D3.2 Documentation of the educational benefits of the project
Contribution December 2008
1. Abstract
The goal of the SchoolCO2web is to give pupils more insight into the carbon cycle and the fluctuations of CO 2 in
the atmosphere. This is a multidisciplinary topic, which includes mathematics, physics, chemistry and biology.
Pupils also learn how to extract valuable information from a large dataset by means of spreadsheet programs
and statistics. Data analysis is an important skill within scientific research. That's why this project creates a
bridge between highschool and university. Moreover, you bring science closer to the pupils, because the
measurements take place on the schools themselves and because the measurements might be useful within
scientific research.
The SchoolCO2web is a European network of schools. This emphasizes the international nature of the
greenhouse gas science and opens up possibilities for project cooperation between pupils from different
countries.
2. Atmosperic CO2 cycles in a nutshell
The higher you get into the troposphere (0 -16 km), the
more mixed the air becomes. So if you want to establish
the average CO2 concentration in the air, which is also
representative for a larger area, it's important to measure at
a high location. The measuring station at Mauna Loa
(Hawaii) is a good example. It is located at a vulcano at an
altitude of 3400 m.
In figure 1 the red line shows the monthly average CO 2
levels as measured at the station. As you can see, the CO 2
levels are oscillating over a period of one year. These are
seasonal effects, due to the increased fixation of CO 2 of
plants from May until September at the northern
hemisphere. As a result, the atmospheric CO2 drops with a
few ppm.
Figure 1 Average monthly CO2 levels at the
Mauna Loa station
The black line shows the monthly average levels
of CO2 corrected for seasonal effects. During the
last years, the average CO2 level increased with
almost 2 ppm per year. This rize is due to the
combustion of phosile fuels.
The closer you get to the earth's surface, the
less mixed the air becomes. The atmospheric
CO2 levels close to earth fluctuate a lot as a
result of photosynthesis by plants and
respiration by animals. We can clearly see this
fluctuations within the measurements of the
SchoolCO2web. In figure 2 you see the CO2
Figure 2 Atmospheric CO2 levels at the Carl-Zeiss-Gymnasium levels of the Carl-Zeiss-Gymnasium in Jena
(DE) and the Maartenscollege in Haren (NL)
in Jena (DE) and the Maartenscollege in Haren (NL)
from the 10th until the 14th of November. You can make these kind of graphs yourself with the SchoolCO2web
tool which you can find on the Carboschools website. Chapter 3 contains a tutorial on how to use this tool.
The grey and white areas of the graph represent the night and day respectively. Especially during the 13 th and
14th of November, you see a big rise of CO2 during the night and a drop during the day. There are two reasons
for this effect, which we call inversion. The first one is that plants only fix CO 2 during daylight, which make the
CO2 levels drop during the day. But a more important reason is that the air is more mixed during the day. When
the sun heats the earth, the earth emits heat to the air. This evokes turbulence and mixture of surface air layers
with layers higher in the atmosphere. During the night the earth cools down rapidly. As a result, the air close to
the surface cools down. But the higher
air layers are still warmer and function
as a blanket to prevent mixture of the
air. As a result, all the CO2 exhaled by
organisms accumulates in the surface
layer.
Although we saw a strong inversion
during the 13th and 14th of November,
this inversion is almost absent during
the 11th and the 12th of November. Why
is that? Figure 3 reveals the answer.
This time, the graph only shows the
CO2 levels of the Carl-ZeissGymnasium. A right y-axis is added with
the wind speed. During the first days of
the period, there was a lot of wind. This
wind causes the atmosphere to mix and
thus prevents inversion. During the last
days there was hardly wind, so
inversion occured.
Figure 3 Atmospheric CO2 levels and wind speed at the Carl-ZeissGymnasium in Jena (DE)
3. Tool to download measurements of the SchoolCO2web
Within the SchoolCO2web a tool has been developped to download or graphically display the CO2 and weather
measurements of the SchoolCO2web. This tool can be found on the Carboschools website
www.carboschools.org, on the SchoolCO2web section.
Operation of this tool is quite easy.
