Comparative evaluation of the ground reflection algorithm in FHWA Traffic Noise Model (TNM 2.5)

Shu, Ning; Cohn, Louis F.; Harris, Roswell A.; Kim, Teak K.; Li, Wensheng

doi:10.1016/j.apacoust.2006.07.004

Cited by 13 publications

(7 citation statements)

References 8 publications

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“…Noise is typically assessed with acoustic models; in the US, the most recent validated version of the Federal Highway Administration Traffic Noise Model (TNM2.5) 33 , 34 uses roads, traffic volumes, pavement type, and mix of vehicle type to output traffic noise as an average day-night sound level: an average noise decibel level over a day with a 10-dB penalty applied from 10:00 pm to 7:00 am. An increase of 10 dB is perceived as being twice as loud, 35 and a typical urban neighborhood residential area in the US ranges from 55 to 70 db.…”

Section: Methodsmentioning

confidence: 99%

Association of the Built Environment With Childhood Psychosocial Stress

et al. 2020

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IMPORTANCE Emerging research suggests that factors associated with the built environment, including artificial light, air pollution, and noise, may adversely affect children's mental health, while living near green space may reduce stress. Little is known about the combined roles of these factors on children's stress. OBJECTIVE To investigate associations between components of the built environment with personal and home characteristics in a large cohort of children who were assessed for perceived stress. DESIGN, SETTING, AND PARTICIPANTS In this cohort study, a total of 2290 Southern California Children's Health Study participants residing in 8 densely populated urban communities responded to detailed questionnaires. Exposures of artificial light at night (ALAN) derived from satellite observations, near-roadway air pollution (NRP) determined from a dispersion model, noise estimated from the US Traffic Noise Model, and green space from satellite observations of the enhanced vegetation index were linked to each participant's geocoded residence. MAIN OUTCOMES AND MEASURES Children's stress was assessed at ages 13 to 14 years and 15 to 16 years using the 4-item Perceived Stress Scale (PSS-4), scaled from 0 to 16, with higher scores indicating greater perceived stress. Measurements were conducted in 2010 and 2012, and data were analyzed from February 6 to August 24, 2019. Multivariate mixed-effects models were used to examine multiple exposures; modification and mediation analyses were also conducted. RESULTS Among the 2290 children in this study, 1149 were girls (50%); mean (SD) age was 13.5 (0.6) years. Girls had significantly higher perceived stress measured by PSS-4 (mean [SD] score, 5.7 [3.4]) than boys (4.9 [3.2]). With increasing age (from 13.5 [0.6] to 15.3 [0.6] years), the mean PSS-4 score rose from 5.6 (3.3) to 6.0 (3.4) in girls but decreased for boys from 5.0 (3.2) to 4.7 (3.1). Multivariate mixed-effects models examining multiple exposures indicated that exposure to secondhand smoke in the home was associated with a 0.85 (95% CI, 0.46-1.24) increase in the PSS-4 score. Of the factors related to the physical environment, an interquartile range (IQR) increase in ALAN was associated with a 0.57 (95% CI, 0.05-1.09) unit increase in the PSS-4 score together with a 0.16 score increase per IQR increase of near-roadway air pollution (95% CI, 0.02-0.30) and a −0.24 score decrease per IQR increase of the enhanced vegetation index (95% CI, −0.45 to −0.04). Income modified the ALAN effect size estimate; participants in households earning less than $48 000 per year had significantly greater stress per IQR increase in ALAN. Sleep duration partially mediated the associations between stress and both enhanced vegetation index (17%) and ALAN (18%). CONCLUSIONS AND RELEVANCE In this cohort study, children's exposure to smoke at home in addition to residential exposure to ALAN and near-roadway air pollution were associated with increased perceived stress among young adolescent children. These associations appeared to be pa...

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Section: Methodsmentioning

confidence: 99%

Association of the Built Environment With Childhood Psychosocial Stress

et al. 2020

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“…The most recent validated version, TNM 2.5, (Shu et al, 2007) uses roads, hourly traffic volume, speed, pavement type, and type of vehicle (cars, heavy trucks, light trucks, buses and motorcycles). Specific data we used as inputs include average daily traffic volume obtained from Kalibrate (http://www.kalibrate.com/) and road segments information from HERE (https://company.here.com/).…”

Section: Methodsmentioning

confidence: 99%

The role of traffic noise on the association between air pollution and children's lung function

Franklin

Fruin

2017

Environmental Research

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Although it has been shown that traffic-related air pollution adversely affects children’s lung function, few studies have examined the influence of traffic noise on this association, despite both sharing a common source. Exposure estimates of noise (Ldn, dB), and freeway and non-freeway emission concentrations of oxides of nitrogen (NOx, ppb) were spatially assigned to children in Southern California who were tested for forced vital capacity (FVC, n=1345), forced expiratory volume in 1 second, (FEV1, n=1332), and asthma. The associations between traffic-related NOx and these outcomes, with and without adjustment for noise, were examined using mixed effects models. Adjustment for noise strengthened the association between NOx and reduced lung function. A 14.5 mL (95 % Cl −40.0, 11.0 mL) decrease in FVC per interquartile range (13.6 ppb) in freeway NOx was strengthened to a 34.6 mL decrease after including a non-linear function of noise (95% CI −66.3, −2.78 mL). Similarly, a 6.54 mL decrease in FEV1 (95% Cl −28.3, 15.3 mL) was strengthened to a 21.1 mL decrease (95% CI −47.6, 5.51) per interquartile range in freeway NOx. Our results indicate that where possible, noise should be included in epidemiological studies of the association between traffic-related air pollution on lung function. Without taking noise into account, the detrimental effects of traffic-related pollution may be underestimated.

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“…Most traffic noise estimation relies on acoustic-based models such as the U.S. Federal Highway Administration’s Traffic Noise Model version 2.5 (TNM2.5), developed to aid in noise limit compliance of federal highway projects . In Europe, there are several noise models that have been harmonized as part of the “Common Noise Assessment Methods in Europe” (CNOSSOS-EU) among which CoRTN (Calculation of Road Traffic Noise) from England and Richtlinien für den Lärmschutz an Straben) from Germany are the most widely used. , Most acoustic models have been developed and optimized for predicting noise on highways and are usually validated with a limited number of fixed-site noise measurements; however, their accuracy decreases as roadways or traffic volumes become smaller.…”

Section: Introductionmentioning

confidence: 99%

Predicting Fine Spatial Scale Traffic Noise Using Mobile Measurements and Machine Learning

Yin

Fallah-Shorshani

McConnell

et al. 2020

Environ. Sci. Technol.

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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 use, 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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Comparative evaluation of the ground reflection algorithm in FHWA Traffic Noise Model (TNM 2.5)

Cited by 13 publications

References 8 publications

Association of the Built Environment With Childhood Psychosocial Stress

Association of the Built Environment With Childhood Psychosocial Stress

The role of traffic noise on the association between air pollution and children's lung function

Predicting Fine Spatial Scale Traffic Noise Using Mobile Measurements and Machine Learning

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