Estimating near-roadway air pollution from multi-frequency noise measurements.

Autor: Fallah-Shorshani M; Department of Population and Public Health Sciences, University of Southern California, 1845 N Soto St, Los Angeles, CA, USA. Electronic address: shorshan@usc.edu., Fruin S; Department of Population and Public Health Sciences, University of Southern California, 1845 N Soto St, Los Angeles, CA, USA. Electronic address: fruin@usc.edu., Yin X; Department of Exposure Epidemiology and Risk Program, Harvard T.H. Chan School of Public Health, 401 Park Dr, Boston, MA, USA. Electronic address: xiaozheyin@hsph.harvard.edu., McConnell R; Department of Population and Public Health Sciences, University of Southern California, 1845 N Soto St, Los Angeles, CA, USA. Electronic address: rmcconne@usc.edu., Franklin M; Department of Population and Public Health Sciences, University of Southern California, 1845 N Soto St, Los Angeles, CA, USA; Department of Statistical Sciences and School of the Environment, University of Toronto, Toronto, Ontario, Canada. Electronic address: meredith.franklin@utoronto.ca.
Jazyk: angličtina
Zdroj: The Science of the total environment [Sci Total Environ] 2024 Sep 20; Vol. 944, pp. 173900. Date of Electronic Publication: 2024 Jun 11.
DOI: 10.1016/j.scitotenv.2024.173900
Abstrakt: Air pollution is a major environmental problem and its monitoring is essential for regulatory purposes, policy making, and protecting public health. However, dense networks of air quality monitoring equipment are prohibitively expensive due to equipment costs, labor requirements, and infrastructure needs. As a result, alternative lower-cost methods that reliably determine air quality levels near potent pollution sources such as freeways are desirable. We present an approach that couples noise frequency measurements with machine learning to estimate near-roadway particulate matter (PM 2.5 ), nitrogen dioxide (NO 2 ), and black carbon (BC) at 1-min temporal resolution. The models were based on data collected by co-located noise and air quality instruments near a busy freeway in Long Beach, California. Model performance was excellent for all three pollutants, e.g., NO 2 predictions yielded Pearson's R = 0.87 with a root mean square error of 7.2 ppb; this error represents about 10 % of total morning rush hour concentrations. Among the best air pollutant predictors were noise frequencies at 40 Hz, 500 Hz, and 800 Hz, and meteorology, particularly wind direction. Overall, our method potentially provides a cost-effective and efficient approach to estimating and/or supplementing near-road air pollutant concentrations in urban areas at high temporal resolution.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
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