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)
This study aimed to examine the validity of a mobile air quality sensor fleet in improving pollution exposure assessments in urban areas. The scope of this study involved experimental setup (sensor validation and calibration), evaluation of spatiotemporal data coverage, and analysis of the representativity of the collected mobile data. The results showed that indicative sensor data quality can be achieved after NO2 co-location calibration, although particulate matter exhibited unsatisfactory performance. An extensive mobile air quality dataset was collected in Antwerp city between February and September 2021, covering 945 km of road by a total of ∼7.9 million data points, yielding an average segment coverage of 1,050 measurements per street segment (median = 62). The collected mobile data were 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 for covering a wide spatial area and collecting many repeated runs (∼200 measurements/segment/month). Monthly maps showed recurring pollution gradients with hotspot locations both at the 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 (five air quality monitoring stations) properly reflected the observed NO2 exposure range (temporal variability), which was documented by the sensor fleet in Antwerp. The spatial exposure variability was improved significantly by the sensor fleet with 59% of the total street length covered after 1 month of mobile deployment (February–March). We required ∼45 repeated passages (31 after post-processing) to derive representative long-term NO2 exposure data from this opportunistic dataset. Our findings suggested that opportunistic data collection using sensors on service fleet vehicles is a valid approach for pollution exposure assessments, through proper validation and calibration strategy. Temporary deployment of mobile sensors was a valuable approach for cities with a less extensive (or lack) air quality monitoring network or those who want a more fine-grained air quality mapping.