Low-cost sensor networks improve neighborhood-scale AQI representation

Abstract

Air quality information is commonly communicated through the air quality index (AQI), which residents often access through the nearest available monitoring site. Motivated by this real-world use pattern, we evaluated how well different monitoring networks represent local AQI across California’s San Joaquin Valley, one of the nation’s most polluted air basins. We used the Global Historical Air Pollution (GHAP) PM2.5 dataset as a spatially continuous reference surface to distinguish point-level concentration agreement from nearest-site AQI representativeness. We found that although regulatory monitors showed the closest overall agreement with GHAP in absolute PM2.5 concentration, their sparse distribution limited their ability to capture spatial variation in AQI. In contrast, the dense low-cost sensor network produced higher match rates with GHAP-derived AQI categories than the regulatory monitor network at daily, weekly, and monthly scales. This contrast was most evident under polluted conditions, when stronger spatial heterogeneity increased the need for finer-scale monitoring coverage. Under such conditions, even uncalibrated low-cost sensors performed more consistently with GHAP in AQI classification than regulatory monitors despite substantial noise and artifacts in the raw data. These findings suggest that dense low-cost sensor networks can improve the spatial relevance of public-facing AQI information and community-scale air quality awareness.