The study of the pollutants needs a better understanding of their extreme behaviours which could potentially cause adverse health effects. When analysing spatial dependence of the pollutant, the dependogram proposed by Arbia and Lafratta is preferred to the traditional correlogram used in the spatial statistics literature because it captures nonlinear relationships in the tails of the joint distributions and helps in detecting a pattern of spatial regularities. In this paper, we present a new method to estimate the spatial dependogram that uses univariate and bivariate threshold models. The method is applied to a set of hourly NO2 data collected by seven monitoring stations in the city of Rome (Italy) during the years 2000 and 2001.
Spatial dependence in the tails of air pollutant distributions: alternatives to spatial correlogram
ARBIA, Giuseppe;LAFRATTA, Giovanni
2009-01-01
Abstract
The study of the pollutants needs a better understanding of their extreme behaviours which could potentially cause adverse health effects. When analysing spatial dependence of the pollutant, the dependogram proposed by Arbia and Lafratta is preferred to the traditional correlogram used in the spatial statistics literature because it captures nonlinear relationships in the tails of the joint distributions and helps in detecting a pattern of spatial regularities. In this paper, we present a new method to estimate the spatial dependogram that uses univariate and bivariate threshold models. The method is applied to a set of hourly NO2 data collected by seven monitoring stations in the city of Rome (Italy) during the years 2000 and 2001.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
