A Traffic-based method to predict and map urban air quality
| Autor: | Rasa Zalakeviciute, Yves Rybarczyk, Adrian Buenaño, Marco G. Bastidas |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Pollution
010504 meteorology & atmospheric sciences Computer science media_common.quotation_subject Air pollution 010501 environmental sciences medicine.disease_cause 01 natural sciences lcsh:Technology pollution mapping Transport engineering lcsh:Chemistry Computer Systems Urbanization Inverse distance weighting medicine machine-learning-based models General Materials Science Instrumentation Air quality index lcsh:QH301-705.5 0105 earth and related environmental sciences media_common Fluid Flow and Transfer Processes lcsh:T Process Chemistry and Technology Decision tree learning General Engineering Traffic flow Miljövetenskap lcsh:QC1-999 Computer Science Applications Datorsystem lcsh:Biology (General) lcsh:QD1-999 lcsh:TA1-2040 urban air quality lcsh:Engineering (General). Civil engineering (General) lcsh:Physics Environmental Sciences Interpolation |
| Zdroj: | Applied Sciences Volume 10 Issue 6 Applied Sciences, Vol 10, Iss 6, p 2035 (2020) |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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