Dataset Information

Evaluation of observation-fused regional air quality model results for population air pollution exposure estimation.

ABSTRACT: In this study, Community Multiscale Air Quality (CMAQ) model was applied to predict ambient gaseous and particulate concentrations during 2001 to 2010 in 15 hospital referral regions (HRRs) using a 36-km horizontal resolution domain. An inverse distance weighting based method was applied to produce exposure estimates based on observation-fused regional pollutant concentration fields using the differences between observations and predictions at grid cells where air quality monitors were located. Although the raw CMAQ model is capable of producing satisfying results for O3 and PM2.5 based on EPA guidelines, using the observation data fusing technique to correct CMAQ predictions leads to significant improvement of model performance for all gaseous and particulate pollutants. Regional average concentrations were calculated using five different methods: 1) inverse distance weighting of observation data alone, 2) raw CMAQ results, 3) observation-fused CMAQ results, 4) population-averaged raw CMAQ results and 5) population-averaged fused CMAQ results. It shows that while O3 (as well as NOx) monitoring networks in the HRRs are dense enough to provide consistent regional average exposure estimation based on monitoring data alone, PM2.5 observation sites (as well as monitors for CO, SO2, PM10 and PM2.5 components) are usually sparse and the difference between the average concentrations estimated by the inverse distance interpolated observations, raw CMAQ and fused CMAQ results can be significantly different. Population-weighted average should be used to account for spatial variation in pollutant concentration and population density. Using raw CMAQ results or observations alone might lead to significant biases in health outcome analyses.

SUBMITTER: Chen G

PROVIDER: S-EPMC4151129 | biostudies-literature | 2014 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Evaluation of observation-fused regional air quality model results for population air pollution exposure estimation.

Chen Gang G Li Jingyi J Ying Qi Q Sherman Seth S Perkins Neil N Sundaram Rajeshwari R Mendola Pauline P

The Science of the total environment 20140417

In this study, Community Multiscale Air Quality (CMAQ) model was applied to predict ambient gaseous and particulate concentrations during 2001 to 2010 in 15 hospital referral regions (HRRs) using a 36-km horizontal resolution domain. An inverse distance weighting based method was applied to produce exposure estimates based on observation-fused regional pollutant concentration fields using the differences between observations and predictions at grid cells where air quality monitors were located. ...[more]

PMID: 24747248

Similar Datasets

Project description:BackgroundThe era of big data has enabled sophisticated models to predict air pollution concentrations over space and time. Historically these models have been evaluated using overall metrics that measure how close predictions are to monitoring data. However, overall methods are not designed to distinguish error at timescales most relevant for epidemiologic studies, such as day-to-day errors that impact studies of short-term health associations.MethodsWe introduce frequency band model performance, which quantifies health estimation capacity of air quality prediction models for time series studies of air pollution and health. Frequency band model performance uses a discrete Fourier transform to evaluate prediction models at timescales of interest. We simulated fine particulate matter (PM2.5), with errors at timescales varying from acute to seasonal, and health time series data. To compare evaluation approaches, we use correlations and root mean squared error (RMSE). Additionally, we assess health estimation capacity through bias and RMSE in estimated health associations. We apply frequency band model performance to PM2.5 predictions at 17 monitors in 8 US cities.ResultsIn simulations, frequency band model performance rates predictions better (lower RMSE, higher correlation) when there is no error at a particular timescale (e.g., acute) and worse when error is added to that timescale, compared to overall approaches. Further, frequency band model performance is more strongly associated (R2 = 0.95) with health association bias compared to overall approaches (R2 = 0.57). For PM2.5 predictions in Salt Lake City, UT, frequency band model performance better identifies acute error that may impact estimated short-term health associations.ConclusionsFor epidemiologic studies, frequency band model performance provides an improvement over existing approaches because it evaluates models at the timescale of interest and is more strongly associated with bias in estimated health associations. Evaluating prediction models at timescales relevant for health studies is critical to determining whether model error will impact estimated health associations.

Project description:BackgroundThe negative effect of air pollution on human health is widely reported in recent literature. It typically involves urbanized areas where the population is concentrated and where most primary air pollutants are produced. A comprehensive health risk assessment is therefore of strategic importance for health authorities.MethodsIn this study we propose a methodology to perform an indirect and retrospective health risk assessment of all-cause mortality associated with long-term exposure to particulate matter less than 2.5 microns (PM2.5), nitrogen dioxide (NO2) and ozone (O3) in a typical Monday to Friday working week. A combination of satellite-based settlement data, model-based air pollution data, land use, demographics and regional scale mobility, allowed to examine the effect of population mobility and pollutants daily variations on the health risk. A Health Risk Increase (HRI) metric was derived on the basis of three components: hazard, exposure and vulnerability, utilizing the relative risk values from the World Health Organization. An additional metric, the Health Burden (HB) was formulated, which accounts for the total number of people exposed to a certain risk level.ResultsThe effect of regional mobility patterns on the HRI metric was assessed, resulting in an increased HRI associated with all three stressors when considering a dynamic population compared to a static one. The effect of diurnal variation of pollutants was only observed for NO2 and O3. For both, the HRI metric resulted in significantly higher values during night. Concerning the HB parameter, we identified the commuting flows of the population as the main driver in the resulting metric.ConclusionsThis indirect exposure assessment methodology provides tools to support policy makers and health authorities in planning intervention and mitigation measures. The study was carried out in Lombardy, Italy, one of the most polluted regions in Europe, but the incorporation of satellite data makes our approach valuable for studying global health.

Dataset Information

Evaluation of observation-fused regional air quality model results for population air pollution exposure estimation.

Publications

Evaluation of observation-fused regional air quality model results for population air pollution exposure estimation.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets