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.

This ZIP file contains the files used to build the interactive version of this report. This document presents an equity analysis of Massachusetts Bay Transportation Authority’s (MBTA) bus performance across three service metrics for the years 2015 to 2017: ontime performance, dropped trips, and overcrowding. These performance metrics are calculated to compare performance of bus lines serving historically underserved populations (i.e. minority, low income) to bus lines serving the rest of the population in the MBTA system. Special attention is paid to Route 111, an important transit connection between Chelsea and Boston, and a line with high percentages of minority and low income bus riders. In general, the analysis shows that minority and low incomes lines experience worse performance for reliability and dropped trips, but these differences do not exceed MBTA thresholds for disparate and disproportionate impacts, which require a difference in service metrics of at least 20%. On the positive side, minority and low income lines experience the same or less overcrowding than other bus lines. Route 111 shows better than average reliability, but is among the worst in terms of dropped trips and overcrowding. The major findings of this analysis are the following: Minority and low income bus riders experienced worse reliability or ontime performance than other bus riders Minority and low income bus riders experienced a higher percentage of dropped trips than other bus riders Minority and low income riders experienced the same or even less overcrowding than other bus riders Systematic differences in bus service for minority and low income riders are clearly evident but are not captured by the MBTA’s threshold for determining disparate or disproportionate impacts Deficiencies in the MBTA’s survey data make equity determinations problematic Details of the analysis, including interactive graphs and maps are presented in the report.