Epidemiological studies have linked both traffic-related air pollution (TRAP) and noise to adverse health outcomes, including increased blood pressure, myocardial infarction, and respiratory health. The high correlation between these environmental exposures and their measurement challenges have constrained research on how simultaneous exposure to TRAP and traffic noise interact and possibly enhance each other’s effect. The objective of this study was to deploy two novel personal sensors for measuring ultrafine particles (UFP, <100 nm diameter) and noise to concurrently monitor real-time exposures. Personal UFP monitors (PUFP, Enmont, LLC) were paired with NEATVIBEwear™ (Noise Exposure, Activity-Time and Vibration wearable), a personal noise monitoring device developed by the authors (Douglas Leaffer, Steve Doroff). A field-test of PUFP monitors co-deployed with NEATVIBEwear logged UFP, noise and ambient temperature exposure levels at 1-s resolution in an adolescent population in Cincinnati, OH to measure real-time exposures in microenvironments (transit, home, school). Preliminary results show that the concurrent measurement of noise exposures with UFP is feasible in a sample of physically active adolescent participants. Personal measurements of UFP and noise, measured prospectively in future studies, will enable researchers to investigate the independent and/or joint-effects of these health-relevant environmental exposures.

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Transportation-derived particulate matter and chronic noise exposure frequently occur concomitantly in urban areas. Noise is an important confounder to be evaluated in epidemiological studies, yet few public health studies have included both air pollution and noise in health effects models due to difficulty in demonstrating epidemiologic causal mechanisms and confounding factors in noise and air pollution sampling and analytical methodologies. This study will present a framework for the development of a traffic-noise frequency and particulate emissions correlation model based on frequency-domain analysis of vehicle noise measurements, compared with particulate measurements sampled concurrently at two Greater Boston urban neighborhoods under varying meteorological conditions. The research will present new methods for evaluating bi-seasonal measurement of both transportation noise and associated particulate emissions, with emphasis on Ultrafine Particulates (UFP, &lt;100 nm diameter) from diesel exhausts. The goal of the paper is to develop a preliminary model demonstrating correlations between transportation source noise frequencies and UFP. The importance of this research is to establish a methodology to disentangle health-based receptor impacts from both pollutants under varying scenarios where meteorological parameters are not favorable for particulate transport to receptors, yet noise is measurably present.

The goal of this study was to characterize transportation noise by vehicle class in two urban communities, to inform studies of transport noise and ultra-fine particulates. Data were collected from April to September 2016 (150 days) of continuous recording in each urban community using high-resolution microphones. Training data was created for airplanes, trucks/buses, and train events by manual listening and extraction of audio files. Digital signal processing using STFT and Hanning windowing was performed in MATLAB, creating audio spectrograms with varying frequency: log vs linear frequency scales, and 4K vs 20K max frequency. For each of the four spectrogram sets, a neural net model using PyTorch was trained via a compute cluster. Initial results for a multi-class model provide an accuracy of 85%. Comparison between a selection of frequency scales and expanding to longer time periods is ongoing. Validation with airport transport logs and local bus and train schedules will be presented.