4.7 Article

Estimating PM10 air concentrations from dust storms in Iraq, Kuwait and Saudi Arabia

期刊

ATMOSPHERIC ENVIRONMENT
卷 35, 期 25, 页码 4315-4330

出版社

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/S1352-2310(01)00159-5

关键词

long-range transport; Southwest Asia; resuspension; aeolian movement; HYSPLIT

向作者/读者索取更多资源

A model for the emission of PM10 dust has been constructed using the concept of a threshold friction velocity which is dependent on surface roughness. Surface roughness in turn was correlated with geomorphology or soil properties for Kuwait, Iraq, part of Syria, Saudi Arabia, the United Arab Emirates and Oman. The PM10 emission algorithm was incorporated into a Lagrangian transport and dispersion model. PM10 air concentrations were computed from August 1990 through August 1991. The model predicted about the right number of dust events over Kuwait (events occur 18% of the time). The model results agreed quantitatively with measurements at four locations in Saudi Arabia and one in Kuwait for one major dust event (> 1000 mug/m(3)). However, for smaller scale dust events (200-1000 mug/m(3)), especially at the coastal sampling locations, the model substantially over-predicted the air concentrations. Part of the over-prediction was attributed to the entrainment of dust-free air by the sea breeze, a flow feature not represented by the large-scale gridded meteorological data fields used in the model computation. Another part of the over-prediction was the model's strong sensitivity to threshold friction velocity and the surface soil texture coefficient (the soil emission factor), and the difficulty in accurately representing these parameters in the model. A comparison of the model predicted PM to spatial pattern with the TOMS satellite aerosol index (Al) yielded a spatial pattern covering a major portion of Saudi Arabia that was quite similar to the observed Al pattern. Published by Elsevier Science Ltd.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据