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首页> 外文期刊>Atmospheric environment >Influence of the sampling period and time resolution on the PM source apportionment: Study based on the high time-resolution data and long-term daily data
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Influence of the sampling period and time resolution on the PM source apportionment: Study based on the high time-resolution data and long-term daily data

机译：采样周期和时间分辨率对PM来源分配的影响：基于高时间分辨率数据和长期每日数据的研究

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摘要

When planning short-term and long-term measurement campaigns of particulate matter (PM), parameters such as sampling period, time resolution, sampling number, etc. are vital. To study their influence and to provide suggestion for the sampling plan of PM source apportionment (SA), ambient and synthetic speciated datasets (including a high time-resolution dataset and a long-term daily dataset) were studied. First, aiming at studying the sampling period required to generate representative and reliable results for SA, high time-resolution ambient samples were collected by online instruments in a megacity in China. Datasets with different sampling periods (four months, two months, one month, two weeks and one week) were modeled by the Positive Matrix Factorization (PMF). Compared with four month results, AAEs (percent absolute errors between true and estimated contributions) ranged from 11.2 to 27.2% (two months), 19.8-44.5% (one month), 21.0-45.9% (two weeks) and 23.9-44.6% (one week), indicating that divergence increased with decreasing sampling periods. To systematically evaluate this problem and investigate if the increasing time resolutions in a short period could enhance the modeling performance, synthetic datasets were constructed. Results revealed that a sufficient sampling period is required to ensure stable results; without sufficient sampling period, the contributions cannot be reliably estimated, even if the number of samples is large. Then, to explore the influence of variability absences, long-term daily datasets with various variability absences were apportioned and compared. The summed AAEs were 102.2% (no winter), 73.6% (no weekend), 138.7% (no weekday) and 165.6% (no autumn, winter or weekends). This general increase of AAEs can indicate that uncertainty enhanced with the increase in variability absences. When planning short-term measurement campaigns, except for number of samples, sampling period that involves sufficient source cycles has significant implications; when planning long-term sampling, more intensive sampling would increase the model performance. (C) 2017 Elsevier Ltd. All rights reserved.
机译：在计划短期和长期的颗粒物（PM）测量活动时，诸如采样周期，时间分辨率，采样数量等参数至关重要。为了研究它们的影响并为PM源分配（SA）的采样计划提供建议，研究了环境和合成指定数据集（包括高时间分辨率数据集和长期每日数据集）。首先，旨在研究产生SA的代表性和可靠结果所需的采样时间，通过在线仪器在中国一个特大城市中收集了高时间分辨率的环境样品。通过正矩阵分解（PMF）对具有不同采样周期（四个月，两个月，一个月，两周和一星期）的数据集进行建模。与四个月的结果相比，AAE（真实和估计贡献之间的绝对误差百分比）的范围从11.2％到27.2％（两个月），19.8-44.5％（一个月），21.0-45.9％（两个星期）和23.9-44.6％ （一周），表明随着采样周期的减少，差异增大。为了系统地评估此问题并调查在短时间内增加时间分辨率是否可以增强建模性能，构建了综合数据集。结果表明，需要足够的采样时间以确保结果稳定；如果没有足够的采样时间，即使样本数量很大，也无法可靠地估算出贡献。然后，为探讨变异性缺失的影响，对具有各种变异性缺失的长期每日数据集进行了分配和比较。总AAE分别为102.2％（无冬季），73.6％（无周末），138.7％（无工作日）和165.6％（无秋季，冬季或周末）。 AAE的总体增加可以表明，不确定性随着不存在变异性的增加而增加。在计划短期测量活动时，除了样本数量外，涉及足够来源周期的采样期会产生重大影响；在计划长期采样时，更密集的采样将提高模型性能。 （C）2017 Elsevier Ltd.保留所有权利。

著录项

  • 来源
    《Atmospheric environment》 |2017年第9期|301-309|共9页
  • 作者

    Tian Yingze; Xiao Zhimei; Wang Haiting; Peng Xing; Guan Liao; Huangfu Yanqi; Shi Guoliang; Chen Kui; Bi Xiaohui; Feng Yinchang;

  • 作者单位

    Nankai Univ, Coll Environm Sci & Engn, State Environm Protect Key Lab Urban Ambient Air, Tianjin 300071, Peoples R China;

    Tianjin Environm Monitoring Ctr, Tianjin 300071, Peoples R China;

    Nankai Univ, Coll Environm Sci & Engn, State Environm Protect Key Lab Urban Ambient Air, Tianjin 300071, Peoples R China;

    Nankai Univ, Coll Environm Sci & Engn, State Environm Protect Key Lab Urban Ambient Air, Tianjin 300071, Peoples R China;

    Nankai Univ, Coll Environm Sci & Engn, State Environm Protect Key Lab Urban Ambient Air, Tianjin 300071, Peoples R China;

    Nankai Univ, Coll Environm Sci & Engn, State Environm Protect Key Lab Urban Ambient Air, Tianjin 300071, Peoples R China;

    Nankai Univ, Coll Environm Sci & Engn, State Environm Protect Key Lab Urban Ambient Air, Tianjin 300071, Peoples R China;

    Tianjin Environm Monitoring Ctr, Tianjin 300071, Peoples R China;

    Nankai Univ, Coll Environm Sci & Engn, State Environm Protect Key Lab Urban Ambient Air, Tianjin 300071, Peoples R China;

    Nankai Univ, Coll Environm Sci & Engn, State Environm Protect Key Lab Urban Ambient Air, Tianjin 300071, Peoples R China;

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  • 正文语种 eng
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  • 关键词

    PM; Source apportionment; PMF; Sampling period; Time resolution;

    机译：PM;源分配;PMF;采样周期;时间分辨率;

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