Abstract
The spatial representativity of monitoring stations plays a major role for the reasonable estimation of air pollutants. The ranking of air pollution monitoring stations based upon their spatial representativity identifies the level of representativeness of the stations and is very useful for developing optimum monitoring networks. In this study, a new ranking method, named RTFI (Ranking Technique based upon Fuzzy Interpolation) is introduced. This ranking method is able to rank air pollution monitoring stations in the urban areas based upon their spatial representativity. Although spatial correlation techniques are often used in the ranking techniques in order to consider spatial representativity, in this ranking technique, the spatial representativity of a station is not limited to its surroundings and is measured independently of its location. RTFI was applied to airborne Particulate Matter (PM) at seven stations in Berlin, and ranked them according to their spatial representativity. The results showed that the Neukolln-Nanenstr station (MC 42) is the most spatially representative station among the studied stations.
Highlights
IntroductionOne of the major objectives of the installation of air pollution monitoring networks in urban areas is the description of spatio-temporal concentrations of pollutants (van Egmond and Onderdelinden, 1981) and the evaluation of the exposure of people and other vulnerable receptors to pollution (Trujillo-Ventura and Ellis, 1991)
One of the major objectives of the installation of air pollution monitoring networks in urban areas is the description of spatio-temporal concentrations of pollutants (van Egmond and Onderdelinden, 1981) and the evaluation of the exposure of people and other vulnerable receptors to pollution (Trujillo-Ventura and Ellis, 1991).Because of both the high intensity of turbulence in the atmosphere and the changes in emissions, air pollution distribution is a function of space and time (Liu et al, 1986)
A new ranking method, named RTFI (Ranking Technique based upon Fuzzy Interpolation) is introduced
Summary
One of the major objectives of the installation of air pollution monitoring networks in urban areas is the description of spatio-temporal concentrations of pollutants (van Egmond and Onderdelinden, 1981) and the evaluation of the exposure of people and other vulnerable receptors to pollution (Trujillo-Ventura and Ellis, 1991). Because of both the high intensity of turbulence in the atmosphere and the changes in emissions, air pollution distribution is a function of space and time (Liu et al, 1986). These stations can be replaced at sites which are more appropriate
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