This introduction will introduce you to the paper that we display through our App. Additonaly, it will explain to you how this app works.
To go to the next step, please click on this icon at the bottom: . To go a step back, you can click on this icon: . If you want to get directly to the app, you can skip the introduction, by using this icon: .
To archive our goal, we use Augmented Reality. With Augmented Reality, we want to make complex variables and insights easier to understand. For example, we can visualize unobservable phenomena, such as airflow. We will use your camera to show you a copy of the actual world you see on the mobile phone. Our app will add virtual objects, that display information. The opportunity for you is to inspect and analyse the information on site.
At the moment there is only one paper to inspect. In the next step, we want to introduce this paper to you.
Since operating urban air quality stations is not only time consuming but also costly, and because air pollutants can cause serious health problems, this paper presents the hourly prediction of ten air pollutant concentrations (CO2, NH3, NO, NO2, NOx, O3, PM1, PM2.5, PM10 and PN10) in a street canyon in Münster using an artificial neural network (ANN) approach. Special attention was paid to comparing three predictor options representing the traffic volume: we included acoustic sound measurements (sound), the total number of vehicles (traffic), and the hour of the day and the day of the week(time) as input variables and then compared their prediction powers.
Please continue reading on the next page.
The models were trained, validated and tested to evaluate their performance. All three input variable options (sound, traffic and time) proved to be suitable and showed distinct strengths for modelling various air pollutant concentrations.
The article was written by Laura Goulier, Bastian Paas, Laura Ehrnsperger, and Otto Klemm. It was published in the International Journal of Environmental Research and Public Health 17, no. 6 (January 2020) page number 2025. The paper is accessible under https://doi.org/10.3390/ijerph17062025
The predicted and measured values refer to the city center of Münster, Germany
Great you made it this far. We hope you understand now what the paper is about. In the next step we will explain what you will see in the app
The air pollution levels are indicated by small dots on your device. The more points displayed, the higher the proportion of air pollutant in the air. The range number of points reaches from 0 to 20000 points. Currently, there are 10000 points dispayed.
Pleasde note: The app needs a few secondes to render all points.
Now the maximum number of points, 20000, is displayed.
Pleasde note: The app needs a few secondes to render all points.
The app contains two views. Actual you see the "overlay" view. On the left side, you see the predicted value for the air pollutant CO2 (red). On the right, the observed value (blue). You can adjust the view by moving the slider in the middle. Try it if you want!
The second view ist the "differences" view. In yellow, you see the observed value (This could change to the predicted, if you change the pollutant or prediction parameter) of the air pollutant CO2. The difference between the predicted and observed value is displayed in purple. The predicted (can also change) value is displayed as an aggregate of the yellow and purple dots.
In the right top corner, you will find a diagram. The left red bar represents the predicted value and the right blue bar the observed value. Note that since the different air pollutant emissions have different units, the left scale changes depending on the air pollutant. On the right axis, the number of points, which is displayed, is shown.
If you want to change the view, air pollutant or prediction parameter, click in the right bottom corner on "options". To select something, click on the previous value. A selection of options opens. Select the requested value. You can close the options with a click on the following icon: Feel free to try it out directly.
Yeah, you are done! We hope you understand everything and enjoy using the app. You can now close the introduction using the following icon:
If you want to read the introduction again, just click on the button in the lower left corner "introduction".
| Calculated: | 17 ppb | ||||
| Difference: | 2 ppb | ||||
| Measured: | Observed | + | Difference | 19 ppb |