Spatial analysis of PM2.5 using a concentration similarity index applied to air quality sensor networks

• Published in: Atmospheric measurement techniques 2024-09, Vol.17 (17), p.5129-5146
• Main Authors: Byrne, Rósín, Wenger, John C, Hellebust, Stig
• Format: Article
• Language: English
• Subjects: Air; Air monitoring; Air pollution; Air quality; Air quality measurements; Burning; Calibration; Clustering; Monitoring; Mortality; Networks; Outdoor air quality; Particulate emissions; Particulate matter; Particulate matter emissions; Performance evaluation; Pollutants; Programming languages; Quality assessment; Residential areas; Residential location; Sensors; Similarity; Solid fuels; Spatial analysis; Spatial variations; Temporal variations; Time measurement; Trends; Urban areas; Urban environments; Winter
• ISSN: 1867-1381; 1867-8548
• DOI: 10.5194/amt-17-5129-2024

Air quality sensor (AQS) networks are useful for mapping PM2.5 (particles with a diameter of 2.5 µm or smaller) in urban environments, but quantitative assessment of the observed spatial and temporal variation is currently underdeveloped. This study introduces a new metric – the concentration similarity index (CSI) – to facilitate a quantitative and time-averaged comparison of the concentration–time profiles of PM2.5 measured by each sensor within an air quality sensor network. Following development on a dataset with minimal unexplained variation and robust tests, the CSI function is used to represent an unbiased and fair depiction of the air quality variation within an area covered by a monitoring network. The measurement data is used to derive a CSI value for every combination of sensor pairs in the network, yielding valuable information on spatial variation in PM2.5. This new method is applied to two separate AQS networks, in Dungarvan and in the city of Cork, Ireland. In Dungarvan there was a lower mean CSI value (x‾CSI, Dungarvan=0.61, x‾CSI, Cork=0.71), indicating lower overall similarity between locations in the network. In both networks, the average diurnal plots for each sensor exhibit an evening peak in PM2.5 concentration due to emissions from residential solid-fuel burning; however, there is considerable variation in the size of this peak. Clustering techniques applied to the CSI matrices identify two different location types in each network; locations in central or residential areas that experience more pollution from solid-fuel burning and locations on the edge of the urban areas that experience cleaner air. The difference in mean PM2.5 between these two location types was 6 µgm-3 in Dungarvan and 2 µgm-3 in Cork. Furthermore, the examination of winter and summer months (January and May) indicates that higher PM2.5 levels during periods of increased residential solid-fuel burning act as a major driver for greater differences (lower similarity indices) between locations in both networks, with differences in mean seasonal CSI values exceeding 0.25 and differences in mean seasonal PM2.5 exceeding 7 µgm-3. These findings underscore the importance of including wintertime PM data in analyses, as the differences between locations is enhanced during periods when solid-fuel burning activities are at a peak. Additionally, the CSI method facilitates the assessment of the representativeness of the PM2.5 measured at regulatory air quality monitoring locat