# A Traffic-Based Method to Predict and Map Urban Air Quality
###### Abstract  
As global urbanization, industrialization, and motorization keep worsening air quality, a continuous rise in health problems is projected. Limited spatial resolution of the information on air quality inhibits full comprehension of urban population exposure. Therefore, we propose a method to predict urban air pollution from traffic by extracting data from Web-based applications (Google Traffic). We apply a machine learning approach by training a decision tree algorithm (C4.8) to predict the concentration of PM\({}_{2.5}\)  
implications of traffic-related particulate pollution, it is crucial to divulge the air quality conditions to the inhabitants of urban conglomerates. Although concentrations of PM\({}_{2.5}\) are higher in the developing countries, the largest share of atmospheric pollution studies and measurements are performed in less traffic-polluted developed countries [1; 9; 10]. Apart from costly scientific research, urban air quality monitoring is either very limited in resolution or simply inexistent, due to expensive and complex reference instruments [11]. In recent years, urban monitoring has been revolutionized by low-cost instruments [12]. As a result, a range of studies on urban air pollution measurements using networks of inexpensive sensors have been published [13; 14; 15; 16; 17]. While this is a more economical way to study air quality in an urban area, the resolution cannot possibly satisfy the full comprehension of health risks that are due to the non-homogeneous urban infrastructure or complex terrain and land use. Thus, another recent alternative is a mobile air quality laboratory, equipped with an array of sensors assessing air quality at the street level [18]. However, again, not all cities can afford mapping air pollution by covering the whole city, especially the ones housing millions of people. Furthermore, urban pollution is a dynamic parameter depending on different factors, such as traffic, which is always fluctuating. Therefore, in order to have representative information, the sampling requires multiple repetitions, including thousands of hours of monitoring. Conventional approaches to local, regional, and even global representation of air pollution conditions are based on air pollution modelling (e.g., Gaussian and chemical transport models (CTMs)) [19; 20]. These models contain a strong comprehension of topography, pollution sources, emissions, deposition, atmospheric conditions, and transport, among others. Different scale CTMs process the characteristics of chemical components of the atmosphere (e.g., emission, transport, mixing, and chemical transformation of trace gases and aerosols) simultaneously with meteorology [20]. On the other hand, these models are computationally costly and require a significant build-up of background and input information. They face additional challenges in regions with complex terrain. Another option is using costly software applications to build static urban pollution (noise, air quality, etc.) models, which require an elaborate list of input parameters [21]. Finally, air pollutants can be associated with predictive geographic attributes such as land use, traffic patterns, or population density to build land-use regression (LUR) models [22; 23; 24]. Alternatively, all cities have access to several applications that show the overall quality of traffic, such as Traffic by Google Maps, Waze, and so forth. Those applications quantify the intensity or severity of traffic on the basis of its corresponding velocities in any city of the world through geo-information (crowdsourcing). While these applications indicate the status of traffic in the city, it also might imply air quality conditions [25]. Our previous research work showed positive results in correlating traffic and urban PM\({}_{2.5}\) pollution in the rapidly growing Ecuadorian capital [26].