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 PM2.5 during the morning pollution peak from: (i) an interpolation (inverse distance weighting) of the value registered at the monitoring stations, (ii) traffic flow, and (iii) traffic flow + time of the day. The results show that the prediction from traffic outperforms the one provided by the monitoring network (average of 65.5% for the former vs. 57% for the latter). Adding the time of day increases the accuracy by an average of 6.5%. Considering the good accuracy on different days, the proposed method seems to be robust enough to create general models able to predict air pollution from traffic conditions. This affordable method, although beneficial for any city, is particularly relevant for low-income countries, because it offers an economically sustainable technique to address air quality issues faced by the developing world.
Highlights
As the world evolves towards global urbanization, 56% of its citiesin developed countries and 98% in low- and middle-income countries violate the World HealthOrganization’s (WHO) recommendations for air quality [1]
Urban PM2.5 Concentrations Based on the Air Quality Network
During the morning hours, the concentrations increase and show some variation, due to the elevated levels in the south of the city. This zone is known for industrial activities and usually shows the highest PM2.5 pollution in the city [10]
Summary
As the world evolves towards global urbanization, 56% of its cities (population over 100,000)in developed countries and 98% in low- and middle-income countries violate the World HealthOrganization’s (WHO) recommendations for air quality [1]. Among the regulated atmospheric pollutants, such as criteria gases (carbon monoxide—CO, nitrogen oxides—NOx , sulfur dioxide—SO2 and ozone—O3 ) and particles, the most complex is fine particulate matter (PM)—PM2.5 (aerodynamic diameter ≤2.5 μm). As it can originate directly and indirectly from anthropogenic activities, such as traffic, industries, and so forth, PM2.5 is a good indicator of the overall air quality and is useful to estimate the health impacts of air pollution exposure, due to its well-known respiratory and cardiovascular health effects [4,5].
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