Greenspace, land use, and PM₂.₅ across pandemic phases: Insights from a community scientist-led mobile sensing campaign.
Regulatory stationary monitoring networks record regional records for PM₂.₅, whereas local mobile monitoring can capture variability in mixed-use municipalities during a targeted sampling campaign. A community scientist-led mobile air quality monitoring campaign was undertaken using low-cost air sensors to measure fine particulate matter (PM₂.₅) across Norwood, Ohio, for two summer periods spanning the initial COVID-19 pandemic phase (2020) and the recovery/reopening phase (2021). A meteorology-adjusted difference-in-differences framework was used to quantify year- and time-of-day-specific shifts in PM₂.₅ levels. The results indicate that higher PM₂.₅ concentrations in 2021 were not fully explained by meteorological factors. To identify local spatial trends, land use regression models were developed at the street level using linear regression. Results showed that in the 2021 afternoon model, PM₂.₅ was higher within 50 m of office and manufacturing areas, while the Normalized Difference Vegetation Index (NDVI) within 100 m was inversely associated with PM₂.₅ in this campaign. Cross-validated R2 ranged from 0.17-0.25 in 2020 to 0.55-0.61 in 2021, with the strongest performance in the 2021 afternoon model. In addition, health survey scores showed that physical functioning and vitality varied across community wards. These scores provided neighborhood-level context for community prioritization, where air monitoring concerns may overlap with broader health-related community needs. This study offers a transferable community science monitoring workflow for neighborhoods with limited regulatory air quality coverage by combining sampling and quality control protocols, low-cost sensor-based meteorological adjustment, and regression modeling.