The 131st Annual Meeting (November 15-19, 2003) of APHA

Abstract #67613

Sacoby M. Wilson, MS and Marc L. Serre, PhD. Environmental Sciences & Engineering, University of North Carolina at Chapel Hill, CB #7431, Chapel Hill, NC 27599, 9199602777, smwilson@email.unc.edu

Confined animal feeding operations (CAFOs) raise large numbers of animals in small spaces and dispose of liquid waste in lagoons and spray fields. Human populations that reside near hog CAFOs may be burdened by health hazards due to the release of air pollutants including ammonia, hydrogen sulfide, and particulate matter. The overall goal of this project is to improve the understanding of human exposure to ammonia (NH3) by characterizing ambient levels present in areas near hog CAFOs via environmental monitoring and GIS analysis in Eastern North Carolina.

2002 NH3 data and data on PM2.5 and SO2 collected by the North Carolina Department of Air Quality and meteorological data obtained from the North Carolina Climate Office was utilized to develop an ammonia concentration model. Limited field data has been collected at different locations in the study area to test the accuracy of the model. ArcGIS and Bayesian Maximum Entropy (BME) mapping are being used to provide a GIS modeling framework to predict NH3 concentrations at different locations in Eastern North Carolina using limited field and modeled data. Preliminary results from semivariogram/covariance modeling show that there is utility in using the monitored and modeled data. The spatiotemporal BME mapping method of modern Geostatistics has been shown to provide a flexible framework that will rigorously process the available monitoring data, equipment measurement error, and CAFO source emission estimates, to produce maps representing the distribution of CAFO air pollutants across space and time, and the resulting exposure on populations in Eastern North Carolina.

Learning Objectives:

• Describe types of pollutants released from CAFOs and related health hazards
• Develop a model that can be used to predict levels of ammonia in areas with a high density of hog CAFOs using meteorological data and source information
• Implement a GIS mapping framework that can be utilized to accurately estimate ammonia levels both spatially and temporally

Keywords: Environmental Health, Geographic Information Systems

Presenting author's disclosure statement:
I do not have any significant financial interest/arrangement or affiliation with any organization/institution whose products or services are being discussed in this session.