Assessing the usefulness of dense sensor network for PM2.5 monitoring on an academic campus area

Low-cost sensors provide an opportunity to improve the spatial and temporal resolution of air quality measurements. Networks of such devicesmay complement the traditional air quality monitoring and provide someuseful information about pollutants and their impact on health. This paper describes the network of 20 nodes for ambient PM2.5 monitoring on a campus area of Wroclaw University of Science and Technology (Wroclaw, Poland). Sensor nodeswere equippedwith optical sensors PMS A003 (Plantower), which showed high reproducibility between units. The distribution of the sensor nodes was characterised by both high density (14 devices on the main campus area) andwide spread across the city (6 devices on peripheral campuses). During themeasurement campaign, signals fromsensor nodes were consistent with results fromregulatorymonitoring stations and sensor deviceswere capable of indicating elevated levels of PM2.5 concentrations. A great advantage of this systemwas the ability to provide up-to-date air quality information to the public. Furthermore, air quality messaging was sitespecific because of the observed differences in PM2.5 concentrations. Data analysis was aimed at assessing variability between locations using Kendall's t metric and assessing the statistical significance of the differences in measurement results from neighbouring sensor nodes using the Kolmogorov-Smirnov test. The analysis showed high importance of the nodes in the middle of the main campus and variations of signals from nodes on the peripheries. Differences in signals from sensors located in close proximity to each other were in some cases significant, but only for short-term averaged data. Nevertheless, highly visible variation in PM2.5 signals was observed in the case of nodes arranged vertically on two buildings. PM2.5 concentrationswere even 2-4 times greater near the top parts of the buildings than near the ground. The effect of stratification of PM2.5 levelswas observed under conditions of temperature inversion. (C) 2020 Elsevier B.V. All rights reserved.