# A Comparative Analysis for Air Quality Estimation from Traffic and Meteorological Data
## 1 Introduction
In detail, monitoring stations are categorised as: (i) urban stations if positioned in central city districts, (ii) suburban stations when localised in the surroundings, or (iii) rural stations in any other case. In most cases, the urban stations group is the most significant indicator of the air quality for major cities and therefore requires a higher degree of attention, translated in both equipment and maintenance costs: without including heavy calibration, deployment and administration estimates, professional devices can reach prices over EUR 10.000 for a single unit. Multiplying this value for the number of pollutants or atmospheric conditions and the number of districts to be monitored in a large city, the yearly expenses can quickly become non-trivial. At the same time, pollution levels in urban environments can typically be traced back to a relatively small subset of factors, with the vast majority of the contribution deriving from vehicle emissions and surrounding industries. Moreover, several studies [5; 9] show a strong correlation between weather condition and trends of various pollutants: ozone levels for instance are directly influenced by warmer climates and solar radiation levels because of photo-chemical reactions induced on other pollutants, while temperature inversion layers can trap colder polluted air on the surface. Given the serious nature of this subject, many studies have been conducted over the years in order to estimate trends of pollutants or provide air quality forecasts for future hours or days. These include statistical approaches such as [10], where spatial correlations and seasonal patterns were exploited to estimate long-term trends of particulate matter (_PM_2.5) using a hierarchical model, based on Partial Least Squares regression. Other studies [11; 12; 13] investigated the applicability of recent machine learning approaches based on neural networks to this domain. In [11] different combination of Multi-Layer Perceptrons (MLP), periodic components and Self-Organising Maps are applied to hourly values of _NO_2 and meteorological features, showing that the best results can actually be achieved by directly applying the MLP on the original data. Other solutions propose to merge features from different domains and leverage the extreme adaptability of deep learning to extract relevant knowledge and thus provide a finer estimate of air quality. As an example in a urban environment, U-Air [12; 14] exploits different kinds of big data thanks to a composite framework where temporal and spatial information are processed by two different models to provide a robust 48-h forecast of the Air Quality Index (AQI). In [13], a similar approach is adopted and improved with the addition of an Attention Pooling layer that dynamically adapts to the most relevant monitoring station for a fine-grained prediction. Despite the accuracy of current state-of-the-art solutions, a major drawback of the listed systems is the high reliance on real-time sensor data, which strongly limits their use to a small part of urban and densely populated areas covered by monitoring stations. With reference to the systems and the maintenance costs, expanding the encompassed areas can be a slow process, if feasible at all. In this work we propose to combine meteorological and traffic-related features with recent machine learning and deep learning models to obtain a robust estimate of both pollutants and AQIAs a case study, we combine three years of sensor and vehicle logs from the city of Milan, from 2013 to 2016, training different kind of models to predict the monitored air pollutants, namely Nitrogen Oxides (\(NO_{2}\), \(NO_{x}\)), Carbon Monoxide (\(CO\)), Benzene (\(C_{6}H_{6}\)), Ozone (\(O_{3}\)), Black Carbon (\(BC\)) and Particulate Matter (\(PM_{10}\), \(PM_{2.5}\)). Expanding on previous work [15], we adopt a series of machine and deep learning models to predict the evolving trends of the pollutants in time.