Environmental Engineering Reference
In-Depth Information
7.7 Spatial Mapping of Air Quality Trends in Europe
Bruce Denby, Ingrid Sundvor, and Massimo Cassiani
Norwegian Institute for Air Research (NILU), P.O. BOX 100, 2027 Kjeller, Norway
Abstract
This paper investigates the spatial mapping of air quality trends in
Europe. Such spatially distributed maps provide information for policy making
and for understanding the spatial character of air quality trends. Previous trend
studies have concentrated on individual, or groups of, monitoring sites, looking at
the average trends of these. In this study use is made of statistical interpolation
methods that combine observed and modeled data in an optimized way, in this
case using residual log-normal kriging with multiple linear regression, to produce
annual maps of air quality indicators for ozone (AOT40) and annual mean SO
2
in
the period 1996-2005. Trends in these maps are then calculated and their
significance and uncertainty are assessed. The methodology is effectively used for
mapping SO
2
trends to a significant level in most of Europe. However, trends in
AOT40 are less clearly defined since the uncertainty is generally of the same order
as the calculated trends. A general north to south gradient in AOT40 trends can be
seen, negative trends in the UK and Scandinavia but positive trends in the
Mediterranean.
1. Introduction
This paper investigates the spatial mapping of air quality trends in Europe, in
particular the mapping of ozone (AOT40) and annual mean SO
2
. Previous trend
studies have concentrated on individual, or groups of, monitoring sites, looking at
the average trends of these. In this study European wide maps of air quality trends
are produced.
To provide full spatial coverage of air quality some form of interpolation of the
measurement data is required. However, the spatial and temporal coverage of the
measurement data alone is generally insufficient for providing maps of sufficient
quality for a long enough period (10 years). Alternatively, good spatial coverage is
available from air quality models but these have also been shown to misrepresent
the trends in air quality data (e.g. Solberg et al., 2009).
To improve the mapping of air quality, statistical interpolation methods may be
used (e.g. Denby, 2008a, b; Horálek et al., 2007). These methods combine
observations, air quality models and other supplementary data to provide maps
of air quality, as well as maps of uncertainty, on an annual (or daily) basis. By
applying these methods to the available measurement and modeling data the best
estimate of the spatial distribution of the air quality can be achieved. In this study
