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Systematic measurement and machine learning-based profile characterization of community noise in a medium-large city in the United States

Journal of Exposure Science & Environmental Epidemiology (2025)Cite this article

Abstract

Background

Community noise pollution can adversely impact health, yet noise has rarely been systematically measured in United States (U.S.) cities for epidemiological research.

Objective

Collaborating with the Multnomah County Health Department, we developed an exploratory measurement campaign to systematically capture community noise in Portland, Oregon, U.S. to inform environmental health research and practice.

Methods

We identified short-term measurement locations using weighted probability sampling and developed a protocol for deploying Class 1 sound level meters at identified sites to measure sound levels continuously for at least five days. We calculated daytime, nighttime, and daily-average noise metrics including day-night average sound levels (DNL), day-evening-night levels (Lden), intermittency ratios (IR), and 10th- and 90th-percentile noise levels (L90, L10). We evaluated noise metrics by built environment and sociodemographic characteristics at the census tract level and performed machine learning-based cluster analysis to identify locations with similar exposure profiles. Nine additional locations were sampled continuously for one year to assess agreement between short- and long-term noise measurements.

Results

DNL ranged from 49.6 to 86.7 decibels across short-term sites (n = 217). DNL exceeded U.S. Environmental Protection Agency guidelines at 78% of sites, and nighttime noise exceeded World Health Organization guidelines at 90%. Short-term sites in census tracts with higher median income and proportion of white population had lower DNL compared to lower median income and proportion of white population census tracts. Cluster analysis revealed four noise profiles: low LAeq/moderate IR sites usually occurring in residential neighborhoods, high LAeq/moderate IR sites adjacent to major roads, moderate LAeq/high IR sites within 1–2 city blocks of major roads, and high LAeq/low IR and low LAeq/low IR sites near highways or parks, respectively.

impact

  • This study reveals a high prevalence of potentially harmful community noise exposure levels in a medium-large city in the United States, particularly in lower-income and racially diverse neighborhoods. By identifying groupings of sites with similar noise exposure profiles, we establish a foundation for exploring built environment drivers of noise and differential health impacts of multidimensional noise exposures. The measurement protocol and database of noise measurements collected provides tools for researchers and communities (available upon request) to investigate noise exposure patterns, environmental justice concerns, and associated health impacts, with further applications for predictive modeling to estimate individual-level exposures in epidemiologic studies.

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Fig. 1: Land use classifications and sampling weights for short-term measurement sites in Portland, OR.
Fig. 2: Day-night average sound level (DNL), intermittency ratio (IR), daytime sound level (Lday), and nighttime sound level (Lnight) measurements of measurement sites in Portland, OR, 2023.
Fig. 3: Scatterplots of noise metrics at short-term measurement sites, colored by Partitioning Around Medoids (PAM) clustering.
Fig. 4: Spatial distribution of short-term measurement site noise profiles, determined by Partitioning Around Medoids (PAM) clustering analysis.
Fig. 5: Association between day-night average sound level (DNL) and intermittency ratio (IR) by census tract median household income and proportion non-white in Portland, OR.

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Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

The authors gratefully acknowledge the contributions of those who made this study possible, including Max Nonnamaker, Dan Trifone, and Abe Moland from the Multnomah County Health Department, Sophie Son and Michael Barzach from the Volpe National Transportation Systems Center, Bruno Paillard from Convergence Instruments, Summya Khatoon from Boston University, and those who volunteered their homes as measurement sites for the SLM comparisons and long-term measurement sites.

Funding

Funding for this work was supported by Multnomah County Intergovernmental Agreement Contract number HD-IGA-E-15536-2023.

Author information

Authors and Affiliations

  1. School of Nutrition and Public Health, College of Health, Oregon State University, Corvallis, OR, USA

    Carson Mowrer, Andrew Larkin, Perry Hystad & Matthew Bozigar

  2. Oregon Health and Science University-Portland State University School of Public Health, Portland, OR, USA

    Charlotte Roscoe

  3. Department of Environmental Health, Boston University School of Public Health, Boston, MA, USA

    Stephanie T. Grady & Junenette L. Peters

  4. Healthy Homes and Communities Program, Multnomah County Health Department, Portland, OR, USA

    Brendon Haggerty

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  1. Carson Mowrer
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Contributions

Carson Mowrer: Data Curation, Formal Analysis, Investigation, Methodology, Project Administration, Software, Visualization, Writing – Original Draft Preparation. Andrew Larkin: Writing – Review & Editing. Charlotte Roscoe: Writing – Review & Editing. Stephanie T. Grady: Investigation, Writing – Review & Editing. Junenette L. Peters: Supervision, Investigation, Writing – Review & Editing. Brendon Haggerty: Conceptualization, Resources, Writing – Review & Editing. Perry Hystad: Supervision, Writing – Review & Editing. Matthew Bozigar: Conceptualization, Funding Acquisition, Investigation, Methodology, Project Administration, Resources, Supervision, Writing – Review & Editing.

Corresponding authors

Correspondence to Carson Mowrer or Matthew Bozigar.

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The authors declare no competing interests.

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All procedures were performed in compliance with relevant laws and institutional guidelines and have been approved by the Oregon State University Institutional Review Board as not human subjects research (Approval Number HE-2023-497, August 1, 2023).

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Mowrer, C., Larkin, A., Roscoe, C. et al. Systematic measurement and machine learning-based profile characterization of community noise in a medium-large city in the United States. J Expo Sci Environ Epidemiol (2025). https://doi.org/10.1038/s41370-025-00794-y

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