• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Environmental Science & Technology >A Method to Quantitatively Apportion Pollutants at High Spatial and Temporal Resolution: The Stochastic Lagrangian Apportionment Method (SLAM)
【24h】

A Method to Quantitatively Apportion Pollutants at High Spatial and Temporal Resolution: The Stochastic Lagrangian Apportionment Method (SLAM)

机译：在高时空分辨率下定量分配污染物的方法：随机拉格朗日分配法（SLAM）

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

We introduce a method to quantify upwind contributions to concentrations of atmospheric pollutants. The Stochastic Lagrangian Apportionment Method (SLAM) carries out the following: (1) account for chemical transformations and depositional losses; (2) incorporate the effects of turbulent dispersion; (3) simulate the locations of the sources with high spatial and temporal resolution; and (4) minimize the impact from numerical diffusion. SLAM accomplishes these four features by using a time-reversed Lagrangian particle dispersion model and then simulating chemical changes forward in time, while tagging and keeping track of different sources. As an example of SLAM's application, we show its use in apportioning sources contributing to ammonia (NH_3) and ammonium particulates (p-NH_4~+) at a site in southern Ontario, Canada Agricultural emissions are seen to dominate contributions to NH_3 and p-NH_4~+ at the site. The source region of NH_3 was significantly smaller than that of p~NH_4~+, which covered numerous states of the American Midwest. The source apportionment results from SLAM were compared against those from zeroing-out individual sources ("brute force method"; BFM). The comparisons show SLAM to produce almost identical results as BFM for NH_3, but higher concentrations of p-NH_4~+, likely due to indirect effects that affect BFM Finally, uncertainties in the SLAM approach and ways to address such shortcomings by combining SLAM with inverse methods are discussed.
机译：我们引入一种方法来量化迎风对大气污染物浓度的贡献。随机拉格朗日分配法（SLAM）进行以下工作：（1）考虑化学转化和沉积损失； （2）纳入湍流扩散的影响； （3）以高时空分辨率模拟源的位置； （4）最小化数值扩散的影响。 SLAM通过使用时间反向拉格朗日粒子分散模型，然后模拟时间上的化学变化，同时标记并跟踪不同来源，来实现这四个功能。以SLAM的应用为例，我们显示了其在加拿大安大略省南部某处的氨（NH_3）和铵颗粒（p-NH_4〜+）贡献源的分配中的用途。农业排放物被认为是NH_3和p-的主要来源。现场NH_4〜+。 NH_3的源区明显小于p〜NH_4〜+的源区，p_NH_4〜+覆盖了美国中西部的许多州。将SLAM的源分配结果与归零单个源的结果进行了比较（“暴力法”； BFM）。比较结果表明，SLAM产生与NH_3的BFM几乎相同的结果，但是p-NH_4〜+的浓度较高，这可能是由于间接影响BFM的结果。最后，SLAM方法的不确定性以及通过将SLAM与逆相结合来解决此类缺点的方法方法进行了讨论。

著录项

  • 来源
    《Environmental Science & Technology》 |2015年第1期|351-360|共10页
  • 作者

    John C. Lin; Deyong Wen;

  • 作者单位

    Department of Atmospheric Sciences, University of Utah, Salt Lake City, Utah 84112-0102, United States;

    Department of Earth and Environmental Sciences, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);美国《生物学医学文摘》(MEDLINE);美国《化学文摘》(CA);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号