Predicting residential ozone deficits from nearby traffic

https://doi.org/10.1016/j.scitotenv.2005.06.028Get rights and content

Abstract

Oxides of nitrogen in fresh traffic exhaust are known to scavenge ambient ozone. However, there has only been little study of local variation in ozone resulting from variation in vehicular traffic patterns within communities. Homes of 78 children were selected from a sample of participants in 3 communities in the southern California Children's Health Study. Twenty-four hour ozone measurements were made simultaneously at a home and at a community central site monitor on two occasions between February and November 1994. Homes were geo-coded, and local residential nitrogen oxides (NOx) above regional background due to nearby traffic at each participant's home were estimated using a line source dispersion model. Measured home ozone declined in a predictable manner as modeled residential NOx increased. NOx modeled from local traffic near homes accounted for variation in ozone concentrations of as much as 17 parts per billion. We conclude that residential ozone concentrations may be over- or underestimated by measurements at a community monitor, depending on the variation in local traffic in the community. These findings may have implications for studies of health effects of traffic-related pollutants.

Introduction

There is an extensive literature describing how ozone varies on a regional scale as a function of sources of upwind ambient NOx, reactive organic gases, and atmospheric photochemistry, and models have been developed to predict downwind ozone concentrations in communities without monitoring stations (National Research Council Committee on Tropospheric Ozone Formation and Measurement, 1991, Diem and Comrie, 2002). However, a recent critique of ozone mapping efforts noted that interpolations from central site monitors to local neighborhoods are not justified by the spatial resolution of available data and have not been validated against the dense network of measurements that would be required to justify the assumption that the distribution of ozone is homogeneous within communities (Diem, 2003). Where monitoring sites have been available in close proximity in urban areas, levels have differed by up to 50% within 5 km of each other (McNair et al., 1996).

One reason for the spatial inhomogeneity of ozone is local variation in traffic, because nitric oxide (NO) present in fresh vehicle exhaust reacts with and consumes ozone. This reaction occurs much more rapidly than the atmospheric photo-oxidation that produces ozone regionally. Thus, ozone is reduced in tunnels, in heavy traffic, and near heavy traffic corridors, compared with nearby fixed site measurements near the traffic corridor (Rodes and Holland, 1981, Chan et al., 1991). Ozone concentrations are generally lower in urban cores with heavy traffic, compared with downwind suburban areas with little traffic related NO to react with and consume ozone (National Research Council Committee on Tropospheric Ozone Formation and Measurement, 1991, Diem and Comrie, 2002, Gregg et al., 2003). In one previous study, concentrations 8 m downwind from a freeway were often less than 10% of background ambient levels, and reduction in ozone concentrations were evident to 500 m (Rodes and Holland, 1981). Although this local variation in traffic is known to modify ozone concentration (Liu et al., 1997, Monn, 2001), there has been little attempt to exploit complex variation in traffic on freeways, arterials, and collector streets within neighborhoods to predict ozone exposure at homes, predictions which would be useful for health studies. Grid-based photochemical air quality models typically have K-theory dispersion algorithms that are not suitable for applications with the fine horizontal resolution (e.g., < 200 m) needed to simulate line-source impact (Seinfeld and Pandis, 1998).

We investigated whether local variation in measured residential ozone in southern California could be predicted based on traffic related oxides of nitrogen (NOx) at the home. NOx from local traffic were estimated from traffic counts available from the California Department of Transportation.

Section snippets

Data and methods

We used an existing data set of ambient ozone measured at homes in southern California and of concurrent measurements of ambient ozone and NOx at community central site monitors (Avol et al., 1998). Residential NOx concentrations outside study homes were estimated for our analysis from traffic patterns near the homes, as described below. We examined the relationship between home ozone and residential traffic-related NOx, after adjusting for simultaneously measured central site ozone and NOx.

Results

Average 24-h ozone measurements were 33 ppb and 34 ppb at the homes and central site monitoring stations, respectively, but there was a large range from 4.5 ppb to over 90 ppb (Table 1). Estimated residential NOx concentrations modeled from traffic were not normally distributed. The median was 11 ppb, but one home 90 m from a freeway had an estimated NOx exposure of 35 ppb. Fifty percent of the estimated concentrations were between 6.2 and 16 ppb; and 80% were between 4.3 and 19 ppb (results

Discussion

There has been little previous research demonstrating the predictable local spatial variability in ozone that we have shown in these study communities as nearby traffic varies. As described above, ozone concentrations at most homes varied by a modest 7.5 ppb or less due to nearby traffic. However, reductions in ozone concentrations of up to 17 ppb were associated with heavy local traffic across the range of estimated residential NOx in this sample. Our results are consistent with one previous

Conclusion

This study suggests that traffic patterns near homes are potentially a useful tool to predict local spatial variation in ozone within communities.

Acknowledgements

Tami Funk and Siana Alcorn of Sonoma Technology contributed to developing traffic-modeled exposures and performed extensive quality assurance of the air pollution data, respectively, used for this study. Daniel Stram and W. James Gauderman made useful suggestions on modeling strategies. Edward Rappaport, Jassy Molitor and Vince Lin provided programming support. We acknowledge the hard work of Helene Margolis and Dane Westerdahl of the California Air Resources Board and the staff at the

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