Abstract
ABSTRACT City-wide air pollution assessments have typically relied on a small number of widely separated regulatory monitoring sites or land use regression (LUR) models built using time-integrated samples to assess annual average population-scale exposure. However, air pollutant concentrations often exhibit significant spatial and temporal variability depending on local sources and features of the built environment. In 2016, the Center for Air, Climate, and Energy Solutions (CACES) Air Quality Observatory was launched at Carnegie Mellon University to better understand urban spatial and temporal pollution gradients on the 8 h) above the regional background. Compared to the non-decomposed total pollutant signal, the short-lived or persistent enhancement pollutant signals, which should come from local sources, were better correlated with covariates used in LUR model construction. For example, Pearson r between total vehicle counts in a 100 m buffer and NO2 increased from 0.57 using the total pollutant signal to 0.83 using the persistent enhancement only. The findings from this study support building more accurate and higher time resolution (e.g., daily, hourly) LURs using low-cost sensors.
Highlights
Traditional urban and regional air quality studies have typically relied on an existing network of regulatory monitoring sites located far from individual sources or usedA significant fraction of air pollutant exposure research has highlighted the substantial health effects attributable to PM2.5, including premature mortality, cardiovascular disease, lung cancer, Zimmerman et al, Aerosol and Air Quality Research, 20: 314–328, 2020 and asthma (Pope et al, 2009; Brook et al, 2010; RaaschouNielsen et al, 2013)
By using real-time, high-spatial resolution sampling with low-cost sensors to span the range of modifiable factors in urban areas, we hypothesize that temporallyresolved land use regression (LUR) can be constructed that improve accuracy of prediction in complex urban areas and that these LURs may be more readily transferred to other urban areas by removing the influence of regional effects
While measurements at different sites were in some cases made in different seasons, we used the Oakland site to assess seasonal variability
Summary
Traditional urban and regional air quality studies have typically relied on an existing network of regulatory monitoring sites located far from individual sources or usedA significant fraction of air pollutant exposure research has highlighted the substantial health effects attributable to PM2.5 (particulate matter with a diameter < 2.5 μm), including premature mortality, cardiovascular disease, lung cancer, Zimmerman et al, Aerosol and Air Quality Research, 20: 314–328, 2020 and asthma (Pope et al, 2009; Brook et al, 2010; RaaschouNielsen et al, 2013). Epidemiological studies frequently demonstrate elevated risks of chronic and acute health effects for populations that reside near major sources, such as roadways (Hoek et al, 2002; Baumgartner et al, 2014) This enhancement in risk near roadways is likely not solely attributed to PM2.5; nearroad concentrations of PM2.5 are only modestly elevated (typically < 1.5 times) above urban background concentrations (Karner et al, 2010; Apte et al, 2011). Other traffic-related air pollutants, including nitrogen oxides (NOx), carbon monoxide (CO), black carbon, and ultrafine particles (UFP; particles 3–5 times) (Karner et al, 2010; Saha et al, 2018) These pollutants, or synergistic effects of exposure to multiple traffic-related pollutants, might explain some portion of the observed risk increment near roadways. The high intra-urban spatiotemporal variability of air pollutants means that urban background concentrations, such as those measured at regulatory monitoring sites, do not adequately represent population exposure for these species
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