Creating a graph exists of the following
steps:
1. Select the parameter you want to
display on the left Y-axis (right now
the label says “y-axis right” but this
is not correct). In this case “CO2
concentration (ppm)”
2. Select the parameter you want to
display on the right Y-axis. In this
case we selected “nothing”
3. Click on the stations from which
you want to display the
measurements. If you want to
select more than one school, keep
the CTRL (Windows) of CMD
(Mac) pressed down during
Figure 4 Tool to download the measurements of the schoolCO2web
selection
4. Enter the start and end date of the period from which you want to display the data
5. Click “Show graph”. It may take a while before the data is being displayed, especially if you select a long
period. Once you displayed a certain range of data, this display will be stored in the cache of the server.
This means that the next time you display exactly this range of data, you will see the graph directly. This
is practical to keep in mind when you work with a group of pupils. If you let them select a range on their
computers, make sure you already displayed this range before. This can save a lot of time
6. You can also download the measurements as a datafile (*.csv) which can be loaded into a spreadsheet.
In this case you will download the data of the stations and during the period you selected. In this datafile,
all the parameters will be stored, so not just the ones you selected for the y-axes.
4. Topics for in the classroom
Atmospheric CO2 cycles
In chapter 2, I already discussed the anual, seasonal and daily oscillations of the CO2 levels. As our database
expands, it will not only be possible to see the effects of inversion within the measurements of the
SchoolCO2web. We will also be able to see seasonal effects and in a few years the anual effects.
Calibration errors
As you can see in figure 2, there is a structural difference of about 12 ppm between the Maartenscollege and the
Carl-Zeiss-Gymnasium. This difference has not been caused by natural effects, but is a result of inaccuracy of
the meters. For the meters to measure accurately, it is important to calibrate them with a calibration gas a few
times per year. You can use this calibration errors to make the pupils aware of the famous expression “never
trust a meter”.
Accurate measurements
Especially in the field of atmospheric CO2 research, performing accurate measurements and calibrations is one
of the more difficult and time consuming tasks. When well calibrated, the meters of the SchoolCO2web are 1
ppm accurate. This is already a lot compared to classroom sensors from Coach, Pasco, etc. They reach an
accuracy of about 25 – 50 ppm. The professional measuring tower from the Center of IsotopeResearch in
Groningen reaches an accuracy of 0.1 ppm and higher. To realize this, the meter automatically calibrates itself.
And not just once every few months. The meters takes a measurement, recalibrates, takes another
measurement and so on.
Sinks and sources
Why is it so important to take such accurate samples? The reason for this is that CO 2 levels between different
regions do not differ that much. In order to still distinguish the differences, accurate measurements are
necessary. And why do we want to know these small differences? In the past, the main aim of carbon science
was to determine the average global CO2 levels, to see whether they increase or not. But recently, the
differences between regions are taken into account. The current challenge of carbon research is to model the
carbon cycle as accurate as possible. Some regions function as a sink for atmospheric CO2, for example when
there is a lot of vegetational growth or water which functions as a sink. Other regions are a source, so more CO 2
is emitted to the atmosphere than compared to the uptake.
How do the meters work
The Vaisala is a so called non-dispersive infrared sensor. The
Vaisala contains a lamp which emits infrared light. This light is
reflected by a mirror to a detector for infrared light. During the
way, the light encounters CO2 molecules in the air, that absorb
a part of the light. The infrared sensor thus will sense less light
that the lamp emitted. This difference in intensity is a measure
for the number of CO2 molecules that absorbed light, and thus
a measure for the CO2 concentration in the air.
On every school a Davis Vantage PRO weather station has
been installed as well. This weather station sends values for the
air pressure, temperature and the humidity to the Vaisala. The
Figure 5 Composition of the Vaisala CO2
