Eunice Y Lee1, Michael Jerrett2, Zev Ross3, Patricia F Coogan4, Edmund Y W Seto2. 1. Division of Environmental Health Science, School of Public Health, University of California, 50 University Hall, Berkeley, CA 94720, USA. Electronic address: izeunice@gmail.com. 2. Division of Environmental Health Science, School of Public Health, University of California, 50 University Hall, Berkeley, CA 94720, USA. 3. ZevRoss Spatial Analysis, 120 North Aurora Street, Suite 3A, Ithaca, NY, USA. 4. Slone Epidemiology Center at Boston University, 1010 Commonwealth Avenue, Boston, MA 02215, USA.
Abstract
BACKGROUND: Traffic-related noise is a growing public health concern in developing and developed countries due to increasing vehicle traffic. Epidemiological studies have reported associations between noise exposure and high blood pressure, increased risk of hypertension and heart disease, and stress induced by sleep disturbance and annoyance. These findings motivate the need for regular noise assessments within urban areas. This paper assesses the relationships between traffic and noise in three US cities. METHODS: Noise measurements were conducted in downtown areas in three cities in the United States: Atlanta, Los Angeles, and New York City. For each city, we measured ambient noise levels, and assessed their correlation with simultaneously measured vehicle counts, and with traffic data provided by local Metropolitan Planning Organizations (MPO). Additionally, measured noise levels were compared to noise levels predicted by the Federal Highway Administration's Traffic Noise Model using (1) simultaneously measured traffic counts or (2) MPO traffic data sources as model input. RESULTS: We found substantial variations in traffic and noise within and between cities. Total number of vehicle counts explained a substantial amount of variation in measured ambient noise in Atlanta (78%), Los Angeles (58%), and New York City (62%). Modeled noise levels were moderately correlated with measured noise levels when observed traffic counts were used as model input. Weaker correlations were found when MPO traffic data was used as model input. CONCLUSIONS: Ambient noise levels measured in all three cities were correlated with traffic data, highlighting the importance of traffic planning in mitigating noise-related health effects. Model performance was sensitive to the traffic data used as input. Future noise studies that use modeled noise estimates should evaluate traffic data quality and should ideally include other factors, such as local roadway, building, and meteorological characteristics.
BACKGROUND: Traffic-related noise is a growing public health concern in developing and developed countries due to increasing vehicle traffic. Epidemiological studies have reported associations between noise exposure and high blood pressure, increased risk of hypertension and heart disease, and stress induced by sleep disturbance and annoyance. These findings motivate the need for regular noise assessments within urban areas. This paper assesses the relationships between traffic and noise in three US cities. METHODS: Noise measurements were conducted in downtown areas in three cities in the United States: Atlanta, Los Angeles, and New York City. For each city, we measured ambient noise levels, and assessed their correlation with simultaneously measured vehicle counts, and with traffic data provided by local Metropolitan Planning Organizations (MPO). Additionally, measured noise levels were compared to noise levels predicted by the Federal Highway Administration's Traffic Noise Model using (1) simultaneously measured traffic counts or (2) MPO traffic data sources as model input. RESULTS: We found substantial variations in traffic and noise within and between cities. Total number of vehicle counts explained a substantial amount of variation in measured ambient noise in Atlanta (78%), Los Angeles (58%), and New York City (62%). Modeled noise levels were moderately correlated with measured noise levels when observed traffic counts were used as model input. Weaker correlations were found when MPO traffic data was used as model input. CONCLUSIONS: Ambient noise levels measured in all three cities were correlated with traffic data, highlighting the importance of traffic planning in mitigating noise-related health effects. Model performance was sensitive to the traffic data used as input. Future noise studies that use modeled noise estimates should evaluate traffic data quality and should ideally include other factors, such as local roadway, building, and meteorological characteristics.
Authors: Jeong C Seong; Tae H Park; Joon H Ko; Seo I Chang; Minho Kim; James B Holt; Mohammed R Mehdi Journal: Environ Int Date: 2011-06-24 Impact factor: 9.621
Authors: Erica D Walker; Jaime E Hart; Petros Koutrakis; Jennifer M Cavallari; Trang VoPham; Marcos Luna; Francine Laden Journal: Environ Res Date: 2017-09-18 Impact factor: 6.498
Authors: Kipruto Kirwa; Melissa N Eliot; Yi Wang; Marc A Adams; Cindy G Morgan; Jacqueline Kerr; Gregory J Norman; Charles B Eaton; Matthew A Allison; Gregory A Wellenius Journal: J Am Heart Assoc Date: 2014-10-01 Impact factor: 5.501
Authors: Martina S Ragettli; Sophie Goudreau; Céline Plante; Stéphane Perron; Michel Fournier; Audrey Smargiassi Journal: Int J Environ Res Public Health Date: 2015-12-29 Impact factor: 3.390
Authors: Lia Seguí; Adina Iftimi; Álvaro Briz-Redón; Lucía Martínez-Garay; Francisco Montes Journal: Int J Environ Res Public Health Date: 2019-08-07 Impact factor: 3.390
Authors: Stéphane Perron; Céline Plante; Martina S Ragettli; David J Kaiser; Sophie Goudreau; Audrey Smargiassi Journal: Int J Environ Res Public Health Date: 2016-08-11 Impact factor: 3.390
Authors: Saeha Shin; Li Bai; Tor H Oiamo; Richard T Burnett; Scott Weichenthal; Michael Jerrett; Jeffrey C Kwong; Mark S Goldberg; Ray Copes; Alexander Kopp; Hong Chen Journal: J Am Heart Assoc Date: 2020-03-09 Impact factor: 5.501