# A Crowdsensing Platform for Monitoring of Vehicular Emissions: A Smart City Perspective
## 6 Results and Discussions
### What Is the Amount of CO\({}_{2}\) Emitted by the Vehicles? For the considered sample set, what is the amount of CO\({}_{2}\) emitted by the vehicles? It has been found that it is possible to identify real situations in which several different vehicles travel daily, and each one contributes to a portion of the amount of emitted CO\({}_{2}\). The vehicles shown in Table 6 have traveled routes with varying times and distances, emitting almost 5 kg of carbon dioxide in total. It is noted that the Toyota Corolla was responsible for almost half of the total amount of CO\({}_{2}\) emitted during the test. This may be indicative of some potential electric or mechanical problem in the vehicle engine or even show that a low-quality fuel was used. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Fuel** & **Sensor** \\ \hline Volkswagen Voyage & Gasoline & MAP \\ Ford Ecospor & Gasoline & MAP \\ Fiat Palio & Alcohol & MAP \\ Peugeot 208 Alure & Alcohol & MAP \\ Honda Fit & Gasoline & MAP \\ Toyota Etios & Gasoline & MAF \\ Toyota Corolla & Gasoline & MAF \\ Nissan Versa & Gasoline & MAF \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Vehicle sensors. Having obtained the values of the amount of CO\({}_{2}\) generated by the engines, it is possible to carry out various analyses and obtain new result, therefore providing information for the generation of new solutions. Table 7 presents results obtained by the MAF method, for the particular case of the Toyota Etios 2015 using gasoline as fuel. For this test, data collection was performed on three different days, with similar schedules, early in the morning. After this test, the combined emission was greater than half a kilogram of CO\({}_{2}\). By mapping the emissions, we can get a better idea of what happened and open the possibility of new information generating better insights and analysis. In Figure 4, we can see the route traveled by one of the vehicles on a given day (17 May), already presented briefly in Table 7. The colors closest to red represent places with the highest emission rates, while the tones nearest to green are the areas with the lowest amount of emissions. In the map, the blue marker represents the starting point of the route, while the green marker indicates its end. In this scenario, the defined route was in the daily routine of the driver. Note that the use of Python libraries, for example folium, allowed us to create simple maps to perform a better analysis of the data obtained. Analyzing Figure 4, we can see that the places with the highest incidence can be identified as intersections and places where the vehicle remained stopped for some time due to traffic lights and slow traffic flow. In stretches where a more fluid flow occurred, as seen near the blue marker, we notice that the amount of CO\({}_{2}\) emitted decreased relative to other points on the same path. We must take into account that the methods presented provide us with estimates of CO\({}_{2}\) emission generated by the engine at the time of combustion and that the actual value released to the atmosphere is greater than this estimated value. This happens because, before reaching the external ecosystem, the pollutant gases pass through the catalytic converter that is responsible for treating the gases and transforming them into gases less harmful to our health, and these are released into the environment [55]. Generally, the catalyst converts the highly toxic carbon monoxide into carbon dioxide, which only affects us in a large quantity, resulting in more CO\({}_{2}\) emission by the vehicle. Even so, the actual places with the highest incidence of CO\({}_{2}\) emitted into the atmosphere will be the locations where the estimate of the levels of CO\({}_{2}\) generated by the engine are also higher.