Route selection for real-time air quality monitoring to maximize spatiotemporal coverage

Air quality monitoring is essential to assess ambient air quality and its impact on commuters' activities and health. The fixed monitoring stations are cost-intensive, laborious, and provide limited data, while mobile monitoring using portable, low-cost air quality monitoring devices can provide high-resolution data. This study proposes a probabilistic approach to select the transit vehicles from the scheduled fleet, on which portable devices can be mounted to collect the spatiotemporal data points and maximize coverage continuously. General Transit Feed Specification (GTFS) and real-world vehicle data are employed. The proposed framework creates the segments, evaluates the overlaps, and prioritizes the routes based on joint probabilities. The bipolar Sigmoid activation function is used for probability estimation. A minimum number of portable devices installed on selected vehicles can provide maximum spatiotemporal coverage with the least overlapping routes for real-time air quality monitoring in larger cities. One of the major limitations of the framework is that it requires a schedule of transport modes on which portable devices are to be installed. The results and inferences can be beneficial to the relevant authorities for the appropriate installation of portable, low-cost air quality monitoring devices.