Low-Cost Air Quality Stations’ Capability to Integrate Reference Stations in Particulate Matter Dynamics Assessment

• Published in: Atmosphere Vol. 12; no. 8; p. 1065
• Main Authors: Brilli, Lorenzo, Carotenuto, Federico, Andreini, Bianca Patrizia, Cavaliere, Alice, Esposito, Andrea, Gioli, Beniamino, Martelli, Francesca, Stefanelli, Marco, Vagnoli, Carolina, Venturi, Stefania, Zaldei, Alessandro, Gualtieri, Giovanni
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.08.2021
• Subjects: Accuracy; Air; Air pollution; Air quality; Air quality measurements; Airborne particulates; Anthropogenic factors; Calibration; Citizen participation; Classification; Deployment; Dynamics; Environmental protection; Gradients; Heating; high-resolution; Industrial sites; Low cost; low-cost sensor; Observatories; Outdoor air quality; Particulate emissions; Particulate matter; Particulate matter monitoring; PM10; Pollutants; Population; population exposure; Quality assessment; Rural areas; Sensors; Spatial variability; Spatial variations; Stations; Suspended particulate matter; Temporal resolution; Traffic; Urban air; Urban air quality; Italy; Europe
• ISSN: 2073-4433; 2073-4433
• DOI: 10.3390/atmos12081065

Low-cost air quality stations can provide useful data that can offer a complete picture of urban air quality dynamics, especially when integrated with daily measurements from reference air quality stations. However, the success of such deployment depends on the measurement accuracy and the capability of resolving spatial and temporal gradients within a spatial domain. In this work, an ensemble of three low-cost stations named “AirQino” was deployed to monitor particulate matter (PM) concentrations over three different sites in an area affected by poor air quality conditions. Data of PM2.5 and PM10 concentrations were collected for about two years following a protocol based on field calibration and validation with a reference station. Results indicated that: (i) AirQino station measurements were accurate and stable during co-location periods over time (R2 = 0.5–0.83 and RMSE = 6.4–11.2 μg m−3; valid data: 87.7–95.7%), resolving current spatial and temporal gradients; (ii) spatial variability of anthropogenic emissions was mainly due to extensive use of wood for household heating; (iii) the high temporal resolution made it possible to detect time occurrence and strength of PM10 concentration peaks; (iv) the number of episodes above the 1-h threshold of 90 μg m−3 and their persistence were higher under urban and industrial sites compared to the rural area.