首页> 外文期刊>Transactions of the ASABE >A SIMULATED APPROACH TO ESTIMATING PM10 AND PM2.5 CONCENTRATIONS DOWNWIND FROM COTTON GINS
【24h】

A SIMULATED APPROACH TO ESTIMATING PM10 AND PM2.5 CONCENTRATIONS DOWNWIND FROM COTTON GINS

机译:从棉G中向下估算PM10和PM2.5浓度的模拟方法

获取原文
获取原文并翻译 | 示例
           

摘要

Cotton gins are required to obtain operating permits from state air pollution regulatory agencies (SAPRA), which regulate the amount of particulate matter that can be emitted. Industrial Source Complex Short Term version 3 (ISCST3) is the Gaussian dispersion model currently used by some SAPRAs to predict downwind concentrations used in the regulatory process in the absence of field sampling data. The maximum ambient concentrations for PM 10 and PM 2.5 are set by the National Ambient Air Quality Standard (NAAQS) at 150 µ g/m 3 and 65 µ g/m 3 (24 h average), respectively. Some SAPRAs use the NAAQS concentrations as property line concentrations for regulatory purposes. This article reports the results of a unique approach to estimating downwind PM 10 and PM 2.5 concentrations using Monte Carlo simulation, the Gaussian dispersion equation, the Hino power law, and a particle size distribution that characterizes the dust typically emitted from cotton gin exhausts. These results were then compared to a 10 min concentration (C 10 ) and the concentrations that would be measured by an FRM PM 10 and PM 2.5 sampler. The total suspended particulate (TSP) emission rate, particle size distributions, and sampler performance characteristics were assigned to triangular distributions to simulate the real-world operation of the gin and sampling systems. The TSP emission factor given in AP-42 for cotton gins was used to derive the PM mass emission rate from a 40 bale/h plant. The Gaussian equation was used to model the ambient TSP concentration downwind from the gin. The performance characteristics for the PM 10 and PM 2.5 samplers were then used to predict what the measured concentration would be for two PSD conditions. The first PSD assumption was that the mass median diameter (MMD) and geometric standard deviation (GSD) were constant at 12 µ m and 2, respectively, and the second scenario assigned a triangular distribution to the MMD and GSD of {15, 20, 25} µ m and {1.8, 2.0, 2.2}, respectively. The results show that the PM 2.5 fraction of the dust emitted under either PSD condition was negligible when compared to the NAAQS for PM 2.5 of 65 µ g/m 3 . The results also demonstrate that correcting for wind direction changes within the hour using the power law reduces the ambient concentration by a factor of 2.45
机译:轧花机必须获得州空气污染监管机构(SAPRA)的经营许可证,该机构负责监管可排放的颗粒物的数量。工业资源综合体短期版本3(ISCST3)是目前一些SAPRA使用的高斯离散模型,用于在缺乏现场采样数据的情况下预测监管过程中使用的顺风浓度。国家环境空气质量标准(NAAQS)将PM 10 和PM 2.5 的最大环境浓度设置为150 µg / m 3 和65 µ g / m 3 (平均24小时)。一些SAPRA将NAAQS浓度用作特性线浓度,以进行监管。本文报告了一种独特方法的结果,该方法使用蒙特卡罗模拟,高斯色散方程,日诺幂定律和质点估算顺风PM 10 和PM 2.5 浓度尺寸分布,表征通常从轧花机废气中排放的灰尘。然后将这些结果与10分钟浓度(C 10 )进行比较,并通过FRM PM 10 和PM 2.5 进行测量采样器。总悬浮颗粒物(TSP)排放速率,粒度分布和采样器性能特征被分配给三角分布,以模拟杜松子酒和采样系统的实际操作。 AP-42中给出的用于轧花的TSP排放因子用于推导40包/小时工厂的PM质量排放率。高斯方程用于模拟杜松子酒顺风向周围TSP浓度。然后使用PM 10 和PM 2.5 采样器的性能特征来预测在两种PSD条件下测得的浓度。第一个PSD假设是质量中值直径(MMD)和几何标准偏差(GSD)分别恒定在12 µm和2,并且第二种情况为MMD和GSD分配了三角形分布{15,20, 25} µm和{1.8,2.0,2.2}。结果表明,与NAAQS的PM 2.5 为65 µg / m 3相比,在任一PSD条件下排放的粉尘PM 2.5 比例都可以忽略不计。 。结果还表明,使用幂定律校正小时内的风向变化会使环境浓度降低2.45倍

著录项

  • 来源
    《Transactions of the ASABE》 |2005年第5期|p.1919-1925|共7页
  • 作者单位

    John D. Wanjura, ASABE Student Member, Graduate Student, Department of Biological and Agricultural Engineering, Texas A&

    M University, College Station, Texas;

    Michael D. Buser, ASABE Member Engineer, Research Engineer, USDA-ARS Southwest Cotton Production and Ginning Laboratory, Lubbock, Texas;

    and Calvin B. Parnell, Jr., ASABE Member Engineer, Regents Professor, Bryan W. Shaw, ASABE Member Engineer, Associate Professor, and Ronald E. Lacey, ASABE Member Engineer, Professor, Department of Biological and Agricultural Engineering Department, Texas A&

    M University, College Station, Texas. Corresponding author: John D. Wanjura, Department of Biological and Agricultural Engineering, Texas A&

    M University, 324 Scoates Hall, MS 2117 TAMU, College Station, TX 77843;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Dispersion modeling; Particulate matter;

    机译:分散建模;颗粒物;

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