Vaisala needs this data to compensate the CO2infrared sensor
measurements.This can be best explained with an example.
When the air pressure in the Vaisala rises, is means that there are more air particles in the same space. Thus
there are also more CO2 molecules. That's why the Vaisala measures more CO2 with a higher air pressure.
Because you want to know the CO2 concentration independently from the air pressure, the weather station
sends the value for the air pressure to the Vaisala. The Vaisala calculates the CO 2 concentration according to a
fixed value for the air pressure. The weather station also keeps track of other factors, like the amount of rain, the
wind speed and wind direction.
5. Pupils work with the measurements in a spreadsheet
With the SchoolCO2web tool you can download the data (see chapter 3) from the schools in the form of a *.csv
file. I advice you to select just one school at a time and merge data of different schools later. Once you
downloaded the *.csv file you can import it into a spreadsheet.
How to import a *.csv file into MS Excel
There is a video tutorial called “Tutorial 01 - How to import csv files in Excel”. Basically importing exists of the
following steps:
 Open MS Excel and create a new empty sheet
 Go to the “Data” menu and select “import external data” and then “import data”
 Open the *.csv file
 The text import wizard opens. Proceed as follows during the different windows:
◦ Step 1: Just click “next”
◦ Step 2: Select as delimiter the “Comma”. Click “finish”
 In the import data window just click “OK”
Now you imported the *.csv file. If Excel uses dots to separate decimals, your sheet is ready for use. If Excel
uses comma's to separate decimals, it will not recognise most of the measurements as numbers, because a dot
has been used to separate the decimals. In this case Excel will see it as text and will align it to the left in the
cells. To change them into numbers, you have to replace the dots with comma's. Go to the menu “Edit” and
choose “Replace”. At “Find what” fill in a dot. At “Replace with” fill in a comma. Then click “Replace All”. Dit kan
simpeler binnen Excel door binnen de import functie bij Advanced aan te geven dat de decimalen door een punt
gescheiden worden.
Correlation between wind speed and CO2 levels
As an example I downloaded the same data as displayed in figure 3. These are the data of the Carl-ZeissGymnasium from the 10th until the 14th of November. As I mentioned before, the CO2 levels rise during the night
as a result of the inversion, unless there is plenty of wind to mix the atmospere. So you expect that the CO2
levels decrease when the wind speed increases. In the video tutorial called “Tutorial 02 - Correlation between
CO2 concentration and wind speed” I show how you can make a graph to visualize the correlation between CO2
levels and average wind speed.
Determining the average CO2 levels
To get a good picture of the average CO2 levels of the atmosphere, it is important to just take those
measurements when the atmospere is well mixed. The atmosphere is well mixed during the day because of
turbulence. Secondly, the atmosphere is well mixed when the wind speed is high. So we will determine the
average CO2 level just on the basis of the measurements taken between 13 – 17 h and at an average wind
speed above 2.5 m/s.
How do we do this in Excel? The first step is to filter the data, to get just the measurements between 13-17 h and
at an average wind speed above 2.5 m/s. The video tutorial called “Filtering CO2 levels in well mixed
atmosphere” explains how to do this. For this tutorial I downloaded the following data with the SchoolCO2web
tool: “NL CIO” from the 3rd of June until the 5th of September.
The second step is to calculate the average CO2 level for all the data and for just the filtered data. Our
hypothesis is that the CO2 levels for the filtered data are lower, because the measurements are taken in a well
mixed atmosphere. The video tutorial “Tutorial 04 – Calculation of the average CO2 levels” shows how to
calculate the average CO2 levels. Although not statistically proved, you can see that the average CO2 level for a
mixed atmosphere is indeed higher (370 ppm) than for all the measurements (377 ppm).
Weerfronten en CO2
Kijk op www.ready.noaa.gov/ready/open/hysplit4.html om te berekenen waar de wind vandaan gekomen is. Volg
de volgende stappen:

Compute trajectories

GDAS (global, 2005-present)

Weekselectie

Coördinaten van 1 locatie

Typ de volgende dingen in het scherm
o Backward
o Runtime