Regression Models to Predict Air Pollution from Affordable Data Collections

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

Bioinspired Implementation and Assessment of a Remote-Controlled Robot

This research was funded by the Universidad de Las Americas, Direccion General de Investigacion.Daily activities are characterized by an increasing interaction with smart machines that present a certain level of autonomy. However, the intelligence of such electronic devices is not always transparent for the end user. This study is aimed at assessing the quality of the remote control of a mobile robot whether the artefact exhibits a human-like behavior or not. The bioinspired behavior implemented in the robot is the well-described two-thirds power law. The performance of participants who teleoperate the semiautonomous vehicle implementing the biological law is compared to a manual and nonbiological mode of control. The results show that the time required to complete the path and the number of collisions with obstacles are significantly lower in the biological condition than in the two other conditions. Also, the highest percentage of occurrences of curvilinear or smooth trajectories are obtained when the steering is assisted by an integration of the power law in the robot's way of working. This advanced analysis of the performance based on the naturalness of the movement kinematics provides a refined evaluation of the quality of the Human-Machine Interaction (HMI). This finding is consistent with the hypothesis of a relationship between the power law and jerk minimization. In addition, the outcome of this study supports the theory of a CNS origin of the power law. The discussion addresses the implications of the anthropocentric approach to enhance the HMI.publishersversionpublishe

Contrasted effects of relative humidity and precipitation on urban PM2.5 pollution in high elevation urban areas

Levels of urban pollution can be influenced largely by meteorological conditions and the topography of the area. The impact of the relative humidity (RH) on the daily average PM2.5 concentrations was studied at several sites in a mid-size South American city at a high elevation over the period of nine years. In this work, we show that there is a positive correlation between daily average urban PM2.5 concentrations and the RH in traffic-busy central areas, and a negative correlation in the outskirts of the city inmore industrial areas. While in the traffic sites strong events of precipitation (≥9 mm) played a major role in PM2.5 pollution removal, in the city outskirts, the PM2.5 concentrations decreased with increasing RH independently of rain accumulation. Increasing PM2.5 concentrations are to be expected in any highly motorized city where there is high RH and a lack of strong precipitation, especially in rapidly growing and developing countries with high motorization due to poor fuel quality. Finally, two models, based on a logistic regression algorithm, are proposed to describe the effect of rain and RH on PM2.5, when the source of pollution is traffic-based vs. industry-based.publishersversionpublishe

Urban Air Pollution Mapping and Traffic Intensity: Active Transport Application

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

A Systematic Review of Usability and Accessibility in Tele-Rehabilitation Systems

The appropriate development of tele-rehabilitation platforms requires the involvement and iterative assessments of potential users and experts in usability. Usability consists of measuring the degree to which an interactive system can be used by specified final users to achieve quantified objectives with effectiveness, efficiency, and satisfaction in a quantified context of use. Usability studies need to be complemented by an accessibility assessment. Accessibility indicates how easy it is for a person to access any content, regardless of their physical, educational, social, psychological, or cultural conditions. This chapter intends to conduct a systematic review of the literature on usability and accessibility in tele-rehabilitation platforms carried out through the PRISMA method. To do so, we searched in ACM, IEEE Xplore, Google Scholar, and Scopus databases for the most relevant papers of the last decade. The main result of the usability shows that the user experience predominates over the heuristic studies, and the usability questionnaire most used in user experience is the SUS. The main result of the accessibility reveals that the topic is only marginally studied. In addition, it is observed that Web applications do not apply the physical and cognitive accessibility standards defined by the WCAG 2.1

Modeling PM 2.5

Outdoor air pollution costs millions of premature deaths annually, mostly due to anthropogenic fine particulate matter (or PM2.5). Quito, the capital city of Ecuador, is no exception in exceeding the healthy levels of pollution. In addition to the impact of urbanization, motorization, and rapid population growth, particulate pollution is modulated by meteorological factors and geophysical characteristics, which complicate the implementation of the most advanced models of weather forecast. Thus, this paper proposes a machine learning approach based on six years of meteorological and pollution data analyses to predict the concentrations of PM2.5 from wind (speed and direction) and precipitation levels. The results of the classification model show a high reliability in the classification of low (25 µg/m3) and low (<10 µg/m3) versus moderate (10–25 µg/m3) concentrations of PM2.5. A regression analysis suggests a better prediction of PM2.5 when the climatic conditions are getting more extreme (strong winds or high levels of precipitation). The high correlation between estimated and real data for a time series analysis during the wet season confirms this finding. The study demonstrates that the use of statistical models based on machine learning is relevant to predict PM2.5 concentrations from meteorological data