Assessment and spatial characterization of ground-level ozone exposure using a low-cost sensor network.

Ground-level ozone (O3) remains a pervasive secondary air pollutant of public health concern, particularly in regions with complex emission profiles and limited monitoring infrastructure. This study investigated the hyperlocal spatial variability of ambient O3 in the Greater Springfield area, Massachusetts using a calibrated network of 13 metal oxide semiconductor-based low-cost sensors (LCS). A machine learning-based calibration model was developed using random forest regression trained on a one-year dataset, which demonstrated robust predictive performance for O3 estimation (R2 = 0.82) relying solely on sensor-derived variables. The model was influenced by the local chemical regime, reflecting the VOC-limited environment in the study region. Analysis of the calibrated sensor measurements revealed that 37.7% of the observed O3 variance was attributable to regional-scale photochemical processes, while localized sources, such as industrial activities and urban traffic, accounted for an additional 22.3%. The spatial patterns exhibited coherence and alignment with known emission sources and land use features. This study demonstrates the potential of leveraging LCS networks to capture spatially resolved dynamics of O3. Operation of these networks in resource-limited communities will facilitate equitable access to air quality information by populations with enhanced vulnerability to the associated health impacts.