Spatiotemporal Interpolation Methods for Air Pollution Exposure
Proceedings of the Thirteenth International Symposium on Temporal Representation and Reasoning
This paper investigates spatiotemporal interpolation methods for the application of air pollution assessment The air pollutant of interest in this paper is fine particulate matter PM2.5. The choice of the time scale is investigated when applying the shape function-based method. It is found that the measurement scale of the time dimension has an impact on the interpolation results. Based upon the comparison between the accuracies of interpolation results, the most effective time scale out of four experimental ones was selected for performing the PM interpolation. The paper also evaluates the population exposure to the ambient air pollution of PM2.5 at the county-level in the contiguous U.S. in 2009. The interpolated county-level PM has been linked to 2009 population data and the population with a risky PM exposure has been estimated. The risky PM2.5 exposure means the PM2.5 concentration exceeding the National Ambient Air Quality Standards. The geographic distribution of the counties with a risky PM2.5 exposure is visualized. This work is essential to understanding the associations between ambient air pollution exposure and population health outcomes.
Li, Lixin, Xingyou Zhang, James B. Holt, Jie Tian, Reinhard Piltner.
"Spatiotemporal Interpolation Methods for Air Pollution Exposure."
Proceedings of the Thirteenth International Symposium on Temporal Representation and Reasoning: 75-82 Barcelona, Spain: Association for the Advancement of Artificial Intelligenc.
source: http://www.aaai.org/ocs/index.php/SARA/SARA11/paper/view/4241 isbn: 9781577355441