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首页> 外文期刊>Environmental Science & Technology >Systematic Evaluation of Land Use Regression Models for NO_2
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Systematic Evaluation of Land Use Regression Models for NO_2

机译：NO_2土地利用回归模型的系统评价

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摘要

Land use regression (LUR) models have become popular to explain die spatial variation of air pollution concentrations. Independent evaluation is important. We developed LUR models for nitrogen dioxide (NO_2) using measurements conducted at 144 sampling sites in The Netherlands. Sites were randomly divided into training data sets with a size of 24, 36, 48, 72, 96, 108, and 120 sites. LUR models were evaluated using (1) internal leave-one-out-cross-validation (LOOCV)" within the training data sets and (2) external "hold-out" validation (HV) against independent test data sets. In addition, we calculated Mean Square Error based validation R~2s. The mean adjusted model and LOOCV R~2 slightly decreased from 0.87 to 0.82 and 0.83 to 0.79, respectively, with an increasing number of training sites. In contrast, the mean HV R~2 was lowest (0.60) with the smallest training sets and increased to 0.74 with the largest training sets. Predicted concentrations were more accurate in sites with out of range values for prediction variables after changing these values to the minimum or maximum of the ranee observed in the corresponding training data set. LUR models for NO_2 perform less well, when evaluated against independent measurements, when they are based on relatively small training sets. In our specific application, models based on as few as 24 training sites, however, achieved acceptable hold out validation R~2s of, on average, 0.60.
机译：土地利用回归（LUR）模型已成为解释空气污染浓度空间变化的流行方法。独立评估很重要。我们使用在荷兰的144个采样点进行的测量开发了二氧化氮（NO_2）的LUR模型。将站点随机分为大小为24、36、48、72、96、108和120个站点的训练数据集。使用（1）训练数据集中的内部“留一法则交叉验证（LOOCV）”和（2）针对独立测试数据集的外部“保持”验证（HV）对LUR模型进行了评估。我们计算了基于均方误差的验证R〜2s，平均调整模型和LOOCV R〜2分别从训练地点数量增加，分别从0.87降至0.82和0.83降至0.79，而平均HV R〜2最小训练集的最低浓度为（0.60），最大训练集的最低浓度为0.74，将这些值更改为在训练中观察到的最低或最大后，在预测变量超出范围的位置，预测浓度更加准确。相应的训练数据集。NO_2的LUR模型基于相对较小的训练集进行独立测量评估时，效果较差。在我们的特定应用中，基于最少24个训练点的模型实现了d可接受的保持验证R〜2s平均为0.60。

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  • 来源
    《Environmental Science & Technology》 |2012年第8期|p.4481-4489|共9页
  • 作者

    Meng Wang; Rob Beelen; Marloes Eeftens; Kees Meliefste; Gerard Hoek; Bert Brunekreef;

  • 作者单位

    Institute for Risk Assessment Sciences (IRAS), Division of Environmental Epidemiology, Utrecht University, Utrecht, The Netherlands;

    Institute for Risk Assessment Sciences (IRAS), Division of Environmental Epidemiology, Utrecht University, Utrecht, The Netherlands;

    Institute for Risk Assessment Sciences (IRAS), Division of Environmental Epidemiology, Utrecht University, Utrecht, The Netherlands;

    Institute for Risk Assessment Sciences (IRAS), Division of Environmental Epidemiology, Utrecht University, Utrecht, The Netherlands;

    Institute for Risk Assessment Sciences (IRAS), Division of Environmental Epidemiology, Utrecht University, Utrecht, The Netherlands;

    Institute for Risk Assessment Sciences (IRAS), Division of Environmental Epidemiology, Utrecht University, Utrecht, The Netherlands,Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands;

  • 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);
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