Zhengyuan Zhu
Department of Statistics and Operations and Research
University of North Carolina at Chapel Hill
Spatial Network Design to Detect Regional Trends in ground level Ozone
One goal of the Clean Air Act Amendments of 1990 (CAAA) is to reduce ambient
concentrations of atmospherically-transported pollutants. Monitoring data
from networks such as the Clean Air Act Status and Trends Monitoring Network
(CASTNet) can be used to estimate regional trends of these pollutants to
evaluate the effectiveness of the CAAA. This paper presents spatial network
design methodology to optimize the network's ability to detect and quantify
future regional trends in air pollution by adding or relocating monitoring
sites. The 1997-2003 CASTNet ozone data is analyzed for trends using a
two-stage approach similar to Holland et. al. (2000) to illustrate the
design methodology. In the first stage, a site-specific trend is estimated
after adjusting for the influence of meteorology and season. In the second
stage, trend is assumed to vary over the eastern U.S. as a realization from
a Gaussian random field, and Bayesian kriging methodology is used to
estimate regional trends and uncertainties. A simulated annealing algorithm
is then used to select the optimal locations for future monitoring stations
under different practical consideration. We use the expected length of a
Bayesian prediction interval as the design criterion to account for the
uncertainty of estimating spatial covariance parameters underlying the
estimates of regional trend. This design criterion is directly related to
the network's capability to detect and quantify trends. Designs that
minimize the Bayesian prediction interval are compared with the original
design and designs that minimize the kriging variance. At the end we discuss
how to determine the sample size of a network for a specific objective.
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