SBIR-STTR Award

We propose developing and validating a novel platform technology that combines the collection andchemical analysis of ultrafine particles using an in-situ multispectral technique. The sample, collected directlyonto the analysis substrate, is analyzed via excitation-emission matrix (EEM) spectroscopy. This approach willbe validated against laboratory combustion-generated aerosols, such as diesel exhaust, wood smoke, tobaccosmoke, and against a mixture of environmental pollutants. Within the respiratory tract, particle size determinesthe region of deposition and tissue uptake; the chemistry of the particle also affects solubility and determines thepotential for biochemical reaction with tissues and cells. There is a growing awareness that exposure scenariosare very complex, consisting of time-varying concentrations and chemical composition over a broad range ofparticle sizes. Long-term exposure to air pollution has also been linked to increased mortality rates for infectiousdiseases, including COVID-19. The proposed research addresses the need for improved personal exposureassessment and characterization of ultrafine particles in the environment. Low-cost, miniaturized exposuremonitoring devices can shed insight into the relationships between exposure to pollutants and health impact.Source apportioned measurements of PM concentration with high temporal and spatial resolution can facilitatethe implementation of optimal air pollution mitigation strategies. The anticipated outcome of this project is thedevelopment of a miniaturized spectroscopic sensor that provides an analysis of the chemical composition ofcombustion-generated ultrafine particles, which both reflects the particle sources and determines their toxicpotential. The machine-learning algorithms will enable the deconvolution of the complex spectra andidentification of the PM source from the EEM analysis. The broader applications of the technology areenvironmental and regulatory monitoring, personal exposure assessment for the consumer market, andepidemiological studies.

Public Health Relevance Statement:
Project Narrative Exposure to complex environmental agents, such as ultrafine particulate matter derived from combustion sources and engineered nanomaterials, is not well understood. The toxic potential of inhaled particles is dependent on their size and chemical composition, and current solutions for personal exposure monitoring are expensive. This proposed effort addresses the need for a better individual exposure assessment, specifically, quantification of the health impact of ultrafine particles, and will facilitate optimal selection and implementation of air pollution control strategies.

Project Terms:

We propose developing and validating a novel platform technology that combines the collection andchemical analysis of ultrafine particles using an in-situ multispectral technique. The sample, collected directlyonto the analysis substrate, is analyzed via excitation-emission matrix (EEM) spectroscopy. This approach willbe validated against laboratory combustion-generated aerosols, such as diesel exhaust, wood smoke, tobaccosmoke, and against a mixture of environmental pollutants. Within the respiratory tract, particle size determinesthe region of deposition and tissue uptake; the chemistry of the particle also affects solubility and determines thepotential for biochemical reaction with tissues and cells. There is a growing awareness that exposure scenariosare very complex, consisting of time-varying concentrations and chemical composition over a broad range ofparticle sizes. Long-term exposure to air pollution has also been linked to increased mortality rates for infectiousdiseases, including COVID-19. The proposed research addresses the need for improved personal exposureassessment and characterization of ultrafine particles in the environment. Low-cost, miniaturized exposuremonitoring devices can shed insight into the relationships between exposure to pollutants and health impact.Source apportioned measurements of PM concentration with high temporal and spatial resolution can facilitatethe implementation of optimal air pollution mitigation strategies. The anticipated outcome of this project is thedevelopment of a miniaturized spectroscopic sensor that provides an analysis of the chemical composition ofcombustion-generated ultrafine particles, which both reflects the particle sources and determines their toxicpotential. The machine-learning algorithms will enable the deconvolution of the complex spectra andidentification of the PM source from the EEM analysis. The broader applications of the technology areenvironmental and regulatory monitoring, personal exposure assessment for the consumer market, andepidemiological studies.

Public Health Relevance Statement:
Project Narrative Exposure to complex environmental agents, such as ultrafine particulate matter derived from combustion sources and engineered nanomaterials, is not well understood. The toxic potential of inhaled particles is dependent on their size and chemical composition, and current solutions for personal exposure monitoring are expensive. This proposed effort addresses the need for a better individual exposure assessment, specifically, quantification of the health impact of ultrafine particles, and will facilitate optimal selection and implementation of air pollution control strategies.

Project Terms: