Multispectral Sensor for Chemical Composition Analysis of Ultrafine Aerosols in Air Quality Assessment

用于空气质量评估中超细气溶胶化学成分分析的多光谱传感器

• 批准号: 10546675
• 负责人: ALEXANDER V MAMISHEV
• 金额: $ 25.96万
• 依托单位: SPECTREE INC.
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-13 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10546675
• 关键词: Address; Aerosols; Affect; Air; Air Pollution; Automation; Awareness; Biochemical Reaction; COVID-19; Carbon; Cells; Chemicals; Chemistry; Childhood; Clinical; Clinical Research; Collaborations; Collection; Communicable Diseases; Complex; Cost Analysis; Cost Savings; Data; Deposition; Development; Diesel Exhaust; Electrostatics; Engineering; Environment; Environmental Pollutants; Environmental and Occupational Exposure; Evaluation; Evidence based intervention; Exposure to; Health; In Situ; Individual; Inhalation; Institutional Review Boards; Intervention; Laboratories; Libraries; Link; Machine Learning; Measurement; Medical; Methods; Miniaturization; Monitor; Optics; Outcome; Participant; Particle Size; Particulate Matter; Performance; Phase; Preparation; Research; Research Design; Resolution; Respiratory System; Sampling; Signal Transduction; Solid; Solubility; Solvents; Source; Specificity; Spectrum Analysis; Techniques; Technology; Testing; Time; Tissues; Tobacco smoke; Ultrafine; absorption; air pollution control; base; commercialization; cost; cost effective; design; detection platform; environmental agent; epidemiology study; implementation facilitation; improved; insight; instrumentation; machine learning algorithm; miniaturize; monitoring device; mortality; nanomaterials; novel; particle; particle counter; personal exposure monitor; pollutant; public health research; pulmonary function; relating to nervous system; sensor; solvent extraction; tool; ultrafine particle; uptake; wood smoke

Project Summary We propose developing and validating a novel platform technology that combines the collection and chemical analysis of ultrafine particles using an in-situ multispectral technique. The sample, collected directly onto the analysis substrate, is analyzed via excitation-emission matrix (EEM) spectroscopy. This approach will be validated against laboratory combustion-generated aerosols, such as diesel exhaust, wood smoke, tobacco smoke, and against a mixture of environmental pollutants. Within the respiratory tract, particle size determines the region of deposition and tissue uptake; the chemistry of the particle also affects solubility and determines the potential for biochemical reaction with tissues and cells. There is a growing awareness that exposure scenarios are very complex, consisting of time-varying concentrations and chemical composition over a broad range of particle sizes. Long-term exposure to air pollution has also been linked to increased mortality rates for infectious diseases, including COVID-19. The proposed research addresses the need for improved personal exposure assessment and characterization of ultrafine particles in the environment. Low-cost, miniaturized exposure monitoring 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 facilitate the implementation of optimal air pollution mitigation strategies. The anticipated outcome of this project is the development of a miniaturized spectroscopic sensor that provides an analysis of the chemical composition of combustion-generated ultrafine particles, which both reflects the particle sources and determines their toxic potential. The machine-learning algorithms will enable the deconvolution of the complex spectra and identification of the PM source from the EEM analysis. The broader applications of the technology are environmental and regulatory monitoring, personal exposure assessment for the consumer market, and epidemiological studies.

项目总结