The spatial relationship between traffic-generated air pollution and noise in 2 US cities

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Abstract

Traffic-generated air pollution and noise have both been linked to cardiovascular morbidity. Since traffic is a shared source, there is potential for correlated exposures that may lead to confounding in epidemiologic studies. As part of the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air), 2-week NO and NO2 concentrations were measured at up to 105 locations, selected primarily to characterize gradients near major roads, in each of 9 US communities. We measured 5-min A-weighted equivalent continuous sound pressure levels (Leq) and ultrafine particle (UFP) counts at a subset of these NO/NO2 monitoring locations in Chicago, IL (N=69 in December 2006; N=36 in April 2007) and Riverside County, CA (N=46 in April 2007). Leq and UFP were measured during non-“rush hour” periods (10:00–16:00) to maximize comparability between measurements. We evaluated roadway proximity exposure surrogates in relation to the measured levels, estimated noise–air pollution correlation coefficients, and evaluated the impact of regional-scale pollution gradients, wind direction, and roadway proximity on the correlations. Five-minute Leq measurements in December 2006 and April 2007 were highly correlated (r=0.84), and measurements made at different times of day were similar (coefficients of variation: 0.5–13%), indicating that 5-min measurements are representative of long-term Leq. Binary and continuous roadway proximity metrics characterized Leq as well or better than NO or NO2. We found strong regional-scale gradients in NO and NO2, particularly in Chicago, but only weak regional-scale gradients in Leq and UFP. Leq was most consistently correlated with NO, but the correlations were moderate (0.20–0.60). After removing the influence of regional-scale gradients the correlations generally increased (Leq–NO: r=0.49–0.62), and correlations downwind of major roads (Leq–NO: r=0.53–0.74) were consistently higher than those upwind (0.35–0.65). There was not a consistent effect of roadway proximity on the correlations. In conclusion, roadway proximity variables are not unique exposure surrogates in studies of endpoints hypothesized to be related to both air pollution and noise. Moderate correlations between traffic-generated air pollution and noise suggest the possibility of confounding, which might be minimized by considering regional pollution gradients and/or prevailing wind direction(s) in epidemiologic studies.

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

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    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.

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