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.

项目摘要 我们建议开发和验证一种新颖的平台技术，该技术结合了集合和 使用原位多光谱技术对超细颗粒进行化学分析。样品直接收集 在分析基板上，通过激发发射矩阵（EEM）光谱分析。这种方法会 通过实验室燃烧生成的气溶胶进行验证，例如柴油排气，木烟，烟草 烟雾，并与环境污染物的混合物混合在一起。在呼吸道内，粒度决定 沉积和组织吸收区域；粒子的化学也影响了溶解度，并决定了 与组织和细胞生化反应的潜力。越来越意识到暴露情况 非常复杂，包括时变浓度和化学成分 粒度。长期暴露于空气污染也与感染性的死亡率提高有关 疾病，包括Covid-19。拟议的研究解决了改善个人接触的需求 对环境中超细颗粒的评估和表征。低成本，微型暴露 监视设备可以深入了解暴露于污染物与健康影响之间的关系。 源代码分配的PM浓度的分配为高时空和空间分辨率可以促进 实施最佳空气污染策略。该项目的预期结果是 开发微型光谱传感器，该传感器提供了对化学成分的分析 燃烧生成的超细颗粒，都反映粒子来源并确定其有毒 潜在的。机器学习算法将使复杂光谱的反卷积和 从EEM分析中识别PM源。该技术的更广泛应用是 环境和法规监测，消费市场的个人暴露评估以及 流行病学研究。