NIH R01 · 2024
Statistical methods for air-pollution studies using low-cost monitors
Project summary/abstract Air pollution research is increasingly adopting emergent cost-effective technologies to measure pollutant levels at spatial and temporal scales finer than that delivered by the geographically sparse network of regulatory monitors. Low-cost air-pollution monitors, while promising, introduce a series of data features like need for field co-location and calibration to eliminate noise, spatio-temporally correlated massive datasets, and repeated mea- sures on exposures. Current statistical methodology for more traditional air-pollution data collection schemes are not optimized to properly exploit the noisy, high-throughput, and spatio-temporally dependent low-cost data.…
From the public funding record at NIH RePORTER. Describes the funded project, not the reviews below.