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.

Prescribed burning (PB) is a prominent source of PM_2.5 in the southeastern US and exposure to PB smoke is a health risk. As demand for burning increases and stricter controls are implemented for other anthropogenic sources, PB emissions tend to be responsible for an increasing fraction of PM_2.5 concentrations. Here, to quantify the effect of PB on air quality, low-cost sensors are used to measure PM_2.5 concentrations in Southwestern Georgia. The feasibility of using low-cost sensors as a supplemental measurement tool is evaluated by comparing them with reference instruments. A chemical transport model, CMAQ, is also used to simulate the contribution of PB to PM_2.5 concentrations. Simulated PM_2.5 concentrations are compared to observations from both low-cost sensors and reference monitors. Finally, a data fusion method is applied to generate hourly spatiotemporal exposure fields by fusing PM_2.5 concentrations from the CMAQ model and all observations. The results show that the severe impact of PB on local air quality and public health may be missed due to the dearth of regulatory monitoring sites. In Southwestern Georgia PM_2.5 concentrations are highly non-homogeneous and the spatial variation is not captured even with a 4-km horizontal resolution in model simulations. Low-cost PM sensors can improve the detection of PB impacts and provide useful spatial and temporal information for integration with air quality models. R² of regression with observations increases from an average of 0.09 to 0.40 when data fusion is applied to model simulation withholding the observations at the evaluation site. Adding observations from low-cost sensors reduces the underestimation of nighttime PM_2.5 concentrations and reproduces the peaks that are missed by the simulations. In the future, observations from a dense network of low-cost sensors could be fused with the model simulated PM_2.5 fields to provide better estimates of hourly exposures to smoke from PB. Implications: Prescribed burning emissions are a major cause of elevated PM_2.5 concentrations, posing a risk to human health. However, their impact cannot be quantified properly due to a dearth of regulatory monitoring sites in certain regions of the United States such as Southwestern Georgia. Low-cost PM sensors can be used as a supplemental measurement tool and provide useful spatial and temporal information for integration with air quality model simulations. In the future, data from a dense network of low-cost sensors could be fused with model simulated PM_2.5 fields to provide improved estimates of hourly exposures to smoke from prescribed burning.

Emissions from on-road mobile sources have historically been an important anthropogenic contributor to ambient air pollution leading to high levels of air pollution near major roadways. The U.S. EPA recently implemented the Near-Road (monitoring) Network to measure NO₂ concentrations by high-traffic roadways in urban centers throughout the U.S., as these locations were believed to characterize worst-case human exposures to traffic-related air pollutants. Many near-road sites also include PM_2.5 and CO measurements, which along with the NO₂ observations, were compared in a pairwise manner against non-near-road monitors located within the city-scale boundary. After controlling for primary emissions from the target highways, we found the PM_2.5 concentration difference (i.e. near-road concentration minus non-near-road site concentration) between the near-road and non-near-road urban sites to be δ = 0.42 µg m⁻³( H₀: µ _diff = 0; H_a: µ _diff > 0 (µ _{non-near-road} > µ _near-road); p = 0.051; α = 0.05, 95% CI: −0.08–0.90 µg m⁻³, n = 35 comparisons). NO₂ and CO levels were on average higher at the near-road sites compared to the non-near-road urban sites by 5.0 (95% CI: 3.4–6.5) ppb (n = 44 comparisons) and 9.2 × 10⁻²( 95% CI: 0.04–0.14) ppm (n = 42 comparisons), respectively. The average PM_2.5 difference found here is 5%, and at 14 of the 35 (∼40%) urban monitor comparisons and 28 of the 72 (∼39%) overall comparisons, PM_2.5 is actually higher at the non-near-road site relative to its near-road pair. Cleaner vehicle fleets, formation of secondary PM from on-road emissions occurring downwind (i.e. away from the road), decreased secondary organic aerosol (SOA) formation rates in the near-road environment, the prevalence of other low-volume vehicular and local, non-vehicular sources of emissions at the non-near-road sites (e.g. railyards, truck yards, ports, biomass-fueled heating, backyard barbecuing, and commercial cooking, etc) and local meteorology (e.g. wind speed and wind direction) explain this finding. The wintertime PM_2.5 concentration difference was higher than the other seasons, likely a result of higher primary PM_2.5 tailpipe emissions and lower temperatures that both reduced near-road PM volatility and decreased photochemical activity resulting in lower SOA production at the urban scale. Further, all near-road NO₂ and CO concentrations were below the annual and hourly NAAQS, while eight (most of which were in wildfire-prone locations) of the 94 PM_2.5 sites used in this study were above the annual National Ambient Air Quality Standards. In addition, strong agreement with both annual average daily traffic and fleet-equivalent AADT were found for near-road NO2 and CO concentrations, while weaker, but still positive relationships were found for near-road PM_2.5 levels. Lastly, same observational data was used to assess on-road mobile source emission estimates from the EPA National Emission Inventory, and analysis of the observations are in rough agreement with the current ratio of NO_x to CO emissions from on-road mobile sources.