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首页> 外文期刊>The Science of the Total Environment >Development and transferability of ultrafine particle land use regression models in London
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Development and transferability of ultrafine particle land use regression models in London

机译:伦敦超细粒子利用回归模型的开发和可转移性

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摘要

Due to a lack of routine monitoring, bespoke measurements are required to develop ultrafine particle (UFP) land use regression (LUR) models, which is especially challenging in megacities due to their large area. As an alternative, for London, we developed separate models for three urban residential areas, models combining two areas, and models using all three areas. Models were developed against annual mean ultrafine particle count cm~(-3) estimated from repeated 30-min fixed-site measurements, in different seasons (2016-2018), at forty sites per area, that were subsequently temporally adjusted using continuous measurements from a single reference site within or close to each area. A single model and 10 models were developed for each individual area and combination of areas. Within each area, sites were split into 10 groups using stratified random sampling. Each of the 10 models were developed using 90% of sites. Hold-out validation was performed by pooling the 10% of sites held-out each time. The transferability of models was tested by applying individual and two-area models to external area(s). In model evaluation, within-area mean squared error (MSE) R~2 ranged from 14% to 48%. Transferring individual- and combined-area models to external areas without calibration yielded MSE-R~2 ranging from -18 to 0. MSE-R~2 was in the range 21% to 41% when using particle number count (PNC) measurements in external areas to calibrate models. Our results suggest that the UFP models could be transferred to other areas without calibration in London to assess relative ranking in exposures but not for estimating absolute values of PNC.
机译:由于常规监测缺乏,因此需要定制测量来开发超细粒子(UFP)土地使用回归(LUR)模型,这在巨大的面积上尤其具有挑战性。作为伦敦的替代方案,我们为三个城市住宅区开发了单独的型号,组合两个区域的模型以及使用所有三个区域的模型。模型是针对从重复的30分钟固定点测量估计的年平均超细粒子计数CM〜(3)的模型,在不同的季节(2016-2018),在每种区域的四十个地点,随后使用连续测量来调整每个区域内或靠近每个区域的单个参考站点。为每个个体区域和各个区域开发了一个型号和10种型号。在每个区域内,使用分层随机抽样分为10组。 10种型号中的每一个都是使用90%的网站开发的。通过汇集每次举行的10%的网站进行禁止验证。通过将个体和两区域模型应用于外部区域来测试模型的可转换性。在模型评估中,在面积内平均平方误差(MSE)R〜2范围为14%至48%。将个体和组合区域模型转移到没有校准的外部区域,产生的MSE-R〜2从-18到0.使用粒子数计数(PNC)测量时,MSE-R〜2的范围为21%至41%外部区域校准模型。我们的结果表明,UFP模型可以转移到其他领域,无需在伦敦校准,以评估曝光的相对排名,但不用于估计PNC的绝对值。

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  • 来源
    《The Science of the Total Environment》 |2020年第20期|140059.1-140059.9|共9页
  • 作者单位

    MRC Centre for Environment and Health Department of Epidemiology and Biostatistics School of Public Health Imperial College London St Mary's Campus London United Kingdom;

    MRC Centre for Environment and Health Department of Epidemiology and Biostatistics School of Public Health Imperial College London St Mary's Campus London United Kingdom;

    MRC Centre for Environment and Health School of Population Health & Environmental Sciences Faculty of Life Sciences & Medicine King's College London United Kingdom;

    MRC Centre for Environment and Health Department of Epidemiology and Biostatistics School of Public Health Imperial College London St Mary's Campus London United Kingdom Centre for Environmental Health and Sustainability & School of Geography Geology and the Environment University of Leicester Leicester United Kingdom;

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