Statistical air quality predictions for public health surveillance: evaluation and generation of county level metrics of PM2.5 for the environmental public health tracking network

International Journal of Health Geographics

Judith R Qualters³  Scott R Kegler²  William Fred Dimmick¹  Ambarish Vaidyanathan³ 

[1] Office of Research and Development, US Environmental Protection Agency, Mail Stop: D343-04, 109 Alexander Drive, Durham, NC, 27711, USA;Centers for Disease Control and Prevention, National Center for Injury Prevention and Control, Mail Stop: F64; 4770 Buford Hwy, Atlanta, GA, 30341, USA;Centers for Disease Control and Prevention, National Center for Environmental Health, Mail Stop: F60; 4770 Buford Hwy, Atlanta, GA, 30341, USA

关键词: Geo-imputation;    Air quality system;    Hierarchical Bayesian;    Tracking Network;    Particulate matter;   

Others  :  810230
DOI  :  10.1186/1476-072X-12-12

 received in 2013-01-09, accepted in 2013-02-24,  发布年份 2013

【 摘 要 】

Background

The Centers for Disease Control and Prevention (CDC) developed county level metrics for the Environmental Public Health Tracking Network (Tracking Network) to characterize potential population exposure to airborne particles with an aerodynamic diameter of 2.5 μm or less (PM_2.5). These metrics are based on Federal Reference Method (FRM) air monitor data in the Environmental Protection Agency (EPA) Air Quality System (AQS); however, monitor data are limited in space and time. In order to understand air quality in all areas and on days without monitor data, the CDC collaborated with the EPA in the development of hierarchical Bayesian (HB) based predictions of PM_2.5 concentrations. This paper describes the generation and evaluation of HB-based county level estimates of PM_2.5.

Methods

We used three geo-imputation approaches to convert grid-level predictions to county level estimates. We used Pearson (r) and Kendall Tau-B (τ) correlation coefficients to assess the consistency of the relationship, and examined the direct differences (by county) between HB-based estimates and AQS-based concentrations at the daily level. We further compared the annual averages using Tukey mean-difference plots.

Results

During the year 2005, fewer than 20% of the counties in the conterminous United States (U.S.) had PM_2.5 monitoring and 32% of the conterminous U.S. population resided in counties with no AQS monitors. County level estimates resulting from population-weighted centroid containment approach were correlated more strongly with monitor-based concentrations (r = 0.9; τ = 0.8) than were estimates from other geo-imputation approaches. The median daily difference was −0.2 μg/m³ with an interquartile range (IQR) of 1.9 μg/m³ and the median relative daily difference was −2.2% with an IQR of 17.2%. Under-prediction was more prevalent at higher concentrations and for counties in the western U.S.

Conclusions

While the relationship between county level HB-based estimates and AQS-based concentrations is generally good, there are clear variations in the strength of this relationship for different regions of the U.S. and at various concentrations of PM_2.5. This evaluation suggests that population-weighted county centroid containment method is an appropriate geo-imputation approach, and using the HB-based PM_2.5 estimates to augment gaps in AQS data provides a more spatially and temporally consistent basis for calculating the metrics deployed on the Tracking Network.

【 授权许可】

   
2013 Vaidyanathan et al; licensee BioMed Central Ltd.

【 图 表 】

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