Chemistry Reference
In-Depth Information
• Longer datasets give possibility to compare distributions of concentrations
(distribution statistics, histograms), which give better view of concentrations,
processes, and variability than short-term comparisons.
• Multiple peaked concentration histograms are usually a sign of multiple types of
airmasses arriving to the station. The model ability to produce all of the peaks is
also dependent on capturing correct advection.
• Particle number concentrations in different sizes in the submicron range are
strongly interrelated. Comparing just one size range can give too optimistic view
of the model performance and multiple size range comparison is useful in
pinpointing the processes needing improvement.
•
It is worthwhile to consider the special environments of some of the stations. In
Arctic, the Arctic haze, in mountains, the transport from lower altitudes and the
common inversion situations in Northern Italy can lead to very hard to reproduce
size distributions, at least in large spatial scale models.
The EUSAAR/ACTRIS and GUAN data together with some of the analyses is
available freely for model-to-measurement comparison uses at
http://www.atm.
4 Conclusions and Outlook for the Future
The submicron aerosol populations in the European background air are variable
from location to location. The concentrations and variability of aerosol distributions
do, however, show similarities over large geographical areas (Fig.
11
). The particle
number concentrations are generally lower in more northern and higher mountain
locations, naturally as they are generally located farther from the emission areas.
Standardized long-term measurements provide reliable information on statistical
behavior of atmospheric aerosols, far beyond what could be obtained in short-term
campaign-wise measurements. Although data from a period of only two years is
shown, the results already provide a previously unavailable variety of information
on the sub-micron aerosol physical properties and variability in Europe. Such
information would also be hard to achieve based on information collected from
separately managed stations, especially if the instrumentation and data handling are
not harmonized.
The similarities within the regions give a good chance of useful air quality
model-measurement comparisons. The actual choice of what should be used for
the models to compare with depends on the application and complexity needed. The
most straightforward way is just to compare one or more mean parameters, such as
median concentrations. This approach is simple to do, but can easily lose many
features of the data, and, in cases of strongly bimodal histograms, can even be
misleading. Comparing modeled histograms to results should pay attention to the
histogram mode location (mean or median concentration), width, and relative
abundance (height) of each mode in the histograms.
