**2.2. Cumulative modeling method**

Air pollution data (PM2.5) were collected in central Quito over a period of 2 months in June and July of 2017 by the city Secretariat of the Environment. Belisario (alt. 2835 m.a.s.l, coord.78°29′24″ W, 0°10′48″ S) measurement station was setup following the criteria of the Environmental Protection Agency of the United States (USEPA). For PM2.5 concentration data Thermo Scientific FH62C14-DHS continuous ambient particulate monitor 5014i was used based on beta rays' attenuation method (EPA No. EQPM-0609-183). For all the data 1 hour averages were calculated, resulting in 1118 instances.

In this work, we present several regression models to provide a reliable estimation of the current level of PM2.5 from data collection methods of different levels of affordability. In Section 3, we describe a prediction of PM2.5 concentrations based on real-time traffic monitoring, only. This type of data does not cost anything to the user as it is based on publicly available worldwide traffic data. Section 4 describes a prediction that adds meteorological factors on top of the traffic data. Most of the meteorological equipment is not as costly as air quality sensors, thus still presenting a viable option for the prediction of PM2.5 concentrations. Subsequently, Section 5 describes a prediction that includes traffic data, meteorological factors and trace gas concentrations. This way we build from the simplest to the most complex model, increasing the equipment costs with every step and improving the prediction performance. Finally, we finish our study by proposing the best simple model based on a feature selection method, letting us to reduce the costs significantly, but still producing a high performance.
