In recent years, miniaturized particulate matter (PM) sensors have been studied intensively as an alternative device for air quality measurement due to their price advantage, moderate accuracy, and portable size. The accuracy of these sensors has been studied by calibration against conventional laboratory instruments. Such sensors have been connected in a network for spatiotemporal air quality measurements or used as personal monitors for exposure estimation. Another important application is combining low-cost PM sensors with drones or other unmanned vehicles for sampling environments where the setup of a static sensor network may not be viable. In this study, a mobile robot cart with a low-cost PM sensor (AAQRL-ROBOPM) was developed to map spatial PM distributions over time. The robot can be moved either manually via Bluetooth inputs from an Android device, autonomously by following preprogrammed instructions, or with basic artificial intelligence (AI) and an algorithm. PM concentration readings are sent to the Android device for monitoring and storage. The mobile sensor module was tested for both indoor and outdoor environments, and effectively found the locations of the highest PM concentrations. Using such a device has advantages over a sensor network, such as lower overall cost and lesser complexity of setup. This mobile sensor module provides a more cost-efficient and time-efficient method of finding PM hotspots. Once hotspots are located in the sampled environment, static sensors can be placed for the greatest effectiveness in measuring PM concentration over time. Furthermore, the mobile sensor module was manufactured with low-cost components, making it broadly affordable.
|Original language||English (US)|
|Journal||Journal of Environmental Engineering (United States)|
|State||Published - Oct 1 2019|
Bibliographical noteFunding Information:
This work was partially supported by the Lucy and Stanley Lopata Endowment. We also thank MAGEEP (McDonnell Academy Global Energy and Environmental Partnership) for partial support of this work.
© 2019 American Society of Civil Engineers.
Copyright 2019 Elsevier B.V., All rights reserved.
- Autonomous sampling
- Handbuilt robot cart
- Indoor and outdoor mapping
- Low-cost particulate matter sensor
- Spatiotemporal particulate matter (PM) distribution