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

项目摘要 我们建议开发和验证一种新的平台技术，该技术将集合和 用原位多光谱技术对超细颗粒进行化学分析。样本，直接采集 在分析衬底上，通过激发发射矩阵(EEM)光谱进行分析。这一方法将 对照实验室燃烧产生的气溶胶进行验证，如柴油废气、木烟、烟草 烟雾，并防止环境污染物的混合物。在呼吸道内，颗粒大小决定 沉积和组织摄取的区域；颗粒的化学也影响溶解度，并决定 与组织和细胞发生生化反应的可能性。越来越多的人意识到暴露的情景 非常复杂，由时间变化的浓度和广泛范围内的化学成分组成 颗粒大小。长期暴露在空气污染中也与传染性疾病死亡率的增加有关。 疾病，包括新冠肺炎。拟议中的研究解决了改善个人接触的需要 环境中超细颗粒的评估和表征。低成本、微型化曝光 监测设备可以深入了解污染物暴露与健康影响之间的关系。 具有高时间和空间分辨率的颗粒物浓度源分配测量可以方便地 实施最优大气污染缓解战略。这个项目的预期结果是 一种小型光谱传感器的开发，该传感器可提供对 燃烧产生的超细颗粒，既反映了颗粒物的来源，又确定了它们的毒性 潜力。机器学习算法将使复杂光谱的去卷积和 根据EEM分析确定PM来源。这项技术的更广泛应用是 环境和监管监测，消费市场个人暴露评估，以及 流行病学研究。

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.