OBJECTIVES Cohort studies of associations between air pollution and health have used exposure prediction approaches to estimate individual-level concentrations. A common prediction method used in Korean cohort studies is ordinary kriging. In this study, performance of ordinary kriging models for long-term particulate matter less than or equal to 10 μm in diameter (PM10) concentrations in seven ...

spatial autocorrelation (sa) is the correlations of the observed data of an area in the form of spatial pattern. the criterion of sa phenomenon occurrence is when the distribution of one observed variable value follows a particular pattern systematically. the sa analysis is most useful and an important tool for investigating the spatial database, this analysis not only itself gives useful infor...

We consider the problem of estimating a density fX using a sample Y1,. .. , Yn from fY = fX ⋆ fǫ, where fǫ is an unknown density. We assume that an additional sample ǫ1,. .. , ǫm from fǫ is observed. Estimators of fX and its derivatives are constructed by using non-parametric estimators of fY and fǫ and by applying a spectral cutoff in the Fourier domain. We derive the rate of convergence of th...

Real-time assessment of the ambient air quality has gained an increased interest in recent years. To give support to this evolution, the statistical air pollution interpolation model RIO is developed. Due to the very low computational cost this interpolation model is an efficient tool for an environment agency when performing real-time air quality assessments. Beside this, a reliable interpolat...

Kriging is a powerful spatial interpolation technique, especially for irregularly spaced data points, and is widely used throughout the earth and environmental sciences. The estimation at an unsampled location is given as the weighted sum of the circumjacent observed points. The weighting factors depend on a model of spatial correlation. Calculation of the weighting factors is done by minimizin...