scholarly journals Optimal control of urban air pollution related to traffic flow in road networks

2018 ◽  
Vol 8 (1) ◽  
pp. 177-193 ◽  
Author(s):  
Lino J. Alvarez-Vázquez ◽  
◽  
Néstor García-Chan ◽  
Aurea Martínez ◽  
Miguel E. Vázquez-Méndez ◽  
...  
2020 ◽  
Vol 10 (6) ◽  
pp. 2035 ◽  
Author(s):  
Rasa Zalakeviciute ◽  
Marco Bastidas ◽  
Adrian Buenaño ◽  
Yves Rybarczyk

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.


2017 ◽  
Vol 68 (4) ◽  
pp. 858-863
Author(s):  
Mihaela Oprea ◽  
Marius Olteanu ◽  
Radu Teodor Ianache

Fine particulate matter with a diameter less than 2.5 �m (i.e. PM2.5) is an air pollutant of special concern for urban areas due to its potential significant negative effects on human health, especially on children and elderly people. In order to reduce these effects, new tools based on PM2.5 monitoring infrastructures tailored to specific urban regions are needed by the local and regional environmental management systems for the provision of an expert support to decision makers in air quality planning for cities and also, to inform in real time the vulnerable population when PM2.5 related air pollution episodes occur. The paper focuses on urban air pollution early warning based on PM2.5 prediction. It describes the methodology used, the prediction approach, and the experimental system developed under the ROKIDAIR project for the analysis of PM2.5 air pollution level, health impact assessment and early warning of sensitive people in the Ploiesti city. The PM2.5 concentration evolution prediction is correlated with PM2.5 air pollution and health effects analysis, and the final result is processed by the ROKIDAIR Early Warning System (EWS) and sent as a message to the affected population via email or SMS. ROKIDAIR EWS is included in the ROKIDAIR decision support system.


2020 ◽  
Vol 1 (3) ◽  
pp. 100047 ◽  
Author(s):  
Donghai Liang ◽  
Liuhua Shi ◽  
Jingxuan Zhao ◽  
Pengfei Liu ◽  
Jeremy A. Sarnat ◽  
...  

Author(s):  
Nikolaos Sifakis ◽  
Maria Aryblia ◽  
Tryfon Daras ◽  
Stavroula Tournaki ◽  
Theocharis Tsoutsos

2021 ◽  
Vol 246 ◽  
pp. 118094
Author(s):  
Erik Velasco ◽  
Armando Retama ◽  
Miguel Zavala ◽  
Marc Guevara ◽  
Bernhard Rappenglück ◽  
...  

2021 ◽  
Vol 55 (8) ◽  
pp. 5579-5588
Author(s):  
Bu Zhao ◽  
Long Yu ◽  
Chunyan Wang ◽  
Chenyang Shuai ◽  
Ji Zhu ◽  
...  

2019 ◽  
Vol 173 ◽  
pp. 23-32 ◽  
Author(s):  
Ana Paula Cremasco Takano ◽  
Lisie Tocci Justo ◽  
Nathalia Villa dos Santos ◽  
Mônica Valeria Marquezini ◽  
Paulo Afonso de André ◽  
...  

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