Predicting Primary PM2.5 and PM0.1 Trace Composition for Epidemiological Studies in California
详细信息 查看全文
- 作者：Jianlin Hu ; Hongliang Zhang ; Shu-Hua Chen ; Christine Wiedinmyer ; Francois Vandenberghe ; Qi Ying ; Michael J. Kleeman
- 刊名：Environmental Science & Technology
- 出版年：2014
- 出版时间：May 6, 2014
- 年：2014
- 卷：48
- 期：9
- 页码：4971-4979
- 全文大小：519K
- 年卷期：v.48,no.9(May 6, 2014)
- ISSN：1520-5851
文摘
The University of California鈥擠avis_Primary (UCD_P) chemical transport model was developed and applied to compute the primary airborne particulate matter (PM) trace chemical concentrations from 900 sources in California through a simulation of atmospheric emissions, transport, dry deposition and wet deposition for a 7-year period (2000鈥?006) with results saved at daily time resolution. A comprehensive comparison between monthly average model results and available measurements yielded Pearson correlation coefficients (R) 鈮?.8 at 鈮? sites (out of a total of eight) for elemental carbon (EC) and nine trace elements: potassium, chromium, zinc, iron, titanium, arsenic, calcium, manganese, and strontium in the PM2.5 size fraction. Longer averaging time increased the overall R for PM2.5 EC from 0.89 (1 day) to 0.94 (1 month), and increased the number of species with strong correlations at individual sites. Predicted PM0.1 mass and PM0.1 EC exhibited excellent agreement with measurements (R = 0.92 and 0.94, respectively). The additional temporal and spatial information in the UCD_P model predictions produced population exposure estimates for PM2.5 and PM0.1that differed from traditional exposure estimates based on information at monitoring locations in California Metropolitan Statistical Areas, with a maximum divergence of 58% at Bakersfield. The UCD_P model has the potential to improve exposure estimates in epidemiology studies of PM trace chemical components and health.