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 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.

Assessment of metals in PM10 filters and Araucaria heterophylla needles in two areas of Quito, Ecuador

“…The investigation was carried out using high-volume samplers installed in two sites of the atmospheric monitoring network of Quito: Belisario (S1, 78 o 29 0 45.1 00 W, 0 o 11 0 04.7 00 S) and Los Chillos (S2, 78 o 27 0 19.5 00 W, 0 o 17 0 48.8 00 S) ( Figure 1). Belisario is an urban area with a high vehicular traffic according to the Google Maps Traffic application and a real traffic sampling using the QGis software (Alexandrino et al, 2020b;Zalakeviciute et al, 2020b). On the other hand, Los Chillos is considered a suburban area influenced by industry activities (Cevallos et al, 2017;Zalakeviciute et al, 2020a).…”

Hydrogen Recovery from Waste Gas Streams to Feed (High-Temperature PEM) Fuel Cells: Environmental Performance under a Life-Cycle Thinking Approach

“…In renewable energy highlights the use of wind energy (17.8%), photovoltaic (17.8%), and hydropower (15.1%). To achieve the Paris Agreement targets of limiting global warming to well below 2 • C above pre-industrial levels and pursuing efforts to limit it to 1.5 • C [1], a more sustainable and low-carbon economy and energy must be contemplated. The use of renewable energy sources in the grid mix will help to achieve these goals.…”

“…As the rural population is progressively moving to urban areas and cities, more and more people are being exposed to pollution levels that exceed the recommended levels for air quality. This situation is more critical in undeveloped and developing countries, but even in developed countries, more than 50% of the cities cannot achieve the air quality levels declared by the World Health Organization [1]. Energy-related activities, such as industrial sectors or buildings, are significant stationary sources of air pollution, specifically for CO 2 emissions [2,3].…”

An Empirical Mode Decomposition Fuzzy Forecast Model for Air Quality

“…Other methods of machine learning can better solve this problem. Zakeviciute et al used traffic vehicle flow data to predict air quality data through the decision tree 7 . The experiment shows that the prediction accuracy is improved when the traffic flow is large in the daytime, and it is suitable for the countries with low economy.…”