Air Quality in Cities: What Do Sparse Sensor Networks Miss?
- 1Department of Civil and Environmental Engineering, Northeastern University, Boston, Massachusetts02115, United States.
- 2Department of Marine and Environmental Sciences, Northeastern University, Boston, Massachusetts02115, United States.
Abstract
Stationary air quality networks are increasingly being used to refine urban air pollution exposure assessments and inform local government decision-making, yet to date, a comprehensive assessment of the extent to which such measurements provide sufficient spatial resolution to yield meaningful insights in these contexts is lacking. In this study, the spatial representativeness of sparse air quality monitoring measurements was characterized by pairing measurements at a fixed station with simultaneous mobile measurements of PM2.5 at a dense number of surrounding locations within a 250 m radius. Data collection was conducted at three contrasting areas across the Boston Metro area (MA, USA) during two seasons (Spring and Summer 2025). Under typical conditions, the mean difference between stationary and mobile measurements across all sites and seasons was 2.0 μg/m3 (median: 1.5 μg/m3, IQR: 0.6-2.8 μg/m3); however, certain dynamic events created differences exceeding 300% in relative terms and 800 μg/m3 in difference terms. In 10.4% of cases, with this proportion reaching as high as 21.8% at one site during spring, measurements were sufficiently different to result in different AQI classifications, indicating that local-scale concentration differences can translate into different public-facing air quality messages. Euclidean distance alone could not explain the differences. Higher relative humidity was associated with larger Center-Mobile discrepancies (r = -0.84), though this association should be interpreted cautiously given the limited number of sampling days. Two observed extreme events (a building smoke plume and a nearby idling truck) produced sharp, localized pollution spikes, providing an opportunity to quantify the extent to which short-lived sources and street-canyon effects can drive steep gradients at small spatial scales. This evidence suggests that while sparse sensor networks may be sufficient to characterize short-term air pollution exposures in relatively homogeneous settings, such an approach may not be sufficient in complex urban environments.
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BibTeX
@article{song2026quality,
title = {{Air Quality in Cities: What Do Sparse Sensor Networks Miss?}},
author = {Ruifeng Song and Nail Bashan and Qi R Wang and Amy V Mueller},
journal = {Environmental science \& technology},
year = {2026},
month = sep,
doi = {10.1021/acs.est.6c03923},
url = {https://europepmc.org/article/MED/42916420},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Air Quality in Cities: What Do Sparse Sensor Networks Miss?
AU - Ruifeng Song
AU - Nail Bashan
AU - Qi R Wang
AU - Amy V Mueller
PY - 2026
DA - 2026/09/01
JO - Environmental science & technology
DO - 10.1021/acs.est.6c03923
UR - https://europepmc.org/article/MED/42916420
AB - Stationary air quality networks are increasingly being used to refine urban air pollution exposure assessments and inform local government decision-making, yet to date, a comprehensive assessment of the extent to which such measurements provide sufficient spatial resolution to yield meaningful insights in these contexts is lacking. In this study, the spatial representativeness of sparse air quality monitoring measurements was characterized by pairing measurements at a fixed station with simultaneous mobile measurements of PM2.5 at a dense number of surrounding locations within a 250 m radius. Data collection was conducted at three contrasting areas across the Boston Metro area (MA, USA) during two seasons (Spring and Summer 2025). Under typical conditions, the mean difference between stationary and mobile measurements across all sites and seasons was 2.0 μg/m3 (median: 1.5 μg/m3, IQR: 0.6-2.8 μg/m3); however, certain dynamic events created differences exceeding 300% in relative terms and 800 μg/m3 in difference terms. In 10.4% of cases, with this proportion reaching as high as 21.8% at one site during spring, measurements were sufficiently different to result in different AQI classifications, indicating that local-scale concentration differences can translate into different public-facing air quality messages. Euclidean distance alone could not explain the differences. Higher relative humidity was associated with larger Center-Mobile discrepancies (r = -0.84), though this association should be interpreted cautiously given the limited number of sampling days. Two observed extreme events (a building smoke plume and a nearby idling truck) produced sharp, localized pollution spikes, providing an opportunity to quantify the extent to which short-lived sources and street-canyon effects can drive steep gradients at small spatial scales. This evidence suggests that while sparse sensor networks may be sufficient to characterize short-term air pollution exposures in relatively homogeneous settings, such an approach may not be sufficient in complex urban environments.
SN - 0013-936X
SN - 1520-5851
ER -