Connecting Air Quality with Emotional Well-Being and Neighborhood Infrastructure in a US City

Cities in the United States have announced initiatives to become more sustainable, healthy, resilient, livable, and environmentally friendly. However, indicators for measuring all outcomes related to these targets and the synergies between them have not been well defined or studied. One such relationship is the linkage between air quality with emotional well-being (EWB) and neighborhood infrastructure. Here, regulatory monitoring, low-cost sensors (LCSs), and air quality modeling were combined to assess exposures to PM(2.5) and traffic-related NO(x) in 6 Minneapolis, MN, neighborhoods of varying infrastructure parameters (median household income, urban vs suburban, and access to light rail). Residents of the study neighborhoods concurrently took real-time EWB assessments using a smart phone application, Daynamica, to gauge happiness, tiredness, stress, sadness, and pain. Both LCS PM(2.5) observations and mobile-source-simulated NO(x) were calibrated using regulatory observations in Minneapolis. No statistically significant (α = 0.05) PM(2.5) differences were found between urban poor and urban middle-income neighborhoods, but average mobile-source NO(x) was statistically significantly (α = 0.05) higher in the 4 urban neighborhoods than in the 2 suburban neighborhoods. Close proximity to light rail had no observable impact on average observed PM(2.5) or simulated mobile-source NO(x). Home-based exposure assessments found that PM(2.5) was negatively correlated with positive emotions such as happiness and to net affect (the sum of positive and negative emotion scores) and positively correlated (ie, a higher PM(2.5) concentration led to higher scores) for negative emotions such as tiredness, stress, sadness, and pain. Simulated mobile-source NO(x), assessed from both home-based exposures and in situ exposures, had a near-zero relationship with all EWB indicators. This was attributed to low NO(x) levels throughout the study neighborhoods and at locations were the EWB-assessed activities took place, both owing to low on-road mobile-source NO(x) impacts. Although none of the air quality and EWB responses were determined to be statistically significant (α = 0.05), due in part to the relatively small sample size, the results are suggestive of linkages between air quality and a variety of EWB outcomes.