High-Throughput Automated Air Sample Analysis Platform for Exposomics and Precision Environmental Health Research

Abstract Study of the sum of environmental influences to which each person is exposed (i.e., “exposomics”) can advance our understanding of how susceptibility to, progression of, and severity of different diseases varies from person to person (i.e., “precision environmental health”). Study of the external exposome, in particular—which includes the pollutant levels in air, water, dust, and soil to which a person is exposed—is necessary to understand when, where, and how people are exposed to specific pollutants and, in turn, identify effective public health interventions to reduce those exposures and the associated incidence of disease. Study of the external exposome requires large-scale sampling and analysis of personal exposures to pollution. Recent technological advances have made it easier to sample personal exposure to air pollution at scale; however, our ability to analyze these samples for multiple constituents (e.g., particle mass, black carbon, organic carbon, metals, microplastics) affordably and at scale is still limited. The objective of this project is to enable large-scale characterization of the air exposome by commercializing a high-throughput, automated sample analysis platform (ASAP) that can analyze hundreds of samples of particulate matter air pollution per day for total mass and multiple sub-constituents. This platform will consist of a climate-controlled enclosure containing a six-axis articulating robotic arm, a microbalance (to analyze samples for total mass), an optical transmissometer (to analyze samples for black carbon and other organics), as well as an experimental beta attenuation measurement (BAM) subsystem. The proposed platform is unique for two reasons. First, it includes a robotic arm with a wide range of motion so that samples can be placed into multiple analytical subsystems to characterize a wider range of constituent pollutants than is possible with robotic platforms that are currently commercially available. Second, the BAM subsystem has the potential to analyze samples for total mass with a reduced limit of detection and at a much lower cost than the microbalance—thus addressing two key barriers to large-scale analysis of low-flow-rate personal air samples. The first aim of this project is to advance an academic proof-of-concept ASAP by: (a) integrating the BAM into the automated sample handling and data acquisition workflows as well as (b) developing Python-based control and data acquisition software to reduce electronic hardware and software licensing costs. The second aim is to: (a) validate gravimetric measurements made using the platform through round-robin testing with external laboratories as well as (b) evaluate the limit of detection, precision, and accuracy of particle mass measurements made using the BAM. The third aim is to: (a) roadmap design-for-manufacturing improvements as well as (b) scope future integration of optical microscopy and Raman spectroscopy into the platform for sample quality assurance, analysis of particle morphology, and detection of microplastics.