2.5 at selected points within multi-state regions covering the eastern U.S.