...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Environmental Science & Technology >Predicting Thermal Behavior of Secondary Organic Aerosols
【24h】

Predicting Thermal Behavior of Secondary Organic Aerosols

机译：预测次要有机气溶胶的热行为

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

Volume concentrations of secondary organic aerosol (SOA) are measured in 139 steady-state, single precursor hydrocarbon oxidation experiments after passing through a temperature controlled inlet. The response to change in temperature is well predicted through a feedforward Artificial Neural Network. The most parsimonious model, as indicated by Akaike's Information Criterion, Corrected (AIC,C), utilizes 11 input variables, a single hidden layer of 4 tanh activation function nodes, and a single linear output function. This model predicts thermal behavior of single precursor aerosols to less than ±5%, which is within the measurement uncertainty, while limiting the problem of overfitting. Prediction of thermal behavior of SOA can be achieved by a concise number of descriptors of the precursor hydrocarbon including the number of internal and external double bonds, number of methyl- and ethyl-functional groups, molecular weight, and number of ring structures, in addition to the volume of SOA formed, and an indicator of which of four oxidant precursors was used to initiate reactions (NO_x photo-oxidation, photolysis of H_2O_2, ozonolysis, or thermal decomposition of N_2O_5). Additional input variables, such as chamber volumetric residence time, relative humidity, initial concentration of oxides of nitrogen, reacted hydrocarbon concentration, and further descriptors of the precursor hydrocarbon, including carbon number, number of oxygen atoms, and number of aromatic ring structures, lead to over fit models, and are unnecessary for an efficient, accurate predictive model of thermal behavior of SOA. This work indicates that predictive statistical modeling methods may be complementary to descriptive techniques for use in parametrization of air quality models.
机译：二次有机气溶胶（SOA）的体积浓度在经过温度控制的进口后，在139个稳态单前体烃氧化实验中进行了测量。通过前馈人工神经网络可以很好地预测温度变化的响应。如Akaike的校正后的信息标准（AIC，C）所示，最简约的模型利用11个输入变量，4个tanh激活函数节点的单个隐藏层和单个线性输出函数。该模型预测单个前驱气溶胶的热行为低于±5％，这在测量不确定性之内，同时限制了过拟合的问题。可以通过简明的前体烃描述符来实现SOA的热行为预测，这些描述符包括内部和外部双键的数量，甲基和乙基官能团的数量，分子量以及环结构的数量，此外到形成的SOA的量，并指示四种氧化剂前体中的哪一种用于引发反应（NO_x光氧化，H_2O_2的光解，臭氧分解或N_2O_5的热分解）。其他输入变量，例如腔室容积停留时间，相对湿度，氮氧化物的初始浓度，反应的碳氢化合物浓度，以及前体碳氢化合物的进一步描述，包括碳数，氧原子数和芳环结构数，铅过度拟合模型，对于有效，准确的SOA热行为预测模型而言，这是不必要的。这项工作表明，预测性统计建模方法可能是用于空气质量模型参数化的描述性技术的补充。

著录项

  • 来源
    《Environmental Science & Technology》 |2017年第17期|9911-9919|共9页
  • 作者

    John H. Offenberg; Michael Lewandowski; Tadeusz E. Kleindienst; Kenneth S. Docherty; Mohammed Jaoui; Jonathan Krug; Theran P. Riedel; David A Olson;

  • 作者单位

    United States Environmental Protection Agency, Research Triangle Park, North Carolina 27711, United States;

    United States Environmental Protection Agency, Research Triangle Park, North Carolina 27711, United States;

    United States Environmental Protection Agency, Research Triangle Park, North Carolina 27711, United States;

    Jacobs Technology, Inc., Research Triangle Park, North Carolina 27709, United States;

    United States Environmental Protection Agency, Research Triangle Park, North Carolina 27711, United States;

    United States Environmental Protection Agency, Research Triangle Park, North Carolina 27711, United States;

    United States Environmental Protection Agency, Research Triangle Park, North Carolina 27711, United States;

    United States Environmental Protection Agency, Research Triangle Park, North Carolina 27711, United States;

  • 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