Correcting air pollution time series for meteorological variability. With an application to regional PM10 concentrations
2003
It is well-known that a large part of the year-to-year variation in
annual distribution of daily concentrations of air pollutants is due to
fluctuations in the frequency and severity of meteorological conditions.
This variability makes it difficult to estimate the effectiveness of
emission control strategies. In this report we have demonstrated how a
series of binary decision rules, known as Classification And Regression
Trees (CART), can be used to calculate pollution concentrations that are
standardized to levels expected to occur under a fixed (reference) set of
meteorological conditions. Such meteo-corrected concentration measures
can then be used to identify "underlying" air quality trends resulting
from changes in emissions that may otherwise be difficult to distinguish
due to the interfering effects of unusual weather patterns. The examples
here concern air pollution data (daily concentrations of SO2 and PM10).
However, the methodology could very well be applied to water and soil
applications. Classification trees, where the response variable is
categorical, have important applications in the field of public health.
Furthermore, Regression Trees, which have a continuous response variable,
are very well suited for situations where physically oriented models
explain (part of) the variability in the response variable. Here, CART
analysis and physically oriented models are not exclusive but
complementary tools.
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