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Many environmental justice communities face elevated exposures to multiple stressors, given biases in urban and environmental policy and planning. This paper aims to evaluate sound level exposure in a densely populated environmental justice city in close proximity to major roadways, a nearby airport and high levels of industrial activity. In this study we collected various sound level metrics to evaluate the loudness and frequency composition of the acoustical environment in Chelsea, Massachusetts, USA. A total of 29 week-long sites were collected from October 2019 to June 2020, a time period that also included the influence of the COVID-19 pandemic, which drastically altered activity patterns and corresponding sound level exposures. We found that Chelsea is exposed to high levels of sound, both day and night (65 dB (A), and 80 dB and 90 dB for low frequency, and infrasound sound levels). A spectral analysis shows that 63 Hz was the dominant frequency. Distance to major roads and flight activity (both arrivals and departures) were most strongly correlated with all metrics, most notably with metrics describing contributing from lower frequencies. Overall, we found similar patterns during the COVID-19 pandemic but at levels up to 10 dB lower. Our results demonstrate the importance of noise exposure assessments in environmental justice communities and the importance of using additional metrics to describe communities inundated with significant air, road, and industrial sound levels. It also provides a snapshot of how much quieter communities can be with careful and intentional urban and environmental policy and planning., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Background: Urban sound levels are a ubiquitous environmental stressor and have been shown to be associated with a wide variety of health outcomes. While much is known about the predictors of A-weighted sound pressure levels in the urban environment, far less is known about other frequencies., Objective: To develop a series of spatial-temporal sound models to predict A-weighted sound pressure levels, low, mid, and high frequency sound for Boston, Massachusetts., Methods: Short-term sound levels were gathered at n = 400 sites from February 2015 - February 2016. Spatial and meteorological attributes at or near the sound monitoring site were obtained using publicly available data and a portable weather station. An elastic net variable selection technique was used to select predictors of A-weighted, low, mid, and high frequency sound., Results: The final models for low, mid, high, and A-weighted sound levels explained 59 - 69% of the variability in each measure. Similar to other A-weighted models, our sound models included transportation related variables such as length of roads and bus lines in the surrounding area; distance to road and rail lines; traffic volume, vehicle mix, residential and commercial land use. However, frequency specific models highlighted additional predictors not included in the A-weighted model including temperature, vegetation, impervious surfaces, vehicle mix, and density of entertainment establishments and restaurants., Conclusions: Building spatial temporal models to characterize sound levels across the frequency spectrum using an elastic net approach can be a promising tool for noise exposure assessments within the urban soundscape. Models of sound's character may give us additional important sound exposure metrics to be utilized in epidemiological studies., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Background: While previous epidemiological studies report adverse effects of long-term noise exposure on cardiovascular health, the mechanisms responsible for these effects are unclear. We sought to elucidate the cardiovascular and stress response to short-term, low (31.5-125Hz) and high (500-2000Hz) frequency noise exposures., Methods: Healthy male (n=10) participants were monitored on multiple visits during no noise, low- or high-frequency noise exposure scenarios lasting 40min. Participants were fitted with an ambulatory electrocardiogram (ECG) and blood pressure measures and saliva samples were taken before, during and after noise exposures. ECGs were processed for measures of heart rate variability (HRV): high-frequency power (HF), low-frequency power (LF), the root of the mean squared difference between adjacent normal heart beats (N-N) intervals (RMSSD), and the standard deviation of N-N intervals (SDNN). Systolic blood pressure (SBP), diastolic blood pressure (DPB), and pulse were reported and saliva was analyzed for salivary cortisol and amylase. Multivariate mixed-effects linear regression models adjusted for age were used to identify statistically significant difference in outcomes by no noise, during noise or after noise exposure periods and whether this differed by noise frequency., Results: A total of 658, 205, and 122, HRV, saliva, and blood pressure measurements were performed over 41 person days. Reductions in HRV (LF and RMSSD) were observed during noise exposure (a reduction of 19% (-35,-3.5) and 9.1% (-17,-1.1), respectively). After adjusting for noise frequency, during low frequency noise exposure, HF, LF, and SDNN were reduced (a reduction of 32% (-57,-6.2), 34% (-52,-15), and 16% (-26,-6.1), respectively) and during high frequency noise exposure, a 21% (-39,-2.3) reduction in LF, as compared to during no noise exposure, was found. No significant (p<0.05) changes in blood pressure, salivary cortisol, or amylase were observed., Conclusions: These results suggest that exposure to noise, and in particular, to low-frequency noise, negatively impacts HRV. The frequencies of noise should be considered when evaluating the cardiovascular health impacts of exposure., (Copyright © 2016 Elsevier Inc. All rights reserved.)