# Accurate Estimation of Air Pollution in Outdoor Routes for Citizens and Decision Making
## 6 Conclusions and Future Work  
Air pollution stands as a significant issue contributing to a diverse array of illnesses, with severe impact on some types of cancer and cardio-respiratory diseases. In this paper, an integrated framework is proposed to assist citizens in their daily trips, before and during outdoor trips of their choice, by analysing the exposure to air pollutants in order to minimise the risk of diseases, based on the citizen's profile (medical history) as a trade-off of a marginal increase in travel distance. Our proposal is based on the World Health Organization's Air Quality (AQ) Guidelines, analysing pollutants and their concentrations independently with the aim to empower citizens to proactively make informed personal decisions about their route choices (by changing speed or trajectory), and identify potentially unhealthy travel environments by showing the pollution levels along the whole journey. The pollution measurement for each pollutant is calculated based on the whole trajectory of the outdoor route using spatial interpolation of these pollutants (with Ordinary Kriging) and a mapping process of these pollutants over a grid superimposed over the city map. This application is based on open-source tools and can integrate any pollutant available at the AQ monitoring stations. We have evaluated the application in the city of Valencia as a use case under different scenarios and we have shown the results to assess the exposure to pollution on citizens' routes. Also in case of respiratory problems, we have included a decision analysis taking into account the information provided by the application. It is seen that in practice, it negatively recommends crowded traffic junctions to reduce the amount of exposure to the pedestrian or rider. As could be expected, pollution is highly correlated with road traffic. In addition, we have seen the evolution of the different pollutants in a day and their interactions, showing that when the primary pollutants grow (NO\({}_{2}\) and SO\({}_{2}\)), O\({}_{3}\) grows later by reducing the primary ones through sunlight reactions. As future work, we are working on two different lines. We are developing low-cost AQ monitoring nodes that will allow us to use the application in environments with few official stations. In addition, we are using these pollution indicators as a new metric (as an alternative to distance) to search for the least polluted route over the city map. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Reference** & **Pros** & **Cons** & **Comments** \\ \hline \multirow{3}{*}{[14; 16]} & limited pollutants & \multirow{3}{*}{no user profile} & European project \\  & any route & & Finland \\  & open source & & \\ \hline \multirow{3}{*}{[10]} & limited pollutants & \multirow{3}{*}{limited scope} & \multirow{3}{*}{on Montreal (Canada)} \\  & cyclist’s route & & \\ \hline \multirow{3}{*}{AirNow [17]} & \multirow{3}{*}{user interface} & only at source and destination & not open source \\  & & for O\({}_{3}\) and PM & only North America \\  & & no user profile & limited to specific roads \\ \hline \multirow{3}{*}{BreezoMeter [18]} & \multirow{3}{*}{intuitive} & AQI only at the monitoring & \multirow{3}{*}{not open source} \\  & & spots, limited to specific roads & \\  & & no user profile & private API \\ \hline \multirow{3}{*}{PlumeLabs [19]} & \multirow{3}{*}{friendly} & AQI at your location & not open source \\  & & no user profile & private API \\ \hline \multirow{3}{*}{ours} & any pollutant & \multirow{3}{*}{AQ stations required} & real time \\  & any route & & every 10 min \\ \cline{1-1}  & open source & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison with the state-of-the-art: pros and cons.