1st International Congress on Environmental Modelling and Software - Lugano, Switzerland - June 2002
Keywords
spatial correlation, fractal dimension, probability mapping, indicator semivariograms, no2
Start Date
1-7-2002 12:00 AM
Abstract
Monitoring atmospheric pollution in urban areas frequently involves mapping techniques that assist the researcher and/or the decision-maker to describe and quantify the pollution at locations where no measurements are available. The preparation of these pollution maps is a complex task, which is only feasible if a spatial correlation of the variable of interest is identified. Furthermore, the spatial correlation may not only change in time and space, but also according to the pollution levels. To illustrate this point, this paper investigates the fractal dimension of the spatial correlation of different levels of annual nitrogen dioxide concentrations [NO2] in the greater area of Milan in Italy for the years 1997-1999. It is shown that levels below 20 ppb and levels higher than 32 ppb of annual [NO2] present less correlation in space than mid levels (20-32 ppb) do. As a result, maps defining areas with high and low probability of exceeding a specific concentration threshold will have an uncertainty that is related to the different NO2 annual levels. In the light of these results, the development of environmental policies related to the EC Directive target is briefly discussed. Rather than trying to define an optimal NO2 sampling network, suggestions are made on how the information provided by the fractal analysis of the spatial correlation could be used to streamline the existing network.
Spatial Correlation Analysis of Nitrogen Dioxide Concentrations in the Area of Milan, Italy
Monitoring atmospheric pollution in urban areas frequently involves mapping techniques that assist the researcher and/or the decision-maker to describe and quantify the pollution at locations where no measurements are available. The preparation of these pollution maps is a complex task, which is only feasible if a spatial correlation of the variable of interest is identified. Furthermore, the spatial correlation may not only change in time and space, but also according to the pollution levels. To illustrate this point, this paper investigates the fractal dimension of the spatial correlation of different levels of annual nitrogen dioxide concentrations [NO2] in the greater area of Milan in Italy for the years 1997-1999. It is shown that levels below 20 ppb and levels higher than 32 ppb of annual [NO2] present less correlation in space than mid levels (20-32 ppb) do. As a result, maps defining areas with high and low probability of exceeding a specific concentration threshold will have an uncertainty that is related to the different NO2 annual levels. In the light of these results, the development of environmental policies related to the EC Directive target is briefly discussed. Rather than trying to define an optimal NO2 sampling network, suggestions are made on how the information provided by the fractal analysis of the spatial correlation could be used to streamline the existing network.
