RESUMO
Fine particulate matter (PM2.5) air pollution exposure is associated with short and long-term health effects. Several studies found differences in PM2.5 exposure associated with neighborhood racial and socioeconomic composition. However, most focused on total PM2.5 mass rather than its chemical components and their sources. In this study, we describe the ZIP code characteristics that drive the disparities in exposure to PM2.5 chemical components attributed to source categories both nationally and regionally. We obtained annual mean predictions of PM2.5 and fourteen of its chemical components from spatiotemporal models and socioeconomic and racial predictor variables from the 2010 US Census, and the American Community Survey 5-year estimates. We used non-negative matrix factorization to attribute the chemical components to five source categories. We fit generalized nonlinear models to assess the associations between the neighborhood predictors and each PM2.5 source category in urban areas in the United States in 2010 (n=25,790 zip codes). We observed higher PM2.5 levels in ZIP codes with higher proportions of Black individuals and lower socioeconomic status. Racial exposure disparities were mainly attributed to Heavy Fuel, Oil and Industrial, Metal Processing Industry and Agricultural, and Motor Vehicle sources. Economic disparities were mainly attributed to Soil and Crustal Dust, Heavy Fuel Oil and Industrial, Metal Processing Industry and Agricultural, and Motor Vehicle sources. Upon further analysis through stratifying by regions within the United States, we found that the associations between ZIP code characteristics and source-attributed PM2.5 levels were generally greater in Western states. In conclusion, racial, socioeconomic, and geographic inequalities in exposure to PM2.5 and its components are driven by systematic differences in component sources that can inform air quality improvement strategies.
RESUMO
We conducted a systematic review of quality assessment tools for urban green spaces to evaluate their replicability, comparability and validity. We assessed the characteristics of 15 published, multi-dimensional, direct observation tools regarding: publication, development, features and included dimensions. Even though all tools show acceptable inter-rater variability, there is a notable variability in their characteristics, from required time to conduct the assessment to the number of included items. Additionally, some specific dimensions are underrepresented, and the study units' definitions are feeble. We showcased the need for new tools that are more comprehensive and include more consistent reporting measures.