Abstract
Air pollution is considered a major health problem in urban areas. Small sensor technology integrated with smart phones can be widely used to collect air quality information in real time using mobile applications. By applying the concept of crowdsensing, citizens and authorities can be aware of exposure to pollution during their daily activities in urban areas. This paper describes an on-road air quality monitoring and control approach based on the crowdsensing paradigm. In addition to collecting air pollution data, we are exploring the possibility of using this technology to effectively detect critical situations and redistribute all information through a proactive decision support framework. This information can be combined with sensed air quality parameters for displaying, on an interactive map, the detected pollutants' concentrations using sensors attached to smart phones. The proposed framework provides users with real-time traffic and air quality information, traffic recommendations and notifications, and environmental conditions. Moreover, the authorities can use this system to improve urban mobility and traffic regulation. Such behavior and movements related to geographic information can provide a better understanding of the dynamics of a road network. In this work, we propose to combine the benefits of the crowdsensing paradigm with both machine learning and Big Data tools. An artificial neural networks model and the A* algorithm are used for air quality prediction and the least polluted path finding. All data processing tasks are performed over a Hadoop-based framework.
Highlights
Most economic activities involving the use of road transport are accompanied by emissions of air pollutants which steadily degrades the environment
In order to manage all the data that the system requires, we propose the use of a crowdsensing technologybased approach and Big Data analysis tools [10] to measure, monitor, and control air quality with a higher spatiotemporal resolution while involving users in monitoring their exposure to pollution through mobile tools to better understand the quality of air breathed by the citizens
Presentation The case study will focus on the prediction of ozone concentrations in different areas of the Marrakesh city, especially a simulation area with heavy traffic
Summary
Most economic activities involving the use of road transport are accompanied by emissions of air pollutants which steadily degrades the environment. With varying climatic conditions influenced by temperature, wind, humidity, pressure, etc., these pollutants affect the quality of the air. As global warming becomes a very important topic in government policy, authorities are increasingly required to monitor and reduce harmful gas emissions in their regions. The development of realistic air pollution control strategies is of crucial importance but at the same time requires knowledge of the costs associated with their implementation, the economic benefits that can result from reducing the quantities
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