Smart City Gnosys
Smart city article details
| Title | Air Quality Sensor Networks For Evidence-Based Policy Making: Best Practices For Actionable Insights |
|---|---|
| ID_Doc | 7179 |
| Authors | Hofman J.; Peters J.; Stroobants C.; Elst E.; Baeyens B.; Van Laer J.; Spruyt M.; Van Essche W.; Delbare E.; Roels B.; Cochez A.; Gillijns E.; Van Poppel M. |
| Year | 2022 |
| Published | Atmosphere, 13, 6 |
| DOI | http://dx.doi.org/10.3390/atmos13060944 |
| Abstract | (1) Background: This work evaluated the usability of commercial “low-cost” air quality sensor systems to substantiate evidence-based policy making. (2) Methods: Two commercially available sensor systems (Airly, Kunak) were benchmarked at a regulatory air quality monitoring station (AQMS) and subsequently deployed in Kampenhout and Sint-Niklaas (Belgium) to address real-world policy concerns: (a) what is the pollution contribution from road traffic near a school and at a central city square and (b) do local traffic interventions result in quantifiable air quality impacts? (3) Results: The considered sensor systems performed well in terms of data capture, correlation and intra-sensor uncertainty. Their accuracy was improved via local re-calibration, up to data quality levels for indicative measurements as set in the Air Quality Directive (Uexp < 50% for PM and <25% for NO2). A methodological setup was proposed using local background and source locations, al-lowing for quantification of the (3.1) maximum potential impact of local policy interventions and (3.2) air quality impacts from different traffic interventions with local contribution reductions of up to 89% for NO2 and 60% for NO throughout the considered 3 month monitoring period; (4) Conclu-sions: Our results indicate that commercial air quality sensor systems are able to accurately quantify air quality impacts from (even short-lived) local traffic measures and contribute to evidence-based policy making under the condition of a proper methodological setup (background normalization) and data quality (recurrent calibration) procedure. The applied methodology and learnings were distilled in a blueprint for air quality sensor networks for replication actions in other cities. © 2022 by the authors. Licensee MDPI, Basel, Switzerland. |
| Author Keywords | air quality; calibration; policy measures; sensors; smart city; traffic; urban |
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