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首页> 外文期刊>Journal of Environmental Science and Health >Estimating airborne pollutant concentrations in vegetated urban sites using statistical models with microclimate and urban geometry parameters as predictor variables: A case study in the city of Athens Greece
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Estimating airborne pollutant concentrations in vegetated urban sites using statistical models with microclimate and urban geometry parameters as predictor variables: A case study in the city of Athens Greece

机译：使用以小气候和城市几何参数作为预测变量的统计模型估算植被市区的空气污染物浓度：以希腊雅典市为例

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摘要

The present study demonstrates the efficiency of applying statistical models to estimate airborne pollutant concentrations in urban vegetation by using as predictor variables readily available or easily accessible data. Results revealed that airborne cadmium concentrations in vegetation showed a predictable response to wind conditions and to various urban landscape features such as the distance between the vegetation and the adjacent street, the mean height of the adjacent buildings, the mean density of vegetation between vegetation and the adjacent street and the mean height of vegetation. An artificial neural network (ANN) model was found to have superiority in terms of accuracy with an R~2 value on the order of 0.9. The lowest R~2 value (on the order of 0.7) was associated with the linear model (SMLR model). The linear model with interactions (SMLRI model) and the tree regression (RTM) model gave similar results in terms of accuracy with R~2 values on the order of 0.8. The improvement of the results with the use of the non-linear models (RTM and ANN) and the inclusion of interaction terms in the SMLRI model implied the nonlinear relationships of pollutant concentrations to the selected predictors and showed the importance of the interactions between the various predictor variables. Despite the limitations of the models, some of them appear to be promising alternatives to multimedia-based simulation modeling approaches in urban areas with vegetation, where the application of typical deposition models is sometimes limited.
机译：本研究证明了使用统计模型通过使用易于获得或易于获得的数据作为预测变量来估计城市植被中空气中污染物浓度的效率。结果显示，植被中的空气中镉浓度对风况和各种城市景观特征具有可预测的响应，例如植被与相邻街道之间的距离，相邻建筑物的平均高度，植被与植被之间的平均植被密度。邻近的街道和植被的平均高度。发现人工神经网络（ANN）模型在精度方面具有优越性，R〜2值约为0.9。最低的R〜2值（约为0.7）与线性模型（SMLR模型）相关。具有交互作用的线性模型（SMLRI模型）和树回归（RTM）模型在准确性方面给出了相似的结果，R〜2值约为0.8。使用非线性模型（RTM和ANN）改善结果，并在SMLRI模型中包括相互作用项，这意味着污染物浓度与所选预测变量之间存在非线性关系，并表明了各​​种变量之间相互作用的重要性预测变量。尽管模型存在局限性，但其中一些似乎是有前途的替代方法，可在有植被的城市地区替代基于多媒体的模拟建模方法，在该地区，典型沉积模型的应用有时受到限制。

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