The spatial relationship between traffic-generated air pollution and noise in 2 US cities☆
Introduction
Researchers have reported associations between chronic exposure to traffic and adverse cardiovascular health effects including hypertension, myocardial infarction, stroke, atherosclerosis, heart disease, and mortality. These associations have been attributed to traffic-generated air pollution (Finkelstein et al., 2004; Hoek et al., 2002; Hoffmann et al., 2007, Hoffmann et al., 2006; Maheswaran and Elliott, 2003; Tonne et al., 2007) or road noise (Babisch, 2006; Babisch et al., 2005; Bluhm et al., 2007; de Kluizenaar et al., 2007; Selander et al., 2009; van Kempen et al., 2002). If air pollution and noise are both linked to cardiovascular effects, the fact that traffic is a major shared source suggests the potential for correlated exposures that may lead to confounding in epidemiologic studies (Schwela et al., 2005).
The potential for confounding is increased by the exposure assessment approaches that are commonly used in epidemiologic studies, in which it is not feasible to measure exposure for every participant. As an alternative to measurements, investigations of road noise and health generally use models to estimate noise exposures (Babisch et al., 2005; Beelen et al., 2008; Bluhm et al., 2007; Calixto et al., 2003; de Kluizenaar et al., 2007; van Kempen et al., 2002). Similarly, some studies of traffic-generated air pollution use dispersion and/or “land use regression” models to estimate concentrations (Ainslie et al., 2008; Jerrett et al., 2005a; Su et al., 2008). However, these air pollution models often require spatially dense monitoring or extensive data on emissions and meteorology prior to model development. As a result, simple roadway proximity-based metrics are commonly used as exposure surrogates, in part because they are easily implemented using readily available data and do not require any air pollution measurements (Adar and Kaufman, 2007; Finkelstein et al., 2004; Hoek et al., 2002; Hoffmann et al., 2007, Hoffmann et al., 2006; Jerrett et al., 2005a, Jerrett et al., 2005b; Maheswaran and Elliott, 2003; Tonne et al., 2007). These surrogate measures are based on correlations between roadway proximity and measured levels of traffic-generated air pollutants (Beckerman et al., 2008; Gilbert et al., 2007, Gilbert et al., 2003; Pleijel et al., 2004; Roorda-Knape et al., 1998; Zhu et al., 2002). However, interpretation of epidemiologic studies that use the proximity approach is complicated by the fact that noise levels are also related to roadway proximity (Hothersall and Chandler-Wilde, 1987). The ability of these roadway proximity metrics to predict measured levels of air pollution and noise has not been directly compared.
The published data on the relationship between noise and air pollution are also very limited and somewhat inconsistent. A study in Madrid evaluated the relationship between 1096 daily measurements of noise (measured at 6 locations) and NO2/NOx (measured at 24 locations) (Tobias et al., 2001). The authors reported noise-NO2 and noise-NOx correlation coefficients of 0.32 and 0.35, respectively. However, while informative for daily time-series studies, these temporal relationships are of limited value in interpreting epidemiologic studies of chronic exposures in which the spatial exposure contrast is of interest. More relevant to chronic effects studies are the findings of Klaeboe et al. (2000), who modeled 24-h Leq and 3-month average NO2 concentrations based on traffic volumes at approximately 1000 locations in Oslo and reported a modest relationship (r=0.46). In a study of chronic noise exposure and hypertension in the Netherlands, de Kluizenaar et al. (2007) reported a correlation coefficient of 0.72 between modeled noise and modeled annual average PM10. In a study in Germany, Ising et al. (2004) reported a strong correlation (r=0.84) between measurements of nighttime (0:00–6:00) traffic noise and 58–93 h measurements of NO2 at 25 locations. A recent study in Vancouver, BC, calculated correlations between 5-min noise and 2-week NO2 and NOx measured at 103 locations. They reported noise-NOx and noise-NO2 correlations of 0.64 and 0.53, respectively (Davies et al., 2009). A study in the Netherlands found a relatively poor correlation between yearly modeled noise and background black smoke (r=0.24) (Beelen et al., 2008). Most recently, Selander et al. (2009) reported a correlation coefficient of 0.6 between long-term modeled estimates of Leq and NO2 in Sweden. Although these studies suggest the potential for confounding, all but the Vancouver work were conducted in Europe where differences in the vehicle fleet, roadway configuration, fuel composition, and urban design, as well as these studies’ frequent reliance on models, may limit the generalizabilty of the results to other settings. In summary, little is presently known about the spatial relationship between traffic-generated air pollution and noise in North America.
