Optimizing Low-Cost Sensor Deployment for Improved Indoor-Outdoor Air Quality Monitoring

Music City Center 

Air pollution, specifically Particulate Matter (PM2.5), disproportionately impacts low-income and high-minority communities in Chicago, where limited fine-spatial resolution data exacerbates the problem. Poor outdoor air quality is strongly linked to indoor air pollution, yet gaps remain in understanding this relationship due to inconsistent monitoring. This research addresses these gaps by optimizing sensor placement to enhance the accuracy and coverage of low-cost air quality monitors already established within the sensor network. Using Chicago-specific data, we prioritize matching indoor sensor placements with existing outdoor sensors to ensure consistency across data sources. Additionally, we strategically pair outdoor and indoor sensor locations to optimize spatial diversity. We model the spatial field using a Gaussian Process, incorporating demographic factors and adverse health outcomes. This approach not only improves data quality but also informs future sensor deployments in underserved areas, contributing to more equitable environmental monitoring and a deeper understanding of pollution-related health disparities.

Spatial Optimization

Low-Cost Sensor Network

Gaussian Process

Environment 

Section on Statistics and the Environment