Air pollution is a contributor to approximately one in every nine deaths annually. To counteract health issues resulting from air pollution, air quality monitoring is being carried out extensively in urban environments. Currently, however, city air quality monitoring stations are expensive to maintain, resulting in sparse coverage. In this paper, we introduce the design and development of the MegaSense Cyber-Physical System (CPS) for spatially distributed IoT-based monitoring of urban air quality. MegaSense is able to produce aggregated, privacyaware maps and history graphs of collected pollution data. It provides a feedback loop in the form of personal outdoor and indoor air pollution exposure information, allowing citizens to take measures to avoid future exposure. We present a batterypowered, portable low-cost air quality sensor design for sampling PM2.5 and air pollutant gases in different micro-environments. We validate the approach with a use case in Helsinki, deploying MegaSense with citizens carrying low-maintenance portable sensors, and using smart phone exposure apps. We demonstrate daily air pollution exposure profiles and the air pollution hotspot profile of a district. Our contributions have applications in policy intervention management mechanisms and design of clean air routing and healthier navigation applications to reduce pollution exposure.
Poor indoor air quality is a significant burden to society that can cause health issues and decrease productivity. According to research, indoor air quality is intrinsically linked with human activity and mobility. Indeed, mobility is directly linked with transfer of small particles (e.g. PM 2.5 ) and extent of activity affects production of CO 2 . Currently, however, estimation of indoor quality is difficult, requiring deployment of highly specialized sensing devices which need to be carefully placed and maintained. In this paper, we contribute by examining the suitability of infrastructure-based motion detectors for indoor air quality estimation. Such sensors are increasingly being deployed into smart environments, e.g., to control lighting and ventilation for energy management purposes. Being able to take advantage of these sensors would thus provide a cost-effective solution for indoor quality monitoring without need for deploying additional sensors. We perform a feasibility study considering measurements collected from a smart office environment having a dense deployment of motion detectors and correlating measurements obtained from motion detectors against air quality values. We consider two main pollutants, PM 2.5 and CO 2 , and demonstrate that there indeed is a connection between extent of movement and PM 2.5 concentration. However, for CO 2 , no relationship can be established, mostly due to difficulties in separating between people passing by and those residing long-term in the environment.
This study looks at how an e-Health System can reduce morbidity (poor health) in unreached communities. The e-Health system combines affordable sensors and Body Area Networking technology with mobile health concepts and is called a Portable Health Clinic. The health clinic is portable because all the medical devices fit inside a briefcase and are carried to unreached communities by a healthcare assistants. Patient morbidity is diagnosed using software stratification algorithm and categorized according to triage color-coding scheme within the briefcase. Morbid patients are connected to remote doctor in a telemedicine call center using the mobile network coverage. Electronic Health Records (EHR) are used for the medical consultancy and e-Prescription is generated. The effectiveness of the portable health clinic system to target morbidity was tested on 8690 patients in rural and urban areas of Bangladesh during September 2012 to January 2013. There were two phases to the experiment: the first phase identified the intensity of morbidity and the second phase reexamined the morbid patients, two months later. The experiment results show a decrease in patients to identify as morbid among those who participated in telemedicine process.
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