Yves Rybarczyk

By Yves Rybarczyk, Philippe Hoppenot, Etienne Colle and Daniel R. Mestre

Part of the book: Human Machine Interaction

By Yves Rybarczyk and Rasa Zalakeviciute

Air quality monitoring is key in assuring public health. However, the necessary equipment to accurately measure the criteria pollutants is expensive. Since the countries with more serious problems of air pollution are the less wealthy, this study proposes an affordable method based on machine learning to estimate the concentration of PM2.5. The capital city of Ecuador is used as case study. Several regression models are built from features of different levels of affordability. The first result shows that cheap data collection based on web traffic monitoring enables us to create a model that fairly correlates traffic density with air pollution. Building multiple models according to the hourly occurrence of the pollution peaks seems to increase the accuracy of the estimation, especially in the morning hours. The second result shows that adding meteorological factors allows for a significant improvement of the prediction of PM2.5 concentrations. Nevertheless, the last finding demonstrates that the best predictive model should be based on a hybrid source of data that includes trace gases. Since the sensors to monitor such gases are costly, the last part of the chapter gives some recommendations to get an accurate prediction from models that consider no more than two trace gases.

Part of the book: Machine Learning

By Rasa Zalakeviciute, Adrian Buenaño, David Sannino and Yves Rybarczyk

Air pollution represents one of the greatest risks to human health, with most of the world’s cities exceeding World Health Organization’s recommendations for air quality. In developing countries, a major share of air pollution comes from traffic, consequently, creating air pollution hot spots inside urban street networks. While the world needs to switch to more active and sustainable ways of commuting in order to reduce traffic emissions and help improve degrading cardiopulmonary health due to increasingly sedentary habits, studies point to the negative effects of physical activity near traffic emissions. Common approaches of urban cycling infrastructure planning rely on space availability and route needs, omitting the most vital aspect—air quality. This study, therefore, combines the worldwide need for active commute and health benefits of the cyclists. Our goal was to produce urban pollution map through the geoprocessing of Google Traffic data, validated through the correlation of street level PM2.5 (particulate matter <2.5 μm) concentrations and traffic intensity in a selected district of Quito, Ecuador. The multidisciplinary approach presented in this study can be used by city planners all over the world to help identify the cycling network based on air quality conditions and, consequently, promoting active travel.

Part of the book: Air Pollution

By Yves Rybarczyk

Part of the book: Assistive and Rehabilitation Engineering