Development of long-term spatiotemporal models for ambient ozone in six metropolitan regions of the United States: The MESA Air study
Introduction
Ground-level ozone is the classic indicator for the mixture of photochemical oxidants originating from anthropogenic and biogenic precursor emissions (EPA, 2006). Ozone itself is a potent oxidizing agent that has clear harmful effects on human health, as has been amply demonstrated in human chamber exposure studies (ISA, 2013). Observational associations between short-term exposure to ozone and respiratory morbidity and mortality have also been documented in the United States and Europe (ISA, 2013, WHO, 2013). Chronic effects of ozone exposure on lung function development, asthma incidence and pulmonary inflammation have been suggested (ISA, 2013). However, compared to the large body of evidence on long-term effects of traffic related pollutants (e.g. nitrogen dioxide and particulate matter), relatively little research has examined health effects related to long-term ozone exposure. In the United States, national-wide ozone levels have decreased steadily in the past decade, although with some heterogeneity between urban and rural areas (EPA, 2014, Chan, 2009, Cooper et al., 2012, Lefohn et al., 2010, Simon et al., 2015).
Attempts to estimate long-term ozone exposure in large populations are scarce and challenging, largely because of the complex spatiotemporal pattern of ozone concentrations at fine scales. Previous epidemiological studies on long-term ozone exposure generally relied on estimates from nearby monitoring sites (Jerrett et al., 2009) or simple spatial interpolation techniques such as inverse distance weighting (Breton et al., 2012, Jerrett et al., 2013, Lipsett et al., 2011). More advanced exposure estimation techniques include chemical transport modeling (CTM) and land use regression (LUR) modeling: CTM approaches with resolution to grids measured in km2s are typically not spatially resolved enough to characterize exposures at very local scales (i.e., meters). Recent LUR models have utilized a large number of covariates, such as traffic characteristics and land use/land cover, to account for spatial distributions of air pollutants (Hoek et al., 2008, Malmqvist et al., 2014); hese models did not take temporal variations into account which is important for ozone, as varying spatiotemporal ozone patterns have been observed in the United States (ISA, 2013). Some spatiotemporal ozone modeling efforts were exclusively based on a limited amount of routinely collected monitoring data over large regions (Adam-Poupart et al., 2014, de Nazelle et al., 2010, Yu et al., 2009). However, routinely collected monitoring data from relatively few sites are unable to capture roadside decrements of ozone caused by scavenging by freshly emitted nitric oxide (NO) in urban areas, resulting in overestimation of ozone exposure for some segments of the population samples used in epidemiological studies.
The Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air) was designed to examine the effects of long-term air pollution exposure on cardiovascular health (Kaufman et al., 2012). Air pollution data including ozone were collected through an intensive measurement campaign in six metropolitan regions (Baltimore, Maryland; Chicago, Illinois; Los Angeles, California; New York, New York; St. Paul, Minnesota; Winston–Salem, North Carolina) in order to better represent spatial and temporal patterns of the air pollutants (Cohen et al., 2009). This paper describes the development and the performance of spatiotemporal models of long-term ozone concentrations in these six metropolitan regions.
Section snippets
Materials and methods
Separate models were developed for each of the six MESA metropolitan regions because of the diversity of study areas and differences in available GIS predictor variables, although an identical procedure for estimating model parameters was employed for all regions. Briefly, the spatiotemporal model, which we describe in more detail below, decomposes space-time ozone concentrations into spatially varying long-term averages, spatially-varying seasonal and long-term trends, and spatially-correlated
Ozone concentrations
Substantial variability of two-week average ozone concentrations measured at the AQS and participant home sites was observed in all six MESA regions (Fig. 1). Observations at the AQS monitors, which captured regional spatial and long-term temporal characteristics of ozone, showed larger variability and higher median concentrations compared with project-specific measurements from home and fixed sites that were made locally within each city. The correlation coefficient between AQS monitor
Discussion
We developed spatiotemporal models for ozone within a novel geo-statistical framework following a uniform set of procedures in six metropolitan regions. These models incorporated rich monitoring data, geographic information, and were capable of predicting spatial distributions of ozone over a long time period (1999–2013).
Conclusion
Using rich temporal data from AQS monitoring sites and a dedicated, study-specific spatial monitoring campaign, we were able to develop spatiotemporal models for ozone that perform well in terms of both prediction accuracy and precision in the six metropolitan regions of the MESA study in the United States. Most prior modeling of ozone concentrations has been based on chemical transport modeling, which incorporates meteorology and emissions information but typically is unable to provide the
Acknowledgments
This publication was developed under STAR research assistance agreement, No. RD831697 (MESA Air) and RD833741 (MESA Coarse) awarded by the U.S Environmental protection Agency and in-vehicle and related measurements from the University of Washington Center for Clean Air Research (UW CCAR, Environmental Protection Agency RD83479601-01). Additional support was provided by U.S. EPA grants RD-83479601-0, National Institute of Environmental Health Sciences (NIEHS) grants K24ES013195 and P30ES007033,
References (31)
- et al.
Mapping of background air pollution at a fine spatial scale across the
Eur. Union. Sci. Total Environ.
(2009) - et al.
A review of land-use regression models to assess spatial variation of outdoor air pollution
Atmos. Environ.
(2008) - et al.
A national fine spatial scale land-use regression model for ozone
Environ. Res.
(2015) - et al.
Characterizing changes in surface ozone levels in metropolitan and rural areas in the United States for 1980−2008 and 1994−2008
Atmos. Environ.
(2010) - et al.
Assessing ozone exposure for epidemiological studies in Malmö and Umeå, Sweden
Atmos. Environ.
(2014) - et al.
Pragmatic estimation of a spatio-temporal air quality model with irregular monitoring data
Atmos. Environ.
(2011) - et al.
Spatiotemporal modeling of ozone levels in quebec (Canada): a comparison of kriging, land-use regression (LUR), and combined bayesian maximum entropy–LUR approaches
Environ. Health Perspect.
(2014) - et al.
Large scale air pollution estimation method combining land use regression and chemical transport modeling in a geostatistical framework
Environ. Sci. Technol.
(2014) - et al.
Childhood air pollutant exposure and carotid artery intima-media thickness in young adults
Circulation
(2012) Regional ground-level ozone trends in the context of meteorological influences across Canada and the eastern United States from 1997 to 2006
J. Geophys. Res. Atmos.
(2009)
Long-term ozone trends at rural ozone monitoring sites across the United States, 1990−2010
J. Geophys. Res. Atmos.
Approach to estimating participant pollutant exposures in the multi-ethnic study of atherosclerosis and air pollution (MESA Air)
Environ. Sci. Technol.
Statistics for Spatial Data
Addendum to the User's Guide to CAL3QHC Version 2.0 (CAL3QHCR User's Guide)
(U.S. Environmental Protection Agency). Air Quality Criteria for Ozone and Related Photochemical Oxidants (2006 Final)
Cited by (35)
Ambient air pollution and rate of spontaneous abortion
2024, Environmental ResearchHigh-resolution modeling for criteria air pollutants and the associated air quality index in a metropolitan city
2023, Environment International