![]() We also estimated associations with sociodemographic characteristics controlling for distance to airport and evaluated between- and within-airport relationships.Īnnual average noise levels from aircraft were estimated around each of the 90 study airports for the decennial census year 2010. Census and American Community Survey (ACS) data to perform an investigation of the sociodemographic distributions around U.S. airports spanning all regions and various hub types. To this end, we used spatially resolved noise measures modeled using the method required for environmental impact assessment and compliance for 90 U.S. Our objective was to investigate whether racial/ethnic minority and low-SES populations in the United States are disproportionately exposed to aircraft noise. Although studies of individual airports are valuable, it is unclear what the sociodemographic patterns of aircraft noise exposure would be across and between multiple airports evaluated using the same noise and sociodemographic measures. ( 2019) performed a systematic review of studies of environmental noise exposure in the World Health Organization (WHO) European Region and found mixed results in the eight studies they evaluated but an indication of higher noise exposure in groups of lower socioeconomic position they proposed that mixed results could be due to differences in how noise and social inequality were measured. 2007 WHO Regional Office for Europe 2010). Alternatively, a study in the Netherlands looking at transportation noise exposures separately found different relationships by noise exposure source, most relatedly, lower social inequality associated with aircraft noise exposure ( Kruize et al. 2004) found “only rather weak evidence” of an association between combined transportation (i.e., road, rail, and airport) noise exposure and ethnicity and socioeconomic deprivation. A UK study in Birmingham ( Brainard et al. Louis-Lambert Field and found a higher prevalence of racial/ethnic minority and lower SES populations living in areas of high noise levels however, they found a lower prevalence of racial/ethnic minority and higher SES populations in areas with even more highly elevated noise levels. ( 2007) reported that the primary predictor of aviation noise around a commercial airport in Arizona was race/ethnicity, followed by poverty. Ogneva-Himmelberger and Cooperman ( 2010) found a greater prevalence of racial/ethnic minority and lower-income populations within areas with aircraft noise levels > 55 dB ( A ) compared with unexposed areas within a 21 -km radius surrounding Boston’s Logan International Airport. A few studies have specifically investigated the distribution of sociodemographic characteristics around airports but, to our knowledge, only around individual airports. An important first step to understanding potential health impacts in the United States is to investigate aircraft noise exposure patterning related to community sociodemographic characteristics.Ī popular method for investigating population impacts of aircraft noise is to evaluate its association with property values (e.g., Nelson 2004) however, most of these analyses do not directly evaluate the distribution of noise exposures as a function of a community’s sociodemographic characteristics. ![]() In addition, studies have related aircraft noise to sleep disturbance, impairments in children’s cognition, negative birth outcomes, and cardiovascular disease outcomes, as well as risk factors such as hypertension (reviewed by Basner et al. Aircraft noise is a major source of annoyance and complaints in communities surrounding airports ( Miller et al. One exposure for which disproportionate burdens may be felt is aircraft noise due to a complex set of factors including land-use patterns connected with airport economies and flight paths. In the United States, disproportionate exposure may reflect procedural injustices in environmental regulations, institutional and individual discrimination, and racist housing policies manifesting in residential segregation and suburbanization, as well as structural factors such as politics and economics that affect facility siting and the racialization of labor markets ( McCartney et al. ![]() Communities with low socioeconomic status (SES) and high prevalence of racial/ethnic minority populations are often exposed to greater numbers and concentrations of environmental hazards ( Mohai et al.
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