Year 2016-2017 we are focusing in a scientific poster and blog.
It is also easy to design an App capable of presenting/collecting data of atmospheric pollution. This project is open to your creativity. Here you have some ideas to work with:
1. Data obtention: To obtain atmospheric pollution data just google “contaminació online” and choose Martorell and another city of the Barcelona Metropolitan Area, geolocated in format shp, uploaded to Fusion tables using SHPescape. To work with these data, you can use different databases 1) Fusion Tables (slow connection) 2) TinywebDB (too tiny) or 3) SQLite (self contained).
2. Google API key: Obtain an API key from Google and activate Google Fusion Tables with your gmail account.
3. Google Fusion Tables: Create a Fusion Table containing pollutants from Martorell and another city. Data from different fusion tables (that is different students or the same students can be merged if they contain one common column data).
5. Database: If you decide to use SQLite databases you could use Ai2LiveComplete, that is a new offline App Inventor 2 you can install in your pendrive using localhost:8888 in your portable Chrome browser and it contain SQLite as an advanced feature and it has no limit in data file size (Remember to link your JAVA_HOME to Ai2LiveComplete in your computer advanced settings).
Download an example of use of SQLite with AppInventor2 with the following QR code
6. Statistical analysis: The App must contain an statistical analysis using Student’s t-test, graphs from Fusion Tables and geocode location data for google maps data presentation. Along with FT graphs you must include graphs with means, standard deviations and statistical differences (* p<0.05, ** p<0.01, *** p<0.001) using trial version of Graph Pad Prism (15 days or less).
7. Pollution calculations:
One of the screen allows the users to enter data and calculate Student’s t-test using local variables, global variables, procedures and mathematical functions like in MathBlaster. You can also let the user the possibity to introduce the type of car, and km and it calculates for example: “Your car emission is 26.52g of NOx, 110.05g of CO and 1.27 g of PM10 when you drive 32 km” when you enter in a TextBox 32 km (emission factor 0.8287g/km NOx, 3.4391g/km CO, 0.0397g/km PM10 already introduced in the formula obtained from here).
Another example could be to calculate an index as “Index Català de Qualitat de l’Aire“, air quality index of Environmental Protection Agency (USA) in a 0-500 scale for individual pollutants, or Multi-Pollution Index (WHO).
For example, MPI (WHO) Measured pollutants mean values (ppm) are: SO2: 63, CO: 12, NO2: 222 and guideline values are respectively 125, 10 and 200. The calculation is MPI=1/3 (65-125)/125)+(12-10)/10)+ (222-200/200) =-0.19 Negative values are good air-quality range values. So the user could introduce data and it obtains MPI from data.
8. Multiple choice quiz: It must also contain 5-8 screens to explain every single measured pollutants and Student’s t- test with 10 multiple choice questions. Download the aia source code from Pura Vida website.
9. Chloropleth map: You must also include in your App heat pollution maps using data from Catalonia, Europe or the world. Data sources include cartographical data from Natural Earth, free cartography, data from Institut Cartogràfic de Catalunya, open data from Generalitat, KML files from Catalonian towns and counties and pollution data from IDESCAT, INE, EUROSTAT, WHO and UN, among many other possible data sources. SHP files could be uploaded to Fusion tables using SHPescape.
10. Animated heat map: In your blog and in your App you must include a gif created with GIMP where it can be observed time variation of pollution heat maps in a time scale (from hours to years) in an area (from small areas as Barcelona metropolitan area or Martorell to Europe or the World). To generate the animated gif you can use images (svg, png or jpg formats) obtained from your previous kml chloropleth map (Google fusion table data) using e.g. indiemapper.
11. Include the results in your App and in your blog. All data must be included for download purposes in the student’s blog.
12. Draw conclusions from your data (compare different cities among them, at night/day times, working days/weekends, compare with WHO or EU maximum average concentration of pollutants) and write bibliographic/web references.
Write in a piece of paper and in your blog the mean and standard deviation of NO2, PM10 and H2S (if available) of cities indicated in your Moodle course: and calculate if there is statiscally significant differences between values from 4 December 2013 (possible pollution peak), 20 February 2014 (working day), 23 February 2014 (Sunday).
In Moodle you will study one specific pollutant and you will create a one/several years heat map using Google Fusion Tables. With several heat maps you can create a gif animation using GIMP.