Method for Fusing Observational Data and Chemical Transport Model Simulations To Estimate Spatiotemporally Resolved Ambient Air Pollution

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• 作者：Mariel D. Friberg ; Xinxin Zhai ; Heather A. Holmes ; Howard H. Chang ; Matthew J. Strickland ; Stefanie Ebelt Sarnat ; Paige E. Tolbert ; Armistead G. Russell ; James A. Mulholland
• 刊名：Environmental Science & Technology
• 出版年：2016
• 出版时间：April 5, 2016
• 年：2016
• 卷：50
• 期：7
• 页码：3695-3705
• 全文大小：829K
• ISSN：1520-5851

文摘

Investigations of ambient air pollution health effects rely on complete and accurate spatiotemporal air pollutant estimates. Three methods are developed for fusing ambient monitor measurements and 12 km resolution chemical transport model (CMAQ) simulations to estimate daily air pollutant concentrations across Georgia. Temporal variance is determined by observations in one method, with the annual mean CMAQ field providing spatial structure. A second method involves scaling daily CMAQ simulated fields using mean observations to reduce bias. Finally, a weighted average of these results based on prediction of temporal variance provides optimized daily estimates for each 12 × 12 km grid. These methods were applied to daily metrics of 12 pollutants (CO, NO₂, NO_x, O₃, SO₂, PM₁₀, PM_2.5, and five PM_2.5 components) over the state of Georgia for a seven-year period (2002–2008). Cross-validation demonstrates a wide range in optimized model performance across pollutants, with SO₂ predicted most poorly due to limitations in coal combustion plume monitoring and modeling. For the other pollutants studied, 54–88% of the spatiotemporal variance (Pearson R² from cross-validation) was captured, with ozone and PM_2.5 predicted best. The optimized fusion approach developed provides daily spatial field estimates of air pollutant concentrations and uncertainties that are consistent with observations, emissions, and meteorology.