| | |  | Haze over Kathmandu | |
Exercise 3
Air Pollution of the Kathmandu Valley Case Study Worksheet exercise 3. This exercise is divided into two parts and requires the use of LEOWorks.
Part 1
Have you noticed that on days with high air pollution, the air is less transparent? As you may already know, this phenomenon is called smog. It decreases visibility, and, consequently, you cannot see very far. On those days, nearby hills you would normally see clearly are hardly, or not at all, visible. In fact, the horizontal visibility is also a good indicator to measure the degree of air pollution.
In order to measure the 'thickness' of the atmosphere, we will analyse images taken from the Envisat satellite. As a test, we will compare the data from space with scientific measurements done on the ground (the P10 measurements of the monitoring stations).
Let's assume that the satellite measurements are representative of a certain surface around the station, and that air pollution influences the transparency of the atmosphere. In fact, each pixel of the satellite image corresponds to 300 m x 300 m.
The advantage of using satellite data is that it is represented by different colours, including infrared, which is not visible to the naked eye. The different colours interact differently in the atmosphere. Generally, blue is more affected by haze, mist, or smog than red. Can you explain why? Ask your Physics teacher!
Below you will find 3 data sets. Each of these includes 5 bands. Band 1 is measured in the blue colour range, band 2 in green, band 3 in yellow, band 4 in red, and band 5 in infrared. The scientific question is: Can any of these bands be used to represent P10 – pollution degree? In other words, let's try to find a correlation between the ground measurements and the different bands.
Download and save the MERIS images of the Kathmandu valley:
- 4 November 2003 - band 1 to band 5
- 7 November 2003 - band 1 to band 5
- 22 December 2003 - band 1 to band 5
All the images are geo-referenced - The parameters are:
Projection: Geographic Lat / Lon (WGS 84).
Download and save the shp file 'P10_monitoring_stations' of the Kathmandu valley, placed in Thamel, Putalisadak, T.U.Kirtipur, Bhaktapur, Matsyagaon, and Patan Hospital.
| | Fig. 11: PM10 Ground Data | |
Open Fig.11, the table for PM10 ground data for the above dates.
In LEOWorks, open the image of 4 November 2003 image Band 1r. Use Enhance>Interactive Stretching (do only moderate contrast enhancement). Now load the GIS file 'P10_monitoring_station'. In the Select the Transformation Method window, select Arbitrary and Map Based. All the six monitoring stations will be displayed on the image.
In the GIS Tool, open Tools>Information and click on Point 2 to see that station name (Putalisadak) and the ground-based measurements on the three days (4 Nov, 7 Nov, and 22 Dec 2003). In the image, the point is highlighted.
Click and open View>Cursor Position/Value. Slowly and carefully go over the point with the cursor and read the value of that pixel measured by the satellite as 'Original data', e.g. 61 for Putalisadak 4 Nov. Band 1. You will notice that in the tables and graphs below you find the same number to be 61.35. In fact, we had to reduce the data amount and therefore truncated the decimals.
1. In Microsoft Excel, create a table and tabulate the corresponding PM10 data and Digital number (DN value). With the help of graphs you have to generate, study the relationship between the DN values and the bands, as well as the relationship between the DN values and PM10. See the example on the next page.
2. Repeat the above process for all the images/bands.
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