# A Comparative Analysis for Air Quality Estimation from Traffic and Meteorological Data
## 4 Conclusions  
This work presents a simple yet effective approach to air quality estimation in urban areas from local environmental an traffic information. We demonstrated that good estimates can be provided for most pollutants by only exploiting meteorological features, such as temperature and humidity, and urban-related features, such as vehicle transits, thanks to the strong correlations among them. Moreover, we thoroughly tested different classes of machine learning models, specifically a linear regressor and a Multi-Layer Perceptron with Bayesian regularization, a Random Forest regressor and a Long-Short Term Memory network. We demonstrated that decent results can still be achieved by simpler models, provided that a robust preprocessing and engineering phase is carried out on the available data. In fact, the linear model achieved scores comparable to more complex solutions such as RF and LSTM, especially when considering shorter window dimensions. For instance, the linear model obtained an F-measure of 0.64, 0.91 and 0.83 over the individual AQI sub-indices, that amounted to 0.82 F1-score for the global AQI in the 24-h window configuration. Notably, the linear model surpassed any other solution by a large margin when considering daily measurements of \(PM_{10}\) and \(PM_{2.5}\), where training data was scarce. For these reasons, this solution can be considered a robust lightweight option in the case of limited resources. Nevertheless, when the latter are available, deep recurrent neural networks such as LSTM can provide a relevant edge in terms of estimation performance, as their inherently sequential structure allow for the examination of longer time periods. This can lead to equal or even better results than standard approaches, as demonstrated by the consistently lower RMSE in most hourly pollutants over the 48-h windows. Despite the heavier computational demands, LSTM does not require extensive preprocessing phases such as the introduction of lagged variables, as the sequence length can be trivially modified. Therefore, LSTM-based solution can be considered a valid and versatile option for many different use cases.  
Compared to most part of the literature where time series of air pollutants are included in the estimation for future forecasts, our approach provides results that are almost on par with other works. However, it has the advantage of providing AQI estimates in a completely sensor-free scenario, thus suggesting a an alternative methodology that can first of all reduce the costs for professional equipment and its maintenance. Furthermore, this technique can be applied in any urban region, after a prior calibration based on a small ground truth of sensor data.  
Future works will involve satellite monitoring to expand the predictions on larger areas. For this task, coarser but more comprehensive aerial information could be exploited, such as data related to air pollutants provided by the Copernicus Sentinel-5P mission by Copernicus, a European project in collaboration with ESA for atmosphere monitoring. The latter could allow a more fine-grained estimation on larger areas, without the need for detailed on-ground measurements.  
Conceptualization, E.A., C.R., and A.F.; methodology, E.A., A.F., and C.R.; software, E.A.; validation, A.F. and C.R.; formal analysis, E.A.; data curation, E.A.; writing--original draft preparation, E.A. and A.F.; writing--review and editing, A.F. and C.R.; visualization, E.A.; supervision, A.F. and C.R.; project administration, C.R.; funding acquisition, C.R. All authors have read and agreed to the published version of the manuscript.  
The APC was funded by LINKS Foundation.  
The authors declare no conflict of interest.