Institute for Risk Assessment Sciences, Utrecht University, P.O. Box 80178, 3508 TD Utrecht, The Netherlands.
Environ Sci Technol. 2013 May 7;47(9):4357-64. doi: 10.1021/es305129t. Epub 2013 Apr 16.
Land use regression models (LUR) frequently use leave-one-out-cross-validation (LOOCV) to assess model fit, but recent studies suggested that this may overestimate predictive ability in independent data sets. Our aim was to evaluate LUR models for nitrogen dioxide (NO2) and particulate matter (PM) components exploiting the high correlation between concentrations of PM metrics and NO2. LUR models have been developed for NO2, PM2.5 absorbance, and copper (Cu) in PM10 based on 20 sites in each of the 20 study areas of the ESCAPE project. Models were evaluated with LOOCV and "hold-out evaluation (HEV)" using the correlation of predicted NO2 or PM concentrations with measured NO2 concentrations at the 20 additional NO2 sites in each area. For NO2, PM2.5 absorbance and PM10 Cu, the median LOOCV R(2)s were 0.83, 0.81, and 0.76 whereas the median HEV R(2) were 0.52, 0.44, and 0.40. There was a positive association between the LOOCV R(2) and HEV R(2) for PM2.5 absorbance and PM10 Cu. Our results confirm that the predictive ability of LUR models based on relatively small training sets is overestimated by the LOOCV R(2)s. Nevertheless, in most areas LUR models still explained a substantial fraction of the variation of concentrations measured at independent sites.
土地利用回归模型（LUR）经常使用留一法交叉验证（LOOCV）来评估模型拟合度，但最近的研究表明，这可能会高估独立数据集的预测能力。我们的目的是评估利用 PM 指标浓度与 NO2 之间的高度相关性来开发的用于二氧化氮（NO2）和颗粒物（PM）成分的 LUR 模型。已经基于 ESCAPE 项目的 20 个研究区域中的每个区域的 20 个站点，为 NO2、PM2.5 吸光度和 PM10 中的铜（Cu）开发了 LUR 模型。使用 LOOCV 和“保留评估（HEV）”评估模型，方法是将预测的 NO2 或 PM 浓度与每个区域的 20 个额外 NO2 站点的测量的 NO2 浓度之间的相关性进行比较。对于 NO2、PM2.5 吸光度和 PM10 Cu，LOOCV R(2)的中位数分别为 0.83、0.81 和 0.76，而 HEV R(2)的中位数分别为 0.52、0.44 和 0.40。PM2.5 吸光度和 PM10 Cu 的 LOOCV R(2)与 HEV R(2)之间存在正相关关系。我们的结果证实，基于相对较小的训练集的 LUR 模型的预测能力被 LOOCV R(2)高估了。尽管如此，在大多数地区，LUR 模型仍然解释了独立站点测量的浓度变化的很大一部分。
