Article: Temporal trends of spatial correlation within the PM10 time series of the AirBase ambient air quality database Journal: International Journal of Environment and Pollution (IJEP) 2015 Vol.58 No.1/2 pp.63 - 78 Abstract: We analyse the temporal variations which can be observed within time series of variogram parameters (nugget, sill and range) of daily air quality data (PM10) over a ten years time frame. Datasets have been obtained from previous geostatistical analysis of country wide datasets from the AirBase ambient air quality database. Applying the Kolmogorov-Zurbenko filtering method, the time series are first decomposed into their short-, mid-, and long-term components. Based on this, we then evaluate the magnitude of the individual spectral signal contributions. Furthermore, the significance of a long term trend component is investigated by a block-bootstrap-based approach combined with linear regression. It is discussed if within these datasets the times series of nugget variance can provide information about the evolution of the measurement uncertainty of the related air pollutant, whereas the sill and the range parameters could contain information about the spatial representativeness of the monitoring stations. Inderscience Publishers - linking academia, business and industry through research

Title: Temporal trends of spatial correlation within the PM₁₀ time series of the AirBase ambient air quality database

Authors: Oliver Kracht; Florencia Parravicini; Michel Gerboles

Addresses: European Commission – Joint Research Centre, Institute for Environment and Sustainability, Air and Climate Unit, Via E. Fermi 2749, I-21027 Ispra, Italy ' Garageeks Ltd., 31 Greenmount Office Park, Harolds Cross Bridge, Dublin 6W, Ireland ' European Commission – Joint Research Centre, Institute for Environment and Sustainability, Air and Climate Unit, Via E. Fermi 2749, I-21027 Ispra, Italy

Abstract: We analyse the temporal variations which can be observed within time series of variogram parameters (nugget, sill and range) of daily air quality data (PM₁₀) over a ten years time frame. Datasets have been obtained from previous geostatistical analysis of country wide datasets from the AirBase ambient air quality database. Applying the Kolmogorov-Zurbenko filtering method, the time series are first decomposed into their short-, mid-, and long-term components. Based on this, we then evaluate the magnitude of the individual spectral signal contributions. Furthermore, the significance of a long term trend component is investigated by a block-bootstrap-based approach combined with linear regression. It is discussed if within these datasets the times series of nugget variance can provide information about the evolution of the measurement uncertainty of the related air pollutant, whereas the sill and the range parameters could contain information about the spatial representativeness of the monitoring stations.

Keywords: air pollution; air quality monitoring; measurement uncertainty; geostatistics; spatial correlation; variogram; time series; model validation; temporal variations; PM10; block-bootstrap; linear regression; particulate matter.

DOI: 10.1504/IJEP.2015.076584

International Journal of Environment and Pollution, 2015 Vol.58 No.1/2, pp.63 - 78

Received: 17 Dec 2014
Accepted: 01 Dec 2015
Published online: 16 May 2016 *

Title: Temporal trends of spatial correlation within the PM10 time series of the AirBase ambient air quality database

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Title: Temporal trends of spatial correlation within the PM₁₀ time series of the AirBase ambient air quality database