Enhancing Personal Air Pollution Exposure Data for Mechanistic Health Research

/ABSTRACT Exposure to fine particulate matter (PM2.5) is a leading global environmental health risk, contributing to millions of premature deaths annually. However, conventional exposure assessments often rely on mass- based metrics that fail to capture critical particle characteristics such as size distribution, which influences respiratory deposition patterns and toxicity. Ultrafine particles (UFPs, <100 nm) are particularly hazardous because they can penetrate deep into the lungs, carry toxic compounds, translocate into the bloodstream then other organs, and initiate systemic oxidative stress and inflammation. This project aims to develop and validate a novel, low-burden method for estimating mean particle diameter using changes in filter pressure drop and gravimetric mass collected by personal PM₂.₅ samplers. We hypothesize that filter pressure drop dynamics, when empirically calibrated, can serve as a proxy for particle size distribution in personal exposure monitoring and that this method will be particularly useful for estimating the proportion of UFPs in a PM2.5 sample. In Specific Aim 1, we will generate a robust dataset by collecting mono- and polydisperse aerosols of known characteristics and measuring filter pressure drop, collected particle mass, and particle size distribution. We will use these data to develop random forest machine learning models that predict mean particle diameter and proportion of UFPs from filter pressure drop and PM mass data collected with personal exposure measurement devices. In Specific Aim 2, we will experimentally validate these models in controlled field conditions using real-world strong PM emissions sources, such as wood smoke and charcoal combustion, which have wide particle size distributions that include UFPs. We will compare predicted particle sizes to those measured with reference- grade instruments. The validated method will enable accurate, size-resolved personal exposure assessments without the cost, complexity, and logistical challenges of traditional instrumentation, thus maximizing the PM exposure information content derived from direct personal exposure measurements. By unlocking additional particle size data from routine monitoring devices, this project will significantly improve the precision of exposure–response relationships in epidemiologic studies. Additionally, the particle size estimation method derived here can be applied retrospectively to data already collected with these devices, providing researchers with additional information with which to investigate exposure–health relationships. The approach directly supports the National Institute of Health’s mission to understand environmental determinants of health and develop tools that inform targeted interventions. Long term, this innovation has the potential to transform public health efforts by improving source attribution, refining risk assessments, and guiding policy to reduce disease burden linked to PM exposure. Project Number: 1R03ES038687-01 | Fiscal Year: 2026 | NIH Institute/Center: National Institute of Environmental Health Sciences (NIEHS) | Principal Investigator: Ryan Chartier | Institution: RESEARCH TRIANGLE INSTITUTE, RESEARCH TRIANGLE PARK, NC | Award Amount: $221,941 | Activity Code: R03 | Study Section: Analytics and Statistics for Population Research Panel B Study Section[ASPB] View on NIH RePORTER: https://reporter.nih.gov/project-details/11350454