Here we present the results from a pilot investigation of the relationship between traffic-generated air pollution and noise in Chicago, IL, and Riverside County, CA. Our primary objective was to assess the potential for confounding in epidemiologic studies of chronic health effects by evaluating the correlations between noise and 3 markers of traffic-generated air pollution: NO, NO2, and ultrafine particles (UFPs). A secondary objective was to evaluate and compare the ability of simple roadway proximity metrics to predict measured levels of air pollution and noise.
Section snippets
Pollution measurements
This work leveraged off of the Multi-Ethnic Study of Atherosclerosis and Air Pollution (“MESA Air”). MESA Air is an ongoing investigation of chronic exposure to fine particulate matter (PM2.5) and other air pollutants in relation to the progression of subclinical atherosclerosis in 9 US communities. As part of its air pollution exposure assessment efforts, MESA Air collected simultaneous measurements of NO2 and NOx at up to 105 locations in each study community. Time-integrated NO2/NOx sampling
Results
We sampled noise at 74 of 103 locations with NO2/NOx measurements during the first sampling session in Chicago (Table 1). Due to equipment problems and a snow storm the 2007 sampling session in Chicago included only 37 locations for Leq and 50 for UFP. In Riverside, we obtained valid noise and UFP measurements at 49 of 50 NO2/NOx measurement sites (fewer NO2/NOx samplers were deployed in Riverside because the MESA Air study area is smaller). Restricting our analyses to only those sites for
Discussion
To our knowledge, this is the first investigation of the relationship between traffic-related air pollution and noise in the US. The temporal variability of noise was found to be much lower than that of NO or NO2 in Chicago, perhaps due to the greater impact of meteorology on air pollution concentrations. In fact, 5-min grab samples of noise repeated at the same location were found to be quite stable over time (between-season r=0.84). This stability was extremely important for this study since
Acknowledgments
Funding for this work was provided by the Simon Fraser University President's Research Grant. The MESA Air study is funded by the US Environmental Protection Agency (Grant R831697). We thank Dr. Bruce Allen for assistance with equipment preparation, Melissa Symon for help with data collection, Anne Ho and Jim Sullivan for data processing, and Amber Pearson for calculation of geographical variables. Mention of trade names or commercial products does not constitute an endorsement or
References (46)
- et al.
A source area model incorporating simplified atmospheric dispersion and advection at fine scale for population air pollutant exposure assessment
Atmospheric Environment
(2008) - et al.
Variability in road traffic noise levels
Applied Acoustics
(2005) - et al.
Correlation of nitrogen dioxide with other traffic pollutants near a major expressway
Atmospheric Environment
(2008) - et al.
The statistical modeling of road traffic noise in an urban setting
Cities
(2003) - et al.
The influence of highway traffic on ambient nitrogen dioxide concentrations beyond the immediate vicinity of highways
Atmospheric Environment
(2007) - et al.
Ambient nitrogen dioxide and distance from a major highway
Science of the Total Environment
(2003) - et al.
Association between mortality and indicators of traffic-related air pollution in the Netherlands: a cohort study
Lancet
(2002) - et al.
Prediction of the attenuation of road traffic noise with distance
Journal of Sound and Vibration
(1987) - et al.
Oslo traffic study—part 1: An integrated approach to assess the combined effects of noise and air pollution on annoyance
Atmospheric Environment
(2000) - et al.
The influence of stressors on biochemical reactions-a review of present scientific findings with noise
International Journal of Hygiene and Environmental Health
(2000)
On the logarithmic relationship between NO2 concentration and the distance from a highroad
Science of the Total Environment
Air pollution from traffic in city districts near major motorways
Atmospheric Environment
An innovative land use regression model incorporating meteorology for exposure analysis
Science of the Total Environment
Cardiovascular disease and air pollutants: evaluating and improving epidemiological data implicating traffic exposure
Inhalation Toxicology
The noise/stress concept, risk assessment and research needs
Noise & Health
Transportation noise and cardiovascular risk: updated review and synthesis of epidemiological studies indicate that evidence has increased
Noise & Health
Traffic noise and risk of myocardial infarction
Epidemiology
Road traffic noise and hypertension
Occupational and Environmental Medicine
Air pollution and development of asthma, allergy and infections in a birth cohort
European Respiratory Journal
Chronic traffic-related air pollution and stress interact to predict biological and clinical outcomes in asthma
Environmental Health Perspectives
Synergistic effects of traffic-related air pollution and exposure to violence on urban asthma etiology
Environmental Health Perspectives
Cited by (0)
- ☆
Funding Sources: Funding for this work was provided by the Simon Fraser University President's Research Grant and the US Environmental Protection Agency (Grant R831697). Joel Kaufman was supported by the National Institute of Environmental Health Sciences through grant K24ES013195.