Journal
ENVIRONMENTAL SCIENCE & TECHNOLOGY
Volume 54, Issue 20, Pages 12860-12869Publisher
AMER CHEMICAL SOC
DOI: 10.1021/acs.est.0c01987
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Funding
- United States Environmental Protection Agency (EPA) [CR-83590201]
- National Institutes of Health [P30ES007048]
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Environmental noise has been associated with a variety of health endpoints including cardiovascular disease, sleep disturbance, depression, and psychosocial stress. Most population noise exposure comes from vehicular traffic, which produces fine-scale spatial variability that is difficult to characterize using traditional fixed-site measurement techniques. To address this challenge, we collected A-weighted, equivalent noise (LAeq in decibels, dB) data on hour-long foot journeys around 16 locations throughout Long Beach, California and trained four machine learning models, linear regression, random forest, extreme gradient boosting, and a neural network, to predict noise with 20 m resolution. Input variables to the models included traffic metrics, road network features, meteorological conditions, and land use type. Among all machine learning models, extreme gradient boosting had the best results in validation tests (leave-one-route-out R-2 = 0.71, root mean square error (RMSE) of 4.54 dB; 5-fold R-2 = 0.96, RMSE of 1.8 dB). Local traffic volume was the most important predictor of noise; road features, land us; and meteorology including humidity, temperature, and wind speed also contributed. We show that a novel, on-foot mobile noise measurement method coupled with machine learning approaches enables highly accurate prediction of small-scale spatial patterns in traffic-related noise over a mixed-use urban area.
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