|
|
|
How can the NO2 measurements go badly wrong?! Here is a simple example during an
episode. Remember that the NO2 air quality standard is based on the number of
hourly mean exceedences of 200 ug m-3 or nearly 105 ppb. You may think that
low concentrations are the hardest to measure and these are not important in
urban areas. However, episodes are the crucial periods and this is when the
NO2 data processing errors are the greatest! Imagine the real ambient hourly mean concentrations
are NOx = 1,000 ppb, NO = 900 ppb and NO2 = 100 ppb
which is
not an
exceedence. Make a small +1% error on the NOx channel
calibration and the measurements now become NOx = 1,010 ppb, NO = 900 ppb and NO2 = 110 ppb
which is an exceedence. The tiny +1% error on the NOx can easily become a +10% error
on the NO2. Similarly a -1% error on the NO will produce the same skewed
result. This tips the NO2 measurement into an exceedence. |
|
Here is a real example with the hourly mean NO2
peaking at 130 ppb and NO 450 ppb. There were 5 exceedences of the NO2 104.6
ppb hourly limit value.
The effect on the NO2 concentrations of a small +1%
error on the NOx and -1% NO sensitivities may appear small. However, the
number of NO2 exceedences has now doubled from 5 to 11. Note that the effect
increases with NO2 concentrations and is worst during an episode when correct
monitoring is most important. Exceedence counts are very sensitive to small
changes near to the limit threshold.
|
|
Where does the 1% error come from? Most organisations scale the data with constant sensitivity
factors until the next calibration. These steps will not follow a drifting
instrument very well. The best method is a ramp between the calibration
points. This plot shows the drifting NOx sensitivity over
two months. The real drift is shown in blue. The site operator is calibrating
the instrument every fortnight and applies a constant sensitivity (green
line) until the next calibration. Clearly there is an error shown in red
between the site operator's estimate and the real sensitivity. Note this
method produces significant errors even during periods of modest drift. The
worked example above shows that tiny 1% errors can be disastrous during an
episode.
Applying a constant sensitivity is common practice.
Actually plotting the sensitivity drift is a novelty for many site operators.
Normally this makes little difference but during episodes, the most important
period, the reported NO2 concentrations can be wildly wrong. The best way to
scale NOx measurements is by a series of ramps between the calibration points
that closely follow the natural instrument drift. This technique is shown in
the plot below. Errors are now less than 0.5% even during periods of rapid
drift. This operation is difficult to perform in a spreadsheet but is very
routine in specially designed software. This is the method I pioneered in the
AURN and have always used since 1988.
|
|
Here are some common examples of bad practice
|
|
A quick look at the NO concentration
plot often shows if the NO2 data processing has been performed correctly.
However, NO is rarely reported or even calculated! Occasionally some NO2
peaks and dips look odd but without the sensitivity plots you just have to
believe the concentrations. Most reported NO2 data are just the
annual mean and exceedence statistics. No one knows if these important NO2
statistics are based on simple data processing using sound methodologies or
are just a waste of time and resources. |
|
Remember a tiny 1%
error can easily overestimate the NO2 concentrations by 10% !! How many AQMAs have
been declared or not declared based on suspect NO2 data? Don't rely on your instrument
or your software to produce reliable concentrations without some manual
expertise. NOx instruments drift, that is a fact of life, and you must track
this drift closely or suffer anomalous peaks and dips. You need an expert
to check, select, smooth and apply the best calibration scalings to your
measurements. |
|
Contact me Geoff.Broughton@aqdm.co.uk |