Opportunistic mobile air quality mapping using sensors on postal service vehicles: from point clouds to actionable insights - Hofman, Jelle; Panzica La Manna, Valerio; Ibarrola-Ulzurrun, Edurne; Peters, Jan; Escribano Hierro, Miguel; Van Poppel, Martine (Frontiers in Environmental Health, 2023)
The aim of our study was to test the useability of a mobile air quality sensor fleet for improved exposure assessments in urban areas. It includes experimental setup (sensor validation and calibration), evaluation of spatiotemporal data coverage and representativity analysis of the collected mobile data. Results show that indicative sensor data quality can be achieved after calibration by colocation for NO2 while PM performed poorly for this use case. An extensive mobile air quality dataset was collected in the city of Antwerp between February and September, 2021, covering 945 km of road by a total of ~ca. 7.9 million datapoints, resulting in an average segment coverage of 1050 measurements per street segment (median = 62). The collected mobile data is made available in an open data repository. From the introduced area (%) and street segment (n) coverage, we can conclude that opportunistic data collection using service fleet vehicles (e.g. postal vans) is an efficient approach to cover a wide spatial area and collect many repeated runs (~200 measurements/segment/month). Monthly maps show recurring pollution gradients with hotspot locations both at suspected (e.g. busy traffic arteries) and unexpected locations, with observed increments greatly exceeding the observed inter-sensor uncertainty. The existing air quality monitoring network (5 AQMS) properly reflects the observed NO2 exposure range (temporal variability) documented by the sensor fleet in Antwerp. The spatial exposure variability was improved significantly by the sensor fleet with already 59% of total street length covered after one month of mobile deployment (Feb-March). In order to derive representative long-term NO2 exposure data from this opportunistic dataset, ~45 repeated passages (31 after postprocessing) were required. Our results provide evidence that opportunistic data collection using sensors on service fleet vehicles can be a valid approach for pollution exposure assessments, under the condition of a proper validation and calibration strategy. Temporary deployments of mobile sensors are a valuable approach for cities that have a less extensive (or lack) air quality monitoring network or are interested in more fine-grained air quality mapping of their city.