Evaluating Multipollutant Exposure and Urban Air Quality: Pollutant Interrelationships, Neighborhood Variability, and Nitrogen Dioxide as a Proxy Pollutant

Abstract

Background: Although urban air pollution is a complex mix containing multiple constituents, studies of the health effects of long-term exposure often focus on a single pollutant as a proxy for the entire mixture. A better understanding of the component pollutant concentrations and interrelationships would be useful in epidemiological studies that exploit spatial differences in exposure by clarifying the extent to which measures of individual pollutants, particularly nitrogen dioxide (NO2), represent spatial patterns in the multipollutant mixture.Objectives: We examined air pollutant concentrations and interrelationships at the intraurban scale to obtain insight into the nature of the urban mixture of air pollutants.Methods: Mobile measurements of 23 air pollutants were taken systematically at high resolution in Montreal, Quebec, Canada, over 34 days in the winter, summer, and autumn of 2009.Results: We observed variability in pollution levels and in the statistical correlations between different pollutants according to season and neighborhood. Nitrogen oxide species (nitric oxide, NO2, nitrogen oxides, and total oxidized nitrogen species) had the highest overall spatial correlations with the suite of pollutants measured. Ultrafine particles and hydrocarbon-like organic aerosol concentration, a derived measure used as a specific indicator of traffic particles, also had very high correlations.Conclusions: Our findings indicate that the multipollutant mix varies considerably throughout the city, both in time and in space, and thus, no single pollutant would be a perfect proxy measure for the entire mix under all circumstances. However, based on overall average spatial correlations with the suite of pollutants measured, nitrogen oxide species appeared to be the best available indicators of spatial variation in exposure to the outdoor urban air pollutant mixture.Citation: Levy I, Mihele C, Lu G, Narayan J, Brook JR. 2014. Evaluating multipollutant exposure and urban air quality: pollutant interrelationships, neighborhood variability, and nitrogen dioxide as a proxy pollutant. Environ Health Perspect 122:65–72; http://dx.doi.org/10.1289/ehp.1306518

Evaluating the application of multipollutant exposure metrics in air pollution health studies

“…An example of a typical marker species is nitrogen dioxide (NO 2 ) for traffic pollution. NO 2 is generally considered a robust marker, because it typically correlates well with the variability of traffic activity and concentrations of other constituents within the traffic mixture (Brook et al, 2007;Levy et al, 2014).…”

“…Levy et al (2014) Marker Species  Compares mobile measurements of NO 2 to different particulate and gaseous traffic-related pollutants  Finds nitrogen oxide species, including NO 2 , to be a good marker of traffic based on high spatial correlation among measured traffic species Lobscheid et al (2012) Intake Fraction  Calculates the intake fraction of conserved pollutants emitted from on-road mobile sources utilizing AERMOD for the conterminous United States  Population-weighted mean  Finds intake fractions for populous urban counties are about two orders of magnitude greater than for sparsely populated rural counties with 75% of the intake occuring in the same county as emissions. Maciejczyk et al (2010) Source Apportionment  Uses FA to identify major sources of PM 2.5 in urban area in toxicological study  Observes a strong association between metals and cellular oxidant generation Mar et al (2006) Source Penttinen et al (2006) Source Apportionment  Uses PCA and multiple linear regressions to identify PM 2.5 sources associated with adverse health outcomes  Determines combustion sources are largely linked to negative respiratory outcomes Plaia et al (2013) Risk-based  Develops multi-site, multipollutant index for PM 10 , NO 2 , CO, and SO 2 by aggregating pollutant concentrations across sites using PCA, then aggregating across pollutants using a power sum with exponent 2  Using simulated data, shows that method is sensitive to highly variable pollutants, particularly those at low concentrations Ruggieri and Plaia (2012) Risk-based  Develops power-sum index with exponent 2 for PM 10 , NO 2 , O 3 , CO, and SO 2 and a variability index to account for situations when one pollutant is much higher than the others  Combines air quality and variability indexes to clarify whether high power-sum index values are due to one or multiple pollutants Sarnat et al (2008) Source Stieb et al (2005) Risk-based  Develops AQHI by weighting pollutant concentrations by epidemiologic effect estimate, summing across pollutants, and scaling to an arbitrary scale of 1-10  Uses mortality effect estimates from a multi-city Canadian study for CO, NO 2 , O 3 , SO 2 , and PM 2.5 Stieb et al (2008) Risk-baseed  Conducts sensitivity analyses on pollutants included in AQHI and appropriateness of using multicity effect estimates  NO 2 , O 3 , and PM 2.5 main drivers of index values; multicity formulation in good agreement with single-city effect estimates Suh et al (2011) Chemical Property  Develops a new approach to link chemical properties of air pollution to adverse health outcomes  Observes an association between adverse health effects and alkanes, transition metals, aromatics, and oxides Swamee and Tyagi (1999) Risk-based  Analyzes methods of summing weighted pollutant concentrations to generate a multipollutant index  Suggests a power-sum method with exponent 2.5 as an To et al (2013) Risk-based  Evaluates association between AQHI and asthma morbidity in Ontario  Observes consistent associations between AQHI and asthma hospitalizations, despite AQHI being developed from mortality studies <...>…”

Neural Models for Imputation of Missing Ozone Data in Air‐Quality Datasets

“…This is due to the small variation in weather conditions during summer as well as low industrial activity and traffic in urban areas due to vacation time. Furthermore, correlation coefficients in more than 20 pollutants analyzed in [42] are higher for measurements in the summer compared with correlations for measurements over all days combined. The season of the year with the worst results in the calculation of the MSE has been the autumn in the case of the two regression techniques and RBFN, although the differences between the three seasons (spring, summer, and autumn) is not significant.…”

Public Interest in Microclimate Data in Knoxville, Tennessee, USA

“…As urbanization increases, so do resource consumption, generation of greenhouse gasses, and disruption of human-environmental cycles with consequences for human health and well-being [1][2][3]. The impacts of urbanization on local humidity [4], air temperature [5], air quality [6], and water quality [7] are substantial. These effects have high spatial and temporal variability due to the complexity and patchiness of the urban landscape.…”