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

Abstract

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 PM2.5 and traffic-related NOx 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 PM2.5 observations and mobile-source-simulated NOx were calibrated using regulatory observations in Minneapolis. No statistically significant (α = 0.05) PM2.5 differences were found between urban poor and urban middle-income neighborhoods, but average mobile-source NOx 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 PM2.5 or simulated mobile-source NOx. Home-based exposure assessments found that PM2.5 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 PM2.5 concentration led to higher scores) for negative emotions such as tiredness, stress, sadness, and pain. Simulated mobile-source NOx, assessed from both home-based exposures and in situ exposures, had a near-zero relationship with all EWB indicators. This was attributed to low NOx levels throughout the study neighborhoods and at locations were the EWB-assessed activities took place, both owing to low on-road mobile-source NOx 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.

Real‐time measurements of PM2.5and ozone to assess the effectiveness of residential indoor air filtration in Shanghai homes

“…These monitors include Plantower PMS3003 sensors for PM 2.5 and Alphasense OX‐A4 sensors for O 3 . These monitors have been thoroughly evaluated for measuring ambient PM 2.5 in both high and low concentration environments 28,43 and have been used for ambient, indoor, personal exposure, and source measurements of PM 2.5 28,44‐46 and O 3 47,48 . In addition, both the performance of the individual Plantower 49‐55 and specifically the Plantower PMS3003 56‐60 have been widely studied.…”

Development and Application of a United States wide correction for PM<sub>2.5</sub> data collected with the PurpleAir sensor

“…Overestimation was observed in our dataset before correction as shown in Figures S2 and S3 with overestimation increasing between 30 and 80%.There are few 24-hr averages above 80% RH so there is more uncertainty in the relationship above that level although it appears to level off. To address this, previous studies often used a nonlinear correction as opposed to a correction that changes linearly with RH (Stampfer et al, 2020;Tryner et al, 2020a;Malings et al, 2019;Kim et al, 2019;Zheng et al, 2018;Lal et al, 2020). A nonlinear RH model was tested by adding a RH 2 /(1-RH) term (see Eq.…”

“…for Plantower sensors and other light scattering measurements (Tryner et al, 2020a;Malings et al, 2019;Chakrabarti et al, 2004;Zheng et al, 2018;Zhang et al, 1994;Day and Malm, 2001;Soneja et al, 2014;Lal et al, 2020;Barkjohn et al, 2020b).…”

Development and application of a United States-wide correction for PM<sub>2.5</sub> data collected with the PurpleAir sensor

“…A best practice is to locate air sensors alongside regulatory air monitors to understand their local performance and to develop corrections for each individual sensor (Jiao et al, 2016;Johnson et al, 2018;Zusman et al, 2020). For optical particulate matter (PM) sensors, correction procedures are often needed due to both the changing optical properties of aerosols associated with both their physical and chemical characteristics (Levy Zamora et al, 2019;Tryner et al, 2019) and the influence of meteorological conditions including temperature and relative humidity (RH) (Jayaratne et al, 2018;Zheng et al, 2018). In addition, some air sensors have out-of-the box differences and low precision between sensors of the same model (Feenstra et al, 2019;Feinberg et al, 2018).…”