Climatology and Environmental Meteorology, Institute of Geoecology, Technische Universität Braunschweig, Langer Kamp 19c, 38106 Braunschweig, Germany.
Saxon State Office for Environment, Agriculture and Geology (LfULG), Pillnitzer Platz 3, 01326 Dresden, Germany.
Sci Total Environ. 2020 Feb 10;703:134570. doi: 10.1016/j.scitotenv.2019.134570. Epub 2019 Nov 3.
Quantification of the exposure of urban residents to ultrafine particle number concentrations (UFP) is challenging due to its high spatial and temporal variability. Hence, statistical models, e.g. generalized additive models (GAM), may be used to estimate time series or spatial characteristics of UFP. The GAM approach allows the representation of non-linear relations of a response variable with explanatory variables without the need to pre-define model functions. Up to now, GAMs were usually fitted to UFP data from a single site or from mobile measurement campaigns with limited temporal coverage. In this study, GAMs were used to determine UFP, accumulation mode particle (ACC) and total number concentration (TNC) at five urban sites in the cities of Leipzig and Dresden, Germany for the period 2011-2013. As explanatory variables, reanalysis data sets of meteorological quantities, urban geometry and traffic volume data were evaluated. Variables causing concurvity, which is the equivalent to collinearity in non-linear model approaches, were neglected to guarantee the interpretability of the final models. The models were then validated in a ten-fold cross-validation approach. The final models contained smooth functions for the building surface fraction, planetary boundary layer height, traffic volume, air temperature, wind direction, atmospheric pressure, relative humidity, global radiation and precipitation. Adjusted coefficients of determination (R) for the final models were R = 0.44 for UFP, R = 0.51 for ACC and R = 0.48 for TNC. Coefficients of determination of the cross-validation were in a similar range (0.44 for UFP, 0.51 for ACC, 0.49 for TNC). Finally, our study shows that GAMs are able to represent important processes that contribute to the particle number concentration from the smooth functions, i.e. emission, dilution, nucleation, deposition and long-range transport.
由于城市居民暴露于超细颗粒数浓度（UFP）的高度时空变异性，因此对其进行量化具有挑战性。因此，可以使用统计模型（例如广义加性模型（GAM））来估计 UFP 的时间序列或空间特征。GAM 方法允许在不预先定义模型函数的情况下，将响应变量与解释变量之间的非线性关系表示出来。到目前为止，GAM 通常适用于单个站点或具有有限时间覆盖范围的移动测量活动中的 UFP 数据。在这项研究中，GAM 用于确定德国莱比锡和德累斯顿市五个城市站点在 2011-2013 年期间的 UFP、积聚模态颗粒（ACC）和总颗粒数浓度（TNC）。作为解释变量，评估了气象数量、城市几何形状和交通量数据的再分析数据集。忽略了导致共曲率的变量，共曲率在非线性模型方法中相当于共线性，以保证最终模型的可解释性。然后，通过十折交叉验证方法对模型进行验证。最终模型包含建筑表面积分数、行星边界层高度、交通量、空气温度、风向、大气压、相对湿度、总辐射和降水的平滑函数。最终模型的调整决定系数（R）分别为 UFP 的 R = 0.44、ACC 的 R = 0.51 和 TNC 的 R = 0.48。交叉验证的决定系数也处于相似的范围内（UFP 的 R = 0.44、ACC 的 R = 0.51、TNC 的 R = 0.49）。最后，我们的研究表明，GAM 能够从平滑函数中代表有助于颗粒数浓度的重要过程，即排放、稀释、成核、沉积和长距离传输。
