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Environ Sci Technol. 2017 Sep 5;51(17):9920-9929. doi: 10.1021/acs.est.7b01864. Epub 2017 Aug 11.

Constrained Mixed-Effect Models with Ensemble Learning for Prediction of Nitrogen Oxides Concentrations at High Spatiotemporal Resolution.

Author information

1
Department of Preventive Medicine, University of Southern California , Los Angeles, California 90033, United States.
2
State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources, Chinese Academy of Sciences , Beijing, China.
3
Sonoma Technology, Inc., Petaluma, California 94954, United States.
4
Department of Epidemiology, University of California , Los Angeles, California 90095, United States.
5
Program in Public Health, College of Health Sciences, University of California , Irvine, California 92697, United States.

Abstract

Spatiotemporal models to estimate ambient exposures at high spatiotemporal resolutions are crucial in large-scale air pollution epidemiological studies that follow participants over extended periods. Previous models typically rely on central-site monitoring data and/or covered short periods, limiting their applications to long-term cohort studies. Here we developed a spatiotemporal model that can reliably predict nitrogen oxide concentrations with a high spatiotemporal resolution over a long time span (>20 years). Leveraging the spatially extensive highly clustered exposure data from short-term measurement campaigns across 1-2 years and long-term central site monitoring in 1992-2013, we developed an integrated mixed-effect model with uncertainty estimates. Our statistical model incorporated nonlinear and spatial effects to reduce bias. Identified important predictors included temporal basis predictors, traffic indicators, population density, and subcounty-level mean pollutant concentrations. Substantial spatial autocorrelation (11-13%) was observed between neighboring communities. Ensemble learning and constrained optimization were used to enhance reliability of estimation over a large metropolitan area and a long period. The ensemble predictions of biweekly concentrations resulted in an R2 of 0.85 (RMSE: 4.7 ppb) for NO2 and 0.86 (RMSE: 13.4 ppb) for NOx. Ensemble learning and constrained optimization generated stable time series, which notably improved the results compared with those from initial mixed-effects models.

PMID:
28727456
PMCID:
PMC5609852
DOI:
10.1021/acs.est.7b01864
[Indexed for MEDLINE]
Free PMC Article

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