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Keywords:

  • Science & Technology
  • Life Sciences & Biomedicine
  • Environmental Sciences
  • Public, Environmental & Occupational Health
  • Toxicology
  • Environmental Sciences & Ecology
  • Air pollution
  • Criteria pollutants
  • Epidemiology
  • Exposure modeling
  • Particulate matter
  • Population-based studies
  • FINE PARTICULATE MATTER
  • EMERGENCY-DEPARTMENT VISITS
  • SOUTHEASTERN AEROSOL RESEARCH
  • MEASUREMENT ERROR
  • SOCIOECONOMIC DEPRIVATION
  • PM2.5 CONCENTRATIONS
  • SPATIAL VARIABILITY
  • ASSOCIATIONS
  • POLLUTANTS
  • ATLANTA

Monitoring vs. modeled exposure data in time-series studies of ambient air pollution and acute health outcomes

Tools:

Journal Title:

JOURNAL OF EXPOSURE SCIENCE AND ENVIRONMENTAL EPIDEMIOLOGY

Volume:

Volume 33, Number 3

Publisher:

, Pages 377-385

Type of Work:

Article

Abstract:

Background: Population-based short-term air pollution health studies often have limited spatiotemporally representative exposure data, leading to concerns of exposure measurement error. Objective: To compare the use of monitoring and modeled exposure metrics in time-series analyses of air pollution and cardiorespiratory emergency department (ED) visits. Methods: We obtained daily counts of ED visits for Atlanta, GA during 2009–2013. We leveraged daily ZIP code level concentration estimates for eight pollutants from nine exposure metrics. Metrics included central monitor (CM), monitor-based (inverse distance weighting, kriging), model-based [community multiscale air quality (CMAQ), land use regression (LUR)], and satellite-based measures. We used Poisson models to estimate air pollution health associations using the different exposure metrics. The approach involved: (1) assessing CM-based associations, (2) determining if non-CM metrics can reproduce CM-based associations, and (3) identifying potential value added of incorporating full spatiotemporal information provided by non-CM metrics. Results: Using CM exposures, we observed associations between cardiovascular ED visits and carbon monoxide, nitrogen dioxide, fine particulate matter, elemental and organic carbon, and between respiratory ED visits and ozone. Non-CM metrics were largely able to reproduce CM-based associations, although some unexpected results using CMAQ- and LUR-based metrics reduced confidence in these data for some spatiotemporally-variable pollutants. Associations with nitrogen dioxide and sulfur dioxide were only detected, or were stronger, when using metrics that incorporate all available monitoring data (i.e., inverse distance weighting and kriging). Significance: The use of routinely-collected ambient monitoring data for exposure assignment in time-series studies of large metropolitan areas is a sound approach, particularly when data from multiple monitors are available. More sophisticated approaches derived from CMAQ, LUR, or satellites may add value when monitoring data are inadequate and if paired with thorough data characterization. These results are useful for interpretation of existing literature and for improving exposure assessment in future studies. Impact statement: This study compared and interpreted the use of monitoring and modeled exposure metrics in a daily time-series analysis of air pollution and cardiorespiratory emergency department visits. The results suggest that the use of routinely-collected ambient monitoring data in population-based short-term air pollution and health studies is a sound approach for exposure assignment in large metropolitan regions. CMAQ-, LUR-, and satellite-based metrics may allow for health effects estimation when monitoring data are sparse, if paired with thorough data characterization. These results are useful for interpretation of existing health effects literature and for improving exposure assessment in future air pollution epidemiology studies.
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