Abstract
In this letter, we present the development and evaluation of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Urban Wellbeing</i> mobile application that employs real-world, momentary assessment of the environment and its link to well-being using multimodel sensor data and self-report well-being. Several industry standard environmental sensors comprised of particulate matter, ozone and nitrogen dioxide, nitric oxide, and nitrogen oxides as nitrogen dioxide are analyzed each hour with the average of them combined and calculated as the air quality index. By using mobile technology and on-board sensors, we simultaneously collected for the first time live feed of data, such as the environment type, exact location, image of the environment, and level of noise, and obtained perceived mental well-being, fused at the point of data collection. Through an extensive assessment approach on real-world data, we are able to demonstrate the link between busy, polluted, and green spaces and its impact on well-being. The results also indicate that in environments whereby air quality is poor and noise is very loud, typically, participants experience a negative well-being.
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